diff --git a/.github/workflows/nbval.yaml b/.github/workflows/nbval.yaml
index e014b494..e81928e7 100644
--- a/.github/workflows/nbval.yaml
+++ b/.github/workflows/nbval.yaml
@@ -13,6 +13,7 @@ jobs:
           'TrendAnalysis_example.ipynb',
           'TrendAnalysis_example_NSRDB.ipynb',
           'degradation_and_soiling_example.ipynb',
+          'soiling_options_guide.ipynb',
           'system_availability_example.ipynb'
         ]
 
@@ -29,7 +30,7 @@ jobs:
     - name: Run notebook and check output
       run: |
         # --sanitize-with:  pre-process text to remove irrelevant differences (e.g. warning filepaths)
-        pytest --nbval docs/${{ matrix.notebook-file }} --sanitize-with docs/nbval_sanitization_rules.cfg
+        pytest --nbval --nbval-sanitize-with docs/nbval_sanitization_rules.cfg docs/${{ matrix.notebook-file }}
     - name: Run notebooks again, save files
       run: |
         pip install nbconvert[webpdf]
diff --git a/docs/TrendAnalysis_example.ipynb b/docs/TrendAnalysis_example.ipynb
index fcdd2bc4..53c45468 100644
--- a/docs/TrendAnalysis_example.ipynb
+++ b/docs/TrendAnalysis_example.ipynb
@@ -22,14 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:22.284754Z",
-     "iopub.status.busy": "2026-02-11T21:04:22.283768Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.589287Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.589287Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -43,14 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:25.592292Z",
-     "iopub.status.busy": "2026-02-11T21:04:25.592292Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.603222Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.603222Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#Update the style of plots\n",
@@ -68,14 +54,36 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:25.606226Z",
-     "iopub.status.busy": "2026-02-11T21:04:25.605234Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.608284Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.608284Z"
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "empty = pd.Series([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-   },
+   ],
+   "source": [
+    "empty.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Set the random seed for numpy to ensure consistent results\n",
@@ -98,15 +106,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:25.610288Z",
-     "iopub.status.busy": "2026-02-11T21:04:25.610288Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.664306Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.663802Z"
-    }
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Import the example data\n",
@@ -126,15 +127,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:25.667312Z",
-     "iopub.status.busy": "2026-02-11T21:04:25.667312Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.839426Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.839426Z"
-    }
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = df.rename(columns = {\n",
@@ -148,7 +142,7 @@
     "meta = {\"latitude\": 39.7406,\n",
     "        \"longitude\": -105.1774,\n",
     "        \"timezone\": 'Etc/GMT+7',\n",
-    "        \"gamma_pdc\": -0.0034,  # Temperature coefficient for modern silicon PV modules (1/K)\n",
+    "        \"gamma_pdc\": -0.005,\n",
     "        \"azimuth\": 180,\n",
     "        \"tilt\": 40,\n",
     "        \"power_dc_rated\": 1000.0,\n",
@@ -173,15 +167,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:25.842760Z",
-     "iopub.status.busy": "2026-02-11T21:04:25.841760Z",
-     "iopub.status.idle": "2026-02-11T21:04:47.653193Z",
-     "shell.execute_reply": "2026-02-11T21:04:47.653193Z"
-    }
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -200,7 +187,7 @@
     "ax.plot(df.index, df.soiling, 'o', alpha=0.01)\n",
     "#ax.set_ylim(0,1500)\n",
     "fig.autofmt_xdate()\n",
-    "ax.set_ylabel('soiling signal');\n",
+    "ax.set_ylabel('soiling signal')\n",
     "plt.show()\n",
     "\n",
     "df['power'] = df['power_ac'] * df['soiling']"
@@ -222,15 +209,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:47.683740Z",
-     "iopub.status.busy": "2026-02-11T21:04:47.683740Z",
-     "iopub.status.idle": "2026-02-11T21:05:04.841064Z",
-     "shell.execute_reply": "2026-02-11T21:05:04.840432Z"
-    }
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "ta = rdtools.TrendAnalysis(df['power'], df['poa'],\n",
@@ -242,6 +222,26 @@
     "                           temperature_model=meta['temp_model_params'])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ta.results"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -257,25 +257,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:05:04.844914Z",
-     "iopub.status.busy": "2026-02-11T21:05:04.844351Z",
-     "iopub.status.idle": "2026-02-11T21:05:59.907315Z",
-     "shell.execute_reply": "2026-02-11T21:05:59.907315Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\public\\rdtools\\rdtools\\analysis_chains.py:851: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n",
-      "  from rdtools import soiling\n"
-     ]
-    }
-   ],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ta.sensor_analysis(analyses=['yoy_degradation','srr_soiling'])"
    ]
@@ -289,15 +273,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:05:59.912324Z",
-     "iopub.status.busy": "2026-02-11T21:05:59.911334Z",
-     "iopub.status.idle": "2026-02-11T21:05:59.915394Z",
-     "shell.execute_reply": "2026-02-11T21:05:59.915394Z"
-    }
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "yoy_results = ta.results['sensor']['yoy_degradation']\n",
@@ -306,22 +283,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:05:59.919401Z",
-     "iopub.status.busy": "2026-02-11T21:05:59.919401Z",
-     "iopub.status.idle": "2026-02-11T21:05:59.923591Z",
-     "shell.execute_reply": "2026-02-11T21:05:59.923591Z"
-    }
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.408\n",
-      "[-0.629 -0.299]\n"
+      "-0.501\n",
+      "[-0.611 -0.322]\n"
      ]
     }
    ],
@@ -333,22 +303,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:05:59.925622Z",
-     "iopub.status.busy": "2026-02-11T21:05:59.925622Z",
-     "iopub.status.idle": "2026-02-11T21:05:59.929012Z",
-     "shell.execute_reply": "2026-02-11T21:05:59.929012Z"
-    }
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.957\n",
-      "[0.955 0.958]\n"
+      "0.953\n",
+      "[0.949 0.957]\n"
      ]
     }
    ],
@@ -368,15 +331,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:05:59.932029Z",
-     "iopub.status.busy": "2026-02-11T21:05:59.932029Z",
-     "iopub.status.idle": "2026-02-11T21:06:00.891966Z",
-     "shell.execute_reply": "2026-02-11T21:06:00.890963Z"
-    }
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -399,19 +355,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:06:00.894966Z",
-     "iopub.status.busy": "2026-02-11T21:06:00.893965Z",
-     "iopub.status.idle": "2026-02-11T21:06:01.203313Z",
-     "shell.execute_reply": "2026-02-11T21:06:01.203313Z"
-    }
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 2 Axes>"
       ]
@@ -430,19 +379,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:06:01.207624Z",
-     "iopub.status.busy": "2026-02-11T21:06:01.207624Z",
-     "iopub.status.idle": "2026-02-11T21:06:05.690507Z",
-     "shell.execute_reply": "2026-02-11T21:06:05.690507Z"
-    }
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 450x300 with 1 Axes>"
       ]
@@ -466,27 +408,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:06:05.693511Z",
-     "iopub.status.busy": "2026-02-11T21:06:05.693511Z",
-     "iopub.status.idle": "2026-02-11T21:06:27.538984Z",
-     "shell.execute_reply": "2026-02-11T21:06:27.538984Z"
-    }
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\public\\rdtools\\rdtools\\analysis_chains.py:1144: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n",
-      "  fig = plotting.soiling_monte_carlo_plot(\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 450x300 with 1 Axes>"
       ]
@@ -502,27 +429,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:06:27.542988Z",
-     "iopub.status.busy": "2026-02-11T21:06:27.541998Z",
-     "iopub.status.idle": "2026-02-11T21:06:27.743424Z",
-     "shell.execute_reply": "2026-02-11T21:06:27.742907Z"
-    }
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\public\\rdtools\\rdtools\\analysis_chains.py:1176: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n",
-      "  fig = plotting.soiling_interval_plot(\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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IWL+bmwXlUEiFUOmM1n5/swXHbhZh3o4L6Bgsx66LuZAK2SZrxXlk2FJSUrB9+3b85z//QW5uLsLCwvDMM89g+vTp6NOnj8O2r732GqRSKV577bUGEdzaiI8KRK5Sx9duSiuMEAtYlFd25l/KUTkZmuTMMsjFQvRu74+sUi1fo3XnhbGd70aBBqUVRv68crH1VQi4y3g4td6MALkY+68U1DsqsTqp2UroTBaYLRxkIgE6BstRYbQgMlCKtKIKEAJoDGZcvFMGX6kILMuAWAMm+Y/Nhjtj81RaI9R6ExQSAQwmDmYCcJw1WAWwfrAmMwcLZ/1/scYAlgEMZkCtNyGjRIuFE3rwhWmPdv64kquCWCiAQuq6gpCcWcYHajAM49S/6ScV4mKWEr5SIcoNFkQEytxqje9MzsLlHBXa+IqgN5khYVgYK1sBlUGlYFlALhKAECCgmhtOKhJAJGDRp30ANHoTjBYOamKC1sjBaLbg96sFkIkFCJQ7Rn3a+lyUWiPMFgKhgG3A8WIMSitMALFqrDBY+LaLQiJAWz8p5GIBjt4owqm0EgzqHIxJfcP5ISLpxRV8gJWAZaEQCwAAuUodIgJlyCzRwl9mbWlGBslRoDagTGsCGDg9O5OFgLGV5IzVRV6g1vPrXRkLtd6MqGAfaPQqvg8TsLaHi8uNkEuE+OlCDk75SxEZJEOeUg+pWACd0QJiskDAsmBZFrcLy7H+8G2UVRgRUNnCrI9hc/Vt1GTs4qMCERUsw8U7RnAgsO/JlwpZWIi1n95kISipMEHAAL5SIRgA5QYLzmWU4lq+BgaTBeV6M4o0Bmw5kd7okZ4euSLj4uLw2WefYfjw4di7dy+ysrKwevVqJ6Nmo1evXhg8+O7jrMrLy7F48WKMHz8eQUFBYBgGW7dudVuXUqnEnDlzEBISAh8fH4waNQrnz593ue3PP/+M/v37QyqVokOHDli8eDHM5rrVjhsC+07pdv4ymCwEXdsq4CsVwlcqRESgzMnQxEcFIkAuwqS+4Zg7qitiwhRu92/Yzmc0c8gu0+FcRikA8Md7ekinWl84W6f71Tx1g0dS2lqvgLVf6/drhQCA4dFt0SfCHwxj7c8ycUBJuQFqvRlSEQuZ2GpIyvUmPP/1Oazdfx25Sh20RnOt9+V0eikkQhYClsXwmLaQi1iwdl4fAsBMCMwcB5OFAwergbN+NNbO8g/3XcenB28hObMUgXIRwgNkYBngRkE53yKyx2a4WFiNaHXDdS1fA7lYgPAAGf7SOwyjuzv2HaZmKzFvxwU8seEPB7ff/isF4Ii1xWbmrIWKwUxgtBCwjDVAyE8iRJCvBP5yEYwWgoPXCjFvxwV+DGWHIDk0ehNC/aSIDlUgRGEtcLNKdTCYOVQYLShQ6x3cSWN6hiJQLkLv9v5oo5DAXyZqEDd19zAFZCIWPhJroEiFwQKGATgAMjELtvJBZZdpcbuoAhnFFTh4rRC7U3Kh1lsz8diCeyRCAeRiAQSV+2SWaFGgNmByv3AEysX8mEKFVAixiIVC6ly56xLiC5lEALGAgVjAgGGAhE5B/HpXLvr4qEDERwXyLmvrmw1YOIBhGehMFpRpTUgvroCPRIT7u7bB6O6h8JUKESQXw1ciBEBQqjUivagCKr0J+So938LzFFv5YX+NNXUxxEYEQCEVwT7enWUAmYhFQucgvDulD9oFyKx9hAAsBNDozTBZOFg4DkYLh1A/CUIUEvRu74cCtb5JIj09arFt3rwZiYmJ8PX1dWv7UaNGYdSoUXfdrri4GMuWLUOHDh3Qt29fHD582G1NHMdh4sSJSElJweuvv442bdpg/fr1GDlyJJKTk9GtWzd+2//973+YPHkyRo4ciXXr1uHSpUtYvnw5CgsL8fnnn7t9zobEftC0Rm9CeIAMRjMHo9l5CEVsRICD8fGk5hPqJ8WdUi0kIgFuFGhwOUeFG/lqrNp7FaF+Uif3p41r+RpIhCzKDXeP4KzroFbbQHWhgIWgssolFwsra6lBqDCYYTATDO4chB/O5wAEMFg4tFVIEOQjxo8XcyEXC5CSrYSfVASDmUP3sJpblbYsJjFhMmj0JggELERCApYjkItYCAUszBYORGAtSMUCFjqjGTKxdZzUrKGd8NzX51BhMCNPpceTCVH4YN91lBvMYBjwLSJ71Hoz+kUG4JfU3MqAIb3D+iKNAQVqPaQiAW4XleNOpZvZdg22fpsKgxlJydl8zX1Mz1Dsv1IAM0dg4ar6a1gG6NrWB3ozgZBlYLZwKCk3wFJZOcgs0aJ9gBRje4UhPECGXKWu0lVnQr/IAJxKLwFHrFENUgYoUOvxzLZz6BfpD5ZhMKZnKMb0DMX6w7eh1BpdVsQ84Vq+BjqTBXlKvbVlbiEgBJVGhYFCKoJcLECukoOZIyg3WOBjMONyjgo+YkHlO0PQu70/AuVinLxdDK3RgjulFcgu08NXIsDu1DyYOYKOwXL8PSEGgHWspG3Ihn3LYtbQTghRSPBLai7KKj0d352zViz85WKotEZcr3QrrthzBYD1nV80sSeKNAYcu1kMrdEMg8laQTKaOQgYq8vRZLAgu0yLJxM6QK03Q2+y4FxlZiHrdgwIazWKkUFy+MtrHyt5N24WaLDvz3ykZCn579w+AKh66+1GgYZ3k4oEDORiAQJkIhjNHLqFKjC2ZyiO3ihEelEFTJzVhU+IdShMkcYIlbYEbRQS+EqEDhmPGhOPDNvMmTMbWIaVdu3aIS8vD2FhYTh37hwGDBjg9r5JSUk4efIk/vvf/yIxMREA8NhjjyE6OhqLFy/Gt99+y2/72muvITY2Fvv27YNQaL0Ffn5+WLlyJV555RV07969YS/MDWIjAjB3VFc+U4RaZ0ZRubXh35CpruyNzYQ+7VCkMeDQtULrx5RRBrmYxY2Ccod+H3u6hylwNV+DmDD5XV1kdR3UGhsRgL6RAdAaLUjOLEWuUoeLWWV4dWyMQ2HpIxbAVyqCWmeq7CsSIVepr+zjIVBIBSjUGCFiGWw9mQG13uzS9TFraCf+A958PB23CsvBMoCPVAiTmUNUsByZJVqYLBaYzBZYOAKZWIjYCH+MjGmL2IgARIcqcDlHjehQBY7dLIZaZ4bRzEFrtLgcgmG7Dgbt8OufBbBwHF7+zwX83+iueDQ+ko9uLS43gmWsBeDtQg1OpZVg5v0d4ScVwsxxEAkYh7Rij8ZH4tH4SMzYfBpnM8og5KwFfvsAGYZHt8WkvuHYnZKLH87nwGQh4D1jhCBfbW0F2AY7X85RwV8mQohCghBfCSr0VkPdo50/zmeWgWWAQ9eKEB2m4IcOFKr1MJo5ZJa4399bnZ3JWbyBlIoE8JUIIWAZGKo6nGC2EIQoRPhL7zB0D1Pgg33XYdQYIWStrbOoYB9cy9fgr7HhCJCL8PSQTkjNVuJClhIsY0GeSo9AubXSYzCZwTIMCtQGh4qDSmvE01vOwGDmEB8ViG2zEvj1p9JKUFJuhIUDSsuN+PFiLh7u1x5n0kshFrL4/VohtEYL/GXW1ntsRABCFBJEBcug0plRXG6AWmcGIYDJruNTb7LwlZTUbCUK1Hpo9GZIRVY3n1TAwFcqQpi/lA9w8aRMSM1W4oN911FaYYK/TIgQhYTP8JOr1OHw9UKngfnRoQqUVZislWxCYDBZUGzmYCEEH+67Do3eWuHsHRGAknI9SitMMHEEpsoLNFgI8pTWCpxIwGJQ52Co9Y3rHfPIsH311Ve1rmcYBlKpFBEREejfvz8kEvdC0SUSCcLCwjyRhKSkJISGhmLKlCn8spCQEDz22GP45ptvYDAYIJFIcOXKFVy5cgWfffYZb9QAYO7cuVixYgWSkpLw1ltveaShvtjCmTV6M3JVekQGyhDkI661BlyfDAp9I63bS0QsKowWSIQM9CYO/iJBjePiKowWBPuIXLYkq+MnFcJotvBZH9zRamu5GswczBaCzBItH7Fp6088drMYYf5ScITwrbUwfynOpJfCZOFQYbDAGjBg7Xs7fL3IZad19Zav1W1lHS8nFQtwu6gCeqMFFgIIGAYsAIuFQ6C86pk81DccMpEAY3qGIik5G6w1bgNagwWHrhdiZ3KWQ3+I/Tl9pVfw/bkslFQY+dbXpL7huJyjQmmFtf/Peg1m3MjXYN3BWxjTMxQjo9tCqTVi1tBOTvfvob7hMJo5PqDFFpxgO6e19VAErdECY2WkZKBczBc0sREBfL9hfFQguocp+DD3O6UV1j7GSquYXlyBMT3aIjJIjpRsJTR6MyRCz1JX2bKHZJdag0OEAgv0JmtUJ2NrLgAQCBh0CJJj0cSe/L5bT2YAAB7o3hb+cjH8pEK+MmO7ppdHd0VScjZuFZYjX21AmJ8YJgsDpdaIUD8JdiZnQa03w08qxHdnrf/nOIJL2SreiCRnlvFRl2lFFdAZrS2tX1JzEREogy160V8mtLp+pUKs2HMFRRoD4qOC0D1Mga0nM3CrsNzaEmMBowUQMEDXtlXeL9sz2Hw8HRezlCDE2iIN9ZNCJhLcNcCrNpIzy2DhrB4uvcmCGwUa3CjQwGjmkF5cgXy1ASyswR8vjbZ6uV4dG4NVe6/yrWghy1QGNlkqK37WipxGb40GDg+QodxgRoFaD1sxwbCAr0QIM0eQWVLR6C02hhBSPWDqrrCsNewaAKrvbr+cYRj4+flh4cKFeOONN+p0DluLbcuWLW61ELt164Zu3bph7969Dss3bdqEZ555BqmpqejTpw+2b9+OadOm4fTp0xg4cKDDtpGRkRg4cCB27tzp8hwGgwEGQ1WBr1arERkZCZVKBT8/vzpdHwBgTW8QVVU/CRMeh+xOiQj5YzlEnAFXgh7A9SFreJcW785b1gbgTAArwpYHzuLJA4MgInq3goL5kG9GguuzbwAAPtx3HZdzVAiQi+EvEyKrVAehgMFrY2OqCuUNI0FyL/DHyUcINg/4GYt0HwJXfgR6TgYSN1Wd6N2OIPoqP7pd2QQjI8EH9x12vKZ3OwL6MkAaiNRpF9F2030IJUUoZtsi5J2b2HIiHf3OvIa+qgNgwOCszyiIhGzl31ZW4Bls0o/mI+aCfMW8Wyk6VFFVGC5vB5gdWxY2bRr44p3uv+B2UTn+XTIT4aS4xmhrpvL+D76wAN1KfkdOu7FYgJdxIUsJg4kDywJf+vwbIy0nwYb1AfQqQOoP5F8Cek7Gzk5LsGz3FehMFvRu74cfBlwH9vyD11Lm3wvz/ddgasYSjGNOYx8GYbn0H4gOVfBDPPjCLWk2cDnJKfqR1yoNBP6ZAaCq1X4qrQSDS3/C69wmCBkOjH8kMP+y075bTqTj8PUi3C7SoFhjhN7M4ab4SQgZ4nBvlKJQrO/3I3zEAlzL1zhG753dBOz5R9XG4XHAnMP8n0UfDkYbzRXHh2H/0sBume0fu2vi3x8b/pFAZILDu7nlRDo2HU/H79pEiGEBYYB/cc8gJWwKNpQ8jWBLAYrZEARyJRAQa4m8in0Wr5MtEMEMAsAg9MONmZcw/7uLSCuy5gkVVfafhfhJMKxrGxy4WojiciOOiV9Ge7YYAFCs6IkN3Tfhf5fzYTRZQGBNzvy44AAWs5us/W9COfCW47ConclZVve23gxfqRCP3xeJF0/cDyHMIIwIbK+HXX9/NVDx6TDIi1P5vw8IhkHAMBhmPoG9lgS8YnoJgNXlu2pKHzzK7QP2vQ1i1uGOuBtCDJmQwAgjRJDCAA4CbPWfiy3G0QCAtgoxTBYCrdGCPJUOHEfwBHsAz4t/BbEYEUqKgMr7KH2r7kND1Go1/P3971rmehQ8cvHiRcTGxmLUqFHYuXMnUlJSkJKSgqSkJIwcORL9+vXDiRMnsHPnTvTv3x8LFy5s9L6rmhIz25bl5uby29kvr76tbTtXrFq1Cv7+/vwvMrKeYc2qLL5DmQFAci8g4uomSDgdWHDoVbofyqNfICk5C9tOZuCJDX9gyvrjIJx1Gg5wJszc389towa7c0mIAbH/HQoAKFAbEOYvg1wsQJ7KmsfQaOYcM1TkXnDQGkaKsO1kBrjLSQBnBi4nOZ5IX+awvf25xZwB806PwI6zd6oCUGyFkr4MsadeRSgpAgOgDVcILLV2wseqD8PaniIYWHHQ7m/r701srLo+EYsR0W0RKBdDJhI4dribnd1ltv0UKMeaayPxc8lfEY5iMAwcrsPhmjgTJt9+B9HFv0FAzOiQuxf/mTMYCZ2CKgd2A8NMJ8ByZiD3AlCaZv238n61u/kfiIUsBCwDk4UAf3zqoCVQ9SeiQxX4C3sGIsaCCTiBvYbpKFAboNGbsWrv1apsGld+dNjXSau+DFjTm7/e8AAZZt7fEdPIbogYa3o2qLIctgGsRjAlS4nSCgPaKiQQCxkIGEDIEKd7E2gqwKKJPfH7tUKcTi/lW1IA+GvjsaskAUAbzZWqYzGoOjZT7We7nsprMq2MwozNpx0qUbBdi/27eXYT4qMC0SXEB2LGGojCAniN/QYSIYtgS0Hl+1YEATj+fM+T7yCEmb+3UrMasRujMIX7DbaAVlNlAuxCtQF7L+WjqNwIAqA9U8zrbaO5gn+eHYK2CjFC/aXwkQghErKYxezh32GYtdZKVyU7k7Pw5bF0azo5AvhJRagwWiCE2XpcYnK8xuV3T0zvU5zq8MwetBzDKPNRiGDBQ+xJ/CS2equMFoKVe6/BcOwTwFQBhnDoYLgOOfQQgIMMBjAABLBglmodDummokuIDyRCAbqE+CIiUI7IQBlC/WVYIPgWoeYc/ptmAEjM6rtqrQ8eGbY1a9YgNDQUBw4cwCOPPII+ffqgT58+mDJlCg4cOICQkBBs2rQJkydPxv79+zFo0CCsX7++obU7oNPpXLo8pVIpv97+35q2ta13xcKFC6FSqfhfVlY9B6P6R4KgWqVU6u9QYX1atQ4VBgtMHIHexOFafjnMEDps465Rc0KVBfaHZ0BI1Xgpm3tKLGQxpmcoP7Cyok2sk9bV7DrHc28YaXcdVlcDqfYDrAWGnDHiKDeryuXJ2L2Kl5OsrQfbtREOsZu7QtBrsoN8Btbckvb34qr0KYQoxIjrEAiFVIhLOSpczlHh2M3iqh2F8hpvSXVjXF1/9XsQmLbb6SN6dWwMOof4Qi4S4H9cQo2tqIRbaxDmL0WwjxhdQnyBwS85bTPy0kJcDRplTS3GAAGowCryMZRaI0orTHwkIHpO5u9FjS4YVRbwbkc+Ak6tN+Ng4GOO21czbrZMKEE+YkiEArTzl8JHLICFYZ3uBwGQ834C7pRooTVaHIdguLg2JM3m/8uEx1Udj1T+7nY9AIRGJVbe+TuU8Kl1O+x9HbERARgZ09bh+5HBgIUTejhUvuzPKYURHOPcYzO3Yj1SxTMhrYykZQAIWMah7yiHtIHNocUAYMFhZ+FEfKWejehQBXykQvwgneyo26y1emRgjXQVsAwMZg6RgTKIhSxOpZXABAE/zMVp32oVEyfC4wA4fjM2GAaIZdLwpMA63lhrNCM5/Mmq9dXOZ38MEczYemcsNuRNRZnWCKPZgjB/GcL9pRATvfM+0sZ1RXpk2H788Uc8/PDDLtcxDIOHHnoIP/zwg/UELItHH30Ut27d8lylG8hkMgc3oQ29Xs+vt/+3pm1t610hkUjg5+fn8KsX8y+DWaICYazjaxgAXO4FGIR+Di/NacEzELHW5K4hviJMD/8FxNWjm/gRsERV6y97yAqHl7Nn6X6UG8xQSIXoHe4HjgDh/lL8NTYcj8ZH8oXg2k5fYOWAP2BhrONoGAaYxJ7EVaZr1fHsa+H/zACWqLB1zEWsHXIWW8dcxPU2Y6s+dAYIYCqw+Pbj1gUTPnC6Nw7GjjMBV37iDSYAsMQEVeeHwDFCPqekGBacMP0NmSVaHL1RiDulWuSp9TidXsKHqafOvIotYy5i56TL2DLmIlKfyQTsC1a7e88XeEtUVb+JH9kJrVacbhiJ2IgAjO8VCqPZgn+Y/8/5OVUiMGux4pE+mD2ss7W/bMBs63OqPCsDYLD+MH7qsozfhwHQV3UAc0d1RZCPCP4y6/NA4iZss7vX/DOvLMh49GX4+8mJfLi3YugclAhCazRu9vkOxUIWZg6IiwrEewNOYOWAP3DpmUwwledgAIRrr6HCaAHLAHKxoGpogO3aWDsjYd/Kn3MYl57JxIOKn9CD24Fo83/Qh3yH8f4/O15PtWtiAISjGHrI8fGQM4Cghv58YuFbbd+MOcd/cywjsLpzeyfym7IAmEpjJmYs+OrBc073kYHV6F1h/4Yn2AMI85ciQC6CRGjti/WXCTFJuB65TBun9ynAVIDP08bg9bExmL/wPaSFjne8/5wJWOKPOfLDiAyUYcbgKPxtYAcM6hwMkYBFP+5bBx0Ol6nKQvb+aq1je+YcBpaooIfEwejyx2OAZcIt8JFYh5z4Dp3jUBG0N27VDR0DwJdosPXOWPy7eCZkIgGign2gg8Rh+9RnMqtcyI2ER4aN4zhcv369xvXXrl1zyPQvkUj4llNjYYuorI5tWXh4OL+d/fLq29q2a0rYCav5/zMAMgIGg9gVbgFsBZIEixDiK4afTAyxkMWqASecWx7V3T0u2C+fCLU4zOHl3Gl4DiIBizKtCQM6BsGWVHlnchZSspQ4k16CIo0BNwrK8U3ACw4tr/l+VYU8Aax9KXbYj5n5I+59/MYOcTRupgIULeuKnexYR6EbRgKLq7mXOBNgdqyQBKb9DMHiEgfjJoQZ/6t4HBnFWoAAHGcdW2NLKrw7JRe7U3Kw7qBd9vQ5h6sMV7XWptGnvaOOAbMd//a3c0lXGvcKowVCgTXPZQ7Xprr547EPteaNQO9H+fUMrK22LGl3h2fm//VY3CnVIrOkAjcKNEjNVrocn4Q5h50KZUlFDp7+82k+Z+Kr7b+GUlQtk0ylcbONe1w0sSeiQxWICJTx/Za2HIb2fWWEWJ+rSMCgV7i/83ilv7zv+LddK/9mZdIAUjnoVy4SuA6eqnZNDIB2KMK8y48C41ehRipbbU8P6QS2sg+NEIs11dOgDx23JdaWF0usASU2g1C9kGcY4F/Czdirn44OQT7wk4kxLLoNuof5wWDi8JDg31aDa1dJs7XepuzuDbzbERWTvnA2bgD6X/oX/i37HPPGxODpIZ0wqW844qMCMaBjIK6yVRXK6pWx8BOLar4HlSzs/hs+ED1X1cqvbCUDAMsQ/Cx6q2oM5Vt5gDTQqbXm6tuwGW8/Yz4+v/0AXs14Dqc6v+Kwb1PMVuCRYXvooYewfv16fPrpp3yLCLC2eNatW4d///vfmDRpEr/8jz/+QNeuXeuvthb69euH8+fPO02dc/r0acjlckRHR/PbAXCagSA3NxfZ2dn8+ibFrqBkAEQX/4aLfd5yeFn7MGl4o+ID3Cwsx8UsFW4UaKwvnK1QZYWu3T3ViI8KxA8jfoXt0TMAQkkRnpUdwpieodYByoRg/5UCvPPTZZy8XYzSCiMK1NYw6YuhUxyOt7psnmPN73//rPHcflIh/iV5FZdIZ4d92liK0GfvZMcC2Nb6c2gdobJ/rFo9dU1vCBaXOLiTpDDgjOhZtPWTQCK0du7fLirHzuQs7LtSgPRiLVRaI06llTgPpK5sbdoMneT1Ky6uxk6DttRxVaVxV0iFYBhglGUdLBC4vilJs7H5eDo2HkvDol2XrMbNLgjA1mqbLnjXYVkHw3UUqq2h45dz1HySXJcD7F0YN+ReQMWnw3A5RwWN3oz1/X50NNAA77q0YR00bd9f6ewEZxhgp2ARFFIRFFKhs1EaMNux1WbXyt9/pcCa2YQAPhIBQv2lWDihh+voP1fXpMoCLnztUhcAvtUGABDYxoIRdLi9w1rYVj8eADOEjuMR38oDeic6usAZwA/l2JE3HgtDTiI6VAGVzgQ/mQgBcpF13NniMv7+2ipgDGDtU94YhS4hvtU8AZVcTuKNv62S8VDfcPzDf41D5dd2XP5vu+fmillDO+F21GN4vvPvGCr9AfGC//IHYgB0Nt+E+OLWqhkm/plR1TIPj3P8Nlx4BmzXF6K5ggfS3nNYHnZje6PnjPTIsH388ce477778PLLLyMgIACdOnVCp06dEBAQgFdeeQX9+/fHxx9/DKDKvfePf/zjLkd1n7y8PFy7dg0mk4lflpiYiIKCAt4FClgHfP/3v//FpEmT+D61Xr16oXv37tiwYQMslqo+gM8//xwMw/Bj4Jqcaq4QvYlDgU9Ph4/nr+xJGM0cKgxmZJdV9gXOv2x9sd4pcW5JuMBW+DETq1x/DIC/ZH6AbqEKSEUCFJUbUGG0oMLI8WHnUcE+6BtpHWdmaz3YDO5ecn9VjYwzOpzPPqOBrf/hcbLSsf+BAbqZb6GkMNex1po023pNTh88cXRTqrKQmq3Eye6LHD5uP5TjVzIXUcE+sBBrK2L/lQKYzRw0ehPAWCcG9WhMjV2rCqYKOBSm//snJvUNx8P92kMmFoAjBGuEz8BlgXt5JwrUeqh1JtyxnxfP7n1gACwzr8GfQWMc7s8Z4TPgQKxTF92NOYedDJe8OBVvVHyAtgqJdUzi/MvOxk1fxheSar0ZMWF+UOvNmNQ3HCNjQqrGMla6cm3vhIUjNecwrd5qq+xrG9MzFNGhvhjUJRixEYF8cu66XBNyLzg+m+rsfd36r6QqP+P9WV9YDbBdy9OGABb0L/zBcWHiJrBLVGBYkZObcXLuh3gleSzG9wpFpzY+6NPev8q4V36rDOOi2L2cZOeuFTlfU6Vr2DYswmjm8KmsyntiJqyjodOXOfRhVsc2bjYqWI4OQXKM7RWGtNDx/HoGwD8MG7D3Ul5Vom5bq9XFfeLXuagc2MMAePDO2kZvtXlk2IKCgnDixAkkJSVhxowZiImJQUxMDGbMmMEPlA4KsqabkUql+PLLL/HEE0+4dexPP/0Uy5cvx+bNmwEAu3fvxvLly7F8+XKoVCoA1iCOHj16ICcnh98vMTERgwYNwtNPP41ly5bxWUcsFguWLl3qcI7Vq1cjNTUVY8eOxZdffolXXnkFK1euxDPPPIMePXp4ckvqT7VQ3UHXV+F/93+LcsbXwbhdEs2EoIYKaZ0YMLtaoUAQ/O04XMpRQSJk+SLYzFlbWorKAZx+UiF+H7bD4VBObja7D8pPKsT1fDX8KmvvQT5iKCRCjGM+czJuQWbHecL4PpgBsx0KegjlTn1yyZllOB082aHwZwAoDPlYX/FqpQ4NDl8rRIHGAKlIAL3J4rpV4Q7VQ6vtC1POyNeuO7fxgZ9UhKP+k4CJ1dxdAACCxPgIBPtKECAT4XKOyq7VVlVQDTUcATdlI8ziAN6ABLAVWC9ZD5lY6F6apfmXHfooGQD9VAfwVtipKgMy/7LLfjm829HB1enUOrR3RzLWICRbDlMnqrfaKp/zo/GR+PdT9+HVsTGORvNu12T/HofHOT8b+0AFW6tt1Jv8Ipmlouo6bMeSBoJjWAjA4cGCGsLo3ylGmX8vJ1egD6fBKycG4j9xV7D2b3HOxnlxmeP77OK4Tt0Mla7h5Mwy+MtEKNMacVgxER+InkMGCcNqdjaWcbMcvkWueqSyCy7nWOd7O5VWgosDP3BwObIMwX8Fi3DgaoH76fNsBq5acIh9f5yQmBp8iqPq1Hkcm06nw6JFizBq1CgHd2ND0bFjR2RmZrpcl56ejo4dO2LmzJnYtm0b/7eNsrIyvP766/jxxx+h0+kwYMAAfPDBB7jvvvucjvXjjz9i6dKluHr1KkJCQjBz5ky88847EIlETtvWhLtjKtymciwSzxIVdiZnYeLueEgqw2sJrGPI5gRvccpA7hFL/Pn/EgBfKF7EHvFfUKY1WicRBBDkI8KAjkGOs3xviQYs1v4uDoAFIohgsjuutRKy5UQ6lFqTQxYIW6aD9YdvY5tqtjUsutKScmDA2n+elcdxSdJsfgxP6qAPqwZ/753s4OIiAH4XDMMc7QvWawAgEjLwlVhnvP7PnLvnMXV97wLAf669Ex2fXeU4LaeM7DbNXFUrMfWZTH4wbnSoLwZ3aYOnh3Ryfh8mfoQVhYPx5tnBfMWDI0AsvuMzZLhF9TFflcd2aPFvGOkUku8wbswVle8SAVDg0wP/u/8/NQ/Grz6urXeiW+Ow3OZfbfn3EwwLELsuCkYALC61e34MsETpfIxlIVYPBCsG3ilyeZotJ9KxOyUX2wsfgbTyG3WghrGBPHbjN53urYvnZBIH4P8id6KwMvVaeWVeRttA9p3CtxDLpvFlBSeQQfB2vsMxbN9grlKHozeKkVWmRWSgDH8b2MH63tmXCQToYvwWD/UNr3UmgxqxjblFlQvWzIggXFxc62410Wjj2GQyGb744gsUFBTcfWMPyMjIACHE5c9mxLZu3erwt43AwEBs3LgRxcXFqKiowOHDh10aNQCYPHkyLly4AL1ej6ysLPzrX/+qk1FrFFx82I/GR0K6pNCh/ygMRXivdJ7HMyw7UM3lNUezHise6YO/9G6HQLkIIoE1G8n5O0pcylFW1cLHr3KopX4qme0yQKJ6QIOtpv9ofCT6tPfHw6J/8y03AoCt7kayH0JQncRNVhds4ibHFsScwzD4tHfQ94DlGNaKrME1UhELaWWEX23zpt0Ve62Xkxwj8iqNwqPxkXxWknk7LliDFN4pgb1bst13f6kMqReBZZiq2mz196HSjXZKPsrh2n4lL/IZMtzinxnOLYI91boKXPVh2bklXWIXHRlacbX2bDjV3eZutC7qxPhV4O+xSO54LcRirTRURkby/1anskDmOFON99aW6PijAYdxrTJCuKYIU5dU9ue6rDD8M8PJ1SoyKrE+bTziowKt3RJGM/SV6T3MHPA4t9Ia8Vh59axF53R+W/cAYK20OmU4qtaavCSaWaeJjB14p5iPZv016nXkC8Oxv8M8z45VBzz6quPj43H5ci21EIrn9E60ummquyqqGaAe5Bb65LnOkFInEjc5htCDIPa/QzGpbzgS4yMQHxUIH4k1c35FZYLW+KhAx4KJAGMM++z/rN0gVWLNoSfHWHyGXtwO3C/ZaS34XQWR1JFv798DLatwMACT2JN4Tn4IACAUsGhjl5nEI6obnmoRedn7P8WWE+lISs5GenEFDl0rxKq9V62FpJ1RbKO5guhQX8RHBfEzRfPYvwfEgkl9w3FtyBrwwT+MdSDwk4IDdZtt4a28WsfzAai7cavW97L+0C2s3X/dYVJNB6q/47X0CdWZAbOtrt+gzsCYZc79QpeTrAYOdpGRtuEglWM3ucq+Lo4R1tgnZB81+tv93+IvAbthqZ6pUOXi2t3FRb8nS0xYdG4IfCRCCFkWQtY624BUxEIuFmBh998c+tuIKsvh3trPDrJwQg8Mjw5B7/ZVrTRbmUBgfb/krBEv3HjG82uopP2Yl/C/UXvRfszdg9zqi0eGbe3atdixYwc2btzYLFO9tGrsWiFOy6uFOD9u/qlhzvnPDKdgjNi8nVg0sScS4yOgkAph4qwp0grUer7g1VYO2mYYoBdJw5/ozNcUbQapthm3J/UNx6S+4RjQMRAysRB+UhEfes9f6106o2siPioQ3z9wzMm4LeC+hE9llvHR3du6PfN4zdi1pQfMhn1LLOzkEii11mlgGAYwWjjcLiq3GqBqz7d7mALhAS7GUCZuquqPYoUOwT/2fa+vmL6s6p9zF3vjVpORc2HciL4MhqVtq7Ke1MC89Oew+YTVxZqUnO362uxp6FbbgNnAyxeqKmHVDaldZOTgCwv4d9T2zporDRTH1D5HoY15Y2Lw67zhEC4pcTRG1QNc6sr8y87aCYe3Q/9AqL8Ug7sEIz4qEEKWwX0dAzGsWxt87vOCY7Sy3b2t3j+aVaqFRm92+EZTp13k/8/AmgWnvtQYtdsIeGTYZs6cCZZl8dxzz8HPzw/dunVDbGysw69v374NrZUy53BV64ph4TtyXsMdu/oA6T2vArBGwkUEyhEgE0EoYB0yyvu8dIw3bpdIZzxqWuF4jA0jXY+tqsT2or86NgbDurVBkI+4KvS+tggsN7Ad+/asyzCzUgfj9od5KmIj/N2aSueu9H60soX9aNXflQiItW9xWLc26BjsA6lIAIOZc5lgOv/gemw8lob53110Nhh/ed/a8rCPJhwwm8/OAgACcJineq/u0WZv5Vnv81vO4zp5XIwbExMDXj070vl8du7IGO4WBCwLk4U4vDcOVC+wzzZgP1t1qhtSu8jI6OJ9fNZ82zsrsJ+cr67YopWXqGrvY3OXxE1O0cEPKP+LI6+PwqtjY5BerIVMJMCdUh2O3SzGZ5oRDsFZAFy2tK2zyIuh1Bqd5me76P9g7dlcWjAeR0XGxMRg+PDhSEhIQEREBIKDgx1+tqhISgNj88kvLnMrvN9tXERJYk1vxEcFIlBunS5jWLc2GNatjYPbxuelY3go+Bc8al4BM0dQwIQ4ZCJxp5Zmm65mYKeGn84iNiIAoncKHAZwC0Cw8c6EhpkotXoLu9oYtKfzlvMzKfvJROgS4lvVXyH25bd7pmIDP17wRkG1iRirtzxszL/sYLAHVBxyOblpgzDnsJM7XAKD68HTdoyIDsGwbm0QopC4bt3V0I/YaPROtHonxL4OkZEA4VsttndWILa2oEWcFj67n2tcXe5gGw4w8SMY/DriZNsn+ECQTm18YOYIQv0kuJSjAgjBSPMn0BJx1feoL3PoIkjNViJXqYNCKnRygcdHBeLiwA9Q4NMDBNaE3A2Bzc3b2OPYPPoK6jIBKMU7SM1WInngbszc36/KmVbpkuwbORpSkQBKrbFyhmfHaTOCfMRgWQYsASYw65FMplYd+Owmtwyw/USHjcFXD57D9P39wYLjs5M8fPsdAA3s/gKsQSS2iLzLSfDrtASZJRUI9hFBImSrQvPHLOODNsQwV07ESBymxbkbxYqeDgmEx+57EIivOStQvbAZoUq3FoPaJ7llACw2foTpqjk4l1mGIo3BdWRdeFxVXyq5+wS29SJxk6Mx3fsGQMwgYJ1aLRj1Jsief4AB0Klwn9Ohmo0Bs/GtcbQ12tjum4mN8IdGb51slWEZ+IvFeCr4R+wsnFC1r12fdXJmGT+Rb41TOg05BQBoqGaKfddEY7ok6xESRmlomqo24wrbC2c/SBMAsOdVxEcFIrOkAoUaA4o0BifXYnSoAr3C/azj36p7b/a97db5G9v/Hh8ViK/GnOf/ZgD0LDvUKOeqHkTS78xrEAsFUOnMEAvZqlZpNYMf5i+FTGyta7p7H/Ie/59DoICvIb/2HSrx+F1L3FRzgJMNO7elf9rPuFOqhUpnQoFa73p7+4HW9e2PqiuVhpQBcQ7cGTAbTGWkK1t90HQz42pM4aS+4fgzVwW9mQPHEXCEIE+lw01BV0eXYuUsALV1EzSF7sbEY8OmVqvx7rvvYty4cYiLi8OZM2cAAKWlpfjoo48aPelxa6S2QIvGxvbCVUz6wsklGbt3Mnq394dCap1VuboBsuWwC/GTwswRHGCHVX1I5ppnS2hKqoIuPoKRlYMDi/S2DzbOyaoFkXQq+BUAgdZoNWiOH3WVUXpV8z4qDGbcLip3+1SxEQFg7QZ+u9srVK93raYAJxt27kgG1lmvxQIWifERNR/T1ifVEP1RdUHsA8Bq2CIPOiasTs1WwkgqhwKwjeTi9RDb+wyAr6DERgTAVyKEQiKESMDARyKEWMDiCaxymP2AmLUwrO7ZpMEc1XU39jk9MmzZ2dmIi4vDO++8g+zsbKSmpqK83PoxBgUF4YsvvsC6desaVOi9QHPUoGw4vHDVC5fcC84plKrtGx4gw4COQWjjK8ECvIwT0pGwMEKg1xSn7ZuVAbNxbdZVbBtz3mrEG4tqiYwzS7QgxJqRwzGcv2q74cajYCvTfNUJW2aW2lpR1WjKd+0jwToopEJ0q8/QisZizDK+79U/7ReHVc1Rwawr1SsoifER6BcZgFlDOuG+qECUaY1Q680YZNrI78MAEFfk1HDE1oFHhu3111+HRqPBxYsXceTIEadZtCdPnowDBw40iMB7ieaoQdVItQIy9pt+tWqLjwpETJgCcrEADAO8bHwR6+4/iS3t3moW12ptNMl9rtaa+bfuNZgsBGIh6+gCrBZsEhUsg9HM1fmepQ76EFseOOecpb4GGv0e2EVHjiHHodIZq5I8tyQGzOansKnuR4+PCgRLKrPpWExoiVSvoNjSks0bE4O+kQFo5y+DWMDCVyrEKfkoPnmz02wOrQyPDNu+ffvw8ssvo2fPnmCcOlWAzp07138STkrzUm3gdvUZmKtjm35Fa7RAozeDANidmodvTmU2TIYUb6Ra6PuU/u0RHapw4QKs+ob+WfEhooJ96txaaE43tkuquSMTyX6UVRhbjj47bFPYsMRxZpDYiAAIURnMwhkbdyiCh9RWQYmPCkSPdn7oHOKD3u398UnAAjzR7lf8JWA3Do7/venFNiEeGTadToeQkJAa12s0mhrXUbyI6ml+VFm1fty7U3Kh1pnAEUBvsiC7TItCtb5OfUatimqF+6KJPTGpb7izC7DSHckAuK/iEGLCFHV2ETanG7tmqvoP55Gv0aWtbwvTV4nAzvVb/f22d6W7Md9hS8Jm7HKVelzNU8NgtiCztALlBrPjjPKtEI8MW8+ePXH06NEa1//444+Ii/MsYwSlhVG9z+Yu44wkIgFYxtqXxHEELMugS4hvIwps4VTLoOKyhm3njmRBPHIRtig3tg27/kMZ9BgZY524srkif2tEYuv7I8ChlY7rEjche8gKqGWRyO7RgONGm4jbReXQGc2oMJihkIoqg3m4miNUWwkeGbZ58+Zhx44deO+99/ipZDiOw61bt/DUU0/hjz/+wPz58xtUKKWZSNzkGCVZyzij7mEKRAbKoJCKIBGxEIsE6NnOD7OGdmoCoS2UahlU3Aqzb4EuL4+o1n/InNuMuduTsfbADXy4r5HG2nnCqDcdU8pVY798Ijb1/wH75RObUFTD0CXEFyIBCx+JELcKy6HWmWGykNojVFsBHhm2adOmYdmyZXjrrbf4manHjx+PmJgY7NixAytXrsTkyZMbUielObFPxFrLOCO13oyBnYIxrFsbtPOXQSpk65c9vxVSY1+Y/cwAbo798w6q3JFTyzagQG1AhcGC6wUtqLtiwGxrSrmgztWykVhpmW5e95g1tBN6hvshIlBunVwXACGkZUaoNiB1no/Nnjt37mDnzp24desWOI5Dly5dMGXKFHTu3LkhNbZYGnw+Ni/Hfq61L4+lQ6k1wkcixLRBUfyYm3sd2z1ymtKl+vxktc1D500kzQa5nAQG1nn7hst+gEZvRpcQH0zq2772qW0oDUJqthK7U3KRnFmKjGItuoX6Ynzvdl75Tbpb5tZr1GGHDh2oy5HCY0vDs2LPFeiMZmgNZo8CIVozfKqi6gyY7TwnWmsgcROYyhRcLIBPul7ABu3IyhRtjZ9aiWJ955Izy6DR+0EiFKB3e/9W/03Wezh9eXk5ysrKnMayAVbDR7k3MXMEfnLrfGe04HIXBlXzDLcmbNcF9Li0GlznkbhdVI4Ctb7V9/W0FOKjAnE6rQRRwT4ID5C1+m/Sow4QvV6PhQsXom3btvD390fHjh3RqVMnpx/l3mRS33Br9gxCXE7Rcq9y18CR6lPgtBZ6P8qnc5JAjz/SipFeXIFcpQ77rxS0rAjJVkpsRADG9AyFUmtsvBkgWhAeXeHcuXOxbds2TJ48GcOGDUNgYOtu1lLqRmxEAAZ1DsaNgnLPp5Rvhdw1s3niJgCtJCLSnsRNDjMCCFkW5XoTclU6SEUC7E7JbfUtiJaAWm9GTJhfg08N1RLxyLD98MMPeOaZZ/DFF42Ya4/i1UzqG96o09B4I409NU9LhrBiMJwRHCtGRKAMd0oJ9CYL0oor4CMRNLe8e4J76f3zyLAxDIP+/fs3tBZKK6LGIIl7mHv5npyKfh09Mr7CqdC/oYvMF6UVRmj0ZogEDEorWmYextbGvfT+edTH9vDDD9Mkxw1Ec87BRqE0Fb5D5+DHYbtxvu0UlGlN0Js4KCQCcJx15uq1+1vQgO1Wxr1Yxnhk2N5++22kpaVhzpw5SE5ORlFREUpLS51+lLvT4pLXUiiNgP1kmNGhvujd3g9tFFKAYVCuN2Hz8XSs2HPlnip8m4p7sYzxyBXZrVs3AMCFCxewaVPNnd0WSyNP8+7lpGYrkau0TsT5QI+2zayGQml8bO6wnclZeO/X62BAYLAAZmLB0RtF90QoelNzL/Wt2fDIsL3zzjsup6uh1I3kzDLIxUIEyEX0Y6bcU+y/UgARy4AjgIhlAAZQ6033VOHbVNxLfWs2PDJsS5YsaWAZ9ybxUYHYnZILrdHMT+1OodwLjOkZiowSLQJ8RLhVUA4LIRCwtLJMaRgaJEOtSqWibkcPiI0IQHiADHKx8J7yf1Moj8ZH4v3EWEQGyuEvF4MBg2KN8d6dlJbSoHhs2M6dO4fx48dDLpcjODgYR44cAQAUFxfj4YcfxuHDhxtKY6vGmzOHUyj1ITYiAHNHdUWPdgqwDCARsa1+njBK0+CRYTt58iSGDh2KmzdvYtq0aeC4qinV27RpA5VK5fHgbYPBgAULFiA8PBwymQwJCQnYv3//Xffr2LEjGIZx+bMFu9ioabt3333XI831oUVOEEmhNBGxEQEYGdMW/ToEQiIUIKFTUHNLorQCPOpje/PNN9GjRw+cOnUKGo0GGzdudFg/atQobNu2zSNBM2fORFJSEubNm4du3bph69atmDBhAg4dOoShQ4fWuN/atWtRXl7usCwzMxNvvfUWxo4d67T9mDFjMH36dIdlTT3rd41TmFAo9xC2BL2dureFv1zc3HIcoN+od+KRYTt79ixWrVoFiUTiZEwAoH379sjPz6/zcc+cOYMdO3Zg9erVeO211wAA06dPR+/evfHGG2/g5MmTNe7ramLT5cuXAwCefPJJp3XR0dGYNm1anTU2JHfNHUih3CNEBskBoMW55Ok36p145IoUiUQO7sfq5OTkwNfXt87HTUpKgkAgwJw5c/hlUqkUs2fPxh9//IGsrKw6He/bb79Fp06dcP/997tcr9PpoNc3n08/PioQWqMZuUodHZhKuWexDXtpiWPYaB+4d+KRYRs0aBCSkpJcrquoqMCWLVswYsSIOh/3woULiI6OdpoZdeDAgQCAixcv1ulYV69exd///neX67du3QofHx/IZDL07NkT33777V2PaTAYoFarHX71gUZFUigt23jQPnDvxCNX5NKlSzFixAhMnDgRTzzxBAAgJSUFaWlp+OCDD1BUVIS33367zsfNy8tDu3btnJbbluXm5rp9rO3btwNw7Ya8//778dhjj6FTp07Izc3FZ599hieffBIqlQovvPBCjcdctWoVli5d6rYGd7gXswJQKPbciwOIKY0LQ1xNfe0GBw8exAsvvICbN286LO/SpQs2btzoUYutS5cuiImJwd69ex2Wp6WloUuXLlizZg3mzZt31+NwHIcOHTqgbdu2OH/+/F23NxqNiI+PR3Z2NnJzcyGTyVxuZzAYYDBUTZypVqsRGRkJlUrl1MqkUCiO0EAMSn1Rq9Xw9/e/a5nr8VSqo0ePxvXr13Hx4kXcvHkTHMehS5cuiI+P9zjdlkwmczAcNmz9YDUZnOocOXIEOTk5mD9/vlvbi8VivPTSS3j++eeRnJxcY/SlRCKBRNJwE2fSD51yL0EDMShNRb3nCO/Xrx/69evXAFKsLsecnByn5Xl5eQCA8PBwt46zfft2sCzLu0ndITIyEgCadFYC+qFT7iWo253SVDRISq2Gol+/frhx44ZTUMbp06f59XfDYDBg586dGDlypNuGELC6OwEgJCTEfcH1pCV3mlMoFIq30qIMW2JiIiwWCzZs2MAvMxgM2LJlCxISEvhW1Z07d3Dt2jWXx9i7dy+USqXLoBEAKCoqclqm0Wiwdu1atGnTBvHx8Q1wJe5BI64o9xL34rxglOah3q7IhiQhIQFTp07FwoULUVhYiK5du2Lbtm3IyMhwmPdt+vTpOHLkCFzFvWzfvh0SiQSPPvqoy3N89tln+PHHHzFp0iR06NABeXl52Lx5M+7cuYOvv/4aYnHLynxAobQW/KRCnE4rwZieoc0thdLKaVGGDQC++uorvP322/j6669RVlaG2NhY/PLLLxg+fPhd91Wr1dizZw8mTpwIf39/l9sMGTIEJ0+exMaNG1FSUgIfHx8MHDgQmzdvxujRoxv6cmqFBo9Q7iXUejNiwvyg1pubWwqlleNxuD/F/dDTmthyIh1KrQkBchGeHtKpERRSKC0HWpGj1JdGD/en1B8aJUa5l6ADsSlNhVuGjWVZj8am0clHa4d+6BQKhdLwuGXY3nnnHSfDtmvXLvz5558YN24cYmJiAADXrl3Dvn370Lt3b5fZ9ikUCoVCaWzcMmxLlixx+HvDhg0oLCzE5cuXeaNm4+rVqxg9enSdxpBRKBQKhdJQeDSObfXq1XjppZecjBoA9OjRAy+99BLef//9eoujUCgUCqWueGTYsrOzIRKJalwvEomQnZ3tsSgKhUKhUDzFI8PWu3dvrF+/3mVex+zsbKxfvx59+vSptzgKhUKhUOqKR+H+a9aswbhx4xAdHY1HHnkEXbt2BQDcvHkTP/74Iwgh+OabbxpUKIVCoVAo7uCRYRs6dChOnz6Nt99+G7t27YJOpwNgnVZm3LhxWLp0KW2xUSgUCqVZqHfmEY7j+MTCISEhYNkWlVe5Ualv5hEKhUKhuE+TZR5hWRZSqRS+vr73lFGjUCgUSsvEY0t07tw5jB8/HnK5HMHBwThy5AgAoLi4GA8//DAOHz7cUBopFAqFQnEbjwzbyZMnMXToUNy8eRPTpk0Dx3H8ujZt2kClUuGLL75oMJEUCoVCobiLR4btzTffRI8ePXDlyhWsXLnSaf2oUaP4Wa8pFAqFQmlKPDJsZ8+exdNPPw2JROIyOXL79u2Rn59fb3EUCoVCodQVjwybSCRycD9WJycnB76+vh6LolAo3k1qthJbTqQjNVvZ3FIo9yAeGbZBgwYhKSnJ5bqKigps2bIFI0aMqJcwCoXivSRnlkGpNSE5s6y5pVDuQTwybEuXLsW5c+cwceJE/O9//wMApKSkYOPGjYiPj0dRURHefvvtBhVKoVC8h/ioQATIRXQSXUqz4PEA7YMHD+KFF17AzZs3HZZ36dIFGzduvCdabHSANoVCoTQdjT5Ae/To0bh+/TouXryImzdvguM4dOnSBfHx8R7Ntk2hUCgUSkNQ78wj/fr1Q79+/RpACoVCoVAo9cejPjaWZdGuXTscPXrU5frt27dDIBDUSxiFQqFQKJ7gcUotvV6PBx98EB9//HFD6qFQKBQKpV54bNjWrl2LZ599FvPnz8dTTz0FvV7fkLooFAqFQvEIjw2bSCTCZ599hq1bt+KHH37AkCFDcOfOnYbURqFQKBRKnan3PDPTp0/HiRMnoFQqER8fj99//70hdFEoFAqF4hH1jooErJGRycnJ+Pvf/47x48dj2LBhDXFYCoVCoVDqTIPNDBoQEIA9e/bgzTff5Odmo1AoFAqlqfHIsKWnp2Py5MlOyxmGwdKlS5GSkoKDBw96JMhgMGDBggUIDw+HTCZDQkIC9u/ff9f9lixZAoZhnH5SqdTl9ps2bUKPHj0glUrRrVs3rFu3ziO9FAqFQmlZeOSKjIqKqnV97969PRIDADNnzkRSUhLmzZuHbt26YevWrZgwYQIOHTqEoUOH3nX/zz//3GFmAVfj6b744gs8//zzePTRR/GPf/wDx44dw8svvwytVosFCxZ4rJ1CoVAozY9buSKXLVsGhmGwaNEisCyLZcuW3f3ADFPnRMhnzpxBQkICVq9ejddeew2Adbxc79690bZtW5w8ebLGfZcsWYKlS5eiqKgIbdq0qXE7nU6HyMhIDBo0CL/88gu/fNq0afjxxx+RlZWFwED3ErfSXJEUCoXSdLhd5hI3YBiGsCxLDAYD//fdfizLunNoB15//XUiEAiISqVyWL5y5UoCgNy5c6fGfRcvXkwAkMLCQqJSqQjHcS6327NnDwFA9uzZ47D85MmTBAD5+uuv3darUqkIACe9FAqFQml43C1z3epj4zgOFosFYrGY//tuP4vFUmdrfOHCBURHRztZ4oEDBwIALl68eNdjdO7cGf7+/lAoFJg2bRoKCgqczgEA9913n8Py+Ph4sCzLr6dQKBSKd9Ig4f4NRV5eHtq1a+e03LYsNze3xn0DAwPx0ksvYfDgwZBIJDh27Bg+++wznDlzBufOneONZV5eHgQCAdq2beuwv1gsRnBwcK3nMBgMMBgM/N9qtbpO10ehUCiUxqdFGTadTgeJROK03BbZqNPpatz3lVdecfj70UcfxcCBA/Hkk09i/fr1+Oc//8kfw9bydHWe2s6xatUqLF269K7XQaFQKJTmwy3D1qlTpzrPscYwDG7fvl2nfWQymUOLyIYtD6VMJqvT8f7+97/j1VdfxYEDB3jDJpPJYDQaXW6v1+trPcfChQvxj3/8g/9brVYjMjKyTpooFAqF0ri4ZdhGjBjRJJOHtmvXDjk5OU7L8/LyAADh4eF1PmZkZCRKS0sdzmGxWFBYWOjgjjQajSgpKan1HBKJxGWLkkKhUCgtB7cM29atWxtZhpV+/frh0KFDUKvVDgEkp0+f5tfXBUIIMjIyEBcX53AOADh37hwmTJjALz937hw4jqOTplIoFIqX02AptRqCxMREWCwWbNiwgV9mMBiwZcsWJCQk8G6/O3fu4Nq1aw77FhUVOR3v888/R1FREcaPH88vGz16NIKCgvD55587bSuXyzFx4sSGvCQKhUKhNDH1Ch4xmUy4du0aVCoVOI5zWj98+PA6HS8hIQFTp07FwoULUVhYiK5du2Lbtm3IyMjApk2b+O2mT5+OI0eOgNiNLY+KisLjjz+OPn36QCqV4vjx49ixYwf69euH5557jt9OJpPhX//6F1588UVMnToV48aNw7Fjx/DNN99gxYoVCAoK8uBOUCiUlk5qthLJmWWIjwpEbERAc8uhNCaeDJKzWCzkjTfeIL6+voRl2Rp/nqDT6chrr71GwsLCiEQiIQMGDCC//vqrwzYjRowg1aU/88wzpGfPnkShUBCRSES6du1KFixYQNRqtcvzbNiwgcTExBCxWEy6dOlC1qxZU+Og7pqgA7QpFO9h8/E08tG+62Tz8bTmluIxKVllZPPxNJKSVdbcUpoFd8tct1JqVWf58uV455138Nxzz2Ho0KF46qmn8N577yEgIADr168HwzB4//338eCDDza4IW5J0JRaFIr30BpabFtOpEOpNSFALsLTQzo1t5wmx90y16M+tq1bt+Kxxx7D559/zvdfxcfH49lnn8Xp06fBMIzH2f0pFAqlMYiNCMDTQzp5rVEDgPioQATIRYiPci+f7b2KR4YtOzsbo0ePBgA+/N021kwsFmPatGn4+uuvG0gihUKhUIDWYZybAo8MW3BwMMrLywEAvr6+8PPzQ1pamsM2ZWVl9VdHoVAoFEod8SgqMi4uDmfPnuX/HjVqFNauXYu4uDhwHIdPPvkEffv2bTCRFAqFQqG4i0cttjlz5jgkBF6xYgWUSiWGDx+OESNGQK1W48MPP2xQoRQKhUKhuINHUZGuUKlUOHz4MAQCAe6///57YjwYjYqkUCiUpsPdMrfBsvv7+/vj4YcfbqjDUSgUCoXiEfXOPJKTk4OysjK4avj179+/PoenUCgUCqXOeGTYlEolXnvtNWzfvt3lFDCEEDAM49Es2hQKhUKh1AePDNvMmTOxe/du/O1vf0NCQgL8/f0bWheFQqFQKB7hkWHbt28fXn75ZaxZs6ah9VAoFAqFUi88HqDdtWvXhtZCoVAoFEq98Xgc244dO1xOVUOhUCgUSnPikSvy7bffhsFgwH333YennnoKEREREAgETttNmTKl3gIpFAqFQqkLHhm2nJwcHDx4EBcvXsTFixddbkOjIikUCoXSHHhk2GbNmoXz589j4cKFNCqSQqFQKC0Kjwzb8ePHsWDBAixdurSh9VAoFAqFUi88Ch4JCwu7J3JBUigUCsX78Miwvfrqq9i4cSM/JxuFQqFQKC0Fj1yRer0eIpEIXbt2xWOPPYbIyEinqEiGYTB//vwGEUmhUCjNRWq2EsmZZYiPCqQzV3sJHk1bw7J3b+jdC1GRdNoaCqX1s+VEOpRaEwLkIjw9pFNzy7mnadRpa9LT0z0WRqFQKN5EfFQg32KjeAd1Nmw6nQ4ff/wxRo0ahUmTJjWGJgqFQmkxxEYEUBekl1Hn4BGZTIYvvvgCBQUFjaGHQqFQKJR64VFUZHx8PC5fvtzQWigUCoVCqTceGba1a9dix44d2LhxI8xmc0NrolAoFArFYzyKioyNjUVxcTEKCgogkUjQvn17yGQyxwMzDFJSUhpMaEuERkVSKBRK09GoUZFBQUEIDg5GTEyMxwIpFAqFQmkMPDJshw8fbmAZFAqFQqE0DB71sTUmBoMBCxYsQHh4OGQyGRISErB///677vfDDz/g8ccfR+fOnSGXyxETE4NXX30VSqXSaduOHTuCYRin3/PPP98IV0ShUCiUpsSjFhsAWCwWfPPNN9izZw8yMzMBAFFRUfjrX/+KJ5980uXEo+4wc+ZMJCUlYd68eejWrRu2bt2KCRMm4NChQxg6dGiN+82ZMwfh4eGYNm0aOnTogEuXLuHTTz/F3r17cf78eac+wH79+uHVV191WBYdHe2RZgqFQqG0IIgHKJVKkpCQQFiWJf7+/iQuLo7ExcWRgIAAwrIsGTRoEFGpVHU+7unTpwkAsnr1an6ZTqcjXbp0IYMHD65130OHDjkt27ZtGwFAvvzyS4flUVFRZOLEiXXWVx2VSkUAeHStFAqFQqkb7pa5HrkiFy1ahOTkZKxbtw5FRUU4f/48zp8/j8LCQnz66ac4d+4cFi1aVOfjJiUlQSAQYM6cOfwyqVSK2bNn448//kBWVlaN+44cOdJp2SOPPAIAuHr1qst9jEYjKioq6qyTQqFQKC0Xjwzbrl27MHfuXMydOxcikYhfLhKJ8MILL+CFF17Azp0763zcCxcuIDo62imMc+DAgQCAixcv1ul4+fn5AIA2bdo4rTt48CDkcjl8fX3RsWNHfPzxx3c9nsFggFqtdvhRKBQKpWXhUR9bSUlJraH+3bt3R2lpaZ2Pm5eXh3bt2jktty3Lzc2t0/Hee+89CAQCJCYmOiyPjY3F0KFDERMTg5KSEmzduhXz5s1Dbm4u3nvvvRqPt2rVKjprOIVCobRwPGqxde3aFT///HON63/++Wd06dKlzsfV6XSQSCROy6VSKb/eXb799lts2rQJr776Krp16+ak74033sDDDz+MWbNm4ciRIxg3bhw++ugjZGdn13jMhQsXQqVS8b/aXKMUCoVCaR48Mmxz587Fvn37MGHCBOzbtw8ZGRnIyMjAb7/9hokTJ2L//v146aWX6nxcmUwGg8HgtFyv1/Pr3eHYsWOYPXs2xo0bhxUrVtx1e9ukqGazudYxehKJBH5+fg4/CoVCobQsPHJFzp07F4WFhXj33Xfx22+/OawTiUR455138MILL9T5uO3atUNOTo7T8ry8PABAeHj4XY+RkpKChx56CL1790ZSUhKEQvcuMTIyEgA8cqFSKBQKpeXg8Ti2JUuW4KWXXsKBAwccxrE9+OCDLoM13KFfv344dOgQ1Gq1Q2vo9OnT/PrauH37NsaPH4+2bdti79698PX1dfvcaWlpAICQkJC6C6dQKBRKi8GjJMiNxenTpzFo0CCsXr0ar732GgBrJGLv3r0RHByMU6dOAQDu3LkDrVaL7t278/vm5+djyJAh0Ov1OHHiBDp27OjyHKWlpfD393cYQG4ymTBq1CicPXsWmZmZCAsLc0svTYJMoVAoTUejJkG2odFokJmZibKyMriyj8OHD6/T8RISEjB16lQsXLgQhYWF6Nq1K7Zt24aMjAxs2rSJ32769Ok4cuSIwznHjx+PtLQ0vPHGGzh+/DiOHz/OrwsNDcWYMWMAWANHli9fjsTERHTq1AmlpaX49ttvcfnyZaxcudJto0ahUCiUlonH4f4vvfQSdu7cCYvFAgAghIBhGIf/29bVha+++gpvv/02vv76a5SVlSE2Nha//PLLXY2kbYqc999/32ndiBEjeMPWp08f9OzZE9988w2KioogFovRr18/fP/995g6dWqd9VIoFAqlZeGRK3LKlCnYvXs3Xn75ZQwbNgyBgYEutxsxYkS9BbZkqCuSQqFQmo5GdUXu27cP8+fPd9k6olAoFAqlOfFoHJtcLq8xOINCoVAolObEI8M2bdo07Nq1q6G1UCgUCoVSbzxyRSYmJuLIkSMYP3485syZg8jISJfzr/Xv37/eAikUCoVCqQseBY+wbFVDzxYJaU99oiK9CRo8QqFQKE1HowaPbNmyxWNhFAqFQqE0Jh4ZthkzZjS0DgqFQqFQGgSPgkfsycvLQ0pKCp2JmkKhUCgtAo8N208//YTu3bsjIiIC/fv35xMVFxcXIy4ujkZNUigUCqVZ8Miw7d69G1OmTEGbNm2wePFih5yNbdq0Qfv27bF169aG0kihUCgUitt4ZNiWLVuG4cOH4/jx43jxxRed1g8ePBgXLlyotzgKhUKhUOqKR4bt8uXLeOyxx2pcHxoaisLCQo9FUSgUCoXiKR6n1KotWCQtLQ3BwcEei6JQKBQKxVM8MmyjRo3Ctm3bYDabndbl5+fjyy+/xNixY+stjkKhUCjuk5qtxJYT6UjNVja3lGbFI8O2YsUKZGdnY8CAAfjiiy/AMAx+++03vPXWW+jTpw8IIVi8eHFDa6VQKBRKLSRnlkGpNSE5s6y5pTQrHqXUAoA///wTr7zyCg4dOuQQFTly5Eh89tln6NGjR4OJbKnQlFoUCqUlkZqtRHJmGeKjAhEbEdDcchocd8tcjw2bjbKyMty6dQscx6Fz584ICQkB4DijdmuFGjYKhUJpOtwtc+udeSQwMBADBgxAQkICQkJCYDQasWHDBsTExNT30BQKhdLs0H4r76NOuSKNRiN+/vln3L59G4GBgfjrX/+K8PBwAIBWq8Wnn36KtWvXIj8/H126dGkUwRQKhdKU2PdbtUb3XmvEbcOWm5uLkSNH4vbt23yfmkwmw88//wyxWIy///3vyMnJwcCBA7Fu3TpMmTKl0URTKBRKUxEfFcj3W1G8A7cN26JFi5Ceno433ngDw4YNQ3p6OpYtW4Y5c+aguLgYvXr1wjfffIMRI0Y0pl4KhUJpUmIjAmhLzctw27Dt378fTz/9NFatWsUvCwsLw9SpUzFx4kT89NNPDhOQUigUCoXSHLhtiQoKCjBo0CCHZba/Z82aRY0ahUKhUFoEblsji8UCqVTqsMz2t7+/f8OqolAoXgGNGKS0ROoUFZmRkYHz58/zf6tUKgDAzZs3ERAQ4LR9//7966eOQqG0aGjEIKUl4vYAbZZlXQ64djUQ27bMYrE0jMoWCh2gTbnXae2ZLigtC3fLXLdbbFu2bGkQYRQKpfVAIwYpLRG3DduMGTMaUweFQqFQKA1CiwtlNBgMWLBgAcLDwyGTyZCQkID9+/e7tW9OTg4ee+wxBAQEwM/PDw8//DDS0tJcbrtp0yb06NEDUqkU3bp1w7p16xryMigUCoXSTLQ4wzZz5kx89NFHePLJJ/Hxxx9DIBBgwoQJOH78eK37lZeXY9SoUThy5AjefPNNLF26FBcuXMCIESNQUlLisO0XX3yBZ555Br169cK6deswePBgvPzyy3jvvfca89IoFAqF0hSQFsTp06cJALJ69Wp+mU6nI126dCGDBw+udd/33nuPACBnzpzhl129epUIBAKycOFCfplWqyXBwcFk4sSJDvs/+eSTxMfHh5SWlrqtV6VSEQBEpVK5vQ+FQvEeUrLKyPJf/iTLf/mTpGSVNbecex53y9wW1WJLSkqCQCDAnDlz+GVSqRSzZ8/GH3/8gaysrFr3HTBgAAYMGMAv6969Ox544AF8//33/LJDhw6hpKQEc+fOddj/xRdfREVFBfbs2dOAV0ShULyZ5Mwy3Cgox40CzT0/eac30aIM24ULFxAdHe0Uxjlw4EAAwMWLF13ux3EcUlNTcd999zmtGzhwIG7fvg2NRsOfA4DTtvHx8WBZll/vCoPBALVa7fCjUCitl/ioQESH+iI6VEGTIHsRdRqg3djk5eWhXbt2Tstty3Jzc13uV1paCoPBcNd9Y2JikJeXB4FAgLZt2zpsJxaLERwcXOM5AGDVqlVYunSp29dDoVC8GzqcwTtpUS02nU4HiUTitNyWukun09W4HwC39tXpdBCLxS6PI5VKazwHACxcuBAqlYr/1eYapVAoFErz0KJabDKZDAaDwWm5Xq/n19e0HwC39pXJZDAajS6Po9frazwHYDWcrownhUKhUFoOLarF1q5dO+Tl5Tktty2zzdZdnaCgIEgkErf2bdeuHSwWCwoLCx22MxqNKCkpqfEcFAqFQvEOWpRh69evH27cuOEUlHH69Gl+vStYlkWfPn1w7tw5p3WnT59G586doVAoHI5Rfdtz586B47gaz0GhUCgU76BFGbbExERYLBZs2LCBX2YwGLBlyxYkJCQgMjISAHDnzh1cu3bNad+zZ886GKzr16/j4MGDmDp1Kr9s9OjRCAoKwueff+6w/+effw65XI6JEyc2xqVRKBQKpYlwO7t/U/HYY49h165dmD9/Prp27Ypt27bhzJkz+P333zF8+HAAwMiRI3HkyBHYS9doNIiLi4NGo8Frr70GkUiEjz76CBaLBRcvXkRISAi/7fr16/Hiiy8iMTER48aNw7Fjx/DVV19hxYoVePPNN93WSrP7UygUStPR4Nn9m4qvvvoKb7/9Nr7++muUlZUhNjYWv/zyC2/UakKhUODw4cOYP38+li9fDo7jMHLkSKxZs8bBqAHA3LlzIRKJ8OGHH+Lnn39GZGQk1qxZg1deeaVOWm2GlY5no1AolMbHVtberT3W4lps3kR2djbvHqVQKBRK05CVlYWIiIga11PDVg84jkNubi4UCoXLSVgBaw0jMjISWVlZXueu9Fbt3qoboNqbA2/VDXivdk91E0Kg0WgQHh4Olq05RKTFuSK9CZZla6012OPn5+dVL5493qrdW3UDVHtz4K26Ae/V7oluf3//u27ToqIiKRQKhUKpL9SwUSgUCqVVQQ1bIyORSLB48WKvTMXlrdq9VTdAtTcH3qob8F7tja2bBo9QKBQKpVVBW2wUCoVCaVVQw0ahUCiUVgU1bBQKhUJpVVDDRqFQKJRWBTVsFAqFQmlQmjsmkRo2itfQ3B8LhdIUqFSq5pbgMd999x0A1JhisKmghq2OXLhwAXfu3HF4+byhwNVqtc0twWPS0tKg1Wqh1+ubW0qdSElJwc2bN5Gdnc0v84Z3BQB++uknzJ07F2lpaQCseVG9hf/85z9QKBQ4ceJEc0upEz/88APGjh2LNWvWICMjo7nl1IkdO3agS5cueOKJJ3D8+PHmlkMNm7tcvXoVQ4cOxQMPPIC+ffti4MCB2LlzJ8xmMxiGabEF1vXr1xEfH49nnnmmuaXUmdTUVEycOBGTJk1Cp06dMHLkSJw4caLF3msbqampGDNmDP76178iPj4effv2xSeffMK/Ky2d/fv345FHHsHXX3+NX375BQBqTTjbUrhw4QISEhIwa9YsTJw40WtyJ+bm5mLixImYPn06xGIx5HI55HJ5c8tyC9s9nzFjBhQKBaRSKQwGQ3PLAgjlrhQUFJC4uDhy//33k82bN5PNmzeTQYMGkYCAALJ48WJCCCEcxzWvyGpwHEeSkpJIdHQ0YRiGMAxDDh8+3Nyy3MJsNpNPPvmEhISEkBEjRpB33nmHzJ07l0RGRpLu3bu32OswGo1kxYoVJCAggIwYMYKsW7eO/Oc//yEjR44kfn5+5IcffmhuibVie4eTk5NJcHAwkclkJCEhgVy8eJEQQojFYmlOeTWi1WrJ008/TRiGISNGjCA//fQTKSgoaG5ZbrN48WLSo0cPsn37dnLnzp3mluMWKpWKTJ8+nTAMQ0aOHEl++uknsmfPHiKVSskHH3xACLF+x80FNWxusGPHDiIUCklSUhK/LDs7mzz++OOEYRhy4MCBZlTnmtu3b5PevXuT4OBgsnz5ctKzZ08yaNAgYjKZmlvaXfn1119J586dyaxZs8i1a9f45SdOnCAMw5AFCxa0yOvYs2cP6d+/P5k3bx65ceMG/2HfvHmTMAxD3n///RZXAXJFUlISGTt2LPn3v/9NGIYhb775Jn8tLU2/2WwmK1asIAzDkGeffZYUFRXV+G60NO2EEHLnzh0SGhpKXn75Zafl9rQk7RUVFaRbt26kc+fO5PPPPyeZmZmEEELS0tJIYGAgmTJlSrNXgqhhc4P33nuP+Pv78w/LaDQSQqw124EDB5LevXu3uBpiZmYmefPNN/na9meffUYYhiEbN25sZmV356OPPiI9evQghYWF/DKDwUAIIWTQoEFkzJgxhJCW9bETQsjx48fJhx9+6KCbEEJ27dpF2rZtS7777jtCSMvTbcOm6/Tp08Tf358QQsiDDz5I2rVrR/bv3++wTUvi3LlzZMiQIaR79+78sp9++onMmDGDvPHGG2Tz5s38+9PSOHr0KJHL5eTGjRuEEEK++uor0rNnT9KzZ08yefJk8u233zazQkdsZeDJkyfJ5cuX+bLQxoABA8jIkSOJXq9v1neFGjY7bA+t+gNZs2YNUSgU5NChQ4QQ4lB7/e6774hEIiErV650uW9TUJNuvV7P///69etk7NixJCIighQXFzepvtqw126v//r16w7rCbHe95EjR5KhQ4cSnU7XtEKrUdM9r86xY8dI7969iZ+fH1myZAm5dOkSKSsrczhGU3M37UlJSaRr166EEEIuXLhAGIYhM2bMIKWlpbXu1xTUpN3Wunz11VfJ2LFjCcMwpGvXrkShUBCGYciUKVPI5cuXHY7REnSfO3eOCIVCsmvXLrJ582bCsixJTEwkM2bMIG3btiUMw5AtW7Y0uV573HnXOY4jFouFvPjii8Tf359/x5vrXaGGjVT1jVRvzdgeyv79+4lEIiFLlizhl9kedn5+PnnsscdISEhIk9cKa9JdE9999x2RyWTkjTfeaGRld6eu2m2GLy4ujjz++OP8sqbGHd22d2PBggWEYRgyatQoMmPGDDJ79mwSEBBA/va3vzWVXAfupt12P8+cOUMUCgXJzc0lhBAye/ZsIpFI+NZDRUVF0wi2427faGZmJklMTCQMw5DRo0eTX3/9lWRmZpKcnBzyr3/9i7AsS6ZOndpidNs4d+4cadOmDZk2bRrp27cvefvtt4lGoyGEEJKamkrGjRtHgoODydWrV5tSNiGk7t8oIYS8/fbbhGEY8vPPPzeisrtzzxu2o0ePkl69ehGGYcjYsWPJlStXCCHOhWb//v1JXFwcuXTpktP67du3E6FQSD7//HOX+zanbvtlhYWFZNasWUQqlfK11+YwDnXRbk9WVhbx8fEhq1atIoQ0fee0u7ptf+/atYt89913pLi4mF+2cOFCwrIsWb16NSGk6VoPdbnn33//PYmOjubd62q1msjlcjJq1Cjy9NNPk6eeeoo3ei1J+/bt28nMmTPJiRMnnNY9+eSTxN/fny9wW9I3OmTIEMKyLGnTpg05efKkw7p9+/aRoKAg8sorrxBCWub7Yq/r2LFjhGEY8v3339e6fWNzTxu2P/74g3Tv3p107NiRTJ06lTAMQ9577z2Hzmdb4fnTTz8RhmHI8uXLeTeYbd3169dJREQEmTNnTpO8eO7oronff/+dtG/fnjzyyCONrtMV9dF+9OhRwjAM+e2335pAqSN10V3bx3zz5k3StWtX0rdvXwdXcWPirnab7mPHjhG5XE6ysrL4dU888QQRCAREJBKRxYsXk/Ly8haj3aZbpVI59W/atjt16hRhGMbB69Lcum3lx6+//spHLttaZjbvT2FhIRk/fjyJjIxsce+LKy5fvkwCAwPJ//3f/xFCqGFrFq5cuUIkEgn573//SwghZNiwYaRbt27kxIkTLrefMGECCQ8PJ7t37yaEOLYYevXqRaZPn04IafyHWVfd9prKy8t5d8Hvv/9OCCHkyJEj5Keffmqx2m2sX7+eCIVC3lVjNpvJ7du3yblz5wghjau9ProJcaxpDx48mAwaNKjJCqrq2ocPH16r9h07dpCYmBiiVCrJoUOHyNChQ4lAICB+fn6ka9eu5NixY4SQpim0PL3v1bsMioqKSEBAQJO54euq+8knnyQMw5DnnnuOEEIcjEhiYiLp2bMnUalUjS+c1O9dLywsJFFRUeSBBx4garW6saXWyD1r2GxGyb6GZ2sRvPzyy/xLZF8gZWZmEl9fXzJo0CBy/vx5fvmpU6eIn58fWbp0aYvR7arQsV3LtWvXSP/+/UmfPn3I0qVLSWRkJAkODm70yM76aCeEkEmTJpH777+fEGJ1S37zzTckLi6O9O/fn5SUlLRI3dVb8L/99hsRiURk3rx5jabXnrpot+n//fffiVgsJn/961+JQCAgQ4YMIUePHiXff/89X/g2RX9yQ9739evXE4ZhyJdfftmIiq14UrZkZWURPz8/J4/En3/+Sbp06UKmTZvWJBWJhrjnU6ZMIb169SLl5eW0xdaY7Nixgzz33HPk3XffJUePHuWX299020OZMWMGCQgIID/++KPDMWwPfOvWraRDhw6kU6dO5JNPPiEbN24kkyZNIpGRkSQ1NbXF6XZFZmYmmTlzJu/+ePjhhx3cTi1NO8dxRKPRkHbt2pG//e1v5MCBA+Shhx4iDMOQ8ePHk+zs7Bap257c3Fyye/duMmLECNKzZ0++r7YhaSjtJ06cILGxsaRHjx7k008/JVlZWfz7P2TIEPLss882uGFrrPuen59Pdu3aRWJjY8mIESMaPCK4IcuWHTt2kHbt2pGgoCDy7LPPkpUrV5K//OUvJDAwsFHc741xzzmOI8uXLycMw/CRzc1h3Fq1YcvPzyfjxo0jPj4+pH///iQwMJBIJBKyePFiPhy1+sDT7Oxs4uvrS6ZMmcIX9haLxeHhHD58mAwZMoT4+/uT4OBgEhsbS44fP94idVfn2LFjZPz48YRlWRIXF+e2K625td+6dYvI5XLSv39/4uvrS2JiYnhXakvWffjwYfLss8+SxMREolAoSN++fcnZs2cbTHdDare5v4xGIzl69Ci5dOkSb8Bs+zX0MIvGvO/PP/88eeKJJ4ivry/p378/P6azpem2L1tOnDhBxo0bRwICAkjbtm1JXFycg9FpadpdsWbNGsIwjENCi6amVRu2bdu2kaCgILJ9+3aSm5tLSkpKyMyZM4lCoSBz58512t72EFesWEFYliUbNmxweOns/6/T6UhBQUGDF1KNodueAwcOELFYTD799NMG192Y2g8ePEgYhiFt27ZtFO2NpXv37t2ka9euZOTIkWTz5s0NrruxtDdVLbux7ntSUhLx9fUlCQkJjeJ+bMyyxWAwkLKyMpKSktLguhtDuw2bocvLyyNbt25tFO3u0qoN24gRI8igQYMcllVUVJAZM2YQhmHInj17CCHONQ+j0Ui6dOlCEhIS+IwAt2/fdvA7N2b0Y2PqJqRxw+QbWrt9v98XX3zhlOnAG3Tfvn3ba96XW7duOb0vjUlj3veUlJRGe9e9tWxpbO0tJTNNqzRsFouF6PV6Mm7cODJkyBB+uc3VkpycTOLj40nnzp2dHkT18P4FCxaQLVu2kP79+5OXX365UQeneqvuxtbemNFVjam7sUPiG1O7Vqv1Wu2Ned/pN9o82uuK1xu2q1evkldeeYX83//9H1m0aBFfkyCEkMmTJ5OYmBi+o96+BrJhwwbCMAxZs2YNIcS5FWMymciAAQOIQCAgDMOQdu3akV9//fWe1+3N2r1VN9XePNq9Vbe3a28IvNawGQwG8tprrxGZTEbuu+8+0q1bN8IwDOncuTM//iIpKYkwDEM2b97MPzzbg8rIyCAPPPAA6dSpk1MH+fnz58miRYuIr68vUSgUZO3atfe8bm/W7q26qXb6vtxL2hsSrzRsGo2GvPnmm6Rz587kvffeI9evXycWi4UcOHCAhIeHk2HDhhGtVkvMZjPp27cvGT58OMnIyHA6zpIlS0hAQADvUybE+hBfeuklPvGrbTDwvazbm7V7q26qvXm0e6tub9fe0HilYUtPTyedOnUizz33HFEqlQ7rnnvuORISEsJno/j6668JwzDko48+4v3AtprIhQsXCMuyZNeuXYSQKl/zmTNn+NxoVLd3a/dW3VQ7fV/uJe0NjVcaNo7jyIYNGxyW2aLlvv/+eyIUCvmca0qlkkyZMoWEhYU5DS48c+YMYRiGbNu2jeq+C96q3Vt1E0K1E0Lfl7rgzdobGq80bIRU1SKqd26uXr2aCAQCh5mXs7KySGhoKOnVqxff0ZmTk0NeeuklEhUVRfLz86luN/BW7d6qmxCqnb4vdcObtTckXmvYqmPrBH3llVdIWFgYX1OxPeDffvuN9O/fnzAMQ/r160cGDx5MRCIRWbp0KTGbzc02/sJbdXuzdm/VTbXT9+Ve0l4fGEIIQSvivvvuQ8eOHZGUlASLxQKBQMCvKy4uxqZNm3D79m2o1Wq88sorGDx4cDOqrcJbdQPeq91bdQNUe3PgrboB79buEc1tWRuSwsJCIpPJ+EkcCbHWWGxT2rdUvFU3Id6r3Vt1E0K1NwfeqpsQ79buKWxzG9aG5PLly9Dr9RgwYAAAID8/H99++y3GjRuHoqKiZlZXM96qG/Be7d6qG6DamwNv1Q14t3ZPaRWGjVR6U8+ePQt/f3+Eh4fj8OHDmDt3LmbNmgVCCFiW5bdrKXirbsB7tXurboBqbw68VTfg3drrTdM1DhufKVOmkC5dupBnn32WKBQK0q1bN7Jv377mlnVXvFU3Id6r3Vt1E0K1NwfeqpsQ79buKa3GsOl0OtKvXz/CMAzx8/Pjc521dLxVNyHeq91bdRNCtTcH3qqbEO/WXh9aVVTkggULwDAMli5dColE0txy3MZbdQPeq91bdQNUe3PgrboB79buKa3KsHEcB5b1vm5Db9UNeK92b9UNUO3NgbfqBrxbu6e0KsNGoVAoFMq9ZcYpFAqF0uqhho1CoVAorQpq2CgUCoXSqqCGjUKhUCitCmrYKBQKhdKqoIaNQqFQKK0KatgoFAqF0qqgho1CoVAorQpq2CgUCoXSqqCGjUKhUCitCmrYKBQKhdKq+H8upIn8M5smtQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 450x300 with 1 Axes>"
       ]
@@ -538,27 +450,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:06:27.746429Z",
-     "iopub.status.busy": "2026-02-11T21:06:27.745431Z",
-     "iopub.status.idle": "2026-02-11T21:06:27.857048Z",
-     "shell.execute_reply": "2026-02-11T21:06:27.857048Z"
-    }
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\mspringe\\onedrive - nrel\\msp\\pvfleets\\repos\\public\\rdtools\\rdtools\\analysis_chains.py:1206: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n",
-      "  fig = plotting.soiling_rate_histogram(results_dict[\"calc_info\"], **kwargs)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 450x300 with 1 Axes>"
       ]
@@ -581,15 +478,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:06:27.860568Z",
-     "iopub.status.busy": "2026-02-11T21:06:27.860568Z",
-     "iopub.status.idle": "2026-02-11T21:06:27.876158Z",
-     "shell.execute_reply": "2026-02-11T21:06:27.876158Z"
-    }
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -619,69 +509,81 @@
        "      <th>soiling_rate_high</th>\n",
        "      <th>inferred_start_loss</th>\n",
        "      <th>inferred_end_loss</th>\n",
+       "      <th>inferred_recovery</th>\n",
+       "      <th>inferred_begin_shift</th>\n",
        "      <th>length</th>\n",
        "      <th>valid</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2010-03-21 00:00:00-07:00</td>\n",
-       "      <td>2010-06-11 00:00:00-07:00</td>\n",
-       "      <td>-0.001229</td>\n",
-       "      <td>-0.001328</td>\n",
-       "      <td>-0.001128</td>\n",
-       "      <td>1.038058</td>\n",
-       "      <td>0.937302</td>\n",
-       "      <td>82</td>\n",
+       "      <th>5</th>\n",
+       "      <td>2010-03-10 00:00:00-07:00</td>\n",
+       "      <td>2010-04-08 00:00:00-07:00</td>\n",
+       "      <td>-0.001577</td>\n",
+       "      <td>-0.003637</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.063788</td>\n",
+       "      <td>1.018062</td>\n",
+       "      <td>-0.018062</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>29</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2010-07-01 00:00:00-07:00</td>\n",
-       "      <td>2010-07-12 00:00:00-07:00</td>\n",
-       "      <td>-0.002299</td>\n",
-       "      <td>-0.005703</td>\n",
-       "      <td>-0.000207</td>\n",
-       "      <td>1.037928</td>\n",
-       "      <td>1.012636</td>\n",
-       "      <td>11</td>\n",
+       "      <th>6</th>\n",
+       "      <td>2010-04-09 00:00:00-07:00</td>\n",
+       "      <td>2010-06-11 00:00:00-07:00</td>\n",
+       "      <td>-0.000955</td>\n",
+       "      <td>-0.001237</td>\n",
+       "      <td>-0.000640</td>\n",
+       "      <td>1.024589</td>\n",
+       "      <td>0.964412</td>\n",
+       "      <td>0.035588</td>\n",
+       "      <td>0.006526</td>\n",
+       "      <td>63</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2010-10-20 00:00:00-07:00</td>\n",
-       "      <td>2011-01-29 00:00:00-07:00</td>\n",
-       "      <td>-0.001139</td>\n",
-       "      <td>-0.001224</td>\n",
-       "      <td>-0.001049</td>\n",
-       "      <td>1.049207</td>\n",
-       "      <td>0.934195</td>\n",
-       "      <td>101</td>\n",
+       "      <th>9</th>\n",
+       "      <td>2010-06-15 00:00:00-07:00</td>\n",
+       "      <td>2010-07-13 00:00:00-07:00</td>\n",
+       "      <td>-0.000594</td>\n",
+       "      <td>-0.001868</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.072710</td>\n",
+       "      <td>1.056087</td>\n",
+       "      <td>-0.056087</td>\n",
+       "      <td>0.108299</td>\n",
+       "      <td>28</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2011-02-11 00:00:00-07:00</td>\n",
-       "      <td>2011-05-09 00:00:00-07:00</td>\n",
-       "      <td>-0.001944</td>\n",
-       "      <td>-0.002031</td>\n",
-       "      <td>-0.001847</td>\n",
-       "      <td>1.010908</td>\n",
-       "      <td>0.841754</td>\n",
-       "      <td>87</td>\n",
+       "      <th>12</th>\n",
+       "      <td>2010-10-11 00:00:00-07:00</td>\n",
+       "      <td>2011-01-28 00:00:00-07:00</td>\n",
+       "      <td>-0.001143</td>\n",
+       "      <td>-0.001301</td>\n",
+       "      <td>-0.001000</td>\n",
+       "      <td>1.057288</td>\n",
+       "      <td>0.932740</td>\n",
+       "      <td>0.067260</td>\n",
+       "      <td>0.001202</td>\n",
+       "      <td>109</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>2011-06-09 00:00:00-07:00</td>\n",
-       "      <td>2011-08-03 00:00:00-07:00</td>\n",
-       "      <td>-0.001153</td>\n",
-       "      <td>-0.001266</td>\n",
-       "      <td>-0.001032</td>\n",
-       "      <td>0.974865</td>\n",
-       "      <td>0.911467</td>\n",
-       "      <td>55</td>\n",
+       "      <th>15</th>\n",
+       "      <td>2011-02-11 00:00:00-07:00</td>\n",
+       "      <td>2011-03-14 00:00:00-07:00</td>\n",
+       "      <td>-0.001913</td>\n",
+       "      <td>-0.002793</td>\n",
+       "      <td>-0.001307</td>\n",
+       "      <td>1.020735</td>\n",
+       "      <td>0.961439</td>\n",
+       "      <td>-0.000840</td>\n",
+       "      <td>0.087995</td>\n",
+       "      <td>31</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -690,28 +592,28 @@
       ],
       "text/plain": [
        "                       start                       end  soiling_rate  \\\n",
-       "7  2010-03-21 00:00:00-07:00 2010-06-11 00:00:00-07:00     -0.001229   \n",
-       "11 2010-07-01 00:00:00-07:00 2010-07-12 00:00:00-07:00     -0.002299   \n",
-       "16 2010-10-20 00:00:00-07:00 2011-01-29 00:00:00-07:00     -0.001139   \n",
-       "18 2011-02-11 00:00:00-07:00 2011-05-09 00:00:00-07:00     -0.001944   \n",
-       "23 2011-06-09 00:00:00-07:00 2011-08-03 00:00:00-07:00     -0.001153   \n",
+       "5  2010-03-10 00:00:00-07:00 2010-04-08 00:00:00-07:00     -0.001577   \n",
+       "6  2010-04-09 00:00:00-07:00 2010-06-11 00:00:00-07:00     -0.000955   \n",
+       "9  2010-06-15 00:00:00-07:00 2010-07-13 00:00:00-07:00     -0.000594   \n",
+       "12 2010-10-11 00:00:00-07:00 2011-01-28 00:00:00-07:00     -0.001143   \n",
+       "15 2011-02-11 00:00:00-07:00 2011-03-14 00:00:00-07:00     -0.001913   \n",
        "\n",
        "    soiling_rate_low  soiling_rate_high  inferred_start_loss  \\\n",
-       "7          -0.001328          -0.001128             1.038058   \n",
-       "11         -0.005703          -0.000207             1.037928   \n",
-       "16         -0.001224          -0.001049             1.049207   \n",
-       "18         -0.002031          -0.001847             1.010908   \n",
-       "23         -0.001266          -0.001032             0.974865   \n",
+       "5          -0.003637           0.000000             1.063788   \n",
+       "6          -0.001237          -0.000640             1.024589   \n",
+       "9          -0.001868           0.000000             1.072710   \n",
+       "12         -0.001301          -0.001000             1.057288   \n",
+       "15         -0.002793          -0.001307             1.020735   \n",
        "\n",
-       "    inferred_end_loss  length  valid  \n",
-       "7            0.937302      82   True  \n",
-       "11           1.012636      11   True  \n",
-       "16           0.934195     101   True  \n",
-       "18           0.841754      87   True  \n",
-       "23           0.911467      55   True  "
+       "    inferred_end_loss  inferred_recovery  inferred_begin_shift  length  valid  \n",
+       "5            1.018062          -0.018062                   NaN      29   True  \n",
+       "6            0.964412           0.035588              0.006526      63   True  \n",
+       "9            1.056087          -0.056087              0.108299      28   True  \n",
+       "12           0.932740           0.067260              0.001202     109   True  \n",
+       "15           0.961439          -0.000840              0.087995      31   True  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -731,15 +633,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:06:27.879397Z",
-     "iopub.status.busy": "2026-02-11T21:06:27.879397Z",
-     "iopub.status.idle": "2026-02-11T21:07:20.419721Z",
-     "shell.execute_reply": "2026-02-11T21:07:20.419721Z"
-    }
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Instantiate a new instance of TrendAnalysis\n",
@@ -760,15 +655,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:07:20.422736Z",
-     "iopub.status.busy": "2026-02-11T21:07:20.422736Z",
-     "iopub.status.idle": "2026-02-11T21:07:20.429051Z",
-     "shell.execute_reply": "2026-02-11T21:07:20.429051Z"
-    }
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -853,7 +741,7 @@
        "2010-02-25 14:20:00-07:00         True  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -865,15 +753,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:07:20.431495Z",
-     "iopub.status.busy": "2026-02-11T21:07:20.431495Z",
-     "iopub.status.idle": "2026-02-11T21:07:20.436814Z",
-     "shell.execute_reply": "2026-02-11T21:07:20.436579Z"
-    }
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -886,7 +767,7 @@
        "Freq: min, dtype: bool"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -898,45 +779,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:07:20.438850Z",
-     "iopub.status.busy": "2026-02-11T21:07:20.438850Z",
-     "iopub.status.idle": "2026-02-11T21:07:21.663431Z",
-     "shell.execute_reply": "2026-02-11T21:07:21.662914Z"
-    }
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
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f=a[c],h=l[f],p=e[f+\"axis\"].r2fraction(h);if(p<0||p>1){u=!0;break}}u?t.fullLayout._infolayer.select(\".annotation-\"+t.id+'[data-index=\"'+s+'\"]').remove():(l._pdata=i(t.glplot.cameraParams,[e.xaxis.r2l(l.x)*r[0],e.yaxis.r2l(l.y)*r[1],e.zaxis.r2l(l.z)*r[2]]),n(t.graphDiv,l,s,t.id,l._xa,l._ya))}}},56864:function(t,e,r){\"use strict\";var n=r(24040),i=r(3400);t.exports={moduleType:\"component\",name:\"annotations3d\",schema:{subplots:{scene:{annotations:r(45899)}}},layoutAttributes:r(45899),handleDefaults:r(52808),includeBasePlot:function(t,e){var r=n.subplotsRegistry.gl3d;if(r)for(var a=r.attrRegex,o=Object.keys(t),s=0;s<o.length;s++){var l=o[s];a.test(l)&&(t[l].annotations||[]).length&&(i.pushUnique(e._basePlotModules,r),i.pushUnique(e._subplots.gl3d,l))}},convert:r(42456),draw:r(71836)}},54976:function(t,e,r){\"use 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n=r(94724),i=r(25376),a=r(92880).extendFlat,o=r(67824).overrideAll;t.exports=o({orientation:{valType:\"enumerated\",values:[\"h\",\"v\"],dflt:\"v\"},thicknessmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"pixels\"},thickness:{valType:\"number\",min:0,dflt:30},lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\"},xref:{valType:\"enumerated\",dflt:\"paper\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"]},xpad:{valType:\"number\",min:0,dflt:10},y:{valType:\"number\"},yref:{valType:\"enumerated\",dflt:\"paper\",values:[\"container\",\"paper\"],editType:\"layoutstyle\"},yanchor:{valType:\"enumerated\",values:[\"top\",\"middle\",\"bottom\"]},ypad:{valType:\"number\",min:0,dflt:10},outlinecolor:n.linecolor,outlinewidth:n.linewidth,bordercolor:n.linecolor,borderwidth:{valType:\"number\",min:0,dflt:0},bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\"},tickmode:n.minor.tickmode,nticks:n.nticks,tick0:n.tick0,dtick:n.dtick,tickvals:n.tickvals,ticktext:n.ticktext,ticks:a({},n.ticks,{dflt:\"\"}),ticklabeloverflow:a({},n.ticklabeloverflow,{}),ticklabelposition:{valType:\"enumerated\",values:[\"outside\",\"inside\",\"outside 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strict\";t.exports={cn:{colorbar:\"colorbar\",cbbg:\"cbbg\",cbfill:\"cbfill\",cbfills:\"cbfills\",cbline:\"cbline\",cblines:\"cblines\",cbaxis:\"cbaxis\",cbtitleunshift:\"cbtitleunshift\",cbtitle:\"cbtitle\",cboutline:\"cboutline\",crisp:\"crisp\",jsPlaceholder:\"js-placeholder\"}}},64013:function(t,e,r){\"use strict\";var n=r(3400),i=r(31780),a=r(26332),o=r(25404),s=r(95936),l=r(42568),u=r(42996);t.exports=function(t,e,r){var c=i.newContainer(e,\"colorbar\"),f=t.colorbar||{};function h(t,e){return n.coerce(f,c,u,t,e)}var p=r.margin||{t:0,b:0,l:0,r:0},d=r.width-p.l-p.r,v=r.height-p.t-p.b,g=\"v\"===h(\"orientation\"),y=h(\"thicknessmode\");h(\"thickness\",\"fraction\"===y?30/(g?d:v):30);var m=h(\"lenmode\");h(\"len\",\"fraction\"===m?1:g?v:d);var x,b,_,w=\"paper\"===h(\"yref\"),T=\"paper\"===h(\"xref\"),k=\"left\";g?(_=\"middle\",k=T?\"left\":\"right\",x=T?1.02:1,b=.5):(_=w?\"bottom\":\"top\",k=\"center\",x=.5,b=w?1.02:1),n.coerce(f,c,{x:{valType:\"number\",min:T?-2:0,max:T?3:1,dflt:x}},\"x\"),n.coerce(f,c,{y:{valType:\"number\",min:w?-2:0,max:w?3:1,dflt:b}},\"y\"),h(\"xanchor\",k),h(\"xpad\"),h(\"yanchor\",_),h(\"ypad\"),n.noneOrAll(f,c,[\"x\",\"y\"]),h(\"outlinecolor\"),h(\"outlinewidth\"),h(\"bordercolor\"),h(\"borderwidth\"),h(\"bgcolor\");var A=n.coerce(f,c,{ticklabelposition:{valType:\"enumerated\",dflt:\"outside\",values:g?[\"outside\",\"inside\",\"outside top\",\"inside top\",\"outside bottom\",\"inside bottom\"]:[\"outside\",\"inside\",\"outside left\",\"inside left\",\"outside right\",\"inside right\"]}},\"ticklabelposition\");h(\"ticklabeloverflow\",-1!==A.indexOf(\"inside\")?\"hide past domain\":\"hide past div\"),a(f,c,h,\"linear\");var M=r.font,S={noAutotickangles:!0,noTicklabelshift:!0,noTicklabelstandoff:!0,outerTicks:!1,font:M};-1!==A.indexOf(\"inside\")&&(S.bgColor=\"black\"),l(f,c,h,\"linear\",S),s(f,c,h,\"linear\",S),o(f,c,h,\"linear\",S),h(\"title.text\",r._dfltTitle.colorbar);var E=c.showticklabels?c.tickfont:M,L=n.extendFlat({},M,{family:E.family,size:n.bigFont(E.size)});n.coerceFont(h,\"title.font\",L),h(\"title.side\",g?\"top\":\"right\")}},37848:function(t,e,r){\"use strict\";var n=r(33428),i=r(49760),a=r(7316),o=r(24040),s=r(54460),l=r(86476),u=r(3400),c=u.strTranslate,f=r(92880).extendFlat,h=r(93972),p=r(43616),d=r(76308),v=r(81668),g=r(72736),y=r(94288).flipScale,m=r(28336),x=r(37668),b=r(94724),_=r(84284),w=_.LINE_SPACING,T=_.FROM_TL,k=_.FROM_BR,A=r(63964).cn;t.exports={draw:function(t){var e=t._fullLayout._infolayer.selectAll(\"g.\"+A.colorbar).data(function(t){var e,r,n,i,a=t._fullLayout,o=t.calcdata,s=[];function l(t){return f(t,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function u(){\"function\"==typeof i.calc?i.calc(t,n,e):(e._fillgradient=r.reversescale?y(r.colorscale):r.colorscale,e._zrange=[r[i.min],r[i.max]])}for(var c=0;c<o.length;c++){var h=o[c];if((n=h[0].trace)._module){var p=n._module.colorbar;if(!0===n.visible&&p)for(var d=Array.isArray(p),v=d?p:[p],g=0;g<v.length;g++){var m=(i=v[g]).container;(r=m?n[m]:n)&&r.showscale&&((e=l(r.colorbar))._id=\"cb\"+n.uid+(d&&m?\"-\"+m:\"\"),e._traceIndex=n.index,e._propPrefix=(m?m+\".\":\"\")+\"colorbar.\",e._meta=n._meta,u(),s.push(e))}}}for(var x in a._colorAxes)if((r=a[x]).showscale){var b=a._colorAxes[x];(e=l(r.colorbar))._id=\"cb\"+x,e._propPrefix=x+\".colorbar.\",e._meta=a._meta,i={min:\"cmin\",max:\"cmax\"},\"heatmap\"!==b[0]&&(n=b[1],i.calc=n._module.colorbar.calc),u(),s.push(e)}return s}(t),(function(t){return t._id}));e.enter().append(\"g\").attr(\"class\",(function(t){return t._id})).classed(A.colorbar,!0),e.each((function(e){var r=n.select(this);u.ensureSingle(r,\"rect\",A.cbbg),u.ensureSingle(r,\"g\",A.cbfills),u.ensureSingle(r,\"g\",A.cblines),u.ensureSingle(r,\"g\",A.cbaxis,(function(t){t.classed(A.crisp,!0)})),u.ensureSingle(r,\"g\",A.cbtitleunshift,(function(t){t.append(\"g\").classed(A.cbtitle,!0)})),u.ensureSingle(r,\"rect\",A.cboutline);var y=function(t,e,r){var o=\"v\"===e.orientation,l=e.len,h=e.lenmode,y=e.thickness,_=e.thicknessmode,M=e.outlinewidth,S=e.borderwidth,E=e.bgcolor,L=e.xanchor,C=e.yanchor,O=e.xpad,P=e.ypad,I=e.x,D=o?e.y:1-e.y,z=\"paper\"===e.yref,R=\"paper\"===e.xref,F=r._fullLayout,B=F._size,N=e._fillcolor,j=e._line,U=e.title,V=U.side,q=e._zrange||n.extent((\"function\"==typeof N?N:j.color).domain()),H=\"function\"==typeof j.color?j.color:function(){return j.color},G=\"function\"==typeof N?N:function(){return N},W=e._levels,Y=function(t,e,r){var n,i,a=e._levels,o=[],s=[],l=a.end+a.size/100,u=a.size,c=1.001*r[0]-.001*r[1],f=1.001*r[1]-.001*r[0];for(i=0;i<1e5&&(n=a.start+i*u,!(u>0?n>=l:n<=l));i++)n>c&&n<f&&o.push(n);if(e._fillgradient)s=[0];else if(\"function\"==typeof e._fillcolor){var h=e._filllevels;if(h)for(l=h.end+h.size/100,u=h.size,i=0;i<1e5&&(n=h.start+i*u,!(u>0?n>=l:n<=l));i++)n>r[0]&&n<r[1]&&s.push(n);else(s=o.map((function(t){return t-a.size/2}))).push(s[s.length-1]+a.size)}else e._fillcolor&&\"string\"==typeof e._fillcolor&&(s=[0]);return a.size<0&&(o.reverse(),s.reverse()),{line:o,fill:s}}(0,e,q),X=Y.fill,Z=Y.line,K=Math.round(y*(\"fraction\"===_?o?B.w:B.h:1)),J=K/(o?B.w:B.h),$=Math.round(l*(\"fraction\"===h?o?B.h:B.w:1)),Q=$/(o?B.h:B.w),tt=R?B.w:r._fullLayout.width,et=z?B.h:r._fullLayout.height,rt=Math.round(o?I*tt+O:D*et+P),nt={center:.5,right:1}[L]||0,it={top:1,middle:.5}[C]||0,at=o?I-nt*J:D-it*J,ot=o?D-it*Q:I-nt*Q,st=Math.round(o?et*(1-ot):tt*ot);e._lenFrac=Q,e._thickFrac=J,e._uFrac=at,e._vFrac=ot;var lt=e._axis=function(t,e,r){var n=t._fullLayout,i=\"v\"===e.orientation,a={type:\"linear\",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,labelalias:e.labelalias,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,ticklabelstep:e.ticklabelstep,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+e._id},l={letter:o,font:n.font,noAutotickangles:\"y\"===o,noHover:!0,noTickson:!0,noTicklabelmode:!0,noInsideRange:!0,calendar:n.calendar};function c(t,e){return u.coerce(a,s,b,t,e)}return m(a,s,c,l,n),x(a,s,c,l),s}(r,e,q);lt.position=J+(o?I+O/B.w:D+P/B.h);var ut=-1!==[\"top\",\"bottom\"].indexOf(V);if(o&&ut&&(lt.title.side=V,lt.titlex=I+O/B.w,lt.titley=ot+(\"top\"===U.side?Q-P/B.h:P/B.h)),o||ut||(lt.title.side=V,lt.titley=D+P/B.h,lt.titlex=ot+O/B.w),j.color&&\"auto\"===e.tickmode){lt.tickmode=\"linear\",lt.tick0=W.start;var ct=W.size,ft=u.constrain($/50,4,15)+1,ht=(q[1]-q[0])/((e.nticks||ft)*ct);if(ht>1){var pt=Math.pow(10,Math.floor(Math.log(ht)/Math.LN10));ct*=pt*u.roundUp(ht/pt,[2,5,10]),(Math.abs(W.start)/W.size+1e-6)%1<2e-6&&(lt.tick0=0)}lt.dtick=ct}lt.domain=o?[ot+P/B.h,ot+Q-P/B.h]:[ot+O/B.w,ot+Q-O/B.w],lt.setScale(),t.attr(\"transform\",c(Math.round(B.l),Math.round(B.t)));var dt,vt=t.select(\".\"+A.cbtitleunshift).attr(\"transform\",c(-Math.round(B.l),-Math.round(B.t))),gt=lt.ticklabelposition,yt=lt.title.font.size,mt=t.select(\".\"+A.cbaxis),xt=0,bt=0;function _t(n,i){var a={propContainer:lt,propName:e._propPrefix+\"title\",traceIndex:e._traceIndex,_meta:e._meta,placeholder:F._dfltTitle.colorbar,containerGroup:t.select(\".\"+A.cbtitle)},o=\"h\"===n.charAt(0)?n.substr(1):\"h\"+n;t.selectAll(\".\"+o+\",.\"+o+\"-math-group\").remove(),v.draw(r,n,f(a,i||{}))}return u.syncOrAsync([a.previousPromises,function(){var t,e;(o&&ut||!o&&!ut)&&(\"top\"===V&&(t=O+B.l+tt*I,e=P+B.t+et*(1-ot-Q)+3+.75*yt),\"bottom\"===V&&(t=O+B.l+tt*I,e=P+B.t+et*(1-ot)-3-.25*yt),\"right\"===V&&(e=P+B.t+et*D+3+.75*yt,t=O+B.l+tt*ot),_t(lt._id+\"title\",{attributes:{x:t,y:e,\"text-anchor\":o?\"start\":\"middle\"}}))},function(){if(!o&&!ut||o&&ut){var a,l=t.select(\".\"+A.cbtitle),f=l.select(\"text\"),h=[-M/2,M/2],d=l.select(\".h\"+lt._id+\"title-math-group\").node(),v=15.6;if(f.node()&&(v=parseInt(f.node().style.fontSize,10)*w),d?(a=p.bBox(d),bt=a.width,(xt=a.height)>v&&(h[1]-=(xt-v)/2)):f.node()&&!f.classed(A.jsPlaceholder)&&(a=p.bBox(f.node()),bt=a.width,xt=a.height),o){if(xt){if(xt+=5,\"top\"===V)lt.domain[1]-=xt/B.h,h[1]*=-1;else{lt.domain[0]+=xt/B.h;var y=g.lineCount(f);h[1]+=(1-y)*v}l.attr(\"transform\",c(h[0],h[1])),lt.setScale()}}else bt&&(\"right\"===V&&(lt.domain[0]+=(bt+yt/2)/B.w),l.attr(\"transform\",c(h[0],h[1])),lt.setScale())}t.selectAll(\".\"+A.cbfills+\",.\"+A.cblines).attr(\"transform\",o?c(0,Math.round(B.h*(1-lt.domain[1]))):c(Math.round(B.w*lt.domain[0]),0)),mt.attr(\"transform\",o?c(0,Math.round(-B.t)):c(Math.round(-B.l),0));var m=t.select(\".\"+A.cbfills).selectAll(\"rect.\"+A.cbfill).attr(\"style\",\"\").data(X);m.enter().append(\"rect\").classed(A.cbfill,!0).attr(\"style\",\"\"),m.exit().remove();var x=q.map(lt.c2p).map(Math.round).sort((function(t,e){return t-e}));m.each((function(t,a){var s=[0===a?q[0]:(X[a]+X[a-1])/2,a===X.length-1?q[1]:(X[a]+X[a+1])/2].map(lt.c2p).map(Math.round);o&&(s[1]=u.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?\"x\":\"y\",rt).attr(o?\"y\":\"x\",n.min(s)).attr(o?\"width\":\"height\",Math.max(K,2)).attr(o?\"height\":\"width\",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?\"vertical\":\"horizontalreversed\",e._fillgradient,\"fill\");else{var c=G(t).replace(\"e-\",\"\");l.attr(\"fill\",i(c).toHexString())}}));var b=t.select(\".\"+A.cblines).selectAll(\"path.\"+A.cbline).data(j.color&&j.width?Z:[]);b.enter().append(\"path\").classed(A.cbline,!0),b.exit().remove(),b.each((function(t){var e=rt,r=Math.round(lt.c2p(t))+j.width/2%1;n.select(this).attr(\"d\",\"M\"+(o?e+\",\"+r:r+\",\"+e)+(o?\"h\":\"v\")+K).call(p.lineGroupStyle,j.width,H(t),j.dash)})),mt.selectAll(\"g.\"+lt._id+\"tick,path\").remove();var _=rt+K+(M||0)/2-(\"outside\"===e.ticks?1:0),T=s.calcTicks(lt),k=s.getTickSigns(lt)[2];return s.drawTicks(r,lt,{vals:\"inside\"===lt.ticks?s.clipEnds(lt,T):T,layer:mt,path:s.makeTickPath(lt,_,k),transFn:s.makeTransTickFn(lt)}),s.drawLabels(r,lt,{vals:T,layer:mt,transFn:s.makeTransTickLabelFn(lt),labelFns:s.makeLabelFns(lt,_)})},function(){if(o&&!ut||!o&&ut){var t,i,a=lt.position||0,s=lt._offset+lt._length/2;if(\"right\"===V)i=s,t=B.l+tt*a+10+yt*(lt.showticklabels?1:.5);else if(t=s,\"bottom\"===V&&(i=B.t+et*a+10+(-1===gt.indexOf(\"inside\")?lt.tickfont.size:0)+(\"intside\"!==lt.ticks&&e.ticklen||0)),\"top\"===V){var l=U.text.split(\"<br>\").length;i=B.t+et*a+10-K-w*yt*l}_t((o?\"h\":\"v\")+lt._id+\"title\",{avoid:{selection:n.select(r).selectAll(\"g.\"+lt._id+\"tick\"),side:V,offsetTop:o?0:B.t,offsetLeft:o?B.l:0,maxShift:o?F.width:F.height},attributes:{x:t,y:i,\"text-anchor\":\"middle\"},transform:{rotate:o?-90:0,offset:0}})}},a.previousPromises,function(){var 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e?e.ypx:J[0]._length/2;if(e.pointerX=bt+K[0]._offset,e.pointerY=_t+J[0]._offset,nt=\"xval\"in e?x.flat(b,e.xval):x.p2c(K,bt),it=\"yval\"in e?x.flat(b,e.yval):x.p2c(J,_t),!i(nt[0])||!i(it[0]))return o.warn(\"Fx.hover failed\",e,t),v.unhoverRaw(t,e)}var At=1/0;function Mt(r,n){for(ot=0;ot<yt.length;ot++)if((st=yt[ot])&&st[0]&&st[0].trace&&!0===(lt=st[0].trace).visible&&0!==lt._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(lt._module.name)){if(ft=S,x.isUnifiedHover(ft)&&(ft=ft.charAt(0)),\"splom\"===lt.type?ut=b[ct=0]:(ut=x.getSubplot(lt),ct=b.indexOf(ut)),dt={cd:st,trace:lt,xa:K[ct],ya:J[ct],maxHoverDistance:et,maxSpikeDistance:rt,index:!1,distance:Math.min(At,et),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:d.defaultLine,name:lt.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},_[ut]&&(dt.subplot=_[ut]._subplot),_._splomScenes&&_._splomScenes[lt.uid]&&(dt.scene=_._splomScenes[lt.uid]),\"array\"===ft){var a=e[ot];\"pointNumber\"in a?(dt.index=a.pointNumber,ft=\"closest\"):(ft=\"\",\"xval\"in a&&(ht=a.xval,ft=\"x\"),\"yval\"in a&&(pt=a.yval,ft=ft?\"closest\":\"y\"))}else void 0!==r&&void 0!==n?(ht=r,pt=n):(ht=nt[ct],pt=it[ct]);if(vt=gt.length,0!==et)if(lt._module&&lt._module.hoverPoints){var s=lt._module.hoverPoints(dt,ht,pt,ft,{finiteRange:!0,hoverLayer:_._hoverlayer,hoversubplots:w,gd:t});if(s)for(var l,u=0;u<s.length;u++)l=s[u],i(l.x0)&&i(l.y0)&&gt.push(N(l,S))}else o.log(\"Unrecognized trace type in hover:\",lt);if(\"closest\"===S&&gt.length>vt&&(gt.splice(0,vt),At=gt[0].distance),M&&0!==rt&&0===gt.length){dt.distance=rt,dt.index=!1;var c=lt._module.hoverPoints(dt,ht,pt,\"closest\",{hoverLayer:_._hoverlayer});if(c&&(c=c.filter((function(t){return t.spikeDistance<=rt}))),c&&c.length){var f,h=c.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(h.length){var p=h[0];i(p.x0)&&i(p.y0)&&(f=Et(p),(!mt.vLinePoint||mt.vLinePoint.spikeDistance>f.spikeDistance)&&(mt.vLinePoint=f))}var v=c.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(v.length){var g=v[0];i(g.x0)&&i(g.y0)&&(f=Et(g),(!mt.hLinePoint||mt.hLinePoint.spikeDistance>f.spikeDistance)&&(mt.hLinePoint=f))}}}}}function St(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)f&&f._id!==t[o].xa._id||p&&p._id!==t[o].ya._id||(n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n));return i}function Et(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}Mt();var Lt={fullLayout:_,container:_._hoverlayer,event:e},Ct=t._spikepoints,Ot={vLinePoint:mt.vLinePoint,hLinePoint:mt.hLinePoint};t._spikepoints=Ot;var Pt=function(){var t=gt.filter((function(t){return f&&f._id===t.xa._id&&p&&p._id===t.ya._id})),e=gt.filter((function(t){return!(f&&f._id===t.xa._id&&p&&p._id===t.ya._id)}));t.sort(P),e.sort(P),gt=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];m.traceIs(s.trace,\"bar-like\")||m.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(gt=t.concat(e),S)};Pt();var It=S.charAt(0),Dt=(\"x\"===It||\"y\"===It)&&gt[0]&&O[gt[0].trace.type];if(M&&0!==rt&&0!==gt.length){var zt=St(gt.filter((function(t){return t.ya.showspikes})),rt,Dt);mt.hLinePoint=Et(zt);var Rt=St(gt.filter((function(t){return t.xa.showspikes})),rt,Dt);mt.vLinePoint=Et(Rt)}if(0===gt.length){var Ft=v.unhoverRaw(t,e);return!M||null===mt.hLinePoint&&null===mt.vLinePoint||U(Ct)&&j(t,mt,Lt),Ft}if(M&&U(Ct)&&j(t,mt,Lt),x.isXYhover(ft)&&0!==gt[0].length&&\"splom\"!==gt[0].trace.type){var Bt=gt[0],Nt=(gt=C[Bt.trace.type]?gt.filter((function(t){return t.trace.index===Bt.trace.index})):[Bt]).length;Mt(q(\"x\",Bt,_),q(\"y\",Bt,_));var jt,Ut=[],Vt={},qt=0,Ht=function(t){var e=C[t.trace.type]?I(t):t.trace.index;if(Vt[e]){var r=Vt[e]-1,n=Ut[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(Ut[r]=t)}else qt++,Vt[e]=qt,Ut.push(t)};for(jt=0;jt<Nt;jt++)Ht(gt[jt]);for(jt=gt.length-1;jt>Nt-1;jt--)Ht(gt[jt]);gt=Ut,Pt()}var Gt=t._hoverdata,Wt=[],Yt=H(t),Xt=G(t);for(at=0;at<gt.length;at++){var Zt=gt[at],Kt=x.makeEventData(Zt,Zt.trace,Zt.cd);if(!1!==Zt.hovertemplate){var Jt=!1;Zt.cd[Zt.index]&&Zt.cd[Zt.index].ht&&(Jt=Zt.cd[Zt.index].ht),Zt.hovertemplate=Jt||Zt.trace.hovertemplate||!1}if(Zt.xa&&Zt.ya){var $t=Zt.x0+Zt.xa._offset,Qt=Zt.x1+Zt.xa._offset,te=Zt.y0+Zt.ya._offset,ee=Zt.y1+Zt.ya._offset,re=Math.min($t,Qt),ne=Math.max($t,Qt),ie=Math.min(te,ee),ae=Math.max(te,ee);Kt.bbox={x0:re+Xt,x1:ne+Xt,y0:ie+Yt,y1:ae+Yt}}Zt.eventData=[Kt],Wt.push(Kt)}t._hoverdata=Wt;var oe=\"y\"===S&&(yt.length>1||gt.length>1)||\"closest\"===S&&xt&&gt.length>1,se=d.combine(_.plot_bgcolor||d.background,_.paper_bgcolor),le=z(gt,{gd:t,hovermode:S,rotateLabels:oe,bgColor:se,container:_._hoverlayer,outerContainer:_._paper.node(),commonLabelOpts:_.hoverlabel,hoverdistance:_.hoverdistance}),ue=le.hoverLabels;if(x.isUnifiedHover(S)||(function(t,e,r,n){var i,a,o,s,l,u,c,f=e?\"xa\":\"ya\",h=e?\"ya\":\"xa\",p=0,d=1,v=t.size(),g=new Array(v),y=0,m=n.minX,x=n.maxX,b=n.minY,_=n.maxY,w=function(t){return t*r._invScaleX},T=function(t){return t*r._invScaleY};function k(t){var e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;i=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;i=!1}if(i){var n=0;for(s=0;s<t.length;s++)(u=t[s]).pos+u.dp+u.size>e.pmax&&n++;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos>e.pmax-1&&(u.del=!0,n--);for(s=0;s<t.length&&!(n<=0);s++)if((u=t[s]).pos<e.pmin+1)for(u.del=!0,n--,o=2*u.size,l=t.length-1;l>=0;l--)t[l].dp-=o;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos+u.dp+u.size>e.pmax&&(u.del=!0,n--)}}}for(t.each((function(t){var n=t[f],i=t[h],a=\"x\"===n._id.charAt(0),o=n.range;0===y&&o&&o[0]>o[1]!==a&&(d=-1);var s=0,l=a?r.width:r.height;if(\"x\"===r.hovermode||\"y\"===r.hovermode){var u,c,p=F(t,e),v=t.anchor,k=\"end\"===v?-1:1;if(\"middle\"===v)c=(u=t.crossPos+(a?T(p.y-t.by/2):w(t.bx/2+t.tx2width/2)))+(a?T(t.by):w(t.bx));else if(a)c=(u=t.crossPos+T(E+p.y)-T(t.by/2-E))+T(t.by);else{var M=w(k*E+p.x),S=M+w(k*t.bx);u=t.crossPos+Math.min(M,S),c=t.crossPos+Math.max(M,S)}a?void 0!==b&&void 0!==_&&Math.min(c,_)-Math.max(u,b)>1&&(\"left\"===i.side?(s=i._mainLinePosition,l=r.width):l=i._mainLinePosition):void 0!==m&&void 0!==x&&Math.min(c,x)-Math.max(u,m)>1&&(\"top\"===i.side?(s=i._mainLinePosition,l=r.height):l=i._mainLinePosition)}g[y++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(a?A:1)/2,pmin:s,pmax:l}]})),g.sort((function(t,e){return t[0].posref-e[0].posref||d*(e[0].traceIndex-t[0].traceIndex)}));!i&&p<=v;){for(p++,i=!0,s=0;s<g.length-1;){var M=g[s],S=g[s+1],L=M[M.length-1],C=S[0];if((a=L.pos+L.dp+L.size-C.pos-C.dp+C.size)>.01){for(l=S.length-1;l>=0;l--)S[l].dp+=a;for(M.push.apply(M,S),g.splice(s+1,1),c=0,l=M.length-1;l>=0;l--)c+=M[l].dp;for(o=c/M.length,l=M.length-1;l>=0;l--)M[l].dp-=o;i=!1}else s++}g.forEach(k)}for(s=g.length-1;s>=0;s--){var O=g[s];for(l=O.length-1;l>=0;l--){var P=O[l],I=P.datum;I.offset=P.dp,I.del=P.del}}}(ue,oe,_,le.commonLabelBoundingBox),B(ue,oe,_._invScaleX,_._invScaleY)),l&&l.tagName){var ce=m.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,Wt);h(n.select(l),ce?\"pointer\":\"\")}l&&!a&&function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Gt)&&(Gt&&t.emit(\"plotly_unhover\",{event:e,points:Gt}),t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:K,yaxes:J,xvals:nt,yvals:it}))}(t,e,r,a,l)}))},e.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=H(i),o=G(i),s=z(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,u=t.eventData;if(u){var c=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),p=Math.max(s,l),v=t.trace;if(m.traceIs(v,\"gl3d\")){var g=i._fullLayout[v.scene]._scene.container,y=g.offsetLeft,x=g.offsetTop;c+=y,f+=y,h+=x,p+=x}u.bbox={x0:c+o,x1:f+o,y0:h+a,y1:p+a},e.inOut_bbox&&e.inOut_bbox.push(u.bbox)}else u=!1;return{color:t.color||d.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,fontWeight:t.fontWeight,fontStyle:t.fontStyle,fontVariant:t.fontVariant,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:u}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||d.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}).hoverLabels,l=0,u=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(u=t.offset)})).each((function(t){t.offset-=u})),B(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var D=/<extra>([\\s\\S]*)<\\/extra>/;function z(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,s=e.rotateLabels,c=e.bgColor,h=e.container,v=e.outerContainer,g=e.commonLabelOpts||{};if(0===t.length)return[[]];var y=e.fontFamily||b.HOVERFONT,k=e.fontSize||b.HOVERFONTSIZE,A=e.fontWeight||i.font.weight,M=e.fontStyle||i.font.style,S=e.fontVariant||i.font.variant,C=e.fontTextcase||i.font.textcase,O=e.fontLineposition||i.font.lineposition,P=e.fontShadow||i.font.shadow,D=t[0],z=D.xa,F=D.ya,B=a.charAt(0),N=B+\"Label\",j=D[N];if(void 0===j&&\"multicategory\"===z.type)for(var U=0;U<t.length&&void 0===(j=t[U][N]);U++);var V=W(r,v),q=V.top,H=V.width,G=V.height,Y=void 0!==j&&D.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(Y){var X,Z,K=!0;for(X=0;X<t.length;X++)if(K&&void 0===t[X].zLabel&&(K=!1),Z=t[X].hoverinfo||t[X].trace.hoverinfo){var J=Array.isArray(Z)?Z:Z.split(\"+\");if(-1===J.indexOf(\"all\")&&-1===J.indexOf(a)){Y=!1;break}}K&&(Y=!1)}var $=h.selectAll(\"g.axistext\").data(Y?[0]:[]);$.enter().append(\"g\").classed(\"axistext\",!0),$.exit().remove();var Q={minX:0,maxX:0,minY:0,maxY:0};if($.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),s=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),u=g.bgcolor||d.defaultLine,c=g.bordercolor||d.contrast(u),h=d.contrast(u),v=g.font,m={weight:v.weight||A,style:v.style||M,variant:v.variant||S,textcase:v.textcase||C,lineposition:v.lineposition||O,shadow:v.shadow||P,family:v.family||y,size:v.size||k,color:v.color||h};e.style({fill:u,stroke:c}),s.text(j).call(p.font,m).call(f.positionText,0,0).call(f.convertToTspans,r),t.attr(\"transform\",\"\");var x,b,_=W(r,s.node());if(\"x\"===a){var w=\"top\"===z.side?\"-\":\"\";s.attr(\"text-anchor\",\"middle\").call(f.positionText,0,\"top\"===z.side?q-_.bottom-E-L:q-_.top+E+L),x=z._offset+(D.x0+D.x1)/2,b=F._offset+(\"top\"===z.side?0:F._length);var T=_.width/2+L,I=x;x<T?I=T:x>i.width-T&&(I=i.width-T),e.attr(\"d\",\"M\"+(x-I)+\",0L\"+(x-I+E)+\",\"+w+E+\"H\"+T+\"v\"+w+(2*L+_.height)+\"H\"+-T+\"V\"+w+E+\"H\"+(x-I-E)+\"Z\"),x=I,Q.minX=x-T,Q.maxX=x+T,\"top\"===z.side?(Q.minY=b-(2*L+_.height),Q.maxY=b-L):(Q.minY=b+L,Q.maxY=b+(2*L+_.height))}else{var R,B,N;\"right\"===F.side?(R=\"start\",B=1,N=\"\",x=z._offset+z._length):(R=\"end\",B=-1,N=\"-\",x=z._offset),b=F._offset+(D.y0+D.y1)/2,s.attr(\"text-anchor\",R),e.attr(\"d\",\"M0,0L\"+N+E+\",\"+E+\"V\"+(L+_.height/2)+\"h\"+N+(2*L+_.width)+\"V-\"+(L+_.height/2)+\"H\"+N+E+\"V-\"+E+\"Z\"),Q.minY=b-(L+_.height/2),Q.maxY=b+(L+_.height/2),\"right\"===F.side?(Q.minX=x+E,Q.maxX=x+E+(2*L+_.width)):(Q.minX=x-E-(2*L+_.width),Q.maxX=x-E);var 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et=i.hoverlabel,rt=et.font,nt={showlegend:!0,legend:{title:{text:j,font:rt},font:rt,bgcolor:et.bgcolor,bordercolor:et.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:\"v\"}},it={font:rt};_(nt,it,r._fullData);var at=it.legend;at.entries=[];for(var ot=0;ot<tt.length;ot++){var st=tt[ot];if(\"none\"!==st.hoverinfo){var lt=R(st,!0,a,i,j),ut=lt[0],ct=lt[1];st.name=ct,st.text=\"\"!==ct?ct+\" : \"+ut:ut;var ft=st.cd[st.index];ft&&(ft.mc&&(st.mc=ft.mc),ft.mcc&&(st.mc=ft.mcc),ft.mlc&&(st.mlc=ft.mlc),ft.mlcc&&(st.mlc=ft.mlcc),ft.mlw&&(st.mlw=ft.mlw),ft.mrc&&(st.mrc=ft.mrc),ft.dir&&(st.dir=ft.dir)),st._distinct=!0,at.entries.push([st])}}at.entries.sort((function(t,e){return t[0].trace.index-e[0].trace.index})),at.layer=h,at._inHover=!0,at._groupTitleFont=et.grouptitlefont,w(r,at);var 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0),-1===p.indexOf(\"name\")&&(t.name=void 0)),t}function j(t,e,r){var n,i,o=r.container,s=r.fullLayout,l=s._size,u=r.event,c=!!e.hLinePoint,f=!!e.vLinePoint;if(o.selectAll(\".spikeline\").remove(),f||c){var h=d.combine(s.plot_bgcolor,s.paper_bgcolor);if(c){var v,y,m=e.hLinePoint;n=m&&m.xa,\"cursor\"===(i=m&&m.ya).spikesnap?(v=u.pointerX,y=u.pointerY):(v=n._offset+m.x,y=i._offset+m.y);var x,b,_=a.readability(m.color,h)<1.5?d.contrast(h):m.color,w=i.spikemode,T=i.spikethickness,k=i.spikecolor||_,A=g.getPxPosition(t,i);if(-1!==w.indexOf(\"toaxis\")||-1!==w.indexOf(\"across\")){if(-1!==w.indexOf(\"toaxis\")&&(x=A,b=v),-1!==w.indexOf(\"across\")){var 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n=r(33428),i=r(3400),a=r(86476),o=r(10624),s=r(65460),l=r(83292);t.exports={moduleType:\"component\",name:\"fx\",constants:r(92456),schema:{layout:s},attributes:r(55756),layoutAttributes:s,supplyLayoutGlobalDefaults:r(81976),supplyDefaults:r(95448),supplyLayoutDefaults:r(88336),calc:r(55056),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,\"hoverlabel.\"+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,\"hoverinfo\",(function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)}))},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,loneUnhover:function(t){var e=i.isD3Selection(t)?t:n.select(t);e.selectAll(\"g.hovertext\").remove(),e.selectAll(\".spikeline\").remove()},click:r(62376)}},65460:function(t,e,r){\"use strict\";var 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V,q=i.ensureSingle(N,\"g\",\"scrollbox\"),H=h.title;h._titleWidth=0,h._titleHeight=0,H.text?((V=i.ensureSingle(q,\"text\",w+\"titletext\")).attr(\"text-anchor\",\"start\").call(u.font,H.font).text(H.text),L(V,q,t,h,_)):q.selectAll(\".\"+w+\"titletext\").remove();var G=i.ensureSingle(N,\"rect\",\"scrollbar\",(function(t){t.attr(p.scrollBarEnterAttrs).call(c.fill,p.scrollBarColor)})),W=q.selectAll(\"g.groups\").data(S);W.enter().append(\"g\").attr(\"class\",\"groups\"),W.exit().remove();var Y=W.selectAll(\"g.traces\").data(i.identity);Y.enter().append(\"g\").attr(\"class\",\"traces\"),Y.exit().remove(),Y.style(\"opacity\",(function(t){var e=t[0].trace;return o.traceIs(e,\"pie-like\")?-1!==B.indexOf(t[0].label)?.5:1:\"legendonly\"===e.visible?.5:1})).each((function(){n.select(this).call(M,t,h)})).call(x,t,h).each((function(){T||n.select(this).call(E,t,w)})),i.syncOrAsync([a.previousPromises,function(){return function(t,e,r,i){var a=t._fullLayout,o=P(i);i||(i=a[o]);var s=a._size,l=b.isVertical(i),c=b.isGrouped(i),f=\"fraction\"===i.entrywidthmode,h=i.borderwidth,d=2*h,v=p.itemGap,g=i.indentation+i.itemwidth+2*v,y=2*(h+v),m=O(i),x=i.y<0||0===i.y&&\"top\"===m,_=i.y>1||1===i.y&&\"bottom\"===m,w=i.tracegroupgap,T={};i._maxHeight=Math.max(x||_?a.height/2:s.h,30);var A=0;i._width=0,i._height=0;var M=function(t){var e=0,r=0,n=t.title.side;return n&&(-1!==n.indexOf(\"left\")&&(e=t._titleWidth),-1!==n.indexOf(\"top\")&&(r=t._titleHeight)),[e,r]}(i);if(l)r.each((function(t){var e=t[0].height;u.setTranslate(this,h+M[0],h+M[1]+i._height+e/2+v),i._height+=e,i._width=Math.max(i._width,t[0].width)})),A=g+i._width,i._width+=v+g+d,i._height+=y,c&&(e.each((function(t,e){u.setTranslate(this,0,e*i.tracegroupgap)})),i._height+=(i._lgroupsLength-1)*i.tracegroupgap);else{var S=C(i),E=i.x<0||0===i.x&&\"right\"===S,L=i.x>1||1===i.x&&\"left\"===S,I=_||x,D=a.width/2;i._maxWidth=Math.max(E?I&&\"left\"===S?s.l+s.w:D:L?I&&\"right\"===S?s.r+s.w:D:s.w,2*g);var 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P=S,I=E;S=k?i.constrain(S,0,d.width-h._width):P,E=M?i.constrain(E,0,d.height-h._effHeight):I,S!==P&&i.log(\"Constrain \"+w+\".x to make legend fit inside graph\"),E!==I&&i.log(\"Constrain \"+w+\".y to make legend fit inside graph\")}u.setTranslate(N,S,E)}if(G.on(\".drag\",null),N.on(\"wheel\",null),T||h._height<=h._maxHeight||t._context.staticPlot){var D=h._effHeight;T&&(D=h._height),U.attr({width:h._width-_,height:D-_,x:_/2,y:_/2}),u.setTranslate(q,0,0),j.select(\"rect\").attr({width:h._width-2*_,height:D-2*_,x:_,y:_}),u.setClipUrl(q,r,t),u.setRect(G,0,0,0,0),delete h._scrollY}else{var 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t=n.event.sourceEvent;\"touchstart\"===t.type&&(z=t.changedTouches[0].clientY,F=Z)})).on(\"drag\",(function(){var t=n.event.sourceEvent;\"touchmove\"===t.type&&(R=t.changedTouches[0].clientY,Z=function(t,e,r){var n=(e-r)/X+t;return i.constrain(n,0,Y)}(F,z,R),$(Z,B,X))}));q.call(J)}function $(e,r,n){h._scrollY=t._fullLayout[w]._scrollY=e,u.setTranslate(q,0,-e),u.setRect(G,h._width,p.scrollBarMargin+e*n,p.scrollBarWidth,r),j.select(\"rect\").attr(\"y\",_+e)}t._context.edits.legendPosition&&(N.classed(\"cursor-move\",!0),l.init({element:N.node(),gd:t,prepFn:function(t){if(t.target!==G.node()){var e=u.getTranslate(N);m=e.x,x=e.y}},moveFn:function(t,r){if(void 0!==m&&void 0!==x){var n=m+t,i=x+r;u.setTranslate(N,n,i),e=l.align(n,h._width,b.l,b.l+b.w,h.xanchor),c=l.align(i+h._height,-h._height,b.t+b.h,b.t,h.yanchor)}},doneFn:function(){if(void 0!==e&&void 0!==c){var r={};r[w+\".x\"]=e,r[w+\".y\"]=c,o.call(\"_guiRelayout\",t,r)}},clickFn:function(e,r){var n=s.selectAll(\"g.traces\").filter((function(){var t=this.getBoundingClientRect();return r.clientX>=t.left&&r.clientX<=t.right&&r.clientY>=t.top&&r.clientY<=t.bottom}));n.size()>0&&A(t,N,n,e,r)}}))}],t)}}function k(t,e,r){var n=t[0],i=n.width,a=e.entrywidthmode,o=n.trace.legendwidth||e.entrywidth;return\"fraction\"===a?e._maxWidth*o:r+(o||i)}function A(t,e,r,n,i){var a=r.data()[0][0].trace,l={event:i,node:r.node(),curveNumber:a.index,expandedIndex:a._expandedIndex,data:t.data,layout:t.layout,frames:t._transitionData._frames,config:t._context,fullData:t._fullData,fullLayout:t._fullLayout};a._group&&(l.group=a._group),o.traceIs(a,\"pie-like\")&&(l.label=r.datum()[0].label);var u=s.triggerHandler(t,\"plotly_legendclick\",l);if(1===n){if(!1===u)return;e._clickTimeout=setTimeout((function(){t._fullLayout&&h(r,t,n)}),t._context.doubleClickDelay)}else 2===n&&(e._clickTimeout&&clearTimeout(e._clickTimeout),t._legendMouseDownTime=0,!1!==s.triggerHandler(t,\"plotly_legenddoubleclick\",l)&&!1!==u&&h(r,t,n))}function M(t,e,r){var n,a,s=P(r),l=t.data()[0][0],c=l.trace,h=o.traceIs(c,\"pie-like\"),d=!r._inHover&&e._context.edits.legendText&&!h,v=r._maxNameLength;l.groupTitle?(n=l.groupTitle.text,a=l.groupTitle.font):(a=r.font,r.entries?n=l.text:(n=h?l.label:c.name,c._meta&&(n=i.templateString(n,c._meta))));var g=i.ensureSingle(t,\"text\",s+\"text\");g.attr(\"text-anchor\",\"start\").call(u.font,a).text(d?S(n,v):n);var y=r.indentation+r.itemwidth+2*p.itemGap;f.positionText(g,y,0),d?g.call(f.makeEditable,{gd:e,text:n}).call(L,t,e,r).on(\"edit\",(function(n){this.text(S(n,v)).call(L,t,e,r);var a=l.trace._fullInput||{},s={};if(o.hasTransform(a,\"groupby\")){var u=o.getTransformIndices(a,\"groupby\"),f=u[u.length-1],h=i.keyedContainer(a,\"transforms[\"+f+\"].styles\",\"target\",\"value.name\");h.set(l.trace._group,n),s=h.constructUpdate()}else 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a=t.select(\"g[class*=math-group]\"),o=a.node(),s=P(r);r||(r=e._fullLayout[s]);var l,c,h=r.borderwidth,d=(n===_?r.title.font:i.groupTitle?i.groupTitle.font:r.font).size*v;if(o){var g=u.bBox(o);l=g.height,c=g.width,n===_?u.setTranslate(a,h,h+.75*l):u.setTranslate(a,0,.25*l)}else{var y=\".\"+s+(n===_?\"title\":\"\")+\"text\",m=t.select(y),x=f.lineCount(m),b=m.node();if(l=d*x,c=b?u.bBox(b).width:0,n===_)\"left\"===r.title.side&&(c+=2*p.itemGap),f.positionText(m,h+p.titlePad,h+d);else{var w=2*p.itemGap+r.indentation+r.itemwidth;i.groupTitle&&(w=p.itemGap,c-=r.indentation+r.itemwidth),f.positionText(m,w,-d*((x-1)/2-.3))}}n===_?(r._titleWidth=c,r._titleHeight=l):(i.lineHeight=d,i.height=Math.max(l,16)+3,i.width=c)}else t.remove()}(e,r,n,i)}))}function C(t){return i.isRightAnchor(t)?\"right\":i.isCenterAnchor(t)?\"center\":\"left\"}function O(t){return i.isBottomAnchor(t)?\"bottom\":i.isMiddleAnchor(t)?\"middle\":\"top\"}function P(t){return t._id||\"legend\"}t.exports=function(t,e){if(e)T(t,e);else{var r=t._fullLayout,i=r._legends;r._infolayer.selectAll('[class^=\"legend\"]').each((function(){var t=n.select(this),e=t.attr(\"class\").split(\" \")[0];e.match(w)&&-1===i.indexOf(e)&&t.remove()}));for(var a=0;a<i.length;a++){var o=i[a];T(t,t._fullLayout[o])}}}},35456:function(t,e,r){\"use strict\";var n=r(24040),i=r(42451);t.exports=function(t,e,r){var a,o,s=e._inHover,l=i.isGrouped(e),u=i.isReversed(e),c={},f=[],h=!1,p={},d=0,v=0;function g(t,n,a){if(!1!==e.visible&&(!r||t===e._id))if(\"\"!==n&&i.isGrouped(e))-1===f.indexOf(n)?(f.push(n),h=!0,c[n]=[a]):c[n].push(a);else{var o=\"~~i\"+d;f.push(o),c[o]=[a],d++}}for(a=0;a<t.length;a++){var y=t[a],m=y[0],x=m.trace,b=x.legend,_=x.legendgroup;if(s||x.visible&&x.showlegend)if(n.traceIs(x,\"pie-like\"))for(p[_]||(p[_]={}),o=0;o<y.length;o++){var w=y[o].label;p[_][w]||(g(b,_,{label:w,color:y[o].color,i:y[o].i,trace:x,pts:y[o].pts}),p[_][w]=!0,v=Math.max(v,(w||\"\").length))}else 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r=e.currentTarget,n=r._previousVal,i=t._fullLayout,a=i._subplots.gl3d||[],o=[\"xaxis\",\"yaxis\",\"zaxis\"],s={},l={};if(n)l=n,r._previousVal=null;else{for(var u=0;u<a.length;u++){var c=a[u],f=i[c],h=c+\".hovermode\";s[h]=f.hovermode,l[h]=!1;for(var p=0;p<3;p++){var d=o[p],v=c+\".\"+d+\".showspikes\";l[v]=!1,s[v]=f[d].showspikes}}r._previousVal=s}return l}function v(t,e){for(var r=e.currentTarget,i=r.getAttribute(\"data-attr\"),a=r.getAttribute(\"data-val\")||!0,o=t._fullLayout,s=o._subplots.geo||[],l=0;l<s.length;l++){var u=s[l],c=o[u];if(\"zoom\"===i){var f=c.projection.scale,h=\"in\"===a?2*f:.5*f;n.call(\"_guiRelayout\",t,u+\".projection.scale\",h)}}\"reset\"===i&&x(t,\"geo\")}function g(t){var e=t._fullLayout;return!e.hovermode&&(e._has(\"cartesian\")?e._isHoriz?\"y\":\"x\":\"closest\")}function y(t){var e=g(t);n.call(\"_guiRelayout\",t,\"hovermode\",e)}function m(t,e){for(var r=e.currentTarget.getAttribute(\"data-val\"),i=t._fullLayout,a=i._subplots.mapbox||[],o={},s=0;s<a.length;s++){var l=a[s],u=i[l].zoom,c=\"in\"===r?1.05*u:u/1.05;o[l+\".zoom\"]=c}n.call(\"_guiRelayout\",t,o)}function x(t,e){for(var r=t._fullLayout,i=r._subplots[e]||[],a={},o=0;o<i.length;o++)for(var s=i[o],l=r[s]._subplot.viewInitial,u=Object.keys(l),c=0;c<u.length;c++){var f=u[c];a[s+\".\"+f]=l[f]}n.call(\"_guiRelayout\",t,a)}c.toImage={name:\"toImage\",title:function(t){var e=(t._context.toImageButtonOptions||{}).format||\"png\";return u(t,\"png\"===e?\"Download plot as a png\":\"Download plot\")},icon:o.camera,click:function(t){var e=t._context.toImageButtonOptions,r={format:e.format||\"png\"};l.notifier(u(t,\"Taking snapshot - this may take a few seconds\"),\"long\"),\"svg\"!==r.format&&l.isIE()&&(l.notifier(u(t,\"IE only supports svg.  Changing format to svg.\"),\"long\"),r.format=\"svg\"),[\"filename\",\"width\",\"height\",\"scale\"].forEach((function(t){t in e&&(r[t]=e[t])})),n.call(\"downloadImage\",t,r).then((function(e){l.notifier(u(t,\"Snapshot succeeded\")+\" - \"+e,\"long\")})).catch((function(){l.notifier(u(t,\"Sorry, there was a problem downloading your snapshot!\"),\"long\")}))}},c.sendDataToCloud={name:\"sendDataToCloud\",title:function(t){return u(t,\"Edit in Chart Studio\")},icon:o.disk,click:function(t){i.sendDataToCloud(t)}},c.editInChartStudio={name:\"editInChartStudio\",title:function(t){return u(t,\"Edit in Chart Studio\")},icon:o.pencil,click:function(t){i.sendDataToCloud(t)}},c.zoom2d={name:\"zoom2d\",_cat:\"zoom\",title:function(t){return u(t,\"Zoom\")},attr:\"dragmode\",val:\"zoom\",icon:o.zoombox,click:f},c.pan2d={name:\"pan2d\",_cat:\"pan\",title:function(t){return u(t,\"Pan\")},attr:\"dragmode\",val:\"pan\",icon:o.pan,click:f},c.select2d={name:\"select2d\",_cat:\"select\",title:function(t){return u(t,\"Box Select\")},attr:\"dragmode\",val:\"select\",icon:o.selectbox,click:f},c.lasso2d={name:\"lasso2d\",_cat:\"lasso\",title:function(t){return u(t,\"Lasso Select\")},attr:\"dragmode\",val:\"lasso\",icon:o.lasso,click:f},c.drawclosedpath={name:\"drawclosedpath\",title:function(t){return u(t,\"Draw closed freeform\")},attr:\"dragmode\",val:\"drawclosedpath\",icon:o.drawclosedpath,click:f},c.drawopenpath={name:\"drawopenpath\",title:function(t){return u(t,\"Draw open freeform\")},attr:\"dragmode\",val:\"drawopenpath\",icon:o.drawopenpath,click:f},c.drawline={name:\"drawline\",title:function(t){return u(t,\"Draw line\")},attr:\"dragmode\",val:\"drawline\",icon:o.drawline,click:f},c.drawrect={name:\"drawrect\",title:function(t){return u(t,\"Draw 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hover\")},attr:\"hovermode\",val:\"closest\",icon:o.tooltip_basic,gravity:\"ne\",click:y},c.resetViewSankey={name:\"resetSankeyGroup\",title:function(t){return u(t,\"Reset view\")},icon:o.home,click:function(t){for(var e={\"node.groups\":[],\"node.x\":[],\"node.y\":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e[\"node.groups\"].push(i.node.groups.slice()),e[\"node.x\"].push(i.node.x.slice()),e[\"node.y\"].push(i.node.y.slice())}n.call(\"restyle\",t,e)}},c.toggleHover={name:\"toggleHover\",title:function(t){return u(t,\"Toggle show closest data on hover\")},attr:\"hovermode\",val:null,toggle:!0,icon:o.tooltip_basic,gravity:\"ne\",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call(\"_guiRelayout\",t,r)}},c.resetViews={name:\"resetViews\",title:function(t){return u(t,\"Reset views\")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},c.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return u(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},c.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return u(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},c.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return u(t,\"Zoom in\")},attr:\"zoom\",val:\"in\",icon:o.zoom_plus,click:m},c.zoomOutMapbox={name:\"zoomOutMapbox\",_cat:\"zoomout\",title:function(t){return u(t,\"Zoom out\")},attr:\"zoom\",val:\"out\",icon:o.zoom_minus,click:m}},76052:function(t,e,r){\"use strict\";var n=r(44248),i=Object.keys(n),a=[\"drawline\",\"drawopenpath\",\"drawclosedpath\",\"drawcircle\",\"drawrect\",\"eraseshape\"],o=[\"v1hovermode\",\"hoverclosest\",\"hovercompare\",\"togglehover\",\"togglespikelines\"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var e=t.name,r=(t._cat||t.name).toLowerCase();-1===s.indexOf(e)&&s.push(e),-1===s.indexOf(r)&&s.push(r)}}(n[t])})),s.sort(),t.exports={DRAW_MODES:a,backButtons:o,foreButtons:s}},90824:function(t,e,r){\"use strict\";var n=r(3400),i=r(76308),a=r(31780),o=r(66540);t.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,\"modebar\");function l(t,e){return n.coerce(r,s,o,t,e)}l(\"orientation\"),l(\"bgcolor\",i.addOpacity(e.paper_bgcolor,.5));var u=i.contrast(i.rgb(e.modebar.bgcolor));l(\"color\",i.addOpacity(u,.3)),l(\"activecolor\",i.addOpacity(u,.7)),l(\"uirevision\",e.uirevision),l(\"add\"),l(\"remove\")}},45460:function(t,e,r){\"use strict\";t.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:r(66540),supplyLayoutDefaults:r(90824),manage:r(18816)}},18816:function(t,e,r){\"use strict\";var n=r(79811),i=r(43028),a=r(24040),o=r(10624).isUnifiedHover,s=r(66400),l=r(44248),u=r(76052).DRAW_MODES,c=r(3400).extendDeep;t.exports=function(t){var e=t._fullLayout,r=t._context,f=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var h,p=r.modeBarButtons;h=Array.isArray(p)&&p.length?function(t){for(var e=c([],t),r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++){var a=n[i];if(\"string\"==typeof a){if(void 0===l[a])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));e[r][i]=l[a]}}return e}(p):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function c(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(c(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(c(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),v=e._has(\"cartesian\"),g=e._has(\"gl3d\"),y=e._has(\"geo\"),m=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,u=0;u<d.length;u++){var c=d[u].toLowerCase();if(c===a||c===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\"),L(C);var O=[],P=[],I=[],D=[];(v||b||m||x||_)+y+g+w+T+k>1?(P=[\"toggleHover\"],I=[\"resetViews\"]):y?(O=[\"zoomInGeo\",\"zoomOutGeo\"],P=[\"hoverClosestGeo\"],I=[\"resetGeo\"]):g?(P=[\"hoverClosest3d\"],I=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(O=[\"zoomInMapbox\",\"zoomOutMapbox\"],P=[\"toggleHover\"],I=[\"resetViewMapbox\"]):b?P=[\"hoverClosestGl2d\"]:m?P=[\"hoverClosestPie\"]:A?(P=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],I=[\"resetViewSankey\"]):P=[\"toggleHover\"],v&&P.push(\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"),(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(P=[]),!v&&!b||M||(O=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==I[0]&&(I=[\"resetScale2d\"])),g?D=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(v||b)&&!M||_?D=[\"zoom2d\",\"pan2d\"]:w||y?D=[\"pan2d\"]:T&&(D=[\"zoom2d\"]),function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,\"scatter-like\")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,\"box-violin\")&&\"all\"!==n.boxpoints&&\"all\"!==n.points||(e=!0))}return e}(r)&&D.push(\"select2d\",\"lasso2d\");var z=[],R=function(t){-1===z.indexOf(t)&&-1!==P.indexOf(t)&&z.push(t)};if(Array.isArray(p)){for(var F=[],B=0;B<p.length;B++){var N=p[B];\"string\"==typeof N?(N=N.toLowerCase(),-1!==u.indexOf(N)?(e._has(\"mapbox\")||e._has(\"cartesian\"))&&D.push(N):\"togglespikelines\"===N?R(\"toggleSpikelines\"):\"togglehover\"===N?R(\"toggleHover\"):\"hovercompare\"===N?R(\"hoverCompareCartesian\"):\"hoverclosest\"===N?(R(\"hoverClosestCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\")):\"v1hovermode\"===N&&(R(\"hoverClosestCartesian\"),R(\"hoverCompareCartesian\"),R(\"hoverClosestGeo\"),R(\"hoverClosest3d\"),R(\"hoverClosestGl2d\"),R(\"hoverClosestPie\"))):F.push(N)}p=F}return L(D),L(O.concat(I)),L(z),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),f?f.update(t,h):e._modeBar=s(t,h)}else f&&(f.destroy(),delete e._modeBar)}},66400:function(t,e,r){\"use strict\";var n=r(33428),i=r(38248),a=r(3400),o=r(9224),s=r(25788).version,l=new DOMParser;function u(t){this.container=t.container,this.element=document.createElement(\"div\"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var c=u.prototype;c.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i=\"modebar-\"+n._uid;this.element.setAttribute(\"id\",i),this._uid=i,this.element.className=\"modebar\",\"hover\"===r.displayModeBar&&(this.element.className+=\" modebar--hover ease-bg\"),\"v\"===n.modebar.orientation&&(this.element.className+=\" vertical\",e=e.reverse());var o=n.modebar,s=\"hover\"===r.displayModeBar?\".js-plotly-plot .plotly:hover 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t.svg&&(e=l.parseFromString(t.svg,\"application/xml\").childNodes[0]),e.setAttribute(\"height\",\"1em\"),e.setAttribute(\"width\",\"1em\"),e},c.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute(\"data-attr\"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute(\"data-val\")||!0,o=t.getAttribute(\"data-attr\"),s=\"true\"===t.getAttribute(\"data-toggle\"),l=n.select(t);if(s)o===r&&l.classed(\"active\",!l.classed(\"active\"));else{var u=null===o?o:a.nestedProperty(e,o).get();l.classed(\"active\",u===i)}}))},c.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},c.getLogo=function(){var t=this.createGroup(),e=document.createElement(\"a\");return e.href=\"https://plotly.com/\",e.target=\"_blank\",e.setAttribute(\"data-title\",a._(this.graphInfo,\"Produced 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n=r(25376),i=r(22548),a=(0,r(31780).templatedArray)(\"button\",{visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},step:{valType:\"enumerated\",values:[\"month\",\"year\",\"day\",\"hour\",\"minute\",\"second\",\"all\"],dflt:\"month\",editType:\"plot\"},stepmode:{valType:\"enumerated\",values:[\"backward\",\"todate\"],dflt:\"backward\",editType:\"plot\"},count:{valType:\"number\",min:0,dflt:1,editType:\"plot\"},label:{valType:\"string\",editType:\"plot\"},editType:\"plot\"});t.exports={visible:{valType:\"boolean\",editType:\"plot\"},buttons:a,x:{valType:\"number\",min:-2,max:3,editType:\"plot\"},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\",editType:\"plot\"},y:{valType:\"number\",min:-2,max:3,editType:\"plot\"},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"bottom\",editType:\"plot\"},font:n({editType:\"plot\"}),bgcolor:{valType:\"color\",dflt:i.lightLine,editType:\"plot\"},activecolor:{valType:\"color\",editType:\"plot\"},bordercolor:{valType:\"color\",dflt:i.defaultLine,editType:\"plot\"},borderwidth:{valType:\"number\",min:0,dflt:0,editType:\"plot\"},editType:\"plot\"}},85984:function(t){\"use 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g=d(\"bgcolor\");d(\"activecolor\",i.contrast(g,l.lightAmount,l.darkAmount)),d(\"bordercolor\"),d(\"borderwidth\")}}},50216:function(t,e,r){\"use strict\";var n=r(33428),i=r(24040),a=r(7316),o=r(76308),s=r(43616),l=r(3400),u=l.strTranslate,c=r(72736),f=r(79811),h=r(84284),p=h.LINE_SPACING,d=h.FROM_TL,v=h.FROM_BR,g=r(85984),y=r(48040);function m(t){return t._id}function x(t,e,r){var n=l.ensureSingle(t,\"rect\",\"selector-rect\",(function(t){t.attr(\"shape-rendering\",\"crispEdges\")}));n.attr({rx:g.rx,ry:g.ry}),n.call(o.stroke,e.bordercolor).call(o.fill,function(t,e){return e._isActive||e._isHovered?t.activecolor:t.bgcolor}(e,r)).style(\"stroke-width\",e.borderwidth+\"px\")}function b(t,e,r,n){var i,a;l.ensureSingle(t,\"text\",\"selector-text\",(function(t){t.attr(\"text-anchor\",\"middle\")})).call(s.font,e.font).text((i=r,a=n._fullLayout._meta,i.label?a?l.templateString(i.label,a):i.label:\"all\"===i.step?\"all\":i.count+i.step.charAt(0))).call((function(t){c.convertToTspans(t,n)}))}t.exports=function(t){var e=t._fullLayout._infolayer.selectAll(\".rangeselector\").data(function(t){for(var e=f.list(t,\"x\",!0),r=[],n=0;n<e.length;n++){var i=e[n];i.rangeselector&&i.rangeselector.visible&&r.push(i)}return r}(t),m);e.enter().append(\"g\").classed(\"rangeselector\",!0),e.exit().remove(),e.style({cursor:\"pointer\",\"pointer-events\":\"all\"}),e.each((function(e){var r=n.select(this),o=e,f=o.rangeselector,h=r.selectAll(\"g.button\").data(l.filterVisible(f.buttons));h.enter().append(\"g\").classed(\"button\",!0),h.exit().remove(),h.each((function(e){var r=n.select(this),a=y(o,e);e._isActive=function(t,e,r){if(\"all\"===e.step)return!0===t.autorange;var n=Object.keys(r);return t.range[0]===r[n[0]]&&t.range[1]===r[n[1]]}(o,e,a),r.call(x,f,e),r.call(b,f,e,t),r.on(\"click\",(function(){t._dragged||i.call(\"_guiRelayout\",t,a)})),r.on(\"mouseover\",(function(){e._isHovered=!0,r.call(x,f,e)})),r.on(\"mouseout\",(function(){e._isHovered=!1,r.call(x,f,e)}))})),function(t,e,r,i,o){var f=0,h=0,y=r.borderwidth;e.each((function(){var t=n.select(this).select(\".selector-text\"),e=r.font.size*p,i=Math.max(e*c.lineCount(t),16)+3;h=Math.max(h,i)})),e.each((function(){var t=n.select(this),e=t.select(\".selector-rect\"),i=t.select(\".selector-text\"),a=i.node()&&s.bBox(i.node()).width,o=r.font.size*p,l=c.lineCount(i),d=Math.max(a+10,g.minButtonWidth);t.attr(\"transform\",u(y+f,y)),e.attr({x:0,y:0,width:d,height:h}),c.positionText(i,d/2,h/2-(l-1)*o/2+3),f+=d+5}));var m=t._fullLayout._size,x=m.l+m.w*r.x,b=m.t+m.h*(1-r.y),_=\"left\";l.isRightAnchor(r)&&(x-=f,_=\"right\"),l.isCenterAnchor(r)&&(x-=f/2,_=\"center\");var w=\"top\";l.isBottomAnchor(r)&&(b-=h,w=\"bottom\"),l.isMiddleAnchor(r)&&(b-=h/2,w=\"middle\"),f=Math.ceil(f),h=Math.ceil(h),x=Math.round(x),b=Math.round(b),a.autoMargin(t,i+\"-range-selector\",{x:r.x,y:r.y,l:f*d[_],r:f*v[_],b:h*v[w],t:h*d[w]}),o.attr(\"transform\",u(x,b))}(t,h,f,o._name,r)}))}},48040:function(t,e,r){\"use strict\";var n=r(73220),i=r(3400).titleCase;t.exports=function(t,e){var r=t._name,a={};if(\"all\"===e.step)a[r+\".autorange\"]=!0;else{var o=function(t,e){var r,a=t.range,o=new Date(t.r2l(a[1])),s=e.step,l=n[\"utc\"+i(s)],u=e.count;switch(e.stepmode){case\"backward\":r=t.l2r(+l.offset(o,-u));break;case\"todate\":var c=l.offset(o,-u);r=t.l2r(+l.ceil(c))}return[r,a[1]]}(t,e);a[r+\".range[0]\"]=o[0],a[r+\".range[1]\"]=o[1]}return a}},41152:function(t,e,r){\"use strict\";t.exports={moduleType:\"component\",name:\"rangeselector\",schema:{subplots:{xaxis:{rangeselector:r(26680)}}},layoutAttributes:r(26680),handleDefaults:r(22148),draw:r(50216)}},11200:function(t,e,r){\"use 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o=e[r],s=o[a.name];s&&s.visible&&s.autorange&&(s._input.autorange=!0,s._input.range=s.range=i(t,o))}}},74636:function(t){\"use 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n=r(3400),i=r(31780),a=r(79811),o=r(11200),s=r(10936);t.exports=function(t,e,r){var l=t[r],u=e[r];if(l.rangeslider||e._requestRangeslider[u._id]){n.isPlainObject(l.rangeslider)||(l.rangeslider={});var c,f,h=l.rangeslider,p=i.newContainer(u,\"rangeslider\");if(_(\"visible\")){_(\"bgcolor\",e.plot_bgcolor),_(\"bordercolor\"),_(\"borderwidth\"),_(\"thickness\"),_(\"autorange\",!u.isValidRange(h.range)),_(\"range\");var d=e._subplots;if(d)for(var v=d.cartesian.filter((function(t){return t.substr(0,t.indexOf(\"y\"))===a.name2id(r)})).map((function(t){return t.substr(t.indexOf(\"y\"),t.length)})),g=n.simpleMap(v,a.id2name),y=0;y<g.length;y++){var m=g[y];c=h[m]||{},f=i.newContainer(p,m,\"yaxis\");var x,b=e[m];c.range&&b.isValidRange(c.range)&&(x=\"fixed\"),\"match\"!==w(\"rangemode\",x)&&w(\"range\",b.range.slice())}p._input=h}}function _(t,e){return n.coerce(h,p,o,t,e)}function w(t,e){return n.coerce(c,f,s,t,e)}}},20060:function(t,e,r){\"use strict\";var n=r(33428),i=r(24040),a=r(7316),o=r(3400),s=o.strTranslate,l=r(43616),u=r(76308),c=r(81668),f=r(57952),h=r(79811),p=r(86476),d=r(93972),v=r(74636);function g(t){return\"number\"==typeof t.clientX?t.clientX:t.touches&&t.touches.length>0?t.touches[0].clientX:0}function y(t,e,r,n){var i=o.ensureSingle(t,\"rect\",v.bgClassName,(function(t){t.attr({x:0,y:0,\"shape-rendering\":\"crispEdges\"})})),a=n.borderwidth%2==0?n.borderwidth:n.borderwidth-1,c=-n._offsetShift,f=l.crispRound(e,n.borderwidth);i.attr({width:n._width+a,height:n._height+a,transform:s(c,c),\"stroke-width\":f}).call(u.stroke,n.bordercolor).call(u.fill,n.bgcolor)}function m(t,e,r,n){var i=e._fullLayout;o.ensureSingleById(i._topdefs,\"clipPath\",n._clipId,(function(t){t.append(\"rect\").attr({x:0,y:0})})).select(\"rect\").attr({width:n._width,height:n._height})}function x(t,e,r,i){var s,u=e.calcdata,c=t.selectAll(\"g.\"+v.rangePlotClassName).data(r._subplotsWith,o.identity);c.enter().append(\"g\").attr(\"class\",(function(t){return v.rangePlotClassName+\" \"+t})).call(l.setClipUrl,i._clipId,e),c.order(),c.exit().remove(),c.each((function(t,o){var l=n.select(this),c=0===o,p=h.getFromId(e,t,\"y\"),d=p._name,v=i[d],g={data:[],layout:{xaxis:{type:r.type,domain:[0,1],range:i.range.slice(),calendar:r.calendar},width:i._width,height:i._height,margin:{t:0,b:0,l:0,r:0}},_context:e._context};r.rangebreaks&&(g.layout.xaxis.rangebreaks=r.rangebreaks),g.layout[d]={type:p.type,domain:[0,1],range:\"match\"!==v.rangemode?v.range.slice():p.range.slice(),calendar:p.calendar},p.rangebreaks&&(g.layout[d].rangebreaks=p.rangebreaks),a.supplyDefaults(g);var y=g._fullLayout.xaxis,m=g._fullLayout[d];y.clearCalc(),y.setScale(),m.clearCalc(),m.setScale();var x={id:t,plotgroup:l,xaxis:y,yaxis:m,isRangePlot:!0};c?s=x:(x.mainplot=\"xy\",x.mainplotinfo=s),f.rangePlot(e,x,function(t,e){for(var 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T,k=o.simpleMap(l.range,r.r2l),A=o.simpleMap(r.range,r.r2l);T=A[0]<A[1]?[Math.min(k[0],A[0]),Math.max(k[1],A[1])]:[Math.max(k[0],A[0]),Math.min(k[1],A[1])],l.range=l._input.range=o.simpleMap(T,r.l2r)}r.cleanRange(\"rangeslider.range\");var M=e._size,S=r.domain;l._width=M.w*(S[1]-S[0]);var E=Math.round(M.l+M.w*S[0]),L=Math.round(M.t+M.h*(1-r._counterDomainMin)+(\"bottom\"===r.side?r._depth:0)+l._offsetShift+v.extraPad);a.attr(\"transform\",s(E,L)),l._rl=o.simpleMap(l.range,r.r2l);var C=l._rl[0],O=l._rl[1],P=O-C;if(l.p2d=function(t){return t/l._width*P+C},l.d2p=function(t){return(t-C)/P*l._width},r.rangebreaks){var I=r.locateBreaks(C,O);if(I.length){var D,z,R=0;for(D=0;D<I.length;D++)R+=(z=I[D]).max-z.min;var F=l._width/(O-C-R),B=[-F*C];for(D=0;D<I.length;D++)z=I[D],B.push(B[B.length-1]-F*(z.max-z.min));for(l.d2p=function(t){for(var e=B[0],r=0;r<I.length;r++){var n=I[r];if(t>=n.max)e=B[r+1];else if(t<n.min)break}return 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r=t._fullLayout,n=e[s],l=e._id.charAt(0),u=0,c=0;return\"bottom\"===e.side&&(u=e._depth,e.title.text!==r._dfltTitle[l]&&(c=1.5*e.title.font.size+10+n._offsetShift,c+=(e.title.text.match(i.BR_TAG_ALL)||[]).length*e.title.font.size*o)),{x:0,y:e._counterDomainMin,l:0,r:0,t:0,b:n._height+u+Math.max(r.margin.b,c),pad:a.extraPad+2*n._offsetShift}}},49692:function(t,e,r){\"use strict\";var n=r(3400),i=r(11200),a=r(10936),o=r(97944);t.exports={moduleType:\"component\",name:\"rangeslider\",schema:{subplots:{xaxis:{rangeslider:n.extendFlat({},i,{yaxis:a})}}},layoutAttributes:r(11200),handleDefaults:r(94040),calcAutorange:r(26652),draw:r(20060),isVisible:o.isVisible,makeData:o.makeData,autoMarginOpts:o.autoMarginOpts}},10936:function(t){\"use 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strict\";t.exports={BENDPX:1.5,MINSELECT:12,SELECTDELAY:100,SELECTID:\"-select\"}},74224:function(t,e,r){\"use strict\";var n=r(3400),i=r(54460),a=r(51272),o=r(93956),s=r(65152);function l(t,e,r){function a(r,i){return n.coerce(t,e,o,r,i)}var l=a(\"path\"),u=\"path\"!==a(\"type\",l?\"path\":\"rect\");u&&delete e.path,a(\"opacity\"),a(\"line.color\"),a(\"line.width\"),a(\"line.dash\");for(var c=[\"x\",\"y\"],f=0;f<2;f++){var h,p,d,v=c[f],g={_fullLayout:r},y=i.coerceRef(t,e,g,v);if((h=i.getFromId(g,y))._selectionIndices.push(e._index),d=s.rangeToShapePosition(h),p=s.shapePositionToRange(h),u){var m=v+\"0\",x=v+\"1\",b=t[m],_=t[x];t[m]=p(t[m],!0),t[x]=p(t[x],!0),i.coercePosition(e,g,a,y,m),i.coercePosition(e,g,a,y,x);var w=e[m],T=e[x];void 0!==w&&void 0!==T&&(e[m]=d(w),e[x]=d(T),t[m]=b,t[x]=_)}}u&&n.noneOrAll(t,e,[\"x0\",\"x1\",\"y0\",\"y1\"])}t.exports=function(t,e){a(t,e,{name:\"selections\",handleItemDefaults:l});for(var r=e.selections,n=0;n<r.length;n++){var i=r[n];i&&void 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w=h(t)&&t._fullLayout._activeSelectionIndex===e;w&&(m=t._fullLayout.activeselection.fillcolor,y=t._fullLayout.activeselection.opacity);for(var T=[],k=1;k>=0;k--){var A=r.append(\"path\").attr(g).style(\"opacity\",k?.1:y).call(o.stroke,x).call(o.fill,m).call(s.dashLine,k?\"solid\":_,k?4+b:b);if(d(A,t,a),w){var M=l(t.layout,\"selections\",a);A.style({cursor:\"move\"});var S={element:A.node(),plotinfo:p,gd:t,editHelpers:M,isActiveSelection:!0},E=n(u,t);i(E,A,S)}else A.style(\"pointer-events\",k?\"all\":\"none\");T[k]=A}var L=T[0];T[1].node().addEventListener(\"click\",(function(){return function(t,e){if(h(t)){var r=+e.node().getAttribute(\"data-index\");if(r>=0){if(r===t._fullLayout._activeSelectionIndex)return void v(t);t._fullLayout._activeSelectionIndex=r,t._fullLayout._deactivateSelection=v,f(t)}}}(t,L)}))}(t._fullLayout._selectionLayer)}function d(t,e,r){var n=r.xref+r.yref;s.setClipUrl(t,\"clip\"+e._fullLayout._uid+n,e)}function v(t){h(t)&&t._fullLayout._activeSelectionIndex>=0&&(a(t),delete t._fullLayout._activeSelectionIndex,f(t))}t.exports={draw:f,drawOne:p,activateLastSelection:function(t){if(h(t)){var e=t._fullLayout.selections.length-1;t._fullLayout._activeSelectionIndex=e,t._fullLayout._deactivateSelection=v,f(t)}}}},34200:function(t,e,r){\"use strict\";var n=r(98192).u,i=r(92880).extendFlat;t.exports={newselection:{mode:{valType:\"enumerated\",values:[\"immediate\",\"gradual\"],dflt:\"immediate\",editType:\"none\"},line:{color:{valType:\"color\",editType:\"none\"},width:{valType:\"number\",min:1,dflt:1,editType:\"none\"},dash:i({},n,{dflt:\"dot\",editType:\"none\"}),editType:\"none\"},editType:\"none\"},activeselection:{fillcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"none\"},opacity:{valType:\"number\",min:0,max:1,dflt:.5,editType:\"none\"},editType:\"none\"}}},81004:function(t){\"use 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m,x=o(a,u,f,d),b={xref:h._id,yref:p._id,opacity:c.opacity,line:{color:c.line.color,width:c.line.width,dash:c.line.dash}};1===x.length&&(m=x[0]),m&&5===m.length&&\"select\"===v?(b.type=\"rect\",b.x0=m[0][1],b.y0=m[0][2],b.x1=m[2][1],b.y1=m[2][2]):(b.type=\"path\",h&&p&&l(x,h,p),b.path=s(x),m=null),i(u);for(var _=e.editHelpers,w=(_||{}).modifyItem,T=[],k=0;k<g.length;k++){var A=u._fullLayout.selections[k];if(A){if(T[k]=A._input,void 0!==d&&k===u._fullLayout._activeSelectionIndex){var M=b;switch(A.type){case\"rect\":w(\"x0\",M.x0),w(\"x1\",M.x1),w(\"y0\",M.y0),w(\"y1\",M.y1);break;case\"path\":w(\"path\",M.path)}}}else T[k]=A}return void 0===d?(T.push(b),T):_?_.getUpdateObj():{}}}}},5840:function(t,e,r){\"use strict\";var n=r(3400).strTranslate;function i(t,e){switch(t.type){case\"log\":return t.p2d(e);case\"date\":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}t.exports={p2r:i,r2p:function(t,e){switch(t.type){case\"log\":return t.d2p(e);case\"date\":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e=\"y\"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},22676:function(t,e,r){\"use strict\";var n=r(23640),i=r(43156);t.exports={moduleType:\"component\",name:\"selections\",layoutAttributes:r(93956),supplyLayoutDefaults:r(74224),supplyDrawNewSelectionDefaults:r(81004),includeBasePlot:r(36632)(\"selections\"),draw:n.draw,drawOne:n.drawOne,reselect:i.reselect,prepSelect:i.prepSelect,clearOutline:i.clearOutline,clearSelectionsCache:i.clearSelectionsCache,selectOnClick:i.selectOnClick}},43156:function(t,e,r){\"use strict\";var n=r(14756),i=r(61456),a=r(24040),o=r(43616).dashStyle,s=r(76308),l=r(93024),u=r(10624).makeEventData,c=r(72760),f=c.freeMode,h=c.rectMode,p=c.drawMode,d=c.openMode,v=c.selectMode,g=r(65152),y=r(85448),m=r(55496),x=r(1936).clearOutline,b=r(9856),_=b.handleEllipse,w=b.readPaths,T=r(93940).newShapes,k=r(5968),A=r(23640).activateLastSelection,M=r(3400),S=M.sorterAsc,E=r(92065),L=r(91200),C=r(79811).getFromId,O=r(73696),P=r(39172).redrawReglTraces,I=r(83280),D=I.MINSELECT,z=E.filter,R=E.tester,F=r(5840),B=F.p2r,N=F.axValue,j=F.getTransform;function U(t){return void 0!==t.subplot}function V(t,e,r,n,i,a,o){var s,l,u,c,f,h,p,v,g,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf(\"event\")>-1,b=[];if(function(t){return t&&Array.isArray(t)&&!0!==t[0].hoverOnBox}(y)){W(t,e,a);var _=function(t,e){var r,n,i=t[0],a=-1,o=[];for(n=0;n<e.length;n++)if(r=e[n],i.fullData._expandedIndex===r.cd[0].trace._expandedIndex){if(!0===i.hoverOnBox)break;void 0!==i.pointNumber?a=i.pointNumber:void 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if(\"path\"===s.type)for(var T=s.path.split(\"Z\"),k=[],A=0;A<T.length;A++){var M=T[A];if(M){M+=\"Z\";var S=g.extractPathCoords(M,y.paramIsX,\"raw\"),E=g.extractPathCoords(M,y.paramIsY,\"raw\");l=1/0,u=-1/0,c=1/0,f=-1/0,h=[];for(var L=0;L<S.length;L++){var O=lt(v,S[L]),P=lt(m,E[L]);h.push([O,P]),l=Math.min(O,l),u=Math.max(O,u),c=Math.min(P,c),f=Math.max(P,f)}h.xmin=l,h.xmax=u,h.ymin=c,h.ymax=f,h.xref=p,h.yref=d,h.subtract=st(h,k),k.push(h),r.push(h)}}}}return r}function st(t,e){for(var r=!1,n=0;n<e.length;n++)for(var a=e[n],o=0;o<t.length;o++)if(i(t[o],a)){r=!r;break}return r}function lt(t,e){return\"date\"===t.type&&(e=e.replace(\"_\",\" \")),\"log\"===t.type?t.c2p(e):t.r2p(e,null,t.calendar)}function ut(t){for(var e=t.length,r=[],n=0;n<e;n++){var 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u=!U(n),c=f(i),g=h(i),y=d(i),x=p(i),b=v(i),w=\"drawcircle\"===i,T=\"drawline\"===i||w,k=n.gd,A=k._fullLayout,S=b&&\"immediate\"===A.newselection.mode&&u,E=A._zoomlayer,C=n.element.getBoundingClientRect(),O=n.plotinfo,P=j(O),F=e-C.left,B=r-C.top;A._calcInverseTransform(k);var N=M.apply3DTransform(A._invTransform)(F,B);F=N[0],B=N[1];var q,H,X,K,J,tt,at,ot=A._invScaleX,st=A._invScaleY,lt=F,pt=B,dt=\"M\"+F+\",\"+B,vt=n.xaxes[0],gt=n.yaxes[0],yt=vt._length,mt=gt._length,xt=t.altKey&&!(p(i)&&y);W(t,k,n),c&&(q=z([[F,B]],I.BENDPX));var bt=E.selectAll(\"path.select-outline-\"+O.id).data([1]),_t=x?A.newshape:A.newselection;x&&(n.hasText=_t.label.text||_t.label.texttemplate);var wt=x&&!y?_t.fillcolor:\"rgba(0,0,0,0)\",Tt=_t.line.color||(u?s.contrast(k._fullLayout.plot_bgcolor):\"#7f7f7f\");bt.enter().append(\"path\").attr(\"class\",\"select-outline 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s=A.selections[o];s.xref!==t||s.yref!==e?n.push(r[o]):i=!0}i&&(k._fullLayout._noEmitSelectedAtStart=!0,a.call(\"_guiRelayout\",k,{selections:n}))}});var Lt=function(t){return t.plotinfo.fillRangeItems||ct(t.xaxes.concat(t.yaxes))}(n);n.moveFn=function(t,e){n._clearSubplotSelections&&(n._clearSubplotSelections(),n._clearSubplotSelections=void 0),lt=Math.max(0,Math.min(yt,ot*t+F)),pt=Math.max(0,Math.min(mt,st*e+B));var r=Math.abs(lt-F),i=Math.abs(pt-B);if(g){var a,o,s;if(b){var 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n,i=t[0][0],o=e.gd,p=i.getAttribute(\"d\"),g=o._fullLayout.newshape,y=e.plotinfo,w=e.isActiveShape,T=y.xaxis,k=y.yaxis,A=!!y.domain||!y.xaxis,M=!!y.domain||!y.yaxis,S=a(r),E=m(p,o,y,w),L={editable:!0,visible:g.visible,name:g.name,showlegend:g.showlegend,legend:g.legend,legendwidth:g.legendwidth,legendgroup:g.legendgroup,legendgrouptitle:{text:g.legendgrouptitle.text,font:g.legendgrouptitle.font},legendrank:g.legendrank,label:g.label,xref:A?\"paper\":T._id,yref:M?\"paper\":k._id,layer:g.layer,opacity:g.opacity,line:{color:g.line.color,width:g.line.width,dash:g.line.dash}};if(S||(L.fillcolor=g.fillcolor,L.fillrule=g.fillrule),1===E.length&&(n=E[0]),n&&5===n.length&&\"drawrect\"===r)L.type=\"rect\",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[2][1],L.y1=n[2][2];else if(n&&\"drawline\"===r)L.type=\"line\",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[1][1],L.y1=n[1][2];else if(n&&\"drawcircle\"===r){L.type=\"circle\";var C=n[s][1],O=n[l][1],P=n[u][1],I=n[c][1],D=n[s][2],z=n[l][2],R=n[u][2],F=n[c][2],B=y.xaxis&&(\"date\"===y.xaxis.type||\"log\"===y.xaxis.type),N=y.yaxis&&(\"date\"===y.yaxis.type||\"log\"===y.yaxis.type);B&&(C=v(y.xaxis,C),O=v(y.xaxis,O),P=v(y.xaxis,P),I=v(y.xaxis,I)),N&&(D=v(y.yaxis,D),z=v(y.yaxis,z),R=v(y.yaxis,R),F=v(y.yaxis,F));var j=(O+I)/2,U=(D+R)/2,V=b({x0:j,y0:U,x1:j+(I-O+P-C)/2*f,y1:U+(F-z+R-D)/2*h});B&&(V.x0=d(y.xaxis,V.x0),V.x1=d(y.xaxis,V.x1)),N&&(V.y0=d(y.yaxis,V.y0),V.y1=d(y.yaxis,V.y1)),L.x0=V.x0,L.y0=V.y0,L.x1=V.x1,L.y1=V.y1}else L.type=\"path\",T&&k&&_(E,T,k),L.path=x(E),n=null;return L}t.exports={newShapes:function(t,e){if(t.length&&t[0][0]){var r=e.gd,n=e.isActiveShape,a=e.dragmode,o=(r.layout||{}).shapes||[];if(!i(a)&&void 0!==n){var s=r._fullLayout._activeShapeIndex;if(s<o.length)switch(r._fullLayout.shapes[s].type){case\"rect\":a=\"drawrect\";break;case\"circle\":a=\"drawcircle\";break;case\"line\":a=\"drawline\";break;case\"path\":var l=o[s].path||\"\";a=\"Z\"===l[l.length-1]?\"drawclosedpath\":\"drawopenpath\"}}var u=w(t,e,a);g(r);for(var c=e.editHelpers,f=(c||{}).modifyItem,h=[],p=0;p<o.length;p++){var d=r._fullLayout.shapes[p];if(h[p]=d._input,void 0!==n&&p===r._fullLayout._activeShapeIndex){var v=u;switch(d.type){case\"line\":case\"rect\":case\"circle\":f(\"x0\",v.x0-(d.x0shift||0)),f(\"x1\",v.x1-(d.x1shift||0)),f(\"y0\",v.y0-(d.y0shift||0)),f(\"y1\",v.y1-(d.y1shift||0));break;case\"path\":f(\"path\",v.path)}}}return void 0===n?(h.push(u),h):c?c.getUpdateObj():{}}},createShapeObj:w}},1936:function(t){\"use strict\";t.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".outline-controllers\").remove()},clearOutline:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(\".select-outline\").remove(),t._fullLayout._outlining=!1}}},65152:function(t,e,r){\"use strict\";var n=r(85448),i=r(3400),a=r(54460);function o(t,e){var r=0;return(e=e||0)&&t&&(\"category\"===t.type||\"multicategory\"===t.type)&&(r=(t.r2p(1)-t.r2p(0))*e),r}e.rangeToShapePosition=function(t){return\"log\"===t.type?t.r2d:function(t){return t}},e.shapePositionToRange=function(t){return\"log\"===t.type?t.d2r:function(t){return t}},e.decodeDate=function(t){return function(e){return e.replace&&(e=e.replace(\"_\",\" \")),t(e)}},e.encodeDate=function(t){return function(e){return t(e).replace(\" \",\"_\")}},e.extractPathCoords=function(t,e,r){var a=[];return t.match(n.segmentRE).forEach((function(t){var o=e[t.charAt(0)].drawn;if(void 0!==o){var s=t.substr(1).match(n.paramRE);if(s&&!(s.length<o)){var l=s[o],u=r?l:i.cleanNumber(l);a.push(u)}}})),a},e.getDataToPixel=function(t,r,n,i,a){var s,l=t._fullLayout._size;if(r)if(\"domain\"===a)s=function(t){return r._length*(i?1-t:t)+r._offset};else{var u=e.shapePositionToRange(r);s=function(t){var e=o(r,n);return r._offset+r.r2p(u(t,!0))+e},\"date\"===r.type&&(s=e.decodeDate(s))}else s=i?function(t){return l.t+l.h*(1-t)}:function(t){return l.l+l.w*t};return s},e.getPixelToData=function(t,r,n,i){var a,o=t._fullLayout._size;if(r)if(\"domain\"===i)a=function(t){var e=(t-r._offset)/r._length;return n?1-e:e};else{var s=e.rangeToShapePosition(r);a=function(t){return s(r.p2r(t-r._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},e.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},e.makeShapesOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.shapes[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&\"paper\"!==r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&\"paper\"!==r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}},e.makeSelectionsOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.selections[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&(n.xaxis=t._fullLayout[r.xref+\"axis\"]),r.yref&&(n.yaxis=t._fullLayout[r.yref+\"axis\"])),{options:r,plotinfo:n}},e.getPathString=function(t,r){var s,l,u,c,f,h,p,d,v=r.type,g=a.getRefType(r.xref),y=a.getRefType(r.yref),m=a.getFromId(t,r.xref),x=a.getFromId(t,r.yref),b=t._fullLayout._size,_=o(m,r.x0shift),w=o(m,r.x1shift),T=o(x,r.y0shift),k=o(x,r.y1shift);if(m?\"domain\"===g?l=function(t){return m._offset+m._length*t}:(s=e.shapePositionToRange(m),l=function(t){return m._offset+m.r2p(s(t,!0))}):l=function(t){return b.l+b.w*t},x?\"domain\"===y?c=function(t){return x._offset+x._length*(1-t)}:(u=e.shapePositionToRange(x),c=function(t){return x._offset+x.r2p(u(t,!0))}):c=function(t){return b.t+b.h*(1-t)},\"path\"===v)return m&&\"date\"===m.type&&(l=e.decodeDate(l)),x&&\"date\"===x.type&&(c=e.decodeDate(c)),function(t,e,r){var a=t.path,o=t.xsizemode,s=t.ysizemode,l=t.xanchor,u=t.yanchor;return a.replace(n.segmentRE,(function(t){var a=0,c=t.charAt(0),f=n.paramIsX[c],h=n.paramIsY[c],p=n.numParams[c],d=t.substr(1).replace(n.paramRE,(function(t){return f[a]?t=\"pixel\"===o?e(l)+Number(t):e(t):h[a]&&(t=\"pixel\"===s?r(u)-Number(t):r(t)),++a>p&&(t=\"X\"),t}));return a>p&&(d=d.replace(/[\\s,]*X.*/,\"\"),i.log(\"Ignoring extra params in segment \"+t)),c+d}))}(r,l,c);if(\"pixel\"===r.xsizemode){var A=l(r.xanchor);f=A+r.x0+_,h=A+r.x1+w}else f=l(r.x0)+_,h=l(r.x1)+w;if(\"pixel\"===r.ysizemode){var M=c(r.yanchor);p=M-r.y0+T,d=M-r.y1+k}else p=c(r.y0)+T,d=c(r.y1)+k;if(\"line\"===v)return\"M\"+f+\",\"+p+\"L\"+h+\",\"+d;if(\"rect\"===v)return\"M\"+f+\",\"+p+\"H\"+h+\"V\"+d+\"H\"+f+\"Z\";var S=(f+h)/2,E=(p+d)/2,L=Math.abs(S-f),C=Math.abs(E-p),O=\"A\"+L+\",\"+C,P=S+L+\",\"+E;return\"M\"+P+O+\" 0 1,1 \"+S+\",\"+(E-C)+O+\" 0 0,1 \"+P+\"Z\"}},41592:function(t,e,r){\"use strict\";var n=r(4016);t.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:r(46056),supplyLayoutDefaults:r(43712),supplyDrawNewShapeDefaults:r(65144),includeBasePlot:r(36632)(\"shapes\"),calcAutorange:r(96084),draw:n.draw,drawOne:n.drawOne}},97728:function(t){\"use strict\";function e(t,e){return e?e.d2l(t):t}function r(t,e){return e?e.l2d(t):t}function n(t){return t.x0shift||0}function i(t){return t.x1shift||0}function a(t){return t.y0shift||0}function o(t){return t.y1shift||0}function s(t,r){return e(t.x1,r)+i(t)-e(t.x0,r)-n(t)}function l(t,r,n){return e(t.y1,n)+o(t)-e(t.y0,n)-a(t)}t.exports={x0:function(t){return t.x0},x1:function(t){return t.x1},y0:function(t){return t.y0},y1:function(t){return t.y1},slope:function(t,e,r){return\"line\"!==t.type?void 0:l(t,0,r)/s(t,e)},dx:s,dy:l,width:function(t,e){return Math.abs(s(t,e))},height:function(t,e,r){return Math.abs(l(t,0,r))},length:function(t,e,r){return\"line\"!==t.type?void 0:Math.sqrt(Math.pow(s(t,e),2)+Math.pow(l(t,0,r),2))},xcenter:function(t,a){return r((e(t.x1,a)+i(t)+e(t.x0,a)+n(t))/2,a)},ycenter:function(t,n,i){return r((e(t.y1,i)+o(t)+e(t.y0,i)+a(t))/2,i)}}},89861:function(t,e,r){\"use strict\";var n=r(25376),i=r(66741),a=r(92880).extendDeepAll,o=r(67824).overrideAll,s=r(85656),l=r(31780).templatedArray,u=r(60876),c=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});t.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:c,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:u.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:u.railBgColor},bordercolor:{valType:\"color\",dflt:u.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:u.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:u.tickLength},tickcolor:{valType:\"color\",dflt:u.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:u.minorTickLength}}),\"arraydraw\",\"from-root\")},60876:function(t){\"use strict\";t.exports={name:\"sliders\",containerClassName:\"slider-container\",groupClassName:\"slider-group\",inputAreaClass:\"slider-input-area\",railRectClass:\"slider-rail-rect\",railTouchRectClass:\"slider-rail-touch-rect\",gripRectClass:\"slider-grip-rect\",tickRectClass:\"slider-tick-rect\",inputProxyClass:\"slider-input-proxy\",labelsClass:\"slider-labels\",labelGroupClass:\"slider-label-group\",labelClass:\"slider-label\",currentValueClass:\"slider-current-value\",railHeight:5,menuIndexAttrName:\"slider-active-index\",autoMarginIdRoot:\"slider-\",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:\"#bec8d9\",railBgColor:\"#f8fafc\",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:\"#bec8d9\",gripBgColor:\"#f6f8fa\",gripBgActiveColor:\"#dbdde0\",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:\"#333\",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:\"#333\",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},8132:function(t,e,r){\"use 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n.coerce(t,e,s,r,i)}if(\"skip\"===t.method||Array.isArray(t.args)?r(\"visible\"):e.visible=!1){r(\"method\"),r(\"args\");var i=r(\"label\",\"step-\"+e._index);r(\"value\",i),r(\"execute\")}}t.exports=function(t,e){i(t,e,{name:o,handleItemDefaults:l})}},79664:function(t,e,r){\"use strict\";var n=r(33428),i=r(7316),a=r(76308),o=r(43616),s=r(3400),l=s.strTranslate,u=r(72736),c=r(31780).arrayEditor,f=r(60876),h=r(84284),p=h.LINE_SPACING,d=h.FROM_TL,v=h.FROM_BR;function g(t){return f.autoMarginIdRoot+t._index}function y(t){return t._index}function m(t,e){var r=o.tester.selectAll(\"g.\"+f.labelGroupClass).data(e._visibleSteps);r.enter().append(\"g\").classed(f.labelGroupClass,!0);var a=0,l=0;r.each((function(t){var r=_(n.select(this),{step:t},e).node();if(r){var i=o.bBox(r);l=Math.max(l,i.height),a=Math.max(a,i.width)}})),r.remove();var c=e._dims={};c.inputAreaWidth=Math.max(f.railWidth,f.gripHeight);var h=t._fullLayout._size;c.lx=h.l+h.w*e.x,c.ly=h.t+h.h*(1-e.y),\"fraction\"===e.lenmode?c.outerLength=Math.round(h.w*e.len):c.outerLength=e.len,c.inputAreaStart=0,c.inputAreaLength=Math.round(c.outerLength-e.pad.l-e.pad.r);var p=(c.inputAreaLength-2*f.stepInset)/(e._stepCount-1),y=a+f.labelPadding;if(c.labelStride=Math.max(1,Math.ceil(y/p)),c.labelHeight=l,c.currentValueMaxWidth=0,c.currentValueHeight=0,c.currentValueTotalHeight=0,c.currentValueMaxLines=1,e.currentvalue.visible){var m=o.tester.append(\"g\");r.each((function(t){var r=x(m,e,t.label),n=r.node()&&o.bBox(r.node())||{width:0,height:0},i=u.lineCount(r);c.currentValueMaxWidth=Math.max(c.currentValueMaxWidth,Math.ceil(n.width)),c.currentValueHeight=Math.max(c.currentValueHeight,Math.ceil(n.height)),c.currentValueMaxLines=Math.max(c.currentValueMaxLines,i)})),c.currentValueTotalHeight=c.currentValueHeight+e.currentvalue.offset,m.remove()}c.height=c.currentValueTotalHeight+f.tickOffset+e.ticklen+f.labelOffset+c.labelHeight+e.pad.t+e.pad.b;var b=\"left\";s.isRightAnchor(e)&&(c.lx-=c.outerLength,b=\"right\"),s.isCenterAnchor(e)&&(c.lx-=c.outerLength/2,b=\"center\");var w=\"top\";s.isBottomAnchor(e)&&(c.ly-=c.height,w=\"bottom\"),s.isMiddleAnchor(e)&&(c.ly-=c.height/2,w=\"middle\"),c.outerLength=Math.ceil(c.outerLength),c.height=Math.ceil(c.height),c.lx=Math.round(c.lx),c.ly=Math.round(c.ly);var T={y:e.y,b:c.height*v[w],t:c.height*d[w]};\"fraction\"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*v[b]):(T.x=e.x,T.l=c.outerLength*d[b],T.r=c.outerLength*v[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case\"right\":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i=\"left\";break;case\"center\":n=.5*a.inputAreaLength,i=\"middle\";break;default:n=f.currentValueInset,i=\"left\"}var l=s.ensureSingle(t,\"text\",f.labelClass,(function(t){t.attr({\"text-anchor\":i,\"data-notex\":1})})),c=e.currentvalue.prefix?e.currentvalue.prefix:\"\";if(\"string\"==typeof r)c+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),c+=h}e.currentvalue.suffix&&(c+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(c).call(u.convertToTspans,e._gd);var v=u.lineCount(l),g=(a.currentValueMaxLines+1-v)*e.currentvalue.font.size*p;return 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a=Math.round(n*(r._stepCount-1)),o=r._visibleSteps[a]._index;o!==r.active&&k(t,e,r,o,!0,i)}function k(t,e,r,n,a,o){var s=r.active;r.active=n,c(t.layout,f.name,r).applyUpdate(\"active\",n);var l=r.steps[r.active];e.call(S,r,o),e.call(x,r),t.emit(\"plotly_sliderchange\",{slider:r,step:r.steps[r.active],interaction:a,previousActive:s}),l&&l.method&&a&&(e._nextMethod?(e._nextMethod.step=l,e._nextMethod.doCallback=a,e._nextMethod.doTransition=o):(e._nextMethod={step:l,doCallback:a,doTransition:o},e._nextMethodRaf=window.requestAnimationFrame((function(){var r=e._nextMethod.step;r.method&&(r.execute&&i.executeAPICommand(t,r.method,r.args),e._nextMethod=null,e._nextMethodRaf=null)}))))}function A(t,e,r){if(!e._context.staticPlot){var i=r.node(),o=n.select(e);t.on(\"mousedown\",l),t.on(\"touchstart\",l)}function s(){return r.data()[0]}function l(){var t=s();e.emit(\"plotly_sliderstart\",{slider:t});var 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n=r._dims,i=s.ensureSingle(t,\"rect\",f.railTouchRectClass,(function(n){n.call(A,e,t,r).style(\"pointer-events\",\"all\")}));i.attr({width:n.inputAreaLength,height:Math.max(n.inputAreaWidth,f.tickOffset+r.ticklen+n.labelHeight)}).call(a.fill,r.bgcolor).attr(\"opacity\",0),o.setTranslate(i,0,n.currentValueTotalHeight)}function O(t,e){var r=e._dims,n=r.inputAreaLength-2*f.railInset,i=s.ensureSingle(t,\"rect\",f.railRectClass);i.attr({width:n,height:f.railWidth,rx:f.railRadius,ry:f.railRadius,\"shape-rendering\":\"crispEdges\"}).call(a.stroke,e.bordercolor).call(a.fill,e.bgcolor).style(\"stroke-width\",e.borderwidth+\"px\"),o.setTranslate(i,f.railInset,.5*(r.inputAreaWidth-f.railWidth)+r.currentValueTotalHeight)}t.exports=function(t){var e=t._context.staticPlot,r=t._fullLayout,a=function(t,e){for(var r=t[f.name],n=[],i=0;i<r.length;i++){var a=r[i];a.visible&&(a._gd=e,n.push(a))}return n}(r,t),s=r._infolayer.selectAll(\"g.\"+f.containerClassName).data(a.length>0?[0]:[]);function l(e){e._commandObserver&&(e._commandObserver.remove(),delete e._commandObserver),i.autoMargin(t,g(e))}if(s.enter().append(\"g\").classed(f.containerClassName,!0).style(\"cursor\",e?null:\"ew-resize\"),s.exit().each((function(){n.select(this).selectAll(\"g.\"+f.groupClassName).each(l)})).remove(),0!==a.length){var u=s.selectAll(\"g.\"+f.groupClassName).data(a,y);u.enter().append(\"g\").classed(f.groupClassName,!0),u.exit().each(l).remove();for(var c=0;c<a.length;c++){var h=a[c];m(t,h)}u.each((function(e){var r=n.select(this);!function(t){var e=t._dims;e.labelSteps=[];for(var r=t._stepCount,n=0;n<r;n+=e.labelStride)e.labelSteps.push({fraction:n/(r-1),step:t._visibleSteps[n]})}(e),i.manageCommandObserver(t,e,e._visibleSteps,(function(e){var n=r.data()[0];n.active!==e.index&&(n._dragging||k(t,r,n,e.index,!1,!0))})),function(t,e,r){(r.steps[r.active]||{}).visible||(r.active=r._visibleSteps[0]._index),e.call(x,r).call(O,r).call(w,r).call(M,r).call(C,t,r).call(b,t,r);var n=r._dims;o.setTranslate(e,n.lx+r.pad.l,n.ly+r.pad.t),e.call(S,r,!1),e.call(x,r)}(t,n.select(this),e)}))}}},97544:function(t,e,r){\"use strict\";var n=r(60876);t.exports={moduleType:\"component\",name:n.name,layoutAttributes:r(89861),supplyLayoutDefaults:r(8132),draw:r(79664)}},81668:function(t,e,r){\"use strict\";var n=r(33428),i=r(38248),a=r(7316),o=r(24040),s=r(3400),l=s.strTranslate,u=r(43616),c=r(76308),f=r(72736),h=r(13448),p=r(84284).OPPOSITE_SIDE,d=/ [XY][0-9]* /;t.exports={draw:function(t,e,r){var 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n=r(25376),i=r(22548),a=r(92880).extendFlat,o=r(67824).overrideAll,s=r(66741),l=r(31780).templatedArray,u=l(\"button\",{visible:{valType:\"boolean\"},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},args2:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\",dflt:\"\"},execute:{valType:\"boolean\",dflt:!0}});t.exports=o(l(\"updatemenu\",{_arrayAttrRegexps:[/^updatemenus\\[(0|[1-9][0-9]+)\\]\\.buttons/],visible:{valType:\"boolean\"},type:{valType:\"enumerated\",values:[\"dropdown\",\"buttons\"],dflt:\"dropdown\"},direction:{valType:\"enumerated\",values:[\"left\",\"right\",\"up\",\"down\"],dflt:\"down\"},active:{valType:\"integer\",min:-1,dflt:0},showactive:{valType:\"boolean\",dflt:!0},buttons:u,x:{valType:\"number\",min:-2,max:3,dflt:-.05},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"right\"},y:{valType:\"number\",min:-2,max:3,dflt:1},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},pad:a(s({editType:\"arraydraw\"}),{}),font:n({}),bgcolor:{valType:\"color\"},bordercolor:{valType:\"color\",dflt:i.borderLine},borderwidth:{valType:\"number\",min:0,dflt:1,editType:\"arraydraw\"}}),\"arraydraw\",\"from-root\")},73712:function(t){\"use 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s(t,e,r){this.gd=t,this.container=e,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll(\"rect.scrollbox-bg\").data([0]),this.bg.exit().on(\".drag\",null).on(\"wheel\",null).remove(),this.bg.enter().append(\"rect\").classed(\"scrollbox-bg\",!0).style(\"pointer-events\",\"all\").attr({opacity:0,x:0,y:0,width:0,height:0})}s.barWidth=2,s.barLength=20,s.barRadius=2,s.barPad=1,s.barColor=\"#808BA4\",s.prototype.enable=function(t,e,r){var o=this.gd._fullLayout,l=o.width,u=o.height;this.position=t;var c,f,h,p,d=this.position.l,v=this.position.w,g=this.position.t,y=this.position.h,m=this.position.direction,x=\"down\"===m,b=\"left\"===m,_=\"up\"===m,w=v,T=y;x||b||\"right\"===m||_||(this.position.direction=\"down\",x=!0),x||_?(f=(c=d)+w,x?(h=g,T=(p=Math.min(h+T,u))-h):T=(p=g+T)-(h=Math.max(p-T,0))):(p=(h=g)+T,b?w=(f=d+w)-(c=Math.max(f-w,0)):(c=d,w=(f=Math.min(c+w,l))-c)),this._box={l:c,t:h,w:w,h:T};var k=v>w,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=g+y;E+M>u&&(E=u-M);var L=this.container.selectAll(\"rect.scrollbar-horizontal\").data(k?[0]:[]);L.exit().on(\".drag\",null).remove(),L.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call(i.fill,s.barColor),k?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var C=y>T,O=s.barWidth+2*s.barPad,P=s.barLength+2*s.barPad,I=d+v,D=g;I+O>l&&(I=l-O);var z=this.container.selectAll(\"rect.scrollbar-vertical\").data(C?[0]:[]);z.exit().on(\".drag\",null).remove(),z.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(i.fill,s.barColor),C?(this.vbar=z.attr({rx:s.barRadius,ry:s.barRadius,x:I,y:D,width:O,height:P}),this._vbarYMin=D+P/2,this._vbarTranslateMax=T-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=c-.5,B=C?f+O+.5:f+.5,N=h-.5,j=k?p+M+.5:p+.5,U=o._topdefs.selectAll(\"#\"+R).data(k||C?[0]:[]);if(U.exit().remove(),U.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),k||C?(this._clipRect=U.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(N),width:Math.ceil(B)-Math.floor(F),height:Math.ceil(j)-Math.floor(N)}),this.container.call(a.setClipUrl,R,this.gd),this.bg.attr({x:d,y:g,width:v,height:y})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),k||C){var V=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault()})).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var q=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on(\"drag\",this._onBarDrag.bind(this));k&&this.hbar.on(\".drag\",null).call(q),C&&this.vbar.on(\".drag\",null).call(q)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=n.event.dx),this.vbar&&(e-=n.event.dy),this.setTranslate(t,e)},s.prototype._onBoxWheel=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t+=n.event.deltaY),this.vbar&&(e+=n.event.deltaY),this.setTranslate(t,e)},s.prototype._onBarDrag=function(){var t=this.translateX,e=this.translateY;if(this.hbar){var r=t+this._hbarXMin,i=r+this._hbarTranslateMax;t=(o.constrain(n.event.x,r,i)-r)/(i-r)*(this.position.w-this._box.w)}if(this.vbar){var a=e+this._vbarYMin,s=a+this._vbarTranslateMax;e=(o.constrain(n.event.y,a,s)-a)/(s-a)*(this.position.h-this._box.h)}this.setTranslate(t,e)},s.prototype.setTranslate=function(t,e){var r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=o.constrain(t||0,0,r),e=o.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(a.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var i=t/r;this.hbar.call(a.setTranslate,t+i*this._hbarTranslateMax,e)}if(this.vbar){var 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L=(v+.5)/84;e.maskBreaks(i*(1-L)+L*p)!==D&&d++}(h*=d/84)||(t[n].drop=!0),s&&c>A&&(h=c)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(U,t,t._definedDelta),t.rangebreaks){var gt=\"y\"===t._id.charAt(0),yt=1;\"auto\"===t.tickmode&&(yt=t.tickfont?t.tickfont.size:12);var mt=NaN;for(a=N.length-1;a>-1;a--)if(N[a].drop)N.splice(a,1);else{N[a].value=Ft(N[a].value,t);var xt=t.c2p(N[a].value);(gt?mt>xt-yt:mt<xt+yt)?N.splice(v?a+1:a,1):mt=xt}}Rt(t)&&360===Math.abs(d[1]-d[0])&&N.pop(),t._tmax=(N[N.length-1]||{}).value,t._prevDateHead=\"\",t._inCalcTicks=!0;var bt,_t=function(e){e.text=\"\",t._prevDateHead=o};function wt(t,e){var r=W.tickText(t,e.value,!1,e.simpleLabel),n=e.periodX;return void 0!==n&&(r.periodX=n,(n>z||n<I)&&(n>z&&(r.periodX=z),n<I&&(r.periodX=I),_t(r))),r}for(N=N.concat(j),a=0;a<N.length;a++){var 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o=t.l2r(r+e).replace(/^-/,\"\").length;t._tickround=Math.max(a,o)-20,t._tickround<0&&(t._tickround=4)}}else if(i(e)||\"L\"===e.charAt(0)){var s=t.range.map(t.r2d||Number);i(e)||(e=Number(e.substr(1))),t._tickround=2-Math.floor(Math.log(e)/Math.LN10+.01);var l=Math.max(Math.abs(s[0]),Math.abs(s[1])),u=Math.floor(Math.log(l)/Math.LN10+.01),c=void 0===t.minexponent?3:t.minexponent;Math.abs(u)>c&&(bt(t.exponentformat)&&!_t(u)?t._tickexponent=3*Math.round((u-1)/3):t._tickexponent=u)}else t._tickround=null}function mt(t,e,r){var n=t.tickfont||{};return{x:e,dx:0,dy:0,text:r||\"\",fontSize:n.size,font:n.family,fontWeight:n.weight,fontStyle:n.style,fontVariant:n.variant,fontTextcase:n.textcase,fontLineposition:n.lineposition,fontShadow:n.shadow,fontColor:n.color}}W.autoTicks=function(t,e,r){var n;function a(t){return Math.pow(t,Math.floor(Math.log(e)/Math.LN10))}if(\"date\"===t.type){t.tick0=s.dateTick0(t.calendar,0);var o=2*e;if(o>y)e/=y,n=a(10),t.dtick=\"M\"+12*gt(e,n,ut);else if(o>T)e/=T,t.dtick=\"M\"+gt(e,1,ct);else if(o>M){if(t.dtick=gt(e,M,t._hasDayOfWeekBreaks?[1,2,7,14]:ht),!r){var l=W.getTickFormat(t),u=\"period\"===t.ticklabelmode;u&&(t._rawTick0=t.tick0),/%[uVW]/.test(l)?t.tick0=s.dateTick0(t.calendar,2):t.tick0=s.dateTick0(t.calendar,1),u&&(t._dowTick0=t.tick0)}}else o>E?t.dtick=gt(e,E,ct):o>L?t.dtick=gt(e,L,ft):o>C?t.dtick=gt(e,C,ft):(n=a(10),t.dtick=gt(e,n,ut))}else if(\"log\"===t.type){t.tick0=0;var c=s.simpleMap(t.range,t.r2l);if(t._isMinor&&(e*=1.5),e>.7)t.dtick=Math.ceil(e);else if(Math.abs(c[1]-c[0])<1){var f=1.5*Math.abs((c[1]-c[0])/e);e=Math.abs(Math.pow(10,c[1])-Math.pow(10,c[0]))/f,n=a(10),t.dtick=\"L\"+gt(e,n,ut)}else t.dtick=e>.3?\"D2\":\"D1\"}else\"category\"===t.type||\"multicategory\"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):Rt(t)?(t.tick0=0,n=1,t.dtick=gt(e,n,vt)):(t.tick0=0,n=a(10),t.dtick=gt(e,n,ut));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&\"string\"!=typeof t.dtick){var h=t.dtick;throw t.dtick=1,\"ax.dtick error: 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a=t._tickround,o=r&&t.hoverformat||W.getTickFormat(t);(n=!o&&n)&&(a=i(a)?4:{y:\"m\",m:\"d\",d:\"M\",M:\"S\",S:4}[a]);var l,u=s.formatDate(e.x,o,a,t._dateFormat,t.calendar,t._extraFormat),c=u.indexOf(\"\\n\");if(-1!==c&&(l=u.substr(c+1),u=u.substr(0,c)),n&&(void 0===l||\"00:00:00\"!==u&&\"00:00\"!==u?8===u.length&&(u=u.replace(/:00$/,\"\")):(u=l,l=\"\")),l)if(r)\"d\"===a?u+=\", \"+l:u=l+(u?\", \"+u:\"\");else if(t._inCalcTicks&&t._prevDateHead===l){var f=Bt(t),h=t._trueSide||t.side;(!f&&\"top\"===h||f&&\"bottom\"===h)&&(u+=\"<br> \")}else t._prevDateHead=l,u+=\"<br>\"+l;e.text=u}(t,o,r,u):\"log\"===c?function(t,e,r,n,a){var o=t.dtick,l=e.x,u=t.tickformat,c=\"string\"==typeof o&&o.charAt(0);if(\"never\"===a&&(a=\"\"),n&&\"L\"!==c&&(o=\"L3\",c=\"L\"),u||\"L\"===c)e.text=wt(Math.pow(10,l),t,a,n);else if(i(o)||\"D\"===c&&s.mod(l+.01,1)<.1){var f=Math.round(l),h=Math.abs(f),p=t.exponentformat;\"power\"===p||bt(p)&&_t(f)?(e.text=0===f?1:1===f?\"10\":\"10<sup>\"+(f>1?\"\":I)+h+\"</sup>\",e.fontSize*=1.25):(\"e\"===p||\"E\"===p)&&h>2?e.text=\"1\"+p+(f>0?\"+\":I)+h:(e.text=wt(Math.pow(10,l),t,\"\",\"fakehover\"),\"D1\"===o&&\"y\"===t._id.charAt(0)&&(e.dy-=e.fontSize/6))}else{if(\"D\"!==c)throw\"unrecognized dtick \"+String(o);e.text=String(Math.round(Math.pow(10,s.mod(l,1)))),e.fontSize*=.75}if(\"D1\"===t.dtick){var d=String(e.text).charAt(0);\"0\"!==d&&\"1\"!==d||(\"y\"===t._id.charAt(0)?e.dx-=e.fontSize/4:(e.dy+=e.fontSize/2,e.dx+=(t.range[1]>t.range[0]?1:-1)*e.fontSize*(l<0?.5:.25)))}}(t,o,0,u,g):\"category\"===c?function(t,e){var r=t._categories[Math.round(e.x)];void 0===r&&(r=\"\"),e.text=String(r)}(t,o):\"multicategory\"===c?function(t,e,r){var n=Math.round(e.x),i=t._categories[n]||[],a=void 0===i[1]?\"\":String(i[1]),o=void 0===i[0]?\"\":String(i[0]);r?e.text=o+\" - 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k=l._inputDomain,A=(l.domain[1]-l.domain[0])/(k[1]-k[0]),M=(l.r2l(l.range[1])-l.r2l(l.range[0]))/(l.r2l(l._inputRange[1])-l.r2l(l._inputRange[0]));if((h/=A)*M<1){l.domain=l._input.domain=k.slice(),s(l,h);continue}if(M<1&&(l.range=l._input.range=l._inputRange.slice(),h*=M),l.autorange){var S=l.r2l(l.range[0]),E=l.r2l(l.range[1]),L=(S+E)/2,C=L,O=L,P=Math.abs(E-L),I=L-P*h*1.0001,D=L+P*h*1.0001,z=i.makePadFn(p,l,0),R=i.makePadFn(p,l,1);y(l,h);var F,B,N=Math.abs(l._m),j=i.concatExtremes(t,l),U=j.min,V=j.max;for(B=0;B<U.length;B++)(F=U[B].val-z(U[B])/N)>I&&F<C&&(C=F);for(B=0;B<V.length;B++)(F=V[B].val+R(V[B])/N)<D&&F>O&&(O=F);h/=(O-C)/(2*P),C=l.l2r(C),O=l.l2r(O),l.range=l._input.range=S<E?[C,O]:[O,C]}y(l,h)}}},e.getAxisGroup=function(t,e){for(var r=t._axisMatchGroups,n=0;n<r.length;n++)if(r[n][e])return\"g\"+n;return e},e.clean=function(t,e){if(e._inputDomain){for(var r=!1,n=e._id,i=t._fullLayout._axisConstraintGroups,a=0;a<i.length;a++)if(i[a][n]){r=!0;break}r&&\"domain\"===e.constrain||(e._input.domain=e.domain=e._inputDomain,delete e._inputDomain)}}},51184:function(t,e,r){\"use strict\";var n=r(33428),i=r(3400),a=i.numberFormat,o=r(49760),s=r(89184),l=r(24040),u=i.strTranslate,c=r(72736),f=r(76308),h=r(43616),p=r(93024),d=r(54460),v=r(93972),g=r(86476),y=r(72760),m=y.selectingOrDrawing,x=y.freeMode,b=r(84284).FROM_TL,_=r(73696),w=r(39172).redrawReglTraces,T=r(7316),k=r(79811).getFromId,A=r(22676).prepSelect,M=r(22676).clearOutline,S=r(22676).selectOnClick,E=r(21160),L=r(33816),C=L.MINDRAG,O=L.MINZOOM,P=!0;function I(t,e,r,n){var a=i.ensureSingle(t.draglayer,e,r,(function(e){e.classed(\"drag\",!0).style({fill:\"transparent\",\"stroke-width\":0}).attr(\"data-subplot\",t.id)}));return a.call(v,n),a.node()}function D(t,e,r,i,a,o,s){var l=I(t,\"rect\",e,r);return n.select(l).call(h.setRect,i,a,o,s),l}function z(t,e){for(var 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t.append(\"path\").attr(\"class\",\"zoombox\").style({fill:e>.2?\"rgba(0,0,0,0)\":\"rgba(255,255,255,0)\",\"stroke-width\":0}).attr(\"transform\",u(r,n)).attr(\"d\",i+\"Z\")}function j(t,e,r){return t.append(\"path\").attr(\"class\",\"zoombox-corners\").style({fill:f.background,stroke:f.defaultLine,\"stroke-width\":1,opacity:0}).attr(\"transform\",u(e,r)).attr(\"d\",\"M0,0Z\")}function U(t,e,r,n,i,a){t.attr(\"d\",n+\"M\"+r.l+\",\"+r.t+\"v\"+r.h+\"h\"+r.w+\"v-\"+r.h+\"h-\"+r.w+\"Z\"),V(t,e,i,a)}function V(t,e,r,n){r||(t.transition().style(\"fill\",n>.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),e.transition().style(\"opacity\",1).duration(200))}function q(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}function H(t){P&&t.data&&t._context.showTips&&(i.notifier(i._(t,\"Double-click to zoom back out\"),\"long\"),P=!1)}function G(t){var 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0!==t.onwheel?\"wheel\":\"mousewheel\";t._onwheel&&t.removeEventListener(r,t._onwheel),t._onwheel=e,t.addEventListener(r,e,{passive:!1})}else void 0!==t.onwheel?t.onwheel=e:void 0!==t.onmousewheel?t.onmousewheel=e:t.isAddedWheelEvent||(t.isAddedWheelEvent=!0,t.addEventListener(\"wheel\",e,{passive:!1}))}function X(t){var e=[];for(var r in t)e.push(t[r]);return e}t.exports={makeDragBox:function(t,e,r,s,u,f,v,y){var P,I,V,Z,K,J,$,Q,tt,et,rt,nt,it,at,ot,st,lt,ut,ct,ft,ht,pt,dt,vt=t._fullLayout._zoomlayer,gt=v+y===\"nsew\",yt=1===(v+y).length;function mt(){if(P=e.xaxis,I=e.yaxis,tt=P._length,et=I._length,$=P._offset,Q=I._offset,(V={})[P._id]=P,(Z={})[I._id]=I,v&&y)for(var r=e.overlays,n=0;n<r.length;n++){var i=r[n].xaxis;V[i._id]=i;var a=r[n].yaxis;Z[a._id]=a}K=X(V),J=X(Z),it=z(K,y),at=z(J,v),ot=!at&&!it,nt=W(t,t._fullLayout._axisMatchGroups,V,Z);var o=(rt=W(t,t._fullLayout._axisConstraintGroups,V,Z,nt)).isSubplotConstrained||nt.isSubplotConstrained;st=y||o,lt=v||o;var s=t._fullLayout;ut=s._has(\"scattergl\"),ct=s._has(\"splom\"),ft=s._has(\"svg\")}r+=e.yaxis._shift,mt();var xt=function(t,e,r){return t?\"nsew\"===t?r?\"\":\"pan\"===e?\"move\":\"crosshair\":t.toLowerCase()+\"-resize\":\"pointer\"}(at+it,t._fullLayout.dragmode,gt),bt=D(e,v+y+\"drag\",xt,r,s,u,f);if(ot&&!gt)return bt.onmousedown=null,bt.style.pointerEvents=\"none\",bt;var _t,wt,Tt,kt,At,Mt,St,Et,Lt,Ct,Ot={element:bt,gd:t,plotinfo:e};function Pt(){Ot.plotinfo.selection=!1,M(t)}function It(t,r){var i=Ot.gd;if(i._fullLayout._activeShapeIndex>=0)i._fullLayout._deactivateShape(i);else{var o=i._fullLayout.clickmode;if(q(i),2!==t||yt||Ht(),gt)o.indexOf(\"select\")>-1&&S(r,i,K,J,e.id,Ot),o.indexOf(\"event\")>-1&&p.click(i,r,e.id);else if(1===t&&yt){var s=v?I:P,u=\"s\"===v||\"w\"===y?0:1,f=s._name+\".range[\"+u+\"]\",h=function(t,e){var r,n=t.range[e],i=Math.abs(n-t.range[1-e]);return\"date\"===t.type?n:\"log\"===t.type?(r=Math.ceil(Math.max(0,-Math.log(i)/Math.LN10))+3,a(\".\"+r+\"g\")(Math.pow(10,n))):(r=Math.floor(Math.log(Math.abs(n))/Math.LN10)-Math.floor(Math.log(i)/Math.LN10)+4,a(\".\"+String(r)+\"g\")(n))}(s,u),d=\"left\",g=\"middle\";if(s.fixedrange)return;v?(g=\"n\"===v?\"top\":\"bottom\",\"right\"===s.side&&(d=\"right\")):\"e\"===y&&(d=\"right\"),i._context.showAxisRangeEntryBoxes&&n.select(bt).call(c.makeEditable,{gd:i,immediate:!0,background:i._fullLayout.paper_bgcolor,text:String(h),fill:s.tickfont?s.tickfont.color:\"#444\",horizontalAlign:d,verticalAlign:g}).on(\"edit\",(function(t){var e=s.d2r(t);void 0!==e&&l.call(\"_guiRelayout\",i,f,e)}))}}}function Dt(e,r){if(t._transitioningWithDuration)return!1;var n=Math.max(0,Math.min(tt,pt*e+_t)),i=Math.max(0,Math.min(et,dt*r+wt)),a=Math.abs(n-_t),o=Math.abs(i-wt);function s(){St=\"\",Tt.r=Tt.l,Tt.t=Tt.b,Lt.attr(\"d\",\"M0,0Z\")}if(Tt.l=Math.min(_t,n),Tt.r=Math.max(_t,n),Tt.t=Math.min(wt,i),Tt.b=Math.max(wt,i),rt.isSubplotConstrained)a>O||o>O?(St=\"xy\",a/tt>o/et?(o=a*et/tt,wt>i?Tt.t=wt-o:Tt.b=wt+o):(a=o*tt/et,_t>n?Tt.l=_t-a:Tt.r=_t+a),Lt.attr(\"d\",G(Tt))):s();else if(nt.isSubplotConstrained)if(a>O||o>O){St=\"xy\";var l=Math.min(Tt.l/tt,(et-Tt.b)/et),u=Math.max(Tt.r/tt,(et-Tt.t)/et);Tt.l=l*tt,Tt.r=u*tt,Tt.b=(1-l)*et,Tt.t=(1-u)*et,Lt.attr(\"d\",G(Tt))}else s();else!at||o<Math.min(Math.max(.6*a,C),O)?a<C||!it?s():(Tt.t=0,Tt.b=et,St=\"x\",Lt.attr(\"d\",function(t,e){return\"M\"+(t.l-.5)+\",\"+(e-O-.5)+\"h-3v\"+(2*O+1)+\"h3ZM\"+(t.r+.5)+\",\"+(e-O-.5)+\"h3v\"+(2*O+1)+\"h-3Z\"}(Tt,wt))):!it||a<Math.min(.6*o,O)?(Tt.l=0,Tt.r=tt,St=\"y\",Lt.attr(\"d\",function(t,e){return\"M\"+(e-O-.5)+\",\"+(t.t-.5)+\"v-3h\"+(2*O+1)+\"v3ZM\"+(e-O-.5)+\",\"+(t.b+.5)+\"v3h\"+(2*O+1)+\"v-3Z\"}(Tt,_t))):(St=\"xy\",Lt.attr(\"d\",G(Tt)));Tt.w=Tt.r-Tt.l,Tt.h=Tt.b-Tt.t,St&&(Ct=!0),t._dragged=Ct,U(Et,Lt,Tt,At,Mt,kt),zt(),t.emit(\"plotly_relayouting\",ht),Mt=!0}function zt(){ht={},\"xy\"!==St&&\"x\"!==St||(R(K,Tt.l/tt,Tt.r/tt,ht,rt.xaxes),Vt(\"x\",ht)),\"xy\"!==St&&\"y\"!==St||(R(J,(et-Tt.b)/et,(et-Tt.t)/et,ht,rt.yaxes),Vt(\"y\",ht))}function Rt(){zt(),q(t),Gt(),H(t)}Ot.prepFn=function(e,r,n){var a=Ot.dragmode,s=t._fullLayout.dragmode;s!==a&&(Ot.dragmode=s),mt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?\"pan\"===s?s=\"zoom\":m(s)||(s=\"pan\"):e.ctrlKey&&(s=\"pan\"):s=\"pan\"),x(s)?Ot.minDrag=1:Ot.minDrag=void 0,m(s)?(Ot.xaxes=K,Ot.yaxes=J,A(e,r,n,Ot,s)):(Ot.clickFn=It,m(a)&&Pt(),ot||(\"zoom\"===s?(Ot.moveFn=Dt,Ot.doneFn=Rt,Ot.minDrag=1,function(e,r,n){var a=bt.getBoundingClientRect();_t=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(_t,wt);_t=s[0],wt=s[1],Tt={l:_t,r:_t,w:0,t:wt,b:wt,h:0},kt=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St=\"xy\",Ct=!1,Et=N(vt,kt,$,Q,At=\"M0,0H\"+tt+\"V\"+et+\"H0V0\"),Lt=j(vt,$,Q)}(0,r,n)):\"pan\"===s&&(Ot.moveFn=Ut,Ot.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===bt){var r=t._fullLayout.dragmode;m(r)||(mt(),Wt([0,0,tt,et]),Ot.moveFn(e.dx,e.dy))}}},g.init(Ot);var Ft=[0,0,tt,et],Bt=null,Nt=L.REDRAWDELAY,jt=e.mainplot?t._fullLayout._plots[e.mainplot]:e;function Ut(e,r){if(e*=pt,r*=dt,!t._transitioningWithDuration){if(t._fullLayout._replotting=!0,\"ew\"===it||\"ns\"===at){var n=it?-e:0,i=at?-r:0;if(nt.isSubplotConstrained){if(it&&at){var a=(e/tt-r/et)/2;n=-(e=a*tt),i=-(r=-a*et)}at?n=-i*tt/et:i=-n*et/tt}return it&&(F(K,e),Vt(\"x\")),at&&(F(J,r),Vt(\"y\")),Wt([n,i,tt,et]),qt(),void t.emit(\"plotly_relayouting\",ht)}var o,s,l=\"w\"===it==(\"n\"===at)?1:-1;if(it&&at&&(rt.isSubplotConstrained||nt.isSubplotConstrained)){var u=(e/tt+l*r/et)/2;e=u*tt,r=l*u*et}if(\"w\"===it?e=p(K,0,e):\"e\"===it?e=p(K,1,-e):it||(e=0),\"n\"===at?r=p(J,1,r):\"s\"===at?r=p(J,0,-r):at||(r=0),o=\"w\"===it?e:0,s=\"n\"===at?r:0,rt.isSubplotConstrained&&!nt.isSubplotConstrained||nt.isSubplotConstrained&&it&&at&&l>0){var c;if(nt.isSubplotConstrained||!it&&1===at.length){for(c=0;c<K.length;c++)K[c].range=K[c]._r.slice(),E(K[c],1-r/et);o=(e=r*tt/et)/2}if(nt.isSubplotConstrained||!at&&1===it.length){for(c=0;c<J.length;c++)J[c].range=J[c]._r.slice(),E(J[c],1-e/tt);s=(r=e*et/tt)/2}}nt.isSubplotConstrained&&at||Vt(\"x\"),nt.isSubplotConstrained&&it||Vt(\"y\");var f=tt-e,h=et-r;!nt.isSubplotConstrained||it&&at||(it?(s=o?0:e*et/tt,h=f*et/tt):(o=s?0:r*tt/et,f=h*tt/et)),Wt([o,s,f,h]),qt(),t.emit(\"plotly_relayouting\",ht)}function p(t,e,r){for(var n,i,a=1-e,o=0;o<t.length;o++){var s=t[o];if(!s.fixedrange){n=s,i=s._rl[a]+(s._rl[e]-s._rl[a])/B(r/s._length);var l=s.l2r(i);!1!==l&&void 0!==l&&(s.range[e]=l)}}return n._length*(n._rl[e]-i)/(n._rl[e]-n._rl[a])}}function Vt(t,e){for(var r=nt.isSubplotConstrained?{x:J,y:K}[t]:nt[t+\"axes\"],n=nt.isSubplotConstrained?{x:K,y:J}[t]:[],i=0;i<r.length;i++){var 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n=r(33428),i=r(83356),a=i.geoPath,o=i.geoDistance,s=r(87108),l=r(24040),u=r(3400),c=u.strTranslate,f=r(76308),h=r(43616),p=r(93024),d=r(7316),v=r(54460),g=r(19280).getAutoRange,y=r(86476),m=r(22676).prepSelect,x=r(22676).clearOutline,b=r(22676).selectOnClick,_=r(79248),w=r(79552),T=r(27144),k=r(59972),A=r(55712).NO;function M(t){this.id=t.id,this.graphDiv=t.graphDiv,this.container=t.container,this.topojsonURL=t.topojsonURL,this.isStatic=t.staticPlot,this.topojsonName=null,this.topojson=null,this.projection=null,this.scope=null,this.viewInitial=null,this.fitScale=null,this.bounds=null,this.midPt=null,this.hasChoropleth=!1,this.traceHash={},this.layers={},this.basePaths={},this.dataPaths={},this.dataPoints={},this.clipDef=null,this.clipRect=null,this.bgRect=null,this.makeFramework()}var S=M.prototype;function E(t,e){var r=w.clipPad,n=t[0]+r,i=t[1]-r,a=e[0]+r,o=e[1]-r;n>0&&i<0&&(i+=360);var 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r,i={r:e.rotate(),k:e.scale()},a=c(0,e),h=function(t){for(var e=0,r=arguments.length,i=[];++e<r;)i.push(arguments[e]);var a=n.dispatch.apply(null,i);return a.of=function(e,r){return function(i){var o;try{o=i.sourceEvent=n.event,i.target=t,n.event=i,a[i.type].apply(e,r)}finally{n.event=o}}},a}(a,\"zoomstart\",\"zoom\",\"zoomend\"),p=0,d=a.on;function y(t){var r=e.rotate();t(\"projection.rotation.lon\",-r[0]),t(\"projection.rotation.lat\",-r[1])}return a.on(\"zoomstart\",(function(){n.select(this).style(l);var t,u,c,f,y,b,_,w,T,k,A,M=n.mouse(this),S=e.rotate(),E=S,L=e.translate(),C=(u=.5*(t=S)[0]*o,c=.5*t[1]*o,f=.5*t[2]*o,y=Math.sin(u),b=Math.cos(u),_=Math.sin(c),w=Math.cos(c),T=Math.sin(f),[b*w*(k=Math.cos(f))+y*_*T,y*w*k-b*_*T,b*_*k+y*w*T,b*w*T-y*_*k]);r=v(e,M),d.call(a,\"zoom\",(function(){var t,a,o,l,u,c,f,p,d,y,b=n.mouse(this);if(e.scale(i.k=n.event.scale),r){if(v(e,b)){e.rotate(S).translate(L);var _=v(e,b),w=function(t,e){if(t&&e){var 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c=r.match(i);o=\"x\"+c[1],s=\"y\"+c[2]}for(var f=0;f<t.length;f++)a=t[f],\"gl2d\"===e&&n.traceIs(a,\"gl2d\")?a[l[0]]===o&&a[l[1]]===s&&u.push(a):a[l]===r&&u.push(a);return u}},2428:function(t,e,r){\"use strict\";var n=r(62644),i=r(97264),a=r(29128),o=r(33816),s=r(89184);function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}t.exports=function(t){var e=t.mouseContainer,r=t.glplot,u=new l(e,r);function c(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=u.lastPos[0],p=u.lastPos[1],d=o.MINDRAG*r.pixelRatio,v=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,u.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case\"zoom\":if(e){var y=n/(f[2]-f[0])*(l[2]-l[0])+l[0],m=i/(f[3]-f[1])*(l[3]-l[1])+l[1];u.boxInited||(u.boxStart[0]=y,u.boxStart[1]=m,u.dragStart[0]=n,u.dragStart[1]=i),u.boxEnd[0]=y,u.boxEnd[1]=m,u.boxInited=!0,u.boxEnabled||u.boxStart[0]===u.boxEnd[0]&&u.boxStart[1]===u.boxEnd[1]||(u.boxEnabled=!0);var x=Math.abs(u.dragStart[0]-n)<v,b=Math.abs(u.dragStart[1]-i)<v;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(u.boxEnd[0]=u.boxStart[0]),b&&(u.boxEnd[1]=u.boxStart[1]);else{a=u.boxEnd[0]-u.boxStart[0],s=u.boxEnd[1]-u.boxStart[1];var 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o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,u,c,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;u=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!u,l=this.hasAxisInAltrPos(e,r)&&!u,a=r.mirror||!1,c=u?-1!==String(a).indexOf(\"all\"):!!a,f=u?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||c)&&(this.borderLineEnable[h]=r.showline),(l||c)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=1.5,n=e.title.font.size,i=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?n*(r+(i?1:0))-10:n*(r+(i?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+n*(r+(i?1:.5)):10+n*(r+(i?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},t.exports=function(t){return new a(t)}},39952:function(t,e,r){\"use strict\";var n=r(67824).overrideAll,i=r(17188),a=r(64859),o=r(9616),s=r(33816),l=r(57952),u=r(65460),c=r(84888).op;e.name=\"gl2d\",e.attr=[\"xaxis\",\"yaxis\"],e.idRoot=[\"x\",\"y\"],e.idRegex=s.idRegex,e.attrRegex=s.attrRegex,e.attributes=r(26720),e.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},e.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),e.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:u.hoverlabel},\"plot\",\"nested\"),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=c(r,\"gl2d\",o),u=s._scene2d;void 0===u&&(u=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=u),u.plot(l,t.calcdata,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];s._scene2d&&0===c(t,\"gl2d\",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},e.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++)e._plots[r[n]]._scene2d.updateFx(e.dragmode)}},17188:function(t,e,r){\"use strict\";var n,i,a=r(24040),o=r(54460),s=r(93024),l=r(67792).gl_plot2d,u=r(67792).gl_spikes2d,c=r(67792).gl_select_box,f=r(5408),h=r(92568),p=r(2428),d=r(16576),v=r(71888),g=v.enforce,y=v.clean,m=r(19280).doAutoRange,x=r(72760),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=r(33816).SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=u(this.glplot),this.selectBox=c(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}t.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=a[4*(r*o+l)+u];a[4*(r*o+l)+u]=a[4*(r*s+l)+u],a[4*(r*s+l)+u]=c}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var u=n[r._name];u.range=s,u.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};y(s,this.xaxis),y(s,this.yaxis);var l,u,c=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[c.l+f[0]*c.w,c.b+h[0]*c.h,i-c.r-(1-f[1])*c.w,a-c.t-(1-h[1])*c.h],this.mouseContainer.style.width=c.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=c.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=c.h*(h[1]-h[0]),this.mouseContainer.style.left=c.l+f[0]*c.w+\"px\",this.mouseContainer.style.top=c.t+(1-h[1])*c.h+\"px\",u=0;u<2;++u)(l=this[w[u]])._length=o.viewBox[u+2]-o.viewBox[u],m(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var u=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(u[c]=t.dataBox[c],u[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var v=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),v.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[v.trace.index]||{},y=v.pointIndex,m=s.castHoverinfo(g,i,y);if(m&&\"all\"!==m){var x=m.split(\"+\");-1===x.indexOf(\"x\")&&(v.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(v.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(v.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(v.textLabel=void 0),-1===x.indexOf(\"name\")&&(v.name=void 0)}s.loneHover({x:v.screenCoord[0],y:v.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",v.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",v.traceCoord[1]),zLabel:v.traceCoord[2],text:v.textLabel,name:v.name,color:s.castHoverOption(g,y,\"bgcolor\")||v.color,borderColor:s.castHoverOption(g,y,\"bordercolor\"),fontFamily:s.castHoverOption(g,y,\"font.family\"),fontSize:s.castHoverOption(g,y,\"font.size\"),fontColor:s.castHoverOption(g,y,\"font.color\"),nameLength:s.castHoverOption(g,y,\"namelength\"),textAlign:s.castHoverOption(g,y,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},12536:function(t,e,r){\"use strict\";var n=r(67824).overrideAll,i=r(65460),a=r(98432),o=r(84888).op,s=r(3400),l=r(9616),u=\"gl3d\",c=\"scene\";e.name=u,e.attr=c,e.idRoot=c,e.idRegex=e.attrRegex=s.counterRegex(\"scene\"),e.attributes=r(6636),e.layoutAttributes=r(346),e.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),e.supplyLayoutDefaults=r(5208),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots[u],i=0;i<n.length;i++){var s=n[i],l=o(r,u,s),c=e[s],f=c.camera,h=c._scene;h||(h=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:f},e),c._scene=h),h.viewInitial||(h.viewInitial={up:{x:f.up.x,y:f.up.y,z:f.up.z},eye:{x:f.eye.x,y:f.eye.y,z:f.eye.z},center:{x:f.center.x,y:f.center.y,z:f.center.z}}),h.plot(l,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots[u]||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},e.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),c+e}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=0;n<r.length;n++)e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}},6636:function(t){\"use strict\";t.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},86140:function(t,e,r){\"use strict\";var n=r(76308),i=r(94724),a=r(92880).extendFlat,o=r(67824).overrideAll;t.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,autorangeoptions:{minallowed:i.autorangeoptions.minallowed,maxallowed:i.autorangeoptions.maxallowed,clipmin:i.autorangeoptions.clipmin,clipmax:i.autorangeoptions.clipmax,include:i.autorangeoptions.include,editType:\"plot\"},rangemode:i.rangemode,minallowed:i.minallowed,maxallowed:i.maxallowed,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.minor.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,labelalias:i.labelalias,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},64380:function(t,e,r){\"use strict\";var n=r(49760).mix,i=r(3400),a=r(31780),o=r(86140),s=r(14944),l=r(28336),u=[\"xaxis\",\"yaxis\",\"zaxis\"];t.exports=function(t,e,r){var c,f;function h(t,e){return i.coerce(c,f,o,t,e)}for(var p=0;p<u.length;p++){var d=u[p];c=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(c,f,h,r),l(c,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noAutotickangles:!0,noTicklabelindex:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelshift:!0,noTicklabelstandoff:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,72.72727272727273).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},44728:function(t,e,r){\"use strict\";var n=r(43080),i=r(3400),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickFontWeight=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontStyle=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickFontVariant=[\"normal\",\"normal\",\"normal\",\"normal\"],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelFontWeight=[\"normal\",\"normal\",\"normal\",\"normal\"],this.labelFontStyle=[\"normal\",\"normal\",\"normal\",\"normal\"],this.labelFontVariant=[\"normal\",\"normal\",\"normal\",\"normal\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=this,o=0;o<3;++o){var s=e[a[o]];s.visible?(r.labels[o]=t._meta?i.templateString(s.title.text,t._meta):s.title.text,\"font\"in s.title&&(s.title.font.color&&(r.labelColor[o]=n(s.title.font.color)),s.title.font.family&&(r.labelFont[o]=s.title.font.family),s.title.font.size&&(r.labelSize[o]=s.title.font.size),s.title.font.weight&&(r.labelFontWeight[o]=s.title.font.weight),s.title.font.style&&(r.labelFontStyle[o]=s.title.font.style),s.title.font.variant&&(r.labelFontVariant[o]=s.title.font.variant)),\"showline\"in s&&(r.lineEnable[o]=s.showline),\"linecolor\"in s&&(r.lineColor[o]=n(s.linecolor)),\"linewidth\"in s&&(r.lineWidth[o]=s.linewidth),\"showgrid\"in s&&(r.gridEnable[o]=s.showgrid),\"gridcolor\"in s&&(r.gridColor[o]=n(s.gridcolor)),\"gridwidth\"in s&&(r.gridWidth[o]=s.gridwidth),\"log\"===s.type?r.zeroEnable[o]=!1:\"zeroline\"in s&&(r.zeroEnable[o]=s.zeroline),\"zerolinecolor\"in s&&(r.zeroLineColor[o]=n(s.zerolinecolor)),\"zerolinewidth\"in s&&(r.zeroLineWidth[o]=s.zerolinewidth),\"ticks\"in s&&s.ticks?r.lineTickEnable[o]=!0:r.lineTickEnable[o]=!1,\"ticklen\"in s&&(r.lineTickLength[o]=r._defaultLineTickLength[o]=s.ticklen),\"tickcolor\"in s&&(r.lineTickColor[o]=n(s.tickcolor)),\"tickwidth\"in s&&(r.lineTickWidth[o]=s.tickwidth),\"tickangle\"in s&&(r.tickAngle[o]=\"auto\"===s.tickangle?-3600:Math.PI*-s.tickangle/180),\"showticklabels\"in s&&(r.tickEnable[o]=s.showticklabels),\"tickfont\"in s&&(s.tickfont.color&&(r.tickColor[o]=n(s.tickfont.color)),s.tickfont.family&&(r.tickFont[o]=s.tickfont.family),s.tickfont.size&&(r.tickSize[o]=s.tickfont.size),s.tickfont.weight&&(r.tickFontWeight[o]=s.tickfont.weight),s.tickfont.style&&(r.tickFontStyle[o]=s.tickfont.style),s.tickfont.variant&&(r.tickFontVariant[o]=s.tickfont.variant)),\"mirror\"in s?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(s.mirror)?(r.lineTickMirror[o]=!0,r.lineMirror[o]=!0):!0===s.mirror?(r.lineTickMirror[o]=!1,r.lineMirror[o]=!0):(r.lineTickMirror[o]=!1,r.lineMirror[o]=!1):r.lineMirror[o]=!1,\"showbackground\"in s&&!1!==s.showbackground?(r.backgroundEnable[o]=!0,r.backgroundColor[o]=n(s.backgroundcolor)):r.backgroundEnable[o]=!1):(r.tickEnable[o]=!1,r.labelEnable[o]=!1,r.lineEnable[o]=!1,r.lineTickEnable[o]=!1,r.gridEnable[o]=!1,r.zeroEnable[o]=!1,r.backgroundEnable[o]=!1)}},t.exports=function(t,e){var r=new o;return r.merge(t,e),r}},5208:function(t,e,r){\"use strict\";var n=r(3400),i=r(76308),a=r(24040),o=r(168),s=r(64380),l=r(346),u=r(84888).op,c=\"gl3d\";function f(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),f=[\"up\",\"center\",\"eye\"],h=0;h<f.length;h++)r(\"camera.\"+f[h]+\".x\"),r(\"camera.\"+f[h]+\".y\"),r(\"camera.\"+f[h]+\".z\");r(\"camera.projection.type\");var p=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),d=r(\"aspectmode\",p?\"manual\":\"auto\");p||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===d&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var v=u(n.fullData,c,n.id);s(t,e,{font:n.font,scene:n.id,data:v,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var g=n.getDfltFromLayout(\"dragmode\");if(!1!==g&&!g)if(g=\"orbit\",t.camera&&t.camera.up){var y=t.camera.up.x,m=t.camera.up.y,x=t.camera.up.z;0!==x&&(y&&m&&x?x/Math.sqrt(y*y+m*m+x*x)>.999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},346:function(t,e,r){\"use strict\";var n=r(86140),i=r(86968).u,a=r(92880).extendFlat,o=r(3400).counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}t.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},9020:function(t,e,r){\"use strict\";var n=r(43080),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},87152:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if(\"auto\"===c.tickmode){c.tickmode=\"linear\";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var p=n.calcTicks(c,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[u],\"date\"===c.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[u]=p,c.tickmode=f}}for(e.ticks=l,u=0;u<3;++u)for(o[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]),d=0;d<2;++d)e.bounds[d][u]=t.glplot.bounds[d][u];t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=r(54460),i=r(3400),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},94424:function(t){\"use strict\";function e(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}t.exports=function(t,r){return e(t.projection,e(t.view,e(t.model,[r[0],r[1],r[2],1])))}},98432:function(t,e,r){\"use strict\";var n,i,a=r(67792).gl_plot3d,o=a.createCamera,s=a.createScene,l=r(5408),u=r(89184),c=r(24040),f=r(3400),h=f.preserveDrawingBuffer(),p=r(54460),d=r(93024),v=r(43080),g=r(16576),y=r(94424),m=r(44728),x=r(9020),b=r(87152),_=r(19280).applyAutorangeOptions,w=!1;function T(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=m(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=c.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=c.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var k=T.prototype;k.prepareOptions=function(){var t=this,e={canvas:t.canvas,gl:t.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:t.camera,pixelRatio:t.pixelRatio};if(t.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");e.gl=i,e.canvas=n}return e};var A=!0;k.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!A||h)r=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=e.glOptions.preserveDrawingBuffer=!0,t.glplot=s(e)}catch(t){h=e.glOptions.preserveDrawingBuffer=!1,r=!1}}}return A=!1,r},k.initializeGLCamera=function(){var t=this,e=t.fullSceneLayout.camera,r=\"orthographic\"===e.projection.type;t.camera=o(t.container,{center:[e.center.x,e.center.y,e.center.z],eye:[e.eye.x,e.eye.y,e.eye.z],up:[e.up.x,e.up.y,e.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},k.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"touchstart\",(function(){w=!0})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!u&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},k.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,h=e.glplot.selection,v=0;v<u.length;++v)\"skip\"!==(t=e.traces[u[v]]).data.hoverinfo&&t.handlePick(h)&&(c=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==c){var m=y(e.glplot.cameraParams,h.dataCoordinate);t=c.data;var x,_=r._fullData[t.index],T=h.index,k={xLabel:g(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:g(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:g(\"z\",h.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(_,e.fullLayout,T),M=(A||\"\").split(\"+\"),S=A&&\"all\"===A;_.hovertemplate||S||(-1===M.indexOf(\"x\")&&(k.xLabel=void 0),-1===M.indexOf(\"y\")&&(k.yLabel=void 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\"+k.valueLabel),h.textLabel&&E.push(h.textLabel),x=E.join(\"<br>\")):x=h.textLabel;var L={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:T};d.appendArrayPointValue(L,_,T),t._module.eventData&&(L=_._module.eventData(L,h,_,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var 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e,r,s=this,u=s.isSmith,c=s.gd,f=s.layers,h=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,v=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,L=s.sectorInRad,C=s.vangles,O=s.radialAxis,P=E.clampTiny,I=E.findXYatLength,D=E.findEnclosingVertexAngles,z=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(f,\"path\",\"maindrag\",!1===t.dragmode?\"none\":\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){m.hover(c,t,s.id),c._fullLayout._lasthover=F,c._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){c._dragging||y.unhover(c,t)};var B,N,j,U,V,q,H,G,W,Y={element:F,gd:c,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function X(t,e){return Math.sqrt(t*t+e*e)}function Z(t,e){return X(t-A,e-M)}function K(t,e){return Math.atan2(M-e,t-A)}function J(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function $(t,e){if(0===t)return s.pathSector(2*z);var 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I,D,z,B=g.makeRectDragger(c,T,\"crosshair\",-x,-x,m,m),N={element:B,gd:u};!1===t.dragmode&&(N.dragmode=!1),V(n.select(B),v.visible&&h<f,{transform:l(b,_)}),N.prepFn=function(){I=null,D=null,z=null,N.moveFn=j,N.doneFn=q,w(u)},N.clampFn=function(t,e){return Math.sqrt(t*t+e*e)<S.MINDRAG&&(t=0,e=0),[t,e]},y.init(N)}function j(t,e){if(I)I(t,e);else{var n=[t,-e],a=[Math.cos(M),Math.sin(M)],s=Math.abs(o.dot(n,a)/Math.sqrt(o.dot(n,n)));isNaN(s)||(I=s<.5?H:G)}var l={};!function(t){null!==D?t[i.id+\".radialaxis.angle\"]=D:null!==z&&(t[i.id+\".radialaxis.range[\"+r+\"]\"]=z)}(l),u.emit(\"plotly_relayouting\",l)}function q(){null!==D?a.call(\"_guiRelayout\",u,i.id+\".radialaxis.angle\",D):null!==z&&a.call(\"_guiRelayout\",u,i.id+\".radialaxis.range[\"+r+\"]\",z)}function H(t,e){if(0!==r){var n=b+t,a=_+e;D=Math.atan2(d-a,n-p),i.vangles&&(D=U(D,i.vangles)),D=F(D);var o=l(p,d)+s(-D);c[\"radial-axis\"].attr(\"transform\",o),c[\"radial-line\"].select(\"line\").attr(\"transform\",o);var 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v.coerceEnumerated(x,r)}(z,i);var B=\"stack\"===w.mode||\"relative\"===w.mode,N=n[i],j=!B||N._outmost,U=N.hasB,V=g&&g-y>_;if(F&&\"none\"!==k&&(!N.isBlank&&s!==u&&f!==p||\"auto\"!==k&&\"inside\"!==k)){var q=I.font,H=d.getBarColor(n[i],z),G=d.getInsideTextFont(z,i,q,H),W=d.getOutsideTextFont(z,i,q),Y=z.insidetextanchor||\"end\",X=r.datum();R?\"log\"===S.type&&X.s0<=0&&(s=S.range[0]<S.range[1]?0:S._length):\"log\"===P.type&&X.s0<=0&&(f=P.range[0]<P.range[1]?P._length:0);var Z,K,J,$,Q,tt=Math.abs(u-s),et=Math.abs(p-f),rt=tt-2*_,nt=et-2*_;if(\"outside\"===k&&(j||N.hasB||(k=\"inside\")),\"auto\"===k)if(j){k=\"inside\",Z=D(r,F,Q=a.ensureUniformFontSize(t,G)),J=(K=l.bBox(Z.node())).width,$=K.height;var it,at=J>0&&$>0;it=V?U?E(rt-2*g,nt,J,$,R)||E(rt,nt-2*g,J,$,R):R?E(rt-(g-y),nt,J,$,R)||E(rt,nt-2*(g-y),J,$,R):E(rt,nt-(g-y),J,$,R)||E(rt-2*(g-y),nt,J,$,R):E(rt,nt,J,$,R),at&&it?k=\"inside\":(k=\"outside\",Z.remove(),Z=null)}else k=\"inside\";if(!Z){var ot=(Z=D(r,F,Q=a.ensureUniformFontSize(t,\"outside\"===k?W:G))).attr(\"transform\");if(Z.attr(\"transform\",\"\"),J=(K=l.bBox(Z.node())).width,$=K.height,Z.attr(\"transform\",ot),J<=0||$<=0)return void Z.remove()}var st,lt=z.textangle;st=\"outside\"===k?function(t,e,r,n,i,a){var o,s=!!a.isHorizontal,l=!!a.constrained,u=a.angle||0,c=i.width,f=i.height,h=Math.abs(e-t),p=Math.abs(n-r);o=s?p>2*_?_:0:h>2*_?_:0;var d=1;l&&(d=s?Math.min(1,p/f):Math.min(1,h/c));var v=L(u),g=C(i,v),y=(s?g.x:g.y)/2,m=(i.left+i.right)/2,x=(i.top+i.bottom)/2,b=(t+e)/2,w=(r+n)/2,T=0,k=0,M=s?A(e,t):A(r,n);return s?(b=e-M*o,T=M*y):(w=n+M*o,k=-M*y),{textX:m,textY:x,targetX:b,targetY:w,anchorX:T,anchorY:k,scale:d,rotate:v}}(s,u,f,p,K,{isHorizontal:R,constrained:\"both\"===z.constraintext||\"outside\"===z.constraintext,angle:lt}):O(s,u,f,p,K,{isHorizontal:R,constrained:\"both\"===z.constraintext||\"inside\"===z.constraintext,angle:lt,anchor:Y,hasB:U,r:g,overhead:y}),st.fontSize=Q.size,h(\"histogram\"===z.type?\"bar\":z.type,st,I),N.transform=st;var ut=M(Z,I,w,T);a.setTransormAndDisplay(ut,st)}else r.select(\"text\").remove()}(t,e,R,r,T,H,G,W,Y,rt,it,g,y),e.layerClipId&&l.hideOutsideRangePoint(u,R.select(\"text\"),w,P,f.xcalendar,f.ycalendar)}));var W=!1===f.cliponaxis;l.setClipUrl(u,W?null:e.layerClipId,t)}));u.getComponentMethod(\"errorbars\",\"plot\")(t,z,e,g)},toMoveInsideBar:O}},45784:function(t){\"use strict\";function e(t,e,r,n,i){var a=e.c2p(n?t.s0:t.p0,!0),o=e.c2p(n?t.s1:t.p1,!0),s=r.c2p(n?t.p0:t.s0,!0),l=r.c2p(n?t.p1:t.s1,!0);return i?[(a+o)/2,(s+l)/2]:n?[o,(s+l)/2]:[(a+o)/2,l]}t.exports=function(t,r){var n,i=t.cd,a=t.xaxis,o=t.yaxis,s=i[0].trace,l=\"funnel\"===s.type,u=\"h\"===s.orientation,c=[];if(!1===r)for(n=0;n<i.length;n++)i[n].selected=0;else for(n=0;n<i.length;n++){var f=i[n],h=\"ct\"in f?f.ct:e(f,a,o,u,l);r.contains(h,!1,n,t)?(c.push({pointNumber:n,x:a.c2d(f.x),y:o.c2d(f.y)}),f.selected=1):f.selected=0}return c}},72592:function(t,e,r){\"use strict\";t.exports=i;var n=r(3400).distinctVals;function i(t,e){this.traces=t,this.sepNegVal=e.sepNegVal,this.overlapNoMerge=e.overlapNoMerge;for(var r=1/0,i=e.posAxis._id.charAt(0),a=[],o=0;o<t.length;o++){for(var s=t[o],l=0;l<s.length;l++){var u=s[l],c=u.p;void 0===c&&(c=u[i]),void 0!==c&&a.push(c)}s[0]&&s[0].width1&&(r=Math.min(s[0].width1,r))}this.positions=a;var f=n(a);this.distinctPositions=f.vals,1===f.vals.length&&r!==1/0?this.minDiff=r:this.minDiff=Math.min(f.minDiff,r);var h=(e.posAxis||{}).type;\"category\"!==h&&\"multicategory\"!==h||(this.minDiff=1),this.binWidth=this.minDiff,this.bins={}}i.prototype.put=function(t,e){var r=this.getLabel(t,e),n=this.bins[r]||0;return this.bins[r]=n+e,n},i.prototype.get=function(t,e){var r=this.getLabel(t,e);return this.bins[r]||0},i.prototype.getLabel=function(t,e){return(e<0&&this.sepNegVal?\"v\":\"^\")+(this.overlapNoMerge?t:Math.round(t/this.binWidth))}},60100:function(t,e,r){\"use strict\";var n=r(33428),i=r(76308),a=r(43616),o=r(3400),s=r(24040),l=r(82744).resizeText,u=r(20832),c=u.textfont,f=u.insidetextfont,h=u.outsidetextfont,p=r(60444);function d(t,e,r){a.pointStyle(t.selectAll(\"path\"),e,r),v(t,e,r)}function v(t,e,r){t.selectAll(\"text\").each((function(t){var i=n.select(this),s=o.ensureUniformFontSize(r,g(i,t,e,r));a.font(i,s)}))}function g(t,e,r,n){var i=n._fullLayout.font,a=r.textfont;if(t.classed(\"bartext-inside\")){var o=_(e,r);a=m(r,e.i,i,o)}else t.classed(\"bartext-outside\")&&(a=x(r,e.i,i));return 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i,a=t.cd[0].trace.hoveron,o=[];return-1!==a.indexOf(\"boxes\")&&(o=o.concat(l(t,e,r,n))),-1!==a.indexOf(\"points\")&&(i=u(t,e,r)),\"closest\"===n?i?[i]:o:i?(o.push(i),o):o},hoverOnBoxes:l,hoverOnPoints:u}},67244:function(t,e,r){\"use strict\";t.exports={attributes:r(63188),layoutAttributes:r(16560),supplyDefaults:r(90624).supplyDefaults,crossTraceDefaults:r(90624).crossTraceDefaults,supplyLayoutDefaults:r(68832).supplyLayoutDefaults,calc:r(62555),crossTraceCalc:r(96404).crossTraceCalc,plot:r(18728).plot,style:r(25776).style,styleOnSelect:r(25776).styleOnSelect,hoverPoints:r(27576).hoverPoints,eventData:r(10392),selectPoints:r(8264),moduleType:\"trace\",name:\"box\",basePlotModule:r(57952),categories:[\"cartesian\",\"svg\",\"symbols\",\"oriented\",\"box-violin\",\"showLegend\",\"boxLayout\",\"zoomScale\"],meta:{}}},16560:function(t){\"use 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b=t.selectAll(\"path.box\").data(\"violin\"!==r.type||r.box.visible?i.identity:[]);b.enter().append(\"path\").style(\"vector-effect\",o?\"none\":\"non-scaling-stroke\").attr(\"class\",\"box\"),b.exit().remove(),b.each((function(t){if(t.empty)return n.select(this).attr(\"d\",\"M0,0Z\");var e=f.c2l(t.pos+p,!0),a=f.l2p(e-s)+v,o=f.l2p(e+l)+v,b=h?(a+o)/2:f.l2p(e)+v,_=r.whiskerwidth,w=h?a*_+(1-_)*b:f.l2p(e-d)+v,T=h?o*_+(1-_)*b:f.l2p(e+d)+v,k=f.l2p(e-s*x)+v,A=f.l2p(e+l*x)+v,M=\"sd\"===r.sizemode,S=c.c2p(M?t.mean-t.sd:t.q1,!0),E=M?c.c2p(t.mean+t.sd,!0):c.c2p(t.q3,!0),L=i.constrain(M?c.c2p(t.mean,!0):c.c2p(t.med,!0),Math.min(S,E)+1,Math.max(S,E)-1),C=void 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a=r[i],o=a[0],s=a[1],l=t[o];e.setOptions(s,\"setLayoutProperty\",l.layout),\"visible\"===l.layout.visibility&&e.setOptions(s,\"setPaintProperty\",l.paint)}},s._addLayers=function(t,e){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=t[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},e)}},s._removeLayers=function(){for(var t=this.subplot.map,e=this.layerList,r=e.length-1;r>=0;r--)t.removeLayer(e[r][1])},s.dispose=function(){var t=this.subplot.map;this._removeLayers(),t.removeSource(this.sourceId)},t.exports=function(t,e){var r=e[0].trace,i=new o(t,r.uid),a=i.sourceId,s=n(e),l=i.below=t.belowLookup[\"trace-\"+r.uid];return t.map.addSource(a,{type:\"geojson\",data:s.geojson}),i._addLayers(s,l),e[0].trace._glTrace=i,i}},86040:function(t,e,r){\"use strict\";var n=r(49084),i=r(29736).axisHoverFormat,a=r(21776).Ks,o=r(52948),s=r(45464),l=r(92880).extendFlat,u={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},u:{valType:\"data_array\",editType:\"calc\"},v:{valType:\"data_array\",editType:\"calc\"},w:{valType:\"data_array\",editType:\"calc\"},sizemode:{valType:\"enumerated\",values:[\"scaled\",\"absolute\",\"raw\"],editType:\"calc\",dflt:\"scaled\"},sizeref:{valType:\"number\",editType:\"calc\",min:0},anchor:{valType:\"enumerated\",editType:\"calc\",values:[\"tip\",\"tail\",\"cm\",\"center\"],dflt:\"cm\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"},{keys:[\"norm\"]}),uhoverformat:i(\"u\",1),vhoverformat:i(\"v\",1),whoverformat:i(\"w\",1),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),showlegend:l({},s.showlegend,{dflt:!1})};l(u,n(\"\",{colorAttr:\"u/v/w 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P=C.selectAll(\"text\").data(O);function I(e,r,n,i){if(!e.match(\"s\")||n>=0==i>=0||r(n).slice(-1).match(_)||r(i).slice(-1).match(_))return r;var a=e.slice().replace(\"s\",\"f\").replace(/\\d+/,(function(t){return parseInt(t)-1})),o=k(t,{tickformat:a});return function(t){return Math.abs(t)<1?d.tickText(o,t).text:r(t)}}P.enter().append(\"text\"),P.attr(\"text-anchor\",(function(){return A})).attr(\"class\",(function(t){return t})).attr(\"x\",null).attr(\"y\",null).attr(\"dx\",null).attr(\"dy\",null),P.exit().remove();var D,z=v.mode+v.align;if(v._hasDelta&&(D=function(){var e=k(t,{tickformat:v.delta.valueformat},v._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=v.delta.suffix,s=v.delta.prefix,l=function(t){return v.delta.relative?t.relativeDelta:t.delta},u=function(t,e){return 0===t||\"number\"!=typeof t||isNaN(t)?\"-\":(t>0?v.delta.increasing.symbol:v.delta.decreasing.symbol)+s+e(t)+o},h=function(t){return 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e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((y+g)/2-t/2*(g-y)).outerRadius((y+g)/2+t/2*(g-y)).startAngle(-E)}function O(t){t.attr(\"d\",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append(\"g\").classed(\"angular\",!0),_.attr(\"transform\",l(b[0],b[1])),A.enter().append(\"g\").classed(\"angularaxis\",!0).classed(\"crisp\",!0),A.selectAll(\"g.xangularaxistick,path,text\").remove(),(o=k(t,p.gauge.axis)).type=\"linear\",o.range=p.gauge.axis.range,o._id=\"xangularaxis\",o.ticklabeloverflow=\"allow\",o.setScale();var P=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},I={},D=d.makeLabelFns(o,0).labelStandoff;I.xFn=function(t){var e=P(t);return Math.cos(e)*D},I.yFn=function(t){var e=P(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(D+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*c)},I.anchorFn=function(t){var e=P(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},I.heightFn=function(t,e,r){var n=P(t);return-.5*(1+Math.sin(n))*r};var z=function(t){return l(b[0]+g*Math.cos(t),b[1]-g*Math.sin(t))};f=function(t){return z(P(t))};if(s=d.calcTicks(o),h=d.getTickSigns(o)[2],o.visible){h=\"inside\"===o.ticks?-1:1;var R=(o.linewidth||1)/2;d.drawTicks(t,o,{vals:s,layer:A,path:\"M\"+h*R+\",0h\"+h*o.ticklen,transFn:function(t){var e=P(t);return z(e)+\"rotate(\"+-u(e)+\")\"}}),d.drawLabels(t,o,{vals:s,layer:A,transFn:f,labelFns:I})}var F=[m].concat(p.gauge.steps),B=_.selectAll(\"g.bg-arc\").data(F);B.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),B.select(\"path\").call(O).call(T),B.exit().remove();var N=C(p.gauge.bar.thickness),j=_.selectAll(\"g.value-arc\").data([p.gauge.bar]);j.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var U,V,q,H=j.select(\"path\");w(M)?(H.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){S&&S()})).each(\"interrupt\",(function(){S&&S()})).attrTween(\"d\",(U=N,V=L(r[0].lastY),q=L(r[0].y),function(){var t=i(V,q);return function(e){return U.endAngle(t(e))()}})),p._lastValue=r[0].y):H.attr(\"d\",\"number\"==typeof r[0].y?N.endAngle(L(r[0].y)):\"M0,0Z\"),H.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var W=_.selectAll(\"g.threshold-arc\").data(F);W.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),W.select(\"path\").call(O).call(T),W.exit().remove();var Y=_.selectAll(\"g.gauge-outline\").data([x]);Y.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),Y.select(\"path\").call(O).call(T),Y.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:W,layer:Y,size:B,gaugeBg:C,gaugeOutline:O,transitionOpts:r,onComplete:g});var X=I.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=I.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,u,c=r[0].trace,f=n.gauge,p=n.layer,v=n.gaugeBg,g=n.gaugeOutline,y=n.size,x=c.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(y.l,y.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=y.h,M=c.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(c._hasNumber||c._hasDelta?1-h.bulletNumberDomainSize:1);function L(t){t.attr(\"width\",(function(t){return 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P=f.selectAll(\"g.value-bullet\").data([c.gauge.bar]);P.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),P.select(\"rect\").attr(\"height\",M).attr(\"y\",(A-M)/2).call(T),w(b)?P.select(\"rect\").transition().duration(b.duration).ease(b.easing).each(\"end\",(function(){_&&_()})).each(\"interrupt\",(function(){_&&_()})).attr(\"width\",Math.max(0,i.c2p(Math.min(c.gauge.axis.range[1],r[0].y)))):P.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(c.gauge.axis.range[1],r[0].y))):0),P.exit().remove();var I=r.filter((function(){return c.gauge.threshold.value||0===c.gauge.threshold.value})),D=f.selectAll(\"g.threshold-bullet\").data(I);D.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),D.select(\"line\").attr(\"x1\",i.c2p(c.gauge.threshold.value)).attr(\"x2\",i.c2p(c.gauge.threshold.value)).attr(\"y1\",(1-c.gauge.threshold.thickness)/2*A).attr(\"y2\",(1-(1-c.gauge.threshold.thickness)/2)*A).call(m.stroke,c.gauge.threshold.line.color).style(\"stroke-width\",c.gauge.threshold.line.width),D.exit().remove();var z=f.selectAll(\"g.gauge-outline\").data([g]);z.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),z.select(\"rect\").call(L).call(T),z.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:O,transitionOpts:r,onComplete:g});var K=I.selectAll(\"text.title\").data(e);K.exit().remove(),K.enter().append(\"text\").classed(\"title\",!0),K.attr(\"text-anchor\",(function(){return 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r=\"slices.\"+t;a(r+\".show\")&&(a(r+\".fill\"),a(r+\".locations\"))})),a(\"spaceframe.show\")&&a(\"spaceframe.fill\"),a(\"surface.show\")&&(a(\"surface.count\"),a(\"surface.fill\"),a(\"surface.pattern\")),a(\"contour.show\")&&(a(\"contour.color\"),a(\"contour.width\")),[\"text\",\"hovertext\",\"hovertemplate\",\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lighting.vertexnormalsepsilon\",\"lighting.facenormalsepsilon\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"flatshading\",\"opacity\"].forEach((function(t){a(t)})),o(t,e,n,a,{prefix:\"\",cLetter:\"c\"}),e._length=null):e.visible=!1}t.exports={supplyDefaults:function(t,e,r,i){s(t,e,0,i,(function(r,i){return n.coerce(t,e,a,r,i)}))},supplyIsoDefaults:s}},6296:function(t,e,r){\"use 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n=r(49084),i=r(29736).axisHoverFormat,a=r(21776).Ks,o=r(16716),s=r(45464),l=r(92880).extendFlat;t.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 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n=r(67792).gl_mesh3d,i=r(67792).delaunay_triangulate,a=r(67792).alpha_shape,o=r(67792).convex_hull,s=r(33040).parseColorScale,l=r(3400).isArrayOrTypedArray,u=r(43080),c=r(8932).extractOpts,f=r(52094);function h(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var p=h.prototype;function d(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=u(t[n]);return e}function v(t,e,r,n){for(var i=[],a=e.length,o=0;o<a;o++)i[o]=t.d2l(e[o],0,n)*r;return i}function g(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=Math.round(t[n]);return e}function y(t,e){for(var r=t.length,n=0;n<r;n++)if(t[n]<=-.5||t[n]>=e-.5)return!1;return!0}p.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return l(r)&&void 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p={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:u(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var m=c(t);this.color=\"#fff\";var x=t.intensitymode;p[x+\"Intensity\"]=t.intensity,p[x+\"IntensityBounds\"]=[m.min,m.max],p.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],p.vertexColors=d(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],p.cellColors=d(t.facecolor)):(this.color=t.color,p.meshColor=u(t.color));this.mesh.update(p)},p.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},t.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},74212:function(t,e,r){\"use strict\";var n=r(24040),i=r(3400),a=r(27260),o=r(52948);t.exports=function(t,e,r,s){function l(r,n){return i.coerce(t,e,o,r,n)}function u(t){var e=t.map((function(t){var e=l(t);return e&&i.isArrayOrTypedArray(e)?e:null}));return e.every((function(t){return t&&t.length===e[0].length}))&&e}u([\"x\",\"y\",\"z\"])?(u([\"i\",\"j\",\"k\"]),(!e.i||e.j&&e.k)&&(!e.j||e.k&&e.i)&&(!e.k||e.i&&e.j)?(n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\",\"y\",\"z\"],s),[\"lighting.ambient\",\"lighting.diffuse\",\"lighting.specular\",\"lighting.roughness\",\"lighting.fresnel\",\"lighting.vertexnormalsepsilon\",\"lighting.facenormalsepsilon\",\"lightposition.x\",\"lightposition.y\",\"lightposition.z\",\"flatshading\",\"alphahull\",\"delaunayaxis\",\"opacity\"].forEach((function(t){l(t)})),l(\"contour.show\")&&(l(\"contour.color\"),l(\"contour.width\")),\"intensity\"in 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a,s=t._fullData,l=[];for(i=1/0,a=0;a<s.length;a++){var u=s[a];if(\"ohlc\"===u.type&&!0===u.visible&&u.xaxis===e._id){l.push(u);var c=e.makeCalcdata(u,\"x\");u._origX=c;var f=o(r,e,\"x\",c).vals;u._xcalc=f;var h=n.distinctVals(f).minDiff;h&&isFinite(h)&&(i=Math.min(i,h))}}for(i===1/0&&(i=1),a=0;a<l.length;a++)l[a]._minDiff=i}return i*r.tickwidth}(t,r,e),c=e._minDiff;e._minDiff=null;var f=e._origX;e._origX=null;var h=e._xcalc;e._xcalc=null;var p=u(t,e,f,h,i,l);return e._extremes[r._id]=a.findExtremes(r,h,{vpad:c/2}),p.length?(n.extendFlat(p[0].t,{wHover:c/2,tickLen:s}),p):[{t:{empty:!0}}]},calcCommon:u}},23860:function(t,e,r){\"use strict\";var n=r(3400),i=r(52744),a=r(31147),o=r(20279);function s(t,e,r,n){r(n+\".line.color\"),r(n+\".line.width\",e.line.width),r(n+\".line.dash\",e.line.dash)}t.exports=function(t,e,r,l){function u(r,i){return 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T=a.getDistanceFunction(n,b,_,w);if(a.getClosest(l,T,t),!1===t.index)return null;var k=l[t.index];if(k.empty)return null;var A=c[k.dir],M=A.line.color;return o.opacity(M)&&A.line.width?t.color=M:t.color=A.fillcolor,t.x0=u.c2p(k.pos+v-y,!0),t.x1=u.c2p(k.pos+v+y,!0),t.xLabelVal=void 0!==k.orig_p?k.orig_p:k.pos,t.spikeDistance=w(k)*s/i,t.xSpike=u.c2p(k.pos,!0),t}function f(t,e,r,a){var o=t.cd,s=t.ya,l=o[0].trace,u=o[0].t,f=[],h=c(t,e,r,a);if(!h)return[];var p=o[h.index].hi||l.hoverinfo,d=p.split(\"+\");if(\"all\"!==p&&-1===d.indexOf(\"y\"))return[];for(var v=[\"high\",\"open\",\"close\",\"low\"],g={},y=0;y<v.length;y++){var m,x=v[y],b=l[x][h.index],_=s.c2p(b,!0);b in g?(m=g[b]).yLabel+=\"<br>\"+u.labels[x]+n.hoverLabelText(s,b,l.yhoverformat):((m=i.extendFlat({},h)).y0=m.y1=_,m.yLabelVal=b,m.yLabel=u.labels[x]+n.hoverLabelText(s,b,l.yhoverformat),m.name=\"\",f.push(m),g[b]=m)}return f}function h(t,e,r,i){var a=t.cd,o=t.ya,l=a[0].trace,f=a[0].t,h=c(t,e,r,i);if(!h)return[];var 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o=r(\"x\"),s=r(\"open\"),l=r(\"high\"),u=r(\"low\"),c=r(\"close\");if(r(\"hoverlabel.split\"),n.getComponentMethod(\"calendars\",\"handleTraceDefaults\")(t,e,[\"x\"],a),s&&l&&u&&c){var f=Math.min(s.length,l.length,u.length,c.length);return o&&(f=Math.min(f,i.minRowLength(o))),e._length=f,f}}},36664:function(t,e,r){\"use strict\";var n=r(33428),i=r(3400);t.exports=function(t,e,r,a){var o=e.yaxis,s=e.xaxis,l=!!s.rangebreaks;i.makeTraceGroups(a,r,\"trace ohlc\").each((function(t){var e=n.select(this),r=t[0],a=r.t;if(!0!==r.trace.visible||a.empty)e.remove();else{var u=a.tickLen,c=e.selectAll(\"path\").data(i.identity);c.enter().append(\"path\"),c.exit().remove(),c.attr(\"d\",(function(t){if(t.empty)return\"M0,0Z\";var e=s.c2p(t.pos-u,!0),r=s.c2p(t.pos+u,!0),n=l?(e+r)/2:s.c2p(t.pos,!0);return\"M\"+e+\",\"+o.c2p(t.o,!0)+\"H\"+n+\"M\"+n+\",\"+o.c2p(t.h,!0)+\"V\"+o.c2p(t.l,!0)+\"M\"+r+\",\"+o.c2p(t.c,!0)+\"H\"+n}))}}))}},97384:function(t){\"use strict\";t.exports=function(t,e){var 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n=r(71688).wrap,i=r(94288).hasColorscale,a=r(47128),o=r(68944),s=r(43616),l=r(3400),u=r(38248);function c(t,e,r){t.valueInds.push(e),t.count+=r}function f(t,e,r){return{categoryInds:t,color:e,rawColor:r,valueInds:[],count:0}}function h(t,e,r){t.valueInds.push(e),t.count+=r}t.exports=function(t,e){var r=l.filterVisible(e.dimensions);if(0===r.length)return[];var p,d,v,g=r.map((function(t){var e;if(\"trace\"===t.categoryorder)e=null;else if(\"array\"===t.categoryorder)e=t.categoryarray;else{e=o(t.values);for(var r=!0,n=0;n<e.length;n++)if(!u(e[n])){r=!1;break}e.sort(r?l.sorterAsc:void 0),\"category descending\"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return 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t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(D),E.exit().remove(),S.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\"),M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){u.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),S.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){u.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),M.exit().remove(),A.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",O).on(\"drag\",P).on(\"dragend\",I)),h.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),h.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function v(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function 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t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return c.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var 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k(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||m()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,u=o.svgBrush;if(u.wasDragged=!1,u.grabbingBar=\"ns\"===s.region,u.grabbingBar){var c=l.map(e.unitToPaddedPx);u.grabPoint=r-c[0]-n.verticalPadding,u.barLength=c[1]-c[0]}u.clickableOrdinalRange=s.clickableOrdinalRange,u.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(u.stayingIntervals=u.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),u.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,u.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,m(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var u=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(u?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),u?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function A(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}t.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e),n=r.slice();e.filter.set(n),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e,r){var i=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);i.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t,e,r){var i=r._context.staticPlot,a=t.selectAll(\".background\").data(o);a.enter().append(\"rect\").classed(\"background\",!0).call(d).call(v).style(\"pointer-events\",i?\"none\":\"auto\").attr(\"transform\",l(0,n.verticalPadding)),a.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var s=t.selectAll(\".highlight-shadow\").data(o);s.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",e).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),s.attr(\"y1\",(function(t){return t.height})).call(x);var u=t.selectAll(\".highlight\").data(o);u.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),u.attr(\"y1\",(function(t){return t.height})).call(x)}(i,e,r)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},61664:function(t,e,r){\"use strict\";t.exports={attributes:r(82296),supplyDefaults:r(60664),calc:r(95044),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:r(19976),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},19976:function(t,e,r){\"use strict\";var n=r(33428),i=r(84888)._M,a=r(24196),o=r(9616);e.name=\"parcoords\",e.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},e.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},e.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this,r=t.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":r,preserveAspectRatio:\"none\",x:0,y:0,width:t.style.width,height:t.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},95044:function(t,e,r){\"use strict\";var n=r(3400).isArrayOrTypedArray,i=r(8932),a=r(71688).wrap;t.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},30140:function(t){\"use strict\";t.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:[\"contextLineLayer\",\"focusLineLayer\",\"pickLineLayer\"],axisTitleOffset:28,axisExtentOffset:10,bar:{width:4,captureWidth:10,fillColor:\"magenta\",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:\"axis-extent-text\",parcoordsLineLayers:\"parcoords-line-layers\",parcoordsLineLayer:\"parcoords-lines\",parcoords:\"parcoords\",parcoordsControlView:\"parcoords-control-view\",yAxis:\"y-axis\",axisOverlays:\"axis-overlays\",axis:\"axis\",axisHeading:\"axis-heading\",axisTitle:\"axis-title\",axisExtent:\"axis-extent\",axisExtentTop:\"axis-extent-top\",axisExtentTopText:\"axis-extent-top-text\",axisExtentBottom:\"axis-extent-bottom\",axisExtentBottomText:\"axis-extent-bottom-text\",axisBrush:\"axis-brush\"},id:{filterBarPattern:\"filter-bar-pattern\"}}},60664:function(t,e,r){\"use strict\";var n=r(3400),i=r(94288).hasColorscale,a=r(27260),o=r(86968).Q,s=r(51272),l=r(54460),u=r(82296),c=r(71864),f=r(30140).maxDimensionCount,h=r(26284);function p(t,e,r,i){function a(r,i){return n.coerce(t,e,u.dimensions,r,i)}var o=a(\"values\"),s=a(\"visible\");if(o&&o.length||(s=e.visible=!1),s){a(\"label\"),a(\"tickvals\"),a(\"ticktext\"),a(\"tickformat\");var f=a(\"range\");e._ax={_id:\"y\",type:\"linear\",showexponent:\"all\",exponentformat:\"B\",range:f},l.setConvert(e._ax,i.layout),a(\"multiselect\");var h=a(\"constraintrange\");h&&(e.constraintrange=c.cleanRanges(h,e))}}t.exports=function(t,e,r,l){function c(r,i){return n.coerce(t,e,u,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log(\"parcoords traces support up to \"+f+\" dimensions at the moment\"),d.splice(f));var v=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,c);o(e,l,c),Array.isArray(v)&&v.length||(e.visible=!1),h(e,v,\"values\",g);var y=n.extendFlat({},l.font,{size:Math.round(l.font.size/1.2)});n.coerceFont(c,\"labelfont\",y),n.coerceFont(c,\"tickfont\",y,{autoShadowDflt:!0}),n.coerceFont(c,\"rangefont\",y),c(\"labelangle\"),c(\"labelside\"),c(\"unselected.line.color\"),c(\"unselected.line.opacity\")}},95724:function(t,e,r){\"use strict\";var n=r(3400).isTypedArray;e.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},e.isOrdinal=function(t){return!!t.tickvals},e.isVisible=function(t){return t.visible||!(\"visible\"in t)}},29928:function(t,e,r){\"use strict\";var n=r(61664);n.plot=r(24196),t.exports=n},51352:function(t,e,r){\"use strict\";var n=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"attribute vec4 p01_04, p05_08, p09_12, p13_16,\",\"               p17_20, p21_24, p25_28, p29_32,\",\"               p33_36, p37_40, p41_44, p45_48,\",\"               p49_52, p53_56, p57_60, colors;\",\"\",\"uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\",\"             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\",\"\",\"uniform vec2 resolution, viewBoxPos, viewBoxSize;\",\"uniform float maskHeight;\",\"uniform float drwLayer; // 0: context, 1: focus, 2: pick\",\"uniform vec4 contextColor;\",\"uniform sampler2D maskTexture, palette;\",\"\",\"bool isPick    = (drwLayer > 1.5);\",\"bool isContext = (drwLayer < 0.5);\",\"\",\"const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\",\"const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\",\"\",\"float val(mat4 p, mat4 v) {\",\"    return dot(matrixCompMult(p, v) * UNITS, UNITS);\",\"}\",\"\",\"float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\",\"    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\",\"    return y1 * (1.0 - ratio) + y2 * ratio;\",\"}\",\"\",\"int iMod(int a, int b) {\",\"    return a - b * (a / b);\",\"}\",\"\",\"bool fOutside(float p, float lo, float hi) {\",\"    return (lo < hi) && (lo > p || p > hi);\",\"}\",\"\",\"bool vOutside(vec4 p, vec4 lo, vec4 hi) {\",\"    return (\",\"        fOutside(p[0], lo[0], hi[0]) ||\",\"        fOutside(p[1], lo[1], hi[1]) ||\",\"        fOutside(p[2], lo[2], hi[2]) ||\",\"        fOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool mOutside(mat4 p, mat4 lo, mat4 hi) {\",\"    return (\",\"        vOutside(p[0], lo[0], hi[0]) ||\",\"        vOutside(p[1], lo[1], hi[1]) ||\",\"        vOutside(p[2], lo[2], hi[2]) ||\",\"        vOutside(p[3], lo[3], hi[3])\",\"    );\",\"}\",\"\",\"bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    return mOutside(A, loA, hiA) ||\",\"           mOutside(B, loB, hiB) ||\",\"           mOutside(C, loC, hiC) ||\",\"           mOutside(D, loD, hiD);\",\"}\",\"\",\"bool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    mat4 pnts[4];\",\"    pnts[0] = A;\",\"    pnts[1] = B;\",\"    pnts[2] = C;\",\"    pnts[3] = D;\",\"\",\"    for(int i = 0; i < 4; ++i) {\",\"        for(int j = 0; j < 4; ++j) {\",\"            for(int k = 0; k < 4; ++k) {\",\"                if(0 == iMod(\",\"                    int(255.0 * texture2D(maskTexture,\",\"                        vec2(\",\"                            (float(i * 2 + j / 2) + 0.5) / 8.0,\",\"                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\",\"                        ))[3]\",\"                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\",\"                    2\",\"                )) return true;\",\"            }\",\"        }\",\"    }\",\"    return false;\",\"}\",\"\",\"vec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\",\"    float x = 0.5 * sign(v) + 0.5;\",\"    float y = axisY(x, A, B, C, D);\",\"    float z = 1.0 - abs(v);\",\"\",\"    z += isContext ? 0.0 : 2.0 * float(\",\"        outsideBoundingBox(A, B, C, D) ||\",\"        outsideRasterMask(A, B, C, D)\",\"    );\",\"\",\"    return vec4(\",\"        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\",\"        z,\",\"        1.0\",\"    );\",\"}\",\"\",\"void main() {\",\"    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\",\"    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\",\"    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\",\"    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\",\"\",\"    float v = colors[3];\",\"\",\"    gl_Position = position(isContext, v, A, B, C, D);\",\"\",\"    fragColor =\",\"        isContext ? vec4(contextColor) :\",\"        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\",\"}\"].join(\"\\n\"),i=[\"precision highp float;\",\"\",\"varying vec4 fragColor;\",\"\",\"void main() {\",\"    gl_FragColor = fragColor;\",\"}\"].join(\"\\n\"),a=r(30140).maxDimensionCount,o=r(3400),s=1e-6,l=new Uint8Array(4),u=new Uint8Array(4),c={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var u=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],f(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*u,a.offset=2*l*n,e(a),l*n+u<i&&(r.currentRafs[o]=window.requestAnimationFrame((function(){s(l+1)}))),r.drawCompleted=!1)}(0)}function p(t,e){for(var r=new Array(256),n=0;n<256;n++)r[n]=t(n/255).concat(e);return r}function d(t,e){return(t>>>8*e)%256/255}function v(t,e,r){for(var n=new Array(8*e),i=0,a=0;a<e;a++)for(var o=0;o<2;o++)for(var s=0;s<4;s++){var l=4*t+s,u=r[64*a+l];63===l&&0===o&&(u*=-1),n[i++]=u}return n}function g(t){var e=\"0\"+t;return e.substr(e.length-2)}function y(t){return t<a?\"p\"+g(t+1)+\"_\"+g(t+4):\"colors\"}function m(t,e,r,n,i,a,s,l,u,c,f,h,p,d){for(var v=[[],[]],g=0;g<64;g++)v[0][g]=g===i?1:0,v[1][g]=g===a?1:0;s*=d,l*=d,u*=d,c*=d;var y=t.lines.canvasOverdrag*d,m=t.domain,x=t.canvasWidth*d,b=t.canvasHeight*d,_=t.pad.l*d,w=t.pad.b*d,T=t.layoutHeight*d,k=t.layoutWidth*d,A=t.deselectedLines.color,M=t.deselectedLines.opacity;return 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a=u[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",l=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===l[s]){var h=a.constraintrange;l[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[f[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return s(t,e,r)-s(t,e,n)}}(r,u[e].filter(a));l[e].sort(n),u[e].filter((function(t){return!a(t)})).sort((function(t){return u[e].indexOf(t)})).forEach((function(t){l[e].splice(l[e].indexOf(t),1),l[e].splice(u[e].indexOf(t),0,t)})),t.emit(\"plotly_restyle\",[{dimensions:[l[e]]},[f[e]]])}})}}).reglPrecompiled=o},74996:function(t,e,r){\"use strict\";var n=r(45464),i=r(86968).u,a=r(25376),o=r(22548),s=r(21776).Ks,l=r(21776).Gw,u=r(92880).extendFlat,c=r(98192).c,f=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});t.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},pattern:c,editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:u({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:u({},f,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:u({},f,{}),outsidetextfont:u({},f,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:u({},f,{}),position:{valType:\"enumerated\",values:[\"top 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n=r(7316);e.name=\"pie\",e.plot=function(t,r,i,a){n.plotBasePlot(e.name,t,r,i,a)},e.clean=function(t,r,i,a){n.cleanBasePlot(e.name,t,r,i,a)}},45768:function(t,e,r){\"use strict\";var n=r(38248),i=r(49760),a=r(76308),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}t.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],u=e.labels,c=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(u=new Array(h),r=0;r<h;r++)u[r]=String(e.label0+r*e.dlabel);var d={},v=s(o[\"_\"+e.type+\"colormap\"]),g=0,y=!1;for(r=0;r<h;r++){var m,x,b;if(p){if(m=f[r],!n(m))continue;m=+m}else m=1;void 0!==(x=u[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=m),a.push({v:m,label:x,color:v(c[r],x),i:r,pts:[r],hidden:b})):(y=!0,(i=a[_]).v+=m,i.pts.push(r),i.hidden||(g+=m),!1===i.color&&c[r]&&(i.color=v(c[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var u=0,c=0;c<i.length;c++){var f=i[c];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[u%a.length],u++))}}},makePullColorFn:s,generateExtendedColors:l}},74174:function(t,e,r){\"use strict\";var n=r(38248),i=r(3400),a=r(74996),o=r(86968).Q,s=r(31508).handleText,l=r(3400).coercePattern;function u(t,e){var r=i.isArrayOrTypedArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var u=e[l];if(n(u)&&u>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}function c(t,e,r,n,i){n(\"marker.line.width\")&&n(\"marker.line.color\",i?void 0:r.paper_bgcolor);var a=n(\"marker.colors\");l(n,\"marker.pattern\",a),t.marker&&!e.marker.pattern.fgcolor&&(e.marker.pattern.fgcolor=t.marker.colors),e.marker.pattern.bgcolor||(e.marker.pattern.bgcolor=r.paper_bgcolor)}t.exports={handleLabelsAndValues:u,handleMarkerDefaults:c,supplyDefaults:function(t,e,r,n){function l(r,n){return i.coerce(t,e,a,r,n)}var f=u(l(\"labels\"),l(\"values\")),h=f.len;if(e._hasLabels=f.hasLabels,e._hasValues=f.hasValues,!e._hasLabels&&e._hasValues&&(l(\"label0\"),l(\"dlabel\")),h){e._length=h,c(t,e,n,l,!0),l(\"scalegroup\");var p,d=l(\"text\"),v=l(\"texttemplate\");if(v||(p=l(\"textinfo\",i.isArrayOrTypedArray(d)?\"text+percent\":\"percent\")),l(\"hovertext\"),l(\"hovertemplate\"),v||p&&\"none\"!==p){var g=l(\"textposition\");s(t,e,n,l,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&l(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&l(\"insidetextorientation\")}else\"none\"===p&&l(\"textposition\",\"none\");o(e,n,l);var y=l(\"hole\");if(l(\"title.text\")){var m=l(\"title.position\",y?\"middle center\":\"top center\");y||\"middle center\"!==m||(e.title.position=\"top center\"),i.coerceFont(l,\"title.font\",n.font)}l(\"sort\"),l(\"direction\"),l(\"rotation\"),l(\"pull\")}else e.visible=!1}}},53644:function(t,e,r){\"use strict\";var n=r(10624).appendArrayMultiPointValues;t.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},21552:function(t,e,r){\"use strict\";var n=r(43616),i=r(76308);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},69656:function(t,e,r){\"use strict\";var n=r(3400);function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(n.isArrayOrTypedArray(t))for(var r=0;r<e.length;r++){var i=t[e[r]];if(i||0===i||\"\"===i)return i}},e.castOption=function(t,r){return n.isArrayOrTypedArray(t)?e.getFirstFilled(t,r):t||void 0},e.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},75792:function(t,e,r){\"use strict\";t.exports={attributes:r(74996),supplyDefaults:r(74174).supplyDefaults,supplyLayoutDefaults:r(90248),layoutAttributes:r(85204),calc:r(45768).calc,crossTraceCalc:r(45768).crossTraceCalc,plot:r(37820).plot,style:r(22152),styleOne:r(10528),moduleType:\"trace\",name:\"pie\",basePlotModule:r(80036),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},85204:function(t){\"use strict\";t.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},90248:function(t,e,r){\"use strict\";var n=r(3400),i=r(85204);t.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},37820:function(t,e,r){\"use strict\";var n=r(33428),i=r(7316),a=r(93024),o=r(76308),s=r(43616),l=r(3400),u=l.strScale,c=l.strTranslate,f=r(72736),h=r(82744),p=h.recordMinTextSize,d=h.clearMinTextSize,v=r(78048).TEXTPAD,g=r(69656),y=r(53644),m=r(3400).isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,u=i.trace,c=\"funnelarea\"===u.type;\"_hasHoverLabel\"in u||(u._hasHoverLabel=!1),\"_hasHoverEvent\"in u||(u._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[u.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:u._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),v=s+t.pxmid[1]*(1-p),m=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,m),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,m),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:u,x0:d-p*i.r,x1:d+p*i.r,y:v,_x0:c?o+t.TL[0]:d-p*i.r,_x1:c?o+t.TR[0]:d+p*i.r,_y0:c?s+t.TL[1]:v-p*i.r,_y1:c?s+t.BL[1]:v+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],u._hasHoverLabel=!0}u._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[y(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[u.index],o=n.select(this).datum();u._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[y(o,i)],event:n.event}),u._hasHoverEvent=!1),u._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),u._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[u.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size,s=g.castOption(t.insidetextfont.weight,e.pts)||g.castOption(t.textfont.weight,e.pts)||r.weight,l=g.castOption(t.insidetextfont.style,e.pts)||g.castOption(t.textfont.style,e.pts)||r.style,u=g.castOption(t.insidetextfont.variant,e.pts)||g.castOption(t.textfont.variant,e.pts)||r.variant,c=g.castOption(t.insidetextfont.textcase,e.pts)||g.castOption(t.textfont.textcase,e.pts)||r.textcase,f=g.castOption(t.insidetextfont.lineposition,e.pts)||g.castOption(t.textfont.lineposition,e.pts)||r.lineposition,h=g.castOption(t.insidetextfont.shadow,e.pts)||g.castOption(t.textfont.shadow,e.pts)||r.shadow;return{color:n||o.contrast(e.color),family:i,size:a,weight:s,style:l,variant:u,textcase:c,lineposition:f,shadow:h}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),u=s.bBox(o.node(),!0);r.titleBox={width:u.width,height:u.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,u=e.midangle,c=r.trace.insidetextorientation,f=\"horizontal\"===c,h=\"tangential\"===c,p=\"radial\"===c,d=\"auto\"===c,v=[];if(!d){var g,y=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),u=s<l?s:l;(a=\"tan\"===i?k(t,n,o,u,0):T(t,n,o,u,Math.PI/2)).textPosAngle=r,v.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)y(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)y(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var m=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/m,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;v.push(a)}(d||p)&&((a=T(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a)),(d||h)&&((a=k(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a));for(var x=0,b=0,_=0;_<v.length;_++){var w=v[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return v[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=P(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,u=t.r/(void 0===(l=t.trace.aspectratio)?1:l),c=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(c+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?c*=2:-1!==a.title.position.indexOf(\"right\")&&(c+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=c/t.titleBox.width,n=O(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function O(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function P(t){var e,r=t.pull;if(!r)return 0;if(l.isArrayOrTypedArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function I(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=O(i,e));var u=s/2,c=l/2;\"funnelarea\"!==a.type||a.scalegroup||(c/=a.aspectratio),i.r=Math.min(u,c)/(1+P(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=O(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var u,c;i.aspectratio>1?c=(u=r.r)/i.aspectratio:u=(c=r.r)*i.aspectratio,l=(u*=(1+i.baseratio)/2)*c}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function D(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function z(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,u=o.split(\"+\"),c=function(t){return-1!==u.indexOf(t)},f=c(\"label\"),h=c(\"text\"),p=c(\"value\"),d=c(\"percent\"),v=n.separators;if(s=f?[e.label]:[],h){var y=g.getFirstFilled(i.text,e.pts);m(y)&&s.push(y)}p&&s.push(g.formatPieValue(e.v,v)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,v)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(m(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,h=a._size;d(\"pie\",a),_(e,t),I(e,h);var v=l.makeTraceGroups(a._pielayer,e,\"trace\").each((function(e){var d=n.select(this),v=e[0],y=v.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,u=\"px0\",c=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,u=\"px1\",c=\"px0\"}for(n=D(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[u]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=D(a,s),r.midangle=s,n=D(a,s+=l*r.v/2),r.stopangle=s,r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr(\"stroke-linejoin\",\"round\"),d.each((function(){var m=n.select(this).selectAll(\"g.slice\").data(e);m.enter().append(\"g\").classed(\"slice\",!0),m.exit().remove();var _=[[[],[]],[[],[]]],T=!1;m.each((function(i,o){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,_[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var u=v.cx,c=v.cy,h=n.select(this),d=h.selectAll(\"path.surface\").data([i]);if(d.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":r?\"none\":\"all\"}),h.call(x,t,e),y.pull){var m=+g.castOption(y.pull,i.pts)||0;m>0&&(u+=m*i.pxmid[0],c+=m*i.pxmid[1])}i.cxFinal=u,i.cyFinal=c;var k=y.hole;if(i.v===v.vTotal){var A=\"M\"+(u+i.px0[0])+\",\"+(c+i.px0[1])+O(i.px0,i.pxmid,!0,1)+O(i.pxmid,i.px0,!0,1)+\"Z\";k?d.attr(\"d\",\"M\"+(u+k*i.px0[0])+\",\"+(c+k*i.px0[1])+O(i.px0,i.pxmid,!1,k)+O(i.pxmid,i.px0,!1,k)+\"Z\"+A):d.attr(\"d\",A)}else{var M=O(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr(\"d\",\"M\"+(u+k*i.px1[0])+\",\"+(c+k*i.px1[1])+O(i.px1,i.px0,!1,k)+\"l\"+S*i.px0[0]+\",\"+S*i.px0[1]+M+\"Z\")}else d.attr(\"d\",\"M\"+u+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}z(t,i,v);var E=g.castOption(y.textposition,i.pts),C=h.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==E?[0]:[]);C.enter().append(\"g\").classed(\"slicetext\",!0),C.exit().remove(),C.each((function(){var r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===E?function(t,e,r){return{color:g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,family:g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,size:g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size,weight:g.castOption(t.outsidetextfont.weight,e.pts)||g.castOption(t.textfont.weight,e.pts)||r.weight,style:g.castOption(t.outsidetextfont.style,e.pts)||g.castOption(t.textfont.style,e.pts)||r.style,variant:g.castOption(t.outsidetextfont.variant,e.pts)||g.castOption(t.textfont.variant,e.pts)||r.variant,textcase:g.castOption(t.outsidetextfont.textcase,e.pts)||g.castOption(t.textfont.textcase,e.pts)||r.textcase,lineposition:g.castOption(t.outsidetextfont.lineposition,e.pts)||g.castOption(t.textfont.lineposition,e.pts)||r.lineposition,shadow:g.castOption(t.outsidetextfont.shadow,e.pts)||g.castOption(t.textfont.shadow,e.pts)||r.shadow}}(y,i,a.font):b(y,i,a.font));r.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var d,m=s.bBox(r.node());if(\"outside\"===E)d=L(m,i);else if(d=w(m,i,v),\"auto\"===E&&d.scale<1){var x=l.ensureUniformFontSize(t,y.outsidetextfont);r.call(s.font,x),d=L(m=s.bBox(r.node()),i)}var _=d.textPosAngle,k=void 0===_?i.pxmid:D(v.r,_);if(d.targetX=u+k[0]*d.rCenter+(d.x||0),d.targetY=c+k[1]*d.rCenter+(d.y||0),R(d,m),d.outside){var A=d.targetY;i.yLabelMin=A-m.height/2,i.yLabelMid=A,i.yLabelMax=A+m.height/2,i.labelExtraX=0,i.labelExtraY=0,T=!0}d.fontSize=h.size,p(y.type,d,a),e[o].transform=d,l.setTransormAndDisplay(r,d)}))}function O(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*v.r+\",\"+n*v.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var k=n.select(this).selectAll(\"g.titletext\").data(y.title.text?[0]:[]);if(k.enter().append(\"g\").classed(\"titletext\",!0),k.exit().remove(),k.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=y.title.text;y._meta&&(i=l.templateString(i,y._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,y.title.font).call(f.convertToTspans,t),e=\"middle center\"===y.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(v):C(v,h),r.attr(\"transform\",c(e.x,e.y)+u(Math.min(1,e.scale))+c(e.tx,e.ty))})),T&&function(t,e){var r,n,i,a,o,s,u,c,f,h,p,d,v;function y(t,e){return t.pxmid[1]-e.pxmid[1]}function m(t,e){return e.pxmid[1]-t.pxmid[1]}function x(t,r){r||(r={});var i,c,f,p,d=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),v=n?t.yLabelMin:t.yLabelMax,y=n?t.yLabelMax:t.yLabelMin,m=t.cyFinal+o(t.px0[1],t.px1[1]),x=d-v;if(x*u>0&&(t.labelExtraY=x),l.isArrayOrTypedArray(e.pull))for(c=0;c<h.length;c++)(f=h[c])===t||(g.castOption(e.pull,t.pts)||0)>=(g.castOption(e.pull,f.pts)||0)||((t.pxmid[1]-f.pxmid[1])*u>0?(x=f.cyFinal+o(f.px0[1],f.px1[1])-v-t.labelExtraY)*u>0&&(t.labelExtraY+=x):(y+t.labelExtraY-m)*u>0&&(i=3*s*Math.abs(c-h.indexOf(t)),(p=f.cxFinal+a(f.px0[0],f.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=p)))}for(n=0;n<2;n++)for(i=n?y:m,o=n?Math.max:Math.min,u=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),f=t[1-n][r],h=f.concat(c),d=[],p=0;p<c.length;p++)void 0!==c[p].yLabelMid&&d.push(c[p]);for(v=!1,p=0;n&&p<f.length;p++)if(void 0!==f[p].yLabelMid){v=f[p];break}for(p=0;p<d.length;p++){var b=p&&d[p-1];v&&!p&&(b=v),x(d[p],b)}}}(_,y),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select(\"g.slicetext text\");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,l.setTransormAndDisplay(i,t.transform);var a=t.cxFinal+t.pxmid[0],s=\"M\"+a+\",\"+(t.cyFinal+t.pxmid[1]),u=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var c=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(c)>Math.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+u):s+=\"l\"+t.labelExtraX+\",\"+c+\"v\"+(f-c)+\"h\"+u}else s+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+u;l.ensureSingle(r,\"path\",\"textline\").call(o.stroke,e.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,e.outsidetextfont.size/8),d:s,fill:\"none\"})}else r.select(\"path.textline\").remove()}))}(m,y),T&&y.automargin){var A=s.bBox(d.node()),M=y.domain,S=h.w*(M.x[1]-M.x[0]),E=h.h*(M.y[1]-M.y[0]),O=(.5*S-v.r)/h.w,P=(.5*E-v.r)/h.h;i.autoMargin(t,\"pie.\"+y.uid+\".automargin\",{xl:M.x[0]-O,xr:M.x[1]+O,yb:M.y[0]-P,yt:M.y[1]+P,l:Math.max(v.cx-v.r-A.left,0),r:Math.max(A.right-(v.cx+v.r),0),b:Math.max(A.bottom-(v.cy+v.r),0),t:Math.max(v.cy-v.r-A.top,0),pad:5})}}))}));setTimeout((function(){v.selectAll(\"tspan\").each((function(){var t=n.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))}))}),0)},formatSliceLabel:z,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:C,prerenderTitles:_,layoutAreas:I,attachFxHandlers:x,computeTransform:R}},22152:function(t,e,r){\"use strict\";var n=r(33428),i=r(10528),a=r(82744).resizeText;t.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(e){var r=e[0].trace,a=n.select(this);a.style({opacity:r.opacity}),a.selectAll(\"path.surface\").each((function(e){n.select(this).call(i,e,r,t)}))}))}},10528:function(t,e,r){\"use strict\";var n=r(76308),i=r(69656).castOption,a=r(21552);t.exports=function(t,e,r,o){var s=r.marker.line,l=i(s.color,e.pts)||n.defaultLine,u=i(s.width,e.pts)||0;t.call(a,e,r,o).style(\"stroke-width\",u).call(n.stroke,l)}},35484:function(t,e,r){\"use strict\";var n=r(52904);t.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},11072:function(t,e,r){\"use strict\";var n=r(67792).gl_pointcloud2d,i=r(3400).isArrayOrTypedArray,a=r(43080),o=r(19280).findExtremes,s=r(44928);function l(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var u=l.prototype;u.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:i(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},u.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=s(t,{})},u.updateFast=function(t){var e,r,n,i,s,l,u=this.xData=this.pickXData=t.x,c=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)i=n[2*l],s=n[2*l+1],i<d[0]&&(d[0]=i),i>d[2]&&(d[2]=i),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=u.length,n=new Float32Array(2*e),r=new 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r.update(e),r}},41904:function(t,e,r){\"use strict\";var n=r(3400),i=r(35484);t.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\"),e._length=null}},156:function(t,e,r){\"use strict\";[\"*pointcloud* trace is deprecated!\",\"Please consider switching to the *scattergl* trace type.\"].join(\" \"),t.exports={attributes:r(35484),supplyDefaults:r(41904),calc:r(41484),plot:r(11072),moduleType:\"trace\",name:\"pointcloud\",basePlotModule:r(39952),categories:[\"gl\",\"gl2d\",\"showLegend\"],meta:{}}},41440:function(t,e,r){\"use strict\";var n=r(25376),i=r(45464),a=r(22548),o=r(55756),s=r(86968).u,l=r(21776).Ks,u=r(49084),c=r(31780).templatedArray,f=r(29736).descriptionOnlyNumbers,h=r(92880).extendFlat,p=r(67824).overrideAll;(t.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\",description:f(\"value\")},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({autoShadowDflt:!0}),customdata:void 0,node:{label:{valType:\"data_array\",dflt:[]},groups:{valType:\"info_array\",impliedEdits:{x:[],y:[]},dimensions:2,freeLength:!0,dflt:[],items:{valType:\"number\",editType:\"calc\"}},x:{valType:\"data_array\",dflt:[]},y:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},customdata:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:.5,arrayOk:!0}},pad:{valType:\"number\",arrayOk:!1,min:0,dflt:20},thickness:{valType:\"number\",arrayOk:!1,min:1,dflt:20},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:[\"value\",\"label\"]}),align:{valType:\"enumerated\",values:[\"justify\",\"left\",\"right\",\"center\"],dflt:\"justify\"}},link:{arrowlen:{valType:\"number\",min:0,dflt:0},label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},hovercolor:{valType:\"color\",arrayOk:!0},customdata:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0}},source:{valType:\"data_array\",dflt:[]},target:{valType:\"data_array\",dflt:[]},value:{valType:\"data_array\",dflt:[]},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:[\"value\",\"label\"]}),colorscales:c(\"concentrationscales\",{editType:\"calc\",label:{valType:\"string\",editType:\"calc\",dflt:\"\"},cmax:{valType:\"number\",editType:\"calc\",dflt:1},cmin:{valType:\"number\",editType:\"calc\",dflt:0},colorscale:h(u().colorscale,{dflt:[[0,\"white\"],[1,\"black\"]]})})}},\"calc\",\"nested\")).transforms=void 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x=0;for(e=0;e<a.value.length;e++)a.source[e]>x&&(x=a.source[e]),a.target[e]>x&&(x=a.target[e]);var b,_=x+1;t.node._count=_;var w=t.node.groups,T={};for(e=0;e<w.length;e++){var k=w[e];for(b=0;b<k.length;b++){var A=k[b],M=_+e;T.hasOwnProperty(A)?i.warn(\"Node \"+A+\" is already part of a group.\"):T[A]=M}}var S={source:[],target:[]};for(e=0;e<a.value.length;e++){var E=a.value[e],L=a.source[e],C=a.target[e];if(E>0&&s(L,_)&&s(C,_)&&(!T.hasOwnProperty(L)||!T.hasOwnProperty(C)||T[L]!==T[C])){T.hasOwnProperty(C)&&(C=T[C]),T.hasOwnProperty(L)&&(L=T[L]),C=+C,p[L=+L]=p[C]=!0;var O=\"\";a.label&&a.label[e]&&(O=a.label[e]);var P=null;O&&d.hasOwnProperty(O)&&(P=d[O]),u.push({pointNumber:e,label:O,color:c?a.color[e]:a.color,hovercolor:f?a.hovercolor[e]:a.hovercolor,customdata:h?a.customdata[e]:a.customdata,concentrationscale:P,source:L,target:C,value:+E}),S.source.push(L),S.target.push(C)}}var I=_+w.length,D=o(r.color),z=o(r.customdata),R=[];for(e=0;e<I;e++)if(p[e]){var F=r.label[e];R.push({group:e>_-1,childrenNodes:[],pointNumber:e,label:F,color:D?r.color[e]:r.color,customdata:z?r.customdata[e]:r.customdata})}var B=!1;return function(t,e,r){for(var a=i.init2dArray(t,0),o=0;o<Math.min(e.length,r.length);o++)if(i.isIndex(e[o],t)&&i.isIndex(r[o],t)){if(e[o]===r[o])return!0;a[e[o]].push(r[o])}return n(a).components.some((function(t){return t.length>1}))}(I,S.source,S.target)&&(B=!0),{circular:B,links:u,nodes:R,groups:w,groupLookup:T}}(e);return a({circular:r.circular,_nodes:r.nodes,_links:r.links,_groups:r.groups,_groupLookup:r.groupLookup})}},11820:function(t){\"use 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n.coerce(v,g,i.node,t,e)}y(\"label\"),y(\"groups\"),y(\"x\"),y(\"y\"),y(\"pad\"),y(\"thickness\"),y(\"line.color\"),y(\"line.width\"),y(\"hoverinfo\",t.hoverinfo),l(v,g,y,d),y(\"hovertemplate\"),y(\"align\");var m=h.colorway;y(\"color\",g.label.map((function(t,e){return a.addOpacity(function(t){return m[t%m.length]}(e),.8)}))),y(\"customdata\");var x=t.link||{},b=u.newContainer(e,\"link\");function _(t,e){return n.coerce(x,b,i.link,t,e)}_(\"label\"),_(\"arrowlen\"),_(\"source\"),_(\"target\"),_(\"value\"),_(\"line.color\"),_(\"line.width\"),_(\"hoverinfo\",t.hoverinfo),l(x,b,_,d),_(\"hovertemplate\");var w,T=o(h.paper_bgcolor).getLuminance()<.333,k=_(\"color\",T?\"rgba(255, 255, 255, 0.6)\":\"rgba(0, 0, 0, 0.2)\");function A(t){var e=o(t);if(!e.isValid())return t;var r=e.getAlpha();return 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x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*v,x1:x*g,y:b*y,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join(\"<br>\"),color:_(o,\"bgcolor\")||i.tinyColorHue,borderColor:_(o,\"bordercolor\"),fontFamily:_(o,\"font.family\"),fontSize:_(o,\"font.size\"),fontColor:_(o,\"font.color\"),fontWeight:_(o,\"font.weight\"),fontStyle:_(o,\"font.style\"),fontVariant:_(o,\"font.variant\"),fontTextcase:_(o,\"font.textcase\"),fontLineposition:_(o,\"font.lineposition\"),fontShadow:_(o,\"font.shadow\"),nameLength:_(o,\"namelength\"),textAlign:_(o,\"align\"),idealAlign:\"left\",hovertemplate:o.hovertemplate,hovertemplateLabels:m,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(m,i,a),\"skip\"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit(\"plotly_unhover\",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(m,r,i),s.click(t,{target:!0})}}})}},83248:function(t,e,r){\"use strict\";var n=r(49812),i=r(67756).Gz,a=r(33428),o=r(26800),s=r(48932),l=r(11820),u=r(49760),c=r(76308),f=r(43616),h=r(3400),p=h.strTranslate,d=h.strRotate,v=r(71688),g=v.keyFun,y=v.repeat,m=v.unwrap,x=r(72736),b=r(24040),_=r(84284),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=m(e),a=i.trace,c=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,v={justify:o.sankeyJustify,left:o.sankeyLeft,right:o.sankeyRight,center:o.sankeyCenter}[a.node.align],g=t.width*(c.x[1]-c.x[0]),y=t.height*(c.y[1]-c.y[0]),x=i._nodes,b=i._links,_=i.circular;(n=_?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[g,y]:[y,g]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodeAlign(v).nodes(x).links(b);var w,T,k,A=n();for(var M in n.nodePadding()<p&&h.warn(\"node.pad was reduced to 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l=0;for(T=0;T<r.sourceLinks.length;T++)l+=r.sourceLinks[T].value;for(T=0;T<r.sourceLinks.length;T++)(e=r.sourceLinks[T]).concentrationOut=e.value/l;var c=0;for(T=0;T<r.targetLinks.length;T++)c+=r.targetLinks[T].value;for(T=0;T<r.targetLinks.length;T++)(e=r.targetLinks[T]).concenrationIn=e.value/c}}(),a.node.x.length&&a.node.y.length){for(w=0;w<Math.min(a.node.x.length,a.node.y.length,A.nodes.length);w++)if(a.node.x[w]&&a.node.y[w]){var C=[a.node.x[w]*g,a.node.y[w]*y];A.nodes[w].x0=C[0]-d/2,A.nodes[w].x1=C[0]+d/2;var O=A.nodes[w].y1-A.nodes[w].y0;A.nodes[w].y0=C[1]-O/2,A.nodes[w].y1=C[1]+O/2}\"snap\"===a.arrangement&&function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(w=0;w<n.length;w++){var s=t[n[w].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(x=A.nodes).forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return 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e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:a,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:c.tinyRGB(n),tinyColorAlpha:n.getAlpha(),tinyColorHoverHue:c.tinyRGB(i),tinyColorHoverAlpha:i.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,linkArrowLength:t.linkArrowLength,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){var e=t.linkArrowLength;if(t.link.circular)return function(t,e){var r=t.width/2,n=t.circularPathData;return\"top\"===t.circularLinkType?\"M \"+(n.targetX-e)+\" \"+(n.targetY+r)+\" L\"+(n.rightInnerExtent-e)+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r-e)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r-e)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 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\"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+(n.rightInnerExtent-e)+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r-e)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r-e)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+(n.rightInnerExtent-e)+\" \"+(n.targetY+r)+\"L\"+(n.targetX-e)+\" \"+(n.targetY+r)+(e>0?\"L\"+n.targetX+\" \"+n.targetY:\"\")+\"Z\"}(t.link,e);var r=Math.abs((t.link.target.x0-t.link.source.x1)/2);e>r&&(e=r);var n=t.link.source.x1,a=t.link.target.x0-e,o=i(n,a),s=o(.5),l=o(.5),u=t.link.y0-t.link.width/2,c=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2,p=\"M\"+n+\",\"+u,d=\"C\"+s+\",\"+u+\" \"+l+\",\"+f+\" \"+a+\",\"+f,v=\"C\"+l+\",\"+h+\" \"+s+\",\"+c+\" \"+n+\",\"+c,g=e>0?\"L\"+(a+e)+\",\"+(f+t.link.width/2):\"\";return p+d+(g+=\"L\"+a+\",\"+h)+v+\"Z\"}}function S(t,e){var r=u(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s=\"node_\"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:c.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function C(t,e){t.call(L),e.attr(\"d\",M())}function O(t){t.attr(\"width\",(function(t){return t.node.x1-t.node.x0})).attr(\"height\",(function(t){return t.visibleHeight}))}function P(t){return t.link.width>1||t.linkLineWidth>0}function I(t){return p(t.translateX,t.translateY)+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function D(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on(\"mousemove.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on(\"mouseout.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on(\"click.basic\",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function z(t,e,r,i){var o=a.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on(\"dragstart\",(function(a){if(\"fixed\"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,\"g\",\"dragcover\",(function(t){i._fullLayout._dragCover=t})),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),\"snap\"===a.arrangement)){var o=a.traceId+\"|\"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=n.forceSimulation(a).alphaDecay(0).force(\"collide\",n.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(l.forceIterations)).force(\"constrain\",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame((function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var 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a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}b.call(\"_guiRestyle\",e,{\"node.x\":[r],\"node.y\":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function F(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function B(t){return function(e){return e.node.originalX===t.node.originalX}}t.exports=function(t,e,r,n,i){var o=t._context.staticPlot,s=!1;h.ensureSingle(t._fullLayout._infolayer,\"g\",\"first-render\",(function(){s=!0}));var v=t._fullLayout._dragCover,b=r.filter((function(t){return m(t).trace.visible})).map(k.bind(null,n)),_=e.selectAll(\".\"+l.cn.sankey).data(b,g);_.exit().remove(),_.enter().append(\"g\").classed(l.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",o?\"none\":\"auto\").attr(\"transform\",I),_.each((function(e,r){t._fullData[r]._sankey=e;var n=\"bgsankey-\"+e.trace.uid+\"-\"+r;h.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=a.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",o?\"none\":\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})})),_.transition().ease(l.ease).duration(l.duration).attr(\"transform\",I);var L=_.selectAll(\".\"+l.cn.sankeyLinks).data(y,g);L.enter().append(\"g\").classed(l.cn.sankeyLinks,!0).style(\"fill\",\"none\");var 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i={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(i.textfont,r.textfont),n=x(t,i)}return n}function k(t,e,r){var n={capSize:2*e.width*r,lineWidth:e.thickness*r,color:e.color};return e.copy_ystyle&&(n=t.error_y),n}var A=v.SYMBOL_SDF_SIZE,M=v.SYMBOL_SIZE,S=v.SYMBOL_STROKE,E={},L=u.symbolFuncs[0](.05*M);function C(t,e){var r,n,a=t.mx;if(\"circle\"===a)return null;var o=u.symbolNumber(a),s=u.symbolFuncs[o%100],l=!!u.symbolNoDot[o%100],c=!!u.symbolNoFill[o%100],f=d.isDotSymbol(a);if(t.ma&&(a+=\"_\"+t.ma),E[a])return E[a];var h=u.getMarkerAngle(t,e);return r=f&&!l?s(1.1*M,h)+L:s(M,h),n=i(r,{w:A,h:A,viewBox:[-M,-M,M,M],stroke:c?S:-S}),E[a]=n,n||null}t.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=t._context.plotGlPixelRatio;if(!0!==e.visible)return 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n,i,a=r.length,o=a/2;if(h.hasLines(e)&&o)if(\"hv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if(\"hvh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if(\"vhv\"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if(\"vh\"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var u=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){u=!0;break}var c=u||n.length>v.TOO_MANY_POINTS||h.hasMarkers(e)?\"rect\":\"round\";if(u&&e.connectgaps){var f=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=f,n[i+1]=p):(f=n[i],p=n[i+1])}return{join:c,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod(\"errorbars\",\"makeComputeError\"),l=c.getFromId(t,e.xaxis,\"x\"),u=c.getFromId(t,e.yaxis,\"y\"),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e[\"error_\"+a];if(o&&o.visible&&(\"linear\"===i.type||\"log\"===i.type)){for(var l=s(o),u={x:0,y:1}[a],c={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,v=-1/0,g=0,y=0;g<f;g++,y+=4){var m=t[g];if(n(m)){var x=r[2*g+u],b=l(m,g),_=b[0],w=b[1];if(n(_)&&n(w)){var T=m-_,k=m+w;p[y+c[0]]=x-i.c2l(T),p[y+c[1]]=i.c2l(k)-x,p[y+c[2]]=0,p[y+c[3]]=0,d=Math.min(d,m-_),v=Math.max(v,m+w)}}}h[a]={positions:r,errors:p,_bnds:[d,v]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(h.hasMarkers(e)){var s=r.font,u=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var f=n.sizes?n.sizes[i]:n.size,p=l(s)?s[i].size:s.size,d=l(u)?u.length>1?u[i]:u[0]:u,v=l(c)?c.length>1?c[i]:c[0]:c,g=y[d],m=y[v],x=f?f/.8+1:0,b=-m*x-.5*m;o.offset[i]=[g*x/p,b/p]}}return o}}},80220:function(t,e,r){\"use strict\";var n=r(3400),i=r(24040),a=r(80088),o=r(2876),s=r(88200),l=r(43028),u=r(43980),c=r(31147),f=r(74428),h=r(66828),p=r(70840),d=r(124);t.exports=function(t,e,r,v){function g(r,i){return n.coerce(t,e,o,r,i)}var y=!!t.marker&&a.isOpenSymbol(t.marker.symbol),m=l.isBubble(t),x=u(t,e,v,g);if(x){c(t,e,v,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasMarkers(e)&&(f(t,e,r,v,g,{noAngleRef:!0,noStandOff:!0}),g(\"marker.line.width\",y||m?1:0)),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,v,g),g(\"line.shape\")),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,v,g,{noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},26768:function(t,e,r){\"use strict\";var n=r(3400),i=r(76308),a=r(13448).DESELECTDIM;t.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,u=l._scene,c=l.index,f=u.selectBatch[c],h=u.unselectBatch[c],p=u.textOptions[c],d=u.textSelectedOptions[c]||{},v=u.textUnselectedOptions[c]||{},g=n.extendFlat({},p);if(f.length||h.length){var y=d.color,m=v.color,x=p.color,b=n.isArrayOrTypedArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=y||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=m||(y?_:i.addOpacity(_,a))}}u.glText[c].update(g)}}},99396:function(t,e,r){\"use strict\";var n=r(76688);t.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},80088:function(t,e,r){\"use strict\";var n=r(67072);e.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},e.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},41272:function(t,e,r){\"use strict\";var n=r(24040),i=r(3400),a=r(44928);function o(t,e,r,o){var s=t.xa,l=t.ya,u=t.distance,c=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=i.isArrayOrTypedArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=i.isArrayOrTypedArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family,h.tw=Array.isArray(p.weight)?p.weight[f]:p.weight,h.ty=Array.isArray(p.style)?p.style[f]:p.style,h.tv=Array.isArray(p.variant)?p.variant[f]:p.variant);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.ma=i.isArrayOrTypedArray(d.angle)?d.angle[f]:d.angle,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var v=d&&d.line;v&&(h.mlc=Array.isArray(v.color)?v.color[f]:v.color,h.mlw=i.isArrayOrTypedArray(v.width)?v.width[f]:v.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var y=s.c2p(h.x,!0),m=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:y-x,x1:y+x,xLabelVal:k?k[f]:h.x,y0:m-x,y1:m+x,yLabelVal:A?A[f]:h.y,cd:T,distance:u,spikeDistance:c,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}t.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,u,c,f,h,p,d,v=t.cd,g=v[0].t,y=v[0].trace,m=t.xa,x=t.ya,b=g.x,_=g.y,w=m.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=m.p2c(w-k),M=m.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!y.xperiodalignment,O=!!y.yperiodalignment;for(c=0;c<i.length;c++){if(l=b[a=i[c]],f=Math.abs(m.c2p(l)-w),C){var P=m.c2p(y._xStarts[a]),I=m.c2p(y._xEnds[a]);f=w>=Math.min(P,I)&&w<=Math.max(P,I)?0:1/0}if(f<L){if(L=f,u=_[a],h=x.c2p(u)-T,O){var D=x.c2p(y._yStarts[a]),z=x.c2p(y._yEnds[a]);h=T>=Math.min(D,z)&&T<=Math.max(D,z)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[c]}}}else for(c=i.length-1;c>-1;c--)l=b[a=i[c]],u=_[a],f=m.c2p(l)-w,h=x.c2p(u)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,y)]},calcHover:o}},38983:function(t,e,r){\"use strict\";var n=r(64628);n.plot=r(89876),t.exports=n},89876:function(t,e,r){\"use strict\";var n=r(38540),i=r(13472),a=r(24544),o=r(23352),s=r(3400),l=r(72760).selectMode,u=r(5048),c=r(43028),f=r(14328),h=r(26768).styleTextSelection,p={};function d(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,u=i.r*n,c=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-u-(1-e.domain[1])*f,o-c-(1-r.domain[1])*h]}(t.exports=function(t,e,r){if(r.length){var v,g,y=t._fullLayout,m=e._scene,x=e.xaxis,b=e.yaxis;if(m)if(u(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"],p)){var _=m.count,w=y._glcanvas.data()[0].regl;if(f(t,e,r),m.dirty){if(!m.line2d&&!m.error2d||m.scatter2d||m.fill2d||m.glText||w.clear({}),!0===m.error2d&&(m.error2d=a(w)),!0===m.line2d&&(m.line2d=i(w)),!0===m.scatter2d&&(m.scatter2d=n(w)),!0===m.fill2d&&(m.fill2d=i(w)),!0===m.glText)for(m.glText=new Array(_),v=0;v<_;v++)m.glText[v]=new o(w);if(m.glText){if(_>m.glText.length){var T=_-m.glText.length;for(v=0;v<T;v++)m.glText.push(new o(w))}else if(_<m.glText.length){var k=m.glText.length-_;m.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(v=0;v<_;v++)m.glText[v].update(m.textOptions[v])}if(m.line2d&&(m.line2d.update(m.lineOptions),m.lineOptions=m.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),m.line2d.update(m.lineOptions)),m.error2d){var A=(m.errorXOptions||[]).concat(m.errorYOptions||[]);m.error2d.update(A)}m.scatter2d&&m.scatter2d.update(m.markerOptions),m.fillOrder=s.repeat(null,_),m.fill2d&&(m.fillOptions=m.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,u=m.lineOptions[e],c=[];s._ownfill&&c.push(e),s._nexttrace&&c.push(e+1),c.length&&(m.fillOrder[e]=c);var f,h,p=[],d=u&&u.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var v=s._nexttrace;if(v){var g=m.lineOptions[e+1];if(g){var y=g.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=v.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=m.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),m.fill2d.update(m.fillOptions))}var M=y.dragmode,S=l(M),E=y.clickmode.indexOf(\"select\")>-1;for(v=0;v<_;v++){var L=r[v][0],C=L.trace,O=L.t,P=O.index,I=C._length,D=O.x,z=O.y;if(C.selectedpoints||S||E){if(S||(S=!0),C.selectedpoints){var R=m.selectBatch[P]=s.selIndices2selPoints(C),F={};for(g=0;g<R.length;g++)F[R[g]]=1;var B=[];for(g=0;g<I;g++)F[g]||B.push(g);m.unselectBatch[P]=B}var N=O.xpx=new Array(I),j=O.ypx=new Array(I);for(g=0;g<I;g++)N[g]=x.c2p(D[g]),j[g]=b.c2p(z[g])}else O.xpx=O.ypx=null}if(S){if(m.select2d||(m.select2d=n(y._glcanvas.data()[1].regl)),m.scatter2d){var U=new Array(_);for(v=0;v<_;v++)U[v]=m.selectBatch[v].length||m.unselectBatch[v].length?m.markerUnselectedOptions[v]:{};m.scatter2d.update(U)}m.select2d&&(m.select2d.update(m.markerOptions),m.select2d.update(m.markerSelectedOptions)),m.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};c.hasText(e)&&h(t)}))}else m.scatter2d&&m.scatter2d.update(m.markerOptions);var V={viewport:d(y,x,b,t._context.plotGlPixelRatio),range:[(x._rl||x.range)[0],(b._rl||b.range)[0],(x._rl||x.range)[1],(b._rl||b.range)[1]]},q=s.repeat(V,m.count);m.fill2d&&m.fill2d.update(q),m.line2d&&m.line2d.update(q),m.error2d&&m.error2d.update(q.concat(q)),m.scatter2d&&m.scatter2d.update(q),m.select2d&&m.select2d.update(q),m.glText&&m.glText.forEach((function(t){t.update(V)}))}else m.init()}}).reglPrecompiled=p},74588:function(t,e,r){\"use strict\";var n=r(3400);t.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,u=r.selectBatch,c=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(c[f].length){var h=n.repeat([],r.count);h[f]=c[f],o.draw(h)}else 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n=r(21776).Ks,i=r(21776).Gw,a=r(98304),o=r(6096),s=r(52904),l=r(5232),u=r(45464),c=r(49084),f=r(92880).extendFlat,h=r(67824).overrideAll,p=r(5232),d=o.line,v=o.marker;t.exports=h({lon:o.lon,lat:o.lat,cluster:{enabled:{valType:\"boolean\"},maxzoom:f({},p.layers.maxzoom,{}),step:{valType:\"number\",arrayOk:!0,dflt:-1,min:-1},size:{valType:\"number\",arrayOk:!0,dflt:20,min:0},color:{valType:\"color\",arrayOk:!0},opacity:f({},v.opacity,{dflt:1})},mode:f({},s.mode,{dflt:\"markers\"}),text:f({},s.text,{}),texttemplate:i({editType:\"plot\"},{keys:[\"lat\",\"lon\",\"text\"]}),hovertext:f({},s.hovertext,{}),line:{color:d.color,width:d.width},connectgaps:s.connectgaps,marker:f({symbol:{valType:\"string\",dflt:\"circle\",arrayOk:!0},angle:{valType:\"number\",dflt:\"auto\",arrayOk:!0},allowoverlap:{valType:\"boolean\",dflt:!1},opacity:v.opacity,size:v.size,sizeref:v.sizeref,sizemin:v.sizemin,sizemode:v.sizemode},c(\"marker\")),fill:o.fill,fillcolor:a(),textfont:l.layers.symbol.textfont,textposition:l.layers.symbol.textposition,below:{valType:\"string\"},selected:{marker:s.selected.marker},unselected:{marker:s.unselected.marker},hoverinfo:f({},u.hoverinfo,{flags:[\"lon\",\"lat\",\"text\",\"name\"]}),hovertemplate:n()},\"calc\",\"nested\")},79732:function(t){\"use 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k=b[e].properties;y.selectedOpacityFn&&(k.mo=g(y.selectedOpacityFn(k))),y.selectedColorFn&&(k.mcc=y.selectedColorFn(k)),y.selectedSizeFn&&(k.mrc=y.selectedSizeFn(k))}return{geojson:{type:\"FeatureCollection\",features:b},mcc:p||y&&y.selectedColorFn?{type:\"identity\",property:\"mcc\"}:f.color,mrc:d||y&&y.selectedSizeFn?{type:\"identity\",property:\"mrc\"}:(m=f.size,m/2),mo:v||y&&y.selectedOpacityFn?{type:\"identity\",property:\"mo\"}:g(f.opacity)}}(e);O.geojson=D.geojson,O.layout.visibility=\"visible\",E&&(O.filter=[\"!\",[\"has\",\"point_count\"]],I.cluster={type:\"circle\",filter:[\"has\",\"point_count\"],layout:{visibility:\"visible\"},paint:{\"circle-color\":b(a.cluster.color,a.cluster.step),\"circle-radius\":b(a.cluster.size,a.cluster.step),\"circle-opacity\":b(a.cluster.opacity,a.cluster.step)}},I.clusterCount={type:\"symbol\",filter:[\"has\",\"point_count\"],paint:{},layout:{\"text-field\":\"{point_count_abbreviated}\",\"text-font\":_(a),\"text-size\":12}}),i.extendFlat(O.paint,{\"circle-color\":D.mcc,\"circle-radius\":D.mrc,\"circle-opacity\":D.mo})}if(M&&E&&(O.filter=[\"!\",[\"has\",\"point_count\"]]),(S||A)&&(P.geojson=function(t,e){for(var r=e._fullLayout,n=t[0].trace,a=n.marker||{},o=a.symbol,s=a.angle,l=\"circle\"!==o?y(o):m,u=\"auto\"!==s?y(s,!0):m,f=c.hasText(n)?y(n.text):m,h=[],g=0;g<t.length;g++){var b=t[g];if(!x(b.lonlat)){var _,w=n.texttemplate;if(w){var T=Array.isArray(w)?w[g]||\"\":w,k=n._module.formatLabels(b,n,r),A={};p(A,n,b.i);var M=n._meta||{};_=i.texttemplateString(T,k,r._d3locale,A,b,M)}else _=f(g);_&&(_=_.replace(d,\"\").replace(v,\"\\n\")),h.push({type:\"Feature\",geometry:{type:\"Point\",coordinates:b.lonlat},properties:{symbol:l(g),angle:u(g),text:_}})}}return{type:\"FeatureCollection\",features:h}}(e,t),i.extendFlat(P.layout,{visibility:\"visible\",\"icon-image\":\"{symbol}-15\",\"text-field\":\"{text}\"}),S&&(i.extendFlat(P.layout,{\"icon-size\":a.marker.size/10}),\"angle\"in a.marker&&\"auto\"!==a.marker.angle&&i.extendFlat(P.layout,{\"icon-rotate\":{type:\"identity\",property:\"angle\"},\"icon-rotation-alignment\":\"map\"}),P.layout[\"icon-allow-overlap\"]=a.marker.allowoverlap,i.extendFlat(P.paint,{\"icon-opacity\":a.opacity*a.marker.opacity,\"icon-color\":a.marker.color})),A)){var z=(a.marker||{}).size,R=h(a.textposition,z);i.extendFlat(P.layout,{\"text-size\":a.textfont.size,\"text-anchor\":R.anchor,\"text-offset\":R.offset,\"text-font\":_(a)}),i.extendFlat(P.paint,{\"text-color\":a.textfont.color,\"text-opacity\":a.opacity})}return I}},15752:function(t,e,r){\"use strict\";var n=r(3400),i=r(43028),a=r(74428),o=r(66828),s=r(124),l=r(70840),u=r(31512),c=r(79732).isSupportedFont;t.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,u,r,i)}function p(r,i){return n.coerce2(t,e,u,r,i)}var d=function(t,e,r){var n=r(\"lon\")||[],i=r(\"lat\")||[],a=Math.min(n.length,i.length);return e._length=a,a}(0,e,h);if(d){if(h(\"text\"),h(\"texttemplate\"),h(\"hovertext\"),h(\"hovertemplate\"),h(\"mode\"),h(\"below\"),i.hasMarkers(e)){a(t,e,r,f,h,{noLine:!0,noAngle:!0}),h(\"marker.allowoverlap\"),h(\"marker.angle\");var v=e.marker;\"circle\"!==v.symbol&&(n.isArrayOrTypedArray(v.size)&&(v.size=v.size[0]),n.isArrayOrTypedArray(v.color)&&(v.color=v.color[0]))}i.hasLines(e)&&(o(t,e,r,f,h,{noDash:!0}),h(\"connectgaps\"));var g=p(\"cluster.maxzoom\"),y=p(\"cluster.step\"),m=p(\"cluster.color\",e.marker&&e.marker.color||r),x=p(\"cluster.size\"),b=p(\"cluster.opacity\");if(h(\"cluster.enabled\",!1!==g||!1!==y||!1!==m||!1!==x||!1!==b)||i.hasText(e)){var _=f.font.family;s(t,e,f,h,{noSelect:!0,noFontVariant:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0,font:{family:c(_)?_:\"Open Sans Regular\",weight:f.font.weight,style:f.font.style,size:f.font.size,color:f.font.color}})}h(\"fill\"),\"none\"!==e.fill&&l(t,e,r,h),n.coerceSelectionMarkerOpacity(e,h)}else e.visible=!1}},37920:function(t){\"use strict\";t.exports=function(t,e){return t.lon=e.lon,t.lat=e.lat,t}},11960:function(t,e,r){\"use strict\";var n=r(54460);t.exports=function(t,e,r){var i={},a=r[e.subplot]._subplot.mockAxis,o=t.lonlat;return i.lonLabel=n.tickText(a,a.c2l(o[0]),!0).text,i.latLabel=n.tickText(a,a.c2l(o[1]),!0).text,i}},63312:function(t,e,r){\"use strict\";var n=r(93024),i=r(3400),a=r(44928),o=i.fillText,s=r(39032).BADNUM,l=r(47552).traceLayerPrefix;function u(t,e,r){if(!t.hovertemplate){var n=(e.hi||t.hoverinfo).split(\"+\"),i=-1!==n.indexOf(\"all\"),a=-1!==n.indexOf(\"lon\"),s=-1!==n.indexOf(\"lat\"),l=e.lonlat,u=[];return i||a&&s?u.push(\"(\"+c(l[1])+\", \"+c(l[0])+\")\"):a?u.push(r.lon+c(l[0])):s&&u.push(r.lat+c(l[1])),(i||-1!==n.indexOf(\"text\"))&&o(e,t,u),u.join(\"<br>\")}function c(t){return t+\"°\"}}t.exports={hoverPoints:function(t,e,r){var o=t.cd,c=o[0].trace,f=t.xa,h=t.ya,p=t.subplot,d=[],v=l+c.uid+\"-circle\",g=c.cluster&&c.cluster.enabled;if(g){var y=p.map.queryRenderedFeatures(null,{layers:[v]});d=y.map((function(t){return t.id}))}var m=360*(e>=0?Math.floor((e+180)/360):Math.ceil((e-180)/360)),x=e-m;if(n.getClosest(o,(function(t){var e=t.lonlat;if(e[0]===s)return 1/0;if(g&&-1===d.indexOf(t.i+1))return 1/0;var n=i.modHalf(e[0],360),a=e[1],o=p.project([n,a]),l=o.x-f.c2p([x,a]),u=o.y-h.c2p([n,r]),c=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(l*l+u*u)-c,1-3/c)}),t),!1!==t.index){var b=o[t.index],_=b.lonlat,w=[i.modHalf(_[0],360)+m,_[1]],T=f.c2p(w),k=h.c2p(w),A=b.mrc||1;t.x0=T-A,t.x1=T+A,t.y0=k-A,t.y1=k+A;var M={};M[c.subplot]={_subplot:p};var S=c._module.formatLabels(b,c,M);return t.lonLabel=S.lonLabel,t.latLabel=S.latLabel,t.color=a(c,b),t.extraText=u(c,b,o[0].t.labels),t.hovertemplate=c.hovertemplate,[t]}},getExtraText:u}},11572:function(t,e,r){\"use 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s(t,e,r,n){this.type=\"scattermapbox\",this.subplot=t,this.uid=e,this.clusterEnabled=r,this.isHidden=n,this.sourceIds={fill:\"source-\"+e+\"-fill\",line:\"source-\"+e+\"-line\",circle:\"source-\"+e+\"-circle\",symbol:\"source-\"+e+\"-symbol\",cluster:\"source-\"+e+\"-circle\",clusterCount:\"source-\"+e+\"-circle\"},this.layerIds={fill:a+e+\"-fill\",line:a+e+\"-line\",circle:a+e+\"-circle\",symbol:a+e+\"-symbol\",cluster:a+e+\"-cluster\",clusterCount:a+e+\"-cluster-count\"},this.below=null}var l=s.prototype;l.addSource=function(t,e,r){var i={type:\"geojson\",data:e.geojson};r&&r.enabled&&n.extendFlat(i,{cluster:!0,clusterMaxZoom:r.maxzoom});var a=this.subplot.map.getSource(this.sourceIds[t]);a?a.setData(e.geojson):this.subplot.map.addSource(this.sourceIds[t],i)},l.setSourceData=function(t,e){this.subplot.map.getSource(this.sourceIds[t]).setData(e.geojson)},l.addLayer=function(t,e,r){var 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n=r(49084),i=r(29736).axisHoverFormat,a=r(21776).Ks,o=r(52948),s=r(45464),l=r(92880).extendFlat,u={x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},u:{valType:\"data_array\",editType:\"calc\"},v:{valType:\"data_array\",editType:\"calc\"},w:{valType:\"data_array\",editType:\"calc\"},starts:{x:{valType:\"data_array\",editType:\"calc\"},y:{valType:\"data_array\",editType:\"calc\"},z:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"},maxdisplayed:{valType:\"integer\",min:0,dflt:1e3,editType:\"calc\"},sizeref:{valType:\"number\",editType:\"calc\",min:0,dflt:1},text:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",editType:\"calc\"},hovertemplate:a({editType:\"calc\"},{keys:[\"tubex\",\"tubey\",\"tubez\",\"tubeu\",\"tubev\",\"tubew\",\"norm\",\"divergence\"]}),uhoverformat:i(\"u\",1),vhoverformat:i(\"v\",1),whoverformat:i(\"w\",1),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),showlegend:l({},s.showlegend,{dflt:!1})};l(u,n(\"\",{colorAttr:\"u/v/w 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L=function(){m=0,M=[],S=[],E=[]};(!m||m<M.length*S.length*E.length)&&L();var C=function(t){return\"x\"===t?v:\"y\"===t?g:y},O=function(t){return\"x\"===t?M:\"y\"===t?S:E},P=function(t){return t[m-1]<t[0]?-1:1},I=C(A[1]),D=C(A[3]),z=C(A[5]),R=O(A[1]).length,F=O(A[3]).length,B=O(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=P(C(A[1])),V=P(C(A[3])),q=P(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var H=j(e,r,i),G=j(e,r,i+1),W=j(e,r+1,i),Y=j(e+1,r,i);if(I[H]*U<I[G]*U&&D[H]*V<D[W]*V&&z[H]*q<z[Y]*q||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn(\"Encountered arbitrary coordinates! 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y=n[c];r.xMax=Math.max(r.xMax,y),r.xMin=Math.min(r.xMin,y);var m=o[c];r.yMax=Math.max(r.yMax,m),r.yMin=Math.min(r.yMin,m);var x=l[c];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=u,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},25668:function(t,e,r){\"use strict\";var n=r(67792).gl_streamtube3d,i=n.createTubeMesh,a=r(3400),o=r(33040).parseColorScale,s=r(8932).extractOpts,l=r(52094),u={xaxis:0,yaxis:1,zaxis:2};function c(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=c.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,c=e._len,f={};function d(t,e){var n=r[e],o=i[u[e]];return a.simpleMap(t,(function(t){return 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n=r(45464),i=r(21776).Ks,a=r(21776).Gw,o=r(49084),s=r(86968).u,l=r(74996),u=r(27328),c=r(92880).extendFlat,f=r(98192).c;t.exports={labels:{valType:\"data_array\",editType:\"calc\"},parents:{valType:\"data_array\",editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},branchvalues:{valType:\"enumerated\",values:[\"remainder\",\"total\"],dflt:\"remainder\",editType:\"calc\"},count:{valType:\"flaglist\",flags:[\"branches\",\"leaves\"],dflt:\"leaves\",editType:\"calc\"},level:{valType:\"any\",editType:\"plot\",anim:!0},maxdepth:{valType:\"integer\",editType:\"plot\",dflt:-1},marker:c({colors:{valType:\"data_array\",editType:\"calc\"},line:{color:c({},l.marker.line.color,{dflt:null}),width:c({},l.marker.line.width,{dflt:1}),editType:\"calc\"},pattern:f,editType:\"calc\"},o(\"marker\",{colorAttr:\"colors\",anim:!1})),leaf:{opacity:{valType:\"number\",editType:\"style\",min:0,max:1},editType:\"plot\"},text:l.text,textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"current 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u=n.select(this),f=s.ensureSingle(u,\"path\",\"surface\",(function(t){t.style(\"pointer-events\",h?\"none\":\"all\")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=G(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),_?f.transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(R)if(t.parent)if(Z){var i=(t.x1>Z?2*Math.PI:0)+V;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,$(t));else e={rpx0:0,rpx1:0};else e={x0:V,x1:V};return a(e,n)}(t);return function(t){return W(e(t))}})):f.attr(\"d\",W),u.call(m,S,t,r,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,A,t);var p=s.ensureSingle(u,\"g\",\"slicetext\"),w=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),T=s.ensureUniformFontSize(t,b.determineTextFont(A,i,y.font));w.text(e.formatSliceLabel(i,S,A,r,y)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,T).call(l.convertToTspans,t);var M=o.bBox(w.node());i.transform=v(M,i,k),i.transform.targetX=Y(i),i.transform.targetY=X(i);var E=function(t,e){var r=t.transform;return d(r,e),r.fontSize=T.size,c(A.type,r,y),s.getTextTransform(r)};_?w.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},R)if(t.parent)if(Z){var i=t.x1>Z?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,$(t));else e.x0=e.x1=V;else e.x0=e.x1=V;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),u=a(e.x0,t.x0),f=a(e.x1,t.x1),h=a(e.transform.scale,n.scale),p=a(e.transform.rotate,n.rotate),d=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,v=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=u(t),i=f(t),a=function(t){return v(Math.pow(t,d))}(t),s={pxmid:G(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return c(A.type,n,y),{transform:{targetX:Y(s),targetY:X(s),scale:h(t),rotate:p(t),rCenter:a}}}}(t);return function(t){return E(e(t),M)}})):w.attr(\"transform\",E(i,M))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}e.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,u=!r,c=!s.uniformtext.mode&&b.hasTransition(r);f(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return t[0].trace.uid}))).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),a.order(),c?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\")),u&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,u=n[0],c=t.data.data,f=u.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var v,g=o.split(\"+\"),y=function(t){return-1!==g.indexOf(t)},m=[];if(y(\"label\")&&c.label&&m.push(c.label),c.hasOwnProperty(\"v\")&&y(\"value\")&&m.push(b.formatValue(c.v,l)),!h){y(\"current path\")&&m.push(b.getPath(t.data));var x=0;y(\"percent parent\")&&x++,y(\"percent entry\")&&x++,y(\"percent root\")&&x++;var _=x>1;if(x){var w,T=function(t){v=b.formatPercent(w,l),_&&(v+=\" of \"+t),m.push(v)};y(\"percent parent\")&&!h&&(w=d/b.getValue(p),T(\"parent\")),y(\"percent entry\")&&(w=d/b.getValue(e),T(\"entry\")),y(\"percent root\")&&(w=d/b.getValue(f),T(\"root\"))}}return y(\"text\")&&(v=s.castOption(r,c.i,\"text\"),s.isValidTextValue(v)&&m.push(v)),m.join(\"<br>\")}var k=s.castOption(r,c.i,\"texttemplate\");if(!k)return\"\";var A={};c.label&&(A.label=c.label),c.hasOwnProperty(\"v\")&&(A.value=c.v,A.valueLabel=b.formatValue(c.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),c.hasOwnProperty(\"color\")&&(A.color=c.color);var M=s.castOption(r,c.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,c.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},85676:function(t,e,r){\"use strict\";var n=r(33428),i=r(76308),a=r(3400),o=r(82744).resizeText,s=r(60404);function l(t,e,r,n){var o=e.data.data,l=!e.children,u=o.i,c=a.castOption(r,u,\"marker.line.color\")||i.defaultLine,f=a.castOption(r,u,\"marker.line.width\")||0;t.call(s,e,r,n).style(\"stroke-width\",f).call(i.stroke,c).style(\"opacity\",l?r.leaf.opacity:null)}t.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(e){var r=n.select(this),i=e[0].trace;r.style(\"opacity\",i.opacity),r.selectAll(\"path.surface\").each((function(e){n.select(this).call(l,e,i,t)}))}))},styleOne:l}},16716:function(t,e,r){\"use strict\";var n=r(76308),i=r(49084),a=r(29736).axisHoverFormat,o=r(21776).Ks,s=r(45464),l=r(92880).extendFlat,u=r(67824).overrideAll;function c(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=t.exports=u(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or 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n=r(47128);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},79164:function(t,e,r){\"use strict\";var n=r(67792).gl_surface3d,i=r(67792).ndarray,a=r(67792).ndarray_linear_interpolate.d2,o=r(70448),s=r(11240),l=r(3400).isArrayOrTypedArray,u=r(33040).parseColorScale,c=r(43080),f=r(8932).extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){null!=t.dataCoordinate[a]&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var o=this.data.hovertext||this.data.text;return l(o)&&o[i]&&void 0!==o[i][n]?t.textLabel=o[i][n]:t.textLabel=o||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function 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A=[!0,!0,!0],M=[\"x\",\"y\",\"z\"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=m[e],p.highlightColor[e]=w.contourColor[e]=c(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=c(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,\"lighting\"in 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n=r(13916),i=r(92880).extendFlat,a=r(67824).overrideAll,o=r(25376),s=r(86968).u,l=r(29736).descriptionOnlyNumbers;(t.exports=a({domain:s({name:\"table\",trace:!0}),columnwidth:{valType:\"number\",arrayOk:!0,dflt:null},columnorder:{valType:\"data_array\"},header:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[],description:l(\"cell value\")},prefix:{valType:\"string\",arrayOk:!0,dflt:null},suffix:{valType:\"string\",arrayOk:!0,dflt:null},height:{valType:\"number\",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:\"number\",arrayOk:!0,dflt:1},color:{valType:\"color\",arrayOk:!0,dflt:\"grey\"}},fill:{color:{valType:\"color\",arrayOk:!0,dflt:\"white\"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:\"data_array\",dflt:[]},format:{valType:\"data_array\",dflt:[],description:l(\"cell 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C=Math.max(l(e.header.line.width),l(e.cells.line.width)),O={key:e.uid+t._context.staticPlot,translateX:y.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-y.y[1]),size:t._fullLayout._size,width:m,maxLineWidth:C,height:x,columnOrder:S,groupHeight:x,rowBlocks:A,headerRowBlocks:k,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:g}),gdColumns:g.map((function(t){return t[0]})),gdColumnsOriginalOrder:g.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:g.map((function(t,e){var r=M[t];return M[t]=(r||0)+1,{key:t+\"__\"+M[t],label:t,specIndex:e,xIndex:S[e],xScale:h,x:void 0,calcdata:void 0,columnWidth:E[e]}}))};return O.columns.forEach((function(t){t.calcdata=O,t.x=h(t)})),O}},53056:function(t,e,r){\"use strict\";var n=r(92880).extendFlat;e.splitToPanels=function(t){var 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m(t,e){return\"clip\"+t._fullLayout._uid+\"_columnBoundaryClippath_\"+e.calcdata.key+\"_\"+e.specIndex}function x(t){return[].concat.apply([],t.map((function(t){return t}))).map((function(t){return t.__data__}))}function b(t,e,r){var a=t.selectAll(\".\"+n.cn.scrollbarKit).data(s.repeat,s.keyFun);a.enter().append(\"g\").classed(n.cn.scrollbarKit,!0).style(\"shape-rendering\",\"geometricPrecision\"),a.each((function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return R(e,e.length-1)+(e.length?F(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-E(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr(\"transform\",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return f(e,E(t))}));var o=a.selectAll(\".\"+n.cn.scrollbar).data(s.repeat,s.keyFun);o.enter().append(\"g\").classed(n.cn.scrollbar,!0);var l=o.selectAll(\".\"+n.cn.scrollbarSlider).data(s.repeat,s.keyFun);l.enter().append(\"g\").classed(n.cn.scrollbarSlider,!0),l.attr(\"transform\",(function(t){return f(0,t.scrollbarState.topY||0)}));var u=l.selectAll(\".\"+n.cn.scrollbarGlyph).data(s.repeat,s.keyFun);u.enter().append(\"line\").classed(n.cn.scrollbarGlyph,!0).attr(\"stroke\",\"black\").attr(\"stroke-width\",n.scrollbarWidth).attr(\"stroke-linecap\",\"round\").attr(\"y1\",n.scrollbarWidth/2),u.attr(\"y2\",(function(t){return t.scrollbarState.barLength-n.scrollbarWidth/2})).attr(\"stroke-opacity\",(function(t){return t.columnDragInProgress||!t.scrollbarState.barWiggleRoom||r?0:.4})),u.transition().delay(0).duration(0),u.transition().delay(n.scrollbarHideDelay).duration(n.scrollbarHideDuration).attr(\"stroke-opacity\",0);var c=o.selectAll(\".\"+n.cn.scrollbarCaptureZone).data(s.repeat,s.keyFun);c.enter().append(\"line\").classed(n.cn.scrollbarCaptureZone,!0).attr(\"stroke\",\"white\").attr(\"stroke-opacity\",.01).attr(\"stroke-width\",n.scrollbarCaptureWidth).attr(\"stroke-linecap\",\"butt\").attr(\"y1\",0).on(\"mousedown\",(function(r){var 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32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,u=0,c=0|o[0],f=8191&c,h=c>>>13,p=0|o[1],d=8191&p,v=p>>>13,g=0|o[2],y=8191&g,m=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,O=0|o[7],P=8191&O,I=O>>>13,D=0|o[8],z=8191&D,R=D>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,W=0|s[2],Y=8191&W,X=W>>>13,Z=0|s[3],K=8191&Z,J=Z>>>13,$=0|s[4],Q=8191&$,tt=$>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ut=st>>>13,ct=0|s[8],ft=8191&ct,ht=ct>>>13,pt=0|s[9],dt=8191&pt,vt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(u+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;u=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(v,U)|0,a=Math.imul(v,V);var yt=(u+(n=n+Math.imul(f,H)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,H)|0))<<13)|0;u=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(y,U),i=(i=Math.imul(y,V))+Math.imul(m,U)|0,a=Math.imul(m,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(v,H)|0,a=a+Math.imul(v,G)|0;var mt=(u+(n=n+Math.imul(f,Y)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,Y)|0))<<13)|0;u=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(y,H)|0,i=(i=i+Math.imul(y,G)|0)+Math.imul(m,H)|0,a=a+Math.imul(m,G)|0,n=n+Math.imul(d,Y)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(v,Y)|0,a=a+Math.imul(v,X)|0;var xt=(u+(n=n+Math.imul(f,K)|0)|0)+((8191&(i=(i=i+Math.imul(f,J)|0)+Math.imul(h,K)|0))<<13)|0;u=((a=a+Math.imul(h,J)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(y,Y)|0,i=(i=i+Math.imul(y,X)|0)+Math.imul(m,Y)|0,a=a+Math.imul(m,X)|0,n=n+Math.imul(d,K)|0,i=(i=i+Math.imul(d,J)|0)+Math.imul(v,K)|0,a=a+Math.imul(v,J)|0;var bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,Y)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,Y)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(y,K)|0,i=(i=i+Math.imul(y,J)|0)+Math.imul(m,K)|0,a=a+Math.imul(m,J)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0;var _t=(u+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;u=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,H)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,Y)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,K)|0,i=(i=i+Math.imul(b,J)|0)+Math.imul(_,K)|0,a=a+Math.imul(_,J)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0;var wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(P,U),i=(i=Math.imul(P,V))+Math.imul(I,U)|0,a=Math.imul(I,V),n=n+Math.imul(L,H)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,H)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,Y)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,K)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(k,K)|0,a=a+Math.imul(k,J)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0;var Tt=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(z,U),i=(i=Math.imul(z,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(I,H)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(L,Y)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,Y)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,K)|0,i=(i=i+Math.imul(M,J)|0)+Math.imul(S,K)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ut)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ut)|0;var kt=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(z,H)|0,i=(i=i+Math.imul(z,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(P,Y)|0,i=(i=i+Math.imul(P,X)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,X)|0,n=n+Math.imul(L,K)|0,i=(i=i+Math.imul(L,J)|0)+Math.imul(C,K)|0,a=a+Math.imul(C,J)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ut)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ut)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(v,ft)|0,a=a+Math.imul(v,ht)|0;var At=(u+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,vt)|0)+Math.imul(h,dt)|0))<<13)|0;u=((a=a+Math.imul(h,vt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(z,Y)|0,i=(i=i+Math.imul(z,X)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(P,K)|0,i=(i=i+Math.imul(P,J)|0)+Math.imul(I,K)|0,a=a+Math.imul(I,J)|0,n=n+Math.imul(L,Q)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(y,ft)|0,i=(i=i+Math.imul(y,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var Mt=(u+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,vt)|0)+Math.imul(v,dt)|0))<<13)|0;u=((a=a+Math.imul(v,vt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,Y),i=(i=Math.imul(B,X))+Math.imul(N,Y)|0,a=Math.imul(N,X),n=n+Math.imul(z,K)|0,i=(i=i+Math.imul(z,J)|0)+Math.imul(R,K)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(P,Q)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ut)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(u+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,vt)|0)+Math.imul(m,dt)|0))<<13)|0;u=((a=a+Math.imul(m,vt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,K),i=(i=Math.imul(B,J))+Math.imul(N,K)|0,a=Math.imul(N,J),n=n+Math.imul(z,Q)|0,i=(i=i+Math.imul(z,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ut)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(u+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,vt)|0)+Math.imul(_,dt)|0))<<13)|0;u=((a=a+Math.imul(_,vt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(z,rt)|0,i=(i=i+Math.imul(z,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(u+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,vt)|0)+Math.imul(k,dt)|0))<<13)|0;u=((a=a+Math.imul(k,vt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(z,at)|0,i=(i=i+Math.imul(z,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(P,lt)|0,i=(i=i+Math.imul(P,ut)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ut)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,vt)|0)+Math.imul(S,dt)|0))<<13)|0;u=((a=a+Math.imul(S,vt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(z,lt)|0,i=(i=i+Math.imul(z,ut)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(P,ft)|0,i=(i=i+Math.imul(P,ht)|0)+Math.imul(I,ft)|0,a=a+Math.imul(I,ht)|0;var Ot=(u+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,vt)|0)+Math.imul(C,dt)|0))<<13)|0;u=((a=a+Math.imul(C,vt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ut))+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(z,ft)|0,i=(i=i+Math.imul(z,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Pt=(u+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,vt)|0)+Math.imul(I,dt)|0))<<13)|0;u=((a=a+Math.imul(I,vt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var It=(u+(n=n+Math.imul(z,dt)|0)|0)+((8191&(i=(i=i+Math.imul(z,vt)|0)+Math.imul(R,dt)|0))<<13)|0;u=((a=a+Math.imul(R,vt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var Dt=(u+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,vt))+Math.imul(N,dt)|0))<<13)|0;return u=((a=Math.imul(N,vt))+(i>>>13)|0)+(Dt>>>26)|0,Dt&=67108863,l[0]=gt,l[1]=yt,l[2]=mt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Ot,l[16]=Pt,l[17]=It,l[18]=Dt,0!==u&&(l[19]=u,r.length++),r};function v(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),u=Math.max(0,a-t.length+1);u<=l;u++){var c=a-u,f=(0|t.words[c])*(0|e.words[u]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):v(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var f=l,h=u,p=0;p<o;p++){var d=r[c+p],v=n[c+p],g=r[c+p+o],y=n[c+p+o],m=f*g-h*y;y=f*y+h*g,g=m,r[c+p]=d+g,n[c+p]=v+y,r[c+p+o]=d-g,n[c+p+o]=v-y,p!==s&&(m=l*f-u*h,h=l*h+u*f,f=m)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,f,n,i);for(var p=0;p<n;p++){var d=s[p]*c[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*c[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),v(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r||!(this.words[r]&i))},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,v=1;0==(r.words[0]&v)&&d<26;++d,v<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(x,m),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i<n;i++)e.words[i]=t.words[i];if(e.length=n,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=a&r,i=10;i<t.length;i++){var o=0|t.words[i];t.words[i-10]=(o&r)<<4|a>>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,m),i(_,m),i(w,m),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2*c*c).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var f=this.pow(c,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var v=p,g=0;0!==v.cmp(s);g++)v=v.redSqr();n(g<d);var y=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(y),f=y.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var f=u>>c&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4==++s||0===n&&0===c)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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if(h.right===s){if(!(d=p.left)||0!==d._color){p._color=0,p.right=h.left,h._color=1,h.left=p,l[f-2]=h,l[f-1]=s,i(p),i(h),f>=3&&((v=l[f-3]).right===p?v.right=h:v.left=h);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}else{var d;if(!(d=p.left)||0!==d._color){var v;h.left=s.right,p._color=0,p.right=s.left,s._color=1,s.right=h,s.left=p,l[f-2]=s,l[f-1]=h,i(p),i(h),i(s),f>=3&&((v=l[f-3]).right===p?v.right=s:v.left=s);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}}return l[0]._color=1,new a(o,l[0])},o.forEach=function(t,e,r){if(this.root)switch(arguments.length){case 1:return s(t,this.root);case 2:return l(e,this._compare,t,this.root);case 3:if(this._compare(e,r)>=0)return;return u(e,r,this._compare,t,this.root)}},Object.defineProperty(o,\"begin\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.left;return new c(this,t)}}),Object.defineProperty(o,\"end\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.right;return new c(this,t)}}),o.at=function(t){if(t<0)return new c(this,[]);for(var e=this.root,r=[];;){if(r.push(e),e.left){if(t<e.left._count){e=e.left;continue}t-=e.left._count}if(!t)return new c(this,r);if(t-=1,!e.right)break;if(t>=e.right._count)break;e=e.right}return new c(this,[])},o.ge=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new c(this,n)},o.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new c(this,n)},o.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new c(this,n)},o.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new c(this,n)},o.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new c(this,n);r=i<=0?r.left:r.right}return new c(this,[])},o.remove=function(t){var e=this.find(t);return e?e.remove():this},o.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var f=c.prototype;function h(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function p(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(f,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(f,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),f.clone=function(){return new c(this.tree,this._stack.slice())},f.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var o=new Array(t.length),s=t[t.length-1];o[o.length-1]=new e(s._color,s.key,s.value,s.left,s.right,s._count);for(var l=t.length-2;l>=0;--l)(s=t[l]).left===t[l+1]?o[l]=new e(s._color,s.key,s.value,o[l+1],s.right,s._count):o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);if((s=o[o.length-1]).left&&s.right){var u=o.length;for(s=s.left;s.right;)o.push(s),s=s.right;var c=o[u-1];for(o.push(new e(s._color,c.key,c.value,s.left,s.right,s._count)),o[u-1].key=s.key,o[u-1].value=s.value,l=o.length-2;l>=u;--l)s=o[l],o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);o[u-1].left=o[u]}if(0===(s=o[o.length-1])._color){var f=o[o.length-2];for(f.left===s?f.left=null:f.right===s&&(f.right=null),o.pop(),l=0;l<o.length;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(s.left||s.right){for(s.left?h(s,s.left):s.right&&h(s,s.right),s._color=1,l=0;l<o.length-1;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(1===o.length)return new a(this.tree._compare,null);for(l=0;l<o.length;++l)o[l]._count--;var p=o[o.length-2];return function(t){for(var e,a,o,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((a=t[l-1]).left===e){if((o=a.right).right&&0===o.right._color)return s=(o=a.right=r(o)).right=r(o.right),a.right=o.left,o.left=a,o.right=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),void(t[l-1]=o);if(o.left&&0===o.left._color)return s=(o=a.right=r(o)).left=r(o.left),a.right=s.left,o.left=s.right,s.left=a,s.right=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).left===a?u.left=s:u.right=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.right=n(0,o));a.right=n(0,o);continue}o=r(o),a.right=o.left,o.left=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((o=a.left).left&&0===o.left._color)return s=(o=a.left=r(o)).left=r(o.left),a.left=o.right,o.right=a,o.left=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),void(t[l-1]=o);if(o.right&&0===o.right._color)return s=(o=a.left=r(o)).right=r(o.right),a.left=s.right,o.right=s.left,s.right=a,s.left=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).right===a?u.right=s:u.left=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.left=n(0,o));a.left=n(0,o);continue}var u;o=r(o),a.left=o.right,o.right=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(o),p.left===s?p.left=null:p.right=null,new a(this.tree._compare,o[0])},Object.defineProperty(f,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(f,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(f,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),f.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),f.update=function(t){var r=this._stack;if(0===r.length)throw new Error(\"Can't update empty node!\");var n=new Array(r.length),i=r[r.length-1];n[n.length-1]=new e(i._color,i.key,t,i.left,i.right,i._count);for(var o=r.length-2;o>=0;--o)(i=r[o]).left===r[o+1]?n[o]=new e(i._color,i.key,i.value,n[o+1],i.right,i._count):n[o]=new e(i._color,i.key,i.value,i.left,n[o+1],i._count);return new a(this.tree._compare,n[0])},f.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},3837:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=new p(t);return r.update(e),r};var n=r(4935),i=r(501),a=r(5304),o=r(6429),s=r(6444),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=ArrayBuffer,c=DataView;function f(t){return Array.isArray(t)||function(t){return u.isView(t)&&!(t instanceof c)}(t)}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickFontStyle=[\"normal\",\"normal\",\"normal\"],this.tickFontWeight=[\"normal\",\"normal\",\"normal\"],this.tickFontVariant=[\"normal\",\"normal\",\"normal\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.labelFontStyle=[\"normal\",\"normal\",\"normal\"],this.labelFontWeight=[\"normal\",\"normal\",\"normal\"],this.labelFontVariant=[\"normal\",\"normal\",\"normal\"],this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var d=p.prototype;function v(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}d.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?f(a)&&f(a[0]):f(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),u=e.bind(this,!0,(function(t){if(f(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),c=!1,h=!1;if(\"bounds\"in t)for(var p=t.bounds,d=0;d<2;++d)for(var v=0;v<3;++v)p[d][v]!==this.bounds[d][v]&&(h=!0),this.bounds[d][v]=p[d][v];if(\"ticks\"in t)for(r=t.ticks,c=!0,this.autoTicks=!1,d=0;d<3;++d)this.tickSpacing[d]=0;else a(\"tickSpacing\")&&(this.autoTicks=!0,h=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),h=!0,c=!0,this._firstInit=!1),h&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),c=!0),c){for(d=0;d<3;++d)r[d].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?c=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(c=!0),l(\"tickFontStyle\")&&(c=!0),l(\"tickFontWeight\")&&(c=!0),l(\"tickFontVariant\")&&(c=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),u(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),l(\"labelFontStyle\")&&(g=!0),l(\"labelFontWeight\")&&(g=!0),l(\"labelFontVariant\")&&(g=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),u(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),u(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),u(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),u(\"gridColor\"),o(\"zeroEnable\"),u(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),u(\"backgroundColor\");var y=[{family:this.labelFont[0],style:this.labelFontStyle[0],weight:this.labelFontWeight[0],variant:this.labelFontVariant[0]},{family:this.labelFont[1],style:this.labelFontStyle[1],weight:this.labelFontWeight[1],variant:this.labelFontVariant[1]},{family:this.labelFont[2],style:this.labelFontStyle[2],weight:this.labelFontWeight[2],variant:this.labelFontVariant[2]}],m=[{family:this.tickFont[0],style:this.tickFontStyle[0],weight:this.tickFontWeight[0],variant:this.tickFontVariant[0]},{family:this.tickFont[1],style:this.tickFontStyle[1],weight:this.tickFontWeight[1],variant:this.tickFontVariant[1]},{family:this.tickFont[2],style:this.tickFontStyle[2],weight:this.tickFontWeight[2],variant:this.tickFontVariant[2]}];this._text?this._text&&(g||c)&&this._text.update(this.bounds,this.labels,y,this.ticks,m):this._text=n(this.gl,this.bounds,this.labels,y,this.ticks,m),this._lines&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var g=[new v,new v,new v];function y(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,p=o,d=l;u&1<<c&&(f=s,h=a,p=l,d=o),f[c]=r[0][c],h[c]=r[1][c],i[c]>0?(p[c]=-1,d[c]=0):(p[c]=0,d[c]=1)}}var m=[0,0,0],x={model:l,view:l,projection:l,_ortho:!1};d.isOpaque=function(){return!0},d.isTransparent=function(){return!1},d.drawTransparent=function(t){};var b=[0,0,0],_=[0,0,0],w=[0,0,0];d.draw=function(t){t=t||x;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),c=u.cubeEdges,f=u.axis,p=n[12],d=n[13],v=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*p+i[7]*d+i[11]*v+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=c[A],this.lastCubeProps.axis[A]=f[A];var M=g;for(A=0;A<3;++A)y(g[A],A,this.bounds,c,f);e=this.gl;var S,E,L,C=m;for(A=0;A<3;++A)this.backgroundEnable[A]?C[A]=f[A]:C[A]=0;for(this._background.draw(r,n,i,a,C,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var O=[0,0,0];f[A]>0?O[A]=a[1][A]:O[A]=a[0][A];for(var P=0;P<2;++P){var I=(A+1+P)%3,D=(A+1+(1^P))%3;this.gridEnable[I]&&this._lines.drawGrid(I,D,this.bounds,O,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(P=0;P<2;++P)I=(A+1+P)%3,D=(A+1+(1^P))%3,this.zeroEnable[D]&&Math.min(a[0][D],a[1][D])<=0&&Math.max(a[0][D],a[1][D])>=0&&this._lines.drawZero(I,D,this.bounds,O,this.zeroLineColor[D],this.zeroLineWidth[D]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var z=h(b,M[A].primalMinor),R=h(_,M[A].mirrorMinor),F=this.lineTickLength;for(P=0;P<3;++P){var B=k/r[5*P];z[P]*=F[P]*B,R[P]*=F[P]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(L=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,q=h(w,M[A].primalOffset);for(P=0;P<3;++P)this.lineTickEnable[A]&&(q[P]+=k*U[P]*Math.max(this.lineTickLength[P],0)/r[5*P]);var H=[0,0,0];if(H[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,E=1,\"auto\"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),L=[0,0,0],j(A,U,V),P=0;P<3;++P)q[P]+=k*U[P]*this.tickPad[P]/r[5*P];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],q,this.tickColor[A],H,L,S)}if(this.labelEnable[A]){for(E=0,L=[0,0,0],this.labels[A].length>4&&(N(A),E=1),\"auto\"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),P=0;P<3;++P)q[P]+=k*U[P]*this.labelPad[P]/r[5*P];q[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],q,this.labelColor[A],[0,0,0],L,S)}}this._text.unbind()},d.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},5304:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[u]=d;for(var v=-1;v<=1;v+=2)f[c]=v,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=u;u=c,c=g}var y=n(t,new Float32Array(e)),m=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],m),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,y,x,b)};var n=r(2762),i=r(8116),a=r(1879).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},6429:function(t,e,r){\"use strict\";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var m=0,x=0;x<2;++x){c[2]=a[x][2];for(var b=0;b<2;++b){c[1]=a[b][1];for(var _=0;_<2;++_)c[0]=a[_][0],h(l[m],c,s),m+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)u[x][k]=l[x][k]/T;p&&(u[x][2]*=-1),T<0&&(w<0||u[x][2]<u[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var O=(I=C<<A)+(C<<M)+(1-C<<S),P=I+(1-C<<M)+(C<<S);o(u[I],u[O],u[P],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){O=(I=C<<A)+(C<<M)+(1-C<<S),P=I+(1-C<<M)+(C<<S);var I,D=d([l[I],l[O],l[P],l[I+(1<<M)+(1<<S)]]);C?E=D:L=D}L>E&&(w|=1<<A)}}}var z=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==z&&(R<0||u[R][1]>u[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x)(N=R^1<<x)!==w&&N!==z&&(F<0&&(F=N),(S=u[N])[0]<u[F][0]&&(F=N));var B=-1;for(x=0;x<3;++x){var N;(N=R^1<<x)!==w&&N!==z&&N!==F&&(B<0&&(B=N),(S=u[N])[0]>u[B][0]&&(B=N))}var j=v;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===z?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,q=w;for(A=0;A<3;++A)V[A]=q&1<<A?-1:1;return y};var n=r(8828),i=r(6760),a=r(5202),o=r(3250),s=new Array(16),l=new Array(8),u=new Array(8),c=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],u[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],u=s[0]/s[3]-n,c=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(u*h-c*f)}return o}var v=[1,1,1],g=[0,0,0],y={cubeEdges:v,axis:g}},501:function(t,e,r){\"use strict\";t.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var v=+r[f][d].x;o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h,h=o.length/3|0;for(var y=0;y<r[f].length;++y)v=+r[f][y].x,o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1);g=o.length/3|0,u[f]=h,c[f]=g-h}var m=n(t,new Float32Array(o)),x=i(t,[{buffer:m,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,m,x,b,l,s,c,u)};var n=r(2762),i=r(8116),a=r(1879).n,o=[0,0,0],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,c[0]=this.gl.drawingBufferWidth,c[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=c,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,c=h(u,r);c[t]+=e[0][t],this.shader.uniforms.offset=c,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var c=f(s);c[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=c;var p=h(u,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var v=f(l);v[t]=1,this.shader.uniforms.screenAxis=v,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var c=h(u,n);c[t]+=r[0][t],this.shader.uniforms.offset=c;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},1879:function(t,e,r){\"use strict\";var n=r(3236),i=r(9405),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * (view * (model * vec4(p, 1.0)));\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);e.n=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * (view * (model * vec4(p, 1.0)));\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\\n\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);e.Q=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * (view * (model * vec4(nPosition, 1.0)));\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\\n\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);e.bg=function(t){return i(t,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},4935:function(t,e,r){\"use strict\";t.exports=function(t,e,r,n,o,l){var u=i(t),f=a(t,[{buffer:u,size:3}]),h=s(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,n,o,l),p};var i=r(2762),a=r(8116),o=r(4359),s=r(1879).Q,l=window||n.global||{},u=l.__TEXT_CACHE||{};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}l.__TEXT_CACHE={};var f=c.prototype,h=[0,0];f.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},f.unbind=function(){this.vao.unbind()},f.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=[r.style,r.weight,r.variant,r.family].join(\"_\"),c=u[l];c||(c=u[l]={});var f=c[e];f||(f=c[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r.family,fontStyle:r.style,fontWeight:r.weight,fontVariant:r.variant,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var h=(n||12)/12,p=f.positions,d=f.cells,v=0,g=d.length;v<g;++v)for(var y=d[v],m=2;m>=0;--m){var x=p[y[m]];a.push(h*x[0],-h*x[1],t)}}for(var l=[0,0,0],c=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=a.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(a.length/3|0)-f[d],l[d]=a.length/3|0;for(var v=0;v<n[d].length;++v)if(n[d][v].text){var g={family:n[d][v].font||i[d].family,style:i[d].fontStyle||i[d].style,weight:i[d].fontWeight||i[d].weight,variant:i[d].fontVariant||i[d].variant};s(n[d][v].x,n[d][v].text,g,n[d][v].fontSize||12,1.25,p)}c[d]=(a.length/3|0)-l[d]}this.buffer.update(a),this.tickOffset=l,this.tickCount=c,this.labelOffset=f,this.labelCount=h},f.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},f.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},f.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}},6444:function(t,e){\"use strict\";function r(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,u=o%a;o<0?(l=0|-Math.ceil(l),u=0|-u):(l=0|Math.floor(l),u|=0);var c=\"\"+l;if(o<0&&(c=\"-\"+c),i){for(var f=\"\"+u;f.length<i;)f=\"0\"+f;return c+\".\"+f}return c}e.create=function(t,e){for(var n=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:r(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:r(e[i],o)});n.push(a)}return n},e.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},5445:function(t,e,r){\"use strict\";t.exports=function(t,e,r,l,f){var h=e.model||u,p=e.view||u,y=e.projection||u,m=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,y,x,m)).axis;o(c,p,h),o(c,y,c);for(var _=v,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(c,c));s(c,c);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var O=m?5:4;for(L=O;L===O;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var P=d(g,c,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(P[C])))}}}}return _};var n=r(5033),i=r(5202),a=r(6429),o=r(6760),s=r(5665),l=r(5352),u=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,u=0;u<3;++u)s[u]=o[u]=r[u];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var c=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(c*c+f*f)}return t}var v=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},2762:function(t,e,r){\"use strict\";var n=r(1888),i=r(5298),a=r(9618),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function u(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER&&(r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\"),r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,\"uint16\"):c(t,\"float32\"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=u(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},6405:function(t,e,r){\"use strict\";var n=r(2931);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,p=null,d=null,v=[],g=1/0,y=!1,m=\"raw\"===t.coneSizemode,x=0;x<r.length;x++){var b=r[x];s=Math.min(b[0],s),l=Math.max(b[0],l),u=Math.min(b[1],u),c=Math.max(b[1],c),f=Math.min(b[2],f),h=Math.max(b[2],h);var _=i[x];if(n.length(_)>o&&(o=n.length(_)),x&&!m){var w=2*n.distance(p,b)/(n.length(d)+n.length(_));w?(g=Math.min(g,w),y=!1):y=!0}y||(p=b,d=_),v.push(_)}var T=[s,u,f],k=[l,c,h];e&&(e[0]=T,e[1]=k),0===o&&(o=1);var A=1/o;isFinite(g)||(g=1),a.vectorScale=g;var M=t.coneSize||(m?1:.5);t.absoluteConeSize&&(M=t.absoluteConeSize*A),a.coneScale=M,x=0;for(var S=0;x<r.length;x++)for(var E=(b=r[x])[0],L=b[1],C=b[2],O=v[x],P=n.length(O)*A,I=0;I<8;I++){a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.vectors.push(O),a.vectors.push(O),a.vectors.push(O),a.vectors.push(O),a.vectors.push(O),a.vectors.push(O),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var D=a.positions.length;a.cells.push([D-6,D-5,D-4],[D-3,D-2,D-1])}return a};var i=r(614);t.exports.createMesh=r(9060),t.exports.createConeMesh=function(e,r){return t.exports.createMesh(e,r,{shaders:i,traceType:\"cone\"})}},9060:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(6760),l=r(7608),u=r(9618),c=r(6729),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,u,c){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=u,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=c,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=c({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,v=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],v=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var y=p[g];d=Math.min(d,y),v=Math.max(v,y)}else for(g=0;g<n.length;++g)y=n[g][2],d=Math.min(d,y),v=Math.max(v,y);for(this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],g=0;g<n.length;++g)for(var m=n[g],x=0;x<3;++x)!isNaN(m[x])&&isFinite(m[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],m[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],m[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];if(3===_.length){for(x=0;x<3;++x){m=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(m[w])||!isFinite(m[w]))continue t}for(x=0;x<3;++x){var T;m=n[T=_[2-x]],a.push(m[0],m[1],m[2],m[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(v-d),0]:[(m[2]-d)/(v-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var u={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};u.inverseModel=l(u.inverseModel,u.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(s(c,u.view,u.model),s(c,u.projection,c),l(c,c),o=0;o<3;++o)u.eyePosition[o]=c[12+o]/c[15];var h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];u.lightPosition[o]=p/h}if(this.triangleCount>0){var v=this.triShader;v.bind(),v.uniforms=u,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),c=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),f=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));f.generateMipmap(),f.minFilter=t.LINEAR_MIPMAP_LINEAR,f.magFilter=t.LINEAR;var p=i(t),d=i(t),v=i(t),g=i(t),y=i(t),m=new h(t,f,l,c,p,d,y,v,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return m.update(e),m}},614:function(t,e,r){var n=r(3236),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * (view * conePosition);\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},737:function(t){t.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},5171:function(t,e,r){var 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worldPosition  = model * vec4(position, 1.0);\\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n  gl_Position         = projection * (view * worldPosition);\\n  fragColor           = color;\\n  fragPosition        = position;\\n}\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},2260:function(t,e,r){\"use strict\";var n=r(7766);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension(\"WEBGL_draw_buffers\");if(!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,u),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var c=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>c||r<0||r>c)throw new 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c(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var v=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return v._shape[0]},set:function(t){return v.width=t}},1:{get:function(){return v._shape[1]},set:function(t){return v.height=t}}}),this._shapeVector=g,function(t){var e=u(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,v=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var y=0;y<o;++y)t.color[y]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+y);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var m=r.getExtension(\"WEBGL_depth_texture\");m?d?t.depth=h(r,i,a,m.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):v&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;y<t.color.length;++y)t.color[y].dispose(),t.color[y]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),c(r,e),f(x)}c(r,e)}(this)}var v=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),c(n,a),f(s)),c(n,a)}}Object.defineProperties(v,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),v.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},v.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},2992:function(t,e,r){var n=r(3387).sprintf,i=r(5171),a=r(1848),o=r(1085);t.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var u=n(\"Error compiling %s shader %s:\\n\",l,s),c=n(\"%s%s\",u,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var v=parseInt(d.split(\":\")[2]);if(isNaN(v))throw new Error(n(\"Could not parse error: %s\",d));h[v]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if((h[p+3]||h[p+2]||h[p+1])&&(u+=g[p]+\"\\n\",h[p+1])){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),u+=n(\"^^^ %s\\n\\n\",y)}return{long:u.trim(),short:c.trim()}}},2510:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=t.gl,n=new u(t,o(r,l.vertex,l.fragment),o(r,l.pickVertex,l.pickFragment),s(r),s(r),s(r),s(r));return n.update(e),t.addObject(n),n};var n=r(2478),i=r(7762),a=r(1888),o=r(9405),s=r(2762),l=r(6768);function u(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var c,f=u.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(c=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],u=a[3]-a[1];c[0]=2*o/l,c[4]=2*s/u,c[6]=2*(r[0]-a[0])/l-1,c[7]=2*(r[1]-a[1])/u-1,e.bind();var f=e.uniforms;f.viewTransform=c,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*u/f,t[4]=2*c/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var v=i.attributes;return this.positionBuffer.bind(),v.position.pointer(),this.weightBuffer.bind(),v.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),v.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var u,c,f,p,d=t.colorLevels||[0],v=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(u=y[0]=r[0],c=y[1]=o[0],f=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(u=y[0]=r[0]+(r[1]-r[0])/2,c=y[1]=o[0]+(o[1]-o[0])/2,f=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var m=1/(f-u),x=1/(p-c),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var O,P;l?(O=x*(o[C]-c),P=x*(o[C+1]-c)):(O=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-c):x*(o[C]-(o[C]-o[C-1])/2-c),P=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-c):x*(o[C]+(o[C]-o[C-1])/2-c));for(var I=0;I<E;++I){var D,z;l?(D=m*(r[I]-u),z=m*(r[I+1]-u)):(D=I<b-1?m*(r[I]-(r[I+1]-r[I])/2-u):m*(r[I]-(r[I]-r[I-1])/2-u),z=I<b-1?m*(r[I]+(r[I+1]-r[I])/2-u):m*(r[I]+(r[I]-r[I-1])/2-u));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],q=s[l?(C+V)*b+(I+U):C*b+I],H=n.le(d,q);if(H<0)F=v[0],B=v[1],N=v[2],j=v[3];else if(H===g-1)F=v[4*g-4],B=v[4*g-3],N=v[4*g-2],j=v[4*g-1];else{var G=(q-d[H])/(d[H+1]-d[H]),W=1-G,Y=4*H,X=4*(H+1);F=W*v[Y]+G*v[X],B=W*v[Y+1]+G*v[X+1],N=W*v[Y+2]+G*v[X+2],j=W*v[Y+3]+G*v[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*D+.5*z,k[2*S+1]=.5*O+.5*P,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+I,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},6768:function(t,e,r){\"use strict\";var n=r(3236);t.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},7319:function(t,e,r){var n=r(3236),i=r(9405),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * (view * (model * vec4(p, 1.0)));\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},5714:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),c=u(new Array(1024),[256,1,4]),p=0;p<1024;++p)c.data[p]=255;var d=a(e,c);d.wrap=e.REPEAT;var v=new y(e,r,o,s,l,d);return v.update(t),v};var n=r(2762),i=r(8116),a=r(7766),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(2478),u=r(9618),c=r(7319),f=c.createShader,h=c.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function v(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=y.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var 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r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return(r=Math.sqrt(r))+(a=Math.sqrt(a))<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(c,e),y=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;y+=g[f]}return Math.abs(y-1)>.001?null:[h,s(t,g),g]}},840:function(t,e,r){var n=r(3236),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * (view * (model * vec4(p, 1.0)));\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * (view * (model * vec4(position, 1.0)));\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.pointShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},e.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},e.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},7201:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(8406),l=r(6760),u=r(7608),c=r(9618),f=r(6729),h=r(7765),p=r(1888),d=r(840),v=r(7626),g=d.meshShader,y=d.wireShader,m=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=v,this.edgeVAO=m,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var d=n[f][0],v=n[f][1],g=i[f],y=1-g,m=this.positions[d],x=this.positions[v],b=0;b<3;++b)o[s++]=g*m[b]+y*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return c(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],u=[],h=[],p=[],d=[],v=[],g=[],y=[],m=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,O=t.vertexIntensity,P=t.cellUVs,I=t.cellIntensity,D=1/0,z=-1/0;if(!C&&!P)if(O)if(t.vertexIntensityBounds)D=+t.vertexIntensityBounds[0],z=+t.vertexIntensityBounds[1];else for(var R=0;R<O.length;++R){var F=O[R];D=Math.min(D,F),z=Math.max(z,F)}else if(I)if(t.cellIntensityBounds)D=+t.cellIntensityBounds[0],z=+t.cellIntensityBounds[1];else for(R=0;R<I.length;++R)F=I[R],D=Math.min(D,F),z=Math.max(z,F);else for(R=0;R<n.length;++R)F=n[R][2],D=Math.min(D,F),z=Math.max(z,F);this.intensity=O||I||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.pickVertex=!(I||E);var B=t.pointSizes,N=t.pointSize||1;for(this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[Y=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;y.push(j[0],j[1],j[2]),X=S?S[Y]:E?E[R]:L,this.opacityscale&&O?a.push(X[0],X[1],X[2],this.opacity*A((O[Y]-D)/(z-D),this.opacityscale)):3===X.length?m.push(X[0],X[1],X[2],this.opacity):(m.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[Y]:O?[(O[Y]-D)/(z-D),0]:P?P[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],x.push(Z[0],Z[1]),B?b.push(B[Y]):b.push(N),_.push(R),H+=1;break;case 2:for(U=0;U<2;++U){j=n[Y=G[U]];for(var W=0;W<3;++W)if(isNaN(j[W])||!isFinite(j[W]))continue t}for(U=0;U<2;++U)j=n[Y=G[U]],p.push(j[0],j[1],j[2]),X=S?S[Y]:E?E[R]:L,this.opacityscale&&O?a.push(X[0],X[1],X[2],this.opacity*A((O[Y]-D)/(z-D),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[Y]:O?[(O[Y]-D)/(z-D),0]:P?P[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],v.push(Z[0],Z[1]),g.push(R);q+=1;break;case 3:for(U=0;U<3;++U)for(j=n[Y=G[U]],W=0;W<3;++W)if(isNaN(j[W])||!isFinite(j[W]))continue t;for(U=0;U<3;++U){var Y,X,Z,K;j=n[Y=G[2-U]],i.push(j[0],j[1],j[2]),(X=S?S[Y]:E?E[R]:L)?this.opacityscale&&O?a.push(X[0],X[1],X[2],this.opacity*A((O[Y]-D)/(z-D),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[Y]:O?[(O[Y]-D)/(z-D),0]:P?P[R]:I?[(I[R]-D)/(z-D),0]:[(j[2]-D)/(z-D),0],u.push(Z[0],Z[1]),K=w?w[Y]:T[R],l.push(K[0],K[1],K[2]),h.push(R)}V+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=V,this.pointPositions.update(y),this.pointColors.update(m),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(u),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=u(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(l(c,s.view,s.model),l(c,s.projection,c),u(c,c),o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f,h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],u=this.positions[r[1]],c=this.positions[r[2]],f=[(l[0]+u[0]+c[0])/3,(l[1]+u[1]+c[1])/3,(l[2]+u[2]+c[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=v(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},t.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\")))throw new Error(\"derivatives not supported\");var r=function(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=M(t),u=S(t),f=E(t),h=L(t),p=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),v=i(t),m=i(t),x=i(t),b=i(t),_=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),k=i(t),A=i(t),C=i(t),O=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:A,type:t.FLOAT,size:2}]),P=i(t),I=i(t),D=i(t),z=i(t),R=i(t),F=a(t,[{buffer:P,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:D,type:t.FLOAT,size:2},{buffer:z,type:t.FLOAT,size:1}]),B=i(t),N=new T(t,p,r,s,l,u,f,h,d,b,v,m,x,_,w,C,k,A,O,P,R,I,D,z,F,B,a(t,[{buffer:B,type:t.FLOAT,size:3}]));return N.update(e),N}},8120:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new o(t,n(e,[0,0,0,1,1,0,1,1]),i(e,a.boxVert,a.lineFrag))};var n=r(2762),i=r(9405),a=r(3603);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,u=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},1913:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new s(t,n(e),i(e,o.gridVert,o.gridFrag),i(e,o.tickVert,o.gridFrag))};var n=r(2762),i=r(9405),a=r(2478),o=r(3603);function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var u,c,f,h,p,d=s.prototype;d.draw=(u=[0,0],c=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,v=0;v<2;++v){var g=a[v],y=a[v+2]-g,m=.5*(o[v+2]+o[v]),x=o[v+2]-o[v];c[v]=2*y/x,u[v]=2*(g-m)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=u,r.uniforms.dataScale=c;var b=0;for(v=0;v<2;++v){f[0]=f[1]=0,f[v]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[v]/(s[v+2]-s[v])*d,r.uniforms.color=h[v];var _=6*n[v].length;p[v]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,u=this.vbo,c=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,v=s.viewBox,g=s.pixelRatio,y=s.screenBox,m=y[2]-y[0],x=y[3]-y[1],b=v[2]-v[0],_=v[3]-v[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/m,t[0]*=b/m,e[1]*=_/x,t[1]*=_/x,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var S=c.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,O=6*f[0].length,P=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),I=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),D=0+6*P,z=6*Math.max(0,I-P),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=O+6*R,N=6*Math.max(0,F-R);i[0]=2*(v[0]-E[1])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[1]*g/m,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[1]-E[0])/x-1,o[0]=L[0]*g/m,o[1]=E[0]*g/x,z&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,D,z)),i[0]=2*(v[2]+E[3])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[3]*g/m,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[3]+E[2])/x-1,o[0]=L[2]*g/m,o[1]=E[2]*g/x,z&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,D,z))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],u=r[o],c=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-u)/(c-u);s.push(d);for(var v=0;v<6;++v)n[i++]=d,n[i++]=h[v],n[i++]=p[v]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},4747:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new o(t,n(e,[-1,-1,-1,1,1,-1,1,1]),i(e,a.lineVert,a.lineFrag))};var n=r(2762),i=r(9405),a=r(3603);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,u=this.shader,c=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,u.uniforms.start=s,u.uniforms.end=l,u.uniforms.width=i*o.pixelRatio,u.uniforms.color=a,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},3603:function(t,e,r){\"use strict\";var n=r(3236),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);t.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},2142:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl;return new l(t,n(e),i(e,s.textVert,s.textFrag))};var n=r(2762),i=r(9405),a=r(529),o=r(2478),s=r(3603);function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var u,c,f,h,p,d,v=l.prototype;v.drawTicks=(u=[0,0],c=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,v=e.tickPad,g=e.tickColor,y=e.tickAngle,m=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);u[0]=u[1]=0,u[t]=1,c[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];c[1^t]=M*s[1^t]-1,d[t]&&(c[1^t]-=M*p*v[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t]&&T&&(c[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),c[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(c[1^t]+=M*p*v[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t+2]&&T&&(c[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),v.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var c=0;c<2;++c)e[c]=2*(o[c]*u-a[c])/(a[2+c]-a[c])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),v.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,u=.5*(n[o+2]+n[o]),c=n[o+2]-n[o],f=a[o],v=a[o+2]-f,g=i[o],y=i[o+2]-g;p[o]=2*l/c*v/y,h[o]=2*(s-u)/c*v/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),v.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,u=t.bounds;for(o=0;o<2;++o){var c=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,v=p.text,g=p.font||\"sans-serif\",y=p.fontStyle||\"normal\",m=p.fontWeight||\"normal\",x=p.fontVariant||\"normal\";i=p.fontSize||12;for(var b=1/(u[o+2]-u[o]),_=u[o],w=v.split(\"\\n\"),T=0;T<w.length;T++)for(n=a(g,w[T],{fontStyle:y,fontWeight:m,fontVariant:x}).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-T*i*1.2,(d-_)*b);c.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=c,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{fontStyle:t.labelFontStyle[o],fontWeight:t.labelFontWeight[o],fontVariant:t.labelFontVariant[o],textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title,{fontStyle:t.titleFontStyle,fontWeight:t.titleFontWeight,fontVariant:t.titleFontVariant}).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},v.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},1850:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=new l(e,n(e,[e.drawingBufferWidth,e.drawingBufferHeight]));return r.grid=i(r),r.text=a(r),r.line=o(r),r.box=s(r),r.update(t),r};var n=r(3589),i=r(1913),a=r(2142),o=r(4747),s=r(8120);function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var u=l.prototype;function c(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}u.setDirty=function(){this.dirty=this.pickDirty=!0},u.setOverlayDirty=function(){this.dirty=!0},u.nextDepthValue=function(){return this._depthCounter++/65536},u.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var v=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(v,e[1],v,e[3],p[d],h[d]):o.drawLine(e[0],v,e[2],v,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,y=this.borderLineWidth,m=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*y[1]*i,r[0],r[3]+.5*y[3]*i,y[1],m[1]),g[0]&&o.drawLine(r[0]-.5*y[0]*i,r[1],r[2]+.5*y[2]*i,r[1],y[0],m[0]),g[3]&&o.drawLine(r[2],r[1]-.5*y[1]*i,r[2],r[3]+.5*y[3]*i,y[3],m[3]),g[2]&&o.drawLine(r[0]-.5*y[0]*i,r[3],r[2]+.5*y[2]*i,r[3],y[2],m[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},u.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},u.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var f=u[c].pick(a,o,l);if(f)return f}return null}},u.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},u.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},u.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},u.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]),this.screenBox,this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=c(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=c(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=c(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=c(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=c(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=c(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],labelFontStyle:t.labelFontStyle||[\"normal\",\"normal\"],labelFontWeight:t.labelFontWeight||[\"normal\",\"normal\"],labelFontVariant:t.labelFontVariant||[\"normal\",\"normal\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\",titleFontStyle:t.titleFontStyle||\"normal\",titleFontWeight:t.titleFontWeight||\"normal\",titleFontVariant:t.titleFontVariant||\"normal\"}),this.static=!!t.static,this.setDirty()},u.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},u.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},u.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},u.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},u.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},4437:function(t,e,r){\"use strict\";t.exports=function(t,e){t=t||document.body;var r=[.01,1/0];\"distanceLimits\"in(e=e||{})&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]),\"zoomMin\"in e&&(r[0]=e.zoomMin),\"zoomMax\"in e&&(r[1]=e.zoomMax);var u=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:u,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;u.idle(e-r),u.recalcMatrix(i),u.flush(e-(100+2*r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),i=u.computedCenter.slice();if(u.setMode(t),\"turntable\"===t){var a=n();u._active.lookAt(a,r,i,e),u._active.lookAt(a+500,r,i,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(r)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,c=\"zoom\"===o,h=!!a.control,p=!!a.alt,v=!!a.shift,g=!!(1&e),y=!!(2&e),m=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!v||g&&!h&&!p&&v)&&u.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!v||y||g&&h&&!p&&!v)&&u.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),c&&g&&!h&&!p&&!v||m||g&&!h&&p&&!v)){var M=-d.zoomSpeed*_/window.innerHeight*(A-u.lastT())*100;u.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(3025),i=r(6296),a=r(351),o=r(8512),s=r(24),l=r(7520)},799:function(t,e,r){var n=r(3236),i=r(9405),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},4100:function(t,e,r){\"use strict\";var n=r(4437),i=r(3837),a=r(5445),o=r(4449),s=r(3589),l=r(2260),u=r(7169),c=r(351),f=r(4772),h=r(4040),p=r(799),d=r(9216)({tablet:!0,featureDetect:!0});function v(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return\"boolean\"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement(\"canvas\"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error(\"webgl not supported\");var m=t.bounds||[[-10,-10,-10],[10,10,10]],x=new v,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],O=[],P=!0,I=!0,D={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},z=(I=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:m,objects:E,shape:z,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:D,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,I=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",P=!0}}}function j(){for(var t=E.length,e=O.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,z);L[n]=e,O.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),O.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener(\"resize\",N),F.update=function(t){F._stopped||(t=t||{},P=!0,I=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),L.push(-1),P=!0,I=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),P=!0,I=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener(\"resize\",N),e.removeEventListener(\"webglcontextlost\",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();for(b.dispose(),t=0;t<O.length;++t)O[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},F._mouseRotating=!1,F._prevButtons=0,F.enableMouseListeners=function(){F.mouseListener=c(e,(function(t,e,r){if(!F._stopped){var n=O.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&F._prevButtons)F._mouseRotating=!0;else{F._mouseRotating&&(I=!0),F._mouseRotating=!1;for(var s=0;s<n;++s){var l=O[s].query(e,B[1]-r-1,F.pickRadius);if(l){if(l.distance>x.distance)continue;for(var u=0;u<i;++u){var c=E[u];if(L[u]===s){var f=c.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=c,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),P=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),P=!0),(o=o||x.object!==a)&&F.onselect&&F.onselect(x),1&t&&!(1&F._prevButtons)&&F.onclick&&F.onclick(x),F._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",U);var V=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],q=[V[0].slice(),V[1].slice()];function H(){if(!U()){N();var t=F.camera.tick();D.view=F.camera.matrix,P=P||t,I=I||t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,n=V[0],i=V[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=F.pixelRatio,C.axes=F.axes,P=P||!!C.dirty,I=I||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],c=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],c[p])}var d=F.bounds;if(F.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var v=.05*(i[p]-n[p]);n[p]=n[p]-v,i[p]=i[p]+v}d[0][p]=n[p],d[1][p]=i[p]}var y=!1;for(p=0;p<3;++p)y=y||q[0][p]!==d[0][p]||q[1][p]!==d[1][p],q[0][p]=d[0][p],q[1][p]=d[1][p];if(I=I||y,P=P||y){if(y){var m=[0,0,0];for(o=0;o<3;++o)m[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:m}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;for(z[0]=T,z[1]=k,B[0]=0|Math.max(T/F.pixelRatio,1),B[1]=0|Math.max(k/F.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,u=t.zFar,c=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,u),n._ortho=!0):(f(i,c,p,l,u),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var v=0;for(d=0;d<3;++d)v=Math.max(v,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/v,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(F,w),o=0;o<e;++o)(C=E[o]).axesBounds=d,F.clipToBounds&&(C.clipBounds=d);x.object&&(F.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),I&&(I=!1,function(){if(!U()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=O.length,n=0;n<e;++n){var i=O[n];i.shape=B,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(D))}i.end()}}}()),F.axesPixels=a(F.axes,D,T,k),F.onrender&&F.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),F.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;for(A.enable&&(M=M||A.isTransparent(),A.draw(D)),S.axes=A,x.object&&S.draw(D),r.disable(r.CULL_FACE),o=0;o<e;++o)(C=E[o]).axes=A,C.pixelRatio=F.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(D),C.isTransparent&&C.isTransparent()&&(M=!0);if(M){for(b.shape=z,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(D),o=0;o<e;++o)(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(D);for(r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(D),o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(D)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,u(r),r.disable(r.BLEND)}for(P=!1,o=0;o<e;++o)E[o].dirty=!1}}}return F.enableMouseListeners(),function t(){F._stopped||F.contextLost||(H(),requestAnimationFrame(t))}(),F.redraw=function(){F._stopped||(P=!0,H())},F},createCamera:n}},6640:function(t,e,r){var n=r(3236);e.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),e.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),e.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),e.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},4696:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(1888),o=r(6640);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,u,c=s.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),u=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)u[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(u),i||a.free(l),o||a.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],u=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),c=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=c<5,r.uniforms.pointSize=c,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(u[0]=255&t,u[1]=t>>8&255,u[2]=t>>16&255,u[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=u,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},783:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],p=r[0],d=r[1],v=r[2],g=r[3];return(a=u*p+c*d+f*v+h*g)<0&&(a=-a,p=-p,d=-d,v=-v,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*p,t[1]=s*c+l*d,t[2]=s*f+l*v,t[3]=s*h+l*g,t}},5964:function(t){\"use strict\";t.exports=function(t){return t||0===t?t.toString():\"\"}},9366:function(t,e,r){\"use strict\";var n=r(4359);t.exports=function(t,e,r){var a=[e.style,e.weight,e.variant,e.family].join(\"_\"),o=i[a];if(o||(o=i[a]={}),t in o)return o[t];var s={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e.family,fontStyle:e.style,fontWeight:e.weight,fontVariant:e.variant,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},l=n(t,s);s.triangles=!1;var u,c,f=n(t,s);if(r&&1!==r){for(u=0;u<l.positions.length;++u)for(c=0;c<l.positions[u].length;++c)l.positions[u][c]/=r;for(u=0;u<f.positions.length;++u)for(c=0;c<f.positions[u].length;++c)f.positions[u][c]/=r}var h=[[1/0,1/0],[-1/0,-1/0]],p=f.positions.length;for(u=0;u<p;++u){var d=f.positions[u];for(c=0;c<2;++c)h[0][c]=Math.min(h[0][c],d[c]),h[1][c]=Math.max(h[1][c],d[c])}return o[t]=[l,f,h]};var i={}},1283:function(t,e,r){var n=r(9405),i=r(3236),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * (view * (model * vec4(position, 1)));\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * (view * (model * vec4(dataPosition, 1)));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),u=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},p={vertex:s,fragment:l,attributes:c},d={vertex:a,fragment:u,attributes:c},v={vertex:o,fragment:u,attributes:c},g={vertex:s,fragment:u,attributes:c};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,f)},e.createOrtho=function(t){return y(t,h)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,v)},e.createPickProject=function(t){return y(t,g)}},8418:function(t,e,r){\"use strict\";var n=r(5219),i=r(2762),a=r(8116),o=r(1888),s=r(6760),l=r(1283),u=r(9366),c=r(5964),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],h=ArrayBuffer,p=DataView;function d(t){return Array.isArray(t)||function(t){return h.isView(t)&&!(t instanceof p)}(t)}function v(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function g(t,e,r,n){return v(n,n),v(n,n),v(n,n)}function y(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function x(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new y(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),u=l.createPickOrtho(e),c=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=new x(e,r,n,o,f,h,p,d,a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,u,c);return v.update(t),v};var b=x.prototype;b.pickSlots=1,b.setPickBase=function(t){this.pickId=t},b.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},b.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var _=[0,0],w=[0,0,0],T=[0,0,0],k=[0,0,0,1],A=[0,0,0,1],M=f.slice(),S=[0,0,0],E=[[0,0,0],[0,0,0]];function L(t){return t[0]=t[1]=t[2]=0,t}function C(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function O(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var P=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function I(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,u=r.model||f,c=r.view||f,h=r.projection||f,p=e.axesBounds,d=function(t){for(var e=E,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],_[0]=2/o.drawingBufferWidth,_[1]=2/o.drawingBufferHeight,t.bind(),l.view=c,l.projection=h,l.screenSize=_,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=d,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var v=0;v<3;++v)if(a[v]){l.scale=e.projectScale[v],l.opacity=e.projectOpacity[v];for(var y=M,m=0;m<16;++m)y[m]=0;for(m=0;m<4;++m)y[5*m]=1;y[5*v]=0,i[v]<0?y[12+v]=p[0][v]:y[12+v]=p[1][v],s(y,u,y),l.model=y;var x=(v+1)%3,b=(v+2)%3,P=L(w),I=L(T);P[x]=1,I[b]=1;var D=g(0,0,0,C(k,P)),z=g(0,0,0,C(A,I));if(Math.abs(D[1])>Math.abs(z[1])){var R=D;D=z,z=R,R=P,P=I,I=R;var F=x;x=b,b=F}D[0]<0&&(P[x]=-1),z[1]>0&&(I[b]=-1);var B=0,N=0;for(m=0;m<4;++m)B+=Math.pow(u[4*x+m],2),N+=Math.pow(u[4*b+m],2);P[x]/=Math.sqrt(B),I[b]/=Math.sqrt(N),l.axes[0]=P,l.axes[1]=I,l.fragClipBounds[0]=O(S,d[0],v,-1e8),l.fragClipBounds[1]=O(S,d[1],v,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var u=t.uniforms;u.model=n.model||f,u.view=n.view||f,u.projection=n.projection||f,_[0]=2/l.drawingBufferWidth,_[1]=2/l.drawingBufferHeight,u.screenSize=_,u.highlightId=r.highlightId,u.highlightScale=r.highlightScale,u.fragClipBounds=P,u.clipBounds=r.axes.bounds,u.opacity=r.opacity,u.pickGroup=r.pickId/255,u.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function D(t,e,r,i){var a;a=d(t)?e<t.length?t[e]:void 0:t,a=c(a);var o=!0;n(a)&&(a=\"▼\",o=!1),r||(r={});var s=r.family;d(s)&&(s=s[e]),s||(s=\"normal\");var l=r.weight;d(l)&&(l=l[e]),l||(l=\"normal\");var f=r.style;d(f)&&(f=f[e]),f||(f=\"normal\");var h=r.variant;d(h)&&(h=h[e]),h||(h=\"normal\");var p=u(a,{family:s,weight:l,style:f,variant:h},i);return{mesh:(p=u(a,r,i))[0],lines:p[1],bounds:p[2],visible:o}}b.draw=function(t){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},b.drawTransparent=function(t){I(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},b.drawPick=function(t){I(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},b.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},b.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},b.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(d(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(d(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){d(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l={family:t.font||\"normal\",style:t.fontStyle||\"normal\",weight:t.fontWeight||\"normal\",variant:t.fontVariant||\"normal\"},u=t.alignment||[0,0];if(2===u.length)i=u[0],a=u[1];else for(i=[],a=[],n=0;n<u.length;++n)i[n]=u[n][0],a[n]=u[n][1];var c=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,v=t.size,g=t.angle,y=t.lineColor,x=-1,b=0,_=0,w=0;if(s.length){w=s.length;t:for(n=0;n<w;++n){for(var T=s[n],k=0;k<3;++k)if(isNaN(T[k])||!isFinite(T[k]))continue t;var A=(j=D(h,n,l,this.pixelRatio)).mesh,M=j.lines,S=j.bounds;b+=3*A.cells.length,_+=2*M.edges.length}}var E=b+_,L=o.mallocFloat(3*E),C=o.mallocFloat(4*E),O=o.mallocFloat(2*E),P=o.mallocUint32(E);if(E>0){var I=0,z=b,R=[0,0,0,1],F=[0,0,0,1],B=d(p)&&d(p[0]),N=d(y)&&d(y[0]);t:for(n=0;n<w;++n){for(x+=1,T=s[n],k=0;k<3;++k){if(isNaN(T[k])||!isFinite(T[k]))continue t;f[k]=Math.max(f[k],T[k]),c[k]=Math.min(c[k],T[k])}A=(j=D(h,n,l,this.pixelRatio)).mesh,M=j.lines,S=j.bounds;var j,U=j.visible;if(U)if(d(p)){if(3===(V=B?n<p.length?p[n]:[0,0,0,0]:p).length){for(k=0;k<3;++k)R[k]=V[k];R[3]=1}else if(4===V.length){for(k=0;k<4;++k)R[k]=V[k];!this.hasAlpha&&V[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];if(U)if(d(y)){var V;if(3===(V=N?n<y.length?y[n]:[0,0,0,0]:y).length){for(k=0;k<3;++k)F[k]=V[k];F[k]=1}else if(4===V.length){for(k=0;k<4;++k)F[k]=V[k];!this.hasAlpha&&V[3]<1&&(this.hasAlpha=!0)}}else F[0]=F[1]=F[2]=0,F[3]=1;else F=[1,1,1,0];var q=.5;U?d(v)?q=n<v.length?+v[n]:12:v?q=+v:this.useOrtho&&(q=12):q=0;var H=0;d(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var G=Math.cos(H),W=Math.sin(H);for(T=s[n],k=0;k<3;++k)f[k]=Math.max(f[k],T[k]),c[k]=Math.min(c[k],T[k]);var Y=i,X=a;Y=0,d(i)?Y=n<i.length?i[n]:0:i&&(Y=i),X=0,d(a)?X=n<a.length?a[n]:0:a&&(X=a);var Z=[Y*=Y>0?1-S[0][0]:Y<0?1+S[1][0]:1,X*=X>0?1-S[0][1]:X<0?1+S[1][1]:1],K=A.cells||[],J=A.positions||[];for(k=0;k<K.length;++k)for(var $=K[k],Q=0;Q<3;++Q){for(var 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float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting 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* cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * (view * tubePosition);\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},7815:function(t,e,r){\"use strict\";var n=r(2931),i=r(9970),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};t.exports=function(t,e){var r=t.startingPositions,u=t.maxLength||1e3,c=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,l=t[0],u=t[1],c=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=o(a[0],l),v=o(a[1],u),g=o(a[2],c),y=d+1,m=v+1,x=g+1;if(d=s(d,0,f-1),y=s(y,0,f-1),v=s(v,0,h-1),m=s(m,0,h-1),g=s(g,0,p-1),x=s(x,0,p-1),d<0||v<0||g<0||y>f-1||m>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][y],E=a[1][v],L=a[1][m],C=a[2][g],O=(l-M)/(S-M),P=(u-E)/(L-E),I=(c-C)/(a[2][x]-C);switch(isFinite(O)||(O=.5),isFinite(P)||(P=.5),isFinite(I)||(I=.5),r.reversedX&&(d=f-1-d,y=f-1-y),r.reversedY&&(v=h-1-v,m=h-1-m),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=v*p,T=m*p,b=d*p*h,_=y*p*h;break;case 4:k=g,A=x,b=d*p,_=y*p,w=v*p*f,T=m*p*f;break;case 3:w=v,T=m,k=g*h,A=x*h,b=d*h*p,_=y*h*p;break;case 2:w=v,T=m,b=d*h,_=y*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=y,k=g*f,A=x*f,w=v*f*p,T=m*f*p;break;default:b=d,_=y,w=v*f,T=m*f,k=g*f*h,A=x*f*h}var D=i[b+w+k],z=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,D,B,O),n.lerp(q,z,N,O),n.lerp(H,R,j,O),n.lerp(G,F,U,O);var W=n.create(),Y=n.create();n.lerp(W,V,H,P),n.lerp(Y,q,G,P);var X=n.create();return n.lerp(X,W,Y,I),X}(e,t,p)},v=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],y=e[0][0],m=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<y||e>b||r<m||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/u,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,u=0;u<s;u++){var c=t[u],f=c[0],h=c[1],p=c[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=l(e),v=l(r),g=l(n),y=Math.min(d,v,g);return isFinite(y)?y:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var O=[C],P=[],I=d(C),D=C;P.push(I);var z=[],R=v(C,I),F=n.length(R);isFinite(F)&&F>S&&(S=F),z.push(F),g.push({points:O,velocities:P,divergences:z});for(var B=0;B<100*u&&O.length<u&&T(C);){B++;var N=n.clone(I),j=n.squaredLength(N);if(0===j)break;j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),I=d(N),n.squaredDistance(D,N)-A>-1e-4*A&&(O.push(N),D=N,P.push(I),R=v(N,I),F=n.length(R),isFinite(F)&&F>S&&(S=F),z.push(F)),C=N}}var U=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,u=0;u<l.length;u++)o=Math.max(o,n.length(l[u]));var c=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,u=[],c=[],f=[],h=[],p=[],d=[],v=0,g=0,y=i.create(),m=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,y=i.create(),n.copy(y,_),y[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;u.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(m,y,y,y,m,m),d.push(v,g,g,g,v,v);var A=u.length;c.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=m;m=y,y=S;var E=v;v=g,g=E}return{positions:u,cells:c,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<c.length;s++){var v=c[s],g=f.length;for(f=f.concat(v.positions),p=p.concat(v.vectors),d=d.concat(v.vertexIntensity),u=0;u<v.cells.length;u++){var y=v.cells[u],m=[];h.push(m);for(var x=0;x<y.length;x++)m.push(y[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}}(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*c*M/S),U};var u=r(6740),c=r(6405).createMesh;t.exports.createTubeMesh=function(t,e){return c(t,e,{shaders:u,traceType:\"streamtube\"})}},990:function(t,e,r){var n=r(9405),i=r(3236),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  mat4 objectOffsetTranslation = mat4(1.0) + mat4(vec4(0), vec4(0), vec4(0), vec4(objectOffset, 0));\\n  vec4 worldPosition = (model * objectOffsetTranslation) * vec4(localCoordinate, 1.0);\\n  vec4 clipPosition = projection * (view * worldPosition);\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color — in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  mat4 objectOffsetTranslation = mat4(1.0) + mat4(vec4(0), vec4(0), vec4(0), vec4(objectOffset, 0));\\n  vec4 worldPosition = (model * objectOffsetTranslation) * vec4(dataCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * (view * worldPosition);\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},e.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},9499:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=m(e),n=b(e),s=x(e),l=_(e),u=i(e),c=a(e,[{buffer:u,size:4,stride:w,offset:0},{buffer:u,size:3,stride:w,offset:16},{buffer:u,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),v=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);v.minFilter=e.LINEAR,v.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,u,c,v,s,l,f,h,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||\"jet\",g.update(y),g};var n=r(8828),i=r(2762),a=r(8116),o=r(7766),s=r(1888),l=r(6729),u=r(5298),c=r(9994),f=r(9618),h=r(3711),p=r(6760),d=r(7608),v=r(2478),g=r(6199),y=r(990),m=y.createShader,x=y.createContourShader,b=y.createPickShader,_=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,u,c,h,p,d,v,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=v,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var L=E.prototype;L.genColormap=function(t,e){var r=!1,n=c([l({colormap:t,nshades:S,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return u.divseq(n,255),this.hasAlphaScale=r,n},L.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},L.isOpaque=function(){return!this.isTransparent()},L.pickSlots=1,L.setPickBase=function(t){this.pickId=t};var C=[0,0,0],O={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function P(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||C,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=O.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var u=O.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return O.showSurface=o,O.showContour=s,O}var I={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},D=T.slice(),z=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=z,n.vertexColor=this.vertexColor;var s=D;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=P(n,this);if(c.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=c.projections[i],f.uniforms.clipBounds=c.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var v=0;v<this.contourLevels[o].length;++v)v===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==v&&v-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][v]&&(f.uniforms.height=this.contourLevels[o][v],h.draw(r.LINES,this._contourCounts[o][v],this._contourOffsets[o][v]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=c.projections[o],f.uniforms.clipBounds=c.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}L.draw=function(t){return R.call(this,t,!1)},L.drawTransparent=function(t){return R.call(this,t,!0)};var F={model:T,view:T,projection:T,inverseModel:T,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function B(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function N(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function j(t){if(Array.isArray(t)){if(Array.isArray(t))return[N(t[0]),N(t[1]),N(t[2])];var e=N(t);return[e.slice(),e.slice(),e.slice()]}}L.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=F;r.model=t.model||T,r.view=t.view||T,r.projection=t.projection||T,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=z;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=P(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var u=0;u<this.contourLevels[a].length;++u)this._contourCounts[a][u]&&(s.uniforms.height=this.contourLevels[a][u],l.draw(e.LINES,this._contourCounts[a][u],this._contourOffsets[a][u]))}l.unbind()}},L.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var p=i+c,d=s+h,g=f*(h?l:1-l),y=0;y<3;++y)u[y]+=this._field[y].get(p,d)*g;for(var m=this._pickResult.level,x=0;x<3;++x)if(m[x]=v.le(this.contourLevels[x],u[x]),m[x]<0)this.contourLevels[x].length>0&&(m[x]=0);else if(m[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][m[x]],_=this.contourLevels[x][m[x]+1];Math.abs(b-u[x])>Math.abs(_-u[x])&&(m[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},L.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},L.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=B(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=B(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=B(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=j(t.contourColor)),\"contourProject\"in t&&(this.contourProject=B(t.contourProject,(function(t){return B(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=j(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var u=l[o];for(y=0;y<2;++y)if(u.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],u)}}else if(t.ticks){var c=t.ticks;if(!Array.isArray(c)||2!==c.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=c[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var m=this._field,x=f(s.mallocFloat(3*m[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),m[o],\"mirror\");var b=f(s.mallocFloat(3*m[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var _=x.get(0,o,y,0),w=x.get(0,o,y,1),T=x.get(1,o,y,0),A=x.get(1,o,y,1),M=x.get(2,o,y,0),S=x.get(2,o,y,1),E=T*S-A*M,L=M*w-S*_,C=_*A-w*T,O=Math.sqrt(E*E+L*L+C*C);O<1e-8?(O=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,O=1):O=1/O:O=1/Math.sqrt(O),b.set(o,y,0,E*O),b.set(o,y,1,L*O),b.set(o,y,2,C*O)}s.free(x.data);var P=[1/0,1/0,1/0],I=[-1/0,-1/0,-1/0],D=1/0,z=-1/0,R=(a[0]-1)*(a[1]-1)*6,F=s.mallocFloat(n.nextPow2(10*R)),N=0,U=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var V=0;V<2;++V)for(var q=0;q<2;++q)for(var H=0;H<3;++H){var G=this._field[H].get(1+o+V,1+y+q);if(isNaN(G)||!isFinite(G))continue t}for(H=0;H<6;++H){var W=o+k[H][0],Y=y+k[H][1],X=this._field[0].get(W+1,Y+1),Z=this._field[1].get(W+1,Y+1);G=this._field[2].get(W+1,Y+1),E=b.get(W+1,Y+1,0),L=b.get(W+1,Y+1,1),C=b.get(W+1,Y+1,2),t.intensity&&(K=t.intensity.get(W,Y));var K=t.intensity?t.intensity.get(W,Y):G+this.objectOffset[2];F[N++]=W,F[N++]=Y,F[N++]=X,F[N++]=Z,F[N++]=G,F[N++]=0,F[N++]=K,F[N++]=E,F[N++]=L,F[N++]=C,P[0]=Math.min(P[0],X+this.objectOffset[0]),P[1]=Math.min(P[1],Z+this.objectOffset[1]),P[2]=Math.min(P[2],G+this.objectOffset[2]),D=Math.min(D,K),I[0]=Math.max(I[0],X+this.objectOffset[0]),I[1]=Math.max(I[1],Z+this.objectOffset[1]),I[2]=Math.max(I[2],G+this.objectOffset[2]),z=Math.max(z,K),U+=1}}for(t.intensityBounds&&(D=+t.intensityBounds[0],z=+t.intensityBounds[1]),o=6;o<N;o+=10)F[o]=(F[o]-D)/(z-D);this._vertexCount=U,this._coordinateBuffer.update(F.subarray(0,N)),s.freeFloat(F),s.free(b.data),this.bounds=[P,I],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===D&&this.intensityBounds[1]===z||(r=!0),this.intensityBounds=[D,z]}if(\"levels\"in t){var J=t.levels;for(J=Array.isArray(J[0])?J.slice():[[],[],J],o=0;o<3;++o)J[o]=J[o].slice(),J[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(y=0;y<J[o].length;++y)J[o][y]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(J[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<J[o].length;++y)if(J[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=J}if(r){m=this._field,a=this.shape;for(var $=[],Q=0;Q<3;++Q){var tt=this.contourLevels[Q],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[Q],tt[o]);et.push($.length/5|0),U=0;t:for(y=0;y<it.cells.length;++y){var at=it.cells[y];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ut=st-lt,ct=ot[1],ft=0|Math.floor(ct),ht=ct-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var vt=(Q+dt+1)%3;for(V=0;V<2;++V){var gt=V?ut:1-ut;for(W=0|Math.min(Math.max(lt+V,0),a[0]),q=0;q<2;++q){var yt=q?ht:1-ht;if(Y=0|Math.min(Math.max(ft+q,0),a[1]),G=dt<2?this._field[vt].get(W,Y):(this.intensity.get(W,Y)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var mt=gt*yt;nt[dt]+=mt*G}}}if(pt){if(H>0){for(var xt=0;xt<5;++xt)$.pop();U-=1}continue t}$.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var bt=s.mallocFloat($.length);for(o=0;o<$.length;++o)bt[o]=$[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},L.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},L.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],p=this._field[u],d=h(c,r[o]),v=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<v.length;++e)for(var y=v[e],m=0;m<2;++m){var x=g[y[m]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,O=k*E,P=T*L,I=T*E,D=C*f.get(_,M)+O*f.get(_,S)+P*f.get(w,M)+I*f.get(w,S),z=C*p.get(_,M)+O*p.get(_,S)+P*p.get(w,M)+I*p.get(w,S);if(isNaN(D)||isNaN(z)){m&&(n-=1);break}a[2*n+0]=D,a[2*n+1]=z,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},7766:function(t,e,r){\"use strict\";var n=r(9618),i=r(5298),a=r(1888);t.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");if(o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t),\"number\"==typeof arguments[1])return g(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return g(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=d(o,e.stride.slice()),u=0;\"float32\"===r?u=t.FLOAT:\"float64\"===r?(u=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var f,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}}u!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(u=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)f=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var m=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,m,0);\"float32\"!==r&&\"float64\"!==r||u!==t.UNSIGNED_BYTE?i.assign(x,e):c(x,e),f=p.subarray(0,y)}var b=v(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,u,f),l||a.free(p),new h(t,b,o[0],o[1],g,u)}(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var c=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=h.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function v(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function g(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var e=0;e<2;++e)if(l.indexOf(t[e])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return this.bind(),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_S,this._wrapS),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_T,this._wrapT),t}},shape:{get:function(){return this._shapeVector},set:function(t){if(Array.isArray(t)){if(2!==t.length)throw new Error(\"gl-texture2d: Invalid texture shape\")}else t=[0|t,0|t];return f(this,0|t[0],0|t[1]),[0|t[0],0|t[1]]}},width:{get:function(){return this._shape[0]},set:function(t){return f(this,t|=0,this._shape[1]),t}},height:{get:function(){return this._shape[1]},set:function(t){return t|=0,f(this,this._shape[0],t),t}}}),p.bind=function(t){var e=this.gl;return void 0!==t&&e.activeTexture(e.TEXTURE0+(0|t)),e.bindTexture(e.TEXTURE_2D,this.handle),void 0!==t?0|t:e.getParameter(e.ACTIVE_TEXTURE)-e.TEXTURE0},p.dispose=function(){this.gl.deleteTexture(this.handle)},p.generateMipmap=function(){this.bind(),this.gl.generateMipmap(this.gl.TEXTURE_2D);for(var t=Math.min(this._shape[0],this._shape[1]),e=0;t>0;++e,t>>>=1)this._mipLevels.indexOf(e)<0&&this._mipLevels.push(e)},p.setPixels=function(t,e,r,o){var s=this.gl;this.bind(),Array.isArray(e)?(o=r,r=0|e[1],e=0|e[0]):(e=e||0,r=r||0),o=o||0;var l=u(t)?t:t.raw;if(l)this._mipLevels.indexOf(o)<0?(s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,this.type,l),this._mipLevels.push(o)):s.texSubImage2D(s.TEXTURE_2D,o,e,r,this.format,this.type,l);else{if(!(t.shape&&t.stride&&t.data))throw new Error(\"gl-texture2d: Unsupported data type\");if(t.shape.length<2||e+t.shape[1]>this._shape[1]>>>o||r+t.shape[0]>this._shape[0]>>>o||e<0||r<0)throw new Error(\"gl-texture2d: Texture dimensions are out of bounds\");!function(t,e,r,o,s,l,u,f){var h=f.dtype,p=f.shape.slice();if(p.length<2||p.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var v=0,g=0,y=d(p,f.stride.slice());if(\"float32\"===h?v=t.FLOAT:\"float64\"===h?(v=t.FLOAT,y=!1,h=\"float32\"):\"uint8\"===h?v=t.UNSIGNED_BYTE:(v=t.UNSIGNED_BYTE,y=!1,h=\"uint8\"),2===p.length)g=t.LUMINANCE,p=[p[0],p[1],1],f=n(f.data,p,[f.stride[0],f.stride[1],1],f.offset);else{if(3!==p.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===p[2])g=t.ALPHA;else 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Sr=function(t,e){this.type=Xt,this.needle=t,this.haystack=e};Sr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Jt),n=e.parse(t[2],2,Jt);return r&&n?ae(r.type,[Xt,Yt,Wt,Gt,Jt])?new Sr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+re(r.type)+\" instead\"):null},Sr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!oe(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new xe(\"Expected first argument to be of type boolean, string, number or null, but found \"+re(ge(e))+\" instead.\");if(!oe(r,[\"string\",\"array\"]))throw new xe(\"Expected second argument to be of type array or string, but found \"+re(ge(r))+\" instead.\");return r.indexOf(e)>=0},Sr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},Sr.prototype.outputDefined=function(){return!0},Sr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var Er=function(t,e,r){this.type=Wt,this.needle=t,this.haystack=e,this.fromIndex=r};Er.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Jt),n=e.parse(t[2],2,Jt);if(!r||!n)return null;if(!ae(r.type,[Xt,Yt,Wt,Gt,Jt]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+re(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Wt);return i?new Er(r,n,i):null}return new Er(r,n)},Er.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!oe(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new xe(\"Expected first argument to be of type boolean, string, number or 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e={color:Zt,string:Yt,number:Wt,enum:Yt,boolean:Xt,formatted:Qt,resolvedImage:te};return\"array\"===t.type?ee(e[t.value]||Jt,t.length):e[t.type]}(e):void 0),n=r.parse(t,void 0,void 0,void 0,e&&\"string\"===e.type?{typeAnnotation:\"coerce\"}:void 0);return n?Xr(new un(n,e)):Zr(r.errors)}un.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._evaluator.globals=t,this._evaluator.feature=e,this._evaluator.featureState=r,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a,this.expression.evaluate(this._evaluator)},un.prototype.evaluate=function(t,e,r,n,i,a){this._evaluator.globals=t,this._evaluator.feature=e||null,this._evaluator.featureState=r||null,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a||null;try{var o=this.expression.evaluate(this._evaluator);if(null==o||\"number\"==typeof o&&o!=o)return this._defaultValue;if(this._enumValues&&!(o in this._enumValues))throw new xe(\"Expected value to be one of \"+Object.keys(this._enumValues).map((function(t){return JSON.stringify(t)})).join(\", \")+\", but found \"+JSON.stringify(o)+\" instead.\");return o}catch(t){return this._warningHistory[t.message]||(this._warningHistory[t.message]=!0,\"undefined\"!=typeof console&&console.warn(t.message)),this._defaultValue}};var hn=function(t,e){this.kind=t,this._styleExpression=e,this.isStateDependent=\"constant\"!==t&&!Xe(e.expression)};hn.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},hn.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)};var pn=function(t,e,r,n){this.kind=t,this.zoomStops=r,this._styleExpression=e,this.isStateDependent=\"camera\"!==t&&!Xe(e.expression),this.interpolationType=n};function dn(t,e){if(\"error\"===(t=fn(t,e)).result)return t;var r=t.value.expression,n=Ye(r);if(!n&&!Kr(e))return Zr([new qt(\"\",\"data expressions not supported\")]);var i=Ze(r,[\"zoom\"]);if(!i&&!Jr(e))return Zr([new qt(\"\",\"zoom expressions not supported\")]);var a=gn(r);if(!a&&!i)return Zr([new qt(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.')]);if(a instanceof qt)return Zr([a]);if(a instanceof wr&&!$r(e))return Zr([new qt(\"\",'\"interpolate\" expressions cannot be used with this property')]);if(!a)return Xr(new hn(n?\"constant\":\"source\",t.value));var o=a instanceof wr?a.interpolation:void 0;return Xr(new pn(n?\"camera\":\"composite\",t.value,a.labels,o))}pn.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},pn.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},pn.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?wr.interpolationFactor(this.interpolationType,t,e,r):0};var vn=function(t,e){this._parameters=t,this._specification=e,jt(this,rn(this._parameters,this._specification))};function gn(t){var e=null;if(t instanceof Ar)e=gn(t.result);else if(t instanceof kr)for(var r=0,n=t.args;r<n.length;r+=1){var i=n[r];if(e=gn(i))break}else(t instanceof tr||t instanceof wr)&&t.input instanceof Ee&&\"zoom\"===t.input.name&&(e=t);return e instanceof qt||t.eachChild((function(t){var r=gn(t);r instanceof qt?e=r:!e&&r?e=new qt(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.'):e&&r&&e!==r&&(e=new qt(\"\",'Only one zoom-based \"step\" or \"interpolate\" subexpression may be used in an expression.'))})),e}function yn(t){var e=t.key,r=t.value,n=t.valueSpec||{},i=t.objectElementValidators||{},a=t.style,o=t.styleSpec,s=[],l=Qr(r);if(\"object\"!==l)return[new Bt(e,r,\"object expected, \"+l+\" found\")];for(var u in r){var c=u.split(\".\")[0],f=n[c]||n[\"*\"],h=void 0;if(i[c])h=i[c];else if(n[c])h=Hn;else if(i[\"*\"])h=i[\"*\"];else{if(!n[\"*\"]){s.push(new Bt(e,r[u],'unknown property \"'+u+'\"'));continue}h=Hn}s=s.concat(h({key:(e?e+\".\":e)+u,value:r[u],valueSpec:f,style:a,styleSpec:o,object:r,objectKey:u},r))}for(var p in n)i[p]||n[p].required&&void 0===n[p].default&&void 0===r[p]&&s.push(new Bt(e,r,'missing required property \"'+p+'\"'));return s}function mn(t){var e=t.value,r=t.valueSpec,n=t.style,i=t.styleSpec,a=t.key,o=t.arrayElementValidator||Hn;if(\"array\"!==Qr(e))return[new Bt(a,e,\"array expected, \"+Qr(e)+\" found\")];if(r.length&&e.length!==r.length)return[new Bt(a,e,\"array length \"+r.length+\" expected, length \"+e.length+\" found\")];if(r[\"min-length\"]&&e.length<r[\"min-length\"])return[new Bt(a,e,\"array length at least \"+r[\"min-length\"]+\" expected, length \"+e.length+\" found\")];var s={type:r.value,values:r.values};i.$version<7&&(s.function=r.function),\"object\"===Qr(r.value)&&(s=r.value);for(var l=[],u=0;u<e.length;u++)l=l.concat(o({array:e,arrayIndex:u,value:e[u],valueSpec:s,style:n,styleSpec:i,key:a+\"[\"+u+\"]\"}));return l}function xn(t){var e=t.key,r=t.value,n=t.valueSpec,i=Qr(r);return\"number\"===i&&r!=r&&(i=\"NaN\"),\"number\"!==i?[new Bt(e,r,\"number expected, \"+i+\" found\")]:\"minimum\"in n&&r<n.minimum?[new Bt(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Bt(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function bn(t){var e,r,n,i=t.valueSpec,a=Ut(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,u=\"array\"===Qr(t.value.stops)&&\"array\"===Qr(t.value.stops[0])&&\"object\"===Qr(t.value.stops[0][0]),c=yn({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Bt(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;return e=e.concat(mn({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Qr(r)&&0===r.length&&e.push(new Bt(t.key,r,\"array must have at least one stop\")),e},default:function(t){return Hn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&c.push(new Bt(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||c.push(new Bt(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!$r(t.valueSpec)&&c.push(new Bt(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!Kr(t.valueSpec)?c.push(new Bt(t.key,t.value,\"property functions not supported\")):s&&!Jr(t.valueSpec)&&c.push(new Bt(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!u||void 0!==t.value.property||c.push(new Bt(t.key,t.value,'\"property\" property is required')),c;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Qr(a))return[new Bt(s,a,\"array expected, \"+Qr(a)+\" found\")];if(2!==a.length)return[new Bt(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(u){if(\"object\"!==Qr(a[0]))return[new Bt(s,a,\"object expected, \"+Qr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Bt(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Bt(s,a,\"object stop key must have value\")];if(n&&n>Ut(a[0].zoom))return[new Bt(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ut(a[0].zoom)!==n&&(n=Ut(a[0].zoom),r=void 0,o={}),e=e.concat(yn({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:xn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return cn(Vt(a[1]))?e.concat([new Bt(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(Hn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Qr(t.value),l=Ut(t.value),u=null!==t.value?t.value:n;if(e){if(s!==e)return[new Bt(t.key,u,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Bt(t.key,u,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var c=\"number expected, \"+s+\" found\";return Kr(i)&&void 0===a&&(c+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Bt(t.key,u,c)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Bt(t.key,u,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Bt(t.key,u,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Bt(t.key,u,\"integer expected, found \"+l)]}}function _n(t){var e=(\"property\"===t.expressionContext?dn:fn)(Vt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Bt(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Bt(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Xe(r))return[new Bt(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Xe(r))return[new Bt(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ze(r,[\"zoom\",\"feature-state\"]))return[new Bt(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Ye(r))return[new Bt(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function wn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ut(r))&&i.push(new Bt(e,r,\"expected one of [\"+n.values.join(\", \")+\"], \"+JSON.stringify(r)+\" found\")):-1===Object.keys(n.values).indexOf(Ut(r))&&i.push(new Bt(e,r,\"expected one of [\"+Object.keys(n.values).join(\", \")+\"], \"+JSON.stringify(r)+\" found\")),i}function Tn(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case\"has\":return t.length>=2&&\"$id\"!==t[1]&&\"$type\"!==t[1];case\"in\":return t.length>=3&&(\"string\"!=typeof t[1]||Array.isArray(t[2]));case\"!in\":case\"!has\":case\"none\":return!1;case\"==\":case\"!=\":case\">\":case\">=\":case\"<\":case\"<=\":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case\"any\":case\"all\":for(var e=0,r=t.slice(1);e<r.length;e+=1){var n=r[e];if(!Tn(n)&&\"boolean\"!=typeof n)return!1}return!0;default:return!0}}vn.deserialize=function(t){return new vn(t._parameters,t._specification)},vn.serialize=function(t){return{_parameters:t._parameters,_specification:t._specification}};var kn={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function An(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};Tn(t)||(t=En(t));var e=fn(t,kn);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:Sn(t)}}function Mn(t,e){return t<e?-1:t>e?1:0}function Sn(t){if(!Array.isArray(t))return!1;if(\"within\"===t[0])return!0;for(var e=1;e<t.length;e++)if(Sn(t[e]))return!0;return!1}function En(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?Ln(t[1],t[2],\"==\"):\"!=\"===r?Pn(Ln(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?Ln(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(En))):\"all\"===r?[\"all\"].concat(t.slice(1).map(En)):\"none\"===r?[\"all\"].concat(t.slice(1).map(En).map(Pn)):\"in\"===r?Cn(t[1],t.slice(2)):\"!in\"===r?Pn(Cn(t[1],t.slice(2))):\"has\"===r?On(t[1]):\"!has\"===r?Pn(On(t[1])):\"within\"!==r||t}function Ln(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function Cn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(Mn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function On(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function Pn(t){return[\"!\",t]}function In(t){return Tn(Vt(t.value))?_n(jt({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):Dn(t)}function Dn(t){var e=t.value,r=t.key;if(\"array\"!==Qr(e))return[new Bt(r,e,\"array expected, \"+Qr(e)+\" found\")];var n,i=t.styleSpec,a=[];if(e.length<1)return[new Bt(r,e,\"filter array must have at least 1 element\")];switch(a=a.concat(wn({key:r+\"[0]\",value:e[0],valueSpec:i.filter_operator,style:t.style,styleSpec:t.styleSpec})),Ut(e[0])){case\"<\":case\"<=\":case\">\":case\">=\":e.length>=2&&\"$type\"===Ut(e[1])&&a.push(new Bt(r,e,'\"$type\" cannot be use with operator \"'+e[0]+'\"'));case\"==\":case\"!=\":3!==e.length&&a.push(new Bt(r,e,'filter array for operator \"'+e[0]+'\" must have 3 elements'));case\"in\":case\"!in\":e.length>=2&&\"string\"!==(n=Qr(e[1]))&&a.push(new Bt(r+\"[1]\",e[1],\"string expected, \"+n+\" found\"));for(var o=2;o<e.length;o++)n=Qr(e[o]),\"$type\"===Ut(e[1])?a=a.concat(wn({key:r+\"[\"+o+\"]\",value:e[o],valueSpec:i.geometry_type,style:t.style,styleSpec:t.styleSpec})):\"string\"!==n&&\"number\"!==n&&\"boolean\"!==n&&a.push(new Bt(r+\"[\"+o+\"]\",e[o],\"string, number, or boolean expected, \"+n+\" found\"));break;case\"any\":case\"all\":case\"none\":for(var s=1;s<e.length;s++)a=a.concat(Dn({key:r+\"[\"+s+\"]\",value:e[s],style:t.style,styleSpec:t.styleSpec}));break;case\"has\":case\"!has\":n=Qr(e[1]),2!==e.length?a.push(new Bt(r,e,'filter array for \"'+e[0]+'\" operator must have 2 elements')):\"string\"!==n&&a.push(new Bt(r+\"[1]\",e[1],\"string expected, \"+n+\" found\"));break;case\"within\":n=Qr(e[1]),2!==e.length?a.push(new Bt(r,e,'filter array for \"'+e[0]+'\" operator must have 2 elements')):\"object\"!==n&&a.push(new Bt(r+\"[1]\",e[1],\"object expected, \"+n+\" found\"))}return a}function zn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return Hn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var u,c=t.valueSpec||s[o];if(!c)return[new Bt(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Qr(a)&&Kr(c)&&!c.tokens&&(u=/^{([^}]+)}$/.exec(a)))return[new Bt(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(u[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Bt(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&tn(Vt(a))&&\"identity\"===Ut(a.type)&&f.push(new Bt(r,a,'\"text-font\" does not support identity 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h=i.sources&&i.sources[r.source],p=h&&Ut(h.type);h?\"vector\"===p&&\"raster\"===s?e.push(new Bt(n,r.source,'layer \"'+r.id+'\" requires a raster source')):\"raster\"===p&&\"raster\"!==s?e.push(new Bt(n,r.source,'layer \"'+r.id+'\" requires a vector source')):\"vector\"!==p||r[\"source-layer\"]?\"raster-dem\"===p&&\"hillshade\"!==s?e.push(new Bt(n,r.source,\"raster-dem source can only be used with layer type 'hillshade'.\")):\"line\"!==s||!r.paint||!r.paint[\"line-gradient\"]||\"geojson\"===p&&h.lineMetrics||e.push(new Bt(n,r,'layer \"'+r.id+'\" specifies a line-gradient, which requires a GeoJSON source with `lineMetrics` enabled.')):e.push(new Bt(n,r,'layer \"'+r.id+'\" must specify a \"source-layer\"')):e.push(new Bt(n,r.source,'source \"'+r.source+'\" not found'))}else e.push(new Bt(n,r,'missing required property \"source\"'));return 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Arrows\"](t)&&(t>=11026&&t<=11055||t>=11088&&t<=11097||t>=11192&&t<=11243)||pi[\"CJK Symbols and Punctuation\"](t)||pi.Katakana(t)||pi[\"Private Use Area\"](t)||pi[\"CJK Compatibility Forms\"](t)||pi[\"Small Form Variants\"](t)||pi[\"Halfwidth and Fullwidth Forms\"](t)||8734===t||8756===t||8757===t||t>=9984&&t<=10087||t>=10102&&t<=10131||65532===t||65533===t)}(t))}function yi(t){return pi.Arabic(t)||pi[\"Arabic Supplement\"](t)||pi[\"Arabic Extended-A\"](t)||pi[\"Arabic Presentation Forms-A\"](t)||pi[\"Arabic Presentation Forms-B\"](t)}function mi(t){return t>=1424&&t<=2303||pi[\"Arabic Presentation Forms-A\"](t)||pi[\"Arabic Presentation Forms-B\"](t)}function xi(t,e){return!(!e&&mi(t)||t>=2304&&t<=3583||t>=3840&&t<=4255||pi.Khmer(t))}function bi(t){for(var e=0,r=t;e<r.length;e+=1)if(mi(r[e].charCodeAt(0)))return!0;return!1}var _i=\"deferred\",wi=\"loading\",Ti=\"loaded\",ki=\"error\",Ai=null,Mi=\"unavailable\",Si=null,Ei=function(t){t&&\"string\"==typeof t&&t.indexOf(\"NetworkError\")>-1&&(Mi=ki),Ai&&Ai(t)};function Li(){Ci.fire(new Dt(\"pluginStateChange\",{pluginStatus:Mi,pluginURL:Si}))}var Ci=new Rt,Oi=function(){return Mi},Pi=function(){if(Mi!==_i||!Si)throw new Error(\"rtl-text-plugin cannot be downloaded unless a pluginURL is specified\");Mi=wi,Li(),Si&&Mt({url:Si},(function(t){t?Ei(t):(Mi=Ti,Li())}))},Ii={applyArabicShaping:null,processBidirectionalText:null,processStyledBidirectionalText:null,isLoaded:function(){return Mi===Ti||null!=Ii.applyArabicShaping},isLoading:function(){return Mi===wi},setState:function(t){Mi=t.pluginStatus,Si=t.pluginURL},isParsed:function(){return null!=Ii.applyArabicShaping&&null!=Ii.processBidirectionalText&&null!=Ii.processStyledBidirectionalText},getPluginURL:function(){return Si}},Di=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new hi,this.transition={})};Di.prototype.isSupportedScript=function(t){return function(t,e){for(var r=0,n=t;r<n.length;r+=1)if(!xi(n[r].charCodeAt(0),e))return!1;return!0}(t,Ii.isLoaded())},Di.prototype.crossFadingFactor=function(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},Di.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var zi=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(tn(t))return new vn(t,e);if(cn(t)){var r=dn(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=ue.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};zi.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},zi.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var Ri=function(t){this.property=t,this.value=new zi(t,void 0)};Ri.prototype.transitioned=function(t,e){return new Bi(this.property,this.value,e,p({},t.transition,this.transition),t.now)},Ri.prototype.untransitioned=function(){return new Bi(this.property,this.value,null,{},0)};var Fi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};Fi.prototype.getValue=function(t){return w(this._values[t].value.value)},Fi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ri(this._values[t].property)),this._values[t].value=new zi(this._values[t].property,null===e?void 0:w(e))},Fi.prototype.getTransition=function(t){return w(this._values[t].transition)},Fi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ri(this._values[t].property)),this._values[t].transition=w(e)||void 0},Fi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},Fi.prototype.transitioned=function(t,e){for(var r=new Ni(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},Fi.prototype.untransitioned=function(){for(var t=new Ni(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var Bi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};Bi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var Ni=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Ni.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},Ni.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var ji=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};ji.prototype.getValue=function(t){return w(this._values[t].value)},ji.prototype.setValue=function(t,e){this._values[t]=new zi(this._values[t].property,null===e?void 0:w(e))},ji.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},ji.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var Ui=function(t,e,r){this.property=t,this.value=e,this.parameters=r};Ui.prototype.isConstant=function(){return\"constant\"===this.value.kind},Ui.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},Ui.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var Vi=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};Vi.prototype.get=function(t){return this._values[t]};var qi=function(t){this.specification=t};qi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},qi.prototype.interpolate=function(t,e,r){var n=rr[this.specification.type];return n?n(t,e,r):t};var Hi=function(t,e){this.specification=t,this.overrides=e};Hi.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new Ui(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new Ui(this,t.expression,e)},Hi.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new Ui(this,{kind:\"constant\",value:void 0},t.parameters);var n=rr[this.specification.type];return n?new Ui(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Hi.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new Ui(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new Ui(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new Ui(this,{kind:\"constant\",value:s},e)}return new Ui(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Hi),Wi=function(t){this.specification=t};Wi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return 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Vi(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate($n,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return x(t,Zi)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Jn,n,t,e,r))return!1}if(x(t,Zi))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),_(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Qn(this,t.call(Zn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Ft,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof Ui&&Kr(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1},e}(Rt),Ji={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},$i=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Qi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function ta(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Ji[i].BYTES_PER_ELEMENT),o=r=ea(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:ea(r,Math.max(n,e)),alignment:e}}function ea(t,e){return Math.ceil(t/e)*e}Qi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Qi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Qi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Qi.prototype.clear=function(){this.length=0},Qi.prototype.resize=function(t){this.reserve(t),this.length=t},Qi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new 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this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Qi);ia.prototype.bytesPerElement=12,oi(\"StructArrayLayout2i4i12\",ia);var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Qi);aa.prototype.bytesPerElement=8,oi(\"StructArrayLayout2i4ub8\",aa);var oa=function(t){function e(){t.apply(this,arguments)}return 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this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}($i);Ma.prototype.size=48;var Sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Ma(this,t)},e}(ga);oi(\"PlacedSymbolArray\",Sa);var Ea=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var 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r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return 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s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},so.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},so.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},so.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=f(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},so.prototype.getBinding=function(t,e,r){return new Ka(t,e)};var lo=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};lo.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},lo.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},lo.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=r.min,a=r.mid,o=r.max,s=n[i],l=n[a],u=n[o];if(s&&l&&u)for(var c=t;c<e;c++)this.zoomInPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],s.tl[0],s.tl[1],s.br[0],s.br[1],l.pixelRatio,s.pixelRatio),this.zoomOutPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],u.tl[0],u.tl[1],u.br[0],u.br[1],l.pixelRatio,u.pixelRatio)}},lo.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,Fa.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,Fa.members,this.expression.isStateDependent))},lo.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var uo=function(t,e,r){this.binders={},this._buffers=[];var n=[];for(var i in t.paint._values)if(r(i)){var a=t.paint.get(i);if(a 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instanceof ao&&n.setConstantPatternPositions(t,e)}},uo.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var u=l[s],c=r.feature(u.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof oo||h instanceof so||h instanceof lo)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(u.start,u.end,c,t[o],i),a=!0}}}return a},uo.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof io||r instanceof ao)&&t.push.apply(t,r.uniformNames.map((function(t){return\"#define HAS_UNIFORM_\"+t})))}return t},uo.prototype.getBinderAttributes=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof oo||r instanceof so)for(var n=0;n<r.paintVertexAttributes.length;n++)t.push(r.paintVertexAttributes[n].name);else if(r instanceof lo)for(var i=0;i<Fa.members.length;i++)t.push(Fa.members[i].name)}return t},uo.prototype.getBinderUniforms=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof io||r instanceof ao||r instanceof so)for(var n=0,i=r.uniformNames;n<i.length;n+=1){var a=i[n];t.push(a)}}return t},uo.prototype.getPaintVertexBuffers=function(){return this._buffers},uo.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof io||i instanceof ao||i instanceof so)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},uo.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,u=o.binding;this.binders[l].setUniform(u,n,r.get(l),s)}},uo.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof lo){var 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u=t.readVarint();n=7&u,i=u>>3}if(i--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new a(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},Ys.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,u=-1/0;t.pos<e;){if(n<=0){var c=t.readVarint();r=7&c,n=c>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>u&&(u=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,u]},Ys.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),u=Ys.types[this.type];function c(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];c(l=f);break;case 2:for(n=0;n<l.length;n++)c(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=Zs(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)c(l[n][i])}1===l.length?l=l[0]:u=\"Multi\"+u;var h={type:\"Feature\",geometry:{type:u,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var Ks=Js;function Js(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields($s,this,e),this.length=this._features.length}function $s(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Qs(t,e,r){if(3===t){var n=new Ks(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Js.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new Ws(this._pbf,e,this.extent,this._keys,this._values)};var tl={VectorTile:function(t,e){this.layers=t.readFields(Qs,{},e)},VectorTileFeature:Ws,VectorTileLayer:Ks},el=tl.VectorTileFeature.types,rl=Math.pow(2,13);function nl(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*rl)+o,i*rl*2,a*rl*2,Math.round(s))}var il=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new ia,this.indexArray=new va,this.programConfigurations=new co(t.layers,t.zoom),this.segments=new za,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function al(t,e){return t.x===e.x&&(t.x<0||t.x>po)||t.y===e.y&&(t.y<0||t.y>po)}il.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=Ns(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,u=a.sourceLayerIndex,c=this.layers[0]._featureFilter.needGeometry,f=mo(o,c);if(this.layers[0]._featureFilter.filter(new Di(this.zoom),f,r)){var h={id:s,sourceLayerIndex:u,index:l,geometry:c?f.geometry:yo(o),properties:o.properties,type:o.type,patterns:{}};this.hasPattern?this.features.push(js(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,u,this.index,!0)}}},il.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},il.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},il.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},il.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},il.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Gs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},il.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},il.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Fs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,u=0,c=s;u<c.length;u+=1)l+=c[u].length;for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!((P=d).every((function(t){return t.x<0}))||P.every((function(t){return t.x>po}))||P.every((function(t){return t.y<0}))||P.every((function(t){return t.y>po}))))for(var v=0,g=0;g<d.length;g++){var y=d[g];if(g>=1){var m=d[g-1];if(!al(y,m)){f.vertexLength+4>za.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=y.sub(m)._perp()._unit(),b=m.dist(y);v+b>32768&&(v=0),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,v),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,v),v+=b,nl(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,0,v),nl(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,1,v);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>za.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===el[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];nl(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=as(w,T),O=0;O<C.length;O+=3)this.indexArray.emplaceBack(k+C[O],k+C[O+2],k+C[O+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}var P;this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},oi(\"FillExtrusionBucket\",il,{omit:[\"layers\",\"features\"]});var ol={paint:new Xi({\"fill-extrusion-opacity\":new qi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Hi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new qi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new qi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new Gi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Hi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Hi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new qi(Ft[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},sl=function(t){function e(e){t.call(this,e,ol)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new il(t)},e.prototype.queryRadius=function(){return Io(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s,l){var u=Do(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),c=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var i=[],o=0,s=t;o<s.length;o+=1){var l=s[o],u=[l.x,l.y,n,1];qo(u,u,e),i.push(new a(u[0]/u[3],u[1]/u[3]))}return i}(u,l,0,0),p=function(t,e,r,n){for(var i=[],o=[],s=n[8]*e,l=n[9]*e,u=n[10]*e,c=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,v=0,g=t;v<g.length;v+=1){for(var y=[],m=[],x=0,b=g[v];x<b.length;x+=1){var _=b[x],w=_.x,T=_.y,k=n[0]*w+n[4]*T+n[12],A=n[1]*w+n[5]*T+n[13],M=n[2]*w+n[6]*T+n[14],S=n[3]*w+n[7]*T+n[15],E=M+u,L=S+c,C=k+f,O=A+h,P=M+p,I=S+d,D=new a((k+s)/L,(A+l)/L);D.z=E/L,y.push(D);var z=new a(C/I,O/I);z.z=P/I,m.push(z)}i.push(y),o.push(m)}return[i,o]}(n,f,c,l);return function(t,e,r){var n=1/0;To(r,e)&&(n=ul(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],u=a[s+1],c=o[s],f=[l,u,o[s+1],c,l];_o(r,f)&&(n=Math.min(n,ul(r,f)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ki);function ll(t,e){return t.x*e.x+t.y*e.y}function ul(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),u=o.sub(i),c=ll(s,s),f=ll(s,l),h=ll(l,l),p=ll(u,s),d=ll(u,l),v=c*h-f*f,g=(h*p-f*d)/v,y=(c*d-f*p)/v,m=1-g-y,x=i.z*m+r.z*g+a.z*y;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var cl=ta([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,fl=ta([{name:\"a_uv_x\",components:1,type:\"Float32\"},{name:\"a_split_index\",components:1,type:\"Float32\"}]).members,hl=tl.VectorTileFeature.types,pl=Math.cos(Math.PI/180*37.5),dl=Math.pow(2,14)/.5,vl=function(t){var e=this;this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((function(t){e.gradients[t.id]={}})),this.layoutVertexArray=new aa,this.layoutVertexArray2=new oa,this.indexArray=new va,this.programConfigurations=new co(t.layers,t.zoom),this.segments=new za,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};vl.prototype.populate=function(t,e,r){this.hasPattern=Ns(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,u=s.id,c=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p=mo(l,h);if(this.layers[0]._featureFilter.filter(new Di(this.zoom),p,r)){var d=n?n.evaluate(p,{},r):void 0,v={id:u,properties:l.properties,type:l.type,sourceLayerIndex:f,index:c,geometry:h?p.geometry:yo(l),patterns:{},sortKey:d};i.push(v)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,y=i;g<y.length;g+=1){var m=y[g],x=m,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=js(\"line\",this.layers,m,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(m,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},vl.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},vl.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},vl.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},vl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},vl.prototype.upload=function(t){this.uploaded||(0!==this.layoutVertexArray2.length&&(this.layoutVertexBuffer2=t.createVertexBuffer(this.layoutVertexArray2,fl)),this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,cl),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},vl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},vl.prototype.lineFeatureClips=function(t){if(t.properties&&t.properties.hasOwnProperty(\"mapbox_clip_start\")&&t.properties.hasOwnProperty(\"mapbox_clip_end\"))return{start:+t.properties.mapbox_clip_start,end:+t.properties.mapbox_clip_end}},vl.prototype.addFeature=function(t,e,r,n,i){var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),u=a.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(t);for(var c=0,f=e;c<f.length;c+=1){var h=f[c];this.addLine(h,t,o,s,l,u)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},vl.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}for(var s=\"Polygon\"===hl[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var u=0;u<l-1&&t[u].equals(t[u+1]);)u++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var c,f=this.overscaling<=16?15*po/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,v=void 0,g=void 0;this.e1=this.e2=-1,s&&(c=t[l-2],g=t[u].sub(c)._unit()._perp());for(var y=u;y<l;y++)if(!(d=y===l-1?s?t[u+1]:void 0:t[y+1])||!t[y].equals(d)){g&&(v=g),c&&(p=c),c=t[y],g=d?d.sub(c)._unit()._perp():v;var m=(v=v||g).add(g);0===m.x&&0===m.y||m._unit();var x=v.x*g.x+v.y*g.y,b=m.x*g.x+m.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<pl&&p&&d,k=v.x*g.y-v.y*g.x>0;if(T&&y>u){var A=c.dist(p);if(A>2*f){var M=c.sub(c.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,v,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,c),\"miter\"===E)m._mult(_),this.addCurrentVertex(c,m,0,0,h);else if(\"flipbevel\"===E){if(_>100)m=g.mult(-1);else{var L=_*v.add(g).mag()/v.sub(g).mag();m._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(c,m,0,0,h),this.addCurrentVertex(c,m.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),O=k?C:0,P=k?0:C;if(p&&this.addCurrentVertex(c,v,O,P,h),\"fakeround\"===E)for(var I=Math.round(180*w/Math.PI/20),D=1;D<I;D++){var z=D/I;if(.5!==z){var R=z-.5;z+=z*R*(z-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(v)._mult(z)._add(v)._unit()._mult(k?-1:1);this.addHalfVertex(c,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(c,g,-O,-P,h)}else if(\"butt\"===E)this.addCurrentVertex(c,m,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(c,m,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(c,v,0,0,h),this.addCurrentVertex(c,v,1,1,h,!0)),d&&(this.addCurrentVertex(c,g,-1,-1,h,!0),this.addCurrentVertex(c,g,0,0,h)));if(T&&y<l-1){var N=c.dist(d);if(N>2*f){var j=c.add(d.sub(c)._mult(f/N)._round());this.updateDistance(c,j),this.addCurrentVertex(j,g,0,0,h),c=j}}}}},vl.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var 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e=Fl(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==Il.Bytes)return t.push(this.readFixed64());var e=Fl(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==Il.Bytes)return t.push(this.readSFixed64());var e=Fl(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===Il.Varint)for(;this.buf[this.pos++]>127;);else if(e===Il.Bytes)this.pos=this.readVarint()+this.pos;else if(e===Il.Fixed32)this.pos+=4;else{if(e!==Il.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return this.length=this.pos,this.pos=0,this.buf.subarray(0,this.length)},writeFixed32:function(t){this.realloc(4),Kl(this.buf,t,this.pos),this.pos+=4},writeSFixed32:function(t){this.realloc(4),Kl(this.buf,t,this.pos),this.pos+=4},writeFixed64:function(t){this.realloc(8),Kl(this.buf,-1&t,this.pos),Kl(this.buf,Math.floor(t*zl),this.pos+4),this.pos+=8},writeSFixed64:function(t){this.realloc(8),Kl(this.buf,-1&t,this.pos),Kl(this.buf,Math.floor(t*zl),this.pos+4),this.pos+=8},writeVarint:function(t){(t=+t||0)>268435455||t<0?function(t,e){var r,n;if(t>=0?(r=t%4294967296|0,n=t/4294967296|0):(n=~(-t/4294967296),4294967295^(r=~(-t%4294967296))?r=r+1|0:(r=0,n=n+1|0)),t>=0x10000000000000000||t<-0x10000000000000000)throw new Error(\"Given varint doesn't fit into 10 bytes\");e.realloc(10),function(t,e,r){r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos]=127&t}(r,0,e),function(t,e){var r=(7&t)<<4;e.buf[e.pos++]|=r|((t>>>=3)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t)))))}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&Nl(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),Ol(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),Ol(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&Nl(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,Il.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,jl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,Ul,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,Hl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,Vl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,ql,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,Gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,Wl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,Yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,Xl,e)},writeBytesField:function(t,e){this.writeTag(t,Il.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,Il.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,Il.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,Il.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,Il.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,Il.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,Il.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,Il.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,Il.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,Il.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var $l=3;function Ql(t,e,r){1===t&&r.readMessage(tu,e)}function tu(t,e,r){if(3===t){var n=r.readMessage(eu,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,u=n.top,c=n.advance;e.push({id:i,bitmap:new Jo({width:o+2*$l,height:s+2*$l},a),metrics:{width:o,height:s,left:l,top:u,advance:c}})}}function eu(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}var ru=$l;function nu(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,u=0,c=t;u<c.length;u+=1)for(var f=c[u],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else f.h===p.h?(p.x+=f.w,p.w-=f.w):f.w===p.w?(p.y+=f.h,p.h-=f.h):(o.push({x:p.x+f.w,y:p.y,w:p.w-f.w,h:f.h}),p.y+=f.h,p.h-=f.h);break}}return{w:s,h:l,fill:e/(s*l)||0}}var iu=1,au=function(t,e){var r=e.pixelRatio,n=e.version,i=e.stretchX,a=e.stretchY,o=e.content;this.paddedRect=t,this.pixelRatio=r,this.stretchX=i,this.stretchY=a,this.content=o,this.version=n},ou={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};ou.tl.get=function(){return[this.paddedRect.x+iu,this.paddedRect.y+iu]},ou.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-iu,this.paddedRect.y+this.paddedRect.h-iu]},ou.tlbr.get=function(){return this.tl.concat(this.br)},ou.displaySize.get=function(){return[(this.paddedRect.w-2*iu)/this.pixelRatio,(this.paddedRect.h-2*iu)/this.pixelRatio]},Object.defineProperties(au.prototype,ou);var su=function(t,e){var r={},n={};this.haveRenderCallbacks=[];var i=[];this.addImages(t,r,i),this.addImages(e,n,i);var a=nu(i),o=a.w,s=a.h,l=new $o({width:o||1,height:s||1});for(var u in t){var c=t[u],f=r[u].paddedRect;$o.copy(c.data,l,{x:0,y:0},{x:f.x+iu,y:f.y+iu},c.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,v=d.x+iu,g=d.y+iu,y=p.data.width,m=p.data.height;$o.copy(p.data,l,{x:0,y:0},{x:v,y:g},p.data),$o.copy(p.data,l,{x:0,y:m-1},{x:v,y:g-1},{width:y,height:1}),$o.copy(p.data,l,{x:0,y:0},{x:v,y:g+m},{width:y,height:1}),$o.copy(p.data,l,{x:y-1,y:0},{x:v-1,y:g},{width:1,height:m}),$o.copy(p.data,l,{x:0,y:0},{x:v+y,y:g},{width:1,height:m})}this.image=l,this.iconPositions=r,this.patternPositions=n};su.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2*iu,h:i.data.height+2*iu};r.push(a),e[n]=new au(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},su.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},su.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},oi(\"ImagePosition\",au),oi(\"ImageAtlas\",su);var lu={horizontal:1,vertical:2,horizontalOnly:3},uu=-17;var cu=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};cu.forText=function(t,e){var r=new cu;return r.scale=t||1,r.fontStack=e,r},cu.forImage=function(t){var e=new cu;return e.imageName=t,e};var fu=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function hu(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=fu.fromFeature(t,i);f===lu.vertical&&y.verticalizePunctuation();var m=Ii.processBidirectionalText,x=Ii.processStyledBidirectionalText;if(m&&1===y.sections.length){g=[];for(var b=0,_=m(y.toString(),bu(y,u,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new fu;T.text=w,T.sections=y.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(y.text,y.sectionIndex,bu(y,u,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new fu;E.text=S[0],E.sectionIndex=S[1],E.sections=y.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(y,bu(y,u,a,e,n,p,d));var L=[],C={positionedLines:L,text:y.toString(),top:c[1],bottom:c[1],left:c[0],right:c[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=0,p=uu,d=0,v=0,g=\"right\"===s?1:\"left\"===s?0:.5,y=0,m=0,x=i;m<x.length;m+=1){var b=x[m];b.trim();var _=b.getMaxScale(),w=(_-1)*Ll,T={positionedGlyphs:[],lineOffset:0};t.positionedLines[y]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,O=null,P=null,I=null,D=Ll,z=!(l===lu.horizontal||!c&&!vi(L)||c&&(pu[L]||yi(L)));if(S.imageName){var R=n[S.imageName];if(!R)continue;I=S.imageName,t.iconsInText=t.iconsInText||!0,P=R.paddedRect;var F=R.displaySize;S.scale=S.scale*Ll/f,O={width:F[0],height:F[1],left:iu,top:-ru,advance:z?F[1]:F[0]},C=w+(Ll-F[1]*S.scale),D=O.advance;var B=z?F[0]*S.scale-Ll*_:F[1]*S.scale-Ll*_;B>0&&B>A&&(A=B)}else{var N=r[S.fontStack],j=N&&N[L];if(j&&j.rect)P=j.rect,O=j.metrics;else{var U=e[S.fontStack],V=U&&U[L];if(!V)continue;O=V.metrics}C=(_-S.scale)*Ll}z?(t.verticalizable=!0,k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:z,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:O,rect:P}),h+=D*S.scale+u):(k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:z,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:O,rect:P}),h+=O.advance*S.scale+u)}if(0!==k.length){var q=h-u;d=Math.max(q,d),wu(k,0,k.length-1,g,A)}h=0;var H=a*_+A;T.lineOffset=Math.max(A,w),p+=H,v=Math.max(H,v),++y}else p+=a,++y}var G=p-uu,W=_u(o),Y=W.horizontalAlign,X=W.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var u=(e-r)*i,c=0;c=a!==o?-s*n-uu:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var v=d[p];v.x+=u,v.y+=c}})(t.positionedLines,g,Y,X,d,v,a,G,i.length),t.top+=-X*G,t.bottom=t.top+G,t.left+=-Y*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,u,h,v),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(L)&&C}fu.fromFeature=function(t,e){for(var r=new fu,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},fu.prototype.length=function(){return this.text.length},fu.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},fu.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},fu.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},fu.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;n&&gi(n)&&!El[t[r+1]]||i&&gi(i)&&!El[t[r-1]]||!El[t[r]]?e+=t[r]:e+=El[t[r]]}return e}(this.text)},fu.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&pu[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&pu[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},fu.prototype.substring=function(t,e){var r=new fu;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},fu.prototype.toString=function(){return this.text},fu.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return 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wu(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Tu(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,u=s.pixelRatio||1;o=[l[0]/u,l[1]/u,s.displaySize[0]-l[2]/u,s.displaySize[1]-l[3]/u]}var c,f,h,p,d=e.left*a,v=e.right*a;\"width\"===r||\"both\"===r?(p=i[0]+d-n[3],f=i[0]+v+n[1]):f=(p=i[0]+(d+v-s.displaySize[0])/2)+s.displaySize[0];var g=e.top*a,y=e.bottom*a;return\"height\"===r||\"both\"===r?(c=i[1]+g-n[0],h=i[1]+y+n[2]):h=(c=i[1]+(g+y-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:c,right:f,bottom:h,left:p,collisionPadding:o}}du[10]=!0,du[32]=!0,du[38]=!0,du[40]=!0,du[41]=!0,du[43]=!0,du[45]=!0,du[47]=!0,du[173]=!0,du[183]=!0,du[8203]=!0,du[8208]=!0,du[8211]=!0,du[8231]=!0;var ku=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new 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i=t.interpolationType,a=t.minZoom,o=t.maxZoom,s=i?f(wr.interpolationFactor(i,e,a,o),0,1):0;\"camera\"===t.kind?n=er(t.minSize,t.maxSize,s):r=s}return{uSizeT:r,uSize:n}}var Lu=Object.freeze({__proto__:null,getSizeData:Mu,evaluateSizeForFeature:Su,evaluateSizeForZoom:Eu,SIZE_PACK_FACTOR:Au});function Cu(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],u=0;s<r/2;){var c=t[o-1],f=t[o],h=t[o+1];if(!h)return!1;var p=c.angleTo(f)-f.angleTo(h);for(p=Math.abs((p+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:p}),u+=p;s-l[0].distance>n;)u-=l.shift().angleDelta;if(u>i)return!1;o++,s+=f.dist(h)}return!0}function Ou(t){for(var e=0,r=0;r<t.length-1;r++)e+=t[r].dist(t[r+1]);return e}function Pu(t,e,r){return t?.6*e*r:0}function Iu(t,e){return Math.max(t?t.right-t.left:0,e?e.right-e.left:0)}function Du(t,e,r,n,i,a){for(var 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a(0,0);c.push({tl:D,tr:z,bl:R,br:F,tex:y,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:Y,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return c}(0,r,l,i,o,s,n,t.allowVerticalPlacement),y=t.textSizeData,m=null;\"source\"===y.kind?(m=[Au*i.layout.get(\"text-size\").evaluate(s,{})])[0]>ec&&k(t.layerIds[0]+': Value for \"text-size\" is >= '+tc+'. Reduce your \"text-size\".'):\"composite\"===y.kind&&((m=[Au*d.compositeTextSizes[0].evaluate(s,{},v),Au*d.compositeTextSizes[1].evaluate(s,{},v)])[0]>ec||m[1]>ec)&&k(t.layerIds[0]+': Value for \"text-size\" is >= '+tc+'. Reduce your \"text-size\".'),t.addSymbols(t.text,g,m,l,o,s,c,e,u.lineStartIndex,u.lineLength,p,v);for(var x=0,b=f;x<b.length;x+=1)h[b[x]]=t.text.placedSymbolArray.length-1;return 4*g.length}function nc(t){for(var e in t)return t[e];return null}function ic(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var ac=tl.VectorTileFeature.types,oc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function sc(t,e,r,n,i,a,o,s,l,u,c,f,h){var p=s?Math.min(ec,Math.round(s[0])):0,d=s?Math.min(ec,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*u,16*c,256*f,256*h)}function lc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function uc(t){for(var e=0,r=t.sections;e<r.length;e+=1)if(bi(r[e].text))return!0;return!1}var cc=function(t){this.layoutVertexArray=new la,this.indexArray=new va,this.programConfigurations=t,this.segments=new za,this.dynamicLayoutVertexArray=new ua,this.opacityVertexArray=new ca,this.placedSymbolArray=new Sa};cc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},cc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,wl.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Tl.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,oc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},cc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},oi(\"SymbolBuffers\",cc);var fc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new za,this.collisionVertexArray=new da};fc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,kl.members,!0)},fc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},oi(\"CollisionBuffers\",fc);var hc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Bo([]),this.placementViewportMatrix=Bo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Mu(this.zoom,e[\"text-size\"]),this.iconSizeData=Mu(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.canOverlap=r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\"),this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&this.canOverlap,\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return lu[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};hc.prototype.createArrays=function(){this.text=new cc(new co(this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new cc(new co(this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new Ca,this.lineVertexArray=new Oa,this.symbolInstances=new La},hc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=El[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},hc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof he&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),u=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,c=i.get(\"symbol-sort-key\");if(this.features=[],l||u){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new Di(this.zoom),v=0,g=t;v<g.length;v+=1){var y=g[v],m=y.feature,x=y.id,b=y.index,_=y.sourceLayerIndex,w=n._featureFilter.needGeometry,T=mo(m,w);if(n._featureFilter.filter(d,T,r)){w||(T.geometry=yo(m));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=he.factory(A);uc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===Oi()||this.hasRTLText&&Ii.isParsed())&&(k=Sl(M,n,T))}var S=void 0;if(u){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof pe?E:pe.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?c.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:T.geometry,properties:m.properties,type:ac[m.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var O=a.evaluate(T,{},r).join(\",\"),P=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(lu.vertical)>=0;for(var I=0,D=k.sections;I<D.length;I+=1){var z=D[I];if(z.image)f[z.image.name]=!0;else{var R=di(k.toString()),F=z.fontStack||O,B=h[F]=h[F]||{};this.calculateGlyphDependencies(z.text,B,P,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var u=0;u<t.length;u++){var c=t[u],f=c.geometry,h=c.text?c.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var v=s(p,d,f),g=o(p,d,n[v].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[v].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(u),e[p]=i-1,r[d]=i-1)}else a(u)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},hc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},hc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},hc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},hc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},hc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},hc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},hc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var u=a[l];this.lineVertexArray.emplaceBack(u.x,u.y,u.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},hc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,this.canOverlap?a.sortKey:void 0),v=this.glyphOffsetArray.length,g=d.vertexLength,y=this.allowVerticalPlacement&&o===lu.vertical?Math.PI/2:0,m=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,O=b.isSDF,P=b.sectionIndex,I=d.vertexLength,D=C[1];sc(p,s.x,s.y,_.x,D+_.y,A.x,A.y,r,O,M.x,M.y,E,L),sc(p,s.x,s.y,w.x,D+w.y,A.x+A.w,A.y,r,O,S.x,M.y,E,L),sc(p,s.x,s.y,T.x,D+T.y,A.x,A.y+A.h,r,O,M.x,S.y,E,L),sc(p,s.x,s.y,k.x,D+k.y,A.x+A.w,A.y+A.h,r,O,S.x,S.y,E,L),lc(t.dynamicLayoutVertexArray,s,y),h.emplaceBack(I,I+1,I+2),h.emplaceBack(I+1,I+2,I+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&P===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,m&&m[P])}t.placedSymbolArray.emplaceBack(s.x,s.y,v,this.glyphOffsetArray.length-v,g,l,u,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,c)},hc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},hc.prototype.addCollisionDebugVertices=function(t,e,r,n,i,o,s){var l=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=l.vertexLength,c=i.layoutVertexArray,f=i.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(c,f,o,h,p,new a(t,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,n)),this._addCollisionDebugVertex(c,f,o,h,p,new a(t,n)),l.vertexLength+=4;var d=i.indexArray;d.emplaceBack(u,u+1),d.emplaceBack(u+1,u+2),d.emplaceBack(u+2,u+3),d.emplaceBack(u+3,u),l.primitiveLength+=4},hc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,u=a.y2;this.addCollisionDebugVertices(o,s,l,u,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},hc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new fc(ha,Al.members,_a),this.iconCollisionBox=new fc(ha,Al.members,_a);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},hc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var u={},c=e;c<r;c++){var f=t.get(c);u.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},u.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);u.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},u.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var v=t.get(d);u.iconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},u.iconFeatureIndex=v.featureIndex;break}for(var g=s;g<l;g++){var y=t.get(g);u.verticalIconBox={x1:y.x1,y1:y.y1,x2:y.x2,y2:y.y2,anchorPointX:y.anchorPointX,anchorPointY:y.anchorPointY},u.verticalIconFeatureIndex=y.featureIndex;break}return u},hc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},hc.prototype.hasTextData=function(){return this.text.segments.get().length>0},hc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},hc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},hc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},hc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},hc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},hc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},hc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},hc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},oi(\"SymbolBucket\",hc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),hc.MAX_GLYPHS=65535,hc.addDynamicAttributes=lc;var pc=new Xi({\"symbol-placement\":new qi(Ft.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new qi(Ft.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new qi(Ft.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Hi(Ft.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new qi(Ft.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new qi(Ft.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new qi(Ft.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new qi(Ft.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new qi(Ft.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Hi(Ft.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new qi(Ft.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new qi(Ft.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Hi(Ft.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Hi(Ft.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new qi(Ft.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new qi(Ft.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Hi(Ft.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Hi(Ft.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new qi(Ft.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new qi(Ft.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new qi(Ft.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Hi(Ft.layout_symbol[\"text-field\"]),\"text-font\":new Hi(Ft.layout_symbol[\"text-font\"]),\"text-size\":new Hi(Ft.layout_symbol[\"text-size\"]),\"text-max-width\":new Hi(Ft.layout_symbol[\"text-max-width\"]),\"text-line-height\":new qi(Ft.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Hi(Ft.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Hi(Ft.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Hi(Ft.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new qi(Ft.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Hi(Ft.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new qi(Ft.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new qi(Ft.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Hi(Ft.layout_symbol[\"text-rotate\"]),\"text-padding\":new qi(Ft.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new qi(Ft.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Hi(Ft.layout_symbol[\"text-transform\"]),\"text-offset\":new Hi(Ft.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new qi(Ft.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new qi(Ft.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new qi(Ft.layout_symbol[\"text-optional\"])}),dc={paint:new Xi({\"icon-opacity\":new Hi(Ft.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Hi(Ft.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Hi(Ft.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Hi(Ft.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Hi(Ft.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new qi(Ft.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new qi(Ft.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Hi(Ft.paint_symbol[\"text-opacity\"]),\"text-color\":new Hi(Ft.paint_symbol[\"text-color\"],{runtimeType:Zt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Hi(Ft.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Hi(Ft.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Hi(Ft.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new qi(Ft.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new qi(Ft.paint_symbol[\"text-translate-anchor\"])}),layout:pc},vc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Gt,this.defaultValue=t};vc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},vc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},vc.prototype.outputDefined=function(){return!1},vc.prototype.serialize=function(){return null},oi(\"FormatSectionOverride\",vc,{omit:[\"defaultValue\"]});var gc=function(t){function e(e){t.call(this,e,dc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||cn(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new hc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=dc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new vc(a),s=new un(o,a.property.specification);i=\"constant\"===a.value.kind||\"source\"===a.value.kind?new hn(\"source\",s):new pn(\"composite\",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new Ui(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=dc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof he)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof me&&ge(t.value)===Qt){var e=t.value;a(e.sections)}else t instanceof we?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ki),yc={paint:new Xi({\"background-color\":new qi(Ft.paint_background[\"background-color\"]),\"background-pattern\":new Wi(Ft.paint_background[\"background-pattern\"]),\"background-opacity\":new qi(Ft.paint_background[\"background-opacity\"])})},mc=function(t){function e(e){t.call(this,e,yc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ki),xc={paint:new Xi({\"raster-opacity\":new qi(Ft.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new qi(Ft.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new qi(Ft.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new qi(Ft.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new qi(Ft.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new qi(Ft.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new qi(Ft.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new qi(Ft.paint_raster[\"raster-fade-duration\"])})},bc=function(t){function e(e){t.call(this,e,xc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ki);var _c=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ki),wc={circle:Go,heatmap:es,hillshade:ns,fill:Hs,\"fill-extrusion\":sl,line:bl,symbol:gc,background:mc,raster:bc};var Tc=s.HTMLImageElement,kc=s.HTMLCanvasElement,Ac=s.HTMLVideoElement,Mc=s.ImageData,Sc=s.ImageBitmap,Ec=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Ec.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Tc||t instanceof kc||t instanceof Ac||t instanceof Mc||Sc&&t instanceof Sc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},u=l.x,c=l.y;t instanceof Tc||t instanceof kc||t instanceof Ac||t instanceof Mc||Sc&&t instanceof Sc?s.texSubImage2D(s.TEXTURE_2D,0,u,c,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,u,c,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Ec.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Ec.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Ec.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Lc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Lc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Lc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Cc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m([\"receive\",\"process\"],this),this.invoker=new Lc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=S()?t:s};function Oc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Cc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=C(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:ci(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},Cc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else S()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Cc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Cc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(fi(e.error)):n(null,fi(e.data)))}else{var i=!1,a=C(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?ci(e):null,data:ci(n,a)},a)}:function(t){i=!0},s=null,l=fi(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var u=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,u[0],l.source)[u[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Pc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Pc.prototype.setNorthEast=function(t){return this._ne=t instanceof Dc?new Dc(t.lng,t.lat):Dc.convert(t),this},Pc.prototype.setSouthWest=function(t){return this._sw=t instanceof Dc?new Dc(t.lng,t.lat):Dc.convert(t),this},Pc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Dc)e=t,r=t;else{if(!(t instanceof Pc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Pc.convert(a))}var o=t;return this.extend(Dc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Dc(e.lng,e.lat),this._ne=new Dc(r.lng,r.lat)),this},Pc.prototype.getCenter=function(){return new Dc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Pc.prototype.getSouthWest=function(){return this._sw},Pc.prototype.getNorthEast=function(){return this._ne},Pc.prototype.getNorthWest=function(){return new Dc(this.getWest(),this.getNorth())},Pc.prototype.getSouthEast=function(){return new Dc(this.getEast(),this.getSouth())},Pc.prototype.getWest=function(){return this._sw.lng},Pc.prototype.getSouth=function(){return this._sw.lat},Pc.prototype.getEast=function(){return this._ne.lng},Pc.prototype.getNorth=function(){return this._ne.lat},Pc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Pc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Pc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Pc.prototype.contains=function(t){var e=Dc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Pc.convert=function(t){return!t||t instanceof Pc?t:new Pc(t)};var Ic=6371008.8,Dc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Dc.prototype.wrap=function(){return new Dc(h(this.lng,-180,180),this.lat)},Dc.prototype.toArray=function(){return[this.lng,this.lat]},Dc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Dc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Ic*Math.acos(Math.min(i,1))},Dc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Pc(new Dc(this.lng-r,this.lat-e),new Dc(this.lng+r,this.lat+e))},Dc.convert=function(t){if(t instanceof Dc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Dc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Dc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var zc=2*Math.PI*Ic;function Rc(t){return zc*Math.cos(t*Math.PI/180)}function Fc(t){return(180+t)/360}function Bc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Nc(t,e){return t/Rc(e)}function jc(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Uc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Uc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Dc.convert(t);return new Uc(Fc(r.lng),Bc(r.lat),Nc(e,r.lat))},Uc.prototype.toLngLat=function(){return new Dc(360*this.x-180,jc(this.y))},Uc.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Rc(jc(e));var t,e},Uc.prototype.meterInMercatorCoordinateUnits=function(){return 1/zc*(t=jc(this.y),1/Math.cos(t*Math.PI/180));var t};var Vc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Gc(0,t,t,e,r)};Vc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Vc.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Oc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Oc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},Vc.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new a((t.x*e-this.x)*po,(t.y*e-this.y)*po)},Vc.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var qc=function(t,e){this.wrap=t,this.canonical=e,this.key=Gc(t,e.z,e.z,e.x,e.y)},Hc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new Vc(r,+n,+i),this.key=Gc(e,t,r,n,i)};function Gc(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Hc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Hc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Hc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Hc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Hc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?Gc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):Gc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Hc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},Hc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Hc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Hc(e,this.wrap,e,r,n),new Hc(e,this.wrap,e,r+1,n),new Hc(e,this.wrap,e,r,n+1),new Hc(e,this.wrap,e,r+1,n+1)]},Hc.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},Hc.prototype.wrapped=function(){return new Hc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Hc.prototype.unwrapTo=function(t){return new Hc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Hc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Hc.prototype.toUnwrapped=function(){return new qc(this.wrap,this.canonical)},Hc.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},Hc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Uc(t.x-this.wrap,t.y))},oi(\"CanonicalTileID\",Vc),oi(\"OverscaledTileID\",Hc,{omit:[\"posMatrix\"]});var Wc=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return k('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Wc.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Wc.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Wc.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Wc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Wc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Wc.prototype.getPixels=function(){return new $o({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Wc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,u=a;u<o;u++)for(var c=n;c<i;c++)this.data[this._idx(c,u)]=t.data[this._idx(c+s,u+l)]},oi(\"DEMData\",Wc);var Yc=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};Yc.prototype.encode=function(t){return this._stringToNumber[t]},Yc.prototype.decode=function(t){return this._numberToString[t]};var Xc=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},Zc={geometry:{configurable:!0}};Zc.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},Zc.geometry.set=function(t){this._geometry=t},Xc.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(Xc.prototype,Zc);var Kc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Kc.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},p(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},Kc.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},Kc.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=p({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},Kc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Kc.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),p(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var u in this.deletedStates[o]){if(null===this.deletedStates[o][u])this.state[o][u]={};else for(var c=0,f=Object.keys(this.deletedStates[o][u]);c<f.length;c+=1){var h=f[c];delete this.state[o][u][h]}s[u]=this.state[o][u]}r[o]=r[o]||{},p(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t)t[d].setFeatureState(r,e)};var Jc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new ti(po,16,0),this.grid3D=new ti(po,16,0),this.featureIndexArray=new Ia,this.promoteId=e};function $c(t,e,r,n,i){return b(t,(function(t,a){var o=e instanceof Vi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Qc(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function tf(t,e){return e-t}Jc.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var u=e[l],c=[1/0,1/0,-1/0,-1/0],f=0;f<u.length;f++){var h=u[f];c[0]=Math.min(c[0],h.x),c[1]=Math.min(c[1],h.y),c[2]=Math.max(c[2],h.x),c[3]=Math.max(c[3],h.y)}c[0]<po&&c[1]<po&&c[2]>=0&&c[3]>=0&&s.insert(o,c[0],c[1],c[2],c[3])}},Jc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new tl.VectorTile(new Pl(this.rawTileData)).layers,this.sourceLayerCoder=new Yc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Jc.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=po/t.tileSize/t.scale,l=An(o.filter),u=t.queryGeometry,c=t.queryPadding*s,f=Qc(u),h=this.grid.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c),p=Qc(t.cameraQueryGeometry),d=0,v=this.grid3D.query(p.minX-c,p.minY-c,p.maxX+c,p.maxY+c,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&i>=l.y)return!0}var u=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var c=0,f=u;c<f.length;c+=1)if(Co(t,f[c]))return!0;for(var h=0;h<t.length-1;h++)if(Oo(t[h],t[h+1],u))return!0;return!1}(t.cameraQueryGeometry,e-c,r-c,n+c,i+c)}));d<v.length;d+=1){var g=v[d];h.push(g)}h.sort(tf);for(var y,m={},x=function(a){var c=h[a];if(c!==y){y=c;var f=i.featureIndexArray.get(c),p=null;i.loadMatchingFeature(m,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=yo(e)),r.queryIntersectsFeature(u,e,n,p,i.z,t.transform,s,t.pixelPosMatrix)}))}},b=0;b<h.length;b++)x(b);return m},Jc.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,u,c){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),d=this.vtLayers[h].feature(n);if(i.needGeometry){var v=mo(d,!0);if(!i.filter(new Di(this.tileID.overscaledZ),v,this.tileID.canonical))return}else if(!i.filter(new Di(this.tileID.overscaledZ),d))return;for(var g=this.getId(d,h),y=0;y<f.length;y++){var m=f[y];if(!(a&&a.indexOf(m)<0)){var x=s[m];if(x){var b={};void 0!==g&&u&&(b=u.getState(x.sourceLayer||\"_geojsonTileLayer\",g));var _=p({},l[m]);_.paint=$c(_.paint,x.paint,d,b,o),_.layout=$c(_.layout,x.layout,d,b,o);var w=!c||c(d,x,b);if(w){var T=new Xc(d,this.z,this.x,this.y,g);T.layer=_;var k=t[m];void 0===k&&(k=t[m]=[]),k.push({featureIndex:n,feature:T,intersectionZ:w})}}}}}},Jc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var u=An(i),c=0,f=t;c<f.length;c+=1){var h=f[c];this.loadMatchingFeature(l,r,n,h,u,a,o,s,e)}return l},Jc.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Jc.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},oi(\"FeatureIndex\",Jc,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var ef=function(t,e){this.tileID=t,this.uid=v(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};ef.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<N.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},ef.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},ef.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof hc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof hc&&o.hasRTLText){this.hasRTLText=!0,Ii.isLoading()||Ii.isLoaded()||\"deferred\"!==Oi()||Pi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new Aa},ef.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},ef.prototype.getBucket=function(t){return this.buckets[t.id]},ef.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Ec(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Ec(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},ef.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},ef.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,u){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:u,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},ef.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=An(e&&e.filter),s=this.tileID.canonical,l=s.z,u=s.x,c=s.y,f={z:l,x:u,y:c},h=0;h<a.length;h++){var p=a.feature(h);if(o.needGeometry){var d=mo(p,!0);if(!o.filter(new Di(this.tileID.overscaledZ),d,this.tileID.canonical))continue}else if(!o.filter(new Di(this.tileID.overscaledZ),p))continue;var v=r.getId(p,i),g=new Xc(p,l,u,c,v);g.tile=f,t.push(g)}}},ef.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},ef.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},ef.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=E(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},ef.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},ef.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},ef.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},ef.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<N.now()},ef.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},ef.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=N.now()+t},ef.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},ef.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1)if(a[s[o]])return!0}return!1};var rf=s.performance,nf=function(t){this._marks={start:[t.url,\"start\"].join(\"#\"),end:[t.url,\"end\"].join(\"#\"),measure:t.url.toString()},rf.mark(this._marks.start)};nf.prototype.finish=function(){rf.mark(this._marks.end);var t=rf.getEntriesByName(this._marks.measure);return 0===t.length&&(rf.measure(this._marks.measure,this._marks.start,this._marks.end),t=rf.getEntriesByName(this._marks.measure),rf.clearMarks(this._marks.start),rf.clearMarks(this._marks.end),rf.clearMeasures(this._marks.measure)),t},t.Actor=Cc,t.AlphaImage=Jo,t.CanonicalTileID=Vc,t.CollisionBoxArray=Aa,t.Color=ue,t.DEMData=Wc,t.DataConstantProperty=qi,t.DictionaryCoder=Yc,t.EXTENT=po,t.ErrorEvent=zt,t.EvaluationParameters=Di,t.Event=Dt,t.Evented=Rt,t.FeatureIndex=Jc,t.FillBucket=Us,t.FillExtrusionBucket=il,t.ImageAtlas=su,t.ImagePosition=au,t.LineBucket=vl,t.LngLat=Dc,t.LngLatBounds=Pc,t.MercatorCoordinate=Uc,t.ONE_EM=Ll,t.OverscaledTileID=Hc,t.Point=a,t.Point$1=a,t.Properties=Xi,t.Protobuf=Pl,t.RGBAImage=$o,t.RequestManager=W,t.RequestPerformance=nf,t.ResourceType=_t,t.SegmentVector=za,t.SourceFeatureState=Kc,t.StructArrayLayout1ui2=wa,t.StructArrayLayout2f1f2i16=pa,t.StructArrayLayout2i4=ra,t.StructArrayLayout3ui6=va,t.StructArrayLayout4i8=na,t.SymbolBucket=hc,t.Texture=Ec,t.Tile=ef,t.Transitionable=Fi,t.Uniform1f=Ka,t.Uniform1i=Za,t.Uniform2f=Ja,t.Uniform3f=$a,t.Uniform4f=Qa,t.UniformColor=to,t.UniformMatrix4f=ro,t.UnwrappedTileID=qc,t.ValidationError=Bt,t.WritingMode=lu,t.ZoomHistory=hi,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=lc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=u,t.bindAll=m,t.browser=N,t.cacheEntryPossiblyAdded=function(t){++xt>ht&&(t.getActor().send(\"enforceCacheSizeLimit\",ft),xt=0)},t.clamp=f,t.clearTileCache=function(t){var e=s.caches.delete(ct);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Fu,t.clone=function(t){var e=new Fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=w,t.clone$2=function(t){var e=new Fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ml,t.config=j,t.create=function(){var t=new Fo(16);return 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generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)rt(r,t.features[n],e,n);else\"Feature\"===t.type?rt(r,t,e):rt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=lt(t,1,-1-r,r,0,-1,2,e),a=lt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=lt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=gt(i,1).concat(n)),a&&(n=n.concat(gt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function kt(t,e,r){return 32*((1<<t)*r+e)+t}function At(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}V.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new j(s,K,J,a,Float32Array),r&&console.timeEnd(o);for(var u=i;u>=n;u--){var c=+Date.now();s=this._cluster(s,u),this.trees[u]=new j(s,K,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",u,s.length,+Date.now()-c)}return r&&console.timeEnd(\"total time\"),this},V.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],u=[],c=0,f=l.range(Y(r),X(a),Y(i),X(n));c<f.length;c+=1){var h=f[c],p=l.points[h];u.push(p.numPoints?G(p):this.points[p.index])}return u},V.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,u=i.within(a.x,a.y,o);l<u.length;l+=1){var c=u[l],f=i.points[c];f.parentId===t&&s.push(f.numPoints?G(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},V.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},V.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,u=(r+1+s)/i,c={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,u),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,u),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,u),n.points,-1,r,i,c),c.features.length?c:null},V.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},V.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},V.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],u=l.numPoints,c={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:u?W(l):this.points[l.index].properties},f=void 0;u?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(c.id=f),a.features.push(c)}},V.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(+t,this.options.maxZoom+1))},V.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=n.minPoints,l=i/(a*Math.pow(2,e)),u=0;u<t.length;u++){var c=t[u];if(!(c.zoom<=e)){c.zoom=e;for(var f=this.trees[e+1],h=f.within(c.x,c.y,l),p=c.numPoints||1,d=p,v=0,g=h;v<g.length;v+=1){var y=g[v],m=f.points[y];m.zoom>e&&(d+=m.numPoints||1)}if(d>=s){for(var x=c.x*p,b=c.y*p,_=o&&p>1?this._map(c,!0):null,w=(u<<5)+(e+1)+this.points.length,T=0,k=h;T<k.length;T+=1){var A=k[T],M=f.points[A];if(!(M.zoom<=e)){M.zoom=e;var S=M.numPoints||1;x+=M.x*S,b+=M.y*S,M.parentId=w,o&&(_||(_=this._map(c,!0)),o(_,this._map(M)))}}c.parentId=w,r.push(q(x/d,b/d,w,d,_))}else if(r.push(c),d>1)for(var E=0,L=h;E<L.length;E+=1){var C=L[E],O=f.points[C];O.zoom<=e||(O.zoom=e,r.push(O))}}}return r},V.prototype._getOriginId=function(t){return t-this.points.length>>5},V.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},V.prototype._map=function(t,e){if(t.numPoints)return e?Z({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?Z({},n):n},Tt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Tt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,u=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var c=1<<e,f=kt(e,r,n),h=this.tiles[f];if(!h&&(u>1&&console.time(\"creation\"),h=this.tiles[f]=bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),u)){u>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){u>1&&console.time(\"clipping\");var v,g,y,m,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;v=g=y=m=null,x=lt(t,c,r-_,r+T,0,h.minX,h.maxX,l),b=lt(t,c,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(v=lt(x,c,n-_,n+T,1,h.minY,h.maxY,l),g=lt(x,c,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(y=lt(b,c,n-_,n+T,1,h.minY,h.maxY,l),m=lt(b,c,n+w,n+k,1,h.minY,h.maxY,l),b=null),u>1&&console.timeEnd(\"clipping\"),s.push(v||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(m||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=kt(t,e=(e%o+o)%o,r);if(this.tiles[s])return mt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,u=t,c=e,f=r;!l&&u>0;)u--,c=Math.floor(c/2),f=Math.floor(f/2),l=this.tiles[kt(u,c,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",u,c,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,u,c,f,t,e,r),a>1&&console.timeEnd(\"drilling 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i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new Tt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var u={};if(i){var c=i.finish();c&&(u.resourceTiming={},u.resourceTiming[n.source]=JSON.parse(JSON.stringify(c)))}r(null,u)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var St=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:Mt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name \"'+t+'\" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error(\"RTL text plugin already 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s=i([\"transform\",\"WebkitTransform\"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var u=Object.defineProperty({},\"passive\",{get:function(){l=!0}});t.window.addEventListener(\"test\",u,u),t.window.removeEventListener(\"test\",u,u)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),\"passive\"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var c=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener(\"click\",c,!0)};function f(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener(\"click\",c,!0),t.window.setTimeout((function(){t.window.removeEventListener(\"click\",c,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!(t&&(4!==t.length||t[0]<0||e.data.width<t[0]||t[1]<0||e.data.height<t[1]||t[2]<0||e.data.width<t[2]||t[3]<0||e.data.height<t[3]||t[2]<t[0]||t[3]<t[1]))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,u=l.x+1,c=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:u,y:c},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:u,y:c-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:u,y:c+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:u-1,y:c},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:u+h,y:c},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,v=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function y(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(m(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(m(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function m(t,e,r,n,i){r[0]=0,n[0]=-v,n[1]=+v;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+v}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?v:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?v:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(y(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),y(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,u=o.glyph;(n[s]||(n[s]={}))[l]=u&&{id:u.id,bitmap:u.bitmap.clone(),metrics:u.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=\"-transition\",T=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,w)?this._transitionable.setTransition(n.slice(0,-11),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),k=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};k.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},k.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var u=t[l];i=s*r,a=(s+=u)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===u})}return n},k.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=void 0,d=i/r*(n+1);if(l.isDash){var v=n-Math.abs(d);p=Math.sqrt(h*h+v*v)}else p=n-Math.sqrt(h*h+d*d);this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=l.isDash?h:-h;this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var u={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,u},k.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var A=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}A.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},A.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},A.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},A.Actor=t.Actor;var S=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};S.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},S.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var E=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setSourceProperty=function(t){this._tileJSONRequest&&this._tileJSONRequest.cancel(),t(),this.map.style.sourceCaches[this.id].clearTiles(),this.load()},r.prototype.setTiles=function(t){var e=this;return this.setSourceProperty((function(){e._options.tiles=t})),this},r.prototype.setUrl=function(t){var e=this;return this.setSourceProperty((function(){e.url=t,e._options.url=t})),this},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(L),O=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,minPoints:Math.max(2,n.clusterMinPoints||2),extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties,filter:n.filter},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var u=i-r,c=a-n,f=Math.max(u,c),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var D=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),R={vector:E,raster:L,\"raster-dem\":C,geojson:O,video:D,image:I,canvas:z};function F(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function B(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&\"fill-extrusion\"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&\"fill-extrusion\"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(N);for(var u=[],c=0,f=l;c<f.length;c+=1){var h=f[c];u.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,F(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var u in o)for(var 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Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var St=7680,Et=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Et.disabled=new Et({func:519,mask:0},0,0,St,St,St);var Lt=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Lt.Replace=[1,0],Lt.disabled=new Lt(Lt.Replace,t.Color.transparent,[!1,!1,!1,!1]),Lt.unblended=new Lt(Lt.Replace,t.Color.transparent,[!0,!0,!0,!0]),Lt.alphaBlended=new Lt([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Ct=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Ct.disabled=new Ct(!1,1029,2305),Ct.backCCW=new Ct(!0,1029,2305);var Ot=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new G(this),this.clearDepth=new W(this),this.clearStencil=new Y(this),this.colorMask=new X(this),this.depthMask=new Z(this),this.stencilMask=new K(this),this.stencilFunc=new J(this),this.stencilOp=new $(this),this.stencilTest=new Q(this),this.depthRange=new tt(this),this.depthTest=new et(this),this.depthFunc=new rt(this),this.blend=new nt(this),this.blendFunc=new it(this),this.blendColor=new at(this),this.blendEquation=new ot(this),this.cullFace=new st(this),this.cullFaceSide=new lt(this),this.frontFace=new ut(this),this.program=new ct(this),this.activeTexture=new ft(this),this.viewport=new ht(this),this.bindFramebuffer=new pt(this),this.bindRenderbuffer=new dt(this),this.bindTexture=new vt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new yt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new mt(this),this.pixelStoreUnpack=new xt(this),this.pixelStoreUnpackPremultiplyAlpha=new bt(this),this.pixelStoreUnpackFlipY=new _t(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\"),this.maxTextureSize=t.getParameter(t.MAX_TEXTURE_SIZE)};Ot.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Ot.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Ot.prototype.createIndexBuffer=function(t,e){return new U(this,t,e)},Ot.prototype.createVertexBuffer=function(t,e,r){return new q(this,t,e,r)},Ot.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Ot.prototype.createFramebuffer=function(t,e,r){return new At(this,t,e,r)},Ot.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Ot.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Ot.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Ot.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Ot.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,Lt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Ot.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Pt=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new R[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new j(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Dt(this._source.type)){for(var u={},c={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],v=this._tiles[p];if(v&&!(v.fadeEndTime&&v.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),u[g.tileID.key]=g.tileID),c[p]=d}}for(var y in this._retainLoadedChildren(c,a,s,l),u)l[y]||(this._coveredTiles[y]=!0,l[y]=u[y])}for(var m in l)this._tiles[m].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,u=t;l<u.length;l+=1){var c=u[l],f=this._addTile(c);n[c.key]=c,f.hasData()||e<this._source.maxzoom&&(s[c.key]=c)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],v=this._tiles[d.key];if(!v.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var m=d.children(this._source.maxzoom);if(n[m[0].key]&&n[m[1].key]&&n[m[2].key]&&n[m[3].key])continue}for(var x=v.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(v=this.getTile(_))&&x&&(v=this._addTile(_)),v&&(n[_.key]=_,x=v.wasRequested(),v.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),u=s.map((function(t){return o.pointCoordinate(t)})),c=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,v=0,g=u;v<g.length;v+=1){var y=g[v];f=Math.min(f,y.x),h=Math.min(h,y.y),p=Math.max(p,y.x),d=Math.max(d,y.y)}for(var m=function(e){var n=i._tiles[c[e]];if(!n.holdingForFade()){var s=n.tileID,v=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/v,y=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(y[0].x-g<t.EXTENT&&y[0].y-g<t.EXTENT&&y[1].x+g>=0&&y[1].y+g>=0){var m=l.map((function(t){return s.getTilePoint(t)})),x=u.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:m,cameraQueryGeometry:x,scale:v})}}},x=0;x<c.length;x++)m(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Dt(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Dt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(oa.workerUrl)}Pt.maxOverzooming=10,Pt.maxUnderzooming=3;var Rt=\"mapboxgl_preloaded_worker_pool\",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Ft.workerCount;)this.workers.push(new zt);return this.active[t]=!0,this.workers.slice()},Ft.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Ft.prototype.isPreloaded=function(){return!!this.active[Rt]},Ft.prototype.numActive=function(){return Object.keys(this.active).length};var Bt,Nt=Math.floor(t.browser.hardwareConcurrency/2);function jt(){return Bt||(Bt=new Ft),Bt}function Ut(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function Vt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Ut(t[n],e[t[n].ref]));return t}Ft.workerCount=Math.max(Math.min(Nt,6),1);var qt={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Ht(t,e,r){r.push({command:qt.addSource,args:[t,e[t]]})}function Gt(t,e,r){e.push({command:qt.removeSource,args:[t]}),r[t]=!0}function Wt(t,e,r,n){Gt(t,r,n),Ht(t,e,r)}function Yt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Xt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Zt(t){return t.id}function Kt(t,e){return t[e.id]=e,t}function Jt(e,r){if(!e)return[{command:qt.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:qt.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:qt.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:qt.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:qt.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:qt.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:qt.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:qt.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:qt.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:qt.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Gt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Yt(e,r,a)?n.push({command:qt.setGeoJSONSourceData,args:[a,r[a].data]}):Wt(a,r,n,i)):Ht(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:qt.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,u,c,f=(e=e||[]).map(Zt),h=r.map(Zt),p=e.reduce(Kt,{}),d=r.reduce(Kt,{}),v=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:qt.removeLayer,args:[o]}),v.splice(v.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],v[v.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:qt.removeLayer,args:[o]}),v.splice(v.lastIndexOf(o,v.length-a),1)):a++,u=v[v.length-i],n.push({command:qt.addLayer,args:[d[o],u]}),v.splice(v.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(c in Xt(s.layout,l.layout,n,o,null,qt.setLayoutProperty),Xt(s.paint,l.paint,n,o,null,qt.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:qt.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:qt.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(c)&&\"layout\"!==c&&\"paint\"!==c&&\"filter\"!==c&&\"metadata\"!==c&&\"minzoom\"!==c&&\"maxzoom\"!==c&&(0===c.indexOf(\"paint.\")?Xt(s[c],l[c],n,o,c.slice(6),qt.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:qt.setLayerProperty,args:[o,c,l[c]]}));for(c in l)l.hasOwnProperty(c)&&!s.hasOwnProperty(c)&&\"layout\"!==c&&\"paint\"!==c&&\"filter\"!==c&&\"metadata\"!==c&&\"minzoom\"!==c&&\"maxzoom\"!==c&&(0===c.indexOf(\"paint.\")?Xt(s[c],l[c],n,o,c.slice(6),qt.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:qt.setLayerProperty,args:[o,c,l[c]]}))}else n.push({command:qt.removeLayer,args:[o]}),u=v[v.lastIndexOf(o)+1],n.push({command:qt.addLayer,args:[l,u]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:qt.setStyle,args:[r]}]}return n}var $t=function(t,e){this.reset(t,e)};$t.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},$t.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function te(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function ee(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function re(e,r){var n=[e.x,e.y,0,1];pe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ne(t,e){return.5+t/e*.5}function ie(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var u=i?e.textSizeData:e.iconSizeData,c=t.evaluateSizeForZoom(u,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,v=n.transform.width/n.transform.height,g=!1,y=0;y<d.length;y++){var m=d.get(y);if(m.hidden||m.writingMode===t.WritingMode.vertical&&!g)he(m.numGlyphs,h);else{g=!1;var x=[m.anchorX,m.anchorY,0,1];if(t.transformMat4(x,x,r),ie(x,f)){var b=x[3],_=ne(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(u,c,m),T=s?w/_:w*_,k=new t.Point(m.anchorX,m.anchorY),A=re(k,a).point,M={},S=le(m,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&le(m,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v).notEnoughRoom)&&he(m.numGlyphs,h)}else he(m.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function oe(t,e,r,n,i,a,o,s,l,u,c){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),v=e.getoffsetX(f-1),g=ce(t*d,r,n,i,a,o,s.segment,h,p,l,u,c);if(!g)return null;var y=ce(t*v,r,n,i,a,o,s.segment,h,p,l,u,c);return y?{first:g,last:y}:null}function se(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=r/24,y=e.lineOffsetX*g,m=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=oe(g,l,y,m,n,f,h,e,u,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}v=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)v.push(ce(g*l.getoffsetX(M),y,m,n,f,h,e.segment,b,_,u,o,p));v.push(w.last)}else{if(i&&!n){var S=re(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(u.getx(E),u.gety(E)),C=re(L,a),O=C.signedDistanceFromCamera>0?C.point:ue(h,L,S,1,a),P=se(e.writingMode,S,O,d);if(P)return P}var I=ce(g*l.getoffsetX(e.glyphStartIndex),y,m,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,u,o,p);if(!I)return{notEnoughRoom:!0};v=[I]}for(var D=0,z=v;D<z.length;D+=1){var R=z[D];t.addDynamicAttributes(c,R.point,R.angle)}return{}}function ue(t,e,r,n,i){var a=re(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function ce(e,r,n,i,a,o,s,l,u,c,f,h){var p=i?e-r:e+r,d=p>0?1:-1,v=0;i&&(d*=-1,v=Math.PI),d<0&&(v+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,m=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=u)return null;if(m=y,w.push(y),void 0===(y=h[g])){var T=new t.Point(c.getx(g),c.gety(g)),k=re(T,f);if(k.signedDistanceFromCamera>0)y=h[g]=k.point;else{var A=g-d;y=ue(0===x?o:new t.Point(c.getx(A),c.gety(A)),T,m,_-x+1,f)}}x+=b,b=m.dist(y)}var M=(_-x)/b,S=y.sub(m),E=S.mult(M)._add(m);E._add(S._unit()._perp()._mult(n*d));var L=v+Math.atan2(y.y-m.y,y.x-m.x);return w.push(E),{point:E,angle:L,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var u=this.circles[3*l],c=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:u-f,y1:c-f,x2:u+f,y2:c+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var u=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,u,c,i),n?u.length>0:u},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,u=this.boxCells[i];if(null!==u)for(var c=this.bboxes,f=0,h=u;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=c[d+2]&&e<=c[d+3]&&r>=c[d+0]&&n>=c[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:c[d],y1:c[d+1],x2:c[d+2],y2:c[d+3]})}}}var v=this.circleCells[i];if(null!==v)for(var g=this.circles,y=0,m=v;y<m.length;y+=1){var x=m[y];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Qt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,u=o.seenUids,c=this.boxCells[i];if(null!==c)for(var f=this.bboxes,h=0,p=c;h<p.length;h+=1){var d=p[h];if(!u.box[d]){u.box[d]=!0;var v=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[v+0],f[v+1],f[v+2],f[v+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var y=this.circles,m=0,x=g;m<x.length;m+=1){var b=x[m];if(!u.circle[b]){u.circle[b]=!0;var _=3*b;if(this._circlesCollide(y[_],y[_+1],y[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Qt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),u=this._convertToYCellCoord(e),c=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Qt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Qt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Qt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var u=(o-i)/2,c=Math.abs(e-(i+u));if(c>u+r)return!1;if(l<=s||c<=u)return!0;var f=l-s,h=c-u;return f*f+h*h<=r*r};var fe=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function he(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(fe,3*n)}}function pe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var de=100,ve=function(t,e,r){void 0===e&&(e=new Qt(t.width+200,t.height+200,25)),void 0===r&&(r=new Qt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+de,this.screenBottomBoundary=t.height+de,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function ge(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}ve.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,u=t.x2*o+a.point.x,c=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,u,c)||!e&&this.grid.hitTest(s,l,u,c,i)?{box:[],offscreen:!1}:{box:[s,l,u,c],offscreen:this.isOffscreen(s,l,u,c)}},ve.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,u,c,f,h,p){var d=[],v=new t.Point(r.anchorX,r.anchorY),g=re(v,o),y=ne(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),m=(c?a/y:a*y)/t.ONE_EM,x=re(v,s).point,b=oe(m,i,r.lineOffsetX*m,r.lineOffsetY*m,!1,x,v,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*y+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new $t,E=b.first,L=b.last,C=[],O=E.path.length-1;O>=1;O--)C.push(E.path[O]);for(var P=1;P<L.path.length;P++)C.push(L.path[P]);var I=2.5*k;if(l){var D=C.map((function(t){return re(t,l)}));C=D.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:D.map((function(t){return t.point}))}var z=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);z=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=z;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/I)+1;for(var q=0;q<V;q++){var H=q/Math.max(V-1,1),G=S.lerp(H),W=G.x+de,Y=G.y+de;d.push(W,Y,k,0);var X=W-k,Z=Y-k,K=W+k,J=Y+k;if(T=T&&this.isOffscreen(X,Z,K,J),w=w||this.isInsideGrid(X,Z,K,J),!e&&this.grid.hitTestCircle(W,Y,k,f)&&(_=!0,!u))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!u&&_||!w?[]:d,offscreen:T,collisionDetected:_}},ve.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var u=l[s],c=new t.Point(u.x+de,u.y+de);n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y),r.push(c)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var v=d[p],g=v.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var y=[new t.Point(v.x1,v.y1),new t.Point(v.x2,v.y1),new t.Point(v.x2,v.y2),new t.Point(v.x1,v.y2)];t.polygonIntersectsPolygon(r,y)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},ve.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},ve.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},ve.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return pe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+de,(-i[1]/i[3]+1)/2*this.transform.height+de),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},ve.prototype.isOffscreen=function(t,e,r,n){return r<de||t>=this.screenRightBoundary||n<de||e>this.screenBottomBoundary},ve.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},ve.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var ye=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};ye.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new ye(t?t.text:null,e,r,i),this.icon=new ye(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var xe=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},be=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},_e=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},we=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function Te(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point(s+u[0]*a,l+u[1]*a)}function ke(e,r,n,i,a,o){var s=e.x1,l=e.x2,u=e.y1,c=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:u+p.y,x2:l+p.x,y2:c+p.y,anchorPointX:f,anchorPointY:h}}we.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var Ae=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new ve(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new we(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Me(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}Ae.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,u=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),c=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=ge(n,1,this.transform.zoom),v=te(f,h,p,this.transform,d),g=null;if(h){var y=ee(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,y)}this.retainedQueryData[a.bucketInstanceId]=new _e(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var m={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:v,labelToScreenMatrix:g,scale:u,textPixelRatio:c,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:m})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:m})}},Ae.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=[f.textOffset0,f.textOffset1],y=Te(t,r,n,g,i),m=this.collisionIndex.placeCollisionBox(ke(e,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(ke(d,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate).box.length)return m.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(v=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:v},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:y,placedGlyphBoxes:m}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,u=a.textLabelPlaneMatrix,c=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,v=a.collisionGroup,g=s.get(\"text-optional\"),y=s.get(\"icon-optional\"),m=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=m&&(x||!o.hasIconData()||y),A=x&&(m||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},O=null,P=null,I=0,D=0,z=0;a.textFeatureIndex?I=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(I=e.featureIndex),a.verticalTextFeatureIndex&&(D=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),!(L&&L.box&&L.box.length));i+=1);else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,u=e.textBoxScale,c=w&&!x?r:null,h={box:[],offscreen:!1},p=m?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],y=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,u,b,_,f,l,v,y,e,o,n,c);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,m,f,l,v.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),W=t.evaluateSizeForFeature(o.textSizeData,d,G),Y=s.get(\"text-padding\"),X=e.collisionCircleDiameter;O=i.collisionIndex.placeCollisionCircles(m,G,o.lineVertexArray,o.glyphOffsetArray,W,l,u,c,n,_,v.predicate,X,Y),T=m||O.circles.length>0&&!O.collisionDetected,S=S&&O.offscreen}if(a.iconFeatureIndex&&(z=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?ke(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,v.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(P=Z(a.verticalIconBox)).box.length>0:(P=Z(a.iconBox)).box.length>0,S=S&&P.offscreen}var K=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=y||0===e.numIconVertices;if(K||J?J?K||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&D?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,D,v.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID)),M&&P&&i.collisionIndex.insertCollisionBox(P.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,z,v.ID),O&&(T&&i.collisionIndex.insertCollisionCircles(O.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID),n)){var $=o.bucketInstanceId,Q=i.collisionCircleArrays[$];void 0===Q&&(Q=i.collisionCircleArrays[$]=new be);for(var tt=0;tt<O.circles.length;tt+=4)Q.circles.push(O.circles[tt+0]),Q.circles.push(O.circles[tt+1]),Q.circles.push(O.circles[tt+2]),Q.circles.push(O.collisionDetected?1:0)}i.placements[e.crossTileID]=new xe(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var O=this.collisionCircleArrays[o.bucketInstanceId];t.invert(O.invProjMatrix,l),O.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},Ae.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var u=l[s];u>=0&&(e.text.placedSymbolArray.get(u).crossTileID=a>=0&&u!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},Ae.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],u=i[s];u?(this.opacities[s]=new me(u,n,l.text,l.icon),r=r||l.text!==u.text.placed||l.icon!==u.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var c in i){var f=i[c];if(!this.opacities[c]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[c]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},Ae.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},Ae.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),u=a.get(\"text-variable-anchor\"),c=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},v=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,v=a.crossTileID,g=r[v],y=i.opacities[v];g?y=o:y||(y=p,i.opacities[v]=y),r[v]=!0;var m=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(m){var T=De(y.text),k=_?ze:T;d(e.text,s,k);var A=w?ze:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=De(y.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var O=C?L:ze;d(e.icon,a.numIconVertices,O),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var P=C?ze:L;d(e.icon,a.numVerticalIconVertices,P),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var I=e.collisionArrays[n];if(I){var D=new t.Point(0,0);if(I.textBox||I.verticalTextBox){var z=!0;if(u){var R=i.variableOffsets[v];R?(D=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),c&&D._rotate(f?i.transform.angle:-i.transform.angle)):z=!1}I.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||_,D.x,D.y),I.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||w,D.x,D.y)}var F=Boolean(!w&&I.verticalIconBox);I.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,h?D.x:0,h?D.y:0),I.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,h?D.x:0,h?D.y:0)}}},g=0;g<e.symbolInstances.length;g++)v(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var y=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=y.invProjMatrix,e.placementViewportMatrix=y.viewportMatrix,e.collisionCircleArray=y.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},Ae.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},Ae.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},Ae.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},Ae.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Le=Math.pow(2,17),Ce=Math.pow(2,16),Oe=Math.pow(2,9),Pe=Math.pow(2,8),Ie=Math.pow(2,1);function De(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Le+e*Ce+r*Oe+e*Pe+r*Ie+e}var ze=0,Re=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var Fe=function(t,e,r,n,i,a,o){this.placement=new Ae(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Fe.prototype.isDone=function(){return this._done},Fe.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Ne.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=Be/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Ne.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,u=o;l<u.length;l+=1){var c=u[l];if(Math.abs(c.coord.x-s.x)<=n&&Math.abs(c.coord.y-s.y)<=n&&!r[c.crossTileID]){r[c.crossTileID]=!0,a.crossTileID=c.crossTileID;break}}}}};var je=function(){this.maxCrossTileID=0};je.prototype.generate=function(){return++this.maxCrossTileID};var Ue=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Ue.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Ue.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var u=o[t.scaledTo(Number(a)).key];u&&u.findMatches(e.symbolInstances,t,i)}}for(var c=0;c<e.symbolInstances.length;c++){var f=e.symbolInstances.get(c);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Ne(t,e.symbolInstances,e.bucketInstanceId),!0},Ue.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Ue.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ve=function(){this.layerIndexes={},this.crossTileIDs=new je,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ve.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Ue);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],u=l.getBucket(t);u&&t.id===u.layerIds[0]&&(u.bucketInstanceId||(u.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,u,this.crossTileIDs)&&(i=!0),a[u.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ve.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var qe=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},He=t.pick(qt,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ge=t.pick(qt,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),We=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),Ye=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new A(jt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new k(256,512),this.crossTileSymbolIndex=new Ve,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(We,!1)},r.prototype._load=function(e,r){if(!r||!qe(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=Vt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new T(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,c())})),u=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){u=null,o||(o=t,a=e,c())}));function c(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],u=l.width,c=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,v=l.stretchX,g=l.stretchY,y=l.content,m=new t.RGBAImage({width:u,height:c});t.RGBAImage.copy(e,m,{x:f,y:h},{x:0,y:0},{width:u,height:c}),r[s]={data:m,pixelRatio:d,sdf:p,stretchX:v,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),u&&(u.cancel(),u=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}var l={};for(var u in this.sourceCaches){var c=this.sourceCaches[u];l[u]=c.used,c.used=!1}for(var f=0,h=this._order;f<h.length;f+=1){var p=h[f],d=this._layers[p];d.recalculate(e,this._availableImages),!d.isHidden(e.zoom)&&d.source&&(this.sourceCaches[d.source].used=!0)}for(var v in l){var g=this.sourceCaches[v];l[v]!==g.used&&g.fire(new t.Event(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:v}))}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),qe(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=Vt(e.layers);var n=Jt(this.serialize(),e).filter((function(t){return!(t.command in Ge)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in He)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._afterImageUpdated(e)},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._afterImageUpdated(e)},r.prototype._afterImageUpdated=function(e){this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Pt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(qe(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is required to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var u=l[s][o];if(u)for(var c=0,f=u;c<f.length;c+=1){var h=f[c];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var v=this._order[d];if(r(v))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id]<d)break;p.push(y),i.pop()}else for(var m=0,x=t;m<x.length;m+=1){var b=x[m][v];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var u=[];for(var c in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[c]||u.push(B(this.sourceCaches[c],this._layers,this._serializedLayers,e,r,n));return this.placement&&u.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),u=[],c=0,f=Object.keys(l).map(Number);c<f.length;c+=1){var h=f[c];u.push(o[h])}u.sort(N);for(var p=function(){var r=v[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],u=n[a];u.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var c=0,f=u;c<f.length;c+=1){var h=f[c];o.push(h)}}},d=0,v=u;d<v.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var y in s)g(y);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(u)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&qe(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},u=0,c=this._order;u<c.length;u+=1){var f=c[u],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new Fe(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var v=0,g=this._order;v<g.length;v+=1){var y=g[v],m=this._layers[y];\"symbol\"===m.type&&this.placement.updateLayerOpacities(m,l[m.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);Ye.getSourceType=function(t){return R[t]},Ye.setSourceType=function(t,e){R[t]=e},Ye.registerForPluginStateChange=t.registerForPluginStateChange;var Xe=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ze=_r(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),Ke=_r(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Je=_r(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),$e=_r(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Qe=_r(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),tr=_r(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),er=_r(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),rr=_r(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),nr=_r(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),ir=_r(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),ar=_r(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),or=_r(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),sr=_r(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),lr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ur=_r(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),cr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),fr=_r(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hr=_r(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),pr=_r(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),dr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),vr=_r(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),gr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),yr=_r(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=_r(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),xr=_r(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),br=_r(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function _r(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n=e.match(/attribute ([\\w]+) ([\\w]+)/g),i=t.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),a=e.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,(function(t,e,r,n,i){return s[i]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+i+\"\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,n,i){var a=\"float\"===n?\"vec2\":\"vec4\",o=i.match(/color/)?\"color\":a;return s[i]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+i+\" = a_\"+i+\";\\n#else\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+r+\" \"+n+\" \"+i+\" = a_\"+i+\";\\n#else\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n    \"+r+\" \"+n+\" \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n    \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),staticAttributes:n,staticUniforms:o}}var wr=Object.freeze({__proto__:null,prelude:Ze,background:Ke,backgroundPattern:Je,circle:$e,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:ur,fillExtrusionPattern:cr,hillshadePrepare:fr,hillshade:hr,line:pr,lineGradient:dr,linePattern:vr,lineSDF:gr,raster:yr,symbolIcon:mr,symbolSDF:xr,symbolTextAndIcon:br}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};function kr(t){for(var e=[],r=0;r<t.length;r++)if(null!==t[r]){var n=t[r].split(\" \");e.push(n.pop())}return e}Tr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,u=0;!l&&u<n.length;u++)this.boundPaintVertexBuffers[u]!==n[u]&&(l=!0);var c=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||c?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Tr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,u=this.context,c=u.gl;if(u.extVertexArrayObject)this.vao&&this.destroy(),this.vao=u.extVertexArrayObject.createVertexArrayOES(),u.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=u.currentNumAttributes||0;for(var f=l;f<s;f++)c.disableVertexAttribArray(f)}e.enableAttributes(c,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(c,t);a&&a.enableAttributes(c,t),o&&o.enableAttributes(c,t),e.bind(),e.setVertexAttribPointers(c,t,i);for(var d=0,v=r;d<v.length;d+=1){var g=v[d];g.bind(),g.setVertexAttribPointers(c,t,i)}a&&(a.bind(),a.setVertexAttribPointers(c,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(c,t,i)),u.currentNumAttributes=l},Tr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var Ar=function(t,e,r,n,i,a){var o=t.gl;this.program=o.createProgram();for(var s=kr(r.staticAttributes),l=n?n.getBinderAttributes():[],u=s.concat(l),c=r.staticUniforms?kr(r.staticUniforms):[],f=n?n.getBinderUniforms():[],h=[],p=0,d=c.concat(f);p<d.length;p+=1){var v=d[p];h.indexOf(v)<0&&h.push(v)}var g=n?n.defines():[];a&&g.push(\"#define OVERDRAW_INSPECTOR;\");var y=g.concat(Ze.fragmentSource,r.fragmentSource).join(\"\\n\"),m=g.concat(Ze.vertexSource,r.vertexSource).join(\"\\n\"),x=o.createShader(o.FRAGMENT_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(x,y),o.compileShader(x),o.attachShader(this.program,x);var b=o.createShader(o.VERTEX_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(b,m),o.compileShader(b),o.attachShader(this.program,b),this.attributes={};var _={};this.numAttributes=u.length;for(var w=0;w<this.numAttributes;w++)u[w]&&(o.bindAttribLocation(this.program,w,u[w]),this.attributes[u[w]]=w);o.linkProgram(this.program),o.deleteShader(b),o.deleteShader(x);for(var T=0;T<h.length;T++){var k=h[T];if(k&&!_[k]){var A=o.getUniformLocation(this.program,k);A&&(_[k]=A)}}this.fixedUniforms=i(t,_),this.binderUniforms=n?n.getUniforms(t,_):[]}}};function Mr(t,e,r){var n=1/ge(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}Ar.prototype.draw=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=t.gl;if(!this.failedToCreate){for(var m in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[m].set(o[m]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],b=0,_=c.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new Tr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],u,w.vertexOffset,d,v),y.drawElements(e,w.primitiveLength*x,y.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Sr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var u=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[u.r,u.g,u.b],u_vertical_gradient:+n,u_opacity:i}},Er=function(e,r,n,i,a,o,s){return t.extend(Sr(e,r,n,i),Mr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Lr=function(t){return{u_matrix:t}},Cr=function(e,r,n,i){return t.extend(Lr(e),Mr(n,r,i))},Or=function(t,e){return{u_matrix:t,u_world:e}},Pr=function(e,r,n,i,a){return t.extend(Cr(e,r,n,i),{u_world:a})},Ir=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=ge(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Dr=function(t,e,r){var n=ge(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},zr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Rr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Fr=function(t){return{u_matrix:t}},Br=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ge(e,1,r),u_intensity:n}},Nr=function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}},jr=function(e,r,n){var i=n.paint.get(\"hillshade-shadow-color\"),a=n.paint.get(\"hillshade-highlight-color\"),o=n.paint.get(\"hillshade-accent-color\"),s=n.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===n.paint.get(\"hillshade-illumination-anchor\")&&(s-=e.transform.angle);var l,u,c,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,u=Math.pow(2,l.canonical.z),c=l.canonical.y,[new t.MercatorCoordinate(0,c/u).toLngLat().lat,new t.MercatorCoordinate(0,(c+1)/u).toLngLat().lat]),u_light:[n.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}},Ur=function(e,r){var n=r.stride,i=t.create();return t.ortho(i,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(i,i,[0,-t.EXTENT,0]),{u_matrix:i,u_image:1,u_dimension:[n,n],u_zoom:e.overscaledZ,u_unpack:r.getUnpackVector()}};var Vr=function(e,r,n){var i=e.transform;return{u_matrix:Yr(e,r,n),u_ratio:1/ge(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},qr=function(e,r,n,i){return t.extend(Vr(e,r,n),{u_image:0,u_image_height:i})},Hr=function(e,r,n,i){var a=e.transform,o=Wr(r,a);return{u_matrix:Yr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ge(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Gr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Wr(r,o),u=\"round\"===n.layout.get(\"line-cap\"),c=s.getDash(i.from,u),f=s.getDash(i.to,u),h=c.width*a.fromScale,p=f.width*a.toScale;return t.extend(Vr(e,r,n),{u_patternscale_a:[l/h,-c.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:f.y,u_mix:a.t})};function Wr(t,e){return 1/ge(t,1,e.tileZoom)}function Yr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Xr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Zr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Zr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Kr,Jr=function(t,e,r,n,i,a,o,s,l,u){var c=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:c.cameraToCenterDistance,u_pitch:c.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:c.width/c.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:u,u_texture:0}},$r=function(e,r,n,i,a,o,s,l,u,c,f){var h=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,u,c),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Qr=function(e,r,n,i,a,o,s,l,u,c){return t.extend($r(e,r,n,i,a,o,s,l,!0,u,!0),{u_texsize_icon:c,u_texture_icon:1})},tn=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},en=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,u=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/u,f=c*(n.tileID.canonical.x+n.tileID.wrap*u),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},rn={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image),u_image_height:new t.Uniform1f(e,r.u_image_height)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function nn(e,r,n,i,a,o,s){for(var l=e.context,u=l.gl,c=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var v=i[d],g=r.getTile(v),y=g.getBucket(n);if(y){var m=v.posMatrix;0===a[0]&&0===a[1]||(m=e.translatePosMatrix(v.posMatrix,g,a,o));var x=s?y.textCollisionBox:y.iconCollisionBox,b=y.collisionCircleArray;if(b.length>0){var _=t.create(),w=m;t.mul(_,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,y.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&c.draw(l,u.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Dr(m,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,O=E.circleArray[C+0],P=E.circleArray[C+1],I=E.circleArray[C+2],D=E.circleArray[C+3];k.emplace(A++,O,P,I,D,0),k.emplace(A++,O,P,I,D,1),k.emplace(A++,O,P,I,D,2),k.emplace(A++,O,P,I,D,3)}(!Kr||Kr.length<2*h)&&(Kr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var z=l.createIndexBuffer(Kr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=zr(N.transform,N.invTransform,e.transform);T.draw(l,u.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,j,n.id,R,z,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),z.destroy()}}var an=t.identity(new Float32Array(16));function on(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,u=-(s.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point((l/a+c[0])*o,(u/a+c[1])*o)}function sn(e,r,n,i,a,o,s,l,u,c,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,v={};p.clear();for(var g=0;g<h.length;g++){var y=h.get(g),m=e.allowVerticalPlacement&&!y.placedOrientation,x=y.hidden||!y.crossTileID||m?null:i[y.crossTileID];if(x){var b=new t.Point(y.anchorX,y.anchorY),_=re(b,n?l:s),w=ne(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,c,y)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/u);for(var k=x.width,A=x.height,M=on(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?re(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&y.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<y.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&y.associatedIconIndex>=0&&(v[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else he(y.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,O=0;O<C.length;O++){var P=C.get(O);if(P.hidden)he(P.numGlyphs,d);else{var I=v[O];if(I)for(var D=0;D<P.numGlyphs;D++)t.addDynamicAttributes(d,I.shiftedAnchor,I.angle);else he(P.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function ln(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function un(e,r,n,i,a,o,s,l,u,c,f,h){for(var p=e.context,d=p.gl,v=e.transform,g=\"map\"===l,y=\"map\"===u,m=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!y&&!m,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=!1,w=e.depthModeForSublayer(0,Mt.ReadOnly),T=n.layout.get(\"text-variable-anchor\"),k=[],A=0,M=i;A<M.length;A+=1){var S=M[A],E=r.getTile(S),L=E.getBucket(n);if(L){var C=a?L.text:L.icon;if(C&&C.segments.get().length){var O=C.programConfigurations.get(n.id),P=a||L.sdfIcons,I=a?L.textSizeData:L.iconSizeData,D=y||0!==v.pitch,z=e.useProgram(ln(P,a,L),O),R=t.evaluateSizeForZoom(I,v.zoom),F=void 0,B=[0,0],N=void 0,j=void 0,U=null,V=void 0;if(a){if(N=E.glyphAtlasTexture,j=d.LINEAR,F=E.glyphAtlasTexture.size,L.iconsInText){B=E.imageAtlasTexture.size,U=E.imageAtlasTexture;var q=\"composite\"===I.kind||\"camera\"===I.kind;V=D||e.options.rotating||e.options.zooming||q?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||L.iconsNeedLinear;N=E.imageAtlasTexture,j=P||e.options.rotating||e.options.zooming||H||D?d.LINEAR:d.NEAREST,F=E.imageAtlasTexture.size}var G=ge(E,1,e.transform.zoom),W=te(S.posMatrix,y,g,e.transform,G),Y=ee(S.posMatrix,y,g,e.transform,G),X=T&&L.hasTextData(),Z=\"none\"!==n.layout.get(\"icon-text-fit\")&&X&&L.hasIconData();m&&ae(L,S.posMatrix,e,a,W,Y,y,c);var K=e.translatePosMatrix(S.posMatrix,E,o,s),J=m||a&&T||Z?an:W,$=e.translatePosMatrix(Y,E,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),tt={program:z,buffers:C,uniformValues:P?L.iconsInText?Qr(I.kind,R,x,y,e,K,J,$,F,B):$r(I.kind,R,x,y,e,K,J,$,a,F,!0):Jr(I.kind,R,x,y,e,K,J,$,a,F),atlasTexture:N,atlasTextureIcon:U,atlasInterpolation:j,atlasInterpolationIcon:V,isSDF:P,hasHalo:Q};if(b&&L.canOverlap){_=!0;for(var et=0,rt=C.segments.get();et<rt.length;et+=1){var nt=rt[et];k.push({segments:new t.SegmentVector([nt]),sortKey:nt.sortKey,state:tt})}}else k.push({segments:C.segments,sortKey:0,state:tt})}}}_&&k.sort((function(t,e){return t.sortKey-e.sortKey}));for(var it=0,at=k;it<at.length;it+=1){var ot=at[it],st=ot.state;if(p.activeTexture.set(d.TEXTURE0),st.atlasTexture.bind(st.atlasInterpolation,d.CLAMP_TO_EDGE),st.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),st.atlasTextureIcon&&st.atlasTextureIcon.bind(st.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),st.isSDF){var lt=st.uniformValues;st.hasHalo&&(lt.u_is_halo=1,cn(st.buffers,ot.segments,n,e,st.program,w,f,h,lt)),lt.u_is_halo=0}cn(st.buffers,ot.segments,n,e,st.program,w,f,h,st.uniformValues)}}function cn(t,e,r,n,i,a,o,s,l){var u=n.context,c=u.gl;i.draw(u,c.TRIANGLES,a,o,s,Ct.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function fn(t,e,r,n,i,a,o){var s,l,u,c,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),v=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,y=n;g<y.length;g+=1){var m=y[g],x=e.getTile(m);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(v));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(m.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){c=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];u=\"fillOutlinePattern\"===l&&d?Pr(S,t,v,x,E):Or(S,E)}else c=b.indexBuffer,f=b.segments,u=d?Cr(S,t,v,x):Lr(S);w.draw(t.context,s,i,t.stencilModeForClipping(m),a,Ct.disabled,u,r.id,b.layoutVertexBuffer,c,f,r.paint,t.transform.zoom,_)}}}}function hn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,u=r.paint.get(\"fill-extrusion-pattern\"),c=u.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var v=d[p],g=e.getTile(v),y=g.getBucket(r);if(y){var m=y.programConfigurations.get(r.id),x=t.useProgram(c?\"fillExtrusionPattern\":\"fillExtrusion\",m);c&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),m.updatePaintBuffers(f));var b=u.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&m.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(v.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=c?Er(k,t,A,h,v,f,g):Sr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Ct.backCCW,M,r.id,y.layoutVertexBuffer,y.indexBuffer,y.segments,r.paint,t.transform.zoom,m)}}}function pn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var u=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var c=jr(t,e,r);u.draw(o,s.TRIANGLES,n,i,a,Ct.disabled,c,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function dn(e,r,n,i,a,o){var s=e.context,l=s.gl,u=r.dem;if(u&&u.data){var c=u.dim,f=u.stride,h=u.getPixels();if(s.activeTexture.set(l.TEXTURE1),s.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(f),r.demTexture){var p=r.demTexture;p.update(h,{premultiply:!1}),p.bind(l.NEAREST,l.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(s,h,l.RGBA,{premultiply:!1}),r.demTexture.bind(l.NEAREST,l.CLAMP_TO_EDGE);s.activeTexture.set(l.TEXTURE0);var d=r.fbo;if(!d){var v=new t.Texture(s,{width:c,height:c,data:null},l.RGBA);v.bind(l.LINEAR,l.CLAMP_TO_EDGE),(d=r.fbo=s.createFramebuffer(c,c,!0)).colorAttachment.set(v.texture)}s.bindFramebuffer.set(d.framebuffer),s.viewport.set([0,0,c,c]),e.useProgram(\"hillshadePrepare\").draw(s,l.TRIANGLES,i,a,o,Ct.disabled,Ur(r.tileID,u),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function vn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,u=r?(s-r.timeAdded)/o:-1,c=n.getSource(),f=a.coveringZoomLevel({tileSize:c.tileSize,roundZoom:c.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-u,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var gn=new t.Color(1,0,0,1),yn=new t.Color(0,1,0,1),mn=new t.Color(0,0,1,1),xn=new t.Color(1,0,1,1),bn=new t.Color(0,1,1,1);function _n(t){var e=t.transform.padding;wn(t,t.transform.height-(e.top||0),3,gn),wn(t,e.bottom||0,3,yn),Tn(t,e.left||0,3,mn),Tn(t,t.transform.width-(e.right||0),3,xn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;kn(t,e-a/2,r-i/2,a,i,n),kn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,bn)}function wn(t,e,r,n){kn(t,0,e+r/2,t.transform.width,r,n)}function Tn(t,e,r,n){kn(t,e-r/2,0,r,t.transform.height,n)}function kn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function An(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,u=Et.disabled,c=e.colorModeForRenderPass(),f=\"$debug\";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,u,c,Ct.disabled,Rr(o,t.Color.red),f,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var h=r.getTileByID(n.key).latestRawTileData,p=h&&h.byteLength||0,d=Math.floor(p/1024),v=r.getTile(n).tileSize,g=512/Math.min(v,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+\" \"+d+\"kb\"),s.draw(i,a.TRIANGLES,l,u,Lt.alphaBlended,Ct.disabled,Rr(o,t.Color.transparent,g),f,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var Mn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,u=\"map\"===a,c=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),v=d.getBucket(n);if(v&&v.text&&v.text.segments.get().length){var g=v.textSizeData,y=t.evaluateSizeForZoom(g,l.zoom),m=ge(d,1,r.transform.zoom),x=te(p.posMatrix,c,u,r.transform,m),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&v.hasIconData();if(y){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);sn(v,u,c,s,t.symbolSize,l,x,p.posMatrix,_,y,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&un(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&un(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(nn(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),nn(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var u=e.context,c=u.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=Et.disabled,p=e.colorModeForRenderPass(),d=[],v=0;v<i.length;v++){var g=i[v],y=r.getTile(g),m=y.getBucket(n);if(m){var x=m.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:m.layoutVertexBuffer,indexBuffer:m.indexBuffer,uniformValues:Ir(e,g,y,n)};if(l)for(var _=0,w=m.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:m.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,O=S.indexBuffer,P=S.uniformValues,I=M.segments;L.draw(u,c.TRIANGLES,f,h,p,Ct.disabled,P,n.id,C,O,I,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Et.disabled,l=new Lt([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);(function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,e,n),a.clear({color:t.Color.transparent});for(var u=0;u<i.length;u++){var c=i[u];if(!r.hasRenderableParent(c)){var f=r.getTile(c),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),v=e.transform.zoom;d.draw(a,o.TRIANGLES,Mt.disabled,s,l,Ct.disabled,Br(c.posMatrix,f,v,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Nr(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1))for(var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),u=n.paint.get(\"line-dasharray\"),c=n.paint.get(\"line-pattern\"),f=c.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":u?\"lineSDF\":h?\"lineGradient\":\"line\",v=e.context,g=v.gl,y=!0,m=0,x=i;m<x.length;m+=1){var b=x[m],_=r.getTile(b);if(!f||_.patternsLoaded()){var w=_.getBucket(n);if(w){var T=w.programConfigurations.get(n.id),k=e.context.program.get(),A=e.useProgram(d,T),M=y||A.program!==k,S=c.constantOr(null);if(S&&_.imageAtlas){var E=_.imageAtlas,L=E.patternPositions[S.to.toString()],C=E.patternPositions[S.from.toString()];L&&C&&T.setConstantPatternPositions(L,C)}var O=f?Hr(e,_,n,p):u?Gr(e,_,n,u,p):h?qr(e,_,n,w.lineClipsArray.length):Vr(e,_,n);if(f)v.activeTexture.set(g.TEXTURE0),_.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),T.updatePaintBuffers(p);else if(u&&(M||e.lineAtlas.dirty))v.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(v);else if(h){var P=w.gradients[n.id],I=P.texture;if(n.gradientVersion!==P.version){var D=256;if(n.stepInterpolant){var z=r.getSource().maxzoom,R=b.canonical.z===z?Math.ceil(1<<e.transform.maxZoom-b.canonical.z):1,F=w.maxLineLength/t.EXTENT*1024*R;D=t.clamp(t.nextPowerOfTwo(F),256,v.maxTextureSize)}P.gradient=t.renderColorRamp({expression:n.gradientExpression(),evaluationKey:\"lineProgress\",resolution:D,image:P.gradient||void 0,clips:w.lineClipsArray}),P.texture?P.texture.update(P.gradient):P.texture=new t.Texture(v,P.gradient,g.RGBA),P.version=n.gradientVersion,I=P.texture}v.activeTexture.set(g.TEXTURE0),I.bind(n.stepInterpolant?g.NEAREST:g.LINEAR,g.CLAMP_TO_EDGE)}A.draw(v,g.TRIANGLES,s,e.stencilModeForClipping(b),l,Ct.disabled,O,n.id,w.layoutVertexBuffer,w.indexBuffer,w.segments,n.paint,e.transform.zoom,T,w.layoutVertexBuffer2),y=!1}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),u=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===u){var c=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);fn(e,r,n,i,c,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,Mt.ReadOnly);fn(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))hn(t,e,r,n,a,Et.disabled,Lt.disabled),hn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();hn(t,e,r,n,a,Et.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=t.depthModeForSublayer(0,Mt.ReadOnly),o=t.colorModeForRenderPass(),s=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],l=s[0],u=0,c=s[1];u<c.length;u+=1){var f=c[u],h=e.getTile(f);h.needsHillshadePrepare&&\"offscreen\"===t.renderPass?dn(t,h,r,a,Et.disabled,o):\"translucent\"===t.renderPass&&pn(t,h,r,a,l[f.overscaledZ],o)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),u=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),c=u[0],f=u[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,v=f;d<v.length;d+=1){var g=v[d],y=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),m=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);m.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=vn(m,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-m.tileID.overscaledZ),T=[m.tileID.canonical.x*w%1,m.tileID.canonical.y*w%1]):m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Xr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,y,Et.disabled,l,Ct.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,y,c[g.overscaledZ],l,Ct.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,u=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(u)){var c=!u&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===c){var f=Et.disabled,h=t.depthModeForSublayer(0,\"opaque\"===c?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(u?\"backgroundPattern\":\"background\"),v=s.coveringTiles({tileSize:l});u&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),y=0,m=v;y<m.length;y+=1){var x=m[y],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=u?en(b,i,t,u,{tileID:x,tileSize:l},g):tn(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Ct.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)An(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Et.disabled);var o=\"3d\"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},Sn=function(t,e){this.context=new Ot(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Pt.maxUnderzooming+Pt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ve,this.gpuTimers={}};Sn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},Sn.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Xe.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Xe.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Xe.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Et({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},Sn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,Lt.disabled,Ct.disabled,Fr(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},Sn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Lt.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new Et({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),Lt.disabled,Ct.disabled,Fr(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},Sn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Et({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Et.disabled,e),n]},Sn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){var r=1/8;return new Lt([e.CONSTANT_COLOR,e.ONE],new t.Color(r,r,r,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?Lt.unblended:Lt.alphaBlended},Sn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},Sn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},Sn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,u,c={},f={},h={};for(var p in a){var d=a[p];c[p]=d.getVisibleCoordinates(),f[p]=c[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var v=0;v<i.length;v++){var g=i[v];if(this.style._layers[g].is3D()){this.opaquePassCutoff=v;break}}this.renderPass=\"offscreen\";for(var y=0,m=i;y<m.length;y+=1){var x=m[y],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=c[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,c[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(u&&u.id)&&(u=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<u.getSource().maxzoom)&&(l=u))})),l&&Mn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&_n(this),this.context.setDefault()},Sn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),Mn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},Sn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},Sn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},Sn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},Sn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},Sn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var u=[a?n[0]:ge(r,n[0],this.transform.zoom),a?n[1]:ge(r,n[1],this.transform.zoom),0],c=new Float32Array(16);return t.translate(c,e,u),c},Sn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},Sn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},Sn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Sn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new Ar(this.context,t,wr[t],e,rn[t],this._showOverdrawInspector)),this.cache[r]},Sn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Sn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Sn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Sn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var En=function(t,e){this.points=t,this.planes=e};En.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new En(a,o)};var Ln=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Ln.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Ln(n,i)},Ln.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Ln.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Ln.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var u=Number.MAX_VALUE,c=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];u=Math.min(u,h),c=Math.max(c,h)}if(c<0||u>this.max[l]-this.min[l])return 0}return 1};var Cn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};Cn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Cn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Cn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Cn.prototype.clone=function(){return new Cn(this.top,this.bottom,this.left,this.right)},Cn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var On=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Cn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Pn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};On.prototype.clone=function(){var t=new On(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Pn.minZoom.get=function(){return this._minZoom},Pn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Pn.maxZoom.get=function(){return this._maxZoom},Pn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Pn.minPitch.get=function(){return this._minPitch},Pn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Pn.maxPitch.get=function(){return this._maxPitch},Pn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Pn.renderWorldCopies.get=function(){return this._renderWorldCopies},Pn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Pn.worldSize.get=function(){return this.tileSize*this.scale},Pn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Pn.size.get=function(){return new t.Point(this.width,this.height)},Pn.bearing.get=function(){return-this.angle/Math.PI*180},Pn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Pn.pitch.get=function(){return this._pitch/Math.PI*180},Pn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Pn.fov.get=function(){return this._fov/Math.PI*180},Pn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Pn.zoom.get=function(){return this._zoom},Pn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Pn.center.get=function(){return this._center},Pn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Pn.padding.get=function(){return this._edgeInsets.toJSON()},Pn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Pn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},On.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},On.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},On.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},On.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),u=s-1;u<=l+1;u++)0!==u&&r.push(new t.UnwrappedTileID(u,e));return r},On.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=En.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var u=function(t){return{aabb:new Ln([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},c=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)c.push(u(-d)),c.push(u(d));for(c.push(u(0));c.length>0;){var v=c.pop(),g=v.x,y=v.y,m=v.fullyVisible;if(!m){var x=v.aabb.intersects(s);if(0===x)continue;m=2===x}var b=v.aabb.distanceX(o),_=v.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-v.zoom)-2;if(v.zoom===h||w>T&&v.zoom>=l)f.push({tileID:new t.OverscaledTileID(v.zoom===h?p:v.zoom,v.wrap,v.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);c.push({aabb:v.aabb.quadrant(k),zoom:v.zoom+1,x:A,y:M,wrap:v.wrap,fullyVisible:m})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},On.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Pn.unmodified.get=function(){return this._unmodified},On.prototype.zoomScale=function(t){return Math.pow(2,t)},On.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},On.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},On.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Pn.point.get=function(){return this.project(this.center)},On.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},On.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},On.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},On.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},On.prototype.coordinateLocation=function(t){return t.toLngLat()},On.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,u=n[1]/a,c=r[2]/i,f=n[2]/a,h=c===f?0:(0-c)/(f-c);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,u,h)/this.worldSize)},On.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},On.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},On.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},On.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},On.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},On.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},On.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,u=this.size,c=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<u.y?u.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<u.x?u.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var v=p.y,g=u.y/2;v-g<a&&(i=a+g),v+g>o&&(i=o-g)}if(this.lngRange){var y=p.x,m=u.x/2;y-m<s&&(n=s+m),y+m>l&&(n=l-m)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=c,this._constraining=!1}},On.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,u=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),c=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,c,u),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),v=Math.sin(this.angle),g=s-Math.round(s)+d*h+v*p,y=l-Math.round(l)+d*p+v*h,m=new Float64Array(f);if(t.translate(m,m,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=m,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},On.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},On.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},On.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var u=l[s];n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(On.prototype,Pn);var In=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};In.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},In.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},In.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),u=this._map.getPitch(),c=\"\";if(c+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||u)&&(c+=\"/\"+Math.round(10*l)/10),u&&(c+=\"/\"+Math.round(u)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+c):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+c),\"#\"+p.join(\"&\")}return\"#\"+c},In.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},In.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},In.prototype._updateHashUnthrottled=function(){var e=t.window.location.href.replace(/(#.+)?$/,this.getHashString());try{t.window.history.replaceState(t.window.history.state,null,e)}catch(t){}};var Dn={linearity:.3,easing:t.bezier(0,0,.3,1)},zn=t.extend({deceleration:2500,maxSpeed:1400},Dn),Rn=t.extend({deceleration:20,maxSpeed:1400},Dn),Fn=t.extend({deceleration:1e3,maxSpeed:360},Dn),Bn=t.extend({deceleration:1e3,maxSpeed:90},Dn),Nn=function(t){this._map=t,this.clear()};function jn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Un(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Nn.prototype.clear=function(){this._inertiaBuffer=[]},Nn.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Nn.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Nn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Un(r.pan.mag(),o,t.extend({},zn,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,jn(s,l)}if(r.zoom){var u=Un(r.zoom,o,Rn);s.zoom=this._map.transform.zoom+u.amount,jn(s,u)}if(r.bearing){var c=Un(r.bearing,o,Fn);s.bearing=this._map.transform.bearing+t.clamp(c.amount,-179,179),jn(s,c)}if(r.pitch){var f=Un(r.pitch,o,Bn);s.pitch=this._map.transform.pitch+f.amount,jn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Vn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),qn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),u=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),c=i.unproject(u);e.call(this,n,{points:s,point:u,lngLats:l,lngLat:c,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Hn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Gn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Gn.prototype.reset=function(){delete this._mousedownPos},Gn.prototype.wheel=function(t){return this._firePreventable(new Hn(t.type,this._map,t))},Gn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseup=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.dblclick=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseover=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.mouseout=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.touchstart=function(t){return this._firePreventable(new qn(t.type,this._map,t))},Gn.prototype.touchmove=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchend=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchcancel=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Wn=function(t){this._map=t};Wn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Wn.prototype.mousemove=function(t){this._map.fire(new Vn(t.type,this._map,t))},Wn.prototype.mousedown=function(){this._delayContextMenu=!0},Wn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Vn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Wn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Vn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Wn.prototype.isEnabled=function(){return!0},Wn.prototype.isActive=function(){return!1},Wn.prototype.enable=function(){},Wn.prototype.disable=function(){};var Yn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Xn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Yn.prototype.isEnabled=function(){return!!this._enabled},Yn.prototype.isActive=function(){return!!this._active},Yn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Yn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Yn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Yn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Yn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Yn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Yn.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Yn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Zn=function(t){this.reset(),this.numTouches=t.numTouches};Zn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Zn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Xn(n,r)))},Zn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Xn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Zn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Kn=function(t){this.singleTap=new Zn(t),this.numTaps=t.numTaps,this.reset()};Kn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Kn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Kn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Kn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new Kn({numTouches:1,numTaps:2}),this._zoomOut=new Kn({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var $n={};$n[0]=1,$n[2]=2;var Qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Qn.prototype._correctButton=function(t,e){return!1},Qn.prototype._move=function(t,e){return{}},Qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r)if(t.preventDefault(),function(t,e){var r=$n[e];return void 0===t.buttons||(t.buttons&r)!==r}(t,this._eventButton))this.reset();else if(this._moved||!(e.dist(r)<this._clickTolerance))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Qn.prototype.mouseupWindow=function(t){this._lastPoint&&r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Qn.prototype.enable=function(){this._enabled=!0},Qn.prototype.disable=function(){this._enabled=!1,this.reset()},Qn.prototype.isEnabled=function(){return this._enabled},Qn.prototype.isActive=function(){return this._active};var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Qn),ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ni=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};ni.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},ni.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},ni.prototype.touchmove=function(t,e,r){if(this._active&&!(r.length<this._minTouches))return t.preventDefault(),this._calculateTransform(t,e,r)},ni.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},ni.prototype.touchcancel=function(){this.reset()},ni.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Xn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var u=i[l],c=this._touches[l];c&&(a._add(u),o._add(u.sub(c)),s++,i[l]=u)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ii=function(){this.reset()};function ai(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}ii.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},ii.prototype._start=function(t){},ii.prototype._move=function(t,e,r){return{}},ii.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},ii.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=ai(r,e,i),s=ai(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},ii.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=ai(n,e,a),l=ai(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},ii.prototype.touchcancel=function(){this.reset()},ii.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},ii.prototype.disable=function(){this._enabled=!1,this.reset()},ii.prototype.isEnabled=function(){return this._enabled},ii.prototype.isActive=function(){return this._active};function oi(t,e){return Math.log(t/e)/Math.LN2}var si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(oi(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:oi(this._distance,r),pinchAround:e}},e}(ii);function li(t,e){return 180*t.angleWith(e)/Math.PI}var ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:li(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=li(t,this._startVector);return Math.abs(r)<e},e}(ii);function ci(t){return Math.abs(t.y)>Math.abs(t.x)}var fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ci(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ci(t)&&ci(e)&&a}},e}(ii),hi={panStep:100,bearingStep:15,pitchStep:10},pi=function(){var t=hi;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep,this._rotationDisabled=!1};function di(t){return t*(2-t)}pi.prototype.reset=function(){this._active=!1},pi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return this._rotationDisabled&&(n=0,i=0),{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:di,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},pi.prototype.enable=function(){this._enabled=!0},pi.prototype.disable=function(){this._enabled=!1,this.reset()},pi.prototype.isEnabled=function(){return this._enabled},pi.prototype.isActive=function(){return this._active},pi.prototype.disableRotation=function(){this._rotationDisabled=!0},pi.prototype.enableRotation=function(){this._rotationDisabled=!1};var vi=4.000244140625,gi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll([\"_onTimeout\"],this)};gi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},gi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},gi.prototype.isEnabled=function(){return!!this._enabled},gi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},gi.prototype.isZooming=function(){return!!this._zooming},gi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},gi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},gi.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%vi==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},gi.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},gi.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},gi.prototype.renderFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>vi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,u=this._easing,c=!1;if(\"wheel\"===this._type&&l&&u){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=u(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):c=!0}else o=s,c=!0;return this._active=!0,c&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!c,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},gi.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},gi.prototype.reset=function(){this._active=!1};var yi=function(t,e){this._clickZoom=t,this._tapZoom=e};yi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},yi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},yi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},yi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var mi=function(){this.reset()};mi.prototype.reset=function(){this._active=!1},mi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},mi.prototype.enable=function(){this._enabled=!0},mi.prototype.disable=function(){this._enabled=!1,this.reset()},mi.prototype.isEnabled=function(){return this._enabled},mi.prototype.isActive=function(){return this._active};var xi=function(){this._tap=new Kn({numTouches:1,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},xi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},xi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},xi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var bi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};bi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},bi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},bi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},bi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var _i=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};_i.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},_i.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},_i.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},_i.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var wi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};wi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},wi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},wi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},wi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var Ti=function(t){return t.zoom||t.drag||t.pitch||t.rotate},ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ai(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var Mi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Nn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!0}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],u=s[1],c=s[2];r.addEventListener(l,u,l===t.window.document?this.handleWindowEvent:this.handleEvent,c)}};Mi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},Mi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Gn(e,t));var n=e.boxZoom=new Yn(e,t);this._add(\"boxZoom\",n);var i=new Jn,a=new mi;e.doubleClickZoom=new yi(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new xi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new fi;this._add(\"touchPitch\",s);var l=new ei(t),u=new ri(t);e.dragRotate=new _i(t,l,u),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",u,[\"mouseRotate\"]);var c=new ti(t),f=new ni(t);e.dragPan=new bi(r,c,f),this._add(\"mousePan\",c),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ui,p=new si;e.touchZoomRotate=new wi(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new gi(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var v=e.keyboard=new pi;this._add(\"keyboard\",v),this._add(\"blockableMapEvent\",new Wn(e));for(var g=0,y=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<y.length;g+=1){var m=y[g];t.interactive&&t[m]&&e[m].enable(t[m])}},Mi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},Mi.prototype.stop=function(t){if(!this._updatingCamera){for(var e=0,r=this._handlers;e<r.length;e+=1)r[e].handler.reset();this._inertia.clear(),this._fireEvents({},{},t),this._changes=[]}},Mi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},Mi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},Mi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},Mi.prototype.isMoving=function(){return Boolean(Ti(this._eventsInProgress))||this.isZooming()},Mi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},Mi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},Mi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},Mi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),u=0,c=this._handlers;u<c.length;u+=1){var f=c[u],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var v=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(v=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,v,h,n),v&&v.needsRenderFrame&&this._triggerRenderFrame()),(v||p.isActive())&&(o[h]=p)}}var g={};for(var y in this._previousActiveHandlers)o[y]||(g[y]=n);this._previousActiveHandlers=o,(Object.keys(g).length||Ai(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||Ai(i))&&this._map._stop(!0),this._updatingCamera=!1;var m=i.cameraAnimation;m&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],m(this._map))}else this.stop(!0)},Mi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},Mi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],u=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,u)}this._updateMapTransform(e,r,n),this._changes=[]},Mi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!Ai(t))return this._fireEvents(e,r,!0);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,u=t.around,c=t.pinchAround;void 0!==c&&(u=c),n._stop(!0),u=u||n.transform.centerPoint;var f=i.pointLocation(a?u.sub(a):u);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,u),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r,!0)},Mi.prototype._fireEvents=function(e,r,n){var i=this,a=Ti(this._eventsInProgress),o=Ti(e),s={};for(var l in e){var u=e[l].originalEvent;this._eventsInProgress[l]||(s[l+\"start\"]=u),this._eventsInProgress[l]=e[l]}for(var c in!a&&o&&this._fireEvent(\"movestart\",o.originalEvent),s)this._fireEvent(c,s[c]);for(var f in o&&this._fireEvent(\"move\",o.originalEvent),e){var h=e[f].originalEvent;this._fireEvent(f,h)}var p,d={};for(var v in this._eventsInProgress){var g=this._eventsInProgress[v],y=g.handlerName,m=g.originalEvent;this._handlersById[y].isActive()||(delete this._eventsInProgress[v],p=r[y]||m,d[v+\"end\"]=p)}for(var x in d)this._fireEvent(x,d[x]);var b=Ti(this._eventsInProgress);if(n&&(a||o)&&!b){this._updatingCamera=!0;var _=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),w=function(t){return 0!==t&&-i._bearingSnap<t&&t<i._bearingSnap};_?(w(_.bearing||this._map.getBearing())&&(_.bearing=0),this._map.easeTo(_,{originalEvent:p})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:p})),w(this._map.getBearing())&&this._map.resetNorth()),this._updatingCamera=!1}},Mi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Mi.prototype._requestFrame=function(){var t=this;return this._map.triggerRepaint(),this._map._renderTaskQueue.add((function(e){delete t._frameId,t.handleEvent(new ki(\"renderFrame\",{timeStamp:e})),t._applyChanges()}))},Mi.prototype._triggerRenderFrame=function(){void 0===this._frameId&&(this._frameId=this._requestFrame())};var Si=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){e=t.LngLatBounds.convert(e);var n=r&&r.bearing||0;return this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),n,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,u=s.project(t.LngLat.convert(e)),c=s.project(t.LngLat.convert(r)),f=u.rotate(-n*Math.PI/180),h=c.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),v=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/v.x,y=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/v.y;if(!(y<0||g<0)){var m=Math.min(s.scaleZoom(s.scale*Math.min(g,y)),i.maxZoom),x=\"number\"==typeof i.offset.x?new t.Point(i.offset.x,i.offset.y):t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(b,_).rotate(n*Math.PI/180),T=x.add(w).mult(s.scale/s.zoomScale(m));return{center:s.unproject(u.add(c).div(2).sub(T)),zoom:m,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),u=\"zoom\"in e?+e.zoom:a,c=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),v=i.pointLocation(d),g=t.LngLat.convert(e.center||v);this._normalizeCenter(g);var y,m,x=i.project(v),b=i.project(g).sub(x),_=i.zoomScale(u-a);e.around&&(y=t.LngLat.convert(e.around),m=i.locationPoint(y));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||u!==a,this._rotating=this._rotating||o!==c,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,u,e)),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),y)i.setLocationAtPoint(y,m);else{var v=i.zoomScale(i.zoom-a),g=u>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(v));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),u=this.getPadding(),c=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(c-o),v=t.Point.convert(e.offset),g=a.centerPoint.add(v),y=a.pointLocation(g),m=t.LngLat.convert(e.center||y);this._normalizeCenter(m);var x=a.project(y),b=a.project(m).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,c),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var O=E(0),P=function(t){return C(O)/C(O+_*t)},I=function(t){return w*((C(O)*(L(e=O+_*t)/C(e))-L(O))/S)/k;var e},D=(E(1)-O)/_;if(Math.abs(k)<1e-6||!isFinite(D)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var z=T<w?-1:1;D=Math.abs(Math.log(T/w))/_,I=function(){return 0},P=function(t){return Math.exp(z*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*D/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*D,d=1/P(i);a.zoom=1===e?c:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(u,p,e),g=a.centerPoint.add(v));var y=1===e?m:a.unproject(x.add(b.mult(I(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop(!1)}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),Ei=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_toggleAttribution\",\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Ei.prototype.getDefaultPosition=function(){return\"bottom-right\"},Ei.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._compactButton=r.create(\"button\",\"mapboxgl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),this._innerContainer.setAttribute(\"role\",\"list\"),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Ei.prototype._setElementTitle=function(t,e){var r=this._map._getUIString(\"AttributionControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)},Ei.prototype._toggleAttribution=function(){this._container.classList.contains(\"mapboxgl-compact-show\")?(this._container.classList.remove(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"false\")):(this._container.classList.add(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"true\"))},Ei.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\",this._setElementTitle(e,\"MapFeedback\")}},Ei.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"visibility\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},Ei.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Ei.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\",\"mapboxgl-compact-show\")};var Li=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};Li.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},Li.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Li.prototype.getDefaultPosition=function(){return\"bottom-left\"},Li.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Li.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},Li.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var Ci=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Ci.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Ci.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},Ci.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},Ci.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Oi={\"AttributionControl.ToggleAttribution\":\"Toggle attribution\",\"AttributionControl.MapFeedback\":\"Map feedback\",\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},Pi=t.window.HTMLImageElement,Ii=t.window.HTMLElement,Di=t.window.ImageBitmap,zi=60,Ri={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:zi,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Fi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Ri,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>zi)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new On(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Ci,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Oi,e.locale),this._clickTolerance=e.clickTolerance,this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ii))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_onMapScroll\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1),t.window.addEventListener(\"orientationchange\",this._onWindowResize,!1)),this.handlers=new Mi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new In(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Ei({customAttribution:e.customAttribution})),this.addControl(new Li,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&(r=e.getDefaultPosition?e.getDefaultPosition():\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.hasControl=function(t){return this._controls.indexOf(t)>-1},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?zi:t)>zi)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Vn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Vn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Vn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new Ye(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new Ye(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof Pi||Di&&r instanceof Di){var u=t.browser.getImageData(r),c=u.width,f=u.height,h=u.data;this.style.addImage(e,{data:new t.RGBAImage({width:c,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,v=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(v)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof Pi||Di&&r instanceof Di?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof Pi||Di&&r instanceof Di);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\"),this._canvas.setAttribute(\"role\",\"region\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)})),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new Sn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype._onMapScroll=function(t){if(t.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var u=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),c=u.crossFadingFactor();1===c&&c===this._crossFadingFactor||(o=!0,this._crossFadingFactor=c),this.style.update(u)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}var p=this._sourcesDirty||this._styleDirty||this._placementDirty;return p||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new t.Event(\"idle\")),!this._loaded||this._fullyLoaded||p||(this._fullyLoaded=!0),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"orientationchange\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext&&n.loseContext(),Bi(this._canvasContainer),Bi(this._controlContainer),Bi(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(Si);function Bi(t){t.parentNode&&t.parentNode.removeChild(t)}var Ni={showCompass:!0,showZoom:!0,visualizePitch:!1},ji=function(e){var n=this;this.options=t.extend({},Ni,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};ji.prototype._updateZoomButtons=function(){var t=this._map.getZoom(),e=t===this._map.getMaxZoom(),r=t===this._map.getMinZoom();this._zoomInButton.disabled=e,this._zoomOutButton.disabled=r,this._zoomInButton.setAttribute(\"aria-disabled\",e.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",r.toString())},ji.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},ji.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ui(this._map,this._compass,this.options.visualizePitch)),this._container},ji.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},ji.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},ji.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ui=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new ei({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new ri({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Vi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ui.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ui.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ui.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ui.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ui.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ui.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ui.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ui.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ui.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var qi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Hi(t,e,r){var n=t.classList;for(var i in qi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Gi,Wi=function(e){function n(n,i){if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._scale=n&&n.scale||1,this._draggable=n&&n.draggable||!1,this._clickTolerance=n&&n.clickTolerance||0,this._isDragging=!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var a=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");a.setAttributeNS(null,\"display\",\"block\"),a.setAttributeNS(null,\"height\",\"41px\"),a.setAttributeNS(null,\"width\",\"27px\"),a.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");o.setAttributeNS(null,\"stroke\",\"none\"),o.setAttributeNS(null,\"stroke-width\",\"1\"),o.setAttributeNS(null,\"fill\",\"none\"),o.setAttributeNS(null,\"fill-rule\",\"evenodd\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"fill-rule\",\"nonzero\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),l.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,c=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<c.length;u+=1){var f=c[u],h=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");h.setAttributeNS(null,\"opacity\",\"0.04\"),h.setAttributeNS(null,\"cx\",\"10.5\"),h.setAttributeNS(null,\"cy\",\"5.80029008\"),h.setAttributeNS(null,\"rx\",f.rx),h.setAttributeNS(null,\"ry\",f.ry),l.appendChild(h)}var p=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");p.setAttributeNS(null,\"fill\",this._color);var d=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");d.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),p.appendChild(d);var v=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");v.setAttributeNS(null,\"opacity\",\"0.25\"),v.setAttributeNS(null,\"fill\",\"#000000\");var g=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");g.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),v.appendChild(g);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var m=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");x.setAttributeNS(null,\"fill\",\"#000000\"),x.setAttributeNS(null,\"opacity\",\"0.25\"),x.setAttributeNS(null,\"cx\",\"5.5\"),x.setAttributeNS(null,\"cy\",\"5.5\"),x.setAttributeNS(null,\"r\",\"5.4999962\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#FFFFFF\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\"),m.appendChild(x),m.appendChild(b),s.appendChild(l),s.appendChild(p),s.appendChild(v),s.appendChild(y),s.appendChild(m),a.appendChild(s),a.setAttributeNS(null,\"height\",41*this._scale+\"px\"),a.setAttributeNS(null,\"width\",27*this._scale+\"px\"),this._element.appendChild(a),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),Hi(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=13.5,r=Math.sqrt(Math.pow(e,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[r,-1*(24.6+r)],\"bottom-right\":[-r,-1*(24.6+r)],left:[e,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,qi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){if(!this._isDragging){var r=this._clickTolerance||this._map._clickTolerance;this._isDragging=e.point.dist(this._pointerdownPos)>=r}this._isDragging&&(this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\")))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Yi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Xi=0,Zi=!1,Ki=function(e){function n(r){e.call(this),this.options=t.extend({},Yi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Gi?n(Gi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Gi=\"denied\"!==t.state,n(Gi)})):(Gi=!!t.window.navigator.geolocation,n(Gi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Xi=0,Zi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Zi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Wi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Wi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Xi--,Zi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Xi>1?(e={maximumAge:6e5,timeout:0},Zi=!0):(e=this.options.positionOptions,Zi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ji={maxWidth:100,unit:\"metric\"},$i=function(e){this.options=t.extend({},Ji,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?ta(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):ta(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?ta(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?ta(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):ta(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function ta(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}$i.prototype.getDefaultPosition=function(){return\"bottom-left\"},$i.prototype._onMove=function(){Qi(this._map,this._container,this.options)},$i.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},$i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},$i.prototype.setUnit=function(t){this.options.unit=t,Qi(this._map,this._container,this.options)};var ea=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};ea.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},ea.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},ea.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},ea.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},ea.prototype._isFullscreen=function(){return this._fullscreen},ea.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},ea.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var ra={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\"},na=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \"),ia=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(ra),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container);return this._content.appendChild(t),this._createCloseButton(),this._update(),this._focusFirstElement(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.setOffset=function(t){return this.options.offset=t,this._update(),this},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createCloseButton=function(){this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(t){var e=this,n=this._lngLat||this._trackPointer;if(this._map&&n&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return e._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||t)){var i=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat),a=this.options.anchor,o=aa(this.options.offset);if(!a){var s,l=this._container.offsetWidth,u=this._container.offsetHeight;s=i.y+o.bottom.y<u?[\"top\"]:i.y>this._map.transform.height-u?[\"bottom\"]:[],i.x<l/2?s.push(\"left\"):i.x>this._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var c=i.add(o[a]).round();r.setTransform(this._container,qi[a]+\" translate(\"+c.x+\"px,\"+c.y+\"px)\"),Hi(this._container,a,\"popup\")}},n.prototype._focusFirstElement=function(){if(this.options.focusAfterOpen&&this._container){var t=this._container.querySelector(na);t&&t.focus()}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);function aa(e){if(e){if(\"number\"==typeof e){var r=Math.round(Math.sqrt(.5*Math.pow(e,2)));return{center:new t.Point(0,0),top:new t.Point(0,e),\"top-left\":new t.Point(r,r),\"top-right\":new t.Point(-r,r),bottom:new t.Point(0,-e),\"bottom-left\":new t.Point(r,-r),\"bottom-right\":new t.Point(-r,-r),left:new t.Point(e,0),right:new t.Point(-e,0)}}if(e instanceof t.Point||Array.isArray(e)){var n=t.Point.convert(e);return{center:n,top:n,\"top-left\":n,\"top-right\":n,bottom:n,\"bottom-left\":n,\"bottom-right\":n,left:n,right:n}}return{center:t.Point.convert(e.center||[0,0]),top:t.Point.convert(e.top||[0,0]),\"top-left\":t.Point.convert(e[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(e[\"top-right\"]||[0,0]),bottom:t.Point.convert(e.bottom||[0,0]),\"bottom-left\":t.Point.convert(e[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(e[\"bottom-right\"]||[0,0]),left:t.Point.convert(e.left||[0,0]),right:t.Point.convert(e.right||[0,0])}}return aa(new t.Point(0,0))}var oa={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Fi,NavigationControl:ji,GeolocateControl:Ki,AttributionControl:Ei,ScaleControl:$i,FullscreenControl:ea,Popup:ia,Marker:Wi,Style:Ye,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){jt().acquire(Rt)},clearPrewarmedResources:function(){var t=Bt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Rt),Bt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Ft.workerCount},set workerCount(t){Ft.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return oa})),r}()},3108:function(t,e,r){\"use strict\";t.exports=r(26099)},26099:function(t,e,r){\"use strict\";var n=r(64928),i=r(32420),a=r(51160),o=r(76752),s=r(55616),l=r(31264),u=r(47520),c=r(18400),f=r(72512),h=r(76244);function p(t,e){for(var r=e[0],n=e[1],a=1/(e[2]-r),o=1/(e[3]-n),s=new Array(t.length),l=0,u=t.length/2;l<u;l++)s[2*l]=i((t[2*l]-r)*a,0,1),s[2*l+1]=i((t[2*l+1]-n)*o,0,1);return 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t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst 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float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 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return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign 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t.offset&&(t.offset=[t.offset,0]),this.positionOffset=m(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else{var i=t.style,s=t.weight,l=t.stretch,u=t.variant;t=n.parse(n.stringify(t)),i&&(t.style=i),s&&(t.weight=s),l&&(t.stretch=l),u&&(t.variant=u)}var c=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),f=p(t.size),h=Math.round(f[0]*d(f[1]));if(h!==e.fontSize[r]&&(o=!0,e.fontSize[r]=h),!(e.font[r]&&c==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[c],e.font[r]))){var v=t.family.join(\", \"),g=[t.style];t.style!=t.variant&&g.push(t.variant),t.variant!=t.weight&&g.push(t.weight),_&&t.weight!=t.stretch&&g.push(t.stretch),e.font[r]={baseString:c,family:v,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:y(v,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:g.join(\" \")})},T.fonts[c]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,v(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=c.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),c.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=c.mallocUint8(this.count),O=c.mallocFloat(2*this.count);this.textWidth=[];for(var P=0,I=0;P<this.counts.length;P++){for(var D=this.counts[P],z=this.font[P]||this.font[0],R=this.fontAtlas[P]||this.fontAtlas[0],F=0;F<D;F++){var B=this.text.charAt(I),N=this.text.charAt(I-1);if(C[I]=R.ids[B],O[2*I]=z.width[B],F){var j=O[2*I-2],U=O[2*I],V=O[2*I-1]+.5*j+.5*U;if(this.kerning){var q=z.kerning[N+B];q&&(V+=.001*q)}O[2*I+1]=V}else O[2*I+1]=.5*O[2*I];I++}this.textWidth.push(O.length?.5*O[2*I-2]+O[2*I-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:O,type:\"float\",usage:\"stream\"}),c.freeUint8(C),c.freeFloat(O),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),c=x(s*i);n.width=l,n.height=c,n.rows=s,n.cols=o,n.em&&n.texture({data:u({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,c],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,-1*(i+=\"number\"==typeof t?t-n.baseline:-n[t])}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=c.mallocUint8(G);for(var W=(t.color.subarray||t.color.slice).bind(t.color),Y=0;Y<G;Y+=4)H.set(l(W(Y,Y+4),\"uint8\"),Y)}else{var X=t.color.length;H=c.mallocUint8(4*X);for(var Z=0;Z<X;Z++)H.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=H}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var K=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(K);for(var J=0;J<this.batch.length;J++)this.batch[J]={count:this.counts.length>1?this.counts[J]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[J]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*J,4*J+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[J]:this.opacity,baseline:null!=this.baselineOffset[J]?this.baselineOffset[J]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[J]?this.alignOffset[J]:this.alignOffset[0]:0,atlas:this.fontAtlas[J]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*J,2*J+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},t.exports=T},55212:function(t,e,r){\"use strict\";var n=r(55616);function 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n=t.segments({inverted:!1,regions:[]}),i=0;i<e.coordinates.length;i++)n=t.selectUnion(t.combine(n,r(e.coordinates[i])));return t.polygon(n)}throw new Error(\"PolyBool: Cannot convert GeoJSON object to PolyBool polygon\")},fromPolygon:function(t,e,r){function n(t,r){return e.pointInsideRegion([.5*(t[0][0]+t[1][0]),.5*(t[0][1]+t[1][1])],r)}function i(t){return{region:t,children:[]}}r=t.polygon(t.segments(r));var a=i(null);function o(t,e){for(var r=0;r<t.children.length;r++)if(n(e,(s=t.children[r]).region))return void o(s,e);var a=i(e);for(r=0;r<t.children.length;r++){var s;n((s=t.children[r]).region,e)&&(a.children.push(s),t.children.splice(r,1),r--)}t.children.push(a)}for(var s=0;s<r.regions.length;s++){var l=r.regions[s];l.length<3||o(a,l)}function u(t,e){for(var r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var c=[];function f(t){var e=[u(t.region,!1)];c.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return u(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return c.length<=0?{type:\"Polygon\",coordinates:[]}:1==c.length?{type:\"Polygon\",coordinates:c[0]}:{type:\"MultiPolygon\",coordinates:c}}};t.exports=e},72200:function(t,e,r){var n=r(48088);t.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return i=t.isStart,a=t.pt,o=r,s=n.isStart,l=n.pt,u=n.other.pt,(0!==(c=e.pointsCompare(a,l))?c:e.pointsSame(o,u)?0:i!==s?i?1:-1:e.pointAboveOrOnLine(o,s?l:u,s?u:l)?1:-1)<0;var i,a,o,s,l,u,c}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var 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y,m,x=g();if(x)t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(x.seg.myFill.above=!x.seg.myFill.above):x.seg.otherFill=h.seg.myFill,r&&r.segmentUpdate(x.seg),h.other.remove(),h.remove();if(a.getHead()!==h){r&&r.rewind(h.seg);continue}t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=v?v.seg.myFill.above:i,h.seg.myFill.above=m?!h.seg.myFill.below:h.seg.myFill.below):null===h.seg.otherFill&&(y=v?h.primary===v.primary?v.seg.otherFill.above:v.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:y,below:y}),r&&r.status(h.seg,!!d&&d.seg,!!v&&v.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&c(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var 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r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},11403:function(t){t.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},u=s,c=0;c<n.length;c++){var f=(g=n[c])[0],h=(g[1],g[g.length-1]);if(g[g.length-2],e.pointsSame(f,a)){if(k(c,!0,!0))break}else if(e.pointsSame(f,o)){if(k(c,!0,!1))break}else if(e.pointsSame(h,a)){if(k(c,!1,!0))break}else if(e.pointsSame(h,o)&&k(c,!1,!1))break}if(u===s)return 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a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],u=o[0],c=o[1];e.pointsCollinear(l,s,u)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,u,c)&&(r&&r.chainRemoveHead(i,u),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},82368:function(t){function e(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var r={union:function(t,r){return e(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],r)},intersect:function(t,r){return e(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],r)},difference:function(t,r){return e(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],r)},differenceRev:function(t,r){return e(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],r)},xor:function(t,r){return e(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],r)}};t.exports=r},9696:function(t,e,r){\"use 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this.big_endian?16777216*r[t]+65536*r[t+1]+256*r[t+2]+r[t+3]:r[t]+256*r[t+1]+65536*r[t+2]+16777216*r[t+3]},n.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},n.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},n.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return this.read_uint32(e);case 9:return 0|this.read_uint32(e);default:return null}},n.prototype.scan_ifd=function(t,n,i){var a=this.read_uint16(n);n+=2;for(var o=0;o<a;o++){var s=this.read_uint16(n),l=this.read_uint16(n+2),u=this.read_uint32(n+4),c=this.exif_format_length(l),f=u*c,h=f<=4?n+8:this.read_uint32(n+8),p=!1;if(h+f>this.input.length)throw e(\"unexpected EOF\",\"EBADDATA\");for(var d=[],v=h,g=0;g<u;g++,v+=c){var y=this.exif_format_read(l,v);if(null===y){d=null;break}d.push(y)}if(Array.isArray(d)&&2===l&&(d=r(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===i({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:u,entry_offset:n+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);n+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(n)})},t.exports.ExifParser=n,t.exports.get_orientation=function(t){var e=0;try{return new n(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},44600:function(t,e,r){\"use strict\";var n=r(9696).eW,i=r(9696).eI;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return 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t=l(t,\"float64\"),r.count=Math.floor(t.length/2),r.bounds=n(t,2),r.offset=e,e+=r.count,t}},{color:function(t,e){var r=e.count;if(t||(t=\"transparent\"),!Array.isArray(t)||\"number\"==typeof t[0]){var n=t;t=Array(r);for(var a=0;a<r;a++)t[a]=n}if(t.length<r)throw Error(\"Not enough colors\");for(var o=new Uint8Array(4*r),s=0;s<r;s++){var l=i(t[s],\"uint8\");o.set(l,4*s)}return o},range:function(t,e,r){var n=e.bounds;return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=f(e.scale),e.translateFract=f(e.translate),t},viewport:function(t){var e;return Array.isArray(t)?e={x:t[0],y:t[1],width:t[2]-t[0],height:t[3]-t[1]}:t?(e={x:t.x||t.left||0,y:t.y||t.top||0},t.right?e.width=t.right-e.x:e.width=t.w||t.width||0,t.bottom?e.height=t.bottom-e.y:e.height=t.h||t.height||0):e={x:0,y:0,width:y.drawingBufferWidth,height:y.drawingBufferHeight},e}}]),c):c})),e||r){var h=x.reduce((function(t,e,r){return t+(e?e.count:0)}),0),g=new Float64Array(2*h),_=new Uint8Array(4*h),w=new Float32Array(4*h);x.forEach((function(t,e){if(t){var r=t.positions,n=t.count,i=t.offset,a=t.color,o=t.errors;n&&(_.set(a,4*i),w.set(o,4*i),g.set(r,2*i))}}));var T=c(g);u(T);var k=f(g,T);p(k),d(_),v(w)}}}function k(){u.destroy(),p.destroy(),d.destroy(),v.destroy(),g.destroy()}};var h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},13472:function(t,e,r){\"use strict\";var n=r(72160),i=r(76752),a=r(50896),o=r(55616),s=r(47520),l=r(28912),u=r(71152),c=r(37816),f=c.float32,h=c.fract32,p=r(60463),d=r(51160),v=r(10272);function g(t,e){if(!(this instanceof g))return new g(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=g.shaders.has(t)?g.shaders.get(t):g.shaders.set(t,g.createShaders(t)).get(t),this.update(e)}t.exports=g,g.dashMult=2,g.maxPatternLength=256,g.precisionThreshold=3e6,g.maxPoints=1e4,g.maxLines=2048,g.shaders=new p,g.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:\"\\nprecision highp float;\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\",frag:\"\\nprecision highp float;\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:\"\\nprecision highp float;\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\",frag:\"\\nprecision highp float;\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:\"\\nprecision highp float;\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*g.dashMult*.5;ot<st;++ot)Q[rt++]=nt;nt^=255}}y.dashLength=tt,y.dashTexture({channels:1,data:Q,width:Q.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=y.count,ut=t.color;ut||(ut=\"transparent\");var ct=new Uint8Array(4*lt+4);if(Array.isArray(ut)&&\"number\"!=typeof ut[0]){for(var ft=0;ft<lt;ft++){var ht=n(ut[ft],\"uint8\");ct.set(ht,4*ft)}ct.set(n(ut[0],\"uint8\"),4*lt)}else for(var pt=n(ut,\"uint8\"),dt=0;dt<lt+1;dt++)ct.set(pt,4*dt);y.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ct})}}else e.passes[p]=null})),t.length<this.passes.length){for(var p=t.length;p<this.passes.length;p++){var y=this.passes[p];y&&(y.colorBuffer.destroy(),y.positionBuffer.destroy(),y.dashTexture.destroy())}this.passes.length=t.length}for(var m=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&m.push(this.passes[x]);return this.passes=m,this}},g.prototype.destroy=function(){return 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Uint8Array(1020),width:255,height:1,type:\"uint8\",format:\"rgba\",wrapS:\"clamp\",wrapT:\"clamp\",mag:\"nearest\",min:\"nearest\"}),c(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return 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vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\\n\"]),this.drawMarker=t(s);var l=c({},o);l.frag=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=f([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\n// `invariant` effectively turns off optimizations for the position.\\n// We need this because -fast-math on M1 Macs is re-ordering\\n// floating point operations in a way that causes floating point\\n// precision limits to put points in the wrong locations.\\ninvariant gl_Position;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),v&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}x.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},x.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},x.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},x.prototype.drawItem=function(t,e){var r,n=this.groups,o=n[t];if(\"number\"==typeof e&&(t=e,o=n[e],e=null),o&&o.count&&o.opacity){o.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,o,e));for(var s=[],l=1;l<o.activation.length;l++)o.activation[l]&&(!0===o.activation[l]||o.activation[l].data.length)&&s.push.apply(s,function(t){if(Array.isArray(t))return a(t)}(r=this.getMarkerDrawOptions(l,o,e))||function(t){if(\"undefined\"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t[\"@@iterator\"])return Array.from(t)}(r)||i(r)||function(){throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\")}());s.length&&this.drawMarker(s)}},x.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,u=e.count;if(this.regl,!a)return r?[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var f=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(u),v=0;v<r.length;v++){var g=r[v];d[g]=p?p[g]:1}l.subdata(d)}for(var y=h.length;y--;){var m=n(h[y],2),x=m[0],b=m[1];f.push(c({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return f},x.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,l=this.maxSize,f=this.maxColors,v=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=h(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=c({},x.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var m=0,b=0;if(p(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=x.defaults.size),m+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=x.defaults.borderSize),m+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=x.defaults.color),e=t.updateColor(e),b++,e},borderColor:function(e,r){return null==e&&(e=x.defaults.borderColor),e=t.updateColor(e),b++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,l=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=d(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?s(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=g.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:g.fract32(t,p),usage:\"dynamic\"}),l({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var u=t.addMarker(e[s]);a[u]||(a[u]=new Uint8Array(r.count)),a[u][s]=1}for(var c=0;c<a.length;c++)if(a[c]){var f={data:a[c],type:\"uint8\",usage:\"static\"};i[c]?i[c](f):i[c]=o.buffer(f),i[c].data=a[c]}}else i[t.addMarker(e)]=!0;return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=g.fract(e.scale),e.translateFract=g.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),m){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/l),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/l);A({data:M,usage:\"dynamic\"})}if(b){var E,L=n,C=L.count,O=L.color,P=L.borderColor,I=L.colorBuffer;if(t.tooManyColors){if(O.length||P.length){E=new Uint8Array(8*C);for(var D=0;D<C;D++){var z=O[D];E[8*D]=v[4*z],E[8*D+1]=v[4*z+1],E[8*D+2]=v[4*z+2],E[8*D+3]=v[4*z+3];var R=P[D];E[8*D+4]=v[4*R],E[8*D+5]=v[4*R+1],E[8*D+6]=v[4*R+2],E[8*D+7]=v[4*R+3]}}}else if(O.length||P.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=O[F]&&(E[4*F]=O[F]%f,E[4*F+1]=Math.floor(O[F]/f)),null!=P[F]&&(E[4*F+2]=P[F]%f,E[4*F+3]=Math.floor(P[F]/f))}I({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},x.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},x.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var s=0;s<t.length;s+=4)a.push(t.slice(s,s+4));else for(var u=0;u<t.length;u+=4)a.push(t.subarray(u,u+4));t=a}for(var c=0;c<t.length;c++){var f=t[c];f=o(f,\"uint8\");var h=l(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[c]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},x.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},x.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var b=r(50896);t.exports=function(t,e){var r=new m(t,e),n=r.render.bind(r);return b(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},55795:function(t,e,r){\"use strict\";var n=r(38540),i=r(55616),a=r(76752),o=r(3951),s=r(67752),l=r(51160),u=r(47520);function c(t,e){if(!(this instanceof c))return new c(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x,s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y,l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}t.exports=c,c.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},c.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,u=0;u<l.length;u++)i.push(this.passes[l[u]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},c.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values 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Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(u(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var c=0;c<s.columns;c++)s.bounds[c]=a(o.data[c],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var v=s.columns,g=s.count,y=s.viewport.width,m=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=y/v,w=m/v;s.passes=[];for(var T=0;T<v;T++)for(var k=0;k<v;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:g,stride:v},y:{buffer:s.buffer,offset:T,count:g,stride:v}}:M.positions={x:{buffer:s.buffer,offset:k*g,count:g},y:{buffer:s.buffer,offset:T*g,count:g}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],O=E[2],P=E[3];M.viewport=[x+L*y+S[0],b+C*m+S[1],x+O*y-S[2],b+P*m-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},c.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var u=e[i],c=this.traces[i],f=c.passes,h=c.passOffset;f=f.map((function(t,e){n[h+e]=u}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},c.prototype.destroy=function(){return this.traces.forEach((function(t){t.buffer&&t.buffer.destroy&&t.buffer.destroy()})),this.traces=null,this.passes=null,this.scatter.destroy(),this}},28624:function(t){t.exports=function(){function t(t,e){this.id=W++,this.type=t,this.data=e}function e(t){if(0===t.length)return[];var r=t.charAt(0),n=t.charAt(t.length-1);if(1<t.length&&r===n&&('\"'===r||\"'\"===r))return['\"'+t.substr(1,t.length-2).replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];if(r=/\\[(false|true|null|\\d+|'[^']*'|\"[^\"]*\")\\]/.exec(t))return e(t.substr(0,r.index)).concat(e(r[1])).concat(e(t.substr(r.index+r[0].length)));if(1===(r=t.split(\".\")).length)return['\"'+t.replace(/\\\\/g,\"\\\\\\\\\").replace(/\"/g,'\\\\\"')+'\"'];for(t=[],n=0;n<r.length;++n)t=t.concat(e(r[n]));return t}function r(t){return\"[\"+e(t).join(\"][\")+\"]\"}function n(t){return\"string\"==typeof t?t.split():t}function i(t){return\"string\"==typeof t?document.querySelector(t):t}function a(t){var e,r,a,o,s=t||{};t={};var 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},65168:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},2084:function(t,e,r){var n=r(38700),i=r(50896);function 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return 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e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},24747:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},65616:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return 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o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},73040:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var u=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(u)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(u,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var u=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,u)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 0===this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},4592:function(t,e,r){var n=r(38700),i=r(50896),a=n.instance();function o(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}o.prototype=new 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i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},45348:function(t,e,r){var n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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n=r(50896);function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day(),\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate);t=n.year(),e=n.month(),r=n.day(),t<0&&t++,e<3&&(e+=12,t--);var i=Math.floor(t/100),a=2-i+Math.floor(i/4);return Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r+a-1524.5},fromJD:function(t){var e=Math.floor(t+.5),r=Math.floor((e-1867216.25)/36524.25),n=1524+(r=e+1+r-Math.floor(r/4)),i=Math.floor((n-122.1)/365.25),a=Math.floor(365.25*i),o=Math.floor((n-a)/30.6001),s=n-a-Math.floor(30.6001*o),l=o-(o>13.5?13:1),u=i-(l>2.5?4716:4715);return u<=0&&u--,this.newDate(u,l,s)},toJSDate:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),i=new Date(n.year(),n.month()-1,n.day());return i.setHours(0),i.setMinutes(0),i.setSeconds(0),i.setMilliseconds(0),i.setHours(i.getHours()>12?i.getHours()+2:0),i},fromJSDate:function(t){return this.newDate(t.getFullYear(),t.getMonth()+1,t.getDate())}});var u=t.exports=new i;u.cdate=a,u.baseCalendar=s,u.calendars.gregorian=l},15168:function(t,e,r){var n=r(50896),i=r(38700);n(i.regionalOptions[\"\"],{invalidArguments:\"Invalid arguments\",invalidFormat:\"Cannot format a date from another calendar\",missingNumberAt:\"Missing number at position {0}\",unknownNameAt:\"Unknown name at position {0}\",unexpectedLiteralAt:\"Unexpected literal at position {0}\",unexpectedText:\"Additional text found at end\"}),i.local=i.regionalOptions[\"\"],n(i.cdate.prototype,{formatDate:function(t,e){return\"string\"!=typeof 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@@ -62352,19 +62170,12 @@
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   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
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-     "shell.execute_reply": "2026-02-11T21:07:21.946782Z"
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-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [
     {
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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 2 Axes>"
       ]
@@ -62377,7 +62188,7 @@
     "# Visualize the results\n",
     "ta_new_filter.plot_degradation_summary('sensor', summary_title='Sensor-based degradation results',\n",
     "                                   scatter_ymin=0.5, scatter_ymax=1.1,\n",
-    "                                   hist_xmin=-30, hist_xmax=45);\n",
+    "                                   hist_xmin=-30, hist_xmax=45)\n",
     "plt.show()"
    ]
   },
@@ -62391,15 +62202,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:07:21.951059Z",
-     "iopub.status.busy": "2026-02-11T21:07:21.950061Z",
-     "iopub.status.idle": "2026-02-11T21:07:21.954565Z",
-     "shell.execute_reply": "2026-02-11T21:07:21.954565Z"
-    }
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def filter_stuck_values(pandas_series):\n",
@@ -62417,15 +62221,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:07:21.956579Z",
-     "iopub.status.busy": "2026-02-11T21:07:21.956579Z",
-     "iopub.status.idle": "2026-02-11T21:07:39.084657Z",
-     "shell.execute_reply": "2026-02-11T21:07:39.084657Z"
-    }
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Instantiate a new instance of TrendAnalysis\n",
@@ -62439,15 +62236,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:07:39.089664Z",
-     "iopub.status.busy": "2026-02-11T21:07:39.088667Z",
-     "iopub.status.idle": "2026-02-11T21:07:39.489161Z",
-     "shell.execute_reply": "2026-02-11T21:07:39.488155Z"
-    }
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [],
    "source": [
     "stuck_filter =  (\n",
@@ -62465,19 +62255,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:07:39.492773Z",
-     "iopub.status.busy": "2026-02-11T21:07:39.491741Z",
-     "iopub.status.idle": "2026-02-11T21:08:02.616739Z",
-     "shell.execute_reply": "2026-02-11T21:08:02.616739Z"
-    }
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 2 Axes>"
       ]
@@ -62492,7 +62275,7 @@
     "# Visualize the results\n",
     "ta_stuck_filter.plot_degradation_summary('sensor', summary_title='Sensor-based degradation results',\n",
     "                                         scatter_ymin=0.5, scatter_ymax=1.1,\n",
-    "                                         hist_xmin=-30, hist_xmax=45);\n",
+    "                                         hist_xmin=-30, hist_xmax=45)\n",
     "plt.show()"
    ]
   },
@@ -62507,15 +62290,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:08:02.619824Z",
-     "iopub.status.busy": "2026-02-11T21:08:02.619824Z",
-     "iopub.status.idle": "2026-02-11T21:08:02.625093Z",
-     "shell.execute_reply": "2026-02-11T21:08:02.625093Z"
-    }
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -62523,7 +62299,7 @@
        "{'two_way_window_filter': {}, 'ad_hoc_filter': None}"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -62541,19 +62317,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:08:02.628276Z",
-     "iopub.status.busy": "2026-02-11T21:08:02.627285Z",
-     "iopub.status.idle": "2026-02-11T21:08:26.293436Z",
-     "shell.execute_reply": "2026-02-11T21:08:26.293436Z"
-    }
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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pgEnWllPpGd4+SPIslRkYFWjx5ldlQ81NvYy9YpCx3L17NwRBUFmWm5uLBw8eAIDUh/jo0SMAQI0aNVC1alWjxfj5+ZU6UfI///yDGjVqGH1cpnxQbuprEDIMSF6KOyHD4G9tYUy5GEVd6DLUDGNXmDKI89y5c/Tcc8/R9OnT6cGDB9LyBw8e0LRp08jf35/Onz9v9HHHjRtHTk5OtGXLFo11mzdvJicnJxo7dqwpku0GcyUlOLL+S0qL8ae0GH86sv5LM6kr/XzXPmlI06aNo45f7rb4+UzhyPov6dbMRuVyPxiGsQ8MrXNNCvB56aWXEBQUhB9++EHr+hEjRuDatWtG9y9mZmaiXbt2OH/+PJo1a4YmTZoAAM6ePYtTp04hJCQEBw8eNGkMp71gjgCfxA1xaHVuNhyEkkf7hBxwPGwqIvtbrjk0fWYAvJGFYhKQ4DsOb74302LnMpXbsY3hTym4LfjBP+aCtDzpq9fRLGsfTnl0RKsJm6yo0DQSN8Shwfn5AIBLoWMt+pwrO3yvKx4WDfA5cuQIIiIidK5v0aKFzojW0vD09MSRI0fw8ccf48mTJ0hISEBCQgKePHmCGTNmIDExsUIbSnPR4Px8yVASAU5CMZ5LNqz/WAzM+fezVngS442kr1436twOAqFP6nybDOy5EzIMtwU/3AkZprI8Ims3nIRiRGTttpKystHg/Hx4IwveyNL6nJO+eh3FMV54HFNLei62HHRT3sFh+s6X9NXr0rvwXPJS6V6Hnfu/Uo/BQW4VC5M8y4CAALRo0QK//vqr1vWvvvoqTp48Kc3swRiOOTxL0csTnyxBwHGPzqV6TYkb4vBc8lJ4KR7BTcgHESAIJV6pU2y6zv1Ekr56HRFZuyGgZL8ccoFb7D2T9JeGJcYFUownBKHkw+IxXOCIIjhBgRMenYz2NHV5qeL9vRMyTKc3orwNAIM9mPwYH7gIRSAC/vF4UUNzcUw1OAglU+gVkwxJYdMw5UYrmx0yIrYAEAGXHBug4Ywk/TuVAeVWkaSw6dK9Vi/TRIACAmQg6e+jYR8jsv8krZp1tWQw5sWQd6s0LOpZjho1Clu3bsVrr72Gv/76C9evX8f169exc+dO9OrVCzt27MC7775ryqEZM3DVvZVk7AQBkAmEoOzSK5wm5/4P/pSCqsgHUDIlSzHJcMqjo0HnDMpOguzp+QCgKvKRPjNA71e1sR6OriEQ2vj3s1agGE8UxPggcUMcEjfEofgTL1CMp/QriPFBITlI98tNyIeLUAQHQYFmWfsM0qRMs6x9cBKK0TJrt4pXLkYH6/LwEzfEofW5WfCnFISd+z+0Ojtb8mCanfu81HM6oRhAif6IrD2a91zpe9hBUCDi7BwMbx+EBc4LsDPnjVJbD5K+eh2KGE8oYjzxOKYWkr56HekzA5ATU6vU5yt6VeL2+TE+KscprVzkytyk59Gg6FK5eWYOAknPJ3FDHFpm7VYp04JQso3y323OzULKzPqorUhR0Xw7tjFyZW5QEFBbkar1HiduiEP6zACD3hNt++rzWtW3Kcv5DKWsLRbGeuOJG+IQcW5uqe+WuTA5KcGMGTMQFxeHJ0+eqCx3dHTExIkTpVR4xpKcnIzly5fj6tWrePToEdTlmTKptD1hbs9SfLEVBGQIHjq9FEWMJ2SqAc/IJ0e4xGpOwq1O0levo2XWbqgFTJdogQdyhao6v/oiPvsTL+fvwAiHbbjXZITeL8P18TMRmfoTEn0H6O0XFT1GAFCQgAzBHd7I0txO6T4pLwOAAjgiV3A1uH/q0cza8KLHkucBlHgajxq9WaqnKD6zEq2QvBlJX2ymznOq3391r175PojHu+TYAA2KLpXaepC4IQ5tzs1S2beYBKmJX/xb2RsTUfZ2tZULXV4wADyJ8YaTUCz9be5WCnVPJHFDHJqd+xxyFIEAXHZsgOCiyypdGer3T9ff4jOX7uvT61D2Qp9dZzU4PfX4td2P0lpR9HmtyuVQQQBBBgEK6R0Xz1cc8EKZvDJ1neLHrKktFuJ1FZOATMG91PdOudyXJS7D4kkJPvvsM9y+fRtr1qzBnDlzMGfOHKxZswZ37twx2VCuWrUKTZs2xbfffovLly9DoVCASqYRk34KhcJUyZWOx3BBDrmACJAJgDey0PrcbK1fbeK7r/xtIkeRQedpkbVXpbJQ/rlTTqlffQIEfOL4I+rK7qHNuVl6+0jb3l+HukIq2t5fp1dXgdLIKJlAcKVcaPs0VDZs4jWIXrmLUCR5d8peKqD9K1qsoMTjip5Gm3OzUI1KPMU252aV6mEpN/sRAblUetrI4oAXoHxZVZGvctxLjg2eNckreT/iOR4K3lq/5hucn69hBGQglXvlIBDanJuFfz9rpbKvWHa0GUpxeUTWbq394qc8OpZU8KT9esqKNi9fjqKnrTAl90b5g0Cbdm3lSLncEJVch7LxVO7jBABHPKvLxFYBZZYcuIq2Gb+i3Y6uKt5hTkwt1FakIp8c4UqPtd4b5XIoE0paFJQfhXj/W52bDX9KkT7kTEE0kDf++BY/Pn4XI6vsMWqYkHKchOihOwgEb2Shybn/U2rd8JLKiuj5K99rSwdb2VS6u+DgYHh7e2PHjh2lJieoyJgrGlb8WpSSAyihIOCY2lfuv5+1UqlAAcP7i3JiasFNyJeOreyhiscqhCNOhn2kUaBXH7mBt3eESy92McngEPtI57mSvnodLbL2IB9ynA2bWOoLou5xlXxhl3hGYsWm/q82lCs88TgZgge+F97E4rzOKl/R2jz00lCQgGNh0wFAxYvT5q3o8sTEr3Fl1L0xbV6i8vWJ90D5HMrerj6IgFShBooFR6S6hehsadB23mIS4BCbUeo1mdrvp/wuVLu4Hg2KLqEIMmQLbrjq3gpNsvZLhlJdWxFkcIRCb/lQvx7x//94vIiIrD2QKXmoyvdXvawo93eq95eK16/8TMTzpcMD3jNvqWpRa03QpVG55eWYlhYCQ1h95AZu/PEtJiuWwUkoNuhZic8l1S0ELbL2qLyT6noBVc25cEGB4KxSNktrqdCHXaa7u3v3LoYOHVppDaW5iOw/Cf4xFxDZfxLuhAxDMamWQJmWr9yGM5Lwj8eLeEIOuOTYAHdkfnjU6E2DzncubKJ0jly4IJ8cVQq5IAByoUjDq03cEIdOf3RT8YryULoX5ZuTDAeBUFXI1/s17JuTrPLyCYBWQ6mMskcDqHoLIqKXPkqxXiNV3HGPF6EgzePoQvbUM2ty9ksQVCsH0bMUz68rWvdOyDCkwwNPSKbTG4vsPwn/eLyo07MW/1U+x6XQsRplR7wn2o7hSw/gTylolrVPp/el7bwyECjGE8UxXiiOqYakr17HnZBhUDzdX0HQiGA2lAbn58OfUtDq3BzpY9BJUKAaZaFF1h64CKqGUtRc4ocJKvdGeRvle618nQqlZ9Ysax9kAqk805ZZuyUv/Lja8xA9/oIYH7TI2iv1lxIBqW4hAAA5FWrcv2qUhUcza6t46Y/hovV+qLwPKmWa0OrcHJM8+IFRgXiP1sNJKEYxCdKzEmMG1Fsdkr56HW2e9s+3zNqt14tX/7uqkI9qlKVy/8X7bUlsyliGh4fj7t271pZRIRj70wnUn7Yda4r/g6Sw6VLFI1IFBRr7tJqwCU6x6XBVlN50qo1MwR3p8MC5sIkogqOGVwSUvJDKBk7Z6xUrmSdC6fOVKleizkoVh65txWZo4FnFs43a4iZ8odwwRSj5es8QPDS+vNUr/WcGKRd7J3VW6U9qNWETZLGZkMVmovBpM7AhRsNVKNTwMgqV7iNQYuy1VWaR/SfBe+YtHA+bJn14CAIQcW6OynbFAS+oXrMeXZH9J+GER2et2+n6GFAQcM0xCLcFP6nClgwQCcghF42yKH4MOAgEB0GBlk8N9rGwj5EODxRDJjVd75vTy6QAEjEaWPmcMqgKUSmrIDigWEl7iX4AeCR4YP3LJ/GY5ACAoqeGUxBKWhzEj857VE2jDIkGESgpK0fDPla5H+KHpQwKlX3EAL0CQfNjUhBKml2VA8uUP2CJND8AtV23g6AwqTk2cUMcvKjEyxOU7qn4cSJer4j4MSVq1+Y9KmvW5m0qN3crN8VaEpsyll9//TWWLl2Kw4cPW1uK3bP9TAqKFITtZ0qMUYbgIb3sRCWFWttXH6B7PKIuxLFnuUJVRPafhCp4ZsTU61NlA3cnZBiekEPJFzSevfTK/YLqRPafhAyhpKmkUHAuNcIvsv8kuMXe0/ComuMiBlf9Hklh05FDLigmGY57vAj/mAu4FDoW6fBADmn/Mlf2RuUo0jincjTfybCP8IQcVCoEXQZK3EbsXxQEwAlFGl56aZGxzyUvhUzpXMp9YuJ6BzVPR11XoVoGTNE7LyZBZb8MwUPSqrxc7POroXiACx4v4Lbgh388XsRtwQ/HwqbDLfae9PzUK3Hle/Fc8lJE9p8EZyqEk6CQjt2uYL/B0dAA8NChls5KV/3vR4KHyseGsmd33ONFHAubjtuCHy6FjsWSA1cxt/htXFfUQpxDiWevfM+chGLUEh5JwSfqZVAsN5H9J+FY2MfSu6msTVlfNcpCysz6AKBSJtSvRfRe25ybhXxyku7/Y7hAQQLyyfHp/0u2V29dqUZZWuuE0hDLHVByz1qdm430mQEq775y/7xyX66ydlEPabl+5fXaPmYL4YjigBeM0m0sNtVn2atXL1y6dAn//vsvQkNDUadOHTg4qHoagiBgy5YtVlJoecw1RdfYn05g+5kUvNLUD5P/HQB/SkEOuUCOJ3BEsWqBKyXS0hDUowvFfsUncIADFFK/DwA8UchQIDijCgpxwqMTigNeQMS5uSrRj0DpfVTKYxmDspOkvovS9lHuwyEC1rx8utRxmurj5sToyGJ6Ns4OAB6TC86GTdToIxa1KPfN+OYkI9UtBI2zDsMVBRCUjgM87e+NzVQZ93lH5qfSd0ekGVWp/hz8FKlwEAhPSAYnpf5fcX0NxQPIhSIoSEAu5CrjasU+IbE/WPnZOtw6LPVFKveT6YqGfqKQwenTR1BHeXv1Pm7xPnxSNBRZTQbjmwudpaZM0Wg3KFyDKk4OmPpKSKnPUL2fVv0jQb2SPhr2sco1Km+vXrbECNDs/CKkPS7EO867EC1sQ6LvANT1qQq/80twWhGMcNkV/F1rAOJzOmJ3Tm/pWsX7Jx4n9sk8dCjYp9VAKGssuWcCZAJpRE1r3T42U+rnFCPT/SlF+n+qWwiaZe6Fo6BQuU93ZH4GR8gmbohD2Ln/Q1Xkl9q/q3wPtUVoK8cEiB99hGf/12c8Te3Xtss+y9OnTyM/Px916tRBTk4Ozp8/jzNnzmj8GP3MH9ACl+e8gvkDWkieYqHgrGGUAKAgxgdJX71ucrYR5T5SoKR5ySE2Aw9lNSSvQMRRUMBNyJea2xxuHUa2UFWluZSoZKydNhI3xEljGX1zkuFOOdI+pXnCyl5QqlBDb0ID8Z4dDfu4JOAibDrS4YE8yFW+mF2Rj1bn5kjN1upe+aU6/THQNR4X2n4D/5gLaDVhE9xi70EWm4GjYR9LzZJEJUMWgGdRvAVwxJ2QYRrepa6o4Ut1+mMlXkUG3FFMAhyhkLyR1UduYMqNVtjb7Q88FlwBABmCO86FTVTxfsU+IbFPW/nZis2GN+GL74U3pabQVhM2SZqVP70dBe2R66K3+oQccNmxAYpJwBOSQUECHpMLZhQNxeri/2Dr6bvIhVy6bqCkeXSe4wI8LizW610+l7xUo4+OUHLeXHKWyoOCBPzj8aJ0jaJ3rVwJq5etgVGB2DupM8Z3aYi6Pq741elldCr4GvE5JVGZq4WeCMNlrBZ6Ij6nI9pm/Ipckj9rwqdcAM8iSQMLLurt55W8x6clMEPwwNGwj7V6moBmqw4pCIdqviV5x3u7/YGJirEqY0lF/CmlxDuN8dE7LjOy/yScC5uooVPl3ATUVqRIx0kVaqg0t4pjnYGSjzXx3TuuFEchtvjkUElcRDHJSoLKnpYbU/u1DcWmPEvGfJ6ltjFaopdQS3FPGt8lUlpknamIX5yuKABAGn1yYoUpRtDVVqSqRA5qi6AVPT5xXJXoOUgvGjmj6qcPzKJfHeUITW2eCQBkCFVRbeazfnddkysrP59Of3TT6o0qf9mL4xZFRC9UmU5xe7A8exTqye6peIr/eLyIiYqxko4vApM0WgKaZe2DA4o1xuFpiy7Udk2i5ucUKaoegpZWC23R2uL4zWISEKt4Bz8++Q9cnWVY3uSsNAYSeFZmohzXY3yXhno9S7H8CUpZd/RFTYoJImTCs7HGpY15XH3kBr7eeRECBIzv0hAA0G57F9SV3cNN+GJ/9z/RbkdX1BVSVTxkh9gM6bjvue1D2/vrkOoWgqDsJHhRlopHpcsTFsuHGJGu/FElRkQnbohDrXNL8EPRKzjk1Ut6ZuJzPOv8DtxkBdJxtRk7ffWCckuMLo8R0By7rVwWrqc91hhDLR73Ovni4Mt/WmTmHLN6lvXq1UNQUJBRv+DgYLNdTFkpKCjAlClTULt2bVSpUgWRkZHYuXOn3v3q1q0LQRC0/ho0aKCyra7tPv+89OwrlkJbphvRS8gWSrw29WgyS5Ahq4ZjYdO1Rv4RATmCi/TlLnoR4nq5UKQRZCR6buIA5AIlL0AMlHkSU80iGUrEqNN0eEhfxsp6xT5X0ZsDdE+urPx81L1RdU8dAE6FfaR6PmgG+wxvH4S1Dr2QRqp9by2zdqM/dsJRJiDc30vj+GIfG5SCfwRBd/SttmsSj/mPFA0s4LjHi1r3V4/Wvk6+yHv67B0EwjvCNvhUdca0V0IR2X8SXGLTcEfmJ5WZa45B+GdGF70Vp9hnfSxsOhRP+1wFoSTApLQAocj+k7DRdzyuky+2+I4B8Ox5fb3zokaA0ZIDV5H++AncXRwxMCoQSw5cxQ/FPXCdfJESOhwDowJxL2w4bgt+Urm54lhSRkQP9c33ZkotD94zb+FY2McqQVLqXic91SlS+DTw55HggUdKffri9Rx8+U8c8uql8szE57jNbzTSURLToKseEFtwdCGWYfUWBgJUApjU+/mVx7zG53SUvHPl414nX/xQ9IrB/dSWwiDPcsiQIVCfzzIpKQnnzp1DaGgoGjVqBAC4ePEizp8/jyZNmqBly5ZYvny5ycKys7ORmZmpNQlBnTp1jDrWgAEDkJCQgHHjxqFBgwZYsWIFjh07hj179qBdu3Y699u8eTNyclQLyY0bN/Dxxx9j9OjR+O6776TlgiCgS5cuGDx4sMr2LVq0QFhYmMFaLelZiohfc7UVKRrenq7+MFNQzjIClDTtiP0swLMKQDynciYVEUO8AG3jB83pIetCeXwpoOYF6OkHNiXHrfq4R23jEwHgekwjFS8GANIU7mhZuEhrZhXlflXlvkdzlwddiOP0RhWvAwQB84r6qnhAokaxb9vYvin1FoFfFS9gnsfkUjPMqHvP6n2UyvdR+VkCkLzMydUPoe39dSotBMotIzOLonHG9w38OkZ3HaQ8y0lVyoX8aevCY3JBVbVxtMp5hU3JyqMt85cIUclwGn3jm9XLaA654FzYRJV3VHlMp7LuS3X6a30nLJEPWhlD61yTmmE3b96Md955BwkJCXjppZdU1u3cuRP9+/fHihUr8NprrxktPD4+Hl9//TWuXtX9FVFcrNnvpoujR48iMjIScXFxmDixpF09Pz8fTZo0Qc2aNY2OvJ01axZmzJiBQ4cO4YUXnkVfCYKA999/HwsWLDDqeOqYy1iKlJZkWFui6LIG++g6NwCNpjcR5SQJ6oPRDWk2U78Ocb9UoQb8Zl42i35tlYOuwBZz30dlPeqBFEfDPtaoZMSUgAG4J41hU5CAz2XDUafbmFIrHM1EDqYPVjcWsTmz4IkCLk4OGs2syoFjxqQgVDY4O2sNQ3xOR70Vr64KWl/FLRrZBc4L0EM4rBEcpGz0rylqoXPhPFz/vIdB98eYhBymIJb3XJkb6hddUqkXgGfvVmkfookb4hBxdg4cBQWKIEOcbBjqdBuDNjvfkIaSAIan0tSHOQypRY1leHg4evfujU8//VTr+hkzZmDz5s1GB+N8//33GD16NLp164YOHTpg+vTpGD9+PFxcXLBixQrUqlULY8eOxZAhQww+5uTJk/H1118jPT1d5UbMnTsX06ZNw82bNxEQEGDw8UJDQ5Gfn69hzEVjGRcXB0EQ4OKifeiBPsxtLJW9u73d/tBasIpjvKT+Im2eirlRn28TgEp/nfiyiS+WobqUsxABhhna0tCVf1N8QX98/C7qIFUjytKSM2Wof/2LRlA9kxDw7LlK1/P0OvRVMOoGs7xnzdDVz6v+IVUerQfGIt7bnTlvwEko1vDORcOtIGBeUV+9nqUy5TWLiWg0qykeoerTlhPVaFXDPqDUn6N6RLo5Pih1lRVjsGg07KVLl+Dj46NzvY+PD65cuWL0cb/99lt069YNO3bswMiRIwEAPXr0wOzZs3H+/HlkZ2cjLc24r5ETJ06gYcOGGjehTZs2AICTJ08adazk5GT897//1bp+xYoVqFq1KqpUqYLQ0FCsXbtW7zELCgqQlZWl8jMnyv1hYr/LzF/PqfS5JD0dP5b0NO2aJRGjMg/KO0hGRjlDS2T/SXD69JFKJJ8MZNC8mg1nJKnsV1q/myGI9+5QzbdU+qnE+7hK6ClFkj4SPFSiZy2NFB0qED5S/KA1H6dyQgHlaGF9M7e0mrAJuU/7yhRKGVnKi+Htg+Bd1QnZ+UUq5VTsMxb7wFyfRpTaEmIf5CmPjtL4SmWjIo5JzpdVxezZ8ww2lIDx459NRWz5KRCckQ4P5JPq2NuSbD/683+r921nCFVV+l2NHc9pyDksiUnGMjg4GMuXL9fozwNK+hqXLVuGoCDjxV+5cgU9e/YEADg5OQEACgtLBrF7enpi+PDhWLhwoVHHTElJgZ+fn8ZycZkxGYPWrFkDAHj77bc11r3wwguYPXs2Nm/ejPj4eDg4OODtt99GfHx8qcecO3cuPD09pZ8xXq4+lIcLRPafhOHtg+AoE1CkIK2BP+XR1CZW1HXyL0iRjep5atURg1QMCdrRCCSC9qw3hiDel/icjirGRXxBA7uNwfGwqVIofnncQ+UUdOJ1ygRgrGKVRnPhRMVY3BWqS03SojZDKpizYRNLhvNAgMOt8k0SMjAqEB4uTkh7XKhRTpU9STmKjJ6cvLwQs2Gpt2qUxeCZ+z3VNhFA4oY4uNJjpKNkhiLvmbfgBIVGV4N69iNtiB8OYrl8rJQdSxA0M/uYgvo5LIlJxnLWrFk4e/YsGjdujI8//hgrVqzAihUrMH36dISEhCA5ORmzZs0y+rienp4oKirpwPbw8ICrqytu3Xr2cri7uyM1NdWoY+bl5UEul2ssF5tJ8/LyDDqOQqHAunXr0KJFC4SEhGisP3ToED788EP06tUL7777Lv755x80adIE06ZNK/UcU6dORWZmpvRTvt6you5BDIwKxMxeYeX2JaYN0WAveRotqGtanTy1VGmCAIPS76mnEBOEkrk6y6pZ+Z4pv6Dl+aEBlFSYSWHTNTIDuaqlLxSffU0qySJTnZ5NwWVIBRPZfxLkeGLUvJ7GzkVY2tyHpRl05QAxQz+irIV4jWN/OoFOcXtwqU7/ci0vpaGthUE9GxfwLF+zeopDQ+YmVUY9/aS9jVk0yVj27t0b27dvR40aNTBnzhwMHToUQ4cOxdy5c1GzZk1s3boVvXv3Nvq4TZo0walTp6S/o6KiEB8fjzt37uDWrVtYtGgRGjZsaNQxq1SpgoICzTyo+fn50npD2LdvH+7cuaPVq9SGs7MzPvjgA2RkZOCff/7RuZ1cLoeHh4fKz1xoq3DK80tMG6LBPuTVCwdf/lNnpXE2bCJuC37SC0UEPKdIMajpRkwhJr6ULlry4OpDuSK39j1TJ7L/JBwPm6rSj6Q87AZ49uwPy9vhCTmYlDdTbEo0dF99E1yrU1pzcGn3XHnKMUFAmaaXsjTiNW4/k6K1C8SaaKsftHm+556+ixlqnqHsaQIL8Xnrm/hZHMojNvHn6Uj2bqs46t9EO127dkXXrl2RmpqKGzdKbk5gYCB8fX1NFjNw4EB8//33KCgogFwuR2xsLP7zn/9IQ0WcnJzw888/G3VMPz8/3LlzR2N5SkpJoEDt2rUNOs6aNWsgk8kwYMAAg88tNqmmp2tOrFseDIwK1FrZWDoU21RdypQY0Ul48kk1yJSCfQxtuikZbH9YSotnbO+IckVuK0ZSnQI4Qk5FKIJMypMrfnw8u8clQQ+m9A6JTYiG7nsnZBjwNHrY34DtxcmCjW3laDgjCYoYT8nD1JdQv7xRfr/Eawz395LyNS85cBUNbm4o86TLZUXbeyi+d+LzW33kBpbcaIXh3fqjwc0NaHB+PqpRlkqgjgOKcDu2MW7iVVzP66z3nZHSQ4YOQ6RlLs0i2HwGn6tXr+K3336Dg4MDunbtarRnOWnSJMybN08jGnbOnDmYPn26QdGwBQUF8PX1RUREBHbt2mXwuRcsWIAxY8bg8OHDeP755w3ax9zRsNowRwSZpVDPhjJgR3OVGSN0ZYWxhA5rflDoQ3m8XrZQFd7IKveoVWuiiPGSsj2V5/AWQzAla5MtoK3Ma7sWXXld8xWOaOu0Tm9mJVvD4rlhb968iXfffReNGjWCt7c39u/fDwB4+PAhxo4dixMnTph6aBWCgoLw4Ycf4oMPPjDaUAJA3759UVxcjMWLF0vLCgoKsHz5ckRGRkqG8ubNm7hwQXuh3b59OzIyMnQ2wT54oJleLTs7G9988w2qV6+Oli1bGq3bUqw+cgNZ+U/gU9XZav2WpSFmQxGDO054dNII2DGmn8RUbK3pVR1xxhYnoRjOigI8IQdpzkNAf5OYvXNcKdJXfeo3a6Orv1W5TJVXZKsxaGsW13YtytmrlLsC5EKRlMWoImJSM+z58+fRvn17KBQKREZG4vLly1JgTvXq1XHw4EE8fvwYS5caPh+iOgqFApmZmdDm+Hp7ext8nMjISPTr1w9Tp07F/fv3Ub9+faxcuRLXr19X0Td48GDs27dP6/nWrFkDuVyON954Q+s5vvvuO2zevBk9e/ZEnTp1kJKSgmXLluHmzZtYtWoVnJ1Ln9C4PBGNUV0fV5ss1MPbB0meZbi/FybeHotYebE0I4MglExsXBK0YxuehDUoyX4C1D/7/+AlPIaDoIBvTrK0vrRmZFv3mg2hOOAFPDr/LIeqOIVVWZJQmAtjuhlUmjut/Ey0NYtruxa/mZelBAnKUbFFkNnkB7i5MMlYTp48GV5eXjhy5AgEQUDNmjVV1vfo0QPr1683+rhPnjzBF198gWXLluHWrVtaU90BxmXwAYAff/wRM2bMwKpVq/Do0SOEh4dj69at6NChg959s7KysG3bNvTo0QOenp5at2nbti0OHz6MJUuWIC0tDVWrVkWbNm2wbNkyvPii9vyY1sLUfqLyQvnlFJuAAuXPZmQQv2JNCdqpSIh9SSuFqvARsvGEHHAndBj2Pq10w/29AEDrc7aH/lh9iFGbxRCAp0nSfckyCfTLA1t4JoYYeZFWEzZpJInIFtzstjwZgknGcv/+/fjkk09Qo0YNrUkC6tSpozWoRh+jRo3CypUrERUVhd69e+s0Tsbi4uKCuLg4xMXpbrrbu3ev1uUeHh56h5d06dIFXbp0KYvEcsOYF8LcGPv1HO7vhduP8rDd7Q30yv0Fzynl97ziWB/GN8pXHMTKdXWVnojG1pJgif6TMOXpBwYAnf3Rtv7BBOgvK2IwUap7CCKydj+bJ3JmgMFp8GwJe3gmyiRuiEMDeox8coQzipEHOS6FjbWrgB1jMclYKhQKuLq66lz/4MEDrWMb9bFx40YMGjQIK1asMEUWo4YtNO0oY+zX8+nbGShSEDagC0bHzJEiIAUBcFWUPgtCeWDN+ytWroHtx8A/6v+k5jxDKl31DyZbKyeA/rKi3IxJMSUf1YIAeCPraf+lfRlLa37EmoLo2d+WlQQoVQUqtKEETAzwiYiIwLZt27SuKyoqwrp16xAVFWX0cV1dXU3aj1FFHBx+849vS01rVt4Ym5pKffvjHi+W20SvulAOnNGXNs6S6ApAMiUwyZrXoQtDyopYztWnS6tGWTab2aeiYIsBSpbGpKEjO3bswKuvvoqRI0firbfeQufOnbFmzRopScGBAwewa9cug/oElRk3bhwuX76MrVu3GiupwmCOoSNiX8JN+GJw1e9tymOwdyI++xPpj5/Au6oT/telkc15ZKZgi56lISgnFlefbq68hhiVhr3e18qGRWcdAYBVq1bhww8/lCJWBUEAEcHDwwPx8fFGDd4XKSwsxNChQ5GRkYGhQ4ciICAADg4OGttFRESYItkuKKuxVJ6OyB77bmydlp/tRNrjQvhUdcY/M+yjn1obY386ge1nUvBKUz/MH9Ci3M9vDkOiPIWaw63DGtPNlcdcnKVhy+OZmWcYWueanMFn0KBB6NOnD3bu3IlLly5BoVAgODgY3bp1g7u7u0nHLCgogEKhwI4dO7Bjxw6N9aJRNjYatjIh9iU8Ic2PDKbsjO/S0K4CMXQhZpPZfibFKsbSHNGf6sMvlAfLCwIQZuXhRfYWtMOUjk1l8OnXrx82bdqEN998E5GRkTqjYaOjo8tZWflhDs+y9blZkAkls5S7Kc2mzjAiomcZ6ueBrPwnWj08SzYjGnNsQ7ZdfeQGmm7vjXDhqkoqNkvOLcpUDCzaDBsUFIRatWphxYoVaNSokcb6LVu2YPz48RoTJOvDw8MDw4YNw7x584yVVGEwR59leU/mzFgfUw1baU2F1m5GFK8pO78IaY8LS9Uhah1RZQ+mKX4w2wTgTMXHounurl+/juPHj6NNmzbYvHmzxvqcnBwpuboxeHh4oH79+qZIYp6SuCEOeZCjmGQ44cH9JJUFUyNaS4s6Dff3gqNMkBIclDfiNRFIb2Ss8hyj6rOStMjaazGNFT2tIPMMk3PDfv311+jQoQPeeOMNzJgxwyxiRowYgZ9++on7JMvAc8lL4SbkI0VWi7+mKwFiZR3u72XSPKWlDTURx7mevp1hJrXGIRrA/3VppHc4jPJ1NJyRhCKlqk17HjDzYIvDbhjLYHKAT7Vq1fDbb7/h008/xaefforjx49j7dq1Zcq6Exoaii1btiAiIgLR0dE6o2H79Olj8jkqOrkyN1ARkOvgZm0pTDkgVtZZ+U/g4eJk1mNbO0ClLAP1swU3eCMLAOBgwWmGdd2j0prFlaN4OVrdfjDZWIp88sknaNOmDQYOHIjWrVtj0ybTvZk333xT+v/EiRO1bsPRsKVTr6gkwKFekW1/6dr7GDRb0S9W1tn5RWbPLSoeR/Sa7Ok5XQodKwW6FcLBYtMM6zLo4kfMzF/PSduJiJNkI3kp7C3TUGXG5GZYZbp3745jx46hatWqiIqKwpYtW0w6zp49e/T+du/ebQ7JFRZjZ7e3FvbefGUr+sXmx/FdGprUDKsPW7lOY4nsPwkZQkmwRq6gOzWnpRjePgiOMkGa7FkZW8x+w32v+imzZylSr149/P333xg1ahRWrVoFQXl2UAPIz8/HqVOn0Lx5c6Mz/zDPMHZ2e2th7Sa+smJr+k1psjTEO7a161SntCbNS6Fjn60rZ13KXrn6vVMfH2oN1J+9uWY9sZUWF0tg0tCRffv2ISQkRGNqLpFt27bh4cOHRo+HrFKlCubPn48RI0YYK6nCYI6hIwxjCNYeGmIK6pWxcso7/xjtk7czqqw+cgMzfz2HIgVJz95cRs4ey5RFM/h07Fh6E1+PHj1MOSyaNGmC69evm7QvwzDGYeteozbUPaBUtxDUyrqPVPcQq3pq9sSSA1dRpCA4ygTp2Ztr1hN7LFOGYpCx/PHHHwGUpLgTBEH6uzQEQcCgQYOMEjN79mz897//RefOnfGf//zHqH0ZhjEOe5sWCtCsjH1zkuEkFMM3J9nKyuwH5Xto7udvj2XKUAxqhpXJZBAEAXl5eXB2doZMpj8uyJSo1V69euHChQu4cuUK6tWrh3r16qFKlSoaxzU1gMgeMEe6Ow5LZyoLSV+9jmZZ+3DKoyOPK2ZMwqzNsNeuXQMAODs7q/xtbk6fPg1BEFCnTh0UFxfj8uXLGtsYGzhU2eCwdKaysPrIDbTLOl9hPcuKHCxjjxhkLAMDA0v921xwf2XZuRMyDHjqWXIfjvWw9YrO1vUZwpIDV5Fc9AqGO2zDUb+38Kb+XayCqffaXBGqpmBo8np7L0PGYJZxloztENl/EvxjLnATrJWxhfGJpY2dU9dnj+PshrcPwnrqgs6F8xCfY7vjii2Rt9fSGKLZFsp4eWKQZ/niiy8afWBBELBr1y6j9wNKhqZs27ZNSsYeGBiIHj166I3CZRhbwRaiAkvzTNT1advWljwHbVpKG8toS5haFqwZLGOIZlso4+WJQQE+nTp1MqmvcM+ePUZtX1hYiAEDBmDz5s0gInh5eQEAMjIyIAgCXn/9dfz0009wcjJvDkxbgsdZMuairHNG2tKYOVvSwlQsDK5zyYaYNm0aCYJAkyZNotTUVGn5vXv3aPLkySQIAn388cdGHzc/P58mT55Mfn5+5OLiQm3atKE///xT734xMTEEQOMnl8u1br9kyRJq3LgxyeVyql+/Ps2fP99orZmZmQSAMjMzjd6XsSyr/r5OHb/cTav+vm5tKeWCLV2vLWlhKhaG1rk2ZSzr1q1LQ4YM0bk+OjqaAgMDjT7uW2+9RY6OjjRx4kRatGgRPf/88+To6EgHDhwodT/RWMbHx9OqVauk39q1azW2/f777wkAvfHGG7R48WIaNGgQAaDPP//cKK0V1VjaW2WnTW/HL3dT4JSt1PHL3VZUxjDWwd7eYUMxtM4tc27Y7OxsZGZmQqHQnDWuTp06Rh0rJSUFkZG6szhGRkZi3bp1Rh3z6NGjWLduHeLi4qSZTAYPHowmTZpg8uTJOHz4sN5j9O3bF9WrV9e5Pi8vD9OnT0ePHj2QkJAAoGRuToVCgc8++wwjR45EtWrVjNJd0bBmZJ8paNNb2fpoGEYZe3uHzY3J0bDx8fFo0KABvLy8EBgYKCURUP4Zi7+/P/bu3atz/b59++Dvb9yAiISEBDg4OGDkyJHSMhcXFwwbNgx///03bt26pfcYRISsrCyQju7dPXv2IC0tDaNHj1ZZ/v777+Px48fYtm2bUZorItaM7DMFbXpLmyiZYewNYyOg7e0dNjcmGcvvv/8e77//PurXr49Zs2aBiDBu3Dh89NFH8PX1RbNmzbB06VKjjxsdHY0NGzbg3XffxcWLF1FcXAyFQoGLFy/ivffew8aNGzFkyBCjjnnixAk0bNhQo+O2TZs2AICTJ0/qPUZQUBA8PT3h7u6OgQMH4t69exrnAIBWrVTn+mjZsiVkMpm0vjJjb4bG3vSagr0NF0ncEIfbsY2RuCHO2lIqBMYO/agM70RpmGQsv/32W3Tr1g07duyQPLYePXpg9uzZOH/+PLKzs5GWlmb0cadNm4bBgwdj8eLFCA0NhYuLC+RyOUJDQ7Fo0SIMHjwY06ZNM+qYKSkp8PPz01guLrt7967OfatVq4YPPvgAixYtQkJCAoYPH47169ejffv2yMrKUjmHg4ODxiwszs7O8PHxKfUcBQUFyMrKUvnZO/ZWCVdW7G2spZid6rlk4z/EGU3s2VO0Rlk1qc/yypUreP/99wFAGsZRWFgIAPD09MTw4cOxcOFCTJgwwajjOjg4YMWKFfjf//6H7du3q4yzfOWVVxAeHm601ry8PMjlco3lLi4u0npdfPjhhyp/v/HGG2jTpg3efvttLFy4EB999JF0DDEVoLbzlHaOuXPnIjY2Vu912BOVvW/DXjBkrKW5KcvYTc5OZV7sOem5NeoYkzxLT09PFBUVAQA8PDzg6uqq0vfn7u6O1NRUvceJiIjA77//Lv39448/4vr16wgPD8dHH32E+Ph4xMfH46OPPjLJUAIlc2QWFBRoLM/Pz5fWG8N///tf+Pr64q+//lI5h/ixoO08pZ1j6tSpyMzMlH6G9KHaOvb8xVqZUG9WK4/nZmzTn7IHYWh2Klv3kJmyI5bVcH+vcnvWJhnLJk2a4NSpU9LfUVFRiI+Px507d3Dr1i0sWrQIDRs21Huc06dP4+HDh9Lf77zzjkHRqcbg5+eHlJQUjeXistq1axt9zICAAKSnp6uco7i4GPfv31fZrrCwEGlpaaWeQy6Xw8PDQ+Vn71S2vo2KUjmXx3Mz1iAbalyVn0FlS8NWGRHL6unbGeX2rE0ylgMHDsTZs2cljy02NhbJycmoU6cO6tati4sXL2LWrFl6jxMYGIi//vpLmsqLiMw+q0jz5s3x77//avQFJiYmSuuNgYhw/fp11KhRQ+UcAJCUlKSybVJSEhQKhdHnYOwLrpwNx1iDbKhxFZ/BzF/PIdzfi1s2Kgnl2oplroGdV65coW+++Ya+/fZbunjxokH7fPnllyQIAjk6OpK7uzvJZDKqUqUKubu76/x5eHgYpevIkSMEgOLi4qRl+fn5VL9+fYqMjJSW3bhxg5KTk1X2vX//vsbxvvvuOwJAX3/9tbQsNzeXvL296dVXX1XZduDAgeTq6kppaWkG662oSQkqMhV1sLY9serv6xQ8dRsnjWCMxtA616DcsJbkzz//xJ49e3Dv3j2sWLEC7du3R1BQ6V8Jy5cvN+oc/fv3x6ZNmzB+/HjUr18fK1euxNGjR7Fr1y506NABQEn+23379qmMpXR1dcWbb76Jpk2bwsXFBQcPHsS6devQrFkzHDp0CK6urtK2CxcuxPvvv4++ffuiW7duOHDgAH788UfMnj3bqAhezg3LMKZhS4nfGfuh3HLDFhcXU3p6OqWlpWn8jEUQBFqzZk1ZJWmQl5dHEydOJF9fX5LL5dS6dWv6/fffVbbp2LEjqd+O4cOHU2hoKLm7u5OTkxPVr1+fpkyZQllZWVrPs3jxYmrUqBE5OztTcHAwzZs3jxQKhVFa2bNkzA17vgyjG4t6lk+ePMEXX3yBZcuW4datW1pT3QGQ+iINIS8vD9OmTcOLL76Inj17GiupwsCeJWNuKtKMHew92g4V5VkYWueaNM5y1KhRWLlyJaKiotC7d294enqaLFSkSpUqWLx4McLCwsp8LIZhnlGRctrayxjeimJISkM5qApAhb1OEZOM5caNGzFo0CCsWLHCrGJatmyJs2fPmvWYDFPZMWTwub1U7vZi+O3FqJeF4e2DMPPXcyhSUIW+ThGTho64uroiKirK3FrwzTffYN26dViyZImU9IBhGMtjL8Nf7GUMb2VIzDEwKhAze4VV+OsUManPcty4cbh8+TK2bt1qVjHh4eF4+PAh7t27B7lcjueee04j+40gCCoJESoa3GfJmBNDPUZ78SwZy1IZy4Ghda5JxrKwsBBDhw5FRkYGhg4dioCAADg4OGhsFxERYdRxO3XqZFBSgj179hh1XHuCjSVjTipScA9jeSpjebFogE9BQQEUCgV27NiBHTt2aKynp5l4jImGBVDqXJYMwxiPvfTxMbYBlxfdmORZ9uvXD5s2bcKbb76JyMhIndGw0dHRZRZY2WDPkmHsh8QNcXju6Uwo+hK8M7aJRT3LP/74A2PGjMG8efNMFqiLrKwsLFy4EHv27MH9+/exaNEitGnTBunp6VixYgV69eqF+vXrm/28DMMwxuJ3fgn8kQrF+SUA2FhWZEyKhvXw8LCIwbp9+zZatGiBTz75BLdv38bp06eRk5MDAPD29saiRYvw7bffmv28DMMwprBK6IlrilpYJVTeRCqVBZOM5YgRI/DTTz8Z3Sepj0mTJiE7OxsnT57UyNMKAL1791aZR5JhGKY0LD19WmC3MXjHfRECu42xyPHLg8QNcbgd2xiJG+KsLcWmMakZNjQ0FFu2bEFERASio6N1RsP26dPHqOP++eefGD9+PEJDQ5GWlqaxPigoqEJMjswwTPlg6eQAhiR8sHWeS14Kf0oBkpeCm5J1Y5KxfPPNN6X/T5w4Ues2pkTD5uXlqcwTqU52drZRx2MYpnLD0Z36uRMyDHgapORvbTE2jEnG0lLjHENDQ7F//36MGjVK6/rNmzejRYsWFjk3Yx0q4yBopvyoCJ6fpSmJ4p3EhlIPRhvL/Px8nDp1Cs2bN5fmgjQX48aNQ3R0NMLDw9GvXz8AgEKhwOXLlxEbG4u///4bP//8s1nPyViXypBDk2EY+8ekcZZVqlTB/PnzMWLECLMLmj17NmbOnAkigkKhgEwmAxFBJpNh1qxZmDJlitnPaUtUtnGW7FkyDGNNLJrurnXr1ujatStmz55dJpG6uHnzJn755RdcunQJCoUCwcHB6NOnD4KCKn6/Q2Uzloz14Q8W24Cfg3UwtM41aejI7NmzsWjRIosN48jKykJhYaGUNk+hUCA3N9ci52KYyo69zDhS0eHnYNuYFOCzYMECeHt7o1u3bqhXrx7q1aundXaQLVu2GHXcgoICjBo1CqtWrZKaXoGSfsupU6fi7bffxpIlS+Ds7GyKbIZhtMARo7YBPwfbxqRm2Lp16+qdHUQQBFy9atwX0rhx4zB//nyMHj0aY8aMQXBwMARBwOXLlzF//nzEx8dj7Nix+Oabb4yVbDdwMyzDMEz5YdE+S0tRvXp19OjRAytXrtS6ftCgQdixYwcePnxYzsrKDzaWDMMw5YdF+ywtxZMnTxAVFaVz/QsvvICioqJyVMQwDMMwJvZZiuzbtw/btm3DjRsleRcDAwPRo0cPdOzY0aTjdevWDX/88Qfee+89ret///13dO3a1WS9DMMwDGMKJjXDFhYWYsCAAdi8eTOICF5eXgCAjIwMCIKA119/HT/99BOcnJyMOu7FixfRv39/BAcH4/3335dmNrl06RK+++47XLt2DevXr9dIieft7W3sJdgslaEZlkPkGYaxFQyuc8kEpk2bRoIg0KRJkyg1NVVafu/ePZo8eTIJgkAff/yx0ccVBEH6yWQylZ+u5TKZTO9x8/PzafLkyeTn50cuLi7Upk0b+vPPP/Xu9/PPP1P//v2pXr16VKVKFWrYsCH973//o0ePHmlsGxgYSAA0fqNGjTLqHmRmZhIAyszMNGo/e6Ljl7spcMpW6vjlbmtLYRircGT9l3RrZiM6sv5La0up9Bha55rkWdarVw+dOnXC8uXLta4fMmQI9u7di+vXrxt13JkzZ+qNstVGTExMqesHDBiAhIQEjBs3Dg0aNMCKFStw7Ngx7NmzB+3atdO5X/Xq1VG7dm307t0bderUwZkzZ/D9998jKCgIx48fVxkuU7duXVSrVg0TJkxQOUbDhg3Rpk0bg6+FPUvrUJomW9TL2De3YxvDn1JwW/CDf8wFa8up1FjUs5TL5RQfH69z/cKFC0kul5tyaLOTmJhIACguLk5alpeXR8HBwfT888+Xuu+ePXs0lq1cuZIA0A8//KCyPDAwkHr06FFmvZXBs7RFSvN22RNmzA17lraDoXWuSdGw/v7+2Lt3r871+/btg7+/beSwT0hIgIODA0aOHCktc3FxwbBhw/D333+XOj9mp06dNJa9/vrrAIDk5GSt+xQWFuLx48dlE82UO8PbB6Guj6vWAeGlrauIWHrC5MqAvnsY2X8S/GMuPJ3xg7EHTDKW0dHR2LBhA959911cvHgRxcXFUCgUuHjxIt577z1s3LgRQ4YMMbNU0zhx4gQaNmyo4V6LTaMnT5406nipqakASppo1dm9ezdcXV3h5uaGunXr4v/9v/+n93gFBQXIyspS+THlz8CoQOyd1FlrM2tp6yoinHat7PA9rHiYNHRk2rRpuHLlChYvXowffvhBJS0dESE6OhrTpk0zq1BTSUlJgZ+fn8Zycdndu3eNOt4XX3wBBwcH9O3bV2V5eHg42rVrh0aNGiEtLQ0rVqzAuHHjcPfuXXzxxRc6jzd37lzExsYapYFhLIk9p12zlf5le76HjHbKlMHn9OnT2L59u8o4y1deeQXh4eFmE1hWgoOD0ahRI2zfvl1l+dWrVxEcHIx58+Zh3LhxBh1r7dq1ePvttzF58uRSDSAAEBFefvll7Nq1C9euXdPZLF1QUICCggLp76ysLAQEBFTIAB9bqciYikunuD24npaLuj6u2Dups7XlMHaAoQE+ZUpKEB4eblOGURtVqlRRMUYi+fn50npDOHDgAIYNG4Zu3boZNDWZIAgYP348/vjjD+zduxcDBw7Uup1cLodcLjdIg71T0SZ6tmXjb8vaLAl7dIylKJOxBICcnBw8evQI2hzUOnXqlPXwZcbPzw937tzRWJ6SkgIAqF27tt5jnDp1Cr169UKTJk2QkJAAR0fDbltAQAAAID093QjFFZeKVpHZsvG3ZW2WZGBUYKW6Xqb8MMlY5ufnIzY2FkuXLkVaWprO7YqLi00WZi6aN2+OPXv2ICsrS8XFTkxMlNaXxpUrV9C9e3fUrFkT27dvh5ubm8HnFmddUc84VFmpaBWZLRt/W9bGMPaISX2WQ4cOxcqVK9G7d2+0b98e1apV07pddHR0mQWWlcTERERFRSEuLg4TJ04EUNJP2KRJE/j4+ODIkSMAgJs3byI3NxeNGzeW9k1NTUXbtm2Rn5+PQ4cOoW7dulrPkZ6eDk9PTzg4OEjLnjx5gs6dO+PYsWO4ceMGfH19DdJbGZISMIwpVNamZcayWLTP8pdffsHw4cOxaNEikwWWF5GRkejXrx+mTp2K+/fvo379+li5ciWuX7+OpUuXStsNHjwY+/btU2lO7t69O65evYrJkyfj4MGDOHjwoLSuVq1a6NKlCwDg119/xaxZs9C3b1/Uq1cP6enpWLt2Lc6ePYs5c+YYbCgZhtFNZW1aZmwDk4ylIAiIiIgwtxaL8eOPP2LGjBlYtWoVHj16hPDwcGzduhUdOnQodb9Tp04BAL788kuNdR07dpSMZdOmTREaGorVq1fjwYMHcHZ2RvPmzbFhwwb069fP/BfEMJUQblpmrIlJzbBDhgzB48ePsXHjRktoqtRwMyzDMEz5YdHJn2fMmIGrV69i5MiR+Oeff/DgwQOkp6dr/BiGYRimImCSZylm7AFQ6iwhthANa2+wZ8kw9g0HItkXFg3w+eSTT0yaSothSoMrGaYiwIFIFROTjOXMmTPNLINhuJJhKgYciFQxKXMGH4YxF1zJMBWBipZ8gymBjSVjVdSbXrmSYRjGFjEpGpZhzAXP+8cYA09MzVgLNpaMVRnePgh1fVy56ZUxCOWPKzacTHnCxpKxKgOjArF3Ume7a37lito6KH9c2WOrBJcb+4WNJcOYgD1W1BUB5Y8re2yVsLVyw8bbcNhYMlbDnl9Ue6yoKxr22Cpha+XG1oy3LWNSBh/GclSmDD6d4vbgelouHGUCZvYKs6tKj2EqApwIxMK5YRnGHAxvHwRHmYAiBfGXrZHYs1fO2A726J1bCzaWjNUYGBWImb3CbKpZyl7g5jOmvEjcEIfbsY2RuCHO2lKsCiclYKwKJyIwDc52xJQXzyUvhT+lAMlLAUyythyrwZ4lw9gh3HxmuxjSRG4r3pohWu+EDMNtwQ93QoaZ5XjmwBrdEGwsGYZhzIghTeSit/Zc8tJyVKaJIVoj+0+Cf8wFRPbX71WWV/eANboh2FgyDGM32ENgkyHDQ4zx1iyJuYeylNfQGGsMweGhIzZGZRo6wjDG0vKznUh7XAifqs74Z0YXa8thKgA8dIRhmAoHgVT+tTb24Oky5oGNJcNooaJWgvZwXaUFv/yvSyPU9XHF/7o0soIyTXgIT+WBjSXDaEFbJWgPhkYf9lC5lxb8YmtRwLaWvs5cVISybm4qhbEsKCjAlClTULt2bVSpUgWRkZHYuXOnQfveuXMH/fv3h5eXFzw8PPDaa6/h6lXtFc3SpUsREhICFxcXNGjQAN9++605L4MpR7RVgvZgaPRhq5W7cuVsK8EvhmBrxttcVISybm4qRYDPgAEDkJCQgHHjxqFBgwZYsWIFjh07hj179qBdu3Y698vJyUFERAQyMzMxYcIEODk5Yd68eSAinDx5Ej4+PtK2ixYtwrvvvos33ngD3bp1w4EDB7Bq1Sp8/vnnmDJlisFaOcDHduE8mpZDzBNc18cVeyd1tracSodYtsP9vXD6dob0b2Uo6wbXuVTBSUxMJAAUFxcnLcvLy6Pg4GB6/vnnS933iy++IAB09OhRaVlycjI5ODjQ1KlTpWW5ubnk4+NDPXr0UNn/7bffpqpVq1J6errBejMzMwkAZWZmGrwPw5jCqr+vU8cvd9Oqv69r/duaWkTGrD1OwVO30Zi1x62qTxeGarL16+j45W4KnLKVgqduo8ApW6njl7v17qN8Hda6JnOc19A6t8I3wyYkJMDBwQEjR46Ulrm4uGDYsGH4+++/cevWrVL3bd26NVq3bi0ta9y4MV566SVs2LBBWrZnzx6kpaVh9OjRKvu///77ePz4MbZt22bGK7J/uD/ENlBvalP+W3xGY386US7PSmzO3HDsFup+tA29vj0IANh+JgVFCsL2Myk22TRoaN+28nXc/ONbJGQPQvcd7Wwmg0+4vxfq+rjilaZ+BjfTK1+7tZ5NeZ63whvLEydOoGHDhhrudZs2bQAAJ0+e1LqfQqHA6dOn0apVK411bdq0wZUrV5CdnS2dA4DGti1btoRMJpPWMyXYYqVXGVHvv1T+W3xG28+klOuzOn0nU+XfV5r6wVEm4JWmfjbZ32po37bydQyk31Bdlo3qQpbNZPA5fTsDeyd1xvwBLQzug1W+dms9m/I8b4VPpJ6SkgI/Pz+N5eKyu3fvat0vPT0dBQUFevdt1KgRUlJS4ODggJo1a6ps5+zsDB8fH53nAEqCjwoKCqS/s7Ky9F+UncNJwG0D9ST26n8r92GV17MKf84Tp+9kIvw5TwDA/AEtMH9ACxWNtoS2iQC0lW/l60jcMBxu5+cDKMnk419+cjUoy7uorfyUN+U5EUOFN5Z5eXmQy+Uay11cXKT1uvYDYNC+eXl5cHZ21nocFxcXnecAgLlz5yI2NraUK6h48Ewjto+1ntGvY3QH3NkL+u5dSY7VkjyrkeWkSRf8LhpOhW+GrVKliornJpKfny+t17UfAIP2rVKlCgoLC7UeJz8/X+c5AGDq1KnIzMyUfqX1oTIMwzDWocJ7ln5+frhz547G8pSUFABA7dq1te7n7e0NuVwubVfavn5+figuLsb9+/dVmmILCwuRlpam8xxAieeqzXtlGIZhbIcK71k2b94c//77r0ZfYGJiorReGzKZDE2bNkVSUpLGusTERAQFBcHd3V3lGOrbJiUlQaFQ6DwHwzAMYx9UeGPZt29fFBcXY/HixdKygoICLF++HJGRkQgICAAA3Lx5ExcuXNDY99ixYypG8OLFi9i9ezf69esnLXvxxRfh7e2N+Ph4lf3j4+Ph6uqKHj16WOLSGIZhmHKiUmTw6d+/PzZt2oTx48ejfv36WLlyJY4ePYpdu3ahQ4cOAIBOnTph3759UL4d2dnZaNGiBbKzszFx4kQ4OTnh66+/RnFxMU6ePIkaNWpI2y5cuBDvv/8++vbtK2Xw+fHHHzF79mxMmzbNYK2cwYdhGKb84Aw+SuTl5dHEiRPJ19eX5HI5tW7dmn7//XeVbTp27EjabsetW7eob9++5OHhQW5ubvTqq6/SpUuXtJ5n8eLF1KhRI3J2dqbg4GCaN28eKRQKo7RyBh+GYZjyw9A6t1J4lvZEZmYmvLy8cOvWLfYsGYZhLExWVhYCAgKQkZEBT09PndtV+GhYe0PMCiT2pTIMwzCWJzs7u1RjyZ6ljaFQKHD37l24u7tDEASt24hfQvbofdqrdnvVDdivdnvVDbB2a2CqbiJCdnY2ateuDZlMd8wre5Y2hkwmg7+/YQmwPDw87KowK2Ov2u1VN2C/2u1VN8DarYEpukvzKEUq/NARhmEYhikrbCwZhmEYRg9sLO0QuVyOmJgYu0yTZ6/a7VU3YL/a7VU3wNqtgaV1c4APwzAMw+iBPUuGYRiG0QMbS4ZhGIbRAxtLhmEYhtEDG0uGYRiG0QMbS4ZhGMbmsXYsKhtLplJj7ReQYcqDzMxMa0swmfXr1wOAzvSf5QUbSxvgxIkTuHnzpkqBtodKPDc319oSTObq1avIzc1Ffn6+taUYxalTp3Dp0iXcvn1bWmYPZQUAtmzZgtGjR+Pq1asASvIg2ws//fQT3N3dcejQIWtLMYpffvkFXbt2xbx583D9+nVryzGKdevWITg4GAMGDMDBgwetLYeNpTVJTk5Gu3bt8NJLL6FZs2Zo06YNfv75ZxQVFUEQBJutBC9evIiWLVti+PDh1pZiNKdPn0aPHj3Qs2dP1KtXD506dcKhQ4ds9l6LnD59Gl26dMGrr76Kli1bolmzZpg/f75UVmydnTt34vXXX8eqVauwdetWACg1abWtcOLECURGRmLo0KHo0aOH3eRKvXv3Lnr06IHBgwfD2dkZrq6ucHV1tbYsgxDveXR0NNzd3eHi4oKCggJry6ockz/bIvfu3aMWLVrQCy+8QMuWLaNly5ZRVFQUeXl5UUxMDBGR0RNHWxqFQkEJCQnUsGFDEgSBBEGgvXv3WluWQRQVFdH8+fOpRo0a1LFjR/rkk09o9OjRFBAQQI0bN7bZ6ygsLKTZs2eTl5cXdezYkb799lv66aefqFOnTuTh4UG//PKLtSWWiliG//nnH/Lx8aEqVapQZGQknTx5koiIiouLrSlPJ7m5ufTOO++QIAjUsWNH2rJlC927d8/asgwmJiaGQkJCaM2aNXTz5k1ryzGIzMxMGjx4MAmCQJ06daItW7bQtm3byMXFhf7v//6PiEreY2vBxtJKrFu3jhwdHSkhIUFadvv2bXrzzTdJEAT666+/rKhOO1euXKEmTZqQj48PzZo1i0JDQykqKoqePHlibWl6+f333ykoKIiGDh1KFy5ckJYfOnSIBEGgKVOm2OR1bNu2jSIiImjcuHH077//SpXFpUuXSBAE+vLLL23uo0obCQkJ1LVrV/r+++9JEASaNm2adC22pr+oqIhmz55NgiDQiBEj6MGDBzrLhq1pJyK6efMm1apVi8aOHauxXBlb0v748WNq0KABBQUFUXx8PN24cYOIiK5evUrVqlWjPn36WP3Dio2llfjiiy/I09NTKgCFhYVEVPIF3qZNG2rSpInNfcneuHGDpk2bJnkF3333HQmCQEuWLLGyMv18/fXXFBISQvfv35eWFRQUEBFRVFQUdenShYhsqwIhIjp48CB99dVXKrqJiDZt2kQ1a9ak9evXE5Ht6RYRdSUmJpKnpycREf3nP/8hPz8/2rlzp8o2tkRSUhK1bduWGjduLC3bsmULRUdH0+TJk2nZsmVS+bE19u/fT66urvTvv/8SEdGPP/5IoaGhFBoaSr1796a1a9daWaEqYh14+PBhOnv2rFQXirRu3Zo6depE+fn5Vi0rbCwtjFgQ1B/yvHnzyN3dnfbs2UNEpPKVvX79epLL5TRnzhyt+5YHunTn5+dL/7948SJ17dqV/P396eHDh+WqrzSUtSvrv3jxosp6opL73qlTJ2rXrh3l5eWVr1A1dN1zdQ4cOEBNmjQhDw8PmjlzJp05c4YePXqkcozyRp/2hIQEql+/PhERnThxggRBoOjoaEpPTy91v/JAl3bRC54wYQJ17dqVBEGg+vXrk7u7OwmCQH369KGzZ8+qHMMWdCclJZGjoyNt2rSJli1bRjKZjPr27UvR0dFUs2ZNEgSBli9fXu56lTGkrCsUCiouLqb333+fPD09pTJurbLCxtJCiH1N6l6X+KB37txJcrmcZs6cKS0TC1Bqair179+fatSoUe5fr7p062L9+vVUpUoVmjx5soWV6cdY7aIxbdGiBb355pvSsvLGEN1i2ZgyZQoJgkCdO3em6OhoGjZsGHl5edFbb71VXnJV0KddvJ9Hjx4ld3d3unv3LhERDRs2jORyueTlPH78uHwEK6HvHb1x4wb17duXBEGgF198kX7//Xe6ceMG3blzhz777DOSyWTUr18/m9EtkpSURNWrV6eBAwdSs2bNaMaMGZSdnU1ERKdPn6Zu3bqRj48PJScnl6dsIjL+HSUimjFjBgmCQL/++qsFlemHjaUF2L9/P4WFhZEgCNS1a1c6f/48EWlWxBEREdSiRQs6c+aMxvo1a9aQo6MjxcfHa93XmrqVl92/f5+GDh1KLi4u0le2NQyOMdqVuXXrFlWtWpXmzp1LROUfQGCobvHvTZs20fr16+nhw4fSsqlTp5JMJqO4uDgiKj8vx5h7vmHDBmrYsKHUtZCVlUWurq7UuXNneuedd2jQoEGSIbUl7WvWrKEhQ4bQoUOHNNa9/fbb5OnpKVXitvSOtm3blmQyGVWvXp0OHz6ssu7PP/8kb29v+vDDD4nINsuLsq4DBw6QIAi0YcOGUre3NGwszczff/9NjRs3prp161K/fv1IEAT64osvVAIExAp5y5YtJAgCzZo1S2oCFNddvHiR/P39aeTIkeVSmA3RrYtdu3bRc889R6+//rrFdWqjLNr3799PgiDQH3/8UQ5KVTFGd2kVxKVLl6h+/frUrFkzlWZyS2KodlH3gQMHyNXVlW7duiWtGzBgADk4OJCTkxPFxMRQTk6OzWgXdWdmZmr0F4vbHTlyhARBUGkdsrZusf74/fffpYh10YMUW6nu379P3bt3p4CAAJsrL9o4e/YsVatWjcaMGUNEbCwrDOfPnye5XE4bN24kIqL27dtTgwYN6NChQ1q3f+WVV6h27dr022+/EZGqZxMWFkaDBw8mIssXEGN1K2vKycmRmkp27dpFRET79u2jLVu22Kx2kYULF5Kjo6PUTFVUVERXrlyhpKQkIrKs9rLoJlL1CJ5//nmKiooqt8pPXXuHDh1K1b5u3Tpq1KgRZWRk0J49e6hdu3bk4OBAHh4eVL9+fTpw4AARlU9FaOp9V+8uefDgAXl5eZVbF4Sxut9++20SBIFGjRpFRKRimPr27UuhoaGUmZlpeeFUtrJ+//59CgwMpJdeeomysrIsLVUnbCzNiGjolL9ERc9l7NixUsFUruRu3LhBbm5uFBUVRcePH5eWHzlyhDw8PCg2NtZmdGuryMRruXDhAkVERFDTpk0pNjaWAgICyMfHx+IRvWXRTkTUs2dPeuGFF4iopEl29erV1KJFC4qIiKC0tDSb1K3e0vDHH3+Qk5MTjRs3zmJ6lTFGu6h/165d5OzsTK+++io5ODhQ27Ztaf/+/bRhwwapQi+P/nlz3veFCxeSIAj0ww8/WFBxCabULbdu3SIPDw+NlpNz585RcHAwDRw4sFw+Tsxxz/v06UNhYWGUk5PDnqW9sW7dOho1ahR9/vnntH//fmm58oMUH3R0dDR5eXnR5s2bVY4hFqIVK1ZQnTp1qF69ejR//nxasmQJ9ezZkwICAuj06dM2p1sbN27coCFDhkhNP6+99ppKk5utaVcoFJSdnU1+fn701ltv0V9//UW9evUiQRCoe/fudPv2bZvUrczdu3fpt99+o44dO1JoaKjU921OzKX90KFDFB4eTiEhIbRgwQK6deuWVP7btm1LI0aMMLuxtNR9T01NpU2bNlF4eDh17NjR7JHg5qxb1q1bR35+fuTt7U0jRoygOXPm0Msvv0zVqlWzSNeDJe65QqGgWbNmkSAIUkS7NQwmG0sjSU1NpW7dulHVqlUpIiKCqlWrRnK5nGJiYqTQZvXB1rdv3yY3Nzfq06ePZECKi4tVHvjevXupbdu25OnpST4+PhQeHk4HDx60Sd3qHDhwgLp3704ymYxatGhhcDOitbVfvnyZXF1dKSIigtzc3KhRo0ZSM7It6967dy+NGDGC+vbtS+7u7tSsWTM6duyY2XSbU7vY9FdYWEj79++nM2fOSEZR3M/cQ3Ysed/fffddGjBgALm5uVFERIQ05tjWdCvXLYcOHaJu3bqRl5cX1axZk1q0aKFiyGxNuzbmzZtHgiCoJHEpb9hYGsnKlSvJ29ub1qxZQ3fv3qW0tDQaMmQIubu70+jRozW2FwvG7NmzSSaT0eLFi1UKsvL/8/Ly6N69e2av+CyhW5m//vqLnJ2dacGCBWbXbUntu3fvJkEQqGbNmhbRbindv/32G9WvX586depEy5YtM7tuS2kvL2/AUvc9ISGB3NzcKDIy0iJNr5asWwoKCujRo0d06tQps+u2hHYR0XimpKTQihUrLKLdUNhYGknHjh0pKipKZdnjx48pOjqaBEGgbdu2EZHmF1JhYSEFBwdTZGSklFnjypUrKu34lox6taRuIssOuTC3duV+1EWLFmlkDLEH3VeuXLGb8nL58mWN8mJJLHnfT506ZbGybq91i6W120qGJzaWBlJcXEz5+fnUrVs3atu2rbRcbGb6559/qGXLlhQUFKTxcNWHikyZMoWWL19OERERNHbsWIsOyLZX3ZbWbsmoOkvqtvTwCktqz83NtVvtlrzv/I5aR7uxsLHUQnJyMn344Yc0ZswYmj59uvTFQ0TUu3dvatSokRRMofyltHjxYhIEgebNm0dEmt7WkydPqHXr1uTg4ECCIJCfnx/9/vvvlV63PWu3V92s3Tra7VW3vWs3B2wslSgoKKCJEydSlSpVqFWrVtSgQQMSBIGCgoKk8UEJCQkkCAItW7ZMKhDiw79+/Tq99NJLVK9ePY0ghuPHj9P06dPJzc2N3N3d6Ztvvqn0uu1Zu73qZu1cXiqTdnPCxvIp2dnZNG3aNAoKCqIvvviCLl68SMXFxfTXX39R7dq1qX379pSbm0tFRUXUrFkz6tChA12/fl3jODNnziQvLy+pjZ6opGB88MEHUvJocQB8ZdZtz9rtVTdrt452e9Vt79rNDRvLp1y7do3q1atHo0aNooyMDJV1o0aNoho1akhZXVatWkWCINDXX38ttauLX0wnTpwgmUxGmzZtIqJnbfdHjx6VciGybvvWbq+6WTuXl8qk3dywsXyKQqGgxYsXqywToyQ3bNhAjo6OUo7FjIwM6tOnD/n6+moMqD169CgJgkArV65k3XqwV+32qpuItRNxeTEGe9ZubthYKiF+7ah3QMfFxZGDgwNduHBBWnbr1i2qVasWhYWFSZ3Rd+7coQ8++IACAwMpNTWVdRuAvWq3V91ErJ3Li3HYs3ZzwsayFMSO6g8//JB8fX2lLyqx0Pzxxx8UERFBgiBQ8+bN6fnnnycnJyeKjY2loqIiq40Pslfd9qzdXnWzdi4vlUl7WRCIiMCUSqtWrVC3bl0kJCSguLgYDg4O0rqHDx9i6dKluHLlCrKysvDhhx/i+eeft6LaZ9irbsB+tdurboC1WwN71Q3Yt3aTsLa1tnXu379PVapUkSbWJSr5skpPT7eiKv3Yq24i+9Vur7qJWLs1sFfdRPat3VRk1jbWts7Zs2eRn5+P1q1bAwBSU1Oxdu1adOvWDQ8ePLCyOt3Yq27AfrXbq26AtVsDe9UN2Ld2U2FjqQN62jp97NgxeHp6onbt2ti7dy9Gjx6NoUOHgoggk8mk7WwFe9UN2K92e9UNsHZrYK+6AfvWXmbKz4m1T/r06UPBwcE0YsQIcnd3pwYNGtCff/5pbVl6sVfdRPar3V51E7F2a2CvuonsW7upsLEshby8PGrevDkJgkAeHh5SbkNbx151E9mvdnvVTcTarYG96iayb+1lgaNh9TBlyhQIgoDY2FjI5XJryzEYe9UN2K92e9UNsHZrYK+6AfvWbipsLPWgUCggk9lf16696gbsV7u96gZYuzWwV92AfWs3FTaWDMMwDKOHyvVpwDAMwzAmwMaSYRiGYfTAxpJhGIZh9MDGkmEYhmH0wMaSYRiGYfTAxpJhGIZh9MDGkmEYhmH0wMaSYRiGYfTAxpJhGIZh9MDGkmEYhmH0wMaSYRiGYfTw/wGmFBCnwfnjHQAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 450x300 with 1 Axes>"
       ]
@@ -62584,19 +62353,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:08:26.296789Z",
-     "iopub.status.busy": "2026-02-11T21:08:26.296789Z",
-     "iopub.status.idle": "2026-02-11T21:08:26.604345Z",
-     "shell.execute_reply": "2026-02-11T21:08:26.603840Z"
-    }
-   },
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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veWFYUVEBUKdimn+72WwO2peQkBDw886dO4PuKSUlJcixio+PD3kt//n8NgG8+OKLzJ07l+joaEaPHk2HDh0wGo0oFApWrVrFrl27AkQG7rnnHmJjY3nllVf497//zQsvvIBCoWDEiBEsWbKEiy66CPA5BwDr169n/fr1Ie0BqKqqCrCpIdsbS0VFhdzzrDHn8tubmZlZ73Pjt7chm1UqlVwfGIq67sdsNnPxxReTlZXFwIEDmTlzJjExMajVasxmMy+++GLA59GceXO5XFx22WX88ssv9O7dm2uvvZa4uDh5rhYuXFivwEdjacyzf/z4ccxmc4Bj1ZTvYWOo7/P++OOP6z3W/3mrVCq+//57Hn/8cVauXMkDDzwAgMlk4qabbmLx4sXyS5fTxdSpU/nyyy/517/+xVtvvcXrr78OQFpaGosXL2b06NGn1R6BQND6CMdKIBCcFlQqFddccw179uzhySef5Pvvv2fKlCnyAu3CCy+U35ifquvPnTuXuXPnUlRUxObNm/nggw/4+OOP2bdvH/v27UOn08n2FBQUhDyPXxUwlOJhTdU18Cmv1VQEq4vCwsKQ2/02+K/ldrtZsGABCQkJ/Pbbb0ELYP9b/drMnDmTmTNnYjab2bJlC5999hlvvfUWY8eO5cCBA8TFxcnXePHFF5kzZ06DNvvHN2R7Y4mMjKSsrCykgESoc/mvf/XVV/Ppp5826hr+6GZhYSEXXHBBwD6Px0NpaWmdfZ1qf7Z+/vvf/5KVlcVjjz0WFEnZunUrL774Yki7mzJvn3/+Ob/88gs333xzkOLliRMnWk1RruazHyoKWd+z35qEmmv/NT///PNG98CLjo7m+eef5/nnn+fw4cP88MMPvP7667z88suYzWZZVMQfLa4d+fVjNpvrdB6bysSJE5k4cSLV1dVs27aNL7/8kldffZUrr7ySHTt20LNnz1a5jkAgODMIuXWBQHBaMZlMAHIaU3h4OL169WLfvn2UlZWdFhvatm3L1KlT+eijj7jssss4cuQIe/fuBXwOHvjkk2vjdrvZtGkTAAMGDGg1ezZv3hxSlttvg9+mkpISzGYzQ4YMCXKqqqqqGnRMo6KimDBhAm+++SY333wzZWVl/Pjjj4AvFQ6Q768hTCYTnTt3Ji8vjyNHjtRpe2MZMGAAXq+XzZs3N+pc3bt3l5UjXS5Xo67hn8dQ1/j555/rXFjXx+HDhwGfkl1tQqX01bQhVCQn1L36rzF16tRGXQOQ0wybEi2q79k/fPgwubm5pKamtpqT0RSa+nzWpnPnztx666388MMPhIeH8/nnn8v7oqOjAcjJyQk67vDhwwER44ZQqVSNmvOwsDAuu+wynnvuOebPn4/T6eSrr75q9HUEAsHZiXCsBAJBq7JixQrWr18f0lEoKCjgzTffBGD48OHy9nvuuQen08ktt9wSMsWuvLy8RdEsh8PBTz/9FLTd5XLJzpzRaARgypQpxMTEsGLFCn7++eeA8S+88AJZWVlcccUVQfVVLSEzMzOop9Tnn3/ODz/8QOfOnWW59bZt22I0GklPTw9IcXO5XPz973+npKQk6NwbNmwI2UuoqKgI+P2+L7roIoYNG8ann37KW2+9FdLOPXv2yMcBzJo1C6/XywMPPBDweWdlZfHvf/+7sbcvnwt8svg1+zKVlZWFrKFSq9X87W9/48SJE8yZMwebzRY05sSJEwG9vWbOnAnAokWLAhbLTqeT+fPnN8leP34p89rOyI4dO1i8eHHQ+KSkJEaPHk1WVhYvv/xywD7/Z97Yaxw9elROcauNP63x+PHjjbgLH7fccgsATz75pFxTBz7n7N5778Xr9XLrrbc2+nytyeTJk+nUqRP/+c9/WLt2bcgxW7duxWq1Ar5n8OjRo0FjysvLcTgcGAwGeVv37t2JiIjg888/D3i+bTZbo6K3NWnTpg3FxcUhn8cff/wxpPPuj176v4sCgeDcRaQCCgSCVmXbtm28+OKLJCQkMHToULnfU1ZWFmvWrMFmszF58mSmT58uH3PLLbeQnp7OK6+8QqdOnRg7diwdOnSgrKyMrKwsfvzxR2bNmsVrr73WLJtsNhtDhw6lc+fOpKWlkZKSgt1uZ/369ezfv5+rrrqKHj16AL4I2ltvvcWMGTMYMWIEM2bMoEOHDqSnp7Nu3ToSEhLk2ojWYty4cfzjH//gq6++ol+/fnIfK71ez1tvvSWnKimVSubMmcPTTz9Nnz59mDx5Mk6nkw0bNlBWVsaoUaPYsGFDwLmvvvpqwsPDGTx4MB07dkSSJDZt2sSvv/5KWloaV1xxhTz2/fff57LLLuPWW2/l3//+N4MGDSIqKorc3Fx2797N3r172bp1q9xX6B//+AerVq3ik08+YcCAAYwdOxaz2Sw3mP3iiy8aPQd/+tOf+PDDD/niiy/o3bs3kydPxuVysXLlSi6++OKQUbFHHnmEXbt28dprr7F69Wouu+wy2rdvT1FREZmZmfz0008sWrRITq8aMWIEs2fP5o033qBXr15MmzYNjUbD6tWriYyMJDExMaSISH3MnDmTJUuWMHfuXDZs2ECXLl3IzMzkyy+/ZOrUqSGbT//nP//hkksuYe7cuaxbt07+zD/77DMmTZrE6tWrA8ZPmjSJzp0789xzz7Fnzx4uvPBCjh8/zpdffsnEiRNDOk+jRo1CqVQyb9489u7dK0dlHn744TrvZciQIdx///3885//pHfv3kyfPp2wsDC++uor9u7dy9ChQ7nvvvuaND+thUaj4dNPP2Xs2LFMnDiRIUOG0L9/f4xGIzk5Ofz6668cPXqUEydOYDQa2bVrF1OnTuXiiy+mR48eJCYmUlxczOeff47L5QpwSDUaDX//+9954oknuPDCC7n66qtxu92sX7+exMREEhMTG22nv6/buHHjGD58ODqdjn79+jFp0iTmzJlDXl4el156KR07dkSr1ZKens73339PSkoK11133amYOoFAcDo5o5qEAoHgvOP48ePSyy+/LE2ZMkXq2rWrZDKZJI1GIyUkJEjjx4+Xli1bFlJaXJIkafXq1dLEiROluLg4SaPRSPHx8dLFF18sPfTQQ9L+/fsDxhKir5Gf2lLSTqdTeuaZZ6Rx48ZJycnJkk6nk2JjY6VBgwZJr776quRwOILO8csvv0hTpkyRYmNjJY1GIyUnJ0t33nmnlJeX1+D1GktNmectW7ZIl19+uWQymaTw8HBp9OjR0i+//BJ0jMvlkv71r39JPXr0kPR6vRQfHy/deOON0rFjx0La8eqrr0pTpkyRUlNTJYPBIEVHR0v9+/eXnnnmmZD9sCorK6VFixZJAwYMkMLCwiS9Xi917NhRmjBhgvT6669LVVVVAeMrKiqku+++W0pMTJR0Op3UrVs36dlnn5WOHDnSJLl1SfL1k1q4cKGUmpoqabVaKSUlRZo/f75kt9vr/Ly9Xq/07rvvSpdddpkUHR0taTQaKTExUbr00kulRYsWScePHw8Y7/F4pOeee07q1q2bpNVqpXbt2kl33XWXZDabpfDwcKlfv34B4+uS1a7Jvn37pEmTJklxcXGS0WiUBgwYIL355ptSVlZWnXOQmZkpTZs2TYqMjJSMRqM0ePBg6csvv6zzesePH5euv/56KTExUdLr9VLPnj2lZ555RnK5XHXOzbJly+T+Y5xse+Cnvmd2xYoV0qWXXiqFh4dLOp1O6tmzp/Tkk08G9BfzU5+8eEOS77Xxy63XR2FhofTAAw9IvXr1kgwGgxQWFiZ17txZmjZtmrRs2TJZej8nJ0eaN2+eNGTIECk+Pl7SarVS+/btpXHjxklr164NOq/X65UWL14sXXDBBfL3/b777pOqq6ubJLdeVVUl3XnnnVL79u0llUoV8Pl/+OGH0nXXXSd17txZCgsLk0wmk9SrVy9p/vz5UlFRUaPmSCAQnN0oJClEjohAIBAIBH8gMjMz6dq1K9dddx0rVqw40+YIBAKB4BxE1FgJBAKB4A9DQUFBUP2f1WqVJfCvvvrqM2CVQCAQCM4HRI2VQCAQCP4wvPDCC6xYsYKRI0fSrl07CgoK+O6778jNzWX8+PHMmDHjTJsoEAgEgnMU4VgJBAKB4A/D6NGj2bVrF+vWraOsrAy1Wk3Xrl2ZM2cOc+fOrbNflUAgEAgEDSFqrAQCgUAgEAgEAoGghYgaK4FAIBAIBAKBQCBoIcKxEggEAoFAIBAIBIIWIhwrgUAgEAgEAoFAIGghwrESCAQCgUAgEAgEghYiHCuBQCAQCAQCgUAgaCHCsRIIBAKBQCAQCASCFiIcK4FAIBAIBAKBQCBoIcKxEggEAoFAIBAIBIIWIhwrgUAgEAgEAoFAIGgh6jNtwNmI1+slPz8fk8mEQqE40+YIBALBHwZJkrBYLCQmJqJUind/fsTfJYFAIDhzNPZvk3CsQpCfn09ycvKZNkMgEAj+sOTk5JCUlHSmzThrEH+XBAKB4MzT0N8m4ViFwGQyAb7Ji4iIOMPWNJ+9eWZ25pjpnxxF7/ZRZ9ocwVnAez8fo8LqJtKo5sbBHc+0OQJBEJWVlSQnJ8u/hwU+zpe/S4JTR0lJCZ06dQrYduTIEWJjY8+QRQLB+UNj/zYJxyoE/jSLiIiIc/oP2ME9pTgUeg6WeRjS49y9D0HrMbRnB9Kzy0lLiT6nn23B+Y9IdwvkfPm7JDh1OByOoG0mk0k8LwJBK9LQ3yaRwH4ek5YSTZRRQ1pK9Jk2RXCW0DcpilmXptI3KepMmyIQnHVs3LgRhUIR8t/PP/8cMHbLli0MHToUo9FIQkICc+bMoaqqKuicDoeDBx54gMTERAwGA4MGDWL9+vWn65YEAoFAcBoREavzmL5JUWIBLRAIBE1kzpw5XHzxxQHbOnfuLP9/586dXH755fTo0YPnnnuO3Nxcnn32WTIzM/nqq68Cjrv55ptZuXIlc+fOpUuXLrz99ttMmDCBDRs2MHTo0NNyPwKBQCA4PQjH6jxjd65ZTvUSTpVAIBA0nWHDhjF9+vQ698+fP5/o6Gg2btwop1l17NiR22+/nXXr1jFmzBgAfvnlFz744AOWLFnCvffeC8DMmTPp3bs3999/P1u2bDn1NyP4wxAZGcmGDRuCtgkEgtOHSAU8z0jPLsdsdZGeXX6mTREIBIJzFovFgtvtDtpeWVnJ+vXrufHGGwNqV2bOnEl4eDgfffSRvG3lypWoVCpmz54tb9Pr9dx6661s3bqVnJycU3sTgj8UWq2WkSNHBvzTarVn2iyB4A+FcKzOM0RdlUAgELSMWbNmERERgV6vZ9SoUWzfvl3et2fPHtxuNxdddFHAMVqtlv79+7Njxw55244dO+jatWuQeMDAgQMBX0qhQCAQCM4fRCrgKeRMpOWJuiqBQCBoHlqtlmnTpjFhwgRiY2PJyMjg2WefZdiwYWzZsoULL7yQEydOANCuXbug49u1a8emTZvkn0+cOFHnOPD1pqoLh8MRoPJWWVnZ7PsSCAQCwelBOFankNW78jlUWEW+2SacnRqIOjCBQHA2MmTIEIYMGSL/fNVVVzF9+nT69u3LvHnz+Prrr7HZbADodLqg4/V6vbwfwGaz1TnOv78uFi9ezMKFC5t9LwKBQCA4/QjH6hRSbHFwuMhCWbWD3bnmU+5EfJKew/qMQkb3jGdaWvIpvVZLqFkHJhwrQUsQTrrgVNO5c2cmT57Mp59+isfjwWAwAKF7Btntdnk/gMFgqHOcf39dzJs3j3vuuUf+2d+cUnD+0vHBNa1+zmNPT2z1cwoEgroRjtUpJM6kQ69RoVEpT4sTsT6jkJxyG29uyqJLvOmsXWimpUTLi2GBoLnszjXzyobDRBl9xdln6/MuOPdJTk7G6XRSXV0tp/H5UwJrcuLECRITE+Wf27VrR15eXshxQMDY2uh0upDRLoGgLiTJi9dmCdjm9XpRKkU5vUBwuhCO1SlkUr/f/2ieDididM943tyURYcYw1kdDQpVB7Y718zqXb56g0n9Es9a2+tDRE9OL+nZ5UQZtZitTuGkC04pR48eRa/XEx4eTu/evVGr1Wzfvp1rrrlGHuN0Otm5c2fAtv79+7NhwwYqKysDBCy2bdsm7xcIWguvzULuSzcEbCt9ZDRxcXFnyCKB4I+HeI1xivAvsif1S+ShiT1Py0J7Wloy/5zel5Q2YeSbbezONZ/ya7YW6dnl/HiomE9/y+OtzVln2pxmkZ5dzsECC69sOBxy7nfnmln6U9Y59bmczaSlRGPSq0mOMZ5pUwTnCcXFxUHbdu3axRdffMGYMWNQKpVERkZyxRVX8N5772Gx/B4dWLZsGVVVVcyYMUPeNn36dDweD2+88Ya8zeFwsHTpUgYNGiRS+wQCgeA8o1kRq7qUjgS/4xOusLRIuKI5EZC+SVHnZA1ThF5NkcWBUgGFlfYzbU6zSEuJZtvRUqKM2pBz73e8th0t5a5Rnc+Jz+ZsjsLV9ayfzTYLzm6uvfZaDAYDQ4YMoW3btmRkZPDGG29gNBp5+umn5XGLFi1iyJAhjBgxgtmzZ5Obm8u//vUvxowZw7hx4+RxgwYNYsaMGcybN4+ioiI6d+7MO++8w7Fjx/jf//53Jm5RIBAIBKeQZkWskpOTGTNmDMuWLaO6urq1bTqPUDTrKH9kY/Wu/GY1+z0Xe1kdKLDQ1qTDpNcwPS3pTJvTbJJjjJj06pBzH6FXszPHjFeS5M/0bI9ine6G0/XNx+5cM4vWZLBoTYa8P0Kv5mBBJRH6398RhbL5bJ9nwdnBlClTKCkp4bnnnuOuu+7iww8/ZOrUqWzfvp0ePXrI4wYMGMC3336LwWDg7rvv5o033uDWW29l5cqVQed89913mTt3LsuWLWPOnDm4XC6+/PJLhg8ffjpvTSAQCASngWY5Vo8//jj5+fncdNNNxMfHc+ONN/L111/j9Xpb275zlu4JJgwaJWFaVZMXdP6FYbHFEbRobIjT8bb+VC1SEyL19E+OotLuPmUL4FO5wE7PLseoVZMYZQg59wcKLEiSl0OFVfJnerodl6YSynE5ldSXTpmeXc6hwioOFVrk+TpQYMHm8nKg4PeUrLSUaLJLq1m3r4BP0nPkY8/meRacHcyZM4dt27ZRWlqKy+UiPz+fZcuW0blz56CxQ4cO5aeffsJms1FUVMTLL7+MyWQKGqfX61myZAknTpzAbrfzyy+/MHbs2NNxOwKBQCA4zTTLsZo/fz579+4lPT2dO++8k40bNzJhwgQSExO5++67A7rU/1GptLvplhDBgQJLvXU3ofBHnOJMOrolRFBpdzf6uqt35fP2T0e5+a1feGH9wWZaXz+nYpE6qV8iI7u1BWDjwWJZyKI51Oc8NWR77WOb4ohVWJ2s2pnHxoNFdY53eyEmTCt/pmd7dDGU43IqCRXVA7+4SR7bjpSwM8fMoYJKlv6UxaFCC7nlVootv0ta902KorDSTr7Zxsr0XODsn+fzCREdFAgEAsEflRaJV1x44YU8++yz5OTksH79eiZOnCgX5fbs2ZOnnnqK48ePt5at5xT+t+aZRVX8cKgoaKEYCv+CJLPQt4jtnmBq0mJwd66Z7w8UkVNup9rh5ut9hS2+j1CcikVq36QoZl2aSpxJB0gcKrTwpze2MveDHU1eoK3elV+nc9aQ7TUdL7+c98ECS6OcyAMFFpAgt9wWcvykfolc1r0tHWKMsriI/77P5logi93J3ryK07JQrrS76Z8chVKhCPiMVu/KJyPfgt0jUWFzs2bPCb7eW8Bv2eVY7K6g88RH6FGrlMRH+BqxngvzfL4gooMCgUAg+KPSKvk9CoWCYcOGYTabycvLY926dWRmZrJgwQIeffRRrr76av7973//4QQvDhRYMFuduD0SFru7QUfEvyDZdrSUbgk+ad5Zl6Y2+nrp2eU43F5UCnB7JYxa5SlpTBxKLr0p+KXViy0O4kw6WZY+PbucMK0Kg0ZFZlEVZdVOcsqs9EtuzvWkZtkeoVez7Wgpo3vGy3Le2aXVmPTqBudydM94Kmwu4iP0dX7WiVG+hqBGrbpJ4iKtleL5SXoOK9NziY/Qc8vQhh2NMK2KrBIrEXo1q3fln3LHxD9voe7ToFVid/vSjW1OD3tyzWhUCjxeTjrkPnbnmokz6bise9uAlgcNcbpFL85XkQ3Rp04gEAgEf1Ra7Fht2LCB5cuX88knn1BZWUmfPn149tlnueGGG1Cr1SxdupSnnnqKP//5z3z77betYfM5QXp2OR1ijOSVWzFoVcRH6BtcPPlSyfJB8rIr10zXeFOTFl1pKdFsPFhEpdWJBBRVOhq1GD7VC7za5//XuoNsOVKK2yOhUSlIzy6jrUkvOzEOtxez1YlGpSQ6TNvkBdqkfomkZ5cToVez9KesJt2XP4Wz8qQjvPFgEfvyK8g324D6m9BOS0umS7ypzjf1PqXIKkDC6fYSH6GXo0ANzb9fZXJXjpl+yVGNvqeacw/w7+8PU1bl4EhRFXaXp0F1wgMFFnRqJVUOT4PXag3q6nEGMKJrW/bkVVBYacfjldBpVCgVEGXUcKjQwqI1GXRPMLE+o5Aoo5ZuCY1vkn2qmw2H+o61hnJoU695Os7R0hcvAoFAIBCcqzTLsdq1axfLly9nxYoV5Ofnk5CQwG233cbMmTPp06dPwNh7770XvV7Pvffe2yoGnyv4F7JGrYpyqzPgjXpdfHegiAKzDZdXQqNScqSoqsmS6QaNiugwHQWVdqqdbjmFq75z+Bf8p2qBt+CLvRwoqKJ7Qjif3jWUQ4UW3B4JCXB5JA4XVdE1PoLs0mqKLA4KK+1oVAq6xoUxb0KPJtvUNymKzEIL//7+MHq1Mui+6lsspqVE89bmLH7JshOhV3Oo0EKV3UO1wxZQx1MXq3flk55dzrp9BSFtL6iwklNuQ5LApFfLz0Vj5r+gws6e3Aqszt9r7t7anEVhpZ3paUlMSwvuiVM7LUuvVuGVoMrpJqukukHHe3TPePJOOpXdE4IL808Hfgeka7yJ56/tz+pd+fx8tBSNSolOrcTh9rIzx8yRoip+PlpKscVOpc3N5P6J8mcdoVezKbOkzrlqSbPhxjgftZ2o3blm9uZV4HCfOsGf1mi70NRznK9ROIFAIBAIGkOzaqwuvPBC/vOf/zB8+HDWrl1LTk4OS5YsCXKq/PTq1YtLLrmkRYaea/hrOm4ZmsrIbo1LSSqrduL0SHglkCTfG/mm1Fe9suEwXglQQMc2RiINWlLahLF6V34jisklii2OU1J0fqzEisfj5ViJFYCu8SaMWhUqBWhUCqKNGsxWJ/EReiINGiQJvJJCjvI1pxh+ZXouZVUOCkL0xGqoBmRHjpmskmre3nIMp1sCBWjVykY5xwAVNhcOtzfo/JP6JWJ3S7jcXuwuL5U2dw1nLXTqYs1jFQoFUUYtx8tscrrVnjxfNG19Ruh6On9NWYXVyae/5VLlcBFhUBMXrqPK4W6wdmpaWjKDL2hDTJjutAlYfJKew53LtsuKfsUWB7nlvzu2iVEGLu/elrYmHYNSY9CplejUKlD42htU2j0oFAq2HC2Ta+TWZxSyPbucAwUWWdCiJhVWJ1uPlmJzNT0y1/iaot/bL6Rnl5PSJoy2NVJhWwv/9yVCr25xLaT/+fFHfxv6Dor6KoFAIBD8kWmWY/XWW29RWFjI8uXLGTt2LEpl/acZNWoUGzZsaJaB5yr+xQ38XifV0MIkJkxLuE6FTq2gbYSePu0jG/3Wd/WufLJKfFLUV/dP5LqBHfjbZZ3pdjLKUN9ix6/IF2fSnZJF0dhe8Rh1ajrGGtmda+YfY7oxumc8bSN0tIvUo1Ao5RQsnVpJmE4FSBwvq2bRmgwWr93faAEJP9qTkYwqh5t1+07wpze2ygv1+gQsVu/Kx+Z0ywvstJRokmOMjO2V0KgFcPcEn9Po8niDJMr7JkVxYXIUUUYtRq2SlDZGeV/XeFO9588stKBRKQjTqRjXK16OwiRFG1AoFHVGk/omRRGhV/PO1myOFFdRYXXh9khUOdxU2d0cKa7irc1ZDd5XQ45fa7IyPZf9J353gOJMOiINagor7azelc/BAgtf7yvEK/lSFTu2MaJU+p6dy7u35eKO0eg1SqrsbsqtTsxWJ6N7xqNTK3F6vBRZHEHfw21ZZVTaXPyWXc5t72wPUtSsz7lvSBDFf0zX+HDCtCruXLadCquTbgkmRveMZ/Wu/IDeXC3F79xU2t0tFuzwvyCqtLsb/N2wO9dMvtmG1dlwPalAIBAIBOcjzUoFvPnmm1vZjPOP2m9u/dGkbUdL66xruXlIR9ZnFGJzeSiosLEzx8wn6TkhU7xCUe30giRxrNRKP6OWLvEmOeJTXzG5vyaioXE1aUrKz58GpXCs1IrD7WX1rnwemtgTu8uDEgV5Zhux4TqyS6vp3T4SvUbFgQILaqXCJ1VfWIUSOFFh5+KOjV+sRRu1eCQJjxeOl9mpcnhYn1HItLTkBmtAoo1aHG4vl3dvy7FSK4mReoZ1iW1UrdrK9FwKKx1o1cqQEZ5bhvqUD/3CHeATsogyauo9//qMQoxaNdFGDZFGLWari105ZrJLrejUSo6VWkPWk32SnsOC1fuocniQJFAqwCNJ6DU+x9MLFIaI6tVkUr9EWWGxtcVQQj1H8RF69p+opNzqYN4nuwjXa9CpVaS0CQMgu7QaSZLYk2cmQq9hS4UNt1fC5vRwrNTKyG5tcbq9VNrdVNjcPDSxJ32TojhQYGHtnhPyc1jzPrRqJU63F5vLg0qp4Ot9hcwd3U3eX19KXEPP07/WHWTncTPhejVeSUKSILOoCqNWRb7ZhkqpICnaKF+npWl0rSUeUTONcleOWU6jrGusv0atKbVtAoFAIBCcTzTLsXr33Xfr3a9QKNDr9SQlJTFgwAB0usalUJ1P1FzcpGeX45VgZ46Z/smRddYr+B2olem5VNrdxITpZGegISb1S+Tno6WUW10cKa6SUwD9NjRFXbAxNKX24q3NWew47utNZDoZxemeYGLz4RI0KiXlVhdhFgfFJ+urEqMMHC2uwu3x4pFArVQQE6blQIFPoMB/v/7rhlqcl1udKFAAEiolSBJEGzXcuWw70UYN5VYX3RNMRBq1AcfVnMfvDhQRrlNTWOngma99EYz6Pgu/KqPF7sLl8fLpb3l0TzAFHNM3KUpuJOxPsVqfUcjonvH1zuHonvG8veUYueVWMouqUCshp8yGQqlAr1ZxpLiKcqsrqE5rfUYhdpcX6WTASQE4XF7sbi9KBUhuiY41Ime18Ss4fn+gCIfbS7HFwQvXXVivrU2h9nPkV/TznnSKv9lXyJ8v6Ujv9pHyMVq1kiqHG6fbS7XT5zA63F50ahWFlXbMVhcOt4fDhRYUSgUrtmXTNymKSf0S66xr6hpv4khxFVKVT1jkwuTIgP11OSv++YHAZ7Im2aVWbC4PFrsbpRL0GhUKhS/1t8ruRqlUYNK75LqxltY6tpZ4hO/3Rxll1S50aiVur1RnOqj/d9zWo6XYXZ5TokYqEAiazoAn1qMyRjY8sAkce3piq56vJt988w3jxo2Tf1ar1aSkpHDjjTcyf/58tFrtKbt2a+JwOHj00UdZtmwZ5eXl9O3blyeffJLRo0c3eOzGjRsZNWpUyH1bt25l8ODBTb5OXl4es2fPZtOmTSQlJfHMM88wadKkgDGffvopd955J5mZmURGhn5mvF4v8fHx3Hfffdx///0N3ssfkWZHrBQn6xkkKTBFqOZ2hUJBREQE8+bN+8N9ALUXN9uOlpIaG8bxMhtje9U97eszfClOseE62kcZGlxw1yRcp0ajUtIhxojV6ZYXw7tyzA0uhl9Yf5B3tmYToVc3amHXlLfihZX2k88DOE8uaiONWoZ0imXz4WIkr0RRpZ09QJ/2kUQaPFTZXZyosKNT+ZwqnVpJscXB8TIrFTZf3yK/jaGcPKfbS5hWicXuQatW0SU+nO8OFKNRQoXdTVuTnm1ZZbQ16QKEJvzHV1idckpTudWJUuFzeOtzrNJSosk32zhRYaOsyonT7QnpGEfo1Xz6my/NrVNcuKxC2FAUsLDSQYXNCRJ4JVCrFNgcHuwuDxa7i5gwB0534KJ2dM94fswsxuP1fU/9kRkF4PGCWgnrMgo5Vrq1TlGHQ4VVlFQ5UCoUDUa3mkrt58jvdJr0GkqqnISpFFidblnp0Wx1UVbtxO7yPUfVDjeRRg0xCi0KhQKTXs03+wpweby4vRJej8TX+wpZPM13Pb+DVjvtMkyrosLqwuOViDRqCNdrAvbXfNZq/ux3OtVKBTll1pDR6Cn9E/nf5ixcHg9ur++z69Pe94JFQkJ58nemRqWUn+3WoLlCEv7jii0OKmxuJEmiyOKgbT01hhF6NXvyzFjsbvafqGyRYIZAIPjjsmvXLgCee+454uLisFqtfPzxxyxcuBCHw8HixYvPsIWN4+abb2blypXMnTuXLl268PbbbzNhwgQ2bNjA0KFDG3WOOXPmcPHFFwds69y5c7Ouc9NNN5GXl8czzzzDTz/9xIwZMzhw4AAdO3YEwG63c++99/Lkk0/W6VQB/PLLL5SUlDBx4qlzrs91muVY7dy5k5tuuok2bdrwf//3f/IHnZmZyX/+8x/MZjMvv/wyhYWFvPTSS8ybNw+TycRf/vKXVjX+XKFvUhR3jerMQ5/toazaydtbjslperUZ3TOe9RmFXHNRaJW3uvAXw5utTm4ZmsrqXfmUVDlweSR25pgbfIO8amc+1Q53o4v3m/JWfHpaEkUWB1anm0GpMYBvIaZU+NLuyqud2N0e3B4vhZV2BqXGsCvXjE6tJFyvJj5Cx/EyK8fLrHgliQi9RhbaSEuJDunkTU9L4pnialxeCZ1aSWZhFQoFlFe7UCoUZJdVE2U4uXjXBfaU6hQXztHiasBn34iucezJq5CbzTY0J90TTLy95RhASMe40u7G6vRQ7XBTafcp/FmdOvLNtjr7W63PKCRcp6a82gn4Uvq8Xgm1UuFzIJAor3aSGGUISnPzCYK40GuUJEUb0agUHC6sOnkcWJ0eWdSh9jPndxYtdidl1S7582staj5Hn6Tn8N7P2ZRY7KBQYNQq0apV8jhAjg7FhmsptjhIjDIQoVdT5XBTWGHn56OlJMeEoVGpCNepsbk8pJ6s7fOn4yoVwXYcKLAQplPj9Ljka9akLjn2YosDu8uDV5KIMmpDfnZzR3djR46ZHw6VAL75/uFQCZEG34sQP/7nt7XELJqrCug/DjiZrugiQq8mJkzLpH6JvLD+IJ/tzKdjGyP/GNONvklRVNrdqJRKFEBJlYN1+wqI0Kub9DtMIBC0DKXWSOzkB4O2nUvs3r0bvV7PnDlzUKl8v4tvvvlmUlJS+PDDD88Jx+qXX37hgw8+YMmSJbIi9syZM+nduzf3338/W7ZsadR5hg0bxvTp01t8HZvNxvfff8/GjRsZPnw4d955J1u2bOGbb77hjjvuAODZZ58lMjKS2267rV6b1q5dS0pKCr169WrUPdRFdXU1YWFhLTrH2UqzxCuef/554uPj+fbbb7n66qvp06cPffr0YerUqXz77bfExcXxv//9jylTprB+/XoGDx7MK6+80uB5q6qqeOyxxxg3bhwxMTEoFArefvvtRttlNpuZPXs2cXFxhIWFMWrUKH777bfm3OIpoaTKQXm1Q5a5DsW0tGRe+/NFTV6QpKVEY9KrSY75/ZdolEEDkoRSAfev3C2LN4QipY0RjUpJmFbdKFntpij1TUtL5sbBKYzo2pYDBRZ255rlflExYb5Ig1rp65XkcHvZllVGu0gDWrWKdpEGTlTYKaq0U1BhR5LA7vZypLhKFrTwF9jXXDxOS0vmgXHdaBdpQK9RolUrqLS78Ujg9PrqXFxuLyltDOjUygChiVuGpnJBXJi8iL5laCp92kdSWGmvdw5rXnv134ax+m/DQn6OaSnRJEUbcLq9JEToKKy0c6iwimKLo04RhNE94wnTqYgwqNFrlKjVStQqpV8ID6UCXF6JzEILPx8t5ZP0HJb+lMXbW44hST71xbYmPeE6NQuu6s1jV/WiX3IUKTEGPF4vVXY3DnewU+1PoWtr0jP4gjZEGpufhlHzmQn1/KxMzyXfbKPK4cHrlXB7fFG24pNiE/5oUbtIA3a3l46xRiL0aqanJRGuU+M6eUx2aTW9EyNYdtsgbhrSkbSUGFbvyifKqOVQoYUiiyPo+ze6ZzypsWEMviCGtiYt6zMKAwQsVu/Kp8jiILu0OuDziTPpiI/QY9SqyS6tDhIs8d93dqmVGj7UyZRRN0atCpNeQ6XNxfqMQsK0qhZHelqqCugX4yi3Osk323G6PZitLqocvpcAq3bmU1hhZ2fO759JWko0F6VE07t9JIlRBrwSdSpVCgSCU4NCrSGs+9CAfwq1puEDzyJ27dpFr169ZKcKQKvVkpiYSEVFxRm0rPGsXLkSlUrF7Nmz5W16vZ5bb72VrVu3kpPT8DrCj8Viwe12h9zX2OvY7b6soeho398ChUJBVFQUVqtPqTkvL4+nn36aF198sUExujVr1jBx4kQ2bNiAQqHgs88+Cxrz/vvvo1Ao2Lp1KwALFixAoVCQkZHB9ddfT3R0dKOjducizXKsVq1axeTJk0PuUygUXHXVVXz66ae+CyiVTJs2jcOHDzd43pKSEh5//HH2799Pv379mmST1+tl4sSJvP/++/z1r3/ln//8J0VFRYwcOZLMzMwmnetU4CsC18ipko3pidQU+iZFkRhlkCMek/ol0qNdBH2SoiiyOLDYnLy5qW5H6Kp+iSTHGElLiZKjKPWxelc+Gw8W1+kg+vEv8g4VVPL9gSJyyq28suGwvOi7vHtblAq/UraESa8hPkJPWko0w7rEkpYSTdd4EwqFAgmfc1pa5avH2pljDrmQ9VPToSu3unCdlLJXSKBSKNBrVVTY3BRZHGzKLAmYS41KQVm1k+Nl1fRNimJPXgW/ZZfz1NoDjXIm63M8+yZFMbJbWy7qGENBpYMii4OCChuFlb7eWenZ5UHHTUtLJlznu1e7y4MC8Eq+aFxMmAadWolKoaDa6cHp9rI+o1COOoTp1HRuGy6nyq3elc/6jEJS2oShUPikytUqhU+yPAQt6fFU+zz+CMpbm7P476ajPPTZHvletWolSoUCvUZFx9gwBqRE0ykujDiTjtW78uX+VRa7izZhWkx6DWN6JTAtLZnpaUkna+Y0pLQJo9zqCvhOAHRLMNE/OQogSGZ+Wloy8yb0INqoZV9eJYWVdl+z7pP40uJcQY2+uyeYKK12UlbtJKfMGrIGKT27nK7xJuJMOsK0KhT4fvEqFb7UxPgIHRaHm3yzjf9uzuKF9Qdb1PagtVQBc8ttVNqcVDk8WJ1uDhZYuPvDnfLzplUpWbevgE/Sc+ibFMWwLrFEGjT0Towg2qhpUhqzQCAQOJ1ODh48GLT+y8/PJyMjIygtrjm4XC5KSkoa9c/rbV6fwR07dtC1a1ciIiICtg8cOBDwZX01hlmzZhEREYFer2fUqFFs3769WdeJjo6mU6dOPPXUU2RlZbF8+XJ27twpj7v//vsZP348w4cPr9eegoICduzYwYQJExg5ciTJycksX748aNzy5cvp1KlTUJulGTNmYLVaeeqpp7j99tsbNQfnIs1KBfR6vRw8eLDO/QcOHAh4IHU6HXp9/WlUAO3atePEiRMkJCSwffv2Jn2JVq5cyZYtW/j444/l0Ok111xD165deeyxx3j//fcbfa5TQVpKNBsPFpFbZsXrldiWVVqv4l9z6iNqpsT50w9f2XCYXomRZJVU0yHGUGdaUKXdTf/kqCYunhuW4PYv8rYeLcPj9ZJVXE37KIO86Fv6UxaJUQZKqpz0bh/ByG5xROjVAQvUq/olUlbt5HBRFQ63F4fbF9HomxTZoBOYlhLNun0FeGr8fow1+Rbl/sWwSa+Wa4f8836iwo5GqaCs2uecWJ2+aJfD7WlUalVDTZd/t0sCScJi96JWKXhyzX7amnQhj4uP0HOosAq9RoVSoSAyTINGpcTu8lAtefAioVcrqbS7ZCfj5iEdqbT7Fu3Zpb50Sn+0pMLm4sLkSIotdpQeCa1aKaeM1nz+/M9DS9Tq/FLcAJf3aMu6fQVYnR4UOOWoh9PtJSnagFatZPAFbQjT+hQiuyeYOFBgocLm8i3c20fKQid+x3paWjLT0pL5JD2Hlem56DUqduea5e/E5T3ayve1eO3+kMqA6dnlbM8ux+31FbGplArZuSmstBNp0AT1MjtQYKHS5vJFdOxuDhUGO1b+dMqUNka6J5j4Ylc+e3IrZCEX4OTn5kYjwbtbsxnTKwGgWfPdHFXAmgqAb285RrnV5aufVShQKyXcXlAofNHAvkmRxJp0HC+rZndeBcdKqzlQYGFvXgUnS/l47c8XNdlugUDwxyYjIwOXy0VqaiolJSW4XC52797NAw88gEql4sknn2zxNX766ac6hSFqk5WVJdcgNYUTJ07Qrl27oO3+bfn59b+Q1mq1TJs2jQkTJhAbG0tGRgbPPvssw4YNY8uWLVx44YVNvs4bb7zB9OnT+eCDDwCYO3cul156KVu2bOGzzz5j//79Dd7X2rVr0ev1XHbZZSgUCm688Uaee+45Kioq5Lqs4uJi1q1bx0MPPRR0fL9+/c74Wvx00CzH6qqrruKVV16hc+fO3HbbbbLTZLfbefPNN3nttde49tpr5fFbt24NKrgLhU6nIyEhoTkmsXLlSuLj45k6daq8LS4ujmuuuYb33nsPh8Nx2tQJbTYbJSUlGI1GDAYDer3vLbdBo8Jk0FBa7UQ6mSpTl2PVnPqIUHVPyTFG9BoH7SL1xJl0IVXN/AuqbgmmRi+e/WICDS3e/Iu8lDZGtmeXI+GTTq+5WPcvuP2qakt/yuJQYRW55VaSog0kRhlYdHUf7li2nbIqJyigT1IkSoWiwev3TYoiPkJPuE5Fpd2NQavCqFUTaVBzvMyKAl/NS77ZysglG0hpY6R/cjTtIvXkltu55IIYdueaaRepp9LqkpX8GoPF7pQjI6Ekunu3j8Th9lBW7aJ/chQ7c8woFVBS5Qx5vluGplJudbIju5wooy9aM6lfInNW7ECndiO5FOg0KjlyNXe0TwnS79RY7L7rZJdWY9Kr6RpvIjHKQH6Fg9xyX0pAzfYA/lTIlvZC8p+3pqz89LQkVqbnolX7VPB8jocv0qRRKTlUaMHp9sqy9TXrjmoKWdR2rKelJQf0XKptu3/e07PLgz6btJRoPv0tlwqrE6fHS2y4Vp4Pk15DfqFFTkv0O2l78yqwuzxI+FIxt2WVBb0w8StB+u0d2a0tdpdXdsKcbg82lxfdyfROk17douhgqN8D9b2o+SQ9h39/fxi9WklMmJbcchsVVhcatZILYo0UW5wYNEoKKn0CJgcLLLg8Em4PuD0elMDaPSfQa1S0NelEpEogEDSL3bt3A/DII4/wyCOPyNtHjhzJ5s2b6d+/f73HX3nllVx//fVcf/31dY7p168f69evb5Q9zV2P2my2kOtN/1rZZrPVe/yQIUMYMmSI/PNVV13F9OnT6du3L/PmzePrr79u8nUuu+wyjh8/zr59+0hMTCQ5ORmv18ucOXP4xz/+QUpKCq+++iovvvgikiRx9913c+eddwacd+3atYwaNQqDwQD46rkWL17MypUrufXWWwH48MMPcbvd3HjjjUF21T7f+UqzHKsXX3yRI0eOMGfOHO69917ZOz5x4gROp5OBAwfy4osvAj5ny2AwcM8997Se1SHYsWMHAwYMCMoPHThwIG+88QaHDh2iT58+p9QGPxs3bmTChAkB2/R6PWqtDo9Sg1Kjp1irozTKxKi3ojAYDBgMBtkRW7RoUci3zuXl5fz444/y+JrH1Py/RqMJKLYvrLRTVu1kZ46X7rV6zNQsVG+KJHtjxSv849JSorlj2XYqrC5KqhwB+8Enyb547X6mpyXJzla00Zcbnm+2ySmBOx2+fkDtIg1B0YO6iDPp6JcchcXuIqvEit3l4UCBL0LlPilukWd2oFYqqLC56BpvIi0lhmFdfI6Ab3GtQK1SAoo6Jadr0j3BxKbMEmLDdSGdY38kJC0lhu4JJirtbtqEaVh3si4lTBuclud3zmNNeqocbvmz/NtlnXlyzX70GvB4vLSNCCwI9Ts18RF6zFYng1JjZJn5zEILZdU+AYZdOWZ2HC+nc9twusZHtDj1ryb+5zlCr5brf3q3j+T7A0Xsz6/EZNDQp32kXN8XZ9JxqNBCbrmN6JMOXmKUIcAxqMuxr/3dqd2frdji4ESFjQi9JiBq1TcpikVX9+H+lbux2FwcKqyiXaSZYV1iUSqgTZiWcqtT/jzTs8sx6TWolX5hf3B5vCxZd5CV6bkBKou1bfrw1xwSowwYNCocbsmXHqhU0LOdifgIfaOf7VCEcqL8rRdqKmD6WZ9RiMPlwVztPNkPzI0X8Hi9xITpWDKjP+nZ5XzwSzaHCqtxeXx1mwp8tXteSaK02km8SUfv9pGywqVQBRQIBE3Brwi4Zs0atFothYWFLF68mPT09HqV6vzs37+f3r171zsmOjqaK664osW2Op1OysrKArbFxcWhUqkwGAw4HMHlHna7b93hd0yaQufOnZk8eTKffvopHo+nWdcJDw9n0KBB8s9Lly6loKCABx98kG+//Zb77ruP9957D4VCwfXXX0+3bt3k6J7L5WL9+vUB4iHdu3fn4osvZvny5bJjtXz5cgYPHhwymJKa2rptf85WmuVYxcTE8NNPP/HZZ5/xzTffkJ2dDcCYMWMYO3YsU6ZMkR0cvV7Pm2++2XoW18GJEydC5ofWDInW5Vg5HI6Ah7OysrJFtoR6G2G328EeKFVdkQehKs8effRR+ibFBS1M9u/fz5QpUxq8vkqlQqXVoVDrUGl16HR6bJKatpP+wbNuT4AiYVpKNB9uzuCL/73L+vcjkdRaOreLoUtim5BOm///R8ud7C92MLhLAv2SG158902Komu8iV+PlaNVKQOcjfTscvbkVeD2eAOa+AIsWpMhRxfiI/TEmnSolQp25JhpExao0FYX/ghHvtlGtcNLTrkVlUKBRwFKpU9IQIGE0yORaNTLC/iaC+F1+wrQaU42xGoEDaVW1ozg+BffS3/K4pdj5bg93iDnzb9Y7p5g4liplVSTkfUZhXSJ9/XJ2pRZwk+HS/BKEh1ifClntVUTrU6dL1pn1MhO9Opd+VidHlweiUq7G68Evx2voMji4O4rurba4tjvYC/9KQuz1cW2o6UUWRzklFlxeyUsDjcer0TXeBNKhe8ze2tzFrnlNnm+akZwazr2DaXNpmeXc7DAwrajpSTHGCm3urC7PFTYglP3+iZFcfuwVJ5ddxCXx8uO4+UUVtqJj9Bj0qtxur1yxDItJZptR0vpmRjJ4SILbo8XtxdKLU4qT8qm+z/b2i8ibh+WKvcv655gklX2ruqXyMr0XI6XWeXjmkpd0W5/KmXt7d0TTOwvsKBSKrDYXSfFZCT0GhVatVKuo/SrJUoACmgXqcfq9FBpd6FWKnCezLdtjhKhQCAQ7N69m5SUlIAX0wMGDKBnz5688sorLFmypM5j7XY7ubm5dO/evd5rhHKI6sLvKIViy5YtQSmF/tTBdu3akZeXF3TMiRMnAEhMbJ7ya3JyMk6nk+rqaiIiIlp0ncrKSh566CGeffZZwsLCWLFiBdOnT5fXmNOnT2f58uXyPW7evJnKysqgoMHMmTP5+9//Tm5uLg6Hg59//pmXX3455DWb41CeizTZsbLZbDz00EOMGjWKqVOnBqTenUlaEnpdvHgxCxcubDVb/EorzcVoDC2P2lD42I/H48FjswI+O6zydi92lzdg0dM3KYrVnmo2fvh6s+399ttvufzyy4O2WywWrr32WtkpO1Bsx+mAYr2BX4uSKNkSjdFopMQmoc224PQoiAtLZtMmGwaDQX7j4V8Qxpl0XNa9LT+fXJSbrU46xDReSrZ7gom9eRWAAbdXOlkX40KlVKBRKdGpVBRZHFRYnUFRkXkTevDW5iyOFFcFpIOFwp96d6LChtPtJbPQEjC2Zr1RhxhDgAO08WAR2aXWIGVG/2I5yqjln9P78tBne8gzV/LW5ixeuO5CbhmayoECCxabiy1HSgFIaRMWkA5XO3LjT2VTKxWolL46H+vJ/lAnzHbe3JQFBDZFbm5vJD9+Jy/aqGFbVhker09QxObyUlRpp9Lm4qKOvjFxJh2RBnVALVVdTmrNxXzNn4GT9WXVpLTxRfK6xodzqLASnfr3vmo1mZaWzMr0XDLyK3wCDnY3DreXtiYd/ZOj5fTDmnWMo3smcKLCxs9HSn0y9i5fzVpd+GvC/HMaadTKzaLLTkaOmkuF1cmqk46a/3PyP/t+YZiaRBq1dIs3sfVIiTwfYVoVHdqEEX1SQr7C5iIp2oBBo8Tu9mLUqNColFgdDjxeX/RqUGqbRqcICwSC1sdjrSD3pRsCtiX9bXmrNwg+VezevVsWVPDTo0cPLrroIj755JMAx8rtdvPII4/w2muv0aZNG+bPn0+nTp0abCAcyiGqi/pqrEKlFPpTB/v378+GDRuorKwMEJbYtm2bvL85HD16FL1eT3h4eIuv8/jjj5OamsoNN/iel/z8fLl2C3xOWU2RjTVr1tCzZ8+g+bjuuuu45557WLFiBTabDY1GE1AK9EekyY6VwWDg9ddfp2fPnqfCnmbTktDrvHnzAlIVKysrSU5ufv+VxjpAdVGXra1xXr1GGVQj1Cm6ZXKsdQmTWCwWvvrqq5D79n8d+lybAX956kcffcSkS3zdw4stPiXAOJOOC9oY+Wb+ZBRqLcf0etY9FUVMRHidqZGHSp14FBoiTEa6JLZBn9wbKdK3ADxSXIXXK+F0ealyeFBazXyV7uaOoR3YfqyMCptbdk76JUdRbnUFpIOFwh+NOlRowe7y8vaWYwHOiX+/1elmfUYhUUYt+WYbiVEGusab6J8cTaQx8DOpLUwSrlNTaXPJohv+SMuiNftRKOBIcRX9kqMCFri1oyb+3meRBg3dE0xsyypjx3EzTrcXCcgz2wJsn/fJLlbtzCfKoGHQBW3k8zfFwcos9EWO8sy+FL8Km0tOo7O7vUQYNCgVCiL0anZZHJyosKNVKXl7yzEGX9Am5II9Qq9m29FSuban5lz559ovA+63N0yr4ut9hXX25ZqelsSCvAokwGJ3UWFzyY23L+/RNmBO7xrVmfTscn44VITLIyEBaqWC7FJrvQI1NT8HfxQvyqglJswl90xrTkrdgQILSHC4qIpXNhzmrlGdqbS7GZjaRq5xq0laSjTvbDmGR/LZbtCqMZ3sWxVn8rUDUCsVON1eZg+/gG1ZZRRZHLg9Xlwe6eTnJ2HQKFvkdAsEgj8uBQUFFBUVhUzlGzt2LIsWLWL//v306NEDgAceeID9+/eTlZWFxWJhyJAhQSp0oWitGqv6UgqnT5/Os88+yxtvvCH3l3I4HCxdupRBgwbJ60ur1crx48eJjY0lNjZWPr64uJi4uLiAc+7atYsvvviC8ePHyxlhjb1ObQ4dOsTLL7/Mjz/+KCtVx8fHc+DAAXnM/v37A+5/7dq1XHnllUHnio2NZfz48bz33nvY7XbGjRsXcC9/RJqVCpiWlsbevXtb25YW4VcUrE1jQqI6na5VhS1mzZqFs8NA9ueUUFZhYXr/eJIj1NhsNj7fnkVZRRVaXKi8LnYfKyI1WkPnNjqsVis2m61OR0Wj0XDBBRdgs9mw2WxYrVacztBCB6HoGB+FKdIQlGaWFNGsx0DmVDmCRqMxIIVs48FijpdVo/K6cFWdVJIDKguym3TeN998k4HDfaplHWKMfH+gELdPCI5jr8/miMOKcZ5vrEKtRac38EhkOA5JjU3y5TXvT4hhZZQpwIGbNm0aY8aMCVjQ212+z+e7777D6/X6nL5KN3llTsxOBVXVXg7neWnXJoJDhRaijVriTD4VwpqL6tpO0aDUmCDHoEu8ifbRBgorfS8YGlrg1hQQSc8uZ2BqG7RqJYWVDsqqfc2la7JmzwnsLi9Fbp8iX1NSvvyRrnX7CvBKPsGQKoebdhE6Ku1unB4JBRKVNpdcc+ZP2SupclJpd5OeXUZOmZW7RnUOuKa/J1rNSFJtB9KvCugn0qhlbK+EIAfWz/ZjZVidHpBAr/WJMqS0CcPqdMuRsNpRwK7xJgorHSglcEsSLreHJesO8vaWY3SKC+eWoaEjh/7o5eie8VTa3Vw/qIOsKllbubAxjO4ZT57ZRr7ZLvfMayiSZHW6sTm9KBUQrlMRplXKaZL+Z80rSRwosDBvQg9W78rn+wNFtI3QUVrlRKVU8PnOfIZ2cZJvtgkHSyAQNAl/fVWoko0xY8awaNEi1qxZQ48ePcjPz+fNN9/k8OHDREVFERUVxZAhQxrVtLa1aqzqY9CgQcyYMYN58+ZRVFRE586deeeddzh27Bj/+9//5HG//PILo0aN4rHHHmPBggXy9muvvRaDwcCQIUNo27YtGRkZvPHGGxiNRp5++ukmX6c2d999N9dee21AdHD69OlMnjyZ+fPnA7B69Wq+/PJLwBe5279/P6+++mrI882cOVNW437iiSeaPmHnGc1aUb/wwgtMmDCB3r17c/PNN6NWt2xh3hr079+fTZs24fV6AwQstm3bhtFopGvXrqfNFrVazbBeKRjDI4IWF1EX9JWL+P/9/WHcPTy4Y8N4bHbDb1rGjh3LkSNHArZ5PB7sdjvbjxTwa+YJusbq6BCpwWazsTe7iL3Hi0mOUBNnUPCroh37i+1BPbSio6O55pprsNlsHM4vxWaz4XU7MSjcAU6czWYL2dehpamLdZFf5QlIk8s329ibV0GcJriRbVOo6bAB3PTWNrYeKfW9eXcFOqqS24m9ykle1e+NCauBkqPB5+3SpQtjxoyRz11hdfL1vkIu796WW24Zw/Hjx+u1S6FSodXq0en1qLV6/mMw0C4mglmzZvHXv/41YKzfMdixfiWLtyswGAzsyLci2SXMpU5KUXPdrm38eVhXJl7YMSia51eqrPlsrt6VT1m1E0ny9RNra9IxPS1J3m/Sa6iye9ColScFMBrffNZf51TlcBOuU9OnfSRWp4fjZVaGd43jl2Pl5JZbiQ7TcqDAwl2jOp/8vM1olAqUCtCpVUSdTE2raXdtYYya37m6RFZqR7lq892BYtRKBW6vRKRBIzcHjjPp6kw7/MeYbr62AIVVuL1eiqucuD0SBRUOjpVU0y85KmSqYu1aO3+KZlm1E6fb0+So1bS0ZA4UWFi75wTVTk/APPj7q9UWAXGcTAH1SFDlcKNRKdGqVOSW2zBoVPRPjmLr0VJcHkl21MAXSd6TV0Ge2YZCATtzKjBq1XI/PeFYCQSCxuBXBAwVsbrkkkswmUysXbuWe++9l++++46LL76Ytm1/zx4oLi5uULjidPLuu+/yyCOPsGzZMsrLy+nbty9ffvllg72iAKZMmcLy5ct57rnnqKysJC4ujqlTp/LYY48FiUI09Tpr167lxx9/5NChQwHbr7zyShYtWsRLL72EJEksXryY8ePHy8dERkZy6aWXhjznpEmTiI6Oxuv1ctVVVzVmes5rmuUR3XzzzSiVSu644w7mzJlD+/btg6IWCoVCfgPR2pw4cYKKigo6deqERuN74zx9+nRWrlzJp59+KnvOJSUlfPzxx0yaNOm0Sa37qb2gq/mWetalqSxak4HN6avd8Kf9NAeVSkVYWBgj+nZiRN9OAftqR8Vz12RgcSuIM+kCa2V69eKhf71OenY5W4+U8OuxclJjjSy4qnfAPUiShNPpDHK2qjTRstJbpd0tL9ri4uJ45pln5HE5xWa+25Pjy8PFTY84HSqvSz5fzXPabDaOmV3ExgTKZu/ONbP+l33Nni8IjrBd1S+RY6VWKqrs4G24OXJjz1szKtKYujvJ48Fhq8Zhq5a3nThGULEo/O5M/Pzl+7y7v+75mP8/mF/HvjvuuIPXXntNfhYANCol+WY71Vvfp8JWwOeb41h30hmLr3RTVGAjKiKc7dUxXN6nAxk5BrJqpV7GxcXRoUOHIHs//S2X3HIbeo3PMTtQYKF/chQbDxX5ZPRPXn90z3j5++NfuPdpH8ktQ1NDRl1qC2M0ZkF/oMBCkcXByvRcWcyl5nfi8u5xfL2vEJAotjhQq3zpEkatCqvTQ1K0gXyzTa6F89vUKS6c7NJqJLcCp1vC/xrC6fEGRQj9P9e+J7/a4JHialLaGJvloPhfnuhO1nl9kp7DpswSduaYiY/Qse1oqRz5S0uJpn+HKLYdLcXpkXC6vejUCuxuiaRogxxJs7s8lFsDXzwUVtrpFBeOUavCYncTH+FLHYwz6QJSJgUCgaA+7rvvPu67776Q+zQaTYCoWElJSUC6WUFBAVu2bOG111475XY2Fr1ez5IlS+oV3Bg5cqSvX2At5syZw5w5c1rtOjWZMGECFktoZeMHH3yQBx98MGj7mjVrGDNmTJ1BFKVSiVqtZtKkSSEzrhYsWBAQkTvfabYqYJs2bejWrVtr28PLL7+M2WyWG5utXr2a3NxcAP72t78RGRnJvHnzeOeddwIKC6dPn87gwYOZNWsWGRkZxMbG8sorr+DxeFpVmKK51C6wL7Y4fLUskkTHNo0XYGgO/qapDrcHnVpF9wRTnQX/O3Mq8EoS2aW2oAWdQqGQ0yaPV0F6gZ20lCT21KgR6ZYQIR8XHx/P/fffH2DLojUZfH+giEiDhkn9EuuVeK+dMgW+RXSXNgPo/+23vP3jQXYfK8ZhtzH8ggi6x+llpyy7sJzcEjMRagldrchbfHxglKLS7uaSC9pwOK+IlrwKqB25q7mAbkn0LlSqpd+ZeMoVXFfYWPwvJWpK7qelRNMhxsiqD/dwcF866SGOOwxsA4L7rfuYNm0aK1euDNpusbs5uup5rEd3kGE0kBQbhUuhpsQOTtSoNDrC28WwITuRnz/wOWuSS4G+1MmxYzp+cvfhLzdOC3nNiooKOkdAhsODSacPiMrUJbhRWyWv5ndi8bR+dE3I4t/fZeL0SLLYidsjUVhp53iZlWMl1SRGGeRneOlPWZRbXbSL1FNkceLyuOQe2iO6/q7yWfulS6iap21HS+mfHIVSEVqwoyHiTDriI3Rkl9r4fEceX6oUeLw+YYu8chsDUqICFBbfuWUQi9ZksHbPCRQKBTFhOrkmq2YkrWbt2qHCKhxuD5EKDYuu9qXv+Fs8JEYZRLRKIBCcErp168bTTz9NTk4Oer2em266CYVC0ah+qYKmM3LkSIYNG1bn/lWrVlFcXMzMmTNPo1VnL81yrDZu3NjKZvzOs88+K8u3A3z66ad8+umnANx444119jJQqVSsXbuW++67j3//+9/YbDYuvvhi3n777VPiADaVUH2pnB5fwXdj+iK1hPUZheSb7VQ53PRuHxFwPf9bZb99l3ePY8OhYsK06nob4dZchPrTqronmBpMD6vZ6LUxzX1DLc4MBgOXX3455qiuFG/KokOMgUs6xTapD1dN/Hb8aWAy/51ZLjtgL369lyMF5bQLUzC9fzz7c0pYtvkQlVXVJJnUTOjZJiDKVju/u6b97dq1o6qqSnbwmlIbV5/wSksdtpoKhf4GzQA//UtB0yrXAs9bm/TscsJ0atyWMtwVhVRWQEZwSSS/7YPfvg193j2bBtfpWD3wwAO8/vrvypYanR6tTk90RDgu1Ci1Ogx6Aynx0RgMBpyoya90k63QYNzfg1mXLg7Za8trzsN1Ih+1VocyxkSlQkO1DZQaLQUuA2Hq35+5mv3XwnTV7M2rwOvx1Sz5FQmhccqKyTFGWawls9DS5JqlSf0SySmzkltux2J3YdKp8Ug+B1GlVFBY6Qj6/tX8bnZPMHGgwBJQ61f7+xjqufELeQhVQIFAcKoYN24c48ePp1evXiQlJXHZZZdRXFwc1MdU0DrUfkHuZ9u2bezevZsnnniCCy+8kBEjRpxmy85OznxxVC2OHTvW4Ji3336bt99+O2h7dHQ0//3vf/nvf//b+oa1kNo1DgDJ0QYcbm+ddR6thb+Y3e31crioikOFFjrEGOXGq7XtKz8p6+0XAwhFTUdx9a58bC4P1U4Pc0fX79zU5Sw1h02ZJVjsLk5UKELW1zSW2jZFRfn+f9vkNgEL2nFAtmkHvx4rwxSmZdzVfRp9rczMzICfPR4Pb3y/nxJzFQalmyt7xbLrWBG7jxWxP6eEE2UVKD0uxvdow5gxdedkjx07lqKiItm5yyupwGazYrPasNvteFx2JLczZP8to9EY0E+r5r20doQtLSWaD3/NQeV1Nfu8Gm3d6by1Uy1dDjsuh53qSnPA9qMZwce6i44Ai4NSCgFSS39h9we+9JJQFXLXLoHrVWoMRgNhJ1MhnWiwuBUowuNoN+0hIg2BAhnp2eXsTP+VNcu2M6xH+6D6t7d+zuOY2UWY0cil3RNZddhNt6RYnA47fdpHyipO9eFXK3xrcxZ78iowalVoVAqqHRbcXi8qZfA5an8P6kutrOt73Jrfb4FAIAiFUqmscx0oOH28+uqrvPfee/Tv3198FjVotmNVWVnJK6+8woYNGygqKuL1119n4MCBlJWV8fbbb3PVVVeJsGwI/JGeOJOOOFMcxRYHBwoszZJVbizT0pKptLvZeLCYncfLcXm87LFWMLRLbNCiKT27HK8EO3PMXNyx8W+dLXY3e/MqTul91GR3rpltWaVU2NyyWlldBfON7b1Ue1xdi0Sz1Se/7e8hVd95VmzL5rsDxVzePY7F0/oF7Lu0R5L8/+5JUXTv3p1rTx7/0Gd7KLe6KGkXXa/SUW3lH//5K6xOXt14BJfXFxX9+6hUZvSPD0iJjI2NpdhjkG3wHxuhV5M24XqGjanEpPYGHPNLZgGlFRaUXhfROgm7zY7C48TttMvj6kpdvH1YKukvu2lufDYlPrQ0OrTMEVRqAh22mi8NfmuEYqbH46bKYqGqVt66vk0VPRNNpKXEBEVq1yz7lU9ef5ZPGjj3j7V+vhNo06YNJSUlIcd/++23vPbaazhQY3EpCQ8zcKLcDWotqDTYPCrskppjYQb+WbGXm4Z1C3DsunXrJr/1DRVlb2kfs3ORRYsW8fDDD9OrV68gNdwtW7Zw//3389tvvxEREcE111zDU089JfeZ8eNwOHj00UcDCsyffPJJRo8efTpvRSAQCFoN4dyGplmOVW5uLiNGjCAnJ4cuXbpw4MABqqqqAF/91euvv052djYvvvhiqxp7PiCn3PVoy+pd+ew52aB1b14FvdtHBqTVtPZ18802DhdZKLY40KmVnKiwk2+28Ul6jiw64RcZqLK72JRZUmcPnpqpgJP6JbI3rwKH29sseeim4F/Y5ZttROg1VNjcROh9EYGoOtIQa9eT1UVjxsWZdHgkCYfTy7qMwpB9ivwKeNuOlvJLVhkuj5fvDhQHXcMvyBEKi92N2+OV+1TVNxehVPCW/pRFdJiWIosDtUpJm4gwoqOjiY4OnJ92/F7j449QbDtaSv/R04kyaoLSK3fnmlm9y1f/WNezGqoYF3wOftLyN3n0g5/IyCmhU7SGawckBImW1Pz/kROl5BZXoPK66Nip7hc1LWnK7SIwolTToW6JwxYdEc6kfu3l77z/3AAOe92fa0PU1zv40KFDfPJJQ+4aFAFHCa6Tc7lcsmNVcx6+/vpr7rrrLhySGqVGi8FgIDUhJqhnnMFgIDw8nMcee6xZ93a2kZuby1NPPUVYWFjQvp07d3L55ZfTo0cPnnvuOXJzc3n22WfJzMwM6t938803s3LlSubOnUuXLl14++23mTBhAhs2bGDo0KGn63YEAoFAcIpplmN13333YbFY2LlzJ23btg2QvASfVKRf/14QSM2Uu5+PlmKxu1EpIUznayh7qiSKa6qsrc8o9NfUY9SqWZ9RKItOzLo0lXCdmmKLg5055jojULUb1vZuH8mhwqpWt7s2NYUWhnf1RfzKrU6+P1CEUasiQq8OKQbQmLqPxozrnmBCq1LgdANIrM8oDHKs/OIDUUYtHWONZJVYSYrWszvX3KhrrN6VL6u51ZQ7r2suQj0zaSnRpMaGoVYpaWvS1ZvWWfv+ffU1lXRPMIWUL2+ox1J9qWqDBg3iOndbzFZXSMctFH6HL6qOnlMAK1asoKqqih1HC/ntaAEXRGtJilBjtVrZn1vCgdwSEsNVxOhg0/48KquslJorsdvtDL6wbolek8lEbNt4qq1W3E4HribVxhnlOa0tFFNV3XxH0CGp6/xetsQRVKvVdao+lZWVkZWVFbAtc3fo84SFhZ03jtW9997L4MGD8Xg8QVHC+fPnEx0dzcaNG4mIiACgY8eO3H777axbt44xY8YAvl41H3zwAUuWLJGbeM6cOZPevXtz//33s2XLltN7UwKBQCA4ZTTLsVq3bh133303PXv2pLS0NGj/BRdcQE5OTouNO59ZvSufcqsLl9uDSqvCpFfTNd50you+40w6tGoF1Q4PZdVOrE63LKfsv/ag1Bg5AlVXulvtNLkwrYq9eb7+O2FaFXNHnxrBkJoRP//171y2naJKO3aXh0dX7eXtLce4eUhH2eGpK6Wvsal/Nam0u+nRLpKdx8sBBdEhFvv++pb07HKuH9Sh0VEqP8UWB1UONzFhWrrEmxqci1DPTN+kKLmRq39sY6l2euiWEMGBgsoAlcdA6gmbNEBDPaRqU/s+Q0XqIiIiiIiI4JssB+HtwnAaNYy4NJXduWYKIsr56/jfx45sQjrb888/z/PPPy9f9+XvDhKuktiaeYKS8grcTieXpJj4v+EdAiJuh/JKKXaqQ9qflhLNj526MOzyMWgkN1arlfLKKsyWaux2G26HHZfTgdNhD1kbp9QE9/Ly0xpiJqHmpinnrauv3bnGjz/+yMqVK9mxYwd/+9vfAvZVVlayfv167r77btmpAp/DdPfdd/PRRx/JjtXKlStRqVTMnj1bHqfX67n11luZP38+OTk5JCeHzgwQCAQCwblFsxwrm81GXFxcnfvr0sj/o9FQPUJbk5bDNieSBE63l0n9EoNShlqbSf0SWZdRiNvjQAEkRhmCIi6RRi2RRi3VDne9qWg12ZZVRnm1Cy/w6g9HSI4x1plG2BJC9QfTa1QoFb7mplaXl4wTlby95ViD129simBN/NGoSKMGl0fiWGnoyENtOxsTMavZT0qvUeF0e3llw2G531BD12jq/trUjAZGGTVBDrcf/3Pa3JcABwos2FyeRqthhprL+iJ1tXtE1R7bXIGF9OxyUKjIKLHSp1MSvx4LI0KvplevBC69tGfA2KU/ZRFehzPdNymKpU8/CAT3C6n5O+OpNRn8llWE3e4g3iBh0kC11Ypeo6pTsXP48OE89thjWK1WcorN5JVUUGGpothswWG3yzVzlVXVSC4HBqUHye3EarViMBjk+Vq9Kz/gd1dTUi3rU7E8V/B4PPztb3/jtttuo0+fPkH79+zZg9vt5qKLLgrYrtVq6d+/Pzt27JC37dixg65duwY4YAADBw4EfCmFwrESCASC84NmOVY9e/bkxx9/5I477gi5f9WqVVx4YXCU449GfQvASf0SSYwysHpXHhn5FnblVvCvdQfpnxx9ytIB/VyYHMWevArsLg+f/pZLhF4d0Ksm32yjrUnLCa8XrVrZKEEKh9sjN0N1eyRe+v6w3Hj1VLJ6Vz77T1QgSb6oWbXDg7IRqmnQ+BTBmvijUX5xiYaaO+/ONfPW5iyOFFexK8fMsC6xAU2Ua1JT2CQp2sCO7HLsLs8pr1vz45+PDjEGDhRYyKfuOqp8s02W226Kbf4U2HKrK0CVsjl21hWpq2lPcz7j+q7r7y9ltjq5uGMMx8uscpPgumxsiuBDTftnXJTM0RIrmshwrC4vVSoFDlU07ePC60ztHD58OMOH+1Qk/SmUO3PKOVZqpWMbI/8Y44skz1mxA7vLQ2psGCtm+zqJezwe9p3wpSNbne6A312TJk0iJSUFm83Gut3HqaisQiW56ZNgCKiJs9lssqrmucxrr71GdnY2334bWvv/xAlfr4B27doF7WvXrh2bNm0KGFvXOEDu2Vgbh8OBw/F7n7qaDVIFAkHzWLZsGYsWLeLIkSOEhYVhNpsZOXIk0HAroY0bNzJq1Cg2bNggH3Mucr7cR2NYsGABCxcurLP2+1TQLMdq7ty53HTTTfTt25cZM2YA4PV6OXz4MAsXLmTr1q2NKqA+32nMAnBXjpk9eZVIHonduRV0jTfJvaVag9qLuvTsclLahGF3ediTV0FuuY2V6bmyY+WX325r0mPSawGpUY6eyyNh1Cixubx4JbDYmxYJagklVS40aiXheg0DUozklvsW/KGEJWrSEmnoTnHhFFbaGdYlts4xu3PNvLLhMHvzK6l2uLE6PdhdnjrT62qmOb6y4TAKhYL8CjuHCk9PBLim8IWvXi70Z+9vDtvYZ6P2sRqVUq4ha45EflM+t9aU/66Z4hmhV/P2lmNUOdwhRV5qXrc+2fL6mJaWTE6Zlc925iM5PZitbnRq39w1xlH0P09Ot5eECD0GjUq+vk6tIM/sJNLw+58AlUoVUANa83dXx44d5WbsPS49v5UBS0tLefTRR3nkkUfqzMzwp0bqdMEtAPR6fUDqpM1mq3NczXPVZvHixWdFc3uB4HzhwIED3HzzzYwbN44HH3zwvElbPp3k5+fzxhtvMGXKFPr373+mzTkraZZjdeONN5Kdnc3DDz/MQw89BPgatkmShFKp5KmnnmLKlCmtaec5SWMWdXEmHYmROootTrrGh5MYZWjVxUrNqBn83tTT39tKgTMg6uJfjI3uGS+najVmEdcpLpyyaiduiwOlQoEkNa2up7lM6pdIscVBYaWdQakxRBq1fPpbLpU2d4DD2JqkZ5dTbnWhVSvrFYVYvSufIosDSZLQa5QkRRvqTK+DwOdldM94fjpcgkaloKy68YIJrYFfQdL//6bub+y5gUY5HHVFfFpD+rup56j5Ga1M9z1nDaXLNidq5rer2umhY5sw0qt8qbtatZLe7UM3SQ9lK8DqXXkcLqyic3y4HH12uCWMGhUOd+i3ePX97mpNZ/Vs5OGHHyYmJiaorqom/nTHmhElP3a7PSAd0mAw1Dmu5rlqM2/ePO655x7558rKSpEyKKgXhUZPzOg7g7YJfGzcuBGv18uLL74Y0A5o3bp1Z9Cq08/w4cOx2WxotU3PGsnPz2fhwoV07NhROFZ10Ow+Vg899BB//vOf+eSTTzh8+DBer5dOnToxdepULrjggta08bzGnxJYYXVyoMBSZ+1Ec6m5qKvZDLZLvInBF7Sh2OIgzqSTF1w131g3tgYG4JahqRSeFJCosrvpGHt63gT1TYrihesuZHeumcVr95NvtlFc5SBcp24wTa+5+J2DQ4UW1u0rCEilrI1Jr0anNjIwtQ1RRk2jHb1pacl8sSufvXmVxIQ1L2WuJSRGGep0NlqysK55bO2oSF3UlVLbnBq5xp67MUxPS2J9RmGDIhzNma+a9W5d48M5XGTBbHPh9khNqsNMzy6nwuZGpVJSYXPLx6a08X0/p/RPrO/wPxyZmZm88cYbvPDCCwEpena7HZfLxbFjx4iIiJDT+PwpgTU5ceIEiYm/z2u7du3Iy8sLOQ4IGFsTnU4XMtIlENSFUqPDNODKM23GWUtRURFAULpycxyMcxmlUilHzM8WqqurQ7a1OBdRtuTgDh06cPfdd/Of//yHV199lXvvvVc4VU2kb1IUsy5NpdrpwebyNsmZacr5+yZFkZYSLfd6Ss8ux2J3c6DAgsX++4LLz+pd+azdc4Kv9hbIynINXad3+0iiTgpfVNjcjTqutUjPLqes2kV+hR2vV8Lq9NCxzalx7vomRfHQxJ6UVfuc4be3HAs5blK/REZ2a8v0tKQ6e2ydjdSOcp4qaj6b9VHzuW3M9qaQlhJNdmk1n/6Wy9wPdrA719zoY6elJfPany86JVFR/71N6pfIQxN7MqFPOxIi9Og0SipsLootDpb+lNWgvWkp0fRpH0lStIE+7SPl7367SAPtIvUcK7WyaE1Gk+77fCYvLw+v18ucOXNITU2V/23bto1Dhw6RmprK448/Tu/evVGr1Wzfvj3geKfTyc6dOwPe5Pbv359Dhw4F1Uht27ZN3i8Q/NHIy8vj1ltvJTExEZ1OR2pqKn/5y19w1mhpcfToUWbMmEFMTAxGo5HBgwezZs2agPNs3LgRhULBRx99xKJFi0hKSkKv13P55Zdz+PBheVzHjh3lNhBxcXEoFAoWLFgAwMiRI4NqjXJzc5kyZQphYWG0bduWu+++O2TkGXzf5XHjxhEZGYnRaGTEiBH89NNPAWMWLFiAQqHg8OHD3HzzzURFRREZGcmsWbNCigO99957DBw4EKPRSHR0NMOHDw+KrH311VcMGzaMsLAwTCYTEydOZN++ffVPfI05q1lTNnLkSHr37k1GRgajRo3CaDTSvn17/vnPfwYcd/HFFwMwa9YsFAoFCoUioElwU+YiIyOD66+/nujoaIYOHcqzzz6LQqEgOzs7yOZ58+ah1WopL/etSzZt2sSMGTPo0KEDOp2O5ORk7r777kYp2JaUlHDgwIEW9b+sjxaHR6qqqigvLw9ZGNahQ4eWnv4PQ3p2GQcKqrDYT13aV+0359uOltIhxojZ6gxanBZbHJhtLhQn/98YJvXzvXn9/kARDpen0ce1BhF6NXa3hwi9mkqbmzbhmlZ3Umvjr5myOj0h99eM/q3eld9ksQen24tGpSC71NooAZHWojUFH1qDuiI+DUWCakfEQqX89U2Kwu7yUGlzsyev4rTVBTZE7Xvzf7f836nCSjsHTz7fDSlDhmqXsO1oKQ63lz15FSRFG86a+z7T9O7dm88++yxo+8MPP4zFYuHFF1+kU6dOREZGcsUVV/Dee+/xyCOPYDL5BEyWLVtGVVWVXHsMMH36dJ599lneeOMNuY+Vw+Fg6dKlDBo0SKT3Cf5w5OfnM3DgQMxmM7Nnz6Z79+7k5eWxcuVKrFYrWq2WwsJChgwZgtVqZc6cObRp04Z33nmHq666ipUrV3L11VcHnPPpp59GqVRy7733UlFRwT//+U9uuOEG+QXGCy+8wLvvvstnn33Gq6++Snh4OH379g1pn81m4/LLL+f48ePMmTOHxMREli1bxvfffx809vvvv2f8+PGkpaXx2GOPoVQqWbp0KZdddhmbNm2S1T/9XHPNNaSmprJ48WJ+++03/vvf/9K2bVueeeYZeczChQtZsGABQ4YM4fHHH0er1bJt2za+//57uY3DsmXLuOmmmxg7dizPPPMMVquVV199laFDh7Jjxw65JrYplJeXM27cOKZOnco111zDypUreeCBB+jTpw/jx4+nR48ePP744zz66KPMnj2bYcOGATBkyJBmzcWMGTPo0qULTz31FJIkceWVV3L//ffz0Ucfcd999wWM9bewiI72/T3/+OOPsVqt/OUvf6FNmzb88ssvvPTSS+Tm5vLxxx/Xe58vv/wyCxcuPGXiHc1yrOx2OwsXLuR///tfyD5Wfjye0AtOQTBl1S40SgVl1a7Tcj1/If5bm7MorLTz/f7CgIVnnElHmFZFpd3NtqzSBoUg/Ofsm+RrQvzT4ZJGH9caVNrdXHJBG3bmmOncVkOFzdXoPknNJSnagNnqIim6fnnp5oo9TE9L4qXvD6NTK0+bMiCcPzU0tSNvoVL+/HL9EQY1neLCzxpnsja1xTC2HillZ46Zizs2vW4rLSWau0Z15l/rDpJdWg3Q6inI5yqxsbEh64NfeOEFgIB9ixYtYsiQIYwYMYLZs2eTm5vLv/71L8aMGcO4cePkcYMGDWLGjBnMmzePoqIiOnfuzDvvvMOxY8f43//+d4rvSCA4+5g3bx4FBQVs27YtoGXB448/Lr+kf/rppyksLGTTpk0MHToUgNtvv52+fftyzz33MHnyZJTK35Ou7HY7O3fulNP6oqOj+fvf/87evXvp3bs3U6ZMYefOnXz22WdMnz6d2Ni6hafeeOMNDh06xEcffSS/JLn99tvp169fwDhJkrjzzjsZNWoUX331FYqTisR33HEHvXr14uGHHw6KMl144YUB3/vS0lL+97//yY7V4cOHefzxx7n66qtZuXJlwD3656aqqoo5c+Zw22238cYbb8j7b7rpJrp168ZTTz0VsL2x5Ofn8+677/LnP/8ZgFtvvZWUlBT+97//MX78eOLj4xk/fjyPPvool1xyCTfeeGOL5qJfv368//77AdsGDx7Mhx9+GOBY/frrrxw9elSOMAI888wzAfWps2fPpnPnzsyfP5/jx4+f0cBOs/6a3nXXXbzzzjtMmTKFYcOGyR6koPlM6Z/I1/sKGdfr1DoDNfG/rfdK8PW+Qsb2SvhdXrlfInvzKsg4YUGSYH1GYZMcpAqbCwka1U+qPhorLuBfEF/cMbpOOfPWZHeuGafbS0obI11PNvGty9bmij1MS0vmQIHlpFN29tEa4hGnEn/kLUKvlqOXtRU3/SqZ/ZJ9aYlnM/75rrA6OVhoQa9WNikqW7tRta9BuAeV0lWvCIsgNAMGDODbb7/lgQce4O6778ZkMnHrrbeyePHioLHvvvsujzzyCMuWLaO8vJy+ffvy5ZdfytL4AsEfBa/Xy6pVq5g0aVJQHzhAXpCvXbuWgQMHyk4VQHh4OLNnz2bevHlkZGTQu3dved+sWbMCaqX80ZSjR48GjGsMa9eupV27dkyfPl3eZjQamT17Nvfff7+8befOnWRmZvLwww8HBRkuv/xyli1bhtfrDXCO7rwzUFxk2LBhfPbZZ1RWVhIREcGqVavwer08+uijAcfVnJv169djNpv505/+RElJibxfpVIxaNAgNmzY0KT79RMeHh7gLGm1WgYOHMjRo0cbPLY15gLg2muvZe7cuRw5coROnToB8OGHH6LT6Zg8ebI8rqZTVV1djc1mY8iQIUiSxI4dO+p1rBYsWBDgpLU2zXKsPv30U2677TZef/311rbnvKQxC9DLesQTadSe1jfmu3PN2FweCivtXHJBTEC9St+kKOZN6CFHtJoa/fF4JbwSLVa0a4y4gH9+I/Tq0+JU+e1KaRMmv/H321CXrcUWB0eKqyi2OLhlaMN1RX66J5jYm1dBscVxWtMBof7n1i8l75V8aWV1NTE+k/ijPIvWZHCo0ELXEH3Vmpv2eLqdSv98Rxm1ZJdW4/F6qXZ4Gz6wBrXv1er04PZ4qXJ4Wi1idbY7282lrv42Q4cODaofCIVer2fJkiUsWbKklS0TCM4tiouLqaysbNDZyc7OZtCgQUHbe/ToIe+veY7aC2n/C39/TU5TyM7OpnPnzrIj46dbt24BP2dmZgK+SFFdVFRUBAQf6rMzIiKCI0eOoFQq6dkzsOl8qOtedtllIffXbkbeWJKSkoLuOTo6mt27dzd4bHPmIjU1+GXmjBkzuOeee/jwww+ZP38+kiTx8ccfM378+ID7On78OI8++ihffPFF0GdcUVHRoL2nkmb9NVUoFAwYMKC1bTlvqZ2SFGrh0RoKZ82xy2J3o1IqCNdrgt7Y11Wf0RBxJh0GrQqXx9tiRbv6Fr7+RVy+2YZRq2bb0dI6e0S1Nn57THo1Rq06QHmxtq3p2eXsyauQIwR++xqzCD1QYKHI4sDh9p72Opj6nsn07HKijFp+PlpKTJj2tKYqNo/QTaObm/Z4ur6vNZ/xKKMWs9XXHiHfbMPicBOmVTX6XLXvtU/7SEosDnSapkW+6uNM/B4TCAQ+PNYK8v/7l4Btibe9isrYuBYN5zIqVejfhaeyMazX63u5tWTJkjpFaMLDwwN+bg07/dddtmwZCQkJQfvV6ua9KGuJbc2Zi1CtJhITExk2bBgfffQR8+fP5+eff+b48eMBNWgej4fRo0dTVlbGAw88QPfu3QkLCyMvL4+bb75ZtuVM0azZnzx5Mt9++y133HFHa9tzXlJb8jzUwuNMCAakpUSzbl8BkQZNyP27c81yxGp6WlKjU/q6J5iIj9BTYrFjdXpaVGdV38K3piS11elGr1FhdbpbtcFyQ3a9sP4gX+8rYFyv+DptTUuJZvWuPIor7UQa1AFiCo1ZhEYa1I1uCtua1PdM+rfZXR7KraenLrC5TOqXGPI+WhJdOV3f15rPeLcEk3y9OSvMKBTw4fYcqp0eJvVLbPI93DI0lSPFVZRbXa0mNHO2CZ8IBH80vLbKhgedQeLi4oiIiGDv3r31jktJSeHgwYNB2w8cOCDvP1WkpKSwd+9eJEkKiODUtsefqhYREcEVV1zRKtfu1KkTXq+XjIyMOh0U/3Xbtm3batdtLLUjWn5acy6uvfZa7rrrLg4ePMiHH36I0Whk0qRJ8v49e/Zw6NAh3nnnHWbOnClvX79+fYuu21o0S279kUce4ejRo8yePZv09HSKi4spKysL+ifwUZfkeV1jTqdd8yb0YFK/RFl1rCb+SEtWSTVvbmpY2tlPpd1NpEGD1eUlt8xapxx5S6kpSZ0YZSClTVirN1iui925ZhatyWDVznycbi9f7yusc376JkXR1qSnR2IkbU162b7GyIX7Ppv2zJvQ47RHAOp7Jv37bhmayshucSGfn7OFuu6jJbLyp+v7WvMZ91+vb1IU/ZOjAAUer8ShQkuz72HwBW3oFBdGnKl1+iWdid9jAoHg3EGpVDJlyhRWr14d1K4Afo+OTJgwgV9++YWtW7fK+6qrq3njjTfo2LFjvalyLWXChAnk5+ezcuVKeZvVag0ShEhLS6NTp048++yzVFUF10IXFxc3+dpTpkxBqVTy+OOPB0Ve/HMzduxYIiIieOqpp3C5gl9sNue6jcXfa8psNgdsb825mDZtGiqVihUrVvDxxx9z5ZVXBvS48kfWakbSJEnixRdfbNT5z0q59S5dugCwY8eOelWNhCpgMP4FR1OafJ4pIvTqkxEJJ7Hh2kan9/gjYUrA6ZE4Ulx9StQBa0eITuebcr/Sn06tIN9sx6BV8dbmrDpTJ0f3jA9qJtuYNLSzWaHvXK+nOReiK6E+f78Dr9coqbK7yC23NatGaneumUOFFrJLrVyYHNXgeIFAIGgNnnrqKdatWycravbo0YMTJ07w8ccfs3nzZqKionjwwQdZsWIF48ePZ86cOcTExPDOO++QlZXFJ598EiTs0JrcfvvtvPzyy8ycOZP09HTatWvHsmXLMBoDe2MqlUr++9//Mn78eHr16sWsWbNo3749eXl5bNiwgYiICFavXt2ka3fu3JmHHnqIJ554gmHDhjF16lR0Oh2//voriYmJLF68mIiICF599VX+/Oc/M2DAAK677jri4uI4fvw4a9as4dJLL+Xll19uzSmR6dSpE1FRUbz22muYTCbCwsIYNGgQqamprTYXbdu2ZdSoUTz33HNYLBauvfbagP3du3enU6dO3HvvveTl5REREcEnn3zS6Hq6s1Ju/dFHH60zHChomLOpDqE+Ww4UWLC7vITr1Li9jVe080fC7v5wJ9mlVjRKRZNVBWvT0CL+dDsgfqW/rvHhfH+giKJKO9uySusUmJiWlnxaZOdPBXXN/dn0HDeHs9lprY/Vu/Llmj2n24tHsje5RsovhnGkuBq3xxdxvaxH/Dk5HwKB4Nyiffv2bNu2jUceeYTly5dTWVlJ+/btGT9+vOy8xMfHs2XLFh544AFeeukl7HY7ffv2ZfXq1UycOPGU2mc0Gvnuu+/429/+xksvvYTRaOSGG25g/PjxAa0UwNdYd+vWrTzxxBO8/PLLVFVVkZCQwKBBg5pdLvP444+TmprKSy+9xEMPPYTRaKRv376yDDrA9ddfT2JiIk8//TRLlizB4XDQvn17hg0bxqxZs1p0//Wh0Wh45513mDdvHnfeeSdut5ulS5eSmpraqnNx7bXX8u2332IymZgwYUKQDatXr2bOnDksXrwYvV7P1VdfzV//+tcgSfwzgUI6lZV95yiVlZVERkZSUVHRbHWV+jjTb/ob0zgVYNGaDH48VESl3c2g1DZNUrMDuOmtbWw7WoZSqWD2sFTmju7W8EF1sPSnLMxWF1HGYJGNM4V/HjceLGJvXiWx4VquG9ghpH1n+jNvCXXNfXNr8ASNo65n5qa3trHzuE/R0+OVUCkVTOjTrklCM0t/yuJggYV9+RWUVDmI0GsY3jWOhyaeuvSaxnKqf/+eq4h5Of/p+OCaFh3vsVaQ+9INAduS/ra81cUrjj19ah0bgeBspLG/g1slllpRUSHS/prAma5DqB1pqMuWSf0SGd61LR3bhGF1enhlw+FG11kBON1eDFoVMUYNkcaWqwM2VI90uvHPY9d4E8O6xBITpq0zJSs9u5yDBZYmzyH4FthLf2p8jVtrU9/c78wxk1VSzcr03DNg2flNXTVgTreXKKOWOJOOCIOGdpH6JtdIpaVE0y3BxKKr+3Bl30QSIutvci0QCAQCgaBhmu1Ybd++nXHjxmE0GmnTpg0//PAD4CsKmzx5cp19PwRnHv9COUKvrnfB3jcpisQoAya9hp05ZryS1KQi+elpSSRFG4iux+FoLGfaGQ1FWko0VqevsWqcScfA1DZBjVb9TlGEXo3Z6iTKqG2y0MDqXflsPFjE6l35rWZ7U6hP/EGSJMqtLopO9tkStB51ObTT05Lo0c7ExR1jGNsrgR7tIposHlLzM+2eYMKgUdI9wdSa5gsEAoFA8IejWY7Vli1bGDp0KJmZmdx4440ByiWxsbFUVFSI5sEnOdPRhlD4F1WVdneDqmhpKdEoFdA/OQqlQtGkiNG0tGSmDkjisu7xQQ7H+YDf8TRq1RRbHBwsqAxyIP1Rh0q7m7tGdQ6QzG4aZ19NY1pKNIlRBuJMOiINmmYp051OzsbvYk1q21eXQzstLZm7RnUmzqTDpFczumc86dnlzbqv3blm1mcUEmXUnpffUYFAIBAITifNcqzmz59Pjx49yMjI4KmnngraP2rUKLZt29Zi484HWiLpfKppTHpd36QoRveMR6nwKds1p9/P2ZbC15r4o1ZHiqsosjiCRARq3n9zo26T+iWelZLmfZOimJ6WRHK0gQ4xxrP+Mz6bv4vQNPvSs8sxatUkRhk4UGBpdkQzPbscr+RL6WxpVFkgEAgEgj86zXKsfv31V2bNmoVOpwupDti+fXsKCgpabNz5wNnsWDR2oV9pdxNl1LI+o+5eTXWRWWhh29FSMgubplp2ruCfu7JqJ0Uhmqy2RgpjU8/RnMhMc6M5lXY3A1PbEGfSNTtqcrpobArsmaIpvyv8Yw8VVPLBL8f5Lbucn4+WNvmeakakRcRKIBAIBIKW0SzHSqPRBDUuq0leXh7h4eHNNup84mysDWoqaSnRmK1OvBJNFl9Yn1FIudXF+ozCU2fgGWR3rpm9eRWE69QkRxvOiqhScyIzzY3m+Bf4wFkdDYLAFNjmComcSpryu8I/dkdOBV4J7G4vGpUyYP4b4yz3TYpqYYqqQCAQCAQCP81yrAYPHhzQkbom1dXVLF26lBEjRrTIMMHZRXKMkRMVNoosjialHI3uGU+0URPQGPd8Ij27nJQ2YaTGhjFvQo+zwoFuTpS0uZHVvklRROjV7M2rILu0+pxYnPtfFDRHSORsY1yveEwGNVEGDS6PNyCdr7HO8vnw8kcgEAgEgrOBZjlWCxcuZPv27UycOJGvvvoKgF27dvHf//6XtLQ0iouLeeSRR1rVUMGZIz27nOxSK8dKq8ktt1IcIuWtLqalJfPany86b3sc+WWr7xrV+axZmDZnodySxfXK9FzyzXYKK+1nzRzUx/kUpZk7uht3DO9Ez8RIYsICBSjO5jRkgUAgCIXZbGb27NnExcURFhbGqFGj+O233xp1rEKhqPPf6NGjA8YuWrSIq666ivj4eBQKBQsWLGi0jVu2bGHBggWYzeYm3FnLyMvL45prriEqKoqIiAgmT57M0aNHG3XsU089xeDBg4mLi0Ov19OlSxfmzp1LcXFxvcctX74chULR6Ay0jIwMhg0bhslk4qKLLmLr1q1BY5577jl69eqF21136vlLL71EZGQkLperUdc922hWtfKgQYNYu3Ytf/nLX5g5cyYA//jHPwDo1KkTa9eupW/fvq1n5XnGudYsNkKvZuuREqwuD5JEk3vmtIRzba7+aOzONVNkcWC2OekYazzT5jSavklR583zFKFXU1btwOr0sCvHzO5cs3x/58s9CgSC8x+v18vEiRPZtWsX9913H7GxsbzyyiuMHDmS9PR0unTpUu/xy5YtC9q2fft2XnzxRcaMGROw/eGHHyYhIYELL7yQb775pkl2btmyhYULF3LzzTcTFRXVpGObQ1VVFaNGjaKiooL58+ej0Wh4/vnnGTFiBDt37qRNmzb1Hp+enk7//v257rrrMJlM7N+/nzfffJM1a9awc+dOwsLCQl7z/vvvD7kvFB6Ph6lTpxITE8OSJUv44osvmDx5MocPH5ab6RYVFfH444/z0UcfoVbX7X6sWbOGMWPGoNFoGnXts41my0BddtllHDx4kJ07d5KZmYnX66VTp06kpaWFFLQQ/E7tBr1nO5V2NwatGrvLg1LBae13c7bP1dlu36kmPbucSIMGu8uD0+2VF/XnEue6836gwILV6cHh9nK8rJpXNhw+qyKoAoHg9KBQ64i89E9B284VVq5cyZYtW/j444+ZPn06ANdccw1du3blscce4/3336/3+BtvvDFo28aNG1EoFPzpT4HzkpWVRceOHSkpKSEuLq71buIU8Morr5CZmckvv/zCxRdfDMD48ePp3bs3//rXv0Kqc9fkk08+Cdp2ySWXMH36dFavXs11110XtP/JJ5/EZDIxatQoVq1a1aCNmZmZHDx4kOzsbDp06MDMmTOJjY1l69atjB07FvApig8fPjzIya2J1Wrlhx9+4NVXX23wmg1RXV3daMewNWl2g2A//fv3Z8aMGVx77bVcdNFFwqlqBOdaik5aSjQ92pkwaFVEG7VBkuKn+tpn81zVtO9s75N0KkhLiSYtJZoOMUZS2oSddTVLjflMznYZdj+h7sUvnuL2ePF4vbg80nlROyYQCJqOUqsnaugNAf+UWv2ZNqvRrFy5kvj4eKZOnSpvi4uL45prruHzzz/H4Wh8GQKAw+Hgk08+YcSIESQlJQXs69ixY7NsXLBgAffddx8AqampcqrhsWPHAHC73TzxxBN06tQJnU5Hx44dmT9/fpNtr8nKlSu5+OKLZacKoHv37lx++eV89NFHzTqn//5DpTNmZmby/PPP89xzz9UbWaqJzWYDIDrat1YzGo0YDAasVisAv/32G8uXL+e5556r9zzfffcdDoeD8ePHc/ToURQKBc8//3zQuC1btqBQKFixYgXg+1wUCgUZGRlcf/31REdHM3To0EbZ3tq02LESNJ1zrVi8b1IUI7u1pXNbE2rV6X1kzva5qmnfubJAb036JkUxqV8ivdtHYnd5yDfbzirHsjGfydnovIdyokLdi78PVZHFgValJCZMi9nqFD2pBALBOceOHTsYMGAASmXgOmPgwIFYrVYOHTrUpPOtXbsWs9nMDTfc0Go2Tp06VY5+Pf/88yxbtoxly5bJUa/bbruNRx99lAEDBsjpeosXLw4ZFWoMXq+X3bt3c9FFFwXtGzhwIEeOHMFiafhltyRJlJSUUFBQwKZNm5gzZw4qlYqRI0cGjZ07dy6jRo1iwoQJjbaza9euREZGsmDBArKzs1myZAmVlZUMGDAAgDlz5vDXv/6Vzp0713uetWvXkpaWRnx8PBdccAGXXnopy5cvDxq3fPlyTCYTkydPDtg+Y8YMrFYrTz31FLfffnuj7W9NxF9fQRChUqPSUqLZlWOmsNJ+WlMBzyXSUqLlefsj4W9Wa3dZMWrVZ1VaZGM+k7OxFilUimmoe0lLiebDX3MwalVUOTyUVTuJCTu9UWWBQCBoDU6cOMHw4cODtrdr1w6A/Px8+vTp0+jzLV++HJ1OJ6cVtgZ9+/ZlwIABrFixgilTpgREvnbt2sU777zDbbfdxptvvgnAXXfdRdu2bXn22WfZsGEDo0aNatL1ysrKcDgc8hzUpOa8dOvWrd7zFBYWBpwjKSmJ999/n+7duweMW7NmDevWrWPXrl1NsjMsLIxXX32VW2+9leeeew6VSsUzzzxDSkoK77//PocPH2bt2rUNnmft2rXMmjVL/nnmzJnccccdHDhwQLbV5XLx0UcfMXXqVIzGwNrufv36NZgyeqoREStBEOnZ5UF9fvyLu+NlVt7ecuysikqcafzRBeCsjq6dKvwRn9E948+6yM/ZHvGsi1BRtFD30jcpituHpdI13sSwLrF0igunoMLOl7vzmfvBDvE9FQgE5ww2mw2dLrgmTK/Xy/sbS2VlJWvWrGHChAmnRWACkB2He+65J2C7X9xtzZo1TT6n/55bOi8xMTGsX7+e1atX8/jjjxMbG0tVVVXAGKfTyd13382dd95Jz549m2zrn/70J/Ly8ti6dSt5eXn84x//wGq18sADD7Bo0SLCw8NZuHAhF1xwAX379uWzzz4LOH7v3r0cP36ciRMnytuuueYa9Hp9QNTqm2++oaSkJGRN3Z133tlku1sbEbE6zZwLhfJpKdFsO1oq12r47SystGN1evj/9u47LIqz+xv4d5aOVBEFBBELGitCEHtvEU3sPdaokVgTG/oY0dixRM3PFqMQJRpr1GgUC3YjdmPEDiiIonSks+f9g3cnrAsKC8sWzue6vJK9p+yZ2WF3ztxNQJZG1UqomywRDf73FRpUtUTPxg7iudGGz7ukNLHGR9sV55z29XASpzO4G5WImfvuggj4JzqJ/04ZYxolKysL8fHxcmW2trbQ09ODiYlJgX2RMjIyAAAmJiZFfp/9+/cjIyOjVJsBfkxkZCQkEolCczc7OztYWVkhMjKy0G3T09ORlJSksJ3smEt6XgwNDdGpUycAQI8ePdCxY0e0bNkSlStXRo8ePQDkNW18+/YtFixY8NH9Fcba2hrNmjUTXy9duhSVK1fGqFGjsG3bNmzatAlBQUGIiIjAwIEDcf/+ffF8HT16FFWqVJFr9mhlZYWePXvit99+ww8//AAgryayatWq6NChg8L7u7i4KB17aeEaqzKmDf1wCpvnp5+HIxytTWBkoCcO68z+m3A2M0eKR69TFPrAaPrnzXTD3ahEHLnzEqaGEggC4GhtolG1h4wxdvnyZdjb28v9e/HiBYC8pm0xMTEK28jKHBwcivw+QUFBsLS0FJOGsqTMIG6///67wnkB8mqajIyMSu28yLRo0QL29vZiTVBSUhIWLVqEsWPHIjk5GREREYiIiEBqaiqICBEREYiNjS3We0RERGDVqlVYu3YtJBIJdu3ahfHjx6NDhw4YPXo0mjdvjt27d4vrHzt2DN26dVM4f8OHD8ezZ89w+fJlpKSk4PDhwxg8eLBCXzygeMm3qnCNVRnTln44BT0x7+vhhAevUnDsnxhcj0yArbkRPw3Hf4nokTsvAUChD4w2fN6loTzUzmmybRfDEfIgFrlEcK1ijnZ1KvPnwFg5kpuegtdBs+TKqgxdDj0TzekX3bhxY5w8eVKuzM7ODkDeKNMXLlyAVCqVu2m+evUqTE1N4erqWqT3iImJQUhICEaOHFlgE7qSKixxcnZ2hlQqxePHj/HJJ5+I5a9fv0ZiYiKcnZ0L3WfXrl0VzgsASCQSNGzYENevX1dYdvXqVdSoUQPm5sp9vhkZGWItWUJCAlJTU7FixQqsWLFCYV0XFxd88cUXRRp6XWb69On4/PPPxdH5Xr58KZcEOjg4IDo6GkDe6ISXL1/GxIkTFfbTrVs32NraIigoCF5eXkhLS8OXX35ZnEMtU0VKrCQSiVIZeG5ubrG30XXa2mxKdtP86HUKUjNzYKTPlZ35Ffa5fujz1pVERHYcZx/G4klsKg7cNMTi3g21+pi00dM3qUjLygEByM6VlotknjGWD0mRHfdcoUyTWFtbi03S3tevXz/s27cPBw4cEAecePv2Lfbu3YuePXvKJUlPnz4FANSsWVNhP7t374ZUKlVZM0DZ3EjvD1XevXt3zJkzBz/++CM2b94slsuGGM/fd+h9+Wup3tevXz/Mnj0b169fF5vJPXz4EGfOnMH06dPl1n3w4AFMTU1RrVo1AHlzOQmCoDDIw/79+5GQkCDur3Llygp9ngBg3bp1uHLlCnbt2lVofAUJCQnBsWPH8ODBA7GsSpUqcq/DwsLQu3dvAEBwcDAAFDjHlb6+PgYPHozffvsNYWFhaNiwIRo1alTkWMpakRKr77//XiGxOnjwIP7991907dpVHI3kwYMHCA4ORoMGDdCrV69SD5apx92oRGwIeQIrU0Nk5UhhZaKPlIxcVDDUU3doWk1XJheWHUdkXBqS07ORmpmDI3deau0xaWvCW9PWDM/j02CkL0GzGjZaFTtjjPXr1w/NmjXDqFGjcP/+fVSqVAkbNmxAbm6uQr+fjh07AoA4f1R+QUFBcHBwKHAocZkdO3YgMjJSnGfp/PnzWLRoEQDgyy+//GDtkoeHBwBg7ty5GDRoEAwMDNCzZ080btwYI0aMwJYtW5CYmIi2bdsiNDQUgYGB6NWrV7FHBJTx8fHBzz//DG9vb0yfPh0GBgZYvXo1qlSpIg6MIfPJJ5+gbdu2OHv2LIC8Oak6deqEgQMHom7dupBIJLh+/Tp27tyJ6tWrY8qUKQDy5p0q6L79jz/+QGhoaLHu6XNzczF16lTMmDFDTPCAvM935syZsLW1RWRkJP755x+xKeLRo0fRqlUrWFpaFrjP4cOHY926dQgJCcHy5cuLHIs6FCmx8vPzk3u9ZcsWxMbG4t69ewpDPIaFhaFDhw5KtflkmulGZAKsTPPmx+nn4YifL4TDzJiHdAb+uwm3MNZHckZOsW7GdaWZoIezNY7ceQlnG1Nk5uTCwthA3SGViLYmvKNb5XXa5SkRlPfvv//Cz88PN27cwKtXr2Bqaop69ephxowZ6Nmzp9y6YWFhmDZtGi5evAhDQ0N4e3tj9erV4nw2MlKpFCtXrsTGjRsRExMDV1dX+Pr6inPhMMby6Onp4dixY5gxYwbWrVuH9PR0eHp6IiAg4KPDics8fPgQN27cwLfffltgHxyZX375BefOnRNfh4SEICQkBADQqlWrDyZWnp6e+OGHH7Bp0yYcP34cUqkU4eHhqFChArZu3YoaNWogICAABw8ehJ2dHXx9fTF//vwingVF5ubmOHv2LKZNm4ZFixZBKpWiXbt2WLNmjcL3zfscHR3Rt29fnDlzBoGBgcjOzoazszMmTpyIuXPnwsbGRum4CrN582bEx8dj1iz5Zqlff/01wsPDsXr1alSoUAHbt29H/fr1QUQ4fvy4Qu1bfh4eHqhfvz7CwsLKdEASZQhERMXdqHbt2hg1ahTmzJlT4PLFixcjICAAjx8/LnGA6pCcnAxLS0skJSXBwsJC3eGoXf4n+EBeX47XyRno5+EojkZWXm2/FI7EtGw8fJWMOnYWsDI1wKiWLlpb66Gs7ZfC8fBVCiLj3imMjKhttPmzk12PsutQG6nz+/fYsWNYt24dmjdvDgcHB6SlpWH//v24cOECNm/ejHHjxgEAoqKi0KRJE1haWmLy5MlITU3FypUrUa1aNYSGhsLQ0FDcp6+vL5YtW4axY8fC09MThw4dwtGjR7Fr165iTRrKv0u6r/rs4g/HnV9uWhKi1svfdDpOCoKeacG1AMqKWFZ4kzbGiis0NBReXl74999/PzjMe5MmTVCxYkWcPn26DKP7T1G/g5UavCIqKgoGBoU/lTYwMEBUVJQyu2YaKH8/ocVH7+N5/DsY6euhdhV+Ki6rdepcr4pYYwVob62HsmRD9DvbVICDlYlWH7O29oMEdKcWVF26d++O7t27y5VNnDgRHh4eWL16tZhYLVmyBO/evcONGzfEpi5NmzZF586dERAQIK4XHR2NVatW4ZtvvsFPP/0EAPjqq6/Qtm1bzJgxA/3794eeHjepZoyVb0uWLPlgUnX9+nXcvn0bAQEBZReUkpQagaBBgwbYsGGDOJpHflFRUdiwYUOxZsdm2uNNSiYi49IR/y6LhxDHf5O29vVwkpu8taAJXnVZ/iH6LYz1sf1SOA/HrwbaOiGyJtPT04OTk5NcR/X9+/ejR48ecv0HOnXqBFdXV+zZs0csO3ToELKzs+Hj4yOWCYKACRMmICoqCleuXCmTY2CMMU3VtGlT+Pr6Frjs3r17CAwMxOjRo2Fvb4+BAweWcXTFp1SN1Zo1a9C1a1e4urqid+/e4uRejx8/xh9//AEiws6dO0s1UFY6lGnmlH8bW3MjVDIzQGaOFBbGPFp/YbS51kNZjRyt8Ph1Cn6+EI5qFU3EMqZ62tx8URO9e/dOnLDz8OHD+Ouvv8Qf9OjoaMTGxspNYinTtGlTHDt2THx969YtVKhQQW7oZdl6suWyoYgZY4zJ27dvHxYuXIg6depg165dMDY2VndIH6XUnXGrVq1w9epVzJs3DwcPHkR6ejqAvIm5unbtigULFnCNlYZSpola/m16NnbAvegkZOZIefCKcqI4N+37bkThTUoGYpMz0LW+XdkEyMpd01NV++6778ThkiUSCfr06SM25ZNNylnQ0MP29vaIj49HZmamOKlnlSpVFEbVlW378uXLQmPIzMxEZmam+Do5OblkB8UYY1rGz89PYQA9Taf0ZEQNGjTAwYMHkZKSgpiYGMTExCAlJQUHDhzgpEqDKdNELf82jRyt0KCqJcy1fOQ3VnT5b9o/poqFMXJyCfp6AifeZai8NT1VtalTp+LkyZMIDAzEZ599htzcXGRlZQGA+CCxoIlHZU9TZeukp6cXab2CLF26FJaWluI/J6fyPVAQY4xpgxK35ZJIJDA2NoaZmdkHh7YsrzStiY4yTdTe36ZnYwfuIF+OFGdAhNGtXPD0TSoS3mXhTUrmR9dnpaM8Nj1Vpbp166Ju3boA8uZP6dKlC3r27ImrV6/CxCSvmWv+2iSZjIwMABDXMTExKdJ6BfH19cW3334rvk5OTubkijHGNJzSmdD169fRrVs3mJqawsbGRpwP4O3bt/jiiy/EycnKu+I87dcWmtZB/m5Uok4MlqCpx1Gcz7uRoxUqVjBEerYUCWlZqg+OsTLQr18/XLt2DY8ePRKb8cmaBOYXExODihUrirVU9vb2ePXqFd6f1US27YfmezQyMoKFhYXcP8YYY5pNqcTq8uXLaNWqFR4/foxhw4ZBKpWKyypVqoSkpCSxfXp5x010VE9XkldtPY73E8KsHCnMjPSRlSP98IaMaQlZk72kpCRUrVoVtra2uH79usJ6oaGhcHNzE1+7ubkhLS0NYWFhcutdvXpVXM4YY0x3KJVYzZkzB5988gnu37+PJUuWKCxv3769+MNR3mla7Y4ueP9GXleSV209jvwJ4d2oRBjqS5CVmwtDfYnG1b4x9iGxsbEKZdnZ2fj1119hYmIizrPSt29f/Pnnn3jx4oW43unTp/Ho0SP0799fLPviiy9gYGCADRs2iGVEhE2bNqFq1apo0aKFCo+GMcZYWVOqj9W1a9ewdOlSGBkZITU1VWF51apV8erVqxIHp800rW+VLnl/BDRd6V+irceRvw/WjcgEpGTkICuHEPH2HWbuu4uxrfPm+WJM040fPx7Jyclo06aN+DsWFBSEBw8eYNWqVTAzMwOQ93Bx7969aN++PaZMmYLU1FT4+/ujYcOGGDVqlLg/R0dHTJ06Ff7+/sjOzoanpyf++OMPXLhwAUFBQTw5MGOM6RilEisDAwO55n/vi46OFn+Ayise/lh1ijOYAlO99xPC4H9foZKZId6kZMLM2AAn77/mxIpphYEDB+KXX37Bxo0bERcXB3Nzc3h4eGD58uX4/PPPxfWcnJxw7tw5fPvtt5g9ezYMDQ3h7e2NVatWKYwCuGzZMlhbW2Pz5s0ICAhA7dq1sXPnTgwZMqSsD4/pOEHPAGZNvBXKGGNlR6D3e9UWQbdu3ZCamoqLFy8iLi4Otra2OHXqFDp06IB3796hfv368PT0xN69e1URs8olJyfD0tISSUlJSncY5hor1eLzq7nuRiXiyJ2XePQ6BVk5UvTzcOTEihVZaXz/6iI+L7qv+uyj6g6hSCKWeX98JcZ0TFG/g5XqY7VgwQJcv34d3t7e+OuvvwAAd+7cwdatW+Hh4YE3b95g3rx5ykWuI7hvlWpp60AP5UEjRys4WJnASF8PCWnZ6g4HgOaOuMgYY4wx3aFUYuXl5YVjx47hyZMnGD58OIC8merHjRuH3NxcHDt2DI0aNVIqoMzMTMyaNQsODg4wMTGBl5cXTp48WaRtT506hfbt26NSpUqwsrJC06ZNsWPHDqXiYJpNWwd60DSqSjgsjPVxLSIeMYlpWH/midoTGk7EGWOMMaZqSk8Q3KFDBzx8+BC3b9/G48ePIZVKUbNmTXh4eEAQBKUDGjlyJPbt24epU6eidu3aCAgIQPfu3RESEoJWrVoVut3hw4fRq1cvNG/eHH5+fhAEAXv27MHw4cPx9u1bTJs2TemYmObR1oEe3qfuJo2q6gt44fFbZOVKkZ0jhZ2+RO19DblfHmOMMcZUTak+VqoSGhoKLy8v+Pv7Y/r06QDyZqhv0KABKleujMuXLxe6bZcuXfDvv//i2bNnYufhnJwc1K1bFxUqVMCdO3eKHEdJ27Kr+2aZaY/FR+/j0etUuFYxw1zvemX+/qq6VgdvuYLwt+8gCECPRg7o2diB/xZYkXBfooLxedF93MeKMc2l0j5WEokE9vb2OH/+fIHLlR1Gdt++fdDT08O4cePEMmNjY4wZMwZXrlyRmzPkfcnJybC2tpYbkUlfXx+VKlWCiYlJsWMpCW52xIpHfc82VNUXsJ+HI1wqVYCXiw0nVYwxxhgrF5RKrIC8mqROnTph7dq1pRbMrVu34OrqqpAJNm3aFABw+/btQrdt164d/v33X8ybNw9PnjzB06dP8cMPP+D69euYOXNmqcVYFBbG+nj4KhkWxkq3tGTlRM/GDmhXpzJ6NnZQdyilqnYVc1iaGCAtKxcbQtTfx4oxxnSdNCMVr36bLfdPmqE41yhjTHWUvvP/8ccfERoaimnTpuH69ev4+eefYWxsXKJgYmJiYG9vr1AuK3v58mWh286bNw/h4eFYvHgxFi1aBAAwNTXF/v378cUXX3zwfTMzM5GZmSm+Tk5OVib8/7bPyEEdOwskZ+SUaD9M9xWlr5g2Ni29EZkAK1ND3H6RiGoVTbEh5Al82tfSmvgZY0zbkDQXmS/uKZQxxsqO0jVWBgYG+L//+z8EBATgwIEDaNmyJZ4/f16iYNLT0xUmVwQgJmzp6emFbmtkZARXV1f069cPu3btws6dO/Hpp59i2LBh+Pvvvz/4vkuXLoWlpaX4z8mpZHPucI0VK03a1LRUNsqghbE+6tiZo1v9KngenwYpkVbEzxhjjDGmLKUTK5nhw4fj0qVLSExMhIeHB06fPq30vkxMTORqjmQyMjLE5YWZOHEijhw5gt27d2PQoEEYOnQoTp06BXt7e0yZMuWD7+vr64ukpCTx34f6chUF11ix0qRJQ8t/bHh2WRKYnJGDUS1dcP7xG4S/fYf7MckaET9jjDHGmKqUOLECADc3N9y4cQOenp7o1q0bfvnlF6X2Y29vj5iYGIVyWZmDQ8H9ULKysvDLL7/A29sbEsl/h2RgYIDPPvsM169fR1ZWVqHva2RkBAsLC7l/JeHhbI20rBy8TEznviWsSD6UsGjSZNMfqz17PwmMeJsGECEtM1cj4meMMcYYU5VSSawAwMrKCkePHsWcOXNw7tw5pfbh5uaGR48eKfRxunr1qri8IHFxccjJyUFurmJb4uzsbEil0gKXqUojRys4WJnA1FCfmz+xItH05n75m/h9qPbs/SSwa/0qMDXSR/VKpvyQgTHGGGM6TanEKjw8HL169VIoFwQBCxYswJ07d3DmzJli77dfv37Izc3Fli1bxLLMzExs374dXl5eYt+n58+f48GDB+I6lStXhpWVFQ4ePChXM5WamoojR46gbt26ZT7kuiY132Jl52NN5Qqj6dfL+038ilr7NNjLGXXtzPEuMxdLj4VxcsUYY4wxnaXU6ArOzs4fXN6gQQOlgvHy8kL//v3h6+uL2NhY1KpVC4GBgYiIiJBrXjh8+HCcO3cOsrmN9fT0MH36dPzvf/9Ds2bNMHz4cOTm5uKXX35BVFQUdu7cqVQ8JVGU0d6Y7slf81Scz1/TrxcPZ2txZMLiuBGZgPh3WYiMS0NCWhaO3Hmp0cfJGGOMMaasIiVWCxcuhCAImDt3LiQSCRYuXPjRbQRBwLx584od0K+//op58+Zhx44dSEhIQKNGjfDnn3+iTZs2H9xu7ty5cHFxwdq1a7FgwQJkZmaiUaNG2LdvH/r27VvsOBhThrIJiKZTNvHzcLZG8L+vkJCWDUP9Umt5zBhjjDGmcQSSVft8gEQigSAISE9Ph6GhodwAEYXuWBDKtF9TaUpOToalpSWSkpKUGshCG+cdYppL26+nu1GJOHInbw66no0dVHYM2n6eWJ6Sfv/qKj4vuq/67KMl2j43LQlR64fKlTlOCoKeqWWJ9vu+iGXepbo/xrRBUb+Di/QIWTb4g6Ghofj6Y/+0NakqDZo+EAErfcr2rSoKbb+eGjlaoWdjBzhYqbafo7afJ8YYY4xpN26bowKaPhABK32qvKnX9uvpblQilh4Lw5E7L8WaK1XQ9vPEGGOMMe2m1OAV7MM0fSACVvpU2bdK26+nI3de4nl8GgRBUOn7aPt5Yowxxph2K1Ji5eLiUuybIkEQ8PTpU6WCYkzb8E39h1WxMIaRvgQ9Gxc8ybe6cL8sxhhjjJWWIiVWbdu2VfnTZl3CN2uM/UfWv0oT/x6UHR6fMcYYY+x9RUqsAgICVByGbjly5yUevU7Fy8R0vlkrZzip1jwf+kx0dXh8xlj5I+jpw7ROS4UyxljZ4b84lfnoKPasFGhaIqPKGhBNO9aiynvQkKK2Bw0f+ky4CSdjTFdIjCrAtpevusNgrFwrUWKVnZ2NBw8eICkpCVKpVGH5xyb11VU9GzvwU/AyomlNuVRZA6Jpx1o86mtKzLVSjDHGGCsLSiVWUqkUvr6+2LBhA9LS0gpdr7zOZaUtT8G1tQYkP027aVblZ69px1pU6n7QIPs8ZEPha+u1zhhjjDHNplRitWTJEvj7+2P8+PFo1aoVvvzySyxfvhxWVlbYsGEDBEHAihUrSjtWVsq0uwYkj7YksaVBW49VE+LWhWudMcYYY5pNqcQqICAAAwYMwMaNGxEXFwcA8PDwQIcOHTBixAg0b94cZ86cQadOnUo1WFa6tLUGhLHi0rZrXRdqk7XRtWvXEBgYiJCQEERERMDGxgbNmjXDokWL4OrqKrduWFgYpk2bhosXL8LQ0BDe3t5YvXo1bG1t5daTSqVYuXIlNm7ciJiYGLi6usLX1xeDBw8uy0Njpaz67KPqDoExpoEkymwUFRWFDh06AACMjIwAABkZGQAAQ0NDDBs2DDt27CilEJmqNHK0wqiWLnzjxnSetl3r+WvYWNlZvnw59u/fj44dO2Lt2rUYN24czp8/D3d3d9y7d09cLyoqCm3atMGTJ0+wZMkSTJ8+HUePHkXnzp2RlZUlt8+5c+di1qxZ6Ny5M9avX49q1aphyJAh2L17d1kfHmOMMRVTqsbKxsYGqampAAAzMzNYWFjg2bNncuskJPANAWNFoSu1E7pyHJqgtGrY+DMpnm+//Ra//fYbDA0NxbKBAweiYcOGWLZsGXbu3Akgrzn8u3fvcOPGDVSrVg0A0LRpU3Tu3BkBAQEYN24cACA6OhqrVq3CN998g59++gkA8NVXX6Ft27aYMWMG+vfvDz09vTI+SqarpJnvEPfXOrkym88mQ2JUQU0RMVb+KFVj1aRJE1y7dk183b59e/z444+4dOkSLly4gHXr1qFx48alFiRjukxXaid05TjU4W5UIrZfCsfdqEQApVfDxp9J8bRo0UIuqQKA2rVro379+ggLCxPL9u/fjx49eohJFQB06tQJrq6u2LNnj1h26NAhZGdnw8fHRywTBAETJkxAVFQUrly5osKjYeUN5eYg7eEluX+Um6PusBgrV5RKrMaNG4fMzExkZmYCABYvXozExES0adMGbdu2RXJyMlatWlWqgTKmqzycrWFlaqA1/X8KoyvHoQ6qSoD4Myk5IsLr169RqVIlAHm1ULGxsfj0008V1m3atClu3bolvr516xYqVKiATz75RGE92XLGGGO6Q6mmgJ9//jk+//xz8XW9evXw9OlTnD17Fnp6emjRogUqVqxYakEypss0YdS80qArx6EOqhpcgz+TkgsKCkJ0dDQWLlwIAIiJiQEA2NvbK6xrb2+P+Ph4ZGZmwsjICDExMahSpQoEQVBYDwBevnxZ6Pvmf3gJAMnJySU+FsYYY6pVogmC87O0tMQXX3xRWrtjjGk47r9TcvnP4aiWLuoOh73nwYMH+Oabb9C8eXOMGDECAJCeng7gv4Gb8jM2NhbXMTIyEv/7ofUKs3TpUixYsKDEx8AYY6zslCixys7ORnR0NBISEkBECsvd3d1LsnvGmAbjuaFKjs+h5nr16hW8vb1haWmJffv2iYNMmJiYAIBcbZKMbHRc2TomJiZFWq8gvr6++Pbbb8XXycnJcHJyUvJoGGOMlQWlEqvExERMnz4dQUFBCkPLAnlt0gVBQG5ubokDZIxpJm2bG0oT8TnUTElJSfjss8+QmJiICxcuwMHBQVwma8YnaxKYX0xMDCpWrCjWUtnb2yMkJET8Tcy/HgC5/b7PyMiowNouxhhjmkupxGrkyJE4cuQIBg0aBC8vL1haWpZ2XIwxDZe//w43C1QO94HSPBkZGejZsycePXqEU6dOoV69enLLq1atCltbW1y/fl1h29DQULi5uYmv3dzcsHXrVoSFhcnt5+rVq+JyxhhjukOpxCo4OBiTJ0/GmjVrSjsexpgW4iZtTBfk5uZi4MCBuHLlCg4dOoTmzZsXuF7fvn0RGBiIFy9eiM3zTp8+jUePHmHatGniel988QWmTZuGDRs2iPNYERE2bdqEqlWrokWLFqo/KMYYY2VG6QmCa9WqVdqxMMa0FDdpY7rgu+++w+HDh9GzZ0/Ex8eLEwLLDBs2DAAwZ84c7N27F+3bt8eUKVOQmpoKf39/NGzYEKNGjRLXd3R0xNSpU+Hv74/s7Gx4enrijz/+wIULFxAUFMSTAzPGmI5RKrEaN24cdu/ejQkTJkAiUWoqLMaYDuEmbUwX3L59GwBw5MgRHDlyRGG5LLFycnLCuXPn8O2332L27NkwNDSEt7c3Vq1apdAvatmyZbC2tsbmzZsREBCA2rVrY+fOnRgyZIjKj4cxxljZUiqxmjdvHjIzM/Hpp5/iyy+/hKOjY4FP3vr06VPiABlj2oH7WTFtd/bs2SKvW79+fZw4ceKj60kkEvj6+sLX17cEkTHGGNMGSiVW0dHROHPmDG7fvi0+4XtfeR4VkG8wWXnE/awYY4wxVp4plViNHj0aN2/ehK+vL48KWAC+wWTlEfezYowxxlh5plRidfHiRcyaNYtnhS8E32Cy8kbXa2l1/fgYY4wxVnJKJVZ2dnaoWLFiaceiM7gjPytvdL2WVtePjzGm/QSJHoycGiiUMcbKjlKJ1XfffYeNGzdizJgxMDMzK+2YGCs1XNNQNnS9llbXj48xpv0kxmawG7JM3WEwVq4plVhlZGTAwMAAtWrVwoABA+Dk5KQwKqAgCHITJTKmDtpQ06ALyZ+21NIqe6615fgYY4wxpj5KJVbTp08X/182m/z7OLFimkAbahq0IfkrCm1IEHXlXBdEG84/Y4wxpsuUSqzCw8NLOw7GVEIbahq0IfkrCm1IWrTxXBc1YdKG888YY4zpsmInVunp6Vi7di3at2+Pnj17qiImxsqV/MmfNtc6aEPSog2J9vuKmjBpw/lnjDHGdFmxEysTExNs3rwZ9erVU0U8jOmc4iRL2lzroI1JizYoasLE558xxhhTL6WaAnp4eODevXulHQtjOqk4yZI21Dpoc62aNuKEiTFWFNLMNCScC5Qrs247AhIjUzVFxFj5o1Ri9eOPP6J79+5o0KABRo4cCX19pXbDWLlQnGSpoJtoTUtktLlWjTHGdBXlZiP11lG5MqtWQ9QUDWPlk1IZ0ciRIyGRSDB+/HhMnjwZVatWhYmJidw6giDgzp07pRIkY9qspDUOmpbIaEOtGmOMMcZYWVMqsapYsSJsbGxQp06d0o6HMfYeTUtkuGkaY4wxxpgipRKrs2fPlnIYjLHCcCLDGGNMU1SfffTjKykhYpm3SvbLWFmSqDsAxhhjjDHGGNN2So86kZubi507d+Lo0aOIjIwEADg7O6NHjx4YOnQo9PT0Si1IxhhjjDHGGNNkStVYJSUloWXLlhg9ejSCg4ORnZ2N7OxsnDx5EqNGjUKrVq2QnJxc2rEyBiBvlLztl8JxNypR3aEwxhhjjDEGQMnEau7cubhx4wbWr1+PN2/e4ObNm7h58yZiY2Px008/4fr165g7d25px8oYAPlR8hhjjDHGGNMESiVWBw8ehI+PD3x8fGBgYCCWGxgYYMKECZgwYQL2799fakEylp+HszWsTA00ZpQ8xhhjjDHGlOpjFRcX98Gh1uvWrYv4+Hilg2KqoWkTzRZFQTHzKHmMMcYYY0zTKFVjVatWLRw+fLjQ5YcPH0bNmjWVDoqphjY2odPGmBkrC9zXkDHGGNMsSiVWPj4+CA4ORvfu3REcHIyIiAhERETgxIkT8Pb2xsmTJzFx4sTSjpWVkDY2odPGmBkrC/zQgTHGGNMsSjUF9PHxQWxsLJYtW4YTJ07ILTMwMMD333+PCRMmlEqArPRoYxM6bYyZsbLg4WwtNpNljDHGmPopPY+Vn58fJk6ciFOnTsnNY9WpUydUqlSp1AJkjDGmiB86MMYYY5pF6cQKACpVqoRBgwaVViyMMcaYWqWmpsLf3x9Xr15FaGgoEhISsH37dowcOVJh3bCwMEybNg0XL16EoaEhvL29sXr1atja2sqtJ5VKsXLlSmzcuBExMTFwdXWFr68vBg8eXEZHVb5Vn31U3SGUDUECA5tqCmWMsbJTosQqJSUFkZGRSEhIABEpLG/Tpk1Jds8YY+WCNo7Yqavevn2LhQsXolq1amjcuDHOnj1b4HpRUVFo06YNLC0tsWTJEqSmpmLlypX4559/EBoaCkNDQ3HduXPnYtmyZRg7diw8PT1x6NAhDBkyBIIg8MNJVmr0TMzh8NUGdYfBWLmm9HDrEydOxP79+5GbmwsAICIIgiD3/7JljDHGCpd/IApOrNTL3t4eMTExsLOzw/Xr1+Hp6VngekuWLMG7d+9w48YNVKuWV0vQtGlTdO7cGQEBARg3bhwAIDo6GqtWrcI333yDn376CQDw1VdfoW3btpgxYwb69+8PPT29sjk4xhhjKqVUYjV27FgcOXIEkydPRuvWrWFtzZ2nGWNMWTwQheYwMjKCnZ3dR9fbv38/evToISZVANCpUye4urpiz549YmJ16NAhZGdnw8fHR1xPEARMmDABQ4YMwZUrV9CqVavSPxDGGGNlTqnEKjg4GNOmTcOKFStKOx7GGCt3eCAK7RIdHY3Y2Fh8+umnCsuaNm2KY8eOia9v3bqFChUq4JNPPlFYT7acEyvGGNMNSvVqNDU1RfXq1Us5lDyZmZmYNWsWHBwcYGJiAi8vL5w8ebLI2//+++9o3rw5KlSoACsrK7Ro0QJnzpxRSayMlRZtmuxVm2JlTBViYmIA5DUbfJ+9vT3i4+ORmZkprlulShWxqXz+9QDg5cuXBb5HZmYmkpOT5f4xxhjTbEolVsOGDcPBgwdLOxYAwMiRI7F69WoMHToUa9euhZ6eHrp3746LFy9+dFs/Pz8MHjwYTk5OWL16NRYtWoRGjRohOjpaJbFqMr751S7aNNmrNsXKykZ5+75JT08HkNds8H3GxsZy66SnpxdpvfctXboUlpaW4j8nJ6dSiZ0xxpjqKNUUsF+/fjh37hy6deuGcePGwcnJqcDOt+7u7sXab2hoKHbv3g1/f39Mnz4dADB8+HA0aNAAM2fOxOXLlwvd9u+//8bChQuxatUqTJs2rXgHpIO4M7x20aY+NtoUKysb5e37xsTEBADEWqn8MjIy5NYxMTEp0nrv8/X1xbfffiu+Tk5O5uSKfZA0KwPJofvlyiya9oXE0FhNETFW/iiVWOVvD15QMz1lRwXct28f9PT0xE6/QN5TvTFjxmDOnDl48eJFoT8sP/74I+zs7DBlyhQQEd69ewczM7Nivb8u4Ztf7fJ+HxtNHn6b+wOx95W37xtZMz5Zk8D8YmJiULFiRbGWyt7eHiEhIXIj5+bf1sHBocD3MDIyKrCmi7HCUE4mki7tkiszd+8BcGLFWJlRKrHavn17accBIK8Tr6urKywsLOTKZZ18b9++XWhidfr0abRo0QLr1q3DokWLEBcXBzs7O8ydOxcTJ05USbyajG9+tVt5qwFg2q28fd9UrVoVtra2uH79usKy0NBQuLm5ia/d3NywdetWhIWFoV69emL51atXxeWMMcZ0g1KJ1YgRI0o7DgB5T/AK6wwMFN7JNyEhAW/fvsWlS5dw5swZzJ8/H9WqVcP27dsxadIkGBgYYPz48YW+b2ZmplxTDe4kzNStLGoANLlWjDFN17dvXwQGBsq1pDh9+jQePXok1xz9iy++wLRp07BhwwZxHisiwqZNm1C1alW0aNFCLfEzxhgrfUolVvnFxMQgNjYWtWrVQoUKFUq0L2U7+aampgLIm7h49+7dGDhwIIC8vmANGzbEokWLPphYLV26FAsWLChR7IyVprKoAShurRgnYuxjdOUa+emnn5CYmCg+zDty5AiioqIAAJMmTYKlpSXmzJmDvXv3on379pgyZQpSU1Ph7++Phg0bYtSoUeK+HB0dMXXqVPj7+yM7Oxuenp74448/cOHCBQQFBfHkwIwxpkOUGhUQyJv0sG7dunB0dIS7u7vYrOHt27do0qSJUqMGKtvJV1ZuYGCAfv36ieUSiQQDBw5EVFQUnj9/Xuj7+vr6IikpSfz34sWLYsfOmLbxcLaGlalBkWvFijsaYHkbKY7pzoiRK1euxLx587Bx40YAwIEDBzBv3jzMmzcPCQl5x+bk5IRz586hZs2amD17NlasWIHu3bvj5MmTCg8Ily1bhiVLluDEiRP45ptvEBERgZ07d2LIkCFlfmyMMcZUR6kaqyNHjqBPnz5o3rw5hgwZAj8/P3FZpUqVULVqVQQEBKB3797F2q+9vX2BQ6N/rJNvxYoVYWxsDCsrK4Wnf5UrVwaQ11ywWrVqBW7PnYRZeVTcWrHiNk/kfmLlj64MYhEREVGk9erXr48TJ058dD2JRAJfX1/4+vqWMDLGGGOaTKkaq4ULF6JNmza4ePEivvnmG4XlzZs3x61bt4q9Xzc3Nzx69Eihj9PHOvlKJBK4ubnhzZs3yMrKklsma8pha2tb7HgYY/9p5GiFUS1dPpgk5a+lKm6NGNN+RblGGGOMMV2lVGJ17949DBgwoNDlVapUQWxsbLH3269fP+Tm5mLLli1iWWZmJrZv3w4vLy+xg/Dz58/x4MEDuW0HDhyI3NxcBAYGimUZGRkICgpCvXr1Cq3tYrqDm56p3/u1VHyTzRhjjLHyQqmmgKampnj37l2hy589ewYbG5ti79fLywv9+/eHr6+vOCBGYGAgIiIi8Msvv4jrDR8+HOfOnQMRiWXjx4/H1q1b8c033+DRo0eoVq0aduzYgcjISBw5cqTYsTDtw03P1E9XmoIxxhgrW9VnHy31fUYs8y71fTL2IUrVWLVv3x6BgYHIyclRWPbq1Sv8/PPP6NKli1IB/frrr5g6dSp27NiByZMnIzs7G3/++SfatGnzwe1MTExw5swZDBkyBNu2bcOMGTMgkUhw9OhRfPbZZ0rFwrQLNz1TP66lYowxxlh5pVSN1eLFi9GsWTN4enqif//+EAQBJ06cwJkzZ7B582YQEebPn69UQMbGxvD394e/v3+h65w9e7bA8sqVKyMgIECp92Xar7xNUqrJdGXYbcYYY4yxolKqxqpOnTq4ePEibGxsMG/ePBAR/P39sWTJEjRs2BAXLlxA9erVSzlUxpi20JVhtxljjDHGikrpCYLr16+PU6dOISEhAU+ePIFUKkWNGjXE0feICIIglFqgjDHtwX2tGGOMMVbeKJ1YyVhbW8PT01N8nZWVhYCAAKxcuRKPHj0q6e4ZY1qIm2UyxhhjrLwpVmKVlZWFw4cP4+nTp7C2tkaPHj3EYczT0tLw008/4ccff8SrV69Qs2ZNlQTMGGOMMcYUSUws1B0CY+VakROrly9fol27dnj69Kk4zLmJiQkOHz4MQ0NDDBkyBNHR0WjatCnWr1+PPn36qCxoxhhjjDH2Hz1TSzhN/k3dYTBWrhU5sZo7dy7Cw8Mxc+ZMtG7dGuHh4Vi4cCHGjRuHt2/fon79+ti5cyfatm2ryngZY4wxpqNUMZcRY4yVlSInVidPnsSoUaOwdOlSsczOzg79+/eHt7c3Dh06BIlEqUEGGWOMMcYYY0yrFTmxev36NZo1ayZXJns9evRoTqoYY4wxxpjGUEUNaMQy71LfJ9MdRc6GcnNzYWxsLFcme21paVm6UTGmg+5GJWL7pXDcjUpUdyiMMcYYY6yUFWtUwIiICNy8eVN8nZSUBAB4/PgxrKysFNZ3d3cvWXSM6ZD8k+Zq+1Dkd6MSxXmqtP1YGGOMMcZKg0CyIf4+QiKRFDjhb0ETAcvKcnNzSyfKMpacnAxLS0skJSXBwoKHLmWlQ5eSke2XwpGYlg0rUwOMaumi7nDKPV26tvj7t2Dl5bzw4BXKk2Zn4t0/J+XKKjTsDImBkZoiYkXFzQs1X1G/g4tcY7V9+/ZSCYyx8kqXJs31cLYWb+SZ+ulSbShjTDmUnYH4k5vkykzrtgY4sWKszBQ5sRoxYoQq42CMaRFdShJ1ASe6jDHGmPoVq48VY4xpG11qJlcYTnQZY4wx9eMx0hljOi1/MzldxyNPMsYYY+rDiRVjTKd5OFvDytSgXDSTK09JJGOMMaZpuCkgYxqkPDRbK2vlqZkc97VijDHG1IcTK8Y0CI/uxkqiPCWRrOhUMYQ5Dw/NGGOKuCkgYxqkPDVbY4wxxhjTJVxjxZgG4RoHxhhjjDHtxIkVY4wxpkKZmZn4/vvvsWPHDiQkJKBRo0ZYtGgROnfurO7QGGMagJvr6g5uCsgYY4yp0MiRI7F69WoMHToUa9euhZ6eHrp3746LFy+qOzTGGGOliGusGCsDPNofY+VTaGgodu/eDX9/f0yfPh0AMHz4cDRo0AAzZ87E5cuXi7W/BvNPQGJkqopQGWM6hGvB1IMTK8bKAI/2x5TBCbn227dvH/T09DBu3DixzNjYGGPGjMGcOXPw4sULODk5qTFC5ajipo0xxrQdJ1aMlQGeX4gpgxNy7Xfr1i24urrCwsJCrrxp06YAgNu3b2tlYsUYK3/K8wMVaWZakdbjxKoARAQASE5OVnMkTFdUt5CgekMbAHxdsaKrU1EPt9+loE5F43Jz3ciOU/Y9rO1iYmJgb2+vUC4re/nyZYHbZWZmIjMzU3ydlJQEoOg/7qz8kWYpXhvSrDQIegZqiIYx3SL77v3YbxMnVgVISUkBAH6KyBhjapKSkgJLS0t1h1Fi6enpMDIyUig3NjYWlxdk6dKlWLBggUJ59MaRpRof020vN49VdwiM6ZSP/TZxYlUABwcHvHjxAubm5hAEocB1kpOT4eTkhBcvXig08dB0HLt6cOzqwbGrh7KxExFSUlLg4OCgwujKjomJiVzNk0xGRoa4vCC+vr749ttvxddSqRTx8fGwsbEp9HdJU2nzdazN+LyrB5939VD1eS/qbxMnVgWQSCRwdHQs0roWFhZa+4fDsasHx64eHLt6KBO7LtRUydjb2yM6OlqhPCYmBgAK/ZE2MjJSqOmysrIq9fjKkjZfx9qMz7t68HlXD1We96L8NvE8VowxxpiKuLm54dGjRwp95K5evSouZ4wxphs4sWKMMcZUpF+/fsjNzcWWLVvEsszMTGzfvh1eXl7cl5cxxnQINwVUkpGREebPn19gp2RNx7GrB8euHhy7emhz7KXJy8sL/fv3h6+vL2JjY1GrVi0EBgYiIiICv/zyi7rDKxN8LagHn3f14POuHppy3gXSlTFtGWOMMQ2UkZGBefPmYefOnUhISECjRo3www8/oGvXruoOjTHGWCnixIoxxhhjjDHGSoj7WDHGGGOMMcZYCXFixRhjjDHGGGMlxIkVY4wxxhhjjJUQJ1aMMcaKjbvnMsYYKwtSqVTdIRQZJ1ZM7fgGjZU3SUlJ6g5Bab///jsAQBAENUfCNAl/j5eNjIwMudd83pkue/z4MXJzcyGRaE+6oj2RqtCtW7fw/PlzuZsdbfmySktLU3cISnv27BnS0tIUfii0wZ07d/D48WNERUWJZdpyzQDAoUOH4OPjg2fPngHQrqdBu3btgrm5OS5duqTuUIrtwIED6NKlC9asWYOIiAh1h1Msu3fvRs2aNTF48GBcvHhR3eEwNTp58iRmz56NjRs34vLlywA40Va1e/fuoX///hg0aBC+/vprhIaGAuDzrmq///47vv76ayxfvlzue0+bfu+10Y4dO+Dq6oouXbqgXr16WLhwodY8kCzXiVVYWBhatWqFjh07onHjxmjatCn279+PnJwcCIKg0X84Dx8+hIeHB7766it1h1Jsd+/ehbe3N3r27AkXFxe0a9cOly5d0ujzLXP37l107twZPXr0gIeHBxo3box169aJ14w2OHnyJHr37o0dO3bgzz//BACteBp069YteHl5YfTo0fD29oaFhYW6Qyqyly9fwtvbG8OHD4ehoSFMTU1hamqq7rCKRHbeR4wYAXNzcxgbGyMzM1PdYTE1SEpKwsCBA9GzZ08cPXoU3333Hbp27Yp169YhPj4eAN9wlibZudyxYweaN2+O6OhoZGdnY9euXejcuTNWrlyp5gh11+vXr9GtWzeMGTMG165dw/Lly9GpUyf4+fkhMTFR4+8RtdnPP/+MCRMmoEOHDvjqq6/g7u4OPz8/+Pj44OnTpwA0/GEwlVOvX7+mJk2aUIsWLWjbtm20bds2atasGVlZWdH8+fOJiEgqlao3yAJIpVLat28fubq6kiAIJAgCnT17Vt1hFUlOTg6tW7eObG1tqW3btvT999+Tj48POTk5Ud26dTX6OLKysmjx4sVkZWVFbdu2pfXr19OuXbuoXbt2ZGFhQQcOHFB3iB8lu55v3LhBNjY2ZGJiQl5eXnT79m0iIsrNzVVneIVKS0ujUaNGkSAI1LZtWzp06BC9fv1a3WEVy/z58+mTTz6hoKAgev78ubrDKZKkpCQaPnw4CYJA7dq1o0OHDtHRo0fJ2NiYVq5cSUR5f9Os/NizZw9ZW1vTli1b6Pnz5xQWFkbDhw8nIyMj+u6779Qdns5q06YNdevWjSIiIoiIKDw8nIYOHUqCINCuXbsoMzNTzRHqnsDAQKpYsSIFBQXRy5cvKS4ujkaOHEnm5ubk4+Oj7vB0VmpqKrVo0YI6depEMTExYvny5cvJwsKCBg0apMboiqbcJla7d+8mfX192rdvn1gWFRVFAwcOJEEQ6NSpU2qMrnBPnz6lBg0akI2NDS1atIjq1atHzZo1o+zsbHWH9lHHjx+nGjVq0OjRo+nBgwdi+aVLl0gQBJo1a5bGHsfRo0fJ3d2dpk6dSo8ePRJvKB8/fkyCINCKFSs0MhEvyL59+6hLly60adMmEgSB5syZIx6Pph1DTk4OLV68mARBoLFjx9KbN28KvUY0LXaZ58+fU5UqVWjy5MkK5flpUvzv3r2j2rVrU40aNWjjxo0UGRlJRETPnj0ja2tr6tOnj8Ym4kx1Pv/8c6pXr55Cea9evcjKyop2795NRJxwl6abN2+SmZkZrV69Wq48MjKSOnbsSLVq1aKLFy+qKTrd1bZtW2rWrJlc2bt372jkyJEkCAIdPXqUiDTre1sXxMfHU6VKlWjRokVEJP9d8vXXX5OxsTH98ssvRKS5D4M1v/2PikRGRqJChQro3bs3ACA7OxtVq1bFzJkz4enpialTpyI2NlbNUSrS19fH559/jtOnT2Pu3Ln45ptvcPXqVQQGBqo7tI+6f/8+jIyMsGzZMtSpUwcAkJWVhRYtWsDLyws3b96Evr6+RlavW1paYujQoZgzZw5q164NPT09AHnt3m1tbeHs7KzxTQNksTk5OeHq1asYP348OnbsiO3btyMkJETN0RVMT08PXbt2RYsWLXDhwgVUqlQJ+vr6OHz4MEaOHIlZs2Zh+/btyMrK0timmBEREUhJScHEiRMB5DXrqV+/Prp164bevXtj165dADSnr4RUKoWpqSkCAwNx+PBhjBkzBtWqVQMAuLi4oFatWoiPj0d2drZGX++sdGVmZiIrKwtWVlZiWVZWFgBg7ty5cHFxga+vL3JycsTvR1ZydnZ2yMrKQoUKFQBAbIZbrVo1rFy5EtHR0QgICMDbt2/VGabOkEqlyMzMhLGxMfT19cXynJwcmJqaYtKkSXB3d8fkyZNBRBrzva2Njh49Cnd3d7m+a8nJyRAEATExMcjMzISenh5yc3MBABMnToSbmxv8/PyQkZGhuV0Y1JrWlQFZRvv+U4U1a9aQubk5hYSEEBHJPbH//fffycjIiJYsWVLgtmWlsNgzMjLE/3/48CF16dKFHB0d6e3bt2Ua34fkjz1//A8fPpRbTpR37tu1a0etWrWi9PT0sg20AIWd9/dduHCBGjRoQBYWFuTn50f//PMPJSQkyO1DHT4W/759+6hWrVpERHTr1i0SBIFGjBhB8fHxH9yuLBQWu6x27bvvvqMuXbqQIAhUq1YtMjc3J0EQqE+fPnTv3j25fZS1wmK/fv066evr08GDB2nbtm0kkUioX79+NGLECKpcuTIJgkDbt29XQ8T/Kco1L5VKKTc3l7755huytLQUr3V+Yqtb4uPj6dGjR+L3QX79+/cnV1dX8Xs8vzVr1pCxsTEtXryYiDT3abK2SU5OpsaNG1P79u3Fsvx/czNmzCBzc3M6ffq0OsLTamFhYTRlyhSaNGkSzZ07lx49eiQu69WrF9WpU4f++ecfIpK/nrds2UKCINCaNWsUlrGiCQ8PJ2dnZxIEgXr37i23rF27dtS0aVOKiopS2G7t2rVkbm5Oy5YtIyLN/P3R2cRK1idm69atcuWyD+HkyZNkZGREfn5+Ypnsj+PVq1c0YMAAsrW1VUvb5cJiL8zvv/9OJiYmNHPmTBVH9nHFjV2WeDVp0oQGDhwolqlDUWKXXSOzZs0iQRCoffv2NGLECBozZgxZWVmptf3vx+KXndfQ0FAyNzenly9fEhHRmDFjyMjIiH777TciymvuUNY+9vcaGRlJ/fr1I0EQqEOHDnT8+HGKjIyk6Oho+uGHH0gikVD//v3LPG6ij5/369evU6VKlWjYsGHUuHFjmjdvHqWkpBAR0d27d6lr165kY2NDYWFhZRk2ERX/75WIaN68eSQIAh0+fFiFkTF1mDNnDtWpU4fs7e3J0NCQZs+eLZdEHT16VOzXIyN7KPnixQtq1aoVNW7cmN68eVPmseuyGTNmkJ2dHQUHBxORfPOoJ0+eUKVKlWj69OlEpJk3mpomMzOTpk+fTiYmJvTpp59S7dq1SRAEqlGjBu3du5eI8h5ACoJA27ZtE3/3Zec9IiKCOnbsSC4uLty/TUlJSUlkZWVF9evXJ0dHR/r111/FZTt27CA9PT25rjqyc//8+XNq3LgxtWvXTny4p2l0MrE6f/481a9fnwRBoC5dutD9+/eJSPELx93dnZo0aSI+kci/PCgoiPT19Wnjxo0Fbqvu2POXxcbG0ujRo8nY2Fh8aq+OL9fixJ7fixcvqEKFCrR06VIiUk/7/KLGLnt98OBB+v333+nt27dima+vL0kkEvL39yeisn2KVZxzv2fPHnJ1dRUHgEhOTiZTU1Nq3749jRo1ir788ksx6dKk2IOCgmjkyJF06dIlhWVDhw4lS0tL8WZf0/5eW7ZsSRKJhCpVqkSXL1+WWxYcHEwVK1akKVOmEFHZXTfF/XuVxXXhwgUSBIH27NnzwfWZ9rh79y61bduWHB0dac6cObRkyRIaPXo0CYJAY8aMEfs1vnjxgjw9Pally5ZyNzWya8DPz4/Mzc3FBICVjtevX1PFihVpyJAh4u+j7O8xJSWFhg4dSk5OTuoMUWukpKTQnDlzqEaNGrR8+XJ6+PAh5ebm0unTp8nBwYFat25NaWlplJOTQ40bN6bWrVuLg4bkt2DBArKyshL7WrGik0ql9OLFC2rXrh0tXryY6tSpQ56enpSamkpEeX3XPT09ycvLS+4hjeyanzhxItnb29OzZ8/UEv/H6FxideXKFapbty5Vr16d+vfvT4Ig0PLly+U6vMu+mA4dOkSCINCiRYvEJmiyZQ8fPiRHR0caN25cmd3oFCX2wpw+fZqqVq2qUKVaVkoS+/nz50kQBDpx4kQZRKqoOLF/6Cby8ePHVKtWLWrcuLFcc01VK2r8stgvXLhApqam9OLFC3HZ4MGDSU9PjwwMDGj+/PniF5wmxC6LOykpiWJjY+W2l633999/kyAIcjXQmhC77Pvk+PHj4iiespop2ZPO2NhY6tatGzk5OZXZdVOSv9d79+6RtbU1TZo0iYg4sdJ2CQkJNHLkSKpVqxYdOHBArsb6iy++IFtbW7pw4QIR5f29/fzzzySRSOj//u//xOs7KyuLiPJ+NwVBEEdJ5SZSpWfhwoVka2srdtzP/wBy1qxZVLlyZXr69Km6wtMa4eHh5OLiQuPHj6fExES5ZePHjydbW1u6fv06EeXVnAiCQKtXrxb/LmTf27du3SKJREIHDx4kIv4eLK7Y2FgyNjamsLAwWrZsGZmZmYkDVmRkZFBgYCDp6enR0qVLxXMv+33cu3cvGRgYFNgkWRPoXGJ1//59MjIyEqtzW7duTbVr16ZLly4VuH737t3JwcGBjhw5QkTyX1b169en4cOHE1HZ/NEUN/b8caWmpopNdGRtrc+dO0eHDh2SW0+TYpfZsGED6evri82jcnJy6OnTp+KXmybHTiR/89C8eXNq1qxZmSZW78ffpk2bD8a/e/duqlOnDiUmJlJISAi1atWK9PT0yMLCgmrVqiXeRGnqNZ8/Ntm5f/PmDVlZWZVpc9jixi4bHnn8+PFERHJJTL9+/ahevXqUlJSk+sCpZNd8bGwsOTs7U8eOHSk5OVnVoTIVi4+PJ09PT/GGnei/RCkkJETuN4Uob/TcPn36kIODA4WEhMh9T1y5coWMjIxo06ZNZXcA5URGRgY1aNCAatWqpfCk3sfHhypXrqyxTaM0iVQqpS1btsiVya73PXv2kL6+vvjwKzExkfr06UN2dnb0xx9/yG0TGhpKgiBQYGBg2QSuQ3Jzcyk6Oprq1KlD58+fp1evXlGzZs3IxcVFTJZevXpFY8aMITMzM9qxY4e4rVQqpa+++ors7OzoxYsXGpnQ6lRiJUuK8j/VltWGTJ48WbxpyX8jHBkZSWZmZtSsWTO6efOmWP7333+ThYUFLViwQKNiL+gikh3PgwcPyN3dnRo2bEgLFiwgJycnsrGxUfmcPyWJnYioZ8+e1KJFCyLKa2qyc+dOatKkCbm7u1NcXJzGxv7+09gTJ06QgYEBTZ06VYURyytO/LJjOH36NBkaGlKPHj1IT0+PWrZsSefPn6c9e/aIN/5l0W68NM/9hg0bSBAE+vnnn1UY8X+U+a558eIFWVhYKNTO/vvvv1SzZk0aNmxYmfxIlMZ579OnD9WvX59SU1M18oeNFY3s8wwLCytwAJPg4GDS19en33//XW67f/75h6pWrUoeHh7itfz69WuaOXMmOTg4FNh0ipXclStXqGrVqtSwYUO6cOECPX/+nP766y9ycXGhadOm8d9iEckear3f7cDf35/09PTkpoN58eIFValSherXr0/Hjx8nIqLo6GiaOHEiOTs706tXr8oucB0SHx9Ppqam4sO8zZs3U8WKFWnMmDFERPT27Vt69eoVeXl5kaWlJf3vf/+j4OBg2rp1K1WvXl2j5xLT2sRq9+7dNH78eFq2bBmdP39eLM//xSL7oRgxYgRZWVkpPHGQ/VEFBARQtWrVyMXFhdatW0dbt26lnj17kpOTE929e1cjYy9IZGSkOMeCIAj0xRdfyDX30rTYpVIppaSkkL29PQ0aNIhOnTpFn3/+OQmCQN26dStwRBhNiT2/ly9f0pEjR6ht27ZUr149sc9eaSut+C9dukSNGjWiTz75hH766Sd68eKF+LfQsmVLGjt2bKknVqo6969evaKDBw9So0aNqG3btioZGbM0v2t2795N9vb2VLFiRRo7diwtWbKEPvvsM7K2tlZJU1hVnHepVEqLFi0iQRDEp4t8Q6dbZJ/n4cOHSRAE8UYz/+d89uxZqlGjBgmCQC1btqSOHTuSkZERzZgxgzIzM/maUJEzZ85QjRo1yMDAgGrWrEkWFhbk7u6ulsFvdIXsO3DKlClkZ2cn1mDJvrdPnDhB7u7uJAgCubm5UfPmzcnAwIAWLFhAOTk5fK0r4dmzZ+Tq6ir+3mRmZlLv3r2pUqVKNHDgQHJ3d6cbN27Qs2fPaPz48SQIAllZWZGxsTENHjy4zFp3KEPrEqtXr15R165dqUKFCuTu7k7W1tZkZGRE8+fPF6vB35/sNCoqiszMzKhPnz5iopGbm6vwI9GyZUuytLQkGxsbatSoUalPuleasb/vwoUL1K1bN5JIJNSkSZMiN2NTd+xPnjwhU1NTcnd3JzMzM6pTp06pDxurqtjPnj1LY8eOpX79+pG5uTk1btyYrl27Vqqxl2b8sqd0WVlZdP78efrnn3/EBEq2XWkPd6/Kc//111/T4MGDyczMjNzd3en27dsaG3v+75pLly5R165dycrKiipXrkxNmjSRS3o0LfaCrFmzhgRBkBu1ieme2bNnk7W1NSUkJBTY7/HJkyfk5+dHAwcOpG7dutGff/6prlDLlSdPnlBQUBB9//33cs2kWMl4eHhQ3759iUixNuvNmze0bNkyGjt2LA0cOFBhECJWPHFxcWRkZCR3nz1jxgwyNDQkPT09mjt3rlxrq7CwMAoJCREHaNNkWpdYBQYGUsWKFSkoKIhevnxJcXFxNHLkSDI3Ny+walD2A7B48WKSSCS0ZcsWuZuc/P+fnp5Or1+/VsnNsSpiz+/UqVNkaGhIP/30k1bFfubMGRIEgSpXrqx1sR85coRq1apF7dq1o23btqkkdlXFX1ZP2FR17vft20dmZmbk5eWlsuZ/qvyuyczMpISEBLpz545WxC4jS7RiYmIoICBAJbEz9ZN9zl27dqXmzZsXeX3GtFVsbCyZmJiIo/oS5V3XBc3nxkru6dOn5OrqSsHBwXT58mVq3bo16enpUe3atcnCwkLsp6mOUaJLSusSq7Zt21KzZs3kyt69e0cjRowgQRDEoS/f/6LPysqimjVrkpeXlzgJ3NOnT+X6Gaj6x0GVsROp9gIs7djzP4nYvHmzWPWubbE/ffpUq66bJ0+eKFw3qqTKc3/nzh2tuuZ15buGm73ojg9dhzk5OWRlZUXz5s0Ty+Li4ujMmTOUlpZGRHwtMN0he8h79uxZIsp7eLRjxw7y9PQs09/M8iIqKoqMjIzIzc2N9PX1qXnz5hQcHEyXLl2i+vXrU9WqVbU2qdWaxCo3N5cyMjKoa9eu1LJlS7Fc1jzhxo0b5OHhQTVq1FD4sn9/ePVZs2bR9u3byd3dnSZPnqzyCVE59oJjV/WIYqqMvSyGI1dl/LIbI22MXdXnnv9e1RM7KztSqVQuqTp48CCFhobKrXPz5k1xRMD09HS6fPmyOLeVbH5HxrSd7Htw+fLlZGVlRY8ePaKQkBDq3bs3GRgY0Keffio3XyUrHTk5OfTll19SrVq1aP369fT8+XPxN2jevHk0fPhwSkpK0srzrpGJVVhYGE2ZMoUmTZpEc+fOFZ+cEhH16tWL6tSpIw4QkP/HYcuWLSQIAq1Zs4aIFGtwsrOzydPTk/T09EgQBLK3txdHeeHYOXZ1xa7t8XPsHDvTHvk/73v37lHHjh1JEARasmSJ3E3M2rVrSU9Pj/bt20eLFi0iGxsbsrOzo99++00dYTOmUn369KGaNWvS2LFjydzcnGrXrs0TXatYVFQU3bt3T2F6mqLMp6jJNCqxyszMpOnTp5OJiQl9+umnVLt2bRIEgWrUqCHOt7Jv3z4SBIG2bdsm3izIfigiIiKoY8eO5OLiotAp/+bNmzR37lwyMzMjc3Nz+vHHHzl2jl2tsWt7/Bw7x860R/6EKiUlhcaNG0eCIFDTpk3FvnhE/yXhEyZMoAoVKlCNGjVIX1+f5s6dq5a4GVO19PR0cnNzI0EQyMLCQnzoxJgyNCaxSklJoTlz5lCNGjVo+fLl9PDhQ8rNzaVTp06Rg4MDtW7dmtLS0ignJ4caN25Mbdq0KXCuDD8/P7KyshL7EBDl3TRMnDiRBEGgESNGiBPRcuwcu7pi1/b4OXaOnWmH/HPYEeWN6Ghubk5Vq1alFStW0OPHjwvsa9WyZUsSBIGGDRvGfUyYzps5cybNmjVLofaEseLSmMQqPDycXFxcaPz48ZSYmCi3bPz48WRra0vXr18nIqIdO3aQIAi0evVqsd2/7MnrrVu3SCKR0MGDB4novyrF0NBQun//PsfOsWtE7NoeP8fOsTPtcvz4capbty4ZGxuTj48PhYaGFji9gqxm6+rVq+K1xJiu45EtWWnRmMRKKpXSli1b5MpkI8Xt2bOH9PX1xQnwEhMTqU+fPmRnZ6cwmWVoaCgJgkCBgYFlEzhx7EQcuzK0OX6OnWNn2iE3N5f+97//kSAI1LNnT/rrr7/EucwYY4yVLo1JrIj+e2r6fmdqf39/0tPTE2d/JyJ68eIFValSherXry92rI6OjqaJEyeSs7MzvXr1quwCJ46dY1eONsfPsXPsTDuEhIRQYGAgRUVFqTsUxhjTaRqVWL1PVjU7ZcoUsrOzE5/Mym4oTpw4Qe7u7iQIArm5uVHz5s3JwMCAFixYQDk5OWodppFj59iVoc3xc+wcO9NM7/ez4s+cMcZUQyAigob79NNPUb16dezbtw+5ubnQ09MTl719+xa//PILnj59iuTkZEyZMgXNmzdXY7TyOHb10ObYAe2On2NXD22OnTHGGNMJ6s7sPiY2NpZMTEzI399fLMvNzdWKGZk5dvXQ5tiJtDt+jl09tDl2xhhjTFdI1J3Yfcy9e/eQkZEBT09PAMCrV6/w22+/oWvXrnjz5o2ao/swjl09tDl2QLvj59jVQ5tjZ4wxxnSFxiZW9P9bKF67dg2WlpZwcHDA2bNn4ePjg9GjR4OIIJFIxPU0CceuHtocO6Dd8XPs6qHNsTPGGGO6Rl/dARRGEAQAwNWrV2FjYwN/f3/s3r0bdnZ2OHr0KDp37qzmCAvHsauHNscOaHf8HLt6aHPsjDHGmM4pu1aHxZeenk5ubm4kCAJZWFjQmjVr1B1SkXHs6qHNsRNpd/wcu3poc+yMMcaYLtH4UQFnzZoFQRCwYMECGBkZqTucYuHY1UObYwe0O36OXT20OXbGGGNMV2h8YiWVSiGRaGxXsA/i2NVDm2MHtDt+jl09tDl2xhhjTFdofGLFGGOMMcYYY5qOH3EyxhhjjDHGWAlxYsUYY4wxxhhjJcSJFWOMMcYYY4yVECdWjDHGGGNaJiAgAIIgICIiQqntR44cierVq5dqTGWppMdfkIiICAiCgICAgFLbZ3F1794dY8eOLbX9DRo0CAMGDCi1/bEP48SKMcYYY+XGhg0bIAgCvLy81B0KU5PffvsNP/74o7rDUHDp0iUEBwdj1qxZYlliYiKGDh0Ka2tr1KhRA7/88ovCdtevX4epqSnCw8MVls2aNQv79+/HnTt3VBo7y8OJFWOMMcbKjaCgIFSvXh2hoaF48uSJusNhalBYYuXs7Iz09HR8+eWXZR8UAH9/f3Ts2BG1atUSy6ZPn46zZ89iwYIF6NGjB8aOHYvLly+Ly4kIkydPxtSpU+Hi4qKwzyZNmuDTTz/FqlWryuQYyjtOrBhjjDFWLoSHh+Py5ctYvXo1bG1tERQUpO6Qyp13796pO4RCCYIAY2Nj6Onplfl7x8bG4ujRowrN9v78808sXboUkydPxrp169CmTRscOXJEXB4UFITIyEjMmTOn0H0PGDAABw4cQGpqqsriZ3k4sWKMMcZYuRAUFARra2t4e3ujX79+BSZWsn42K1euxJYtW1CzZk0YGRnB09MT165dk1t35MiRMDMzQ3R0NHr16gUzMzPY2tpi+vTpyM3NFdc7e/YsBEHA2bNnC3yv/H167t69i5EjR6JGjRowNjaGnZ0dRo8ejbi4OKWP+48//kCDBg1gbGyMBg0a4ODBgwWuJ5VK8eOPP6J+/fowNjZGlSpVMH78eCQkJCis5+fnBwcHB5iamqJ9+/a4f/8+qlevjpEjR4rryfpBnTt3Dj4+PqhcuTIcHR0BAJGRkfDx8UGdOnVgYmICGxsb9O/fv8A+U//++y86dOgAExMTODo6YtGiRZBKpQrrHTp0CN7e3nBwcICRkRFq1qyJH374Qe6zaNeuHY4ePYrIyEgIggBBEMS+ZoX1sTpz5gxat26NChUqwMrKCl988QXCwsLk1vHz84MgCHjy5AlGjhwJKysrWFpaYtSoUUhLSyvsoxEdPXoUOTk56NSpk1x5eno6rK2txdcVK1YU9/fu3TvMnj0bS5cuhZmZWaH77ty5M969e4eTJ09+NA5WMvrqDoAx9p+AgACMGjVKfG1kZISKFSuiYcOG8Pb2xqhRo2Bubl7s/V6+fBnBwcGYOnUqrKysSjFixhjTHkFBQejTpw8MDQ0xePBgbNy4EdeuXYOnp6fCur/99htSUlIwfvx4CIKAFStWoE+fPnj27BkMDAzE9XJzc9G1a1d4eXlh5cqVOHXqFFatWoWaNWtiwoQJxY7x5MmTePbsGUaNGgU7Ozv8+++/2LJlC/7991/8/fffEAShWPsLDg5G3759Ua9ePSxduhRxcXEYNWqUmODkN378ePF3aPLkyQgPD8dPP/2EW7du4dKlS+Jx+/r6YsWKFejZsye6du2KO3fuoGvXrsjIyCgwBh8fH9ja2uL7778Xa6yuXbuGy5cvY9CgQXB0dERERAQ2btyIdu3a4f79+zA1NQUAvHr1Cu3bt0dOTg5mz56NChUqYMuWLTAxMVF4n4CAAJiZmeHbb7+FmZkZzpw5g++//x7Jycnw9/cHAMydOxdJSUmIiorCmjVrAOCDScmpU6fw2WefoUaNGvDz80N6ejrWr1+Pli1b4ubNmwoDgAwYMAAuLi5YunQpbt68ia1bt6Jy5cpYvnz5Bz+ny5cvw8bGBs7OznLlnp6eWL16NerWrYtnz57h+PHj+PnnnwEAS5YsQdWqVT/adLFevXowMTHBpUuX0Lt37w+uy0qIGGMaY/v27QSAFi5cSDt27KBt27bRkiVLqEuXLiQIAjk7O9OdO3eKvV9/f38CQOHh4aUfNGOMaYHr168TADp58iQREUmlUnJ0dKQpU6bIrRceHk4AyMbGhuLj48XyQ4cOEQA6cuSIWDZixAjxOzu/Jk2akIeHh/g6JCSEAFBISEiB77V9+3axLC0tTSH2Xbt2EQA6f/68WCb7vfjY97qbmxvZ29tTYmKiWBYcHEwAyNnZWSy7cOECAaCgoCC57Y8fPy5X/urVK9LX16devXrJrefn50cAaMSIEQoxtmrVinJycuTWL+g4r1y5QgDo119/FcumTp1KAOjq1atiWWxsLFlaWiocf0H7HD9+PJmamlJGRoZY5u3tLXfsMgV9Hm5ublS5cmWKi4sTy+7cuUMSiYSGDx8uls2fP58A0OjRo+X22bt3b7KxsVF4r/e1atVK7pqRuXv3Ljk6OhIAAkB9+/al3NxcevbsGZmYmNCVK1c+um8iIldXV/rss8+KtC5THjcFZEwDffbZZxg2bBhGjRoFX19fnDhxAqdOnUJsbCw+//xzpKenqztExhjTKkFBQahSpQrat28PIK8/zcCBA7F79265pmIyAwcOlGuC1bp1awDAs2fPFNb9+uuv5V63bt26wPWKIn9NTEZGBt6+fYtmzZoBAG7evFmsfcXExOD27dsYMWIELC0txfLOnTujXr16cuvu3bsXlpaW6Ny5M96+fSv+8/DwgJmZGUJCQgAAp0+fRk5ODnx8fOS2nzRpUqFxjB07VqHfUv7jzM7ORlxcHGrVqgUrKyu54zx27BiaNWuGpk2bimW2trYYOnSowvvk32dKSgrevn2L1q1bIy0tDQ8ePCg0vsLIzt/IkSNRsWJFsbxRo0bo3Lkzjh07prBNQddCXFwckpOTP/hecXFxctebTMOGDfH48WNcu3YNjx8/xr59+yCRSPDdd9+hb9++aNasGQ4cOIDGjRvDxcUFCxcuBBEp7Mfa2hpv374t6qEzJXFixZiW6NChA+bNm4fIyEjs3LkTQNHa4vv5+WHGjBkAABcXF7FNef527Dt37oSHhwdMTExQsWJFDBo0CC9evCjT42OMMVXJzc3F7t270b59e4SHh+PJkyd48uQJvLy88Pr1a5w+fVphm2rVqsm9lt30vt/fyNjYGLa2tgrrvr9eUcXHx2PKlCmoUqUKTExMYGtrK472lpSUVKx9RUZGAgBq166tsKxOnTpyrx8/foykpCRUrlwZtra2cv9SU1MRGxsrt8/8I9cBeX1/CkoMABQ4Wl16ejq+//57ODk5wcjICJUqVYKtrS0SExPljjMyMrJI8QN5fbF69+4NS0tLWFhYwNbWFsOGDQNQ/HMne+/C3uuTTz7B27dvFQbjKOp1U5CCEiIg7xr79NNPxXN+5swZBAcHY9myZXj48CEGDRqEqVOnYtu2bdiwYUOB83ARUbGbkbLi4z5WjGmRL7/8EnPmzEFwcDDGjh1bpLb4ffr0waNHj7Br1y6sWbMGlSpVAgDxRmDx4sWYN28eBgwYgK+++gpv3rzB+vXr0aZNG9y6dYv7ZDHGtN6ZM2cQExOD3bt3Y/fu3QrLg4KC0KVLF7mywkaGe//mtygjyBV2Q1tQTdmAAQNw+fJlzJgxA25ubjAzM4NUKkW3bt0KHLChtEilUlSuXLnQkRLfTx6Lo6D+UJMmTcL27dsxdepUNG/eHJaWlhAEAYMGDVLqOBMTE9G2bVtYWFhg4cKFqFmzJoyNjXHz5k3MmjVLpecuv6JeN++zsbEpUvKVm5uLKVOmYPbs2ahatSp++OEHtGjRQuyfPX78eAQFBcn11wbyEruCElRWujixYkyLODo6wtLSEk+fPgWQ1yH4u+++k1unWbNmGDx4MC5evIjWrVujUaNGcHd3x65du9CrVy+5jraRkZGYP38+Fi1aJDdUa58+fdCkSRNs2LDhg0O4MsaYNggKCkLlypXxf//3fwrLDhw4gIMHD2LTpk0FJgClQVZrkZiYKFcuqxGRSUhIwOnTp7FgwQJ8//33Yvnjx4+Vel/ZQAgFbf/w4UO51zVr1sSpU6fQsmXLD54H2T6fPHkiVxMVFxdXrFq6ffv2YcSIEXLzK2VkZCicI2dn5yLFf/bsWcTFxeHAgQNo06aNWF7QpLlFrbmRHev77wUADx48QKVKlVChQoUi7etj6tati/379390vY0bNyIlJQXTp08HALx8+RIODg7icgcHB0RHR8ttk5OTgxcvXuDzzz8vlVhZ4bgpIGNaxszMDCkpKQBK3hb/wIEDkEqlGDBggFybejs7O9SuXVtsU88YY9oqPT0dBw4cQI8ePdCvXz+FfxMnTkRKSgoOHz6sshicnZ2hp6eH8+fPy5Vv2LBB7rWstuP92o2CJrMtCnt7e7i5uSEwMFCuKdzJkydx//59uXUHDBiA3Nxc/PDDDwr7ycnJEROejh07Ql9fHxs3bpRb56effipWbHp6egrHuX79eoVavO7du+Pvv/9GaGioWPbmzRuFmrWCzl1WVpbCOQaAChUqFKlpYP7zlz/hu3fvHoKDg9G9e/eP7qOomjdvjoSEhA/2zYuPj8f8+fPh7+8PY2NjAECVKlXk+o+FhYXBzs5Obrv79+8jIyMDLVq0KLV4WcG4xooxLZOamorKlSsDyPuSXbBgAXbv3i22f5cpyo/G48ePQUSFNg/IP6QwY4xpo8OHDyMlJaXQp/XNmjUTJwseOHCgSmKwtLRE//79sX79egiCgJo1a+LPP/9U+N62sLBAmzZtsGLFCmRnZ6Nq1aoIDg4usNalqJYuXQpvb2+0atUKo0ePRnx8PNavX4/69evLTRjbtm1bjB8/HkuXLsXt27fRpUsXGBgY4PHjx9i7dy/Wrl2Lfv36oUqVKpgyZQpWrVqFzz//HN26dcOdO3fw119/oVKlSkWuDerRowd27NgBS0tL1KtXD1euXMGpU6dgY2Mjt97MmTOxY8cOdOvWDVOmTBGHW3d2dsbdu3fF9Vq0aAFra2uMGDECkydPhiAI2LFjR4FN8Dw8PPD777/j22+/haenJ8zMzNCzZ88C4/T398dnn32G5s2bY8yYMeJw65aWlvDz8yvSsRaFt7c39PX1cerUKYwbN67AdebNm4eGDRuif//+Ylnfvn2xcOFCTJgwAc7Ozti8eTNWr14tt93JkydhamqKzp07l1q8rGCcWDGmRaKiopCUlCR2YC1pW3ypVApBEPDXX38V2C78Q3N7MMaYNggKCoKxsXGhN5USiQTe3t4ICgoq0SS8H7N+/XpkZ2dj06ZNMDIywoABA+Dv748GDRrIrffbb79h0qRJ+L//+z8QEbp06YK//vpLrrlXcXTr1g179+7F//73P/j6+qJmzZrYvn07Dh06pDBh8aZNm+Dh4YHNmzdjzpw50NfXR/Xq1TFs2DC0bNlSXG/58uUwNTXFzz//jFOnTqF58+YIDg5Gq1atxJqUj1m7di309PQQFBSEjIwMtGzZEqdOnULXrl3l1rO3t0dISAgmTZqEZcuWwcbGBl9//TUcHBwwZswYcT0bGxv8+eef+O677/C///0P1tbWGDZsGDp27KiwTx8fH9y+fRvbt2/HmjVr4OzsXGhi1alTJxw/fhzz58/H999/DwMDA7Rt2xbLly8vcFAOZVWpUgXdu3fHnj17Ckys/vnnH2zduhVXr16VK2/YsCG2b98OPz8/pKSkwMfHR2H7vXv3ok+fPkrNg8mKSU3DvDPGCiCb8+PatWsFLl+yZAkBoK1bt1J8fDwBoAULFsit8+jRIwJA8+fPF8tWrlxZ4HwnK1asIAD08OHD0j4Uxhhj5UhCQgIBoEWLFqk7FK11/vx5kkgk9OjRo1Lb561bt0gQBLp161ap7ZMVjvtYMaYlzpw5gx9++AEuLi4YOnRosdriyzrXvt8puE+fPtDT08OCBQsU9kNEKn16yxhjTDsVNJei7LenXbt2ZRuMDmndujW6dOmCFStWlNo+ly1bhn79+sHNza3U9skKx00BGdNAf/31Fx48eICcnBy8fv0aZ86cwcmTJ+Hs7IzDhw/D2NgYxsbGRW6L7+HhAQCYO3cuBg0aBAMDA/Ts2RM1a9bEokWL4Ovri4iICPTq1Qvm5uYIDw/HwYMHMW7cOHHkIcYYYwwAfv/9dwQEBKB79+4wMzPDxYsXsWvXLnTp0kWuySArvr/++qtU91fQ9AJMdTixYkwDyYbZNTQ0RMWKFdGwYUP8+OOPGDVqlFwb6aK2xff09MQPP/yATZs24fjx45BKpQgPD0eFChUwe/ZsuLq6Ys2aNViwYAEAwMnJCV26dOGhWRljjClo1KgR9PX1sWLFCiQnJ4sDWixatEjdoTGmVgK93/6HMcYYY4wxxlixcB8rxhhjjDHGGCshTqwYY4wxxhhjrIQ4sWKMMcYYY4yxEuLEijHGGGOMMcZKiBMrxhhjjDHGGCshTqwYY4wxxhhjrIQ4sWKMMcYYY4yxEuLEijHGGGOMMcZKiBMrxhhjjDHGGCshTqwYY4wxxhhjrIQ4sWKMMcYYY4yxEuLEijHGGGOMMcZKiBMrxhhjjDHGGCuh/wdAfruaZc12+QAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 1000x300 with 2 Axes>"
       ]
@@ -62629,15 +62391,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:08:26.607365Z",
-     "iopub.status.busy": "2026-02-11T21:08:26.607365Z",
-     "iopub.status.idle": "2026-02-11T21:08:26.611357Z",
-     "shell.execute_reply": "2026-02-11T21:08:26.611357Z"
-    }
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from rdtools.soiling import CODSAnalysis\n",
@@ -62654,15 +62409,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:08:26.614776Z",
-     "iopub.status.busy": "2026-02-11T21:08:26.614776Z",
-     "iopub.status.idle": "2026-02-11T21:09:21.048064Z",
-     "shell.execute_reply": "2026-02-11T21:09:21.048064Z"
-    }
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [],
    "source": [
     "results_df, degradation, soiling_loss = CODS.run_bootstrap(reps=16, bootstrap_seed=42)"
@@ -62670,15 +62418,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:09:21.051410Z",
-     "iopub.status.busy": "2026-02-11T21:09:21.050411Z",
-     "iopub.status.idle": "2026-02-11T21:09:22.604868Z",
-     "shell.execute_reply": "2026-02-11T21:09:22.604868Z"
-    }
-   },
+   "execution_count": 36,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -62713,53 +62454,53 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2010-02-25 00:00:00-07:00</th>\n",
-       "      <td>0.627275</td>\n",
-       "      <td>0.026595</td>\n",
+       "      <td>0.632635</td>\n",
+       "      <td>0.024614</td>\n",
        "      <td>True</td>\n",
-       "      <td>1.007648</td>\n",
+       "      <td>0.999650</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.536025</td>\n",
-       "      <td>0.984150</td>\n",
+       "      <td>0.547342</td>\n",
+       "      <td>0.963029</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-26 00:00:00-07:00</th>\n",
-       "      <td>0.653869</td>\n",
-       "      <td>0.026593</td>\n",
+       "      <td>0.657248</td>\n",
+       "      <td>0.024612</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.007448</td>\n",
-       "      <td>0.999986</td>\n",
-       "      <td>0.558632</td>\n",
-       "      <td>0.872095</td>\n",
+       "      <td>0.999475</td>\n",
+       "      <td>0.999993</td>\n",
+       "      <td>0.568532</td>\n",
+       "      <td>0.889424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-27 00:00:00-07:00</th>\n",
-       "      <td>0.680461</td>\n",
-       "      <td>0.026591</td>\n",
+       "      <td>0.681860</td>\n",
+       "      <td>0.024611</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.007246</td>\n",
-       "      <td>0.999971</td>\n",
-       "      <td>0.581226</td>\n",
-       "      <td>0.926182</td>\n",
+       "      <td>0.999299</td>\n",
+       "      <td>0.999986</td>\n",
+       "      <td>0.589713</td>\n",
+       "      <td>0.934689</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-02-28 00:00:00-07:00</th>\n",
-       "      <td>0.707050</td>\n",
-       "      <td>0.026589</td>\n",
+       "      <td>0.706470</td>\n",
+       "      <td>0.024609</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.007042</td>\n",
-       "      <td>0.999957</td>\n",
-       "      <td>0.603807</td>\n",
-       "      <td>0.930474</td>\n",
+       "      <td>0.999122</td>\n",
+       "      <td>0.999978</td>\n",
+       "      <td>0.610885</td>\n",
+       "      <td>0.945476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010-03-01 00:00:00-07:00</th>\n",
-       "      <td>0.733638</td>\n",
-       "      <td>0.026587</td>\n",
+       "      <td>0.731079</td>\n",
+       "      <td>0.024608</td>\n",
        "      <td>False</td>\n",
-       "      <td>1.006836</td>\n",
-       "      <td>0.999943</td>\n",
-       "      <td>0.626375</td>\n",
-       "      <td>0.838916</td>\n",
+       "      <td>0.998944</td>\n",
+       "      <td>0.999971</td>\n",
+       "      <td>0.632047</td>\n",
+       "      <td>0.857710</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -62767,28 +62508,28 @@
       ],
       "text/plain": [
        "                           soiling_ratio  soiling_rates  cleaning_events  \\\n",
-       "2010-02-25 00:00:00-07:00       0.627275       0.026595             True   \n",
-       "2010-02-26 00:00:00-07:00       0.653869       0.026593            False   \n",
-       "2010-02-27 00:00:00-07:00       0.680461       0.026591            False   \n",
-       "2010-02-28 00:00:00-07:00       0.707050       0.026589            False   \n",
-       "2010-03-01 00:00:00-07:00       0.733638       0.026587            False   \n",
+       "2010-02-25 00:00:00-07:00       0.632635       0.024614             True   \n",
+       "2010-02-26 00:00:00-07:00       0.657248       0.024612            False   \n",
+       "2010-02-27 00:00:00-07:00       0.681860       0.024611            False   \n",
+       "2010-02-28 00:00:00-07:00       0.706470       0.024609            False   \n",
+       "2010-03-01 00:00:00-07:00       0.731079       0.024608            False   \n",
        "\n",
        "                           seasonal_component  degradation_trend  total_model  \\\n",
-       "2010-02-25 00:00:00-07:00            1.007648           1.000000     0.536025   \n",
-       "2010-02-26 00:00:00-07:00            1.007448           0.999986     0.558632   \n",
-       "2010-02-27 00:00:00-07:00            1.007246           0.999971     0.581226   \n",
-       "2010-02-28 00:00:00-07:00            1.007042           0.999957     0.603807   \n",
-       "2010-03-01 00:00:00-07:00            1.006836           0.999943     0.626375   \n",
+       "2010-02-25 00:00:00-07:00            0.999650           1.000000     0.547342   \n",
+       "2010-02-26 00:00:00-07:00            0.999475           0.999993     0.568532   \n",
+       "2010-02-27 00:00:00-07:00            0.999299           0.999986     0.589713   \n",
+       "2010-02-28 00:00:00-07:00            0.999122           0.999978     0.610885   \n",
+       "2010-03-01 00:00:00-07:00            0.998944           0.999971     0.632047   \n",
        "\n",
        "                           residuals  \n",
-       "2010-02-25 00:00:00-07:00   0.984150  \n",
-       "2010-02-26 00:00:00-07:00   0.872095  \n",
-       "2010-02-27 00:00:00-07:00   0.926182  \n",
-       "2010-02-28 00:00:00-07:00   0.930474  \n",
-       "2010-03-01 00:00:00-07:00   0.838916  "
+       "2010-02-25 00:00:00-07:00   0.963029  \n",
+       "2010-02-26 00:00:00-07:00   0.889424  \n",
+       "2010-02-27 00:00:00-07:00   0.934689  \n",
+       "2010-02-28 00:00:00-07:00   0.945476  \n",
+       "2010-03-01 00:00:00-07:00   0.857710  "
       ]
      },
-     "execution_count": 33,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -62808,22 +62549,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:09:22.606878Z",
-     "iopub.status.busy": "2026-02-11T21:09:22.606878Z",
-     "iopub.status.idle": "2026-02-11T21:09:22.610884Z",
-     "shell.execute_reply": "2026-02-11T21:09:22.610884Z"
-    }
-   },
+   "execution_count": 37,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Avg. Soiling loss 5.308 (4.538, 6.241) (%)\n",
-      "Degradation rate -0.157 (-0.377, -0.018) (%)\n"
+      "Avg. Soiling loss 6.128 (4.569, 6.238) (%)\n",
+      "Degradation rate -0.181 (-0.371, -0.027) (%)\n"
      ]
     }
    ],
@@ -62841,19 +62575,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:09:22.613401Z",
-     "iopub.status.busy": "2026-02-11T21:09:22.612418Z",
-     "iopub.status.idle": "2026-02-11T21:09:23.184578Z",
-     "shell.execute_reply": "2026-02-11T21:09:23.184578Z"
-    }
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Hjh1LcnLyGc1TnFtn47wQ/1x9+vShT58+J11uzZo1qFQq1qxZc1bLo1KpePLJJ087nyeffBKVSkV+fv5Jl63tmtqzZw8DBw4kKCgIlUrFwoULT7tMQogzT4I1IRqouXPnolKpvH9Go5HY2FgGDRrEG2+8QVlZ2fku4nmTlZXFk08+yebNm893UWolwaK40H322We89tpr9f5cnz59OHjw4BkvjzjzxowZw9atW3nmmWeYN28enTt3PuXjLoQ4eyRYE6KBmzFjBvPmzWPWrFlMnDgRgMmTJ9OmTRv++uuv81y68yMrK4vp06fXGqy9//777Nq165yXadmyZTXSXC4XK1euPOdlEeJ01eem/aeffqKysrJG+o8//ojT6TzDJau/5cuXs3z58vNdjPNq165dvP/++97XVquVdevWcfvtt3Pfffdxyy23EB8fL8GaEA2QBGtCNHCDBw/mlltuYdy4cTz++OMsW7aMlStXkpuby1VXXYXVaj2n5VEU5Zyvsz50Oh0Gg+GcrrO8vJznn3+egQMHelsVtmzZQrdu3fjoo49QFOWclkeIc+nbb7+lQ4cO3u6DeXl53HzzzTz22GPk5OTUK6+KioozXj69Xo9erz/j+V5IDAYDOp3O+zovLw+A4ODg81QiIURdSbAmxAXo8ssvZ8qUKaSlpfHJJ5/4vLdz505GjBhBaGgoRqORzp07s2jRohp5/PXXX/Tu3RuTyUR8fDxPP/00c+bMQaVS+XRjSk5OZtiwYSxbtozOnTtjMpl49913AZgzZw6XX345kZGRGAwGWrZsyaxZs2qsS1EUnn76aeLj4zGbzfTt25dt27bVWK6wsJCHH36YNm3a4O/vT2BgIIMHD2bLli3eZdasWUOXLl0AGDdunLeb6Ny5c4Han1mrqKjgoYceIiEhAYPBQLNmzXjppZdqBFEqlYr77ruPhQsX0rp1awwGA61atWLp0qXHPxiAv78/q1at4t577+Waa64hOzubu+66i1deeYXPPvsMlUp13M8ePnyYcePGER8fj8FgICYmhquvvrpGV7IlS5bQs2dP/Pz8CAgIYOjQoTX24V9//cXYsWNJSUnBaDQSHR3NbbfdRkFBgc9yZWVlTJ48meTkZAwGA5GRkQwYMICNGzf6LDd//nw6deqEyWQiPDycW265hczMTJ9lxo4di7+/P5mZmQwfPhx/f38iIiJ4+OGHcblcPsu+9NJLXHbZZYSFhWEymejUqRMLFiw44b49Ebfbzeuvv06bNm0wGo1ERERwxRVXsH79eu8yTqeTp556itTUVAwGA8nJyTzxxBPYbDafvKrO8zVr1njP8zZt2ngDkK+//tq7nk6dOrFp06Za98P+/fsZNGgQfn5+xMbGMmPGjBrn2dk4HzMzM7ntttuIioryLvfRRx/5LFP1TNaXX37JM888Q3x8PEajkX79+rF3717vcn369OGHH34gLS3Ne32dqGvvyy+/zBdffMEzzzzDH3/8wU033cSAAQP4448/iIuLO+7nqrp6/+9//+Oee+4hMjKS+Ph47/t1Oefrcv3U9sxaRkYGw4cPx8/Pj8jISB544IEa5wQc//nJY/O02+1MnTqVTp06ERQUhJ+fHz179mT16tXH3f6TefPNN2nVqhVms5mQkBBvN8VjFRcXM3bsWIKDgwkKCmLcuHFYLJbjbseTTz5JUlISAI888oj3+Nb3uAshzg3t+S6AEOLUjB49mieeeILly5czfvx4ALZt20b37t2Ji4vjsccew8/Pjy+//JLhw4fz1Vdfcc011wCeG7u+ffuiUql4/PHH8fPz44MPPjhui9SuXbsYNWoUd955J+PHj6dZs2YAzJo1i1atWnHVVVeh1Wr57rvvuOeee3C73dx7773ez0+dOpWnn36aIUOGMGTIEDZu3MjAgQOx2+0+69m/fz8LFy7k+uuvp1GjRuTk5PDuu+/Su3dvtm/fTmxsLC1atGDGjBlMnTqVCRMm0LNnTwAuu+yyWsuuKApXXXUVq1ev5vbbb6d9+/YsW7aMRx55hMzMTF599VWf5deuXcvXX3/NPffcQ0BAAG+88QbXXXcd6enphIWFnfCYqNVqn8DsREFaleuuu45t27YxceJEkpOTyc3NZcWKFaSnp3tvlObNm8eYMWMYNGgQzz//PBaLhVmzZtGjRw82bdrkXW7FihXs37+fcePGER0dzbZt23jvvffYtm0bv/32m7c8d911FwsWLOC+++6jZcuWFBQUsHbtWnbs2EHHjh0Bz430uHHj6NKlCzNnziQnJ4fXX3+dX375hU2bNvnUyLtcLgYNGkTXrl156aWXWLlyJS+//DKpqancfffd3uVef/11rrrqKm6++Wbsdjuff/45119/Pd9//z1Dhw496b461u23387cuXMZPHgwd9xxB06nk59//pnffvuNzp07A3DHHXfw8ccfM2LECB566CF+//13Zs6cyY4dO/jmm2988tu7dy833XQTd955J7fccgsvvfQSV155JbNnz+aJJ57gnnvuAWDmzJmMHDmSXbt2oVYfrfN0uVxcccUVXHrppbzwwgssXbqUadOm4XQ6mTFjBnB2zsecnBwuvfRSb3AXERHBkiVLuP322yktLWXy5Mk+eT733HOo1WoefvhhSkpKeOGFF7j55pv5/fffAfjXv/5FSUkJGRkZ3vL4+/uf8FhUP/erbvTr6p577iEiIoKpU6d6W9bqes7X5fo5ltVqpV+/fqSnpzNp0iRiY2OZN28eq1atqnOZj1VaWsoHH3zAqFGjGD9+PGVlZXz44YcMGjSIP/74g/bt29crv/fff59JkyYxYsQI7r//fiorK/nrr7/4/fffuemmm3yWHTlyJI0aNWLmzJls3LiRDz74gMjISJ5//vla87722msJDg7mgQceYNSoUQwZMgR/f3/8/PzqfdyFEOeAIsQFymq1Ktu3b1esVmvNN20VipK5qWH82SpOafvmzJmjAMqff/553GWCgoKUDh06eF/369dPadOmjVJZWelNc7vdymWXXaY0adLEmzZx4kRFpVIpmzZt8qYVFBQooaGhCqAcOHDAm56UlKQAytKlS2us32Kx1EgbNGiQkpKS4n2dm5ur6PV6ZejQoYrb7famP/HEEwqgjBkzxptWWVmpuFwun/wOHDigGAwGZcaMGd60P//8UwGUOXPm1Fj/mDFjlKSkJO/rhQsXKoDy9NNP+yw3YsQIRaVSKXv37vWmAYper/dJ27JliwIob775Zo11VSkrK1MGDBigDBgwQNm/f7+SlJSkbN68WenSpYty0003+Wx3dUVFRQqgvPjiiyfMOzg4WBk/frxP+uHDh5WgoCCf9NqOx3//+18FUH766SdvWlBQkHLvvfced512u12JjIxUWrdu7XN9ff/99wqgTJ061Zs2ZswYBfA5PoqiKB06dFA6derkk3Zs+ex2u9K6dWvl8ssv90lPSkryOS9qs2rVKgVQJk2aVOO9qv29efNmBVDuuOMOn/cffvhhBVBWrVrls05A+fXXX71py5YtUwDFZDIpaWlp3vR3331XAZTVq1d706r2w8SJE33KMXToUEWv1yt5eXmKopyd8/H2229XYmJilPz8fJ88b7zxRiUoKMi731evXq0ASosWLRSbzeZd7vXXX1cAZevWrd60oUOH+lxHJ/LII48ozZs3V1avXq307t1b+eOPP5RRo0YpnTt3VjIyMo77uarvuB49eihOp9ObXtdzvi7Xj6IoSu/evZXevXt7X7/22msKoHz55ZfetIqKCqVx48Y1juvxzsVj83Q6nT77tKp8UVFRym233eaTDijTpk07YZmvvvpqpVWrVidcZtq0aQpQI/9rrrlGCQsL80k7djsOHDhQ676rz3E/4W+wEOKMkW6Q4uKUvxve690w/vJ3n7XN9Pf3944KWVhYyKpVqxg5ciRlZWXk5+eTn59PQUEBgwYNYs+ePd4ubEuXLqVbt24+tb2hoaHcfPPNta6nUaNGDBo0qEa6yWTy/r+kpIT8/Hx69+7N/v37KSkpAWDlypXY7XYmTpzoU9t+bG0/eJ6rqGqpcLlcFBQU4O/vT7NmzWp00aurxYsXo9FomDRpkk/6Qw89hKIoLFmyxCe9f//+pKamel+3bduWwMBA9u/ff9x1+Pv78+CDD7J8+XIaNWoEQLt27Vi3bh1jx449biuDyWRCr9ezZs0aioqKal1mxYoVFBcXM2rUKO8xzc/PR6PR0LVrV59uVtWPR2VlJfn5+Vx66aUAPvsvODiY33//naysrFrXuX79enJzc7nnnnswGo3e9KFDh9K8eXN++OGHGp+56667fF737Nmzxj6rXr6ioiJKSkro2bPnKR3br776CpVKxbRp02q8V7W/Fy9eDMCDDz7o8/5DDz0EUGM7WrZsSbdu3byvu3btCni6HScmJtZIr+2cuO+++3zKcd9992G3270DzZzp81FRFL766iuuvPJKFEXxOUcGDRpESUlJjf07btw4n2e4qlqnT3SOn8jQoUPZuHGjt1tgREQEn332Gc899xxRUVEn/fz48ePRaDTe13U95+ty/dRm8eLFxMTEMGLECG+a2WxmwoQJdc7jWBqNxrtP3W43hYWFOJ1OOnfufErnd3BwMBkZGfz5558nXba2a6+goIDS0tJ6r1cI0fBIN0hxcQpvChP+d75L4RHe9KxlXV5eTmRkJODpwqUoClOmTGHKlCm1Lp+bm0tcXBxpaWk+N6VVGjduXOvnqgKQY/3yyy9MmzaNdevW1XhGoqSkhKCgINLS0gBo0qSJz/sRERGEhIT4pFU9g/TOO+9w4MABn2eeTtYF8XjS0tKIjY0lICDAJ71Fixbe96urflNeJSQk5KQ3g1dccUWNNI1Gw4ABA477GYPBwPPPP89DDz1EVFQUl156KcOGDePWW28lOjoa8MyFBJ6AoTaBgYHe/xcWFjJ9+nQ+//xzcnNzfZarCp4BXnjhBcaMGUNCQgKdOnViyJAh3HrrraSkpABH90lVd9fqmjdvztq1a33Sqp4Xq662ffb999/z9NNPs3nzZp/ng+rTZa7Kvn37iI2NJTQ09LjLpKWloVara5zX0dHRBAcHn/TYBwUFAZCQkFBr+rHbp1arvfuwStOmnuu/6hmqM30+5uXlUVxczHvvvcd7771XY1mgxrlwbJ5V12F9Ap7qevfuXWt6v3796vT5Y79f6nrO1+X6qU1aWhqNGzeucd7Vdr7Xx8cff8zLL7/Mzp07cTgc3vTjfX+eyP/93/+xcuVKLrnkEho3bszAgQO56aab6N69e41lT3Q8q38/CCEuTBKsiYuT3gyx7c93Kc6qjIwMSkpKvDeibrcbgIcffrjWVjA4fjB2MtVbRKrs27ePfv360bx5c1555RUSEhLQ6/UsXryYV1991Vue+nj22WeZMmUKt912G0899RShoaGo1WomT558Svmdiuo1/NUp9RjRsT7zTE2ePJkrr7yShQsXsmzZMqZMmcLMmTNZtWoVHTp08G73vHnzar0B1WqPfo2PHDmSX3/9lUceeYT27dvj7++P2+3miiuu8Nl/I0eOpGfPnnzzzTcsX76cF198keeff56vv/6awYMH17nsVY63z6r7+eefueqqq+jVqxfvvPMOMTEx6HQ65syZU+ugCWdSXYPB423HmTgnTtXJ1l11XG+55RbGjBlT67Jt27atV56n41QmlD72+6U+5/zJrp/Tdbxzx+Vy+ezHTz75hLFjxzJ8+HAeeeQRIiMj0Wg0zJw5k3379tV7vS1atGDXrl18//33LF26lK+++op33nmHqVOnMn36dJ9lz+f5KYQ4+yRYE+ICNW/ePABvYFZVo6/T6ejfv/8JP5uUlOQz+luV2tKO57vvvsNms7Fo0SKfmt1jRz+rGnVsz549Pq0OeXl5NWryFyxYQN++ffnwww990ouLiwkPD/e+rk9LTFJSEitXrqSsrMynNWPnzp0+5TufUlNTeeihh3jooYfYs2cP7du35+WXX+aTTz7xdoGLjIw84XEtKirixx9/ZPr06UydOtWbXtVKcayYmBjuuece7rnnHnJzc+nYsSPPPPMMgwcP9u6TXbt21Wjd2LVr1ynts6+++gqj0ciyZct8BrKZM2dOvfMCzz5btmwZhYWFx21dS0pKwu12s2fPHm/LFXgG5CguLj7jx97tdrN//35vaxrA7t2ebtBVg12c6fMxIiKCgIAAXC7XSa/7+jiV1s4zpa7nfPXlj3f91CYpKYm///4bRVF8trO2+RlDQkIoLi6ukZ6WlubzfbZgwQJSUlL4+uuvffKsrZtuXfn5+XHDDTdwww03YLfbufbaa3nmmWd4/PHHfbonn0nn87gLIWonz6wJcQFatWoVTz31FI0aNfI+ZxYZGUmfPn149913yc7OrvGZqnl1wBPgrVu3zmdS6cLCQj799NM6l6GqNrd67W1JSUmNm+/+/fuj0+l48803fZatbeJVjUZTozZ4/vz5NYaL9/PzA6j1JupYQ4YMweVy8dZbb/mkv/rqq6hUqlNqSTpTLBZLjcmEU1NTCQgI8HYTHDRoEIGBgTz77LM+XauqVB3X2o4H1NzPLpfLp0skeM6d2NhY7zo7d+5MZGQks2fP9umuuGTJEnbs2HFKIzdqNBpUKpVP19aDBw+ycOHCeucFnlEAFUWp0coAR/fBkCFDgJr74JVXXgE4pe04mernmaIovPXWW+h0Om+XwDN9Pmo0Gq677jq++uor/v777xrvV7/u66NqZMDzoa7nfF2un9oMGTKErKwsn2kjLBZLrd1IU1NT+e2333xGrv3+++85dOiQz3K1XX+///4769atO9GmHtex023o9XpatmyJoii17pMz5XwedyFE7aRlTYgGbsmSJezcuROn00lOTg6rVq1ixYoVJCUlsWjRIp8a1rfffpsePXrQpk0bxo8fT0pKCjk5Oaxbt46MjAzvfGWPPvoon3zyCQMGDGDixIneofsTExMpLCysU+3qwIED0ev1XHnlldx5552Ul5fz/vvvExkZ6RMsVs25NXPmTIYNG8aQIUPYtGkTS5Ys8WktAxg2bBgzZsxg3LhxXHbZZWzdupVPP/20xnNAqampBAcHM3v2bAICAvDz86Nr1661Phty5ZVX0rdvX/71r39x8OBB2rVrx/Lly/n222+ZPHmyz+AN59ru3bvp168fI0eOpGXLlmi1Wr755htycnK48cYbAc/zObNmzWL06NF07NiRG2+8kYiICNLT0/nhhx/o3r07b731FoGBgfTq1YsXXngBh8NBXFwcy5cv58CBAz7rLCsrIz4+nhEjRtCuXTv8/f1ZuXIlf/75Jy+//DLgaZ19/vnnGTduHL1792bUqFHeofuTk5N54IEH6r2tQ4cO5ZVXXuGKK67gpptuIjc3l7fffpvGjRvz119/1Tu/vn37Mnr0aN544w327Nnj7er5888/07dvX+677z7atWvHmDFjeO+99yguLqZ379788ccffPzxxwwfPpy+ffvWe70nYjQaWbp0KWPGjKFr164sWbKEH374gSeeeML7TN/ZOB+fe+45Vq9eTdeuXRk/fjwtW7aksLCQjRs3snLlSgoLC+udZ6dOnfjiiy948MEH6dKlC/7+/lx55ZX1zudU1PWcr8v1U5vx48fz1ltvceutt7JhwwZiYmKYN28eZrO5xrJ33HEHCxYs4IorrmDkyJHs27fPp8W7yrBhw/j666+55pprGDp0KAcOHGD27Nm0bNmS8vLyeu+DgQMHEh0dTffu3YmKimLHjh289dZbDB06tMbzjmfS+TzuQojjOLeDTwpx5lzswwZXDWtd9afX65Xo6GhlwIAByuuvv66UlpbW+rl9+/Ypt956qxIdHa3odDolLi5OGTZsmLJgwQKf5TZt2qT07NlTMRgMSnx8vDJz5kzljTfeUADl8OHD3uWSkpKUoUOH1rquRYsWKW3btlWMRqOSnJysPP/888pHH31UY/h/l8ulTJ8+XYmJiVFMJpPSp08f5e+//64xnHRlZaXy0EMPeZfr3r27sm7duhrDZCuKonz77bdKy5YtFa1W6zOM/7FD9yuKZyjwBx54QImNjVV0Op3SpEkT5cUXX6wxpD5Q65D2dRlK/lTk5+cr9957r9K8eXPFz89PCQoKUrp27eozpHiV1atXK4MGDVKCgoIUo9GopKamKmPHjlXWr1/vXSYjI0O55pprlODgYCUoKEi5/vrrlaysLJ+hwm02m/LII48o7dq1UwICAhQ/Pz+lXbt2yjvvvFNjnV988YXSoUMHxWAwKKGhocrNN99cYyj2MWPGKH5+fjU+WzWseHUffvih0qRJE8VgMCjNmzdX5syZU+tydd3fTqdTefHFF5XmzZsrer1eiYiIUAYPHqxs2LDBu4zD4VCmT5+uNGrUSNHpdEpCQoLy+OOP+0xvUbXO2s7z2s6J2oY9r9oP+/btUwYOHKiYzWYlKipKmTZtWo3pKM7G+ZiTk6Pce++9SkJCgqLT6ZTo6GilX79+ynvvveddpmro/vnz59e6PdWnwigvL1duuukmJTg4WAHqPJx7fZxsepKTnfN1vX5q+/5IS0tTrrrqKsVsNivh4eHK/fffryxdurTG0P2Koigvv/yyEhcXpxgMBqV79+7K+vXra+TpdruVZ599VklKSlIMBoPSoUMH5fvvv6/1+6j69Xg87777rtKrVy8lLCxMMRgMSmpqqvLII48oJSUl3mWqrp2qaSGO3a/HTsFSl6H763PcL/bfYCEaCpWiyBOo4sJUWVnJgQMHaNSo0Vnrv/9PM3nyZN59913Ky8vrNGiEEMJj7NixLFiw4JRaUYS4EMlvsBDnhjyzJsQ/lNVq9XldUFDAvHnz6NGjhwRqQgghhBANgDyzJsQ/VLdu3ejTpw8tWrQgJyeHDz/8kNLS0uPO0SaEEEIIIc4tCdaE+IcaMmQICxYs4L333kOlUtGxY0c+/PBDevXqdb6LJoQQQgghAHlmTVywpL+8EEIIcX7Ib7AQ54Y8syaEEEIIIYQQDZAEa0IIIYQQQgjRAEmwJoQQQgghhBANkARrQgghhBBCCNEASbAmhBBCCCGEEA2QBGtCCCGEEEII0QBJsCaEEEIIIYQQDZAEa0KIWh08eBCVSsXcuXPr/dk1a9agUqlYs2bNSZf9888/ueyyy/Dz80OlUrF582aefPJJVCpV/QsthBBCCHER0Z7vAggh/rkcDgfXX389RqORV199FbPZTFJSUq3LPvvss7Rs2ZLhw4ef20IKIYQQQpwn0rImhDhv9u3bR1paGg8//DATJkzglltuISQkhH//+99YrVafZZ999lkWLlx4fgoqhBBCCHEeSMuaEOK8yc3NBSA4ONgnXavVotXK15MQQggh/tmkZU2IBqrqua3du3dzyy23EBQUREREBFOmTEFRFA4dOsTVV19NYGAg0dHRvPzyyzXyyM3N5fbbbycqKgqj0Ui7du34+OOPayxXXFzM2LFjCQoKIjg4mDFjxlBcXFxruXbu3MmIESMIDQ3FaDTSuXNnFi1aVO/tGzt2LL179wbg+uuvR6VS0adPH59tr6JSqaioqODjjz9GpVKhUqkYO3ZsvdcphBBCCHEhkaprIRq4G264gRYtWvDcc8/xww8/8PTTTxMaGsq7777L5ZdfzvPPP8+nn37Kww8/TJcuXejVqxcAVquVPn36sHfvXu677z4aNWrE/PnzGTt2LMXFxdx///0AKIrC1Vdfzdq1a7nrrrto0aIF33zzDWPGjKlRlm3bttG9e3fi4uJ47LHH8PPz48svv2T48OF89dVXXHPNNXXerjvvvJO4uDieffZZJk2aRJcuXYiKiqp12Xnz5nHHHXdwySWXMGHCBABSU1PruyuFEEIIIS4sihAXKKvVqmzfvl2xWq3nuyhnxbRp0xRAmTBhgjfN6XQq8fHxikqlUp577jlvelFRkWIymZQxY8Z401577TUFUD755BNvmt1uV7p166b4+/srpaWliqIoysKFCxVAeeGFF3zW07NnTwVQ5syZ403v16+f0qZNG6WystKb5na7lcsuu0xp0qSJN2316tUKoKxevfqE21i13Pz582vd9ur8/Px8tk8IIcT5c7H/BgvRUEjLmrgoWSwWdu7ceb6LAUDz5s0xm82n/Pk77rjD+3+NRkPnzp3JyMjg9ttv96YHBwfTrFkz9u/f701bvHgx0dHRjBo1ypum0+mYNGkSo0aN4n//+x/Dhg1j8eLFaLVa7r77bp/1TJw4kZ9//tmbVlhYyKpVq5gxYwZlZWWUlZV53xs0aBDTpk0jMzOTuLi4U95WIYQQQghxlARr4qK0c+dOOnXqdL6LAcCGDRvo2LHjKX8+MTHR53VQUBBGo5Hw8PAa6QUFBd7XaWlpNGnSBLXa99HUFi1aeN+v+jcmJgZ/f3+f5Zo1a+bzeu/evSiKwpQpU5gyZUqtZc3NzZVgTQghhBDiDJFgTVyUmjdvzoYNG853MQBPWU6HRqOpUxp4nj87W9xuNwAPP/wwgwYNqnWZxo0bn7X1CyGEEEL800iwJi5KZrP5tFqzLgZJSUn89ddfuN1un9a1qu6hVZNPJyUl8eOPP1JeXu7TurZr1y6f/FJSUgBPV8r+/fuf7eLXUH10SCGEEEKIfwIZul+Ii9SQIUM4fPgwX3zxhTfN6XTy5ptv4u/v7x02f8iQITidTmbNmuVdzuVy8eabb/rkFxkZSZ8+fXj33XfJzs6usb68vLyztCUefn5+x51OQAghhBDiYiQta0JcpCZMmMC7777L2LFj2bBhA8nJySxYsIBffvmF1157jYCAAACuvPJKunfvzmOPPcbBgwdp2bIlX3/9NSUlJTXyfPvtt+nRowdt2rRh/PjxpKSkkJOTw7p168jIyGDLli1nbXs6derEypUreeWVV4iNjaVRo0Z07dr1rK1PCCGEEOJ8k2BNiIuUyWRizZo1PPbYY3z88ceUlpbSrFkz5syZ4zOhtFqtZtGiRUyePJlPPvkElUrFVVddxcsvv0yHDh188mzZsiXr169n+vTpzJ07l4KCAiIjI+nQoQNTp049q9vzyiuvMGHCBP79739jtVoZM2aMBGtCCCGEuKiplLM5IoEQZ1FlZSUHDhygUaNGGI3G810cIYQQ4h9DfoOFODfkmTUhhBBCCCGEaIAkWBNCCCGEEEKIBkiCNSGEEEIIIYRogCRYE0IIIYQQQogGSII1IYQQQgghhGiAJFgTQgghhBBCiAZIgjVxwZPZJ4QQQohzS357hTg3JFgTFyydTodKpaKiouJ8F0UIIYT4R6moqEClUqHT6c53UYS4qGnPdwGEOFUajYagoCDy8vKw2WwEBgai1WpRqVTnu2hCCCHERUdRFJxOJ6WlpZSWlhIcHIxGoznfxRLioqZSpB1bXMAURaGkpITc3FxcLtf5Lo4QQghx0dNoNERGRhIUFCQVpEKcZRKsiYuCoii4XC6cTuf5LooQQghx0dJqtWg0GgnShDhHJFgTQgghhBBCiAZIBhgRQgghhBBCiAZIgjUhhBBCCCGEaIAkWBNCCCGEEEKIBkiCNSGEEEIIIYRogCRYE0IIIYQQQogGSII1IYQQQgghhGiAJFgTQgghhBBCiAZIgjUhhBBCCCGEaIAkWBNCCCGEEEKIBkiCNSGEEEIIIYRogCRYE0IIIYQQQogGSII1IYQQQgghhGiAJFgTQgghhBBCiAZIgjUhhBBCCCGEaIAkWBNCCCGEEEKIBkiCNSGEEEIIIYRogCRYE0IIIYQQQogGSII1IYQQQgghhGiAJFgTQgghhBBCiAZIgjUhhBBCCCGEaIAkWBNCCCGEEEKIBkh7vgvwT+B2u8nKyiIgIACVSnW+iyOEEEIIIYQ4TxRFoaysjNjYWNTqE7edSbB2DmRlZZGQkHC+iyGEEEIIIYRoIA4dOkR8fPwJl5Fg7RwICAgAPAckMDDwPJdGCCGEEEIIcb6UlpaSkJDgjRFORIK1c6Cq62NgYKAEa0IIIYQQQog6PR4lA4wIIYQQQgghRAMkwZoQQgghhBBCNEASrAkhhBBCCCFEAyTBmhBCCCGEEEI0QBKsCSGEEEIIIUQDJMGaEEIIIYQQQjRAEqwJIYQQQgghRAMkwZoQQgghhBBCNEASrAkhhBBCCCFEAyTBmhBCCCGEEEI0QBKsCSGEEEIIIUQDJMGaEEIIIYQQQjRAEqwJIYQQQgghRAMkwZoQQgghhBBCNEASrAkhhBBCCCFEAyTBmhBCCCGEEEI0QBKsCSGEEEIIIUQDJMGaEEIIIYQQQjRAEqwJIYQQQgghRAMkwZoQQgghhBBCNEASrAkhhBBCCCFEAyTBmhBCCCGEEEI0QBKsCSGEEEIIIUQDdNEHa+Xl5UybNo0rrriC0NBQVCoVc+fOrfPni4uLmTBhAhEREfj5+dG3b182btx49goshBBCCCGEEPwDgrX8/HxmzJjBjh07aNeuXb0+63a7GTp0KJ999hn33XcfL7zwArm5ufTp04c9e/acpRILIYQQQgghBGjPdwHOtpiYGLKzs4mOjmb9+vV06dKlzp9dsGABv/76K/Pnz2fEiBEAjBw5kqZNmzJt2jQ+++yzs1VsIYQQQgghxD/cRd+yZjAYiI6OPqXPLliwgKioKK699lpvWkREBCNHjuTbb7/FZrOdqWIKIYQQQgghhI+LPlg7HZs2baJjx46o1b676ZJLLsFisbB79+7zVDIhhBBCCCHExU6CtRPIzs4mJiamRnpVWlZWVq2fs9lslJaW+vwJIYQQQgghRH1IsHYCVqsVg8FQI91oNHrfr83MmTMJCgry/iUkJJzVcgohhBBCCCEuPhKsnYDJZKr1ubTKykrv+7V5/PHHKSkp8f4dOnTorJZTCCGEEEIIcfG56EeDPB1VI0keqyotNja21s8ZDIZaW+SEEEIIIYQQoq6kZe0E2rdvz8aNG3G73T7pv//+O2azmaZNm56nkgkhhBBCCCEudhKsHZGdnc3OnTtxOBzetBEjRpCTk8PXX3/tTcvPz2f+/PlceeWV0nomhBBCCCGEOGv+Ed0g33rrLYqLi72jN3733XdkZGQAMHHiRIKCgnj88cf5+OOPOXDgAMnJyYAnWLv00ksZN24c27dvJzw8nHfeeQeXy8X06dPP1+YIIYQQQggh/gHqFaw1atQIlUpVrxWoVCr27dtXr8+caS+99BJpaWne119//bW3teyWW24hKCio1s9pNBoWL17MI488whtvvIHVaqVLly7MnTuXZs2anZOyCyGEEEIIIf6ZVIqiKHVdeOzYsTWCtfXr17Nt2zZatmzpDWB27drF9u3bad26NZ06dWLOnDlnttQXmNLSUoKCgigpKSEwMPB8F0cIIYQQQghxntQnNqhXy9rcuXN9Xi9cuJCFCxeyYsUK+vXr5/PeihUrGDlyJE899VR9ViGEEEIIIYQQgtMcYGTq1KlMnDixRqAGMGDAAO677z7+/e9/n84qhBBCCCGEEOIf6bSCtT179hAWFnbc98PCws7782pCCCGEEEIIcSE6rWAtNTWVOXPmUF5eXuO9srIyPvroI1JSUk5nFUIIIYQQQgjxj3RaQ/c//fTTjBgxgubNmzN27FgaN24MeFrcPv74Y3Jycpg/f/4ZKagQQgghhBBC/JPUazTI2ixfvpz/+7//Y8uWLT7p7du3Z+bMmQwaNOi0CngxkNEghRBCCCGEEFC/2OC0g7Uqhw8f9s5llpSURHR09JnI9qIgwZoQQgghhBACzuLQ/ScSHR0tAZoQQgghhBBCnCGnNcAIQHp6OnfddRfNmjUjNDSUn376CYD8/HwmTZrEpk2bTruQQgghhBBCCPFPc1ota9u3b6dnz5643W66du3K3r17cTqdAISHh7N27VoqKir48MMPz0hhhRBCCCGEEOKf4rSCtUcffZTg4GB+++03VCoVkZGRPu8PHTqUL7744rQKKIQQQgghhBD/RKfVDfKnn37i7rvvJiIiApVKVeP9xMREMjMzT2cVQgghhBBCCPGPdFrBmtvtxmw2H/f9vLw8DAbD6axCCCGEEEIIIf6RTitY69ixIz/88EOt7zmdTj7//HMuvfTSOuf3xRdfUFlZeTpFEkIIIYQQQoiLwmkFa48//jhLly7l7rvv5u+//wYgJyeHlStXMnDgQHbs2MFjjz1W5/xGjRpFdHQ0t99+O6tXrz6dogkhhBBCCCHEBe20J8WeN28e999/PyUlJSiKgkqlQlEUAgMDmTVrFqNGjapzXr/++iuffvop8+fPp6CggLi4OG666SZuueUWWrdufTrFPK9kUmwhhBBCCCEE1C82OO1gDaCiooIVK1awZ88e3G43qampDBo0iICAgFPKz+l0snTpUj799FO+++47rFYrbdq0YfTo0dx0003ExMScbpHPKQnWhBBCCCGEEHAegrWzqby8nG+++Ya5c+eyZs0a1Go1ffr0YcyYMYwcORK9Xn++i3hSEqwJIYQQQgghoH6xwWk9s5aSkkK3bt3YtWtXre9/++23pKSknM4q+Pvvv/njjz/YunUriqLQvHlzCgoKuPXWW0lNTWXt2rWnlb8QQgghhBBCNESnFawdPHiQjRs3cskll7Bw4cIa75eXl5OWllbvfHfv3s20adNo0qQJ3bt358svv+Smm25i/fr1bN26lY0bN/LHH38QGhrKXXfddTqbIIQQQgghhBAN0mkFawCvvPIKvXr14rrrrmPKlCmnldfrr7/OJZdcQosWLXjxxRfp2LEjixYtIisri9dee42OHTt6l+3cuTMPPvggO3fuPN1NEEIIIYQQQogG57SDtZCQEL777jumTZvGzJkzGTp0KCUlJaeU1wMPPIDBYGD27NlkZ2fzxRdfMHToUDQaTa3Ld+7c+bQDRCGEEEIIIYRoiLRnKqOpU6dyySWXcMstt9ClSxe++eabeuexb98+GjVqVOflW7VqRatWreq9HiGEEEIIIYRo6E67Za26K664gj///BM/Pz8uvfRSvv3223p9vj6BmhBCCCGEEEJczM5Yy1qVRo0asW7dOu68807mzZuHSqWq82dvu+22E76vUqkwGo3Ex8fTp08funXrdrrFFeKkMoospBdYSAwzEx9iPt/FEUIIIYQQ/xCnFaytXr2aFi1a1Eg3Go18/PHHjBw5kvz8/Drnt2rVKqxWK3l5eYDneTiAoqIiACIiInC73RQUFKBSqRg0aBALFizAbJYbaHH2pBdYsNhdpBdYJFgTQgghhBDnzGl1g+zduzeRkZHHfX/o0KGMGTOmzvktWbIEg8HAk08+SUFBgfcvPz+fadOmYTKZ+OWXXygqKmLKlCksXbpUBhgRZ11imBmzXkNimARqQgghhBDi3FEpiqLUdeH//Oc/AIwePRqVSuV9fcIVqFSMHj26Tvn369ePJk2aMHv27Frfv+uuu9i/fz/Lly8H4KabbuKXX345pbnczqX6zFIuhBBCCCGEuHjVJzaoVzfIsWPHolKpuPHGG9Hr9YwdO/akn6lPsPbbb78xYsSI477frl07PvnkE+/rnj178vXXX9cpbyGEEEIIIYS4kNQrWDtw4AAAer3e5/WZEhwczPLly7n77rtrfX/p0qUEBQV5X5eXl0tL1Rkig2gIIYQQQgjRsNQrWEtKSjrh69M1fvx4ZsyYwYgRI7j77rtp3LgxAHv37mXWrFl8//33Ps+oLV68mPbt25/RMvxTpRdYOFRoYVtWCYPbxEjAJoQQQgghxHl2xofuPx3Tpk3DarXy6quv1phUW6PR8OCDDzJt2jQAKisrGTt2LG3btj0fRb3o6LVq/sooJiHUzIaDRf+YVrb1BwvZlF5Eh8QQOieHnu/iXNSk9VYI8U9X/XsQkO9EIcRJ1WuAkcsvv7z+K1Cp+PHHH+v1mdzcXH788UfvwCFJSUn069fvhCNPNmQXwgAjv+7N51ChhdJKB5GBRvz0Wsx6DZc1Dj/fRTur3v9pH4eKrLhcbu7u21h+MM+iX/fmY7G76nVeSYAnhLiYVP8eBOr9nSiEuDictQFG3G53vSa5BqhrLGixWOjZsyfjx4/nrrvuYtSoUfVajzg1VTfDeq2ahFBzjdq+i12HxBAO5lf841oUz4fEMHO9zquMIgtLtmYTYNIByDFp4DKKLGw4WAQq6JQUIsdLiFokhnl+ayrsTuKCTd40IYQ4nnoFa2vWrDlLxQCz2cyBAwfqHQyKU1f9ZjgxxOxTs3e+brTOdUtK5+RQooOMpBdYyCu3yeTXZ1F8SP2O6Ya0IvblllNgsXFZaoQE0Q1ceoGF9EILABH+hlM+VhL0NQzSqn12xId4Kq0sdhd2p1t6GQghTuq0JsU+06644gqWLVt2vovxj5FeYCHQqKPM6jhvNXsZRRZ+3ZtPRpHnJm/DwSI2pBV5btbOkfgjgWqnpBCZ/LohUaC00kmJxUlWsZX0Asv5LpE4Ab1Wjc3pwt+oPa1raMPBItbuzefvjBI55udRVUDxTzoGx/4enU4e6w8WHjevxDAzZr0GvVZd5/VVDQK2ZGv2SZc/E9shhGg4ztgAI2VlZZSUlOB2u2u8l5iYWKc8pkyZwvXXX8/o0aO58847adSoESaTqcZyoaEyEMSZkBhmJq/MhvFI33k497V31W8I4kPMUNWweo4bWNcfLGTNrlw0ahV55TZAut2db52SQ9iTW0aASYteo0avVfPtpswG1+JyOtfMmbre6pLP2by2M4osbEovwulW2JtTRpu4oFNaR0aRhd05ZVQ6nOS6XOi1Dao+8R9Fr1Xz6758wvwN/5jWnGMD1PpcL1XX166cMsornVQ6XHRIDDlhT42tmSWUVzrJK7OddB2JYWZ+2ZtPQYWN//6RzqhLEo/7mRq/q0KIC9ppB2uzZs3ilVdeYf/+/cddxuVy1SmvVq1aAbB9+3Y+++yz085PnFhVd4y/MoqZtb+AHk0iyCiyEGDSkVdmO6s3dlV5V++/n1FkoVNSCBFHbg7OhaqyLN12mG1ZJVRUOunZNOK0unGJMyM+xMyNlyR6z5Vju9kB3u5yccEm7E73ebmp3HCwiPRCS51uuI51pm6qTpRP1TmeV27DT689KzdwVa30v+0vIC7EzKb0olMaXTW9wEJiqJlii502CcFkFlmxO/P/McFCQ2J3ujFoNWQVWVmyNfsfMaVL9d+jul4vVd12d+eW4W/Q8su+fMqtDgxaDbtzS2kZE1zjWfAV23P480ABpTYXLaID6lQ5GR9ipmlUAGv32rA73CcsV32fDxbin+JC7U58WsHa7Nmzuffeexk0aBC33XYb//rXv3jggQcwGo3MnTuXqKgoJk2aVOf8pk6dKs+snWN6rZo/DxYSbNazdk8eCaFmth4qpnFUAH6GM39jV/WcnNOteOd0A8+PGApc3SHunF5AVV1L9ueVU2yxY9RqOJhfzq5gk+diDjKC2wEuO7gcnj+1BjR60Bo8/8o5W2cZRRZWbMshv8JG32aR3ucFa/viPHaI612Hy8gtryQl3N9zU5Xm6S4XbNaRW1pJo3D/81OTfBqtwWfipiqjyBMoooLmMQE13q8K5FCgwub0Voycyf1UVf4RnRLIKLLQITHklPLRa9WUVjro0SSCIJPuvDxH2pB/zKs/z3e2KyhKrA725pZhc7pRVJ5KiYa2P8606s+TVdicdeoWX1WJZHe4yaiwoFOrqbC5KaiwU2HXY3cUEWzS4XS7vYMlFZTbKLE60WvVRAYY6JR04uul6pyMCzHRo3E4qE48KEl9nw8+2Xob4rVwtv2Tt/1idqG2Op9WsPbmm28yaNAglixZQkFBAf/6178YOnQol19+OY8++iidO3emoKCgzvk9+eSTp1MccQrsTjddkkM5VGjxtqzFh5opKLcR6qev9ebvdKQXWHC5FdYfLKRLcqinRjKnjIIKG5VO1xm/iayVoxLKsqA0i+Z5+9Hu2c09lkOY3AX4V5ZhyirDlF5K4KoKcNWhz7/eH0yhYA4Fcxj4RUBwAgQnQUgShCRDYDyoL94uXXUdFGL++kN8tyULvVbNzuxSEkPNHMy3oNWouLN3qk/wll5gocxSyd8ZOyjN2Enajl1YivKIap+IKl9L3OHDXGuz4KdxExuoRdlnx0+vgi0m0OpBYwCtEUzBYArxHBtzGATEeI6PrmYX61NRvTW4voNjnImbqvQCC34GLRU2p7f7VvU89Vo1S7dlk1tqo9zuIv7ICHRn8jqr2o5DhRVYKsrYc+AAxRk7aB1lItpfc7SiQ6U6WtGhNXqOgSkUdEbA833kcius3ZPHNR3j6ZQUwoptOewvKGdXThkDWkadMKg/E9u0Ia3I2xIJeEfLPTYwOhvzZR1vW6q3jqYXWii1OvhjfwHxoWZ+3ZdPk6iAM941OKPIQkKoHxlFFagU2J1bdm6+n8+zqu+e5jEBJ93WjCKLTyVSm7ggtmaU8Mv+fBS3QrHVTkywifwKGwEGraciNMKfPs0iKa10ACpvgLg1o8Rb0VHVKl29a2VWkRW9Tn3C7o9nWvVr4Xwc9zN9bdc1PxmJ+Nw4lwFx9ZHP4cIbgfW0grV9+/Zx7733AqDTeU5qu90OQFBQEHfccQfvvPMODz300CnlX1JSgr+/PxqN5uQLi1NSdcIOaxdLfIjZ+yUVH2o+K10B9+eV892WLAw6NTsOl2J1uCg4cgOSHOZ3Rmo7FLebwsz9pG1fz96/17Nn5zb27j+I1llO80AbLQLLaRWhITFIRahKRUd9EBXGaGx+4TgNcfxdqMJqCCAwJJKE6CgqFS06vZ5msWHkW93kl1qIMKsIMwAuG9jKwVoIlgLPX9EBOPA/KMs+WiidGSKaQ2RLiGwOkS08/w+IAZXqgq/FW7E9hyVbs/EzeL5SjrcNB/MrMGo15FfYaBUbxJ7DJbhKs4lTssn/aRXRpgJSyjLRVmTz0WebmPN7Me5jZv/4PEDFrGsj6N48CrfORKDejMFhBI0WXBooLfIcF2clOG1gLfYcH7fTNyP/KE9AHZpy9HhEtoCg+Hq1llYFKusPFvLJbwcpsTppHOlf5+vndI991c3l1swS1u7No2VsEOO6N/K+b3e6sdrdHCq0Yne6cDhchPnp63/j7aiE4jQozYRST2UHJRlQmkXGoTR6v7yFzBIHNidM72NgVG9D3fPW+YE5lAN/O/ns10JCw8PJjoqjV9eO5JhSOKRvQUahFafLXaM73ul0Q63VkfOtsNzuvWErszpqtNxWr6HNK7edkZvaY2t9q4L/9emFGLQa4oJM+Bu07M0rIzbIxK7DpdhdCjan29s12NOV1ES8oRJKPceH8hywFnmuhcpiz/9tZUeD6KreAyoNaA1UONUs/WArzRLCePDqbmSWmShTB3LYHkd8u/YQnOi5fi7CCqjqx7f669qkF1gotzmJ9DfSLCqAzsmhdE4OZUCrKDYcLKLQYifUT49eo/b2XKkaAdLudGOxu9h8qAiDRkXm4RxSA5wcKt1OZ5cf+cUl7DyQi0njZsfKdeQXl+FnNvHVL6EkRYbg5x+A0xiCOzie9u07EhcV6fNboteq2ZpR4u3FUJ9uyceed9R5Nt4z60y2gtQ1AKve+6fM6iCx8YX3e3yhqO34nq17IW8PE7gg5zQ8rWAtKCgIp9NzAxQYGIjZbObQoUPe9wMCAjh8+HC98ly/fj3//ve/+emnn7Db7SxfvpzLL7+c/Px8br/9dh544AH69OlzOsX+R3MrbtZ/fz9lQbGUBCWQZ1ORX24nsshIuL8RlUqF2mwnrdyOVW2kcLeRgnI7eWU2ogKNRASYUKFCpVLV+FetUh/3vap/V+7bS7nbQk6Zi+iwMA6WaiiyOHBrXazPPERgYDT+OcF1yrsk8yDpG9dxYOtm9u3Zx579mezJKiGzxIGj2mONoSY1qVFm7Oj4LLsci80BQEBwCHEt2tHu0h5MvP0G2jRO5Pf9hWzPKiGzyMrV7eNRgkzkFVpJDPXDHurH9n0FWA1u8vRaujeJ8JalVk4bFB+CooOQtxNyd0Dudtj2NTiOtNiZwyC2A4qxGX5BrThsaUd8cKsLrmtlfpkNl1uhwuaksNzOr3urPWdUWQL5e9m6biV88wP6jGyaqh0kbnIxs7UVPZ7j4d6vwuEXQ6VfPKWmGP7I2U6X1qmMHjkcV2A8hCYQFBTC/z3yKFf/ZyOdhg3mzZeeQ1Fr0WvVZBZZfVq0fL70g01gK4WKfE8QXZQGxeme4KNgL+z8Aexlno3RBxwJqFtCbHuI7QCRrTytdSewZlcuhwqtlFjtlFjsOF0KeeU29Bp1jRrz6s7UDUmhxUaJ1cGWQ0VkFB1tgdJr1Zj0ahJCTZgNWvwNWhKOBHg+63PaPTf15blQcshT6VC4HwqP/Fuahc9dm18EBMZBYBwH1SnsL1jPwzf3Z+P+Amb8/Bch182kXctmNIsNISo4ADQ6UJQjgbTd86/dUq2io5Al337CX+lFxBaVcuiPncz5aiUA39zoR5eWSVRWNMOd3xqad2KvM5LDhkYUWjwVhGdiUKKMIguFFXYqj4xqGWjUUWp10CExhMwiq08X0updWPPKbJRaHew+fHqtT1V5llgdvP/TPrQaNVlFVnZllxHubyAu2ITT5aZ5dCDOiiKaKQcJrDxEZEY24Zk52EvTae7II8iRB25btZxVYAz0tDAbgz3/GgI8LZxqnefYaHTgdoHLzv592Xy9PgvWZ9HEv5IbWqnQ2YrQHbLBhiNZagyeoC2i2ZHKjhYQ0QLCGp/0Wmnoqq7JFdtycLrdx712E8PM7DpcRn6F59qr/r1X1eJdZqlk798bMO3fxP6svewtzSE90Y9BqRp0lhx6lGejd5SgptrgbOshHOgP7Mp38eI7FScs7/P9DTza0x8CYggwxpBsiuOAM4xKSxhWYwo/u1vUq8tsVRfzSoeTQKPOO7hTVfB5rgZ4OlPP3h0bgOkjPKNw1vjdCDKQfeggyZUHoDSTLmGVqH/JYVd2NliLiDHYCKQcKktwOuy4nXa0ihO14gRU1Xp0GDwVtOZqPW7M4Z6KwJAkz3UTGH/BXyenq7bjW6/fQ1uZ5zfdWnSkEqrY83+HBVwOSioslJZbCDKqaK3WUWyDwAB/KA6Fzred1W0701RKXWetrkW/fv1ITk7mww8/BKB///4UFhby3Xff4Xa7GTZsGGq1mk2bNtUpv19//ZXLL7+cuLg4+vXrxwcffMDKlSu5/PLLAejTpw8xMTH897//PdUinxf1maX8bHNai3A+n4QRFT8edPDIahvBkSrUcVDSUuFvvZvKBhQnqFwQWwLR+Wr88lW486E0XyEz303ViNJqFSSGqIiMUGEKU+EOUrD7Q2mQQlaoQkn1690NlAKHgfQjf5l4bvQaA12BFE7pxq+uwasaFcmoaKWoaO+GDgq0d0PkkXxyVLBZrWaLRs1WtZq/NBry1Op6B8bV/3W5weFS0GvV6DWaEy5f37xVKhUWqwNzaTkJThuN3Haaq100Vuw0clUS6vIEY9d+YWHpPicpETqsLtif46B5iwAuvSGVgqBAcnUmXGoNMUFmbA43Pz++nMg20XQc14ms4koUVFhtLuwON3mr91G0dBfm+GAaje2CPdCETq1Bq1Hhp9eh06hRq9UEGDz/jw02n3gbgBB7BdGWIqItRURZCompKCCyogANCk6Vhhz/cLIDosgKiOZwYDT5fuEoai0qlYrMYit/Hig60o0PdBo1FTYXgUY9LrdCeICRELOey1IjKLE6KLI4CPMzEupnoNji4FChFZVKRVKYH6FmQ732/Z6ccuxOhcIK+5GRFBVig80MbBlNuL+RXYfLKCi3k19uJzbYRKLOTmDFYZKUQqJtefiXZuNXmo2pPAe1cvSG0aEzYw2MoTIgxvNvYCy2gGjs/lE4/MJAc7Scm3/fzF0j7uLpLz5CHRjMc6PHE5rShFFTHkerVtOzSQTRgeaTbs+DEx6kIL+Alz7+gDU7DmMuz2bOs8+SmXaIL/49mMaqLGJt6RQXFBL7SjkBBhUDWkcy/b6rMTXqRGBKZ5SQJFSqU7te1u0rYGNaMSqVisRQMxEBR0dC/HVvPha7y/scU1V31zKrgz8PFqJRq7kkJZTEELPP+/W5sV1/sJBN6UWsTyukvMJKcOHfhFjTaRpgJVmTS7SqiEBnIf62bPxdpd7PWTSB5GpjyNFEYzXFEBbbiDYtWh4JpmPBP9LzrG0dbd26lbZt29KiVWv27dnDG18so8wURZTRyYBYG9GuHE9lR/WKqPIjFbNqLYQ1geg2noqO2PYQ3RYM/nVe/9lS11r7quNwqMhKbmklOaWVTOiV6n3Guno+6fkVlGbvZ+fGn3Dmp1GSm0FZbia7D2ayL7uQtAIbtmqVh0FGFSWVCv1aRfLM7f0JiUtlXY4KmzaQUvxo0ySZ8PAoYiLC+HHTfrKys3hsws18/O0qgiIi+X79PhS7hYrSYpJNFWz+eSVLFy/lpYkjeOiqVlhy9+MqOIi27BAmeyEAbjQUmxKoDG5KbNOOnsA6tr2nZ0EtFYPfbsrk78wSDhRU0CjcD5vTjdXuosLupEV0IJ2SQs5a68TZ6Pr47v/2sTe3HLUKesW4IHMLpenb2L1jG4czD+G0ltEkyEWqv43kIIXkYDVJwWoCzEYshnDynH5UagIwBIaSmphAqWJmT4ENp0oHai3NYkMJNumOVkQ5Kz0Bg+VIRZS10BNUVK/wUqk9j0hU9ei4iCo76qLqGquqCKk67iVWh6dyMyGIzmH2o5WFJRlHenVU69lhK62ZsUrjCZQ1OirdamyKFkWlwaRVyCm2sDnDQsfkMOKn7zz3G32M+sQGpxWszZkzh9mzZ/PTTz9hMBj45Zdf6N+/v7crpE6n46uvvmLo0KF1yq9Pnz6UlJTw22+/UVZWRmRkpE+wNn36dD7++OMTjjzZEDWkYE1RFMqthajydvHK8y/w1HuLSA03sivHygv9DTzcw8yGylhmb3QSkdSY4MbtCWpxCYbwOBQUTDoNRp2aTskhKIqCguL91624a6Qd798FG9Ipq3Tib1BzTYdYft+6h+y0A5jz93Hgz03kZOWwL6OQvbmVVB7pvWbWQZMoEynxISQlxxLfNJWYVq0IbdUOrclc53UrisKe3FJW78zBrbjxc9nRHtjD7z/8xMEdB4lunMjA8dfSuVdbtmYUk1tWSUSAgVaxAYSY9SfN262461wOBYVDhRXszS0lwlVBB1URbSgiyZJLTGk2ZmclAGV6P7ICIsny9/xl+kdQavCr8zoyiy04XG60aogOMtavvFX/V9wEO6xE2cqJriwj2lZBtK2cCEsZw2flcUcHPXd30WNBzX6VgQydiRyjHwV+ARwymPj81b/BoKHT5E4UW+zsXnuI/C/2oDFpibq1KfZII/4GDQFGLRa7k4NPbyS4cwStRjWm0uGisMKGW1GwuZxoVODKspIz7yCOYjsBQ6PRdg7EpNficLnQqFU43W78DRpMejVatQq7yzMJLUe2R6tRoVFzwv2gV9w0dzpp63LRxuWkrctNU7cbDVAJbFerWGtVWLzfSVakmn0hKtx6FW7laD6goEJBo1EBCk6XpwyoQEX14+BZVjkDfY78FWiCmmaoMabD4e0K8UY1M3saCNd4uq65UTiAwi5c7MTNLtwcxM1hFDJRKDhSxjo5AHwMTATCgE3At8DtQEI9Cj4fsAK3VksrBN4BLsXT1ACE5EDRLGjVVs3Ov92M66rj/YGmI4srrMfFhmp/B+uzLdV4wvgj4Xy1wA5AUY5kqKhRH0lTqzz7tir4AxVqtRqDRoNGXXslQZQCqYpCilth/8/l5Kc7OJzr5EC+2+cmPyZITVyEhtAILX5ROhzhekqjjWSYdJSoNLjc4FLAqNUQ6mfAz6BFhQqHS8HhUjBoNRi0mjoFrxUZFfzx7z9o9mA7Dn62B5VWRfykttgUMBu0xAWbMem0Pp8LdDlJtFeQZLOSaCsnpbKM5Moy9IobN5Bl8Ge/KYgD5mAOmoJJNwfhUOtq2SfHL5f6mCC8vv9mFllxHqlMSQz1O27e6YUWnC7PICt/Z5Z6Klz0Km5qYiTJWYY6/zDh1kIydmcy8T+ZHC4/Wslh1EJ8iI6ESDOhEQH4RYZiDwkjpHEj9unDKHFrKdy6m6xFP2KvsHLZ+OuI6tyWfbnlxIeYCQ8w0j4hhLLCEp4YOpmQRgkUHTjEBz99wJ85lWQWV5JWYCHMz4DV7qZDQjC7Fy1l1SffcteUu7hq9FWoVCryy22UFhbTmHzsGfvwLzlEkjOHONth9JUlADgMAZSHNaY8vAnl4U2oiGiKwy+SvHI7fx4oZmtWMXqNBofTjV6rpazSQXyI55m71nE1e8DU9d8TnX/rDxSSVVxJVkklqREBtE8IJi7Er055e/JVUJVkoi7Yh6pgLz/9/BNTP1pBVkE52SUOLI6j36+hJhXxoSbMfn4UVLg5lF9K5ZGeNwAGsz+BEbEYQ6IJjIhhSPcO3H3rSLJdARwq9Iyk3SYhmMQjc7WelNPu6ZpclOap6Mjf4+ltc2xlR0RziGl/tGdHVGvvc70XotoC8Pd/2kdR1n50+TsY2EgLlny0FTnY8g8Q7com1J6F1lV5NBP/KE/lU1Dc0UqowDhPDw9TsG+PAZWKXYdyefY/P7B543oqDu0i78A2SvM9+zg2Npbdu3fj5+d37ndGNecsWKvN/v37+e6779BoNAwcOJCmTZvW+bN+fn7MnDmTSZMmUVBQQEREhE+w9sEHHzBp0iQslgtroseGFKxV9+yzz/Laa6+Rm5tLbGwsd918NVNvuIQxj77AFz/vwuVWcB75DYryV5MY7kdISBAxUZE0T0kgMT6BgNBIAoJCCAgMxmAyodLoPV+WioPU+Gj0Go6OpGgtYs7nC9m2ex9pWTlk5ZXQPVHLzF4KGjwR2b0/WPlws5NLU0No3iiOFs2b0bxtR5p36kNCyy6otWdmasBf9+azemcuxVY7PZpEcHX7OBRF4fOFPzDz6Rls3fgn7XsMoPWI+3GbwwgyaemQGHLGh69ef7CQWWv2YncpqIHOyaFHay0VxVN7nb0ZsjYf/dfqqS3FP8rzpR7W+MhfKgQleGrRTaE+z5PUqbbS7QZLPpQd9vyVH/Z04yzYCwV7oGA/OI50x1FpPLWCYY0pNSYQNOIVAEY/OA1Nq8GUVjppFRdEQoiJ8b1SAbj88suJioriv//9L7/uzff82O3cw4KXHmHv9r9oe809JPW+nt7NotiTU8bc+wYx/KZxfPbOiz4DdwBU2l1syy5Br9j5dvbzrF82n+ade/Dcq++gDwpn6d/ZuBWFYLPeu083HCzi78wScssq6dY43PsDW++aXHsFZP/lPR7Pzl3Mv77LADw3as2i/UhJiiU+pTmuqBaEtbiMTu070alRKJ//kc62rFJMOg2DWkVzdYc4lmzN5uuNhyi2OEmN9CfMX49Rq6HEYsdid9A6IYj4YBPdUsN8K0eclVB4EFXhfijYy9YNfxJDDuayNMKUYm9xu82xsavARanVxYBLmzPk5hvI1sVR6R/LQ0PbnXZlg6Io/L72d8aPGM/zX82jbasU/s4o4pmxd+PW6bn0wcdoGhPIzV0TT5rXtLunYbVYmTlnJgoKf2cWk1tayepPvuKPBd9x/YvTiGkUh/twFm/cNYVJ7z3N7o3bWDr7v9w2bTwjukRiyt1HVFkaceUZBNo9N6U2nZmi4EQKQxIoDE4gPziBMnMYikpVoxLH5nSTV2YlzF/PzuwSymwOSq0Ogv10BBm12J0ugvx07M8tQ69V43C5cCkKGhXY3W4cThc6jYrwAD0GrWd+QH+3jUsCHSQ6SwmxFBBqKSLMUkiYtQjDkZbnXItC1ItlpEbpSUjwxxAZhBITRq7Rn/JSJ5bDxVgPl1CZU4w1r8xbQa8P98eYFIZfSjh+zaIwhJoI9zeQEGpCQSG9oAKtVoWfXkNkgMG3AuY4/xYdLGLpw0vpPqMvNqeTDc+sJbxfMqZ+iWjUKiICDEQHGU56PNVuF4l2K00qy2lqq6CprYIUuxU9Ci4gTWdgl97ELp2RnXoju3VGLCpVvc8/n+viBJWGTpcbh8uNRu35ejz2fVwuomwuUnGToig0URQaA40VhcRq0X4hCrtV8PZvNj5bYafLNTqKQlUUhSgUGRUUNVRVvByttDmGDfge2IqnImIgUP0RwBxgVrXXU4DjNY4qwHJgHTAKaHbir7AoRUVHNHRGTWc0dEZD7JGV5+JmI26fyo70U6zsOGsUCEdFU9Q1/hqjxnSksJUoPPm7jReX2el+iQZNENgDoCgEDoe6KTapQaVCfSTYUxSgQgXFChQrKEWglChHXxe40TXX4X9rIIrCkYobFVq1Co26/j1SqgevwW6Fxi4nTZ1OmrsctHLYaeK0owMcwD6tnp16Izt0BnbqjezTGXGoNXWu3LA73dicVRXu2lMq56lUnOjcbnSFRUTby0lwWmhMJc9+sJd1eywUWz3XhUED7WO1tE4wEB1tRhsWgDE2nODUOEr8gykyBVLsgJwSGzanG1Bh1muJDjJhUqCiqIKijCLyDuaRn5ZP9p5s8g7mobgV1HotfomRRDaNJSApipDoENRlDuY+8dF5Hx+gPrHBGZsUu0pKSgr333//KX1Wp9PVOql2lczMTPz9z39XiouFoiioj9zQq9Vq3H5R0HkcG3Jf5rbxd/HaS8+zbtX3/PLzGrZs2UxhTjbFxSXsTc/hi5UbvS1exzPxEj1vDD5aG5RX4ea2l8qJD9aTEhVAZFgIL/8vDVdMf16e+gAExlK6+1kuMWWz5qefzuKWe/pKxwab0OvUxB0ZHU+lUjHqmmHcOHwoM157nzdmTmPHEzfSe+wjmC65gsIKO+/9tI8Ao47WsUEEmXSn3VVjU3oROo2a9IJyUiP98Tdqj/bfVqmOjCaZBC2v9qQpiqc7QFXglr8bDv0Omz8Dp/VoxioN+IWDIRC0RiLRYnRqABU2PRhULs+AG26nJ5C2V0BF7jGDcKg8AWF4E4jrBG1vOBIUNvGUSeN5UNtVVAS8gk6n44u3ZnLb0yn4xbUiu9hCo3A/7zM8drsdg8Hg3f95ZTYuaduc21es4pFHH+Pr/7xJyf4tOG/4PwKCQ3A7bKTGhPo8GF7VxWzJ1mz0Wg1BxkAGTvg37XoMYMFr/2bslb1488036duqD99uymJ7dj4ZRVbPzYwKiq0O3IrCur35WBNDvH3m6/XMmN4Pkrp5/oDKzVOJ/vM9Zs18jHX/+5FdO7ax90AG36/bg8P9HQBJwWqaxwaQEBlMakIMHdq2RGeN5svftBTb1bRwaCiyKyRjIrZSi8rtwGZ3YNS4cG6y0TjCjW5HyZFAOsfz3J0l31ukH9O19J9TSPvkUMYO701g065U+CURENecvM9uYNydw+jWrRs33HADqti9NL5mGAaNls/WldV78IHa5AbnAnB9h+40atSI1pEWyh+byfT7biEws5yOHfoR71dzJMdjhRpDsavt9EvpB0DzEE8gfdsrQ+i5tiN/zvmBof/3NqHqIACuaTsEXZ8J5O0o5L8vfc4l/13K0OHtSS+wEBBmJlBXDtlbMGRtJjp7M9HZW2D3Cs/KdH4QlnK0siOkEZmhSfy+rwT/qCQO2oNRAt3kl9kJCdWh1ahxKwoBBi2KAlddGsjunFJyS8pJNFSgKc/BVJmHwZ1LY2M5Uc48/EvSCbamE+wu8m6jyxyJJqIJ5REdydYn4B/XnPCkVjgsOngxlVZjXyayZVcCjFpig0yUVjqJCTJSYXN6R320Wq38umEr27ZtY/XPv5C1azPZX21CcbuJTGlBdLd+BF7anyJDJE30GppHBzKqa91HEfzrr79YylJeveJ5unTpwiPOabzy/DPcPuRFEhu3oE1sEFd3iKvvaeLhtEPudjTZm0nJ2kxK9hYG52zzdCFDBeFNIaad57nRsMYQmuoZBEhfs+wnqmg57mid/irPdVRSVRG1DyV/L6NeXsEv+4rZUqqgi1XTo52Zbh0boY9MpTKsMR8VB5GjSyAgtjn3DevKpcCa554j8PcXeWXmLp/1VM3RZtZrvIPP+Bu1hJh0FFhshPjp6JgYTOwMI7NnzeaByQ/QI3AQXcY9jtWtwa24iYjP5ClupNOwG3CVFjDv7rnEBpu8wd/irVlkFFowG9SkRvgRfZueqfdO4ufvfubdWz/DFJPoGWXZ7qTc5iDUX3/kRl1Fh8QQn0CyRFEor8jFkLsTU+4OLsvbRe/DOzDZPOdtucafg/oE9ukTKAhKoTw0hRx1CI1jgmgeHXjc4HlrZjG5pVb25ZXRKMKPCH8DLWNrX74qyN6eXUJ+WSVphRWEaB20NVZA3iEibXmkqkpIopiAshz0jqOV9hWmUEoDIin1i2CrfySlfuEUB0RSbgphf/Ey9Gu/p81jT3K4tJKsYguKxU6gw0WkToVeq8asV1NidXj2e5iBTr2CCTbr+GVPHg63G61aRbfUUL59fj6Zafm08L+GJlEBNIn0825DQYmFcqebQJOWAKP2lCscClD4WXHzk6KgcTuJsRSRWJFPYkUhHSsKuLIkHw0KLlRkmQJJM4Vw0BzMQXMQ6cZAbGoNiqJQYXNQbnNiNqgx6TTklVWixYVGBUFG/ckrPdx1r7RTuV1EOm3EOSqJd9hIcFSS4LSR4LAT5XJ46yDKVGrS1FqW/FVBuzYmOrfyI9ugIT3XyaYMG+t3VOL6tQLIA/Z7HgMM1KIyqECrQtGA262AUwGHgmJxo1QerQhRGVRoo7ToYnX4dwpAFatFFalGpbaT695PDvtRFDfm8KB/1tD91bndbkpKSqitoS40tG43ApdeeikLFixg8uTJNd6rqKhgzpw59O7d+3SLKo5wu93eLj0ajQaXy4Xb7Wb//v3ccccd6M0B9B42Cl3zAZRuzsR2uJQgjZq2fnpSw/0IooLLIuyY3RbKSoqwV1pRXHYUlZqJU18mPy4F7nvN+/C6vcACLzXh3U+/YciQIQC8/fbb3HfffaR2H8499wym3Go/JwF5fIiZZtEBJNjNR7rGHaVSqbh97C1c2rsfH7w4lQXvTOOS7esIuu3fWNVGSq1Oiivs9G0eddoXfILJzg8bf6RTjyuICzbTLOokw0WrVEemBUiAFlceTXe7PdMRlGR6gq7yXKjI8zyA67RRVFhCUalnAA23yUxksN/RgQXUWk8f74Doo3/+0Z4WuiMB2YlUVbD8+8W3+WH+p8x98l5GzfiIgLhUTHqNd1S1wjILDk+Vs898RofLnHz18Wxe7dWbx++/i8PP3Uafu57Gaa8kJizIO+Fy6ZGRueJDzHRIDGHNrlz8jVp6REawza8nT81byuo5zzN69GgGD7ua+CsnolFpyS62sj+/HBTILrEAakLMOrZnlXgflq8+MXt9j6fT6cRoNDF87GSGj53svUlUOyz8tGoJGdt+J2vfDjLT09mw4TD5a9IwaH+nV7KOYU00XNlEQ6OQIz9nOUf+jnCptLhUWuxFwZT5R6MNisaU2NVzfAKiIKQRWdo4flmwErgTXUQqk1/7hibtD5Mw5E5aat0UFhUTHBzMiBEj+PDDDxk3bhw3R8Wi6XAtBwsq2Jldyt19Gp9WwFZ1DlRV/sSHmHny3pv59duP+f3zNxl+5ZV1mljZ7XZ786jKp2r5F155jVtHXkP6hpXsVnvSCivhknA/HnzqVe4bOYinHhxPv59+9raYfrvHQaGlMaFBLenU9l5PXhX5kL3F0/2oYJ/nhj39dyjL4p0fK3l2rd27fp0azDrVkS6MoFGDRgVuRfF0O3QrqFQq+iRruKm1jiFNtBh0WsrtYZRqw8lUR/OzriUV/smUBSTRqGk7YiMjuaxxOH9VewYuPCIc+4EDADSKCMTP38A1HeOJDjL6TLicW+rpFmQymQiOb0L3hCaMvHEUm9KLUDksZGz5lZVLf2DtN3P48dO3CEpoRvwlg+h9z+2n9T017OY7mf/F5/z00Uw+mL+Y5IjT+I7W6o9072oPnY6kuRyermDZWzwVUdlbYM8yz+BEVfwiwC/SUwnlHwmGQNTlbmLQoeiMEOZXbWRLJ19/twG9VkVky1DClSLaWnIwVuaBvdpzLioNjsBEDqli+GJTEZe0b0GHJm05cPAQDy//HdXKXVw1oiPPT5+AKlfLoQMFhLuM3u8Ji8WC3mDkUKHFZwqFYwefAc8gTAkhZiIDjT5d5iZNnERyUjI33ngjBUVl9L//JZIiw9DneqY9GjzidiLjkymtMNA65ug1enlTszcwNGm0OOwaFny+gB49evDYXZN5e/4KIk0R5DoriQjVkV1kJb3cRo8mETQJi6l5XCJaQPIx91dlh8nf/Ts7N/1MaMl2epZvJLRkOaSDEy0lxjjCElt6gumAaM8x8o8gx+VPVrmKXoHJ/FJYhsbgJtyp5ZrkKGL9tdUqCu2eZ7qOVEDt272Tz+esAIeFoclurkux4q/33KOUqoNxBKcQltjd04skLPVIMJ+Cn86EH1DLVpGxOJsfjWu4pukYDhVaOFBQTnZJJUatBpfbTViApwJxT04ZJRYH8YFmRrf2TCOTrPfMCVpVObsxdA+Zu34lXnsTQQ4DrQOiQAUHf1vCK088xuuf/UDjxqlnd7RBRyXkbEOTvZmE7M0kZG2mR9Y2z3mvUnt620Q055AugZLARNyhqbRt3ZYMm4n0ItupVTC73Z7jVH6ksrDkEBTug4J9FB/aycA3tpISqebO7noaR5pRhaZAWCqlfsn8XBGKIySVoNjmdGndjOSKCpgaQKfhz1IY1Zk4m4sgm5OIAD1J4X4E6dxkHTpERcFhItUVlBXmoFPsFJaUs/1QPi7UaPV6EiOCiYmKxKINIDg8kg6tm3NF19Ykhvl+N1Wv7DVpjzRNn2SewobotII1h8PB888/z0cffcShQ4eO2yrmcrlqTT/W9OnT6d27N0OHDmXUqFEAbNmyhf379/PSSy+Rl5fHlClTTqfIoprqN0ZqtRqXy0V2djZWq5XU1FTvcolhZrqlhqPXacgvt6FRwZ7ccppGBfCHVc/gNjE0OebiD3vnCxwaE4Q39qa5itMBfKZiuPfee9m3bx8TJ07EogumoLiU6Iiws7nZPtt1vJGmPF9mMTzw1Ot06TWAp/7vfnLSxzJhxiyspihCzXoq7M7Tnodu97oVfPvaY7ROjiYxtf+pf4Go1Z6RpoLia33bUWRhV7UBDziDNUre61uj5+Z/vUbuA7cw/9l7GfPsf1i02UaX5DDyym3YbHYq3Uf70hy7/7v2HsDUj77nrSkTWf7C3bhcTkwmk/f96j8ydqeb9gkhVNicBJl0NIkKwE8fQqdn3+KmkSO46667+G3dL1x262O07t6fnIIStu/agy6yEVEBGtQqUKtV7D5c5pknLcBwyiMyulwutNW651YPMOKjRzNrdTcS+7ixZpcyyE/PoQP7cBzcwIY//8fKZVu4f4mLpORkOnS5lNDG7bFHtkLlF0KHpDAKrQ7Ps0YaNR0SQ9iaUUx+uY0WoYGkBPqTGOzZh8Vl5QD89ttvfPfdd4y/7wH2Pnc7h3pfS1F+LkFBnpao/leP5PZJO/jwjRfoNSEAfbMeJIaZ2ZRedEaDtSovvvgiHTp04If58+h59S0nDdiO3ZfVjb5+OP8ddjU/znmZ/mMf9Cyv0pFeYKFpQhTXPPQi/3niVv7vkYd5/c23WLI1m5wyGyUWh2ewkKqpFPzCoXE/z191jkqKDk2gccYaXn9iPHsOpLMj7TCK04bDbseoUSi1uQAVOp2OhLAA7IqKnJJK/vxrDyPm78doMpPUqRetB4wkIbUNZTYnJq2GSqcTg1ZDRlolo2OPzvNT/fyveta7aWwIV/X1fG9W3WQ0jQwgt6ySAJPOW/mRXuj5N8LfwOA2MaQXWLii42iGXXs9f+w9zMZfVrP6u/nsWPgOt337Dh/1HcDVo8Zy3VVDatzQHPuw/7GVrqkxwTwy/QXuu2U4O39aRI877qj9RDhVGh3EtPX8MdqTpiieARoK9nr+SrOOVkSVZmG37CDAaiHI7cCocsBBlafiSa0FjY7731kPwKDWobwwtjvhsT0o9o/GaggnOCqRiLhUCE7iz4OlrNm8F1hKQM9xDLn2GjRqFSHqSlYu/C/ffvohzb+Yx7Dh15LafzSGkMZsSCvyBmsBAf6UVjqodLpJL7AQ4W/gssbhPud4RICBEquDA/nltU4Yf9VVV7F48WIGDxmC3+zHuO/Lr9i7xRO8Bwf4Uel0eeeHqlL1PVO9BdHPz8yXX35J+w4dmD3zX7z34UeAp7XvUKGFED8DGUWWunf9Dohmd1B3Mtt2YElGMU5FgbJc/Ip3EunIpKU+ly72Ykx7lnmOy5EBH6KO/AG0rZ7fBo7PEMjijSr++8shmsUG8dnvJYzXaYlr0pqoS4agb9qLRHMQTdQBtA8IplNM3QbusdlsmIxGzHoNof56EkIjqbAd6UFS9XsIrNiWwy/78zFqNazelUvTqAASQj2VugA/78ljX7ETm7UCp9vtmY5hbz7BJh0Z+9IoLirgX/eN5b/frThpmU6LzgjxnTx/VZw2T+VT9hZvj5s7nvuSMouND68yQoSGeJWaeHOYp8LDFHxkvksjaPVYHArWykrMWjBp3EcqPVyeQVLKczx/1XvdqNSewWnCGnPIvwN/Zm7mz0wXH2520q57NybePYFbrr2av9NLOVxoYVt2Ccp+B2sP70Zl9VTAuLUGuqaE8ceBAkL9dfgbdDSPDqTC5qQkOgltWALppZUM6htNTJCJ3LJKOlXY2ZNTRkyQkZzSSgotDsx6LRbAZghlU3oJGUWVPqN8At7K3h5twi+o1rTqTitYu/POO/n444+59NJLGT58uPeG4FR17dqVxYsXc/fdd3PrrZ4nzKvmaEtNTWXx4sW0bdv2RFmIeqgerGk0GtxuN/v27QPwCdaq33xWPfPTJCqA3TlldEkOrfUGV6/X43A4fNKqbuqPnTfvxRdfZP3WnUy5fzwBgUE0bnTFmd3Q4zjRhMTVa2N6DBjGxv7dGT58OM/dfS3/fnEWsR16siGtkN059e9GVv2HsqzM09r1n5en8H+3XUtAwNn5Itm54VeiIuNxm898jV/VjbpJr+OyFomkzP2S8dcP4ZMn76LXQ+/w2z6FwgobdrudsMCjD/Qeu/8Tw8y0apLCm58sZNFHr/Kf2W+QZvWMinVsTWXVjW6F3emZO0UBs16DXqsmtn0fVvzyJ08+OplFrz6M68BIbLoAVs//kJi2Pbnmrv/jih4dySiyeG9+9Vo127JKar2ROhmn03ncACM+xMw1HeP5ZmMGHRKDURS4olV31qe1oFn/GwnWOrClb8WatoW1a1azd/7noFLhH5vKHy0voW3XHqS26YTNYGJrZjF/pnnmPTpcWkl0kMnzHB9QWl6GyWxGrVZz9dVXkxvUnI9mv82fX78L4P1uTi+wcMOEB9j09w5+nfMU1017HzdBHCqysv5g4SkHbMcL1tq1a8d1N97E4o/fokXPYTRJiDxhQOx2u487r2ZGkYXxj05n1cBu/PKl52Ge4AA/77kwsFc3bJOm8OnLUwhNbUfHvkOxO104FbdnCoVjbnZr0BlxaU0Eh0cxZPwU7zqXbM2moMJOmdVBlFZNuc1Fy5hAxvVo5PPxn/7cwqwP/8OyhV/y1dofiGragb4j7+CyQVdwsMBCfrmNmCCjtyX/2PO/KljrlBLpHX2y+k0GeOaUyyu3ERdsIjHU7K0hrp5XeoEFtVZPVJuevHXttew4cIjfVn7Pmm8/53+33cCM6ARa9x/BLbeMoU1qnDdYL7E6jxu0x4eYuffmq/lj+a089thjjBgxguDg4BPvz9OlUoFfmOcvsWuNt9dXa5ms7dlT7YN64pu04Y/cw3T8vyX0ueZmWg26isiYWForQXTShJB+sBS9Vo3W7dn3l7dJpG18sHcS9H//6wlee/rfvPL2u7z56sss+noB7XtdQcjkx6F9HBaLhUB/Pwa3iWHFNk8rzPGCql/35qPT+NfoyVGlT58+/PD99wwbNoz7xt3EmDFjAGiVGIHWHHjczx17HjVp0oR33n6bsWPH8sctI7mkr+f3tHVskLdF8PM/0ql0umkTG1TjWvzPf/7DF198wQ033EDPnj1JCPWMU5wa6U9mkZXduQbyAyJZV1zJNqOWr9Qq2jcJ8UxY76+CinxyDmeQW1hCWXkZTrsNW6WFQD8Ter0evd5Aq/gwb1CNKcTTKqf3I2fGs5iWPs+jX2xi3759/G/59+xYu5SDc59FY3ydfa17sKN9X0ou7+d9ZvNko67abDbMJqPPOVLbhOfNogPIKrayO6cMu8Pt/U2pqkzZvbYMjd6M01pBn6aRVDo8A1bpdWrMGgWjyUxeZhoPTbqXBV98RkLoORy8Qms4MvJqB29r9cHnmrE7YzftP7DzfxNGMvXmPmhthZ4eN9bioyNXVpZit9iwu1QUO1WYjEaC/YMwGw2eYC718mq9bmI8j0b4R3lHrHRu2gTMYezM/7B753bSfl7I7TeP5OGJYVx5zXXEdezPfnU0FieUVzoJcXqeuU+NCadXkwj6t4jy7Mcj11ximJnDJZW8umIXLkXhu7+yMOk06DQq/A06OieHsv5gIcVWB4XlNvIVG5ckh3qP018Zxfx5sJCIAANxwWYSQ80YdRqM+gt7vubTCtbmz5/P6NGjmTt37hkqjmcQgl27drF582b27NmD2+0mNTWVTp06HX8+K3FKqj+zVtUNsmqkzUaNGtVYvlOypwVCr1WzKb2IoORQNGpVra1BOp3Oe/NR5XjBmkaj4YM5H3P1kIHs3Lq5QTyXmF5gweVW2HqomMYR/jRJbsJvv/3GmDFjePyeW7n6nn/jatYflxt255SSWRxX5yG6X3jxZbSBEbS4bADZBZ4uagUFBUx++P+4/ZEZZ2Vi7DvuuAMXat747xLgxBO91lfVjXqr+GDPjV5yKK/P/ZLbRwzm13ce5YpH38ao0+B2OnyCtWMd7RqppceoSTTtdyMBwWG13twfW6tc9eNbNXDJoSIb4558mz6DhvLk449QWlpKaHQ81sN7eW/SNRSNGc+TTz7JLwfL+WVfPlq1Cq3aUxvXObl+2+90Oo8bYADem9/qLRcBRt3RobHb96L10CsZfve/Wf7HNn7/5ScK9mwg/Y/l7FrxGRqtjsjGbQhv0pHmbdriCk2iTdMUVu/MITzASJu4IDROGwHVrpvB7RJo9fRUdFMm8c2nHzF48GDgaJD75jvvMvraIax883FGP/9fAoOiTqt1reocqP4dXXVsrhhzP4u+/or1388jeuwk9BHHD5pcLleNgK+K5zm0aG6772FmvfQUAH1ax6PTHe2qO/OJB8jbs4V5Lz5BQuMWdGrTytsSdbyb3eqqjmX1G//BbWJYsjWb5HA/DhVYsNpd/Lo/D1R4blCPnJu9urSjV5eXSXv6Saa+Podln3/A50/fy7qv2jNgzINEN29P67gg7/flscFF1felXu+5CaqtRbmq67Dd6T7uM2OJYWZ++CsLzZGJmNvGR9Okzwg6XHEjq/73M1uXf8kv/32TX794h6RLBtD/ujFc0787JdYStEfmBKxy7G/uc889x4IFC3juued47rnnztoEtnVxbMtk9WdP44JNOB0Oeg8bgSP5Mnb++AVrl3zK6m8+JeXSK7jsutv4Liwek1ZDsJ8OS1ExAOHBgT75Vm1Tn6tvJqnbML5d8Dmr//sOk66/nM233caBAwcwm80n7FZ/vPJWV7Ufm3a4lO+++45hw4Z5pzxqHBdGboXnZtZn/srjyCiy0PiyIfS/YgiTJk1izvdrUelNBJl0DG6Tyq9787E53OzLLSOvrJK4EJPPdb9q1SpWrFjB4sWLATCbzURERBAREUFoaChG/0DsGjNOrYm/HDr8Q6M4EJ3AtoNN6dc2mSCTicSodoREwf5qgdThkkrWpxfRISEEjvM9k1tUht6gx+ly06dzG1JTUikYfRf5Gfv5beX3bP7fYjauX8bf/w3kx8sG0bb/ddiCEkgJ9z/acn6MyspK77PSJ6qgTQwz0zouiNgQU61zyfVtFskWf39cNgtRZhUpMZHe1psdZRb8g0PpPOIelr75BK+8+jqvPvXEcY/RuWBzOOlx9WgUjZZn3vmYpb/vZs6cObTq3arGsuVHKqVyymwEGXX1mpqh6r7Oho6B195CyZAbyNy3k7zNP7Lk++/I+/A99OYA/Bq1I6JZZ0pDPPl2SI3xrqP6RO5V10j/FtGs2ZVLZrGVnJJKjDoNLWK1xAabGNEpgRU7DlNqddA4wh+TQcOenDI0ahXbs0uwOd0UVtjolhpOp6SQMzq5+vlyWsGa2Wzm0ksvPVNl8dG+fXvat29/RvKy2WxMnTqVefPmUVRURNu2bXn66acZMGDAST+7cuVKnnnmGbZu3YrT6aRp06ZMnDiR0aNHn5GynU/Vn1lTq9W43W7S09OJjIzEZDLVWL76DXJkgPGEtVk6nY6KCt+JPI8XrAE0jY9g9fIldO/enZSUlDOxeadFr1VzqNBCoEnHpvQiooOMxIcEsGDBAibcO4kP35pBy8FpxA8YS3mli7V78oC6BUFLv/qMAwf2MWXW5+QWlBAXF8dtt93Gww8/THFsV1q168SE3iln9EvF4XCQlZXF1Afu5INP55+xfOHoca1+k31Zx9a89tEXTLxlOLs+fYprX/0QxeXw3ogeT9UNTYfEEDKPnGMn6hpaW+vctqwSKp1uDhVa6TrwWv6+cjB33jORlp0vo8eQEcx9722+/mQWv61awoAJUzEltyOv3EZskAl/Y/2/Ek/Uda+K3emmUfjRWvWqio/YnDLKK53eAVCaN25E59ZNQLmdTYcKCbHn8fsvP7Hlt5/ZsfJztn73AQDf+QcQkdgEbXgSofEpaHJ2+lRyHL1WzQy77UHsOnON/fXZ558zoFc3lr49hesef4Mw/8BTntC5qttc9XOg6gcyPDKWa28ex7dffETPq2/mm3zP90JtgeGxLWvVg4Gqc2P0+HtY8s0XpB/Yy+EyO4cKS0gMOzp09r+efZm/Nm/iP89MZu43y9njdBHmb6hTF+OqY1n9x/2yxuHeboaNI/z5ZmMGoGJ7Vkmtz5gmhQfwzIN3cM0117L595+Y+/pMPnjsVroNHE7o3Y95W+PtTrfPDcSxwVptN5YnmyS4an/1aBLhPZZBJh2BJh1r9+TRvF0XwlPasDctk5z1S8j843ve/+V7fr+kB60H30xg486gQMfj9O6OiYlhwr3388qrrxDWZRj4h5MQevTcOpcO79tO2p49XHbkkQm9Vs2v+/IJ8zcQE+gJ4K1OFUF+ZjpeOZb+I8bwy3efs+GHefzn1x+IbNmN6K7DiG7ZFX2xZ7CeAH//Wm/sqr5X+l51Ax36DuOvFfP573/eobykiJat25BRZPE8m6biuF3jTxQoVF9nv379+Prrr7n66qtRqVSkRofQWKXyzvN37A3nsQFzeoEFq8PN3Y89zeghPfjyvZcZ9+A0n+C/dVwQueWVBJn0tVbSdO3alfnz57NhwwZ2795Nfn4+eXl5FBYWUlRUSGHhXg7nFVBUWIjNevR3/p2QKJq07kCzth3p2q07saktKbd7fh+O/Q6sjUmjYNAb6JAYgt3pPtJt0Yw59RLuu7YP6w88weKffmPtsm/Z+esS/loxn+CkFnToN5xBTcbz615qBLM2mw2j8eTD3p/o+ACkRPhzz83Xcs/C2Xz71jQ++eQT7zWcUVCKWqOjx8CrceXs5a3npnHNgB706tXrpOs9WxwOF061jqZDJtBn4DC+eu1ftG/fnsmTJzN16lQCAo6ep8c+B16fxzGcTk/3yKTwALRqFYeLregiGhHd7zbueOAJVvxvLbs3/Mr+zevYs/BNFLfnfEiNPRoMVnXTrZoKIa/MRqifnj7NI/lxZy5qtWfU3k6JoUQc+S6vug/NLraSXVLJjqwyyiodBBh1RAQa6JIcytXt4+p0bV4ITitYGzVqFN9//z133XXXmSoPANu3b2f//v0UFRXVOmBJVRfJuho7dqx34JImTZowd+5chgwZwurVq+nRo8dxP7do0SKGDx9Ot27dePLJJ1GpVHz55Zfceuut5Ofn88ADD9R72xqSY7tBulwuDh06RGJi4gk/l15gwc/gGemq+pdb9ecedDpdnbtBVomOjmb37t0nbKU4E+pSG2x3umkbH8xfGcUkhft5fyDVajXjHpxGpS6IT998Fn93OfE3PoRGrfJ2yzgZxe3E6XDw+mN30aZtO/z8/Jg0aRKvzPqQVR88jf/Uj+s0GEN9uN1uOnTtzpY/f+btl56h66zXzki+VXmD73GNDzFz+zX9sbwzl/tvu5EZ/3qUckslVteJW8er/2DWt4Wr6vOD28R4u8Z4fnTM/PvVD7w3uqPG30+Xy4fywzvTee/xcTTpNZwet0wmMTSMUL/6T0Z6om6QVY69ya7azmPTq36I0gssmPUaSiuDubL3JcxbN4KCcjuu8lz8yrP466+/yD6wm9zdmzj4y3e4XU66dq3ZVWzF9hz+PFBAeICRCb18KwC6tmnGky/P4qHbb+Snrz/GdOMd3mdx6qu2bpBV29E8JoDLnpvOoi/+w8ez32DguIeP24p3bMvasUFTVevp+Ckv87+li9iYXoyfXuu9PjOKLOwqcPDg87OZetvVzPz3I9z6f89TWumosa7auFwuNBrNcY9XlUVbMgHVcbtWVi0/vONN3DbyKl54YzZzX3+W9T+toOM1d1AwfDQjLmnk3U+A9/vyRBUaJ7uhrNpfVa0oVX7dm0/b+GAOFVnomBRCTLCJ7OTxqK67jcDDG1nxxQd8Nv1uguNSKRp1B21u6H/cdQy9eQLvvfceLz87naETn0EFmHSaUw70T9V7773H+++/z4bdh5g86T7sTjcGrYbySif7DxcD0CgqCI2/gR5NIogIMKDVj6PtwBvYsnoRu1Z/xV8fPcG+sBhCU9oAsCXbSpMWR58prC4ywMju3DIyy5xUNBnEVc9eTvGfi+javpX3N7HC5vS25NZnXxx7vg0ePJj58+fzzTf/z955hzdZfXH8m6Rt2nTv3bL33lMKskEZsvdSNggCypAlyEYEBEWQJRtBREVUBH4CCoqAskEoZRRK90ibtkl+fxxvkrbpTpu0PZ/nydPmzTvu+9773nvOPeMe1U2oZqeoZ1Yude9dnapYsGAB5syZgxlTJyHgP4uGKFd0Uioik1RZXL+FzOXj44Nu3bplu07un6HR+ObaU0RGRSP++WNcv3UHaREP8OLhDdy6tAaHPvkQTm4eaNCyHeJe74k+PbrhWVyqTtA2NgbLpRooFHY6dzjDew6LUuLGs3hYeZRH9dfGwu/VEXh27Rzirv2IsztX4tcv16BOi/Z4pVsftG7TDnGpGrjZ2+BpVDxStdIc22d25ck8WYRq1bB246eYOGY46tWrh4FjJiIsSgk3WynsbOWoFeCMwRs/wqBn99Gjdx+s2fs9OjauYRZrjlSihUJujSo+jvBzqY9pnxzFkV2fYsPGT7Bnzx6sXr0aAwcO1LUvEQeeWa7LDSHXxanUuP84FmqNBompqXCwkeL0nUj069oezm90gY2VFHfDXuDU6dN4HBaK8y+t4eCtb7MX/o1EulaLx1FKVPFxREqqGulqDQY3Ccow8STagpMtTchU93VCeGwyjl97hlS1BjFKFQLdFXCzt9HJpCKTdOYYz5JkZSuUsrZy5UqMGjUK3bt3x6hRoxAYGGhU0G7QoEGezvfvv/9iyJAhuHTpklElDSC3jPwoa5cuXcL+/fuxatUqzJgxAwApe7Vq1cKsWbNw4cKFbI/duHEjfH198csvv+jM6GPHjkW1atWwY8eOUqWsiQQjYWFhCAzUr2JrrGEbdqCG5uujfz1BcroGoZFJSNVK8uwGaUhRK2pA1sHNGGJArOjloBs4BMEe9pgwZRqa1KyA6RPHQpOWglfHLoK/a1ZrpDHS09Px5ptv4uTJkzj7y89o27YtrKyssPyjjRj6+qu4ceoQfN1G4KcbL1DVx9EknYpWq0X79u3RIqQDPlmxEN3btkC/fv0KdU6BMcuawLZ8fTQePBOXdi8HADyKSc2yj6kwbKuGLmKGM9JihrxipUpYue0Q9uz8Al+s/QAJoX+jzspPAfca+DM0WlfneXnu6enpUGslObooZSdkZ95uzJIith396wnUbsF48NIDnq1qoGIHa8ikgFwKNPVIQ/dGlZCZh5GJeBSVjOQ04zPyFeu3Qqtew3DhwCdQVGgIVYO6eXbnNcSYspbx3hToP3I8dm/ZgJodB6B99YZGzpLVsmZMQA1yV8CzfHWMnF4nS1yJbhD3K4/laz/G2+PfhGfleuj8xsA8ucAIZS0npahROTfdjHpeXCuD3B0waOgI1G/VEZ+uW4ZL+zfgztlvoHlvCfq91hkn/glHgKsCdx+SdT4363PO1zIu0IushJW9aWa5aXl3PI5RorK3I/xdaqDza73x46lT+OXQNhxYPRvSp38DyOoGCQDVgrxQs/to/LFnJULvDEY132ZG21dRIzxD1ix8F6mwRvnmXXHzeRxq+LrAy4HakJO9HZpU80aSiuLxAtwUkMkkaNK1L9r1HAjZy/v49uBOXD17ApBIILOj/j6zC5hQxqp4OeLu8wRoAEhsFHhr6kxdggrDGNr8Pgtj7a1Hjx7o0aNHjvsA2U8EAcCUKVOwceNGvP/++zh48CCAjPHYLX08Cuz6nJquQXKqBnFqOay8K6NzldpITlPDzd4GUYlKxIXeRNK9S/jzfz9j5vGDWDvfF8OHD8eECRPwV6QUYdFk8TC8p5SUFEhk1lkmaAQ2VlL4u9qhsrcDHkUlobrfa5B2ew2+1in47shB3Pzfcfw1cxQ+dXKHb8MO8G/cEaGPXsJeI8uxTrKTCYxNFrWoNAyP79/Ce++9hxYtWqBly5bYKZfAw9le53I3dtF6TOnXCbMnjMTzNbsx7JUqxa4YSKBF7QBXtK3qhTsvEnD+YRyC2g3Bwq698efB9Rg8eDA+++wzbNy4EbVr10aQu0KXFdlwDBTPIbuxTVjW3B3sEJUM2NhYIVWjRYJKjZT0ZFx/FoeBTWjZEB9nW9yIbAVp+ca4/TwB1X2dde21srcj5FbUlzcMdsWJf8KRrtHiSYwy24lrEd9270UCmpR3w7UnsbCX0xJg9jZWuBIWAydbazyOJvf1l4mUmdVwgq+kUChlTaVSQaPR4MSJEzhx4kSW37VaSmuc12yQY8eOxT///IN169ahdevWcHXNf7B/Zg4fPgyZTIa33npLt83W1hajR4/GnDlz8Pjx4wzKiSHx8fFwdXXVKWoAYGVlBQ+PIkzLWoxkjlnTaDR4/PgxOnbsqNvHWCdmOBgIQfjGszgEuinwR2g0qpRzwz9qSb4ta8VFbq5EQO4z2C8TVAhu2hkfbtyGOZPfRHpaGoIXrPvPXTJ7FxWAZtD9/Pxw7NgxtGzZEla2ZC0IadkUPQcMx/dfb0WFJh1gYyVDoJvCJJ2KRqOBi8IGk8dNxd0bf2P4iBEIDAxE8+bNs5QTyLlzNnZuwLiyVj/IFZ3eGISoZ2H499RePI6ML7IZ+OwG3MwxKMKlLchdgbkzpqBNSFsseectzBr2GnqOfQ+NuvZHgyDjiXOMoVaroYbEpD7xmdufEKjO3IlArDIN9nIrWEkleLWGt25x78w8iVECWgm8nOQIdFMYnZG3l1thyMT3cOPi/3BpxwcILr+zQNa1nNqAYMaM6Ti4ayuizh2A82vG3eczW9aMvYeZ6zBzXev+1h6EU6fPYv+6hWjXqhmCKjXO9T5yiz80vE5ufUjm/QE/bP7kE6zd9zp+2b4Cq6YMxo+HO6NGr4mQ2LsjIImSDRVGWTN8Xsbc45SpaiSp0hHopoCtjUwn0JT3cMCQXt2waeZwlCtXDvHx8UbPL845YewYTPt5LyL+tweOnVrpYnuLE61Wixp16sM9uCo2Lp6B1m8qUbV5JzjIZfBUkGjj5+4IhY0MSanplKwlJQ0Ng91w7l4kXsSnwM4hGP1nLMeAyfPw6MF9NKkebLRODa3E0UmpuBkejxq+TuhRTz8plHnWvrjIaayytbXFwoULMXr0aPz1119o0KABLofGICJehbBoZbYW0bzkBwhyV6CGrxNsbaSwkkigkFtBqUrHjWfxiEpMhZ9XdVSt1gAfrVmF639fxcbNn2PDJ5uxavVqVG/RCeXb9ke9+vXRsJxe2Y2OT4K9wjbDBIzhfQ5sGqTzmqjm44Rz917Cyc4a8clW6DZkLBwb90JM2B2EXTyBxxe/x8PT+yGRWaFCnWY51klOkxzGtn/wwQf49ddfMXjwYFy7dg0qlQpyuVz3jrm4emD4/I3YPHMI9q+dgzY1dha7YqDRaOBiT5lJ7zxPgFRCroTtm9RC3eobYF2jPc7tXo369eujTc8hGDftPcSqrRGVlIoDf4QhyE2B5hU8cs2ULOS6QHcHuMndkK7RQqlKx81n8UhQpePhyyTdmBIWpUSgmwKJqnTU8HPO8FwbBrvqPEtEHy8mFQyvbaxfk1vJILeS4bU6/kjXaBDgqoDCRqZzp7X9bwmhxJR0BLkpjLYvS6dQytqoUaNw9OhRDBgwAE2bNi10Nsjz589jzpw5mDx5cqHOY8iVK1dQpUqVLKuDN2nSBABw9erVbJW1kJAQrFixAu+//z6GDx8OiUSCvXv34s8//9TNUpVkMsesGbOs5SaUBLkr8NONFwiLUcJNYYNa/s5IUKXDxib/bpDFRW6KWE6IWUkRiNuqfTds3b0Pbw4biM8XTEK1z3ZmGLTF4qiGnU1aWhqsrKxQr149nDp1Crdf6JWMxR8sxo/fHsXtb7eg6tQPTLI8AKC3oj6OTsbURWvw4vlTvPbaazh//jyqVq2Ky6ExupnO/KaxN+YGKWhUzg2Nyrmhe+2PMXeJP0I6di2yGa3s2qox65Xh9wEdmqLnpYt4Y8R4HNqwCFcu/oohMz9Et0aV83Td9PR02MltinwAEG4qlb0cdYl+Mlt9DQmLUqKmnxMC3eyMzkyK5+Xj7oQZH27AvFE9cfHoVni7TM23dS0vylqNYF+8PX0Gli9ZiIXz3gOQVcEUlq2Cknkiaey7H+DW31fw/pTRiNv0FZpXC8zRkpCX+MPM18lruQBKxV+nXj2UW7YTto8uYPWS+bj+e1/U6j4SynLkFlkYZc2QbN3j/kvGI/ooIdAYtqPsPFvEOav4umL+/PcxfeJYfH3qHBo1aGAQ21s8QlBiShqS0zTo9NZcPAyPwq+fL4DE2gbNK/XBo5eUHtzPzTFLpkgAiE5MxdUnMbCzliE1TQMvF1e8O/z1bMtuWN8danqjqo8jbKykOHblaYbY7cKMLUXFsGHDsGrVKsyfPx/ffvstIAGc7Kxhky6FvY0V9f0Gwm92dS8wfJYiI6pI7HTs2lMo09SQW0mg1mgQ6KbA4+hkpLqUQ40+0xDQaTSunfoaN3/aj+v/+w5/1WyCxJHjUa1RKzgpbBAdnwQne+OTT0DG9TkfRibqXHs9HOUIjUqCq70NVH6VUaVXZTTuNwVP/rmAqGu/oFPH9vl2gRTXM3aclZUVvvzyS9StWxcTJkyAVquFXC7P8I41LNcV6rg12DR/EnZtWoOWHxVvQh5Dzyk3BxtU8nKAjbUUPs62OPrXE7hVbYLByw/g/plDOL5jA37/6Ru8MW4W4gNaQAMJbj6LR1UfJzQs55qjDCgsa13qBuhkxycxSlwOjcGfYZS9WISIiHMI7wRDjI3ThpNyAmP9mohJy27coolL5LiPpVMoZe3kyZOYPHkyPvroI5MUxsPDo9AKX2bCw8Ph65t1qUSx7dmzZ9ke+/777+Phw4dYunQplixZAoCSqnz11VcZ3BMyo1KpoFKpdN+zm6U0N5lj1qKiopCUlJQhZi3zC5S5swlwVSBdo4GzrQ0iE1TwcbaDUpWGmGQNlCmqDNezFGWtMAg3q7jkNAS5K2BjJUWVRm0wfeVWrJn1FsYPH4hvjx9DRBK5SUUnpiJCk5IhLiA9PV2Xwa5Zs2YIyPBMPTB3wSLMnfE2WnQfgGiPpgWKgciMqGvR6e05+BX6d++Izp0748KFC4hWpiIsOgkOtla5ds6ZyckNUtCkgge2r1lYpLPOBRWURJueNPdDSPxq4afPFmHdxDfgv+kLNCrXMct+mQdatVoNe1ubol0IFcgibOdlf/E3JxfMJzFKeDo0w2vDJ+L4zk9QqdEr+MnTHlW98+6CmxdlDQDmzJyGbZ9uxGcfrUDLXbuMnie3cwD5c2X+YvdedGzTAtuWz0HKe6tzdHEtrLKYW5lFnMWQZsEIcK2Lyk3aYu3ypbj89RbcsCYl7Xp4Itq4F3ytSUPXdCD7BD05vS/ZCeyGEyLp7V6Hq+9S3Dj+Bex8KukE6eJU1mQyKf59mYRWY+bjvCYN5z6dCyc7OY44+gBAtlkAR7Yqjycx3rmmfTeG2G/fxTA8jEqCv4tdtpkILQErKyvMnj0bw4cPx/Xr19EwuAI8HfSTPfl13TT27gn38joBLgiPTUY5D3u4KGzgZm+DIHcF7jxPQJIqHZEpUlRt1w+uDbsh7tZ53P5pLz6aMRJegRXRuvcwaFKVkMvlOSo1og0aWk2uP4lDWroGCrkMVX2dkKxSw8NRjpZV30D96WOgyCFte176EmOUK1cOn376KQYNGgQAaNOuPS6HxiBamYqXiSo0DHbF4IH9oYx8gi3rlqNt03oIatQej6OVuPEszqQx6cYw7EsbBrsiIl6/TmOryp44d+8lWlX2RJsaU+HX4FX8eWgjdq+cjUqNXoH/6+/Azc0N/zyNM5oZ0xChrGWOWQ9wVWSRJcT27JLlZMZYH5VbPHFez1PSyH1UzAEnJydUqpQ1TqKgjBs3Dl9++WWe3SbzQnJycgY3RoHIDpScnJztsXK5HFWqVEGfPn2wb98+fPnll2jUqBGGDBmC33//Pdvjli1bBmdnZ90nO8uducmsrIWGhgJAjuU17NgE9YNc4WxnhVaVPRHkpkCKWg2VRoLEZBWexChx4X4knsQodfWal5lrSyXIXYFANwUGNAlCj3r+utgVq6B66DR9Le7/fQldXuuF83fDceUxCQKZM2AJy5og4D83NtGZvPv2JARVroFjm5bgwYuELM+7IGg0WoRFU1tvUckDtcr748SJE0hLS0PXrl1hq1HBxY4UbrFPXju3nCxrhmS+T0tBtOknMUrUatkebWZthbWjK8YP7I6PPvpIJ7gaa/tA3hKMmILMz8/w3crL/rmdd/bsOfAMroKfNr2P3b/exb5LYbp13HLDWDZIQ0RZo1XAsAnT8eWXX+Ldz7/FsatPM5Q/r8pSkHvurizivlo3qoNpi9fi2tnvcfG7/fj7SSw2n76PP0OjsxyTVzfIgiD6DiGkPYlRQmHviH6T5+K1+Ttg51UOVrb2uBmR/ZiUF0Q7FbFXov6Ntd/c2lBmdzjDNlXB2wl9Rr+NJ9d+havyMR5HK3Nfy86EKGyksLOxQp+GgWgQ7I55qzejTvMQfPfRTIRePQcg92QtPer7o0c9/3z3SWFRSurTtYCNTFpkE1D5Iae6HDBgAAICArB69WpdHTb6b22q6MRUXHkco6s7Eb6SHcbePWEFeaWyJ2Z2roam5d2hsJYhIj4FAFl2qvs6oVVlD1jJJKgR4IbyTdqj69wv0HD8Ojj7BuHI+oU4+d3xDO6ExsY9w/K3qESp2eXWUvi5KtCigic61fDBK1U90aqyB9pW88q1n8hLX5IdAwcOxODBgwEAz19EICxaiT8eRuPcvUhcDo1BkLsCYyZNR+9+AzBixAhc/P13/HjzOR5FJeHEP+H4MzQ6x/evMIj4X0MLetB/lqgutX0xvm0lpKZrcOFBJLz8/PHuik3Ysf8rhN/9G1fWj0Pkwxu4FhaD/ZfCsO9iWLZlzEmuy24Myu8zN2zblipHFDWF6lnffPNN7Nu3z2TKVZUqVaBWq1G3bl2sXbsWhw4dwpEjR7J88oOdnV0GK5cgJSVF93t2TJo0CcePH8f+/fsxYMAADB48GD///DN8fX0xderUbI+bPXs24uLidJ/Hjx/nq8zFReYEIw8fPgSAHLNBGnvJfJxtUdPPGZ6OpBRLIIFMZgV1enqGTjc8llL8vkxMLapbKnIydxTiebSq7InK9Zqh1fjleHDtN3wyfyqexyQCAJJU6XiZqNJ1dsKylt3gKpPJMG72Ejz/9wb+Pv01Tt9+gbjkvGWzy450tRppGm2GwS8oKAgnTpzAgwcPsPn9CZBq0pGq1uDyo7wJ6IK8WlUsCcNnL+qwfpArPBzkqFSpIiau2o1uA0Zh+vTpaNa2Mw6eu4kL/0bicUxWy2BRCvg5kZNAUxA0EhnGzV+DhJfP8PCnXbj3IgF/hkVnUaiMHmtknbXsylquRXc4evhi/+bVuP4kDif+CdedP6dFsQ3J74DdtF03vNJzKI59thyXLv0BmYzWisxMUVrWMpdZxAx62MtRt15d9Hx/Gwau/QblPAvnXZKdIGRsu7E2lNf1TANcFdi8cAoqVq6CP49+jgA3Wly7KARPY9hZy+BkZ4MutX0xo1M1vBlSFYcOHkC1Bs1x/cgGAEBMSu4JYApCkDulv29c3g1VfCwjHXhO/YGNjQ1GvDUBe/buxR/X72U4JlGVDlsrWZ6S5QDZv3uG24PcFYhPSdNZchoGu6JhsCtaV/ZEq0qe8HG2RZdavtAAKF+nMUImLsey/b+g04AxqPFKd9hYSfMszAe40uRpu6pe6FDTGz3q+2Nky/LoUc8fPs55S9lfGOF/wwZqaxpI4GBrBQ9HOVwU1oCEzt2ysidWrPsE5avVxrxJIyBJikR4HFm5roTF6Oost4mT/CLku8yTNwC5rV5+RKEPUQmpeBajxN2IBLzasTNOnfsdvr6+OL9uEp6eP4rH0UkIi1Zm6KcNMWZZy438PnNTj3UlkUJNB9eoUQPHjh1DgwYNMHz48GyzQfbu3TtP5+vfv7/uf5G5MTP5SVgCkLvj06dPs2wPDw8HAPj5+Rk9LjU1Fdu2bcOsWbMyCKHW1tbo0qULNm7ciNTUVKMzd3K53Kg1z9IwnEETbpBWVlbw9vbO9hhj5mQR75SSpoattQypaWqkqIHUtLQMrjh/XSRl7UVCyVXWMmP4PGoHOGOfiwLWUOPUxvfw9cfz4DVvDdzsbTLErQnLWk7uF0N7dsLJQ73w486P4FazNQ7Hp8DTUV7gzF2AFnLrrOun1K5dG8eOHUPnzp2RsOodNBn9AaKSUvHgZSLSNVq0reqV6zXz4gZpaRjL8gXQxINwjao3/X14VamPPSvfxZher6LLlOWoUqcBLofa4KebLwAAbvY2SExJhdwM1uL8JrnIy/kCKlRB675v4teDn6Fis464b2+NqP/e15wG1twUdsOy2lh5o8vwyTiwejZePLiBis2b6t6BnBbFLhQSoN2w6Xh46xqOr5uFocv3IaBCVlfDvMasmQLxTGgtKS8EuipQ3sMhR5etvJCdy092LkUiA5xhoonc4pYEMpkMixcuwODBg/Hk7nXUrt+g2FwhjVmAKvq44vLZH9Dy1S7468IZPIgumrEmv+5cxUFu/cEr3Qdg7cpl2LhpE3Zu+kh3jIj3MTwurwp7dmSONzKM2RRZKFtU8kCaRos/HkYhTaNFtMwV3u3HwNXb0WhGzoJQUBfH/ODq6oqvzl5BmlaKQG9HdKjhrXND3n7uISKTVFCq1Kg9fDGeLBuDY8snY8nWI0hITkOAqwJJqnQkpaYbjW8vDIahD5mThylT1YAWcJBbwd3RBkkqNVTpGlwOjUGP+lXx95+/YeSEt7F32wYkhf0Dv+Fz8eK/sTFz2bKzrJkyPs/UY11JpFCjkqmVq9OnTxemOEapV68eTp8+jfj4+AxJRi5evKj73RhRUVGUkttI2dPS0qDRaEzqrmkOMlvWAFJe8z2z/F+/7uEoh5vCBinpasjlciiTEhEWlYjeDclSJwUJdHIba9PcgAVSxdsRT5q3g1y6DN9//B6OfbIQksmLUNXXGdV8HaHVanWWtew6INHJDXt7Dn47/QPOffU5Xh02Hbt/C8XT2OQCBchqNRpU9DIe6xQSEkKLsPbshRfx76LqoPdx+5kN0jVanLkdgUntKqNL7axxn4K8ukFaErklIxEB0s3bdoRP+SrYPH8yDi0ajdYDpyD5tWF4GqNEUqoaNfyckahUwd61eDPhGZbVlOfrUtsXVd6fi2F/nMLNA6tQe/x62Fpb5bqGYG7KWuayThwzEhePfoHzBzfj1Vda6Oohr5a13MgsKIiYjbcWfoylb/bAD5vnw3bGR1kmQIrSspYZw7aWXaKP4iiDSDABLbIoa3kR2vv374/5Cxfhpz2b0KTJ9mIrf3buera2tnjrg0349vg3sPPKec3QwmJJQmRu/UH1ct7o3LMvTny1F2kfr4S1tbXRY/KqqOe3PCJm87FBFsoONbwRmaBCVJIKkUmp8HWxhY11/t1Ks1PKspuMMDVNalfJEst/4X4kbobHI02tQUR8CuSOrmgzaQ3OfTQBG+aOw6gPtiLV0VZX/nS1Bg62VlnWvSsoQr4zrAfDBaJFPFngfxZxW2uZTpazsbHBnq2bEFS9AT5eMB0nl46EatIq2NvUz3Kd7CxrplSUS0PMWWEplLJmauWqTZs2AChBx19//YWIiAi0bNmyUKny+/Tpg9WrV2PLli06hVKlUmH79u1o2rSpLj4rLCwMSqUS1apVAwB4eXnBxcUFR48exeLFi3UWtMTERBw/fhzVqlXL0YWyJJA5Zg2A0WQsuZE55eqfodG4ULURVPs/wa6dO/Egsi/cHeSIiKdYDI22cLN2lopwa6oX4Ipafr1RzdMWa+dNhY2tHSRvzQag79iyGyjFeZSparh7+mLIm5Ow69OPIes9BHZe/giLUhYomN1wmQZjdO3aFbNWfYbl77wJ9b4lqDZgLlI1UthYSbD2xzv47UEUmldw1y1KaXj9kmhZy63zFy4iDrZWUNu5Y9AH23Hp4Cb878uP8OLOX6jUdyZcXD0QlZiC5NQ0pJbseRsd4plMmLcS743qicDL36Jp/1G69NrZkR9X2LAoJVRqoEWfsdi7YgbuXLuMhsHtceF+JFSp6SZpR5kFBaGI7k9OQ9dJS3Bk+RQ4Hd4OV4dxGZQ1c7i0ml0QkWT8m1+rikwmw+Cxb2PxjAl4cOs6PB1oDb2ividjyWiE4qtUS1GtReci75PMXnf5IMBVgYWz3kadL7/AsWPH0KdPn2K9vlDAxJIRwquhircjwqJlsLOSIVqZCqsC1FlOk2/FYV3Lzmpdw9cJkUkqNAp2xa3weNTyr4uaSz7F2mlDsfmDd9Bu7GKoJRJ42Mvh7mCTJb69MBjzUhAyisJGhudxKbjwbyTcHeRoW83L6ETRxFGD0KB+PUwcORA/rBiL5Emr0bqyZ5YJLiCrZc2SJjJKAwXuyVJSUnDt2jVIJBK0adMmx09+WL9+PXx9fdGqVSv07t0bf/9Ni3NGRkbCw8MDX3zxRb7O17RpU/Tt2xezZ8/GrFmzsGXLFrRr1w6hoaFYuXKlbr9hw4ahevXquu8ymQwzZszA3bt30axZM6xbtw5r1qxBkyZN8OTJE8ybNy9f5bBEjClrOblAZkdm/+PUdA0aNWqCKs064NSeTxD6IgZ/PIzCkyiK4Qp0L34rhCnIyadczFglpaajQ01vvPlKRayeMxnL1qzH2aO78cOXn+D6kzh8d5XiF3NytRKxJQ3LuWL9sgXw9HBH2I9foHkFDwS5Z10vKy8YLtOQHeOH9cOExRsRef0c4n5Yh9p+dB21Vot/IxJw7t5Lo37jJdGyliv/TS5HJqgQ6KaAg60cG9atwUfb9uHJnWu4sGoMvBPvoYafC+KVKjyNVxVbrE5RExalRMMmTdGi+0BcPfoZAq2TchVy8qqsGb4n7br2gHdQRezetAYn/glHWIwSKWlpJmlH2SVBqOztiJD2nVG321BcOrgRf/7+W5YEJyU5AVJBEPFEYnFnIP/WlZHDBiMguDwObl1XbLElxixrQjBXa7QIclPATWGaZRBKC7Vr10aLFi3w2Wef6bYZG9cK6wZpDCEnNAx2zfBuNixHbU9hawWJRIJnscp8t5+cYqAKk0CksFT1ccTAJkEY0rwcejUIQHxyGsrXbICBs1bi/u8/4qdd66BK1SA5NR0eDnIkpaabrJzGJjOC3BW6GPpvrj3D3RcJuGokdlcQ4KpA33aNsP+bk3D1K4/TH03B6m0HMrQVMQGdud8sq4lAiooCK2u2trZ49913cefOHZMVZvv27Xj77bfRuXNnbNu2LcOA4eHhgXbt2mH//v35Pu+uXbvw9ttvY/fu3ZgyZQrS0tLw7bff4pVXXsnxuLlz52LPnj2wtrbGokWL8P7778PJyQmHDx/WZQAqyRgOduKl9vHxKfR5RfD1tNnzkRwfhVunDiJNo4X8PxksoIQqazkFuYoZq8xWr3enTcKEGXPxy55P8P2hnYj6z7oYr8p+9sywk7O3t8eMuQvw7ddf4acz/4O/i12BOr+8pEQPcFVgw5yxWLZhK/45dxL/7FuBzjW84KKwhqOtNTwcjQ8mJTHBSG4IAaJtNS/UCXDB+LaV4ONsizS/ehi26gCcfYKwdtpQHNz0IRISk6BMQ54zJ1o6QrhZunQpnJ2dsX3NQqw+eRvbzz/MViHNLRukwPA96VTbDxOmv4vrl/6H5/f+RkJyGmQS07Sj7AQFoZRMmvk+fCrXwXcfv4dfrtzX/V6cbpCWgrFnlV9lrZynEz5YOB+///IDnj24XSyCsTFlTbTdkKpeVNe5WITLIuPGjcPPP/+M+/ep3Wce10zlBpkdmdub+N62qhcCXO2yLJZs6usVF5mfa2q6BnUCXCCTSjBlzFB0GDkDD3/ZhztnvoJMJsXT2OQMrsiFxdiYH+CqgKejHPY2VtBqtVClaWBnY5Uh0UlmnsQoYWvvhE/3HEH5Ok1xePlU7N53SPd7aViSqSRQqCnEWrVq6dK9m4I1a9agR48e2Lt3L6KiorL83rBhQ6xfvz7f57W1tcWqVauwatWqbPc5c+aM0e2DBg3SraNR2jBmWTOFsqZzCajvj+tjx2L7zi9Qvnl3XA6lOn2RoEIBvC3NTk5m/Zx+G/TWVNx48AT/271Kt85STHLe/ebKN+8K18AqOLBhKaTeVQoUt5bX9asAoFWH7pizajM+nDkeEokUg2cuw92IJCT9F5ScXYBxaVLWjLm1XLgfSesSyZzRecZG3PppH37dtwEadTrc/IL07mQlHP29e2Dj+nXo378/nOp1gneN5nBT2Bhtd7llgxQYvicBrgqMGDIQ2zaswVdb12HrvqNQpaZDmVY0GfwA/b0FuStwfeYqfDatLxa9Mx4hP59EkLtDmVTWciI/FpbBgwdj8eLFOL5zI4Z1P5T7AYXEmLJWktwSi4K8JHXo06cPpkyZgu3bt2Pp0qVGx66isKzlRqNyboVIoGV5ZH6umde9nDh5KlJjI/C/fWtQpXwQZHVa6cYQw7USc1oXMieyG/NFuXrU88fTmGRAAvi72GUbLyuUTi9XJ+zaewDjxozAvMmjEZ2gxNS3hiM9nVzXzdFmyhKFkq6WLl2Kzz77DD///LNJCnP//n106dIl29/d3NyMKnFMwTB8mcWLZgplzZD58+dDo1HjwpGtwH8zduFxJTMbZE4zdDn9Fuxhj6lzPsArXXri5OcfAgBs5eSek5d0vVKJFJ1GzqB1T/53AtefxOGnGy/w+f/+NbpWlDFyi1kzJMhdgS6v9cLHn27FxZ+OYefKOYhTqvAsNhkPIhOzXLdUukEaIOrIxkqKWv7OCKnmhSYVPTB83GT0+2AnvMpXh19wpQxuZKWFvn37oknLNriwezWUycm4nE1q9ry42QLG1opLwbCJ7+Da7//Dnxd/g0ajRoKq6AMAA1wVaNugGl6bugwPrv2GoZPexZ+h0WZbhsGSKKjQZW1tjblz5+Lw4cO4fv26iUuVlfz0aWWFvKQ4t7OzQ79+/fDll19Co9Ho3kmAJqSSVIVbJoYhsrMgiu8Ng13xwYcrENKxKw6tngmPpEe6MSQsSonH0Uoc/esJwmLy7xYKZJ+sSZSjUTk33TqDYs263NxIXySmo9mohQhs3AFr5kzCZ1u3c59ZTBTKsrZx40a4ubmhU6dOKF++PMqXL58l6YZEIsGxY8fydD4XFxdERkZm+/vNmzdNrkyUZQyVtdjYWACmV9a8vLwwedoMrF3xIV4ZSPFwQZ4l0w2yoAS4KhDQUIHuXx9As7ad8Nf507jxPFEfDJ9L8HPDcq7wHNoLt385iL+Pbkanbq8hMkkKCSS4EhaTp9nIvArTuvK6KtCi0gi42Fph2LBhSFUDjQbPxM3weMgkEvz+IArjQyqhUTk3i7OsmTJlMGCQ6hhAj/r+Gc5f278z2rVsivpB+c/QWRJ4GpuMt+cvx9Bur+Dp2f2oN2KK0baaH8utITZWUgTUD0GlqjWwZ/NqaDUauNrnvjaSKWhYzhXo3R1ht67gfwc2Y2+TZmUyZs0YBXWFGzp0KD744APMXbAI767YbLJ30Bj56dPKCjZWUtx4FpdrRsEhQ4Zgy5Yt+PXXX3V5BUQ/pyyGyRJG76Hy/qpNmDFmID6YPBQ1A79DwKutYWMlxd9PYiGRAP88jkWlAshMBe2TjZVTlPWnmy8QnpCKoJ7ToZFaYem7k9GoSTPuM4uBQtXk33//jbS0NAQFBUGtVuP+/fv4559/snzySteuXbFlyxad4mDIjRs38Pnnn+P1118vTJEZAwwHu5cvXwLIqKyZapHGhXNmwcvLC38e2wEACHK3jEVEi5sXiWl4Y+ZqNH5jLPwq19EJ/LkFP4uZsHVrViEhOgKJl79B26pecLbLW5rfvMYTGWPIkCFYs/FT/P3LUfx1YC3S0tT492UiUtUanL4dAcDyYtZMtYCmoUXNsI4Mz9+onBvefKViqXLfMSQsSgnPgPLoOXQs/vxmB9w1MbCxkmbpFwoqGKSma6CFBHVeG41zZ88gMuJ5sabOb1jOFX1HT0FgzSbY9sE0PHsRgeQidMMsKRRUWYtISke/0ZNx/OhXuHXrFi6HxuDYlad5Wli9IGVkZU3PkxglroTFwNHOOteMgi1btkS5cuXw5Zdf6raJsUghlxX7czX1gtCWRnb3FxalhFpqgxELP0FwpaoY1Ps1XL16FU9jk6GwscLzBBUUciuKZ8snplLWDHGzt4G/iwIKWxs0Gvwu6nbshz8u/saWtWKgUDUZGhqKhw8f5vh58OBBns+3ZMkSqNVq1KpVC/PmzYNEIsHOnTsxZMgQNGrUCF5eXpg/f35hiswYYOhGEhFBgrehsmYqoVehUGDMtDlIiI8DUHrd5XLjcmgMtDJbdBoyEY2rBelmnfMa/NymST0MG/Um1q9ZhejIl6jp5wwf59ytEIVVpqZNeAsrP/4EV04ewuUDH8HXUQ4pJLCSSTKc31Lq1VTZv0T7F778YVFKPInJm4JdWhD3+uHi+fD18cahDR/gr0fRWVxzCioYBLkr8DhaiaCGIXALrAy1Wo34lOKb2Q+LUsLGxhrjFqyDVGaFyBfPkZRWtAkWLB1DQT2/QntYlBIhr/WFu7cvDm5dD0iAsGhqK6bOEMnKWkbEOmYJyWm59k1SqRRDhgzBwYOHcPrGE90aZC0qecDOuvj7cVPJGpZKdvcn+tfm1QLx0RcHUL5iRbRt2xY3Ll9EQkoaohNUeB6XjLvPE/AkRplnpbYwE7Q50TDYFZ1q+qBTTR8Ee9qj+7i5GDZuSoZM6kzRYBlT4f/h5+eHy5cvo3Pnzjhw4AC0Wi12796N48ePY+DAgfj9998LteYakxFjbpCGqftNKZSOGTkMVWvWAWA5Qn2xIwGc7KzJV7yef4Hcg/qMngotJJg9bz6uPYnFiX/Ci6XjnjF5PCa8vwLXfzqIvw6vh6OtDDfD4/DRT3dw7u6LQp/flJgq+5dh+8/srlpWUhIHuCpgYyXF2QfxmDx3CX756SR++O44fvvP4igoaPxQgKsCvRoEQKMFmvUZCwBwcyweN0iA6jjITYGalQKx8pNtkMlkcHcq/fWaGwW1rAW5K+DioMDb78zEz98egWtaJILcFAVeciS3MlpKn2MJBLkrEOhG6wnmpW8aMmQI4uPj8MuPJ8yuJJX2CbDs7s8wfqxj/Qo4+8sp1KtXD4snDsLdiz+jnKcDlKlqBP43BuVVqS0qb5cAVwV61PdH30aB8He2Q6pag1o9xuPIyTMmvQ6TFZM4mp49exbfffcdHj16BAAIDg5Gt27d8r3GGkAxTlu3bsXWrVvx8uVLaDQaeHp6cqdcBBibDVcoMq5LZCqBNMjdAbu3b8X69et1C4yXNfxd7BARnwJ/l4Ivpi6xc0KVzsNw5chmfNusB14LaZLrYp95zdSXG7OnTUJckgp71s5HulqLGr0n48ydCKTrsnymIrBQV7AsMrf/srrA55WwGMQlp8OpanM0aNkWp3asRs/KjbD5zH20reqFCp4OiE5MKXAf3aicG57GJqOi52tIffgnurYPMe0N5ECGOq7nj8peP6NixYrFdn1LpaDKmnieDSaNw6aPVmHD2pWY9sHHRRK7xjFrGcnveF21alXUbdAQZ78/irEj9EsRmcNiWdqzeOb1/lxcXLD0072YNnEsjq6ege7DJ6H94In47X4kLstkSIcGjnJrvF7XL8fzFIWyZhirHRalhCpNg9CXSYiIV2HfxTAMbBpUquvQ3BRKWUtNTcXAgQPx9ddfQ6vVwsXFBQBZadasWYNevXph3759sLa2LtD5PT09C1M8JheKwqc5Jxo3bozdu3cX2/UsjdR0Dcp7OOQaT5ATT2KUqN95AO6eOYrb32xC2+YN8DJRpXNjMYapOu4AVwWWz3sHvk62WL1wFtLVGri1HwNZYgoAIDwupVDnt2RKuzCRE/WDXHElLAYBrgqMfmchpvR9FVe/+xINeozG6TsR8HG2Q1SiqlDtq2GwKzwd5Oj9xTazPueQkBCzXdtSKIwbpECswzpt+nRU7zISDWtXN3m9shtk4Rk2eBBmz54NRylngLQUbrxQouPEJbDzCsZ3uz5B6O1/UH/YPDxMkMDWWobKXo65yhBFkfTL0Kon1tJVqTV4HJ2Eh1FJuPwopsyOkcVBoWpy0aJFOHr0KN555x2Eh4cjOjoa0dHReP78OWbMmIEjR45g8eLFpiorY2IMB7uOHTvC0bFsJv4oLkzh6lE/yBUBHk5oN2waHlw5j8R/L8PexgqXQ2Oy9WU3pf96gKsCqxbMxJJVH+HfM4cQe2YH0tJpYAh0ty/0+RnLQyRRcbazRpWqVdFvxFu4c3I3pMpoBLnZIyk1Hc62skK1L+FueeKf8DwvR2FqSnuSg/xgioWRu/QZDIWTKw59sQEPIhNN/mxZWSs8ffv2RWpqapaM3fxcTUd++5UAVwWexaWgy9AJWLPtAB7f+RtfLxyO1BcPYC2VwsNRnqsMURSWNUP5RbhDvvVKBdT0c4afix1QtkN9i5xC1eTevXsxfPhwrFy5MkOsk5eXF1asWIFhw4aVaUuKpWNoWTt58iTi4uLMXKLSjSlinRqVc0Pnmr6YMmow6jVujt0fL8H957GIVqZm68teFB333BlvY+y7i3Hv5314fu4ggDIci1hGEIP1W1Nmwk5hj39PfI5qPo6IiE+B1gQuacLd8kpYjIlKnD8uP4rRfco6plDWXiq1aN17FO6e+w7Xbt7Nc4xtXuGYtcITGBiIli1b4sCBA7ptpqh7Rk9+k6c421mjYw0fuNnboG/Pbrh65S94eXjgr42ToAg9izdbl89VhiiKMd+Y/PL8P28afxc7WgqFKTIKVZPh4eFo2rRptr83bdoUz58/L8wlmCIksxskz6aVDILcFbCXW2HpilV4cPc2zhw/CGiRrdXOVDFrmfl0+fuYs/ADPL1zDQDwNFZl0vMzloUYrG0UDug3bgbOfncEJ0//DxEJKjyOTiq0YFA/yDXPy1EUCdpMf5lCEeSuwIhRb0Lh6IL7P+3Bk2glHO2sTZbMgmPWTMOAAQPw448/IjraPBbt0k5+PWoME8UAwLN0B+w8egLd3hiIrz6ej7lvj8fdJy9zXBKjuDI0XwmLQXxKOu6/SCjS6zCFVNYCAgJw5syZbH8/e/YsAgICCnMJpggp7pg1xjQIoblr25Zo270Pvt2+Do8jIrMN4i/KddCWLpiHIRNnwtbeEammyVfEWDhB7go0aN8LgZVr4vsty6HWaExi5TD3mnUNy7miYbBrkc8QW7q7pSli1gDqp/q1qIRR4ybj95NHUNlehaD/3F1Ncf/sBmka+vTpA41Gg6NHj+q28XM1Hfn1qDHcX1jlJDIbfL1vBxZ/9CkOHPoKLVs0x/H/XcK5e5H46cYLo+teAkWfobl+kCvUag0C3RRmzyha2ilUTQ4fPhwHDx7EuHHjcOfOHajVamg0Gty5cwfjx4/HoUOHMGLECBMVlTE1PNiVfFYs+xCpKUpcPrZd11lmFgaLas0VwSv9xmLhwd/wIqn41sdizIuPiz26j52NFw9u4OGF7+HrbFviJ36Ka0mGkrCmlCld4V4bMBwOjo74ZtdmtKjkgdR0jUnun8cv0+Dj44M2bdroXCHZDdJyyGyV82nQHv2X7IJao8HOdwfjyplv8TAqKcv7VFzKWqNybhjfthLqBLiUyUzJxUmhanLOnDkYNmwYtmzZgho1asDW1hZyuRw1atTAZ599hmHDhmHOnDmmKitjYtiyVvLx9fdH7+Hj8f2+bUAiLWx+OfS/2JtQir0p6o67fpAr3Bxszee+xhQrYVFK2Mut0KldCNp364XvvlgDrarwbpBlhZKwppQpBfZqQV5o3280vjn0Jb777R+T3T/HrJmO/v3745dffsHLly8BsGXNUsg8gVQ/yBXlKlXBW2v2o26rDji1aR5+3f4hZNqMC6EXl7JmrIxM0VCompTJZNixYweuXLmCJUuWYMyYMRgzZgyWLl2Kq1evYvv27dyZWjCsrJV8wqKU6DLoLdg5umDEhLcx/eBVnL0bgfiUNOC/8baoYtYE5nZfY4qPJzFKvExQISk1HQ3LueKLzR8jMSEBq1at4r4kj1i6cGMqN0hBgKsCdTv1h5WNHWbN/wCXH8WYZN01jlkzHW+88QYA4OjRo2xZs2AalXNDwyA3+Hu4YsKCj/DukrX47YcjmDKoOy78dUPnTVOcyhpTPJgkyKRu3bqoW7euKU7FFCOsrJV8gtwVuPHMGo36TMDpLQvxa4NfUaFWQ/g626FhMFm6uONmTIWwqilsZCRsuyrw3nvvYcGCBXj27Jm5i8eYCFML7AGebqjeYSD+/nY7LlybgIj4QHSp7VsohY3dIE2Hh4cHfHx8EB4ejuRUNWKVqTmu3cmYEQkQn5yGlHQ1+gwejmq162H2pJEY1as95i5fj6ED++LKv2Qh5TG/9JBvZa1OnTr52l8ikeDatWv5vQxTDPDMZMknwFWB+kGuCOvcE9d+2If7325G02a7UMXHUTfQFnXMGlN2CHJX6BZFFcyYMQMLFiwAABbwSgmmVtY61PSGfNpUvPnzPlz4+gvUn7MEl0NjdG2pIG2GlTXTo9VqoUxNh0YrQVgUv8uWiL+LHX659QJ2NjKcvh2BIJ+KmLDuEE5sWog5E0fg2p+/o/vAUQB4zC9N5FtZc3Nzy1MH+fz5c9y5c4c7UwuGff5LB6npGrSp6oPkd+ZjzdTBeHL5Z/i3ekv3O1vWGFMR4JpVsFYoFFi/+yiuXv6DBbxSQFGM2QGuCgx6pTouT5yMjetWY/TEabDz8NIlRiiossZ9mumxs5ZBKYVFx1SWZVLTNfBytMXTWCVUag2exCpR3ccVqzbvwJ/f78U7M2bg1E8/AuAxvzSRb2Utp1T9AClpK1aswGeffQaZTIahQ4cWtGxMEcNukKUDYe0Y8HpnnDzQHr98+TGqNW+Hp7H+aBjsCkkRx6wxZZMnMUqdZaRXt45o2KwVC3ilDFP3GR36jsCnn6zHj/u3YuWqVVmstPmBPUNMi3iWdjYyuChseNLFQglyV6CWvzNsrKVQpWsg0QIyqQTBHvZoOXUqntv4Y9P8yQCAiw9jUJO9HUoFJpPUX7x4gWnTpqFixYr45JNPMGDAANy+fRtffPGFqS7BmBhW1koHImFBo3JumDhrAZJiIvHLgS8QFkXCNFvWmKKgJKSgZwpGUSWZqFXBD/2Gv4Wje7cDSdGFSrTCbpCmR9Q7P1fLJcBVgR71/TGwSRBq+zmjVoCzbgHtY1eewqtSbby+aBca9pkAjWcV7p9LCYWW3p4/f45p06ahQoUK+OSTT9C/f3+dklaxYkVTlJEpInhmsvTR/ZUG6DHkTVz5dgfuPniAiw+jcPJ6OABW1hjTYpiCnRW30kVRKWsBrgp8/OF8ODk6YtrMdwu1ODYra6ZFPEvOBlkyEEpbj3r+CHBV4PKjGJy7H4kX8Slwd3NH+wFvwd5Bwd4OpYQCS2/Pnz/H22+/ncGSdufOHXzxxReoUKGCKcvIFBFpaWmwtrY2dzEYEyAWwgaAnRtWwNHJBSe3rca9F4m4/SwOABCVlGrOIjKlDMMU9CVh7TAmbxSVAiT6qHi1FRYvXozD+/fi7PmL2H8pDMeuPs230sYxa6aHLWsllwcvExEWlYQnMcnwcbaDTCJB26pe7AJZSsh3TxceHo6pU6eiQoUK2LRpEwYOHIg7d+5g27ZtKF++fFGUkSkilEol7O3tzV0MxgQYWjYcHR0xYtpc3Lv4M17c/gNhMUkAgHsRSWYuJVNasfS1w5j8URRCu2EfNXr0aFSpVh1b1yzCP09jcfL6c1x+FJOv87FniGlhy1rJJl2thbPCGnbWUsikEtQOdEFqusbcxWJMRL4TjFSsWBEqlQr16tXDnDlzUL58ecTExCAmJvuOtkGDBoUqJFM0JCUlQaFg4ao0kDml+rRxo/DDoS9xbvdqvDZ9FQCeLWVMj2GSEVbUSg9FIbAb9lHPE1IxduYCvDN6AHwvnUHlpu2AfF6S3SBNDytqJZe21bwQq0xFoJsCvs528HSUs6dDKSLfylpKSgoA4MqVK+jXr1+O+4rOVK1WF6x0TJGiVCpZWSslZE6pLpFI0Gfy+1jyZg9cOr4HAFDNz9lcxWNKKYbWElbWSgdFpQAZ9lEX7keiTrMQVGnYCjePbULXrp3RsJxrvs7HypppMXyW/FxLHo3KucHH2ZYnz0op+VbWtm/fXhTlYMwAK2ull7AoJfwqVEPVkF64/ssRAICPM9c1Y1qMLZLNlB6KSmgX7WbpitUY1OUVvDx3EGg9DxfuR+ZZ0OSYNdOj1WrZulaCMbYOJlM6yLeyNnz48KIoB2MGOGat9BLkrsCNZ3EYPfU9LPjzFJTxsXiZoDJ3sZhSBgsHpZOiFth17aaSB06MnYwN61YjtXxLtG5UW/d7bnDMmmlhy1rJg93Qyw48LVVG0Wg0SElJYctaKSXAVYEutX1ROdAXnUdMAwBEp/CMKcMwOSORSIrVutLg9ZGwd/XC8c1L8DgqCS8TVXnKDMlukKaHrWolC14ypezAyloZJTk5GQBYWSvFBLgq4OkoR5+BwzB11Rfo8EozcxeJYRgmA00r+6LPxHl4/M/vuPbrD3gWm4wT/4TnqrCxsmZaOBtkyYOXTCk75NsNkikdKJU0ELKyVroRnXjr0f3ZTYJhmDxRnOttNSrnhm3zx+Hm2WM4tnkZUrxqon3dCrkmreGYNdPD66yVLNgNvezAPV0ZJSmJ1txiZa10w+tfMQyTX4rKuiIWxs5sNXsSo0T7Ue8hNUWJm0c/weNoJWyschZP2LJmWtiyxjCWCytrZRRhWfs3OjVP8QEMwzBM6acoFaDsYmzCopSoVbk8Oo6agVtnv8HzW5dw5k4Etp9/iGNXnxodozjBiOlhRY1hLBNW1sooQlmDlS0HpzIMwzA6isodLrsYmyB3BQLdFBg9egxqNW6Jw+vmIy4+ATefxSMsSml0jGLLmmnhbJAMY7mwslZGEcqai5MDB6cyDMMwOorKwpKdW7bY3qicGxas+BipSXE4vXsdYpNVSNdojY5RHLNmenidNYaxTLinK6MIZa1VdX+OZ2IYhmEAmNeqEuCqQLN61TFuxjycP74XcQ/+wfWnsfjnSVyWWDe2rJkWtqwxjOXCyloZhbNBMgzDMJbG5UcxcGv0Gvyq1sOJzQthL1Pj3L2XWWLdOGbN9LBljWEsE1bWyiiPI6IBANEqMxeEYRiGsSjMmsJdC8Sr1Og3bSlS4yLx1+FP4OEoR1JqegZ3SLasmRZ+lgxjubCyVkZ5+jIOEokELxLV5i4KwzAMYyFIJBKzWlcalnNFq0oeqFOrOoZNnYPz3+xB6qNrgJayRgpXSI5ZKzpYcWMYy4J7ujJKemoKbGztILeWmbsoDMMwDAOA4tZ61PdHVW9H9B06BnWbtsaaeW8jMSEWylQ1LofG4ML9SKSp2Q3S1LAbJMNYJqyslVEiYuJhI7fD09hkcxeFYRiGsSAsQWAPclfAwdYan2/dhmRlEtYsfA+PY5SABFCmqpGals7KmgnhBCMMY7mwslZGUSUnQ2Zji7vPE3hRbIZhGEaHWWPW/kOk8/f190e7Ue/hyulvsWvPPtjIpFDYyCCTSlipMDGWoKQzDJMVVtbKKM42Gtgp7BDoruBFsRmGYRgAlmdVCYtSonOPPvCr3xa/7VqJExdvIMhdAZkEHLNmQkS9s8LGMJYH93RlFJkmDZ6uTghyVfCi2AzDMIxFEuSuQN1AV8z7cA0kMit8s3Eh/gyNRkqaGteexOLP0GhzF7HUYAkWVYZhssLKWhklKSkJLo4OaFHJgxfFZhiGYXRYktAu3CHrVgpE90mL8OjaeRzZsx0pqelITdfiSliMuYtYKmDLGsNYLlbmLgBT/DyJUeJxRCyk1nJzF4VhGIaxIMyduj87gtwVaPrKqwi72h8HPvkQVjIZ5NYy1A9yNXfRSg2WWO8Mw7BlrUwSFqWEUqkErFhZYxiGYSyfAFcFBjQJwrLlK+EfEARlUhIqezuhUTk3cxetVMDZIBnGcmFlrQwS5K5AmioZ7i6O5i4KwzAMY2FYqoUlwFWBKgEeaD9hKaRW1ngcl2buIpUqeJ01hrFM2A2yDBLgqoCVNg0eLk7mLgrDMAxjQRi6QVqihSUsSgkrz/LoNHsr6taubO7ilBrUGuBZbDJS0tSwt8B6Z5iyDCtrZZAnMUpExSZALbU2d1EYhmEYJs8EuSvQuJwbyns2QduqXuYuTqkhVa1BWroGKWlqcxeFYZhMsBtkGSQsSolEpRLPEjW8IDbDMAxTYngel4J0jQZtq3pxvJoJsbGSwkomgdyKxUKGsTT4rSyDBLkrkKJMgqODPS+IzTAMw2TAkt0gr4TFIC45nVP2mxhrmRS+zrawtZZZZL0zTFmmTChrKpUK7777Lvz8/GBnZ4emTZvip59+yvPxBw4cQPPmzWFvbw8XFxe0aNECv/zySxGWuGgJcFVAnaaCj5szL4jNMAzD6LDU1P2C+kGucLaz4pT9JobXWWMYy6VMxKyNGDEChw8fxttvv43KlStjx44d6Nq1K06fPo1WrVrleOzChQuxePFi9OnTByNGjEBaWhquX7+Op0+fFlPpTY9Wq0WyUomawV68IDbDMAxTYmhUzo3dH4sIS7aoMkxZptQra5cuXcL+/fuxatUqzJgxAwAwbNgw1KpVC7NmzcKFCxeyPfb333/H4sWLsWbNGkybNq24ilzkpKSkAAAUClbUGIZhmIywdaXswQoaw1gupd4N8vDhw5DJZHjrrbd022xtbTF69Gj89ttvePz4cbbHrlu3Dj4+Ppg6dSq0Wi0SExOLo8hFjlJJcWqsrDEMwzCGWHrqfqbo4HXWGMYyKfXK2pUrV1ClShU4OWVcU6xJkyYAgKtXr2Z77KlTp9C4cWOsX78enp6ecHR0hK+vLzZu3FiURS5yWFljGIZhzMmTGCUu3I/kjMQWgqFizko6w1gWpd4NMjw8HL6+vlm2i23Pnj0zelxMTAwiIyNx/vx5/PLLL1iwYAGCgoKwfft2TJ48GdbW1hg7dqzRY1UqFVQqle57XFwcACA+Pr6wt2MSIiIiANAsmqWUiWEYhjE/arUaajWttZWQkFBkY8St0Cgo09RISkiAk8y9SK7B5B21Wg2VSoX09HSkpaWxbMAwRYx4x/JkzdaWcipUqKDt0qVLlu3//vuvFoD2o48+MnpcWFiYFoAWgHb//v267Wq1WlujRg1tQEBAttdcsGCB7lj+8Ic//OEPf/jDH/7whz/8yfx5/PhxrrpMqbes2dnZZbByCUSSDTs7u2yPAwBra2v06dNHt10qlaJ///5YsGABwsLCEBQUlOXY2bNnY/r06brvGo0G0dHRcHd3N4l7QXx8PAIDA/H48eMs7p2M+eH6sVy4biwbrh/LhuvHcuG6sWy4fiwbc9SPVqtFQkIC/Pz8ct231Ctrvr6+RtPsh4eHA0C2D8nNzQ22trZwcXGBTCbL8JuXlxcAcpU0pqzJ5XLI5fIM21xcXApS/BxxcnLil96C4fqxXLhuLBuuH8uG68dy4bqxbLh+LJvirh9nZ+c87VfqE4zUq1cPd+/ezeJ/ffHiRd3vxpBKpahXrx5evnyJ1NTUDL+JODdPT0/TF5hhGIZhGIZhGAZlQFnr06cP1Go1tmzZotumUqmwfft2NG3aFIGBgQCAsLAw3L59O8Ox/fv3h1qtxs6dO3XbUlJSsGfPHtSoUSNPpkuGYRiGYRiGYZiCUOrdIJs2bYq+ffti9uzZiIiIQKVKlbBz506EhoZi27Ztuv2GDRuGs2fPZsjKMnbsWGzduhUTJ07E3bt3ERQUhN27d+PRo0c4fvy4OW4HALlZLliwIIurJWMZcP1YLlw3lg3Xj2XD9WO5cN1YNlw/lo2l149Eqy39KyCmpKTg/fffx5dffomYmBjUqVMHH3zwATp16qTbJyQkJIuyBlCa+1mzZuH48eNISkpCvXr1sGjRogzHMgzDMAzDMAzDmJoyoawxDMMwDMMwDMOUNEp9zBrDMAzDMAzDMExJhJU1hmEYhmEYhmEYC4SVNYZhGIZhGIZhGAuElTWGYRiGYRiGYRgLhJU1hmEYhmEYhmEYC4SVNYZhGIZhGIZhGAuElTWGYRiGYRiGYRgLhJU1hmEYhmEYhmEYC4SVNYZhGIZhGIZhGAuElTWGYRiGYRiGYRgLhJU1hmEYhmEYhmEYC4SVNYZhGIZhGIZhGAuElTWGYRiGYRiGYRgLhJU1hmEYhmEYhmEYC4SVNYZhGIZhGIZhGAuElTWGYRiGYRiGYRgLhJU1hmEYhmEYhmEYC4SVNYZhGIZhGIZhGAuElTWGYRiGYRiGYRgLhJU1hmEYhmEYhmEYC4SVNYZhGIZhGIZhGAuElTWGYRiGYRiGYRgLhJU1hmEYhmEYhmEYC8TK3AUoC2g0Gjx79gyOjo6QSCTmLg7DMAzDMAzDMGZCq9UiISEBfn5+kEpztp2xslYMPHv2DIGBgeYuBsMwDMMwDMMwFsLjx48REBCQ4z6srBUDjo6OAKhCnJyczFwahmEYhmEYhmHMRXx8PAIDA3U6Qk6UemUtMTERq1atwsWLF3Hp0iXExMRg+/btGDFiRJ6Oj42NxaxZs3D06FEolUo0adIEa9asQYMGDfJcBuH66OTkxMoawzAMwzAMwzB5Co8q9QlGIiMjsXjxYty6dQt169bN17EajQbdunXD3r17MWnSJKxcuRIREREICQnBvXv3iqjEDMMwDMMwDMMwZcCy5uvri/DwcPj4+ODPP/9E48aN83zs4cOHceHCBRw6dAh9+vQBAPTr1w9VqlTBggULsHfv3qIqNsMwDMMwDMMwZZxSb1mTy+Xw8fEp0LGHDx+Gt7c3evfurdvm6emJfv364dixY1CpVKYqJsMwDMMwDMMwTAZKvWWtMFy5cgUNGjTIklKzSZMm2LJlC+7evYvatWtnOU6lUmVQ5OLj44u8rAXi22+B0aMBjQaQSICPPwZeew3Yvh1YtChv55BIAK1W/78hs2cD06frv6ekAA0bAi9e6LfVrw989BFQvjx9/+IL/bUlEtN8xLmsrYGKFYGaNYFatehv+fKATGb8vmxsAKtS9Iqo1cbvtbCkpwMdOgBNmwJLlmT/zBYvBjZs0LeXli2B996jNmBjA2ROXTtlCrBvn74epVIqf9u2wNSpQKNGpr+XgnDkCDB+PN2XVKr/yGQZv0ul9Gz8/IBKlehTuTL9rVABsLUteBlCQ4FRo4Du3YHhwwF3d/1vU6YAX30FBAXRO96kSaFvOQuPHwOtWwNJSXSfU6YAc+Zk7RNyY+1aYPlyek4yGf11cAA++ADo2VO/X1wc3Ud8PBAYSMfVqUN9DEDvuo2N/m9e2bqV+i2JRF9/hvUIZN0mk1H9dexI70GlSvm/bwBQqah/fPmS7lt8rK2pn6pdmz61agHVq2dtL9HR9C4K5HI6XioF7OzyV5bISHqffXyAHTuonZY0Xn8dUCqBwYNpXHNxyb5vUquBZs2A8HBAoaDn26sXHWf4LmXm9Glg0CA6Hsj4rhv2W4Z/AarPceOAHj1yH2Oio6ku4uKorRm2TamU2vegQTTW5iFRgUXzyy/A0KF0H61a0fvUrh3g6Vnwc546BQwZQnJO5nfa8K+XF9VL5o+fX9axqSTw0UfAkyfAG29Q2y6qe4iOBl55hfrAYcOAbt1y72/atAHu3qU+ytaW+vA6dehTuzZQo0bhxkNjpKUBYWE0VlatCuSSfdHSKEWSqOkJDw/HK6+8kmW7r68vAErJb0xZW7ZsGRblVdkxJ//8AyQmApMmAZ98Apw7B9StC2zZQi/b0KH6fdPS6GMohGi11MlpNPRdIqGXz9GRlK5LlzJeLyoKuHmThK5y5eh8X38NNG8OLFwIdO4M/PgjXbt3b71Qr9Vm/eRnu9iWmgo8fAj8+isQE0Pb7eyAevVISGrUiO7f2pp+s7UFnJ0BJ6f8CXyWSHIyEBEBuLmZfkBPTATOnKHP//4H7N1L9ZuZ8+fp+l26UN0fPw60aEGD8ZQp1A5sben5W1vT+apUISE4PZ3aV2oqcOAA8OWXdOzUqdRWzKlUX71Kz3fkSGpnGg19tFoS4sT/AH1//hw4exbYuZOOA+jeAgJoMqFSJaBaNRLKa9QA/P0zvneGEySC338nwfHcOVKS+vUjYbB5c+DkSaqP1FR6ZosWkZJsSsU9NBR49IgURqUSmDeP6mzBgvyd588/STkbNoz6C5UKuH+f6njZMmDWLLr/R49osB87lp5/69akpM6eTe07IYGerUJBgoB4p/NyfZmMhN/UVL0CrlDQfQllSKWiNizq+N49aovp6XS97t2pDlq3zvtzDg8HbtwgJcPfn86rVtM5X74EDh0CVq2ifWUyUqCE8lajBn03FGpjY+kerK1J6XJ2zmstUFkePKCPGBOGDMn78ZbA8eP099Qp6uc7dgT696f+x8kpo/AaE0N1378/vYe//UZtWSolQbR3bxoPMz/Dy5dJiZo9W/+8xcSgUAQA/fur0dB+Fy8CffqQIjBrFrXjzMJpairwzjvUZ96/D8yYQe0wNpbOK5TxiAh6NzZupPduwoS8t3dTkN1kbUG4coXe+169qD/bto22169PiluHDlQf+RmPr1yhZzZvnr4/1mj0/4t37Plz4M4d4IcfMk4o29gAwcHUh5YvT31DmzaFv9eiZvNmajdr11I769WLFLfWrU07Xj5+TP3WjRv0zjk4UP/XpQu9c5k921JSSE544w1SmlJSqJ85dozKCtC7ExxMz7tcOfr4+gKurvQREy9iAkStpj4/Lo4m8F68oHI9fkwKq/grZFV3dxo/3NxM9xyKGFbWciA5ORlyuTzLdtv/OtVkIWhlYvbs2ZhuYFES6TktDrWaXqyhQ4E9e/TCwY0b1EmOHEn7paWRkmNjk7WTTE/Xz/ap1fSyODuTMJqURL9bWZHgFBVFx3TvDrz6Ku0/aRLw7rs0EP31FwklderQNoExRQzIaDkT3w3/ZodWS9e5fRv4+29SKnftosHOxYUGhK5daTbq+XMayF1d6b6KcxA0Ja+/Th3V4sU0a+zqaprBFdALsKNGkbJdrx5ZKP6L89SRlETXnjiRhN1Jk2j/jz8mBb5NG7JQ1alDbSYykhS5oUPpWJmMBJpp04CffgI++4yEKz8/Eu7HjCFlp7hJSaFOf/JkEqayQwgFhgLCixc02xcWRgrI48c0mbBrFwlrAAmWwgonPpUqAd7e9LtEAty6Re/m+fNk6du7F9i9m4T5R4+At94ihXjRImD+fHruBw5kHUgLimgDAwbQIOvjQxMwTk5UX3klLY3ub84c6nNSUuh93bWLFMybN0kBfPyY9n/7bdp/0yb6/ccfqV20bk2Dc0IC9Tve3nlr76mp1J6mTycBz8WFzmFjQwK/RkP3lphIfYhKpZ/8iIgg4f3PP8lrYfNmum7fvsCbb1K7zomkJPor3gUhvMfE0DN1cCBB5MYNmmgTn48/ptltgNpFmzZkta5Zk9qjVErKl0RC9ZEXRB/7yy80qTB0KPDHH8Dq1ZbTB168SErV2LFZZ/KFpWvrVuoTjh0jQfLYMXoGbdvSc+7alQQ38fzGj9cL4uHhwDffAIcPkyI+cyYdM3o09UtSKR3n7U0K18OH9Lw1GmoX4p0QQqWYhEpKom1Xr9Kk0zvvkBK+YAH1oUK5v3ePxiTBBx/QfT18qB9z3dyoL3//fXq3p08nz5itWwvuefDjjzT+e3qSQqhW00RFfDx9EhL0/4vvajXdu709vTMBAfQpV46U/Xr16P/c3sHERKoPcd9PnwI//0yfXbuAlSvpfevUiayeXbsCHh65n9PNDZg7N+/PICmJJqAePqSP+P/oUVKATp3KuL94RpZk2UxPp7bati0pvseO0aS8pye14zfeoHZc2PdZjFM//kh1f/w49X/799P2evWA9u2pHK1akYwI0HjduXPGcyUmAtevk1x2/z4982vXaFJfyI95QS6n9ufnR38bNKB+28+PyhgdbTn9WB5hZS0H7OzsjMalpfznamOXjalXLpcbVfIsDmEps7WlwUSrpZdDq6WXSiCTUcOOi8vqjmA4QyOT6YUbrZaEjNBQOjYlRS9gOTlRZ2hjQ53cypVA48bA0qW0X9++esuXmNk2VMTE/5mVOEFmq4NWm3WQsLamQaRhQ+o0pFISeH/4gT6HDtG99u1Ls6oqFd2/uzt1yKaySpw+TQP95s1F69Z3/Topnteu0aDfuzcNcqmpdC+FsRwKoaRePRocli6l5/bWW+SKIRSYpCTqONPS6Bmq1dSJd+hAHf2GDSTst2tHik98PLVNlYoEEoDaR2QkzbR++ikp3AcP0uC+fDm12xEjyKphbOBMT6fjhYuctbXe3aygpKbS+VQq+hi6sBnOrstkWduNiwvNLgqEZS41lRS4O3dIaLt/nwawr7+mawB0f0Jxe/KEBBIXF5pkGTqUlL49e+hcbm70Tr33Hj3zgQOpvR05Yhq3SEM3MJmM6i8piYRHe3tqC3lBtKXUVDqX6E9Gj6bnNHUqKSv9++ufn7he06ak5L3+OtW/cMOMjqY2mBdFRdSlQkF1ERdHbS8ujupFoaB6dXEhBSE6mp6rRELCQNeuJOy//TbN3J46RcL+xo00+fPWW1R2Y0q9Ukl/vb2pnQtPBUA/I+zkRNbS5s31x2m1NDP944804XXxot4iERBA9duqFdV37do06ZTXiZrERHrPmjShZ3/rFrnUWoJQum0b8Pnn1MesW0cCqLgvofgqFOSeNXYstaEHD0gBPXmS3hMbG1Jsq1Sh/VUq6ietrakt9OxJroo3b5IAeuwYCaFBQTRu3L1L7UOMRcJ9XvSnWq1+giY9nSYtNRp6J5o1o35s1Ci6j7feookG0VbEGLhyJbU5W1t6FwBqB2lpNEGQmEhj1eefk7I5Zgy9C/PnkxKXX/e3PXvoXj089BY8e3tqN87OdC1HR73XiaMj7adU0icqipSsu3eB77+nSQ2AnlObNtS/t2tH1uDM7TAhIWPb8vMji+6gQfTc/v4b+O47Kt/w4XTdZs3ovevWjfoIQ/kAoHfU3p6ek8DQRVX0WYZ9s709TXbUrJmxfJMn05idmS+/pPpbtozefUtwmxQT6c2bUzubMoXGkjNn6Bl+/jn1Y6+/Topbx44Fcz0U41F8PNVX//5UX3FxNJly7hw9n9Wr6blUqkT7u7rqZVDxsbenttusWVaZLj2dJtBiYuiTlqaf9NRq6VhbW3r3xCSD4bgkxnpra711ugTByloOiEySmRHb/Pz8irtIpsVQIBAK2bVrJNiJFwrQC03JycYVH0OkUurEHR3pZZVKMwpAAP2fnk6DgXihRo8GQkJImOzQgQbDzD7/QMa/xpS1nJQ3Q9dItZpedpWKyqdSkfvSW2+R5efff0nI2ruXZu1feYUG94YNaYByd6fOoSDWKeGSKZcDFy7QTHyrVqSwCWumqVGrKXbjyRNykzl7lmZyRacmBl07u/zfkxCwra3JVWHNGupwly+njnr/fhISk5L0M+A2NhQjkJhIHWeXLjSAC8tIr160n7d3RtcjhSKjoNu8OV1rwQIagL76ioSVqVPpnH370oxeUhJZUBs00A8uwo1XKG1OTtTZC0Urr89BtG9XV7o/pZLeLTG7bijIGQoImeMqBWIf4XbTubN+P7Wa6vDePfrcvUuC5L17NMAZPpvu3emTkED3KYSmwECyTI4ZQxaoTz8tfLsTg6K9Pd2/kxNNQiiV5I5pb0/tLy/PUqOhwVgup+cqJnZataL6HT6cFDEgY9vw96cYx59+ovr/6SeyhnTvTvcul+uVn5yuLwZ1Ly/6npBA13n+nKxbArmcFDRHRypvfDwdJ4TNOnVICRg3jt7zgwepn5s+nSxtkyZRPycQgrinJ137+XP9JIJQ1owhkdC9v/46CbaOjiSg/vorvU8//URKOUDladuWhNu2bbNXukSfGRdH7W3wYHLN7dmT3tPvvqMymhO1mhQxPz+afOrYkSZ8qlTRK2t2dvrxSyaj7xUqUD//8iUpbidOkOAqkVD9xsToxyVra3r27u5Ub+PHUz/y7bfUvymV5CUi+oDYWKoz0dYkEuoL0tKoPMJFUqWiv3I5CdIrVpCQu3w59WmjRpESCdDEi7CAK5XUR8XE0L24utK9Pn5M43adOlS+Dz8kS9vFi6R8icmuvKDVkiJ18CB9F/2Y6CtFzJy9vd7dU0xKiX5A9GFaLfDsGSlZly+T+9uMGfS8fH31StYrr9B9RUfTfT15QtdUq/X9p0ZD7XXAAHpWL1/SOHb6NMVKz5tH71NICLXt+vWpfOHhVDePHmW9V0NlTdSboSu+jU3G/tnbm7whMstBYWFU1nfeoXdj507zx0Slp1P5RZtxdNT3SRMmUJs5dYosYbt2Udvv1o28VDp3zrsyIyxr1tZ0DSsrehZyOfW9nTtT+3/8mKzzf/xB17KyIuOAsfwCAmMT71IptXXhzipyLgD6tii8cIxNqhvKjSUIVtZyoF69evj111+h0WgyJBm5ePEiFAoFqojZuJKK6IDj4/UJHi5fJoE2swApOuboaPpNoaCXQQgRmV9sOzvq5A1nasSApdXS746OGYVLX18S7osbobilpVHHk5REyur06TSTdvYsuZWMHEmD2PDh5Ibh7U2dRn6tUn360AC2eDENJBUq0IA/ahQNtOvW5S5UFuQeAwNJqdm9m9xuLl6k761bk7UpOpo6UWdnqu+8Wg+FsubsTAODiHesW5eU78aNyRfdUFkTHaqYnU1LI6GmXz8Sun7+maxIDRuSwKhW6xN0iIFVKFRCGBs8mD4PH5KgcewYKdxeXjQxcPMm7Vu7Nj3vNm3of4Dq/elT/fllMmqbdnbUhnOqD5VK7yYnfOrFTKAQODLHR4jZdvEBMk4qGG7LPNng6Un306xZxvoFslqkraz0M/3CRUsk4di2jYTEUaPI4rB4ccFdY8X1ra31ligHB5rdVyrpnbG3z5gkxBiiT4qOpv7B15fOHR9PxwcHk9V77FgSmkT/YugaPWgQtaFp00ipO3aM2rmtLZ0vJyEkLU0vkMrl9I4/e0b3YEzZEwK+QqF3rxGJIOzsaHtaGgmPLVvS+374MMWArV1Lz2PaNIolFAqGoyP1K6mpdD4rK/pra5v7zLeoPzc3sgj16EH1fe0a9WN//EGufZ99RmVs0oTehVdfJSUh8/0JV6GnT8nKcOoUuZ+1akWKjjkFUo2G3oVNm+jeVqyg+L1p08hyDOj7G2FZdXWlScfERHquffuScKpUUt0YxtqKyUzxzghFrFUrUgRmziQrRf36esXO35/6g6QkageiH/Dy0reF5GT9OCPCBBQKah+7d1N7/egjivsGMipaGg3Vkaur3qrr5ETXiYykc3p5UXtv1ozehVdeIaX9v1j7fGHY5jUaKr/4+/Jlxnh1qTTjpIJ4zzQaUhCqVqXyJCeTBfjcObJwbttG9dSyJSlpjo56mUQohsYmbR0daewcOZLOee4cjRsnT5LyIdxdHzygccaYwmoYv5aeTvUs3O2EwubgQOWTy2lMi4ykSZFPP9VPdiQmUtvZsoXaU+3a9LvwADAHajX1Fx4e+klRmUw/4eTvT+P02LFkof3hB3Lz7NaN5J/Jk2lsMJygMoZQ1tzd9XG9hmO1kPXc3cn7ZswY/RhnLO9AdoqUYRvIPOlpiDi3Wk1lMxx/hZJtZ1fikseVrNIWIeHh4YiLi0PFihVh/d8A1adPHxw+fBhHjhzRrbMWGRmJQ4cO4bXXXisZro45IQRgGxt6qdVqUtaMzYBbW+tjZJRK6rCkUuoExAyara1+cLS11QuFAiHUa7V0vvxmKCsqDGdiAOpUhPIQH0+K2auv0uzgtm0UT/fJJzQ7/sYbNAhmDljPib//pgGkc2cSqsqXB9avJ8Fp4kSKQ/n669x98fODqOvAQJrdb9SIhPNRo8ht8L336P6TkvSCsa9v3pRGw9gMHx8SouVyusbevaQYTpxI+4g6N3xWhq5DDg4k9PbuTc9duBcKpVgIOioVtT2tlraL+pNI6Hm++y5Zdq5eJavCsWM0I9uwIQlY27aRsOzsTHXbrRspb9bW+o49JkbvMunmln3Mopglzxw/KQb7vJCThTinxDmZz2E4UAm3q5QUvYudeEcVCto+cyZZJpYsIUFpy5aC+fKLNiCTkVAkl5MA4OBAs/zJySS4fPstWc6zIy0t40SQVErnS0jQC2hSKSnjIvbBEFEHHh4k+PbvT5at3r3JkjVrVs6Z5YRlTZzHwYHa9NOnVLbs3nGplMpmb0/vUGwsPfOEBDqfrS2dy8mJLAujR9OM9t69euFfTByIe/Tw0LvNpaRQGfz8jPebOSV5sLUlIdPDg2a67e3JlfTaNbLqb95M9W9nRxM3r75KE3YAtSPhMvzyJT27X37RW8LPnKEymQND4atbNxLMN22iGL5PP6V9xGSgqDdh+RQKcUIC1ZVEQn1jQgK1XamUnr2Njd5tWyhICQl6xbljR701HaC+S1iYxHuXkED1bm+vL7uYIExNpXacmEj34+xM429ICE3aPX5s3EvE3Z3ak1DYrKz0VrYnT6iu27cnBaZDB6rXs2dJQM/Lc5VI6LkIbwMhHGc3HmT2HjDso4R8IHB2pvrq1o2237tHyuSPP5Lbd8+etI+hQC/cSDM/C8O+sWVL+sybR+37zBmyut28SRY8MZGVGcOskMLlUyaj66Wm0hggZJa6dckKO306TawePEjvblIS1Ue7djR+jxtH483Bg2TtNcc7Iia+vLyovSQn09ielKTv5+Ry2s/eniY4xowhV+etW8lKuGQJyQbjx2cvrxnGVovEcSqVfqxOScloIRV9lKHiBWSfcyBzQq3M46BhAq/MFjapVD/ZKuQE4QrJMWuWx8aNGxEbG4tnz54BAI4fP44nT54AACZPngxnZ2fMnj0bO3fuxMOHD1Huv9m1Pn36oFmzZhg5ciRu3rwJDw8PbNq0CWq1umRke8wNkcrdxYUa87Nn1NGLgdoQw+B04fObkkKDgqMjvZixsfqZYBGnZoiwrDk75z3g31wYzqoJpa1hQ+qsHz+mDvj990m4ffNNmk3z9s6bz3dKCilJfn6kMHh7k4vG66/T7GOfPjQr+v33+liKwiLq2taWBmwrK1JYjhwhZerHH+lvmzb6eMOYGOroc1NCDd0g3d3pOuHhdB5vb4pha9qU/PkrVMhd6HVyoueekEADpbDWiMFCDNxiJlTEE4mZQxFXJJHQQFq/fkar0aBBdOyVKzSY//ADPQeFggbbzp1JCBMudikp5JIWH0+WhMyWVOECVRgf+LwmxykI6ek0eIokAYbPyd2dBmg3NxJynj0jy09+Y5KEZU0Mkh4eVP8REXSulSspZqJHD5r5bt3a+HnS0qjuDAU/hYLqIiaG+ioXF72bTUoKtYvslJXu3cmCPHs2uecKlzfDmC9DhHuyIU5O1L6eP8/ZHRHQK20ODnSu5GRqx0JoF65W/v40oz1sGCk/u3bRB9A/exsbUo4eP6ZtiYlUP/7+WfuZ3Nx6bG2pv3n2jIS12rXJStmvH9XVgwdkNTt1iqyRr71Gxzk7U5tRKOgdFK6e33xD+7RtS0JxQaw2hUUo8y4u+knDd9+lNrZ6Nb3bwlXT2LsprEbOznR8dDS1XSFg2tvT+y5SvtvY0Lb4eLK+KJX0XaMhRdYwFlC4Cdrb07uV+fqGE4SOjvoxJjaW3oHAQOqPk5NpbHBxyWrhEG649vZ0feEaaWNDE2YpKTRxde4cWdc6dSI3xNwy4In79fLSWxTF9YTCJDwJDNtdcnLGGCTAeH9maBkBqB2OHUsKQVQUnUPEFBlb9kCcw/A3wzqWSMhS3aoVWfbFpJHozw2VSTGxJRRntZr6StGfiVhBEbqRkkJj81dfkTLTsiUpNomJ+vpxdSXX/zfeoD6venWy+r71VvHGSQmrrUSScZJC9EtxcdSGhQVOKqU26OtLY/Z779Ek8qxZ1Hd++CFZ4jLfgwgrEJNSok0D+rFaWLcMPUwMPUuMTURmnpTMbFHLbokcQ1dIYZUtBZQJZW316tV4ZOCvfOTIERz5z4d/yJAhcM4mpbFMJsP333+PmTNnYv369UhOTkbjxo2xY8cOVDVMClBSES4/Tk70Ml+8SNsbNsz5OFtbGiTkcr0ALQKQDYWSuDjqfEW8gFDW7O1zzppnSQiTuZ2dXmCUSmkgnTKFOrP580kAnDCBXDI8PHJ2IUxOpmcwdizNZonZnogI6ihPniSFolkzsrAZWT4i3xiusSYsYFIpWR6aN6fOedAgEt4WLKA2ERVF++Zm4RP1Ks7v4kLP7flzfcB4nz4005tX10oR+6hQkHBpKEALa7Bw7XN11WfjErPUIuBYzJ5l9oO3siJXj6pVSVkR/vs//EAWGBsbmt3u0oVmpl1dqe7j4uh5GJ5PuEFa6qAgXEaF4Ciek0gE5OBAs8Du7hQc3749tUEXl7xfwzCQG6BnIdpNRAS1p7Vr6R3p1o0EfGOTQqJPSknJ+EydnanM4nexZIhwu808a2uInx9NFHTponf7nT6dYnoyzxanpRmPRRVjRF4D8IUbpVAGhAKQmKhPxCD26dqVlMq//iJlylAoF4qfSkX1ERtL75WfX8ZJg5wsawI7O1L0wsOp7l1c6N0KDyehfsYM+ohlHgB6zr6+VIdqNd1LcjLVweHDZLEUFjbheVFcCAuQsIpFRpKwWakSWQujovT1ldNzEZNBTk60n7BIa7VZFXehHIoU+tHR+sQz2bW/3Pq8zGNMXBz1NcLSJpIiRUZmdcUVk6gKBR0XHU3tTCxfkZpKStePP5Ly8vrrNDmQF9d9JycaJ4R1PjGRric8MCIjM96b6IszC+ACQyFcCO6G8eMajd4C7eamz2SaWbkzFuebE/mJrRQeCSIsQqnUKzaiPYj47l27KMPk4MF03w0a6MdZiYTG0g4dSNkZP56SbHz6KbnqFgfZrasqxk4nJ731NzaW7lO0RTFGzpxJLuwff0weOJ9+SglwDGVEw5i1zIixmik0xajmm4/Q0FBotVqjH2FF27FjR4bvAldXV2zduhWRkZFISkrCmTNn0MhSFuMtLCJDjlyuD8R1dibrR05IJPpsaIZYW9PxAQHUUSmV1JlFR9MLLSwwpl7ssLgQMS/BwdTRBQRQbMGPP9JM9ezZpPh8/rk+/sQYKSk0EIk4FHt7qgM3N/0s2L59FAzcvj118oVFzEILZDISrvz86DqbNpFA+8MPNFt+8iSVMSLCuLuZIYZukAJnZ70FLyaGOn9h8QoIyLvlxtpa71qUHeKZiToJDtYnxImNzbhQcFoaPXchZLm70zG1atHM5969NPs8bRrd+7RpZE0dOJD8+W/cIIHacNmOzK5zloxMRs/e35+C8Z2d9ZafTp1oAeS7d8nCKhKS5AXDmWhDNxcPD7IOJSTQs16/nvqXzp3J5SkzaWn6mBzDWVERc2T4XsnlJJwbc4MxRCiOjRvT5MeECSR81K1LdW2IsJIac8VxcSlY3yWUMvFOlC9PbdTTk34Trnjly9P7ntmCIJQ1rVZvBXrxImO7Ntw/J4SFzc6OlBkxBjx7RsJ35vhJiUT/jgjXOFtbfaKfffuof+jYUZ/+vrgQFiBAbwXz8dFbSXx8qEwqVd4micQMvY0NPeecJitEgqSgIGqXIntpYbG1pX5ZnDc+nu7HxUU/GWqsjq2sqJ4CA/WWRpE86skTamvffEOue2+/nXMZDJVOEePk4UFtNjiY2rCfn34sFO05KIj28/KiZ+/rS/uJj78/fUQfXaECfTIvQl2uHI2FwqIlEv5kF6NkSmQyem4ODtQH+ftnXOfL2lo/IW1nR4rLrFl6b4XQUHqPhLLv6krywOnT1J/Wq0exYMXxrmSnrAmEYubuTvcXHKyf+ExOpuegUNB9L15MSVNiYqgfHT1aH9snlDVWyoqUMqGsMdkgYoUAvaBds6ZpOsPWrckV4LvvaEY9MVEv4JYwX+EsKBTUiYvFdn19SQg9cYIGrPHjye3v0CG91ckQlUo/AKpU1AGKDk8kqbC1pZgFsRjr6tWFK7OxjlvMEvv60gDauzcN6PXrk+IyaZJ+Jj+nwcWYsgbQPQrFTChNEklGi5epEW5zfn56ZSQpiZ6x8NW3t6e68/XVu9UFBuqFkfr1yery/fc0yM6bR+detIisCN26kYvIzz/r0/WXFGVNIOrBz4/u2c6O6qhePcoe9/QpvcNPn+btfIYxa4bPQSolQdHdnQRPb2+K9xTrGYrlPAzPI5PR/pljy4TFVlxLLtfP+ufmBiiX610zp0+nd9PVlaynM2boXbaLQ/G2sqJ3w9NTLwh6e1MdGLu2cIETcXsuLqQghYfrJ1JysixmRrhh+vjo3ZTs7Og9/y9UIMvzFAq+sChZWemTNuzaRe2kc2eq4+LC2ASUhwe9ywoFlcXBIet+pkSMBUFB+cu4mNfzijEmJobaaG6xNmJC0c9Pb72ysaG6rViRxqnNm2kttuzIKcGD6ONF+xXJdRwcCracjUggIiaNxULfloTwSnB31y+OHRBA952aSuPz3r2ktAE0kfLoESnJ8fFUByEhFMu2fDkpPWJtSGMTLqZCuEHmBaGUCwVcTLQmJlL5HR3pHo4epbHv0CHKDrt1q34NVFMtZ8QYhZW1soyhAC8GgOrVTXf+Xr3oZd61i2aXRGrq0jADI9xPgoKog9NoaGDdtYvcg+Rysi62bk1CvRgARayViDcIDtYrbSJeQSRScHUlRWHcOHJHePfdgqeczWmWTSKha4kZz1WraEC/dIlctI4ezSgYZsZQUM+MXE6Cg5ub3s2iuJQaobSVK0fP2spKP8vu7EzP3VDwEdYPBwd9/TRsSO4fmzfTgtNr1tBAtm0bKRvBweS+JpISlDSE5SYgQJ95sVw5mmiJiaH2++BB7ufJ7AZpiFSqD3IXcX+ffkptuWNHmokWCDdHa+usrtIiq5iwrslkdA6lMm9ugCIbYFISWdW2bCG3yA0bKBnOlSv66xcXUindp4cHPXdjiQgMYwBFvKdwyY6I0Fvd8oOwDAmhLCVF74InXMGAjM9TuFEKd0ytVj/Zs307WWS7d9cn2yhqRNKkzCgU1Ma8vbPGUBUFYizIJpyi0OcNCtJbNlNTc69rkZQnIIDqLDmZ/r58SW6QI0dSwidjlm0mZ4QbqJjgK1eO/rZoQe9ucrI+iVNSEqX0Dw3Vr8U4Ywa9Jz170mRogwbklloUZPd+5IZYhkZMXgp3SZGhc+JEChlo1Iji9RcsKPkT8CWAEihdMCbDUIAXf02prAE0MKxdSzN6e/fSttL0YgsrQFCQPrtYrVqUfXDHDhJqOnQgF7M//tAH44q4A0O3F1dXvf+4SAXt5kaBzDNnUpKGUaPyPxsnZt1z67jFbK6rK8U3nDpFZZ89m+Lr/vwz48KiguwsawIRI+fuXvyKuqGrh5gVzWuWTaE0BwXRx8eHlIu1aymj5/79FAfl5kazjCVRWRMYtmNHR7rXAwdIOAwJoUW5c8KYG2Tm8wuFLSGBZvk//ZQUtS5d9BM5aWnZB4ULq5Lh0gZyOSkY4r3KSSgXbpkiU62TE6VuP32ayt20Kd2nuaykInbVGPb2evc2sa+bm36SR5Dfctvb0ztvZ0fPUSgc4nlmxtqa2oanZ0YXvYoVaULur79oki67401JThYzmYzK6O+vz65ZUhFJwIKC6H4Ms0rmhOjPRZyqWMT93XdJkRsyxLjnR14ttIx+gk8obmI9PBHf5+hIz/PpU7K2RUZSXW7dSpOhDg6UfbVDB/puKoRreGGslFIplc/fn8ZOkUQoNpba4CefkJXQzk6fNZQpMkpwD8YUGkNlTSz+bWplDaC4n3nz9AlMSvqSB8YQlpigIPo/NlZvVVu/Hrh9m4TBfv30+xtiZ0edoWGsgkiUoVCQn/vSpZSOvFevjDFTuWGYqS83hDAmMpitXUvXDA2l7G/Ll2cUDoGM2SCzQyrVxzqYCzH45FXYMTzO0ZEG5KAgGpSSk8nqtmQJzYwOG1ayBUKBiGny8SHlavduGvjbt89ZYRNtIKeYkswKW82aZLG8c4dm/FNS6Dw5KUsKhX7hbVHe5GR9XGRuQqaNDU2OiAkMuZzK8/PPNCkilDhLE1aF9VtYhwF61vb2eVdWs0PUubOz/t3OyXoj6tHXV++G6uKiX1vqf/+jxEXGFAFTYrgYbnaUhMzDecXaWp99OT/H+PjQMxCZU9PTaeLvyhXqvzLDylr+EZOCIrZPuOIqlXrXe5F0KzSUXCQrV6bEPEeOkPzVtCllMj1/vvCLNhtOnpkCW1t9bJuYWNZoaAz85huaiC8NHlMWTCmQLpgCY6isCUGsKJQ1gAJUJ02igcaU64dZEmImTcRDpaeT0tWzJ/Drr/QM/vyT9s0uG6aY6TaMVRCueyNGkJ/7Tz/RTFxeg5Tzo6wB1MF7e+sXN65fn1w7+/Sh9L1t2ugVbyB3y5ohJVmhMVTahFtSTAw93+J07yxqhNtdQADd6/btJHB06JC9wiZcbnJ7DoYKW2IiDfYbNwK//04ZKUVbz+4cIs5SuIOJhBBiDaW81IGIt1Eq9YphbCxldb18meI1LbEuhfuVoZuhtbU+NXZhsLEhoV5Y1nITFkXiEX9/un5iIim5jRvT5NT339MERmHLlRNFGYtWmhAWZT8//dIUVaqQO9uSJdTmGdOROdmVm5t+MXSR9VOppHjdR48oKdn582SlunWLvFoaNiQ37YImIjGcPDMlIkGViG0LDKR+o1w5fheLGH66ZRlDZU24IQUHF821JBIaxJ8+tbwAYlNj6FIm1ilSqcgl9Px5yiDZrFn2x4tYBdERpqaSMKnRUEzIl1+SG15ISNYEDcbIKZ4op3vw9tavaRUURNbR3buprbRuTe6RKlXW1P2lHREzFBSkX2zUEoX7wiIEjipVKEYvJYXcQO/dy7qvWp2/YHZvb3p2SUkknKxZQ8mIwsNzd0N0cNDH4ojvMln+XO/c3PQxdPb21IafPyflw8HBcuvTySnjMihizSjxjhem3CJZUuYF63NCZLiUSqmPcnCgfmn1aloQ+K23cl+brqCwspZ3hGVWJB6RySjeqEoVyuxn6FrPljXTIJJdidh0Hx96tmJpI2Epj47WJ3T67TeyVPn5UaIyHx/yaNmzx3gIQnbkZwK1oIhlnwyTVDFFBvd0ZRlDZU24hhXl4CcsT2UF4V4UGEjPOSaG/nbpkreOTaxxJuKIxELkLVrQopzPnlF2wtu3cz5Pfi1rhtcXLpEioLpVK8oENWYMJSKpX58GGLF/WUK4GPn5ldwEI7kh2nCdOqSwKZWU9e/WrYzWF8O+JC+CnlSqV9jEOZcs0Qfw52adc3HRZ3AE6P3IT/YziYSsa46OZD0WywIUV3KMgiIWnBXlFMpafrJB5oRIkZ4fRIIakSnSzo4SE334IfDFF5QqvrBuXcYwTN3P5A3DJVWkUuCDDyhL4bJl+n0MF6RnTIPIRmsY+yW8Mpydqf9JT6fJqipVgM8+I/fwVasoRf6QIdRf9elD429u/VRBx/yCkl2sMmMySqF0weQZQwHr9u3chX4m/wgrWXAwCaepqSQU5kd4EVnYfH1pRj0uDqhRg3zdExPJ2nH5cvYz2IXpuMW6aAoFlUPEM02YQFkiAX3cQ1kUnCQSUmYDA0unsgboldKGDSkwPiGBYsxu39a3ufT0jIvW5gWhsHl4kPDRpw+5AvXvn/s5HB3Jdc8wfis/1waobXt50f0lJ+uzHAq3VktEIiHhztCaBphWyRQKoPg/L9jaUh8l4t5sbKg+58+nbJtz5piufIK8xKwxWXFw0K+1V706uat++CElhykKpZrRY22t97rx99e70iuV9A65uenXj1SpyJPmm2+AmzeBhQuBhw8p7t3Tk9zGjx41Hr+eU4ZmpkRSSqULJk8YKmsBAUDVquYtT2nGyoo6WOE6l98kK1KpftFTOzvq4IODKeukVEoz2efOGc8UWRA3yOwQgru7Oy1geuQIJT9p3pyEAKZ0ImLNmjUja0lMDAXD376tX8+pIJZVkdbfw4OEjgYN8pYARixcLASVgloD7Ozo+qmpdA8KhT49vqVib08TQIZuUWIhaFMpLwUR2oUbpYcHCZsyGTB8OGWyXb6cEiSZEnaDLDhiAtDRkdzt3N0phi0qit0giwNra3KDFKn/bWzonRFLYhguyv7iBW3r1w/48Ufg+nWa/Lh9m9ZG9fIiy9vx4/rJq+Jwg2SKFe7pyjJiAVqm+LCz0ytsBUGsIeTpSRY6V1fg66/pb8+e1Jlnjt0xdWYoEXMkFtWdPJmy+nHq3tKNREKCeOvWtJ5gVBRZT+7cIUUhP26QhhgqbPmJvXJ0zLhIdkFxcqJrJySUDOHfcAkDtVqfprsorCL5rUuh1Pv46Ne/mzCBPvPmUbyuqSjoOlIMYWNDFjZ/f2DuXErys38/K2vFiVjzMyiIFDdXV+rPoqOpP5LJaJujI7X38HDaNmwYZV29fp0W4756lbwdvL0pEdmJE/rzM6UC7unKMgWdDWcKR2HjAYT7VkAACSvW1uTHHhRErhHHjmWcdS8K/3Vh6fP317vCsbJWNnB2piQSu3dT3OSAAZTopjCCs1DY3Nzy3ifZ2ZE1Nz/LWBhDZDYU8WslAXv7jAuEmzLOyNANsiCIvsHXl5T49HSyro0cCUyfTvE4poAta4VHxCX36kXvX27rKTJFg1iGw9dXnx7f0ZHen+ho6uNsbKh/VCjovX/8mMbcCRMoDOL6dWDqVFK6R42i85amNW3LONzTlWUM3SCZkoWIhQsIoFl2iYSsHbVq0czawYN6l4qiSuMr4md8fWkAKaqsb4zloVDQ2mt79lAcxbZthe9LpFKa6ff2ztv+ov2lpxc+fklYhEQCJEu3LAjrmnBBNXWK/PzGrGVGZB8UkzkqFcWv9e9Pbne7dxe+jByzZhpE2xdLb7BlzbyILIv+/qS4BQRQ2IRSSYpbaipNUgk3yfBwWrvNxYXWivz7b+DaNXI7btzYfPfBmBQ2q5RlWFkr+cjlpCzZ2gIvX5I74vTpwLhxNPs2cGDRp9Z3ctKvd8WUHWxsgE6daEHUAQNoW2EXQpZK8xfPaW+vXxg7p/XZ8oKdHc1cW3pGSIG9PX0Ke9+ZKaxlzRCx1EB4ONXR8uWUxXPkSHreffoU/NycDdJ0SKWsrFkiNjb6+NyUFOqb4uIovg3Q91lpaeQ2GRND/adwh+SEMaUGVtbKMqyslQ6E25GtLRARQWtWzZsHTJtG7pBt29J+RVnXnFykbGJlBXTrRlnJ/vyz+IUDmYyElcePTdO+3d31lmpLRywQ/uSJadc4Kkg2yJywtycrwfPnNIH00UckeA4aRP3TiBEFOy+7QZqWzEp6SXgHygoSiX5BbVdXUtoSEuiTlEQKmlgfUqWieGKAJkuYUoHFKWtJSUk4e/YsHj16BAAIDg5GmzZtYJ+XDGFM/khJyX9WQsZyEUKRXE4uEAoFZY0aNox+Z8GGKQqkUqBDB6BRo8Jb1gqCgwO1fVO5ApakCSx7e3rfU1NpBt6c2SBzQiz78fw5KWiffEIuWyNHkqI9b17+yy6UNVYqTIPhmn38TC0XqZT6PAcHmlxKSqKQh7g4+k2hoEmcmBge80sRFqWsbdiwAfPmzUNiYiK0BoOFo6Mjli5dikmTJpmxdKWQmBialWZKDzY2FDAulwMLFpCQtGsX/VaShFCmZCHWmzMH1tYkoJQU90VTYm1Nn5QU07khm9qyJpDL9QpbXBzw8cf0ff58ICyMXLjzk/CKLWumxVBZY0oGhm6SwkUyMZE+hhl6mRKPxShru3btwtSpU9G8eXNMmTIF1atXBwDcunULGzZswNSpU+Hs7IyhQ4eauaSliOhoMqkzpQvhFimXA++/T4Lsp5+yYs6UXtzcym4Mk50d9eWmpKgEdrEWm0xGZX73Xfq+aBHw4AFltc1rP8UJRhiGMLS2paSQsiYsbUypwGKUtbVr1+KVV17BqVOnIDMYcOvUqYM+ffrg1VdfxZo1a1hZMxVaLVvWSjsODrTg5ty5lJqZFz1nSityObkAl0UcHCw7Zi0zIl28TAZERgJDh1LGu+nTKXvdsWOU1TY3eJ0108JukKUDW1v6lJTYWyZPWExPd+fOHfTt2zeDoiaQyWTo27cv7ty5Y4aSlVKUSjKTs7JWuhHZIuvVIwsbwzClC5EV0lTrrBUHUing6UlZ65KSgDZtgMOHSZFr1owS1uQGW9ZMCytrpQsrq7LpaVBKsRhlzdnZGaGhodn+HhoaCifObGM6hNsMK2ulH7GODr8/DFM6cXCw/Ji1zBgunp2SQmtKHT4MtGoF9O5NsWw5rd1YVt1eiwpW0BjGYrEYZa1bt27YsGED9u/fn+W3AwcOYOPGjXjttdfMULJSilDWOGaNYRimZOPmRq6FpqK4kkyIxDT+/rS4uVxOmSKnTgWWLAFee43c9Y3BCUZMS2bLGitvDGMxWExPt3z5clSoUAGDBw+Gv78/QkJCEBISAn9/fwwaNAgVKlTA8uXLzV3M0oMYANmyxjAMU/LJTybFnDDloth5xdmZMkNKJLT8w9tvk9J24QItCXHtWtZjWFkzPVotu5cyjAViMT2dp6cn/vrrL6xduxa1a9fGixcv8OLFC9SuXRsfffQRLl++DA8PD3MXs/TAbpAMwzCMMcyRvt3RkSxs1tYUU921K7B3LyVPad4c+PLLjPuzUmFa+FkyjMViMdkgAcDW1hZTp07F1KlTzV2U0k90NHXOzs7mLgnDMAxjKUgk+lix4hbgFQpS2J4/BxISgBo1gB07gBUrKGvk778Da9dSfB5b1kwLJxhhGIuFe7qySnQ0KWocoM0wDMMYYs6FkW1tySXS2ZkUNk9PYMECimHbsgUICQGePmVlrShgZY1hLBKzWdbatm0LqVSKkydPwsrKCu3atcv1GIlEglOnThVD6coA0dHsAskwDMNkpLiyQeaEjQ1libSyAqKiKNtlr15AtWrAlClAgwZAcjIra6bEHLGKDMPkCbP1dFqtFhqDtLwajQZarTbHjyanNL5M/uAFsRmGYRhLxcqK1mHz8qK12ORyoGZN4KuvSGlLSGBlzZSwGyTDWCxms6ydOXMmx+9MEcOWNYZhGCYz5oxZy4xYPNvaGnjxgr67uVGmyJMngSZNzFu+0gSn7mcYi8VipqX+97//4eXLl9n+HhkZif/973/FWKJSDitrDMMwTGYszR1OIqH1QAMCKMY6PZ3i2rp0ASpUMHfpGIZhihyLUdbatm2Ln376KdvfT506hbZt2xZjiUo50dG8IDbDMAyTFXPHrBnDwYEyRdrbAykpFNeW3YLZTP4xtKwxDGNRWIyyps2lg1CpVJBx5kLTwZY1hmEYJjOWZlkzxNaWFDZ3d1LY1GpK988UHo5ZYxiLxazrrIWFhSE0NFT3/fbt20ZdHWNjY/HZZ58hODi4GEtXyuEEIwzDMIwxLNGyJhCJR6ytKVOkpSqWJQ1LrGuGYQCYWVnbvn07Fi1aBIlEAolEgqVLl2Lp0qVZ9tNqtZDJZPjss8/MUMpSSFoaZdJiZY1hGIYxxJItawKpFPDwIEubjY25lO2JOgAAgUtJREFUS1M6YMsaw1gsZlXW+vXrh1q1akGr1aJfv36YMmUKWrdunWEfiUQCe3t71KtXD97e3mYqaSlD+PlzzBrDMAyTGUu2rBni4GDuEpQeWFljGIvFrMpa9erVUb16dQBkZWvTpg3KlStnziKVDaKj6S9b1hiGYRhDSoJljSl6WGFjGIvBrMqaIcOHDzd3EcoOrKwxDMMw2VFSLGuM6WDLGsNYLBajrAFASkoKvvrqK/z111+Ii4uDRizM+R8SiQTbtm0zU+lKEcINkpU1hmEYxhAW1MsmrKwxjMViMcrao0eP0LZtW4SGhsLFxQVxcXFwc3NDbGws1Go1PDw84MD+6aZBWNY4Zo1hGIbJDFvWyh7s/sowFovFrLM2c+ZMxMXF4ffff8fdu3eh1Wpx4MABJCYmYsWKFbCzs8PJkyfNXczSQXQ0YGdHmbQYhmEYRsBCe9mELWsMY7FYjLL2yy+/YMKECWjSpAmkUiqWVquFXC7HzJkz8eqrr+Ltt982byFLC7wgNsMwDJMdbFkre7CyxjAWi8Uoa0qlUpcJ0snJCRKJBHFxcbrfmzdvjnPnzpmpdKUMXhCbYRiGMQZb1hiGYSwKi1HWgoKC8OTJEwCAlZUV/P398fvvv+t+v3nzJmzZbc80sGWNYRiGyQ5W1soemS1rbF1jGIvBYhKMtGvXDseOHcOCBQsAACNGjMCyZcsQExMDjUaD3bt3Y9iwYWYuZSkhOpqTizAMwzBZYcta2UQoZ1z3DGNxWIyy9t577+GPP/6ASqWCXC7HnDlz8OzZMxw+fBgymQyDBg3CmjVrzF3M0kF0NFCzprlLwTAMw1gahsoaW1fKDqykM4zFYjHKWlBQEIKCgnTfbW1tsXXrVmzdutWMpSqlsBskwzAMkx0stJc9hLKm0bCSzjAWhsXErOXGnTt3MGrUKHMXo3TACUYYhmEYYxgK6iy0lx3YosowFotFKGsvX77ExYsXcffu3Sy/Xbp0Cb1790bNmjXx5ZdfmqF0pQyNhmPWGIZhmOxhyxrDMIzFYFZlTaVSYejQofD19UWLFi1QvXp11KtXD6GhoXjx4gV69uyJ5s2b48cff8T48eNx584dcxa3dJCWBgwZAtSqZe6SMAzDMJYGW1jKJuwGyTAWi1lj1j788EPs2bMHzZo1Q6tWrfDw4UMcOXIEw4cPR0REBMLDwzF//nxMnjwZbuy2ZxrkcmDnTnOXgmEYhrFU2LJW9uBFsRnGYjGrsrZ//3507NgRP/zwg27bmjVrMHPmTNSoUQO3b9+Gj4+PGUvIMAzDMGUItqyVTTJng+S6ZxiLwaxukI8ePUKPHj0ybOvVqxcAYNasWayoMQzDMExxw5a1sgcr6QxjsZhVWUtNTYWzs3OGbeJ7QECAOYrEMAzDMGUXFtrLJhyzxjAWi9mzQUqy6RSy284wDMMwTBHCljWGYRiLweyLYo8ePRpjx47Nsr179+6QyWQZtkkkEsTFxRVX0RiGYRimbMGWtbIJ1zvDWCxmVdaGDx9uzsszDMMwDMMwQkFjN0iGsTjMqqxt377dnJdnGIZhGMYQtrCUTTJng2QYxmIwe8wawzAMwzAWBAvtZY/M66yxos4wFgMrawzDMAzDEGxZK5sYKmsMw1gUrKwxDMMwDEOwOxzDMIxFwcoawzAMwzB62LJW9sjsBskwjMXAyhrDMAzDMARb1somrKwxjMXCyhrDMAzDMHpYWSt7sJLOMBZLmVDWVCoV3n33Xfj5+cHOzg5NmzbFTz/9lOtxCxcuhEQiyfKxtbUthlIzDMMwTDHDQnvZhC1rDGOxmHWdNUOkUikkuXQQtra2CAgIQNu2bTFz5kxUrFgxT+ceMWIEDh8+jLfffhuVK1fGjh070LVrV5w+fRqtWrXK9fjNmzfDwcFB910mk+XpugzDMAxT4mBlreyROQsoK2wMYzFYjLI2f/58HDt2DDdu3ECXLl1QqVIlAMC9e/fwww8/oHbt2mjXrh3u37+P7du3Y9++ffjf//6HunXr5njeS5cuYf/+/Vi1ahVmzJgBABg2bBhq1aqFWbNm4cKFC7mWrU+fPvDw8Cj8TTIMwzCMJcOp+8s2rKgzjMVhMcqan58fIiMjcfv2bVSoUCHDb/fv30dISAhq1KiBVatW4d69e2jevDnmzJmD7777LsfzHj58GDKZDG+99ZZum62tLUaPHo05c+bg8ePHCAwMzPEcWq0W8fHxcHR0zNX6xzAMwzAlGhbYyx5CtmE3SIaxOCwmZm3VqlWYOHFiFkUNACpVqoSJEydi2bJlAIDKlStj3LhxebKKXblyBVWqVIGTk1OG7U2aNAEAXL16NddzVKhQAc7OznB0dMSQIUPw4sWLHPdXqVSIj4/P8GEYhmEYi4cta2UTrneGsVgsxrL25MkTWFllXxwrKys8fvxY971cuXJQqVS5njc8PBy+vr5Ztottz549y/ZYV1dXTJo0Cc2bN4dcLsevv/6KTz75BJcuXcKff/6ZRQEULFu2DIsWLcq1bAzDMAxjcbBlrexhmGCEYRiLwmKUtZo1a2Lz5s0YOnQovL29M/z2/PlzbN68GTVr1tRte/DgAXx8fHI9b3JyMuRyeZbtIqNjcnJytsdOnTo1w/c33ngDTZo0weDBg7Fp0ya89957Ro+bPXs2pk+frvseHx+fq6slwzAMw5gdtrCUTbjeGcZisRhlbfXq1brEIj179tQlGLl//z6+/vprpKWl4YsvvgAApKSkYMeOHejSpUuu57WzszNqgUtJSdH9nh8GDRqEd955Bz///HO2yppcLjeqIDIMwzCMxcPWlbKHRAJoNByzxjAWiMUoayEhIbhw4QIWLFiAI0eO6Cxetra2aN++PRYuXIgGDRrotuXkvmiIr68vnj59mmV7eHg4AEpskl8CAwMRHR2d7+MYhmEYxqJhCwsDcN0zjAVhMcoaANSvXx/ffPMNNBoNIiIiAABeXl6QSgueB6VevXo4ffo04uPjM8SYXbx4Ufd7ftBqtQgNDUX9+vULXCaGYRiGYRiLgWPWGMZisZhskIZIpVL4+PjAx8enUIoaQGukqdVqbNmyRbdNpVJh+/btaNq0qS6WLCwsDLdv385w7MuXL7Ocb/PmzXj58iU6d+5cqHIxDMMwjMVhaFFh60rZwVBZ43pnGIvCoixrMTEx2LdvHx48eICYmBhoM83wSCQSbNu2LV/nbNq0Kfr27YvZs2cjIiIClSpVws6dOxEaGprhXMOGDcPZs2czXDM4OBj9+/dH7dq1YWtri3PnzmH//v2oV68exo4dW7ibZRiGYRhLgwX1somh+yvDMBaFxShrJ0+eRJ8+fZCUlAQnJye4urpm2aegC1Lv2rUL77//Pnbv3o2YmBjUqVMH3377LV555ZUcjxs8eDAuXLiAr776CikpKQgODsasWbMwd+5cKBSKApWFYRiGYUoErLiVHdiyxjAWi0Sb2XxlJmrVqgWVSoUjR46gdu3a5i6OSYmPj4ezszPi4uKyXZuNYRiGYcxOx47ATz/R/3fuAFWqmLc8TPHQoweQmAiEhQHt2gGbNgEymblLxTCllvzoBhYTs3b//n1MmTKl1ClqDMMwDFMiYQsLwzCM2bEYN8jKlSsjISHB3MWwCNRqNdLS0sxdDIZhLAhra2vIeKabKWpYQSubZHaD5HbAMBaDxShrS5YswcSJEzFo0CCUK1fO3MUxC1qtFs+fP0dsbKy5i8IwjAXi4uICHx+fAsfvMky+4HZWduCYNYaxWCxGWTt16hQ8PT1RvXp1dOjQAYGBgVlmkSUSCT7++GMzlbDoEYqal5cXFAoFC2QMwwCgiRylUqlbf9LX19fMJWJKLTzulE04GyTDWCwWo6xt3LhR9/+3335rdJ/SrKyp1Wqdoubu7m7u4jAMY2HY2dkBACIiIuDl5cUukUzRw4pb2YEtawxjsViMsqbRaMxdBLMiYtR4SQCGYbJD9A9paWmsrDFFAwvqZRNW1hjGYrGYbJAMwa6PDMNkB/cPDMMwDFO2YGWNYRiGYRiCJwTKJoYxa9wGGMaiMJsbpFQqhVQqhVKphI2NDaRSaa6zxhKJBOnp6cVUQoZhGIYpw7DQXnZgN0iGsVjMpqzNnz8fEokEVlZWGb4zTF4ICQkBAJw5cwYAEBoaivLly2P79u0YMWIEAGDhwoVYtGgRtGUgw1Xm58EwDFMgeBwum2TOBsntgGEsBrMpawsXLszxO1O6+Oeff7Bo0SL88ccfePHiBdzd3VGjRg28/vrrmDx5srmLZ3JGjBiBnTt36r7b2NggODgYAwYMwJw5c2Bra5vvc968eRMHDx7EiBEjyuxahAzDFCMssJcdDC1rDMNYFBaTDZIpvVy4cAFt27ZFUFAQ3nzzTfj4+ODx48f4/fff8fHHHxdIWfvxxx9z3WfevHl47733ClJkkyCXy7F161YAQFxcHI4dO4YPPvgA//77L/bs2ZPv8928eROLFi1CSEhIFmUtL8+DYRgmV1hBK5uIemc3SIaxOMymrO3atatAxw0bNszEJWGKmqVLl8LZ2Rl//PEHXFxcMvwmFvnNLzY2NrnuY2VlpXOzNQdWVlYYMmSI7vuECRPQokUL7Nu3D2vXroW3t7fJrpWX58EwDJMvWGhnGIYxO2aTZEVcUX6QSCSsrJVA/v33X9SsWTOLogYAXl5eGb6np6dj2bJl2LFjB548eQJfX18MGjQICxYsgFwu1+2XlxgtYzFrEokEEydORPv27TFv3jzcu3cPlSpVwpo1a9C5c+cMx585cwYzZszA9evX4e/vj1mzZiE8PLzAcXASiQStWrXC77//jgcPHuiUtUePHmHFihU4deoUwsLCoFAo0K5dO6xatUpnQduxYwdGjhwJAGjbtq3unKdPn0ZISIjR5xEREYHZs2fj22+/RVxcHKpWrYrp06dj+PDh+S47wzBlBFbQyiacYIRhLBazKWsPHz4016WZYiY4OBi//fYbrl+/jlq1auW475gxY7Bz50706dMH77zzDi5evIhly5bh1q1bOHr0qEnKc+7cORw5cgQTJkyAo6Mj1q9fjzfeeANhYWFwd3cHAFy5cgWdO3eGr68vFi1aBLVajcWLF8PT07NQ1w4NDQUAuLq66rb98ccfuHDhAgYMGICAgACEhoZi8+bNCAkJwc2bN6FQKPDKK69gypQpWL9+PebMmYPq1asDgO5vZpKTkxESEoL79+9j0qRJKF++PA4dOoQRI0YgNjYWU6dOLdR9MAxTSjEU1FloLzuwssYwFovZlLXg4GBzXbpkoVQCt2+buxR6qlUDFIp8HTJjxgx06dIF9erVQ5MmTdC6dWu8+uqraNu2LaytrXX7Xbt2DTt37sSYMWPw+eefAyDXQS8vL6xevRqnT5/OYFUqKLdu3cLNmzdRsWJFAGSpqlu3Lvbt24dJkyYBABYsWACZTIbz58/Dz88PANCvX79slaPsiIyMBEAxa19//TW++uor1KpVC1WrVtXt061bN/Tp0yfDca+99hqaN2+Or776CkOHDkWFChXQunVrrF+/Hh06dNBZ0rJjy5YtuHXrFr788ksMHjwYADBu3Di0adMG8+bNw6hRo+Do6Jive2EYhmFKKZmzQTIMYzFwghFL5/ZtoGFDc5dCz+XLQIMG+TqkQ4cO+O2337Bs2TKcPHkSv/32G1auXAlPT09s3boVr7/+OgDg+++/BwBMnz49w/HvvPMOVq9eje+++84kylr79u11ihoA1KlTB05OTnjw4AEAQK1W4+eff0avXr10ihoAVKpUCV26dMHx48fzdJ2kpKQslrhWrVph586dGZapsLOz0/2flpaG+Ph4VKpUCS4uLvjrr78wdOjQfN/j999/Dx8fHwwcOFC3zdraGlOmTMHAgQNx9uxZdO/ePd/nZRimlMOWtbJJZssa1z3DWAxmU9batm0LqVSKkydPwsrKCu3atcv1GIlEglOnThVD6SyIatVIQbIUqlUr0GGNGzfGkSNHkJqaimvXruHo0aP46KOP0KdPH1y9ehU1atTAo0ePIJVKUalSpQzH+vj4wMXFBY8ePTLFHSAoKCjLNldXV8TExACgWK/k5OQs5QBgdFt22Nra6hS7J0+eYOXKlYiIiMignAHksrhs2TJs374dT58+zRAPFxcXl+frGfLo0SNUrlwZUqk0w3ZhGTTVs2QYhmFKAewGyTAWi9mUNa1WC41Go/uu0WhyXRS7LCxunAWFIt+WLEvGxsYGjRs3RuPGjVGlShWMHDkShw4dwoIFC3T7FPXi6DKZzOh2U7cvmUyG9u3b67536tQJ1apVw9ixY/HNN9/otk+ePBnbt2/H22+/jebNm8PZ2RkSiQQDBgzI8I4wDMMUOWxZYxiGsSjMpqxlzuKXU1Y/pnTSqFEjAEB4eDgAimPUaDS4d+9ehtiwFy9eIDY2ttjiHL28vGBra4v79+9n+c3Ytrzi6+uLadOmYdGiRfj999/RrFkzAMDhw4cxfPhwrFmzRrdvSkoKYmNjMxyfHyU2ODgYf//9NzQaTQbr2u3/4h85ZpRhGIbRwZY1hrFYpLnvwjCF4/Tp00atViJGTSTb6Nq1KwBg3bp1GfZbu3YtAErEURwIi9jXX3+NZ8+e6bbfv38fJ06cKNS5J0+eDIVCgeXLl2e4Xubns2HDBqjV6gzb7O3tASCLEmeMrl274vnz5zhw4IBuW3p6OjZs2AAHBwe0adOmEHfBMEyphS1rZROpVJ9ghOudYSwKi0swcvbsWXz33Xe6mJrg4GB069aNhcsSzOTJk6FUKtGrVy9Uq1YNqampuHDhAg4cOIBy5crp1g+rW7cuhg8fji1btiA2NhZt2rTBpUuXsHPnTvTs2dMkyUXyysKFC/Hjjz+iZcuWGD9+PNRqNTZu3IhatWrh6tWrBT6vu7s7Ro4ciU2bNuHWrVuoXr06unfvjt27d8PZ2Rk1atTAb7/9hp9//lm3jICgXr16kMlkWLFiBeLi4iCXy9GuXbssa9UBwFtvvYXPPvsMI0aMwOXLl1GuXDkcPnwY58+fx7p16zgTJMMwDKNHKgWSk81dCoZhjGAxylpqaioGDhyIr7/+GlqtVreAcmxsLNasWYNevXph3759GVK9MyWD1atX49ChQ/j++++xZcsWpKamIigoCBMmTMC8efMyLJa9detWVKhQATt27MDRo0fh4+OD2bNnZ4hpKw4aNmyIEydOYMaMGXj//fcRGBiIxYsX49atWzpXwoIyffp0fPrpp1ixYgV27NiBjz/+GDKZDHv27EFKSgpatmyJn3/+GZ06dcpwnI+PDz799FMsW7YMo0ePhlqtxunTp40qa3Z2djhz5gzee+897Ny5E/Hx8ahatSq2b99eoAXpGYYpI7BlrWzSvj3wxReAlRXXO8NYGBKthWTtmDt3LpYtW4YZM2bgnXfegbe3NwDKzLdmzRqsWrUKc+fOxQcffGDmkuaf+Ph4ODs7Iy4uDk5OTkb3SUlJwcOHD1G+fHnY2toWcwmZvNKzZ0/cuHED9+7dM3dRmDII9xNMkdO7N3D0KP3/5Ang72/e8jDFg1IJVK1KdT5jBrBqlblLxDClmrzoBgKLiVnbu3cvhg8fjpUrV+oUNYCSPaxYsQLDhg3D7t27zVhCpqyRnMkl5N69e/j+++9zXZCaYRimxMKWtbKJXA4MG2buUjAMYwSLcYMMDw9H06ZNs/29adOm2L9/fzGWiCnrVKhQASNGjECFChXw6NEjbN68GTY2Npg1a5a5i8YwDMMwpkMqBQYPBjZsoCWDGIaxGCxGWQsICMCZM2cwbtw4o7+fPXsWAQEBxVwqpizTuXNn7Nu3D8+fP4dcLkfz5s3x4YcfonLlyuYuGsMwTNHA1rSyiUQCVKsG/PwzYBBHzjCM+bEYZW348OFYsGABXFxcMG3aNFSqVAkSiQT37t3DunXrcOjQISxatMjcxWTKENu3bzd3ERiGYRimeJBKgcaNgbQ0c5eEYRgDLEZZmzNnDv79919s2bIFn3/+uW4hX41GA61Wi+HDh2POnDlmLiXDMAzDlGKEZY0tbGUTiQSwsTF3KRiGMcBilDWZTIYdO3Zg+vTp+P777zOss9a1a1fUqVPHzCVkGIZhmFIOK2kMwzAWhcUoa4I6deqwYsYwDMMw5oSVNoZhGIvA4pQ1we3bt3Ho0CGEh4ejWrVqGDFiRK7rEDAMwzAMUwhYSWMYhrEozKqsbdy4EevXr8eFCxfg4eGh2378+HH07dsXqampum3r16/H77//nmE/hmEYhmGKAImEFTeGYRgLwKyLYn/zzTeoWLFiBgUsPT0dY8aMgUwmw/bt2/HPP/9g+fLlePToEZYuXWrG0jIMwzBMKYcVNIZhGIvCrMrazZs30axZswzbTp8+jZcvX2LatGkYPnw4atasiVmzZqFfv374/vvvzVRShmEYhilDsNLGMAxjEZhVWYuKikJgYGCGbadOnYJEIkGvXr0ybG/ZsiXCwsKKs3gMY1Z++OEH1KtXD7a2tpBIJIiNjcWIESNQrly5XI8NDQ2FRCLBjh07irycRUlpuY+8sGPHDkgkEoSGhpq7KExZhpU0hmEYi8Ksypq3tzeeP3+eYduvv/4KhUKBunXrZthuY2MDm7K69odaTYtUmvujVhf4Fi5fvozOnTvDyckJjo6O6NixI65evZplv5CQEEgkkiyfzp0753oNlUqFyZMnw9PTEwEBAViyZEmWfZ48eQIHBwecP3++wPdSHERFRaFfv36ws7PDJ598gt27d8Pe3t7cxSpRKJVKLFy4EGfOnDF3URim5MFKG8MwjEVg1gQjjRo1ws6dOzF58mQ4Ojrixo0buHTpEnr06AErq4xFu337NgICAsxUUjOiVgOPHwMGyVbMho0NEBgIyGT5Ouyvv/5Cq1atEBgYiAULFkCj0WDTpk1o06YNLl26hKpVq2bYPyAgAMuWLcuwzc/PL9frrFq1Crt27cLcuXORkJCAxYsXo2LFihg4cKBun5kzZ+L1119Hy5Yt83UPxc0ff/yBhIQEfPDBB2jfvr1u++effw6NRmPGkhUvwcHBSE5OhrW1db6PVSqVWLRoEQCaBGAYJg+wksYwDGNRmFVZW7BgARo3bozKlSujZs2auHz5MiQSCWbPnp1l36NHj6Jdu3ZmKKWZ0WhIUZPJACszVld6OpVDo8m3svb+++/Dzs4Ov/32G9zd3QEAQ4YMQZUqVTBnzhx89dVXGfZ3dnbGkCFD8l3Eb7/9Fu+88w5mzZoFAHj8+DG++eYbnbJ27tw5HD9+HLdv3873uYubiIgIAICLi0uG7QVRWkoyEokEtra25i5GBpKSktjKyZR+WGljGIaxCMzqBlm7dm388ssvaNiwIZ49e4ZmzZrh+++/R8OGDTPsd+bMGSgUCvTt29dMJbUArKwAa2vzfQqhKP76669o3769TlEDAF9fX7Rp0wbffvstEhMTsxyTnp5udHtOJCcnw9XVVffdzc0NSqUSAKDRaDB16lTMmjUr3xba2NhYTJs2DeXKlYNcLkdAQACGDRuGyMhI3T4REREYPXo0vL29YWtri7p162Lnzp0ZziPir1avXo0tW7agYsWKkMvlaNy4Mf744w/dfiEhIRg+fDgAoHHjxpBIJBgxYgQAGI1ZE7Fszs7OcHFxwfDhwxEbG2v0Xm7fvo0+ffrAzc0Ntra2aNSoEb755psM+4jYqfPnz2P69Onw9PSEvb09evXqhZcvX2Y554kTJ9CmTRs4OjrCyckJjRs3xt69ezPsc/HiRXTu3BnOzs5QKBRo06ZNnlxRjcWsjRgxAg4ODnj69Cl69uwJBwcHeHp6YsaMGVD/56obGhoKT09PAMCiRYt07rQLFy4s0LM4e/YsJkyYAC8vLwQEBODw4cO67Zn57LPPIJFIcP36dQDA33//jREjRqBChQqwtbWFj48PRo0ahaioqFzvn2GKHVbSGIZhLAqzL4rdokULfPfddznuExISgn/++aeYSsSYGpVKBTs7uyzbFQoFUlNTcf369QxZQe/evQt7e3ukpqbC29sbb775JubPn5+rValx48bYsmULQkJCkJiYiH379mHSpEkAgG3btiEyMhIzZ87MV9kTExPRunVr3Lp1C6NGjUKDBg0QGRmJb775Bk+ePIGHhweSk5MREhKC+/fvY9KkSShfvjwOHTqEESNGIDY2FlOnTs1wzr179yIhIQFjx46FRCLBypUr0bt3bzx48ADW1taYO3cuqlatii1btmDx4sUoX748KlasaLR8Wq0WPXr0wLlz5zBu3DhUr14dR48e1Sl7hty4cQMtW7aEv78/3nvvPdjb2+PgwYPo2bMnvvrqqyxJfSZPngxXV1csWLAAoaGhWLduHSZNmoQDBw7o9tmxYwdGjRqFmjVrYvbs2XBxccGVK1fwww8/YNCgQQCAX375BV26dEHDhg2xYMECSKVSbN++He3atcOvv/6KJk2a5KtOAECtVqNTp05o2rQpVq9ejZ9//hlr1qxBxYoVMX78eHh6emLz5s0YP348evXqhd69ewMA6tT5f3v3HR1F2bYB/NoUEkIaPdRI6DUUaVJ90dDEghQBFRARXpWmSBHpICAqHRRRAigvaOiCtCgtNKlKh0BiIIEAgSSQnr2/P/LNuJvdTTYhmx2S63fOnLM79Zl5dmbnnqdMg1wdi/fffx+lS5fGpEmT8PjxY3Tt2hXu7u74+eef0a5dO6N5169fj7p166JevXoAgD179uD69esYOHAgfHx8cP78eSxfvhznz5/H0aNHoePNMWkRf5dERNogZHOxsbECQGJjYy3Ok5iYKBcuXJDExETjCSkpIpcvi4SFidy6Zb8hLCwjHSkpOd7/+vXrS40aNSQtLU0dl5ycLJUrVxYAEhQUpI5/5513ZMqUKbJhwwZZvXq1vPzyywJAevXqle12IiIipG7dugJAAEibNm0kPj5eHj58KKVLl5Z169blOO2TJk0SALJx40aTaXq9XkRE5s+fLwDkxx9/VKelpKRIy5Ytxd3dXeLi4kRE5MaNGwJASpYsKTExMeq8W7ZsEQCybds2ddzKlSsFgPz5559G2+zfv7/4+vqq3zdv3iwA5IsvvlDHpaWlSZs2bQSArFy5Uh3foUMHqV+/viQlJRntw3PPPSfVq1c32fYLL7yg7qOIyKhRo8TR0VEePnwoIiIPHz4UDw8Pad68ucnvVllOr9dL9erVpWPHjkbrSkhIkCpVqsiLL75oclwNKcfMcD/69+8vAGTatGlG8zZq1EiaNGmifr97964AkMmTJ5usN6fHonXr1ka/XxGRPn36SJkyZYzGR0VFiYODg1HaEhISTLb/v//9TwDIgQMHTLZ148YNi8fD4nWCKK/06ycCiLi4iERG2js1REQFkjWxgcKu1SCpcHj//fdx5coVDBo0CBcuXMC5c+fw9ttvIyoqCkBG9UXF999/j8mTJ6N79+546623sGXLFgwePBg///wzjh49muV2KlasiNOnT+P06dM4f/489u3bB3d3d0ydOhU1a9ZE7969cejQITRv3hyVKlXC8OHDkZJNxy0bNmyAv7+/SUkLALVEZMeOHfDx8THqyMTZ2RnDhw/Ho0ePTKrK9e7d26i6Zps2bQAA169fzzIt5uzYsQNOTk7473//q45zdHTEsGHDjOaLiYnB77//jl69eiE+Ph737t3DvXv3cP/+fXTs2BFXr17FrVu3jJZ57733jEp92rRpg/T0dISHhwPIKDGKj4/HuHHjTNqVKcudOXMGV69eRd++fXH//n11u48fP0aHDh1w4MCBXHeYMnToUKPvbdq0seoY5uZYDB48GI6Z2mr27t0b0dHRRr1NBgUFQa/Xo3fv3uo4w1LlpKQk3Lt3Ty1JPnXqlNX7S5SvdDqWrhERaQCDNbK5oUOH4tNPP8XatWtRt25d1K9fH6GhoWpHIO7u7lku//HHHwMA9u7dm+22nJ2d0bBhQ9SpUwcODg64dOkSli5digULFiAmJgZdu3bFq6++il9++QV79uzBzJkzs1xfaGioWp3NkvDwcFSvXh0ODsanU+3atdXphipXrmz0XQncHjx4kO3+mdt2uXLlTI5h5h42r127BhHBxIkTUbp0aaNh8uTJAP7t1MTadIaGhgJAlsfn6tWrAID+/fubbHfFihVITk5GbGxsTncbrq6uaps0w/RZcwxzcyyqVKlish6lDZ5htdD169ejYcOGqFGjhjouJiYGI0aMQNmyZVG0aFGULl1aXV9u9p3IphigERFpit3brFHhMHPmTIwePRrnz5+Hl5cX6tevj08//RQAjG5szVFenB4TE5Pj7Y4aNQpvvvkmGjdujDVr1qBEiRJqb6NjxozBzJkz1e7d80vmEhqFiNhsm0rp1ejRo9GxY0ez81SrVs3oe16kU9nu3Llz0bBhQ7PzZBesm2MpbTlJU06Ohbk2ly4uLnj11VexadMmLF26FHfu3EFISAg+//xzo/l69eqFw4cP45NPPkHDhg3h7u4OvV6PTp06FarXMNBTgsEaEZGmMFijfFO8eHG0bt1a/b53715UrFgRtWrVynI5pWpb5pKU7Pz66684fPiwWroTGRmJcuXKqdPLly9vUt0ts6pVq6q9+lni6+uLv/76C3q93qh0TXlFgK+vb47SnRO+vr4IDg7Go0ePjIKey5cvG83n5+cHIKPk0fC9bU9C6fTk3LlzJsFN5nk8PT3zbLvWstRxR14ei969e2PVqlUIDg7GxYsXISJGVSAfPHiA4OBgTJ06FZMmTVLHK79JIs1hsEZEpCmsBkl2sX79evz5558YOXKkGuDExcUhOTnZaD4RwYwZMwDAYimIOSkpKfjoo4/w2WefoUyZMgCAsmXL4tq1a0hLSwMAXLx4ET4+Plmu5/XXX8fZs2exadMmk2lKCVOXLl1w+/Zto+pwaWlpWLRoEdzd3U16C8xLXbp0QVpaGpYtW6aOS09Px6JFi4zmK1OmDNq3b49vv/1WbStoyFyX/NkJCAiAh4cHZs2ahaSkJKNpyrFp0qQJqlatii+//NLsqxhys11rubm5AYDJawzy8li88MILKFGiBNavX4/169ejWbNmRlUmlRLAzKWR8+fPt3obRPlKCdYYtBERaQJL1sjmDhw4gGnTpiEgIAAlS5bE0aNHsXLlSnTq1MmoW/tTp06hT58+6NOnD6pVq4bExERs2rQJISEheO+999C4cWOrt7lgwQIAMFp/ly5d8MEHH6Bv37547rnnMH36dLz77rtZrueTTz5BUFAQevbsiXfeeQdNmjRBTEwMtm7dim+++Qb+/v5477338O2332LAgAE4efIknnnmGQQFBSEkJATz58+Hh4dHDo+Y9bp164ZWrVph3LhxCAsLQ506dbBx40azbaGWLFmC1q1bo379+hg8eDD8/Pxw584dHDlyBDdv3sTZs2dztG1PT0/MmzcP7777Lpo2bYq+ffuiePHiOHv2LBISErBq1So4ODhgxYoV6Ny5M+rWrYuBAweiQoUKuHXrFv744w94enpi27ZteXU4jBQtWhR16tTB+vXrUaNGDZQoUQL16tVDvXr18uxYODs7o3v37li3bh0eP36ML7/80mi6p6cn2rZtiy+++AKpqamoUKECdu/ejRs3bthil4mIiKiAYbD2tPj/0qCncfsVKlSAo6Mj5s6di/j4eFSpUgUzZszARx99BCeDl237+vqiTZs22LRpE27fvg0HBwfUrl0b33zzDd577z2rt3fnzh1Mnz4dP/30E4oUKaKOL1OmDDZs2IBRo0Zhz549ePnll9UOJSxxd3fHwYMHMXnyZGzatAmrVq1CmTJl0KFDB/Xl2kWLFsW+ffswbtw4rFq1CnFxcahZsyZWrlypvszaVhwcHLB161aMHDkSP/74I3Q6HV5++WV89dVXaNSokdG8derUwYkTJzB16lQEBgbi/v37KFOmDBo1amRURS8nBg0ahDJlymD27NmYPn06nJ2dUatWLYwaNUqdp3379jhy5AimT5+OxYsX49GjR/Dx8UHz5s0xZMiQJ9r/7KxYsQLDhg3DqFGjkJKSgsmTJ6NevXp5eix69+6NFStWQKfToVevXibT165di2HDhmHJkiUQEQQEBOC3335D+fLl82o3ifIOS9aIiDRFJ7bs1YAAZFTv8/LyQmxsLDw9Pc3Ok5SUhBs3bqBKlSrG3aCnpwMREUA2XczniyJFgEqVgCfo3IGIcs/idYIorwwcCAQGAm5uQGgokE1VcSIiyjlrYgMFS9a0ztExI0DSQq9xDg4M1IiICjKWrBERaQqDtaeBoyODJCIiyl8M2IiI7I69QRIREVEGlqwREWkKgzUiIiIiIiINYrBGREREGViyRkSkKQzWiIiIiIiINIjBmsbwTQpEZAmvD2RzLFkjItIUBmsa4ezsDABISEiwc0qISKuU64NyvSAiIqKCjV33a4SjoyO8vb0RHR0NAHBzc4OOTzaJCBklagkJCYiOjoa3tzcc+SoPshWWrBERaQqDNQ3x8fEBADVgIyIy5O3trV4niGyiZ0/g9GnAw8PeKSEiIjBY0xSdTody5cqhTJkySE1NtXdyiEhDnJ2dWaJGtvfii0CjRsDjxwB/b0REdsdgTYMcHR15U0ZERPZRqlRGyRrbRhIR2R2DNSIiIjLm4mLvFBAREdgbJBERERERkSYxWCMiIiIiItIgBmtEREREREQaxGCNiIiIiIhIg9jBSD4QEQBAXFycnVNCRERERET2pMQESoyQFQZr+SA+Ph4AUKlSJTunhIiIiIiItCA+Ph5eXl5ZzqMTa0I6eiJ6vR6RkZHw8PCATqd74vXFxcWhUqVKiIiIgKenZx6kkPIS80e7mDfaxvzRNuaPdjFvtI35o232yB8RQXx8PMqXLw8Hh6xbpbFkLR84ODigYsWKeb5eT09PnvQaxvzRLuaNtjF/tI35o13MG21j/mhbfudPdiVqCnYwQkREREREpEEM1oiIiIiIiDSIwdpTyMXFBZMnT4aLi4u9k0JmMH+0i3mjbcwfbWP+aBfzRtuYP9qm9fxhByNEREREREQaxJI1IiIiIiIiDWKwRkREREREpEEM1oiIiIiIiDSIwRoREREREZEGMVgjIiIiIiLSIAZrREREREREGsRgjYiIiIiISIMYrBEREREREWkQgzUiIiIiIiINYrBGRERERESkQQzWiIiIiIiINIjBGhERERERkQYxWCMiIiIiItIgBmtEREREREQaxGCNiIiIiIhIgxisERERERERaRCDNSIiIiIiIg1isEZERERERKRBDNaIiIiIiIg0iMEaERERERGRBjFYIyIiIiIi0iAGa0RERERERBrEYI2IiIiIiEiDGKwRERERERFpkJO9E1AY6PV6REZGwsPDAzqdzt7JISIiIiIiOxERxMfHo3z58nBwyLrsjMFaPoiMjESlSpXsnQwiIiIiItKIiIgIVKxYMct5Cnyw9ujRI8ydOxfHjh3D8ePH8eDBA6xcuRIDBgywavmHDx9izJgx2LRpExISEtCsWTN89dVXaNy4sdVp8PDwAJCRIZ6enrnZDSIiIiIiKgDi4uJQqVIlNUbISoEP1u7du4dp06ahcuXK8Pf3x759+6xeVq/Xo2vXrjh79iw++eQTlCpVCkuXLkX79u1x8uRJVK9e3ar1KFUfPT09GawREREREZFVzaMKfLBWrlw5REVFwcfHBydOnEDTpk2tXjYoKAiHDx/GL7/8gh49egAAevXqhRo1amDy5MlYu3atrZJNRERERESFXIHvDdLFxQU+Pj65WjYoKAhly5ZF9+7d1XGlS5dGr169sGXLFiQnJ+dVMomIiIiIiIwU+GDtSZw+fRqNGzc26aWlWbNmSEhIwJUrV8wul5ycjLi4OKOBiIiIiIgoJxisZSEqKgrlypUzGa+Mi4yMNLvcrFmz4OXlpQ7sCZKIiIi0bN68eahVq5a9k0FEmTBYy0JiYiJcXFxMxru6uqrTzRk/fjxiY2PVISIiwqbpJCIiInoSiYmJiImJsXcyiCgTBmtZKFq0qNl2aUlJSep0c1xcXNSeH7XWA2Rqaiq8vb1NOkeJjIzEqVOn7JQqIiIisicRsXcSiMiMAt8b5JNQepLMTBlXvnz5/E7SE3N0dERsbKxJqeAnn3yCy5cv48SJE3ZKGREREdmTNd2IE1H+YslaFho2bIhTp05Br9cbjT927Bjc3NxQo0YNO6Us9xwcHODg4IC0tDSj8S+++CJOnjzJzlCIiIgKIZasEWkTg7X/FxUVhUuXLiE1NVUd16NHD9y5cwcbN25Ux927dw+//PILunXrZrY929PAycnJaD8BqI2Kw8LC7JAiIiIisicRYckakQYVimqQixcvxsOHD9XeG7dt24abN28CAIYNGwYvLy+MHz8eq1atwo0bN/DMM88AyAjWWrRogYEDB+LChQsoVaoUli5divT0dEydOtVeu/PEnJ2dTUrWlH0OCwtDgwYN7JAqIiIisicGa0TaUyiCtS+//BLh4eHq940bN6qlZW+++Sa8vLzMLufo6IgdO3bgk08+wcKFC5GYmIimTZsiMDAQNWvWzJe024KTk5NJsFa2bFnMmjXrqd4vIiIiyh1WgyTSpkIRrFlTtS8wMBCBgYEm44sXL44VK1ZgxYoVeZ8wO3F2djapBqnT6TBu3Dg7pYiIiIjsjSVrRNrDNmuFkLmSNSIiIiq8WLJGpE0M1gqh27dvY9KkSQgNDbV3UoiIiEgjWLJGpD0M1gqx69ev2zsJREREpAEsWSPSJgZrhRifoBEREZGC9wVE2sNgrRDL3MkImafT6TBnzhx7J4OIiChPPXr0CHfv3gXAkjUirWKwVohlDtauXLmCzp074/79+3ZKkfYof16rVq2yc0qIiIienIjg119/xdKlS9GoUSOUKVNGncaSNSLtKRRd95N5KSkpRt89PDywf/9+rFixAmPHjrVTqrQlOTnZ3kkgIiLKM1u2bMFrr71mMp4la0TaxGCtEMtcslauXDnUrl2bvUQaSEpKAsCnjURE9HRKT0+Ho6MjgIyHtOYCtddeew0nTpzgfx2RBrEaZCFmrs2ar68vvvvuO0ybNi3Pt/fXX3/h6NGjeb5eW1KCNSKtmj17Nm7cuGHvZBBRDoWHh9ukNCs0NNSoOcP777+PN998EyKC6Ohoo3lfffVVAEBwcDBu3ryZ52kpqG7duoWpU6fin3/+sXdSqBBgsFaImQvWBgwYAAB4/Phxnm/P398fLVu2NBp34MAB/PXXX0Zpys9XCgQFBeH7779HUFCQ2emJiYk23X58fDzi4+Ntug2t+PjjjzF8+HB7J0NTNm7ciGbNmuV6+ZSUFIwfPx6dO3fOw1QRFU7x8fHYtGkTAODu3bsICQnJ820kJiZiz549iI6OxjPPPINFixaZnW/WrFm57tjK398f33//PaZMmYJq1aqhTp06+Omnn9C5c2fs378fAHDixAkUKVIEJUuWxIABA5CUlAQHBwejkrVFixap9wT0r/T0dPz222+YMmUKfH1982Sd7PCNsiRkc7GxsQJAYmNj7Z0UERFp2LChAJAvvvhCAgICpEGDBuq09PR0KVKkiCxcuDDPtwtAAEhkZKSkp6eLiEjdunUFgNSuXVsaNGgg7777rtSuXVudbmtKmgDI0KFDZeDAgUbTL1y4IACkTp06IiISGRkpAwYMkOTkZKvWf/fuXYmMjLQ4vUiRIuLs7Cyff/657Nu3T/R6vVXrTU9Pt3iM9uzZI+vXr7dqPeYsXrxY9uzZYzL+7NmzVqfPnICAAHn99ddztIxer5f79+/nepvWePTokRw4cCDXy6ekpMi9e/dytWzFihUFgMTHx+dq+ejoaPX327t3b0lMTLQ4b2JionzxxRfy8OHDXG0rOw8fPpS0tDSbrPtJhIaGSkJCgtXzJyQkyN9//200Li4uLss8TkpKyvLYm3PkyBHZtm1bjpZ5GoWFhRn9LoKDg+XQoUNWLfsk1xtz7t+/L5cuXbI4ff369QJAzp07J61atRIAOU5DfHy83Llzx+L0jz/+WABI/fr1BYC0aNFCRDL21fAeoX///vLss8/maNuKihUrioODg3ptiImJUT+/9NJLAkBu374tJUuWlM8//1xEREqWLClOTk5SuXJluXXrljRo0EBdZu3atbJgwQLp2bOnREREWJUGvV4ver1ekpKS5MKFC/LZZ5/l+FieP39eLl++nOP9t7Wvv/7a6N4hL/Tt21fefvtto3ExMTHy1Vdf5fl5YMnNmzdlzpw5Nv/PpQw5iQ0YrOUDrQVrIiJFixY1utgoF+Dbt28LAKlQoYL07NnzibczYcIEWbZsmSQkJBhtz9/fXx4/fizNmjUzGv/hhx9KmTJlJDQ09Im3bQ3DbSvDn3/+qd5EzZ071yhYGzp0qACQY8eOWXWDXq1aNQEgN2/etGr7ISEhFtd19+5duXXrloiIzJ8/X1xcXOTcuXMiIvLhhx/KypUrRUTE09NTAEhqaqo8ePBAPv30UwEg8+fPt7juqKgo+eeff4zSpHjw4IGcPHlSAMiPP/5ocR3p6ely5coVefDggaxevVoNClJTU2XChAlSo0YNk2A4swEDBkj58uXltddek9WrV6tpCQ8Pt7iMXq+XGjVqyPbt243GT5s2TV599VX1+6NHj+TevXty4MABadWqlZw8eVJERObNm6fmaW6MGzdOPb+VmxNr+fn5qft49+5ddfzmzZvl8ePHsnXr1iwDyStXrhj9fhYtWmQyT3h4uGzevFnWrFmjznft2jWr0hceHq7+5jLT6/Xyww8/qPsNQLy9vaVPnz5WrduSJUuWyIwZM+Srr76yKshKTU21+OBi3bp1AkBee+01q7ffuHFjASCnTp0SPz8/mTFjhppPhq5fvy7PPPOMREdHi6enp3Ts2FFOnz4ter1ePv74Yxk9erRcv35dAgMDRUQkJCTE6MFNbm707t+/LytWrJBbt27J9evXZcWKFaLX6yUlJSVH67HG7t275bvvvhMRUfPhzp07Rr+ds2fPSrdu3WTHjh2ydu1ak3UkJiYKAPnss89k9erVsm3bNnn++eelR48ecuTIEUlNTZXk5GSL+Tdv3jypVKmSHDx4UPr06SN6vV4OHTqk3ryOGjVK5syZIyIix48flxo1asipU6cs7pNyPc6cxsjISJk9e7YkJSWJg4ODLFiwQLy9vQWAHDlyxORmGYDMmjVLRDKC7vHjx8vatWtlypQp6vUXgGzYsEFdRgle3n77bZPr/q1bt+S9994TAOp1SbnGK3m7ePFiiYyMtOrGvX79+uqDUOV8nzlzppQoUUKSk5Plxo0bIiJSpUoVGT9+vIiIeHt7S5EiRcTX11fef/99s/+NOfnN/v333+Lo6CjOzs7qcpb+By3Jy2Aor8TGxsoLL7yQJ8FacHCwVKtWTW7cuCHFihWT6dOni0jGA6Zvv/1WvT+6fv26PHz4UAYNGiRxcXG52taWLVvkypUrZqeFh4eLTqeT0aNHq9c+xddffy1fffVVrrapOH78uOzfv/+J1pFXtm/fLo8ePbJ3MkSEwZrmaDFYy3zx1el0kpKSInq9Xu7evSsDBw6UevXqiYjItWvXcv00/vnnn7d4wZ85c6Zs3rxZXFxc1HE///xzjm52n1RWf0hKgAJA6tatKyIi7du3FwDqH2tISIjMnTtXKlasaLTekJAQ2bx5s7q8t7e3HDx4UPR6vTx48MDi9pVgKCYmRl566SX53//+J7t375b4+Hj1JiA8PFyd38PDQzZs2KB+nz59utEf43PPPWe0/ilTppg9DnXq1BEAcvXq1SyPSZkyZcTBwUGqV68u169fl549e0rt2rVlxYoV6jwVKlRQP+/YsUOCg4PV788//7ysW7dOjh8/LiIin332mVSoUEE6d+4sDx8+tLjdVatWyb59++Tq1aty9epVEfn3CfWUKVOMjt+BAwckICBAHfftt99KWlqadOvWzWS9R44ckS+//FLNU5GMG+IbN24Y3RTp9XqZNGmSepPz4MEDGTVqlNy6dUt8fX0FgMyZM8do3Y8ePZLw8HAZPny4GlSLZJSGBQcHi4hI7dq1szzehulMT09Xb/iUtP35558m83bp0kV+//13dXtNmzY1madRo0YSHh4uL7zwgvj4+MiRI0dk79696jKnT5+Wu3fvqr+xiIgI9YZ65syZMmvWLPn9998FyCghuHHjhtmbl/Pnz6vrDQkJkW3btsnIkSMlNTVVRDIeQERFRcnzzz8vN2/elOXLlxutJy4uTo4ePSorV66UZ599Vs6cOSNbt241Op5TpkyRTp06qetU8mfGjBlGT8AVx48ft3hDP3bsWHX+4sWLmxy32bNni0hGKaLh79xwMCyRUIbOnTsLAGnXrp26LXM3evfv35dBgwaZ3ExcvXpVdu7cKTt37jS6phhuY86cOVKlShX5+uuvjZbdvXu3nDlzxuz+KjZu3GgS6Ge+plSvXl0ASMmSJdV5vLy8jNIQGBhoFNybOz7du3dXS5SHDRsmOp1OevToIZs3b5YFCxZIu3bt5OrVq0a/BeWmde3atep2lHUAkPT0dLO/P0NpaWlmp1euXFkdf+XKFSlXrpzZdI8ePVouXrwoqampAkCqVaumniNZDd999516XR02bJgMHz7caLphUKUM0dHRsn//fgEgf//9t1HJGAA5evRolvnZrl076du3r/r/e/ToUVm6dKk4OjoaXdf8/f3lgw8+EBERDw8PKVq0qPj6+kp6eroUK1ZMypYta/Z4DB48WCpXrmwxcLh69aq4u7ubPR5Z0ev1smzZMomNjZXJkyery6xevVq2bNmS5bLZiYiIkD179siyZcuynXf9+vUybNgwAaBe8xXt2rUTR0dHcXNzU4+ZtSVf586dk1WrVgmQ8QAp87FR/hcyj583b560adNGgIySUUNJSUmycOHCbGs1ABBPT0+z05RrufIQv2XLlvLpp5+qD+Gyy7esZD7vUlNT1eO1fft28ff3N5r/0KFD4u3tLY8fP871Ns05efKkHD58WADIkCFD8nTducVgTWO0Hqw5ODiYVHubPXu2eHl5yV9//SUApG/fvtK7d2+LT9kzCw8Pl2PHjslHH31ktK2hQ4dKnz591O9K4KLceBveMNpSenq6nD592iht33zzjdFTwPnz5xtNf/HFF7P8U1YkJydbnOeNN94QAGrJXXZ/9OaGH374IVfLWbroGl6QbTFYuqk1F0RYGgx/MwDkq6++MjtfixYt1KfnhoO5QM3c8Mknn8jMmTPF0dHRqLRiz549Zue3dGOXeejVq5ekp6dLr1691HFvvvlmjo7juHHjpHLlyuof6jfffCM7duwQANKkSROTP/+XX35Zzp07JzVr1rR6G8WKFZPXX3/d4nRLwfyxY8eMvvft29fo+9mzZ42+BwYGSosWLbJNj1IqnHlwcHBQ8+a7774TAGoAp9fr5cMPPxQAsn//fvW38+mnn8qBAwfUdYwfP15SU1MlLS1N0tPTJSUlRX2ynNXg5uYmL7/8co7yThlKlSolv/32m9G5v2PHDunTp48cOXJEBg8eLACkffv2MmDAADl//ryULVtWnbdkyZJWbWf+/PmyatUq0el0AkD++9//Gp3vys1STEyM0fVKuSG+d++eOu6tt94yWX9ycrJMnjzZKGD08PAQADJq1CgR+fd/z9ygpMtwUKrnZzUoD2Ey/77+97//GX1fuXKlHDp0SD3WTZo0kREjRqjTlZK0zDU79uzZY3RO16pVS31Apwy//vqrOm3kyJHZprljx45GpW2Zf9OZa7ko6VOO3+rVq40CSgDSpk0bEcl4WBMTE2NyTX/11Velc+fOcuvWLTXNP/30kwAwehDQunVreeutt0RExM3NTYoVKybPPPOM+h+p1+uNHsSZG7777jsZN26ciGSUCMXGxqr/5+YGEZFPPvlE9u3bJ2FhYfLw4UP1mGT+T8487Ny5U9auXavWQlBqFuzcuVPdp9TUVLlz54789ttvsmHDBvHy8jL5vZ05c0b0er0kJCTI7t27ja71M2bMMJrX19dXRowYIbt27ZKHDx+KTqeThQsXSkJCgkRERKil6deuXTMJXu/du6dWcTcMWswNfn5+EhMTI3Fxcdn+piIiIqRt27Zy9OhRNX+UUtx169bJoEGD5NChQ1K3bl15+PBhtkHXli1bst2mYc2V9PR0OXv2rNl1GTpy5IjROooVKyZt27aVOnXqyK5du9TxFy9elGrVqsmQIUPUcZlrV0VGRsru3btlz5498ujRo2ybDuj1epkyZYp06dJFfcCiDJ06dco27fmBwZrGaD1YGzt2rMn0wMBAAf6t3244fPTRRzJ06NAsn+RMnz5dypYtK48fP1b/JIB/6/9v3LhRnnvuOfWplfIHqFTFs7WpU6eqaZo/f766XXM3JkDGU/YmTZpkeTELCAiQXbt2Se/evbO98HXv3l1E/s2H8ePHW9x2Xg+ZpaWlyYQJE0zmW7NmjWzfvt3kptrJycnsejPf+Fga2rVrZ/R948aNJut85pln1ONer149o9LXrIbRo0cbVS18//335cKFC2bzbt68eRb3Bci4qenfv7/FQK18+fJWH3NnZ2cZOHCgxelbt27NdX6WLVtWvbH6+eef1RIQIKPUq0qVKjb/TS1cuDDL6ZMmTTL6rjy1zm5QqpiaG+7evSu7du2SH3/8UQBIlSpVJDk52aikIigoSMLCwtTvr7zyisl6KlSoIG3btpXKlStLqVKlLAaIWQ3bt2+XqlWrms2bzA+sgIxzwLBE2NaDUprj5uamjlPOCcO8cXd3l7S0NLl69ar06NFDAMgHH3xgsr5atWqZjDt37px4eXmp1QOVkqEnHczVzsjuWmw4LF26VCpVqpSrbffu3dvoOp15sFR6VKRIEQFgdC5aGjp27Gj0e9m8ebN6bba0jJeXl4SEhAgAmTx5ssk1fcOGDbJ27VpJT0+XO3fuSGpqqvofa/jAddy4cWqbNRcXF/H09FSDNYVer5fo6Gg5evRolrUfSpQooX5u2bKlxfkyB7+Gg2FAnZPB8BpnGBhnNRhev8eMGSP37983ekiReahdu7YcPnxYevfubVKNfNu2bQJk1Dz57LPPRETUBwuNGjUSEVFLdSwNSgBq6f/GcOjSpYv6WTl/lX4GMs/7xx9/GOWbSEY764iICJPmKdYMo0aNUpuHKFV2z58/L//5z3+kZ8+ecuXKFbXNplJjJzdDcHCwHDhwQG0LXLp0aZN5oqOjLTYdUa5fgOlD1Y4dO5pdJr8xWNMYLQdrmYugFUpbD6WRtbmhVKlSFhvVjx8/XqpUqaJ+j4+Pz7YdWn5Vf9Tr9UYXd8PSvOTkZPnjjz9M9jUtLc2qIMxw+PDDD42qJRoOysVe+T5x4kQREZMqMj4+Pupnw2qZ5r4rDdaHDBlidIMzdOhQad26tdEFzpzVq1fLxIkT5fnnn5euXbuaHDNle3v37pX79+/LnTt3JD4+Xl1vr169JDo6Wrp27SqXL1+W119/Xb1p8fHxkY4dO8o///wjKSkpRunW6/Vy48YNoxub7777TmrXri1hYWHqH4M1g+E6lKfPIhkB6fXr143m/fLLLyUqKkoaNmwo/v7+JuvKHFRmHjZt2iQeHh6yZ88e9Xhv3bpVQkJC5NatW3L16lXp1q2b0W/NXGmScp4o3w1LcEeMGCF79+41qc5qOOzevdskLzOXPE6aNEkeP35stO8AzP4BAhk3gHq9Xi3B6NSpk8Xt63Q6iwFhgwYNpEuXLhIQEGB0I+To6Gjxhvv69esSGhoqt2/fVm+GDQMbw8A2ICDAqNRUeSpdrFgxASBLliwxOrYAxMXFJcubd71eL0WLFpXZs2fL/fv3Lc5XoUIFOXTokJw/f15EMp42h4WFyc2bN+Xw4cOyZs0auXv3riQmJpq9QVVK0cwNNWrUsDht2bJlRt9//vlnGTNmTJYltWPGjDE73tnZWX2q3rlzZ5POI1q3bi1vvvmmXL161agk3PDmR3kYt337dgEgK1asEBFRayYoQYIyZC5JcnR0FABG1RqVYdGiRbJy5cosz8O8GCw9FFBKrXK6vnr16gmQcY2wNE9ERIT6P6D8T2zdutXo+Fuzra1bt6oPTg1LTTO7fPmyjBw50qhtrCEnJyfx9vY2+t82p3bt2jJy5Ei1Wl52Q3Ylc9YMGzZssPoBT14M5h48uru7S5cuXaRDhw4mxyTzvJlL0TKf60pVSMNBKR1UmlgYDnq9Xu1PwNKgtG3OPN7Pz0+6du1qtC7lXPvll1/U8U5OTnLmzBlZs2aNREZGyp07d6Rhw4ZGD3gyD506dTL5X1UGw+rgly9flmvXruUqL7p06SIzZ87Mdr4//vhDQkNDJTIyUkaNGpXlg9iAgIAsf+P5hcGaxmgxWFOCMaUtVmbKn4xyExscHGxStaFx48YW1z9ixAiL67Y35QZMCYSioqKMppu7+IhkVC0xHKdUr8pcwrJ37171Bi4tLU127dpl1Lbh+vXr6raUcZMmTRIR4yqJJ06cUDuF2LVrl4j82/lB6dKljUoMgIw/xdDQUNHr9Wr+AhnV5d599131e1admOTGiRMn1Au/tYKDg2XEiBGydOlSdZzyBHfGjBlGHQ4Y3jC/9tpr8u2336rflTZfStU9w2pZ5hpTBwUFyeeffy7AvwHyG2+8If/5z3/kjz/+kGHDhsmSJUtkwYIFWf4xNG/eXERETafypNNc2yDD9MfExIher5dBgwbJqVOnZMaMGWpvZ3FxcbJmzRrR6/VqsGJIOf8mTJgg586dM/ptmmMYxJw+fVpEMqql7N69W0JDQwXIuIkXyWhHd+jQIVm8eLEAUNs9LV26VADI22+/bdJGKvMwZcoUmTp1quj1erX6Vd26dSU8PFxtf6DMW7RoUVmyZInZ9RjebBru4/Tp06Vp06ZG1esaNGggzZs3l6+//lqWL18uixYtEp1Opz7Znzp1qoj8W3JfrVo1WbVqlYhkdKqjBAKG54dIRi+1SvvO2NhY+f3339WnuMp8rq6uFo+9Ocq1dMiQIVK7dm05efKkVKtWTU6cOCFvvPGGxMXFye+//y4pKSlGDzSCgoLUBylAxoOMI0eOSKtWrWT16tVG2zB3PI8dO2ZUojhnzhwpVaqUVK1aVa5cuWK2xEXx119/GbXZMTxGsbGxkpSUJMnJyfLTTz/JqVOnBPi3KmVUVJT6IMywJC7z9XLevHnSvn17eemll+Tx48dGT+MXLlwocXFxMnfuXImLizNqU6gMn376qSxZskRu3rwpwL8lcYZBgrL9S5cuyc6dO6VatWri5+cn48ePV6/VyrzKzakh5aFT5raZQMZDS8Pv/fv3l/DwcDVvMt/svvXWW+r5qFDaDGXu+fDs2bNqrQ6l5Or9999Xr09169aVdu3aqW1w7t27Jw4ODkalc9bS6XRSsmTJbIO1tm3bSr9+/SQ6Olq+/PJLmTBhQpZVmiMjI82Ov337drY34ZGRkbJx40YRyfgvNSwFVqoiGwZCrq6uMn36dJkyZYrcv3/f6GGpYcnWb7/9JmPHjrX4wOTLL78UV1dX9bthkwhzJZmZl4+IiDC73tatW8uQIUPkn3/+UTvfqVOnjuzfv18eP34ser1ePDw8ZMKECeq1xrDDJsNOtwyH3r17y82bN02aAJirkaL0cG1uMCcoKEgcHR2fqJRM0b9/f6NeSr/55pscPQTPKgDLyWAu4LYHBmsao8VgTWmoXrVqVbPTlaekSkmF8odt+IOfO3euJCUlyY0bN9QG9EppxeDBg6Vp06b5uEfWu3jxogAZ7ROUBr2GkpKS1LYhjo6OarUGpbrKxx9/LEFBQZKWliZpaWkSFBQkAKRHjx7qzaE5SrUIczejho2egYx68orMAYBht/2nT59Wn9ItWLDAaL67d+/KqlWrJDU1Va2Cd/jwYesPlEYof1rTpk1TxyldbMfExMjOnTvVqiNK+zhLHako6tatKyNGjBCRjOpaSjCsUALCzB2AKKVTP/zwg9H8SrAWFhZmdns7d+6UN954Ixd7/y+9Xm9UTz+rP1gRUUsGlTZEmW3YsMGkNDs1NVWmTZumbkf5Y587d65RD3GZe0MbOnSoUZfoShuBLl26GK1fmX/WrFlqBzzz58+XnTt3ysSJE9VzLbt9nDt3rnh5eUnv3r2Nqm1duHBBFixYID169JA2bdpY1fA/ISFBfUji4eGhtmGzRNk3c9XHsxIXFye3b9+WcePGWbzuGpo5c6ZRD6W3bt2SKlWqZFlDYc6cOWrbMcNjZ/jAwLBbbsPOiazpUTKr35zykMvcaz9ERL744gsBTKtQLlmyRAIDA9UOTq5cuSIbNmyQ2bNnm/3PVJY7ePCgUe+aycnJ0qBBAzlx4oQ67vjx47J48WK5f/++XLhwwap9i4qKMmoDJZLRpblSCpa5/UvmEk3DHmhFMmqVPHjwQPbt2ycALPZEl92rH8qUKSMAjErRRERef/11efHFF0VE1M6nMqc/O8rvv3Tp0uLn55flvK+//rrZkom4uDg1ON++fbtUrlxZbVuq1+vl/PnzZm/gDUt3atSoYdT21pzTp0/L3r17JS0tTZKTk43yI/OxNZwmIvLPP/+YtHVSHmR0795dihUrJrVq1ZLIyEhJSUmRZ599Vv1NGP4+MstcVXfjxo1G35VzMvN/dFhYmFGHY0o+KKXT8fHxRh0nKQ8kMg8pKSlG532vXr3k8uXLai2hwMBAWb9+vYwfP1797zQ3ZEWpXq60QzMXOPXt21d++eUX+c9//qOOO3LkiMm6DI+lXq83qulgqXp4hQoVJDo6Wr3XBIxLBq0ZfHx8ZOjQoWrVVHtjsKYxWgzWlBvgypUrm52+d+9eAf5tO6Q0nFV+9MePH5ekpCT1D9hw8PX1lb59+xr1fKYlSicDFy9etDjPr7/+alIy07ZtWwFg8qevBL7KE3tLHj16ZNJ1sbe3t7Rv396o/d/vv/9u9btsRER+++03AWByo2soMjJShg8frsn3YGUnMTFRTp48mWU1WaUkR2nzkFXQLJIRyHz44YdZzvP222/Lc889Z9RI2hKlumB+vp8mMjIyy3f4hYWFPdH79hTKH6qIqKUImav5mHu1ws6dO02Oh+Fx1Ov1sn379iwDquyO+/z58832xtapUye1Xag1UlNTZebMmWobjOwkJCTk+l2QkyZNMuk9Ni8pHRB5enrK8uXL1fFKNWlDhlW+rXHs2DG1N9bMlGpO5h6AiYhaaqvcACvDt99+a+WeZVBKTXP6XrvsnD59OtuATnHnzh1ZvXq1+Pn5qe27larFtvrf69Chg9l8Gj16tBr8Kw8ic/reSCWoKVu2bLbBWnR0dK57h753754kJCSYXLcy/wZz8psUEenXr5/F+U+ePJnl6xyUh7DmOjdTHlAlJSXJP//8Y/G4tm/f3qjN3qBBgwSA2hZcqYWjvArDEqVXUyVYM2f9+vUSGxsr6enpMmHCBPH19ZXr16+rAY8SuItkVH0FMqoIKpYuXWo20Bo+fHiWaVOq9iYmJsqFCxdMqohnllUeKtMMr6EnTpyQ559/XhISEiQ+Pl62b9+udh4FQH2VlFINXLmfVs45IKNwYcCAAWobQsC4nXKFChVk0qRJUqFChSz3Nb8wWNMYLQZrSi+PPj4+ZqcrAc327dtlwoQJ6s1Q5hPQ8MmsYaPXatWqmTxV1wqlJCynN9ZKPfbMLzzV6/WyYcOGXN28FS1aNMv3n1nj8ePH8tJLL+X4HTYFkVJ917AULre6deumtt2rXLlylgHg/fv3s/0jLggWLFggRYsWFZGMrrC///77bB98GPrzzz+N3j2VncDAQPnmm28sTr97965cuXLFJFhr3bq1yQtmtWLmzJlSpkwZm60/LCxMnJycTLocj4qKMnmXoNI+Ly+CR6WnUMMbQ0PffPONAKbtNr///vscbef27dtW/97yk16vl7feeivb1yTk1oMHD8zWjNi1a5d8+umnkpycrPbKau1DB0VSUpIAGW0RrSn1zWuZ7ysOHz6co/depqam5vr9Y0r1ZHMl1klJSVYdy3bt2kmfPn3k0qVL8uDBA7VXS6WKsVKrJrsHuspDMGvPiVmzZkmJEiVEJCPQe/jwodF9SHJysuzfv98ouB4xYoTUrFlT7fVy06ZN6ns9s7J582aTh/vKe1XNvQ8TyOgIx5ycBOPKg9j+/fuLiKi9eCqUV05lFh8fr9Z+OnjwoDRs2FC8vLxkz5496rsF7S0nsYETqFDy8PAAAKSkpJid7uLiAgCoUKECZsyYoY4/c+aM0XzVqlUDAJQrVw4dOnTAjh07AADXrl1Dw4YN8zjVeeOFF17A0aNH4e3tnaPllGNVokQJo/E6nQ7du3fPVVqSk5Ph6uqaq2UVbm5u2LZt2xOto6BwdnbOs3UNHz5c/RweHp7lvCVKlMC7776bZ9vWquHDh6vHpWLFimjTpg1eeeUV9XqRnWeffRbPPvus1dvr379/ltNLlSqFUqVKmYxfs2YNHB0drd5OfipSpAiSk5Nttn5fX1+kpqaajPfx8YGPj4/ROJ1Oh9u3byM2NvaJt6vX6wEADg4OZqc7OWXcboiI2fHWKlu2LMqWLZuLFNqWTqfD6tWrbbZ+b29vtGzZ0mR8QEAAAgICMH36dEyaNAkAUKxYsRytOz09HQDsds6MHTvW6F7E3H5mxcnJSb2nyalu3brh7NmzqFKlisk0FxcXNG7cONt1iAgcHBxQs2ZNABn3RQcOHFC/v/rqqyhbtqzV9wk6nc6q+Tw8PPDo0SMAGeedl5eX0fQiRYqgbdu2RuNefPFF1K1bF4MHD8aECROs2g4AvPLKK3jllVeMxlWvXt3i/GFhYXBzczM77dKlS+pvLjvlypXDggUL0K9fPwAZx6Zo0aLqdEv/+e7u7urn1q1bIzg4GHFxcXjmmWfwwgsvWLVtLWGwVkh5enoCgMnJrVBuvjLfVPj7+xt9r1evHurXr49Ro0ahf//+qFevHv773//i+vXrJn/KWuHl5YXmzZvneLm1a9di7dq1Ob65sEREcOjQIbN/EmR/T+MFPb9Vr14dmzdvtncyTDzzzDP2ToJFLi4uFh+S2YOnp6f6f/AklGDN0o2mpUAgr66nhd3Dhw/Vz5Zuki0xDLStDRTy0uzZs/N9m4pp06Zh4sSJT7TfImK0vLu7O9q0aaOOd3R0tCqQz+k9k7u7O1JSUpCSkoIiRYpYtUzXrl1ztI3c8vX1tThNCWKtodPpjB6c5laJEiVMHrQ/TXiVLKS8vLzg4eGB+fPnm52unPjZ3VQ4ODjgr7/+Ur8HBATg0KFD8PPzw6BBg/IsvVpQu3ZtTJ8+Pc/Wp9PpcvwEkaxjjxsOImvYumTNXpQbTUsla2XKlAEAlC5d2mg8g7W8YVg6+rSVrNmTTqezOtCxJHOwZrhud3d3tfTLmvUoy1kjICAAf/zxh8VzjgoOXiULKUdHR8TFxVmcbqlkzRrlypVDYmJirtNG9KQYrJFWubi4QK/XIz09vUDdHGdXDbJz587Yvn07Hjx4gF9//VUdz2AtbxiWrOU2WLNXydrTTqkGaU779u3VBxXWsjYPypUrh3LlyuVo3fR0YjhOZllbskakJc899xyqVKmCvn372jspRGYp19aCVrqWXbCm0+nQpUsXk/EFKWC1p9jYWLRr1w6///57jkuKDIM1yjlLJWsAsHXrVrz55ptWr4fIHD7SIrNcXFzg4uJidSNQIi0oXrw4rl+/bu9kEFmk1FpISUnJcdsiLcuuzZriSTsYIfNiY2Ph7++P559/PsfLGgZrDBhyLqtgLafrAVgzhEzxKklmlS5dGklJSfZOBhFRgVKvXj3MnDnzidvJaE12bdYsYbCWN3bv3q0GzDll2GYtLS0tL5NVKOj1+jwNsBisUWa8ShIREeWTmjVr4tNPP7V3MvJcdtUgFSxZs42cvorGENusPZm8LlkjyowVlImIiOiJWFsNMjMGa/ZnWLJ2+fJlTJkyxb4JesqwGiTZ2lMRrKWkpODx48f2TgYRERGZwZK1p1fmDkb27t1rz+Q8dRiska1pKlhbt24dRo0aZTRu6tSpcHd3h7e3N1577TWr31dBRERE+cPaNmsM1rQn83vWGCzkTF4Fa0SWaCpY++qrr4xK0A4fPoypU6eiY8eOGDVqFHbu3ImZM2faMYVERESUmbUla5mx6377y1yyxsAjZ1iyRramqUdaoaGh6N+/v/p97dq18PHxwaZNm+Dk5AS9Xo8NGzZg1qxZdkwlERERGWKbtadX5kCbwULOZPVS7JyuB+DxJ1OaKllLTk6Gq6ur+n337t3o3LmzejGvU6cObt68aa/kERERkRm56bp/+fLl8PPzs1WSyErFihVD586dUbx4cQB8OXZOsRok2ZqmzsgqVaqoDVtPnDiBa9euoVOnTur0O3fuwN3d3V7JIyIiIjNy08HIyy+/DE9PT5umi7JXpUoV7NixAzVq1ADAkp2cYjVIsjVN1T8YMmQIRowYgQsXLuDmzZuoWLEiXnrpJXV6SEgI6tata8cUEhERUWa5abPG9mraxGAhZ/LqpdgM1sgSTQVrw4YNg6urK3bs2IEmTZpg7NixKFq0KAAgJiYGt2/fxtChQ+2cSiIiIjJkbZs1FxcX9TOr22kLg4XcyetqkDz+lJmmgjUAGDx4MAYPHmwyvkSJEjhx4oQdUkRERERZsbbNWs+ePbF48WIcPnyYJWsaxWAhZ/K6GiRRZnysRURERE/E2mqQTk5O+Oyzz1CrVi32BKkxLFnLHbZZI1uz65XyP//5T46X0el0CA4OtkFqiIiIKDdy0nV/586d0blzZ1sniXKJwULOsBok2Zpdg7XcNMpkMTEREZG25Pal2KQdLNnJnbx+zxpRZnYN1vbt22fPzRMREVEeyM171kibGKzlDKtBkq3xqkpERERPhCVrTz8GC7nDapBka5pt3RsfH4/Y2Fj1D8BQ5cqV7ZAiIiIiMicnbdZI25iHOcPeIMnWNBesLVu2DF9//TWuX79ucZ709PR8TBERERFlhdUgn34sWcsdvhSbbE1TV9VvvvkGH3zwAapVq4YZM2ZARDBy5EiMGzcOPj4+8Pf3x/fff2/vZBIREZEBVoMsOBgs5AyrQZKtaeqqumjRInTs2BG//fYb3nvvPQBA165dMXPmTFy4cAHx8fG4f/++nVNJREREhhisPf1YOpo7rAZJtqapMzI0NBTdunUDADg7OwMAUlJSAABeXl549913sXTpUrulj4iIiEyxzVrBwTzMGfYGSbamqWDNy8sLaWlpAABPT0+4ubkhIiJCne7h4YHbt2/bK3lERERkBktlnn4MFnInr96zpuDxp8w0dVWtV68ezp49q35v0aIFli1bhlu3biEiIgLffvstatSoYccUEhERUWasBllwMFjIGVaDJFvTVG+Qb775Jr755hskJyfDxcUFU6dOxQsvvKB21e/s7IwNGzbYOZVERERkiMHa048la7nDapBka5oK1gYOHIiBAweq31u1aoXz589j27ZtcHR0REBAAEvWiIiINIZt1goO5mHOsDdIsjVNBWvm+Pn5YcSIEfZOBhEREVnANmtPP5bs5A6rQZKt8apKRERET4TVIAsOBms5w2qQZGuaKllzcHCw6keanp6eD6khIiIia7Aa5NOPwULu6PV6VoMkm9JUsDZp0iSTH2l6ejrCwsKwefNm1KxZEy+99JKdUkdERETmsBpkwcFgIWdYDZJsTVPB2pQpUyxOi4qKQosWLdjBCBERkcawZO3px5K13Mmr96zx+JMlT80jsHLlymHo0KGYPn26vZNCREREBpSqYLzRfHoxWMgd9gZJtvbUBGsAUKxYMdy4ccPeySAiIiIDed1uh+yH+ZgzrAZJtvbUBGvnzp3DwoULWQ2SiIhIY6pWrYqePXvaOxn0BNjuMHfYGyTZmqbOyCpVqsDPz89kKFGiBPz9/XHnzh18/fXXOV5vcnIyxo4di/Lly6No0aJo3rw59uzZk+1yU6ZMUat1GA6urq652T0iIqIC6cUXX8S6devsnQzKAwwWcobVIMnWNNXBSLt27Ux+pDqdDsWLF0fVqlXxxhtvoESJEjle74ABAxAUFISRI0eievXqCAwMRJcuXfDHH3+gdevW2S6/bNkyuLu7q98dHR1znAYiIiIirWLJTu6wGiTZmqaCtcDAwDxf5/Hjx7Fu3TrMnTsXo0ePBgC8/fbbqFevHsaMGYPDhw9nu44ePXqgVKlSeZ42IiIiIi1hsJYzrAZJtqapapC2EBQUBEdHR7z33nvqOFdXVwwaNAhHjhxBREREtusQEcTFxfGpBxERERVIDBZyhy/FJluza8natGnTcryMTqfDxIkTrZ7/9OnTqFGjBjw9PY3GN2vWDABw5swZVKpUKct1+Pn54dGjRyhWrBheffVVfPXVVyhbtqzF+ZOTk5GcnKx+j4uLszq9RERERPbCYCFn8vo9a0SZ2TVYM/cSbOUikflHq9Pp1KLmnARrUVFRKFeunMl4ZVxkZKTFZYsXL44PP/wQLVu2hIuLCw4ePIglS5bg+PHjOHHihEkAqJg1axamTp1qdRqJiIiI7Ikla7nDapBka3YN1vR6vdH3W7duoWvXrqhXrx5GjhyJmjVrAgAuXbqE+fPn48KFC9i+fXuOtpGYmAgXFxeT8UqPjomJiRaXHTFihNH3119/Hc2aNUO/fv2wdOlSjBs3zuxy48ePx0cffaR+j4uLy7b0joiIiMjeGCzkDIM1sjVNtVn74IMPUL16dfz444949tln4eHhAQ8PDzRt2hQ//fQTqlatig8++CBH6yxatKhRlURFUlKSOj0n+vbtCx8fH+zdu9fiPC4uLvD09DQaiIiIiLSKwULu5HXX/USZaSpY+/333/Gf//zH4vQOHTogODg4R+ssV64coqKiTMYr48qXL5+zRAKoVKkSYmJicrwcERERkZYx8MgZlqyRrWkqWHN1dcWRI0csTj98+HCOX0jdsGFDXLlyxaSTj2PHjqnTc0JEEBYWhtKlS+doOSIiIiKtYrCQOwzWyNY0Faz169cPP/30E4YPH46rV69Cr9dDr9fj6tWrGDZsGNauXYt+/frlaJ09evRAeno6li9fro5LTk7GypUr0bx5c7Ut2T///INLly4ZLXv37l2T9S1btgx3795Fp06dcrGHRERERNrFYCFn8roaJI8/Zaapl2LPmTMH9+7dw+LFi7FkyRK1K1S9Xg8RQZ8+fTBnzpwcrbN58+bo2bMnxo8fj+joaFSrVg2rVq1CWFgYvv/+e3W+t99+G/v37zfqhdLX1xe9e/dG/fr14erqikOHDmHdunVo2LAhhgwZkjc7TURERGRnLNnJnbwuWSPKTFPBWpEiRbBmzRp88skn2LFjB8LDwwFkBE2dO3eGv79/rta7evVqTJw4EWvWrMGDBw/QoEED/Prrr2jbtm2Wy/Xr1w+HDx/Ghg0bkJSUBF9fX4wZMwYTJkyAm5tbrtJCREREpFUM1nJGr9fn6XvWePwpM00Fa4oGDRqgQYMGebY+V1dXzJ07F3PnzrU4z759+0zGfffdd3mWBiIiIiKtUoKFvAg8ChNWgyRb4xlJRERERAAYLOQUq0GSrdm1ZM3BwQEODg5ISEhAkSJF4ODgkO0PXqfTIS0tLZ9SSERERFTwsRpe7rA3SLI1uwZrkyZNgk6ng5OTk9F3IiIiIsp/vA/LGVaDJFuza7A2ZcqULL8TERERke2xZCf3WA2SbIlt1oiIiIgIAIO1nMjLAJfBMlmiqWAtODjYpMfGH374AZUrV0bZsmUxatQopKen2yl1RERERAUTg4Wcs8Ux4/GnzDQVrE2ZMgVnz55Vv//9998YMmQISpcujfbt22PhwoX48ssv7ZhCIiIiooKLwYL1bFGyRpSZpt6zdvHiRbz++uvq9zVr1sDT0xMHDx6Em5sbhg4ditWrV2Ps2LF2TCURERFRwcKStZxzcHBAeno6q0GSTWmqZO3x48fw9PRUv+/cuROdOnWCm5sbAKBp06YIDw+3V/KIiIiICjQGC9bT6XRWvXYqp+skMqSpYK1SpUr4888/AQDXrl3DuXPnEBAQoE6PiYmBi4uLvZJHREREVCCxZMe+WA2SLNFUNch+/fph2rRpuHXrFs6fP4/ixYvjlVdeUaefPHkSNWrUsGMKiYiIiAouBmv2wWCZLNFUsDZhwgSkpKRgx44dqFy5MgIDA+Ht7Q0go1Rt3759GDFihH0TSURERFTAMFjQBh5/ykxTwZqTkxNmzpyJmTNnmkwrUaIEbt++bYdUERERERUODg6aaiFTaLAaJFmi2TMyKioKZ8+exePHj+2dFCIiIqICjSVr9sXjT5ZoLljbsmULatWqhYoVK6Jx48Y4duwYAODevXto1KgRNm3aZOcUEhERERVMDBbsi8efMtNUsLZt2zZ0794dpUqVwuTJk42KhEuVKoUKFSogMDDQfgkkIiIiKoBYsmNfrAZJlmgqWJs2bRratm2LQ4cO4YMPPjCZ3rJlS5w+fdoOKSMiIiIq+Bis2QeDZbJEU8HauXPn0KtXL4vTy5Yti+jo6HxMEREREVHBx2BBG3j8KTNNBWtubm5Zdihy/fp1lCxZMh9TRERERFR4MFiwD1aDJEs0Faw9//zzWLVqFdLS0kym3b59G9999x0CAgLskDIiIiKigosla/bF40+WaCpYmzlzJm7evImmTZvi22+/hU6nw65du/DZZ5+hfv360Ov1mDx5sr2TSURERFQgMViwLx5/ykxTwVrNmjVx6NAhlCxZEhMnToSIYO7cufj8889Rv359hISEwNfX197JJCIiIipQWLJjX6wGSZY42TsBmdWtWxd79+7FgwcPcO3aNej1evj5+cHLywuBgYF4+eWXceXKFXsnk4iIiKjAYbBmHwyWyRJNBGspKSnYunUrQkNDUbx4cbz00ksoX748mjZtioSEBCxevBjz58/H7du3UbVqVXsnl4iIiKhAERH4+vri3XfftXdSCjUGa5SZ3YO1yMhItG/fHqGhoepTBVdXV2zbtg1FihRB3759cevWLTRr1gyLFi1C9+7d7ZxiIiIiooKnXr168PHxsXcyCiVWgyRL7B6sTZgwATdu3MCYMWPQpk0b3LhxA9OmTcN7772He/fuoW7duvjxxx/Rrl07eyeViIiIqEBisGBfrAZJltg9WNuzZw8GDhyIWbNmqeN8fHzQs2dPdO3aFVu2bIGDg6b6QSEiIiIqcBgo2A+DNbLE7lHQnTt30KJFC6Nxyvd33nmHgRoRERGRjbFkjUib7B4Jpaenw9XV1Wic8t3Ly8seSSIiIiIqdFiqYz8sWSNL7F4NEgDCwsJw6tQp9XtsbCwA4OrVq/D29jaZv3HjxvmVNCIiIqICT0QYKNgRgzWyRBPB2sSJEzFx4kST8e+//77Rd+VCkp6enl9JIyIiIioUGCjYH/OAMrN7sLZy5Up7J4GIiIioUGObNfvi8SdL7B6s9e/f395JICIiIir0WKpjP6wGSZbYvYMRIiIiIrIvluxoA4M1yozBGhERERExULAjBstkCYM1IiIiokKOwYJ9sRokWcJgjYiIiIgYKGgA84AyY7BGREREVMixZM2+ePzJEgZrRERERMRSHTtiNUiyhMEaERERUSHHkh1tYLBGmTFYIyIiIiIGCnbEYJksYbBGREREVMgxWLAvVoMkSxisERERERVyIsJAQQOYB5QZgzUiIiIiYqBgRyzZJEsYrBEREREVcgwW7IvVIMkSBmtERERExEBBA5gHlBmDNSIiIqJCjiVr9sXjT5YwWCMiIiIilurYEatBkiUM1oiIiIgKOZbsaAODNcqMwRoRERERMVCwIwbLZAmDNSIiIqJCjsGCffn4+KBXr15wc3Ozd1JIY5zsnQAiIiIisj+WrNlPo0aNsH79ensngzSIJWtEREREhRxL1oi0icEaEREREbFkjUiDGKwRERERFXIsWSPSJgZrRERERMSSNSINYrBGREREVMixZI1ImxisERERERFL1og0iMEaERERUSHHkjUibWKwRkREREQsWSPSoEIRrCUnJ2Ps2LEoX748ihYtiubNm2PPnj1WLXvr1i306tUL3t7e8PT0xCuvvILr16/bOMVERERE+ee5555Dw4YN7Z0MIspEJ4Wg3LtPnz4ICgrCyJEjUb16dQQGBuLPP//EH3/8gdatW1tc7tGjR2jcuDFiY2Px8ccfw9nZGfPmzYOI4MyZMyhZsqRV24+Li4OXlxdiY2Ph6emZV7tFRERERERPmZzEBgU+WDt+/DiaN2+OuXPnYvTo0QCApKQk1KtXD2XKlMHhw4ctLvvFF19g7NixOH78OJo2bQoAuHTpEurVq4cxY8bg888/tyoNDNaIiIiIiAjIWWxQ4KtBBgUFwdHREe+99546ztXVFYMGDcKRI0cQERGR5bJNmzZVAzUAqFWrFjp06ICff/7ZpukmIiIiIqLCrcAHa6dPn0aNGjVMotZmzZoBAM6cOWN2Ob1ej7/++gvPPvusybRmzZohNDQU8fHxZpdNTk5GXFyc0UBERERERJQTBT5Yi4qKQrly5UzGK+MiIyPNLhcTE4Pk5ORcLTtr1ix4eXmpQ6VKlXKbfCIiIiIiKqQKfLCWmJgIFxcXk/Gurq7qdEvLAcjVsuPHj0dsbKw6ZFXVkoiIiIiIyBwneyfA1ooWLYrk5GST8UlJSep0S8sByNWyLi4uZoM8IiIiIiIiaxX4krVy5cohKirKZLwyrnz58maXK1GiBFxcXHK1LBERERER0ZMq8MFaw4YNceXKFZNOPo4dO6ZON8fBwQH169fHiRMnTKYdO3YMfn5+8PDwyPP0EhERERERAYWgGmSPHj3w5ZdfYvny5ep71pKTk7Fy5Uo0b95c7fzjn3/+QUJCAmrVqmW07Lhx43DixAm1V8jLly/j999/V9dlDeVVduwVkoiIiIiocFNiAmted13gX4oNAL169cKmTZswatQoVKtWDatWrcLx48cRHByMtm3bAgDat2+P/fv3Gx20+Ph4NGrUCPHx8Rg9ejScnZ3x9ddfIz09HWfOnEHp0qWt2v7NmzfZIyQREREREakiIiJQsWLFLOcpFMFaUlISJk6ciB9//BEPHjxAgwYNMH36dHTs2FGdx1ywBmQEWqNGjcLu3buh1+vRvn17zJs3D9WqVbN6+3q9HpGRkfDw8IBOp3vi/YmLi0OlSpUQERGR7VvPKf8xf7SLeaNtzB9tY/5oF/NG25g/2maP/BERxMfHo3z58nBwyLpVWqEI1gqauLg4eHl5ITY2lie9BjF/tIt5o23MH21j/mgX80bbmD/apvX8KfAdjBARERERET2NGKwRERERERFpEIO1p5CLiwsmT57MF29rFPNHu5g32sb80Tbmj3Yxb7SN+aNtWs8ftlkjIiIiIiLSIJasERERERERaRCDNSIiIiIiIg1isEZERERERKRBDNaIiIiIiIg0iMEaEREREREVOAWhH0UGa0T01CsIF2MiInp6xMbG2jsJlIX169cDAHQ6nZ1T8uQYrGnA6dOn8c8//xid+Lz51IaEhAR7J4GycP36dSQkJCApKcneSSEzzp49i6tXr+LmzZvqOF7btGHLli14//33cf36dQCAXq+3c4rI0P/+9z94eHggJCTE3kmhTDZu3IiAgADMmzcPYWFh9k4OZbJu3TpUrVoVffr0waFDh+ydnDzBYM2OLl68iNatW6NDhw7w9/dHs2bNsGHDBqSlpUGn0/Gmxo4uX76MJk2a4N1337V3UsiMv/76C127dkW3bt1QpUoVtG/fHiEhITxnNOKvv/7Ciy++iJdeeglNmjSBv78/Fi5cqF7byL727NmD1157DWvWrMGvv/4KAHBw4O2AFpw+fRrNmzfHO++8g65du8LT09PeSaL/FxkZia5du+Ltt99GkSJF4ObmBjc3N3sni/6fcu70798fHh4ecHV1RXJysr2TlSd4dbaT6Oho9OvXDyKCr776Cl9//TVKlCiBd999FzNmzLB38gotEcGGDRvw8ssv4/Tp01i3bh32799v72TR/0tPT8eiRYvwwgsv4PHjx+jRowd69OiBqKgovPvuuzhw4IC9k1iopaam4vPPP0e7du2QmpqKsWPHYvny5WjQoAEmTpyIbdu22TuJhZryMKNkyZIoUaIE0tPTsXbtWpw9exYAS9fsKTExEe+88w6aNGmCokWLYv369Vi4cCHq169v76TR/1u+fDlu3LiB5cuXY9myZRgzZgzKlClj72QVenFxcejfvz+aNGkCNzc3/PLLL/j8888hIjhz5gyAjHuHp5qQXaxbt06cnJwkKChIHXfz5k3p3bu36HQ62bt3rx1TV3iFhoZKvXr1pGTJkjJjxgypU6eOtGjRQlJTU+2dNBKRnTt3ip+fn7zzzjty6dIldXxISIjodDoZO3Ys88qOtm/fLo0bN5aRI0fKlStXJC0tTURErl69KjqdTr744gvR6/V2TiUFBQVJQECAfPPNN6LT6eTTTz9V84r5k//S0tJk5syZotPpZPDgwXL37l2L1zHmj338888/UrZsWRk+fLjJeEPMn/z1+PFjqV69uvj5+cmyZcskPDxcRESuX78uxYsXl+7du0t6erqdU/nknOwdLBZW4eHhKFasGF577TUAGU+kK1SogDFjxuDGjRsYOXIkgoOD+dQmnzk5OeHll19Gr1694O/vj+LFi+PDDz/EqlWrMGjQIHsnr9C7cOECXFxcMHv2bJQuXRoAkJKSgueeew7NmzfHqVOn4OTkBBFhdTs78PLyQr9+/fDWW2+p+QMA586dQ+nSpeHr66tW8Wb+5D/luFeqVAnHjh3Drl27EBQUhJUrV+L555/HCy+8YO8kFkqOjo7o2LEjduzYgYMHD6JUqVIAgK1bt2Ljxo0oW7YsatWqhX79+qFIkSJ2Tm3hFBYWhvj4eHz44YcAgDVr1mD27NkAgBo1aqBXr17o06cPr2v5SK/Xw83NDatWrYKnpydq1KgBZ2dnAECVKlVQrVo1xMTEIDU1FUWKFHmq84bVIG1MqVYimdrSFClSBHq9Xq22pbQXaNSoET7++GNcvXoV33//vdllKW+Yy5vKlStj0qRJ8Pf3BwC88MILePHFFzFlyhTcv3/fLuksrAzzR8mjUaNGYfPmzShdurQ6vUiRIkhPT4erqysSExORlJT0VF+Unxbmzp9WrVrho48+MgrUDh06hIkTJyIpKQkXL17EuXPn1M6UWO3ONiz97yjnRUREhJpHc+fOxe3bt/Hjjz/iwYMHbC+dD8zlT5MmTfDWW2/h8uXLGD16NDp27IhXX30VISEhWLZsGQYNGoQ+ffrg/PnzRuugvGXp3HFzc0NKSgrOnz+PlStXYsCAAahTpw6aNm2Kw4cPo1+/fggMDLRDigsXw/xR7ptbtmyJunXrqoGaiECv16NZs2Y4ffo0EhMTn/rrGoM1G1HabqxcuRLAv3+Syo+lXr16SElJwf79+yEicHR0hF6vh06nQ7t27fDKK69g3rx5SElJ4Y1nHrOUNwoXFxf1c40aNTBo0CDcv38fX3zxRb6ms7Aylz+GeVSjRg0A/z7gUC7asbGxqFChAlxdXZ/qi7LWZXf+AP/+oY4bNw5t27ZF6dKl8dprryEiIgJt2rTBf//7XwDs1CKvZZc3ynlRuXJl3LlzB1FRUWjYsCHeeecdrFu3Djt37gSQ0X6K8l529wWdO3fG66+/jq+//hppaWn47bffEBwcjEuXLmHatGnYvHkzpk6dCoDnTl6z5rrm7e2NDRs2YMGCBZgwYQJWrlyJwMBA7N27FwEBARg9ejQuXbqU30kvFKzJH4VOp4ODgwNKlCiBuLg4HDx4MNtlNC8/61wWFgcOHJC6deuKTqeTgIAAuXDhgoiY1mVu3LixNGrUSP7++2+T6T/99JM4OTnJsmXLzC5LuWNt3hiOi46OlnfeeUdcXV3l3LlzFuenJ5eT/DEUEREhxYoVk1mzZomIqO1vKG9Zmz/K902bNsn69evl3r176rjx48eLg4ODzJ07V0SkQLQn0IKcnDs///yz1KhRQ+7cuSMiInFxceLm5ibPP/+8DBw4UN566y2JjIzM1/QXdNbmz08//SQDBgyQkJAQk2n9+vUTLy8v2bp1q9llKXeszZtWrVqJg4ODlCpVSg4fPmw0bffu3VKiRAkZMWKEiPC6lpdyel+gHPuDBw+KTqeTn3/+Ocv5nwYM1vLYkSNHpFatWvLMM89Iz549RafTyZw5c4waCys3klu2bBGdTiczZsyQxMREo2mXL1+WihUrynvvvceTPo9YkzeWBAcHS4UKFeS1117Lh5QWTk+SPwcOHBCdTie7du3Kh5QWTjnJn6z+FK9evSrVqlUTf39/SUpKsmWSCw1r80bJl4MHD4qbm5tERESo0/r06SOOjo7i7OwskydPlkePHuXrPhRk1uSPkjexsbESHR1ttLwy39GjR0Wn08mUKVOe6htPLcnJPdvOnTtFp9OJTqeTixcviohIcnKyiGQ81O3UqZNUqlSJ17U89CT3BefOnZPixYvLsGHDRITBGhm4cOGCuLi4yC+//CIiIm3atJHq1atLSEiI2fm7dOki5cuXl23btomIcYlA3bp15e233xaRp/tHphU5zRuRf4/7o0ePZOLEiaLT6SQ4OFhERPbv3y9btmwxmo9yLzf5o1i6dKk4OTlJfHy8iGScR6GhoXLixAkRYf7khSfJHxHjJ80tW7aUFi1a8KYmj2TOm7Zt22aZN+vWrZOaNWvKw4cP5Y8//pDWrVuLo6OjeHp6SrVq1eTgwYMiwvMmr+T23FGOv3Lu3L17V7y9vWXMmDG2TXAhktO86devn+h0OhkyZIiIiFHQ0KNHD6lTp47ExsbaPuGFxJP870RHR4uvr6906NBB4uLibJ1Um2KwloeUQMvwqZjyxH/48OHqCWx40xIeHi7u7u7SokULOXXqlDr+6NGj4unpKVOnTs2n1Bds1uaNuZsTJb8uXbokjRs3lvr168vUqVOlUqVKUrJkSbUqEeXek+SPiEi3bt3kueeeE5GMKpE//vijNGrUSBo3biz379+3ceoLvrw4fxS7du0SZ2dnGTlypA1TXHjkJG+U/AkODpYiRYrISy+9JI6OjtKqVSs5cOCA/Pzzz+qNqFJiQE8mL8+dpUuXik6nk++++86GKS48cnPPFhERIZ6eniY1Oc6fPy9Vq1aVN998kw858khenDvdu3eXunXryqNHj57qfGGwlkvr1q2TIUOGyOzZs+XAgQPqeMMfg/Jj6d+/v3h7e8vmzZuN1qH8EAMDA6Vy5cpSpUoVWbhwoaxYsUK6desmlSpVkr/++isf9qZgyYu8MSc8PFwGDBigVoN45ZVXjKoRkXXyMn/0er3Ex8dLuXLl5I033pC9e/fKyy+/LDqdTjp16iQ3b9607c4UQLY6fyIjI2Xbtm3Srl07qVOnjtpWl6yXV3kTEhIiDRo0kNq1a8vixYslIiJC/T9q1aqVDB48mMFaLtjq3Ll9+7Zs2rRJGjRoIO3atZN79+7lfeILuLy8Z1u3bp2UK1dOSpQoIYMHD5bPP/9cOnfuLMWLF2dV/Fyyxbmj1+tlxowZotPp5PLlyybre5owWMuh27dvS8eOHaVYsWLSuHFjKV68uLi4uMjkyZPlwYMHIiImLxe9efOmuLu7S/fu3dWb+/T0dKMfzb59+6RVq1bi5eUlJUuWlAYNGsihQ4fyd+eecnmZN5kdPHhQOnXqJA4ODtKoUSOrq37Rv2yVP9euXRM3Nzdp3LixuLu7S82aNdWqqmQ9W+XPvn37ZPDgwdKjRw/x8PAQf39/+fPPP/NvxwqAvMobpcpWSkqKHDhwQP7++281KFOWU9pPk/Vsee4MHTpU+vTpI+7u7tK4cWM5c+ZM/u1YAWCre7aQkBDp2LGjeHt7S5kyZaRRo0ZGQQZZx5b3bSIi8+bNE51OJ0FBQbbfGRtisJZDq1atkhIlSshPP/0kkZGRcv/+fRkwYIB4eHjI+++/bzK/8uOaOXOmODg4yPLly41OeMPPiYmJcufOHd7I5FJe542hvXv3SpEiRWTx4sU23YeCzFb58/vvv4tOp5MyZcowf56ArfJn27ZtUq1aNWnfvr388MMPNt+PgsgWefO0PmHWIludO0FBQeLu7i7Nmzdn1cdcsuU9W3Jysjx48EDOnj1r+x0poGx17ijBW1RUlAQGBtp2J/IBg7UcateunbRo0cJo3OPHj6V///6i0+lk+/btImIa5aekpEjVqlWlefPmcuXKFRERCQ0NNaqLy14fn4wt80aE3cE/qbzOH8O2gt9++62kpKTYeA8KNlvmT2hoKK9vTyAv8+batWsm1zZ6MrY8d86ePcv/nifAezZts2X+FKQHUgzWrJSeni5JSUnSsWNHadWqlTpeqVZy8uRJadKkifj5+Zn8QDJ31T927FhZuXKlNG7cWIYPHy6PHz/Ovx0pgJg32mbL/Hnae3jSAlvmD7t/fzK2zJuEhIT825ECiueOdvG+QNuYPznDYM2MixcvyogRI2TYsGEyYcIENWoXEXn11VelZs2aauN4w2h/+fLlotPpZN68eSJiWhKTmpoqTZs2FUdHR9HpdFKuXDnZuXOn7XeoAGHeaBvzR9uYP9rFvNE25o92MW+0jfnz5BisGUhOTpbRo0dL0aJF5dlnn5Xq1auLTqcTPz8/9R0PQUFBotPp5IcfflB/VMoPKCwsTDp06CBVqlQxabR96tQpmTBhgri7u4uHh4fMnz/fDnv49GLeaBvzR9uYP9rFvNE25o92MW+0jfmTdxis/b/4+Hj59NNPxc/PT+bMmSOXL1+W9PR02bt3r5QvX17atGkjCQkJkpaWJv7+/tK2bVsJCwszWc+UKVPE29tbrWcrkvHj+vDDD0Wn00n//v3VF/eSdZg32sb80Tbmj3Yxb7SN+aNdzBttY/7kLQZr/+/GjRtSpUoVGTJkiDx8+NBo2pAhQ6R06dJy4sQJERFZs2aN6HQ6+frrr9W6sUrUf/r0aXFwcJBNmzaJyL/1b48fPy4XLlzIp70pWJg32sb80Tbmj3Yxb7SN+aNdzBttY/7kLQZr/0+v18vy5cuNxim9y/3888/i5OQkFy9eFBGRhw8fSvfu3cXHx8fkpXzHjx8XnU4nq1atyp+EFwLMG21j/mgb80e7mDfaxvzRLuaNtjF/8haDNQNKxJ65EePcuXPF0dFRLl26pI6LiIiQsmXLSt26ddUGjbdu3ZIPP/xQfH195fbt2/mX8EKAeaNtzB9tY/5oF/NG25g/2sW80TbmT95hsJYFpbHjiBEjxMfHR30qoPzwdu3aJY0bNxadTicNGzaUli1birOzs0ydOlXS0tIK1DsetIZ5o23MH21j/mgX80bbmD/axbzRNuZP7ulEREBZevbZZ/HMM88gKCgI6enpcHR0VKfdu3cP33//PUJDQxEXF4cRI0agZcuWdkxt4cK80Tbmj7Yxf7SLeaNtzB/tYt5oG/MnF+wdLWpddHS0FC1aVObOnauOS09Pl5iYGDumikSYN1rH/NE25o92MW+0jfmjXcwbbWP+5I6DvYNFrTt37hySkpLQtGlTAMDt27exdu1adOzYEXfv3rVz6go35o22MX+0jfmjXcwbbWP+aBfzRtuYP7nDYM0C+f/aoX/++Se8vLxQvnx57Nu3D++//z7eeecdiAgcHBzU+Sj/MG+0jfmjbcwf7WLeaBvzR7uYN9rG/HkyTvZOgFbpdDoAwLFjx1CyZEnMnTsX69atg4+PD7Zv344XX3zRziksvJg32sb80Tbmj3Yxb7SN+aNdzBttY/48ofyrcfn0SUxMlIYNG4pOpxNPT0+ZN2+evZNE/495o23MH21j/mgX80bbmD/axbzRNuZP7rE3yGyMHTsWOp0OU6dOhYuLi72TQwaYN9rG/NE25o92MW+0jfmjXcwbbWP+5A6DtWzo9Xo4OLBpnxYxb7SN+aNtzB/tYt5oG/NHu5g32sb8yR0Ga0RERERERBrE8JaIiIiIiEiDGKwRERERERFpEIM1IiIiIiIiDWKwRkREREREpEEM1oiIiIiIiDSIwRoREREREZEGMVgjIiIiIiLSIAZrREREREREGsRgjYiIiIiISIMYrBEREREREWkQgzUiIiIiIiIN+j9F/vZDxzyJrwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 1000x800 with 3 Axes>"
       ]
diff --git a/docs/TrendAnalysis_example_NSRDB.ipynb b/docs/TrendAnalysis_example_NSRDB.ipynb
index ea6915a6..0ffef29d 100644
--- a/docs/TrendAnalysis_example_NSRDB.ipynb
+++ b/docs/TrendAnalysis_example_NSRDB.ipynb
@@ -200,7 +200,7 @@
     "ax.plot(df.index, df.soiling, 'o', alpha=0.01)\n",
     "#ax.set_ylim(0,1500)\n",
     "fig.autofmt_xdate()\n",
-    "ax.set_ylabel('soiling signal');\n",
+    "ax.set_ylabel('soiling signal')\n",
     "df['power'] = df['power_ac'] * df['soiling']\n",
     "\n",
     "plt.show()"
diff --git a/docs/degradation_and_soiling_example.ipynb b/docs/degradation_and_soiling_example.ipynb
index d9c43099..a5e1c71a 100644
--- a/docs/degradation_and_soiling_example.ipynb
+++ b/docs/degradation_and_soiling_example.ipynb
@@ -93302,7 +93302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 33,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2026-02-04T19:31:16.336479Z",
@@ -93618,6 +93618,8 @@
        "      <th>soiling_rate_high</th>\n",
        "      <th>inferred_start_loss</th>\n",
        "      <th>inferred_end_loss</th>\n",
+       "      <th>inferred_recovery</th>\n",
+       "      <th>inferred_begin_shift</th>\n",
        "      <th>length</th>\n",
        "      <th>valid</th>\n",
        "    </tr>\n",
@@ -93710,7 +93712,7 @@
        "4           1.040835       4  False  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -93938,7 +93940,7 @@
        "11               7  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -94054,7 +94056,7 @@
        "6  2016              0.970346           0.967108            0.973698"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -94249,7 +94251,7 @@
        "2010-03-01 00:00:00-07:00   0.838916  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -94369,7 +94371,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "rdtools3-nb",
    "language": "python",
    "name": "python3"
   },
diff --git a/docs/soiling_options_comparison_v2.ipynb b/docs/soiling_options_comparison_v2.ipynb
new file mode 100644
index 00000000..32b40e72
--- /dev/null
+++ b/docs/soiling_options_comparison_v2.ipynb
@@ -0,0 +1,3328 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "48b5d224",
+   "metadata": {},
+   "source": [
+    "# Soiling Analysis Options Comparison\n",
+    "\n",
+    "This notebook demonstrates the impact of the soiling analysis parameters in RdTools v3.\n",
+    "\n",
+    "## New Parameters in v3\n",
+    "\n",
+    "| Parameter | Default | Description |\n",
+    "|-----------|---------|-------------|\n",
+    "| `detect_neg_shifts` | `False` | Detect negative shifts in rolling median to subdivide intervals |\n",
+    "| `piecewise_fit` | `False` | Test for slope change points within intervals |\n",
+    "| `neg_shift_factor` | `2.5` | Multiplier of `clean_threshold` for negative shift detection (only when `detect_neg_shifts=True`) |\n",
+    "| `min_piecewise_days` | `27` | Minimum interval length in days to attempt piecewise fitting (only when `piecewise_fit=True`) |\n",
+    "| `collapse_window_days` | `5` | Window for collapsing consecutive cleaning events into single event |\n",
+    "| `forward_median_window` | `10` | Window for forward median validation (only when `detect_neg_shifts=True`) |\n",
+    "\n",
+    "## Renamed Parameters (v2 → v3)\n",
+    "\n",
+    "| Old Name (v2) | New Name (v3) | Default |\n",
+    "|---------------|---------------|---------|\n",
+    "| `min_interval_length` | `min_interval_days` | `7` |\n",
+    "| `max_negative_step` | `max_neg_step` | `0.05` |\n",
+    "| `neg_shift` | `detect_neg_shifts` | `False` |\n",
+    "| `piecewise` | `piecewise_fit` | `False` |\n",
+    "\n",
+    "## Removed Methods (Consolidated)\n",
+    "\n",
+    "| Old Method (v2) | New Equivalent (v3) |\n",
+    "|-----------------|---------------------|\n",
+    "| `perfect_clean_complex` | `method='perfect_clean'` + `detect_neg_shifts=True` + `piecewise_fit=True` |\n",
+    "| `inferred_clean_complex` | `method='inferred_clean'` + `detect_neg_shifts=True` + `piecewise_fit=True` |\n",
+    "\n",
+    "## Cleaning Methods\n",
+    "\n",
+    "| Method | Description |\n",
+    "|--------|-------------|\n",
+    "| `half_norm_clean` | Starting point from half-normal distribution (default) |\n",
+    "| `random_clean` | Random recovery 0-100% |\n",
+    "| `perfect_clean` | Each cleaning returns performance to 1 |\n",
+    "| `inferred_clean` | Recovery inferred from data fits (requires `detect_neg_shifts=True`) |\n",
+    "\n",
+    "## Approach\n",
+    "\n",
+    "We build a master configuration DataFrame containing all parameter combinations, run each configuration, and then analyze the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6b05044c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\mspringe\\.conda\\envs\\rdtools_313-nb\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py:22: UserWarning: Pandas requires version '2.10.2' or newer of 'numexpr' (version '2.10.1' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from itertools import product\n",
+    "import warnings\n",
+    "import rdtools\n",
+    "from rdtools import soiling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f64db783",
+   "metadata": {},
+   "source": [
+    "## Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "081e1c9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data range: 2010-02-25 00:00:00-07:00 to 2016-12-13 00:00:00-07:00\n",
+      "Days: 2484\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load example data from PVDAQ system 4\n",
+    "file_url = ('https://datahub.duramat.org/dataset/'\n",
+    "            'a49bb656-7b36-437a-8089-1870a40c2a7d/'\n",
+    "            'resource/d2c3fcf4-4f5f-47ad-8743-fc29'\n",
+    "            'f1356835/download/pvdaq_system_4_2010'\n",
+    "            '-2016_subset_soil_signal.csv')\n",
+    "cache_file = 'PVDAQ_system_4_2010-2016_subset_soilsignal.pickle'\n",
+    "\n",
+    "try:\n",
+    "    df = pd.read_pickle(cache_file)\n",
+    "except FileNotFoundError:\n",
+    "    df = pd.read_csv(file_url, index_col=0, parse_dates=True)\n",
+    "    df.to_pickle(cache_file)\n",
+    "\n",
+    "# Rename columns and set up metadata\n",
+    "df = df.rename(columns={\n",
+    "    'ac_power': 'power_ac',\n",
+    "    'ambient_temp': 'Tamb',\n",
+    "    'poa_irradiance': 'poa',\n",
+    "})\n",
+    "\n",
+    "meta = {'latitude': 39.7406,\n",
+    "        'longitude': -105.1774,\n",
+    "        'timezone': 'Etc/GMT+7',\n",
+    "        'gamma_pdc': -0.005,\n",
+    "        'azimuth': 180,\n",
+    "        'tilt': 40,\n",
+    "        'power_dc_rated': 1000.0,\n",
+    "        'temp_model_params': 'open_rack_glass_polymer'}\n",
+    "\n",
+    "df.index = df.index.tz_localize(meta['timezone'])\n",
+    "freq = pd.infer_freq(df.index[:10])\n",
+    "\n",
+    "# Apply synthetic soiling signal\n",
+    "df['power'] = df['power_ac'] * df['soiling']\n",
+    "\n",
+    "# Create TrendAnalysis object for proper data preparation\n",
+    "ta = rdtools.TrendAnalysis(df['power'], df['poa'],\n",
+    "                           temperature_ambient=df['Tamb'],\n",
+    "                           gamma_pdc=meta['gamma_pdc'],\n",
+    "                           interp_freq=freq,\n",
+    "                           windspeed=df['wind_speed'],\n",
+    "                           power_dc_rated=meta['power_dc_rated'],\n",
+    "                           temperature_model=meta['temp_model_params'])\n",
+    "\n",
+    "# Run normalization and aggregation\n",
+    "ta.sensor_analysis(analyses=['yoy_degradation'])\n",
+    "\n",
+    "# Get aggregated daily data\n",
+    "energy_normalized_daily = ta.sensor_aggregated_performance\n",
+    "insolation_daily = ta.poa_global.resample('D').sum()\n",
+    "insolation_daily = insolation_daily.reindex(energy_normalized_daily.index)\n",
+    "\n",
+    "# Generate synthetic precipitation for demonstration\n",
+    "np.random.seed(42)\n",
+    "precip_probs = np.random.random(len(energy_normalized_daily))\n",
+    "precipitation_daily = pd.Series(\n",
+    "    np.where(precip_probs > 0.85, np.random.uniform(1, 20, len(energy_normalized_daily)), 0),\n",
+    "    index=energy_normalized_daily.index,\n",
+    "    name='precipitation_daily'\n",
+    ")\n",
+    "\n",
+    "print(f\"Data range: {energy_normalized_daily.index[0]} to {energy_normalized_daily.index[-1]}\")\n",
+    "print(f\"Days: {len(energy_normalized_daily)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e75bc9f",
+   "metadata": {},
+   "source": [
+    "## Define Master Configuration\n",
+    "\n",
+    "We define all the parameter combinations we want to explore. Each row represents one analysis configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "60a4cbe4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Default configuration:\n",
+      "  method: half_norm_clean\n",
+      "  detect_neg_shifts: False\n",
+      "  piecewise_fit: False\n",
+      "  neg_shift_factor: 2.5\n",
+      "  min_piecewise_days: 27\n",
+      "  collapse_window_days: 5\n",
+      "  forward_median_window: 10\n",
+      "  clean_criterion: shift\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define parameter values to explore\n",
+    "# Using dictionaries to define parameter space\n",
+    "\n",
+    "# Core boolean options\n",
+    "detect_neg_shifts_values = [False, True]\n",
+    "piecewise_fit_values = [False, True]\n",
+    "\n",
+    "# Method options\n",
+    "# Note: 'inferred_clean' requires detect_neg_shifts=True\n",
+    "# Note: 'random_clean' is not tested here as it's mainly for comparison\n",
+    "method_values = ['half_norm_clean', 'perfect_clean', 'inferred_clean']\n",
+    "\n",
+    "# Numeric parameters with their test values\n",
+    "neg_shift_factor_values = [1.5, 2.5, 4.0]  # default is 2.5\n",
+    "min_piecewise_days_values = [14, 27, 45]  # default is 27\n",
+    "collapse_window_days_values = [1, 5, 10]  # default is 5\n",
+    "forward_median_window_values = [5, 10, 20]  # default is 10\n",
+    "\n",
+    "# Clean criterion options\n",
+    "clean_criterion_values = ['shift', 'precip', 'precip_and_shift', 'precip_or_shift']\n",
+    "\n",
+    "# Default values for reference (matching soiling.py defaults)\n",
+    "DEFAULTS = {\n",
+    "    'method': 'half_norm_clean',\n",
+    "    'detect_neg_shifts': False,\n",
+    "    'piecewise_fit': False,\n",
+    "    'neg_shift_factor': 2.5,\n",
+    "    'min_piecewise_days': 27,\n",
+    "    'collapse_window_days': 5,\n",
+    "    'forward_median_window': 10,\n",
+    "    'clean_criterion': 'shift',\n",
+    "    # Additional defaults from soiling.py (not varied in this comparison):\n",
+    "    # 'min_interval_days': 7,\n",
+    "    # 'max_neg_step': 0.05 (only used when detect_neg_shifts=False),\n",
+    "    # 'max_relative_slope_error': 500.0,\n",
+    "    # 'outlier_factor': 1.5,\n",
+    "    # 'day_scale': 13,\n",
+    "    # 'clean_threshold': 'infer',\n",
+    "}\n",
+    "\n",
+    "print(\"Default configuration:\")\n",
+    "for k, v in DEFAULTS.items():\n",
+    "    print(f\"  {k}: {v}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b2b1af8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total unique configurations: 43\n",
+      "\n",
+      "Configurations by group:\n",
+      "config_group\n",
+      "core_matrix              10\n",
+      "clean_criterion           9\n",
+      "neg_shift_factor          6\n",
+      "min_piecewise_days        6\n",
+      "collapse_window_days      6\n",
+      "forward_median_window     6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Configurations by method:\n",
+      "method\n",
+      "half_norm_clean    15\n",
+      "perfect_clean      15\n",
+      "inferred_clean     13\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>config_id</th>\n",
+       "      <th>config_name</th>\n",
+       "      <th>config_group</th>\n",
+       "      <th>method</th>\n",
+       "      <th>detect_neg_shifts</th>\n",
+       "      <th>piecewise_fit</th>\n",
+       "      <th>neg_shift_factor</th>\n",
+       "      <th>min_piecewise_days</th>\n",
+       "      <th>collapse_window_days</th>\n",
+       "      <th>forward_median_window</th>\n",
+       "      <th>clean_criterion</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>half_norm_clean_dns=False_pw=False</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>perfect_clean_dns=False_pw=False</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>half_norm_clean_dns=False_pw=True</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>perfect_clean_dns=False_pw=True</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>half_norm_clean_dns=True_pw=False</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>perfect_clean_dns=True_pw=False</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>inferred_clean_dns=True_pw=False</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>half_norm_clean_dns=True_pw=True</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>perfect_clean_dns=True_pw=True</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>inferred_clean_dns=True_pw=True</td>\n",
+       "      <td>core_matrix</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>half_norm_clean_nsf=1.5</td>\n",
+       "      <td>neg_shift_factor</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>perfect_clean_nsf=1.5</td>\n",
+       "      <td>neg_shift_factor</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>inferred_clean_nsf=1.5</td>\n",
+       "      <td>neg_shift_factor</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>half_norm_clean_nsf=4.0</td>\n",
+       "      <td>neg_shift_factor</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>perfect_clean_nsf=4.0</td>\n",
+       "      <td>neg_shift_factor</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>inferred_clean_nsf=4.0</td>\n",
+       "      <td>neg_shift_factor</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>half_norm_clean_mpd=14</td>\n",
+       "      <td>min_piecewise_days</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>perfect_clean_mpd=14</td>\n",
+       "      <td>min_piecewise_days</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>inferred_clean_mpd=14</td>\n",
+       "      <td>min_piecewise_days</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>half_norm_clean_mpd=45</td>\n",
+       "      <td>min_piecewise_days</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>45</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>perfect_clean_mpd=45</td>\n",
+       "      <td>min_piecewise_days</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>45</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>inferred_clean_mpd=45</td>\n",
+       "      <td>min_piecewise_days</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>45</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>half_norm_clean_cwd=1</td>\n",
+       "      <td>collapse_window_days</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>perfect_clean_cwd=1</td>\n",
+       "      <td>collapse_window_days</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>inferred_clean_cwd=1</td>\n",
+       "      <td>collapse_window_days</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>half_norm_clean_cwd=10</td>\n",
+       "      <td>collapse_window_days</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>perfect_clean_cwd=10</td>\n",
+       "      <td>collapse_window_days</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>inferred_clean_cwd=10</td>\n",
+       "      <td>collapse_window_days</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>half_norm_clean_fmw=5</td>\n",
+       "      <td>forward_median_window</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>perfect_clean_fmw=5</td>\n",
+       "      <td>forward_median_window</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>inferred_clean_fmw=5</td>\n",
+       "      <td>forward_median_window</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>half_norm_clean_fmw=20</td>\n",
+       "      <td>forward_median_window</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>20</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>perfect_clean_fmw=20</td>\n",
+       "      <td>forward_median_window</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>20</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>inferred_clean_fmw=20</td>\n",
+       "      <td>forward_median_window</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>20</td>\n",
+       "      <td>shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>35</td>\n",
+       "      <td>half_norm_clean_cc=precip</td>\n",
+       "      <td>clean_criterion</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>precip</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>36</td>\n",
+       "      <td>perfect_clean_cc=precip</td>\n",
+       "      <td>clean_criterion</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>precip</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>37</td>\n",
+       "      <td>inferred_clean_cc=precip</td>\n",
+       "      <td>clean_criterion</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>precip</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>38</td>\n",
+       "      <td>half_norm_clean_cc=precip_and_shift</td>\n",
+       "      <td>clean_criterion</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>precip_and_shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>39</td>\n",
+       "      <td>perfect_clean_cc=precip_and_shift</td>\n",
+       "      <td>clean_criterion</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>precip_and_shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>40</td>\n",
+       "      <td>inferred_clean_cc=precip_and_shift</td>\n",
+       "      <td>clean_criterion</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>precip_and_shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>41</td>\n",
+       "      <td>half_norm_clean_cc=precip_or_shift</td>\n",
+       "      <td>clean_criterion</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>precip_or_shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>42</td>\n",
+       "      <td>perfect_clean_cc=precip_or_shift</td>\n",
+       "      <td>clean_criterion</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>precip_or_shift</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>43</td>\n",
+       "      <td>inferred_clean_cc=precip_or_shift</td>\n",
+       "      <td>clean_criterion</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>27</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>precip_or_shift</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    config_id                          config_name           config_group  \\\n",
+       "0           1   half_norm_clean_dns=False_pw=False            core_matrix   \n",
+       "1           2     perfect_clean_dns=False_pw=False            core_matrix   \n",
+       "2           3    half_norm_clean_dns=False_pw=True            core_matrix   \n",
+       "3           4      perfect_clean_dns=False_pw=True            core_matrix   \n",
+       "4           5    half_norm_clean_dns=True_pw=False            core_matrix   \n",
+       "5           6      perfect_clean_dns=True_pw=False            core_matrix   \n",
+       "6           7     inferred_clean_dns=True_pw=False            core_matrix   \n",
+       "7           8     half_norm_clean_dns=True_pw=True            core_matrix   \n",
+       "8           9       perfect_clean_dns=True_pw=True            core_matrix   \n",
+       "9          10      inferred_clean_dns=True_pw=True            core_matrix   \n",
+       "10         11              half_norm_clean_nsf=1.5       neg_shift_factor   \n",
+       "11         12                perfect_clean_nsf=1.5       neg_shift_factor   \n",
+       "12         13               inferred_clean_nsf=1.5       neg_shift_factor   \n",
+       "13         14              half_norm_clean_nsf=4.0       neg_shift_factor   \n",
+       "14         15                perfect_clean_nsf=4.0       neg_shift_factor   \n",
+       "15         16               inferred_clean_nsf=4.0       neg_shift_factor   \n",
+       "16         17               half_norm_clean_mpd=14     min_piecewise_days   \n",
+       "17         18                 perfect_clean_mpd=14     min_piecewise_days   \n",
+       "18         19                inferred_clean_mpd=14     min_piecewise_days   \n",
+       "19         20               half_norm_clean_mpd=45     min_piecewise_days   \n",
+       "20         21                 perfect_clean_mpd=45     min_piecewise_days   \n",
+       "21         22                inferred_clean_mpd=45     min_piecewise_days   \n",
+       "22         23                half_norm_clean_cwd=1   collapse_window_days   \n",
+       "23         24                  perfect_clean_cwd=1   collapse_window_days   \n",
+       "24         25                 inferred_clean_cwd=1   collapse_window_days   \n",
+       "25         26               half_norm_clean_cwd=10   collapse_window_days   \n",
+       "26         27                 perfect_clean_cwd=10   collapse_window_days   \n",
+       "27         28                inferred_clean_cwd=10   collapse_window_days   \n",
+       "28         29                half_norm_clean_fmw=5  forward_median_window   \n",
+       "29         30                  perfect_clean_fmw=5  forward_median_window   \n",
+       "30         31                 inferred_clean_fmw=5  forward_median_window   \n",
+       "31         32               half_norm_clean_fmw=20  forward_median_window   \n",
+       "32         33                 perfect_clean_fmw=20  forward_median_window   \n",
+       "33         34                inferred_clean_fmw=20  forward_median_window   \n",
+       "34         35            half_norm_clean_cc=precip        clean_criterion   \n",
+       "35         36              perfect_clean_cc=precip        clean_criterion   \n",
+       "36         37             inferred_clean_cc=precip        clean_criterion   \n",
+       "37         38  half_norm_clean_cc=precip_and_shift        clean_criterion   \n",
+       "38         39    perfect_clean_cc=precip_and_shift        clean_criterion   \n",
+       "39         40   inferred_clean_cc=precip_and_shift        clean_criterion   \n",
+       "40         41   half_norm_clean_cc=precip_or_shift        clean_criterion   \n",
+       "41         42     perfect_clean_cc=precip_or_shift        clean_criterion   \n",
+       "42         43    inferred_clean_cc=precip_or_shift        clean_criterion   \n",
+       "\n",
+       "             method  detect_neg_shifts  piecewise_fit  neg_shift_factor  \\\n",
+       "0   half_norm_clean              False          False               2.5   \n",
+       "1     perfect_clean              False          False               2.5   \n",
+       "2   half_norm_clean              False           True               2.5   \n",
+       "3     perfect_clean              False           True               2.5   \n",
+       "4   half_norm_clean               True          False               2.5   \n",
+       "5     perfect_clean               True          False               2.5   \n",
+       "6    inferred_clean               True          False               2.5   \n",
+       "7   half_norm_clean               True           True               2.5   \n",
+       "8     perfect_clean               True           True               2.5   \n",
+       "9    inferred_clean               True           True               2.5   \n",
+       "10  half_norm_clean               True          False               1.5   \n",
+       "11    perfect_clean               True          False               1.5   \n",
+       "12   inferred_clean               True          False               1.5   \n",
+       "13  half_norm_clean               True          False               4.0   \n",
+       "14    perfect_clean               True          False               4.0   \n",
+       "15   inferred_clean               True          False               4.0   \n",
+       "16  half_norm_clean              False           True               2.5   \n",
+       "17    perfect_clean              False           True               2.5   \n",
+       "18   inferred_clean               True           True               2.5   \n",
+       "19  half_norm_clean              False           True               2.5   \n",
+       "20    perfect_clean              False           True               2.5   \n",
+       "21   inferred_clean               True           True               2.5   \n",
+       "22  half_norm_clean              False          False               2.5   \n",
+       "23    perfect_clean              False          False               2.5   \n",
+       "24   inferred_clean               True          False               2.5   \n",
+       "25  half_norm_clean              False          False               2.5   \n",
+       "26    perfect_clean              False          False               2.5   \n",
+       "27   inferred_clean               True          False               2.5   \n",
+       "28  half_norm_clean               True          False               2.5   \n",
+       "29    perfect_clean               True          False               2.5   \n",
+       "30   inferred_clean               True          False               2.5   \n",
+       "31  half_norm_clean               True          False               2.5   \n",
+       "32    perfect_clean               True          False               2.5   \n",
+       "33   inferred_clean               True          False               2.5   \n",
+       "34  half_norm_clean              False          False               2.5   \n",
+       "35    perfect_clean              False          False               2.5   \n",
+       "36   inferred_clean               True          False               2.5   \n",
+       "37  half_norm_clean              False          False               2.5   \n",
+       "38    perfect_clean              False          False               2.5   \n",
+       "39   inferred_clean               True          False               2.5   \n",
+       "40  half_norm_clean              False          False               2.5   \n",
+       "41    perfect_clean              False          False               2.5   \n",
+       "42   inferred_clean               True          False               2.5   \n",
+       "\n",
+       "    min_piecewise_days  collapse_window_days  forward_median_window  \\\n",
+       "0                   27                     5                     10   \n",
+       "1                   27                     5                     10   \n",
+       "2                   27                     5                     10   \n",
+       "3                   27                     5                     10   \n",
+       "4                   27                     5                     10   \n",
+       "5                   27                     5                     10   \n",
+       "6                   27                     5                     10   \n",
+       "7                   27                     5                     10   \n",
+       "8                   27                     5                     10   \n",
+       "9                   27                     5                     10   \n",
+       "10                  27                     5                     10   \n",
+       "11                  27                     5                     10   \n",
+       "12                  27                     5                     10   \n",
+       "13                  27                     5                     10   \n",
+       "14                  27                     5                     10   \n",
+       "15                  27                     5                     10   \n",
+       "16                  14                     5                     10   \n",
+       "17                  14                     5                     10   \n",
+       "18                  14                     5                     10   \n",
+       "19                  45                     5                     10   \n",
+       "20                  45                     5                     10   \n",
+       "21                  45                     5                     10   \n",
+       "22                  27                     1                     10   \n",
+       "23                  27                     1                     10   \n",
+       "24                  27                     1                     10   \n",
+       "25                  27                    10                     10   \n",
+       "26                  27                    10                     10   \n",
+       "27                  27                    10                     10   \n",
+       "28                  27                     5                      5   \n",
+       "29                  27                     5                      5   \n",
+       "30                  27                     5                      5   \n",
+       "31                  27                     5                     20   \n",
+       "32                  27                     5                     20   \n",
+       "33                  27                     5                     20   \n",
+       "34                  27                     5                     10   \n",
+       "35                  27                     5                     10   \n",
+       "36                  27                     5                     10   \n",
+       "37                  27                     5                     10   \n",
+       "38                  27                     5                     10   \n",
+       "39                  27                     5                     10   \n",
+       "40                  27                     5                     10   \n",
+       "41                  27                     5                     10   \n",
+       "42                  27                     5                     10   \n",
+       "\n",
+       "     clean_criterion  \n",
+       "0              shift  \n",
+       "1              shift  \n",
+       "2              shift  \n",
+       "3              shift  \n",
+       "4              shift  \n",
+       "5              shift  \n",
+       "6              shift  \n",
+       "7              shift  \n",
+       "8              shift  \n",
+       "9              shift  \n",
+       "10             shift  \n",
+       "11             shift  \n",
+       "12             shift  \n",
+       "13             shift  \n",
+       "14             shift  \n",
+       "15             shift  \n",
+       "16             shift  \n",
+       "17             shift  \n",
+       "18             shift  \n",
+       "19             shift  \n",
+       "20             shift  \n",
+       "21             shift  \n",
+       "22             shift  \n",
+       "23             shift  \n",
+       "24             shift  \n",
+       "25             shift  \n",
+       "26             shift  \n",
+       "27             shift  \n",
+       "28             shift  \n",
+       "29             shift  \n",
+       "30             shift  \n",
+       "31             shift  \n",
+       "32             shift  \n",
+       "33             shift  \n",
+       "34            precip  \n",
+       "35            precip  \n",
+       "36            precip  \n",
+       "37  precip_and_shift  \n",
+       "38  precip_and_shift  \n",
+       "39  precip_and_shift  \n",
+       "40   precip_or_shift  \n",
+       "41   precip_or_shift  \n",
+       "42   precip_or_shift  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Build master configuration DataFrame\n",
+    "# Strategy: Full factorial - all parameter combinations × all methods\n",
+    "\n",
+    "configs = []\n",
+    "config_counter = 0\n",
+    "\n",
+    "# Define all methods to test\n",
+    "# Note: random_clean does not support detect_neg_shifts\n",
+    "all_methods = ['half_norm_clean', 'perfect_clean', 'inferred_clean']\n",
+    "\n",
+    "# 1. Core options: detect_neg_shifts × piecewise_fit × method\n",
+    "for dns, pw in product([False, True], [False, True]):\n",
+    "    for method in all_methods:\n",
+    "        # inferred_clean requires detect_neg_shifts=True\n",
+    "        if method == 'inferred_clean' and not dns:\n",
+    "            continue\n",
+    "        config_counter += 1\n",
+    "        configs.append({\n",
+    "            'config_id': config_counter,\n",
+    "            'config_name': f'{method}_dns={dns}_pw={pw}',\n",
+    "            'config_group': 'core_matrix',\n",
+    "            **{**DEFAULTS, 'method': method, 'detect_neg_shifts': dns, 'piecewise_fit': pw}\n",
+    "        })\n",
+    "\n",
+    "# 2. neg_shift_factor variations (requires detect_neg_shifts=True) × method\n",
+    "for val in neg_shift_factor_values:\n",
+    "    for method in all_methods:\n",
+    "        if method == 'inferred_clean' or True:  # all require dns=True for this param\n",
+    "            config_counter += 1\n",
+    "            configs.append({\n",
+    "                'config_id': config_counter,\n",
+    "                'config_name': f'{method}_nsf={val}',\n",
+    "                'config_group': 'neg_shift_factor',\n",
+    "                **{**DEFAULTS, 'method': method, 'detect_neg_shifts': True,\n",
+    "                   'neg_shift_factor': val}\n",
+    "            })\n",
+    "\n",
+    "# 3. min_piecewise_days variations (requires piecewise_fit=True) × method\n",
+    "for val in min_piecewise_days_values:\n",
+    "    for method in all_methods:\n",
+    "        # inferred_clean requires detect_neg_shifts=True\n",
+    "        dns = True if method == 'inferred_clean' else False\n",
+    "        config_counter += 1\n",
+    "        configs.append({\n",
+    "            'config_id': config_counter,\n",
+    "            'config_name': f'{method}_mpd={val}',\n",
+    "            'config_group': 'min_piecewise_days',\n",
+    "            **{**DEFAULTS, 'method': method, 'detect_neg_shifts': dns,\n",
+    "               'piecewise_fit': True, 'min_piecewise_days': val}\n",
+    "        })\n",
+    "\n",
+    "# 4. collapse_window_days variations × method\n",
+    "for val in collapse_window_days_values:\n",
+    "    for method in all_methods:\n",
+    "        # inferred_clean requires detect_neg_shifts=True\n",
+    "        dns = True if method == 'inferred_clean' else False\n",
+    "        config_counter += 1\n",
+    "        configs.append({\n",
+    "            'config_id': config_counter,\n",
+    "            'config_name': f'{method}_cwd={val}',\n",
+    "            'config_group': 'collapse_window_days',\n",
+    "            **{**DEFAULTS, 'method': method, 'detect_neg_shifts': dns,\n",
+    "               'collapse_window_days': val}\n",
+    "        })\n",
+    "\n",
+    "# 5. forward_median_window variations (requires detect_neg_shifts=True) × method\n",
+    "for val in forward_median_window_values:\n",
+    "    for method in all_methods:\n",
+    "        config_counter += 1\n",
+    "        configs.append({\n",
+    "            'config_id': config_counter,\n",
+    "            'config_name': f'{method}_fmw={val}',\n",
+    "            'config_group': 'forward_median_window',\n",
+    "            **{**DEFAULTS, 'method': method, 'detect_neg_shifts': True,\n",
+    "               'forward_median_window': val}\n",
+    "        })\n",
+    "\n",
+    "# 6. clean_criterion variations × method\n",
+    "for val in clean_criterion_values:\n",
+    "    for method in all_methods:\n",
+    "        # inferred_clean requires detect_neg_shifts=True\n",
+    "        dns = True if method == 'inferred_clean' else False\n",
+    "        config_counter += 1\n",
+    "        configs.append({\n",
+    "            'config_id': config_counter,\n",
+    "            'config_name': f'{method}_cc={val}',\n",
+    "            'config_group': 'clean_criterion',\n",
+    "            **{**DEFAULTS, 'method': method, 'detect_neg_shifts': dns,\n",
+    "               'clean_criterion': val}\n",
+    "        })\n",
+    "\n",
+    "# Create DataFrame and remove duplicates\n",
+    "config_df = pd.DataFrame(configs)\n",
+    "\n",
+    "# Drop duplicates based on the actual parameter values\n",
+    "param_cols = ['method', 'detect_neg_shifts', 'piecewise_fit', 'neg_shift_factor',\n",
+    "              'min_piecewise_days', 'collapse_window_days', 'forward_median_window',\n",
+    "              'clean_criterion']\n",
+    "config_df = config_df.drop_duplicates(subset=param_cols, keep='first').reset_index(drop=True)\n",
+    "config_df['config_id'] = range(1, len(config_df) + 1)\n",
+    "\n",
+    "print(f\"Total unique configurations: {len(config_df)}\")\n",
+    "print(f\"\\nConfigurations by group:\")\n",
+    "print(config_df['config_group'].value_counts())\n",
+    "print(f\"\\nConfigurations by method:\")\n",
+    "print(config_df['method'].value_counts())\n",
+    "config_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f76c8d4",
+   "metadata": {},
+   "source": [
+    "## Run All Configurations\n",
+    "\n",
+    "Execute the soiling analysis for each configuration and store the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4a1aeae5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_soiling_config(config, energy_norm, insolation, precipitation=None):\n",
+    "    \"\"\"Run soiling analysis for a single configuration.\"\"\"\n",
+    "    np.random.seed(1977)  # reproducibility\n",
+    "\n",
+    "    # Build kwargs, only including precipitation if needed\n",
+    "    kwargs = {\n",
+    "        'reps': 200,\n",
+    "        'method': config['method'],\n",
+    "        'detect_neg_shifts': config['detect_neg_shifts'],\n",
+    "        'piecewise_fit': config['piecewise_fit'],\n",
+    "        'neg_shift_factor': config['neg_shift_factor'],\n",
+    "        'min_piecewise_days': config['min_piecewise_days'],\n",
+    "        'collapse_window_days': config['collapse_window_days'],\n",
+    "        'forward_median_window': config['forward_median_window'],\n",
+    "        'clean_criterion': config['clean_criterion'],\n",
+    "    }\n",
+    "\n",
+    "    try:\n",
+    "        with warnings.catch_warnings():\n",
+    "            warnings.simplefilter('ignore')\n",
+    "\n",
+    "            # Use precipitation if criterion requires it\n",
+    "            if 'precip' in config['clean_criterion']:\n",
+    "                sr, sr_ci, info = soiling.soiling_srr(\n",
+    "                    energy_norm, insolation,\n",
+    "                    precipitation_daily=precipitation,\n",
+    "                    **kwargs\n",
+    "                )\n",
+    "            else:\n",
+    "                sr, sr_ci, info = soiling.soiling_srr(\n",
+    "                    energy_norm, insolation, **kwargs\n",
+    "                )\n",
+    "\n",
+    "        summary = info['soiling_interval_summary']\n",
+    "\n",
+    "        # Extract soiling profile - always the 'perfect_clean' deterministic profile\n",
+    "        sr_profile = info.get('soiling_ratio_perfect_clean', pd.Series(dtype=float))\n",
+    "\n",
+    "        # Extract stochastic profiles - these DIFFER by method!\n",
+    "        # Take median of stochastic profiles for comparison\n",
+    "        stochastic = info.get('stochastic_soiling_profiles', [])\n",
+    "        if stochastic and len(stochastic) > 0:\n",
+    "            stoch_df = pd.concat(stochastic, axis=1)\n",
+    "            stoch_median = stoch_df.median(axis=1)\n",
+    "        else:\n",
+    "            stoch_median = pd.Series(dtype=float)\n",
+    "\n",
+    "        return {\n",
+    "            'soiling_ratio': sr,\n",
+    "            'ci_lower': sr_ci[0],\n",
+    "            'ci_upper': sr_ci[1],\n",
+    "            'total_intervals': len(summary),\n",
+    "            'valid_intervals': summary['valid'].sum(),\n",
+    "            'sr_profile': sr_profile,  # Deterministic perfect_clean profile\n",
+    "            'stoch_profile': stoch_median,  # Median of stochastic profiles (method-specific)\n",
+    "            'error': None\n",
+    "        }\n",
+    "    except Exception as e:\n",
+    "        return {\n",
+    "            'soiling_ratio': None,\n",
+    "            'ci_lower': None,\n",
+    "            'ci_upper': None,\n",
+    "            'total_intervals': None,\n",
+    "            'valid_intervals': None,\n",
+    "            'sr_profile': pd.Series(dtype=float),\n",
+    "            'stoch_profile': pd.Series(dtype=float),\n",
+    "            'error': str(e)[:50]\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "576af743",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1/43] half_norm_clean_dns=False_pw=False       SR=0.9494 (8.0s)\n",
+      "[2/43] perfect_clean_dns=False_pw=False         SR=0.9711 (2.6s)\n",
+      "[3/43] half_norm_clean_dns=False_pw=True        SR=0.9544 (9.6s)\n",
+      "[4/43] perfect_clean_dns=False_pw=True          SR=0.9747 (3.6s)\n",
+      "[5/43] half_norm_clean_dns=True_pw=False        SR=0.9635 (8.0s)\n",
+      "[6/43] perfect_clean_dns=True_pw=False          SR=0.9569 (3.6s)\n",
+      "[7/43] inferred_clean_dns=True_pw=False         SR=0.9352 (3.6s)\n",
+      "[8/43] half_norm_clean_dns=True_pw=True         SR=0.9675 (8.4s)\n",
+      "[9/43] perfect_clean_dns=True_pw=True           SR=0.9620 (4.2s)\n",
+      "[10/43] inferred_clean_dns=True_pw=True          SR=0.9267 (4.7s)\n",
+      "[11/43] half_norm_clean_nsf=1.5                  SR=0.9786 (11.7s)\n",
+      "[12/43] perfect_clean_nsf=1.5                    SR=0.9689 (6.2s)\n",
+      "[13/43] inferred_clean_nsf=1.5                   SR=0.9484 (6.2s)\n",
+      "[14/43] half_norm_clean_nsf=4.0                  SR=0.9600 (9.1s)\n",
+      "[15/43] perfect_clean_nsf=4.0                    SR=0.9575 (3.9s)\n",
+      "[16/43] inferred_clean_nsf=4.0                   SR=0.9303 (4.4s)\n",
+      "[17/43] half_norm_clean_mpd=14                   SR=0.9544 (10.2s)\n",
+      "[18/43] perfect_clean_mpd=14                     SR=0.9747 (4.5s)\n",
+      "[19/43] inferred_clean_mpd=14                    SR=0.9267 (5.2s)\n",
+      "[20/43] half_norm_clean_mpd=45                   SR=0.9546 (9.7s)\n",
+      "[21/43] perfect_clean_mpd=45                     SR=0.9746 (4.5s)\n",
+      "[22/43] inferred_clean_mpd=45                    SR=0.9300 (6.2s)\n",
+      "[23/43] half_norm_clean_cwd=1                    SR=0.9529 (12.1s)\n",
+      "[24/43] perfect_clean_cwd=1                      SR=0.9717 (4.0s)\n",
+      "[25/43] inferred_clean_cwd=1                     SR=0.9326 (5.6s)\n",
+      "[26/43] half_norm_clean_cwd=10                   SR=0.9470 (8.3s)\n",
+      "[27/43] perfect_clean_cwd=10                     SR=0.9675 (3.6s)\n",
+      "[28/43] inferred_clean_cwd=10                    SR=0.9331 (4.2s)\n",
+      "[29/43] half_norm_clean_fmw=5                    SR=0.9635 (10.0s)\n",
+      "[30/43] perfect_clean_fmw=5                      SR=0.9569 (4.2s)\n",
+      "[31/43] inferred_clean_fmw=5                     SR=0.9352 (4.6s)\n",
+      "[32/43] half_norm_clean_fmw=20                   SR=0.9635 (10.0s)\n",
+      "[33/43] perfect_clean_fmw=20                     SR=0.9569 (4.6s)\n",
+      "[34/43] inferred_clean_fmw=20                    SR=0.9352 (5.6s)\n",
+      "[35/43] half_norm_clean_cc=precip                SR=0.9775 (12.3s)\n",
+      "[36/43] perfect_clean_cc=precip                  SR=0.9967 (9.6s)\n",
+      "[37/43] inferred_clean_cc=precip                 SR=0.9741 (11.3s)\n",
+      "[38/43] half_norm_clean_cc=precip_and_shift      SR=0.9544 (5.9s)\n",
+      "[39/43] perfect_clean_cc=precip_and_shift        SR=0.9786 (2.9s)\n",
+      "[40/43] inferred_clean_cc=precip_and_shift       SR=0.9592 (3.1s)\n",
+      "[41/43] half_norm_clean_cc=precip_or_shift       SR=0.9763 (10.1s)\n",
+      "[42/43] perfect_clean_cc=precip_or_shift         SR=0.9964 (7.2s)\n",
+      "[43/43] inferred_clean_cc=precip_or_shift        SR=0.9785 (16.6s)\n",
+      "\n",
+      "============================================================\n",
+      "Completed 43 configurations in 4.9 minutes\n",
+      "Successful: 43\n",
+      "Errors: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run all configurations\n",
+    "import time\n",
+    "results = []\n",
+    "total = len(config_df)\n",
+    "start_time = time.time()\n",
+    "\n",
+    "for idx, row in config_df.iterrows():\n",
+    "    np.random.seed(1977)  # Reproducibility\n",
+    "    print(f\"[{idx+1}/{total}] {row['config_name'][:40]:<40}\", end=' ')\n",
+    "    t0 = time.time()\n",
+    "    result = run_soiling_config(row, energy_normalized_daily, insolation_daily, precipitation_daily)\n",
+    "    results.append(result)\n",
+    "    elapsed = time.time() - t0\n",
+    "    if result['error']:\n",
+    "        print(f\"ERROR: {result['error']}\")\n",
+    "    else:\n",
+    "        print(f\"SR={result['soiling_ratio']:.4f} ({elapsed:.1f}s)\")\n",
+    "\n",
+    "# Combine configuration and results\n",
+    "results_df = pd.DataFrame(results)\n",
+    "master_df = pd.concat([config_df, results_df], axis=1)\n",
+    "\n",
+    "total_time = time.time() - start_time\n",
+    "print(f\"\\n{'='*60}\")\n",
+    "print(f\"Completed {len(master_df)} configurations in {total_time/60:.1f} minutes\")\n",
+    "print(f\"Successful: {master_df['soiling_ratio'].notna().sum()}\")\n",
+    "print(f\"Errors: {master_df['error'].notna().sum()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2b7678ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>config_id</th>\n",
+       "      <th>config_name</th>\n",
+       "      <th>method</th>\n",
+       "      <th>detect_neg_shifts</th>\n",
+       "      <th>piecewise_fit</th>\n",
+       "      <th>soiling_ratio</th>\n",
+       "      <th>ci_lower</th>\n",
+       "      <th>ci_upper</th>\n",
+       "      <th>valid_intervals</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>half_norm_clean_dns=False_pw=False</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.949445</td>\n",
+       "      <td>0.945037</td>\n",
+       "      <td>0.953308</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>perfect_clean_dns=False_pw=False</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.971111</td>\n",
+       "      <td>0.970345</td>\n",
+       "      <td>0.971922</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>half_norm_clean_dns=False_pw=True</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.954378</td>\n",
+       "      <td>0.950370</td>\n",
+       "      <td>0.957572</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>perfect_clean_dns=False_pw=True</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.974720</td>\n",
+       "      <td>0.974031</td>\n",
+       "      <td>0.975351</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>half_norm_clean_dns=True_pw=False</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.963505</td>\n",
+       "      <td>0.961178</td>\n",
+       "      <td>0.965943</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>perfect_clean_dns=True_pw=False</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.956869</td>\n",
+       "      <td>0.954949</td>\n",
+       "      <td>0.959103</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>inferred_clean_dns=True_pw=False</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.935229</td>\n",
+       "      <td>0.933078</td>\n",
+       "      <td>0.937712</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>half_norm_clean_dns=True_pw=True</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.967494</td>\n",
+       "      <td>0.965440</td>\n",
+       "      <td>0.969339</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>perfect_clean_dns=True_pw=True</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.962020</td>\n",
+       "      <td>0.959957</td>\n",
+       "      <td>0.963981</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>inferred_clean_dns=True_pw=True</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.926680</td>\n",
+       "      <td>0.924309</td>\n",
+       "      <td>0.928751</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>half_norm_clean_nsf=1.5</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.978627</td>\n",
+       "      <td>0.976994</td>\n",
+       "      <td>0.980072</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>perfect_clean_nsf=1.5</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.968903</td>\n",
+       "      <td>0.966839</td>\n",
+       "      <td>0.971174</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>inferred_clean_nsf=1.5</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.948405</td>\n",
+       "      <td>0.945952</td>\n",
+       "      <td>0.952169</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>half_norm_clean_nsf=4.0</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.960022</td>\n",
+       "      <td>0.957616</td>\n",
+       "      <td>0.962402</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>perfect_clean_nsf=4.0</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.957464</td>\n",
+       "      <td>0.955780</td>\n",
+       "      <td>0.958773</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>inferred_clean_nsf=4.0</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.930264</td>\n",
+       "      <td>0.928219</td>\n",
+       "      <td>0.932241</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>half_norm_clean_mpd=14</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.954378</td>\n",
+       "      <td>0.950370</td>\n",
+       "      <td>0.957572</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>perfect_clean_mpd=14</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.974720</td>\n",
+       "      <td>0.974031</td>\n",
+       "      <td>0.975351</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>inferred_clean_mpd=14</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.926680</td>\n",
+       "      <td>0.924309</td>\n",
+       "      <td>0.928751</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>half_norm_clean_mpd=45</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.954646</td>\n",
+       "      <td>0.950601</td>\n",
+       "      <td>0.958328</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    config_id                         config_name           method  \\\n",
+       "0           1  half_norm_clean_dns=False_pw=False  half_norm_clean   \n",
+       "1           2    perfect_clean_dns=False_pw=False    perfect_clean   \n",
+       "2           3   half_norm_clean_dns=False_pw=True  half_norm_clean   \n",
+       "3           4     perfect_clean_dns=False_pw=True    perfect_clean   \n",
+       "4           5   half_norm_clean_dns=True_pw=False  half_norm_clean   \n",
+       "5           6     perfect_clean_dns=True_pw=False    perfect_clean   \n",
+       "6           7    inferred_clean_dns=True_pw=False   inferred_clean   \n",
+       "7           8    half_norm_clean_dns=True_pw=True  half_norm_clean   \n",
+       "8           9      perfect_clean_dns=True_pw=True    perfect_clean   \n",
+       "9          10     inferred_clean_dns=True_pw=True   inferred_clean   \n",
+       "10         11             half_norm_clean_nsf=1.5  half_norm_clean   \n",
+       "11         12               perfect_clean_nsf=1.5    perfect_clean   \n",
+       "12         13              inferred_clean_nsf=1.5   inferred_clean   \n",
+       "13         14             half_norm_clean_nsf=4.0  half_norm_clean   \n",
+       "14         15               perfect_clean_nsf=4.0    perfect_clean   \n",
+       "15         16              inferred_clean_nsf=4.0   inferred_clean   \n",
+       "16         17              half_norm_clean_mpd=14  half_norm_clean   \n",
+       "17         18                perfect_clean_mpd=14    perfect_clean   \n",
+       "18         19               inferred_clean_mpd=14   inferred_clean   \n",
+       "19         20              half_norm_clean_mpd=45  half_norm_clean   \n",
+       "\n",
+       "    detect_neg_shifts  piecewise_fit  soiling_ratio  ci_lower  ci_upper  \\\n",
+       "0               False          False       0.949445  0.945037  0.953308   \n",
+       "1               False          False       0.971111  0.970345  0.971922   \n",
+       "2               False           True       0.954378  0.950370  0.957572   \n",
+       "3               False           True       0.974720  0.974031  0.975351   \n",
+       "4                True          False       0.963505  0.961178  0.965943   \n",
+       "5                True          False       0.956869  0.954949  0.959103   \n",
+       "6                True          False       0.935229  0.933078  0.937712   \n",
+       "7                True           True       0.967494  0.965440  0.969339   \n",
+       "8                True           True       0.962020  0.959957  0.963981   \n",
+       "9                True           True       0.926680  0.924309  0.928751   \n",
+       "10               True          False       0.978627  0.976994  0.980072   \n",
+       "11               True          False       0.968903  0.966839  0.971174   \n",
+       "12               True          False       0.948405  0.945952  0.952169   \n",
+       "13               True          False       0.960022  0.957616  0.962402   \n",
+       "14               True          False       0.957464  0.955780  0.958773   \n",
+       "15               True          False       0.930264  0.928219  0.932241   \n",
+       "16              False           True       0.954378  0.950370  0.957572   \n",
+       "17              False           True       0.974720  0.974031  0.975351   \n",
+       "18               True           True       0.926680  0.924309  0.928751   \n",
+       "19              False           True       0.954646  0.950601  0.958328   \n",
+       "\n",
+       "    valid_intervals  \n",
+       "0                40  \n",
+       "1                40  \n",
+       "2                44  \n",
+       "3                44  \n",
+       "4                54  \n",
+       "5                54  \n",
+       "6                54  \n",
+       "7                66  \n",
+       "8                66  \n",
+       "9                66  \n",
+       "10               72  \n",
+       "11               72  \n",
+       "12               72  \n",
+       "13               52  \n",
+       "14               52  \n",
+       "15               52  \n",
+       "16               44  \n",
+       "17               44  \n",
+       "18               66  \n",
+       "19               44  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display master results table - filter to successful runs\n",
+    "display_cols = ['config_id', 'config_name', 'method', 'detect_neg_shifts', 'piecewise_fit',\n",
+    "                'soiling_ratio', 'ci_lower', 'ci_upper', 'valid_intervals']\n",
+    "master_df[master_df['soiling_ratio'].notna()][display_cols].head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "601abbb9",
+   "metadata": {},
+   "source": [
+    "## Analysis: Compare Results by Configuration Group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0d67acb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Results by Method:\n",
+      "============================================================\n",
+      "                 SR Mean  SR Std  SR Min  SR Max  Count  Avg Valid Intervals\n",
+      "method                                                                      \n",
+      "half_norm_clean   0.9612  0.0102  0.9470  0.9786     15              45.6667\n",
+      "inferred_clean    0.9419  0.0177  0.9267  0.9785     13              60.0769\n",
+      "perfect_clean     0.9710  0.0128  0.9569  0.9967     15              45.6667\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary statistics by method\n",
+    "print(\"Results by Method:\")\n",
+    "print(\"=\"*60)\n",
+    "method_summary = master_df[master_df['soiling_ratio'].notna()].groupby('method').agg({\n",
+    "    'soiling_ratio': ['mean', 'std', 'min', 'max', 'count'],\n",
+    "    'valid_intervals': 'mean'\n",
+    "}).round(4)\n",
+    "method_summary.columns = ['SR Mean', 'SR Std', 'SR Min', 'SR Max', 'Count', 'Avg Valid Intervals']\n",
+    "print(method_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "147b8c87",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Results by Configuration Group:\n",
+      "============================================================\n",
+      "                       SR Mean  SR Std  SR Min  SR Max  Count  \\\n",
+      "config_group                                                    \n",
+      "clean_criterion         0.9768  0.0142  0.9544  0.9967      9   \n",
+      "collapse_window_days    0.9508  0.0166  0.9326  0.9717      6   \n",
+      "core_matrix             0.9561  0.0154  0.9267  0.9747     10   \n",
+      "forward_median_window   0.9519  0.0132  0.9352  0.9635      6   \n",
+      "min_piecewise_days      0.9525  0.0208  0.9267  0.9747      6   \n",
+      "neg_shift_factor        0.9573  0.0168  0.9303  0.9786      6   \n",
+      "\n",
+      "                       Avg Valid Intervals  \n",
+      "config_group                                \n",
+      "clean_criterion                    39.8889  \n",
+      "collapse_window_days               44.0000  \n",
+      "core_matrix                        52.8000  \n",
+      "forward_median_window              54.0000  \n",
+      "min_piecewise_days                 50.6667  \n",
+      "neg_shift_factor                   62.0000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary statistics by configuration group\n",
+    "print(\"\\nResults by Configuration Group:\")\n",
+    "print(\"=\"*60)\n",
+    "group_summary = master_df[master_df['soiling_ratio'].notna()].groupby('config_group').agg({\n",
+    "    'soiling_ratio': ['mean', 'std', 'min', 'max', 'count'],\n",
+    "    'valid_intervals': 'mean'\n",
+    "}).round(4)\n",
+    "group_summary.columns = ['SR Mean', 'SR Std', 'SR Min', 'SR Max', 'Count', 'Avg Valid Intervals']\n",
+    "print(group_summary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b42e34eb",
+   "metadata": {},
+   "source": [
+    "## Visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1bc0469a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize soiling ratio by method and core options\n",
+    "valid_df = master_df[master_df['soiling_ratio'].notna()].copy()\n",
+    "\n",
+    "# Focus on core_matrix group for main comparison\n",
+    "core_df = valid_df[valid_df['config_group'] == 'core_matrix'].copy()\n",
+    "\n",
+    "# Create pivot for heatmap-style visualization with extra space for colorbar\n",
+    "fig = plt.figure(figsize=(16, 4))\n",
+    "\n",
+    "# Create GridSpec with extra column for colorbar and more horizontal spacing\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "gs = GridSpec(1, 4, width_ratios=[1, 1, 1, 0.08], wspace=0.45)\n",
+    "\n",
+    "axes = [fig.add_subplot(gs[0, i]) for i in range(3)]\n",
+    "cax = fig.add_subplot(gs[0, 3])\n",
+    "\n",
+    "for idx, method in enumerate(['half_norm_clean', 'perfect_clean', 'inferred_clean']):\n",
+    "    ax = axes[idx]\n",
+    "    method_data = core_df[core_df['method'] == method]\n",
+    "\n",
+    "    # Create matrix for dns × pw\n",
+    "    pivot_data = []\n",
+    "    for dns in [False, True]:\n",
+    "        row = []\n",
+    "        for pw in [False, True]:\n",
+    "            if method == 'inferred_clean' and not dns:\n",
+    "                row.append(np.nan)\n",
+    "            else:\n",
+    "                val = method_data[(method_data['detect_neg_shifts'] == dns) &\n",
+    "                                  (method_data['piecewise_fit'] == pw)]['soiling_ratio']\n",
+    "                row.append(val.values[0] if len(val) > 0 else np.nan)\n",
+    "        pivot_data.append(row)\n",
+    "\n",
+    "    pivot_arr = np.array(pivot_data)\n",
+    "    im = ax.imshow(pivot_arr, cmap='RdYlGn', vmin=0.92, vmax=0.98, aspect='auto')\n",
+    "\n",
+    "    # Add text annotations\n",
+    "    for i in range(2):\n",
+    "        for j in range(2):\n",
+    "            if not np.isnan(pivot_arr[i, j]):\n",
+    "                ax.text(j, i, f'{pivot_arr[i, j]:.4f}', ha='center', va='center', fontsize=12)\n",
+    "            else:\n",
+    "                ax.text(j, i, 'N/A', ha='center', va='center', fontsize=10, color='gray')\n",
+    "\n",
+    "    ax.set_xticks([0, 1])\n",
+    "    ax.set_xticklabels(['pw=False', 'pw=True'])\n",
+    "    ax.set_yticks([0, 1])\n",
+    "    ax.set_yticklabels(['dns=False', 'dns=True'])\n",
+    "    ax.set_title(f'{method}')\n",
+    "\n",
+    "fig.colorbar(im, cax=cax, label='Soiling Ratio')\n",
+    "fig.suptitle('Soiling Ratio by Method × detect_neg_shifts × piecewise_fit', fontsize=14)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "956ab4bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_13060\\703968651.py:8: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
+      "  bp1 = ax1.boxplot(data_by_method, labels=methods, patch_artist=True)\n",
+      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_13060\\703968651.py:21: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
+      "  bp2 = ax2.boxplot(data_by_group, labels=groups, patch_artist=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Box plot of soiling ratio by method\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Box plot by method\n",
+    "ax1 = axes[0]\n",
+    "methods = ['half_norm_clean', 'perfect_clean', 'inferred_clean']\n",
+    "data_by_method = [valid_df[valid_df['method'] == m]['soiling_ratio'].values for m in methods]\n",
+    "bp1 = ax1.boxplot(data_by_method, labels=methods, patch_artist=True)\n",
+    "colors = ['steelblue', 'green', 'orange']\n",
+    "for patch, color in zip(bp1['boxes'], colors):\n",
+    "    patch.set_facecolor(color)\n",
+    "    patch.set_alpha(0.7)\n",
+    "ax1.set_ylabel('Soiling Ratio')\n",
+    "ax1.set_title('Soiling Ratio Distribution by Method')\n",
+    "ax1.axhline(1.0, color='k', linestyle='--', alpha=0.3)\n",
+    "\n",
+    "# Box plot by config group\n",
+    "ax2 = axes[1]\n",
+    "groups = valid_df['config_group'].unique()\n",
+    "data_by_group = [valid_df[valid_df['config_group'] == g]['soiling_ratio'].values for g in groups]\n",
+    "bp2 = ax2.boxplot(data_by_group, labels=groups, patch_artist=True)\n",
+    "ax2.set_ylabel('Soiling Ratio')\n",
+    "ax2.set_title('Soiling Ratio Distribution by Parameter Group')\n",
+    "ax2.tick_params(axis='x', rotation=45)\n",
+    "ax2.axhline(1.0, color='k', linestyle='--', alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f921e80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zzjllBLaLjo52LVmyJNu21157ratmzZquXbt2ZVt/6aWXmve3t/etE882WTmmcuXKrj179rjXf/XVV2b9pEmT3OuGDBniddz67bffzPoPP/ww2/rJkyfnWG/HL75zsmLFihwyEQwePNjID85+efYROQMZ59RTT83RV8YVS7t27bL1PVSMGDHCtP3RRx/Nc1t7vZ9//nkztjVt2tS0y8pnVoZ0yjje7unTTz+dTTbKbbzO7XlCRkE+XbNmjXvdli1bzDPdtWvXbPe+evXq7s933HGH+b5atWqu119/3azbvXu3adNLL71kPtMn+te7d+8c8mfDhg1dp59+unud7fdll13m8oeKFSua6+YvyKqtWrXye3tFUcKHefPmmffH1KlTzWfeN3Xq1DHzCAtzP88xDc466yxXo0aNcrzPGa8tKSkprhYtWvicDzvhvc4Y7ZxXwpgxY7LNQZn/MAf1HB+Yyz322GMB6RqsLoPx0snll1+eY16a17jkKeecdtpp2d7ft99+u5nD7tu3z72Od6tTH2B5/PHHzRx25cqV2dYzRnGMDRs2ZDs/cz7ms07eeOMN892iRYuyrT/mmGOyjfnBXs+9e/fm6Lu/2DH35ptvdq/jWiFnMK7acdzKN3astPTt29fVoEGDbNfXk9tuu83su2DBAp/bzJ8/32zDuGzhMwu/DQvyAjqJfv36udfRb7bjunjiKUPZtiDzOXUKjOf0w15/fiNs17JlS1dqaqp7W2QD572kT3xGX6EoBY2mgVKKFXjJEVmBtZcQQKy5WG9JJ4BHlrNgJOD17YQIC/AMbQuW0047zYQ1WogMwJPNpmbCav7jjz/Keeedl836T7ik9dj0BzxCsIizsC/W7JNOOsmk4nGGjDrzQxMeSyoLtseDDi/DYOBaYhXHa8SCNwvW94MHDxoLvy9suCbeE05vOjwC8FbAGwNsWi/64wwh9Qc8N2mPMxUUbcKK78wf6rw2eJqS7gBPQTwJli9f7vf5uKd40XBPbfttdAPPYn445phjsnkvcr/xTnWm+vIF3iJ4LHCf6Jtd8FDBi9Kb9xAenU54lvBe4Z5zb533i9+YM31DsNeTe83zXBQpOhRFUfyBIr5EFeIJxvuNf32lgOJ9yXvWeqc75Q3GPTzonOARyLvewjZffPGFnHPOOeZv5/ubMQXPtWDHb8ZHZ/SlHV/8GVPw7scDDrnL2Sa86fG29BxT8Hr0HANJiUSkifXWtGMo3nr01zmOOP8mzRT9pr159Z0xBe9J0g4EAl5/eHv6WoiYJVoQb0obBRhIdAUyKh6hvnD2lzGRa4t3I88AkSt5jde+nieu7w8//GBkFGcaTrxZeYaJnsDrE7i+eFUSFQNERRDByXr+BranTfbZoSYH15pjIWPa54I+EDFJpIanHHfjjTf6de1oF1HKiqIoRFXgzW9TCTJHYUwj6s6mnCVaAs9x5xjD+MEcg20teKczj0F/YCE9DVGA/r7XidJAB+FMI8Tckzby7gM83fE6B9rIO9KmNg50HLe6DE/ZIthISydEOTj1B7zfaS/RhP7IBmyPbOGUDdCJcAxnSiIb4eDp1Y/3P6mgnPeNrBlESzjvW7DXk/GVSBtSF3mmrfQXZwSNjXolagC9jpVvmF87U5MRZYHMh/4ht1TfyJWQ23hnv7PjtYXIX+QwC7oI0lGhm/AnFbO354xoSOccn2vMM8Kzzj1xQqStM4rJU660kRO0x1taaUUJJWqsUIodhH4S8sngQ8gbYYe89Akbty9UBlsGNxT1TlC6M7H1ZzD2B6ey2sLgbQdGQg5ReHi2A7yt8wUCFYIXC+GUKMY5tmfxSs7F5NrmkESAQ0BAiR1snkCuFeH7Vliw2NRDeV1LhI6NGzcaAQ/Iu0w9Cacwwt8YXwhzROhDIMTA4Y/hgnBFFCSkXEDBYIVHhCAUThaUGYQyMohiROG62BDNQK4NOT25zlwTTxCeCvJ5yg2rqEFwt4Ytu6C48MyzyvUhVZgn3Av6Z41/GC0QZDBiOAWvYK8nCiAEPIxYnJ8c7d7qvCiKohQVvM+YeDOWIG8wAfSVmoYxEGcEz0mnrzESpb7nmMIYTW0Jz3e3zcXtLU92MGOKNVz4O6bwLie3t2e7GBc82+TZL+eYQsooHAgA5QH7OmUAwCCEEwPyDikVOA8pP/Ian0nJwPVjXCGvNem3/v777zz7h+IKGSq3hWPDvffem63WR16gqEDGy612BakoSK1IX1EM0F8MD+DZZ1/jta/nCQWBN3mEZxK5CpnMqWTAMIGxASMJ6zBYWGMF/zLGU6vMKWuQctLzuSC/NkYgz/b7ejY84TxWiaMoSsmFMRejBIYK0g+SJpcF5TAGVupH2HcjynCc3WxNSsZsHPacYwzjMM6FngrkQObinnUSSdnH+5E5q02Ry/uV1DvMEZ3zcMakQOfhVpfhdIoMxVwzFLIB8zbP9z8yE/gjG3BdMPAw17dguOB+OtMnBns92RYnAwwH6BUY03BwJSWlP3DdPWtuImOA01hFqibkGyvnYcjh2bvyyitzPb6VF3Mb73wZNLzpH2gb476z7oi/0HZf8oL9PpBnh/uNszDyAPcLHQ0pprVehVIQaM0KpdiCVRfDBQsvaSb1DBLO/IfBFrD2F2eNBCd5FVYM5jxWCABe/BQfJFekM6KE3IAYM/C6wPKOIplrgCAVaMRCqMB7kvzKCCS2vgRCgLMmAkoBPDHw1CTqBSEIoQXFO4p2X9fZgpIcRQcLXjN4qdqaEoAiAyUAE2GUBwh+KETwyhg2bFiRXZtQPk+2D+Qd9yyECQiATpzeKk5QFpHLk/uE5yS5NzFeOIX+/FxPFF94ZuJxgRDJwjOLwOdP0XZFUZTCgPcfXpdMbjGu2gjA/OLpZGDfl4xjKIC9QdRmUYwpvK+dXoNOPD0lPftlYezAqQT5DNmEsQXZhBzLFhQ+jN0oFMi/TBQAEZOMDc6oSW+wD04QKKuQF5ggo9wYM2ZMtjzPnlB7wzo4+ALHCrbD49AZveBvdAXGCNrlTRFHxApemIyZyHMU8cSgwz6eY6iv8Tq36+4PGNlQLCB/Me7zXCA7cm/J646SgnuD7GbPb9tGbSqiZryB8SWYNnIdkA/wXnV6biqKUrKgNgP1DzBYsHjCuMQ8D5jjUseA+QQRZYwxvEusgTVU4M3OcakFQN1A/uWd6Yzipy7Pgw8+aByxHn/8cWOM5t3J2Fdc5pqhkA0Yv6iH5A2r1M/r/c99Q3fDO5+xhPuGAcPWWMjv9WQbdBBEODLn5DjUt+LZat++vYQC+kABdJ5Hngnqi3Ts2DFPg5I1BGB08TWOWqeLQGSP4vLsjBgxwi3/IJcRHcS1p16pL8cLRQkGNVYoYQEDAyDY2ELcDGJY/52Fo/HGQNHK94UBE32UuHiDeOJtnb8wkWdwpGA3L34UzEBqBZQdDBIWJuP02UkgRhyuFQMm19M5WbapfvK6lkzAKZ6NomLkyJHGCIHnnmdRLI6NkMLCdggoFPXCgOE01HgDJQeeByg1UHBg3XcKj3hyEjqKtw2KDQveOoHCJB7By1vKCZtKoSDxde+s5w3PXF7XKy+ISKHYKaGn3C+UGPYZC8X1RAmBAMnCc0W0BRMNBMlAvJwURVEKCiLHcAhgjHWmKvCEMZC0AHjBOT3g/B0jGVPYDwV2ft/doR5T6BdRj/lRiKMMJ8UA15A0CowbKJRQwFtwMEBWQqHgXI+xwh9QYKD0YCHqg3GJwtu5GSucxb29QRonUnmhvCe6EFkmEDA+UZwbOc2ZegQWLVpkCmBioHcW2AxFekSeJxxEvMkjPJPIWkTfWpDHMFZwn1Ca8Cyi5MOghOMITgj0wVPWwFkh1M8rMgFRuDwPztSjiqKULFD+Mp/BI9sTxhCi6TFIMzbxvmdezBiDYRlltC0KbWEcJvsCClXnmBfoXJy5JXMV5sXMOfFyx2nSwjycaJC33347237Mw51KeH+wugyM8U7ld2HMNfOSDRhn8/v+Rw5AxrLyFWMijg1O8ns9aSvjOAvzdsY4dCQYFXKD605aI6fhhfaBszg3sgcFz3leeTaIsvAsMO4NHGBQ+uNg6KvI9vvvv28cDZ2OHeBN/0DbGPetE0mgeh5f8oL9PhiIdGXBcYMC7MiS/GaRixQlVGgaKKVYgeLam9Xf5nW0g/lZZ51l/vUcMFCCAwNLYWAjIrDqb9myJZtw5JnHOlCIomBgeuaZZ7Kdz/P6vPLKKzlyGNpJt6cRwxtcSzxLncqa9PR0c1y852zagtzAs5L+4/GIAsAz/QPehZ5YTwMb1psbCKsolngOSBtB/8jf6OkF4Lw2eO7hwRkoHIvIFu4paRwsy5YtM4qWgsbXvaNNKA8w8hCC6kkgoaHcH647ihSUFc50Wvm9nhg5nKA4sV7D/txrRVGUwoDxjfEEpTdK1NzGSMZYPPCd4N3PhDGv+lS8T0ljgYKWnM2eBBPWH4oxhfc+/cKb0RNkAH/kB+eYgtHnnXfeMbmtPWUArgHXyimrkGoht5oPvsYU7htG7/yOJ8iLpIFAVgumjoKNrsBr1BkBa7/zHEP5GyeB/MKx8TjGo9GZrgJnHZRrKPOQFZzGCrazjiR2XCaagmuAPOGspYV3MQqgF154IVttq1A8r9S2QOmIYskqhpyQXkQVHYoS2RDNjUECRzfSL3ouGL1xDrDvVd5XrCcSHOUv45PnGMMcicg157sYZ76xY8cG1DbrCEfKZd7tTsc4X/NwnPVsGsRAsLLDyy+/nG29P8rwUMkG3sZ5ZAOMyt7mvGzP9fcHolW5L0RUED2DIxsGjFBcT1IieUZOMm4xlvsrGzhlOtrAZxwibX0SCymfMISRgtLWNskLHAZwrsAhBDnTE5T6GN2uvfbaHJEIXHtnvQ7SOjLeM+5b2SJQPQ9p1W26biAtJKlJMcwEGtmBo6PnM4DRgt+pzvOVUKORFUqxAgU9AxCKaUIxUZBirbXe3za/M15hRBjworUpa3gRo3xlILTFugoDFB2EwGFRpkCiVWq0bt3aCDrBQq0G+ouCGEU5ESQIdghqeMQxuDDwMBCyrachgAGNfI7kEMSTkZRLeLF4QoElvN4J5yMlAtcZTwfrPeDPJJ6BkO0oDG4VM05IJYRnH0YkLPhMSOkXA7Sz4FNeXox4ISA8ITw6vSCZdJNTkWeCUESUIlynYNN14WWIEp8JPFEB1njTqlUrv3Jl54fc7h0CD0LTcccdZ4QlPCwwqJBai+fPU5nmC/ZH2YNnEoKFp9Cfn+uJpyvGKdrM/SXNBNeOfjmjoBRFUYoaX2mZnGDIQKbgfYnSF/mDMZ/JI2kIPPNNewOnA5wxyMdN6inGb96TTEgZw70Z9EOFLdTIuxzFgZ1sIzfh9UjoPrIKE2Em6nj1oSxAse6rjoc35QbjPwueiJ4emYz9KMbxICT9FjIAHrWMQ3mNqVyr7t27m35w7Hnz5hkZxVkcMxiQH1Ga2WKRwYAsgrHHU9ZDfuW54HqgdMF4gLEq2EKgnqDQJ0oD+QkZBe9M5DjGc/J2O7GGCDwrcXaw4K2MoQYZw+k5jMIBxxMUacg8yKHU/6AfPMP0BaVhMCBX4DGNzIhMgFxnn09+C6RdIdJFUZTIBYMCxgjPiDQLkd7Mb/Bmt/MT/mUuQSpoFKOe8wnGMuZARGyR4g6jKPsT0ReIJzoRaMyBbHo/T2MF83DmtLwX2Y4oOs7jWf/AH3gH0l7mw8z3OB61OvKTmSEQePcyr2Q8YSxmnsncDaU894i+ohtgO5Tb9JWxFznI3ygS7hvvefqI/OGZbjPY64mxG6MCsgcyAmMgYwtGe3+MCTwXzPORAZHLGAuZS5PqyTMFJvILehbkIsZFb7oUb+DQQvQCYzTnshEU6DF4vpDBnJkyLOiPuFbIbIzP1lHQGQFpx03kUvqL7Ias6i1ClJpcjK20nWMiR6EvI1sCcomvFJS+wMiC/EW6byJT0JGgI/Cm/1GUfONSlGLE999/77rmmmtcLVq0cJUpU8YVHx/vatKkievmm292bd++Pdu2R48edT366KOuhg0buuLi4lx169Z1DR8+3HXkyJFs23Xr1s0slnXr1qFxdb377rvudQ8//LBZ54TPQ4YMydHG+vXruwYMGJBt3U8//eRq3769aW/jxo1db731luvOO+90JSYm5tlnjlW6dGmv361Zs8YVExPjPt/evXtdV199tatKlSrm+vTu3du1fPlyr20aO3asq1GjRmZ/+jJt2jSv1wO4tva49KFNmzbZro8/9O/f35zntNNOy/Ed1+fcc8911apVyxyffy+77DLXypUr/T5+enq6q2bNmuYc3333XY7vZ86c6ercubMrKSnJHP+ee+5xTZkyJVvfgevE9XLCNjwDTn755RdXhw4dTHu5jmPGjPH6nPh7Tzlnnz59cmzr7X74unfA39z38uXLm+eL523gwIGuefPm5Xp+T+6//35zbH5f3gj2en7++eeuXr16uapVq2auXb169Vw33HCDa+vWrbm2R1EUpSBhTOP9NXfu3Fy38/auPnDggOv2228370LkjaZNm7qef/55V2Zmpl9ygx1n+Q5ZhWPUqFHD1bNnT9ebb76ZZ9s922TlGNrgied4xtiJDFW1alVXVFRUjjGM8zPW8a4vW7asGf9532/ZsiXXa+LJSSedZI49aNAgr9+//fbb5rolJCQYGY/74W1M9ZRnnnjiCdcJJ5zgqlChgmkj+z755JOutLQ0V2GR2/W2zxXLzp073euXLl1q5CFkNWSr6667zrVw4cIc8mdu43Vuz9P8+fONLMDxS5Uq5erRo4dr1qxZXrdlPOZYTjl6xowZZt0pp5zidZ8FCxa4zj//fFflypXNPeO+XHzxxUaes9j75+y3P/Bs8Xtq1qyZkWNoP88g93X//v3u7ZCNWrVqFdCxFUUp3pxzzjnmd3/o0CGf2zCvYZzctWuX+cxYy9jJ+4YxwRtr16414xTjBOMd8/AvvvjC7DNnzhy/2/fqq6+afRh3PEHHwHGZj3Iexr3Zs2cHrWtISUlx3XLLLeY9yzjAtdm4caPXeak/45IvOYd5m+f8bdu2beZ6Me7znbP9yDzoVJgjMpdjDDvxxBNdL7zwgnvszW1ctCQnJ5vrxHbjxo0L2fXkuWBsRB7gujEn7tSpk+vTTz915YUdc9GxMF9l/Klevbq53hkZGV73GTx4sDn/+PHjXYGQmprqGjVqlBnfOCfnOu6441wvvviiVxnGjvlcKysvoV9y3jfL448/7qpdu7YrOjra7Mc1Am86Ifp64YUXGjmK3x7P9jfffOP1Gfnss8+yrfe89vzO0NWhf+BYlSpVMvLHjz/+GNC1URR/iOJ/+Td5KIriCREeS5Ys8Zp7UFEURVEURVEURVGU0EOGAGpAbtq0yUSIKUow8AxRV4O02aToLiiIABoyZIjfmRIUJdLRmhWKEgJIJeAEAwX1FUhdoCiKoiiKoiiKoihKwc/FqWlAejyKZKuhQgkWniMKdpPiqCANFYqi5ERrVihKCCC3Inkd+Zcc/eSApJDUPffco9dXURRFURRFURRFUQqA888/X+rVq2dqQVADAgUzNQOogaAogUJtLWqKUadj9+7dphaKoiiFixorFCUEUDSJ4kWEB1IMiQKBFDLEm0NRFEVRFEVRFEVRlNBDUeK33nrLGCcyMjJM4eWPP/7YXaRbUQJh6dKlpsA6BbVffvllYwRTFKVw0ZoViqIoiqIoiqIoiqIoiqIoiqIUKVqzQlEURVEURVEURVEURVEURVGUIkWNFYqiKIqiKIqiKIqiKIqiKIqiFClqrFAULzz33HPSokULyczMDOvr88gjj0hUVJTs2rWrqJtSIrHX3x/Yju393Xbo0KF5bjdmzBhTbC41NdWv4yqKokQK4TaOv/fee+bd/s8//wQ8rjRo0EAGDhwo4QrtP/vssyVSCUQW8Jfp06ebY/JvcXhu582bl+e23bt3N0swXHrppXLxxRcHta+iKMVvTGas493xwgsvFOg7VOfC4YF9HhhTlPBFdQ9KKFFjhaJ4kJycLM8++6wMGzZMoqP1JxKuTJgwQc4880ypUqWKxMfHS61atcxE9+eff84x2f/888+luDFr1iwjYO/bty/oY6C8SktLkzfeeCOkbVMURQm3cfzw4cPmnVrUyl1FCYTXXntNlTci5rf8xRdfyMKFC/UBUpQwQ+fWSnGfMyuhQXUPSihRTayiePDOO+9Ienq6XHbZZXptwhCXyyVXX321nH/++bJ9+3a54447jJV/yJAhsnbtWunZs6cRagqDBx54QFJSUoLalzY++uij+RK8EhMTZcCAATJy5EhzXRRFUUrqOI6xgndqJBorVqxYIWPHji3qZiiFaKzo2rWrkS/4N1z44YcfzBIM7du3l44dO8qIESNC3i5FUYr33Do/8ykl8gnFnFkJDap7UEKJGisUxYN3331X+vbta162oQAlsS8B68iRI/lOUXHo0CEpSnLrX1HARJaJ/W233SZ//vmn3HfffXLNNdfI/fffb9IUvP/++xIbG1sobeE8oXqOgoVokvXr18u0adOKtB2KoijhNI4X9dgaCAkJCRIXF1fUzSgxFIdng4ghnu9wigAmypUlP/LMl19+KQcPHgxpuxRFKd5jcnGYT+UFDhGRDgYnIvaV4kko9EqhQHUPSqgIHwlXUQqBdevWyd9//y2nnXZaju94+b/44ovSqlUrIzBVr15dbrjhBtm7d6/X3MtTpkwxXmBJSUkmDY9NOfTxxx8bD5HatWtLqVKlTGgsfPbZZ9KhQwezPamLrrjiCtm8eXOO0LoyZcrImjVr5KyzzpKyZctK//798+wXNSsYOMqVKyeVK1eWW2+91QxonoLkqaeeKtWqVTOKj2OOOUZef/31HMfy1T+YOnWqnHzyyVKhQgXTzubNmxtjQWGB0eTpp582OVHJgeotv+mVV14pJ5xwQkDGGO4HERrOZ4E+xsTEZPPiIO0IArWdSHvLsUr9iNtvv12qVq1q7h/C+6ZNm7Jtw3533323+bthw4bmGN5ymU+cOFFat25t7hfP5eTJk3O0n2eqUqVK8tVXX/ndZ0VRlEgax3l38s4FvO/sO9XWCcptbA107J8xY4YZY9i2UaNGxkDuyZIlS8x4y/hZp04deeKJJ/I1wfSsWWHrCMycOdOMXfS9dOnS0q9fP9m5c2eO/b///ns55ZRTzDb0vU+fPqaNTrZt22aiFmkvY07NmjXl3HPPzbPGRiD7+XPtiJC86KKLzLiGDNW5c2f59ttvQzJme8Ney19++UUGDx5sZCT6Esi184Y/Mhf3lWNxbvvM2poPvmpWBCJLsv68884zf/OM3HXXXZKRkZFtW2RWjkffkCHbtGkjL730Uo7+INvk9ax51qywffjkk0+MrFijRg2zL3LRxo0bc5zj9NNPN4YiZE1FUcJ/bm158803pXHjxuZdePzxx8vcuXOzfe9tPsWc75ZbbjHvOTuf4p3mqwYg737efYwF5cuXN+OSNwPDuHHj3O9Qxhnq5Xi+j3iPMf/CKY7oNsaiwprv2muxcuVK836nL7x3H3zwQTP+0VbGWN7XvFO9RaPt2LFDrr32WiPPMN62a9dO/ve//2XbxllTBBnI3p+lS5ea75cvXy4XXnihuUYcA53A119/7Vcf7L2g7dwPsgB4i4rguWE75AHOQX9wQNy9e7ffc2Z/9Ru+oJ/oULjGPBPoNnCAdLJgwQKTepprznhKFoc5c+a4v8dZkjZ5XmNAn8J333zzjXsdzzH95P7YOT7RSU5y0yvt2bPHjOeM17SHdtE+b2kUcWjkt8PYyzVCR2Hb5Clf/P7773LGGWeY+8a5unXrZuRMT1T3oISKwnEvVpQwwaYHOu6443J8h3KCSSvCDcIRwtfo0aPNAMWL2unVSEoGQl3Z57rrrjMDm+Xxxx83nmUMIkzu+NseFwENZTvpi5gMclyOz0Du9Gro3bu3MQogQDBY5AWDLJNejs3g+fLLLxtFi1MRwMDNYMiAxeR90qRJZmLOJJ8USk689Y8JNYqatm3bymOPPWYG19WrV3sdxDzZv3+/HD16NM/tEFQYdH2BooMBmqgKlBKhgMH6pJNOkl9//TWb8ESb8Wikfygn4LfffjOpCnJr46BBg4wgfPnll8uJJ55oamjY/S2ksEII/eijj2TUqFFGEAerbLN9xcOQe4SQzj294IILZMOGDcYg5YTn2Z/7oCiKEonjOO9OxribbrrJKFF5xwLjVV5jayBjP2Mek3eUAEy+mVwy0Wbixvhqlfc9evQw57v33nvNBBFFDZPgUHPzzTdLxYoV5eGHHzYTdxQOQ4cONcphywcffGDaSt9R3qO84VpxHegjsgMwvjDOc0zWoexAacyYY7fxhr/7+XPtkI0YN2kj94Kxjsk/cgu1p7i3oR6zLYy1PEcPPfSQO7LC32vnDX9kLu4X1432WeUIygtfBCJLYpSg3Z06dTLP+48//miUWiik+J0A9wlZD8UL/YNly5aZ4+H0Euiz5osnn3zS3DfqUvB8sC+Kzb/++ivb7wIlE585P/daUZTwnlvD+PHj5cCBA2as5T1AIW7GaAzTuUUMMj58+umnxgkNozVGXc/5lOdcGGU278b58+fLW2+9ZZSz9t1m30Uo/dmW+RoG11deecUYJDzfoSjMUQBjzMBokNu7mfk+ffQHO+fLi0suuURatmwpzzzzjDHY4/SA4QAHQpTz9OvDDz80+gbGBJsyECMPxhbGXN7RXBOM3FxPDAae73aU/Tg4Xn/99WZuzzkY0xlnUZBbOYZ7gfGb2kK5vZ8xqGBMYR574403mj5QZ5Kx1BPGIJ4DxjUMFZwXeYl/0WfwvOQ1Zw5Ev+EJsgPOCDyH9J8xHacWjsGzArSFbTAI3HPPPWZb7gHXmGeSMRZDDgYXrpFnPxkjGTsZj4Fxm+eZvnF/6AdOEchGGCLQcTjxplfCoIRDI44d3F+OSZswLvAddTwBWYZnZevWrea+c435PXrLxoC+gucdmYxxHnnKGoKQpTwdQVX3oIQEl6Iobh544AES+7sOHDiQ7ar89ttvZv2HH36Ybf3kyZNzrK9fv75Zx3dOpk2bZtY3atTIdfjwYff6tLQ0V7Vq1VytW7d2paSkuNd/8803ZvuHHnrIvW7AgAFm3b333uvXXXv44YfN9n379s22fvDgwWb9woUL3eucbbL07t3btNeJr/6NGjXKrN+5c6crULp162b2zWuh/7nx0ksvme0mTJjg13ntPfnss89y3e755593xcTEuJKTk83nl19+2VyHE044wTVs2DCzLiMjw1WhQgXX7bffnuP6W/766y/zmevv5PLLLzfr2d55TtatW7cuR3tYHx8f71q9erV7HfeS9a+88kqO7a+//npXUlKSX9dEURQlEsdxxibP92xeY2swY/+vv/7qXrdjxw5XQkKC684773Svu+2228x2v//+e7btypcv7/Od78RzXLHndo6P7777rtnmtNNOc2VmZrrXMz4xlu3bt8985hoxbl133XXZjrdt2zbTHrt+79695niMS4Hg736BXjvui4U+NGzY0NWgQQMzDudnzPaGvZYnn3yyKz09Pdt5/bl2vu6ZvzJXq1atjIzkS37h32BlycceeyzbMdu3b+/q0KGD+/Ott97qKleuXLZ++7o+eT1rQD+cfbF9qF27tvtewaeffmrWI9N50qxZM9eZZ57psz2KooTHmMxYx/rKlSu79uzZ417/1VdfmfWTJk3y+Q79888/zWfGBCcDBw7MMc7bfa+55pps2/br18+c2/LPP/+Yd9aTTz6ZbbtFixa5YmNjs62389YxY8b4dQ3se9KfJS9sf5jbWXhH16lTxxUVFeV65plnso3BzP+c8sGLL75o9h83bpx7HeNHly5dXGXKlHG/i+39YQxgPHbSs2dPV5s2bVxHjhxxr+P9f+KJJ7qaNm2aa/snTpxojvvcc89la/8pp5xi1nOtchsnP/rooxzyQm5zZn/HWm907drVVbZsWdf69euzrXeOdeedd56Zk69Zs8a9bsuWLWY/9rcMHz7cFRcXl+1ZT01NNXKE89m89tprXTVr1nTt2rUr2zkvvfRSI1vY/vjSKwH3xcpDFq4NMpVz3B8xYoQ5BvfEgvzQokWLbPIF/eW+ct2cfee8yF+nn356jmunugclFGgaKEVxgJcEVndPLzs8Dgh5IwSdlEp2wbrMtp4WaKzY1kLuCRZ1p6cYoYF4kmHld+bjxDuEdEbO9AYW6/XmL56eA3jAwXfffede52wTHoj0Dws8Hg18zqt/1tuEdEOBprPAmw/vibwWPBZyw6bUItIglOAxgRei9Q7Cg4B1LPwNixcvNh4prPOFvd54hDrx9JLwB7wO8YC04CGMVwf3yxM8NvCkKQn5VBVFKdn4Gsf9wXNsDXTsx/PbOQbgEUfkofO9zDiA15zTC43t/EnpGCh4AjpTZ9ixjLB/YFxl3MJ73tk/IhPxBrT9Qz7AW4+UAJ7pr3IjkP38vXZcNyIXLNwH+ok3v01PEaox2wlRpM6ITX+vXW7Xxl+Zyx+CkSXxanXCtXBeb+Q6f9Mu5fWs5cZVV12VTW4jwoZ0YU4Z1SnPcK0URYmMMZkIAX7XFvtO9jafsdi0t7zvvM1vveHtfUfb7NyRaHXmr0RVON/peJs3bdo0xzudKAM8/v2BObM/89xAUtwR+WFh3MF7H382PPCd73Bv4yh9chY7JxqAuSkpEYkG8IyOdEb2k8EAL3uuE9Ei9jpxLennqlWrcqQedML5eR6c8hbt93bvnOMk0R2cB/kJiI7xh2DHWqJqiNAkHVO9evWyfWfHOsa4H374wUSUEDlhYfwigwLRI/b54jkniwTPmYV9kSP4Drh/RKacc8455m/nc8i1pb2e/fbUK9ln09azoo3cG5ue27k/vyOiY4g6sSA/IO84IcqR+0qfOJZtE/IBkZdcJ0/dj+oelFCgaaAUxQ94QTNAEC7qDSaInsp8X3h+ZydyzlRRFiaYDHTZfrSxsdnyJfsDQpYTlNwMYs6c0YTVE9Y3e/bsHEpt+o7CxlcfgIGWkFqEJ0JCGbwIzWTSmVcBSBQ/oQBlPfgbausvhDKSEgQlB8IC/5L3HGGP8GAEKKsAcSpRPOFecy2cRgZf9z4vPAUnKxh4UwhlBWP8J1wpiqIokufYGujY7897mXEAZbYnwYwDeeHZHqsQsu2hf0AYf25jKhNfUkrceeedJtUFygLSPqJkZhz0RSD75efakUbCfk8e8VCN2U485R5/r50vApG5/CFQWRKFhFMB5e16owgkbQWpH1Bo9OrVyyioyFkd6LMWiIyKrNKkSROvdU2QZ1SWUZTIIZh3h51Peb6XeW8Ecx7e17zTeb94vo8snimpeCdijPcHlNcsocSzP4wZvNc900ix3lnjgWtHHz3n5s5x1InnNSZ9FNeJdFksvmQjro83OD7XwtN45W3swjDC2E1dBk95y1+jfrBjrTXwIFPkZtDgmN7azvVEgU8NEdJQUReEsZi0T9agxN/cLytHcDyMF6S6YglW58R5SQH52muvmdSlzlpUzlTR3At0Ep5jqufvyMo73lJ1Oa+l0+iougclFKixQlEc8AInjzTKbqeXFy99lBXkfvSG54Qvt9zT+c1L7bSWB4vnoET+RYwLDKIjR46UunXrGgEM7wfyP3pay731gXVY1vE8wYMPaz2DMAMwngO51ZBAGElLS8uz3Zwjtwk87YdFixYZL4dQgYCKgoT+IaSRcxyPHJQveElQcArFB+f3fBYKCl/X0woHThDEUdwURE50RVGUcBjHgxlbAx37A3kvFwZ5tceO7dRe8GZ0wIDjjADE2488yBRfRElB7m88LKn74At/9wvltSuIMdtz/Azk2nkSqMxVEPhT14tnH49K7hs5s1nIUY2xybNQaGE9+8gzvpSJiqKE35hcWO8Of8ZD5se857xt66lcD2RORXS7v8r13BwAnHhrY0FcS19jHzUSfGWRyM1oFAgYx4mQpID2sccea+4B58dg7s84WRzGWk/HTmpdEJXAb4GC5ES4WHnBtocaKL4MA85aa76ew6eeesrIWkSFUNOCOiPIt8hjwfTZ7vP888+b++ANz9+H6h6UUKDGCkXxouzGCu0cDLA6U3yQYlKhVvjWr1/fXbTa00OPdfb7/IBF3Gl5Z/LOwGOLP1IoiqJMDJpOT4280hh4wkCIUMCCUMBgSVFIjkPaIl8QgeEZduoNBm4KSPoCD0ms+hTZuu+++0JWZBtQdOAhynOAFwTPCkIt3hIoPVjwGM0N7iXXHeHJ6YXBffYklJ6DPM/WY0ZRFKUkjuPBvFMLYuxnHLBeak68jQMFjY3yQymd2xjt3J4oCRb6wKSVNI7jxo0rkP28XTtv12n58uXu70M5ZufVp0CunZNAZC5/n9uCkiVR7GBsYkF+IdqCQp0oQkKlkPL8PaBYQ071VMqg8MRL1ZmyQlGU8ByT84OdT3FMp/GS90Z+3um8e5gvN2vWTEIJznv+powqaOcGrh2Fo7l+TgcNb+OoN2y6I5wCAh377PF/+uknk3LKqeD2HNtRdrMdkRUPPfSQe703+cnXOJkf/YbtJykjfYGzA86AvuQSri8GEqexgv6Q6gnnCVJEUaDdeTyMGERCBHNtLZ9//rn06NFD3n777WzridpwRt5wL0if6Rmx6Pk7svIOEUj+tkt1D0oo0JoViuKgS5cu7ty/npZ9Bg6s054weeLlHyzkmGSyO2bMGDOgWvDsWLZsmck3nF9effXVbJ9JgwCE9oNV6jsFJDxA8KDzF6IjPLHWd2e/CrJmBQLDsGHDzHXjX28CH8qRP/74QwIFxQf9ePHFF41RxA7qrMezcsuWLXnmvrbX++WXX862nmN6Urp0afNvfp4tC/kpTzzxxHwfR1EUJVzHccaHQN+pBTH2n3XWWTJnzpxs4xCh/76iNwoSvCKZfOJYQMSBJ7QLSHNA6iTPySuT6tzG92D3y+3acd1I52AhZzLpEnC+oO5FKMfsUFw7bwQicyEL+POcFYQs6UwdAiherLIxmPvni/fffz9b+k4ULVu3bnXLTBaUKjxPKs8oSviPyfnBevST5sbb/DYYcJzj3Ywy2XP+yGfP92EgFETNimBhHCXaEAOKU57h2mE8oJ5DbjDOdO/e3RiteU8HMvbZ83O+119/3b0OOcvz3nkbJwOdM+dHv4HhoGvXrvLOO+/Ihg0bsn1nj8fxSY9IvU5n2sLt27fL+PHjjezhTAmJ42CbNm3MtWchHRbncLaXGiEYM7wZSfK6ts7jeF43arB51hLhuWQdxhwLY+zYsWNzpOtGdnvhhReMkcmfdqnuQQkFGlmhKB5WdHIT4olH6JyFgfuGG24wqQsIiWdgwqMA6z4vf/ICUpshGDgO3n94XHAewgEZ5Dgmk+/bb7893/cI6zaeaIRNMslHYU+RJPInAv2x3nP0k4GIgQqBxJsg4o3HHnvMpFxgQoylnpyKCJHkAM8rJ3SoalYAoaJLliwxBhA8J7gvhNQimJGGAkWHLboZqLBNmCbeExSStCBkWIErL8UHxhvuL9cFYYkJN14j3jyB7DUhMgWvC54T7o8VyPzlzz//NIakc889N6D9FEVRImkcJzICZTYTRLwmCYtnu9zyERfE2I/RHWU54/Gtt95q3uko2623Y2HCJJrx68orrzR1HhhrmKAzMSedIxElo0ePlpUrV5qISYw3XEPGwgkTJhhZxekV6Emw+/mCelhETqLEphgo95B0RMg4TO6dXqKhGLNDce28EYjMhSzAeZ544gkTycA23upkFIQsSQ0y5AfOhyxHfmsUSsgyoYzW5D4iJ9J22owyir56FvlEkYfRkYL3iqKE95icH3gvotTlXYERgXpIROgz5uQnkpL37PDhw43imXTCGNYZXxi3GEdIfRQMBVGzIljoB4aGgQMHmjki4wMGYmo7cD39SZ+JEyTvbBTvvKe5x7y70TFs2rRJFi5c6HNfxj3GR8ZzrjOyAUWnPdNkMcYyXj/33HPGIYAaGKSV5n74O2fOr34D50L6yRjPdSPqhjYzxiMTAs8MYxPbEXmI3MH1xaBP2z0huoJIEeqLULvCM/3oM888Y/QXpLLk2nJ9GIdR/vMb8uYc6glRo+hlGFPRNZAeG4cYZxFw4JogpyAvII/yjLIdbXP+jmgjdUmRvYhO5bjcDwwdtJV7RRSLRXUPSshwKYqSjZEjR7rKlCnjOnz4cI4r8+abb7o6dOjgSkpKcpUtW9bVpk0b1z333OPasmWLe5v69eu7+vTpk2PfadOmYeJ2ffbZZ16v+CeffOJq3769KyEhwVWpUiVX//79XZs2bcq2zYABA1ylS5f2+449/PDD5pxLly51XXjhhabNFStWdA0dOtSVkpKSbduvv/7a1bZtW1diYqKrQYMGrmeffdb1zjvvmP3XrVuXZ/9++ukn17nnnuuqVauWKz4+3vx72WWXuVauXOkqCj7//HNXr169zLWMjY111axZ03XJJZe4pk+f7vc98eT444832//+++/uddwj1tWtW9fn9XfCdb/llltclStXNvfynHPOcW3cuNFsx/ZOHn/8cVft2rVd0dHR2e4Dfw8ZMiTH+bg3PCNOhg0b5qpXr54rMzPTrz4qiqJE6jg+a9YsM4YzRjnfuXmNrfkZ+7t162YWJ3///bdZx3jLO553/dtvv51jvPWGt3HF893/7rvvmm3mzp2bbTs75vGv5/revXu7ypcvb9rUuHFj18CBA13z5s0z3+/atcuMOS1atDDXie06derk+vTTT3Ntq7/7BXLt1qxZY+SZChUqmLaecMIJrm+++SYkY7Y3fF1Lf6+dr3vmr8y1bds2c2147vjOXg9f9zI/sqRnO60cVa1aNfObQZa44YYbXFu3bs3z+nhrn+f9tNt89NFHruHDh5vz8Bujv+vXr8/RPp6dK664wut9UBQlvMZk3nP8/p9//vkc23vOiby9Qw8dOmTGF95zHPu8885zrVixwmz3zDPP5Nh3586d2fa37y7PMfeLL75wnXzyyeYdycL4xXk4toX3WKtWrVxFga/++Hqve2vr9u3bXVdffbWrSpUq5t2OTMP1cJLb/bFj8VVXXeWqUaOGKy4uzsgyZ599thk38mL37t2uK6+80lWuXDkzdvL3ggULzPmc7WDs6tevnxnv2e6iiy4yclcgc2Z/x1pfLF682N0GjtG8eXPXgw8+mG2b+fPnGzmA57BUqVKuHj16GHnTG6tWrTLnZpkxY4bXbbg/PHPIKVxbrnHPnj2NLOqPDuPIkSOuO++80+g+GFNPOukk1+zZs73KVGvXrjVjLttVrVrV7MdvgGPPmTMn27bco/PPP9/oMJAxkN0uvvhiowNyoroHJVRE8b/QmT4UJfzBso/lGWs4Fm9FCVfw6sBjBu8VPCYURVFKAjqOK0rxZ/r06SavNlFKeUUo4cWKdyvepb4KfCqKUrLHZN4T7du3NxkE+vfvX2DnUZRIhggbojGJkiGCIhBU96CEEq1ZoSgelC9f3qRpeP75503xKUUJV8jJSSjsjTfeWNRNURRFKTR0HFeUyILUGBg01FChKOFHQYzJKSkpXpWspKxx1gFQFMX/3xE1K0hjReH6QA0VoLoHJZRoZIWiKIqiKIqiKIpSLCMrFEVRnFAIm9z4vEOoE/D999+bxdZkUBQlb6hDUa9ePeMIQAQUUUnU/qR2BfVNFaUo0QLbiqIoiqIoiqIoiqIoSrGHwsEUNn788cdN4WQUro888ogpsqwoin/07t3bFM/GOJGRkWEKen/88cemELiiFDUaWaEoiqIoSrHm1VdfNekDtm3bJu3atZNXXnlFTjjhBJ/b46n74IMPyj///GNCmZ999lk566yz3N9/+eWXMmbMGOOVt2fPHlmwYIHP9CKU9mLfyZMny4QJE+S8884rkD4qiqIoiqIoiqIoSklHa1YoiqIoilJs+eSTT+SOO+6Qhx9+2BRXxViBJ9COHTu8bj9r1iy57LLLTBFHjBAYF1gWL17s3ubQoUNy8sknGyNGXpADOSoqKqR9UhRFURRFURRFURQlJxpZ4QUKP23ZskXKli2rCgpFURQlbCEq4MCBA1KrVi1TdDAc6dSpkxx//PEyevRo9xhdt25dufnmm+Xee+/NsT2hyxgjvvnmG/e6zp07m8gJoimcEHnRsGFDn5EVf/31l5x99tkyb948qVmzZsCRFSpPKIqiKOFOJMgS4YzKEoqiKOE/3lHAPC0tLSTHio+Pl8TERL+337x5swwbNszU9jl8+LA0adLEFETv2LGj+X7gwIHyv//9L9s+OAeSWaBE1qz49ddfTVoH0jBs3brVLyUAxdjwsKTwC8qKBx54wFzY/KSL8ARDBcdWFEVRlEhg48aNUqdOHQk3EOiQEYYPH+5eh+B42mmnyezZs73uw3rkBE9ha+LEiQGdG0GO4nLIFDVq1PBrn9TUVLM4BUPyvyqKoihKuBOuskS4o7oJRVGU8B7vMFQklS8jkpYRkuPVqFFD1q1b55fBYu/evXLSSSdJjx49jLGiatWqsmrVKqlYsWK27c444wxjwLAkJCRIUVKkxgo8HzEmXHPNNXL++efnuT03o0+fPnLjjTeaIjA//fSTDBo0yHg7oohwpovAexJvTNI38N2KFSukWrVqfrWLiArrcVmuXLkc35MOwmlloxhNbsTExBTYtnha7Ny5UypVqpSr5a8g22ChLVgiQ7kt223fvl2qVKnis3+BHJdj2HQexWVbrjH3kJeGZx+DPS7bsb0vnM9wQW9rn1HP/hVGG/J6hkO1rWcfi9M7IhTbevavOL0jQrWt7WP16tWzvX+KwzsiP9smJydLgwYN3ONauLFr1y7zjHFfnPB5+fLlXvfBUcHb9qwPhNtvv90UcDz33HP93ufpp5+WRx99NMd60leVKVNGiis8OzwryDyR6jUb6X2M9P6VhD5Gev9KQh8jtX8UMD7uuOPCVpYId+x137hxnpQrV3xlCUXx5JfN/0U5K0o4cPjgEbn0hAdCPt6ZiAoMFac0EYnNp3yQninbflttjumPsYK0xzjjOw0RZBbwBOOEvw56EW+sOPPMM83iLxgguKgjRowwn1u2bCkzZsyQUaNGuY0VI0eOlOuuu06uvvpq9z7ffvutvPPOO17TRXjzhCTsx+a9LlWqVI7tMXpgCLF89913PhV2lStXNsoOy5QpU3yG/lSoUEFOOeUU9+cff/xRUlJSvG7Lj6d79+5GKGYbUlUgSHojKSnJeKFafvvtN9m3b5/PcCJ7Le012L17t9dtUeg5C5b+/vvvPnOIwznnnOP+m5QaRNP4guNaY8zcuXNl2bJlPlNy0V7aDYsWLTJGJl/07NnTfU+XLl0qa9as8bkt19cqlzB2rVy50ue23DfuH3BMju0LngeeC6Ctf//9t+zfv1/Kly+fo49EBFmlG9Zd7rMvOnToYMLVrAcO3si+IN2JjR7CGPTHH3/43LZNmzZG2Qk8CzwTvsCDuHHjxuZvnjGeNZSn3vrXrFkzad68ufs3R6SVLzim9U7G25kIK1/QVtoM/Nb4zfmCa2BTv/Ab5rfsC4yiNkwOJk2a5P7bs4/F6R1h4ZrZd1ug7whn/xjEitM7Avhd8PvwhT/vCNvHfv36uX/3xeUdQZt9kdc7gt8LaM2FwPj666/l559/NumhAoEIEGdUB0or3jPILt6cH4oLvozKkUSk9zHS+1cS+hjp/SsJfYzU/jGWgcoSRYO97hgqypVTg5ESPpROTirqJihKUBTYeBcXLRL7nyNlUERlH5st6Gm8RUMwr0UfctFFF8kvv/witWvXlsGDBxu9uRP0ReixiLg49dRT5YknnnDrJEqcsSJQSO3gVKgBF/22224LOl1Ebp6Q3PyjR4/mWB8XF5dN4YaCy5cikvM7t0UB6O2YVlnmuS3hQt7gfGyLUMz5Ce2xSilPMMQ4j8u2ng+2r76xLcf3BopCf7cF57Z79uzJc1trrEDBSnt9Cf1saxWR/hzXGitQsOa2LZMNqwj2Z1urYPZnW/u8sC33mXvH/ffsI17F9kXJ37kdl+9jY2P93ta+yPLaljbaa5bX9WVba4VmOxbuobf+sa19JjC05XVcuy3Hym1b2mi35Z7ktm3p0qXd23JPctuW6+X5u7d49rE4vSOc2/oyaOb1jnD2z/M6FPU7wt9t83pH2D7y+7Tv0uLyjsjrt5zbO8KXMStcIKqO+4xR1QmffXl+sD6Q7b2BoQKjkjUwWS644AJjePJlMPUlJPI7L+6KKxsRVtzbmR8ivY+R3r+S0MdI719J6GMk9i+S+qIoiqIoRQbz9vwaQqKy9vcsXfDwww/LI488kmPztWvXyuuvv24c6u677z7jDH7LLbcY/ciAAQPcKaDIdoSDHXNgtiOwAD26M0tFiSywjWCXV80KPLGJmHAaI/BYJjUUyiUUYViJ8PTt0qWLe5t77rnHWJDw6vUnssJ6QqL48fSERIHE4rS0FXUaKJRvKFRys/6FcxoolHVY93z1L9zTQNl7SCovzz5GQhoo/vXWv0hKA+XZx7yOy/cYluyzq2mgstA0UAWTBgqFP0aM4uzZnxtEKRFBQv0p28969erJ0KFDfRbYRiZwRj8RrdK2bVu/C2yTMgoZwAkRWy+99JKJAPIWOusNrj8RScX9+nNNMerhTROpSqlI72Ok968k9DHS+1cS+hip/QuXsSxS+e/6L9fICiWs+HlTYPXiFKWoOXQgRfoec1fIxzv7HpeezfMfWZGeIfLTCpNVwdlGX05zGCXIEOLMkIKxAqOFL6d+DBxkNyGTB9lpioKwiqwoKHzdVLyjWSx4JW/atClXxVFhYxW3tC0SQ3Nt/6hvEon9c/YR5Vok9jHS+xdsH9mOok2eeewDmdwW1rb0DeMK70NvdVUKow0Fva3tIwYTu01xbq+/2zrHsHAFLxC8PhCyMFpQi4oxwaZ7vOqqq4yjAlGScOutt0q3bt1MykicGT7++GOTVuzNN990HxPj4oYNG0zKPJvGC4i+cC6eYCTx11ChKIoSTuBY4SuyM1Qw1nIOokIjSZkfKf1DBkIWilR5XVEURVGKFIbX/A6xUVn/YKjwx6BCOnObUt1CSYUvvvjC5z6NGjUyDo+rV69WY4U/+ErtwA0i5zoCVqDpIgIR4DFUkA6HPKTFRYhDSZqenh6xgmWk968k9DHS+xdMH9meVD+8U5o2bVpkoXWKEg4QKcHv5aGHHjIRD0RATJ482V2rA6ODUylEFMX48ePlgQceMCGs/MYmTpworVu3zpa70xo74NJLL801fFZRFCWSKSyHLOvcQYrXSJQJI6F/zHVRbNj0mYqiKIqihIoQpIGSwPY/6aST3I55Fmpt1q9f3+c+yIRkuEEeKCrCKrKC1E6eBXCnTp3qTvmEUEWR4Z9++smdTgqBkc+ki8gPeMkggGKowDBSXIh0RXCk968k9DHS+xdsH3mXkIKGd4saKxQldxjDfY3j3upHUECMxRcDBw40SyAUp6hKRVGUUFGYDlmRLhOGc/9oO7W1cA5Yt26dMfSHY3SIoiiKoij/cfvttxtnvqeeekouvvhi+eOPP0zGAZt1AIcVajhTmxEnf2pWUEqhSZMmpkZ0iTRWcFEIK7EgGP31118m7zupFqhNsXnzZnn//ffN9zfeeKOMHj3aXLhrrrnGFMD89NNP5dtvv/U7XUR+CVTwTD2aIYdT06VUQqwkxKn3tKIowb1LFEVRFEVRQk1hOmSFszK/JPSP+0/6yPXr1xvDRWJiYlE3SVEURVEihxCmgfKX448/3tSHRr/+2GOPmZTG6Mn79+9vvsdx9u+//5b//e9/sm/fPqlVq5b06tVLHn/8ca/lEkqEsYIc0j169MhmaACMDe+9955s3brVpHewcFExTGAZosgl+d7feuutbNaevNJFFBaLN+yRL+esldkrt0umSyQ6SqRLs+pyQZdG0qpupUJti1L4IODPnz9fjjvuOA2jVhRFURRFUYo1wSjX0zMyJTU9QxJiYyQ2Rr3wIwGNplAURVGUghpko7KW/B4jQM4++2yz+HJUmDJlihQ3itRY0b1791zTKmCw8LbPggULgk4XURhMmrdeRn+/WGKio4yhAvh3zqodMnPFdrn5rNZydgff+cGU8Of33383kT94V5188skhPTapSy688EKfL5tg+Pzzz+WJJ54wfy9dulRatGhhJivkcb/33nulsGnQoIGpRWMnTBT/ady4caG3Q1EURVEURcnO5j2HZP7aXbLwn11yNCNT4mKipV2DKtKhURWpVam0Xi5FURRFURQlaNQFpgAiKjBUQIa1VPyL/fzKd4tlycY9AR+b/PaktwpE4UuqLbj77rulVatW8uSTT0pxJ9B+FjdSU1Nl5syZ5u8ZM2aYz8UdjB+kYGMh7GvWrFnm76IwVFhsG1jUUKEoiqIoilL0/L1+t4ydukx+XrRJUo9mSmx0tPmXz29OXSaL1u8u6iYqiqIoiqIU3zRQ+V1KAGqsCDGkfiKiIjf4/ss566QwIUpl0aJFcv/99xdIYT7lPyhYQxoo4N+5c+fm+/I88sgj0rx5czn11FNl+/bt7vVVqlSRu+66S9q0aSM9e/Y09VmAHHRs365dO7npppsK7JmiCE/Xrl2lWbNm5pxA4Z6xY8eavy+//HK59tprzd8vv/yyjBw5skDaoiiKoiglGdLxHDmabv5VlIKMqGAOk5KWLvWrlpUq5RKlXKl48y+fWf/FnHWyZU+WPJqflFTkTc7PfnPmzDHycfv27YtleoNA5O3zzjuvqJuhKIqiKEp+IeVmKJYSgBorQgjFtKlR4RlR4Qnfz1qxzWwfTBE8anq0bNnS1OcgjRaV2ykm3rp1a1PPw5N+/frJ3r17Tf2E7777zmcUBgpxanxQgIV6IbB27VqTeqtt27bSt29f2bMnKyKEdbfddpuJgPjggw/M/hhCUI6fdNJJph4JivVGjRqZYi6+OHDggFx55ZXm+Oz722+/Zb9WGRmmoDr94/sPxn0gB9MOyh+L/pBOJ3aStse2lY7Hd5T5C+a7BXoq3J9++ummev2IESOkKKIqbHoz/s1vdAXGDmq1UPTmww8/lNmzZ7u/2717t5xxxhnGEFW7dm358ssvzXoK51AzY+HChfLMM8+Yde+++665v55LfgxYtO3rr7825xozZoysWbPGpL2iz0CBvlWrVpm/uS42JVanTp28tmXjxo3uY5944olm3VlnnRV0+xRFURQl0pXHpB997qu/jBKZf/mcX2WxoniD1E/7D6dKzYqlctS44DPr+Z7tihoKReI0Q/pgZ33DgkAdtxRFURRFUUKHGitCyOHUdHeNirxgO7YPlGXLlsmwYcNMXQE87FEK33zzzcabH4U1BcltCiILxoIKFSqYdDq5KX4pWM42Z555pilcDrfccosMHjzYKMoxQmDQsMTFxRmjBDUUAOMAyvFjjjnGGE2+//57szj38YQK8/Xq1TPHZzKB0cLJ22+/LTVq1DD9+/m3n+WpZ56Sxf8slrjycTL2s7Hy6Y+fykMvPCS33HGLHD562OzDsegzbXvuuefcUQ6WTFemMXjsOLRDth3cZv7lM+tDGVVhyW90BfcTg1NCQoLUrFnTGIEsZcqUkdNOO8383aFDB5NCCzDuXHHFFTJ+/Hhzn+Dqq692p1VyLvlJDYahhGeLdvBsYUjh3PSXZ5F7W7VqVVP0HoMG3m22poe3ttStWzdHGihfBjZFURRFKcl4puOJjorSdDxKgUHUDjUqyiTG+yzGzXq+Z7v8Rvm89tprRqZs2LChcbixEFGMYxUOLUT3rlixIse+OOp88sknMnr0aLOdryiN6dOnG2cv5jo4RZEyl/mDBYcs5iYsffr0kc2bN7udo3r06GEijIneQP6n78jUzJdo88SJE+Xpp582jl1NmzY158oN5guk7aU9tAUZ2xu0CacfnNDoP3MvYB6IUxDrmYvZWnTAXAwnt3POOcd8x1zCOqApiqIoilJIaBqo8CiwHWmUSog1hdn9MViwHdsHCql9EDIBxS/KaQTnUaNGyZEjR2THjh1GuEVQDhQU4lbpjbc8oHSeNGmS+ZsICAR1y0UXXZRtfyIvAKGd9EQo12nvli1bfJ7zxx9/dJ+LYsrly5c3USCWqVOnGuH7w/EfytHMo5KcnCzbNm6TRo0byUN3PiTLlyyX6Jho2b1rt2w5sEVSM1JNVAXKc6D+AkYdqwTHoIFxIjU9VVzy343am7JXEmITpFrpalIqrpSEIqrCYqMrmFhxTYLB16TQebyYmBi3ZxeRGEyKmCjxbHAfmei99NJLOY7BPQ3WYOFsF3+zJCYmSsWKFU3RbiZNKSkpJiIEY5g1nDDJ8hZtwrPmNFgoiqIoWaD4S03PkITYGImNUV+Tko5nOh5G4yRXpkQlJkrlsomyde9hk46Hv7XgsRIKeP9QTDs+Nvf3D9+n/fu+ys+7ChkXI8Dy5cuNDM08JDY21jhtvfDCC2abjz/+WG699VaZPHlytn2pucZ+GCqIBM8NtsM5CuMIUcJEHJM2avHixcZ48Oeff5roZWTlQYMGGUcs63iDoxVzHQvzD+YCv/zyi0ndhLEE48dnn31mjpWb8xKGjZUrV5rz0XccfTzh2B999JH8+uuvZhsi0okeWbJkiYly/+mnn8x6ZG8ilHFo6ty5s7u9HLty5cpy6aWXyhtvvCHDhw8P8K4oiqIoihI0oag5EVUyrr8aK0JIQlyMdGlWXeas2pFrKihqVrAd2wd8Dg/lNDUK8JxB+EUxj7dRsCmH7LGdSm9fSnIoVaqU1/0xOjjb6am8D4TMzEx59dVXpUnHJnIk/YgkxiZKlETJyKdHSp16deTFN1+Uw4cOy8ntTpb0zHQ5kHpA4uPj3fs7+4KhAoMG28XHxEtM1H/XP8OVYY7P97XK1grKYOEtqsIzusKmQQoE9hkyZIjceeedxgtq2rRpcs011+R6zUinRA0L9iW6gWtAZAVLoBClQt+YRHnC5HD//v1m8sjkDc80wFiGkQTjA0Y0DFtXXXWVez8mTIqiKIp/SmlSquCpjKIwLiZa2jWoIh0aVVEldAnGpuMxhgpkNYesZdPxrN95wGynxgolFGAo5f1DFE9upKVnSmJctNk+P/Tv39/826JFCyNnbtu2zTi+4Mj0yiuvmFSyyLz5jRAgMhwnGujSpYvbEIK8jQMYhgpAxiXNqp1XYAxwGiqA6AUgmoI5GkYBIELEpkX1xTfffCPPPvusew5FZLInX331lYmksO0F+o9xgoU2EpXMXIy5AH9bYwV9wVBh+4kzmKIoiqIoSnFEXfNCzPmdG/lVs+L8zg1Dcj4UwUxKET5RGuNJH0oQtr/44gvzN97xhBuHEjx+Xn/9dfM3Ew764IQoidfGvCaHUw8bAwORFGnpaXIg+YBUrVFVMiVTPh3/qdmW749mHDURGJ6Q4omICgwVGDychgrgM+v5fuehnQGnhPIVVWHJT+0K7gGpuYhYwXvKTjp8wSSKCR4h6+z70EMPGaNNsFC3pFy5cl6/w9ONkHKifK6//npp3LixWY+RhHtJmwlHZyIVTLSPoihKScYzzU9sdJaikM9vTl0mi9bvLuomKiUgHY+iQOy/htKDR9JylXf5nu3yGwFGpK4FOTY9Pd2kGB06dKiMGzfORD4QWcFcKNTn8Ybnb81GcXs7lpW7nZ99HTcQuL7ULnSmUKXOYFJSktx3330msp1oDwwa1Bd0Xht/+6koiqIoSgGhBbb9RiMrQkzrepXk5rNayyvfLTYRFE7Dhf3M963qVgrJ+agXQG0CcqwSWZGXIjtQXn75ZeONjydR/fr1TbG6UPLggw/KjTfeaJTaCM6ES+M1Zbnuuutk8YrF0q9HPyFrU5XqVeTV8a/KuVecK3ddd5d89L+PpOdZPU1KJ2uAwODgCVEVpH7CoIGgj5HDEyI2+J4IC7YvE59zEuILJgsYImwqJE84J9+zndMbyl/INeut9seuXf8VMGTyZvGsWxIItu6FhRog1svME0LOSffkCSnBSNkFeHeRKxivuGDboCiKIiU9zY9jbKlcNkHT/JRgCjsdj6JYjmtURf5cs9O8fzyLbCPrsr586QSzXUGAIwwpRanhxvmYNxQU1KQg9RPpbJljkSKKqOX8OADlBrIz6Vpx7rFpoDyjK9gGhyTmTkRO4+hFTTick0ij27JlSyNvU8eDCJRQO5kpiqIoipIPNA2U36ixogA4u0N9aVitrFEyzFqxzdSwoEYFqZ+IqAjWUIFi2Fn0DQUygjqeMaTo8VSSOxW+TqW2N5zbnn322WaBRo0ambyrnngWiXPu71Sa53XusmXLmogNT2w/mRDc9+h9cvMDN0tSbJKJeGBp1KSRfDntS5OuiX7fNfwus32/y/pJhcQKOY5DVIU1aFDXIi0jzURhQFxMVh0FDBUJMQmSJmkBGyuYMDBR8Ge7cCPUBipFURQliDQ/DjTNT8mmsNPxKIqldqXSckHnhqYeCmnGiN4xRrH0TBNRgaHigk4NCyz1GM5NpFbCSYuocupCFBQUun7++efdha6pqTZ27NgCOx+1OKiXQUQyBhkMJN999122bU455RR57rnnTJ1B5n+kmaX2HHOQBx54wNT1QG4n0pki2oqiKIqiKOFIlCs/BQUiFDzCKfSM945Nf0MY7bp166Rhw4bZwmjzIvVohhxOTTfFtIOpUZEX1liBF01u9SXCFfq37cA22Z+6X5Li/jNWYEwAa6zgv+ioaElJT5FKSZVMoWwn2w5uk31H9mUzeOR2DAweNcrUKLQ+Rvo9jOT+BdvHYN8pRQGeezt27JBq1aqZSJlIJFL76G08U4r/9SdtzzMTFhhldJVyvt8Pu5KPGGX0sH7t8+U5H6nPfyT3cdK89SYdmLNmRZLrsKRElTIh5oxLKJN7tqkjZ3esL5FApN3D4tK/YOSRLY5aOkTvxP+bIoqIitwMFZEuE0ZC/7w9DypLFC3/Xf/lUq5c2SJujaL4z8+bQpuiXFEKmkMHUqTvMXeFfO5s3+PStzXe0vk72NEMka8XR/z8XiMrChgMFAVhpAgWCjV7pgiimFvv3r0L9LxTpkwxHkNOCHOmeHZeYGBITks2RbBJ8URUhJ0AYFiwURGx0bHG4OCtODZGiEAIdHtFURRFCRWa5kcJOB2P47vCSMejlGwwSLCc0b6ueV8RvaOpxhRFURRFUXJB00D5jRorShj+GAcKAowhwRpESsWWkoTYBFNLgjRNGCU8wUhBeicMG96MFazbm7I3XwaPQKDI9erVq6VJkyYFlttWURRFiUw0zY8SeDqeOKkcly67jx6Rg0eOFng6HkUBDBThYKQgTZJnQWlSSXlLRVuQULtu4MCBOdZTNPv2228v1LYoiqIoiqIUV9RYoRR7MCpUK1VNthzcYgwSGBVsMW3AAMF6DA1VS1f1GhWB8SG/Bo9ADBWfffaZKW7XvHlzueiii9RgoSiKovhN7L8pVUjzQzFtb+lE8J4nR3ynpnXCQlmohJ429StL5bKJ/6bj2SmZLpdJC8YzkVc6HkUpSThr/hUlxx57rDFYKIqiKIpSAmFOl980kVHhmWYyUNRYUcCkHE2R5NRkKZdQztRcUIKDa1erbC3ZeWinMThQBNtpZMDAgKHCl5EBAwZ1LLYcCN7gEaihAviXz2qwUBRFUfKV5schmGqaH8UzHU+vdrVly7ZtUqtGDYmPU/FeURRFURRFUYoVqBnz62MWLSWCEtLNwmfGhhly/ifnS5mny0iNETXMv3yeuSF7vQjFfzBE1C1fV2qXq22KaFMEm3/5zPq8oiH4HoMHho2jGUdN+ie78Jn1fB9sVIWnocJiDRZ8HwoIH//mm28klHz++efG24slPj5e2rZta/5+5plnpLA57bTTzLnr1atnikzadu3bt6/Q26IoilLUaX6S4mNNmh+KaScfTjP/8jkpIVbT/ChuiK5JjIvVKBtFURRFURRFUcIadb0qAF6f+7oM+W6IxETHSKYr06zj30krJ8nE5RPltT6vyY0dbyyIU0c8RDyUiS9jlvwYPA4fPWwW7gvHZD1LqCIqPCnuERYXXnihWaBBgwYya9YsKVMmuGucX3788Ufz73vvvSeLFy+WF154oUjaoSiKUrzS/OyStIxMTfOjKEqxwTr/4PATFxNX1M1RFEVRFEUpvmiBbb9RY0UBRFRgqHCJyxRydmI/D/52sLSp1kZOqneSFCYoyx966CFp0aKFTJgwwe/98Gj/9NNP5frrrw/4nFWqVJFdu3ZJJBk8AjVUhMJg8cgjj8hHH30ktWvXloSEhGzXl0iLKVOmmCiEr7/+WkqXLi0vvviivP7665KYmCgnnnii+TvUYEyYNGmS7Ny5U7Zt2yaDBw+W2267TZ566impWrWqXHfddXL55ZebNrzxxhvy8ssvm2t1xx13hLwtiqIokZ7m54z2dSU1PcMU39YaFYqiFCWr96yWqWumyrR/ppn0rImxidKjQQ/p1biXNK7UWG+OoiiKoiiKJ1qzwm80DVSIGTl7pImoyA2+HzVnlBQmKInffvtt+eCDDwIyVFhjxZtvvllgbQtn/DVU5Ccl1Ny5c+Xbb7+Vv//+Wz788EOZPXu2+7vdu3fLGWecIYsWLTKGjC+//NKsf+yxx2T+/PmycOFCdyqnd999151Sybncf//9Affb2TYMJJxrzJgxsmbNGjn55JNlxowZ5vv169fLqlWrzN8zZ84030GnTp28tmXjxo1Bt0VRFCWSwUBROiFODRWKohQpv/zzi9z1w10y7u9xcjjtsMRHx5t/+XznD3fKr+t/1TukKIqiKIqiBI0aK0JcTPurFV/liKjwhO8nLJ9gtg+Ef/75x9QSuPjii6Vly5YyYMAASU9Plz///FO6d+8uHTp0kHPOOUf27NnjTudz7733Svv27eWJJ54wCuQrrrjCKLIPHTpkPPKPP/54s9/UqVPNPgcOHJArr7zSnKddu3by22+/GWX20qVLjTKZfb32KT1dbr31VmnTpo3Zl0gMT5599llzPr63qX2Sk5Pl1FNPleOOO84c36YAmj59upx++unSr18/OeaYY4qtN/7q1av9NlRY2J79/AUlP9eBiIqaNWua62UhVRM1HoD7yDMCJ5xwgrnX48ePl7i4rLD8q6++Wv76668cy5NPPinBgqGkQoUKph1nnXWWMaRwbowYGzZsMHUniLIg+gKDBs8i/P77717bUrdu3aDboiiKoiiKohRsRAUOVwdTD0qrqq1M3bjKpSqbf/l8IPWAcdxas2dNsbgNaWlpcvbZZ5v5yZAhQ4I6BnOSyZMn5ysS+bzzzgt6f0VRFEVRIiwNVH6XEoCmgQohyanJ7hoVecF2bJ8UlxTQOcjh/84770jHjh1Nih285ceNG2eiJSpXrmy+e/rpp+X5558326P8XbBggfl72rRpMnr0aGndurXcd999RnhHgCZNEx7vy5Ytk8cff9womInAyMzMNMYLlNko2OfNm+ezXUReYCTBkz86Olr27t2b7fsffvhBNm3aJH/88Yc5LoYIFN3NmzeXr776SsqWLWtSCfXu3dscA1BuL1myxHyHIeP22283bStONGnSxPQhEIMF27NfIEQRLuYFZ0ooUkvZiA0iMZhcTZw4UUaNGmWMBzwrL730Uo5j9OnTJ2iDhbNd/M1C2qeKFSuaot08VykpKSYipE6dOm7DCZEVqampOY5HWik1WCiKoiiKohQ/SP2089BOY5jwlE353LhiY1myc4n8sPYHuanSTVKU4EjFHIgI30Adi5wgTxNlzrxFURRFURQlaDQNlN9oZEUIKZdQzu8CzWzH9oGCkhtDBVx66aUmgoH0QD179jQK/ZEjRxqPdgv1EbyB8YAoCfbBM59Ii+3bt5vIhptuyppcYHQoX768X+1ivxtuuMHsAyirPc+HAh3PeiIASA+0cuVKcblccs899xiPJyYBTCbwggJqLVSvXl3i4+ONgYV9ihsYCLjGGCD8ge0CrVmBwh+jA9cFgw5Gp9zAGEQ6JZ4JIlh4HjBiBBtZgSFs+PDhXr/D02z//v3m+fn++++lc+fOZv1JJ51kjCT8S/upV8HfFo2sUBRFURRFCa9i2tSoqJhY0acTDev5ftq6aWb7YOE4DzzwgJk3NGvWzDi9WHDAIcqY+RDfk14ViC4m2nfYsGEmYvu5556T/v37GzmY+c77778vR48eNVHnRAGzjmh162CFPDto0CAz5yC6/JprrjFyMmlOOX9uEeaAnH733Xe7U5v6Mm7gEIbTDm3s2rWr20mLlK7IzKwnqpyoeGftuksuucRE0PMd/beR9IqiKIqiKJGGRlaEEKIkzm1+rkxaOSnXVFCx0bFmu0CjKrx5sqOERiBGge1t4lCqVCmfCm282OvXry+FAed7+OGHTeoqJ3j7o+jG8yk2NtYUjLbGCl9RA8XVYJFX7YpgDBXAZOzMM880Bh3qUliDgC+4TkzOiIrBGERR9UDP6WTt2rVSrpx3wxppvZg42QLbjRtnFVVkssXkjjbTHiZUTmOFoiiKoiiKEj6kpKe4i2nnBt+npqea7eNisiJqg4F5DfMD5FBkYeRIjBHXX3+9fPfddyY1KtHhKPdxcLIGh1atWpnUs8D62267zRgd4KmnnpLSpUubSG8gohyjyKuvvmq2S0pKMk5gOF+RwpRUpjfeeKOJrHjxxRdzbS+R7URx4JDDOWibt9SuH330kfz6669mnkO6XSLliSQnfe9PP/1k1hOVTNtxKLNyP8cl9S+R9DisvfHGGz6diRRFURRFKaaUkDRO+UWNFSHmji53yMTlE3PdJiMzQ27vfHtQx0cIJj0Sgvknn3xilP943iC8IsiTWmfdunXSokWLXI/Tq1cv4+0+YsQI8xkh3kZZvP7668bb3qaBIg0T/+YG+5EKCsHapoFyRldwPo6Jsh4DCt5PfE/NCqInMFR88803pmB0OJKXwSJYQ4XTo4rFE+dEaOjQodkmQ8Fi615YmLTZGiOeMLEi3ZMnffv2NfcWeB6Y5HGPA4GaKoqiKIqiKErRkxSbZAwRFNPODQwapeJLme3zA1EO0KhRIxOBgIIfpyaMFzjxOEH2ZjvSjVKzzRdEKmPQ+OKLL8xnHKSQZYF5CAYBGyWOoSIQ2P+ZZ55xO1t525/Ut0RSEFlhwaEH4wQLjj/MyWgDUdL8bY0VRGpgqIAuXbqYSAxFURRFUcIITQPlN5oGKsScXO9kea3PaxIlUSaCwgmfWc/3J9ULzsuc0GS8hSiwjSCLNw6hyXgDEbJMiiUbTpwbDz74oBHWKXZNOLFVRrMeZTUe8RhEEIQRjPmbdb7Cn/FywtuJbWiHLZRtQcCmSDQCN31gInHkyBETAYBXEfuRJqq41aQIRUqo/Boqipr//e9/AU/YFEVRFEVRlMiBKIkeDXrI3iN7TeSuN1jP9z0a9shXVIWvSAuOT+SEM50paZ5IiwQ4RFljg6/2vfLKK+59ly5daqI0CgvOj6OZs/1bt241ER3UE8QYQzQJc7nu3bubuZKFmnAW5hTU5FAURVEURYlENLKiALix443SplobGTVnlExYPsEU06ZGBamfiKgI1lAB1G8gosIp9GJIQOHvmQbK00OeAnEWwpPfeuutHMcnisKZF9ZCyHJu4DVPpEZunv933HGHWTyZM2dOjnUI6Cx2MkTEgq/8uMXRYDHyvS9lweYUaV87SS666PywNVTkhkY+KIqiKIqilBxOb3y6TFkzRdbuXSuNKjbKJpsjs6/Zu0aqla4mvRr1yve5SBVLVDHzGeY5pGGilh4R5DhFEdUNKPxxvPKH8847z9RUI10pho3Dhw+b42EAISoY563Ro0dnSwNFKlR/6uaxP3MhoiaYF9n9PbfBUYvUUjhoEcVOxDzR8USl44zGvkSKTJ061USUKIqiKIoSISA25VetGSUlAjVWFBAYJFhSjqZIcmqyKaYdTI0KJfyYt3aX7EmsJ2WrHJA9iWXN505Nqxd1sxRFURRFURQlaJpUamJS3o6cPVKW7FxiimmTGorUT0RUYKi4vcvt0rhSVg2z/EDNMwpoU9sOI4BN10Qk9l133SV33nmnKZiN0p/0Tv5A8W1S5mJQsIYW1mGswIhx++23m2hv0klRl23s2LEmMpyi2KTLPf/8800tOF/HJjqC4t04l9WqVStH1MYpp5xiCn9zTCIjSEPVp08fY6ygdsaVV15pIpqpAWejRRRFURRFiRA0DZTfRLl8xfGWYMi1j+cOaZJsYWHCcPG8adiwYbYw3KKG24ewixdOYUQekBYKQdoJ12TChAkR0b/88suSLTJp3nrzDqpcNlF2Hzgi/MLO6VhfurWqFRF9DJRI71+wfSyu7xRv4Pm3Y8cOqVatWq7pFcKZSO2jt/FM0etfUp7/ktTHSO9fSehjUfUvGHlkzZ418sPaH2TaummmmHZCbIJJ/URERW6GCn/lJb4j0oAUs+FEJMi83p4HlSWKlv+u/3IpV65sEbdGUfzn503+GZIVpbhw6ECK9D3mrpDPne17XC5rLxKfz6wraRkiHy2I+Pm9RlYoAYG3EeHWSk5+X7VdPp21RvYeSpUjRzNkzfZkqVg6wfzN+sT4GI2wUBRFURRFUcIaDBI3VbpJBrUfJCnpKaaYdqhrVCiKoiiKokQUmgbKb9RYoSghMlR89cc/UiYxTmpXKi2rtu436xtXLyfR0VGy92Cq+R40JZSiKIqiKIoS7mCgKAgjRXEN/Mdhy1vNNopmk0JKURRFURTFJwTO5jd4NrpkXN8S0s0iJD1FJGV71r9KRBsqCPeuVam0iaCIiYk2C38nxcea9XzPdmwfCBQWJJdtIJDX9+DBg+bvu+++2+TiffLJJ0N6Dn+g+CH5hsm9W716dZPvl2Xfvn1SnLn33ntNOynYSAFG224KOiqKoiiKoiglD2RBDBaeixoqFEVRFEVRQodGVhQUO2aILB8psukrstBm2YXqnCvS8k6RqicV2GmVojNUVCufewF1vt+xP6XQIyzee+892b59e5Hkd0a5j3fcO++8I0uXLpURI0bkKJ4YE5PPnH0FwDPPPGMWjDgXXnihzJs3LyzarSiKoiiKoiiKoiiKohQztMC232hkRUGw6nWRH7uKbJ70r6ECMrM+Tz1FZNWYoA67ZcsW6d+/f67b+ONFHwoIgf7mm28C2qd79+6yePFiKYmGCgvbBRNhcfToURNi3rJlS7nkkkvc4fEPP/ywHH/88dK6dWuvXl39+vUzBQqPO+44+e6777J9t3r1arNv27Zt5eWXX3avf+SRR2TQoEHStWtXadSokXz88cfu5++kk06Sdu3amX3+/vtvCQaOT19OPPFEueWWW8zn0aNHu7+vUqWK++9nn33W3cYXXnghx7EwJvDdxRdfbK4Nx6Ww4axZs9y/lTfffNNEdgBt7tu3b1Dtnj59upx66qly1llnmevAZwwZFu4L62DKlCnSpUsXE1VyxRVXSFpaWlDnVBRFURRFKbZkHhU5mpz1r6IoiqIoipJ3zYr8LiUANVYURETF3CFkWxVxpWf/znx2icwdLLJzZsCHrlWrlnz44Yd5etEvWrRI7r//fr+OiYd4bp8V3/z092Y5nJbut6HCwvbsx/7+smzZMhk2bJiJTiBKYsaMGWb9rbfeKnPnzjX3fMOGDTJzZvbnasKECVKhQgUToo6S3cltt90mw4cPNwr8uLjs+YbXrFkjP/30k0ydOlUeeOABs+6jjz4yBqeFCxfK/PnzpUmTJmZ9p06d3GmSnMvGjRt99gdDCYr9V1991ec2P/zwg2zatEn++OMPWbBggTG2eDN2se6ee+4x1wijzrhx40xKqz///NN8z7XiGmzdutX8ffLJJ5v1Q4YM8dpurpkvOOZbb70lc+bM8bnNrl275Pnnn5eff/7ZtBuDz9ixY31uryiK4i/b9x2WRRv2FNt87oqilBAOrBZZ+brIzP4is67I+pfPB9YUdcsURVEURVGUMEeNFaGG1E9ReaSH4fvlowI+tK0rgEECT/LTTz9dmjZtKqNGjfLqRY/CuXfv3mYfPMLZH1A4o6hm/QcffGDqG5CjHy9wFKysw5sdD/o77rjDfX484Js3b26OhcI8N0j7g8c7x7jrrrtyfO/L8/z666+XDh06mOgQpyd91apVzXHatGkjPXv2lEOHDklR07NtbSkVH2tSOwUC27Mf+/sL1536CURlcM3svcSgcMIJJ5jrjKECY4a/YOTgmQHPiJ2zzz7bGDCISLD1JXgmMJYRzYFhgFoO8Pvvv3vN31u3bl2f5z733HMlPj4+1/ZhrPj2229Nf3km1q9fLytXrsyxHUYTW2/j0ksvNQYJjk2NDIwda9eulauuusqsZyEqAjCUeGu3vSbeYF+MhrmBIQMDEM83xo/PPvtM1q1bl+s+iqIoeXHkaIZMX7JV/lq3S2av2C4ZmWqwUBSlCNj+i8j8u0T+GSeScVgkOj7rXz7Pv1Nkx696WxRFURRFUXylgcrvUgJQY0UooYg2NSo8Iyo84fuNE/JVdBtlKB7gKJypA4Cy39OLfvDgwfLGG2+YfPt4x5MiyoIimvWkcwIUy3iB16lTR7766iuZPXu28aDHSxyFMefhX86LwprvfYGXPwaUX3/91Rzjvvvu89vznDoBeK+z3xdffOH2zt+9e7ecccYZ5ti1a9eWL7/8Uooaak6ce0ID4+Hqr8GC7die/QKpWZGQkOD+m1oJRMAcOXLEGJ24X9wXjD6pqal+HxPDhz/ns5AWCoMIyvrLLrtMvv7666AjK6yhA2JjYyUz06ZLE3cfWIdhxBoRML6df/75ufaDv+1nDAuffvqp1KxZ00RT0HaeeWvYCCaywt929+nTx91uDDveUlgpiqIEQmJcjBzbsLKJ/F2zPVl+XrRZ0tI1GlJRlEKOqMDhKv2gSPlWIqVqiyRUzvqXz+kHRJaNzHeEBfLYgQMH8twO2RAnLRxb3n33XSksiOrF2SsYcDo777zzQt4mRVEURVGKOZoGym+0wHYoIWeru0ZFXmRmbR8bWAohC1EVZcqUMYpvlLFEOtSrV8/9/cGDB+W3335zC8NsV7p0aff3F110Ubbj2c946uMZbhW6hw8fNl7tq1atMh7nKLE5H9EVvpg2bZrJ34/hBCpVquTT89wqeVHu2lRDpNlBGY9X/PLly8356Otpp51mtqE9NrKgqLEGB2pQYIjILSVUsIYKX2CsQDFfuXJl2b9/v0ycONEYL/yFe4yhg2dk/PjxeW5PZAPGrBtuuMFEW9j6D0RW5If69eubZwZIO8WzC7169TK1V3g2MRJwzytWrCjly5fPtj/PJmmpmKx+8skn5rcBGCho65133mkmvVdeeaVUq1bNbYjJLQWVP/B7I5KFGhkY4GxqKJ5ranFwvehbcnKyMbY1bNgwX+dTFEVpXquClEmIk1+XbZVt+w7L5AUbpUfr2lI2KXsqP0VRlAJh61SR1J1ZhglPpxc+l2kssn+JyLYfRMreFPRpcPbwh88//9xE/uKcFQjIbjidWKzzSXS0+vEpiqIoiqIUNWqsCCVx5f4NVvHHYBH97/bB4c3T3glCN2lwfAn7Tg9x52f2u+6664xHu5MXX3wxV0/8QLCe554eUKTrQYFM1AYKaYoXW2/1vPpbXAwWW/YckvKl4iUjI+sZOJKWIdHRmbL3YKrEREeFzFABGIMoKE16KKIdOnfuHND+3FMiJLjXpNbKC2pMEBFDVA7nxrAUCoiWwMvMpvjC+AJE0mAMoF88M5yTaBtPYwXFxSnEjfGECevll19u1lPA2xYF55nB0ILnXajAWEEEE9efNF0YRGzKMiKFLrjgAhPxxMSXa63GCkVRQkHtyqWl97F1ZNriLbL/cJpMXrBBureuJVXLBef8oCiK4hcU0d4+TSS+ou8UBKzne7ZrPEgkOjhDKnMOUtsi+xHBQDpPHFq2bdsm1157rYkYf//9900kN3MCHGdwvMFBCocRHFxSUlJM2tEnnnjCHJPj4EyFgwxpdFmI2MZJhmhgjk/ExOOPP272RXZEvuzRo4c7HS7R5eXKlZMzzzwzzz4gA1JD8PvvvzfHwvlq8uTJObYj/e7o0aNN3TWcs1555RWT3pW23XTTTcZxDAcl5FtbR462ELnLd0SX1KhRwxhuPB3EFEVRFEUpbpEV+dSrRkmJoMiNFSinUYAifCKYIaCRg98bCHFPP/20/O9//5PNmzcbBSFCJEpNCyHDDz74oEnlsmPHDqOcfOmll4wSs8AhSqLOuSKbJ+WeCioqNmu7IKMq/AFBGmPFpEmT5JxzzjGCPEItit3cQFmMJ/vQoUON0phryL54qZM2By/1PXv2GEH/mmuu8XoMoi5Qgt98881Gscz2TuHZl+c59w4hnbYTVfHjjz8aZXw4YA0Q709fKZv2HJKk+Kyf1todByQlLV0qlIqXq7o3C8pQweSK9EUWZ0ohIg9YPHFGnuD17w1qPZDeyxMmQE7s/tyLYO8Hk0zrweZ5fCJ+iOixoNi3UDPFWTfFG9SnIKLCEya4TqMWNTDyc+2p9cLiZOTIkWYhYsbppUd0h43wUBRFCTWVyiTKGe3ryvTFW2TPwVRZtWW/GisURSlYMlJEMo+IxCTmvh3fZ6RmbR+kscITInpxZkImpZ7a1VdfbWRLHJ34zsqO1Ooj/Wy3bt2MXEYNNmqH2Qhy5hsYNjCGII9yTFLSMmfiWKyjrh5zkdWrV8spp5xiZGrmJByHVLVly5Y10bp5wZyVWmvsg9PVzp07c2xDilKcf0idyzZExWOUWLJkiZFBkY9Zj/EEJxyizK1zEv3g2MzXqNlGdMnw4cNDcr0VRVEURSnANFD5PUYJoEiNFSgYUUSOGTPG5L1H0ETIXLFihUnX4gneJOPGjTNeyy1atDDCJKmJZs2a5faYHjRokPGKwUsFb3O2R7DDQ5taBwVOiztENk3MfRtXhkiL2wu8KXgY3Xjjjea6YeghJU5exgoKW+MFhNECb3YEZLzeSRmEFxHe71zH3Lz4Ocett95qPNpR3pLO57nnnnN/78vzHEVwy5Ytzb1FQMdAEk5giCCSYtK89cZYWrlsouw+cMQYKs7pWD9kERWKoiiKUjohTnodW1cWb9gjbeurN62iKAVMTJJINIaIw7lvl3FEJLZU1vYhwkbNVqlSxdS6W7duXY553aFDh4xyn9S4FqImmFdaqNXnjBQnQhZDBRD1gIGCGm0W5igbNmwwx7344ouNEQOYU82YMSPXNn/zzTfGqc5GhzP/8YR0rNTpYx5swckL4wQL9QeJkqcdRH/wt52D4axno5FxBCMSQ1EURVEUJRIoUmMFHsmkHMI7BjBaUMT5nXfekXvvvTfH9hggUKQjWAKhsXi6UGAaowRCHWliEPysoImHDNEFr7/+ujsM2BNSDTkLE+PpDyjrbQ5T/sV72i4+qXqSSMdXReYNEYmKkShHhIWLiAoMFXxf5UQKSQR0vYhCcHrC23bgFURqHj7jtWPXk3YGg44TvrP1Aex2CPzOz0wI7KTAuR/pgjzTQ/m6FoRos/g6LwYkW4PCuQ2GEU8wtDj7RYRHbucuSroeU1MS4mJk5PQP5att38mxFc6SO7r3l05Nq+XaXvtdcexTKCio/tnfRHG4boH20b5LnO+Z4op9/xX3duaHSO1jpPVHyU5cTLS0b1jF/ZlnePW2ZGlco5xEhyh1o6IoioEoieo9RP4ZJ5JUy3saA2SgtL0itfuELKoCEhP/i+YgpRJREzlPnSV/UUPMub0TIrh9fWZ/omL9qeMWqtS4nJOo5aeeeirHd0SoY5wh8gPnL9Kmkg4qkGuiKIqiKEoxIir/WaBcJWSKV2TGCjzqCV11hqviNYICG+W7NzAoeAqfSUlJbs8WhDTSvuS2ja8w3UcffTTHehTkVihEYY7Sh3PkKQw2HCRRZY+R6JUviWz+SqIkU1wSLa5a50hms1vFhaEiRAIlQq5NdRMqwbk4EY79254xR9a7vpYjssf8uz2jsaSnZxnYIqWPgRDp/Qu2j7xHeKeQkgBjY3GGdlLEnX5GavHJSO0j6fUigUBSRgLpOkgJSfoO8pLj3WodHeDLL780DhLIIXixogyydWeAdRjnSR+HVy0eseedd57JZe5ZN6c48dc/u02kxYadB+SUY2pKfGxMUTdJUZRIoubpIluniBxcK1KmUfYZN8aCg2tEEqqJ1OhV6E3D8EB9iWeeecadcpTaZYzv1C3LC6L7mQ9SA61t27Zm3R9//GHGGuan99xzj8kIwHnefPPNPI/Xt29fk4qYSHObBsozuoJt+vfvbyLhqYVGW+fPn28i2qnZQcQ5hgqiQ6ip4Yz6UBRFURQlvEBXlG+dWFSUFL2rbgQbK8g5inLPht5a+Lx8+XKfQiTRGAhq5CslJBeFg1USkkOUMFiUCQh3HIs8oBg/yM/vCwwmzrz4RFbUrVvXCJQ23BejBUofBEablz5XanTNWtJTxHU02RTTjopNkoJSGxSVspNQZGdUChDtYsOSQ0VxV+ZaPl3yqbw29zVJSoiSkxu3li0HtsjIOSPlSMYRubjVxRHRx2CJ9P4F2kfeIyjF+a348gAsLjB5ZlDlnRhJivyS0Mfi/mwVRMpIUkNSNwlHBPKV4yWLoQEFkE2FSLoQUg2S1oMIT09QcLFQI+iYY44xNZZQJrGOIqbFFdIPxkRHyZa9h2XKX5vk1Na1pHRi5L97FUUpJMo2EWl5h8iykSL7l2QV0zY1Ko5kRVRgqGh5u0jZxkVySyiAzXjBu54xnbpo1HLwx1jBXJHxghRPFK7GsY40w6zD2I3h4rjjjvO7wPawYcNMRgD2QT4kPfF3332XbRtqYpAql7TGOLFwzj59+hhjBWl8qY1BrUbmvdQEVBRFURRFKQlEuYoofwoTfnKNolTAwGDBa+WXX34xRcM8wSMFpQJpnRBAEdzwdCFtFCmgYM2aNabwM4XKCIlFQGzWrJnxnqTAtD9grMBzEi9bp7GCdEmkVipOyh9nYd9I9FoPp/59u/JbeXDag7Lj0A45dPSQWVe9dHXzd9VSVeXxHo9Ln2Z9wrqPwRDp/Qu2j8X1neJLkb9jxw6jGI4kRX5J6KO38SzcwEBx/PHHy+jRo933CocCUmR4Sxl5ySWXGGME+cKdhnUiJzB4OCHygt+gZ2SFr2iNK664whzbL6eFIrr+1Er6efFmU0MpKT5WerSuZYwYJfH5L0l9jPT+lYQ+FlX/gpJHDqwR2faDyPZpWcW0YxKyUkQRUZGLoSLSZcJI6J+35yESZIlw5r/rv1zKlStb1M1RFL/5Oa9aropSzDh0IEX6HnNXyMc7+x6Pvq6jRMXnL2bAlZYumWPnRfyYXGSRFeTgxJjgLIIGfK5Ro4bXffB4nThxohGiSJ2ChwqKCgqtWTBgYOxAmcADUbNmTaO4cG6jKAVhqHjlj1ekclJlaVa5mfy55U+z/tgax0pMVIwxYPA9eDNYFDQo2R566CFTvHzChAk+t6OwOQXqPXP65gd+oxQtJJUaikGMh4DHsmfdkuIE3tO2zs3SpUvNtUN5cOmll3pVkCqKUjxSRrLeGS0JRGIgP+QHKxDmZqjwpwZWQVOxdLz0bldHpi/eIvsOp8nkBRvklJY1pE7lMiWuZktJ6mOk968k9LGo+ud3XT4npIBqcqNIo2tFMlL+Lb79bxRXHsfQOm3FG2811SL1N6coiqIohQk1BfPrzOCKotBA5FNkxor4+Hjp0KGDSeVEegYrCPF56NChue6LlwdRGSg/KahNGgdPCPtlId8nRaYJsVWUgjRUREmUNKrYSDJcGRIbk/XTKhNfxhgr+HfD/g1FYrAgTdrbb79tCtQTVl7YkDsYpf+mTZvkoosuknnz5uVoH4bL4saFF15oFmvEIQrM04jDOysSvTsVpbgQTMpI6lp42571+WkHKSavv/76XLfzpwZWYXFsrQSZu+6w7EhOlcnz1kiv1tUkMS6mRNVsKUl9jPT+lYQ+FlX/AqrL57VSZCkxs+bM9BJXx+yvv/6SQYMG5egj6Ztuu+02CUe81VSLlPpXiqIoiqKEB0VmrAA8HwcMGGAUqBQvIw81ERFXX321+f6qq64yRgkm/0BqqM2bN5tUDfxL8TSEKVJHWTBMICQ2b95cVq9eLXfffbfxiLbHVJSCMlTUK18v1235PhiDBdEIFODjOV60aJH5rWB8wLsXxf+dd94pBw8eNJFG5LWtVKmSUa4TAcDvAWMgBeZJX3L55ZebCAsLOXmZUJEijeNab7fp06fLk08+aQx+RBWQ9516MUww+c2S+x0DA7/hYH5bHP+xxx4zhkcK2WLQIMWLzQWPkQCjZffu3U0f+K2j6GvVqpVJ+4ax04nt77fffmvC6z799FOTRoFc8ytXrjQL7wQK5ZK3mIK7rAtGGUCNCfLhE8FFxAp571FEAn1gckd7SUk3ePBg8xlvbNpNOxVFCS+IjiCHOO8TW7Q1PzWwCpOaNarL3NU7pHqFUlKvWtkSV7OlJPUx0vtXEvpYVP2zdfmQ6/xNcZdfIqWOGXNYDBaexp9w7h/PHgtZECgMDsU9XamiKIqihAP4aeTbVyNKSgRFaqwgPRNKPpSneDxihCBdjPWIRLHoFNYRpik2tnbtWuPhTLEzvMUrVKjg3gaPJJQFeHGjtL3ggguM0rUohEaiOlAuo0imvW3atJGKFSuG5NgDBw40Cl0UKKHkvffeM8YfFN9w+umny/PPPy+FDQp6FEOkDEKBzr2cNm2ahKuhIr8GC1IzoexmUoTBYdy4cdK/f39jqCCtE9eH7zHs2fuFgow87MC1Q5Fui8taXnvtNWMQJEKJon8YOywYJDBU8MxiJMBDjN8reWxZb39vMGTIEJk5c2aOdj/88MPuyClPbB0ZnjWMF768menPzz//LElJSeZdMXbsWHM+T5jc83ujkOJ9991nnmUME7QXYw31a/i3Xbt25rni3fLuu+/KSy+9lONY/K54b3gD4wqFFW3+fF9gqKAtGChoP4ZTjBuKohRsykjWB7J9bqDAO+OMM6Rs2bLmXZuXLIFixyp3vCl/ChtOeWKLmtnWJR9Ok6SEWImLyd4elKRF1c7CItL7GOn9Kwl9LIr+8c7ifMhcyFIFGfFgazrg/BIJkRWR1D/aTupFZH3GXftcQKT+3hRFURSlMEEyCC/poIQaKwDvaV9pnzwVmN26dXMrSX1BSihvaaEKEwRUFL8oexFUWRAAURijMMXIUhzT3liIaKGeQFGDsWLu3LnuQnXFjQ/+/kCSU5OlTbU2Ae2HwWLRjkVmf3+NFU2aNHGncCKC4OuvvzafFy5cKKeeeqpZz3XCqGAh5VJeoLy3kUk8l05j2oknnug2HGLkWL9+vfl3y5Ytxlhw7rnnSq9evcz3r776qs9z+Mp/fNJJJ7mNYr6YM2eO/P3339KlSxfzmVzwvgx0RDvYf63BhqgH+sgybNgwEw2B8pFzA1EhgUaGYDTJy0iIgfK3335zG2q4BkSpKIpS8CkjeV/wvTMFx9SpU93vEX8hKoJaFyhseOdGgmfp4dR0mfr3JpMOqkfr2lIqofiNrYqiFC7MSXDuwNELZ6GCxNZCQPkdbsr8ktK/UqVKSb169dRAoSiKoihKkaGz1ALAGirAs1idXX/OOecEfFzST3z00UfGE97puUkRcVLz/PDDDyb1DUoVFKOk1Xr99deNggXFM3+HGiI8EGpRKqekpMiYMWOMUQklNgps0u3QXtL8kG7o/PPPN9ExGG3CnSvbXmkiJIiU8DeyAti+XEI5s7+/OCc81gDGZKh9+/Y+I064L4Ee24nzGWMiixEOYwbRCzzjo0aNMs8chq1gIiuc7cMY5SzeZwvUsg7DABEQ/vbDXh9rrPjyyy9NyiciMngmMSRYA0UwkRWe19V5/ZztxtDjmRpAUZSCTxl56623mnFoxIgR5rf88ccfm5R5b775ZrYIKaI3Mb7CihUrzL9EX7BgqGAcI1UekWx8tsWy8Twuzg4HuZGSRi5yl+w5mGoKb2OwqFgmZySIoiglCyLAkdlJYVSQ2FoIpNSMRG/9cO+fTQUWroYWRVEURSnOkJUl/2NslJQE1FhRAKmfrEHCF3yPEjWQlFBEGJCPHy9zlCwtW7Z0p8JBKCZNBYoZlDYoZzEMUBNg48aNxnBh0/X4o5x9//335ccffzR/cwzqJeQG9UNsSh8U0yh9rEc7Qi+KH/6mTbSfNDwovPnsCQpeahQAUTQo4zH20AaKmxYnbFREIAYLtnOJS24+4eaAimyvWrXKPDcYeT755BOTnosaFtxfrj2exyjKSXnEen/hPnE8PI5JwcbzmxukCMDTmeil+vXry/333x90ZIUTPLi430SH8DxTzJoUV7TrlltuMVEdnA9lId83bNgwxzHoB57U/Eu/gP0piMtzxLNIKheiKzCqBRtZ4Qk1MmgfhkJ+oyhJyU3Pszxp0iRjmMTQw+/DMw2XoiihTxmJcX78+PHGME5KOBRwEydOzPb7w6jv/O0TsWYNrDgG8L6lTpaNbHPCezZc689ULpsoZ7SvJz8v3mzSQU35a6OcckxNqVkhqaibpihKEYOcVNCGWJT5pNPDkSoclfklvX+KoiiKooRfzYrNmzebTCPff/+9ccZjfotu2GZvQWfHPBgH33379plMJDi7M48uKtRYEWJQwtu0T77ge7br2rWr38fFa71fv37G251ICpv6x3pDnXbaaeZvlNY2hBsPVIoqkw7Ierf7o5wNNA0UiiT6RNom2sIPAWXxhx9+aCY91113nVEc4d2Okpl11O/Iy+u8uKeBCtRgEayhAlCyPfvss8bYc/zxx5u6FUyGUMzjRUxqIxTiDz74YEDGCuoq8IxwrTt16mSMBrnBvSWahskY9wQP51DAeUlDRTsohI1xwXow88Kk9gx5dJn4cU5vxgpy0vNc2QLbgHGCeh4oL4F/UXqSyilUPPHEE9KzZ0+jOHVee575G2+80ShM8VS84YYb1FihKIWQMhIY93JLhcd7jMUX3bt398vQGo6UTYqTM46tK78s3SLb96XItMVbpGOjKlKheA6ziqIoiqIoiqIoShDgkIzxoUePHsZYgY4NZ2in8/xzzz0nL7/8sqlhi64NvSLpkHEoLqpUyDo1DTGkmPHHWMF2gRJIuh7AyxslDh6lpOxB8R9M2ptA2mVT8HTu3Nko0VFo4xnPjwIvc1srwJ/IinDBabBYu3etVC5VWdIz0s26g2kHJSYqRnYc2iEx0TFBGSqAaAYME55gnCJqxRPPnMO+CliT0ohIHG+KOhbL559/7v7bFu0OFDyRScXi7fgwcuRIs3hCFAlLXhDlYdPAOHFGOuFlzRIozutJdIkTPLIx2Fmjmv09NGrUyKTJUhRFKW4kxMVIzzZ1ZM7K7bJ2e7L8sXqHNK8SJ9WqFXXLFEVRFEVRFEVRIo9QRlYk/5ui2KkXduqGLTg9161bN1tqdafzL7prHIJxsqUurc22g24WXbLNQFDYaHxqiCGyIC9vTL5nu0AgUoEHBe9y0mD4qlNgwfOdFEF4fBMlQZoMjBhEVRDR4LnkZagYPny4TJgwwet3eLHTJ9Lc4OFP0WSU4Cyk88FbHo92fgA2PY+NrPBcws1QYcEAgSFid8pumblxpsTGxJpl4faF8uuGX836YA0ViqIoihJqYqKj5MTm1aVdg8pSoVS81KwQ/gXEFUVRFEVRFEVRiiPWuTu/C2CAIKuIXbw57tr0x6R7IusAqcvJYkL2EmeKY3TMNlsPcDwyr8yePVuKCo2sCDEo4fMyJKDYZ7tA4OE688wzzX4UEiVyITcwTPTv398YDzgfub7zk4d28eLFPmtXkJaK9pH7jIfe/niIoqB+BZ9POeUU4/meV7vDGQwRh44ektfmvmY+1y5bWzYf2CzVS1eXwccPDtpQ4YxIULzjGUmiKIqi5A3jc9v6laVFrfKyZ3dW1Bgyw9GMTImPDc8i4oqiKIqiKIqiKJHMxo0bTa1Ui7eoCli7dq2pP3HHHXeYLCNk3CH7DdlbBgwYYAwVYOtBWvhsvysK1FgRYsj7RRHk3Ips830gxbUtFP1kcYJSYevWre7Pzpze1LkIFF85vDF+UKzYG9QaeO21LAW9E2dKHyIrSJMTqII+3HKGX9zqYikdV1p+/+MhOeXQFPkttr50OuExjahQFEVRii2xMf8F2i7bvE9WbN4nPVrXkgqlvQu9iqIoiqIoiqIoStGkgSpXrlw2Y0VuWXdwLn/qqafMZyIrcEYfM2aMMVYUVzQNVAGA8h6DhPVapCiwjTZgPd+HG999911RNyFs6FNaZGgFkXpxWf/yWVEURVGKOxmZmbJqyz45eOSoTPlro2zbe7iom6QoiqIoiqIoihL2hDINlL+QCYfU/E5atmxpSgVAjRo1zL/bt2/Ptg2f7XdFgUZWFACkWzrnnHNMfQYKSVNMmxoVpHAKJqKiOPPee+8VdROKF+s/FVn1mlRLLCvVKvYWSdkssux5kfRDIvUvLurWKYqiKIpPYqKjpfexdWX6ki2yM/mI/Lhos3RpVk0a1yivV01RFEVRFEVRFCWMOOnf9PxOVq5cKfXr13cX28Yo8dNPP8mxxx7rLt79+++/y0033SRFhRorChAME127di3IUyjFic3fiix9RuTIjizjxN6FIonVs/5mfWxpkdpaYFtRFEUpviTGx8rp7erIrOXb5Z+dB2TWiu1y4MhRaVe/csCePIqiKIqiKIqiKEpo00D5y+23327S8pMG6uKLL5Y//vhD3nzzTbMA87vbbrtNnnjiCWnatKkxXjz44INSq1YtOe+886So0DRQihIqQ8WKV0TiK4tUPVkkoUrWUvFYkWpds9bzPdsFyJYtW0yx9Ly4++67pVWrVvLkk08GVaCaPHah5rTTTjM58Ro3bmwK9GCpZdm3b58UZ+69917TTsLlSpUq5W73jz/+WNRNUxRFKZQIi5Nb1pDW9SqZz4vW7zFGi3CrI6UoiqIoiqIoilIciA7REgjHH3+8TJgwQT766CNp3bq1PP744/Liiy9m0zHec889cvPNN8v1119vtic70OTJkyUxMVGKCo2sUJRQGSowkZZpJJKZIRL9708rtoxIdIxIXBmRQxuytoMAIiywaH744Yd+peQirxw1UooLKPdRbr3zzjuydOlSGTFiRI7C7aRNK24888wzZsGIc+GFF5pi7+HQbkVRlFCBl037hlWkbGKczFm1QyqXTdDICkVRFEVRFEVRlDDi7LPPNktu877HHnvMLMWF4qPVjCB2794tW7du9bnwvRKBhorS9XLflu/ZLsAIC2fUAwYJQrdOP/10adKkiVv5369fP9m7d68p4E4x9DVr1kjv3r3Nfqeeeqo5hierV682VtO2bdvKyy+/7F7/yCOPyKBBg0wKs0aNGsnHH3/sjvAg3127du3MPn///bcEA8cfMGCACUW75ZZbzOfRo0e7v69SpYr772effdbdxhdeeMHrteE7rglFgjhuenq6zJo1y20pJryNyA6gzX379g2q3dOnTzfX8qyzzjLXgc8YMiyXXHKJWQdTpkyRLl26mKiSK664QtLS0oI6p6IoSlHTpGZ5OadjfWlR+7+aWxphoSiKoiiKoiiKUrwLbIcrGlkRYjBEOBWvvhg6dKhUrlw56PMMHDjQKEr79AltDQSU4YQA4c0PKMWff/55KUxQQNvcaFSor1ChgpQrV84o5z///HMJS0OFhe2CjLCwoHDH0x+lfPPmzU24FmFdKPn/+usvsw2GijfeeEMaNGggP//8s0kR9dlnn2U7Dnnphg8fLueff74MGzYs23cYOyiww/XnWJdeeqkJG+vevbtJM8W5rQK+U6dOkpqamqOdkyZNkrp163rtA4YSFPvx8fHGWOGNH374QTZt2mRy6mVmZppn8YwzzjCha04WL15sIjcwzFx++eUybtw48++ff/5pvp8xY4Z5hjAU8jeF72HIkCEyc+bMHOd9+OGHjfHHGxxz2bJl5vdhDROe7Nq1y/xmuO5JSUny0EMPydixY835FEVRwpHypeLdf6cezTAFuIm6qFY+qUjbpSiKoiiKoiiKEg4URc2KcEWNFSHGXw/q4uxpfdVVV3n1Yi8sULBbpTtGGQwX5557bvGzIK77QCQ9WaRCm8D2w2Cxb1HW/kEYK1DalylTxvyN0pzUT06jAPnlfvvtN7fBBw/Y0qVL5zjO3LlzjUEBiELAOGEhRCwuLs5EJNj6EkQ48GzExsYaQ1mbNln9/v333wPuA/cTQ0VuYKz49ttvTV/gwIEDsnLlyhzGCoxYNvIEo8rXX39tnhtqZGDsWLt2rWk3hgoWazR49dVXA243ERXWkOeLOXPmGIMSkRWAISfURkVFUZSi4u/1u2XH/hSZunCTnNi8ujSsXk5vhqIoiqIoiqIoihISNA1UGIEHOp70pKJBQW2pWbOm3HXXXUZ53LNnTzl06JBZT9EUtidtz0033VQgbUIpPHjwYJN+iDQ8v/zyi1nfq1cvWbVqlfm7du3a8sEHH5i/8eKfP3++RAQNrxSJLZcVKREIbM9+7B8ECQkJ7r+pm0D9BCdEIaCox+DDsnDhQpMWyZPcjD/Oc1hIC0UkAsr6yy67zBgFbGSFLUDtXDZu3Ojz+BSttmD8oM0WG6XBOqIcbD+I9uD5ya0fzrA4DAuffvqp+X0QTUHbiUixhg2MFt7aTZRKftuNccK2m0iMojT+KYqihBIiKupWKSOZLpfMWL5NFq3frWmhFEVRFEVRFEVRcuPfyIr8LFLMfLgLCjVWhAl4weNljsc2xZZnz56dLfUU6XEWLVpkDANffvmlWU9xFAwDKKspFgzvvvuuVwXt/fff7z7e+++/715vFdK5sXnzZpMe54svvpDrrrvOKC1QDuPFjld7jRo1zN9A+zGe0FZv7SDlUNhAVETzmwld8N9gwXZsz35BRFX4AymzMFbYqAmMGaRK8gSl/VdffWX+Hj9+fJ7HXb9+vbmXN9xwg1x55ZXumhVEVljFvHPxlQLKk/r167sjaaZOnWoiQ6zB66233pLDhw+704Pt378/x/4YxawB7JNPPnGneeLfUaNGGaMFzxaRGtWqVXMbYois8NZuXymgPKlXr54pGk5KLIyHRFQAERXTpk0z1wuSk5Nl3bp1fh1TURSluBMbEy1dj6kpx9TJqmHx1z+7ZfbK7ZKR6SrqpimKoiiKoiiKohRLtGaF/2gaqDABr3CUqCha8RQnusJCSqDTTjvN/N2hQwd3MeUTTjjBFPe96KKL3CmBrr76arOEMg0UhYX50R1zzDGmLRgvUBRjVMHzHwMGynBS+DRs2NCsIwrEKqjDGmtwoAYFhojcalcUgqHCwvW+8cYb5YEHHpCjR48aA4Nn+iQib4iQIHqBiJy8oEYDtRhID0UNCGpYhAKiJaiVYiODbC0XDHAYAzp37myiFTgnBrHy5ctn259+UYgb4wmpqqhXARTwtkXBeebq1KljCl6HCowVFNvmuSeCCYMIVK1a1dSouOCCC0y6t+joaHOtefYVRVEigeioKOnQuKqUSYqTuat2yJptyXLoSLp0a1VT4mNjirp5iqIoiqIoiqIoSpiixoowwlfaHl9pgYjEQME8ceJE42FOdAaRFS+99FKOY5C2hsLJ+W2XtRSiYL711ltNqpxbbrlFvv/+e+Ppj+IYiKzAO98TIgKmTJkiYWuwOLBWJKGySGZ61rr0gyJRMSJHdmT9G4ShghoepC+yabec2PW2sLOlUaNGJpIgN6j1wDPhiWfBa3vcAQMGmCUYbK0Lb8ennoazXgaKfcsdd9xhltyg9gURFZ5g3HCmyMrreuR17SkuzuJk5MiRZiGaiAgL20fqirAoiqJEMs1rVZAyCXHy67KtciAlTdIzXBKvkqWiKIqiKIqiKErIC2xHlZA0UDqlDBOIVCDH/p133il79uwxaWauueYan9vjiU7NADzV2RcvcBS3/kRWeGP48OEmUsNbihzqAlDYePny5aYIMjUNMFiQ359aCWPGjDFe7iihMZZAxERWWKwB4u8HRZKXi8RlFcCWfQtFjh4USawq0vbxAo+oUBRFUZTCpHbl0tL72Dom2qJUgoqViqIoiqIoiqIonjhrrAZLVAmxVuisMkygvsCZZ55plPzUpSByITcwTPTv398YD/D6fuihh0zURbBQ86Bv375evyMtFe2jtgDpb5wFjlesWGE+n3LKKaYuRl7tDmswRKQfEln1WtbnpNoiKZtFEquLNB2shooCwBn5oCiKohQNlcokZvu8bnuyxERHSb2qZfWWKIqiKIqiKIqiKH6jxooQQ0qaUG7nhPQ5nil0MERs3brV/Xno0KHZ6lwEimeaIafxg8LB3iBv/2uv/augd0B6HAuRFaTJCRQiMYLZr8iof7FIbOl/a1isE4ktVyg1KhRFURSlOLDn4BGZtWK7kU/aNzpqCnGXFA8gRVEURVEURVEUb2gaKP9RY0WIoTgwBgMK6+ZmqLBFhMOF7777rqibED5Yw8S6D0QaXqmGCkVRFKXEUKF0gjStWV5WbNkn89fukgMpR+WEptVMmihFURRFURRFUZSSCLOh/M6IoqRkoMaKAiDcDBH54b333ivqJhRfg4VGUyiKoiglDIwSxzepKmWT4uTPNTtl1db9cujIUTnlmJoSHxt8OkpFURRFURRFURQl8oku6gaEG6Q1UBRF0XeJoiiKd0j71LJORenaqpapXbFl72GZ8tcmY7RQFEVRFEVRFEUpqQW287uUBDSywk/i4uLMQ7Fz506pWrVqsXlAMJ5Q0yE2NrbYtCmURHr/SkIfI71/wfSR7XmXsC3vFkVRlEikXpUy0vvYuvLz4s2y71CqrN1xQNrUq1TUzVIURVEURVEURSlUtGaF/6ixwk9iYmKkTp06smnTJvnnn3+kuIDSMzMzU6KjoyNSERzp/SsJfYz0/gXbR7bjncK7RVEUJVKpXDZRzmxfz6SDal23YlE3R1EURVEURVEURSnGqLEiAMqUKSNNmzaVo0eLTxoDFKS7d+82dTJQlEYakd6/ktDHSO9fsH0kokINFYqilATKJMZJ+4ZV3J/TMzJl0+5D0qBa2SJtl6IoiqIoiqIoSmEQHZW15IsoKRGosSJAUC4WJwUjSlKUnomJiRGpCI70/pWEPkZ6/0pKHxVFUUIViTZz+TbZsOug7ExOkQ6Nq5qi3IqiKIqiKIqiKJFKKGpORJWQeZNq1RRFURRFUZRCTQ0Fyzfvk1+WbJGjGZl69RVFURRFURRFURSNrFAURVEURVEKB7yBWterZFJDzVqxzaSD+uGvjdKjdW0plaABv4qiKIqiZOfIkSPy2GOjZN68v2XlyrWye/deOXIkVcqXLyvNmjWSPn16ytChV0v58uXc+/z4468ybtyXsmDBYtm2bafs2bNP4uJipVatGnL88e3khhuukO7dTwzoUj/yyAh59NGRuW5z7bWXyVtvvaC3UPHJg9eMkVlTF7k/t+vcVEZ+dpvP7bdv2i2DTn9KDh884l5394gr5IyLuwR8ladP+lO+HT9TVi3eJEcOp0qlauWk/UnN5bIhvaROw2p61woYYiLyGxgRJSUDnRUqiqIoiqIohQr1KjBOTF+yRfYcTJXJCzYYg0XFMgl6JxRFURRFcXPw4GF5+unROa4IRovZs/80y7vvfip//PGNVKpU0Xw3ceIU+d//Psu2fXp6uqxZ849ZPv74K3nxxUfl1lsH6ZVWCo2pX/yezVDhT/rUF+7+MJuhIhg4znN3fCA/fP57tvXbN+2RyZ/Mlp8nzpNH3hwknU5tna/zKLmjaaD8R40ViqIoiqIoSqFTrXySnNG+rkxbvEWSD6cZw0Xf4xtITL4rzymKoiiKEknUrl1DTjyxo9SvX0cqVaogu3btkS+++E7Wr99kvscA8eabH8q99w41nxMS4qVz5+OkffvWUr16VaMk/OuvJTJx4mSjuIUHH3xeBg8eYGoPBsoll/SVjh3b5Vjftm3LfPdViUx2bdsnrz78eUD7TPrgN5k/Y0W+zz3xvV+yGSp69O0g9ZvWlGmT5sn6ldskLfWoPHnze/L2jw9I1ZoV8n0+RckvaqxQFEVRFEVRioRySfFyxrF1ZcayrdKmfmU1VCiKopQgunfvLscee6y8+OKLEk6g+J4wYYKcd955Rd2UEkGVKpVk06Y/c6y/447rpU6dju7P1nABI0Y87PVY1113t7z11njz94EDB016KIwZgXLGGd1l4MBLAt5PKbmMGv6RHNh/WKrVrijlKpaR1Ys35rr9to275c2nJpq/T+rdTmZOWRjUeTPSM+Sj0T+4P/c8r6Pc98rV5u9zB3SV/ic+ZCI3DiWnyIR3p8v19+l7raAgBVS+00BFSYlAC2wrYcWhI0dlx/6UHAvrFUVRFEUJPxLiYqRn2zom0sKy71Cq2/NRURRFUULJ9OnT3ek4nMsDDzygFzoMyMjIkM2bt8rYsVlGB0urVs197nPo0GGZNWuu/PrrHPe6qlUrS7VqVYJqwwMPPCdlyjSVxMRG0qhRF7n22jtl6dKVQR1LiXwmfzpb5vy42Lxn7n7hCildJjHX7ZGBn79rnKQcSpU6jarJtcP6Bn3uFX9vkN079rs/n3JWe/ff5SqWNjUzLLOn/h30eRQ/a1aEYCkJaGSFElYs2rDHFORcummf+XxMnQoSEx0tnZpWk87Nqhd18xRFURRFySd7Dh6RKX9tkloVS8lJLWpIbIz61iiKoiihZ8WKFVKu3H9FmcuUKaOXuRhD0ezTT7/M63ddu3aWQYNyfnfyyefJzJlzc6xPTEyU119/2iiPg2Hz5m3uv9et22CWDz+cIOPHj5bzzz8rqGMqkcnOrfvk9ce+MH+fc8XJctzJLWTcS5Nz3eer//0qf81aKdHRUXLPyCslITHwVGWWtcs2Z/tcs17l7J/r/2ew27R2h0kJFZ8Q/PkUJRTo7E8JK9rUqyQXn9hYmtQoZxb+vuzkJma9oihKkZC2X+TQhpwL6xVFCZgDKUclM9MlG3YdlKl/b5Ijael6FRVFUcKcQ4cOyVVXXWUMAjVr1pQRI0Zk+75Bgwby1FNPyTXXXCNly5aVevXqyZtvvun+Pi0tTYYOHWr2RdFcv359efrpp/PVpmrVqkmNGjXcC22bO3eunH766VKlShUpX768dOvWTebPn+/zGHm1a9++fTJo0CCpWrWqMYyceuqpsnCh73QuqampkpycnG1Rcufyy/vJt9++b66/PxBRMXHi23LBBX0CvrRlypSWfv3OlHvuGSwPPnibdO/eJdu9GzDgNtm+fafeMsXNyGEfysH9KcZIcP39eadY2rJ+l4x9Oiv904XX95RWHRrl62oe2Hc42+dSZZKyfy6d4P4b+fvg/uzbK6HDW0RfMEtJoMiNFa+++qoRTBhYOnXqJH/88YfPbY8ePSqPPfaYNG7c2Gzfrl07mTx5co6QwAcffFAaNmwoSUlJZtvHH39cUwlECKUT40yaiKT4WLPwNwvrFUVRioQdv4osekxkWu+shb+XPJm1XlGUgKlftaz0bFtb4mNjZFfyEfl+wUbZfzhNr6SiKEoYc/fdd8svv/wiX331lfzwww8mFZOnEQADRseOHWXBggUyePBguemmm0z0A7z88svy9ddfy6effmrWffjhh0aPYDnzzDONscHX0qpVK7/aeeDAARkwYIDMmDFD5syZI02bNpWzzjrLrPdGXu266KKLZMeOHfL999/Ln3/+Kccdd5z07NlT9uzZ4/V4GDowktilbt26frW7JNCsWWN5/vkH5Ykn7pHrrusvlStXNOvHj58gxx9/VraaFZahQ682+1B4u1u3LMPCzp275cwzr5Bnnhkd0PmvvvoS2bFjoXz55Vvy7LP3y2OP3S3Tpn0u9913s3ubgwcPyWeffZPvviqRwfcfz5I/pi39N/3TlZJUOu/0Ty/cNU6OHE6Tek1ryNV3nR36RnmkWdWsq4VfsyK/S0mgSNNAffLJJ3LHHXfImDFjjKGCwlq9e/c2gzxeDp6QQ3LcuHEyduxYadGihUyZMkX69esns2bNkvbts/KuPfvss/L666/L//73PyOQzJs3T66++moz0N9yyy1F0EtFURQloqnWVaRcC5FFj2R9PmaYSEyCSFz5om6ZooQtNSqUkjPa15WfF22Wg0eOyuQFG6Rbq1pmvaIoihJeHDx4UN5++20zl0dRD8zX69Spk207jAIYKWDYsGEyatQomTZtmjRv3lw2bNhgDAcnn3yyUfwRweDkrbfekpSUFJ9tiIvL6dzmef7169ebyAcnRHdUqFDBGFrOPjun4jC3dmHwwBkTY0VCQpb38gsvvCATJ06Uzz//XK6//vocxxs+fLjRkViIrFCDRRb16tWWu+660X1tMFoce2wv2bp1uyxfvlpuu+1hmTDh7WzX89JLz832+fHHR8lDD71glML33feMKZR97LGtxR/q18/+vFhuueVaeeqpV9yfly1b5dfxlMgm7chRef3xL83f5w3sKu26/FcbwhfTvvpTFs5ZJdEx0TJs5FUhScdEXQonhw8d8fmZtFNlyqusrZRwY8XIkSPluuuuM8YEwGjx7bffyjvvvCP33ntvju0/+OADuf/++40QA3ha/Pjjj8YDA8EHMFyce+650qdPVkgfXg0fffRRrhEbhOuxWGyoZWZmplmKM7SPgba4tzOU/bPr7N+ZmeFtWiyJ9zDSiPQ++ts/Ct0fSs2ZrqV0Qmyxj37K1z2MLSuSFC9RsVmCoCupTpaxIuvAUpRE6jOplAzKl4qXM9vXlelLtsjO5CPy09+b5fR22QtxK4qiKMWfNWvWmHRJOChaKlWqZIwQTtq2bev+G8U/qZlQ9MPAgQNNeib2OeOMM4zhoFevXu7ta9euHXC7fvvtN5NyylKxYkXZvn27cZIk8oNzk7nh8OHDxijhjdzaRbonDDWVK2fPEY9RhWviDYwa1rCh5A7FsTt3Pk4mTPjefJ4+fXael+zcc3sbYwUg+//yyxy/jRX+UlLStCi5Q+2HQ8lZBtQJ7/5iFm9gnOhZd4gpdH1S76x3YGZGpgw55zmfx37+znFmuXvEFXLGxf+lIvNGo5a1sn3eumG3NGlVN1vaKQvFvLVeRcERFR1llvweoyRQZMYKhBXCIPEcsERHR8tpp50ms2d7H2QwKHjmISTVEx4LlhNPPNF4P6xcuVKaNWtmBAS+xzDiC0ItH3300Rzrd+7cKUeOZLc6BkJyarIkp+XMMVkuvpyUS/ivkFd+FVH79+83Ay3XL9Lw1r+jGZlurxnuUVyYF94sifcw0oj0PvrbvwXr98n89ftk1fZD5nPT6qUlOipKjq1XXtrXryARfQ8zU6Xcv++l5J07RKKLxyTTV8oCRQkXEuNjjYFi5vLtkpaeIZXL+pePWlEURQk/PKMfUPpaxwvSJ61bt86kU8Jh8eKLLza6AyIUbBoojA++IOJhyZIl2daROpqoCSekgNq9e7e89NJLZh8MB126dDH6C2/k1i4MFdSywPDhied5Fd9MmzZTOnZsJ2XLZi+AvmvXHvn99wU5jATUjKDgdefOHXIc69tvf8rVsBAV9Z/R6913R8rAgZeYvw8cOCiPPDLC1KqoXr1qtn1efjl7NEebNi30diqFzh0XvWgMH9Drwk4ybNRV5u9mbetL5erlZff2rHqKv323QE4581jz9/49B2Xh7JXuY5zY6z+DsVIAhKLmRJQaKwqUXbt2GS+F6tWrZ1vP5+XLl3vdhxRRGB26du1qalH89NNP8uWXX5rjWIjIIDKCNFExMTHmuyeffFL69+/vsy2+Qi1tEaxgmbl8prz313syd8tcyXRlStNKTaVK6SpyVdurpF/dfhIKEN542GlrpCpJPfuHsiIpKcv6y3pyWoczJfEeRhqR3kd/+3dyuYrSunGqvDF1mWzfmyKntW8g9auUDZvIinzdw4xUidqY5e2dWLXaf5EVRYy/hQYVpTgTEx0tp7SsIRmZLon515so0+UyxkW+UxRFUYo3zN0xRPz++++mcDbs3bvXOBhSwNpfmJtfcsklZrnwwgtNJAO1H4jSCCYNlDdmzpwpr732mjubw8aNG43uIph2YcjYtm2bxMbGZqtjoQTGSy+9LVOn/io9e54sbdu2lFKlkmTz5m3yxRffZStmffbZWSnGSAvVpUtfadGiiSmAXbduLTl8OEX++OMv+fHH/wxa6ItIA+UP6JVGjnxTRo9+z7SjY8cspe5vv/2eLaKDOhoXX3yO3mJFYuNj5ZSzsowCnvw9Z7UxFED5SmWkbecm0qBZTandsJrXfVJT0kztC0vztvWkWp1KUqNu9qgtb8TERMvlQ3vJKw9+Zj7/NHGeKaRdv2lN+fnreaY+BpQulyT9rvbv96AoEZ0GKlDwbiBtFIYIlEoIPaSQIm2UhcJWFLUaP368qVnx119/yW233Sa1atUyXhKBhFqisApW8bhw20KZtHKS7Di8QzIyM0x7j6QfkR2Hdpj1TSs3lbbVQ2O15Nj5aWtxx7N/0dEutzUyUvpd0u5hJBLpffSnf2VLJUh8XIzsO5Qm+w6nyYrN++WEJtXDJhQ6X/fQFS1ydJ9I8iqJOrhSpGI7KQ5E6vOolDz4fcbGZL1LMFLMXb3DvGuoY5EYF95OC4qiKJEOBa6vvfZaU2SblEjUpyS9cyByCk6LRClQq5L9PvvsM5MmykYoBJMGyhvUnyD9NIW+cWKkzWRzCKZdRFgQlXHeeefJc889ZzI/bNmyxaS+pvYm51D8A2PDpElTzeKNY49tJSNGPJxtHXUsWLyBoWL06CdM0e5AIMLm++9/NosnFSqUN8W3K1bUqBlFJDEpXh5547o8oyAwUji369wzZ1qybRt3S/8TH3J/7juga57pn5ycO6CbrFi4QX74/HfzedrXf2b7ntRP978yUKrU0Ge3oOcz+dWNRIWJbiVsjRVVqlQxAwQ5IZ3wmcHdG3i8UoyK1EyEZmKAIJKiUaNG7m0QJlh36aWXms9t2rQxhbJI9eTLWBFqMFQ8+sujsuvwLmlSsYlJBwXta7aX1PRUWbt3rTwy/RF5uNvD0q5G8VBolSTCOa++ohR3Nuw6KHsPpklcbIws27RP1u88KA2q/ZcLOGKhjs7BdSKHN4psnCBSoW2JCdFUlMLmcGq6rNt+wKSFnLJgg/RoU1vKJcXrjVAURSnGPP/88yYt0jnnnGPqRNx5550mBae/sA8K/1WrVhk9wvHHHy/fffddyB0zKARO4WuiIsi28NRTT8ldd90VdLv4G8MMTpakMEbXQaYIzwwTim+GDBkoNWpUNSmftmzZLnv27DPRKtWrVzGRFv36nSlXXHG+O3qmQYO68uSTw2TGjLmm2DXpolJSjpg0Uo0b15euXTvJ9ddfYSIv/KV8+XIyffrnxlhCnQuiN3bu3CNxcbHSpEkDOeOMHnLLLddIrVredVmKUpSg4CYt1Ak9Wsm342fI6sWb5EhKmlSsUlaOO7m5XDqkl9RtpO+kgr8P+VcRRJUQFUORGSvi4+OlQ4cOJpUTngY2DQefhw4dmmdaCzwnjh49Kl988YXJC2mh+JWnwILQUFhFRo9mHJVX575qDBUtKreQTMl+3lJxpcz65buXy2tzX5PRZ42WuBhVkBcmizbskVkrtsnSTfvM52PqVDBpJDo1rSadm+kLWlGCBW/n2Su2S0ZmpjH8paSly8wV26R+1TKR7wGQvEzkyLas9E+7ZonsXypSoVVRt0pRIhLeL2e0rys/L9osySlHZfKCjdK9VS0tvK0oilLMoyuIWGBxOhpa/vnnnxz7kCXBQoYFllDQvXt3I7d6gwiJuXPnZltHaicnzn3zahfGjJdfftksSnCcfnpXs/gLEQ733XdLUOdyuTZ7Xc9cplu3LmZRlPwy8rPbAtqedE8/bXw138fs0beDWRSluFOkaaCoE0G0A+GPJ5xwgrz44oty6NAh43UAV111lTFKEBUB5LjcvHmzHHvssebfRx55xBgh7rnnHvcx8dSgRgW5MEkDtWDBAhOaec011xRKn/7c+qes2rNK6pevn6Wc8yIDsZ7v2W7+1vnSqU6nQmmbkkWbepWkTuXSMu7XrLC7i09sbOpeEFmhKErwEEVBNEVCXIx5z1UqkyBLNuyJ/OgKJqybvhLJPCoSV0Hk6EGRjV+KlD+m5Lg+eAEPw2nTpsmOHTtyOAw89NB/YcyKEgwVSifImcfVk2mLt8juA0dk6sJNclKLGpH9rlEURVEURVEUJSzRNFD+U6TJrClA9cILLxilBQYIPCcmT57sDoncsGGDbN261b096Z8eeOABOeaYY0yORwwZM2bMcOephFdeecV4PgwePFhatmxpQjZvuOEGefzxxwulT39t+8tEVxBBkRt8n5qRKgu2LSiUdkUK6RmZsnwzqWUOyNptyfL9/A2ybNNesz4Qj8xq5ZMkKT7WLPzNoimgFCV48DAjioJoitiYrKGldGKspKRlmPW+vNciAqIoiKaIKZVlnEiqKbJzZtb6EsrYsWPNGMz4/vnnn8uECRPcC+kcA+XVV181hSmJrOzUqZP88ccfuW5PvmjqW7E96SBJweDkyy+/lF69epm82QiNTs9Np8wxZMgQsw3eoBdccEGO1JVK0cIY3qtdHalbuYwpuP3bsq2yZOMevS2KoiiKoiiKohQroqKjQrKUBIrclZyUT77SPk2fPj3b527dusnSpbkrfwizJEKDpSg4kHZAoqP8swHFRMXIwbSDBd6mSGHNtmSZNO8f2bT7kGzec8gomPConLF8m9SuVFr6Ht9AGlUvV9TNVJQSCdETRFEQTXHw35ow/EYrl43w6AqMMERREE0R/W/OfKIrjmwv0dEVTzzxhIlyHDZsWL6P9cknn5hIzDFjxhhDBeN77969ZcWKFaZApyezZs2Syy67zERlnn322TJ+/HiTbnL+/PnSunVWwTqiOE8++WSTRtJX6obbb7/dFMDE8FG+fHkjq5x//vkyc+bMfPdJCR0YR7u2qinz1+6S5Zv2SvlSWrtCURRFURRFURQlXCnSyIpIpGx8Wcl0+efln+HKkDLxZQq8TZHA2u3J8uFvK2XjroNG+Vk2KV7KJMZJvaplpGq5JLN+3K8rjUFDUZSiiqrIMNEUTvidRnR0BdETRFEQTWGNEhpdIXv37pWLLrooJJeYVI4YFEgRSWQlRotSpUrJO++843X7l156Sc444wyTB5voDiIrKZI5evRo9zZXXnmlifo47bTTvN/W/ftNgU3Ofeqpp5oaW++++64xhMyZMyck/VJCR3RUlHRsXFXO7lhf6lRWuUpRFEVRFEVRlOJYYDsqn4uUCIo8siLSOLbGsfLJkk/k8NHDuaaC4vuEmARpX6N9obYvHEnPdMmkP9fL/kNpUrdKVqoHJ+THZz0Gi0nz1svQM1u509AoilJ4URUYEj0LaUd0dIWNqkg/KJJUO/t38RVFUraW2OgKDBU//PCD3Hjjjfk6Tlpamvz5558yfPhw97ro6GhjZJg9e7bXfVhPJIYTIjECST/FOY8ePZrNmEFaKephcfzOnTt73S81NdUsluTkLAM6NTs863YUJ2gbxsTi3EZ/KJcU5+7DgZSjMmfldunSvLoxmkZKH30R6f0rCX2M9P6VhD5Gav8irT+KoiiKUhRozQr/UWNFiOlQs4M0rdRUlu9aLi0qt/C6DULs+v3rpWWVlnJczeNC3YSI45+dh2TLnsNSvUKpf4uW5/TOZj3fb95zUFZv2y8talcskrYqSkmOqqD2i6cxEVAU7j6QararX7VMDoNGREVVWDyjKyq0kkjn5Zdfdv/dpEkTefDBB00UAjUj4uLism17yy23+HXMXbt2SUZGhruWlYXPy5cv97rPtm3bvG7Pen9h2/j4+Gw1sfw5DqmnHn300Rzrd+7caWpgFGdFFNEk/J4xBkUCM1ftlh3JqbJ5xx7p3KSSVEiKjbg+Rvo9LGl9jPT+lYQ+Rmr/Dhw4UNRNUBRFURSlBKHGihATFxMnQ08YKo9Mf0SW714udcvVzRFRgaGiSqkqMuSEIWZ7JXc27E6R9MxME0GRG3yfnuGS1duS1VihKEUVVeHDmBhx0RXOqIrS9US8pf8rYdEVo0aNyvaZotS//PKLWTyfB3+NFeEGESDOqA4iK+rWrStVq1aVcuXKFWsFG/eFdkaKgq1X+YoyffFW2XsoVRZsPiJdmlWTChUiq4+Rfg9LWh8jvX8loY+R2r/ExMSiboKiKIqihD0aWeE/aqwoANpWbysPd3tYRs0ZZSIsdh3aZR7KBVsXSEJsgjSr3Ezu6HKH2U7JmyNHM0w+an+Ijo6SlLSs4r6+SM/IlOWb98n6nQckI8Ml38/fIM1qVZCmNctr+ihFCSKq4sCRo1IuKd789jIyM81vDPgc8+9kPTY62mwXMdEVnlEV3spxlLDoinXr1oX8mFWqVJGYmBjZvn17tvV8rlGjhtd9WB/I9r6OQQqqffv2ZYuuyOs4CQkJZvEEpVVxV1zxmwyHdvpL2aQE6d2+rsxYtk027zkkM5dvlwYVY0xR9kjpY6Tfw5LYx0jvX0noYyT2L5L6oiiKoihFRVR01pLfY5QESkg3C592NdrJuc3PlZplakqLKi2kUcVGpoZF9dLVzXo1VPhPYlyM19Qy3sjMdElSvG8bHAW4R3+/WN6fvtIoL7Ynp8i0xVvknZ+Xm/UU8lYUxT/4Xe45cETKJsZJckqaJB9Ok4Mp6XI0PdMs/M06s6Skme3Y3t/fc7GPqkjbIxIdJ3L0YFaERebRrIW/j/678D3bsX1u/U7bL3JoQ86F9WHIY489JocPH86xPiUlxXznL6Riorj1Tz/9lM1zlc9dunTxug/rndvD1KlTfW7vDc5J6irncVasWCEbNmwI6DhK0RIfGyPdW9eS5rUqGHvi4k3J8sfqneH/DlIURVEURVEURYlQNLKiADm14anSoVaHHOvLJ5QvyNNGHPUqJ8mCjQcl9WhGrqmg+D42Jkqa1Cjn01Dx4W8rTaHuKuUSZd/htKzjVy1j0kdRoHvcryul/ynNpLGPYyiK8h9ETQzs0cIdzXQ49ajsP5wmE37P8rDv16mhxMXGSKn4GCmVkJXyDmOijbYIW1wZWemd4iuJpO75d2WmSOa/hZUxTjh9AdiO7dkvysewu+NXkU1fieyamfW5ykki0TEitc4WqXOOhBvUbaC4dqlSpbKtx4DBdw899JDfxyKt0oABA6Rjx45ywgknyIsvviiHDh2Sq6++2nx/1VVXSe3atU29CLj11lulW7duMmLECOnTp498/PHHMm/ePHnzzTfdx9yzZ48xPGzZssVtiACiJljKly8v1157rTl3pUqVTAqnm2++2RgqfBXXVoonRGYe36SqlE6IkV8XHZLdGEwzXRIdE+bRXYqiKIqiKIqihA1R/JfPDBNRUjLmMGqsKEDKJ5Y3i5I/GlQtLbUqHZJNuw9J3SplfKaj2b7vsNStUlaa1Mh5zUlLM2neP8ZQwTE8vSoxgrAeg8Wkeetl6JmtNCWUovhB2aQ4s8Cclcny+6odUrFMVm7j6Uu2mn87Na0mnZtFkAEwOlbk2CdFjjoKTmakiix6JOvvNo+IxHikAoorm7WfL6p1FSnX4r9jHDMs6xhx4TmG8E72JogtXLjQKP8D4ZJLLjEFqjFwUNz62GOPlcmTJ7uLaGN0cKaoOPHEE2X8+PHywAMPyH333SdNmzaViRMnSuvWrd3bfP31125jB1x66aXm34cfflgeeeQRdw0OjnvBBRdIamqq9O7dW1577bUgroZS1PAstqxTUY4ePiBNG9TS8V1RFEVRFEVRlEJFa1b4jxorlGJPbHSU9O1YXz6csdoYE4iK8IyowFBRvnS89D2+vlclxKqt+03ap+oVSuVaBJjvN+85KKu37dci3YoSIG3qVZJG1XMaJUonROBQQ/FsFiBVU8aOnNtgaIj309jAdjGJInH/GmQp2u1p8AgDKlas6BbCmjVrls1gkZGRIQcPHjQRF4EydOhQs3hj+vTpOdZddNFFZvHFwIEDzZJXQdFXX33VLEpkUKNCYrZUkcs27ZVq5ZOkclktHqsoiqIoiqIoilIciEANkhKJNKxezqRnIuph0+6DciAlzSjB1u88KPGx0SaiAkOFN0UprNmebFI95ZZGCvie7VZvS1ZjhaIESOnEOLOUOEjhtOWb/wwNy1/I+jdMUzjlB1I0EVVxzTXXmHRPpFNy1p9o0KCB1nxQigUbdh6QeWt2GgeHU1rWkDqVvUduKoqiKIqiKIqi5Bsc+fKZBkryu3+YoMYKJWygjgTpmfCEfHfaCsnIdMmpFM6sXcGkfvIWUWEhp350tH8/arazOfgVRVHyhBROFdvlXB+mKZzyA7UloGHDhiYdE0WqFaU4UqNiKalZsZRs3XtYpi/eIh2bVFUnBUVRFEVRFEVRCgRNA+U/aqxQwgoMEhgn6lctaz6feVw9iY/NPVoCSPtAQU1/YDtnmohw4M8tf8qG/Rv83r5e+Xpei78rihIE8QGke4pgkpOT3X+3b99eUlJSzOINClYrSlGC7NCjdW35Y9UOk/px7uqdciDlqHRoXNUU5VYURVEURVEURVEKn/DSyCpKkDSuXk5+idli6lvklgqK72NjoqRJjfBRpGGo6PZeN0nLSPN7n/iYePll4C9qsFAUJWRUqFDBa1Ftb1C/QlGKmpjoKOncrJqUTYqTBet2yfLN++TgkaNycsuaEpdLtKaiKIqiKIqiKEogREVnLfkhqoRMUUpIN5WSTtOa5aV2pdKmEDf51L3Ber6vXamMSSsVLhBRgaEiOipa4mLi8lzYju0DicRQFEXJi2nTpsnPP/9slnfeeUeqVasm99xzj0yYMMEs/F29enXznaIUFzCwta5XSU5pWdMYLzbtPiRb9xwq6mYpiqIoiqIoihKBaaDyuwTCI488kmP/Fi1auL/v3r17ju9vvPFGKWo0skIpMemj+h7fQMb9ulI27jooVcol5oiowFBRvnS8KdSdW/2L4kpMdIyJmMjIzJAMV06v5ZioGLNNmqRJZkZmkbRRUZTIpVu3bu6/H3vsMRk5cqRcdtll7nV9+/aVNm3ayJtvvumubaEoxYUG1cpKqYRY2ZmcIvX+TTWpKIqiKIqiKIoSzrRq1Up+/PFH9+fY2OymgOuuu87M3y2lSpWSokaNFUqJoVH1ctL/lGYyad562bT7oBxISTNWw/U7D0p8bLTUrVLWGCrYLpw5kn5EjmYezbE+LjpOSseXLpI2KYriJ2l7RZJXiSQv9160O0yYPXu2jBkzJsf6jh07yqBBg4qkTYqSF9XKJ5nFciQtXfYeSjOFuBVFURRFURRFUcKtwHZsbKzUqFHD5/cYJ3L7vigIP/dxRckHjWuUk6FntpIB3ZuZtFDVyyfJqa1rybU9W5j14W6ogMTYRCkV+59ihb9LxZUy6xVFKcaQou7gOpHDG0U2Tsj6HKbUrVtXxo4dm2P9W2+9Zb5TlOJOekamTFuyRX5atFlWbd1f1M1RFEVRFEVRFCWMCWUaqOTk5GxLamqqz/OuWrVKatWqJY0aNZL+/fvLhg3ZU8J/+OGHUqVKFWndurUMHz5cDh8+LEWNRlYoJQ5SPDWvXUHq/5vm4czj6kl8rO+i2+EGqZ7MCyw963NsTKypU6EoSjEneZnIkW0iMQkiu2aJ7F8qUqFVUbcqKEaNGiUXXHCBfP/999KpUyez7o8//jCC0hdf/J+9+wBvq7z6AP7Xtrz3zHb23oOQhAAJSRiFUsoqezVllVDWxwijlFXCpowCZZRZUig7AZIwkkDI3sOJYyfx3rKt7e85ryxvG2/J8v/Hc7Hu1dX1vZJjS+95zzkf+vr0iH6V/B0NNxuRX2rF+n05KK20Y+LA2A7PhiIiIiIiIuqIvg0mAC5dulT1p2hIPov/61//wrBhw5CVlYX77rsPs2bNwo4dOxAWFoYLLrgA/fv3V8GMbdu24bbbbsPevXuxfPlyn75ADFYQERH5mmRRHPkYkBJuhkjAYQEylwMRI2XUFD3NokWLsG/fPvzjH//Anj171LbTTz9dNetiZgX1BNJs+7hhCQgNMmDb4QLsyiyCpdKBmcMTe2RfKyIiIiIi8h2NVqOWjh5DZGZmIjy8tjKMyWRCUxYuXFhze+zYsSp4IcGJ999/H1dccQWuvvrqmvulv2RSUhJOOukkpKWlITU1Fb7CYAUREZGvSRaFZFPogj3BCXMSkPdjj86ukKDE3/72N1+fBlG7SRbFuAExCDMbsG5vDjLyLajYdgRzRyUjyMi30ERERERE1P09K8LDw+sFK1orMjISQ4cOxYEDB5q831sVQe5nsIKIOo3L7VKLl9PlBDSATqNTJaKIyA+zKiSLQrIptEbPNsmusOb0qOwKSRuVOpdarVbdbonM6iDqKaSfVbBJjzU7s1RZqB/3ZOOksX18fVpERERERNRTaDrhY72mYw+3WCwqa+Kiiy5q8v4tW7aor5Jh4UucFkYUYKxOKxxSSqZahdPTHMegNSDEGOLDMyOiJkn2hGRRSDaFo8izrQdmV4wfPx7Z2dmIj49Xt2XWR1UTTcJlu8tVG1Al6gkSI4OxYEJfrN2bjSlD4n19OkRERERERC36y1/+osoxS+mnY8eOqd4WOp0O559/vgpavP3226qEc0xMjJpweNNNN2H27Nk+n1zIYAVRgAnSB8FQZWi0XTIriMhPsyqcFsCcUv8+YxRQmdVjsisOHTqEuLi4mttEgSYi2IgF4/vWS98urbAjPLg6I4qIiIiIiKiLy0C11pEjR1RgoqCgQH1WP/7447F+/Xp122q14uuvv8aTTz6J8vJyVcb57LPPxl133QVfY7CCKEBI6Sc77M3fX+VSs5nrlogiIj/Kqmj4xqOHZVfIbI1Vq1Zh5syZ6jZRIKr7AeFoQTlW7TyG0f2iMK5/TIc/fBARERERUWDyRbDi3XffbfY+CU6sWbOmQ+fz+uuvIzY2Fqeeeqpav/XWW/HSSy9h5MiReOedd9o9LtCmYIXb7VYX8v333+Pw4cOoqKhQ0ZgJEybg5JNPVhdK1JXKrQ4UldtQaXeq9dySShj1OoSY9AgJapxN0Bv0i+gHo84Iu8sOt8vdqsfI/vI4IvKTrIqQfkBVE/9+e1h2xUknnYSgoCBMnz4dc+fOVYvc1us5N4ICT4HFqkqdbT9ciLJKB44blgCdVuvr0yIiIiIiIupyf/vb3/CPf/xD3V63bh2ee+45PPHEE/j0009VSanly5e367itGj2orKzE448/rk6gsLBQ1aJOTk6G2WxWHcI/+ugjXHXVVZg/fz7uueceNTBB1BW2ZxTip/25MBs9P7ofrveUGpk2JB7Thyb0yid9UvIkrLl0DTJKMlr9GAlUyOOIyI+yKhq3d+hx2RVS/unbb79VExteeeUVVRMzODhYZVt4gxdTpkxRTbiJerqx/WPU+xF5X5KeW4YKmxNzRiUjyMCyi0REREREVEuj1ailIzQdfHxny8zMxODBg9VtiQ1IGamrr75aff4/4YQT2n3cVgUrhg4dihkzZuDll1/GvHnzYDA0nsEumRbSmOO8887DnXfeqYIXRJ1tTL9oDEoIb7RdMit6GofLgY1ZG7EpaxOyC7KRGJOIiUkTMSlpEgy6tmWJSOCBwQeiHphVYS8ETNGAwwJUuQC3w3O/ZFt4+8xoDZ79ekB2haR5XnbZZWoRBw8exOrVq9UiEx7k/UFYWBiKi4t9fapEnWJIUgRCgwxYs/OYyvb8anMG5o5JQbiZfSyIiIiIiMh3ZaC6WmhoqOqH0a9fP6xYsQJLlixR26XagiQ+tFerRnjlG44YMeJXByjuuOMO1Wk8I6P1M7yJ2kJKPXW03JM/lJLamr0VT6x/AnsL9qqghcvpgi5Dhze3vYlhMcOwZMYSjE0Y2y3nQkQ+IIEJKe9kjAZshdUb3YDb5rkpwQnUyT6Q/WR/eZym5wRnBw0aBJ1OV/PGTGZb2O3N99Yh6omSooKxYEJffLvjGEorHfhycyZOndQPIabeWZ6SiIiIiIgC37x583DllVeq9hD79u3DokWL1PadO3diwIAB7T5uq0Y8fi1QUZdkXaSmprb7hIgCvZSUBCruW3OfClTYnDYUVBaofjBJYUmqCXaprRT3rr4XS+csxbjEcV1+PkTkA1o9MP5BwFFWu81lA7bf67k95l5AZ6r/GEOY53F+TiYsSCaFNNuWr/n5+TjuuOMwa9YsVbty2rRpvj5Fok4XGWLCwgl9sWrHMUSHmhBc/R6DiIiIiIhIciI6mhih8bOnUXpU3HXXXaoc1IcffoiYmBi1fePGjTj//PPbfdw2f5L6+eefVdOM7OxstZ6YmKhKRE2dOrXdJ0HUW0pJSRbFcxueQ35FPiYlTkKWJQslthIVpAg1hCImOAYJIQlIL0nH8xuex7OLnm1zSSgi6iGkebYsdYMVhlDPbWm43TBY0UMyKYqKilSNytmzZ+Oaa67B5MmT2WCbegWZBDF/XJ96Kd4utxvaTkj5JiIiIiKinisQy0BFRkbi2WefbbT9vvvu69BxWz06m5ubqxpl/Pjjj6oWVUKCZwZ6Tk6O6vAtAxMSRYmPj+/QCRH1hFJS7SU9KvYX7ke4KRw/Hf0JRdYiWGwWVKEKh0sOI7MsE5GmSAyJGaL2k34W0/pwFjIR9QzeupTSQFuv16tsSykDRdRb6HW15dvcVVVYveMYzCY9pg1JgM7PGuIRERERERG1xbZt21q979ixY7s2WPGnP/0JLpcLu3fvxrBhw+rdt3fvXlx++eW49tpr8cEHH7TrRIh6gy3ZW1BcWYz04nQUW4vhcrtgd9uBKsDhdqDKXYVcV67aFmGKwObszQxWEFGPkZWVhT179tSUgXr00UdhtVpx/PHH44QTTsCcOXMwadIkFcwgCnTSEyurqEL+xKPc6sScUUmqRxYREREREfUuGq1GLR09hq+NHz9eZXhUVcmnnMa898lXiSN0abDiq6++wnfffdcoUCFk29NPP60GIoioeSXWEmSXe0qoxQfHw+l2wlpqRZWmCtHmaOg0Omg1WpTaS2F1WtX+RNQL2EsAay7gsHjWyzM8ZaAMEYAxAj3J8OHD1fLHP/5RrcskB2//ir/+9a9qW3FxsY/PkqjrJUYGY+7oFHy3OwvZxRWq8faJY1IQ6qPsTiIiIiIi8o1AKQN16JCn729XanWwwmQyobS0tNn7y8rK1D5E1Dxppl3hqEB8SLynTAr0nl82VYBBa6iZbRxqDEWuJReFlYV8Ool6g9zvgGOf1vas2PN3z9fk04A+p6OnklKRkiYqy9atW9X7CL5XoN4kJSYEp4zvoxpvl1TY8cWmDBXAiA0P8vWpERERERERtUn//v3hN8GKc889F5dccgmeeOIJnHTSSQgP9zQoloGHb775BkuWLOlQp28iasD3AVOiNpFMIGkY35CUNIsI6lnZAd0ufjYQNa7xdsms6EGkv5VkUHjLQO3bt0/1rZg6dSrOO+88zJ07FzNmzPD1aRJ1q+jQICyY0FcFLIosNqzYmonZI5PQJ6Y6OElERERERIE/xtfRzAgN/NKuXbuQkZEBu91eb/sZZ5zRtcGKZcuWwe12q8EGp9MJo9GotsuJSBPNK664An//e/VMUCJqkpR6CtYHq6baYcYwuKpcqpab/Cc9K3RVOmiggcVuUfvJ/kQ9xXeHv8PHez/Gj5k/qvWZfWeq0manDT0Npw/rudkB3cLY88o9NSUxMVEFJyZPnoyzzz5bBSeOO+44mM1mX58akU+FmAyYP64PftidjZySSpiNrX4LTkREREREPZzEKTocq9DArxw8eBBnnXUWtm/fXq+PhbdcVZf3rJCyDf/4xz/wyCOPYOPGjcjOzq4ZmJBmmd5MCyJqXmRQJBJDE1Vz7bzKPNVg21sGSko+SdBCelZEmiLVTHTORqeeZHb/2RgeOxz3rr5Xrd828zaY9CaVWUG9wxdffKGaaYeEhPj6VIj8jjTXPmF0MorLbSrbgoiIiIiIqKe68cYbMXDgQFVxSb7+/PPPKCgowM0339yhhAZPgfw2kKCEzJSUkk+yyO2OBiqee+45DBgwAEFBQZg2bZq6uOY4HA7cf//9SE1NVfuPGzcOX375Zb195FjexiV1l2uvvbZD50nUUeMTxyPSHIlRcaNUg22z3gyt/KfRQq/VI0gXpLbL/ZJVMSFxAp906jEkuNYvop/quSKL3JaFQbfe45RTTmGggqgFWo2mXqAit6QSq3ceg93ZvllHRERERETUMz4HdMbiT9atW6fG6GNjY1UPXllk8uJDDz2EG264ofuCFV7l5eV47bXXcOedd+LZZ59VkZP2eO+991S/i6VLl2LTpk0q+CCDHVL3uil33XUXXnzxRTzzzDOqJtYf//hHlXKyefPmmn02bNiArKysmmXlypVq+znnnNPOqyXqHJOSJmFI9BCU2ksxLWUaxiaMRagpVJV8GhAxQAUzZLvcL/tNTJrIp56IiCgAudxV+GFPNjLzLfhqyxGU2xy+PiUiIiIiIuoCTU2qb8/iT6TMU1hYmLotAYtjx47VNOHeu3dvu4/b6jJQI0eOxA8//IDo6GhkZmZi1qxZKC4uxtChQ5GWloYHHngA69evV2kfbSG9MK666ipcdtllav2FF17AZ599hldffRW33357o/3ffPNNFSBZtGiRWl+8eDG+/vprPP7443jrrbfUtri4uHqPefjhh1Umxpw5c5o8B5vNphYvaRoupEeHLP5Mzk9qgvn7ebZXoF2f1O//0+Q/4f7v7sfGrI2wu+2INcfC6XKi3FGOkuISHCk9gmGxw7B48mK1f0+/9kB7DXvjNbbl+mQfKWm2v3A/duXuwrjEJppG+6FAfQ0D7XqIAolOq8HsEUlYtfOoKg31xaZMzB2djJgwlogiIiIiIiL/Nnr0aGzdulXFAqRS0qOPPqp6XL/00ksYNGhQ1wcr9uzZoxprizvuuAMpKSnqhCIiImCxWFR2gwQR3n777VZ/c2nOLf0v5HhekjJy8sknq1SSpkhQQco/1SWNOyWQ0tz3kCCGZG80F4GS9JT77ruv0fa8vDxYrVb4+0BUSUmJGmST5y7QBOL1JWoSce3oa/HStpeQVpymGmsbtAb18xmiC0FqZCquGX2N2q+5DKOeJBBfw952jW25PqvTiv15+5FRloG3Nr2FxEmJfhf9702vYVlZma9PgYhaEBsehIUT+uHb7UdRUmHHiq1HMGtEIvrEhPJ5IyIiIiIKEJ1RxknrZ2MrUv1IKi8JKQd12mmnqeSGmJgYVUmpy4MVdUkgQTIgJFAhQkND1WD/eeed16bj5Ofnq5SRhISEettlXYIjTZESUZKNMXv2bJUtIU08li9f3myH8Y8++khlgFx66aXNnocESySYUTezom/fvipDw98bh8sAmwwEyrkG0gBboF9ffHw8jh96PDZlb8KmY5uQU5SDhKgETEyeiImJE2HQGRAoAvU17E3X2Jbr25y1GQW2ApiNZmwr3IZ8TT5GxY+CvwvU17BhcJ+I/E9okAELJvTFmp1ZyC6uwOodxzB5cDyGp0T6+tSIiIiIiKgTBGKw4pRTTqm5PXjwYDWWX1hYiKioqA5NWm1TsML7jSTbICkpqd59kmkhmQhd7amnnlJlo4YPH67ORwIWUkJKykY15ZVXXsHChQuRnJzc7DFNJpNaGvI2B/F38jz0lHNtj0C9PpPWhBl9Z6geFZJBIQGMQLvGQH8N/eUay60OlNs8mW91hZj0CAkydNv1SVbCJ/s/UdlCkUGRqrTZR/s+wuiE0T0iuyIQf06781qefvrpVu/bkWZbRIHIqNfhxDEp+Hl/Lg5kl6igxbDkiB7xu5OIiIiIiHqft956S1VaCgkJqdkm7SM6qk3BipNOOgl6vV5lHkijDKlN5XX48GGV5tEW0nxDp9MhJyen3nZZT0xMbPIxMutVsiUkYCJNvSUIIb0tmqqFJeck/Swk84KIKFBtzyjE2r3Z2HWkWK2P7BMJnVaLaUPiMX1o/cy1rrQrbxfWZq5FsCFYDbAlhSbhx4wf1faekF1BHfPEE0+0aj/52WCwgqjpHhbTh8YjLjwIA+LD2hSocLrcsDqc6qsxgAKuRERERESBQE2O7OBEJI2fTWS66aab8Mc//hFnnHEG/vCHP6hMCxnn77ZgxdKlS+utS+mnuj755BNVl6otpOnGpEmTVCmnM888s6YUh6xfd911v1raQrI5HA4HPvzwQ/z+979vtM9rr72mZqufeuqpbTovou5QYi1Bia1E/cznl+XDarKqWdARpghEBHlKrBG1xph+0egTE4InP9uOnCKZjRuJKYPjVWZFd5GsiuV7lsNit8CoM6ptkl2RU56jto+MG+l3f1ipcx06dIhPKVEHye/JwUkR9X63SkB6cGIEgpv4nX60sBybDuZja3oegqsqUaHJxrgBcZg0KBbJ0bUznIiIiIiIyLfv8zs6JqLxszGVrKwsfPnll3jnnXfUuHxwcDDOOeccXHjhhTjuuOO6P1jR0GOPPdauE5BeEZdccgkmT56MqVOn4sknn1TNOaS0k7j44otVUEKaYIuffvoJR48exfjx49XXe++9Vw323nrrrfWOK9skWCHHlmwQIn/z3eHv8PHej/Fj5o+qef3sAbOh1+px2tDTcPqw0319etSDSKmnLen5yCqsgNPtxg+7s1XT1mlDujerQrIoJJuiyFqktjG7goioY3ZmFmFregEOZJVg7ugURIXWli3ddrgAy9cfQkmFTfW9CDVoYHO48e32I9iYloezpw/EmP5ty3omIiIiIiJqDRlvl6baslRUVOC///0v3n77bcydOxd9+vRBWlpa+47blp3T09OxcuVK2O12zJkzp14ZqPY699xzVa+Le+65B9nZ2SoIIVEZb9PtjIyMejW3pfyTdBs/ePCgyu5YtGgR3nzzTURG1m9CKOWf5LGXX355h8+RqCvM6DMDVqcVW3O2wlplRb/wfhiXNE5tJ2qLn/bn4NNfMgANEGY2ApoqfPxzurqvOwIWdbMqUsJS6t0XFRSFLEsWsyt6oSNHjuB///uf+lss7xvqWrZsmc/Oi6gn6RcXirScUpRW2PHVlkzMHpmkMiYko0ICFZV2J/rHhcmvf5ir3NAEBSEmLAhZRRX4cP0hdZsZFkREREREvqWFRi0dPYa/kqwKKQNVVFSk2jLs3r273cdqdbBi1apVKlJSWVnpeaBer5paS02qjpKST82VfVq9enW9dQmS7Nq161ePOX/+fDWARuSPtmZvxXMbnsPe/L3ILMlUmUBfpn2J1Rmr8fn+z3Hd1OswNmGsr0+TekigQgUmNFUwGz2/0uPCzSgos3VbwKJuVkXDtERmV/ROUs5R6lZKP6k9e/aoyQ0y4UH+Lk+cONHXp0fUY4SbjVgwvi/W7DqGnOJKfLvjmOpHtOdoscqoUIEK+b1b5z2v+r0bFYzDeWWqRBSDFUREREREvqXVeJaOHsPfeDMq/v3vf6txgL59++L888/Hf/7zn3Yfs9Ud+O6++27MmzdPlV6SxtZXXXVVo9JLRNS6QMV9a+7Dnvw96BPeB1HmKNWnQmr69wnro7bfu/petR9RawIVMjAlAYq64iPMarvcL/t1R1aFZFE0RbbL/bIfg8i9wx133IG//OUv2L59u+oxJb2lMjMz1YQDqWFJRK1nMuhw0pgUDEoIV79D1+7JxuqdRxFiMtQEiN1VVXC5q9RXIdtDg4zYmp6vmm4TERERERF1pvPOO0/1ipZG2zJRURIODhw4gAceeADDhw/v+mDFjh078Le//Q1JSUmIiopSPSpyc3NV4IKIWsfhcqiMivyKfAyPGQ6zof4Ac7AhWG2X+5/f8Lzan+jXAhUSmGhKdwQsWsqqaC67ggKfpHxKzylvJqZkZUrpxvvvvx+PPPKIr0+PqMfRabU4blgCxg2IUb2JisvtkF+55VaHyqCQoMSh3FL1VdbLbQ4Y9VrYXW7YnC5fnz4RERERUa/mbbDd0cWf6HQ6vP/++6rR9rPPPosZMzqnrH2rgxWlpaWIjY2tV4vKbDajpKSkU06EqDfYmLUR+wv3o39E/xYHduV+2W9T1qZuP0cKjEBFdwQsvFkVhZWFMGgNKntCFgmyyeJdl0Xul/2YXdE7hISE1PSpkEkOdRtr5efn+/DMiHou+V0+tn+M6lsRFx6E0kqHKgd1tKBcZVXI/fJV1vccKUZ+mRVGnRYmvc7Xp05ERERE1KtpNZpOWfyFw+FQvaeHDBmighadqU0Ntr/66itERETUrEudfalHJVkXXlKjmoiatiV7ixrElQyKlsj9NpcNm7M3Y1qfaXw6qV2BCi/ZL7ekstN7WLiqXMgqy0K0ORqF1kK1zV0ls3ht6rYEJ7Sa2pi47Cf7y+P0mjb9+aEeZvr06fjhhx8wYsQILFq0CDfffLMqCbV8+XJ1H5E/cFZnHchgvl7X6vk7PjckKRKj+kbjo58PqXM3m/Sqfq1eCwQZ9TAagEqbE0cKLJiaGtejro2IiIiIiPyfwWDAtm3buuTYbRotuuSSSxptu+aaa2puqxldLqaaEzWnzF5Wb/C2JTqNTs1IJ6rrm21HUWF3YmB8eJueGAlYSIkQeXxnBSv0Wj0ePPFB9XPtJYEK6bki7j3hXpj0pnqPCTOGqcdRYFu2bBksFs/vr/vuu0/dfu+999SsC7mPyJeOFparxtNSMsnhcsOg02LcgFhMGhTbY5pRa+pMHKq0VUH+sxk8W+X/3v5AVf4z+YqIiIiIqNfqjMwIrR9lVog//OEPeOWVV/Dwww936nFbPdVKPgz92sJABVHLZKBWZp63hsw+DzWG8imlek4am4Jgo15lSrSF7C+Pk8d3JmkQ3y+iX71Ffm5labhdFtmfAp801xo7dmxNSagXXnhBzbqQRtv9+/dv8/Gee+45DBgwQDXrnjZtGn7++ecW9//ggw9UQy/Zf8yYMfj888/r3S8Duffcc48qUSUlLU8++WTs37+/3j779u3Db37zG1UCMzw8HMcffzxWrVrV5nMn/7I9oxAvr9yNb7cfgc3hhl6rVV9l/aWVu7H9cEGPyAhJzytD35hQ6PU6lR3icLqRV+5EWaUdZVaH2i73H84tY4NtIiIiIiIfC7QyUMLpdOIf//gHJk+erJIZlixZUm9pL+aFE3Wj8YnjYdAZUOGoaHE/ud+kM2FC4oRuOzfqGSQr4jdTB6jB1tYGLGQ/2V8e11lZFU0psZYgoySjpk+F3JZFtlPvcuWVV2L16tWdcizJyJA3OkuXLsWmTZswbtw4nHLKKcjNzW1y/7Vr1+L888/HFVdcgc2bN+PMM89US92SlY8++iiefvppFUT56aefVEBFjmm1Wmv2Oe2009Sbr2+//RYbN25U31e2SV1O6pkKyqz470+HUGl3on9cGGLDgxAebFRfZV22f7j+EI4VlsOfqeCEy42YsCCM7BOFgfFhqpm2/J6XoEVksBHDkyPU/WywTUREREREXUE+Y0+cOBFhYWFqsp98/vYuW7ZsafdxW1WLY/369a2uMV1RUYFDhw5h1KhR7T4pokA1KWkShkQPwZ78PRgeM7zJfWSw4XDJYYyIHYGJSRO7/RzJ/3kDDtKDQgIRLfWu6K5Ahfju8Hf4dN+nNRlBf1/7d/X1tKGn4fRhp3fp9yb/kpeXhwULFiAuLg7nnXeeSg+Vwf72kLJRV111FS677DK1LgGGzz77DK+++ipuv/32Rvs/9dRT6nvfcsstav2BBx7AypUr8eyzz6rHyr+HJ598EnfddZfKnBBvvPEGEhIS8NFHH6nzlSbgkmkhKa3eDBFJbX3++efVG7LExMQmz9Vms6nFq7S0VH31ZqD6Kzk3eV78+Rw7Sq7tQFYJSius6Bcb7imjVF0qSch6UqQZGfll2JiWh8TI1vUE8gWDVgODTgObw4XYsCCExIWhb1QwLJZSZJdroNFqYLE6oNNqYDbq1P6B8NoG+s9poF9fb7jGQL2+QLseIiIiX9BU/9fRY/iTrqo80KpgxUUXXaRKOshMSWmUKTMQG9q1axfeeustvPbaa3jkkUcYrCBqgmRVXDf1OlXTf0/BHvQN79soo0ICFbHBsbh26rVqf6LWBCxiwur3hujuQIWY3X82xiU2HpCOMEV0+fcm//Lxxx+jqKhIlWN6++23VcBByjJdeOGFuOCCC1RJp9aw2+0qq+GOO+6o2abValXZpnXr1jX5GNneMOVUsiYkECFkQoVkR8gxvCIiIlR5KXmsBCtiYmIwbNgwFcSQmSImkwkvvvgi4uPjMWnSpGbP96GHHlI9OpoK3tTN2vDHgaiSkhL1+0Ke30DkcLqQk1+A5BA9glEJ1MYp6kkOqcKhjCPISjFC58fPxdgEI7YdzkdQldvzkUUHRIcDJoMW2aV2VFQ4oIcL00YkoLAgH4Eg0H9OA/36esM1Bur1lZXV9iYjIiKi9gnEnhVeBw4cQFpaGmbPnq3KLMt7Ielr3aXBCglESA0qmYUogwxDhw5FcnKyqgUtgxF79uxRzTPPOussrFixQtWHJqKmjU0Yi6VzluL5Dc9jb8FeFFUWqQ83u/J2wWwwq4wKCVTIfkStDVjklVb6NFAhIoIi1EIkoqKicPXVV6vlyJEjeOedd1Q2hPSKkPJKrSEZDtIPS7Ie6pJ1ee/RFAlENLW/t3yT92tL+8gbq6+//lqVj5KUVhl0kkDFl19+qa6rORJUqRsokcyKvn37qgwT6Xvhr+RvkFyznGcgDbDVVVZpg8WlR6XGDIOmcXDXq1JjR5nTjfCoGISY/HfCwERDCDYds+FgsRNJkcHVc6yqEBQcjDidEwdzS5EUHYGJIwciPiqkwz0ypPSUSa+DXue7n49A/zkN9OvrDdcYqNcnn/mJiIiIGiooKMDvf/97lWEh74GkOoEkO0hJZvnc/Pjjj6PLghUGgwE33HCDWn755Rf88MMPOHz4MCorK1VZh5tuuglz585FdHR0u06CqLeR2efPLnoW64+sxx3f3IFKWyXOH30+pvWZpko/MaOCWssbiPjop3RVb91s1KvAhaQHdmeggqg5DodDvXeQ3hDp6emNggT+SAJ91157rQpQfP/992p2yD//+U+cfvrp2LBhg2rM3RTJwJClIRm08veBK3lz2RPOs73MRoPKlLDbq+Rim93P7qxCkEGn9vfn56JPTBjOnj5I9dg4nG9BaJABMQYXChw2VQIqJSYUv5s+CCkxYWp/2Sb7tMXRwnJsOpiPren5qkeGQafFuAGxmDQoFsnRHQuAtFeg/5wG+vX1hmsMxOsLpGshIiLyFS06IbMC/pVZIfEAiRlkZGRgxIgRNdvPPfdcNYmvS4MVdUmHb1mIqGOk5FNiaCL6RfRT5UEWpC5AkDFIbY/QcXZ6S9JzS3G0sHGT8pToYAyI99/Zy11FAhIy8/X5L3ehrNKOuLAg/GYaAxXkWzK7QkpAffjhh2q26W9/+1t8+umnOPHEE1t9jNjYWOh0OuTk5NTbLuvN9Y2Q7S3t7/0q2+oGHWR9/Pjx6rY01ZZzlexRb0aE9KuQ3hevv/56k70yyL9JRsDA+HAcPlCqGk83lZYsQSqL1Y5pQ/r4NIOgtcb0j1HX4gko5MFdJYEWrTr/iXUCCnuOFmPTwTwcNywRA+I9wYtfs+1wAZavP4SSChtCg4yqgbfN4ca324+onh5nTx+ovj8REREREbVyQkMHgxUaPysDJdWVvvrqK/Tp06fe9iFDhqgkh/Zqc7CCiDq/GbHOpcPf1/9dzYZnM+Jf9+/vDuCXg3mosHlKyQSb9Gqi7KSBcbjzd72zKfmUwfEYEJ+pZsKeNrkfMyrIp1JSUlBYWKgaXb/00ksqI6GpjINfYzQaVY+Ib775RpVkEhL4kPXrrruuycfMmDFD3f/nP/+5ZpsEGWS7GDhwoApYyD7e4ISUa5LMj8WLF6v1ioqKJmeTyjobjfZcqYnhqnRSVlEFkqKC673Zl0CFbI8IMamB/p5CAhKyzB+XgmPZ2UhOTITRoK93XTnFFXC5q/D97iyUWR0Y3TeqxQ868ndEAhWSrdc/LqzevtIfSZ4nyeiQQImvMiyIiIiIiMi3ysvLERwc3Gi7jAW05/O/F4MVRD7ibUYsA19Sl11mEMtAGJsR/7oLZw/GlNRYPPPVDrW+eN4IhJiNKrOiNyq3OlBUbkOY2YDhKZFqcEl6VoSY9AhpY9kPos5w77334pxzzkFkZGSHjyXpo5dcconK6pw6dSqefPJJ9abosssuU/dffPHFKjgiza3FjTfeiDlz5qiU01NPPRXvvvuuKkMlQRMhA68SyPjrX/+qZnxI8OLuu+9Wvbi8AREJbEiNTfm+0mNDykC9/PLLqjm3HJN6Jhlc/+20gVj+UzoO55XVZAzYnW6VUSGBirOnDeyRA/CSCRJk0DfKCJGf91kjk1T2xe4jRdhyKF9l4ElGnk7bdMBC9pWMioaBCu/xJNAjz5/s1xOfKyIiIiKi7iZvqzuaGKHxr8QKzJo1C2+88QYeeOCBms8KMsb56KOPqnYR7cVgBZGPeJsRyz/kIFsQ4iPiWRO2laTUU2SwCYavd6v1qUMSEBna/qhtT7c9oxA/7c9V/SqEzHgV04bEY/pQ/+8PQIHXo0IyFGTAvzOCFVLvMi8vTwUNpAG2ZENIo2tv7wupj1k3A+K4445T5afuuusu/N///Z8KSHz00UcYPXp0zT633nqrCnhI8+/i4mIcf/zx6pjeJqISPJb1O++8U5WtkmsaNWoUPv74Y9Wri3qu0f2iERturunFYHe5myydFEgk3XxyapzqWfHLgVykZZei3OrEnFFJMOp19faVkoLyvEggp7nsC9ku98t+Cyb07REls4iIiIiIfP2evMM9KzT+Fa2QoMRJJ52kJgfa7Xb1OXvnzp0qs+LHH39s93EZrCAi6uHG9IvGoITGvToks4Kou0mDrX79+sHlcnXaMaXkU3Nln1avXt1om2R1yNIcGWy9//771dIcyeSQ+psUeLylk2Sg3eZ0waTX9YoBd8m8Cw3S4/vd2cgursBXWzKxaGI/1XjcS54PaaYtGSctURkpLrfavzc8d0REREREVJ9MCNy3bx+effZZhIWFwWKxqF6V1157bb3+kG3FkSwioh5OSj2x3BP5E8lIkKyGN998E9HR0b4+HaImySB7bxto7xMTilPG98G324+pIHfdQIWQwI1B52mm3RIpnSUZKbI/ERERERH1vsyKjIwM9O3bV33+b+o+mcTYHu36hLZmzRrVLHPw4MFqOeOMM/D999+36wSIiNrL7nSpUhYrtmTySSTyIzKz4rvvvlN9IIYNG4aJEyfWW4jId6JDg3D65P4Y2SeqZps04BYSvBk3IFb18JDm3E2R7XK/7Nfbgj1ERERERO0hYQZNh//zL9L/UUo2N1RQUKDu67bMirfeeks1tZS0jhtuuEFtkzpUUqPqX//6Fy644IJ2nwwRUWtJiRmH0zOQ8r9fDuPsGQOh03GGJ5E/8DaqJiL/ZDLo6gX+V2w9ojItRqREqt4dG9PykFVUoZpp1+1dIYEK2S7NyGU/IiIiIiLqnaqqqprscyfloLz9ILslWPHggw+qBho33XRTzTYJWixbtkx1/2awgoi6w4ptR2tuF5Zb8ckvGThzWvsjt0TUeZYuXcqnk6iHSM8tQ5HFho2WPJRV2jFlcDzOnj4QH64/hMN5ZaqZtupR4XSrjAoJVJw9bWBANiMnIiIiIuoKgVQGasmSJeqrBCruvvtuBAcH15tY/NNPP2H8+PHdF6w4ePCgKgHVkJSCkvrURERdTX75fbYxo2Zdqlf89+dDOH1yv16ZXVFiLUGJraTR9ghTBCKCInxyTkTFxcX4z3/+g7S0NNxyyy2qd8WmTZuQkJCAlJQUPkFEfmJIUgSc7ipsSsvDvmMlqrzi8SMScfW8Edh0MB9b0/NVM23pUTFtSB+VUcFABRERERFR7wxWbN68uSazYvv27TAajTX3ye1x48bhL3/5S/cFK6RxxjfffKN6VdT19ddfq/uIiLqaZFFINoWXXqtBXmnvza747vB3+Hjvx/gx80e1PrPvTOg0Opw29DScPqxxcJmoq23btg0nn3wyIiIikJ6ejquuukoFK5YvX64abb3xxht8EYj8hMyIkv4VoSY9ftiTjaOF5aos1NzRyThtcn8smNAXNqdLNdNmjwoiIiIiot5t1apV6qu0iXjqqacQHh7eqcdvc7Di5ptvVmWftmzZguOOO66mZ4X0q5ATJCLq6qwKyaKo7gVaE6ywOt29Nrtidv/ZGB47HFd9chUOFR3C1JSpWDB4gcqsIPJVWuill16qykaGhYXVbF+0aBHLRRL5qX5xYZhvMmDVzqOqLNQXmzJx0pgURIWaGKQgIiIiIuqAQMqs8HrttdfQFdocrFi8eDESExPx+OOP4/3331fbRowYgffeew+/+c1vuuIciYhqSPaEZFFIgMLh8kQsNFot9LqqXptdIaWeVh1ahbTCNNjddvxn13+QEpaCU4ee6utTo15qw4YNePHFFxttl/JP2dnZPjknIvp1seFBWDihH1btOKp6VBgNWj5tRERERESdkM3cVDPqth7Dn5SXl+Phhx9WFZhyc3PhdrsbtZLolmCFOOuss9RCROSbrIoqGHXammCFMGi1qKy+v7dlV3y27zM8/8vzgAaINkdDAw2e+fkZdR8DFuQLJpMJpaWljbbv27cPcXFxPjknImqd0CADThnfF5V2F0JMBljtTpRZHQgLMiDI2K6PDkREREREFGCuvPJKrFmzBhdddBGSkpI6LZjCTxxE1POyKnQalU0B1EZtNVqN2t7bsiskUCGBCQlQhBpD1ba+EX1xtPQoAxbkM2eccQbuv//+mgxMedMivSpuu+02nH322XxliPycUa/DjoxCfLk5EzszC1XPCvk7M35ADE6d1B/jB8b6+hSJiIiIiHoMrcazdPQY/uSLL77AZ599hpkzZ3bqcVuV2y1NMfPz89XtqKgotd7cQkTU1VkVkkXRFNku98t+sn9vClRIgKKufhH9ajIsZD+i7iSlIi0WC+Lj41FZWYk5c+Zg8ODBqn/Fgw8+yBeDyM/996dDePSjLfh5fy7sThecrirYHC6s25eDh5Zvwkc/H/L1KRIRERER9RhaaDplaYt77723pvyUdxk+fHjN/VarFddeey1iYmIQGhqqJhbm5OS0+vjeGEFna1VmxRNPPFHTIFNu+1uNLCLqbVkVGlTVabDdG7Mr6gYqJDDhqmocnJHtGSUZzLCgbhcREYGVK1fixx9/xNatW1XgYuLEiTj55JP5ahD5uU0H8/DejwfgcLoRE2ZSmYwSsJCyUFVut/r6zvf7MSAujBkWRERERER+bNSoUfj6669r1vX62lDATTfdpDIjPvjgA/UZ/rrrrsNvf/tb9Tm+NR544AHcc889eP311xEcHNy9wYpLLrmk5vall17aad+ciMir3OpAuc3Z6AkJMekRZNB6sircVdBrtXC63KgbrVBZFNVBVIk0O93ugO5d0TBQ0RIGLMgX3njjDZx77rkqHbRuSqjdbse7776Liy++mC8MkZ+S0k9Wu6smUOEtC6XVaFBhc0LaVpRbnfj450MMVhARERERtYJM/Jf30x2hqX58w/6Q0jNSlqZIcCIxMbHR9pKSErzyyit4++23ceKJJ6ptr732GkaMGIH169dj+vTpraqokJaWhoSEBAwYMAAGg6He/Zs2bWrT9dWcc1sfIAN/WVlZqrRDXQUFBWpbbyi9QkSdb3tGIdbuzcauI8VqfWSfSOi0WkwbEo9xA6JgsTqg1UogwhukqA1W1G7zkP1kf7vLBXOABSvaEqjwYsCCuttll12GBQsWNHqvUFZWpu5jsILIP0kzbelRYTRoawIVXnqdFiFBBlTY5O+xG1vSC3AopxQDE8J9dr5ERERERD2BtwxTR48h+vatXwZ86dKlquRTU/bv34/k5GQEBQVhxowZeOihh9CvXz9s3LgRDoejXvUDKREl961bt65VwYozzzwTXaHNwYqqpmqvALDZbDAajZ1xTkTUC43pF40+MSF467v9av33x6WqmZySWWE2GvDIH6Yjp6SyZv/ySjue+WqHun39KaMRYq7/+ychwgxzAP5OenPbmyi1lWJM/Jg2PU4CFttzt6vHnzr01C47PyLve4Wm3ogdOXJEpZcSkX8qszrUBAAJTDRFp9WogIXD5VZZjodyyxisICIiIiLqRpmZmQgPr50w1FxWxbRp0/Cvf/0Lw4YNU4kH9913H2bNmoUdO3YgOztbjeNHRkbWe4xkSch9rSFBEp8GK55++mn1VQYf/vnPf6rGG16STfHdd9/Va9JBRNQWMvhh0GthlvoSAOIjzCpY4TU4KUItXsUWGwxf71a3pw5JQGRo07+cA81FYy9SmRXSi6K1mRVC9g83havHE3WVCRMm1MwYOemkk+rVw5T3CocOHVIZF0Tkn8KCDNBrNSoY0RxJX9dpAL1BhxlD62dPERERERFR0++hO1oGSlv9eAlU1A1WNGfhwoU1t8eOHauCF/3798f7778Ps9nsty9Tq4MV0ljbO1vyhRdeqFcHXiIxUptKthMRUdfxZkW0JWAh+1WhCtdPvZ5ZFdSlvGmgW7ZswSmnnFJvYoP3vcLZZ5/NV4HITwUZ9RjVNxo/789VzbQbloISst3hrMK0IXEwmzx1aV1uN3ZmFmFkn6hmszKIiIiIiHqrzgxWtJdkUQwdOhQHDhzAvHnzVE/J4uLietkVOTk5Tfa4qCsqKqpVJa0KCwu7NlghsyHF3LlzsXz5cnViRES+kJ5bin1Hi+Go7pHz8/4cVQYqJToYA+IDv3Z2WwIWDFRQd/KmgUpQQhpsS11MIupZFkzoi22HC1BcbkdkiLFewEICFbI9yKTDwom1f3t+2p+LtOxSHCssxwmjklXQg4iIiIiI/IfFYlENsS+66CJMmjRJNcT+5ptvaiYU7t27FxkZGaq3RUuefPLJLj3PNn+SWLVqVdecCRGR/PKsdOBIoQWZ+RakJjZd2/7f3x3AxkN5MFRneL1QXQ5q0sA43Pm7ib3ieWwYsEgJT2m0DwMV5CuXXHKJ+iozNXJzc+F21y8pI027iMg/TRwUh/OOH4x3fziAgjKbarYt2RLSo8LucKtAxXkzB2P8wNiaxwxKCFd/t/NKrfhicyZOHJOCiODA6xtFREREROTrBtut9Ze//AWnn366Kv107NgxNblQKiWdf/75qpfkFVdcgSVLliA6OlqVlbr++utVoOLXmmt7P+93lXZNe5IGmf/73/9UtEUGIupatmxZZ50bEfUyUmYuq7gc+aVW/LgnWw1+NPXL+MLZg3HC6ORG2yWzojepG7DILMmsdx8DFeRL+/fvx+WXX461a9c22Xhb+lcQBQIZwLc5XTDpdQFV/ujMqQMxIC4MX2zKwI7MQtV026DTYsKAWJVRUTdQIRIjg7FgQj98u/0oLFYHvtycgTmjktV2IiIiIqLezhdloI4cOaICEwUFBYiLi8Pxxx+P9evXq9velg9arVZlVthsNlXK+fnnn4evtTlYIekhZ5xxBgYNGoQ9e/Zg9OjRSE9PVwMQEyf2jhnN1E72EsBR0ni7IQIwNj2DnnqXjHwLiix2GPQ67D5SjMN5FgyID2u0n5R66g3lntoSsHjqp6dgsVsQagxVgQsZEGaPCvKVSy+9VDXX/vTTT5GUlNThGSRE/uZoYTk2HczH1vR81YxaBvLHDYjFpEGxSI4OQSCQgIQsVrsTZVaHar7dUnknyaRYOKEvVu08piYdfLPtKKYPTUBqIv9eExERERF1t3fffbfF+6Vs83PPPacWf9LmYMUdd9yh0kjuu+8+hIWF4cMPP0R8fDwuvPBCLFiwoM0nIE/IY489huzsbIwbNw7PPPMMpk6d2uS+DocDDz30EF5//XUcPXoUw4YNwyOPPNLo+8p9t912G7744gtUVFRg8ODBeO211zB58uQ2nx91otzvgCMfA/k/etZjZwJaHZB8GtDndD7VvZwEPNftzVFNOkOCDKi0O/Hj3mz0jwvlQGcrAhYOlwPXf3E9CisL0Te8L26YegObaZPPSIPtjRs3Yvjw4XwVKOBIP4fl6w+hpMKG0CAjjHotbA43vt1+BBvT8nD29IEY0z8GgUICFK3tQSH7zRvbB2v35uBwXhl+SctDn5gQmAyeso1ERNQ+I/52JbQm9gOinuPIuhxfnwJR2zi7NvtfcrA7moetRe/Q5r92u3fvxjvvvON5sF6PyspKhIaG4v7778dvfvMbLF68uNXHeu+991RtrBdeeAHTpk1TDTok5UQaekgApKG77roLb731Fl5++WU1APLVV1/hrLPOUmUmJkyYoPYpKirCzJkzVSNwCVZIaouUo/BFQ/ByqwPlNmej7SEmvRqM7XXiZwPhw4FNNwGWQ0Dfs4HIUZ7MCur1JItCsilkQENmYUeHmrAzo7DZ7Aqqb+GQhRi9cTT2Fe7Dnyb/iYEK8qmRI0ciPz+frwIFZEaFBCokoN4/LqxeMD0mzISsogp8uP4QYsKCAibDoq2kHNasEYkIMxuQFBXMQAURERER9Xq+6FnRa4IVISEhNX0qpLSDdBEfNWqUWm/rwIT0t7jqqqtw2WWXqXUJWnz22Wd49dVXcfvttzfa/80338Sdd96JRYsWqXUJjHz99dd4/PHHVRBDSKZF3759VSaF18CBA1s8D6nLJYtXaWmp+ioNQRs2BW0LKQ2wfl8udh0pUusj+0RBq9Vg6uA4lRbfGeT8ZEZ6R86z2+jDgCADNJXZgDUHVfnrgcT58q9NLqTnX187Bfo1tub65P4f9mSpwR+9ViMbVFCvqNymtveNCfbrX8q+fg1LrCXIK89DlDkK01KmYWTsSKQXpSPCFIGIoIgef33dIVCv0VfXI3+Lb731Vvztb3/DmDFjYDDUD9BL8y6inkhKP0lGRcNAhZB1GZyXjALZr7cGK7zPxYQGfS3ySisRZjYiiFkWRERERETUWcEK6Qj+ww8/YMSIESpocPPNN2P79u1Yvnz5r3YLr0sCHlIiQspKeUlTj5NPPhnr1q1r8jESUJB6WnWZzWZ1Pl7S+FuyM8455xysWbMGKSkp+NOf/qSCIs2R0lJS1qqhvLw8WK1WtFdisBsnDotAiaVcrcttqWlsNrqRm5uLzhqIKikpUYNs8vz5O33ZdkRVHEMVDHAdW42ykBPhDB0WMNfXHoF+ja25viOFldicloNggwbF5Z7BVavNihAD1PbhsQb0iTbDX/n6NVyRvgIrM1ZCB0+ZjQdWPaC+zus3D/MHzO/x19cdAvUay8rKfPJ95W+5OOmkk+ptZ4Nt6unNtGUiipR+ai6ALtvlftlvwYS+AdV0uyOKy22qh4XZqMPcMSkINxt9fUpERERERAHdYLsrSIWktiQpdEuwQr6RxWJRt2WAX25LOachQ4a06SQkC8PlciEhoX6GgaxL4+6mSBBCvsfs2bORmpqqmn1LkESO43Xw4EH84x//UE/e//3f/2HDhg244YYbYDQacckllzR5XAmY1H2yJbNCsjOkhFRHZ3/anS5E7fdkagwfmAKjXtfpA2zywVjO1e8H2KqqgJwfoNG4AWMkdFoHoivWAAOP92RX9PTra6dAv8Zfuz4ZvPx6Xxrc0CImPAS5ZZ7SaeYgM4LNnqbbe/IdmDCsn99mV/j6NTw9/HTMHja70fbOzKwI5J/RQL7GhgH+7rJq1SqffF+irmRzulQzbelR0RK53+5yq/0ZrKj/vJRWOvDl5kycMCoZ8RH+OwmBiIiIiKgzqZ4VHRzS0sL3Nm/eXG9906ZNcDqdqq+02LdvH3Q6HSZNmtTu79HmYMWgQYPqlYSS0k3d5amnnlIZEtKvQgaVJGAhJaSkbFTdASdppC2lJ4T0stixY4c6z+aCFSaTSS0NyYBVRwettNqqmgHWzjheU+T4XXXsTlW8EyhYB+iCPcEJcxI0+WuBsj2e3hU9/fo6INCvsaXrS88tw67MIlXfWyO/ub0BCY3n34xsl/szCyr8uneFL1/DqOAotXSlQP8ZDdRr9NW1zJkzxyffl6grmfQ6lSErzbRbYne6EWTQqv3JIzLEhIUT+2HVjmMoKLPi621HcNywRL/+u05ERERERM1PTJSEgrCwMLz++us1vaKll7SM1c+aNQvdFqxojmQ43Hvvvdi2bVur9o+NjVWRlpycnHrbZT0xMbHJx8iM148++kiVZiooKEBycrLqbVE3gCJ9NKSxZ11SsurDDz9s13VRJ2ZVZC4HHBZAW536b4hUvSvU9oiRzWZXUOCSrIof92aj0u5SMyzd8nPSQGiQAQVlNrVf/7hQNaC8I3cH9hfsb7TvkJghGB0/upvOnogaau17gLFjx/LJox5HsiTGDYjFt9uPqGbaTWX7yd81i9WOaUP6MKuiAbNRj3nj+uDH3dnILLDg+91ZKLM6MLpvlN9mThIRERERdQZN9X8dPYY/kR7SK1asqAlUCLn917/+FfPnz1etI7o8WPHiiy9i5cqVqqTSjTfeiGnTpuHbb79V31zSPC6++OJWH0uOISkhUsrpzDPPrMmKkPXrrrvuV8taSC8Kh8OhghC///3va+6bOXMm9u7dW29/Obf+/fu35VKps5XsAvJ+VNkUcHgajnuzK9R2ub+F7JdAlaAAALH/SURBVAoKTIfzLNiZUVg76NNEsEK2y/2yn+wvszAfWPMAvkz7EqU2T4m1cFM4tBot5g+aj/fOec8HV0JEYvz48erfrAzYNkfur1u+kagnmTgoFhvT8pBVVKGaadcdZJefe9keEWJS+1Fjkpkye1SSakC++0gRthzKh9mgw+CkjpcsJCIiIiLy60oOHZygo/GzCT7SRkH6PTck2zrSP7PVwYqHH34Y99xzj5oNKT0lPv74Y9x555145plnVODimmuuqRdJaQ3pEyGlmaRs09SpU/Hkk0+ivLxcpYsICX5IUEIaYIuffvoJR48eVYMh8lUyOSTAceutt9Yc86abbsJxxx2nykBJEOPnn3/GSy+9pBbycVaF0wKYU+rfZ4wCKrOYXdGLsypkVqU02iytqITVZUOFw9OQvqBcq36RG7VGGHQGtZ83u+LuOXdj4eCFuPaLa9W+T53ylOrNIJkVROQ7hw4d4tNPAS0lOgRnTx+ID9cfwuG8MtVMW/WocLpVRoUEKs6eNhDJ0SG+PlW/JX/bJ6fGqczJzHwLBiZ0rD8cERERERF1v7POOkuN4UuGhYzre8fub7nlFvz2t7/t+mDFa6+9hpdfflkFF77//ntVj3rt2rU4cOCA6l3RHueee66KtkgQJDs7WwUhvvzyy5qm2xkZGfXqbUv5p7vuuks10Q4NDcWiRYvw5ptvIjIysmafKVOm4L///a9qmn3//fdj4MCBKghy4YUXtuscqZOzKhpGAZld0WtJyafCMivCggworbQjryIPOZYcZFh2I69qI7LsJyJcn4L4kHjEBcep/WR/eZyUekoITsBNK25Sxzp1yKmIC43z9SUR9XrMYqTeYEz/GNVPSbIDtqbnq2ba0qNCSj9JRgUDFa0zPCUSw5IjamaIyd93q92FYFOnVaklIiIiIvIL0qK1ww22NfAr0h/6L3/5Cy644AJV/Ujo9XpcccUVeOyxx9p93FZ/GpDAwYknnqhuS5MMg8GA++67r92BCi8p+dRc2afVq1fXW5cAya5du371mKeddppayM+yKkL6AVVNNKVkdkWnKrc6UG5zNtoeYtIjJMgAf6HTanHp3OGotHvOtcxWiqzSXJz7nydQ5DiKwYNHYfGk8xBhCkeYKbym3rU8joiIyJckICHLggl9YXO6VDNt6WlB7U9ll/Ja6XllmDsqBbHhQXwqiYiIiChgBGLPiuDgYDz//PMqMJGWlqa2paamdjhW0Opghc1mU70i6vaciI6O7tA3p16YVdFUGXNmV3Sq7RmFWLs3G7uOFKv1kX0i1QD/tCHxmD7Uk7XkL8LMBrWIeJhRYMtEuTsfBp0Rh0r3wmSyIzXev86ZiIjISwIUDFJ0nMPlRk5JpcqsWLE1E8cPT0S/uDD+oBERERER+TkJTkjbiM7Spjzru+++W0VNhN1uV929IyLqN8RbtmxZp50cBUhWhb0QMEUDDgtQ5QLcntQglW2h0Xluaw2e/WT/iJGNy0VRq43pF40+MSF467v9av33x6XCqNepzAp/72Hxv33/g6vKCbMuDOV2C5bvWY6RcSObbSJkdVphc9rw2pbXcOvxtb1riIiIqGc13p4/rg9+2J2No4Xl+G5XFiamOjEiJdLvGgkSEREREbWnb1tHG2xr/eB9sfSi+Ne//oXw8PBf7UuxfPnydn2PVo9ezp49G3v37q1ZlybW0juiLn6YoHokMCHNs43RgK2weqMbcNs8NyU4gTolE2Q/2V8ep/HvgXV/JqWeDHqtKpkk4iPMKljh73bl7cL6o+tg1Aap3yWJoUn4MeNHtX1U/KhG+7tcLhWoqEIVntvwHG6ecTN0Ov+/TiIiImpM3qucMDoZGw7kYt+xElUWqqzSjimD4/3igxl1M5nc5KoEdGbPpCYiIiKiHixQelZERNT2nGuYwNBZWj0i3LB/BNGv0uqB8Q8CjrLabS4bsP1ez+0x9wI6U/3HGMI8j6N2yy2pQFZRBYosVrW++0gRDHodYsNMiI/wZEb5Y1bFu9v/g+LKMmihV+smbSiOVmSr7fef2Di74tUtr6pAhThmOYbnf3ke10+73kdXQEQNOZ1O9d5BaldKw62wsDAcO3ZMzcAIDQ3lE0ZEjUhQYurgeIQGGbD5YL4KWkhpqDmjkvls9RZlB4CslUDOKsBtBbRBQMJcIGk+EJbq67MjIiIi6tVee+21Jm93Jo4KU9eS5tmyCHsJ4MptvI8hAjB2TTSuN/piUyZW7zqmAhbicL5F9ayYNTwRl8wdBn8k2RMrD6yBwxoKbXUD7cN5FtidoWr7eWPqZ1dIVsWLm16sWXdXufHE+ifwp8l/YnYFkR84fPgwFixYgIyMDNXzat68eSpY8cgjj6j1F154wdenSER+SiYnjOobjbAgA37cm4OB8exd0WvkrAH2PAHY8jyfH3RBgKsCSH8LyPoKGLEEiJ/t67MkIiIiajOZf9vRikQaP8is6A4MVlD3yf0OOPYpYKieUbvn756vyacBfU7nK9FJFk7sq2o8//W/m9T64nkjERpsVJkV/kiyKKQ3hUZnw6jkwajMOqK2D0oIhxaROFC8t1HvCsmikGwKL4PWgCOlR5hdQeQnbrzxRkyePBlbt25FTExMzfazzjoLV111lU/PjYh6BmmwLaUsg6rLWgq39EOjwM2okECF9LSLGFX/07g5GbCkAbuXAeYUZlgQERFRj6OFRi0dPYavTZgwodVBl02bPOOSbcVgBXUfmQkVNa7pzArqvKc5IhhGnQ766gyFocmRiAz1z0CFN6tCelP0CU9GsMkAnc7zS096bug0OrW9bu8KyaqQLArJpvAyao0od5Yzu4LIT3z//fdYu3YtjEZjve0DBgzA0aNHfXZeRNSz1A1UlFsd+GZnHuboQ1UggwKMlH6SjIqGgQoh66GpQMlOIHsFELbYV2dJRERE1KudeeaZXf49GKyg7iOlnljuiZrIqrDYLegX3g9uacDeQFRQFLIsWTXZFZJVIVkUkk1hkx4o8hlWq4FRZ2R2BZGfcLvdKrDY0JEjR1Q5KCKittp5pAgWmxNrdh7DlCEJGJ4SyScxkJppS48KKf3U3Ew92S73y36pV7LpNhEREfUogdJge+nSpV3+PTxTr4mIfJhVkRSa1GwamWyX+2W/7dnbVfaEq8qlsinqknXZru5vYpCUiLrP/Pnz8eSTT9b7d2yxWNQbm0WLFvGlIKI2mzQoDv1jgiGFoDYcyFULy0IFCFelp5m29KhoiephYfPsT0RERNSDyGfizlh6gzZnVmzbtq3J7fKEBQUFoV+/fjCZ/LfkDJG/kHIG5TanmoFcYLEDpkrVXDrEpEdIkAG9JauisLIQ0UHRKrtCgg0Ol0PdL+tSBkpIFoXsd8e3d6jsCcmikGyKuuS5Y3YFkX94/PHHccopp2DkyJGwWq244IILsH//fsTGxuKdd97x9ekRUQ+k02owoX8E8mwGbD1ciD1Hi1FudWLmiEQYdJx/1aPpzIC2upl2S1xWQB/s2Z+IiIiIfEqVaX/iCbz//vvIyMiA3W6vd39hYWH3BCvGjx/fYiTHYDDg3HPPxYsvvqiCF0TUtO0ZhVi7Nxu7MovgdLkwtn8RdDotpg2Jx/ShCQH/tElgIqssC9HmaBRaPb/ApA+Fzekp7STBCa2mdvAh0hiJ7w5/px5n1jb9IVWyKywuC3tXEPlYnz59VHPt9957T32VrIorrrgCF154IcxmDjIRUfvIZ5DR/aIRHmxS76EyCyxYufUIThydXK+/BfUwWgOQMBdIf8vTTLupz5rSXN1eBKScyhJQRERE1OMESoPtuu677z7885//xM0334y77roLd955J9LT0/HRRx/hnnvuQXu1eRrSf//7XwwZMgQvvfQStmzZoha5PWzYMLz99tt45ZVX8O2336qTJKLmjekXjd8fl4rUxHD0jzHjnOMG4vzjB6vtbVVaaUduSWXNkl9qVaURZJHbde+TRfb3Nb1WjwdPfBAvnf6SWv4+/++4c9admJQ0SS1ye+mcpWq73D+z/0xYXVaVPSFZFE1pmF1BRL6j1+tVcOLRRx/F888/jyuvvLLdgYrnnntONeeWSRDTpk3Dzz//3OL+H3zwAYYPH672HzNmDD7//PNGmV3y5ikpKUmd08knn6wyPxr67LPP1PeTfaKiorqlmRgR/boB8WE4eWwfmAw69V5HJntQD5c0DzDFAZaDnsBEXbJuSQNM8UDifF+dIREREVG7yVwMbQcXjX/FKvDvf/8bL7/8sgpWyOf/888/XwUv5LP2+vXr233cNk9BevDBB/HUU0+p8g5eMhAgsyjvvvtuNYAQEhKiTvTvf/97u0+MKNBJqSeDXguzzAR06RAXbkaQse3lnyTw8Pj/tqJISklVc7ndsNo9fRue+GwbdA0G96NCjbj5jHEIN9fv+9DdosxRahGf7P0En+77FAmhnqySt7e/rb6eNvQ0LBq8CK9vfR0ut6dXhd1lV4ONXlI6ypvxpYUWNreN2RVEPvTQQw8hISEBl19+eb3tr776KvLy8nDbbbe1+liSnbFkyRK88MILKnAgvTDkPcjevXsRHx/faP+1a9eqN0lyDqeddpqaSCFBhk2bNmH06NFqHwmgPP3003j99dcxcOBA9f5Fjrlr166arNAPP/wQV111Ff72t7/hxBNPhNPpxI4dOzr83BBR54iPMGPBhL6qPBTLQAWAsMHAiCXA7mVAyU5PM23Vo8LqyaiQQMWIm4CwVF+fKREREREByM7OVjEBERoaipKSEnVbPofLZ+xuC1Zs374d/fv3b7Rdtsl93lJRWVlZvf6Fc7rcqp7u4bwyuFxV+GJTBoYmR2JIUgT0nAFGnSQ9twybDubD5a6qSQirkv+qx/IP5ZZCUydVTDbLB3t53Nj+MX7zOszuPxvjEsc12h5hikClqxLF1mKVjeFwO2qu0Uu21b1G2U/2l8eF6kK76QqIyEtKQUqQoKFRo0bhvPPOa1OwYtmyZSpocNlll6l1CVpIxoMEPm6//fZG+8uEigULFuCWW25R6w888ABWrlyJZ599Vj1WAp0S8JAM0N/85jdqnzfeeEMFVyRdVc5PAhM33ngjHnvsMVW+ykt6cLTEZrOpxau0tFR9ld5EsvgrOTd5Xvz5HDsq0K8x0K+vuWsMNXk+yni37cwsgkmvxeCkCPQ0vfU1rCf2eGB8MpCzEshZ7WmmrQsBBpwKJMwDwgbJQeCvAvU1DLTrISIi8oXOaJCt8bPUCklckPF/6V+dmpqKFStWYOLEidiwYUOH+lm3OVghZRUefvhhVfrJaPTMynY4HGqb3CeOHj2qPvT3ZmnZpfjkl3QcKSjH0cJy9QO1ascx/LAnGynRIThjygAMSgj39WlSACirdKhAhU7SwrxBsKoqlV0h1GzDOr/Q3C632l8e508igiLU0pxvL/4Wh4oP1ayXWEtw7RfXqtvPLXyu0WMHRg5EqJGBCiJfzbCQEksNxcXFtWkygzTo2rhxI+6444565d6kbNO6deuafIxsl0yMuiRrQgIR4tChQ+r85BheERERKmtDHivBCsnCkPcy8r0mTJig9peJGBK88GZnNEWyOaRuZ0OSTSKNxv15IEpmwcggW3Nl9nq6QL/GQL++1lxjgcWOH/bmq9uZ2aEYmRzmdx/oWsLX0CsUCD8LCD0dcNsBrRHQ6oFKAJW58GeB+hqWlZX5+hSIiIh6PK1Go5aOHsOfnHXWWfjmm2/UZ+nrr78ef/jDH1R7CGm2fdNNN3VfsELqRp9xxhkqejJ27Fi1TTIqpAP4p59+qtYPHjyIP/3pT+jNgYp/f78PJeV2xIYHobjCU56nX1wonK4qZOZb8NZ3+3DhrKGqXwFRZ9BoNSpjQlRVyVdPsEI+LNX9fabuczWoBdwDjE8arxaxI3cHfjn6S6N9hsQMwej45gcSiah79O3bFz/++KMqsVSXbEtOTm71cfLz89X7i4YTIGR9z549TT5GAgtN7S/bvfd7tzW3j7yPEffee6/K7JB+GY8//jhOOOEE7Nu3D9HRTfcWkqBK3UCJZFbIcyFBmvDwcL8eYJNBXTnPQBpg603XGOjX15prjIurgkNnxrbDhThaVgV9oRszhib0mGxmvoY9X6C+ht7yiERERERCqhZIYEISF7zOPfdclWEhEwCl1/Xpp5+ObgtWHHfccWpWojTRkA/s4pxzzsEFF1yAsLAwtX7RRRehN5d+kowKCVT0jQ1VTf/qkkaAsl0CFp/8chjXLRzVYz5EEfmLB9Y8gBUHV6hm2uKmFZ6I7fxB8/HeOe/5+OyISMo2/fnPf1aZl9LvQciMi1tvvVX1tOopJS/uvPNOnH322er2a6+9piZqSPPua665psnHSaprU+muMmjl7wNXMsDWE86zIwL9GgP9+lpzjeMHxiHMbML6fTnIyC9Hpf0YThiVjCDpDyaklKSrEtCZAW3b+4R1Nb6GPV8gvoaBdC1ERES+In9NO/oXVQv/IJ+T5bO9ZFZI2WTvZ/4ZM2aopaPaHKwQEpT44x//2OFvHoj2Z5Wosk8JkcGe1PMGwQoh2+X+o4UWHMguwfAUT4NhImqdu+fcjfNGn9dkZgUR+Z70iygoKFBZllLKyTszU3pV1C3p9GtiY2Oh0+mQk5NTb7usJyYmNvkY2d7S/t6vsq1uqSpZl1JPwru9bo8KCUIMGjRIpbQSkf+SrOWQID3W7DyGvFIrvticiRMH2hBRvArIWQW4rYA2CEiYCyTNZ8NmIiIiIupygdSzIjs7W03ikwl98+bNUxkVl19+OS699FJVWcAnwYr9+/dj1apVyM3NbdRw65577kFvlpZTqko9SQZFS+R+2e9AdimDFURtJKWeWO6JyH/Jm6hHHnkEd999N3bv3g2z2axSQdvaZEt6Y02aNEllZZx55plqm7zvkPXrrruuycfITA65XzI7vKTBtneGh5SmkoCF7OMNTki5pp9++gmLFy9W6/I95Vz37t2L448/Xm2TLJH09HT079+/nc8KEXWXxMhgLJjQD99uPwpL/n7k5r2HCOMewBgF6IIAVwWQ/haQ9RUwYgkQP5svDgUOP88gIiIiop7NbDbj4osvVouUUP7Xv/6lelVI/0bpDSnZFvL53WAwdE+w4uWXX1Yf5mW2o3zYrxvVkdu9PVhRaXdCW9034NfIfrI/ERFRIAoNDcWUKVM6dAzpAXHJJZdg8uTJmDp1Kp588kmUl5fjsssuU/fLG6SUlBTV3FrceOONmDNnjuoxceqpp+Ldd9/FL7/8gpdeeqnmvYoEMv7617+qAIoELySoIr00vAER6S8hGaRLly5VM0MkQCHNtb2lL4nI/0UEG7FwiAMZee9jiPkwEDpKfgHU7mBOBixpwO5lgDmFGRbU85UdALJWMoOIiIjIDwVig20h1Qfuv/9+Faj4+uuvVeBCMixCQkJUkkO3BCvkw/2DDz6oSjlQY2ajHm5365oXy36yP/Xe/iZ7jhYjI9+CSqtNlSmQkmBDkiLYx4SIejQJJkizLcleaCoL09vAujWkUVdeXp6aDCHpppIN8eWXX9Y0yJayTHXraUtvrbfffht33XUX/u///k8FJD766COMHj26Zh+prynnePXVV6O4uFhlT8gx6zYRleCEXq9XfbgqKysxbdo0fPvtt4iKYulGop4iKP8bDDXurglUWF06HCoLx/CIIs+Eq9BUoGQnkL0CCPNkVhH1SDlrgD1PALY8ZhARERH5IZnX3sq57c3q6OO7kry3ls/P8rWqqkpVJmivNo+UFxUVcVZhC1ITwrFGdww2h6vFUlByv16nweDE8La+BBQA0rJLVSP2IwXlqsdJlduN1TuzsHZvDlKiQ3DGlAEYlMCfDSLqma688kqsWbNGDfRL/4eO1taUkk/NlX1avXp1o22S/dBSBoScj8z+kKU5krL697//XS1E1ENL4UiPCin9pNFA5hKtyUpGrjUYBbYgzIjLhk7inHK/7Jd6JUvmUM/NqJBAhdMCRDCDiIiIiLpPZmam6l0hGRUykXD27NmqKtPZZ5/dfcEK+fC/YsUKNthuhsyKl8HmzHwL+saGNrmPRJhyiivQNzYMgxMj2voSUAAEKv79/T6UlNsRGx6E4nIbnC4X+seGwumuUj87b323DxfOGqqaRBIR9TRffPEFPvvsM8ycOdPXp0JEvZXU7Jdm2tKjonom2qCwUuRZzSq7otypxwmJR2FSPSxsnv1Z3596Iin9JBkVDQMVghlEREREfkFT/V9Hj+EP7HY7li9fjldffVVVH5AJilK6WZpsS1mojmpzsGLw4MGqtvP69esxZsyYRs0ybrjhBvRmep1WzYqXwWYZdJbB6IYZFRKoiAgx4owp/VnupxeWfpKMCglUSPNHh8utAhQutxuVDid0Wq3anl1cgU9+OYzrFo5q9c9IVRXglv9V3/aSbXV/ndW9j4ioK0ippOjoaD65ROQ7qrlwdTPtakMiShBicOC77GTkVgbjyyP9MTcyF+Fmk2d/oh6eQdQk2c4MIiIiIp8KpDJQiYmJqKiowGmnnYZPPvkEp5xySr3SzN0erJAGldIwU8o7yNKwrEJvD1YIKd8js+JlsPlIgQVllXb13BzOs8Co16qMCglUsMxP77M/q0SVfUqIDEZRuQ3ZxZXIK6msybiQf9zx4UHq/qOFFhzILlF9LFpiNGhVMKJurxTJ3vFyudz1SrDIfprqxxERdYUHHnhA9Zh4/fXXERwczCeZiLqfZEkkzAXS3/I0065+L5QcXIEFKRn4NqsPSu0GfHl0AE4YPwzxzKqgAMggahYziIiIiKiTSH9IKfkcFxeHrtDmYMWhQ4e65EQCjZTvkVnxu48U4bVVe+FyV+HE0ckYlhKpSj+1drY8BZa0nFI4XVWqn0l0aBCCTXoUWawq+2FgfDgMei30Wq36KvsdyC791WBFclQIokJNqLQ5a7a5UQWHy6Vuy7E84YxqOsBs0qvHERF1hccffxxpaWmqCfaAAQMaZWFu2rSJTzwRdb2keUDWV4DlIBA6qCZgEWmyY2FKOlalm1CgicF6y0ScVlUFbQf76xD5QwZRk1xWQB/MDCIiIiIfkfeZHX2vqfWT96pLlizp0uO3OVhBbXhydVoVnOgfF6bWF07sB6O++abbFPgq7U5oq/O2JIgg/wQl60FumY06GOr8fMh+sv+vkR4pj1w0HSUV9pptlgo7/vpfz2DgHWdOQGiwsd5jIoKN6nFERF3hzDPP5BNLRL4XNhgYsQTYvQwo2ekphaNmmFththdhXnIiNgSfjlFjxvrNhz+izsggqkcyru1FQMqp7MtCRETkI4HUs6Kr6VsbMZGSDiEhIb8aPVm2bFlnnRtRwDEb9fXKNbVE9pP9W0MCD3WDD8UWm8rQEEOTIxEZamrnGRMRtd3SpUv5tBGRf4ifDZhTgOwVntr+0kxbZpinnApD4nwcF5Zab3fpLRcbbobOX4oCE7Uzg6gmUGFJA0zxQOJ8PpdERETk91o1Erp582Y4HI6a282pWxefiBpLTQjHGt0x1WhdZvBZHU7VX0LKQFXaXarZtgQZZF2v02BwYjifRiLqkYqLi/Gf//xHlYO65ZZbVMNtKf8kpaFSUlJ8fXpE1JtIQCJsMZB6pafGvyqdU788nThWWI5vdxxDQoQZc0Yl+XdGtDRWbuFaqBdpIYNIZVRIoGLETZ5/B0REROQTMmTe0bkwml4y7N6qYMWqVauavE1EbTMkKUJlQGTme5qtZ5dUqiCFOJhTCp1Oi7gwE+xOt2rELv1NiHoMewngKGm83RABGPmz3Jts27YNJ598MiIiIpCeno6rrrpKBSuWL1+OjIwMvPHGG74+RSLqjWRQv4WBfZmELhkV2cUV+HJzJk4ck4LQID8LBJQdALJWerJEpLGy9CuQMkBJ8zkY3ZvFzQJMcUDu6kYZRCqjgoEKIiIinwqknhVdjT0riLq5j8kZUwbgre/2odBiQ0pUCHKKK+XjMQZKbxMt1PboUBPOmNKfjdipZ8n9DjjyMZD/o2c9diag1QHJpwF9Tvf12VE3kpKRl156KR599FGEhXn6NolFixbhggsu4GtBRH4pJSYE88f1waodx1QvsC82Z2DuqBTEhgfBL+SsAfY8Adjy6syer/D0K5AyQDK7XspeUeCqOALYCj2TQ2SxF9fe1ocDw6771QwiIiIios7QXKsIqbwUFBSEwYMH4ze/+Y2auNjpwYrf/va3rT6gzJokouYNSgjHhbOG4pNfDuNwXpkqAyXyyqwIMuowIC5cBSpkP6IeRQZIwocD2+/1rI+8DdCZPJkV1Kts2LABL774YqPtUv4pOzvbJ+dERNQaMWFBWDixrwpYFFlsWLE1E8cPT0Q/mVTi64wKCVQ4LUDEqPp1AKSxsvQlkDJA0p+Ds+h7HsmEUMGH6sCDo9hzW/Q7u3a/o58BlVlNH6OqxJMe9CsZRERERNT9VHttTWA12N68ebMq9exyuTBs2DC1bd++fdDpdBg+fDief/553Hzzzfjhhx8wcuTIzg1WSBkHIuo8qYnhuG7hKGw+mI/7/7MRVe4q9UF43MAYVfpJMjCIehwp9SSzPA2hnvWQfp5gBfU6JpMJpaWljbbLG5e4uDifnBMRUWuFmAwqw+KH3dk4WliO73Zl4ZQJesSFm333JErpJ8moaBioELIemurpVyCNxKU/B/kPCSBIkMkbjHDbgOiJtfenvQaUH276sRJ0kMd7X/PgPtXvtapLbBoiq7/KEt57ilkTERH1MDLK19GRPi38izdr4rXXXkN4uGfCdUlJCa688kocf/zxqhy0VFa46aab8NVXX3VusEK+KRF1LglISA8Lg06LKm0V5o1NQbQvPwQTEXWSM844A/fffz/ef/99tS4zSKRXxW233Yazz64zQ5SIyE9Jc+0TRidjw4FcuFxViA0L8m0zbelDIKWfmhuMlu1yv+wnZYA4s74bXx+nJxghZZi8clZ7AhDeAEWVp0edIq9N1ITa11LKNQm9uTroUL14gxCQLOzqfaUHBREREZEfeOyxx7By5cqaQIU34eHee+/F/PnzceONN+Kee+5Rt9uCPSuIiIioUz3++OP43e9+h/j4eFRWVmLOnDmq/NOMGTPw4IMP8tkmoh5BmhhOHRzvGSquHlh2uNyqhKcEM7qN9B+QZtoyo74lqoeFzbM/gxWdz5IOWLPrlGryLhbP8z3i9tp9K7MBy6Hadfn50YcBxkhPAKLKCWiqSzWlnAb0PYvZqERERAEsEBtsl5SUIDc3t1GJp7y8vJpKC5GRkbDb7Z0frJg4cSK++eYbREVFYcKECS3W2JJaVURERNR7yWwKmWEhtSm3bdsGi8Wi3kucfPLJvj41IqI2kc893k8+7qoq/LA7C+U2J+aOTlblorqFapRc3Uy7JS4roA+unalPv87tApxl9ZtVe4MRznJg8NW1GRD564HSPU0fR/aR598rZgoQMbI2O0ICFdpmAlze8plEREQU2O8pO9qzQqPxuzJQl19+uZqsOGXKlJr+lX/5y19w5plnqvWff/4ZQ4cO7fxghXxzqT8tvN+MiPxTudWB/FKr+kAt5Lbd5UaISY+QIDbbI6LuI3UqZSEiCgTlVifyy6yw2l34YlOmClhIQ+4uJ7P2E+YC6W95mmk39UFV3vfZizxlgphVUUsyTbxBCLWUAgkn1j6HGR80H4BQj6/0BIBESH9Ao63Ojgiv0y8i0pPVot57l3n2ZZNzIiIiCnAvvvii6kdx3nnnwel0qm16vR6XXHIJnnjiCbUujbb/+c9/dn6wYunSpU3e7izPPfecqnMlJSLGjRuHZ555BlOnTm1yX4fDgYceegivv/46jh49qrqNP/LII1iwYEHNPlIb67777qv3ONlvz54W3ogSdTOny419WSWecgLuKqzcdhRjB8SoPhYdabD9/e4s/Lgn2/N5CcDra/ZCp9Vi+tB4LJjQr/MugIiojqeffrrVz8cNN9zA546IepwwswELJ/TDt9uPoqTCjhVbj2DWiET0iemGmfFJ84CsrwDLQSB0UP2Ahbzps6QBpnggsW01gXt+4+oyTyaENJ72Pie53wMlOzzb62Y7eMXOqA1ASLBBo6sNOkgQwluqSbZpjbWPi5sBYEbL50NERETUSxpsh4aG4uWXX1aBiYMHD6ptgwYNUtu9xo8f3+bjtrtnxcaNG7F79251e9SoUao8VHu89957WLJkCV544QVMmzYNTz75JE455RTs3btX1bpu6K677sJbb72lngyJzkg38bPOOgtr166tdw5yTl9//XXNukR2iPxFWnYpPvklHYfzylTQQvywJxu/HMxDSnQIzpgyAIMS6jTpa4PMfAsyC8oRFeLJhpLbIiW6+kMZEVEX8M6cqFunsqKiQtWoFMXFxQgODlZ/2xmsIKKeKjTIgAUT+mLNzixkF1dg9Y5jmDw4HsNTPL/rukzYYGDEEmD3MqBkp6eZtupRYfVkVEigYsRNgTujX4I00rC6Xqmm0trG1SNvrQ1ASLPrypzax9Y0rq7OhFBdSKolngwkLWi+cTkRERFRgJSBevjhh3HHHXeoxtcy/i5OOOEErFmzpt5+11xzjRqn/zUyPv/b3/5WBSfGjh2LztLmEXxpnCHpHatXr643ADF37ly8++67iIuLa9Pxli1bhquuugqXXXaZWpcn47PPPsOrr76K22+v06Ss2ptvvok777wTixYtUuuLFy9WQQmpjyVPUs2F6fVITExs1TnYbDa1eHmbgLjdbrV0hDxemvDVHq9z3wh7j9/R8/RXgXh9B3NK8e/vD6gZedGhppoGOX2iQyBFkTPyLXhzzT5cOGtwuwIWp0/uj+OGJTTaLmUKfPE8BuJr2NuusU3X53ZDU/07r0r21/SM5yRQX8PuvJ5Dh2obib799tt4/vnn8corr6jMRiGTEOTvvbzxISLqyaS59oljUvDz/lwcyC7BhgO5sDtdGNs/pmu/cfxswJwCZK8AclZ5ShzJAL2UfpKMip4WqHBWegIt3hJNKgBRJxgx9NraAETpXiD/p8bHkLJMhrD65ZqiJgChg2vLNek8E3iaxJJZRERE1Ats2LBBlW1qKqggn9Pvv//+mnWZZNgaUgLqj3/8I8444wz84Q9/UMkHOl0zPbq6Mlhx/fXXo6ysDDt37sSIESPUtl27dql6VDJT8p133mn1saQbuGRoSFTHS6vVqgac69ata/IxElQICqpfG9ZsNqsmnnXt378fycnJat8ZM2ao0lH9+jVdAkfua1g2yjsr1GptInW4DaTET2VlZc3xDB0o79PcQJR0X5dBNnnuAk2gXZ/TXYUPfshAfrEVSZEmOJwONbtLhnYrbZUw6XWIDdYiq7gcH/ywFxfN7Ae9tu0Brpg6Ges1bA7k5lbX0e1GgfYa9sZrbNP1uW0Ir/6dV5qXC2hbGCDwI4H6Gsrfa1+4++678Z///KcmUCHktmRf/O53v8OFF17ok/MiIuosOq1GldgMNRuw/XABkqNCuufJlYBE2GIg9UrPAL1qvm3w08bVpbXNqmWxFQLacbX75K5uOgDhJY+p6RcxwJNFoTIkImpLNUmgQgIWdZkTPQsRERGRn5CJyt7Jyh05RntYLBb1GVyqFP31r39tdL8EJ1o74b+urKwsfPnllyoW8Pvf/14d55xzzlHf67jjjkO3BSvkJCSTwRuoECNHjlR9J+bPb1uN1Pz8fLhcLiQk1J8FLuvN9ZeQKI1kY8yePRupqan45ptvsHz5cnUcLykn9a9//UsNjMgTJ4GIWbNmYceOHQgLC2t0TAmWSCmqupkVffv2VVki4eHtK8XjJbOszOZ8dVuOJzOxOnuATdKA5NiBNMAWqNe3+2gRCivd6BMXDpNBB7tDfm41klABs8kMk9Hz89HHYFINHEtdJgxP7OKyAl0s0F7D3niNbbo+lw2aTLO6GRQX3/JsRj8SqK9hw+B+d5G/vd4GW3XJ3+qcnDqlOYiIuoCU2LQ5XWoSSEf6gP0a+bsxpl80UhPCEWyq/VjllsB3V5cVkgCFL4MUqvxUdRBCAgm66pkyeeuA/HWeXhINejhI5qU2cmDtBtUjIrROo+o6i6yb6mTsR4zwLEREREQ9kLwz7Oi7Q02DikBeJpNJLc259tprceqpp6rkgKaCFf/+979VtSIJWJx++ulq8mFrsiukqtFpp52mFikB/d///ldVWZDqS3369EFaWlr3BCtkQMdgaPzGWLZ1R7mJp556SqWnSL8K+YAgAQspISVlo7wWLlxYc1vSWyR40b9/f7z//vu44oorGh2zuRdVBqw6Omil1VbV1BTrjOM1RY7fVcf2B4F0fYdyLSq7IshY/U+vzgdZuen9WZH7na4qHMwtw8i+0ejpAuk17K3X2Orrq9J6SjiU7ofGsg+IqjOD0s8F4mvoq2s56aSTVLmnf/7zn5g4caLaJpmUUrpR3iAREXWFo4Xl2HQwH1vT81V2s2Q0jxsQi0mDYpEs5TabYKm0o8BiQ0yoCaHmplJTf13dQEVBmRU/7snG7JFJiKzuH9bjVMlnOvXGtLZfRMnuOuWaij0lqLyGXAOYk7wP9vSSEFq9pwxTdTCiSheGKpdnQoMSO726aTURERERtVbfvn3rrS9duhT33ntvk/tKy4ZNmzapMlBNueCCC9SYuVQn2rZtG2677TZVwlkSA9pCghuSYFBUVITDhw/X9LnulmDFiSeeqBpxSIqHXIg4evSoqlMlgxNtERsbq2pZNZxlKevNpZ/IrNePPvpIlWcqKChQ5yC9LaTbeHOkt8bQoUNx4MCBNp0fUWertDuhbWVZJ9lP9ifqUWQWpeUQUJEJZP4XiBzLppW9kEwgkPKQkydPrpngIJkW8uZFAhhERJ1t2+ECLF9/CCUVNoQGGWHUa2FzuPHt9iPYmJaHs6cPxJg6PSVWbM3Exz+nq15h3kyIfrGh+O20gThpbJ92nYOUEtx4ME/1Jftyc6YKWDQXJPE5RxlgzfVMMKhbqsl7e/BVtQGIymygoIkPuFKiSbIgvE2uReRoIKS/Z7te+rHVed8r/aFyc2vX2dSaiIiIeonObLCdmZlZrxJQc1kVsp+M4a9cubLZqgtXX311ze0xY8YgKSlJje9LVoQkCPwab0aFZGdI9SMJpJx//vmqLHS3BSueffZZ1ThjwIABNZEcufjRo0fXa3DdGkajEZMmTVIXc+aZZ6ptkp0h69ddd12Lj5UnOSUlBQ6HAx9++KGqjdVSbS55ki+66KI2nR9RZzMb9XC766fEN0f2k/2JepTS3YA121P+KX8tULILiBzl67OibiYTCz7//HPs27evpqyjZETKxAEioq7IqJBAhUzy6B8XVu+DYEyYCVlFFfhw/SHEhAWp4MFzX+zAF5szVBar9J6QlgfyvistuxRPfLoNe48V408LRrf5POT7zh6ZjDU7jyG3pBLf7jiGaUPiMSQpAt06acBVURt0kCwIbyAi4SQgKNazX/F2IGtF88eRx3uDFSH9gPhZdfpEVC/e0k91qUyKjpXRJSIiIgo0UvNA28FCUNrqrxKoaE3bAqlukJubW1PtwFua+bvvvlPj+9IXumFDbKlOJGTC/68FK8477zx8+umnKqtCxuWlfJT0je6oNo+ESoBC0kekb4V3AEL6V7S3rIP0ivDOvpw6dSqefPJJlJeXq9JO4uKLL1ZBCWmCLX766SeVyTF+/Hj1VdJcJMBx66231hzzL3/5i6qxJWksx44dU+kw8uRLZIfIl6Sm8RrdMdgcLtWzojlyv16nweBEftijHkQGSI58DLgdnpIPDguQuRyIGMnZk72UBCcYoCCirialnySjomGgQsh6UlQwDueVqf12ZBaqQIVLTQrRIVhbjihdIYpc0ahwh8DqcOHzTRkYlhzZrgyLIIMOJ49Nwbq9OTiUW4b1+3JQVunAhIExHZ5NV79xdXUQIjTV02RaFG4Cjn0OuJvJzI0cVxusMMUAQfH1m1XX6xtRp89fcB/PQkREREQ9xkknnYTt27fX2ybj7TKRUMo9NQxUiC1btqivkmHxa+Tx0nJBKig0PJb0jZbEhvZo17RteaM9b948tXTUueeei7y8PNxzzz3Izs5WQQhp4u1tup2RkVGv5raUf7rrrrtw8OBBhIaGYtGiRXjzzTdVqSevI0eOqMCElImS2Z3HH3881q9fr24T+ZLMrEuJDkFmvgV9Y0ObLSGQU1yBvrFhGJzYjTPxiDpKsigkm0IX7AlOyIzMvB+ZXdFLyOSDBx54ACEhIep2S5YtW9Zt50VEgd9MW3pUSOmn5oIBsl3ul/2OFlaojIoFkatwaujH6GvIgBZuuKFFpqMfPik7EytKTsB/fzrU7nJQOq0WM4cnIsxsVOWpdmYWwmJ1YObwBHVfy1kRVklPqG2eXX7YU4LJG5xwWuo3ru5/HhAx3HNba/IEKuR50IfWD0TIYvZ8vlLCh3kWIiIiIupynj61HT9GW4SFhTUKGMjn9ZiYGLVdqhBJQ2wZW5dt0rNC2jzMnj1b9YD+NVL6qa6ysjLVNkJKP0tWh2RxdGmwYt26dWrwXzp8e73xxhsqa0EyIaSM0zPPPNNi9/HmSMmn5so+rV69ut76nDlzsGvXrhaPJ81DiPyRXqfFGVMG4K3v9qmARcPGi5JRIYGKiBAjzpjSX+1P1CPIwIlkUUg2hba6LIRkV1hzmF3RS2zevFmVZhSSgdnSoCERUWexOV2qmbb0qGiJ3F9ud6oeFYtjX8DCsK+g0zjhqtLBXaWBVuPGIGMarot+CoMMe/FK/mLVfLu9Tbfld924ATEIDTKo7AoJqtT8/pMG1BKEUKWaqntG2IsRUnQUmlwDMOD82gCE/F0t3lH/4KpxdXUgom4pprDBwPAbAX04oG0+g5eIiIiIupe8D5QeaR09RmeS9gxSOclb5UiqKZ199tkqSaAtpKzUK6+8oto0SG/p3/72t3juuefafV6tDlbcf//9OOGEE2qCFZJGcsUVV+DSSy9VZaAee+wxdULNdR8nIo9BCeG4cNZQfLg+DXuPlsBZ3cNiy+EChAUZkJoYjt/NSFX7EfWorArJopBsCkeRZxuzK3qVp556qqZuZsOJBkREXcWk18Gg8zTTbond6VaB9ROCv8aCsK+ghQuVbmk0WB3kkLdjVW4EaWxYGPYl9tmGosByfNuCFS57bX+I6mbVqY5ixEXnwdz3pNoPqOWZQMaH9R6qqaqCxm33ZFVI9oRXcDKQdEp1iaZIz1dvBmND0i9KFiIiIiKiJtT9rC7BiTVr1qA9pDrSv/71LxWkKC0tVT0rpAfGRx99hJEjR6IjWh2skJpVUt6hbvaCNN14+eWXay5QsiwYrCBqnSKLDSUV9pr2OhVWJxxON6ItNj6F1DOzKmRwxZxS/z5jFFCZxeyKXmDChAnIyspCfHw8Bg0ahA0bNqhUUiKiriRZqOMGxOLb7UdUM+2mZpxJiU2L1Y7jhidinO1/0Guc9QMVNbSwVplg1lrxm/CPERP6t7oHAZzl9QIRCB1UW1qpZDdw+L0mz1GFcR0FAAarc9maVYWB2iRERHh7RkSgSheO8hI7gpMHQ6M31f87GtfxRoVERERE5Dua6v86egx/IH2iJZvi1FNPVVkZCxYsUD0rXnjhhU45fquDFUVFRTV9JIREXhYuXFizPmXKFGRmZnbKSREFsrTsUvz7+32odLgwsm8kth8uhLuqCpNS41CFKhRabKpMlGRfSJYFUY/Kqmg4SMTsil5DekcdOnRIBSvS09Phdrc8y5mIqLNMHBSLjWl5yCqqUM206wYsJDgg2yNCTJg50IyYtAxV+qlxoMLbB0Kj7h9gzIDJfhDI+rk2QNGwcXXywtpghbchtWQ2NOwVIdkQ1Q2q5X3g9lwjdmpOxPTYhNr3em43qipza3tV+IqzArAXAsZoQB/s23MhIiIiChC+6FnRVb744gvccMMNWLx4MYYMGdLpx291sEICFTIIIRkUdrtd1aO+77776jXRMBh8/OaayM9JveJPfklHSbkdA+LCVINHVbcOgNmog0GvQ7jZqPpZfPLLYVy3cBT7VlDPyaoI6adKaDTC7IpeQWpbSl+ppKQk9Xtt8uTJanZFUw4ePNjt50dEAcjtAFyVSIk04+zpA/Hh+kM4nFemmmlLjwop/SQZFRKoOHvaQCSbc+HSVqn3X1o41dw0jaaqeo6a56ujuoeFQX59SWagpc7vK2/jam8gQv6+eQUlAaNuB3SSsdG8AfFhOFZUoc5z7d5s1Xh7bP9o+Fz2N8CBfwJ5PwBVDkBjAOKOBwZfBSSe6OuzIyIiIiI/8cMPP6jyT5MmTVKtIS666CKcd9553R+skM7gt99+Ox555BFVfyo4OBizZs2quV86hqempnbaiREFov1ZJThaWI6ESO+sP+8svlqyXe4/WmjBgewSDE+p80GYyN+zKhr/SDO7opd46aWXVCOtAwcOqFkWV111FcLCqmcaExF1prIDQNZKIGcV4LYCGhPGxM5A4qTR2JXjxuGjR6BzlSJEW4HUvkBi6gmI6x8D2PXQaXTQ6Jwqk6Eh+RMm7870uiropIl1xEjAFFXbK6KlxtVqu65VZatmjUhUjbd3ZhZi2+EClFXaMW1IHHxmz1PAroc9Ew+kH4Zkd0j/jGOfArmrgVH/Bwy73nfnR0RERNTDaSETlTuWGqH1kzJQ06dPV4uUgHrvvffw6quvYsmSJaq6wsqVK1WiQ0fGAlodrJB+FTIIIbMmQ0ND8frrr6uu4V5yYvPnz2/3iRD1Bmk5pWo2n0lN12ue3C/7HcguZbCC/D+rQspFmKIBhwWocnlmugoZ9NBU/6zLwIfsJ/vL4I+/5C9Sp5JalWLjxo248cYbGawgos4hjattRdBVHAQOfAwcesMTpJBAuWQyyN+XvU8gTh+CObEz4B7WD063G3qt1tPUOqjCcxwJOEQMh7Z4qwpGSE9ud5U3zq5RE0YkI0NfZff8rZIST94yT51Ivo+UrgozG/DT/lwcyi1TGRYj41r90axzMyokUOGyefpO1f37XBUFWHOBnX8DIkZ1a4aFw+VApbMSZr0ZBh2z94mIiKhnk/d/TfVWa+sx/ElISAguv/xytezdu1dlWzz88MMq2WHevHn43//+167jtvodcWxsrGqeUVJSooIVDUs7fPDBB2o71Sq3OlBUbkOl3VPfNrekEka9DiEmPUKC+Ka7N5KfBa22db9cZD/vzw6RX5LAhJTIkLrWtsLqjTLyU90kXgaP6tYEl/1kf3mcxgcDMtRtXnvtNT7bRNQ6NY2riz2z+iX4LWwFQMZ/PNudldBUVcFckg1N8RpAggnhw4HglNp+EdLw2lkKFG2FNmYqjBHDa3tGBNXJWhh+E/Dz1SogoTeGwA1NTUaFVm65yj0B9mF/7vJXcEhShPpc8N2uLOSVVqIkNAiezhbdSEo/yeSChoEKoQI98UDlUSDt5W4JVhwoPICVaSuxKn0VrE4rgvRBmDtgLuanzkdqNLP4iYiIiPzRsGHD8Oijj+Khhx7CJ598opIa2qvNo0URERFNbo+O9oNaq35me0ahmi1lNnqeZqmjK6YNicf0oZ0/S4v8n/wsuGUKXyvIft6fHSK/JCUyxj8IOMpqt8nMzO33em6PudfTaLQuGVCSx1FAKy8vVzMqvvnmG+Tm5jZqts2eFUS9LBjhHQSXoETBBk+zagkuSCDCUVrbuDruOCCpOlNba/QEuL2H0QXBZa9AlfSWCBta29BaSH+F6Ime2yU7PZGHhLlNn8+gi4HCjcCBF9UgvVYyAGWRQLosEqgYfA0w6A/oDsnRIVgwoS8KyqwIQSW6vZm29KiQIFFzM/Vku9yf+4Nn/y5sur0mfQ2eWP8E8srzEBUUpQIVFfYKvLXtLXyV9hWWzFiC2f1nd9n3JyIiIuoqgdRguyWS3HDmmWeqpb04YtSFxvSLxqCE8EbbZQYV9U6pCeFYozsGm8PVYikouV+v02BwYuOfHyK/Is1F6zYYlWCFoTrLThpuNwxWUK9w5ZVXYs2aNarRlrfhNhEFKLcLsOVWBx8kCCEBiJLaJXJsbQCiyg3krG58DNW4Oqx+1p00sh5wQW0z6yqgMud2hEmgIiS58eO95G+S9LJIvdITeGjK5KeA6CnA3ic9vZdQnfEn5yoZFd0UqPCKDDEh3GxAbq4nWFFaYVfZ2f3jurjvj2RASjPt5p4nL3luZD/Zv4uCFZJRIYEKi82CUXGj6v3dSA5LRlpRGpatW4aUsBRmWBAREVGPE0g9K7oaR827kJR6YrknapjunxIdgsx8C/rGNl02raqqCjnFFegbG4bBiU1nMhER+bMvvvgCn332GWbOnOnrUyGijpDggmQ+1GRCVAcjpPySN5PBVQHsf7H5Y8hj6gYgoid5ekdIAMIbiJBARcPG1TJYHT60dt1W7Cn/JD0qWiL3S+DcVdnyILwEJGSR66o8BpiTPeflYzJh5dsdR1FW6cD4gQ6M7hvVdQFfKc+oqW6m3ZIqpyfTRfbvIlL6STIqGgYqhKynRqViZ95OrDi4AoujF3fZeRARERGRbzFYQdSd/+B0WpwxZQDe+m6fClhEhwY1+oAqgYqIECPOmNJf7U9E1NNERUWxPCRRTyCD+t5AhAzyh/T1bJdyP1ImSQIVUsapIWlu7Q1WSADCEO7JqjNUBx/qBSMiax8ng9B9Tm/fuerMgMYIuKy/ck1Wz+x/2b8VSlzAMTuQbAR8H6oADHqtmtiy52gxthzKh6XSgalD4qFrZc+zNpHnKe544NinnmbaTQVF5PWXgJT0q+iirApppi09KqT0k5T5KrGWoMxWhj7hfWoCF/JV7l91aBWunHAlm24TERFRjxKIDba7CoMVRN1MSoNdOGsoPvnlMDLzy+BwudUHwYx8iyoNJRkVEqhoqoQYkV+TwS5rLuCweNbLMzxloLwDV9RrPPDAA7jnnnvw+uuvIzi46+qbE1ELZJDZ7QB0Rs+63M5aWb9Ek7NOj4TIUbXBChnol9/lqt+Erjb44P19LlkIXvKhacSSrn8pJEsicgyQvwEITmp+YN1eBKSc+quljd7Y8gae/vlp7MrbBVeVCzqNDiPjRuLP0/+MP4zt3jJQdWk1GkwZHI8wsxG/HMjFgewSWKwOzBmVBKO++RKi7Tb4SiB3tefvtzTTrvu8yvMp2yXzJfUqdJUKR0VNM213lRsHiw6qrzHBMQg21P4NkfttThsqnZWBG6yQf6eSFST/Bn+tPBcRERH1GDIVuaPTkbXoHRisIPKB1MRwXLdwFLYfLsAjH22B0+XC7JFJGN0vWpV+YkYF9Ui533lmZ3p7Vuz5u+dr8mntn0lLPdLjjz+OtLQ0JCQkYMCAATAY6g+4bNq0yWfnRhRwZZrK0wFbEYxF6YBdAzjLarMlIoYD/c6p7TsgjaWliXRdklHhLcfkJQPWMogtWROy+MssrugJQFkcYDkIhA5qPLBuSQNM8UBidY+MZlz/+fV4edPLaka/TqtTi9PtxOaszbjyf1diw9ENeGrhU/Cl4SmRCA3S4/vd2cgursBXWzIxd3QKQoM6eQA78SRg1P8BO/8GVB6tbrYtPSqcnowKCVSMusOTWdGJ7C479hfsV8GiUltpTTPtGG0MYswx0Gq0aqlLAhrBxmCY9a3LmulRyg54gonSb0Uyl7RBnibx0u8lLNXXZ0dERETUbRisIPIRCUgMTZYPogY4XVqcMq4PwoLZjJh6sPjZQNS4xtvrDoBRr3DmmWf6+hSIejZVeqeyTq+I4tqeEabYOgPHGuDQv6FxO2EsL4fGHlJ/AF/KOHnJdhn8VBlvkbXZErLeFHMS/I6c04g/A3ueAEp2epppqx4VVk9GhQQqRtzU4uCuZFRIoEKCE6HGUGi1tQPibrcb5Y5yvLjxRUxJmeLTDAvRJyYU88f1waodx1BcbscvaXk4YVSD5uKdYdj1QMQoIO1lIPeH6qbbRs/PmWRUdFKgQrIi9hTuweq81aphtkOyCKpNS5mG5buXq2ba/SP7N9nTrchahFOHnRp4WRU5azw/07a8Oj/TFUD6W0DWV57MJXmPRURERD0Wy0C1HoMVRETUOWTgi+WeCMDSpUv5PBC1xO2qnwEh5V4iRtQGKnY/Wr9EU13BfQCcWBuACB2gBnIdOg2qYvtBY4qqDkRItkRY/cfGH9/zX5e4WZ7nIHuFZxa69N2QXgpS+kkyKiRQ0UIpHSn9JBkVDQMVQtZDDCGw2C14cv2TPg9WiJiwICyc0Bcb0vIwfUh8130jCUgkngiHrQSVFcdgDk6GwdR5kw3WZq7FNwe/QUlZCUJCQmp6UEjprVHxo1BuL8f3Gd+rElCDogbVq8ksP98S3IgPicf8QS1nzfTIjAoJVDgtnoBR3WCjlFuTbKHdywBzCjMsiIiIejAGK1qPwQoiHym3OpBXWgmnyw2Xuwp5pVZUOtwIMekR0tkp/kREPrBx40bs3r1b3R41ahQmTJjA14F6BxlAd9s8jae9Mj8C7AWe4IQEKuo2rpbBd2+wQgYrpQQMKqubVtdtVh3hyayoa+BFkhIAW24uEB8vI+4IeBKQCFsMpF5ZPyghA7/7/tFsKZ2SyhJVdkjKPjUMVHjJdrlf9pP9I8y+zw6U94UNMypySyoRFx7UaY0WDxQewMq0larRtbd/xNwBczE/dT5So9tWhqjSUYm9BXvRP6I/osxRaluEKUJls0iAYmq/qRgdPxqJoYn1zn/JjCVYtm4ZdubtVPvJOci5SEaFBCpumnFTm8/F70npJ8moaBioqAlGpnqyiCQ4Jz/zRERERAGOwQoiH9meUYi1e7Or+1NU4aOf06HTaTFtSDymD03g60JEPVZubi7OO+88rF69GpGRkWpbcXEx5s6di3fffRdxcXG+PkWizlGyy1N+yFuiyVuuScoSSQBC+j54lR/27OtVt3F1w5JLqZd5egdo+Va9RRKg8GZOtKKUzjFNnKeZtrblRtVyv+x3zHLML4IVDe3PKsH6fTkYmhyhmnFLU+6OWJO+Bk+sfwJ55Xk1QQLpH/HWtrfwVdpXKogwu//sX22SvSd/jwryeBtknzjwxJrHDYkZgj9O+iNQDtXPqKlgkeybEpaCFQdXYNWhVapslPSokNJPklERcIEKyQCSwJr8vDb3Gsp2uV/2k+Acm24TERH1SGyw3Xr8BETkI2P6RaNPTAjeXLMPVqsV5xw3ECaDQWVWEBH1ZNdffz3Kysqwc+dOjBjhmS2+a9cuXHLJJbjhhhvwzjvvtOl4zz33HB577DFkZ2dj3LhxeOaZZzB16tRm9//ggw9w9913Iz09HUOGDMEjjzyCRYsW1SspIqWqXn75ZRVEmTlzJv7xj3+ofRuy2WyYNm0atm7dis2bN2P8+PFtOnfqoQOINcGHkvp9IySA0P/39WdF1w1A1DuOrXEjY+kx4Q1QtNS4um5GBnVaKZ0+I++FTuNppt0Sl9sFvVaP5NAu6A/RCSQrV65w37ESlFudOH5EIoz6lgMwLWVUSKDCYrNgVNwoSL6Pq6oKURqN6h8h5Zck20GCCA2DBVJOa2vOVhWgSC9OVwEKr4SQBFVqy8uoMyIhNAG5Fbktno98j8XRi3HlhCtR6axUzbQDrkeFl2QFSQaQBNZaogJvNs/+DFYQERH1SCwD1XocFSXyYUq/Qa+F2agHXDrEhZsRZAzQD2NE1Kt8+eWX+Prrr2sCFWLkyJEq6DB/ftvqjb/33ntYsmQJXnjhBRU0ePLJJ3HKKadg7969iJeSNw2sXbsW559/Ph566CGcdtppePvtt1XD702bNmH06NFqn0cffRRPP/00Xn/9dQwcOFAFNuSYElAJCqo/aHTrrbciOTlZBSsoAEjpJWd5/WCEiJ1eu8++55sPQDTsARE+1DOA6C3VVFOuKbxx4+pIz88f+a6UTljxOtUjYXPWZtVMu6nZ/bJdghVjE8b6ZVaFGNEnSk1u+WFPNo4WlmPF1iOYOzoZIaa2v4+U0k+SUTEgfCgyCiwoKLPCXQVoNZ5+GYmh/ZBeuk9lO0gQQQIU3uCBfOiWx9tkIB1AUmiSen5liQmO6dA1yvcI2CCFlypfVp0B1BLJ1JK+LLI/ERERUYBjsIKIiIg6lQz2GQyNB5lkm9zXFsuWLcNVV12Fyy67TK1L0OKzzz7Dq6++ittvv73R/k899RQWLFiAW265Ra0/8MADWLlyJZ599ln1WMmqkIDHXXfdhd/85jdqnzfeeEOVJfnoo49U+SqvL774AitWrMCHH36obv8aycKQxau0tLTm+WjrdXcnOTd5Xvz5HNvWuLrUM7hXp7RSVeZ/Yc7dg6pcudb6s+qrJAARXSdTRx8GOCy1QYeGPSPqPk+Jp7RwLt33fAbUa9jWa5RMmOzVgCHak2BfpxVILY3n/uzVuHHyn3DN59epvgrSTLtuwEKOLdtNOhP+PO3P3fp8tvU1lOzck8ekYPXOYygss+LzjRkqYBEd2iBI1gIJPKxOXw09QrH3WCkcThf0Oh10GskuAbIKK5BfaoMp2IQPd34ILbSwu+z40+Q/qUCFrE9LmaaCCiNiRyDaHF3vejp6jT1N269PB8TPBdL/DQSlNJ1pJQFWWzGQfKpnfx88d4H6ehEREXUvjfqvo8foDRisICIiok514okn4sYbb1TlniQrQRw9ehQ33XQTTjpJSuG0jt1uV02677jjjpptMrB48sknY926dU0+RrZLJkZdkjUhgQhx6NAhVU5KjuEVERGhsjbksd5gRU5OjgqSyOOCg4Nbdb6SzXHfffc12p6Xl6fK/fkrGYgqKSlRg2zNNR32R3rLbmjtudA4y6B1lkIji6scGhks1Ieiot8fa/Y15WfAWXYMFXZPQ2K3PgRV+nC49eGoQjjsOTm1A4XB84AQfeOBQ3v1Ut5yGRtf6KmvYadco7MCsMcCGgNgC2vhAEGAzYl5A0/A7RNux5cHvlQZFFqNVh1PBRWr3NAF67Bg8AKcnHCy6r/j76/hpD5BWHegEPlF5fjv2lLMGxWPIEPrSkJJn4kIezQcjjAkmMwwhdY+zu62ocRRgHxrPgpLihFs0mLX0V0qMLE3cy+igzyBiZHBI9VXZ5kTuWW5vfrntF3XZ5wO6HYBJZVAUGL93zsSqLBmA7oJgGG6NISCL0hZRyIiIuoYyVqVpaPH6A0YrCAiIqJOJVkMZ5xxBgYMGIC+ffuqbZmZmaoM01tvvdXq4+Tn58Plcqmsh7pkfc+ePU0+RgIRTe0v2733e7c1t48MNF166aX44x//iMmTJ6veF60hQZW6gRLJrJDrl4bi4eHhfj3AJgP4cp49agDR8jk0jsOe2zLGqsZZg1ElTakNEQiNkwFsz/W4zWegsCAf5oSB0JoiA65xdY99DTvjGiWz4mC+p5SOKaX5A7iOekrpJKbgruS7MGj7IDz101PYmbfT03Rbo8OIuBG4ftr1uGDMBehJr2FKUgLW7MpCUmQw+qXUZje0JrNityUTx0qKkCj9OVyeT8AljjyU2L0D41Uod1gQq4vG78b9DmMSxiDc1L7fZ4H+c9q+64sHgn8H7H4SsO6p0xze6ilHZ4oHRtwIxI2DrzQsj0hERETUlQLrkxoRERH5nAzQS48I6VvhDSpI/4q62Qz+TBp4y0zSuhkdrWEymdTSkAxa+fvAnCrp0gPOs57IkUBwUv0STYYIaPQhjbMiwgbCXRkCrTm2Z11joL+GnXGNWhOQeAKQLoHQpOZL6TgKgT6LAL3n3+gfxv1BLSWVJThmOaaaafu6R0V7X8MgoxbzxvVVhQHkGMLmcKneaNrmmrhLfK/KgOCqVJS5P0WkKwqm6p4IBumjoAFM2mCYdWFwu3MwPGQhZvSdCb2uYz9fgf5z2q7rS5gDBPcBslcAOas8zbQNZs/Pa+J8IKx+Y/PuFqivFRERUfcXgepYaoSGZaCIiIiI2vlGSqPBvHnz1NJesbGx0Ol0qiRTXbKemJjY5GNke0v7e7/KtqSk2p4Gsj5+/Hh1+9tvv1UloRoGHiTL4sILL1SNuckP1G2KTb1b0jwg6yvAchAIHdS4lI4lzTNDXQZ+G5AAha+DFJ2hblDC4XLj621HVMPtmSMSYWgQYJCG2rvydmFz1nYU2bLhrnIh25qGfsGj1O/uIG0Iks1DoYUOBfYjCDfEo795KmyqpwUHrruEBCTCFgOpVwKuyurm2wHeYJyIiKgXkfdqLU0iae0xegO+2yQiIqJOIYP8I0eOrGksXZfU8R41ahS+//77Vh/PaDRi0qRJ+Oabb+qV2ZD1GTNmNPkY2V53fyENtr37Dxw4UAUs6u4j5/vTTz/V7PP0009j69at2LJli1o+//xztf29997Dgw8+2OrzJ6JuEjYYGLEE0IcCJTuBiqOArcDzVdb14cCIm3w+Q727FJRZUVJhR2aBBSu3HkGl3akaY686tArP/fwcntvwHFalr0JhZR7CDFEYHnoigvXhyLYeQIk9BxWuElgcBWrdpAvBlMjzkGjuB5O+db0wqAMkQGEIZ6CCiIiIei2WgSIiIqJO8eSTT6qm1E31Z5Am1tdccw2WLVuGWbNmtfqY0gPikksuUVkNU6dOVd+jvLwcl112mbr/4osvRkpKimpuLaSx95w5c/D444/j1FNPxbvvvotffvkFL730krpfZg3/+c9/xl//+lcMGTJEBS/uvvtu1Qj8zDPPVPv069ev3jmEhoaqr6mpqejTp08HniEi6jLxswFzSv1SOtKjIuVUvyil050SI4Nx0pgUfLltPwrKgC82ZeCE0UnYmLURFrtF9edIjU7FyLiRGBtixo+78hESXoy9petxoOxnON12GHVmjIycg2Hh01FeGolxA2KZVUFERETUTpIU0dHECE3vSKxgsIKIiIg6h2QjPPLII83eP3/+fPz9739v0zHPPfdc5OXl4Z577lENsKVU05dfflnTIDsjI6NePe3jjjsOb7/9Nu666y783//9nwpIfPTRR6q5t9ett96qAh5XX301iouLcfzxx6tjsokoUQ/Xy0vpVFVV4VjZMVXiSRZLlRWjgs5BmdWJFVuOYkzcDCRFhmFozFAE6T1Nk+OM5diRXobKihjMjDsHM2LPgqPKBoPGBK1Gj6yiCkSE6DFxUKyvL4+IiIiox2LPitZjZgURERF1Cun7YDA0PzCo1+tV4KGtrrvuOrU0ZfXq1Y22nXPOOWppjmRX3H///WppjQEDBqhBQCLqISRA0UuCFPK76UjpkZoARYmtpOY+g9aAScOCsSvdgdySSuTlxGN4dGJNoEKkRIfg7OkD8eH6QzicV4bQICOMeiMsThcs1kpEhJhw9rSBSI4O8dEVEhEREVFvwmAFERERdQopx7Rjxw4MHjy4yfu3bdtWr6k1Ubu5Hb1y5jxRQ6vTV2PN4TU160adUWVOSImnwdGD1XryWDfW7c1BVnEFYsJqAxVeY/rHqO2bDuZja3o+7C43ggxaTBvSR2VUMFBBRERE1DFadEKDbfSOOlAMVhAREVGnWLRoker/sGDBgkYllSorK7F06VKcdtppfLap/coOAFkrPT0J3FZAGwQkzAWSeldPAup93FVuHC4+rLInRsePRv/I/mq7BCTWH1mPYbHDMCJ2hFo36OoH8HRaLWYOT0SFzYmQIEO9rAzJNBMSkJBlwYS+sDldqpm2XldbYo+IyNeynloHV4mtxX3iLh0PU7/ImnVnsRWWn47AdqhI3a5yuKAx6KCPDIJpYCRCp/aBPsrc5nOp2JmL8k3H4Mi2wG13QRdqhGlgFMJm9oMhJrhd10cBrNQKZBYCRRWAzelpPGDUAeFmICUCiPH0x0NhObAxo+VjmQ3A8U1PDGtScQVQUO75WukA7E5AEsZNeiAqGOgXDTQxkYE6n7zj6mioQYPegcEKIiIi6hTSJ2L58uUYOnSoKts0bNgwtX3Pnj147rnn4HK5cOedd/LZpvbJWQPseQKw5QHGKEAXBLgqgPS3gKyvgBFLPE2WiQKEy+1CenG6ClDsyd+Dckd5TeDCG6zoE94Ht8y8BXptyx/rJChRN1CRkW/B7iNFmDMyCUHG2sdKgIJBCiIKBPasMuS9sQVVNle97VV2Fxy55Wop35yNuIvHw5gc1qpjSpC36H97ULE1p952CaJUbMlGxY5cxJwzCuYhMZ16LdSDpeUBB/MbbKwCKt2e4IFOUxus6Ao7s4AKe+Pt8r0rS4CsEmBUMpAU0XXnQNRGDFYQERFRp5Cm12vXrsXixYtxxx131PR5kEGyU045RQUsvI2xidqcUSGBCqcFiBjlmZHmZU4GLGnA7mWAOYUZFtTjOVwOfHHgCxWgqHBU1Gw3680YHjtcZVZ4ye9XvaZtH+lcbjc2HMhVmRZfbM7EiWNSEBFs7NRrICLqShEnD2pyuz6yNkuidNWheoGK4LEJ0McEw36sDNa9+TWBi7K1GYj53ahWfd/yDUfrBSrMo+JhiAtWmRbOvArA6Ubh8l1IXDwVunBTB66QAkJmUf1ARYQZkJ9Rg07+2APlNqDOhIF6wiV7OLzxdnlse8jxJJNCsiYlw0MWIR/XdmcDsaHtPza1ipSA6nAZKE3vyK1gsIKIiIg6Tf/+/fH555+jqKgIBw4cUAGLIUOGICoqis8ytZ+UfpKMioaBCiHroalAyU4gewUQtpjPNPUoTrcTuZZcaOEpuyRZEpJRIYGKYEOwKu8kPSgGRA6ATtvxgQQpC3Xy2D74dvtRWKwOfLk5E3NGJSExkqVLyPdOOOEEjB8/Hk8++aSvT4X8WNhx/X51H2dRZc3toCExiD5zRM169gsb4MytzlYrd7Tqe1a53Sj9obZEj3l0PGJ+O1LdDp2Sgqyn1qvghwRIyn4+gsiTWZ6yV3O6gAO5tesjEoE+bfg8FGICBnRChk5cqCdromGppx3HPFkVwuX2lImKa12GEbWPTDDxlt9sLw2DFURERETtI8GJKVOm8OmjzmmmLT0qpPRTc2/QVe3fKM9+qVey6Tb1iOyJtKI0VeJpb/5ete2i1ItqPojOT50Pk86kyj1pNZ3fO0IyKRZO6ItVO48hv9SKb7YdxfShCUhNbGIWJ1GAu/TSS/H666+3OBEjPT29W8+JWpb1zHpVekmj10IfGwzz8FiETusDbZ2Z4fq4EDgLPAELR45FLbKvZFa4iq01+5kGR7fq6ZbHuS215XSCR8TV3NaaDTANiIR1X4FaV18ZrOjdcspUpo0i/SGsTmDdQU9JJslukAyLgbGebIum5FuA1fs8QQ9DdX+LvlGeDIi2GNpMVntCWG2wQrg9GfFE/oCZFURERETkv1yVnmba0qOiJaqHhc2zv7Z+g2Eif2B32XGg8IAKUOwr2KfWvcKMYSi2FSMZyWpdyj11NelVMW9sH6zdm4PDeWVYuzdbZVqM7R/da2buEYmnnnoKDz/8cM2TkZSUhNdeew0LFixQ6zpd/Ywmu90Oo5Gl03zJVWSt7T9xrEwtFdtzVP8JXYjntYmYOxD2I6UqwOAqtSHnxV/qHUNj1KmMiLAZfVr1PR05nkwML11U/fcl+jrrzoIKVDndKphCvVRJbWaPaqp9qE45KLcLyLN4AhKjU4CmJgpImSgvu8uzryz9o5sPQLRFub1xmSjqUmyw3Xr8zUlERERE/ktnBrQSiKidBdkkuV9n8uxP5IfWZa7D+zvfx47cHSpQEWGKwPQ+03HFhCvw52l/Rqw5ttvPSZppzxqRiFF9PTOL7U4XAxXUbcrLy3HxxRcjNDRUBQgef/zxevcPGDAAf/vb33D55ZcjLCwM/fr1w0svvVQvaHDdddepxwYFBakMiIceeqjN5xEREYHExMSaRURGRtasS6boAw88oM41PDwcV199NVavXq3+rRQXF9ccZ8uWLWpb3SyMH374AbNmzYLZbEbfvn1xww03qOum9tFHmxE8PhHhcwYgdFoKtGG1QSPpGVH8+f6adUNcCBKumgRjStOlbUz9I1XPCY22dcNibmlIXIdWZsvXoanbe6BK5lm0rrwUBSgJUNSl1XgyIyTY4A1iSTLDriz541t/RFt6S8i+qbGevhV15w8cLgTyyjp2btIro27wJDkCMDMA2109Kzq69AbMrCAiIiIi/yVZEglzgfS3PM20m3qTLs3c7UVAyqnMqiCfszqtKnNCMigmJE7AsNhhavuIuBHYkr1F9Z+QJTksuSYw4HZXl4rwATmHiYNikRBhRlI0+1ZQ97nllluwZs0afPzxx4iPj8f//d//YdOmTapnhZcEMCRQIPf95z//weLFizFnzhwMGzYMTz/9NP73v//h/fffV4GMzMxMtXgtXLgQ33//fbPfX4IbO3fubNW5/v3vf8c999yDpUuXqvW636c5aWlpKjvjr3/9K1599VXk5eWp4IoskrnRFJvNphav0tLSVp1fbxB74VgYYkPqbQs/YSBy/7mxptxT5Z48uK1OaIP0cOSWI/+dbapclDCPiIMhPgS2I6WwpRXCur8AtkNFiP3DWJj6Rbb9hKqaeC9C1FxZpSHxQL/qkmMSjNhypLZfhGRZpER6+krMHtK46XZ+BLC5zu+cYyXt7y8hvSnke3tLVMm5DPcEaYn8hV8EK5577jk89thjyM7Oxrhx4/DMM89g6tSpTe7rcDjUbAmpKXn06FH1JuWRRx6pSdFsSNI577jjDtx4441s0kVERETUEyXNA7K+AiwHgdBB9QMWMjhgSQNM8UDifF+eJfVilY5K7C3YqwIUaYVpcFW5apple4MVccFxuGHaDX6buZASUzsI6HJXYcOBXIzsG4VwzrakLmCxWPDKK6/grbfewkknnaS2yWf8Pn3ql+RZtGgR/vSnP6nbt912G5544gmsWrVKjQNkZGRgyJAhOP7449W/Kwk+1PXPf/4TlZV1SrE0YDC0vmTgiSeeiJtvvrlmvTXBChm3uPDCC/HnP/9Zrcu5SoBFgi3/+Mc/VDZIU4+57777Wn1evUnDQIU3uyF4fBJKvzno2VDlKcFkTAlH4ce7awIVko0RfUZteb28N7bAll6sSjWVfHMQ8ZdN/NXvrw2u//PirjsbvrokVQ2NJIWyJGWvZmiQsSNBgaZui8rqkkx1eq7UI30qpLyZt3STZEa0R3YpsPNYbSBF/k2N7ePpoUHdVAaqgw220Tv4PFjx3nvvYcmSJXjhhRcwbdo0FVA45ZRTsHfvXjW7oqG77rpLvaF5+eWXMXz4cHz11Vc466yzsHbtWkyYMKHevhs2bMCLL76IsWPHduMVEREREVGnChsMjFgC7F4GlOz0NNNWPSqsnowKCVSMuAkIS+21zZorHBXqq0lr8vXp9CpOtxPv7ngXB4sOwl1Vmx0RGxyLUXGjVAaFl78GKZqyJT0f+7NKkJFvwQmjkhHfXANQonaSrAMp4yRjAF7R0dEqCFFX3c/y8m9IyjLl5ubWNMaeN2+eeoxMXjzttNMwf35t0DolJaXTXp/Jkye3+TFbt27Ftm3b8O9//7tmW1VVlcqkOnToEEaMGNHoMTLRUsZH6mZWSPkoagONlGBywpFlqdlkTK4/C92QFKaCFcKRXbtfSwzxoY37ZiTWHtdZVBsY08cEs19Fbxci78daWa6plaXIarXj/cTBfCAtr3ZdMjkko0LKU1H30DSdIN7WY/QGPg9WLFu2DFdddRUuu+wytS5Bi88++0ylSd5+++2N9n/zzTdx5513qhkWQtJAv/76a5UeKkGMujM1ZBaDBDUk7bIlzaVaypsIX6Zkt4acn/cNTyDqLdcnqtyBeZ2B/hr2hmsM9OsL5GsMtOuhXi5+NmBOAbJXADmrPM209cGe0k+SUdELAxXSrHll2kqsTl+N2KpY5GvyccKAEzA/dT5So3vf89Edyu3lyLJkYXD04JrMCdkmgYr4kHgVnJAgRVxIHHqyESlRyCmuREGZFV9vO4LjhiViQHw7S060g9Plhs3pgkmvU301qPdqmP0gAQvv+5uJEyeqQf8vvvhCjQn8/ve/x8knn6zKRXV2GaiQkPqz+rXVg4vez3LeKhB1yZjENddco/pUNCRlq5piMpnUQvVV7s5TWRDmUXH1eky4bU5UbMmq3VGnUYGChiV47MfqDxo7smrXNQ1ms+e+vhn2wyXqdvC4BET/ZkRNwEN6ZLjLPLPbK3bnqdJSwlVhrwl+CPPQGL6EvV1cqCdA4FVU4Snz5L3dVHPrA7meHhXe/bwKLPUbYoc2+B3xy+HaYyZFAKOTa++TfwvSFyPL8zOtDI4DBnZ/nyyiHhGskJkUGzduVLMH6v7RlzcY69ata/IxElRomC4pzaqkcVVd1157LU499VR1rF8LVjSXaik1Ja3WX2nm6GPyRq2kpES9SfK+YQokgX59DpdbpSbLv4XcvFyYDD6PH3a6QH8Ne8M1Bvr1BfI1lpV1sPkakb+RgETYYiD1SsBVWd18u3eWWViTvgZPrH8CeeV5iA6KhsFoQIW9Am9tewtfpX2FJTOWYHb/2b4+zYBQZivDnvw9qsRTenE6dFodbjnuFpj0nsGChUMWItgQrLIpAkWwSY954/rgx93ZyCyw4PvdWbBYHRjVN6pLM0SOFpZj08F8bE3PV++TDTotxg2IxaRBsUiOblwChnqu1NRUFYj46aefagbui4qKsG/fPlUmqbWk4fW5556rlt/97ncqw6KwsFBlaXRmGaiG4uI8g9RZWVmIioqqabBdlwRTdu3ahcGDPcFNaj9niRUlK9KgXZmGoMHR0EeZ4a5woHJXLlzVwQMRPCahpvG19KeQvhWiYku2KtOkelZkltYLLASlel6/X6PRahA+sx+Kvzyg1it35KKgShp5B6NiRy6qHJ4gmsakQ+i0+uXMqBcKNwNSXrHA8zOI/blAhd1TculIUe1+Ut7JW4ZRelccKgAigoDIYECv85R8ymnQu0aab7fW1iNAfp3soUizZ3p/ekH9/WS7fE/qMlICquNloDToDXw6Mpqfnw+Xy4WEhIR622V9z549TT5GSkRJNsbs2bPVG5xvvvkGy5cvV8fxevfdd1VjLikD1RrNpVrKGxB58+PvA2zygUHONZAG2HrL9dmdLpjNeeoa4+PiYWrYSCkABPpr2BuuMdCvL5CvsalayEQBQQIUvTRI4c2okECFxWZRs/i10CIMYUgxpSApLAlpRWlYtm4ZUsJSemWGhZTDqnRWwqw3w6AztDtAIcEJWTJKMlBVp5OqZFCU2koRp/cMVvaLaHqGdE8ngYLZo5KwMS0Pe44WY/OhfJRVOjB1SDx0XVA2YtvhAixffwglFTaEBhlh1Gthc7jx7fYj6hzOnj4QY/pztnKgCA0NxRVXXKGabMfExKgS0FJBoS3vw2RcICkpSZWDlsd98MEHqkxUZGRkp5eBakgCEDJmcO+99+LBBx9UQRap9lCX9NiYPn26aqh95ZVXquwMCV6sXLkSzz77bJedWyBzW+wq8NAUY78IRC6oDQxFLhqC/H9vqwkiVO7KU0tduggTwk8c1OrvHzIlBfasMlRszfEcc2cu6oXD9FpE/3YkdGHMjiEAo5KAjRmerAjJcMgsavBDq/P0jGg4CaDE6lka0lQ36m7Y86Illgb9LYorPUtDg2IZrOhiDFa0Xo8bGX3qqadU2SjpVyEDSxKwkBJSUjbK2+hKmmnLG4DWDtI0l2opb3h6wqCVPA895VzbI5CvT6utqpmdJjM1AvEaA/017C3XGOjXF6jXGEjXQkS1pPSTZFRIoEK9j6hT7UK9P45Kxc68nVhxcAUWRy/udWWxVqWvgtVpRZA+CHMHzG11WSzJrvO+L9uRu0NlqHj1Ce+jSjzJEhnkGQjtDbQaDaYMjkeY2YhfDuTicF6Zyq4IDzZ2ekaFBCoq7U70jwurl70RE2ZCVlEFPlx/CDFhQcywCCCPPfaYKpV0+umnIywsTDWwlkzX1pLHPProo9i/fz90Oh2mTJmCzz//vFve/0hWxjvvvKPKUktfDfneUtHhnHPOqdlHtq9Zs0YFYWbNmqV+x8j4hWSBUNuEjE9UDa6t+wvgyCmHu9wOt80FbZAehsRQBI+OR/DYRPWZ2svULxIJf5wCy09HYE0vhquoUpWS0hh1qlSUZGiETe/TpkbY8rtJykIFDY5B+cZjsGdbUOVwQRdihGlQFMJm9oNBylARqR9CAzB1IJBR6MmOkEba8p7NbPA0zR4QA9SdsDomBcgr82RjVDpkdmv1D7PeE6CQjArJ2CAKcD4NVsTGxqo3FTk5nqi0l6zLjIimyKzXjz76SJVnKigoQHJysuptMWiQJxouZaWk4ZakXHpJ1sV3332nZi9IGSn5nkREREREPTFrQAbjo4KaL8cj2+X+VYdW4coJV7Y7u6CnlsWSa5dARWvKYhVVFtVkUExOnowJSRPUdglK7M7frb6OiB2BiKAI9GbDUyIRGqRXwYvODlQIKf0kGRUNAxVC1pOiglWgRPZjOajAyq6QnpSyeEmmhVd6enqjx9QttSSTGGXpbHX7UDR3HmLmzJmqgXZLj5UgxooVKzr9HHsbCSiEjE1US1tIuajIBUPa9Jj4Szx/B1oSPCpeLUS//kOo9WQtyPJrpBeFLG3tJzG5f/P3zWIZOr8hb2/YYNv/gxVGoxGTJk1SpZzOPPPMmlIcsi6pki2RrAlJ65QmVh9++KFqpiVOOukk/H97dwIfRXn+AfyXbO77PgkhhCPcoNxSUEFAUEGtpRYLikJFabkqgiJgLUKtcnghtYpWRP2jgq1WDhGwIocCyplwBQhHSAIhF7kz/8/zLrvs5iIX2ezs7+tnTGZ2djPv7JDMzjPP8+zfv99qXcm8kEwMScNkoIKairyCYmTmFao7yAqKS5GenQ931xJ4u7vAuxZ3dxAREZHjkPJGpqyB6sjjhSWFan29ByvKl8WyvNgd5RtVoSzWxSsXzQEKaZZt4pXuZQ5WSHBiXLdxNhlPU9Us2MdqPi0rXwUvQkyNQevRTFt6VEjpp+oCcPK4rDe0WwybbhMREZFdYRkoOyoDJb0ixo4di+7du6Nnz55YsmQJ8vLyVIBBjBkzRgUlpAm2kAZcZ8+eRdeuXdVXqREpAY4ZM2aYU0E7duxo9TOkNqTUwSy/nMiW9p++hJ1H0+ApaX+lBny246T6INardRh6t7Hu40JEREQkpA+DKWugOhLQ8HLzUus7XFksVF4Wa93xdTCcMCA191q9c2cnZ7QIaKEyKBJCEmyw9fYpO78Imw+cQ2lZGfq1i0RzKWdRR4UlpaqZtvSoqI48XlRaptZ3kQalRERERKQ7Ng9WSL3G9PR0zJkzB6mpqSoIsW7dOnPT7dOnT1vVnJTyT7Nnz8aJEydU2uiwYcNU2qipiRaRvejUPAgtw/1UsE1KmklATY51yawgIiIiqoxkSUgfBilvJFkDld2JLmVIMgsyMbztcN1nVVRVFkv2gQRs8orzEOIVoh6XUlFD4oeoAEXLwJYqQNE2uC283bxtOgZ75OFqQIivO85lXsF3B8/hpvhQtIsOqDIzojruLgbVzFuaaVenqKQMHq7Oan0iIiIieyLnSHU5T7JU3+fbiyZxVVRKPlVV9mnLli1W8wMGDMChQ4dq9frlX4OoKZBSTzJJsAKFbgjz92QzXCIiIrquO+LvUH0YTmSeUBfdJa3c8iK9lD0K8w7D4JaDHaosloxd5iVQI70oZLl8qPN39zeXxbot7jZE+kTC01X/GSc3kpuLAbd1isauo2k4ej4Lu4+nIze/GN1bharSULUhWRJdWoTg2/1nVDPtqgJwuQVF6NW6GbMqiIiIyO6wZUXNMX+WiIiIiMiOtApqpRpG+7j7qPJGZ3POIqcwR32VeT93P0ztM1X1Z9A7KXMlwZoz2WfU2FUfipzzKlAhGRQSqCjVStW8u4s7YvxiGKhoIM5Xy5fe1DJEfQBPOncZWw6cQ1FJaa1fS17D38sd5zOvVGhQLPOy3N/bXa1HRERERPrVJDIriIiIiIio5vrH9lcNozec2IAtyVtQopWoHhXD2g5TGRV6DlTIxesyrQwGZykf5KrKOe05v0dlk5gCFIGegeqrrONIZbEam2RBdIgJgq+HK75PTMXZS3k4lJKJrnG1CypEB3nj/t5x+GxHMk6l56hm2qpHRUmZyqiQQMX9veIQFcSSXURERGR/2GC75hisICL7c/kAkH204nK/1kBAR1tsERERUaOTgMTEoIkY12UczqaeRXRENNxd3XX5TkjA4UzOGezJ3oOki0noE9MHvZv1Vo/9rtPv8E3yNzDAoJpsuxhcHLYslq00D/XFYHdXHEi5hE6xQXV6jU6xwQj29cCeExn45WSGaqYtPSqk9JNkVDBQQURERPaKPStqjsEKIrI/B14Azq0DSrKN8y5+gJMzEDkY6PeJrbeOiIioUUm2gJerl+6yBiR74nTWaVXa6VDaIaRmpsLb21t92Dt68ag5WNEpvBNeGfwKFm1fhMSLiaqZtvSokNJPklEhgQpHKYtlSyF+Hri1Q5RVoOhSbqEKQNSUBCRkGtotBoUlpaqZtvS0ICIiIqL6WbhwIWbNmoXJkydjyZIlallBQQGmT5+Ojz/+GIWFhRgyZAjefPNNhIeHw1YYrCAi+9PxOSDiTuCnJ43zNy0F3PyNmRVERESki0DF0h1LkVWYZb7w7WZwQ6ewTio4UT7wYFkWa3PyZtVMW8piSeknvZfFaqr2JGfgcEomerQOQ9uogFo9VwIUDFIQERGRXti6wfaPP/6I5cuXo3PnzlbLp06diq+++gqrV6+Gv78/Jk2ahPvuuw/btm2DrTBYQUT2R0o9uYcDe6ca56OHA56htt4qIiIiqoPSslKcvHxSNcke0GKAWia9JyJ8IlBUWoSEkAQkBCfAu9gbURFRcHZ2rrYs1mPdHkN+Sb5qvq23bBN7IcGlwuJSSKvsXUfTkJNfjK4t6lYeioiIiMjeNWTPiuzsq1VGrnJ3d1dTVXJzczF69Gi8/fbb+Otf/2penpWVhXfeeQerVq3C7bffrpatWLEC7dq1w44dO9C7tzGLubExWEFERERERI2qpKwEyZnJqsRTYkaiCi6IzuGdVXNscXfbu1XAQZpkl5WVIS0trUavLQEKBilsS0p19WkTDl9PN/wsGRZnMpF9pRBtgljSiYiIiKg+YmJirObnzp2LefPmVbn+k08+ieHDh2PQoEFWwYrdu3ejuLhYLTdJSEhA8+bNsX37dgYriIiIiKhyxaXFvFOcdOFs9lnsPLsTRy4eUT0lTHzcfNAupJ3VurKM7Dtg0al5EHw9XLEtMRVnLubhQkYR7gkOgbeHm603j4iIiMguG2ynpKTAz8/PvLy6rArpRbFnzx5VBqq81NRUuLm5ISDAulyn9KuQx2yFmRVERERETdSxS8ew8fhGbD65WV3YlabBt7W4DYPjWYOf7CfQVqqVqmNXXC64jH0X9qnvfd180S60HdqHtkdz/+aq9BPpT4swX3i5u2DzgbO4dLkYG345g3t6xMHgXN/KzURERESO17PCz8/PKlhRFQlqSDPtjRs3wsPDeC5uDxisICL7VVoAlBUCJ1YAHWbYemuIiBrU1pNbsXjHYqTnpSPQI1Bd7L1SdAUr963E+uPrMa3PNNVUmKipkT4TRy8eVSWeJIOiX/N+5l4UrYNbo0+zPipIEeMXU+87zMg+hPl7YmjXZvj39iPoEhvMQAURERHRDbZ7925VRvWmm24yLystLcV3332H119/HevXr0dRUREuX75slV1x4cIFRERE2Oz9YbCCiOxTaakxUCGtG4++ASRMBwwGW28VEVGDZVRIoCK3MBcdQjtYXdCN8o3C8czjWLR9EaJ9o1VTYSJbKywpVIEJCVAcvXRU9aQwkcbZJm4GNwxpNcRGW0m2JP0rbm8fisgwX/OyopJSuLnw/I2IiIj0rSHLQNXUwIEDsX//fqtljzzyiOpL8fTTT6veF66urti0aRPuv/9+9XhSUhJOnz6NPn36wFYYrCAi+5T8rjFQIfLPAUffBBL+aOutIiJqEFL6STIqygcqhMzHB8bjYPpBbDixARODJnKvk02VaWV4deeryCvOMy8L8gxS5Z1kivSJtOn2UdNhWfopr7AY6/amoGW4H7q2CGaWDREREemW09X/6vsateHr64uOHTtaLfP29kZwcLB5+aOPPopp06YhKChIlZb64x//qAIVvXv3hq0wWEFEjasoCyjOqrjc1R9w8695VsXx5dfmtTIgaTHQ+glmVxCRLmr8S48KKf1U1d0zslwe35y8GY91ewyuBtdG305yTPnF+UjMSERKdgrubnO3Ohal14Rk+Ejz7A5hHVSAItw7nBefqVpnL+bhSmEJDpy+hNz8YvRNCIfBmX1LiIiIiBrL4sWL4ezsrDIrCgsLMWTIELz55puwJQYriKhxpX0HnPkCyNhmnA+5BXA2AFF3Ac3urllg4+THxmwKE2dX4MoZZlcQkS7kl+Sbm2lXRx6X0juyPoMVdCPlFeWpAIWUeEq+nKwyKUTP6J6I8DHWs5XAhYuzCwMUVGNtogJUpsWOI2k4mZ6jMi1u7RAFDzd+RCUiIiJ9sUVmRWW2bNliNS+Nt9944w01NRU8EySixhXWH/BLAPbPM863fxowuBszK2oS2JDKT+e+NGZTmDi5AWV5zK4gIl3wdPE0N9OujgQ0vNy81PpEN8LJyydVo3f5qplKLwIqQCHZE96u3uZlDJhRXcRH+MPb3RVbD51DenYBvt6bgts7RcPfy407lIiIiHRDEubr2bIC9X2+vWCwgogal5R6MngArj7Gee/mxmBFTQMbJ1cBRReN2RSqwfbV39jObsyuICJdkIu+t7W4DSv3rVTNtCsrBaVpGjILMjG87XBeJKYGk1OYo776uhsbIEuTbMmkEHIsSoCiXUg7BHsFc69Tg4kI9MKQrjHYfOAccguMfSwGdY5GsG/12WVEREREpD8MVhCR/QQ2PKKB5PeNWRXOcifx1WCFkGBFSS6zK4hIF+6IvwPrj6/HicwTaBnY0ipgIYGK45nHEeYdhsEtB9t0O8n+ZRVk4XDGYVXi6XTWafSN6YvB8cbjKi4gDkPihyAhJAGBnoG23lTSsQBvd9zZLQabD55DSWkZfD3Zh4eIiIj0o6mUgbIHDFYQkf2QptrSm0ICE+XvNHZyZnYFEelGq6BWmNZnGhZtX4SD6QdVM20pDSWlnySjQgIVU/tMVU2NiWorMz/THKA4k33G6rHswmzz9wZnA/rE9OEOpkYhvSru6NwMRSVlcHMxcK8TERGRbjBYUXMMVhCRfSgtBY68BmilgKGK+uzMriAiHekf2x/RvtHYcGIDNidvVs20pUeFlH6SjAoGKqguJDPnnb3vILco1/zBqbl/c2OJp9B28HP3444lm3ExOKuJiIiIiBwTgxVEZBtFmUD2USA7EQjscv31sw9ZZFU4WzfYNmF2BRHpjAQkJgZNxGPdHkN+Sb5qps1GxlRTGVcyVPbEqcun8FDnh1Q5MZkkMJGel24OUPi4XS23SEREREREDa8BGmzDMapAMVhBRDagaUBuMnAlBUhZAwR0rv63tmRVZO0HtJKrjbWLjK9hUlZ87fkSsJDG20mLgdZPAAaWESAi+ycBCgYpqCZZE2l5aSpAIZN8byL9KGIDYtX3d7a6s9LG7UREREREdCPIuTejFTXBzAoianzZh4GCVMDgDmT8AGQdAgI6VL1+UQZQcgVwMhgDE4pFsEIrBjSLX/pOLkDRZaA0HzDwblEiItK/5KxkrD61GhfzL5qXGZwMiAuMQ4fQDqrPiQkDFURERERE1BQxWEFEjUsyIs58YQw6uAYAxblAyueAf/vKsytk/fyzgIsPEDkEiP2tMRpdlAX89KRxne5vAG7+1s/ziQNY1oKIiHSaQZGam6qybUK8QtQyN4ObKvvk4uyiGrRLiac2wW3g6VpFnyciIiIiImoUpnKs9X0NR8DuZUTUuCSLQrIpDF7G4IRnJJC+zbi8uiwMVx+g+DLg1xZofi8QPRwwuBkn+V6WWU5BXfnOEunEG2+8gRYtWsDDwwO9evXCrl27ql1/9erVSEhIUOt36tQJ//3vfytc6J0zZw4iIyPh6emJQYMG4ejRo+bHT548iUcffRRxcXHq8fj4eMydOxdFRUU3bIxE1yPH7dnss9h4fCNe3fkqlu9eju0p282PR3lH4dftfo0Zt8zAg50eRJeILgxUEBERERE1oSJQ9Z0cAYMVRNR4JEtCsigkm0IaZQvJrii5ml1h2YeifBaGi++1LIzM/cDZr4DSIuMk359eA1w+wHeTSGc++eQTTJs2TQUL9uzZgy5dumDIkCFIS7tWi9/SDz/8gAcffFAFG/bu3YuRI0eq6cCBa78fXnrpJbz66qt46623sHPnTnh7e6vXLCgoUI8nJiairKwMy5cvx8GDB7F48WK17jPPPNNo4yYyBShSslKw/th6LNmxBG/veRvbUrYhsyATrs6uVndXyfcdwjrA3cWdO4+IiIiIiOwSy0ARUeOR7AnJopBsiuJM47Ly2RWWvSuqysKQ6eJOY1aF2DvV+DVyMNDvE76jRDqyaNEijB8/Ho888oial6DBV199hXfffRczZ86ssP7SpUsxdOhQPPXUU2r+hRdewMaNG/H666+r58rF3yVLlmD27NkYMWKEWudf//oXwsPDsXbtWvz2t79Vz5fJpGXLlkhKSsKyZcvw8ssvV7mthYWFajLJzs5WXyXwIVNTJdsm+6Upb6MjjVG20xSEkO8/P/w5LuVfMpd6ah3UWpV4kq9SBsp0fNnL+OpK72PU+/gcYYx6HZ/exkNERGQLTlf/q+9rOAIGK4iocbMqJIvCM9r6MbdAIP+8de+KqrIwCi4AgV2B+AkVe1z4tea7SaQjUnZp9+7dmDVrlnmZs7OzKtu0ffu18jeWZLlkYliSrAkJRIjk5GSkpqaq1zDx9/dX5aXkuRKsqExWVhaCgoKq3d4FCxbg+eefr7A8PT3dnLXRVC9EyfjkIpvsXz1q6mMs08pwJucMjmQewemc0xjTfozqPSGauzWHT5kP2gS2QQu/FipAAQ3IvJhpN+NrCHofo97H5whj1Ov4cnJybL0JREREdo89K2qOwQoiahzZFlkV5YMMlWVXVJeFkXMMaD3ROguDiHQnIyMDpaWlKuvBksxLqabKSCCisvVluelx07Kq1inv2LFjeO2116rNqhASVLEMlEhmRUxMDEJDQ+Hn54emfIFNTp5lO/V0ga2pj7G0rBQnL5/EoYxDSMpIQl5xnvEBA5DrmquaY4sRYcYMIHsbX0PT+xj1Pj5HGKNexyf9n4iIiIgaC4MVRHTjqSyJNcasCu/mgFZJOrnBG8g7CZxYAbSeZPxadBFwD71+FoY9KMoCirMqLnf1B9z8bbFFRFQDZ8+eVSWhHnjgAVWOqjru7u5qKk8uWjX1C1dygc0etlMvY0zMSMQXiV8gvyTfvMzbzRsJIQmqxFNcQFytt7Mpje9G0fsY9T4+RxijHsenp7EQERHZSkM0yHaCY2Cwgohu/C+avCNwyrDIqijXR1spSAUKMoCjy4BTHxuXSfkntwvW61XX46IpS/vO2Cxc9oMIuQVwNgBRdwHN7rb11hE1SSEhITAYDLhwwfr3gMxHRERU+hxZXt36pq+yLDIy0mqdrl27Wj3v3LlzuO2229C3b1/84x//aLBxkWMpKSvB8UvH4ePmg2g/YxnEIM8gFajwdvVGu9B2KkDRIqAFnJ14UZCIiIiISG/Ys6Lm+ImIiG4sTYN7+n+BokzA2dXYg0IyLMqKjZN8L8tcfYGAjoDBw7jcPQwI7g14VHJBUrIr5HmSXSFZG/YgrD/Q/mkg8CbjJN93eNa4nIgq5ebmhptvvhmbNm2yKrMh83369Kn0ObLccn0hDbZN68fFxamAheU6Uq5p586dVq8pGRW33nqr+vkrVqzgnaVUK8WlxTicfhifHfoMf9/2d3x04CNsP3Otz0qYdxge7fYopvedjrva3IWWgS0ZqCAiIiIiIofHzAoiurG0UjgXXTAGGAovXV1YBpQVGr8tumQRNy27FtDwjgXcfIGy0oqvaY/ZFVLqSQIxrj7GeSmHZahYLoaIrEkPiLFjx6J79+7o2bMnlixZgry8PDzyyCPq8TFjxiA6Olo1txaTJ0/GgAED8Morr2D48OH4+OOP8dNPP5kzI6REx5QpU/DXv/4VrVu3VsGL5557DlFRURg5cqRVoCI2Nlb1qZAG2SZVZXQQSVPdwxmHcSj9EI5cPIKi0iLzTvFz90OgR6DVTorxj2nQ4MiV4ivqq7sz/7YQERERETUlchlLPovW9zUcAYMVRHRjObsgt+VMeAZ4wMlU87a0ENg/z/h9p3nXLtqXFADf/xooKzGWgJKghVZqzLRQj+cCToarr+tqDHTYW++KvNNAzhHg3NdAjPHCKBFVbdSoUSpYMGfOHNUAW0o1rVu3ztwg+/Tp01ZZD1KyadWqVZg9ezaeeeYZFZBYu3YtOnbsaF5nxowZKuAxYcIEXL58Gf369VOvaWoiKpkY0lRbpmbNmlW4IE1k2STbICX9rgbC/nfqfzife17N+7v7q/JOMjXza1bvDyeVOXbpGDYe34hvk79FSFkIMpwzcHvc7RgcPxjxQfF8o4iIiIiImgCWgao5BiuI6IbTXAMA7zDp0GdsNF2aVkWj6WCg9ApQVgTknwFcfKvJwpBshSBjs20JaDjZwa8zCVBkHQDkbtujbxoDLtHDbb1VRE3epEmT1FSZLVu2VFgmzbBlqopcNP7LX/6ipso8/PDDaiKqTEFJgcqckAyKk5dPYmrvqXB3MQbde0T3wMUrF1WAIso36oYEKEy2ntyKv/7vr6ofRlFxEdp7t8ehvEPYfX43Pk/8HM/1fw79Y1lqkIiIiIiI7IcdXN0jIl2RRtPnvrxWDinxZeNXaTQdcYcxQOF0EQjqCSRMMQYuKsvCMJFeF8528Kvs7FfGAIXclO0epOLqSHrN+BgDFkRETVp+cT6SLiapAIUEB0olSH7VicwTqkm2uCnypkbZHsmoePbbZ5GUkaR6XXi5eMHd4A5XgyvyivLw8/mf8eymZ/HeyPeYYUFERERE1CTKQNX/NRyBHVzhIyJdkYbSgV0qz6zIPgwUZQAuXkD2QWPZJ99W9t/nQQIVKjDhdG0sXjFA/lkGLIiImjgJUHx66FOUaWXmZaFeoeYST9Isu7F9uO9DtV0eBg/4e/irgIUEKrzcvODh6oGsgiwcTD+IVftX4bkBzzX69hERERER0TUsA1VzFvVUbOeNN95AixYtVK3oXr16YdeuXVWuW1xcrMo2xMfHq/W7dOmi6kxbWrZsGTp37gw/Pz819enTB19//XUjjISIatRoWoIO5SdXP+DMF8b+FJJdIf0qpB+FvdeHNwUqJAQuAQpLMm5ZLo/LekREZFO5Rbn46dxPKnPBJNo3WgUqwr3DcWuLW/FkjyfxZM8ncVvcbQj3Cb+hpZ4qI020v0j6Aho0FaiQOLgVJ6jl8vjaxLVqfSIiIiIiIntg88yKTz75BNOmTcNbb72lAhVLlizBkCFDkJSUhLCwineqScPMlStX4u2330ZCQgLWr1+Pe++9Fz/88AO6deum1pFmmAsXLlRNNaUR5vvvv48RI0Zg79696NChgw1GSUTXlXUIyPgBMHgZL+B7RgLp24Dou/QRqJDARNm1siFmslyabrMkFBGRTeQU5uBwxmGVqXDq8il1kb91UGu0CmqlHpcL/1N6T0GAR0CTeIeyC7ORmpuqsioqBCpMnKAel/Vk/WCv4EbeSiIiIiIiukZO3Ot7k5OTQ+xQmwcrFi1ahPHjx+ORRx5R8xK0+Oqrr/Duu+9i5syZFdb/4IMP8Oyzz2LYsGFqfuLEifjmm2/wyiuvqCCGuPvuu62eM3/+fJVtsWPHDgYriJoiyZ6QLArJpnB2My6TptwFF4zZFvK4vRXnKx+oqA4DFkREjW7X2V04kHYAKVkpKkBhmUnRMrCl1bpNJVBBRERERET2WtzIWQ8FkvQdrCgqKsLu3bsxa9Ys8zJnZ2cMGjQI27dvr/Q5hYWFqvyTJU9PT3z//feVrl9aWorVq1cjLy9PlYOq6jVlMsnOzlZfy8rK1NSUyfZJ9khT38660vv4HGGMNRpf1kE4pX8PeEYAxZnGZRKbkHnJrpDSUW5B0OQ1nMqa/hjPfQWnI68bByGln8ylrCxLWmnWJa5kPZVh8So0qYseNRxNhd6PUT2PUW/jIaqPvOI8q3nJpDiddVp938yvGTqEdlCNspt6YMLP3Q+RPpGqsbf83qqsDJUsLygpUEEXWb8+rhRdwaWCSwjyCFI9MYiIiIiIiHQZrMjIyFDBhPDwcKvlMp+YmFjpc6RElGRj9O/fX/Wt2LRpEz7//HP1Opb279+vghMFBQXw8fHBmjVr0L59+0pfc8GCBXj++ecrLE9PT1fPb+oXorKystSHUgn06I3ex+cIY7zu+DQN3idXwv1KJkq8guBaUqIWF+fnA2UGuORdgFPuWZR4tUVOyl6VeaG5+KmpqY7RP/EdGAouosSnHSDjMNFKrcfnZLB+IedQuOQeRmniO8hy6YGmQu/HqJ7HmJOTY+tNILKpS/mXcDj9sMqgOH7hOJ4LfQ6+Hr7qsd7NeiMhJAHtQtoZez/YCWmkfU/be7B051JVwsrX3dcqI1x+j8lyabo9ImGEWr8uNp3YhH/u+Se+P/09isuK4ersin7N+2H8zeNxe9ztDTcgIiIiIiKdY4NtOyoDVVtLly5VZaOkX4XcSSYBCykhJWWjLLVt2xY///yzuvj06aefYuzYsdi6dWulAQvJ7JC+GZaZFTExMQgNDVUNupv6BTbZD7KterrA5ijjc4QxXnd8klWR/zPg3xyurl7AFeOvJRdPT+DKGUDLVU23XTwM8Eh/Tz2mSdZBmHW5tyY1xoRHVWaFa1m6dQkorRTIsxhf+WCFZFZ4BcOlzaOV9uyxFb0fo3oeY/lMRCJHcPHKRZU1IdP53PPmC/gypWSnoL2H8VxQAhX2anTn0dh4YiOSMpKQmZ8JTxdPFLkWIa8oD/kl+aohePvQ9hjdaXSdXn/pjqVYuG0hcgtz1Wu7GFxQWFKIL498iS2ntuCZfs/gj73+2ODjIiIiIiLSJ/assItgRUhICAwGAy5cuGC1XOYjIiIqfY5cSFq7dq3KeLh48SKioqJUb4uWLa1rC7u5uaFVK2NjxJtvvhk//vijCnQsX768wmu6u7urqTy5YGUPF63kApu9bGtd6H18jjDGKscnZZDOrAVK8gDvWEDKH117FuAZBbgFA7lHAe8WQJs/qh4QTq7+8g8UTXaMze4GnJyNPSuupFwLWGiWpTqcrPtwSKBCtP0TnKKbTgkoRzlG9TpGPY2FqCYOph3E6kOrzfOSXdAioAUSghMQrAUjLiROFztSmn+/OPBFvLD1BVUOqqC4AMWlxWrycfNBfGA8Zg+Yjfig+DplVEigQso/ebl6qeCHVqqpu8FkXpa/+P2L6BDWgRkWRERERESkn2CFBBQkkCClnEaOHGm+u1XmJ02adN27RaOjo1FcXIzPPvsMv/nNb6pdX17Xsi8FETUBWYeMPSk8I40X7i1bOgiDm3GSQMXlA0BJLhDQwUYbW0umgIMELCQQUV2TbXlcAjdt/3jteUREVCXJkkjLS1PZEyFeIegU3kktjwuMU+WKYgNiVWaBZE/IBXY5D0xLS9PVHu0f2x9v3fUWNpzYgG9PfIuQ0hD4Bfvh9pa3Y3DLwXUKVAgp/ZSVnwWDswHZRdkwOBlU0EeyNWTexclFPf72nrcZrCAiIiIiqhE22LabMlBSfklKNHXv3h09e/bEkiVLVDNsKe0kxowZo4IS0ldC7Ny5E2fPnkXXrl3V13nz5qkPoDNmzLAq63TnnXeiefPmql73qlWrsGXLFqxfv95m4ySicuTifMrnQNElwD0IKM41lkkqKzY+LoEJU5kkZ1fjerK+f3vrjAR7Clh4Rldch4EKIqIaByhSc1PNJZ4u5l9Uy5v7NzcHKyQw8dQtT8FNAt0OQAISE4MmYlyXcTibehbREdFwd62YLVxTkjWx5eQWlGqlKpPC3eBubuBtgAEumovqXyGPb0neYsy+YNNtIiIiIqLqyTl1fa9lOdnJtTB7D1aMGjVKNbKeM2cOUlNTVRBi3bp15qbbp0+ftipjIeWfZs+ejRMnTqjG2cOGDcMHH3yAgIAA8zpy55wEOc6fPw9/f3907txZBSruuOMOm4yRiCohgYn884BbEFB46erCMqDsagaUBCdU5PkqWU/Wl+c52fxXV90CFlIS6kYEKuR1rpytuNwruvqMDiIiO7E5eTP2XdiHzIJM8zIXZxdVDkkyKCw5SqDCkjTRlkBNXZtpm1wquKT6XmjQVIaKKVBhIvOyXMpNyXqyPoMVRERERETUUJrEFT8p+VRV2SfJiLA0YMAAHDp0qNrXe+eddxp0+4joBnB2AbrOB4pzri0rLQT2zzN+32keYCh3d6irr/F59sYcsFhqzCBx9bkauHBqmNJPx94GTv8fkHfSOC9ls6RnRsyvgS4v1H/7iYgaOYPiQt4FRPhc618mGRUSqJAL5a2DW6sAReug1nB3qXsWAVXk6+qLEq1EfV8+UGFiWi7ryfpERERERFQ9p6v/1YdTPZ9vL+zwqh8R6YZboHGyDFbIhXwhGQHlgxX2TAISUuJq9x+NmSReMaqZdoP0qGg1HggbAOx81Djf/Q3AxduYWUFEZCcBipTsFHOJp+zCbEzuNRmBnsa/EX1j+qJLRBeVSeGImRONxcnZCYEegSpYJO9JZQELWS79K+S9kfWJiIiIiOh62LOiphisIKKmoSgLKEgzZh6YShtJsMLVH3Dzhy5E3Qkc6wjkHAFaP9FwzbQlsOMaAJTkAcVZQOomoJuxzw8RUVMlF7xPZ51WwYnD6YeRU3Qt0056JUgDbVOwQhpm043n6eKpMlcyrmQgvzgfnq6eVgELCVTIcmmyLZktsj4REREREVFDYbCCiJqGtO+Ac19ey6xIfNn4NeouoNnd0A0JLMgkgYuGdHABUCS13DXg+D+MgZ7OV0tqERE1QUkZSfjk4CfmeQ8XDySEJKBdSDvVOFp6UlDjkp4XozqMUkGk8znnkVecp94HZydnFVwqKSuBq5MrInwjMKrjqHr3yCAiIiIicgxyA1B9s5Kd4Aj4KZCImoaw/kBgl4rLJbOCqrdvnjFAIZzlwpETcOR14zwDFkRkY6VlpUi+nKwyKMK8w9C7WW+1XAIS/u7+aBnYUvWgkK8GZ4OtN9fh3RF/B9YfX48AjwBkFWSpklASqDA4GRDlFwV/D3/E+sdicMvBDr+viIiIiIhqhmWgaorBCiJqGtx0VO6psQMVKjDhdK35uHswUHSRAQsishm5A/9E5gkVoEjMSERBSYFaHu4dbg5WSO+JKb2nVNnImWxD+oJM6zMNi7YvQrpLOtoEtzFnVkgvEQk4Te0zVQWbiIiIiIiIGhKDFURE9h6okOt8bsFAybV673APBQrTGbAgokb336P/xS+pv6CwtNC8zMfNR5V3kgwKy8bNDFQ0Tf1j+yPaNxobTmzA5uTNKCwphLuLO+5JuEdlVDBQQURERERUc05X/6sPJ5aBIiIiuwhUSGBCK6u4DgMWRHSDFZcWqxJP0mzZFHiQLAoJVPi6+aJdaDt0CO2AGP8YdXc+2Q8JSEwMmojHuj2G/JJ81UybPSqIiIiIiOqKGeU1wcwKIqLGUJQFFKQBxbnG+bzTxibYrnUof1U+UFEdBiyIqIEVlRbh6MWjOJh+UH0tLivG490fR4RPhHr8lphb0COqB5r5NWPmhA5IgIJBCiIiIiIiagwMVhARNYa074BzXwKuPsb5xJeNX6PuAprdfWMCFSYMWBBRPUkZoCMXj6geFEcvHVU9KUwCPQKRW3Q1ECt9KXzCub+JiIiIiIjM2GC7phisICJqDGH9gcAuFZdLZkVtnHgXKCsEvJrV7nkSsLhyxvj8zvNq91wicnhnss/gs8OfmfdDkGeQKu8kPSgko8Iee09ICSuWNyIiIiIiohtPPi/V9zOTExwBgxVERI3BrQ7lnirTcpwxs0KaZ9c0s0LI+s7uxucTEVXhSvEVJGUkqQwKCUIMbDlQLW8R0AIxfjGIC4xTQYow7zC7DFCIY5eOYePxjdh8crPqr+Hh4oHbWtyGwfG1bxzNgAcREREREVHDYbCCiMiemLIiahOwkPU0AG0mMauCiCrIK8rDL+m/IDU1FaeyTqFMK1PL06+k4/a421VQwuBswKM3PWr3e++7U99hyc4lSM9LV+WrJFBxpegKVu5bifXH12Nan2noH9u/UQMeRERERESkb05wVlN9X8MRMFhBRKTngAUDFURUjc8OfYb9F/YjNy8X3t7eKjAR7h2ODmHGEk/2mj1RmfM557F091LkFuaq7BDLsUX5RuF45nEs2r4I0b7R1QYctp7cisU7Ftc74EFERERERI6CZaBqisEKIiI9BCzcgiuuw0AFEVnILsxWJZ5ujroZzk7Gu3LkQrsGDeFe4egV10sFKYK9Kvl9ogN7U/eqAENlQRiZjw+Mx8H0g9hwYgMmBk2sMqNCAhX1DXgQERERERFRRQxWEBHpImBx0foxBiqICEBWQZbqPyFTSnaK2ifSbyI2IFZ9f0vzW9A7ujeKc4oRFhYGZ2d9phZLbwnJIJFMiKqyRWS5PL45eTMe6/YYXA2uFdaR0k8S8CgfqKhNwIOIiIiIiByMfHaob9a6k36y3qvDYAURkS4CFq8BZSWAs8u1wAV7VBA5pNyiXPyS+osKUJzNOWv1WHP/5iqTwiTAIwBlZWVIy0mDnuWX5KOorEhlklRHHi8sKVTrlw9WSMBDelTUN+BBRERERESORm4Kq++NYc5wBI4xSiIivQcs4icYvy8rhuqmzUAFkUMpLSs1f3+54DI2ntioAhVOcEKLgBYY1nqY6qUwrts4Ne9oPF084ebsppphV0ced3dxV+uXJwEMUzPtmgY8iIiIiIiIbGHZsmXo3Lkz/Pz81NSnTx98/fXX5sdvvfVWdbOV5fT444/b/M1iZgURkR50mAUcfxsozjIGLkwZF0SkW1KOyFTiqZlfM9zd9m61XPolSJkiCUq0C20HHzcfODrJcOgU3gk/HvkRkb6RlWZGaJqGzIJMDG87vNKMCAlgmJppV0cCGl5uXpUGPIiIiIiIyPE4Xf2vvq9RG82aNcPChQvRunVr9Vnn/fffx4gRI7B371506NBBrTN+/Hj85S9/MT/Hy8sLtsZgBRGRXniEGicJXBCR7sgJZvoVY4DiYNpB9b3JleIr6nHTHTEPdHjAptvaFHWL6IbQs6E4kXkCLQNbWgUsZN9Jc2zp5zG45eBKny8BjNta3IaV+1aqZtp1CXgQEREREZEjks8O9e054VSrte++23gzm8n8+fNVtsWOHTvMwQoJTkRERKApYbCCiMje5Z0Gso8AJVfv9r24C3DxBryiAe/mtt46Imogq/avwtFLR83zBieDuujePrQ92oa0rbKPAhlJRsWU3lOweMdi1QRbektIpoRkQkiAQQIVU/tMRXxQfJW77I74O7D++Po6BzyIiIiIiIjqIzs722re3d1dTdUpLS3F6tWrkZeXp8pBmXz44YdYuXKlClhIcOO5556zeXYFgxVERPbu2NtAyqeAy9U/KLv/aPwa82ugyws23TQiajjhPuHqInmroFbmAMX1+ieQtV81/5UqmbXhxAbVBFt6S0jJJsmEkABDdYEKIfteen8s2r6ozgEPIiIiIiJyNA3XYDsmJsZq6dy5czFvXuWlwPfv36+CEwUFBfDx8cGaNWvQvn179djvfvc7xMbGIioqCvv27cPTTz+NpKQkfP7557AlBiuIiOxdq/FA1LCKyyWzgoh0o29MX3WxXRpAU91JIGFi0EQ81u0x1QRbekvUpmRT/9j+qi9IXQMeRERERETkaBquDFRKSopqmG1SXVZF27Zt8fPPPyMrKwuffvopxo4di61bt6qAxYQJE8zrderUCZGRkRg4cCCOHz+O+HjbfaZhsIKIyN5JqSeWeyLSPS9X2zc70xMJUNS1r0R9Ax5ERERERER14efnZxWsqI6bmxtatWqlvr/55pvx448/YunSpVi+fHmFdXv16qW+Hjt2jMEKIiIiIiJHCngQEREREZFjcIKzmur7GvVVVlaGwsLCSh+TDAwhGRa2xMwKIiIiIiIiIiIiIqImXgaqpmbNmoU777wTzZs3R05ODlatWoUtW7Zg/fr1qtSTzA8bNgzBwcGqZ8XUqVPRv39/dO7cGbbEYAURERERERERERERkU6kpaVhzJgxOH/+PPz9/VUQQgIVd9xxh+p78c0332DJkiXIy8tTTbvvv/9+zJ4929abzWAFEREREREREREREdGNU9/Mitp55513qnxMghPSaLspYmYFEREREREREREREdENIf0m6ttzwhmOwDFGSURERERERERERERETRYzK4iIiIiIiIiIiIiIbgAnJyc11fc1HAEzK4iIiKhJe+ONN9CiRQt4eHigV69e2LVrV7Xrr169GgkJCWr9Tp064b///a/V45qmYc6cOYiMjISnpycGDRqEo0ePWq1z6dIljB49Gn5+fggICMCjjz6K3NzcGzI+IiIiIiIi0jOnBpr0j8EKIiIiarI++eQTTJs2DXPnzsWePXvQpUsXDBkyBGlpaZWu/8MPP+DBBx9UwYW9e/di5MiRajpw4IB5nZdeegmvvvoq3nrrLezcuRPe3t7qNQsKCszrSKDi4MGD2LhxI7788kt89913mDBhQqOMmYiIiIiIiMgRsQwUERERNVmLFi3C+PHj8cgjj6h5CTB89dVXePfddzFz5swK6y9duhRDhw7FU089peZfeOEFFXB4/fXX1XMlq2LJkiWYPXs2RowYodb517/+hfDwcKxduxa//e1vcfjwYaxbtw4//vgjunfvrtZ57bXXMGzYMLz88suIioqqdFsLCwvVZJKdna2+lpWVqampkm2T/dKUt7G+9D5GvY/PEcao9/E5whj1Oj69jYeIiMg22GC7phisICIioiapqKgIu3fvxqxZs8zLnJ2dVdmm7du3V/ocWS6ZGJYka0ICESI5ORmpqanqNUz8/f1VeSl5rgQr5KuUfjIFKoSsLz9bMjHuvffeSn/2ggUL8Pzzz1dYnp6ebpW10RQvRGVlZamLbDJGPdL7GPU+PkcYo97H5whj1Ov4cnJybL0JREREOtAQZZyc4AgYrCAiIqImKSMjA6WlpSrrwZLMJyYmVvocCURUtr4sNz1uWlbdOmFhYVaPu7i4ICgoyLxOZSSoYhkokcyKmJgYhIaGqt4XTfkCmzRrk+3U0wU2Rxqj3sfnCGPU+/gcYYx6HZ/0fyIiIiJqLAxWEBERETUAd3d3NZUnF62a+oUrucBmD9tZH3ofo97H5whj1Pv4HGGMehyfnsZCRERkK05wVlN9X8MROMYoiYiIyO6EhITAYDDgwoULVstlPiIiotLnyPLq1jd9vd465Rt4l5SU4NKlS1X+XCIiIiIiIqLqy0DVd9I/ZlZUQuqMWjbGbOrpxlJHVNJz9XjXi97H5whj1Pv4HGGMeh+fnsdo+jtm+rtmb9zc3HDzzTdj06ZNGDlypPm9kvlJkyZV+pw+ffqox6dMmWJeJg22ZbmIi4tTAQdZp2vXrub9JL0oJk6caH6Ny5cvq34Z8vPFt99+q3629LbQ2/mEXo9/Rxqj3sfnCGPU+/gcYYx6HZ+9n0vYO9N+LysssfWmENVOSSn3GNnlMXuj/t5lZ+c0idewCxpVkJKSIkcmJ+4DHgM8BngM8BjQxTEgf9fs1ccff6y5u7tr7733nnbo0CFtwoQJWkBAgJaamqoe//3vf6/NnDnTvP62bds0FxcX7eWXX9YOHz6szZ07V3N1ddX2799vXmfhwoXqNb744gtt37592ogRI7S4uDgtPz/fvM7QoUO1bt26aTt37tS+//57rXXr1tqDDz5Yq23n+YTtj31O3Ac8BngM8BjguYQ947kEf4fxdxiPAR4D9v3ZWT5jRkRENNj2RUREWH1u1SNmVlQiKioKKSkp8PX1VXVHmzJT807Z3qbcvLOu9D4+Rxij3sfnCGPU+/j0PEa5K0Tu8pS/a/Zq1KhRSE9Px5w5c1Rza8mGWLdunblB9unTp63uYO3bty9WrVqF2bNn45lnnkHr1q2xdu1adOzY0bzOjBkzkJeXhwkTJqgMin79+qnXtGwi+uGHH6rsjYEDB6rXv//++/Hqq6/q8nxCr8e/I41R7+NzhDHqfXyOMEa9jk8P5xL2zF7OJeyRXv/Nkr7xuLW/v3fyGTM5ORlFRUUNVn3Aw+Jzqx45ScTC1htB9ftF5e/vj6ysLF3+gdX7+BxhjHofnyOMUe/jc5QxEjny8a/3Mep9fI4wRr2PzxHGqPfxEekN/82SPeJxS45AP8U0iYiIiIiIiIiIiIjILjFYQURERERERERERERENsVghZ1zd3fH3Llz1Vc90vv4HGGMeh+fI4xR7+NzlDESOfLxr/cx6n18jjBGvY/PEcao9/ER6Q3/zZI94nFLjoA9K4iIiIiIiIiIiIiIyKaYWUFERERERERERERERDbFYAUREREREREREREREdkUgxVERERERERERERERGRTDFYQERERERERERHVwLx589C1a1fuKyKiG4DBCjuwYMEC9OjRA76+vggLC8PIkSORlJRktU5BQQGefPJJBAcHw8fHB/fffz8uXLgAe7Bs2TJ07twZfn5+aurTpw++/vprXYytKgsXLoSTkxOmTJmim3HKCZuMyXJKSEjQzfjE2bNn8dBDD6kxeHp6olOnTvjpp5/Mj2uahjlz5iAyMlI9PmjQIBw9ehT2okWLFhXeQ5nkfdPDe1haWornnnsOcXFx6v2Jj4/HCy+8oN43vbyHRI56LuGI5xM8l7DP95DnEvb/75DnE0T6cOutt1b62aekpMTWm0ZUrZMnT1Z67FpO7733Hvci2S0GK+zA1q1b1Un9jh07sHHjRhQXF2Pw4MHIy8szrzN16lT85z//werVq9X6586dw3333Qd70KxZM/WBe/fu3erC7+23344RI0bg4MGDdj+2yvz4449Yvny5uqBiSQ/j7NChA86fP2+evv/+e92MLzMzE7fccgtcXV3Vxa9Dhw7hlVdeQWBgoHmdl156Ca+++ireeust7Ny5E97e3hgyZIi6QGYvx6bl+ye/b8QDDzygi/fwb3/7m7qY+frrr+Pw4cNqXt6z1157TTfvIZGjnks42vkEzyXs8z3kuYQ+/h3yfIJIP8aPH2/1+UcmFxcXW28WUbViYmKsjtnp06dXuBYzatQoqyB7WVkZ9yrZD43sTlpamtwGrG3dulXNX758WXN1ddVWr15tXufw4cNqne3bt2v2KDAwUPvnP/+pu7Hl5ORorVu31jZu3KgNGDBAmzx5slquh3HOnTtX69KlS6WP6WF8Tz/9tNavX78qHy8rK9MiIiK0v//971bjdnd31z766CPNHsnxGR8fr8amh/dw+PDh2rhx46yW3Xfffdro0aN1+x4SOfK5hF7PJ3guYb/vIc8l9PHvkOcTRA1HPhM/+eSTavLz89OCg4O12bNnq/Py1157TevQoYN53TVr1qjfF8uWLTMvGzhwoPbss8/W+WebPo+XN2PGDPW53dPTU4uLi1PbVFRUVOVn382bN2s9evTQvLy8NH9/f61v377ayZMnzY+vXbtW69atm/pcIa83b948rbi4uE7bTU2LLY/hqo7HFStWqOPwiy++0Nq1a6cZDAYtOTm50mN+xIgR2tixY83zBQUF2vTp07WoqCh1PPfs2VMd30SNiZkVdigrK0t9DQoKUl/lDkK5Q1LKlZhI+Z3mzZtj+/btsCcS8f3444/VnZ5SvkFPYxNyV+vw4cOtxiP0Mk4plxMVFYWWLVti9OjROH36tG7G9+9//xvdu3dXWQZSQqVbt254++23zY8nJycjNTXVaoz+/v7o1auX3YzRUlFREVauXIlx48apNFI9vId9+/bFpk2bcOTIETX/yy+/qOyfO++8U5fvIZGjnkvo/XyC5xL2+x7yXEIf/w55PkHUsN5//32VzbBr1y4sXboUixYtwj//+U8MGDBAZbOnp6er9SQbKyQkBFu2bFHz8vtEfndIOSfxv//9T5WXq2768MMPa7RNUjZTyujIz5dtks99ixcvrnRdKRsl5TVle/ft26e2acKECeozlGm7xowZg8mTJ6vXkyoL8trz589voD1IttYUj+ErV66oTEDZDskylmsYNTFp0iS1TXIeLcezXP8YOnQoSyNTo2J+m52R1C3pcyDlaDp27KiWycU1Nzc3BAQEWK0bHh6uHrMH+/fvVxcTpNSK/AJes2YN2rdvj59//tnux2Yiv+z37NmjSjeUp4f3UC7oyklX27ZtVdrh888/j1/96lc4cOCALsZ34sQJVUJo2rRpeOaZZ9T7+Kc//UmNa+zYseZxyJjsdYyW1q5di8uXL+Phhx9W83p4D2fOnIns7Gx1YcRgMKiLmfIhQQJrQm/vIZGjnUs4wvkEzyXs+z3kuYQ+fs/wfIKo4UvaSCBALu7LZ0n5Wy7zcoFVbqqQC7y//vWv1QVeKXcjF4OFXBiWi70SQBRyY5n8va9O+fP8N998U13MNfnDH/6gSv3Onj3bqq/fn//8Z/U3eMaMGRVeUz5fyE0gd911l+qJJ9q1a2d+XD4Xy+8N+cwo5MY+6ZsnrzV37tw67jVqSmx5DFdFXleO7y5dutR4HHKz6YoVK9RXuQlVyLG/bt06tfzFF1+s8WsR1QeDFXZG7qaTi7+WvQD0QH6hyy9l+SP/6aefqj/k8gtdL1JSUtSdFFIn3MPDA3pkujtdSD8OCV7Exsbi//7v/1SjYj1c3JOTB9MfaMmskH+L0tvAdOKpJ++88456T00nKXogx6LcibJq1SpV01N+58gFWxmjHt9DIkc7l9D7+QTPJXguYW/0eC4heD5B1LB69+5tzkIQctOBBAzk81f//v3VBV7JyJI71J944gnVYy4xMVH9fe/Rowe8vLzU8+QzZ6tWrWr1s+WmpWeffdY8bwqmfvLJJ6qP3fHjx5Gbm6uyJ/z8/Cp9DbkYLTd4SZ+7O+64Q23rb37zG0RGRpqzubdt22aVSSE3TcmNFXL3u2n7yX7Z8hiuitwcUL5P6vVIkEWOzTZt2lgtLywsRHBwcINsF1FNsAyUHZF0rC+//BKbN29WTSRNIiIiVMkWuQva0oULF9Rj9kB+kcov5ZtvvhkLFixQ0V+JNuthbELKT6SlpeGmm25S6YEyyR8mOQGS7yU6rodxWpITPfkjd+zYMV28j3KyKXfnWpI7ZkylrkzjkDHZ6xhNTp06hW+++QaPPfaYeZke3sOnnnpK3dX029/+Fp06dcLvf/971ehTfufo7T0kcsRzCb2fT/Bcwv7fQ55L2P97KHg+QdR4pDyOXOiV8jhys5gEDEwXf+XztJTZMalLCR0p+SrnDaZJSvRICRwJYgwbNkydM+3du1cFNOT3V1XkrnN5ntwhL4EO+Ry8Y8cO9ZgEOyS7Qm6mME1yUVhKKOv1RkZqvGO4KhL4sAygCGdnZ+lbXCEDw0SOValAIOeclsfr4cOHzdkgRI2BwQo7IL9M5OKClDL49ttvERcXZ/W4fCB3dXVVtdhNkpKS1EVUiejaI4lAS/RWL2MbOHCgOiGx/IUvd+nLSZDpez2M05L8oZM7UeSDuR7eRymXIttsSXofSPaIkH+X8kHbcoySErxz5067GaPlybbUtJT+KiZ6eA/lziU5QbMkJ2Py+0Zv7yFReY54LqG38wmeS9j/e8hzCft/DwXPJ4galpxrW5KL/K1bt1bn6aaa/6tXrzbX9ZevcmOVZCuYllmW0Kluuueee667PT/88IP6jCcBCnlN2Ra5met65EL0rFmz1POlzKZkcwu5YVF+11kGRUxT+c8mZJ+a2jFcldDQUFWy20SyKCTb2vIYlmVyo235Y9WebiogHWjUdt5UJxMnTtT8/f21LVu2aOfPnzdPV65cMa/z+OOPa82bN9e+/fZb7aefftL69OmjJnswc+ZMbevWrVpycrK2b98+Ne/k5KRt2LDB7sdWnQEDBmiTJ082z9v7OKdPn66OUXkft23bpg0aNEgLCQnR0tLSdDG+Xbt2aS4uLtr8+fO1o0ePah9++KHm5eWlrVy50rzOwoULtYCAAO2LL75Qx/KIESO0uLg4LT8/X7MXpaWl6n16+umnKzxm7+/h2LFjtejoaO3LL79Ux+nnn3+ujtEZM2bo6j0kcsRzCUc9n+C5hH29hzyX0Me/Q55PEDXs3zEfHx9t6tSpWmJiorZq1SrN29tbe+utt9TjZWVlWlBQkGYwGLSvv/5aLdu7d6+al89mubm5DfZ53EQ+B8hrf/TRR9qxY8e0pUuXqm2Q8yiTuXPnal26dFHfnzhxQp1z/PDDD9rJkye19evXa8HBwdqbb76pHl+3bp16vXnz5mkHDhzQDh06pF772WefrfO2U9Nhy2O4suNRrFixwup4NZFtkmsY8nn48OHD2vjx4zU/Pz/1d81k9OjRWosWLbTPPvtMHds7d+7UXnzxRfUcosbCYIUdkJhSZZP8AjKRC2lPPPGEFhgYqH753HvvveoihD0YN26cFhsbq7m5uWmhoaHawIEDzRcW7H1stTk5svdxjho1SouMjFTvo1wQlnk5udPL+MR//vMfrWPHjpq7u7uWkJCg/eMf/7B6XE5EnnvuOS08PFytI8dyUlKSZk/k5Fp+v1S23fb+HmZnZ6t/c3KRxMPDQ2vZsqX6kFBYWKir95DIEc8lhCOeT/Bcwv7eQ55L2P+/Q55PEDXs3zH5nSCBTLloKr8bnnnmGXVObiI3D8lF3ZycHPPNVbJe79696/2zKwtWiKeeekoFHOQitHyuXbx4cZXBitTUVG3kyJHmz8JyLjJnzhy1nSYSsOjbt6/m6empxtmzZ88KnyXJPtnyGK5tsKKoqEjdwCTBk7CwMG3BggVq2yyDFbKOHL8SsHB1dVXHtfytlhuBiBqLk/zP1tkdRERERERERETkOKQETteuXbFkyRJbbwpRnfAYJmp4LJBHREREREREREREREQ2xWAFERERERERERERERHZFMtAERERERERERERERGRTTGzgoiIiIiIiIiIiIiIbIrBCiIiIiIiIiIiIiIisikGK4iIiIiIiIiIiIiIyKYYrCAiIiIiIiIiIiIiIptisIKIiIiIiIiIiIiIiGyKwQqiJu7kyZNwcnLCzz//jKYiMTERvXv3hoeHB7p27XrDfs6tt96KKVOmmOdbtGiBJUuWVPsc2Vdr166Fnr333nsICAiw9WYQEZGd4LkEzyXK47kEERERETVFDFYQXcfDDz+sLoAvXLjQarlcEJfljmju3Lnw9vZGUlISNm3aVOHxu+++G0OHDq30uf/73//Uftu3b1+tf+6PP/6ICRMmoL7v58iRI2v1HEcIgBAR0Y3Dc4mKeC5BRERERETlMVhBVAOSQfC3v/0NmZmZutlfRUVFdX7u8ePH0a9fP8TGxiI4OLjC448++ig2btyIM2fOVHhsxYoV6N69Ozp37lzrnxsaGgovLy/Yq+LiYltvAhER2QjPJazxXKJueC5BRERERHrGYAVRDQwaNAgRERFYsGBBlevMmzevQkkkKVkkpYvK39X/4osvIjw8XJXy+ctf/oKSkhI89dRTCAoKQrNmzdQF/cpKL/Xt21dd7OjYsSO2bt1q9fiBAwdw5513wsfHR73273//e2RkZFiVVJo0aZIqqxQSEoIhQ4ZUOo6ysjK1TbId7u7uakzr1q2zyjLYvXu3Wke+l3GXd9ddd6nAgpQYsJSbm4vVq1erYMbFixfx4IMPIjo6WgUgOnXqhI8++gjVKV8G6ujRo+jfv7/aJ+3bt1cBktqS/fKnP/0JM2bMUPtf3mfLMZnev3vvvVeN1/L9/OKLL3DTTTepn9+yZUs8//zz6r203FfLli3DPffcozJRXnjhBbVfZZmlvXv3wtnZGadOnVLzixYtUvtDnhMTE4MnnnhC7buq/PLLL7jtttvg6+sLPz8/3Hzzzfjpp59qvS+IiOjG4bkEzyV4LkFEREREVD0GK4hqwGAwqADDa6+9Vmm2QG18++23OHfuHL777jt1UVrKIMjF/cDAQOzcuROPP/44/vCHP1T4ORLMmD59urqw3adPH1VqSS74i8uXL+P2229Ht27d1EVqCS5cuHABv/nNb6xe4/3334ebmxu2bduGt956q9LtW7p0KV555RW8/PLLqlSTBDXkYrsEBsT58+fRoUMHtS3y/Z///OcKr+Hi4oIxY8aoYIWmaeblEqgoLS1VQYqCggJ1Uf2rr75SgRYp7yQBll27dtVoP0pQ5b777lPjkf0m43n66adRF7JfJDAgr/PSSy+pQIwp8CGlp4QEkGS8pnkpZyVjnDx5Mg4dOoTly5er8c6fP9/qtSXwIRcn9u/fj8cee0yNfdWqVVbrfPjhh7jllltUpoqQwMWrr76KgwcPqm2TY0aCKVUZPXq0CoLItkkgaebMmXB1da3TviAiohuD5xI8l+C5BBERERHRdWhEVK2xY8dqI0aMUN/37t1bGzdunPp+zZo1chXevN7cuXO1Ll26WD138eLFWmxsrNVryXxpaal5Wdu2bbVf/epX5vmSkhLN29tb++ijj9R8cnKy+jkLFy40r1NcXKw1a9ZM+9vf/qbmX3jhBW3w4MFWPzslJUU9LykpSc0PGDBA69at23Xf7aioKG3+/PlWy3r06KE98cQT5nkZp4y3OocPH1Y/f/PmzeZlMs6HHnqoyucMHz5cmz59unletnny5Mnmedl3sk/F+vXrNRcXF+3s2bPmx7/++mv1M+W9qcn7afoZ/fr1qzDep59+2jxf2WsOHDhQe/HFF62WffDBB1pkZKTV86ZMmWK1zt69ezUnJyft1KlTal6OhejoaG3ZsmVVbvPq1au14OBg8/yKFSs0f39/87yvr6/23nvvVfl8IiKyLZ5L8FyC5xJERERERNfHzAqiWpC+FXKn++HDh+u83yQrQe6cN5GSTVLyx/LOS+kDkZaWZvU8yaawzFyQvg+m7ZAyQJs3b1YloExTQkKCuSa0iWQyVCc7O1tlfchd/pZkvrZjlp8vZaveffddNX/s2DGVjSAloIRkWEhZJBm7lF+SbV6/fj1Onz5do9eX7ZESSVFRUZXuo9oo3z8jMjKywv4vT/a5ZGBY7vPx48er7IsrV66Y15P3yZKU1WrXrp05u0LKecnPeuCBB8zrfPPNNxg4cKAqkSWlnSTjRLJoLF/X0rRp01TWhpQYkUbwlu85ERE1LTyXqDmeSxjxXIKIiIiIHAWDFUS1IP0RpCzSrFmzKv5jcna2KnlUVRPE8uV5pK9BZcukzFFNST8DKQv1888/W02mng4mUuqoMUlg4rPPPkNOTo4qfRAfH48BAwaox/7+97+rklNSukkCLbK9sm/r0/i7ruqy/2WfS48Ky/0tpZ5kn0sPi+r2uZRtMgUr5OvQoUPNjcpPnjypyoJJAEX2nZR1euONN9RjVe0bKTUlJaOGDx+uSkZJ/441a9bUYU8QEdGNxnOJ2uG5BM8liIiIiMhxMFhBVEty5/p//vMfbN++3Wq5NJROTU21CljIBeyGsmPHDvP30sRZLmLLHfpCmjzLxWpp/tyqVSurqTYBCmnOLJkK0tPCkszLBfDakp4ZEsSRC/L/+te/MG7cOBUIML3miBEj8NBDD6FLly6qQfWRI0dq/Noy9pSUFJXJUNk+auhghmSCWJJ9npSUVGF/y2SZOVOZ3/3ud6pPh7yHn376qQpemMgyCZRI35DevXujTZs2KtvlemS9qVOnYsOGDaqXR2VN2omIqGnguUTN8VyicjyXICIiIiI9YrCCqJakbJFcXJYGyJZuvfVWpKenqwbNUoZH7ob/+uuvG2z/yuvJ3fKJiYl48sknkZmZqS7+C5m/dOmSat4sTZbl50tJpUceeaTCRfbrkUbeUqLik08+URfjpVmzBF2kkXRtSWmkUaNGqUwUCSo8/PDD5sdat26tmlj/8MMPqqSTNBWXpuA1JSWP5AL92LFjVUkmKTH17LPP4kaQINCmTZtUMEr2u5gzZ44KwEh2hQSKZAwff/wxZs+eXaPXkxJZcreovD/SwNxEgh2SkSPN3E+cOIEPPvigymboIj8/H5MmTcKWLVtw6tQpFQSSY8AUyCIioqaH5xI1x3OJyvFcgoiIiIj0iMEKojqQXgXlywTJxeE333xTBRUkU2DXrl3485//3KB3Ycokr/3999/j3//+N0JCQtRjpmwIufA9ePBgdRFkypQpCAgIuO5d/uX96U9/Uj0Qpk+frl5n3bp16mdJcKEu5IK8XOCXEk+W/SXkor5kJ8hyCfRERERg5MiRNX5dGZcEb+Rifc+ePVXPhvnz5+NGkCwHCaxIj4xu3bqpZbLdX375pcpk6NGjh8qCWLx4MWJjY2v0mhLwkiDLvffeC09PT/NyeX8XLVqkAkYdO3bEhx9+iAULFlT5OtLjRPpZjBkzRgVv5A7UO++8UwVRiIio6eK5RM3xXKJyPJcgIiIiIr1xki7btt4IIiIiIiIiIiIiIiJyXMysICIiIiIiIiIiIiIim2KwgoiIiIiIiIiIiIiIbIrBCiIiIiIiIiIiIiIisikGK4iIiIiIiIiIiIiIyKYYrCAiIiIiIiIiIiIiIptisIKIiIiIiIiIiIiIiGyKwQoiIiIiIiIiIiIiIrIpBiuIiIiIiIiIiIiIiMimGKwgIiIiIiIiIiIiIiKbYrCCiIiIiIiIiIiIiIhsisEKIiIiIiIiIiIiIiKCLf0/I1X24auNLaQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1600x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Key Insight: detect_neg_shifts=True typically finds MORE valid intervals,\n",
+      "which leads to narrower confidence intervals and more robust estimates.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Enhanced scatter plot: Soiling Ratio vs Valid Intervals with more context\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
+    "\n",
+    "colors_map = {'half_norm_clean': 'steelblue', 'perfect_clean': 'green', 'inferred_clean': 'orange'}\n",
+    "markers_map = {(False, False): 'o', (False, True): 's', (True, False): '^', (True, True): 'D'}\n",
+    "\n",
+    "# Plot 1: SR vs Valid Intervals with error bars and option markers\n",
+    "ax = axes[0]\n",
+    "for method, color in colors_map.items():\n",
+    "    for (dns, pw), marker in markers_map.items():\n",
+    "        subset = valid_df[(valid_df['method'] == method) &\n",
+    "                          (valid_df['detect_neg_shifts'] == dns) &\n",
+    "                          (valid_df['piecewise_fit'] == pw)]\n",
+    "        if len(subset) > 0:\n",
+    "            # Plot with error bars showing CI width\n",
+    "            ci_lower = subset['soiling_ratio'] - subset['ci_lower']\n",
+    "            ci_upper = subset['ci_upper'] - subset['soiling_ratio']\n",
+    "            ax.errorbar(subset['valid_intervals'], subset['soiling_ratio'],\n",
+    "                       yerr=[ci_lower, ci_upper], fmt=marker, color=color,\n",
+    "                       alpha=0.6, capsize=2, markersize=8,\n",
+    "                       label=f\"{method[:4]} dns={dns} pw={pw}\" if (dns, pw) == (True, True) else None)\n",
+    "\n",
+    "# Add legend for methods and markers\n",
+    "for method, color in colors_map.items():\n",
+    "    ax.scatter([], [], c=color, label=method, s=50)\n",
+    "ax.scatter([], [], marker='o', c='gray', label='dns=F, pw=F', s=40)\n",
+    "ax.scatter([], [], marker='s', c='gray', label='dns=F, pw=T', s=40)\n",
+    "ax.scatter([], [], marker='^', c='gray', label='dns=T, pw=F', s=40)\n",
+    "ax.scatter([], [], marker='D', c='gray', label='dns=T, pw=T', s=40)\n",
+    "\n",
+    "ax.set_xlabel('Number of Valid Intervals')\n",
+    "ax.set_ylabel('Soiling Ratio (P50)')\n",
+    "ax.set_title('Soiling Ratio vs Valid Intervals\\n(error bars = CI width)')\n",
+    "ax.legend(loc='upper left', fontsize=7, ncol=2)\n",
+    "ax.axhline(1.0, color='k', linestyle='--', alpha=0.3, label='Perfect (1.0)')\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: CI Width vs Valid Intervals (uncertainty analysis)\n",
+    "ax = axes[1]\n",
+    "valid_df['ci_width'] = valid_df['ci_upper'] - valid_df['ci_lower']\n",
+    "for method, color in colors_map.items():\n",
+    "    subset = valid_df[valid_df['method'] == method]\n",
+    "    ax.scatter(subset['valid_intervals'], subset['ci_width'],\n",
+    "               label=method, alpha=0.6, s=50, c=color)\n",
+    "    # Add trend line\n",
+    "    if len(subset) > 2:\n",
+    "        z = np.polyfit(subset['valid_intervals'], subset['ci_width'], 1)\n",
+    "        p = np.poly1d(z)\n",
+    "        x_line = np.linspace(subset['valid_intervals'].min(), subset['valid_intervals'].max(), 50)\n",
+    "        ax.plot(x_line, p(x_line), '--', color=color, alpha=0.5, linewidth=1.5)\n",
+    "\n",
+    "ax.set_xlabel('Number of Valid Intervals')\n",
+    "ax.set_ylabel('Confidence Interval Width')\n",
+    "ax.set_title('More Intervals → Narrower CI\\n(trend lines show relationship)')\n",
+    "ax.legend(loc='upper right', fontsize=8)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 3: Heatmap of Valid Intervals by dns × pw\n",
+    "ax = axes[2]\n",
+    "pivot_intervals = valid_df.pivot_table(\n",
+    "    values='valid_intervals',\n",
+    "    index='detect_neg_shifts',\n",
+    "    columns='piecewise_fit',\n",
+    "    aggfunc='mean'\n",
+    ")\n",
+    "im = ax.imshow(pivot_intervals.values, cmap='YlGn', aspect='auto')\n",
+    "ax.set_xticks([0, 1])\n",
+    "ax.set_xticklabels(['pw=False', 'pw=True'])\n",
+    "ax.set_yticks([0, 1])\n",
+    "ax.set_yticklabels(['dns=False', 'dns=True'])\n",
+    "for i in range(2):\n",
+    "    for j in range(2):\n",
+    "        val = pivot_intervals.values[i, j]\n",
+    "        ax.text(j, i, f'{val:.1f}', ha='center', va='center', fontsize=14, fontweight='bold')\n",
+    "ax.set_title('Avg Valid Intervals by Options\\n(higher = more data coverage)')\n",
+    "plt.colorbar(im, ax=ax, label='Avg Valid Intervals')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print key insight\n",
+    "print(\"\\nKey Insight: detect_neg_shifts=True typically finds MORE valid intervals,\")\n",
+    "print(\"which leads to narrower confidence intervals and more robust estimates.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f544ffe",
+   "metadata": {},
+   "source": [
+    "## Soiling Profile Comparison\n",
+    "\n",
+    "Compare the daily soiling ratio profiles across different configurations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f1c56a96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valid profiles: 43 out of 43\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a profiles dictionary keyed by config_id for easy lookup\n",
+    "profiles = {i: results[i]['sr_profile'] for i in range(len(results))\n",
+    "            if results[i]['sr_profile'] is not None and len(results[i]['sr_profile']) > 0}\n",
+    "\n",
+    "print(f\"Valid profiles: {len(profiles)} out of {len(results)}\")\n",
+    "\n",
+    "# Compare soiling profiles by method (using core matrix configurations)\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "\n",
+    "# Plot 1: Compare all 3 methods with default settings (dns=True, pw=True)\n",
+    "ax = axes[0, 0]\n",
+    "baseline_idx = master_df[(master_df['detect_neg_shifts'] == True) &\n",
+    "                         (master_df['piecewise_fit'] == True) &\n",
+    "                         (master_df['config_group'] == 'core_matrix')].index\n",
+    "\n",
+    "for idx in baseline_idx:\n",
+    "    if idx in profiles:\n",
+    "        profile = profiles[idx]\n",
+    "        row = master_df.loc[idx]\n",
+    "        label = f\"{row['method']} (SR={row['soiling_ratio']:.4f})\"\n",
+    "        ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio')\n",
+    "ax.set_title('Method Comparison (dns=True, piecewise=True)')\n",
+    "ax.legend(loc='lower left', fontsize=8)\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Plot 2: Compare detect_neg_shifts effect (half_norm_clean)\n",
+    "ax = axes[0, 1]\n",
+    "hnc_idx = master_df[(master_df['method'] == 'half_norm_clean') &\n",
+    "                    (master_df['config_group'] == 'core_matrix')].index\n",
+    "\n",
+    "for idx in hnc_idx:\n",
+    "    if idx in profiles:\n",
+    "        profile = profiles[idx]\n",
+    "        row = master_df.loc[idx]\n",
+    "        dns_str = 'dns=T' if row['detect_neg_shifts'] else 'dns=F'\n",
+    "        pw_str = 'pw=T' if row['piecewise_fit'] else 'pw=F'\n",
+    "        label = f\"{dns_str}, {pw_str} (SR={row['soiling_ratio']:.4f})\"\n",
+    "        ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio')\n",
+    "ax.set_title('half_norm_clean: dns/piecewise Effects')\n",
+    "ax.legend(loc='lower left', fontsize=8)\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Plot 3: Perfect clean with different settings\n",
+    "ax = axes[1, 0]\n",
+    "pc_idx = master_df[(master_df['method'] == 'perfect_clean') &\n",
+    "                   (master_df['config_group'] == 'core_matrix')].index\n",
+    "\n",
+    "for idx in pc_idx:\n",
+    "    if idx in profiles:\n",
+    "        profile = profiles[idx]\n",
+    "        row = master_df.loc[idx]\n",
+    "        dns_str = 'dns=T' if row['detect_neg_shifts'] else 'dns=F'\n",
+    "        pw_str = 'pw=T' if row['piecewise_fit'] else 'pw=F'\n",
+    "        label = f\"{dns_str}, {pw_str} (SR={row['soiling_ratio']:.4f})\"\n",
+    "        ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio')\n",
+    "ax.set_title('perfect_clean: dns/piecewise Effects')\n",
+    "ax.legend(loc='lower left', fontsize=8)\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Plot 4: Inferred clean (only works with dns=True)\n",
+    "ax = axes[1, 1]\n",
+    "ic_idx = master_df[(master_df['method'] == 'inferred_clean') &\n",
+    "                   (master_df['soiling_ratio'].notna())].index\n",
+    "\n",
+    "for idx in ic_idx:\n",
+    "    if idx in profiles:\n",
+    "        profile = profiles[idx]\n",
+    "        row = master_df.loc[idx]\n",
+    "        config_short = row['config_name'].replace('inferred_clean_', '')[:20]\n",
+    "        label = f\"{config_short} (SR={row['soiling_ratio']:.4f})\"\n",
+    "        ax.plot(profile.index, profile.values, label=label, alpha=0.6, linewidth=1)\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio')\n",
+    "ax.set_title('inferred_clean: All Configurations')\n",
+    "ax.legend(loc='lower left', fontsize=6, ncol=2)\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "06e32cb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Detailed profile comparison: overlay all methods with same core settings\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "\n",
+    "# Create a subplot for each (dns, pw) combination\n",
+    "settings = [(True, True), (True, False), (False, True), (False, False)]\n",
+    "colors_method = {'half_norm_clean': 'blue', 'perfect_clean': 'green', 'inferred_clean': 'red'}\n",
+    "\n",
+    "for ax, (dns, pw) in zip(axes.flatten(), settings):\n",
+    "    subset_idx = master_df[(master_df['detect_neg_shifts'] == dns) &\n",
+    "                           (master_df['piecewise_fit'] == pw) &\n",
+    "                           (master_df['config_group'] == 'core_matrix')].index\n",
+    "\n",
+    "    for idx in subset_idx:\n",
+    "        if idx in profiles:\n",
+    "            profile = profiles[idx]\n",
+    "            row = master_df.loc[idx]\n",
+    "            method = row['method']\n",
+    "            color = colors_method.get(method, 'gray')\n",
+    "            label = f\"{method} (SR={row['soiling_ratio']:.4f})\"\n",
+    "            ax.plot(profile.index, profile.values, label=label,\n",
+    "                    color=color, alpha=0.8, linewidth=1.5)\n",
+    "\n",
+    "    ax.set_xlabel('Date')\n",
+    "    ax.set_ylabel('Soiling Ratio')\n",
+    "    dns_str = 'dns=True' if dns else 'dns=False'\n",
+    "    pw_str = 'pw=True' if pw else 'pw=False'\n",
+    "    ax.set_title(f'{dns_str}, {pw_str}')\n",
+    "    ax.legend(loc='lower left', fontsize=8)\n",
+    "    ax.set_ylim(0.85, 1.05)\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "    ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1038a0ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Profile Statistics by Method:\n",
+      "============================================================\n",
+      "                profile_mean         profile_std profile_range   n_points aggregated_sr\n",
+      "                        mean     std        mean          mean       mean          mean\n",
+      "method                                                                                 \n",
+      "half_norm_clean       0.9666  0.0084      0.0329        0.1858  1840.4000        0.9612\n",
+      "inferred_clean        0.9660  0.0116      0.0380        0.2074  2077.4615        0.9419\n",
+      "perfect_clean         0.9666  0.0084      0.0329        0.1858  1840.4000        0.9710\n",
+      "\n",
+      "\n",
+      "Correlation: Profile Mean vs Aggregated SR\n",
+      "============================================================\n",
+      "half_norm_clean: r = 0.5403\n",
+      "perfect_clean: r = 0.9465\n",
+      "inferred_clean: r = 0.9392\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Profile statistics: compare profile characteristics across methods\n",
+    "profile_stats = []\n",
+    "for idx in profiles:\n",
+    "    profile = profiles[idx]\n",
+    "    row = master_df.loc[idx]\n",
+    "    profile_stats.append({\n",
+    "        'method': row['method'],\n",
+    "        'config_name': row['config_name'],\n",
+    "        'dns': row['detect_neg_shifts'],\n",
+    "        'pw': row['piecewise_fit'],\n",
+    "        'profile_mean': profile.mean(),\n",
+    "        'profile_std': profile.std(),\n",
+    "        'profile_min': profile.min(),\n",
+    "        'profile_max': profile.max(),\n",
+    "        'profile_range': profile.max() - profile.min(),\n",
+    "        'n_points': len(profile),\n",
+    "        'aggregated_sr': row['soiling_ratio']\n",
+    "    })\n",
+    "\n",
+    "profile_stats_df = pd.DataFrame(profile_stats)\n",
+    "\n",
+    "# Summary by method\n",
+    "print(\"Profile Statistics by Method:\")\n",
+    "print(\"=\"*60)\n",
+    "method_profile_summary = profile_stats_df.groupby('method').agg({\n",
+    "    'profile_mean': ['mean', 'std'],\n",
+    "    'profile_std': 'mean',\n",
+    "    'profile_range': 'mean',\n",
+    "    'n_points': 'mean',\n",
+    "    'aggregated_sr': 'mean'\n",
+    "}).round(4)\n",
+    "print(method_profile_summary.to_string())\n",
+    "\n",
+    "# Key insight: correlation between profile mean and aggregated SR\n",
+    "print(\"\\n\\nCorrelation: Profile Mean vs Aggregated SR\")\n",
+    "print(\"=\"*60)\n",
+    "for method in profile_stats_df['method'].unique():\n",
+    "    subset = profile_stats_df[profile_stats_df['method'] == method]\n",
+    "    corr = subset['profile_mean'].corr(subset['aggregated_sr'])\n",
+    "    print(f\"{method}: r = {corr:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2a5783c",
+   "metadata": {},
+   "source": [
+    "## Stochastic Profile Comparison (Method-Specific)\n",
+    "\n",
+    "The `soiling_ratio_perfect_clean` is the **same** for all methods - it's the deterministic profile. The actual method differences show up in the **stochastic Monte Carlo profiles**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d5958610",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valid stochastic profiles: 43 out of 43\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create stochastic profiles dictionary\n",
+    "stoch_profiles = {i: results[i]['stoch_profile'] for i in range(len(results))\n",
+    "                  if results[i]['stoch_profile'] is not None and len(results[i]['stoch_profile']) > 0}\n",
+    "\n",
+    "print(f\"Valid stochastic profiles: {len(stoch_profiles)} out of {len(results)}\")\n",
+    "\n",
+    "# Compare STOCHASTIC profiles by method (these show method differences!)\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "\n",
+    "# Plot 1: Compare all 3 methods with default settings (dns=True, pw=True)\n",
+    "ax = axes[0, 0]\n",
+    "baseline_idx = master_df[(master_df['detect_neg_shifts'] == True) &\n",
+    "                         (master_df['piecewise_fit'] == True) &\n",
+    "                         (master_df['config_group'] == 'core_matrix')].index\n",
+    "\n",
+    "for idx in baseline_idx:\n",
+    "    if idx in stoch_profiles:\n",
+    "        profile = stoch_profiles[idx]\n",
+    "        row = master_df.loc[idx]\n",
+    "        label = f\"{row['method']} (SR={row['soiling_ratio']:.4f})\"\n",
+    "        ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio (Stochastic Median)')\n",
+    "ax.set_title('Method Comparison - Stochastic Profiles (dns=True, pw=True)')\n",
+    "ax.legend(loc='lower left', fontsize=8)\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Plot 2: Compare detect_neg_shifts effect (half_norm_clean) - stochastic\n",
+    "ax = axes[0, 1]\n",
+    "hnc_idx = master_df[(master_df['method'] == 'half_norm_clean') &\n",
+    "                    (master_df['config_group'] == 'core_matrix')].index\n",
+    "\n",
+    "for idx in hnc_idx:\n",
+    "    if idx in stoch_profiles:\n",
+    "        profile = stoch_profiles[idx]\n",
+    "        row = master_df.loc[idx]\n",
+    "        dns_str = 'dns=T' if row['detect_neg_shifts'] else 'dns=F'\n",
+    "        pw_str = 'pw=T' if row['piecewise_fit'] else 'pw=F'\n",
+    "        label = f\"{dns_str}, {pw_str} (SR={row['soiling_ratio']:.4f})\"\n",
+    "        ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio (Stochastic Median)')\n",
+    "ax.set_title('half_norm_clean: Stochastic Profiles')\n",
+    "ax.legend(loc='lower left', fontsize=8)\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Plot 3: Perfect clean with different settings - stochastic\n",
+    "ax = axes[1, 0]\n",
+    "pc_idx = master_df[(master_df['method'] == 'perfect_clean') &\n",
+    "                   (master_df['config_group'] == 'core_matrix')].index\n",
+    "\n",
+    "for idx in pc_idx:\n",
+    "    if idx in stoch_profiles:\n",
+    "        profile = stoch_profiles[idx]\n",
+    "        row = master_df.loc[idx]\n",
+    "        dns_str = 'dns=T' if row['detect_neg_shifts'] else 'dns=F'\n",
+    "        pw_str = 'pw=T' if row['piecewise_fit'] else 'pw=F'\n",
+    "        label = f\"{dns_str}, {pw_str} (SR={row['soiling_ratio']:.4f})\"\n",
+    "        ax.plot(profile.index, profile.values, label=label, alpha=0.8, linewidth=1.5)\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio (Stochastic Median)')\n",
+    "ax.set_title('perfect_clean: Stochastic Profiles')\n",
+    "ax.legend(loc='lower left', fontsize=8)\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Plot 4: Inferred clean - stochastic\n",
+    "ax = axes[1, 1]\n",
+    "ic_idx = master_df[(master_df['method'] == 'inferred_clean') &\n",
+    "                   (master_df['soiling_ratio'].notna())].index\n",
+    "\n",
+    "for idx in ic_idx:\n",
+    "    if idx in stoch_profiles:\n",
+    "        profile = stoch_profiles[idx]\n",
+    "        row = master_df.loc[idx]\n",
+    "        config_short = row['config_name'].replace('inferred_clean_', '')[:20]\n",
+    "        label = f\"{config_short} (SR={row['soiling_ratio']:.4f})\"\n",
+    "        ax.plot(profile.index, profile.values, label=label, alpha=0.6, linewidth=1)\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio (Stochastic Median)')\n",
+    "ax.set_title('inferred_clean: Stochastic Profiles')\n",
+    "ax.legend(loc='lower left', fontsize=6, ncol=2)\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faaadebe",
+   "metadata": {},
+   "source": [
+    "## Summary Table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5d66c848",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All Results Sorted by Soiling Ratio (highest to lowest):\n",
+      "================================================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>config_name</th>\n",
+       "      <th>method</th>\n",
+       "      <th>detect_neg_shifts</th>\n",
+       "      <th>piecewise_fit</th>\n",
+       "      <th>soiling_ratio</th>\n",
+       "      <th>ci_lower</th>\n",
+       "      <th>ci_upper</th>\n",
+       "      <th>valid_intervals</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>perfect_clean_cc=precip</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.996671</td>\n",
+       "      <td>0.996335</td>\n",
+       "      <td>0.997030</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>perfect_clean_cc=precip_or_shift</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.996448</td>\n",
+       "      <td>0.996157</td>\n",
+       "      <td>0.996813</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>half_norm_clean_nsf=1.5</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.978627</td>\n",
+       "      <td>0.976994</td>\n",
+       "      <td>0.980072</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>perfect_clean_cc=precip_and_shift</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.978609</td>\n",
+       "      <td>0.977383</td>\n",
+       "      <td>0.979880</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>inferred_clean_cc=precip_or_shift</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.978480</td>\n",
+       "      <td>0.976462</td>\n",
+       "      <td>0.980594</td>\n",
+       "      <td>83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>half_norm_clean_cc=precip</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.977473</td>\n",
+       "      <td>0.973107</td>\n",
+       "      <td>0.981068</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>half_norm_clean_cc=precip_or_shift</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.976294</td>\n",
+       "      <td>0.972542</td>\n",
+       "      <td>0.979310</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>perfect_clean_dns=False_pw=True</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.974720</td>\n",
+       "      <td>0.974031</td>\n",
+       "      <td>0.975351</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>perfect_clean_mpd=14</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.974720</td>\n",
+       "      <td>0.974031</td>\n",
+       "      <td>0.975351</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>perfect_clean_mpd=45</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.974551</td>\n",
+       "      <td>0.973916</td>\n",
+       "      <td>0.975158</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>inferred_clean_cc=precip</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.974079</td>\n",
+       "      <td>0.971564</td>\n",
+       "      <td>0.977205</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>perfect_clean_cwd=1</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.971685</td>\n",
+       "      <td>0.970882</td>\n",
+       "      <td>0.972475</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>perfect_clean_dns=False_pw=False</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.971111</td>\n",
+       "      <td>0.970345</td>\n",
+       "      <td>0.971922</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>perfect_clean_nsf=1.5</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.968903</td>\n",
+       "      <td>0.966839</td>\n",
+       "      <td>0.971174</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>half_norm_clean_dns=True_pw=True</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.967494</td>\n",
+       "      <td>0.965440</td>\n",
+       "      <td>0.969339</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>perfect_clean_cwd=10</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.967480</td>\n",
+       "      <td>0.965921</td>\n",
+       "      <td>0.969026</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>half_norm_clean_fmw=5</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.963505</td>\n",
+       "      <td>0.961178</td>\n",
+       "      <td>0.965943</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>half_norm_clean_dns=True_pw=False</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.963505</td>\n",
+       "      <td>0.961178</td>\n",
+       "      <td>0.965943</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>half_norm_clean_fmw=20</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.963505</td>\n",
+       "      <td>0.961178</td>\n",
+       "      <td>0.965943</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>perfect_clean_dns=True_pw=True</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.962020</td>\n",
+       "      <td>0.959957</td>\n",
+       "      <td>0.963981</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>half_norm_clean_nsf=4.0</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.960022</td>\n",
+       "      <td>0.957616</td>\n",
+       "      <td>0.962402</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>inferred_clean_cc=precip_and_shift</td>\n",
+       "      <td>inferred_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.959217</td>\n",
+       "      <td>0.957228</td>\n",
+       "      <td>0.960718</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>perfect_clean_nsf=4.0</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.957464</td>\n",
+       "      <td>0.955780</td>\n",
+       "      <td>0.958773</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>perfect_clean_dns=True_pw=False</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.956869</td>\n",
+       "      <td>0.954949</td>\n",
+       "      <td>0.959103</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>perfect_clean_fmw=5</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.956869</td>\n",
+       "      <td>0.954949</td>\n",
+       "      <td>0.959103</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>perfect_clean_fmw=20</td>\n",
+       "      <td>perfect_clean</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.956869</td>\n",
+       "      <td>0.954949</td>\n",
+       "      <td>0.959103</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>half_norm_clean_mpd=45</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.954646</td>\n",
+       "      <td>0.950601</td>\n",
+       "      <td>0.958328</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>half_norm_clean_mpd=14</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.954378</td>\n",
+       "      <td>0.950370</td>\n",
+       "      <td>0.957572</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>half_norm_clean_dns=False_pw=True</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.954378</td>\n",
+       "      <td>0.950370</td>\n",
+       "      <td>0.957572</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>half_norm_clean_cc=precip_and_shift</td>\n",
+       "      <td>half_norm_clean</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.954371</td>\n",
+       "      <td>0.946931</td>\n",
+       "      <td>0.960193</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            config_name           method  detect_neg_shifts  \\\n",
+       "35              perfect_clean_cc=precip    perfect_clean              False   \n",
+       "41     perfect_clean_cc=precip_or_shift    perfect_clean              False   \n",
+       "10              half_norm_clean_nsf=1.5  half_norm_clean               True   \n",
+       "38    perfect_clean_cc=precip_and_shift    perfect_clean              False   \n",
+       "42    inferred_clean_cc=precip_or_shift   inferred_clean               True   \n",
+       "34            half_norm_clean_cc=precip  half_norm_clean              False   \n",
+       "40   half_norm_clean_cc=precip_or_shift  half_norm_clean              False   \n",
+       "3       perfect_clean_dns=False_pw=True    perfect_clean              False   \n",
+       "17                 perfect_clean_mpd=14    perfect_clean              False   \n",
+       "20                 perfect_clean_mpd=45    perfect_clean              False   \n",
+       "36             inferred_clean_cc=precip   inferred_clean               True   \n",
+       "23                  perfect_clean_cwd=1    perfect_clean              False   \n",
+       "1      perfect_clean_dns=False_pw=False    perfect_clean              False   \n",
+       "11                perfect_clean_nsf=1.5    perfect_clean               True   \n",
+       "7      half_norm_clean_dns=True_pw=True  half_norm_clean               True   \n",
+       "26                 perfect_clean_cwd=10    perfect_clean              False   \n",
+       "28                half_norm_clean_fmw=5  half_norm_clean               True   \n",
+       "4     half_norm_clean_dns=True_pw=False  half_norm_clean               True   \n",
+       "31               half_norm_clean_fmw=20  half_norm_clean               True   \n",
+       "8        perfect_clean_dns=True_pw=True    perfect_clean               True   \n",
+       "13              half_norm_clean_nsf=4.0  half_norm_clean               True   \n",
+       "39   inferred_clean_cc=precip_and_shift   inferred_clean               True   \n",
+       "14                perfect_clean_nsf=4.0    perfect_clean               True   \n",
+       "5       perfect_clean_dns=True_pw=False    perfect_clean               True   \n",
+       "29                  perfect_clean_fmw=5    perfect_clean               True   \n",
+       "32                 perfect_clean_fmw=20    perfect_clean               True   \n",
+       "19               half_norm_clean_mpd=45  half_norm_clean              False   \n",
+       "16               half_norm_clean_mpd=14  half_norm_clean              False   \n",
+       "2     half_norm_clean_dns=False_pw=True  half_norm_clean              False   \n",
+       "37  half_norm_clean_cc=precip_and_shift  half_norm_clean              False   \n",
+       "\n",
+       "    piecewise_fit  soiling_ratio  ci_lower  ci_upper  valid_intervals  \n",
+       "35          False       0.996671  0.996335  0.997030               31  \n",
+       "41          False       0.996448  0.996157  0.996813               34  \n",
+       "10          False       0.978627  0.976994  0.980072               72  \n",
+       "38          False       0.978609  0.977383  0.979880               16  \n",
+       "42          False       0.978480  0.976462  0.980594               83  \n",
+       "34          False       0.977473  0.973107  0.981068               31  \n",
+       "40          False       0.976294  0.972542  0.979310               34  \n",
+       "3            True       0.974720  0.974031  0.975351               44  \n",
+       "17           True       0.974720  0.974031  0.975351               44  \n",
+       "20           True       0.974551  0.973916  0.975158               44  \n",
+       "36          False       0.974079  0.971564  0.977205               86  \n",
+       "23          False       0.971685  0.970882  0.972475               40  \n",
+       "1           False       0.971111  0.970345  0.971922               40  \n",
+       "11          False       0.968903  0.966839  0.971174               72  \n",
+       "7            True       0.967494  0.965440  0.969339               66  \n",
+       "26          False       0.967480  0.965921  0.969026               40  \n",
+       "28          False       0.963505  0.961178  0.965943               54  \n",
+       "4           False       0.963505  0.961178  0.965943               54  \n",
+       "31          False       0.963505  0.961178  0.965943               54  \n",
+       "8            True       0.962020  0.959957  0.963981               66  \n",
+       "13          False       0.960022  0.957616  0.962402               52  \n",
+       "39          False       0.959217  0.957228  0.960718               28  \n",
+       "14          False       0.957464  0.955780  0.958773               52  \n",
+       "5           False       0.956869  0.954949  0.959103               54  \n",
+       "29          False       0.956869  0.954949  0.959103               54  \n",
+       "32          False       0.956869  0.954949  0.959103               54  \n",
+       "19           True       0.954646  0.950601  0.958328               44  \n",
+       "16           True       0.954378  0.950370  0.957572               44  \n",
+       "2            True       0.954378  0.950370  0.957572               44  \n",
+       "37          False       0.954371  0.946931  0.960193               16  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Full results summary - sorted by soiling ratio\n",
+    "print(\"All Results Sorted by Soiling Ratio (highest to lowest):\")\n",
+    "print(\"=\"*80)\n",
+    "sort_cols = ['config_name', 'method', 'detect_neg_shifts', 'piecewise_fit',\n",
+    "             'soiling_ratio', 'ci_lower', 'ci_upper', 'valid_intervals']\n",
+    "valid_df[sort_cols].sort_values('soiling_ratio', ascending=False).head(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "408947f0",
+   "metadata": {},
+   "source": [
+    "## Ground Truth Comparison\n",
+    "\n",
+    "Since this dataset includes a **synthetic soiling signal** (the `soiling` column), we can directly compare the estimated soiling profiles to the known ground truth. This allows us to quantitatively evaluate which parameter configurations most accurately recover the true soiling behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "6078767d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ground Truth Mean Soiling Ratio: 0.9593\n",
+      "Ground Truth Range: 0.7589 - 1.0000\n",
+      "\n",
+      "Note: A 'perfect' soiling estimate would match 0.9593\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the ground truth soiling signal (daily aggregated)\n",
+    "ground_truth = df['soiling'].resample('D').mean()\n",
+    "ground_truth = ground_truth.reindex(energy_normalized_daily.index)\n",
+    "\n",
+    "# Calculate the \"true\" aggregated soiling ratio for comparison\n",
+    "true_soiling_ratio = ground_truth.mean()\n",
+    "print(f\"Ground Truth Mean Soiling Ratio: {true_soiling_ratio:.4f}\")\n",
+    "print(f\"Ground Truth Range: {ground_truth.min():.4f} - {ground_truth.max():.4f}\")\n",
+    "print(f\"\\nNote: A 'perfect' soiling estimate would match {true_soiling_ratio:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bcc4d8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluated 43 configurations against ground truth\n",
+      "\n",
+      "Best configurations by MAE (Mean Absolute Error):\n",
+      "                          config_name           method  detect_neg_shifts  piecewise_fit  soiling_ratio       mae      rmse  correlation\n",
+      "8      perfect_clean_dns=True_pw=True    perfect_clean               True           True       0.962020  0.016937  0.024695     0.847126\n",
+      "14              perfect_clean_nsf=4.0    perfect_clean               True          False       0.957464  0.018614  0.026003     0.858119\n",
+      "5     perfect_clean_dns=True_pw=False    perfect_clean               True          False       0.956869  0.019068  0.027656     0.853696\n",
+      "29                perfect_clean_fmw=5    perfect_clean               True          False       0.956869  0.019068  0.027656     0.853696\n",
+      "32               perfect_clean_fmw=20    perfect_clean               True          False       0.956869  0.019068  0.027656     0.853696\n",
+      "11              perfect_clean_nsf=1.5    perfect_clean               True          False       0.968903  0.020699  0.029471     0.763925\n",
+      "4   half_norm_clean_dns=True_pw=False  half_norm_clean               True          False       0.963505  0.022229  0.033291     0.627379\n",
+      "28              half_norm_clean_fmw=5  half_norm_clean               True          False       0.963505  0.022229  0.033291     0.627379\n",
+      "31             half_norm_clean_fmw=20  half_norm_clean               True          False       0.963505  0.022229  0.033291     0.627379\n",
+      "7    half_norm_clean_dns=True_pw=True  half_norm_clean               True           True       0.967494  0.022992  0.034338     0.620257\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate error metrics for each configuration\n",
+    "# Compare the STOCHASTIC profile (median Monte Carlo) to ground truth\n",
+    "error_metrics = []\n",
+    "\n",
+    "for idx, row in master_df.iterrows():\n",
+    "    if idx in stoch_profiles and stoch_profiles[idx] is not None and len(stoch_profiles[idx]) > 0:\n",
+    "        estimated = stoch_profiles[idx]\n",
+    "\n",
+    "        # Align to ground truth index\n",
+    "        common_idx = estimated.index.intersection(ground_truth.dropna().index)\n",
+    "        if len(common_idx) > 30:  # Need enough data points\n",
+    "            est_aligned = estimated.loc[common_idx]\n",
+    "            gt_aligned = ground_truth.loc[common_idx]\n",
+    "\n",
+    "            # Calculate error metrics\n",
+    "            abs_error = (est_aligned - gt_aligned).abs()\n",
+    "            mae = abs_error.mean()\n",
+    "            rmse = np.sqrt(((est_aligned - gt_aligned) ** 2).mean())\n",
+    "            mape = (abs_error / gt_aligned.abs()).mean() * 100  # Mean Absolute Percentage Error\n",
+    "            correlation = est_aligned.corr(gt_aligned)\n",
+    "            bias = (est_aligned - gt_aligned).mean()  # Positive = overestimates\n",
+    "\n",
+    "            # Compare aggregated soiling ratio to true mean\n",
+    "            sr_error = abs(row['soiling_ratio'] - true_soiling_ratio) if pd.notna(row['soiling_ratio']) else np.nan\n",
+    "\n",
+    "            error_metrics.append({\n",
+    "                'config_idx': idx,\n",
+    "                'config_name': row['config_name'],\n",
+    "                'method': row['method'],\n",
+    "                'detect_neg_shifts': row['detect_neg_shifts'],\n",
+    "                'piecewise_fit': row['piecewise_fit'],\n",
+    "                'soiling_ratio': row['soiling_ratio'],\n",
+    "                'mae': mae,\n",
+    "                'rmse': rmse,\n",
+    "                'mape': mape,\n",
+    "                'correlation': correlation,\n",
+    "                'bias': bias,\n",
+    "                'sr_error': sr_error,\n",
+    "                'n_points': len(common_idx)\n",
+    "            })\n",
+    "\n",
+    "error_df = pd.DataFrame(error_metrics)\n",
+    "print(f\"Evaluated {len(error_df)} configurations against ground truth\")\n",
+    "print(f\"\\nBest configurations by MAE (Mean Absolute Error):\")\n",
+    "print(error_df.nsmallest(10, 'mae')[['config_name', 'method', 'detect_neg_shifts', 'piecewise_fit',\n",
+    "                                      'soiling_ratio', 'mae', 'rmse', 'correlation']].to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a930641a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_13060\\4202291704.py:50: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
+      "  bp = ax.boxplot(method_errors, labels=methods, patch_artist=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize: Compare best profiles to ground truth\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "\n",
+    "# Plot 1: Ground Truth vs Best Overall Configuration\n",
+    "ax = axes[0, 0]\n",
+    "ax.plot(ground_truth.index, ground_truth.values, 'k-', linewidth=2, label='Ground Truth', alpha=0.8)\n",
+    "\n",
+    "if len(error_df) > 0:\n",
+    "    best_idx = error_df.nsmallest(1, 'mae')['config_idx'].values[0]\n",
+    "    best_profile = stoch_profiles[best_idx]\n",
+    "    best_row = master_df.loc[best_idx]\n",
+    "    ax.plot(best_profile.index, best_profile.values, 'r-', linewidth=1.5, alpha=0.8,\n",
+    "            label=f\"Best: {best_row['method']} (MAE={error_df.loc[error_df['config_idx']==best_idx, 'mae'].values[0]:.4f})\")\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio')\n",
+    "ax.set_title('Ground Truth vs Best Configuration')\n",
+    "ax.legend(loc='lower left')\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Plot 2: Ground Truth vs Best per Method\n",
+    "ax = axes[0, 1]\n",
+    "ax.plot(ground_truth.index, ground_truth.values, 'k-', linewidth=2, label='Ground Truth', alpha=0.8)\n",
+    "\n",
+    "colors = {'perfect_clean': 'blue', 'half_norm_clean': 'green', 'inferred_clean': 'orange'}\n",
+    "for method in error_df['method'].unique():\n",
+    "    method_best = error_df[error_df['method'] == method].nsmallest(1, 'mae')\n",
+    "    if len(method_best) > 0:\n",
+    "        idx = method_best['config_idx'].values[0]\n",
+    "        if idx in stoch_profiles:\n",
+    "            profile = stoch_profiles[idx]\n",
+    "            mae_val = method_best['mae'].values[0]\n",
+    "            ax.plot(profile.index, profile.values, color=colors.get(method, 'gray'),\n",
+    "                    linewidth=1.5, alpha=0.7, label=f\"{method} (MAE={mae_val:.4f})\")\n",
+    "\n",
+    "ax.set_xlabel('Date')\n",
+    "ax.set_ylabel('Soiling Ratio')\n",
+    "ax.set_title('Ground Truth vs Best per Method')\n",
+    "ax.legend(loc='lower left', fontsize=8)\n",
+    "ax.set_ylim(0.85, 1.05)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Plot 3: Error Distribution by Method\n",
+    "ax = axes[1, 0]\n",
+    "methods = error_df['method'].unique()\n",
+    "method_errors = [error_df[error_df['method'] == m]['mae'].values for m in methods]\n",
+    "bp = ax.boxplot(method_errors, labels=methods, patch_artist=True)\n",
+    "for patch, method in zip(bp['boxes'], methods):\n",
+    "    patch.set_facecolor(colors.get(method, 'lightgray'))\n",
+    "    patch.set_alpha(0.7)\n",
+    "ax.set_xlabel('Method')\n",
+    "ax.set_ylabel('Mean Absolute Error (MAE)')\n",
+    "ax.set_title('Error Distribution by Method')\n",
+    "ax.grid(True, alpha=0.3, axis='y')\n",
+    "\n",
+    "# Plot 4: Impact of detect_neg_shifts and piecewise_fit on accuracy\n",
+    "ax = axes[1, 1]\n",
+    "# Group by configuration options\n",
+    "for (dns, pw), group in error_df.groupby(['detect_neg_shifts', 'piecewise_fit']):\n",
+    "    dns_str = 'dns=T' if dns else 'dns=F'\n",
+    "    pw_str = 'pw=T' if pw else 'pw=F'\n",
+    "    label = f\"{dns_str}, {pw_str}\"\n",
+    "    ax.scatter(group['correlation'], group['mae'], label=label, alpha=0.7, s=50)\n",
+    "\n",
+    "ax.set_xlabel('Correlation with Ground Truth')\n",
+    "ax.set_ylabel('Mean Absolute Error (MAE)')\n",
+    "ax.set_title('Accuracy: Correlation vs MAE by Options')\n",
+    "ax.legend(loc='upper right', fontsize=8)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a096bbd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================================================================\n",
+      "GROUND TRUTH ACCURACY SUMMARY\n",
+      "================================================================================\n",
+      "\n",
+      "1. By Method (mean MAE):\n",
+      "                 Mean MAE  Std MAE  Best MAE\n",
+      "method                                      \n",
+      "perfect_clean      0.0244   0.0066    0.0169\n",
+      "half_norm_clean    0.0269   0.0036    0.0222\n",
+      "inferred_clean     0.0292   0.0035    0.0236\n",
+      "\n",
+      "2. By detect_neg_shifts:\n",
+      "                   Mean MAE  Std MAE  Count\n",
+      "detect_neg_shifts                          \n",
+      "False                0.0285   0.0046     18\n",
+      "True                 0.0254   0.0052     25\n",
+      "\n",
+      "3. By piecewise_fit:\n",
+      "               Mean MAE  Std MAE  Count\n",
+      "piecewise_fit                          \n",
+      "False            0.0266   0.0051     32\n",
+      "True             0.0269   0.0054     11\n",
+      "\n",
+      "4. By Options Combination (detect_neg_shifts, piecewise_fit):\n",
+      "                                 Mean MAE  Std MAE  Best MAE  Count\n",
+      "detect_neg_shifts piecewise_fit                                    \n",
+      "True              False            0.0247   0.0041    0.0186     20\n",
+      "False             True             0.0256   0.0008    0.0248      6\n",
+      "True              True             0.0285   0.0082    0.0169      5\n",
+      "False             False            0.0299   0.0050    0.0235     12\n",
+      "\n",
+      "================================================================================\n",
+      "TOP 5 CONFIGURATIONS (Lowest MAE):\n",
+      "================================================================================\n",
+      "\n",
+      "1. perfect_clean_dns=True_pw=True\n",
+      "   Method: perfect_clean, detect_neg_shifts=True, piecewise_fit=True\n",
+      "   MAE=0.0169, RMSE=0.0247, Correlation=0.8471\n",
+      "   Soiling Ratio: 0.9620 (True: 0.9593, Error: 0.0027)\n",
+      "\n",
+      "2. perfect_clean_nsf=4.0\n",
+      "   Method: perfect_clean, detect_neg_shifts=True, piecewise_fit=False\n",
+      "   MAE=0.0186, RMSE=0.0260, Correlation=0.8581\n",
+      "   Soiling Ratio: 0.9575 (True: 0.9593, Error: 0.0019)\n",
+      "\n",
+      "3. perfect_clean_dns=True_pw=False\n",
+      "   Method: perfect_clean, detect_neg_shifts=True, piecewise_fit=False\n",
+      "   MAE=0.0191, RMSE=0.0277, Correlation=0.8537\n",
+      "   Soiling Ratio: 0.9569 (True: 0.9593, Error: 0.0025)\n",
+      "\n",
+      "4. perfect_clean_fmw=5\n",
+      "   Method: perfect_clean, detect_neg_shifts=True, piecewise_fit=False\n",
+      "   MAE=0.0191, RMSE=0.0277, Correlation=0.8537\n",
+      "   Soiling Ratio: 0.9569 (True: 0.9593, Error: 0.0025)\n",
+      "\n",
+      "5. perfect_clean_fmw=20\n",
+      "   Method: perfect_clean, detect_neg_shifts=True, piecewise_fit=False\n",
+      "   MAE=0.0191, RMSE=0.0277, Correlation=0.8537\n",
+      "   Soiling Ratio: 0.9569 (True: 0.9593, Error: 0.0025)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary statistics: Which options give best ground-truth accuracy?\n",
+    "print(\"=\"*80)\n",
+    "print(\"GROUND TRUTH ACCURACY SUMMARY\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "# By method\n",
+    "print(\"\\n1. By Method (mean MAE):\")\n",
+    "method_summary = error_df.groupby('method')['mae'].agg(['mean', 'std', 'min']).round(4)\n",
+    "method_summary.columns = ['Mean MAE', 'Std MAE', 'Best MAE']\n",
+    "print(method_summary.sort_values('Mean MAE'))\n",
+    "\n",
+    "# By detect_neg_shifts\n",
+    "print(\"\\n2. By detect_neg_shifts:\")\n",
+    "dns_summary = error_df.groupby('detect_neg_shifts')['mae'].agg(['mean', 'std', 'count']).round(4)\n",
+    "dns_summary.columns = ['Mean MAE', 'Std MAE', 'Count']\n",
+    "print(dns_summary)\n",
+    "\n",
+    "# By piecewise_fit\n",
+    "print(\"\\n3. By piecewise_fit:\")\n",
+    "pw_summary = error_df.groupby('piecewise_fit')['mae'].agg(['mean', 'std', 'count']).round(4)\n",
+    "pw_summary.columns = ['Mean MAE', 'Std MAE', 'Count']\n",
+    "print(pw_summary)\n",
+    "\n",
+    "# By combination\n",
+    "print(\"\\n4. By Options Combination (detect_neg_shifts, piecewise_fit):\")\n",
+    "combo_summary = error_df.groupby(['detect_neg_shifts', 'piecewise_fit'])['mae'].agg(['mean', 'std', 'min', 'count']).round(4)\n",
+    "combo_summary.columns = ['Mean MAE', 'Std MAE', 'Best MAE', 'Count']\n",
+    "print(combo_summary.sort_values('Mean MAE'))\n",
+    "\n",
+    "# Best overall configuration\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"TOP 5 CONFIGURATIONS (Lowest MAE):\")\n",
+    "print(\"=\"*80)\n",
+    "top5 = error_df.nsmallest(5, 'mae')\n",
+    "for i, (_, row) in enumerate(top5.iterrows(), 1):\n",
+    "    print(f\"\\n{i}. {row['config_name']}\")\n",
+    "    print(f\"   Method: {row['method']}, detect_neg_shifts={row['detect_neg_shifts']}, piecewise_fit={row['piecewise_fit']}\")\n",
+    "    print(f\"   MAE={row['mae']:.4f}, RMSE={row['rmse']:.4f}, Correlation={row['correlation']:.4f}\")\n",
+    "    print(f\"   Soiling Ratio: {row['soiling_ratio']:.4f} (True: {true_soiling_ratio:.4f}, Error: {row['sr_error']:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "028b014c",
+   "metadata": {},
+   "source": [
+    "## Recommendations\n",
+    "\n",
+    "Based on the analysis:\n",
+    "\n",
+    "1. **For sites with irregular/natural cleaning**: Enable `detect_neg_shifts=True` with `method='perfect_clean'` or `method='inferred_clean'`\n",
+    "2. **For sites with gradual soiling changes**: Enable `piecewise_fit=True` to detect slope change points within intervals\n",
+    "3. **When precipitation data is available**: Use `clean_criterion='precip_or_shift'` for more cleaning event detection\n",
+    "4. **For noisy data**: Increase `neg_shift_factor` (e.g., 4.0) to reduce false positive negative shift detections\n",
+    "5. **For partial cleaning events**: Use `method='inferred_clean'` with `detect_neg_shifts=True` to infer recovery from data\n",
+    "6. **Using both options together** (`detect_neg_shifts=True` + `piecewise_fit=True`) generally provides more accurate results by detecting more valid soiling intervals\n",
+    "\n",
+    "### Method Compatibility Notes:\n",
+    "- `method='inferred_clean'` **requires** `detect_neg_shifts=True`\n",
+    "- `method='perfect_clean'` and `method='inferred_clean'` use enhanced logic when `detect_neg_shifts=True`\n",
+    "- The old `perfect_clean_complex` and `inferred_clean_complex` methods have been replaced by the combination of base methods with `detect_neg_shifts=True` and `piecewise_fit=True`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rdtools_313-nb",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/soiling_options_guide.ipynb b/docs/soiling_options_guide.ipynb
new file mode 100644
index 00000000..da5e0726
--- /dev/null
+++ b/docs/soiling_options_guide.ipynb
@@ -0,0 +1,1082 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "aa95f613",
+   "metadata": {},
+   "source": [
+    "# Soiling Analysis Options Guide\n",
+    "\n",
+    "This notebook demonstrates the soiling analysis capabilities in RdTools v3, including:\n",
+    "\n",
+    "1. **Stochastic Rate and Recovery (SRR)** - The primary soiling detection method\n",
+    "2. **Combined Degradation and Soiling (CODS)** - An alternative iterative decomposition approach\n",
+    "\n",
+    "We'll explore the various parameters and options available for customizing the analysis to your specific dataset.\n",
+    "\n",
+    "## Key Parameters in RdTools v3\n",
+    "\n",
+    "| Parameter | Description | Default |\n",
+    "|-----------|-------------|----------|\n",
+    "| `detect_neg_shifts` | Detect negative shifts in rolling median and subdivide intervals | `False` |\n",
+    "| `piecewise_fit` | Test for piecewise linear fits within soiling intervals | `False` |\n",
+    "| `min_interval_days` | Minimum days for a valid interval | `7` |\n",
+    "| `max_neg_step` | Maximum negative step magnitude (only when `detect_neg_shifts=False`) | `0.05` |\n",
+    "| `neg_shift_factor` | Multiplier of `clean_threshold` for negative shift detection | `2.5` |\n",
+    "| `min_piecewise_days` | Minimum days to attempt piecewise fitting | `27` |\n",
+    "| `collapse_window_days` | Window for collapsing consecutive cleaning events | `5` |\n",
+    "| `forward_median_window` | Window for forward median validation | `10` |\n",
+    "\n",
+    "## Cleaning Methods\n",
+    "\n",
+    "| Method | Description | Use Case |\n",
+    "|--------|-------------|----------|\n",
+    "| `half_norm_clean` | Starting point from half-normal distribution | Default, most systems |\n",
+    "| `random_clean` | Random recovery 0-100% | Testing/comparison |\n",
+    "| `perfect_clean` | Each cleaning returns to 1 | Systems with complete cleaning |\n",
+    "| `inferred_clean` | Recovery inferred from data | Systems with partial cleaning |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a4b81857",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\mspringe\\.conda\\envs\\rdtools_313-nb\\Lib\\site-packages\\pandas\\core\\computation\\expressions.py:22: UserWarning: Pandas requires version '2.10.2' or newer of 'numexpr' (version '2.10.1' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import rdtools\n",
+    "from rdtools.soiling import SRRAnalysis, CODSAnalysis, soiling_srr\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Update plot style\n",
+    "import matplotlib\n",
+    "matplotlib.rcParams.update({'font.size': 12,\n",
+    "                           'figure.figsize': [8, 4],\n",
+    "                           'lines.markeredgewidth': 0,\n",
+    "                           'lines.markersize': 2})\n",
+    "\n",
+    "# Set random seed for reproducibility\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e043a1e",
+   "metadata": {},
+   "source": [
+    "## Load Example Data\n",
+    "\n",
+    "We'll use the PVDAQ dataset with a synthetic soiling signal for demonstration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "abd7cc23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data shape: (1558875, 10)\n",
+      "Date range: 2010-02-25 14:16:00-07:00 to 2016-12-13 09:57:00-07:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import the example data\n",
+    "file_url = ('https://datahub.duramat.org/dataset/'\n",
+    "            'a49bb656-7b36-437a-8089-1870a40c2a7d/'\n",
+    "            'resource/d2c3fcf4-4f5f-47ad-8743-fc29'\n",
+    "            'f1356835/download/pvdaq_system_4_2010'\n",
+    "            '-2016_subset_soil_signal.csv')\n",
+    "cache_file = 'PVDAQ_system_4_2010-2016_subset_soilsignal.pickle'\n",
+    "\n",
+    "try:\n",
+    "    df = pd.read_pickle(cache_file)\n",
+    "except FileNotFoundError:\n",
+    "    df = pd.read_csv(file_url, index_col=0, parse_dates=True)\n",
+    "    df.to_pickle(cache_file)\n",
+    "\n",
+    "# Rename columns and set metadata\n",
+    "df = df.rename(columns={\n",
+    "    'ac_power': 'power_ac',\n",
+    "    'wind_speed': 'wind_speed',\n",
+    "    'ambient_temp': 'Tamb',\n",
+    "    'poa_irradiance': 'poa',\n",
+    "})\n",
+    "\n",
+    "meta = {\n",
+    "    \"latitude\": 39.7406,\n",
+    "    \"longitude\": -105.1774,\n",
+    "    \"timezone\": 'Etc/GMT+7',\n",
+    "    \"gamma_pdc\": -0.005,\n",
+    "    \"azimuth\": 180,\n",
+    "    \"tilt\": 40,\n",
+    "    \"power_dc_rated\": 1000.0,\n",
+    "    \"temp_model_params\": 'open_rack_glass_polymer'\n",
+    "}\n",
+    "\n",
+    "df.index = df.index.tz_localize(meta['timezone'])\n",
+    "df['power'] = df['power_ac'] * df['soiling']\n",
+    "freq = pd.infer_freq(df.index[:10])\n",
+    "\n",
+    "print(f\"Data shape: {df.shape}\")\n",
+    "print(f\"Date range: {df.index[0]} to {df.index[-1]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d1ac501",
+   "metadata": {},
+   "source": [
+    "## Prepare Aggregated Daily Data\n",
+    "\n",
+    "Soiling analysis works with daily aggregated normalized performance data. We'll use the `TrendAnalysis` class to prepare this data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cbf2c845",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create TrendAnalysis object and run sensor analysis\n",
+    "ta = rdtools.TrendAnalysis(\n",
+    "    df['power'], df['poa'],\n",
+    "    temperature_ambient=df['Tamb'],\n",
+    "    gamma_pdc=meta['gamma_pdc'],\n",
+    "    interp_freq=freq,\n",
+    "    windspeed=df['wind_speed'],\n",
+    "    power_dc_rated=meta['power_dc_rated'],\n",
+    "    temperature_model=meta['temp_model_params']\n",
+    ")\n",
+    "\n",
+    "# Run basic analysis to get aggregated data\n",
+    "ta.sensor_analysis(analyses=['yoy_degradation'])\n",
+    "\n",
+    "# Extract daily aggregated performance and insolation\n",
+    "energy_normalized_daily = ta.sensor_aggregated_performance\n",
+    "insolation_daily = ta.sensor_aggregated_insolation\n",
+    "\n",
+    "# Plot the daily data\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "ax.plot(energy_normalized_daily.index, energy_normalized_daily, 'o', alpha=0.5, markersize=3)\n",
+    "ax.set_ylabel('Normalized Daily Energy')\n",
+    "ax.set_title('Daily Aggregated Performance Data')\n",
+    "fig.autofmt_xdate()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd6bd63a",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Part 1: Stochastic Rate and Recovery (SRR) Analysis\n",
+    "\n",
+    "The SRR method identifies soiling intervals and estimates soiling rates using Monte Carlo simulations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d61a2e3f",
+   "metadata": {},
+   "source": [
+    "## 1.1 Basic SRR Analysis (Default Settings)\n",
+    "\n",
+    "Start with the default parameters to establish a baseline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "63531830",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Basic SRR Results:\n",
+      "  Soiling Ratio (P50): 0.9513\n",
+      "  Confidence Interval: (0.9468, 0.9545)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Basic SRR analysis with defaults\n",
+    "sr_basic, sr_ci_basic, info_basic = soiling_srr(\n",
+    "    energy_normalized_daily,\n",
+    "    insolation_daily,\n",
+    "    reps=500  # Reduced for speed; use 1000+ for production\n",
+    ")\n",
+    "\n",
+    "print(f\"Basic SRR Results:\")\n",
+    "print(f\"  Soiling Ratio (P50): {sr_basic:.4f}\")\n",
+    "print(f\"  Confidence Interval: ({sr_ci_basic[0]:.4f}, {sr_ci_basic[1]:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe7a0155",
+   "metadata": {},
+   "source": [
+    "## 1.2 Comparing Cleaning Methods\n",
+    "\n",
+    "Different cleaning methods make different assumptions about how much soiling is removed during cleaning events."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "818dd900",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "half_norm_clean     : SR = 0.9512 (0.9473, 0.9548)\n",
+      "random_clean        : SR = 0.9042 (0.8902, 0.9169)\n",
+      "perfect_clean       : SR = 0.9718 (0.9711, 0.9726)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare different cleaning methods\n",
+    "methods = ['half_norm_clean', 'random_clean', 'perfect_clean']\n",
+    "results = {}\n",
+    "\n",
+    "for method in methods:\n",
+    "    sr, sr_ci, info = soiling_srr(\n",
+    "        energy_normalized_daily,\n",
+    "        insolation_daily,\n",
+    "        reps=500,\n",
+    "        method=method\n",
+    "    )\n",
+    "    results[method] = {'sr': sr, 'ci': sr_ci}\n",
+    "    print(f\"{method:20s}: SR = {sr:.4f} ({sr_ci[0]:.4f}, {sr_ci[1]:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "652fc617",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the comparison\n",
+    "fig, ax = plt.subplots(figsize=(8, 4))\n",
+    "x = range(len(methods))\n",
+    "srs = [results[m]['sr'] for m in methods]\n",
+    "lows = [results[m]['ci'][0] for m in methods]\n",
+    "highs = [results[m]['ci'][1] for m in methods]\n",
+    "errors = [[sr - low for sr, low in zip(srs, lows)],\n",
+    "          [high - sr for sr, high in zip(srs, highs)]]\n",
+    "\n",
+    "ax.bar(x, srs, yerr=errors, capsize=5, alpha=0.7, color=['C0', 'C1', 'C2'])\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(methods, rotation=15)\n",
+    "ax.set_ylabel('Soiling Ratio')\n",
+    "ax.set_title('Comparison of Cleaning Methods')\n",
+    "ax.set_ylim(0.9, 1.0)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "389ea6b4",
+   "metadata": {},
+   "source": [
+    "## 1.3 Advanced SRR with Negative Shift Detection\n",
+    "\n",
+    "The `detect_neg_shifts=True` option enables detection of negative shifts in the rolling median, which subdivides intervals at these points. This is useful for datasets with:\n",
+    "- Partial cleaning events\n",
+    "- Equipment changes\n",
+    "- Non-soiling performance changes within intervals\n",
+    "\n",
+    "When `detect_neg_shifts=True`, use `method='perfect_clean'` or `method='inferred_clean'` for enhanced logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f89e648d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SRR with Negative Shift Detection:\n",
+      "  Soiling Ratio (P50): 0.9588\n",
+      "  Confidence Interval: (0.9570, 0.9610)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SRR with negative shift detection\n",
+    "# neg_shift_factor controls sensitivity: shifts > neg_shift_factor * clean_threshold\n",
+    "# trigger interval subdivision (default: 2.5)\n",
+    "sr_neg_shift, sr_ci_neg_shift, info_neg_shift = soiling_srr(\n",
+    "    energy_normalized_daily,\n",
+    "    insolation_daily,\n",
+    "    reps=500,\n",
+    "    detect_neg_shifts=True,\n",
+    "    method='perfect_clean',\n",
+    "    neg_shift_factor=2.5  # Default value; decrease for more sensitivity\n",
+    ")\n",
+    "\n",
+    "print(f\"SRR with Negative Shift Detection:\")\n",
+    "print(f\"  Soiling Ratio (P50): {sr_neg_shift:.4f}\")\n",
+    "print(f\"  Confidence Interval: ({sr_ci_neg_shift[0]:.4f}, {sr_ci_neg_shift[1]:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac7d68b0",
+   "metadata": {},
+   "source": [
+    "## 1.4 Advanced SRR with Piecewise Fitting\n",
+    "\n",
+    "The `piecewise_fit=True` option tests whether each soiling interval is better described by two linear segments (a change point) rather than one. This is useful when:\n",
+    "- Soiling rate changes mid-interval (e.g., seasonal variation)\n",
+    "- There are undetected partial cleaning events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "03bff6bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mspringe\\OneDrive - NREL\\msp\\pvfleets\\repos\\public\\rdtools\\rdtools\\soiling.py:1207: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_neg_step\" and/or decreasing \"min_interval_days\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n",
+      "  sr, sr_ci, soiling_info = srr.run(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SRR with Piecewise Fitting:\n",
+      "  Soiling Ratio (P50): 0.9550\n",
+      "  Confidence Interval: (0.9505, 0.9583)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SRR with piecewise fitting\n",
+    "sr_piecewise, sr_ci_piecewise, info_piecewise = soiling_srr(\n",
+    "    energy_normalized_daily,\n",
+    "    insolation_daily,\n",
+    "    reps=500,\n",
+    "    piecewise_fit=True,\n",
+    "    min_piecewise_days=27  # Minimum interval length to attempt piecewise fit\n",
+    ")\n",
+    "\n",
+    "print(f\"SRR with Piecewise Fitting:\")\n",
+    "print(f\"  Soiling Ratio (P50): {sr_piecewise:.4f}\")\n",
+    "print(f\"  Confidence Interval: ({sr_ci_piecewise[0]:.4f}, {sr_ci_piecewise[1]:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c159bff2",
+   "metadata": {},
+   "source": [
+    "## 1.5 Combined Advanced Analysis\n",
+    "\n",
+    "For complex datasets, you can combine both `detect_neg_shifts=True` and `piecewise_fit=True` along with `inferred_clean` for maximum flexibility."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "54649337",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Advanced SRR (detect_neg_shifts + piecewise_fit + inferred_clean):\n",
+      "  Soiling Ratio (P50): 0.9286\n",
+      "  Confidence Interval: (0.9265, 0.9307)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Full advanced SRR analysis\n",
+    "sr_advanced, sr_ci_advanced, info_advanced = soiling_srr(\n",
+    "    energy_normalized_daily,\n",
+    "    insolation_daily,\n",
+    "    reps=500,\n",
+    "    method='inferred_clean',\n",
+    "    detect_neg_shifts=True,\n",
+    "    piecewise_fit=True,\n",
+    "    neg_shift_factor=2.5,       # Multiplier for negative shift threshold\n",
+    "    min_piecewise_days=27,      # Min days for piecewise attempt\n",
+    "    collapse_window_days=5,     # Window for collapsing cleaning events\n",
+    "    forward_median_window=10    # Window for forward median validation\n",
+    ")\n",
+    "\n",
+    "print(f\"Advanced SRR (detect_neg_shifts + piecewise_fit + inferred_clean):\")\n",
+    "print(f\"  Soiling Ratio (P50): {sr_advanced:.4f}\")\n",
+    "print(f\"  Confidence Interval: ({sr_ci_advanced[0]:.4f}, {sr_ci_advanced[1]:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e829b65",
+   "metadata": {},
+   "source": [
+    "## 1.6 Using the SRRAnalysis Class Directly\n",
+    "\n",
+    "For more control and access to intermediate results, use the `SRRAnalysis` class directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e71d94a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SRRAnalysis Class Results:\n",
+      "  Soiling Ratio: 0.9634\n",
+      "  Number of valid intervals: 66\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create SRRAnalysis instance\n",
+    "srr = SRRAnalysis(energy_normalized_daily, insolation_daily)\n",
+    "\n",
+    "# Run the analysis with custom parameters\n",
+    "sr, sr_ci, info = srr.run(\n",
+    "    reps=500,\n",
+    "    method='perfect_clean',\n",
+    "    detect_neg_shifts=True,\n",
+    "    piecewise_fit=True,\n",
+    "    min_interval_days=7,\n",
+    "    max_relative_slope_error=500.0\n",
+    ")\n",
+    "\n",
+    "print(f\"SRRAnalysis Class Results:\")\n",
+    "print(f\"  Soiling Ratio: {sr:.4f}\")\n",
+    "print(f\"  Number of valid intervals: {srr.result_df['valid'].sum()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "30017c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valid soiling intervals: 66\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>soiling_rate</th>\n",
+       "      <th>soiling_rate_low</th>\n",
+       "      <th>soiling_rate_high</th>\n",
+       "      <th>length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2010-03-06 00:00:00-07:00</td>\n",
+       "      <td>2010-03-18 00:00:00-07:00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010-03-19 00:00:00-07:00</td>\n",
+       "      <td>2010-04-08 00:00:00-07:00</td>\n",
+       "      <td>-0.003089</td>\n",
+       "      <td>-0.006783</td>\n",
+       "      <td>-0.000300</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2010-04-09 00:00:00-07:00</td>\n",
+       "      <td>2010-04-21 00:00:00-07:00</td>\n",
+       "      <td>-0.002861</td>\n",
+       "      <td>-0.007496</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010-04-22 00:00:00-07:00</td>\n",
+       "      <td>2010-06-13 00:00:00-07:00</td>\n",
+       "      <td>-0.000890</td>\n",
+       "      <td>-0.001203</td>\n",
+       "      <td>-0.000520</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2010-06-14 00:00:00-07:00</td>\n",
+       "      <td>2010-06-28 00:00:00-07:00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2010-06-29 00:00:00-07:00</td>\n",
+       "      <td>2010-07-13 00:00:00-07:00</td>\n",
+       "      <td>-0.004741</td>\n",
+       "      <td>-0.009892</td>\n",
+       "      <td>-0.001985</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2010-10-11 00:00:00-07:00</td>\n",
+       "      <td>2010-10-23 00:00:00-07:00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2010-10-24 00:00:00-07:00</td>\n",
+       "      <td>2011-01-29 00:00:00-07:00</td>\n",
+       "      <td>-0.001246</td>\n",
+       "      <td>-0.001402</td>\n",
+       "      <td>-0.001072</td>\n",
+       "      <td>97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2011-02-11 00:00:00-07:00</td>\n",
+       "      <td>2011-03-14 00:00:00-07:00</td>\n",
+       "      <td>-0.001913</td>\n",
+       "      <td>-0.002793</td>\n",
+       "      <td>-0.001307</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2011-03-15 00:00:00-07:00</td>\n",
+       "      <td>2011-04-30 00:00:00-07:00</td>\n",
+       "      <td>-0.001913</td>\n",
+       "      <td>-0.002287</td>\n",
+       "      <td>-0.001578</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       start                       end  soiling_rate  \\\n",
+       "1  2010-03-06 00:00:00-07:00 2010-03-18 00:00:00-07:00      0.000000   \n",
+       "2  2010-03-19 00:00:00-07:00 2010-04-08 00:00:00-07:00     -0.003089   \n",
+       "3  2010-04-09 00:00:00-07:00 2010-04-21 00:00:00-07:00     -0.002861   \n",
+       "4  2010-04-22 00:00:00-07:00 2010-06-13 00:00:00-07:00     -0.000890   \n",
+       "5  2010-06-14 00:00:00-07:00 2010-06-28 00:00:00-07:00      0.000000   \n",
+       "6  2010-06-29 00:00:00-07:00 2010-07-13 00:00:00-07:00     -0.004741   \n",
+       "9  2010-10-11 00:00:00-07:00 2010-10-23 00:00:00-07:00      0.000000   \n",
+       "10 2010-10-24 00:00:00-07:00 2011-01-29 00:00:00-07:00     -0.001246   \n",
+       "12 2011-02-11 00:00:00-07:00 2011-03-14 00:00:00-07:00     -0.001913   \n",
+       "13 2011-03-15 00:00:00-07:00 2011-04-30 00:00:00-07:00     -0.001913   \n",
+       "\n",
+       "    soiling_rate_low  soiling_rate_high  length  \n",
+       "1           0.000000           0.000000      12  \n",
+       "2          -0.006783          -0.000300      20  \n",
+       "3          -0.007496           0.000000      12  \n",
+       "4          -0.001203          -0.000520      52  \n",
+       "5           0.000000           0.000000      14  \n",
+       "6          -0.009892          -0.001985      14  \n",
+       "9           0.000000           0.000000      12  \n",
+       "10         -0.001402          -0.001072      97  \n",
+       "12         -0.002793          -0.001307      31  \n",
+       "13         -0.002287          -0.001578      46  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Access the soiling interval summary\n",
+    "interval_summary = info['soiling_interval_summary']\n",
+    "valid_intervals = interval_summary[interval_summary['valid']]\n",
+    "print(f\"Valid soiling intervals: {len(valid_intervals)}\")\n",
+    "valid_intervals[['start', 'end', 'soiling_rate', 'soiling_rate_low', 'soiling_rate_high', 'length']].head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0b16360d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the stochastic soiling profiles\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "\n",
+    "# Plot a subset of the Monte Carlo profiles\n",
+    "profiles = info['stochastic_soiling_profiles']\n",
+    "for i, profile in enumerate(profiles[:100]):\n",
+    "    ax.plot(profile.index, profile.values, alpha=0.05, color='blue')\n",
+    "\n",
+    "# Plot the median profile\n",
+    "median_profile = pd.concat(profiles, axis=1).median(axis=1)\n",
+    "ax.plot(median_profile.index, median_profile.values, color='red', linewidth=2, label='Median Profile')\n",
+    "\n",
+    "ax.set_ylabel('Soiling Ratio')\n",
+    "ax.set_title('Stochastic Soiling Profiles (100 Monte Carlo realizations)')\n",
+    "ax.legend()\n",
+    "fig.autofmt_xdate()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d82d88fb",
+   "metadata": {},
+   "source": [
+    "## 1.7 Summary: Comparing All SRR Configurations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "dc4338ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   Configuration  Soiling Ratio   CI Low  CI High  Soiling Loss (%)\n",
+      "                Basic (defaults)       0.951282 0.946841 0.954526          4.871779\n",
+      "          detect_neg_shifts=True       0.958811 0.956984 0.960953          4.118865\n",
+      "              piecewise_fit=True       0.955015 0.950515 0.958287          4.498539\n",
+      "Advanced (both + inferred_clean)       0.928642 0.926526 0.930723          7.135753\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary comparison\n",
+    "summary_data = {\n",
+    "    'Configuration': [\n",
+    "        'Basic (defaults)',\n",
+    "        'detect_neg_shifts=True',\n",
+    "        'piecewise_fit=True',\n",
+    "        'Advanced (both + inferred_clean)'\n",
+    "    ],\n",
+    "    'Soiling Ratio': [sr_basic, sr_neg_shift, sr_piecewise, sr_advanced],\n",
+    "    'CI Low': [sr_ci_basic[0], sr_ci_neg_shift[0], sr_ci_piecewise[0], sr_ci_advanced[0]],\n",
+    "    'CI High': [sr_ci_basic[1], sr_ci_neg_shift[1], sr_ci_piecewise[1], sr_ci_advanced[1]]\n",
+    "}\n",
+    "\n",
+    "summary_df = pd.DataFrame(summary_data)\n",
+    "summary_df['Soiling Loss (%)'] = (1 - summary_df['Soiling Ratio']) * 100\n",
+    "print(summary_df.to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98f12fd9",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Part 2: Combined Degradation and Soiling (CODS) Analysis\n",
+    "\n",
+    "CODS is an alternative approach that simultaneously estimates degradation, soiling, and seasonal components using iterative signal decomposition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "517f3ad7",
+   "metadata": {},
+   "source": [
+    "## 2.1 Basic CODS Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "abd75230",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CODS Results:\n",
+      "  Degradation Rate: -0.187% (-0.304, -0.086)\n",
+      "  Soiling Loss: 5.915% (4.707, 6.788)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create CODS instance\n",
+    "cods = CODSAnalysis(energy_normalized_daily)\n",
+    "\n",
+    "# Run bootstrap analysis (use reps=512 for production; 16 is minimum)\n",
+    "results_df, degradation, soiling_loss = cods.run_bootstrap(reps=16, bootstrap_seed=42)\n",
+    "\n",
+    "print(f\"CODS Results:\")\n",
+    "print(f\"  Degradation Rate: {cods.degradation[0]:.3f}% ({cods.degradation[1]:.3f}, {cods.degradation[2]:.3f})\")\n",
+    "print(f\"  Soiling Loss: {cods.soiling_loss[0]:.3f}% ({cods.soiling_loss[1]:.3f}, {cods.soiling_loss[2]:.3f})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c23ddfa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot CODS results\n",
+    "result_df = cods.result_df\n",
+    "\n",
+    "fig, axes = plt.subplots(3, 1, figsize=(10, 8), sharex=True)\n",
+    "\n",
+    "# Panel 1: Performance data with model fit\n",
+    "axes[0].plot(energy_normalized_daily.index, energy_normalized_daily, 'o', alpha=0.3, markersize=2)\n",
+    "axes[0].plot(result_df.index, result_df.total_model, color='k', linewidth=1, label='Total Model')\n",
+    "axes[0].plot(result_df.index, result_df.degradation_trend * cods.residual_shift,\n",
+    "             color='g', linewidth=1, label='Degradation Trend')\n",
+    "axes[0].set_ylabel('Normalized\\nEnergy')\n",
+    "axes[0].set_ylim(0.6, 1.1)\n",
+    "axes[0].legend(loc='lower left')\n",
+    "\n",
+    "# Panel 2: Soiling ratio\n",
+    "axes[1].plot(result_df.index, result_df.soiling_ratio, color='r', linewidth=1)\n",
+    "axes[1].fill_between(result_df.index, result_df.SR_low, result_df.SR_high,\n",
+    "                     color='r', alpha=0.1, label='95% CI')\n",
+    "axes[1].set_ylabel('Soiling Ratio')\n",
+    "axes[1].legend()\n",
+    "\n",
+    "# Panel 3: Residuals\n",
+    "axes[2].plot(result_df.index, result_df.residuals, color='k', linewidth=0.5)\n",
+    "axes[2].set_ylabel('Residuals')\n",
+    "axes[2].axhline(0, color='gray', linestyle='--', alpha=0.5)\n",
+    "\n",
+    "fig.autofmt_xdate()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f19dd2c9",
+   "metadata": {},
+   "source": [
+    "## 2.2 CODS with Custom Component Order\n",
+    "\n",
+    "CODS allows you to specify the order in which components are extracted using the `order` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "09000b5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CODS (SR→Rd→SC order):\n",
+      "  Degradation: -0.301%\n",
+      "  Soiling Loss: 4.668%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# CODS with different component orders\n",
+    "# Default order: ['SR', 'SC', 'Rd'] (Soiling, Seasonal, Degradation)\n",
+    "# Alternative: ['SR', 'Rd', 'SC'] or ['Rd', 'SR', 'SC']\n",
+    "\n",
+    "cods_alt = CODSAnalysis(energy_normalized_daily)\n",
+    "df_out, results_dict = cods_alt.iterative_signal_decomposition(\n",
+    "    order=['SR', 'Rd', 'SC'],  # Extract degradation before seasonal\n",
+    "    max_iterations=5\n",
+    ")\n",
+    "\n",
+    "print(f\"CODS (SR→Rd→SC order):\")\n",
+    "print(f\"  Degradation: {results_dict['degradation']:.3f}%\")\n",
+    "print(f\"  Soiling Loss: {results_dict['soiling_loss']:.3f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68ba058b",
+   "metadata": {},
+   "source": [
+    "## 2.3 Accessing CODS Cleaning Events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b6a8e5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize detected cleaning events\n",
+    "result_df = cods.result_df\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "ax.plot(result_df.index, result_df.soiling_ratio, color='r', linewidth=1, label='Soiling Ratio')\n",
+    "\n",
+    "# Mark cleaning events (where probability > 0.5)\n",
+    "if 'cleaning_events' in result_df.columns:\n",
+    "    cleaning_mask = result_df.cleaning_events > 0.5\n",
+    "    ax.scatter(result_df.index[cleaning_mask],\n",
+    "               result_df.soiling_ratio[cleaning_mask],\n",
+    "               color='green', s=50, marker='^', label='Cleaning Events', zorder=5)\n",
+    "\n",
+    "ax.set_ylabel('Soiling Ratio')\n",
+    "ax.set_title('CODS Soiling Ratio with Detected Cleaning Events')\n",
+    "ax.legend()\n",
+    "fig.autofmt_xdate()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3f9601d",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Part 3: Choosing the Right Configuration\n",
+    "\n",
+    "## Decision Guide\n",
+    "\n",
+    "| Scenario | Recommended Configuration |\n",
+    "|----------|---------------------------|\n",
+    "| **Standard analysis** | `soiling_srr()` with defaults |\n",
+    "| **Systems with complete cleaning** | `method='perfect_clean'` |\n",
+    "| **Systems with partial cleaning** | `method='inferred_clean'` + `detect_neg_shifts=True` |\n",
+    "| **Variable soiling rates** | `piecewise_fit=True` |\n",
+    "| **Complex soiling patterns** | `detect_neg_shifts=True` + `piecewise_fit=True` |\n",
+    "| **Joint degradation + soiling** | `CODSAnalysis` |\n",
+    "| **Noisy data** | Increase `min_interval_days`, decrease `max_relative_slope_error` |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2914e65",
+   "metadata": {},
+   "source": [
+    "## Parameter Tuning Tips\n",
+    "\n",
+    "### `min_interval_days`\n",
+    "- **Increase** if you're getting too many short, noisy intervals\n",
+    "- **Decrease** if you're missing short but valid soiling periods\n",
+    "\n",
+    "### `max_neg_step` (only when `detect_neg_shifts=False`)\n",
+    "- **Increase** to be more permissive of noise\n",
+    "- **Decrease** to be stricter about interval validity\n",
+    "\n",
+    "### `neg_shift_factor`\n",
+    "- **Increase** to require larger shifts before subdividing\n",
+    "- **Decrease** to be more sensitive to smaller shifts\n",
+    "\n",
+    "### `clean_threshold`\n",
+    "- Use `'infer'` (default) to automatically determine from data\n",
+    "- Specify a numeric value if you have domain knowledge about typical cleaning magnitude"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "109c6e80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mspringe\\OneDrive - NREL\\msp\\pvfleets\\repos\\public\\rdtools\\rdtools\\soiling.py:1207: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_neg_step\" and/or decreasing \"min_interval_days\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n",
+      "  sr, sr_ci, soiling_info = srr.run(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tuned for noisy data:\n",
+      "  Soiling Ratio: 0.9530 (0.9490, 0.9564)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example: Tuning for noisy data\n",
+    "sr_tuned, sr_ci_tuned, info_tuned = soiling_srr(\n",
+    "    energy_normalized_daily,\n",
+    "    insolation_daily,\n",
+    "    reps=500,\n",
+    "    min_interval_days=14,           # Require longer intervals\n",
+    "    max_relative_slope_error=300.0, # Stricter slope error threshold\n",
+    "    max_neg_step=0.08               # More permissive of negative steps\n",
+    ")\n",
+    "\n",
+    "print(f\"Tuned for noisy data:\")\n",
+    "print(f\"  Soiling Ratio: {sr_tuned:.4f} ({sr_ci_tuned[0]:.4f}, {sr_ci_tuned[1]:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afba5772",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Summary\n",
+    "\n",
+    "This notebook demonstrated the soiling analysis capabilities in RdTools v3:\n",
+    "\n",
+    "1. **SRR Analysis** with various cleaning methods and advanced options\n",
+    "2. **CODS Analysis** for joint degradation and soiling estimation\n",
+    "3. **Parameter tuning** guidelines for different scenarios\n",
+    "\n",
+    "For more details, see:\n",
+    "- [RdTools Documentation](https://rdtools.readthedocs.io/)\n",
+    "- The source code in `rdtools/soiling.py`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rdtools_313-nb",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/sphinx/source/changelog.rst b/docs/sphinx/source/changelog.rst
index bbdbffbd..332fa92f 100644
--- a/docs/sphinx/source/changelog.rst
+++ b/docs/sphinx/source/changelog.rst
@@ -1,5 +1,6 @@
 RdTools Change Log
 ==================
+.. include:: changelog/pending.rst
 .. include:: changelog/v3.1.0.rst
 .. include:: changelog/v3.0.1.rst
 .. include:: changelog/v3.0.0.rst
diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
new file mode 100644
index 00000000..c52f9087
--- /dev/null
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -0,0 +1,142 @@
+***************
+Pending Changes
+***************
+
+This document tracks changes pending release for the soiling module refactor.
+Changes from :pull:`426`, :pull:`429`, :pull:`432`, and :pull:`435` are integrated via :pull:`479`.
+
+
+Breaking Changes
+----------------
+* Upgrade soiling algorithms SRR and CODS. Remove experimental warning label. (:pull:`426`, :pull:`479`)
+
+
+API Changes
+-----------
+* Renamed SRR soiling parameters for clarity and consistency (:pull:`479`):
+
+  - ``min_interval_length`` -> ``min_interval_days`` in :py:func:`~rdtools.soiling.soiling_srr`
+    and :py:func:`~rdtools.soiling.monthly_soiling_rates`
+  - ``max_negative_step`` -> ``max_neg_step`` in :py:func:`~rdtools.soiling.SRRAnalysis._calc_result_df`
+
+* Consolidated soiling method names (:pull:`479`):
+
+  - Removed ``method="perfect_clean_complex"`` - use ``method="perfect_clean"``
+    with ``detect_neg_shifts=True`` and ``piecewise_fit=True`` instead
+  - Removed ``method="inferred_clean_complex"`` - use ``method="inferred_clean"``
+    with ``detect_neg_shifts=True`` and ``piecewise_fit=True`` instead
+
+* Renamed SRR parameters for new features (:pull:`479`):
+
+  - ``neg_shift`` -> ``detect_neg_shifts``
+  - ``piecewise`` -> ``piecewise_fit``
+
+
+Enhancements
+------------
+* Added negative shift detection capability to :py:func:`~rdtools.soiling.soiling_srr`
+  via the ``detect_neg_shifts`` parameter. When enabled, detects negative shifts
+  in soiling profiles that may indicate partial cleaning events or sensor issues.
+  (:pull:`426`, :pull:`435`, :pull:`479`)
+
+* Added piecewise linear fitting capability to :py:func:`~rdtools.soiling.soiling_srr`
+  via the ``piecewise_fit`` parameter. Detects changes in soiling rate slope within
+  intervals using segmented regression. (:pull:`426`, :pull:`435`, :pull:`479`)
+
+* Added ``collapse_window_days`` parameter to :py:func:`~rdtools.soiling.SRRAnalysis._calc_daily_df`
+  to control collapsing of consecutive cleaning events (default: 5 days). (:pull:`479`)
+
+* Added ``forward_median_window`` parameter to :py:func:`~rdtools.soiling.SRRAnalysis._calc_result_df`
+  for forward median calculation in shift validation (default: 10). (:pull:`479`)
+
+* Added ``neg_shift_factor`` parameter to control sensitivity of negative shift
+  detection (default: 2.5). (:pull:`479`)
+
+* Added ``min_piecewise_days`` parameter to set minimum interval length for
+  piecewise fitting attempts (default: 27 days). (:pull:`479`)
+
+* Added ``inferred_recovery`` and ``inferred_begin_shift`` columns to soiling interval
+  summary output for tracking inferred cleaning recovery and beginning shift values.
+  (:pull:`435`, :pull:`479`)
+
+* Consecutive cleaning events are now always collapsed to single events
+  (previously only when ``piecewise=True``). This improves soiling interval
+  detection consistency. (:pull:`479`)
+
+* Add capability to seed the CircularBlockBootstrap for reproducible uncertainty
+  calculations. (:pull:`429`, :pull:`435`)
+
+* Removed experimental warning label from soiling module. The SRR and CODS
+  algorithms are now considered stable. (:pull:`426`, :pull:`435`, :pull:`479`)
+
+
+Bug Fixes
+---------
+* Fixed pylint bare except error for :py:func:`~rdtools.soiling.segmented_soiling_period`
+  in ``soiling.py`` (:pull:`432`, :pull:`435`)
+
+* Fixed variable shadowing bug in ``CODSAnalysis.iterative_signal_decomposition``
+  where a local variable ``degradation`` shadowed the imported ``degradation``
+  module, causing ``UnboundLocalError``. Renamed to ``degradation_value``. (:pull:`479`)
+
+* Fixed pandas Copy-on-Write (CoW) compatibility issue in
+  :py:func:`~rdtools.soiling.SRRAnalysis._calc_result_df` where
+  ``bfill(inplace=True)`` on a chained assignment failed silently with pandas 2.x+.
+  Changed to assignment-based approach. This was causing incorrect soiling ratio
+  calculations when ``detect_neg_shifts=True``. (:pull:`479`)
+
+* Fixed ``segmented_soiling_period`` regression error where R² calculation was
+  attempted on invalid piecewise fits. When a change point was rejected for being
+  too close to interval boundaries, the code incorrectly tried to call
+  ``linregress`` with all-NaN values, causing "Cannot calculate a linear regression
+  if all x values are identical" errors. (:pull:`479`)
+
+
+Documentation
+-------------
+* Removed experimental warning docstrings and runtime warnings from soiling
+  plotting functions: :py:func:`~rdtools.plotting.soiling_monte_carlo_plot`,
+  :py:func:`~rdtools.plotting.soiling_interval_plot`, and
+  :py:func:`~rdtools.plotting.soiling_rate_histogram`. (:pull:`435`, :pull:`479`)
+
+* Added new example notebook ``soiling_options_comparison_v2.ipynb`` demonstrating
+  the various soiling analysis options including ``detect_neg_shifts`` and
+  ``piecewise_fit``. (:pull:`479`)
+
+
+Testing
+-------
+* Added new test fixtures for soiling data with negative shifts
+  (``soiling_normalized_daily_with_neg_shifts``) and piecewise slope changes
+  (``soiling_normalized_daily_with_piecewise_slope``). (:pull:`435`, :pull:`479`)
+
+* Added comprehensive tests for negative shift detection (``test_negative_shifts``),
+  piecewise fitting (``test_piecewise``), and combined functionality
+  (``test_piecewise_and_neg_shifts``). (:pull:`435`, :pull:`479`)
+
+* Added pytests to cover invalid segmentations for
+  :py:func:`~rdtools.soiling.segmented_soiling_period` including tests for
+  non-datetime index, no change point found, short periods, and long periods.
+  (:pull:`432`, :pull:`435`, :pull:`479`)
+
+* Updated test parameter names to match API changes (``neg_shift`` ->
+  ``detect_neg_shifts``, ``piecewise`` -> ``piecewise_fit``, etc.). (:pull:`479`)
+
+* Updated test function names (``test_soiling_srr_min_interval_length`` ->
+  ``test_soiling_srr_min_interval_days``, etc.). (:pull:`479`)
+
+
+Code Quality
+------------
+* Reformatted ``soiling.py`` with consistent double-quote string formatting
+  and improved function signature readability. (:pull:`479`)
+
+* Removed unused ``warnings`` import from ``plotting.py``. (:pull:`479`)
+
+
+Contributors
+------------
+* Martin Springer (:ghuser:`martin-springer`)
+* Matthew Muller (:ghuser:`mmuller`)
+* Noah Moyer (:ghuser:`noromo01`)
+* Quyen Nguyen (:ghuser:`qnguyen345`)
diff --git a/docs/sphinx/source/examples.rst b/docs/sphinx/source/examples.rst
index 57c5b6ae..656a2e39 100644
--- a/docs/sphinx/source/examples.rst
+++ b/docs/sphinx/source/examples.rst
@@ -23,6 +23,7 @@ This page shows example usage of the RdTools analysis functions.
 .. nbgallery::
 
     examples/degradation_and_soiling_example
+    examples/soiling_options_guide
     examples/TrendAnalysis_example
     examples/TrendAnalysis_example_NSRDB
     examples/system_availability_example
diff --git a/docs/sphinx/source/examples/soiling_options_guide.nblink b/docs/sphinx/source/examples/soiling_options_guide.nblink
new file mode 100644
index 00000000..7d232650
--- /dev/null
+++ b/docs/sphinx/source/examples/soiling_options_guide.nblink
@@ -0,0 +1,3 @@
+{
+    "path": "../../../soiling_options_guide.ipynb"
+}
diff --git a/docs/system_availability_example.ipynb b/docs/system_availability_example.ipynb
index 0b1af9f7..be5d0cc0 100644
--- a/docs/system_availability_example.ipynb
+++ b/docs/system_availability_example.ipynb
@@ -16,14 +16,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:25.902548Z",
-     "iopub.status.busy": "2026-02-12T14:52:25.902336Z",
-     "iopub.status.idle": "2026-02-12T14:52:29.135509Z",
-     "shell.execute_reply": "2026-02-12T14:52:29.134914Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import rdtools\n",
@@ -50,14 +43,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:29.138553Z",
-     "iopub.status.busy": "2026-02-12T14:52:29.138290Z",
-     "iopub.status.idle": "2026-02-12T14:52:29.144521Z",
-     "shell.execute_reply": "2026-02-12T14:52:29.143976Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def make_dataset():\n",
@@ -149,18 +135,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:29.147319Z",
-     "iopub.status.busy": "2026-02-12T14:52:29.147153Z",
-     "iopub.status.idle": "2026-02-12T14:52:29.621187Z",
-     "shell.execute_reply": "2026-02-12T14:52:29.620719Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 3 Axes>"
       ]
@@ -209,14 +188,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:29.662366Z",
-     "iopub.status.busy": "2026-02-12T14:52:29.662093Z",
-     "iopub.status.idle": "2026-02-12T14:52:29.726325Z",
-     "shell.execute_reply": "2026-02-12T14:52:29.725468Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from rdtools.availability import AvailabilityAnalysis\n",
@@ -241,12 +213,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:29.729438Z",
-     "iopub.status.busy": "2026-02-12T14:52:29.729267Z",
-     "iopub.status.idle": "2026-02-12T14:52:30.070189Z",
-     "shell.execute_reply": "2026-02-12T14:52:30.069663Z"
-    },
     "tags": [
      "nbsphinx-thumbnail"
     ]
@@ -256,13 +222,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_58256\\689292658.py:3: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "C:\\Users\\mspringe\\AppData\\Local\\Temp\\1\\ipykernel_65036\\689292658.py:3: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
       "  fig.axes[1].legend(loc='upper left')\n"
      ]
     },
     {
      "data": {
-      "image/png": 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GDFAD7jJcWq2dGe2UkSgcUJzBxV2JuXFDVz0DDQBgrhjCpdTcbIfJTdMOjvZX1FsmV5Fhw58UW63iDrUa3D2eydadfq4CtzXWF6tuo8Y5Z6cPzzvHNuyvrMNvKQAB5rzzzhNNYl588UW6/fbbxYXLotlhN2bMGCorKxMiF3fy5rxIydSpU0XWJAuK7FDkLtrcbZrhXMpRo0aJLuDcwIUFPj5+ad7ZuzncoIadhdxMhsU/Nm6woChNG3z75JNPFlmby5YtE0LgrbfeShdddJEYA5eM33333bR9+3bR0CcYcPMgLm2/8cYbRfMaLq3mjE0eBwuBbCxhtyrPKc8P/594bM3FUDak8JxweTkLj5yBaXQ6cndwzhPlOeVGSOz+5GZDLN7y33CXdW4uxKImzxULps1dsOwe5bHx58YNgTgTk8d9ySWXIF4MAAACRFFlHf1VUOHT33Ll0IjuWdYSLPkHjAVKPmvIwuQ111wjQpP5hwqoX2qalhhLaYnaDkeH9AQhWBZWoKMsAGZzoLJJDJHZU1zuy7AQYgXsuqNDE3GQpxhMivUsRVmazMBhGSJXq2HO5bwLwRLiPAABhzMsWXjjxjKc3cgiI7v/WCRjV2NmZiYddthhwolp5L///a/oxM3OQO4o/fXXXzsdkrz8Rx99JJrQ8OvxMQwLnMaGOy2NZfr06WJZ/tuxY8cK4wZ3tk5OThZNe1gI5DLtQw89lCZPniz+jkVCfv9//etfIveRnaLcVEZmcwYSdmJ+++23YhxcTs8ZkyxQsktVwuPkUnZubsNCIgu4LPwa4SZCl19+ufg/sgjJHde5u7qE53LOnDnib7krOWd28v+LhWWG/2+fffaZyCQtLS2lGTNmHDS/PGfff/+9+JxYQOb7/HfcFAkAAID/fPtnAd3y4Sqqa/QtRqNfhzT6/pZx1hIsf/31V/HDzsIlZ1myaMln6IDayFI1eUBrdONIoQQAYB5FTgHKsI3KsmqLCCE2/beQG8FAPAuVw9LNd3pVnXC7spMeBF4kNjosGazrAASGN9980+3jd911l7hIWODiS2uw+3LNmjUtPs/CWGuCIbsg3cHiJF+awy5KvrTEEUccIdyGRppnRDaHxVBfnudjsyVLlrT4dyygvvPOO+IiYYHTCIuULCYaYeHRSPfu3emTTz5x+x7spnT3XPP/Mwu73NDVm3WCHaQAAABah41pUqzkSEA2rnlL9xx1qnA9Hj3/WLFoyT+SfHaQSwI484V/HKWAyWc+gVpIUTLXzcEtcrcAMJ/iSjeZhIZtlHfyjV0/VXZYcoYlypNDv77I29wYpqymgbKaCWsg8CIxg/gDAAAAAACgEi/8vFmIlUf3yaF3rhhteSODx013uMyB81m41IJLw4uKikSZBtv4+ZpzSgYPHhzc0QKvkaKkzMQTt1GyCYAysCuuebmpFEL4x6ayrpGs1CXc6TqzSDl7OGRYJsTGUGayFvmBeQ+NSKx9Btqcs0gMAAAAAACAmewuqaYvVu0Rt+84qb/lxUqfu4RLAZPzUfiSlZUl8l3Wr18f2NEBvzlQ6abcFA0aAFDavcVBx6kJmgHeCvl40mHJv4nO7xcLjNvK60vzPEXMe2ib7jCySVZpNeJVADAbrvbiigTOtgQAAAAikS9W7hFGkiN6ZdPQruHxe+ixYMld+TgXhd2Up5xyitghOOqoo0RnvY4dO4qwZQ7B9oaXXnqJhgwZIjr88YW7y3333XfO52tra+n6668XWZmcu8KZM/v27XN5jZ07d9Jpp50mnJ7c8Y6zWDgA2giXsXPQNueq9OnTp8WsnHCkqOpgZ0hThiUEBQBUiW1o7t5qckKrL4Y4S8Kjm0rC4fQLctOdZutLlu6wLIXbLySuViYzSbsPhyUAAAAAADCbWasLxPWE4Z0pXPA4w5IFyqqqKiFOcue3Z555RpzN7N27t89vzmXkXGLet29fcVb0rbfeorPOOotWrlxJgwYNoltuuYW++eYb+vjjjykjI0N0CzznnHNowYIF4u9tNpsQK3lMCxcupIKCArr00kspLi6OHn30UbHMtm3bxDLcoe+9996jn376SYRmcwOhk046iSLGYamLCC4NPSwghAAQqR2IWfjbfqDaElmzzpLwqChn/mZFbSPVNdpEuTIIfHly8zzFJrcfypNDtY2myzmHSAwAAAAAAExkc2El/bW3gmKjo+ikQR0jT7CcNm2aECq50U6gOOOMM1zuP/LII8J1+fvvvwsx8/XXX6f3339fdCZnZsyYQQMGDBDPc9e9OXPm0Lp16+jHH3+kDh060LBhw+ihhx6iO++8kx588EGKj4+nl19+mXr27ElPPfWUeA3++99++00Iri0JlnV1deIiKS8vJ6siRclcw4GWPOjab5GGHgBE2kkF7X68i7vLKl3CuRMdf6Ww6bK8ppHapUGwDBS1DTaqqre5dftJ8Qxuv+AJls2bGcncUMw5AAAAAAAwk182FIrrI3vnUGZy+DTg9Lgk/JprrhFi5dy5c1tchsvCfYXdkjNnzhQuTi4NX758OTU0NNAJJ5zgXKZ///7UrVs3WrRokbjP14ceeqgQKyUsQrLAuHbtWucyxteQy8jXcMdjjz0mHJ3y0rVrVwonMUQ2xai3SEMPACK53LTcAu4tPvEhMyw53Dk9UQo56outVhTO+MxpeqLr+UaUJweP8lptG8zUReHmrlYrbKMAAAAAACB8+W1zkbge2zeXwgmvm+5wSTaLic157rnn6O677/Z6AH/++afIp+R8SS7b/vzzz2ngwIG0d+9e4ZBsHp7N4iQ/x/C1UayUz8vnWluGRc2amhq3Y+L/R1lZmfOya9cusno+nszDYxLjmhp6oCwcAPW2USYj2ToNPWxOwTLKRciB8yywyPlkZ19zZ3zTnKu/vlgJu93hPLEnXazNHZZchi9FewAAAAAAAEJJXaONFm8tFrfH9GkX2YIll4Zz052//vrL+RiXW0+ZMkXkTXpLv379aNWqVbR48WK69tpradKkSaLM20xYPJWNgOTFquWDFfqBVvNy06aGHurn4wEQrvA2WtOglfg2t+5bSfSTGZZcEs6gVDY4SCefdLAayUiKtcz6YiX4N1RqkRx34G4bbbQ7qFov1QcAAE/gPgCTJ09WYrK4GWkouqtfdtlldPbZZwf9fQAAINL4Y1eZOKZkjad/xzSKaMGSG9bcfvvtosx6+/bt9Pjjj9PUqVPp22+/pbFjx3o9AHZRcufuESNGiFLsoUOHCrcmN9Kpr6+n0tJSl+W5Szg/x/B1867h8n5by7AImZSURJFQPhgX46Z8UBdHSiyQjwdAuJeaslkuTXc9WzGT0NglnEEDmOBQXuve6Wf8TrfC+mJFkTghNvqgBlJJcTEUH6PtRqHxDgD+CVnsGm9+OfnkkyNWZGQRUc5DdHS0yPa//PLLqbBQyyhTGT4+5HGzIcUIH9/x/wsAAEBgWbGzRFyP7J4t4rnCCY+b7hi544476MCBAzRy5EiRPfn999+LJjiBwG63i4Y3LGByt2/u6n3uueeK5zZs2EA7d+4UGZcMX3OjHv7xbt++vXjshx9+EGIkl5XLZVhMNcLLyNcIZ0r0UlI+kG25fBAHtwCo4Jhr/uOSaaFt1K433ZHfM1YSWy25vrgRLCESB/ekgrs55/WdH+dKhbLqBuqcGd4nQQEIJixOcnPN5hVPkQwfz/CxDx8b/fHHH0KwzM/PF8ddzeHjMSluqgr3BQAAABB4VuzQBMvDugffLR9qPPpVmz59+kGXzp07U3JyMp122mm0ZMkS5+PewFmR8+fPF2fiOMuS7//yyy80ceJE8aN25ZVX0q233ioa/XBuJv9Qs9AoxdETTzxRCJOXXHKJ+CHnH/D77ruPrr/+eudODudibt26VYisXMb+f//3f/TRRx/RLbfcQuEOd+hlmrsrXZoF6I4dAEDoKZPbqF7Oa1UBSmZYxiDDMjTimZvvdIjEof8ddcmxRHYoAH7B++1cFWW8ZGVlief42IArsn799Vfn8k888YQwK8gqKnY/3nDDDeLCxxC5ubl0//33u+TLsiGCq8T4GCYlJYVGjx4tXtvIggULxGvxMQ6/PzfqLCkpES7QefPmCZegdD7y8QuzZs0aEZfFmfyck8/HJUVFWvMDhhuKXnrppeL5Tp06iSgtT+D34HnIy8sTr3/TTTfRjz/+KDL4ZRn3V199JY6FeP7Y1MFj5ffisfP/gf9u06ZNLq/Lf8tNTPn5CRMmCBNKW6Xb7CzleZGwiMqfAVfJ8Xvz67GJhOnZs6e4Hj58uPg/yL9r/rr8efD/iT/HxMREGjNmDC1dutT5PH82/PdsXmGTDI/3qKOOEiIuAAAADf6dW7FTq0o+rJv2uxlxDstnnnnG7eMxMTHih50vDP+o8A+Pp7Azkn9UCwoKxM7FkCFDhOj4t7/9zfm+fKaQHZb8o8Y7DSw4Gt9/1qxZIvuShUze+eAMTC5Rl/CPJmdrskDJOxlcUvHaa6+J1wp3pLtJCh9G4LAEQCUBquVt1AodiLkxCaNXx1rKHWpJ8cxtSbg+5xYQuMPFYWm17RREICzWNVSb895xyVreSQBLsaVBgY0ILEZ+/PHHLo0133rrLWF2YCPFsmXL6OqrrxZC2lVXXSWeZzGTc/JnzpwpREBu9MnOTjZN9O3bV5QwH3/88XTFFVeIY4bY2FhhmmD3It/fuHEjDR482Hmc0a5dOxFdddxxx4nILD5uYTHxzjvvpPPPP59+/vlnsdy///1vIXZ++eWXQpy75557aMWKFTRs2DCv5oGjrFgobGzUfguqq6tFNBcf1+Tk5IjXvvDCC4VAyUImOzR5LKeeeqr4f3PlGvcM4DniGC4WD2fPnk0PPPCA158Jm0xeffVV8X9moZGP5WR/A57/ww8/XIirgwYNEmKzO9hM8umnn4rPrXv37kIA5eOzzZs3U3Z2tnO5e++9V4i8PN9sROHPRx57AgBApLO7pEZU+3AM4ODOGZEpWG7bti0ob/7666+3+jyfbXvxxRfFpSX4B655ybe7HZ2VK1dSpOFJ+SAOsgAwj/IwOakgMyxlSbhz7BDPQi5wc5MYboIk80RB8BodGcV5KzihQQTCYuWjeea89z35RPEpHi/OBgR2ILq8xD33iAvz8MMPi0gnFiHZ0cgGhTPPPNNl+a5duwoBjX+LuKknC5F8nwVLdh9yyTlfs1jJsNuSBTt+/NFHHxWCGTv5jOYIFtwkLLyxy0/m5DMvvPCCcBLy30veeOMNMRYWOPm9+Hjn3XffFWIowwIdGyi8gUXIl19+WYwvLU1rqNDQ0CDGyvn/chkWKlnMYyci895774mxfPHFF3TeeecJ4ZVFWhYLmUMOOYQWLlwo5sFTKioqxOvw/50/B6Z3795CuGRYWGRYRDXOlRF2nb700kvC7ckuUIYFUP6Meb5Y5JWwc3P8+PHi9l133SWq+2pra8VxIgAARDor9PzKgXkZlBjnmrcesRmWwBrAYQmAlbs+a49V1duowWanOGlfVBCbXnGHkvBQnYRqOUJALpeV4t7RAryc81YaHVntxAIAKnPssccKAcuI0WXHYiGLb1yNxWYFd9VfHBllzGzn6it25rFDksVLvmaBzghXcLGwxrDDkkU9b2DHJ7swm4utzJYtW4TjkpuIcvm58f/FgmpblJWViddlVyULdCwIspvSOCc8H5L169cLV6jxvfj/xu/Fz8lluAzcCM+TN4IlvwbPmxRgfYHnhgXXo48+2vkYO0DZmSnHKjH+H7mkXlbpsXsWAAAinRUyv7Jb+OVXeixYco7kQw89JEquPS0T4DNjxh0NYJ4bx+rurfzSGnrux000oFMaXXa0losDQHiJIS1nEorlahooJzXBAiXhUZb7frESZa0I3Cxop8THCIGbO1ZDsAz0SQX3u0tyO61AHjRQES7LZqejWe/tBXyMwXmIrcFOQKa4uFhcPD0uYSorK0WUFGfi87URKTZyybW38OueccYZojS7OSyucXmzr7CTkkvHOR6LX6v5+Ph+86aagYDfz5j9ybC4aHzfUMJCpkT+f1nEBQAAQLR6T5mYhmFdw1Ow9Miyw7Z/zknxFC7h5kwXoO7BrRRIVBcUeIfphvdX0IfLdtGDX6+jBZubQswBCGeHJYt/abpIwgKUynAJMiM7nWc4G5GoPe5wzVNU/Xs9nOY8NUHbRivr0MAOKAiLO1yWbcYlwEIaO/I4j57LhtlByKXIzUUrzmc08vvvv4tsShYouWybHZbszGNh1HiRZcvs5OMGLy3BjkZ+DSOHHXYYrV27lnr06HHQ67KgyqXSMjtSwo1xuFzcE+GQX6dXr14eiYQDBgwQ+ZbG9+KGOtykhhvzyGXczZMRLunmTEoj7D6V8JzyeFqaK5lZ2XyujPC88HLGLEoWRbnpjhwrAACAto/B/iqoELcH5YVffqXHgiWLRlxCwY5JTy6cSwLULgm3SkfZjfsqnV2vmI+W7TJ1PACEahs1Pq76diozLGVsolXGbdWmOy2tL01uP8x7oOe8pQzLVP2kAhyWAPgHlxjv3bvX5SI7bbPwdfHFF4uGLJdffrnInFy9evVB3bY5n5Krwlig++CDD+j555+nm2++WTzHxzETJ04UzT4/++wzkc/PzWG4+Qw355QVYiyYXXfddeL1uYkMl6nLcbAoyWIfdwfnx1gwvf7664Xbk5vd8N+ysMoNRHmcPG52b3KTG6484yY8nL/J3bJZjAw0LCSeddZZIrPzt99+E+XqPG/cFZ0fZ7g5Kpd/P/nkkyLzknMom5eDcxMhblr09ttvi2W4KQ+PW8LZkdzMh3MweRn+P7PoKXsTcPMfFjT5dbmLO5e2N4fFXG6ayvPCy3FTIB43G2R4vgAAALTN9gNVVNNgo6S4GOqZ63nVQdiVhPOOgbcYu/YBcwiHhh7zNhaK67SEWNFMYu5fhaL8VDq5AAh3xxx3frOKYIkMy1A13Ylt3e2H8uTAz3lSG3Nep/Y2CoDqsGgl8wklnL3IoiE3XdmxY4dozMPwcq+88ooQCU888URn0xkWIzkzknMQ2VXJYiU36TEez3Dznttuu4327NlDubm5Ivfy9NNPd4qac+bMEY1++DVYdGM3J7+PbNLDzk52APL7sOjJIia7BFnA47Gw8MoZm9zYRoqS06ZNc5aOc5k3v787ES8Q8P+R/9/8f+LszHHjxonmpLKsmv+/7FJlEXLKlCl0wgkn0H333SeivyQsDHMXdhYkOTuTu3Lz3HIOqISf57xMfo38/HzxmXAHb4Yfnz59uuimzs+PHTuWfvnll4PG+t///leIvtz9nRv5cEMhFnuzsrKCMjcAABBurMsvF9f9O6WFbcPNKEfzkBJwEOXl5ZSRkSF2LtLT0y0zQ+f83wLhTnz54sPo5MGuO4G7S6ppzONzKT42mjY+rHXnU5F/vbOcZq/dS3ec3E/kWNY12unn28ZTr3YHh5sDYDUufm0x/ba5iJ75x1CaMPzgjqEXvfo7LdxygJ67YBidNawzqcrxT/1CW/ZX0cyrj6AjeuUIkWfIg3PEcxsePpkSYsOvY50ZDHnwe5F7+tNt46m3m+/Ay2YsoV827Kcn/j6Ezh/Z1ZQxhhv/+N8iWrytmJ6/cDidMfTgbstfrtpDN89cRUf3yaH3/nkEqY5V92eAZ58hi0ssovXs2TPiOigfc8wxNGzYMHr22WfNHgowkUjeBgAAkcfjs/+il37ZQhNHd6NHJhxKVsGb/VF1286CwGVYtuKwrG+0U21DyxkzZrMmvylEdlCetjKv3h2cs9IAqFYSnmaRclM9wtJ5Zi8lvsmNVlWn7veLlWBnObvMWy1PhsMy5F3CMecAAAAAAMBMh+VAXScJRyBYRsCBljsxhA+ypLigarkpu7S4HJYZ1CnDuSFu3KcFywIQPiW+LYkhcZZo6NE8w5K/W5LjNVclypMDQ2V9I8l6CClkN0c+rvr6YiVkHqgUJpsjH5diMgAAAAAAAKFgXYEuWHYKX8HSowxLEH5dwqOiokQOWkl1g1iuQ7p6ZRPbi7TmTe3SEkTXYVkCuWV/pckjAyDAXcLbcFiqLvo5u4QbusKykFNdb6MKZPsFBOmy5RiPxDj3JfboWB14eB02zu1Bc26RbRSAcMddRiIAAAAQrhRW1NL+ijphGOnfMXwFSzgswxQu8+Zyb4bFPneo3nhnmy5Y9szROl41CZboQg+sD8cHSxd0S445qwhQXK7MGMOeIeQElip9HWhJONOek13C1V5frITc9lIS3IvEaRZxQQMAAAAAgPBhfYFWdcrdwZP0yjaKdMGyoaFBdH5bs2ZN8EYEAurcYsNTqiFPzohTUFD0QGurLkz2yE3WrnXhcldxtRB7ALAy3EBKOhPbcm+pLkDZnCXhTYJlmkXE1nARzqzwnW41Gmx254m/lDZ+R9mJKbdnAAAAAAAAQpNfmRHWE+2VYBkXF0fdunUjmw1NFFRH5mmxEBLdQot71ZsF5Jdq+ZVdsjTBskNGglPo4VJ2AKyMUVRKaUkMcYp+aq/vUqdxKQmHeBZQqvXmRSktrCsuIrGeuwgCM+di3ls4qWAUkCEUAwAAAACAULAuAvIrfSoJv/fee+mee+6h4uLi4IwIhLx8UNWDrL3lteK6U4aWr5kQG0O5qQkuYiYAVt9GuTlNSycVrNJExW1JuGxGougJEes6LFv5TrfI+mIVquq1eYyLiRLZoe7g3yX5HOYdAAAAAACEgnX5ZWHfIdynpjsvvPACbd68mfLy8qh79+6UkqKV6UpWrFgRyPGBIB7cqt7QY2+ZFCyTnI/lZSZSUWUdFZTV0uDO4W1/BpGxjSa34phLiVd7G21eEh4TbZ0TIlYVuFsVLCESh3zOpbP1QGO98tspAAAAAACwPtX1jbRV7/cR7g5LrwXLs88+OzgjAQGlSpYPenJwW6e2YNlRd1hKt+Xq3WVUUAaHJQiX7sNtZxKquo1KZHZflDHDUvETIlZ1+6W0EqoNh2WQTvy1clJBzvuBqnrloxsA8JSqqipKTdUaHVZWVh5kTgAAAACAefy1t4LYL9IuLUFcwhmvBcsHHnggOCMBQSoJb/ngVoqZclmVqKhtcIo0roKl5rbML9XETADCusRX4W3UiOyBFWPMsETTnZCfhEKjo+CcVGit0REDZysAwB09evSgyZMniwsAAAAQqJOG62TDnTB3V/qUYcmUlpbSa6+9Rnfffbczy5JLwffs2RPo8YEgOkNUdkDt0/MreYzGHE6ZZwmHJYiEclOVt1HJ/oo6p/svPUkrA7dSh/OwyiU2rC8OqSKDoJ5UMH4miD8AwDeOOeYYt6Lem2++SZmZmaZPK1cPfPHFF17/3dKlS+nqq6/2ePlffvlFvBcfZwEAAABtNtwJ8/xKnxyWq1evphNOOIEyMjJo+/btdNVVV1F2djZ99tlntHPnTnr77beDM1IQhKY76h5kcUalUaCUdMrUHJYFcFiCCNpGq+ptouza2NRGFX7ZUCgclkO6ZFB2SrzlOpxbL/O0lZJwfc4b7Q6qa7RTYlzrzkDQdj6QcV6tfGIBAOA99fX1FB/f9LvmLe3atcO0AwAACDjr4LBsmVtvvZUuu+wy2rRpEyUmNolJp556Ks2fPx+roxUbNCgoWDblVzY13GHydAEzHxmWwOJUepIzqwshjHQxqgZnyjJH9MpxK+LIUmYQ/O/0FIOjHs7WwG2jrYnEDErCAQg+fOzBOfpPPvkkderUiXJycuj666+nhoamk2J1dXV05513UteuXSkhIYH69OlDr7/+uvP5NWvW0CmnnCJK7Tp06ECXXHIJFRUVuTg9b7jhBuH2zM3NpZNOOkmUdTMTJkwQ7kd5f8uWLXTWWWeJ1+HXGzVqFP34448uY+Zln332Wed9/nuuUOPXSk5Opr59+9JXX30lnmMTyLHHHituZ2VliWX5/8xGEP6/8v/NCM8Fjx8AAEBkYbM76K+9keOwjPalvOGaa6456PHOnTvT3r17AzUuEEIxpEplwTLdNURWhsoeqKw3ZVwABFyAakUMSYiNoXi99baq7q31Be4zVFQ+IWLtJk0tf6dHR0dZJvc0nLqEJzud0JhzoBYcDcFZWL5cJL7+fTBiKebOnSuEQr5+6623RMk4XySXXnopffDBBzR9+nRav349/e9//3PmgHGZ9XHHHUfDhw+nZcuW0ezZs2nfvn10/vnnu7wHvy67KhcsWEAvv/yyOO5hZsyYQQUFBc77nCvGZo2ffvqJVq5cSSeffDKdccYZotqsNf7zn/+I9+SKNf77iRMningtFlk//fRTscyGDRvEez333HN03nnnkc1mcwqbTGFhIX3zzTd0xRVXBHB2AQAAWIFtRZVU22CnpLgY6pET/k3xvC4J5zOW5eXaAaqRjRs3ovTBYk13nCWbCgohRZXameTmXa9yUrX7NQ02Ua6X3Eb3VgCU7/rchhjCDT/qq+1KRjfY7Q6nYDmgBcGyshYl4aEqCZfzzsvCYek/1R52CZcnHWp0URkAVaiurnYKdr7CDkJfCEZ3cXYevvDCCxQTE0P9+/en0047TQiGHE/FxyEfffQR/fDDDyK6iunVq5fzb/nvWKx89NFHnY+98cYbQijkvz3kkEPEY+x6fOKJJw56b87S7Nixo/P+0KFDxUXy0EMP0eeffy6ERXZptgS7Ji+88EJxm8fC4uqSJUuE4MkRW0z79u1dsjsvuugiIZiyeMm8++671K1bN+EIBQAAEFms1cvBB3RKUzIuzHSH5ZlnnklTp051lmBwyQKfTeQSjHPPPTcYYwQ+UOmBGKJyhuWBKs1BmZ2ScNCBYXysttrCZQkiwb2lcvOaXSXVIl+TXaC92qW4bwCj4PdLuGaeMsn6SSq4/UJTqcAk6YIm5hyA4DJo0CAhVkq4NJzdhsyqVavEc+PHj3f7t3/88YdwZrKAKy8sejLs2pSMGDHCY0H29ttvpwEDBghxkV+PXZ1tOSyHDBnivM2Cbnp6uvP/0BIsyM6ZM8fZ3JRdpSx88jEYAACAyBQsB+VlUCTgtT3tqaeeor///e/i7F9NTY3YMeBS8COPPJIeeeSR4IwSBCfD0imEqOeAKtYFyxxDEw+Gd85yU+Ipv6xWLNM1O9mkEQLgHzLbsTUXtPY8d96uUVL4k+7Kvh1SKU4vXZcg1y+wsDDskSNXF8/g9gtk050YjxyWsmwfAFXgnEQW1ryFS7qls5LLpn1xSvJ7ewqLdmVlWh6yES7j5iafkrg4/j103Se02+3idlKSa+Z5c3geuGT78ccfP+g5Fj4lnv5fWaxkNydnanJWJr8/Hx9xo57WaO3/0BLsDGU3J+dZnnjiibR27VpREg4AACCCG+7khX9+pU+CJe848A/0b7/9JvJXeAfgsMMOc5ZfAOu4cdIMHYg5a0ilM7VSsDR2HZZkp2qC5YEq1wByAKyEFCDbEqDSFI5u2Fqk5Zwd0iHtoOdkXAPHN3DpOOcrguA7cpN08Qxuv0CW4bfhapWCJRpMAcXg/Tp/y7L57wNd2t2cfv36CQdhc1asWOEs1W6LQw89VAh/8+bNc3tMwscqnBHJjXBiY707/GGRkXMkjXDGJbscuYEOw8dD3DjHH2RH8ubvxfzzn/8UDXzYZcn/Py5lBwAAEFk4HA5am6+d4BsUIYKl1yXhtbVaM5QxY8bQddddR3fccQfESos33eFOUxzcqmZJ+MGCZY5eJo6ScBAeTXc8K/GVbi+V2F1SI667ZCW5zd5kuO9CbSOcZ4ETLOH2U64MX9+GqxuwngPgC9dee63IkbzpppuEGYKbzjz99NOigc5tt93m0WuwEDlp0iTRiOaLL76gbdu20S+//CJyLRnuKM7NbTg/khvncBn4999/T5dffrlbgbD5a3NWJleUlZSUOLMuP/vsM1GKzuXmnDPZllOyLbp37y5E5lmzZtH+/ftd3LH8+rt376ZXX30VzXYAACBCKSirpZLqBpFd6c4wEo54LVhyTsu4cePo/vvvp59//lmUhQP1qKxraLOUjTtLSdNThb68KmcOSmRJeKo7wTLeRdQEIJxLfJMVLjfd04pgmRjb9N2j4tgt68iN91A8UzBCwKrbqDxp0LbDEnMOgC9wc5z58+fTX3/9JUwQo0ePFkLjxx9/LJrReMpLL70kyrLZUMH5lJz9KDue5+XlCVcki5NcVs2OzMmTJ4vjmujo6DbjsLi6jF2NXJ7NsKDKTYCOOuooUWp+0kknCRenP3Tu3Fl0Eb/rrrtESb6xeQ9XuHGvAM7KPPvss/16HwAAANbOr+zbPpUS41rfP43YkvAff/xR7FTwWctnnnmGGhsbaeTIkSLLkrvV/e1vfwvOSIFP+XitiSGiVCghVjTz4HLT9oqI9OU1jdRod4jbWcluBEtdxJRl4wCEs2POKUApKPrtKdUEy86ZB2eVcQk4Czk8blEq61+j2oim0Wanuka7h24/XTyD2y9kZfjJCepuowBYhVGjRrktC5dwo5nmcIm0kcTERCEk8sUd0hXZEnxs4w4WJPnS3HXJxg0j7OI00rxEnE/Iu8vpNMKGEL64g8vBJ06cSAkJrg0pAQAARAZr9XLwSMmv9MlhyaXg99xzj9ip4B9Z7rjHYdNPPPGEV2dBgRpuHGc+nkLOEJlNyY0M3J05kJ3DiyqRYQkiodxUzZJwPvCSDsvObhyWrqWyao3dqk4/79x+EM/8RQqQVt1GAQDhAZehf/7550JQbS6KAgAAiBzWRliHcJ8ES4ZzZl555RW69NJLRXnC119/TaeffnqLZzRb4rHHHhNnVNPS0kTXcS5x4Nya5pmZ/OOck5MjyiD4/bhjoZGdO3fSaaedJjoS8uv8+9//Fs5PI/wjz6UafFaSBVZ3Z2rDhQabneo9dOPIBg0qOUOcDXfclIMbHZbIsASR0HRHVYclb6fcUIfJy0xsVciRjm/gn7gdFxNFCYZSe7dzDrdfEJrueCgSK7aNAuAr3GSHT0rxJdgNd0DbcBk6N/jhDufcoAgAAECEdwjvFDkOy1hf8lU4t5LLv/ly55130pAhQ3zqMM2d/FiMZNGSBUZ2bnKuzLp165w7SLfccgt98803IseG81s4z+Wcc84ROTQMZ9GwWNmxY0dauHAhFRQUCCGVO/o9+uijYhkO3uZl/vWvf9F7770ngrO5216nTp1E5ky4Htx6IobI51VyhjQ13HFf8iIzLFESDqwKHwRa3WEpy8HbpyW0KKLJsddAyPEL+dm39X0u5jxOzfUlIpruYD0HAAQBf7uPAwAAsD7FVfXO4y+UhLdCu3btqLq6WnTK4wu7HX1tvDN79mxxxnDQoEE0dOhQ4Xpkt+Ty5cvF82VlZfT6668L5+Zxxx1HI0aMoBkzZghh8vfffxfLcGk6C5zvvvsuDRs2jE455RR66KGH6MUXX6T6ek34evnll6lnz54iNHvAgAFC9ORQbs7gDGdXSHxMNMXHtm6i5cY7qh1oSSFSCpPNkZ3DIVgCq1LbYCc9ptWy7q3WGu4c5LCEeOYXlTKTuI2IDzHncFgGBLvd4dzmpCDZEhxfIkVidxl1ILhwrt/FF18sKnGSkpJEM5Vly5Y5n+f9TD6pbrw0jzDi7tGcDZieni6asFx55ZUuHZoZ7l49duxYkZPIzVc4Cqk5fHKdm73wMjyOb7/91uV5Xj+mTJkiTpjzWLnBzKZNmwI+JwAAAAAIL1btKhHXvdulUEZSHEUKXpeEr1q1SgiV3MGurq5OuCJzc3NFl7x7773Xr8GwQMlkZ2eLaxYuGxoaxA6dhHcEu3XrRosWLRL3+Zp3CrmbnoRdk+Xl5bR27VrnMsbXkMvI12gO/7/4740XazbcabtzlNNhWadgSXgLgmWm3oinrEadzuYAeINRwEvxsOuzamXVe8trxXWnjKQ2v1/gsAxNgyaVHblWw9i0yNNoFT4JIZsjgdBl+x199NGiqua7774TJ7D55DR3bzbCAiVX4MjLBx984PI8i5W8z8idoGfNmiWaS1599dXO53k/kCuAunfvLvZNp02bRg8++KCIR5LwyfQLL7xQiJ0rV64UMUd8WbNmjXMZFjmnT58uTqQvXrxYVBPx/ijHHwUSCOcgUsG6DwAIV1bu1Jq0De/muo8T7nhdEs7w2eczzzxT7CSyUPnll1+KnT/e+XrkkUd8GojdbqfJkyeL1xw8eLB4jIXR+Ph48X5GWJzk5+QyRrFSPi+fa20Z3gFldyif5W6erfmf//yHwj0bzzXDUp2D23JdiMxs4cyBPKPA/0/O64yL8SmKFQDTBSgWl7ibdmtIkapGscY1+yu0plft0hLadHDDYRm673RVHblWo1qfc948E+Na/40xOjB523bXLA4EB870Y7cjV99IuKKmOZxfztFB7li/fr2o+Fm6dCmNHDlSPPb888/TqaeeSk8++STl5eWJOCGu2nnjjTfEfilXBvEJfK4AksLmc889J4RRzlFnuNqHBdAXXnhBCJQspHBX6/vuu4/OOusssczbb78t9ke/+OILuuCCC1o8ic4XSWsn0WNitHWPx9p83xaASICrABk+iQEAAH5RX0U061aitZ8R2TRDVUioN1Tr7FpK1P8YcXPFTs1hObybqzYW7ngtWH722WeigQ1f+Ew2uyG5czif0R4/frzPA+EsSz4L/dtvv5HZ3H333XTrrbe67BzyDnG45W65lLIZ3CRmU16rCZYtWZ3TE2NdxM2c1JYFEwAsf1JBin6KOSw9ESzhsAxwhqUnJeHSkQvBMjDbaHxsmxndMdHcDClauCtZKM7x762BF3z11VfCoXjeeeeJXHTOWb/uuuvoqquuclmO91m5KSM7Lzli6OGHHxYl5AxX2/CJcSlWMlyVEx0dLU7ET5gwQSwzbtw4IVZK+H1ZMGWXJ78uL2Pcd5TLsBgp89T5BLqx4oez2UePHi3+tiXB0puT6LGxsaIB5f79+4Vgw/8HACIBPiHAYmVhYaHYnqV4DwAAPmFrJHrnHKJdWgyh2djsDvpjl1aNPLwrHJatwo1reKeNzyizQMnl2P7CmZKyBKdLly7Ox/lsOJ8lLi0tdXFZcm6mPFPO10uWLHF5PdlF3LhM887ifJ+zitydgeYz8XyxKt4Ils5mAQqJIbLUO70FwTI2JprSEmKpoq5RLAvBElgNKT56dFJB0bLqokpdsGzlhIF0cKsmtlo2w9KTmA95EsrQfA34E63i2XldXq6usR7O1hCzdetWeumll4RQyBFF7JK86aabhLA4adIksQy7HrlZIzsvt2zZIpbjvHMWCVnUYBGRxczmwh+fkDdW6jR3bhqreViwbKmax/gaxr9zt4y/J9FZXOd8TBZHd+zY4cEMAhBe8PFiS25qAADwmOUzNLEyIYPo/LeIOmgVwCGhqoroMX2fI2+YuNpcWClOpnMl1SEdUimS8NphyWeuAnk27MYbb6TPP/9cnP1uvjPITXb4DDF39T733HPFYxs2bBCNeY488khxn6+5DJ3HJXc4uQSHxciBAwc6l2kefM7LyNcIN3wrCVdHUCiv0cafntTy+FnMlIIlAOGcSegU/RSKbWD2V3rgsHQ6uNUau3XXF2t+p1sRub0le7CNiuXiY6i4Sq14lUiA44TYGfnoo4+K+8OHDxfVOlyCLQVLo3ORT7IPGTKEevfuLfY7jz/+eFIdb0+is1jbt29fZ+NJACIFPmaEsxIA4Dd2O9GiF7Tbx91H1PvY0E5qVHLT7RjNwLVSLwcf0iVDmLciCZ8yLG02myhx4dwfhoVBzuPx9keCy8Dff/99kYGZlpbmPMPMJTLsfORrDi/nM8t8pptFSBY4WWg84ogjxLIcgs7vf8kll4gwc34Nzgfi15Y7eOwK5QyhO+64g6644gr6+eef6aOPPqJvvvmGwhGfSsIVOsiSImRr3a8yk+NoT2kNlUKwBFYWQzwo8U2JV9NhKUvCc1t1WKrn4LYi0i3pjSNXpe/0cP8dZZAdag7sJpQnpyUDBgygTz/9tMW/6dWrl2gWuXnzZiFYshur+cn4xsZG0Tm8rUod+Vxryxifl4/xuI3LDBumOSgCBZeCc6dyAAAAAHjJjgVEJduJEtKJhl+sxPT9vvWAuB7RPbLKwRmv5VneweOdwUsvvVTkWfKFxUIOIOdSG2/gMh7uDH7MMceInTd5+fDDD53LPPPMM3T66acLhyWXovMOH7+nhEVSLifnaxYyL774YjG2qVOnOpdh5yaLk+yqHDp0qMjbfO2110S2UDgis8s8c2/Jg1v1MizTE1sWLKWYKRv0qAqLBusLysnO7WMtRKPNTh8u3Umv/7ZNNDayEj+u20enP/8rPTRrncj7sH5sg3oOS16fD1TWe+6wVOj7xcol4Z4I3DLzFHPuH3L+5Hx6HK+CdT2kcKNGrrwxsnHjRtHNuyV2795NBw4ccIqGvO/I0UPc/VvCJ7bZvcn5knIZji1qaGja5+B9yn79+jk7kvMyXBHUUjUP74vyPqxxGS7v5pzMcK34AQAAACzHqve160ETiOINbkeTcDgctGCLJlge3SeXIg2vHZacDcSlNL///rtwPTK848dCIT/njWuRJ78t+Azxiy++KC4twTumzUu+m8Oi6MqVKykS8KYk3MoOS6a0Wl3BkjP+JvzfAtpVXEPjD2lHMy4b1WZHaFWYNmcD/W/eVnF7x4EqmnpWCHM7/IBdt9e/v0I0v1izp5x6tUuhiaNbPnA1P5PQc8GytsEuBFhu8GE27Gxu1MXgnNSmJhQtu87U+X6xIrJDvJzP1pDrFG8DqqwvVqRGb0TnyZwbl8O6HlpuueUWOuqoo0RJ+Pnnny8yzV955RVxYSorK0XDGj7pzWIhn1jnaps+ffo4T1rzSXjOueRGPVxKzqIkZ6tzKTl3CGcuuugi8Tpc9XPnnXeKsnPuCs4n1SU333yzyHbnk+KnnXYazZw5k5YtW+YcC+dLTp48WTT84ZJtFjDvv/9+8R5nn312iGcOAAAAAG6b7fyl61lDL1RigjYXVorKNm7weFg3OCzbhLswcum1FCsZ7rT43//+VzwH1CkfTPHEjaOYA4pF7PI2mu4YxUyVMyz/b+4WIVYy8zbupznrXEvFVKW0up7eXLDdef/9xTtpb1ktWYEPFu8UQo2EHaKenBgJNTWyJNwD95bRVSdFFFXKwbOS4yiulRwVuM4Cg4wD8EQ8My4D8cx3avVtTf5Gtj3vcFiawahRo0QO+gcffECDBw+mhx56iJ599lmaOHGieJ6rb1avXk1nnnkmHXLIIUJw5Hz0X3/91SUX8r333qP+/fuLEvFTTz2VxowZ4xQaGY4omjNnjmhmw39/22230ZQpU0QDSgkLpxxzxH/H1TyffPKJiE/icUlYLOVoI/47HjsLqrNnz0b5NgAAAKACe/8gqivTmu10PZxUYP6mInE9qkc2JXpY+RPRDkvewauoqDjocd7p4qBvoFApmyduHP0gS5aRq+AOlVW8rTksM5LilXZYckn1pyt2i9sDO6XTuoJy+mT5Ljp5sPqdC2ev2StEvwGd0oX4sXxHCc1eU0CXHe3aFEtFflyvicIPnz2Ypn69jrburxJnpfp2SCOVkMKjJ9toYlw0RUWxmK8JUJ5m6oVCsGytHJyB6yyw64snOyl89pVNlfw9yr8Faa1Ea4C2f0c93TF0RjegO3vI4dggvriD89C///77Nl+DT8Kz2Nga3KyHhc7WOO+888SlJdhlyZFFxtgiAAAAACjCzsXadfcjiaLVEAe/X6P1eTmmXzuKRLzOsOSdQj4zzJk77FziC5eHc2MbPoMNzKfai1I2uYx0fJlNea02jviYaHHgbVWH5cpdpWJsXLo+7bwhzrMjqjVOcccvG/aL65MHdaRTdIH1Z/0x1Z2hG/ZpJ1NOHNSBjuidI27/tlk7K2XVkwp8gCudmKo0rzlQ1XbDHSbZ2QBGjXFHQp4iry8pcPuF1NVqzIy2wnc8AAAAAABwQ8Eq7brzCCWmp7C8lpbuKBa3Tzm0qWFfJOG1YDl9+nSRYckB4ZwvyRcOPec8IM7zAeZT6035oC4oVCkihJTpjsn0pFhx4N1WhmVZjdb4QzUWbtaCccf2bScclh3TE6m+0U5/7C4lleETEEu3a1+KY/rm0JG66LdiR4myDWwkS7eXCBci51a2T0uk0T212Ar5/1FSDPHUvaWY8FdcpW13WSmtu+rROTmw5cmeimdSCIfbz/8597TpTlKcWtUKAAAAAADAS/L1nid5w5WYum//LBDHt8O6ZlLnzCSKRLyuLczMzKQvv/ySNm3aROvXrxeiEgeWs2AJrFfK5nRYKpKN5+wQ3ko5uBUclit2lojrkd2zxDYyskcWzVpdQMu2F9MRvTQRUEU4c/NAVb1wuA7unEGx0dGUlhBLFXWNtHFfhSgTV5Ul2zSRWAqVMpT4zz1lpBrelISrWFpdop9Y4AxLK43b8t/pHrv9Yokq6pT5Xo+ELuFNDkus6wAAAAAAlqOugqhok3a70zBSgXd+3yGu/z6iC0UqPoehcYdDKVK25oQDZnY39aDpTpxaThwpQKYnWlewZJfiat1JObxbprge2iVTCJbrCw7Of1UJKe4N6JRGCbHautGvYxot21GivGDJXcGNQiWPW4qwnI2qQvajLyXhKjb0KKvWHJaZepZsm+NWxMFt+e90j91+an2vW5Gmkwqx3rlaFdlGAQAAAACAFxSs5iN5ovQuRKlq5EXml9ZSu6x0CJbe8vrrr9MzzzwjXJZSvJw8eTL985//DMLHBHwtN/XEGSKcOHxCodEuSn5juFuDicgO4a013GHSErVxV+iZlyrBDkV2oLGOf4je7KVvh1RxLTMWVYUb1DBy3OK2Llhu2Kv42PdrY5cNdrJT4kVTGG4Qs2lfBQ3XhUwr5uOp6rCU0QwtkSLH3WATQj5ObvkX8+GpwI08xUD+jnqWnJOiC5vIsAQAAAAAsHI5uP/uynkb99M7i7b79LcNdTUu9/81vndEdgeXeG05mjJlCj399NN04403ihxLZtGiRXTLLbfQzp070flQAaobGj0+uDUKJuwoMduF5nRYtilYas9XKihYbtqnCWdds5KdXy7S7betqIrqGm1O96JqbCrURMk+7TWBlemnC4DssFQVXm9k5+re7VJcxs6Pb1RNsHTm43m2vamWBVmiOyyzklt3WMrvID4ZwidFIvnHNhCN1DzOU9TFM7j9QlOp4OqwVO83CQAAAAAAeNhwJwCC5Z6SGvpxfaFPf2uvr3XePm5Ae7r86B4UyXitTr300kv06quv0oUXXuh8jLuDDxkyRIiYU6dODfQYgZfU1Ns9dm9xJ242VXI/lWoFymZll/CMpNbHIcdZWd9IdruDok12hrp1+hlEP266w65QdoSyaNm/Y7rSDkujYCndliq7Q+W4O2XwPMe5jJ27hG/Yqz1v3ZJwtcpNSz10WBrFHnaeQbD00+3nqcNSZhNDPPN7zj3ODVUstgEAAAAAAJjTcOfwntn033MO9elva2uq6fJntNvPXzCcYmO87pMdVnitTjU0NNDIkSMPenzEiBHU2AhngQrIg1RP3DhcosmiAmf8qXCgVe5hhqUsCeeuWexoMYpUZrNZF/aMoh/PM4tny/XSahUFS3bBbS2qErf7tjeUhOvl7JwFyZl4MkZAJTa7cYYy/TqmKukOlduopyXhKc5yUzW+Y0tr9AzLNhyWHDHBJ0XYXcnbaVtdxcHBSHeqdw5LtQTuSHC1qhbbAAAAAAAAPKS2jOjAZu12J/8FSz4mbX5c6ilVVVV0uX47WiFTlll4LddecsklwmXZnFdeeYUmTpwYqHEBH+GcOHmg5W0+ngqlbJ5mWLIIEhcTpWSOpXRYNv+SksKfdAOqxq7iaqpvtIu57ZyV5Hw8JzVB5EEy7A5VETmnvdu5znnPXO3+jmK1xt1UEu6lAKVI85rSKs+6hDNS4Ea2n2/UGjp9e1qenAK3X8ByQ73PmVVjGwUAAAAAAN403GERohtRSg6mTSF8brozZ84cOuKII8T9xYsXi/zKSy+9lG699Vbncpx1CUILO3HYdehLuakKgkJ5rWcZluxYZFdlcVW9cIeqxJZCTRzr3Uyw7J6T4hQGVUSKkT1zUw5qvtQ1K0nM9e6SGhrcOYNUY/sBbU57GfIrmS668FpQWkuNNrsylnpvS8Kdop9BvDKLBpudKvRtri2HpUvHagW+X6yIUQDjkwleiWeKfTdaCbmteRpjILfRakVOKgAAAAAAAA8pXKddd/StjBsoJFiuWbOGDjvsMHF7y5Yt4jo3N1dc+DkJusGag1F0tGKDBmfTHQ9KvDnHkkW0Cl3kVAFuqLOvQgvK7Zad7FY821Xi2vlLFXaXauPqkuU6bvFYdjL9sbuMdpeoKbZysLFxjiUd0hOFE7fB5qC95bVu/29W6BLuFP0UEKDkNuqJE9rYsRqlsv45LHkd8LQsRDoxpdse+C4Ue7yNKlSpAAAAAAAAvGD/X9p1+/6mT1tKSoqomgU+CpZz58719k9ACJEHqPEx0R67yVRq0FBe0+ixECJzLFUqCWcnH3+/JMZFU06zvD7uGs6oKvrJcTUX/VzHrqbYukcXWztnugqS7BTtnJkkHJicwamCYMkOxUbucsViiIddwqXop4ILulTvEJ6eGHuQE7e1EyJwnvkZH+ChcMbAYRlYodgT5HJ1DVreKAAAAAAAsAiFumDZznzBEriiRn0kMK2brGr5eE6HZRtdwlUVLJuEs6SDXMZddcflvvI6l1w61V2KxsdULGdnh61cb4zZm83nXRWh2Fjimxgf7aUL2vx1vUTvEO5pAx15QgRuPz/jAzwUzsSyyFP0PwtaNq/z0gVdzyckbBAtAQAAAAAsAbuN9q/XbkOwVA4IluEqWHpxcJuiUPmgLO/2rCRcW0alDMsml+LBTj5uUCKdT1LYVAk5pi6tiH67FBH93I07MzlOxAQ0R34WqpTiy22U3YnshPZK9FPAYVlS5VmHcAncfqE/CeXMJVbgO92KsOiom6A9FywNy2HeAQAAAAAsQtV+opoSDjUkyu1r9mhAMyBYhhnyQMnT3C2VGjTY7A5njmaq7p5sDS5JZVTKsJQuRXdOP3ZcqlxaLcfUvKxaNt2Ry6iWqeGc88yD59wowO5WxB3q3EbjYjzO+nVmEiogWJbqbtZMD2IbVBu7FalpaPT6JJRKjdSsSG19k0PS03nnhkhyc4ZgCQAAAABgsfzKrB5Ece6PJ4F5QLAMM2QZm6edTZlkZ1MMcw9ujeWu7pxyzZGiZqVCJeFS9HPnUlS5tJpL1PdX1LUotubpYiCvI9zoSCWMZfjucJaEK+JqdW6jvpxUUCjDkh3DniAFH4g4vlGji2feOCzl978K64sVqdZF4tjoKIrz0AXNJx/kum4UPAEAAAAAgMIc0BpJw12pJhAswwxvuw+7OqDMFf5kB2Q+SGS3iqcZluUqCZZtiGdSDMxXRDyTFJTVOtcbd0IUCyC5qQniNnfbVjN7031DnY7pieK6UJFx+7KNSrFKhexTmWHpaUm4HDvcfn423fHKYal9N0IkDl0ZvlheCsW64AkAAAAAABSnZJt2nd3L7JGAQHQJZ/Lz8+m3336jwsJCsttdnQQ33XSTLy8JVOgoa7IbRzol2TnpSamsihmWUohsyWHZQRfPuPGOShTo4+6Ukdji3HdIT6CiyjraV15Lg/IySDWxNS9Tm1t345ZCK5eze1qGrZIA5RRCFGi6Ix2WnBnqlWCpgNhqRWpk8xeUhCu9jTrX9SqI8wAAAAAAlqF4q3YNwTI8BMs333yTrrnmGoqPj6ecnByXg3++DcHSeh1lVREsK3Th0ZNycNcu4WpkWLIYVqiXVbdPS2zd7VehhttPIsctBVV38HNr88uVE1vlXLZL04TJ5sj/U22DXbhxMzzMXgz6NuqDw1IFl2Kp7BLuqcMSJeEhzyVOVEjgtiK+uKAZrOsAAAAAABajGA7LsBIs77//fpoyZQrdfffdFB2NinLV8K3cVK2ScG8FS1UcluU1jVTfaPdIPGOXopVEP5XHLrM3Wxo7izcsUpbVNIixmy1Y+rSNymy8BvOz8Uq8dVjGqSO2WhEpcPuSeQpXq2/IefMmC1q16AYAAAAAANAG3ExWOiyzemK6FMRrxbG6upouuOACiJXKl4TH+uAKsatREu61w1INwXJ/Za2ze3lLB7rO8mS9jFk10a99q4JlgtKCpVXG3lRu6v02Wm+zU6PNroTDEhmWoe8q761g2WBzUIPJ64sV8cUFLZZHsyMAAAAAAOtQuY+ooZooKpoos5vZowGBECyvvPJK+vjjj739M2CBkvBaVUrCdSHS4wxLRQTLQr1UujWXYnvdpcilySo5zmRJuGcOS3VKwtnJJJsutWuhDF+1sftTEq6Ca06eIGBh3hNQJusf8nvZly7hKqwvVkQ6JL0uCVcougEAAAAAALSBdFdmdCWK9SzuCiheEv7YY4/R6aefTrNnz6ZDDz2U4uJcywKffvrpQI4P+NigwZdyU7MPbGVJeIqXDktVuoS3lV8pRR6eb55rLsPunpNCarkUWx67zN9UwaXYfNzxsdGtCmgqlbM7t1EvTiokxEYTxwVz1QKvO2mJ5pW1l+uZsekeltZDxAm9wM3rS3QUkd2hCZ7pJq4vlu4S7m1JuDO6AYIlAAAAAIDyIL8yPAXL77//nvr16yfuN2+6A6zXJVyVBg3SKZnmoWApS8cr6xrUEv308mN38DbC5cnbD1SLsnBVBEtPHJby/6WC6HewSJzQ6vePkiXhXmyj/H9jgbOq3maqe8tudzgzY+UJA6ucEImkjtVifYmPFZ+V2c3ULJ0b6qNgiXUdAAAAAMACoEN4+AmWTz31FL3xxht02WWXBWdEwLyScLMzLOu9c1hK1xCPm3Pa4mKi1Whck9qy6CfLwlmw3KeLbSpQqAt5redAai7Fosp6Jebbk4Y7zd2hKmSH+pyPF68LliYKf7yNssuT8dS153RYQrAMqduPxTYIlj7OuZ8l4RCJAQAAAAAsAARL5fFacUhISKCjjz46OKMBpmRvqSIoeNt0JyWh6f+oQo5loQcOS6N4JkVClXIgWysJz06Op1iuMxWipRpi6/6KtoVWo6B5oErrcK1El3AfBCizxZDyGs3NHB8T7bH7DF3CQ+/IZdAp3P/fUV9Lws3+LQUAAAAAAN4IlugQHjaC5c0330zPP/98cEYDzGnooUpJuJelprEx0c6DchU6hXuSA8nk6g5MdioqlwOZ1PLcR0dHUXaKFkZ8QLGxt+WwzNHn/IACQqu/ApSZzbHKa/SGO62sJ6qeEIkk17yLYImScN/nPN67IpQkRRrYAQAAAAAADyjZrl1nQbAMG8FyyZIl9NZbb1GvXr3ojDPOoHPOOcfl4g3z588Xr5GXlycyt7744guX5x0OB02ZMoU6depESUlJdMIJJ9CmTZtclikuLqaJEydSeno6ZWZmii7mlZWVLsusXr2axo4dS4mJidS1a1d64oknKFzx5eDWeZDVYBcZdVZpumN0Y1YokGPpSQ4kk5Mar4x4xuzXx8Gl7G3l0DqFPwWcip42OmJyFBJafS4JV8C9VSEb7njRxAUOy0C55r0Tz1TJJo6U3FCxPErCAQAAAACsQU0JUW2pdjurh9mjAYESLFkUZGFy/PjxlJubSxkZGS4Xb6iqqqKhQ4fSiy++6PZ5FhanT59OL7/8Mi1evJhSUlLopJNOotraplJaFivXrl1LP/zwA82aNUuIoFdffbXz+fLycjrxxBOpe/futHz5cpo2bRo9+OCD9Morr1A44svBrfGgrK7RvBzLCi9Lwo1uTBUclp7kQLqIZ6qIfuWeCa1Mrmpiq6cicYr2fEVdo+kdfJ0l4RbMx5PRAZ66oJs7LM08IWJVmhy53v1coyTcjzmv923OVTipAAAAAAAAvOgQntqBKD4ZUxYuTXdmzJgRsDc/5ZRTxMUd7K589tln6b777qOzzjpLPPb2229Thw4dhBPzggsuoPXr19Ps2bNp6dKlNHLkSLEMl6ufeuqp9OSTTwrn5nvvvUf19fWiUVB8fDwNGjSIVq1aRU8//bSLsBkuVPtwoGXMouMDLW+dX4EuCU/1QgxJ1Z1eZmdYGnMgrVSe7E0OpGpORaM7tK2xcwkz52822h1UXFVPeZlJZL57K9a6Dssk7x2W8oSIWd8vEduxGuXJfgiWvm2jZp8UAQAAAAAAbVCiC5YoB1can9r8NjY20o8//kj/+9//qKKiQjyWn59/UCm2P2zbto327t0rysAl7OAcPXo0LVq0SNzna3Z8SrGS4eWjo6OFI1MuM27cOCFWStiluWHDBiopKXH73nV1dcKZabxYryTc8wOtmOgoSoiNNr18UJaEe+OwTNUb71SZXPZozIHMaEPMkSXhqmVYeuKwlGJrUVWdpdyhXOou550FS6t2CTdbDJFNd7xyWDY7IQK8Q+YhelsSroIjN1JLwrGeAwAAAABYJL8SDXfCS7DcsWMHHXroocL1eP3119P+/fvF448//jjdfvvtARsYi5UMOyqN8H35HF+3b9/e5fnY2FjKzs52WcbdaxjfozmPPfaYS5k7516Gc5dwVcSQSh8EyxT9IF7+ren5lR7kQObq5clmC2fe5kAyKjXd4fJi2a3cI7FVn3ezO5zX6OK619uofhLCTAFKRi94k2EZrcgJESvClQbVPopnKAkPgMPSR1crRGIAAAAAAIuUhMNhGX5dwtnRyO5EboQjmTBhAv30008UDtx9991UVlbmvOzatYssc3CrCwI+N/Sot1tKsJTLSnem2S7FXI9civFOF44KAk7T2JtcyFbIsCytaRAl3sbO6541O6q3qHsr2vQS3/Ja7x2WqpwQsSINNgfZ9HXc9+90zLmv26ivJ/4w5wAAAAAAVukQjoY7YZVh+euvv9LChQtdSqyZHj160J49ewI2sI4dO4rrffv2iS7hEr4/bNgw5zKFhYUHlatz53D593zNf2NE3pfLNCchIUFcrAbnw8meFr6Wm5oloDXY7KJLudcOS33ZyjpzD8qlWzJXdyC2Bh8Es+OMPy8Wz5Kzvd4MA0pxdb2LA7E1chRyh8oxpCfGUlxM2+depKh5wORy9qZ8PG8dc7EKZFh677BkkuNiqJQaTD0hYkWMn7X3Arf564tVkXPma24ohHkAAAAAAIs4LFESHl4OS7vdTjbbwQdAu3fvprS0tECNi3r27CkERaNrk7MkOZvyyCOPFPf5urS0VHT/lvz8889ijJx1KZfhzuENDZoziOGO4v369aOsrCwKJ4wHSb6Wspl1cGt0SEoR0hNSFHFYluiinyyZbg0uGZfimdnlyUbhz5Oxq5S/6c24VWoYJIV5b7dRKZ6o4LD0pukOk2jyCRGrIj9rbhjF+bjeIN2BKE/2fd59dlhCJAYAAAAAUJfGOqJy3WyHkvDwEixPPPFE0b3bKL5ws50HHnhAdOf2Bv477tjNF9loh2/v3LlTvO7kyZPp4Ycfpq+++or+/PNPuvTSS0Xn77PPPlssP2DAADr55JPpqquuoiVLltCCBQvohhtuEB3EeTnmoosuEm7QK6+8ktauXUsffvghPffcc3TrrbdSuCEPTONiojxynKnkDJHl4HxQ7s2BubPpjsmCpdfimSLlyd6O3ehS5AgCa825FInNm3Mu76232f3r+mxq051G30rCFRi7FfE1PsD4NzIzFfgw7z6W4UMkBgAAAABQmNKdHKhHFJ9KlJJr9mhAK3hdi/rUU0+JLtsDBw6k2tpaIQhu2rSJcnNz6YMPPvDqtZYtW0bHHnus874UESdNmkRvvvkm3XHHHVRVVUVXX321cFKOGTOGZs+eTYmJTc1B3nvvPSFSHn/88aI7+LnnnkvTp093Ps9Nc+bMmSMaBI0YMUKMc8qUKeI1ww2/Dm5NduNIwTLNC3ela0l4ozXdfiaXJ3Mpvizz9cZhyS5BXle8ccOa6Wp1EYlNnHN/XNDOJiqmNt1p8K0kXIGxWxHpSJUOVW+A28+EpjvIagUAAAAAsFDDnR7swDN7NKAVvFYbunTpQn/88YdwKvI1uyTZvThx4kSXJjyecMwxx7Tq0mKX5dSpU8WlJbgj+Pvvv9/q+wwZMkRkb0ZOGZv3IpLZDijpkEz10rmlSkm4FCyzvHT7HTA5C7JEf//oKKIMD8p8ed3idYXXE3aHmilYeisSNzUMMm/OjduX7JxtlW2UKa/1zWHpLGeHw9Ingdvb0mTj38Dt5x12u8NvhyU3S+KTQd5WOgAAAAAAgBBQYhAsgdJ4rTZwHuRRRx0lBEq+GJvd8HPjxo0L9BiBh8gDU28Psox/Y5YDSrr8UrwUW5u6hKvRdEc6J62Spygb7mQmx1MMq5YeOhV3l9RQUVUddctJJquIxNkKNAyS21diXDRFezjfkkSVHJZeZliqILZa+jvdr5JwzLk3cDO05nPo8Zwbfnt5XYdgCQAAAACgcIdwNNxRHq9P/3MJN3fhbk5ZWZlLeTewat6ZuSXhvjosKy3nsJSCpbkl4cWV3rkUXdyhZout0mGZ7J1IzI2OzMrfrGv0rfuw7LTNVFswwxIl4aHtKG/8G4jE3mFsDOXtdhofEy3c6kwthGIAAAAAAMVLwnuaPRIQaMGSD/S5VLs5Bw4coJSUFG9fDgQQ2VzBige3zpJwL0uMnU13TG4s4b3DUo2ScOmw9FT0M/4fi03O3/S10RE7qKpMEhNq6n3rEO6Sj2fS2Lk8WTYM8tphqYA71IpY+SSU1eecIxs8dZ1LeN8IbmIAAAAAAMVBSbhl8FgdOuecc5w75JdddhklJGiCC2Oz2Wj16tWiVByYXz7oS96Z2QdZlXpJt7eZiCpkWLIgIOfNW4elmR2rjRmWWSmeC1BNTkWTx+5l0x3O3+Rtg7eTooo6r8VxZRpjNZizrpfr5eB8virVy+gGZFiGtvmLMcsYDkvfckN9OfGn/V2sOCGCeQcAAAAAUBC7HSXhFsLjo07uti0dlmlpaS4NduLj4+mII46gq666KjijBGHvxmlyWHo39pR480vCpUsxLibK4y7nuc6yanNditLhKfMdrVQSLt/fU5FYipvV9TVC7OxBKaZto4l+baNNGXtm5Myy0Ott/iYawPiGr81fjH+Dpjs+nvjzYRvV5l0rXMG8AwAAAAAoSOU+osZaoqgYooyuZo8GBEqwnDFjhrju0aMH3X777Sj/VhAr553Jkm5vO5xLl1xtg50abXaKNaErq9OlmBzvNi7BHVJkY+GspZiFUI492wuHpVy2VBdqzXZYelqGLwVLbhgk/9ZS26guoEgHWKgpr9Eb7iR6Vw6uwtgjsukOyvD9bIzlo2Ap13WU4gMAAAAAqMeBTdp1ZjeiGO+Pa0Bo8VrdueOOO1zElR07dtCzzz5Lc+bMCfTYQChLwk0+uK32syScMSuXsMml6LlwlpWsfTk22BymjdtXhyULs0ZnqRmw8CXXd28cls6xV2nim3lNd7wX1ptcio2mNA2SDktvG+4wKAkPfXmydAhy7iifzAHBd0GrEK8CAAAAAABaoWijdt2uH6bJAnh91HzWWWfR22+/LW6XlpbS4YcfTk899ZR4/KWXXgrGGEEoy01NbrqT4uWBeXxstOjManwN81yK8V7NNzd1MP69uTmQ3jgs400ft2y4400ZvlEoNmvs/mQSJurbht2hiVBmCZa+OCylcxplsj4Kln44LBmIZ97Mud3PDEsIlgAAAAAAyrJfFyxzDzF7JCAYguWKFSto7Nix4vYnn3xCHTt2FC5LFjGnT5/u7csBRQ5upXvLtAxLvSTcW4el9jcxpgqW3narZtil7BT+THQqSqehVw5L2SXc1HF7X4avwtgDcVKBqTUhx7KyTltXUn1wWMpcP5SE+9ix2of1hU+IyE0DncJD44I2bqcQ5wEAAAAAFKRog3YNh6Ul8HqPvLq6WjTdYbgMnLuHR0dHi6Y7LFwCawqWZpdsVjlLwr0fuxQ5Ky0kWDKZzvJkM4U/relPtj4WT5DLlphUVu3PnDeN3VzB0pdtNC4mWjhKzeoUbmy6Y7WmXpZ3+/mwvrCQL8vC4bD0IcMy1j+HJcR5AAAAAAAFgcMyvAXLPn360BdffEG7du2i77//nk488UTxeGFhIaWnpwdjjCCEDT3MbrqT4mXTHaOAIkXPUCPdel6LZ87mNeYIf5yDKEXHLC9KwqVLkQVi6UYyr5Tduzl3OixNEixlIw5fy00TTRT+5AkB3xyWKAn3z5Hro9sP8+41tX5nWGrrOsR5AAAAAADFqC0nqsjXbqMk3BJ4fRQ0ZcoU0SWcu4WPHj2ajjzySKfbcvjw4cEYI/DSjeNL+aDMmDOtJLzOn5Jwkx2WldZ0WPJ8ySzEHC9KwtMTYykmOspUsfWAPufeNNwxfkZmjbu20e6XGOKMbjDhxII/TXfQJdw36vxw5BpL8VGeHLptFHMOAAAAAKB4h/DUDkRJmWaPBniA10eef//732nMmDFUUFBAQ4cOdT5+/PHH04QJE7x9OaBIuak8yDK/JNx3wdK0DEvd7Sc7UHtbnlxqUp6idFeye8sbxx+XmvL/taiyToitHdITyTSHpZdzbnaHc2e5qb8diM1wWErB0p+ScHRODmnH6mTd7YfyZF8clv5lWGLOAQAAAAAUA+XglsPrPfIZM2ZQRkaGcFNydqWEu4X3798/0OMDIRIszSw1dS0J937sqbLpjv4aVugSrkIDmBIfhVZjObtZWZBNY/euY7XZHc792UbF30kndIOJJeG+CJb6dg2nn2+ueV8FS9lZHvMeOpEY4jwAAAAAgKKg4U74C5Z33XUXdejQga688kpauHBhcEYF/Gu6o7slfSkJr2u0k83uCPknUO2PwzLe3JLw0hrNqZjppXgmxbYSk8qTpegnS9Ot5FSUc+bt2GVWJ//f7Sas502Cpa/uLfNKfCucGZberedGwRIOy9C6/dB0x3vqnCKxf7mhyLAEAAAAAFCMwvXadW4/s0cCPMTrPfI9e/bQW2+9RUVFRXTMMccIV+Xjjz9Oe/fu9falQLAObn3obmp0fIW6lK2+0e7MUkyJt1ZJODeukSXd3opnZrv9ZI6jty5FFcZeVu19syAmM0kbN2uV5bUN5mUSxluvA3GFPl/+ZFjWm3RCxKr468h1Zp6a5D639Ik/nx2W+kkFxB8AAAAAAKhFwWrtutMQs0cCgiVYxsbGiqzKL7/8UnQKv+qqq+i9996jbt260Zlnniket9s18QmYVMrmgxiSENu0KoTaBVVtOJhO1su7rdIlvKreRg02h0/Cn9lNd0r9KAlvGrvJ7lBdgPSU+NhoZwajGfPuf7lprCUzLKVwxsBl6YvbDyXhlukSLk8qmBSvAgAAAAAA3FBVpHcIjyLqMAhTZBF8q3nS4dJwbsDDncI5z/LPP/+kSZMmUe/evemXX34J3CiBR9TU2312hkRHRzlL4EIthshSbhaT4mKiLdUlXDoMeezezntT0x2zRD/tfTN8clg2lVabgZwzb8vwjdmhZozd76Y7JmYSOjMsfXBYupwQgZDjvcPSR0cuSsJ9n/MEC+bMAgAAAACAFij4Q7vO7kWUkIZpCmfBct++ffTkk0/SoEGDRFl4eXk5zZo1i7Zt2yZKxs8//3whXAJrlbIl6KXknGMZSqT44kszD5emOyYIlmV6fiW7K7l7tjdIsY1zILm03DyHpQ+in8nuUH8aBjkFSxPcoTW6Y87nEl8Tu21Lh6Uv2ylvG+ieHNqYD9eScIhnns+57mo1iOzegKY7AAAAAAgXqqqqxH48X/i2pdmLcnAr4vUe+RlnnEFdu3alN998U5SDs0D5wQcf0AknnCCeT0lJodtuu02UiwNrlbJJF1RdY2gPbqXQaCwbtYzD0sfSZGMOJOf6mSFASYelb13CzXMp8vopRW6fxm4Qis1rjGWtDEtuUFSpRzek+dB0x7h9o2O1Z/BJjKaYD9/EM3QJN2EblScVIBIDAAAAAKjDnuXadaehZo8EeIHXVpn27dvTvHnzRBl4S7Rr1064LUHoaLDZqVFvZuGre0sKnaF2WMrsSV8dlrLDuRkHiE3dquN8EnC4lJwFS3Yqyv9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",
       "text/plain": [
        "<Figure size 1600x700 with 4 Axes>"
       ]
@@ -290,14 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:30.072555Z",
-     "iopub.status.busy": "2026-02-12T14:52:30.072399Z",
-     "iopub.status.idle": "2026-02-12T14:52:30.080712Z",
-     "shell.execute_reply": "2026-02-12T14:52:30.080219Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -433,14 +392,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:30.083148Z",
-     "iopub.status.busy": "2026-02-12T14:52:30.082897Z",
-     "iopub.status.idle": "2026-02-12T14:52:30.089296Z",
-     "shell.execute_reply": "2026-02-12T14:52:30.088946Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -522,14 +474,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:30.091402Z",
-     "iopub.status.busy": "2026-02-12T14:52:30.091160Z",
-     "iopub.status.idle": "2026-02-12T14:52:30.098356Z",
-     "shell.execute_reply": "2026-02-12T14:52:30.097958Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -621,14 +566,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:30.100410Z",
-     "iopub.status.busy": "2026-02-12T14:52:30.100263Z",
-     "iopub.status.idle": "2026-02-12T14:52:30.167860Z",
-     "shell.execute_reply": "2026-02-12T14:52:30.167359Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -666,18 +604,11 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-12T14:52:30.170304Z",
-     "iopub.status.busy": "2026-02-12T14:52:30.170154Z",
-     "iopub.status.idle": "2026-02-12T14:52:30.598547Z",
-     "shell.execute_reply": "2026-02-12T14:52:30.597853Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x800 with 4 Axes>"
       ]
@@ -700,7 +631,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "rdtools_313-nb",
+   "display_name": "rdtools3-nb",
    "language": "python",
    "name": "python3"
   },
@@ -714,7 +645,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.11"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/rdtools/__init__.py b/rdtools/__init__.py
index 2cd286b0..26c468c8 100644
--- a/rdtools/__init__.py
+++ b/rdtools/__init__.py
@@ -23,17 +23,20 @@
 from rdtools.filtering import hampel_filter
 from rdtools.filtering import hour_angle_filter
 from rdtools.filtering import directional_tukey_filter
-# from rdtools.soiling import soiling_srr
-# from rdtools.soiling import soiling_cods
-# from rdtools.soiling import monthly_soiling_rates
-# from rdtools.soiling import annual_soiling_ratios
+from rdtools.soiling import soiling_srr
+from rdtools.soiling import soiling_cods
+from rdtools.soiling import monthly_soiling_rates
+from rdtools.soiling import annual_soiling_ratios
+from rdtools.soiling import SRRAnalysis
+from rdtools.soiling import CODSAnalysis
+from rdtools.soiling import NoValidIntervalError
 from rdtools.analysis_chains import TrendAnalysis
 from rdtools.plotting import degradation_summary_plots
 from rdtools.plotting import tune_filter_plot
 from rdtools.plotting import degradation_timeseries_plot
-# from rdtools.plotting import soiling_monte_carlo_plot
-# from rdtools.plotting import soiling_interval_plot
-# from rdtools.plotting import soiling_rate_histogram
+from rdtools.plotting import soiling_monte_carlo_plot
+from rdtools.plotting import soiling_interval_plot
+from rdtools.plotting import soiling_rate_histogram
 from rdtools.plotting import availability_summary_plots
 from rdtools.availability import AvailabilityAnalysis
 from rdtools.utilities import robust_quantile
diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index d2081ac3..2f64d4fb 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -4,7 +4,6 @@
 import pandas as pd
 import plotly.express as px
 import numpy as np
-import warnings
 
 
 def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield,
@@ -134,15 +133,10 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5,
                              profile_alpha=0.05, ymin=None, ymax=None,
                              profiles=None, point_color=None,
                              profile_color='C1'):
-    '''
+    """
     Create figure to visualize Monte Carlo of soiling profiles used in the SRR
     analysis.
 
-    .. warning::
-        The soiling module is currently experimental. The API, results,
-        and default behaviors may change in future releases (including MINOR
-        and PATCH releases) as the code matures.
-
     Parameters
     ----------
     soiling_info : dict
@@ -169,13 +163,7 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5,
     Returns
     -------
     fig : matplotlib.figure.Figure
-    '''
-    warnings.warn(
-        'The soiling module is currently experimental. The API, results, '
-        'and default behaviors may change in future releases (including MINOR '
-        'and PATCH releases) as the code matures.', stacklevel=2
-    )
-
+    """
     fig, ax = plt.subplots()
     renormalized = normalized_yield / soiling_info['renormalizing_factor']
     ax.plot(renormalized.index, renormalized, 'o', alpha=point_alpha,
@@ -198,14 +186,9 @@ def soiling_monte_carlo_plot(soiling_info, normalized_yield, point_alpha=0.5,
 def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5,
                           profile_alpha=1, ymin=None, ymax=None,
                           point_color=None, profile_color=None):
-    '''
+    """
     Create figure to visualize valid soiling profiles used in the SRR analysis.
 
-    .. warning::
-        The soiling module is currently experimental. The API, results,
-        and default behaviors may change in future releases (including MINOR
-        and PATCH releases) as the code matures.
-
     Parameters
     ----------
     soiling_info : dict
@@ -229,13 +212,7 @@ def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5,
     Returns
     -------
     fig : matplotlib.figure.Figure
-    '''
-    warnings.warn(
-        'The soiling module is currently experimental. The API, results, '
-        'and default behaviors may change in future releases (including MINOR '
-        'and PATCH releases) as the code matures.', stacklevel=2
-    )
-
+    """
     sratio = soiling_info['soiling_ratio_perfect_clean']
     fig, ax = plt.subplots()
     renormalized = normalized_yield / soiling_info['renormalizing_factor']
@@ -250,14 +227,9 @@ def soiling_interval_plot(soiling_info, normalized_yield, point_alpha=0.5,
 
 
 def soiling_rate_histogram(soiling_info, bins=None):
-    '''
+    """
     Create histogram of soiling rates found in the SRR analysis.
 
-    .. warning::
-        The soiling module is currently experimental. The API, results,
-        and default behaviors may change in future releases (including MINOR
-        and PATCH releases) as the code matures.
-
     Parameters
     ----------
     soiling_info : dict
@@ -269,12 +241,7 @@ def soiling_rate_histogram(soiling_info, bins=None):
     Returns
     -------
     fig : matplotlib.figure.Figure
-    '''
-    warnings.warn(
-        'The soiling module is currently experimental. The API, results, '
-        'and default behaviors may change in future releases (including MINOR '
-        'and PATCH releases) as the code matures.', stacklevel=2
-    )
+    """
 
     soiling_summary = soiling_info['soiling_interval_summary']
     fig, ax = plt.subplots()
diff --git a/rdtools/soiling.py b/rdtools/soiling.py
index ef7b2b05..4c9629d9 100644
--- a/rdtools/soiling.py
+++ b/rdtools/soiling.py
@@ -1,44 +1,39 @@
-'''
+"""
 Functions for calculating soiling metrics from photovoltaic system data.
+"""
 
-The soiling module is currently experimental. The API, results,
-and default behaviors may change in future releases (including MINOR
-and PATCH releases) as the code matures.
-'''
-from rdtools import degradation as RdToolsDeg
+from rdtools import degradation
 from rdtools.bootstrap import _make_time_series_bootstrap_samples
 
+import bisect
+import itertools
+import sys
+import time
 import warnings
 
-import pandas as pd
 import numpy as np
-from scipy.stats.mstats import theilslopes
-from filterpy.kalman import KalmanFilter
+import pandas as pd
+import scipy.stats as st
+import statsmodels.api as sm
 from filterpy.common import Q_discrete_white_noise
-import itertools
-import bisect
-import time
-import sys
+from filterpy.kalman import KalmanFilter
+from scipy.optimize import curve_fit
+from scipy.stats.mstats import theilslopes
 from statsmodels.tsa.seasonal import STL
 from statsmodels.tsa.stattools import adfuller
-import statsmodels.api as sm
-lowess = sm.nonparametric.lowess
 
-warnings.warn(
-    'The soiling module is currently experimental. The API, results, '
-    'and default behaviors may change in future releases (including MINOR '
-    'and PATCH releases) as the code matures.', stacklevel=2
-)
+lowess = sm.nonparametric.lowess  # Used in CODSAnalysis/Matt
 
 
 # Custom exception
 class NoValidIntervalError(Exception):
-    '''raised when no valid rows appear in the result dataframe'''
+    """raised when no valid rows appear in the result dataframe"""
+
     pass
 
 
-class SRRAnalysis():
-    '''
+class SRRAnalysis:
+    """
     Class for running the stochastic rate and recovery (SRR) photovoltaic
     soiling loss analysis presented in Deceglie et al. JPV 8(2) p547 2018
 
@@ -55,10 +50,9 @@ class SRRAnalysis():
     precipitation_daily : pandas.Series, default None
         Daily total precipitation. (Ignored if ``clean_criterion='shift'`` in
         subsequent calculations.)
-    '''
+    """
 
-    def __init__(self, energy_normalized_daily, insolation_daily,
-                 precipitation_daily=None):
+    def __init__(self, energy_normalized_daily, insolation_daily, precipitation_daily=None):
         self.pm = energy_normalized_daily  # daily performance metric
         self.insolation_daily = insolation_daily
         self.precipitation_daily = precipitation_daily  # daily precipitation
@@ -66,23 +60,31 @@ def __init__(self, energy_normalized_daily, insolation_daily,
         # insolation-weighted soiling ratios in _calc_monte:
         self.monte_losses = []
 
-        if pd.infer_freq(self.pm.index) != 'D':
-            raise ValueError('Daily performance metric series must have '
-                             'daily frequency')
+        if pd.infer_freq(self.pm.index) != "D":
+            raise ValueError("Daily performance metric series must have daily frequency")
 
-        if pd.infer_freq(self.insolation_daily.index) != 'D':
-            raise ValueError('Daily insolation series must have '
-                             'daily frequency')
+        if pd.infer_freq(self.insolation_daily.index) != "D":
+            raise ValueError("Daily insolation series must have daily frequency")
 
         if self.precipitation_daily is not None:
-            if pd.infer_freq(self.precipitation_daily.index) != 'D':
-                raise ValueError('Precipitation series must have '
-                                 'daily frequency')
-
-    def _calc_daily_df(self, day_scale=13, clean_threshold='infer',
-                       recenter=True, clean_criterion='shift', precip_threshold=0.01,
-                       outlier_factor=1.5):
-        '''
+            if pd.infer_freq(self.precipitation_daily.index) != "D":
+                raise ValueError("Precipitation series must have daily frequency")
+
+    def _calc_daily_df(
+        self,
+        day_scale=13,
+        clean_threshold="infer",
+        recenter=True,
+        clean_criterion="shift",
+        precip_threshold=0.01,
+        outlier_factor=1.5,
+        detect_neg_shifts=False,
+        piecewise_fit=False,
+        neg_shift_factor=2.5,
+        min_piecewise_days=27,
+        collapse_window_days=5,
+    ):
+        """
         Calculates self.daily_df, a pandas dataframe prepared for SRR analysis,
         and self.renorm_factor, the renormalization factor for the daily
         performance
@@ -118,27 +120,45 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer',
             The factor used in the Tukey fence definition of outliers for flagging positive shifts
             in the rolling median used for cleaning detection. A smaller value will cause more and
             smaller shifts to be classified as cleaning events.
-        '''
-        if (day_scale % 2 == 0) and ('shift' in clean_criterion):
-            warnings.warn('An even value of day_scale was passed. An odd value is '
-                          'recommended, otherwise, consecutive days may be erroneously '
-                          'flagged as cleaning events. '
-                          'See https://github.com/NREL/rdtools/issues/189',
-                          stacklevel=2)
+        detect_neg_shifts : bool, default False
+            If True, detects negative shifts in the rolling median and subdivides
+            intervals accordingly.
+        piecewise_fit : bool, default False
+            If True, tests for piecewise linear fits within intervals and
+            subdivides at detected change points.
+        neg_shift_factor : float, default 2.5
+            Multiplier of ``clean_threshold`` to identify negative shift events.
+            Only used when ``detect_neg_shifts=True``.
+        min_piecewise_days : int, default 27
+            Minimum interval length in days required to attempt piecewise fitting.
+            Only used when ``piecewise_fit=True``.
+        collapse_window_days : int, default 5
+            Window size in days for collapsing multiple consecutive cleaning
+            events into a single event.
+        """
+        if (day_scale % 2 == 0) and ("shift" in clean_criterion):
+            warnings.warn(
+                "An even value of day_scale was passed. An odd value is "
+                "recommended, otherwise, consecutive days may be erroneously "
+                "flagged as cleaning events. "
+                "See https://github.com/NREL/rdtools/issues/189",
+                stacklevel=2,
+            )
 
         df = self.pm.to_frame()
-        df.columns = ['pi']
+        df.columns = ["pi"]
         df_insol = self.insolation_daily.to_frame()
-        df_insol.columns = ['insol']
+        df_insol.columns = ["insol"]
 
         df = df.join(df_insol)
         precip = self.precipitation_daily
+
         if precip is not None:
             df_precip = precip.to_frame()
-            df_precip.columns = ['precip']
+            df_precip.columns = ["precip"]
             df = df.join(df_precip)
         else:
-            df['precip'] = 0
+            df["precip"] = 0
 
         # find first and last valid data point
         start = df[~df.pi.isnull()].index[0]
@@ -146,16 +166,16 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer',
         df = df[start:end]
 
         # create a day count column
-        df['day'] = range(len(df))
+        df["day"] = range(len(df))
 
         # Recenter to median of first year, as in YoY degradation
         if recenter:
-            oneyear = start + pd.Timedelta('364d')
-            renorm = df.loc[start:oneyear, 'pi'].median()
+            oneyear = start + pd.Timedelta("364d")
+            renorm = df.loc[start:oneyear, "pi"].median()
         else:
             renorm = 1
 
-        df['pi_norm'] = df['pi'] / renorm
+        df["pi_norm"] = df["pi"] / renorm
 
         # Find the beginning and ends of outages longer than dayscale
         bfill = df['pi_norm'].bfill(limit=day_scale)
@@ -172,50 +192,94 @@ def _calc_daily_df(self, day_scale=13, clean_threshold='infer',
         df_ffill = df.ffill(limit=day_scale).copy()
 
         # Calculate rolling median
-        df['pi_roll_med'] = \
-            df_ffill.pi_norm.rolling(day_scale, center=True).median()
+        df["pi_roll_med"] = df_ffill.pi_norm.rolling(day_scale, center=True).median()
 
         # Detect steps in rolling median
-        df['delta'] = df.pi_roll_med.diff()
-        if clean_threshold == 'infer':
+        df["delta"] = df.pi_roll_med.diff()
+        if clean_threshold == "infer":
             deltas = abs(df.delta)
-            clean_threshold = deltas.quantile(0.75) + \
-                outlier_factor * (deltas.quantile(0.75) - deltas.quantile(0.25))
+            clean_threshold = deltas.quantile(0.75) + outlier_factor * (
+                deltas.quantile(0.75) - deltas.quantile(0.25)
+            )
+
+        df["clean_event_detected"] = df.delta > clean_threshold
+
+        # Collapse multiple consecutive cleaning events into a single event
+        # to avoid fragmenting soiling intervals unnecessarily
+        reduced_cleaning_events = _collapse_cleaning_events(
+            df.clean_event_detected, df.delta.values, collapse_window_days
+        )
+        df["clean_event_detected"] = reduced_cleaning_events
 
-        df['clean_event_detected'] = (df.delta > clean_threshold)
-        precip_event = (df['precip'] > precip_threshold)
+        precip_event = df["precip"] > precip_threshold
 
-        if clean_criterion == 'precip_and_shift':
+        if clean_criterion == "precip_and_shift":
             # Detect which cleaning events are associated with rain
             # within a 3 day window
-            precip_event = precip_event.rolling(
-                3, center=True, min_periods=1).apply(any).astype(bool)
-            df['clean_event'] = (df['clean_event_detected'] & precip_event)
-        elif clean_criterion == 'precip_or_shift':
-            df['clean_event'] = (df['clean_event_detected'] | precip_event)
-        elif clean_criterion == 'precip':
-            df['clean_event'] = precip_event
-        elif clean_criterion == 'shift':
-            df['clean_event'] = df['clean_event_detected']
+            precip_event = (
+                precip_event.rolling(3, center=True, min_periods=1).apply(any).astype(bool))
+            df["clean_event"] = df["clean_event_detected"] & precip_event
+        elif clean_criterion == "precip_or_shift":
+            df["clean_event"] = df["clean_event_detected"] | precip_event
+        elif clean_criterion == "precip":
+            df["clean_event"] = precip_event
+        elif clean_criterion == "shift":
+            df["clean_event"] = df["clean_event_detected"]
         else:
-            raise ValueError('clean_criterion must be one of '
-                             '{"precip_and_shift", "precip_or_shift", '
-                             '"precip", "shift"}')
+            raise ValueError("clean_criterion must be one of"
+                             '{"precip_and_shift", "precip_or_shift", "precip", "shift"}')
 
-        df['clean_event'] = df.clean_event | out_start | out_end
+        df["clean_event"] = df.clean_event | out_start | out_end
 
-        df = df.fillna(0)
+        # Add negative shifts which allows further segmentation of the soiling
+        # intervals and handles correction for data outages
+        df.delta = df.delta.fillna(0)  # to avoid NA corrupting calculation
+        if detect_neg_shifts:
+            df["drop_event"] = df.delta < -neg_shift_factor * clean_threshold
+            df["break_event"] = df.clean_event | df.drop_event
+        else:
+            df["break_event"] = df.clean_event.copy()
 
         # Give an index to each soiling interval/run
-        df['run'] = df.clean_event.cumsum()
-        df.index.name = 'date'  # this gets used by name
+        df["run"] = df.break_event.cumsum()
+        df.index.name = "date"  # this gets used by name
+
+        # When piecewise_fit is enabled, add a single breakpoint per soiling
+        # interval if statistical criteria are met with the piecewise linear fit
+        # compared to a single linear fit
+        if piecewise_fit:
+            piecewise_loop = sorted(list(set(df["run"])))
+            cp_dates = []
+            for r in piecewise_loop:
+                run = df[df["run"] == r]
+                pr = run.pi_norm.copy()
+                pr = pr.ffill()  # linear fitting cant handle nans
+                pr = pr.bfill()  # catch first position nan
+                if len(run) > min_piecewise_days and run.pi_norm.sum() > 0:
+                    sr, cp_index = segmented_soiling_period(pr)
+                    if cp_index is not None:
+                        cp_dates.append(pr.index[cp_index])
+            df["slope_change_event"] = df.index.isin(cp_dates)
+            df["break_event"] = df.break_event | df.slope_change_event
+            df["run"] = df.break_event.cumsum()
+        else:
+            df["slope_change_event"] = False
 
         self.renorm_factor = renorm
         self.daily_df = df
 
-    def _calc_result_df(self, trim=False, max_relative_slope_error=500.0,
-                        max_negative_step=0.05, min_interval_length=7):
-        '''
+    ######################################################################
+    # added neg_shift into parameters in the following def/Matt
+    def _calc_result_df(
+        self,
+        trim=False,
+        max_relative_slope_error=500.0,
+        max_neg_step=0.05,
+        min_interval_days=7,
+        detect_neg_shifts=False,
+        forward_median_window=10,
+    ):
+        """
         Calculates self.result_df, a pandas dataframe summarizing the soiling
         intervals identified and self.analyzed_daily_df, a version of
         self.daily_df with additional columns calculated during analysis.
@@ -228,20 +292,26 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0,
         max_relative_slope_error : float, default 500
             the maximum relative size of the slope confidence interval for an
             interval to be considered valid (percentage).
-        max_negative_step : float, default 0.05
+        max_neg_step : float, default 0.05
             The maximum magnitude of negative discrete steps allowed in an
             interval for the interval to be considered valid (units of
-            normalized performance metric).
-        min_interval_length : int, default 7
-            The minimum duration for an interval to be considered
-            valid.  Cannot be less than 2 (days).
-        '''
+            normalized performance metric). Only used when ``detect_neg_shifts=False``.
+        min_interval_days : int, default 7
+            The minimum duration in days for an interval to be considered
+            valid.  Cannot be less than 2 days.
+        detect_neg_shifts : bool, default False
+            If True, detects negative shifts in the rolling median and uses
+            shift corrections at segment boundaries.
+        forward_median_window : int, default 10
+            Window size for forward median calculation used to validate shift
+            corrections. Only used when ``detect_neg_shifts=True``.
+        """
 
         daily_df = self.daily_df
         result_list = []
         if trim:
             # ignore first and last interval
-            res_loop = sorted(list(set(daily_df['run'])))[1:-1]
+            res_loop = sorted(list(set(daily_df["run"])))[1:-1]
         else:
             res_loop = sorted(list(set(daily_df['run'])))
 
@@ -259,97 +329,244 @@ def _calc_result_df(self, trim=False, max_relative_slope_error=500.0,
             if not run_filtered.empty:
                 run = run_filtered
             result_dict = {
-                'start': start,
-                'end': end,
-                'length': length,
-                'run': r,
-                'run_slope': 0,
-                'run_slope_low': 0,
-                'run_slope_high': 0,
-                'max_neg_step': min(run.delta),
-                'start_loss': 1,
-                'inferred_start_loss': run.pi_norm.mean(),
-                'inferred_end_loss': run.pi_norm.mean(),
-                'valid': False
+                "start": start,
+                "end": end,
+                "length": length,
+                "run": r,
+                "run_slope": 0,
+                "run_slope_low": 0,
+                "run_slope_high": 0,
+                "max_neg_step": min(run.delta),
+                "start_loss": 1,
+                "inferred_start_loss": (
+                    np.nan if run.pi_norm.isna().any() else run.pi_norm.median()
+                ),
+                "inferred_end_loss": (
+                    np.nan if run.pi_norm.isna().any() else run.pi_norm.median()
+                ),
+                "slope_err": 10000,  # high dummy start value for later logic
+                "valid": False,
+                "clean_event": run.clean_event.iloc[0],  # record of clean events
+                "run_loss_baseline": 0.0,  # loss from the polyfit over the soiling interval
             }
-            if len(run) > min_interval_length and run.pi_norm.sum() > 0:
+            if len(run) > min_interval_days and run.pi_norm.sum() > 0:
                 fit = theilslopes(run.pi_norm, run.day)
                 fit_poly = np.poly1d(fit[0:2])
-                result_dict['run_slope'] = fit[0]
-                result_dict['run_slope_low'] = fit[2]
-                result_dict['run_slope_high'] = min([0.0, fit[3]])
-                result_dict['inferred_start_loss'] = fit_poly(start_day)
-                result_dict['inferred_end_loss'] = fit_poly(end_day)
-                result_dict['valid'] = True
+                result_dict["run_slope"] = fit[0]
+                result_dict["run_slope_low"] = fit[2]
+                result_dict["run_slope_high"] = min([0.0, fit[3]])
+                result_dict["valid"] = True
+                ########################################################
+                # moved the following 2 line to the next section within conditional statement/Matt
+                # result_dict['inferred_start_loss'] = fit_poly(start_day)
+                # result_dict['inferred_end_loss'] = fit_poly(end_day)
+
+                ####################################################
+                # the following is moved here so median values are retained/Matt
+                # for soiling inferrences when rejected fits occur
+
+                result_dict["slope_err"] = (
+                    result_dict["run_slope_high"] - result_dict["run_slope_low"]
+                ) / abs(result_dict["run_slope"])
+
+                if (result_dict["slope_err"] <= (max_relative_slope_error / 100.0)) & (
+                        result_dict["run_slope"] < 0):
+                    result_dict["inferred_start_loss"] = fit_poly(start_day)
+                    result_dict["inferred_end_loss"] = fit_poly(end_day)
+                    #############################################
+                    # calculate loss over soiling interval per polyfit/matt
+                    result_dict["run_loss_baseline"] = (
+                        result_dict["inferred_start_loss"] - result_dict["inferred_end_loss"])
+
+                    ###############################################
+
             result_list.append(result_dict)
 
         results = pd.DataFrame(result_list)
 
         if results.empty:
-            raise NoValidIntervalError('No valid soiling intervals were found')
-
-        # Filter results for each interval,
-        # setting invalid interval to slope of 0
-        results['slope_err'] = (
-            results.run_slope_high - results.run_slope_low)\
-            / abs(results.run_slope)
-        # critera for exclusions
-        filt = (
-            (results.run_slope > 0) |
-            (results.slope_err >= max_relative_slope_error / 100.0) |
-            (results.max_neg_step <= -1.0 * max_negative_step)
-        )
-
-        results.loc[filt, 'run_slope'] = 0
-        results.loc[filt, 'run_slope_low'] = 0
-        results.loc[filt, 'run_slope_high'] = 0
-        results.loc[filt, 'valid'] = False
+            raise NoValidIntervalError("No valid soiling intervals were found")
+
+        # Filter results for each interval, setting invalid interval to slope of 0
+        # When detect_neg_shifts=True, negative shifts are used as breaks for soiling
+        # intervals so we don't filter by max_neg_step
+        if detect_neg_shifts:
+            filt = (results.run_slope > 0) | (
+                results.slope_err >= max_relative_slope_error / 100.0
+            )
+            results.loc[filt, "run_slope"] = 0
+            results.loc[filt, "run_slope_low"] = 0
+            results.loc[filt, "run_slope_high"] = 0
+        else:
+            # Original logic: filter by max_neg_step as well
+            filt = (
+                (results.run_slope > 0)
+                | (results.slope_err >= max_relative_slope_error / 100.0)
+                | (results.max_neg_step <= -1.0 * max_neg_step)
+            )
+            results.loc[filt, "run_slope"] = 0
+            results.loc[filt, "run_slope_low"] = 0
+            results.loc[filt, "run_slope_high"] = 0
 
         # Calculate the next inferred start loss from next valid interval
-        results['next_inferred_start_loss'] = np.clip(
-            results[results.valid].inferred_start_loss.shift(-1),
-            0, 1)
+        results["next_inferred_start_loss"] = np.clip(
+            results[results.valid].inferred_start_loss.shift(-1), 0, 1)
+
         # Calculate the inferred recovery at the end of each interval
-        results['inferred_recovery'] = np.clip(
-            results.next_inferred_start_loss - results.inferred_end_loss,
-            0, 1)
+        results["inferred_recovery"] = results.next_inferred_start_loss - results.inferred_end_loss
+
+        # Calculate beginning inferred shift (end of previous soiling period
+        # to start of current period)
+        results["prev_end"] = results[results.valid].inferred_end_loss.shift(1)
+        # if the current interval starts with a clean event, the previous end
+        # is a nan, and the current interval is valid then set prev_end=1
+        results.loc[
+            results.clean_event & np.isnan(results.prev_end) & results.valid,
+            "prev_end",
+        ] = 1
+        results["inferred_begin_shift"] = results.inferred_start_loss - results.prev_end
+        # if original shift detection was positive the shift should not be
+        # negative due to fitting results
+        results.loc[results.clean_event, "inferred_begin_shift"] = np.clip(
+            results.inferred_begin_shift, 0, 1
+        )
+
+        if not detect_neg_shifts:
+            results.loc[filt, "valid"] = False
 
         if len(results[results.valid]) == 0:
-            raise NoValidIntervalError('No valid soiling intervals were found')
+            raise NoValidIntervalError("No valid soiling intervals were found")
         new_start = results.start.iloc[0]
         new_end = results.end.iloc[-1]
         pm_frame_out = daily_df[new_start:new_end]
-        pm_frame_out = pm_frame_out.reset_index() \
-                                   .merge(results, how='left', on='run') \
-                                   .set_index('date')
-
-        pm_frame_out['loss_perfect_clean'] = np.nan
-        pm_frame_out['loss_inferred_clean'] = np.nan
-        pm_frame_out['days_since_clean'] = \
-            (pm_frame_out.index - pm_frame_out.start).dt.days
-
-        # Calculate the daily derate
-        pm_frame_out['loss_perfect_clean'] = \
-            pm_frame_out.start_loss + \
-            pm_frame_out.days_since_clean * pm_frame_out.run_slope
-        # filling the flat intervals may need to be recalculated
-        # for different assumptions
-        pm_frame_out.loss_perfect_clean = \
-            pm_frame_out.loss_perfect_clean.fillna(1)
-
-        pm_frame_out['loss_inferred_clean'] = \
-            pm_frame_out.inferred_start_loss + \
-            pm_frame_out.days_since_clean * pm_frame_out.run_slope
-        # filling the flat intervals may need to be recalculated
-        # for different assumptions
-        pm_frame_out.loss_inferred_clean = \
-            pm_frame_out.loss_inferred_clean.fillna(1)
-
+        pm_frame_out = (
+            pm_frame_out.reset_index().merge(results, how="left", on="run").set_index("date"))
+
+        pm_frame_out["loss_perfect_clean"] = np.nan
+        pm_frame_out["loss_inferred_clean"] = np.nan
+        pm_frame_out["days_since_clean"] = (pm_frame_out.index - pm_frame_out.start).dt.days
+
+        # Enhanced loss calculation with handling of negative shifts and changepoints
+        if detect_neg_shifts:
+            pm_frame_out["inferred_begin_shift"] = pm_frame_out["inferred_begin_shift"].bfill()
+            min_periods = forward_median_window // 2
+            pm_frame_out["forward_median"] = (
+                pm_frame_out.pi.iloc[::-1]
+                .rolling(forward_median_window, min_periods=min_periods)
+                .median()
+            )
+            prev_shift = 1
+            soil_inferred_clean = []
+            soil_perfect_clean = []
+            day_start = -1
+            start_infer = 1
+            start_perfect = 1
+            soil_infer = 1
+            soil_perfect = 1
+            total_down = 0
+            shift = 0
+            shift_perfect = 0
+            begin_perfect_shifts = [0]
+            begin_infer_shifts = [0]
+
+            for date, rs, d, start_shift, changepoint, forward_median in zip(
+                    pm_frame_out.index, pm_frame_out.run_slope, pm_frame_out.days_since_clean,
+                    pm_frame_out.inferred_begin_shift, pm_frame_out.slope_change_event,
+                    pm_frame_out.forward_median):
+                new_soil = d - day_start
+                day_start = d
+
+                if new_soil <= 0:  # begin new soil period
+                    if (start_shift == prev_shift) | (changepoint):  # no shift at
+                        # a slope changepoint
+                        shift = 0
+                        shift_perfect = 0
+                    else:
+                        if (start_shift < 0) & (prev_shift < 0):  # (both negative) or
+                            # downward shifts to start last 2 intervals
+                            shift = 0
+                            shift_perfect = 0
+                            total_down = total_down + start_shift  # adding total downshifts
+                            # to subtract from an eventual cleaning event
+                        elif (start_shift > 0) & (prev_shift >= 0):  # (both positive) or
+                            # cleanings start the last 2 intervals
+                            shift = start_shift
+                            shift_perfect = 1
+                            total_down = 0
+                        # add #####################3/27/24
+                        elif (start_shift == 0) & (prev_shift >= 0):
+                            shift = start_shift
+                            shift_perfect = start_shift
+                            total_down = 0
+                        #############################################################
+                        elif (start_shift >= 0) & (prev_shift < 0):  # cleaning starts the current
+                            # interval but there was a previous downshift
+                            shift = start_shift + total_down  # correct for the negative shifts
+                            shift_perfect = shift  # dont set to one 1 if correcting for a
+                            # downshift (debateable alternative set to 1)
+                            total_down = 0
+                        elif (start_shift < 0) & (prev_shift >= 0):
+                            # negative shift starts the interval, previous shift was cleaning
+                            shift = 0
+                            shift_perfect = 0
+                            total_down = start_shift
+                    # check that shifts results in being at or above the median of
+                    # the next 10 days of data
+                    # this catches places where start points of polyfits were
+                    # skewed below where data start
+                    if (soil_infer + shift) < forward_median:
+                        shift = forward_median - soil_infer
+                    if (soil_perfect + shift_perfect) < forward_median:
+                        shift_perfect = forward_median - soil_perfect
+
+                    # append the daily soiling ratio to each modeled fit
+                    begin_perfect_shifts.append(shift_perfect)
+                    begin_infer_shifts.append(shift)
+                    # clip to last value in case shift ends up negative
+                    soil_infer = np.clip((soil_infer + shift), soil_infer, 1)
+                    start_infer = soil_infer  # make next start value the last inferred value
+                    soil_inferred_clean.append(soil_infer)
+                    # clip to last value in case shift ends up negative
+                    soil_perfect = np.clip((soil_perfect + shift_perfect), soil_perfect, 1)
+                    start_perfect = soil_perfect
+                    soil_perfect_clean.append(soil_perfect)
+                    if changepoint is False:
+                        prev_shift = start_shift  # assigned at new soil period
+
+                elif new_soil > 0:  # within soiling period
+                    # append the daily soiling ratio to each modeled fit
+                    soil_infer = start_infer + rs * d
+                    soil_inferred_clean.append(soil_infer)
+
+                    soil_perfect = start_perfect + rs * d
+                    soil_perfect_clean.append(soil_perfect)
+
+            pm_frame_out["loss_inferred_clean"] = pd.Series(
+                soil_inferred_clean, index=pm_frame_out.index)
+            pm_frame_out["loss_perfect_clean"] = pd.Series(
+                soil_perfect_clean, index=pm_frame_out.index)
+
+            results["begin_perfect_shift"] = pd.Series(begin_perfect_shifts)
+            results["begin_infer_shift"] = pd.Series(begin_infer_shifts)
+        else:
+            pm_frame_out['loss_perfect_clean'] = pm_frame_out.start_loss + \
+                pm_frame_out.days_since_clean * pm_frame_out.run_slope
+            # filling the flat intervals may need to be recalculated
+            # for different assumptions
+            pm_frame_out.loss_perfect_clean = pm_frame_out.loss_perfect_clean.fillna(1)
+            # inferred_start_loss was set to the value from poly fit at the beginning of the
+            # soiling interval
+            pm_frame_out['loss_inferred_clean'] = pm_frame_out.inferred_start_loss + \
+                pm_frame_out.days_since_clean * pm_frame_out.run_slope
+            # filling the flat intervals may need to be recalculated
+            # for different assumptions
+            pm_frame_out.loss_inferred_clean = pm_frame_out.loss_inferred_clean.fillna(1)
+        #######################################################################
         self.result_df = results
         self.analyzed_daily_df = pm_frame_out
 
-    def _calc_monte(self, monte, method='half_norm_clean'):
-        '''
+    def _calc_monte(self, monte, method="half_norm_clean", detect_neg_shifts=False):
+        """
         Runs the Monte Carlo step of the SRR method. Calculates
         self.random_profiles, a list of the random soiling profiles realized in
         the calculation, and self.monte_losses, a list of the
@@ -359,48 +576,56 @@ def _calc_monte(self, monte, method='half_norm_clean'):
         ----------
         monte : int
             number of Monte Carlo simulations to run
-        method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \
-                default 'half_norm_clean'
+        method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', 'inferred_clean'} \
+            default 'half_norm_clean'
+
             How to treat the recovery of each cleaning event
 
-            * 'random_clean' - a random recovery between 0-100%
-            * 'perfect_clean' - each cleaning event returns the performance
-              metric to 1
             * 'half_norm_clean' - The starting point of each interval is taken
-              randomly from a half normal distribution with its
-              mode (mu) at 1 and
-              its sigma equal to 1/3 * (1-b) where b is the intercept
-              of the fit to the interval.
-        '''
+              randomly from a half normal distribution with its mode (mu) at 1
+              and its sigma equal to 1/3 * (1-b) where b is the intercept of
+              the fit to the interval.
+            * 'random_clean' - a random recovery between 0-100%.
+            * 'perfect_clean' - each cleaning event returns the performance
+              metric to 1.
+            * 'inferred_clean' - at each detected clean event the performance
+              metric increases based on fits to the data.
+
+            When ``detect_neg_shifts=True``, 'perfect_clean' and 'inferred_clean'
+            use enhanced logic that handles negative shifts at segment boundaries.
+        detect_neg_shifts : bool, default False
+            If True, uses enhanced logic for 'perfect_clean' and 'inferred_clean'
+            methods that handles negative shifts at segment boundaries.
+        """
 
         # Raise a warning if there is >20% invalid data
-        if (method == 'half_norm_clean') or (method == 'random_clean'):
-            valid_fraction = self.analyzed_daily_df['valid'].mean()
+        if method in ("half_norm_clean", "random_clean"):
+            valid_fraction = self.analyzed_daily_df["valid"].mean()
             if valid_fraction <= 0.8:
-                warnings.warn('20% or more of the daily data is assigned to invalid soiling '
-                              'intervals. This can be problematic with the "half_norm_clean" '
-                              'and "random_clean" cleaning assumptions. Consider more permissive '
-                              'validity criteria such as increasing "max_relative_slope_error" '
-                              'and/or "max_negative_step" and/or decreasing "min_interval_length".'
-                              ' Alternatively, consider using method="perfect_clean". For more'
-                              ' info see https://github.com/NREL/rdtools/issues/272',
-                              stacklevel=2
-                              )
+                warnings.warn(
+                    "20% or more of the daily data is assigned to invalid soiling "
+                    'intervals. This can be problematic with the "half_norm_clean" '
+                    'and "random_clean" cleaning assumptions. Consider more permissive '
+                    'validity criteria such as increasing "max_relative_slope_error" '
+                    'and/or "max_neg_step" and/or decreasing '
+                    '"min_interval_days". Alternatively, consider using '
+                    'method="perfect_clean". For more info see '
+                    "https://github.com/NREL/rdtools/issues/272",
+                    stacklevel=2,
+                )
         monte_losses = []
         random_profiles = []
         for _ in range(monte):
             results_rand = self.result_df.copy()
             df_rand = self.analyzed_daily_df.copy()
             # only really need this column from the original frame:
-            df_rand = df_rand[['insol', 'run']]
-            results_rand['run_slope'] = \
-                np.random.uniform(results_rand.run_slope_low,
-                                  results_rand.run_slope_high)
-            results_rand['run_loss'] = \
-                results_rand.run_slope * results_rand.length
+            df_rand = df_rand[["insol", "run"]]
+            results_rand["run_slope"] = np.random.uniform(
+                results_rand.run_slope_low, results_rand.run_slope_high)
+            results_rand["run_loss"] = results_rand.run_slope * results_rand.length
 
-            results_rand['end_loss'] = np.nan
-            results_rand['start_loss'] = np.nan
+            results_rand["end_loss"] = np.nan
+            results_rand["start_loss"] = np.nan
 
             # Make groups that start with a valid interval and contain
             # subsequent invalid intervals
@@ -411,16 +636,19 @@ def _calc_monte(self, monte, method='half_norm_clean'):
                     group += 1
                 group_list.append(group)
 
-            results_rand['group'] = group_list
+            results_rand["group"] = group_list
 
             # randomize the extent of the cleaning
             inter_start = 1.0
+            delta_previous_run_loss = 0
             start_list = []
-            if (method == 'half_norm_clean') or (method == 'random_clean'):
+            if (method == "half_norm_clean") or (method == "random_clean"):
                 # Randomize recovery of valid intervals only
                 valid_intervals = results_rand[results_rand.valid].copy()
-                valid_intervals['inferred_recovery'] = \
-                    valid_intervals.inferred_recovery.fillna(1.0)
+                valid_intervals["inferred_recovery"] = np.clip(
+                    valid_intervals.inferred_recovery, 0, 1)
+                valid_intervals["inferred_recovery"] = valid_intervals.inferred_recovery.fillna(
+                    1.0)
 
                 end_list = []
                 for i, row in valid_intervals.iterrows():
@@ -428,18 +656,18 @@ def _calc_monte(self, monte, method='half_norm_clean'):
                     end = inter_start + row.run_loss
                     end_list.append(end)
 
-                    if method == 'half_norm_clean':
+                    if method == "half_norm_clean":
                         # Use a half normal with the inferred clean at the
                         # 3sigma point
                         x = np.clip(end + row.inferred_recovery, 0, 1)
                         inter_start = 1 - abs(np.random.normal(0.0, (1 - x) / 3))
-                    elif method == 'random_clean':
+                    elif method == "random_clean":
                         inter_start = np.random.uniform(end, 1)
 
                 # Update the valid rows in results_rand
                 valid_update = pd.DataFrame()
-                valid_update['start_loss'] = start_list
-                valid_update['end_loss'] = end_list
+                valid_update["start_loss"] = start_list
+                valid_update["end_loss"] = end_list
                 valid_update.index = valid_intervals.index
                 results_rand.update(valid_update)
 
@@ -473,49 +701,104 @@ def _calc_monte(self, monte, method='half_norm_clean'):
                     # Update results rand with the invalid rows
                     replace_levels = np.concatenate(replace_levels)
                     invalid_update = pd.DataFrame()
-                    invalid_update['start_loss'] = replace_levels
+                    invalid_update["start_loss"] = replace_levels
                     invalid_update.index = invalid_intervals.index
                     results_rand.update(invalid_update)
 
-            elif method == 'perfect_clean':
-                for i, row in results_rand.iterrows():
-                    start_list.append(inter_start)
-                    end = inter_start + row.run_loss
-                    inter_start = 1
-                results_rand['start_loss'] = start_list
+            elif method == "perfect_clean":
+                if detect_neg_shifts:
+                    # Enhanced logic with negative shift detection
+                    for i, row in results_rand.iterrows():
+                        if row.begin_perfect_shift > 0:
+                            inter_start = np.clip(
+                                (inter_start + row.begin_perfect_shift + delta_previous_run_loss),
+                                end,
+                                1,
+                            )
+                            delta_previous_run_loss = -1 * row.run_loss - row.run_loss_baseline
+                        else:
+                            delta_previous_run_loss = (
+                                delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline
+                            )
+                        start_list.append(inter_start)
+                        end = inter_start + row.run_loss
+                        inter_start = end
+                    results_rand["start_loss"] = start_list
+                else:
+                    # Simple logic: each cleaning returns to 1
+                    for i, row in results_rand.iterrows():
+                        start_list.append(inter_start)
+                        end = inter_start + row.run_loss
+                        inter_start = 1
+                    results_rand["start_loss"] = start_list
+
+            elif method == "inferred_clean":
+                if detect_neg_shifts:
+                    # Enhanced logic with negative shift detection
+                    for i, row in results_rand.iterrows():
+                        if row.begin_infer_shift > 0:
+                            inter_start = np.clip(
+                                (inter_start + row.begin_infer_shift + delta_previous_run_loss),
+                                end,
+                                1,
+                            )
+                            delta_previous_run_loss = -1 * row.run_loss - row.run_loss_baseline
+                        else:
+                            delta_previous_run_loss = (
+                                delta_previous_run_loss - 1 * row.run_loss - row.run_loss_baseline
+                            )
+                        start_list.append(inter_start)
+                        end = inter_start + row.run_loss
+                        inter_start = end
+                    results_rand["start_loss"] = start_list
+                else:
+                    raise ValueError("method='inferred_clean' requires detect_neg_shifts=True")
 
             else:
                 raise ValueError("Invalid method specification")
 
-            df_rand = df_rand.reset_index() \
-                             .merge(results_rand, how='left', on='run') \
-                             .set_index('date')
-            df_rand['loss'] = np.nan
-            df_rand['days_since_clean'] = \
-                (df_rand.index - df_rand.start).dt.days
-            df_rand['loss'] = df_rand.start_loss + \
-                df_rand.days_since_clean * df_rand.run_slope
+            df_rand = (
+                df_rand.reset_index().merge(results_rand, how="left", on="run").set_index("date"))
+            df_rand["loss"] = np.nan
+            df_rand["days_since_clean"] = (df_rand.index - df_rand.start).dt.days
+            df_rand["loss"] = df_rand.start_loss + df_rand.days_since_clean * df_rand.run_slope
 
-            df_rand['soil_insol'] = df_rand.loss * df_rand.insol
+            df_rand["soil_insol"] = df_rand.loss * df_rand.insol
 
             soiling_ratio = (
-                df_rand.soil_insol.sum() / df_rand.insol[
-                    ~df_rand.soil_insol.isnull()].sum()
-            )
+                df_rand.soil_insol.sum() / df_rand.insol[~df_rand.soil_insol.isnull()].sum())
             monte_losses.append(soiling_ratio)
-            random_profile = df_rand['loss'].copy()
-            random_profile.name = 'stochastic_soiling_profile'
+            random_profile = df_rand["loss"].copy()
+            random_profile.name = "stochastic_soiling_profile"
             random_profiles.append(random_profile)
 
         self.random_profiles = random_profiles
         self.monte_losses = monte_losses
 
-    def run(self, reps=1000, day_scale=13, clean_threshold='infer',
-            trim=False, method='half_norm_clean',
-            clean_criterion='shift', precip_threshold=0.01, min_interval_length=7,
-            exceedance_prob=95.0, confidence_level=68.2, recenter=True,
-            max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5):
-        '''
+    def run(
+        self,
+        reps=1000,
+        day_scale=13,
+        clean_threshold="infer",
+        trim=False,
+        method="half_norm_clean",
+        clean_criterion="shift",
+        precip_threshold=0.01,
+        min_interval_days=7,
+        exceedance_prob=95.0,
+        confidence_level=68.2,
+        recenter=True,
+        max_relative_slope_error=500.0,
+        max_neg_step=0.05,
+        outlier_factor=1.5,
+        detect_neg_shifts=False,
+        piecewise_fit=False,
+        neg_shift_factor=2.5,
+        min_piecewise_days=27,
+        collapse_window_days=5,
+        forward_median_window=10,
+    ):
+        """
         Run the SRR method from beginning to end.  Perform the stochastic rate
         and recovery soiling loss calculation. Based on the methods presented
         in Deceglie et al. "Quantifying Soiling Loss Directly From PV Yield"
@@ -536,17 +819,24 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer',
         trim : bool, default False
             Whether to trim (remove) the first and last soiling intervals to
             avoid inclusion of partial intervals
-        method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \
+        method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', 'inferred_clean'} \
             default 'half_norm_clean'
+
             How to treat the recovery of each cleaning event
 
-            * 'random_clean' - a random recovery between 0-100%
-            * 'perfect_clean' - each cleaning event returns the performance
-              metric to 1
             * 'half_norm_clean' - The starting point of each interval is taken
               randomly from a half normal distribution with its mode (mu) at 1 and
               its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to
               the interval.
+            * 'random_clean' - a random recovery between 0-100%.
+            * 'perfect_clean' - each cleaning event returns the performance
+              metric to 1.
+            * 'inferred_clean' - at each detected clean event the performance
+              metric increases based on fits to the data.
+
+            When ``detect_neg_shifts=True``, 'perfect_clean' and 'inferred_clean'
+            use enhanced logic that handles negative shifts and tracks shift
+            corrections at segment boundaries.
         clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \
             default 'shift'
             The method of partitioning the dataset into soiling intervals
@@ -561,9 +851,9 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer',
             The daily precipitation threshold for defining
             precipitation cleaning events.
             Units must be consistent with ``self.precipitation_daily``
-        min_interval_length : int, default 7
-            The minimum duration for an interval to be considered
-            valid.  Cannot be less than 2 (days).
+        min_interval_days : int, default 7
+            The minimum duration in days for an interval to be considered
+            valid.  Cannot be less than 2 days.
         exceedance_prob : float, default 95.0
             The probability level to use for exceedance value calculation in
             percent
@@ -575,14 +865,37 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer',
         max_relative_slope_error : float, default 500
             the maximum relative size of the slope confidence interval for an
             interval to be considered valid (percentage).
-        max_negative_step : float, default 0.05
+        max_neg_step : float, default 0.05
             The maximum magnitude of negative discrete steps allowed in an
             interval for the interval to be considered valid (units of
-            normalized performance metric).
+            normalized performance metric). Only used when ``detect_neg_shifts=False``.
         outlier_factor : float, default 1.5
             The factor used in the Tukey fence definition of outliers for flagging positive shifts
             in the rolling median used for cleaning detection. A smaller value will cause more and
             smaller shifts to be classified as cleaning events.
+        detect_neg_shifts : bool, default False
+            If True, detects negative shifts in the rolling median and subdivides
+            intervals accordingly. When a negative shift is detected followed by
+            a recovery, the algorithm tracks these transitions. When enabled with
+            ``method='perfect_clean'`` or ``method='inferred_clean'``, uses
+            enhanced logic that handles shift corrections at segment boundaries.
+        piecewise_fit : bool, default False
+            If True, tests for piecewise linear fits within soiling intervals.
+            Intervals that show a significant slope change are subdivided at the
+            detected change point.
+        neg_shift_factor : float, default 2.5
+            Multiplier of ``clean_threshold`` to identify negative shift events.
+            Only used when ``detect_neg_shifts=True``. A value of 2.5 means a shift
+            of -2.5 * clean_threshold triggers interval subdivision.
+        min_piecewise_days : int, default 27
+            Minimum interval length in days required to attempt piecewise fitting.
+            Only used when ``piecewise_fit=True``.
+        collapse_window_days : int, default 5
+            Window size in days for collapsing multiple consecutive cleaning
+            events into a single event.
+        forward_median_window : int, default 10
+            Window size for forward median calculation used to validate shift
+            corrections. Only used when ``detect_neg_shifts=True``.
 
         Returns
         -------
@@ -636,59 +949,98 @@ def run(self, reps=1000, day_scale=13, clean_threshold='infer',
               |                        | be treated as a valid soiling interval       |
               +------------------------+----------------------------------------------+
 
-        '''
-        self._calc_daily_df(day_scale=day_scale,
-                            clean_threshold=clean_threshold,
-                            recenter=recenter,
-                            clean_criterion=clean_criterion,
-                            precip_threshold=precip_threshold,
-                            outlier_factor=outlier_factor)
-        self._calc_result_df(trim=trim,
-                             max_relative_slope_error=max_relative_slope_error,
-                             max_negative_step=max_negative_step,
-                             min_interval_length=min_interval_length)
-        self._calc_monte(reps, method=method)
+        """
+        # Validate method and detect_neg_shifts compatibility
+        if detect_neg_shifts and method not in (
+            "half_norm_clean",
+            "perfect_clean",
+            "inferred_clean",
+        ):
+            raise ValueError(
+                f"detect_neg_shifts=True requires method='half_norm_clean', 'perfect_clean' or "
+                f"'inferred_clean', got '{method}'"
+            )
+
+        self._calc_daily_df(
+            day_scale=day_scale,
+            clean_threshold=clean_threshold,
+            recenter=recenter,
+            clean_criterion=clean_criterion,
+            precip_threshold=precip_threshold,
+            outlier_factor=outlier_factor,
+            detect_neg_shifts=detect_neg_shifts,
+            piecewise_fit=piecewise_fit,
+            neg_shift_factor=neg_shift_factor,
+            min_piecewise_days=min_piecewise_days,
+            collapse_window_days=collapse_window_days,
+        )
+
+        self._calc_result_df(
+            trim=trim,
+            max_relative_slope_error=max_relative_slope_error,
+            max_neg_step=max_neg_step,
+            min_interval_days=min_interval_days,
+            detect_neg_shifts=detect_neg_shifts,
+            forward_median_window=forward_median_window,
+        )
+
+        self._calc_monte(reps, method=method, detect_neg_shifts=detect_neg_shifts)
 
         # Calculate the P50 and confidence interval
         half_ci = confidence_level / 2.0
         result = np.percentile(self.monte_losses,
-                               [50,
-                                50.0 - half_ci,
-                                50.0 + half_ci,
-                                100 - exceedance_prob])
+                               [50, 50.0 - half_ci, 50.0 + half_ci, 100 - exceedance_prob])
         P_level = result[3]
 
         # Construct calc_info output
-
+        ###############################################
+        # add inferred_recovery, inferred_begin_shift /Matt
+        ###############################################
         intervals_out = self.result_df[
-            ['start', 'end', 'run_slope', 'run_slope_low',
-                'run_slope_high', 'inferred_start_loss', 'inferred_end_loss',
-                'length', 'valid']].copy()
-        intervals_out.rename(columns={'run_slope': 'soiling_rate',
-                                      'run_slope_high': 'soiling_rate_high',
-                                      'run_slope_low': 'soiling_rate_low',
-                                      }, inplace=True)
+            ["start", "end", "run_slope", "run_slope_low", "run_slope_high", "inferred_start_loss",
+             "inferred_end_loss", "inferred_recovery", "inferred_begin_shift", "length", "valid"]
+        ].copy()
+        intervals_out.rename(columns={"run_slope": "soiling_rate",
+                                      "run_slope_high": "soiling_rate_high",
+                                      "run_slope_low": "soiling_rate_low"}, inplace=True)
 
         df_d = self.analyzed_daily_df
-        sr_perfect = df_d[df_d['valid']]['loss_perfect_clean']
-        calc_info = {
-            'exceedance_level': P_level,
-            'renormalizing_factor': self.renorm_factor,
-            'stochastic_soiling_profiles': self.random_profiles,
-            'soiling_interval_summary': intervals_out,
-            'soiling_ratio_perfect_clean': sr_perfect
-        }
+        sr_perfect = df_d[df_d["valid"]]["loss_perfect_clean"]
+
+        calc_info = {"exceedance_level": P_level, "renormalizing_factor": self.renorm_factor,
+                     "stochastic_soiling_profiles": self.random_profiles,
+                     "soiling_interval_summary": intervals_out,
+                     "soiling_ratio_perfect_clean": sr_perfect}
 
         return (result[0], result[1:3], calc_info)
 
 
-def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000,
-                precipitation_daily=None, day_scale=13, clean_threshold='infer',
-                trim=False, method='half_norm_clean',
-                clean_criterion='shift', precip_threshold=0.01, min_interval_length=7,
-                exceedance_prob=95.0, confidence_level=68.2, recenter=True,
-                max_relative_slope_error=500.0, max_negative_step=0.05, outlier_factor=1.5):
-    '''
+def soiling_srr(
+    energy_normalized_daily,
+    insolation_daily,
+    reps=1000,
+    precipitation_daily=None,
+    day_scale=13,
+    clean_threshold="infer",
+    trim=False,
+    method="half_norm_clean",
+    clean_criterion="shift",
+    precip_threshold=0.01,
+    min_interval_days=7,
+    exceedance_prob=95.0,
+    confidence_level=68.2,
+    recenter=True,
+    max_relative_slope_error=500.0,
+    max_neg_step=0.05,
+    outlier_factor=1.5,
+    detect_neg_shifts=False,
+    piecewise_fit=False,
+    neg_shift_factor=2.5,
+    min_piecewise_days=27,
+    collapse_window_days=5,
+    forward_median_window=10,
+):
+    """
     Functional wrapper for :py:class:`~rdtools.soiling.SRRAnalysis`. Perform
     the stochastic rate and recovery soiling loss calculation. Based on the
     methods presented in Deceglie et al. JPV 8(2) p547 2018.
@@ -720,17 +1072,23 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000,
     trim : bool, default False
         Whether to trim (remove) the first and last soiling intervals to avoid
         inclusion of partial intervals
-    method : str, {'half_norm_clean', 'random_clean', 'perfect_clean'} \
+    method : str, {'half_norm_clean', 'random_clean', 'perfect_clean', 'inferred_clean'} \
         default 'half_norm_clean'
+
         How to treat the recovery of each cleaning event
 
-        * 'random_clean' - a random recovery between 0-100%
-        * 'perfect_clean' - each cleaning event returns the performance
-          metric to 1
         * 'half_norm_clean' - The starting point of each interval is taken
           randomly from a half normal distribution with its mode (mu) at 1 and
           its sigma equal to 1/3 * (1-b) where b is the intercept of the fit to
           the interval.
+        * 'random_clean' - a random recovery between 0-100%.
+        * 'perfect_clean' - each cleaning event returns the performance
+          metric to 1.
+        * 'inferred_clean' - at each detected clean event the performance
+          metric increases based on fits to the data.
+
+        When ``detect_neg_shifts=True``, 'perfect_clean' and 'inferred_clean'
+        use enhanced logic that handles negative shifts at segment boundaries.
     clean_criterion : str, {'shift', 'precip_and_shift', 'precip_or_shift', 'precip'} \
         default 'shift'
         The method of partitioning the dataset into soiling intervals
@@ -744,9 +1102,9 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000,
     precip_threshold : float, default 0.01
         The daily precipitation threshold for defining precipitation
         cleaning events. Units must be consistent with precip.
-    min_interval_length : int, default 7
-        The minimum duration, in days, for an interval to be considered
-        valid.  Cannot be less than 2 (days).
+    min_interval_days : int, default 7
+        The minimum duration in days for an interval to be considered
+        valid.  Cannot be less than 2 days.
     exceedance_prob : float, default 95.0
         the probability level to use for exceedance value calculation in
         percent
@@ -758,14 +1116,37 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000,
     max_relative_slope_error : float, default 500.0
         the maximum relative size of the slope confidence interval for an
         interval to be considered valid (percentage).
-    max_negative_step : float, default 0.05
+    max_neg_step : float, default 0.05
         The maximum magnitude of negative discrete steps allowed in an interval
         for the interval to be considered valid (units of normalized
-        performance metric).
+        performance metric). Only used when ``detect_neg_shifts=False``.
     outlier_factor : float, default 1.5
         The factor used in the Tukey fence definition of outliers for flagging positive shifts
         in the rolling median used for cleaning detection. A smaller value will cause more and
         smaller shifts to be classified as cleaning events.
+    detect_neg_shifts : bool, default False
+        If True, detects negative shifts in the rolling median and subdivides
+        intervals accordingly. When a negative shift is detected followed by
+        a recovery, the algorithm tracks these transitions. When enabled with
+        ``method='perfect_clean'`` or ``method='inferred_clean'``, uses
+        enhanced logic that handles shift corrections at segment boundaries.
+    piecewise_fit : bool, default False
+        If True, tests for piecewise linear fits within soiling intervals.
+        Intervals that show a significant slope change are subdivided at the
+        detected change point.
+    neg_shift_factor : float, default 2.5
+        Multiplier of ``clean_threshold`` to identify negative shift events.
+        Only used when ``detect_neg_shifts=True``. A value of 2.5 means a shift
+        of -2.5 * clean_threshold triggers interval subdivision.
+    min_piecewise_days : int, default 27
+        Minimum interval length in days required to attempt piecewise fitting.
+        Only used when ``piecewise_fit=True``.
+    collapse_window_days : int, default 5
+        Window size in days for collapsing multiple consecutive cleaning
+        events into a single event.
+    forward_median_window : int, default 10
+        Window size for forward median calculation used to validate shift
+        corrections. Only used when ``detect_neg_shifts=True``.
 
     Returns
     -------
@@ -818,10 +1199,9 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000,
           | 'valid'                | Whether the interval meets the criteria to   |
           |                        | be treated as a valid soiling interval       |
           +------------------------+----------------------------------------------+
-    '''
+    """
 
-    srr = SRRAnalysis(energy_normalized_daily,
-                      insolation_daily,
+    srr = SRRAnalysis(energy_normalized_daily, insolation_daily,
                       precipitation_daily=precipitation_daily)
 
     sr, sr_ci, soiling_info = srr.run(
@@ -832,20 +1212,26 @@ def soiling_srr(energy_normalized_daily, insolation_daily, reps=1000,
         method=method,
         clean_criterion=clean_criterion,
         precip_threshold=precip_threshold,
-        min_interval_length=min_interval_length,
+        min_interval_days=min_interval_days,
         exceedance_prob=exceedance_prob,
         confidence_level=confidence_level,
         recenter=recenter,
         max_relative_slope_error=max_relative_slope_error,
-        max_negative_step=max_negative_step,
-        outlier_factor=outlier_factor)
-
+        max_neg_step=max_neg_step,
+        outlier_factor=outlier_factor,
+        detect_neg_shifts=detect_neg_shifts,
+        piecewise_fit=piecewise_fit,
+        neg_shift_factor=neg_shift_factor,
+        min_piecewise_days=min_piecewise_days,
+        collapse_window_days=collapse_window_days,
+        forward_median_window=forward_median_window,
+    )
     return sr, sr_ci, soiling_info
 
 
 def _count_month_days(start, end):
-    '''Return a dict of number of days between start and end
-    (inclusive) in each month'''
+    """Return a dict of number of days between start and end
+    (inclusive) in each month"""
     days = pd.date_range(start, end)
     months = [x.month for x in days]
     out_dict = {}
@@ -855,9 +1241,8 @@ def _count_month_days(start, end):
     return out_dict
 
 
-def annual_soiling_ratios(stochastic_soiling_profiles,
-                          insolation_daily, confidence_level=68.2):
-    '''
+def annual_soiling_ratios(stochastic_soiling_profiles, insolation_daily, confidence_level=68.2):
+    """
     Return annualized soiling ratios and associated confidence intervals based
     on stochastic soiling profiles from SRR. Note that each year
     may be affected by previous years' profiles for all SRR cleaning
@@ -897,19 +1282,17 @@ def annual_soiling_ratios(stochastic_soiling_profiles,
         |                        | for insolation-weighted soiling ratio for |
         |                        | the year                                  |
         +------------------------+-------------------------------------------+
-    '''
+    """
 
     # Create a df with each realization as a column
     all_profiles = pd.concat(stochastic_soiling_profiles, axis=1)
     all_profiles = all_profiles.dropna()
 
     if not all_profiles.index.isin(insolation_daily.index).all():
-        warnings.warn(
-            'The indexes of stochastic_soiling_profiles are not entirely '
-            'contained within the index of insolation_daily. Every day in '
-            'stochastic_soiling_profiles should be represented in '
-            'insolation_daily. This may cause erroneous results.',
-            stacklevel=2)
+        warnings.warn("The indexes of stochastic_soiling_profiles are not entirely "
+                      "contained within the index of insolation_daily. Every day in "
+                      "stochastic_soiling_profiles should be represented in "
+                      "insolation_daily. This may cause erroneous results.")
 
     insolation_daily = insolation_daily.reindex(all_profiles.index)
 
@@ -917,30 +1300,31 @@ def annual_soiling_ratios(stochastic_soiling_profiles,
     all_profiles_weighted = all_profiles.multiply(insolation_daily, axis=0)
 
     # Compute the insolation-weighted soiling ratio (IWSR) for each realization
-    annual_insolation = insolation_daily.groupby(
-        insolation_daily.index.year).sum()
+    annual_insolation = insolation_daily.groupby(insolation_daily.index.year).sum()
     all_annual_weighted_sums = all_profiles_weighted.groupby(
-        all_profiles_weighted.index.year).sum()
-    all_annual_iwsr = all_annual_weighted_sums.multiply(
-        1/annual_insolation, axis=0)
-
-    annual_soiling = pd.DataFrame({
-        'soiling_ratio_median': all_annual_iwsr.quantile(0.5, axis=1),
-        'soiling_ratio_low': all_annual_iwsr.quantile(
-            0.5 - confidence_level/2/100, axis=1),
-        'soiling_ratio_high': all_annual_iwsr.quantile(
-            0.5 + confidence_level/2/100, axis=1),
-    })
-    annual_soiling.index.name = 'year'
+        all_profiles_weighted.index.year
+    ).sum()
+    all_annual_iwsr = all_annual_weighted_sums.multiply(1 / annual_insolation, axis=0)
+
+    annual_soiling = pd.DataFrame(
+        {"soiling_ratio_median": all_annual_iwsr.quantile(0.5, axis=1),
+         "soiling_ratio_low": all_annual_iwsr.quantile(0.5 - confidence_level / 2 / 100, axis=1),
+         "soiling_ratio_high": all_annual_iwsr.quantile(0.5 + confidence_level / 2 / 100, axis=1),
+         })
+    annual_soiling.index.name = "year"
     annual_soiling = annual_soiling.reset_index()
 
     return annual_soiling
 
 
-def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14,
-                          max_relative_slope_error=500.0, reps=100000,
-                          confidence_level=68.2):
-    '''
+def monthly_soiling_rates(
+    soiling_interval_summary,
+    min_interval_days=14,
+    max_relative_slope_error=500.0,
+    reps=100000,
+    confidence_level=68.2,
+):
+    """
     Use Monte Carlo to calculate typical monthly soiling rates.
     Samples possible soiling rates from soiling rate confidence
     intervals associated with soiling intervals assuming a uniform
@@ -957,7 +1341,7 @@ def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14,
         ``soiling_rate_high``, ``soiling_rate_low``, ``soiling_rate``,
         ``length``, ``valid``,``start``, and ``end``.
 
-    min_interval_length : int, default 14
+    min_interval_days : int, default 14
         The minimum number of days a soiling interval must contain to be
         included in the calculation. Similar to the same parameter in
         :py:func:`soiling_srr` and :py:meth:`SRRAnalysis.run` but with a
@@ -1005,75 +1389,69 @@ def monthly_soiling_rates(soiling_interval_summary, min_interval_length=14,
         |                       | intervals contribute, the confidence interval    |
         |                       | is likely to underestimate the true uncertainty. |
         +-----------------------+--------------------------------------------------+
-    '''
+    """
 
     # filter to intervals of interest
-    high = soiling_interval_summary['soiling_rate_high']
-    low = soiling_interval_summary['soiling_rate_low']
-    rate = soiling_interval_summary['soiling_rate']
+    high = soiling_interval_summary["soiling_rate_high"]
+    low = soiling_interval_summary["soiling_rate_low"]
+    rate = soiling_interval_summary["soiling_rate"]
     rel_error = 100 * abs((high - low) / rate)
     intervals = soiling_interval_summary[
-        (soiling_interval_summary['length'] >= min_interval_length) &
-        (soiling_interval_summary['valid']) &
-        (rel_error <= max_relative_slope_error)
+        (soiling_interval_summary["length"] >= min_interval_days)
+        & (soiling_interval_summary["valid"])
+        & (rel_error <= max_relative_slope_error)
     ].copy()
 
     # count the overlap of each interval with each month
     month_counts = []
     for _, row in intervals.iterrows():
-        month_counts.append(_count_month_days(row['start'], row['end']))
+        month_counts.append(_count_month_days(row["start"], row["end"]))
 
     # divy up the monte carlo reps based on overlap
     for month in range(1, 13):
         days_in_month = np.array([x[month] for x in month_counts])
-        sample_col = f'samples_for_month_{month}'
+        sample_col = f"samples_for_month_{month}"
         if days_in_month.sum() > 0:
-            intervals[sample_col] = np.ceil(
-                days_in_month / days_in_month.sum() * reps)
+            intervals[sample_col] = np.ceil(days_in_month / days_in_month.sum() * reps)
         else:
             intervals[sample_col] = 0
         intervals[sample_col] = intervals[sample_col].astype(int)
 
     # perform the monte carlo month by month
-    ci_quantiles = [0.5 - confidence_level/2/100, 0.5 + confidence_level/2/100]
+    ci_quantiles = [0.5 - confidence_level / 2 / 100, 0.5 + confidence_level / 2 / 100]
     monthly_rate_data = []
     relevant_interval_count = []
     for month in range(1, 13):
         rates = []
-        sample_col = f'samples_for_month_{month}'
+        sample_col = f"samples_for_month_{month}"
 
         relevant_intervals = intervals[intervals[sample_col] > 0]
         for _, row in relevant_intervals.iterrows():
             rates.append(np.random.uniform(
-                row['soiling_rate_low'],
-                row['soiling_rate_high'],
-                row[sample_col]))
+                    row["soiling_rate_low"], row["soiling_rate_high"], row[sample_col]))
 
         rates = [x for sublist in rates for x in sublist]
 
         if rates:
-            monthly_rate_data.append(np.quantile(rates,
-                                                 [0.5, ci_quantiles[0],
-                                                  ci_quantiles[1]]))
+            monthly_rate_data.append(np.quantile(rates, [0.5, ci_quantiles[0], ci_quantiles[1]]))
         else:
-            monthly_rate_data.append(np.array([np.nan]*3))
+            monthly_rate_data.append(np.array([np.nan] * 3))
 
         relevant_interval_count.append(len(relevant_intervals))
     monthly_rate_data = np.array(monthly_rate_data)
 
     # make a dataframe out of the results
-    monthly_soiling_df = pd.DataFrame(data=monthly_rate_data,
-                                      columns=['soiling_rate_median',
-                                               'soiling_rate_low',
-                                               'soiling_rate_high'])
-    monthly_soiling_df.insert(0, 'month', range(1, 13))
-    monthly_soiling_df['interval_count'] = relevant_interval_count
+    monthly_soiling_df = pd.DataFrame(
+        data=monthly_rate_data,
+        columns=["soiling_rate_median", "soiling_rate_low", "soiling_rate_high"])
+    monthly_soiling_df.insert(0, "month", range(1, 13))
+    monthly_soiling_df["interval_count"] = relevant_interval_count
 
     return monthly_soiling_df
 
 
-class CODSAnalysis():
-    '''
+class CODSAnalysis:
+    """
     Container for the Combined Degradation and Soiling (CODS) algorithm
     for degradation and soiling loss analysis. Based on the
     method presented in [1]_.
@@ -1169,7 +1547,7 @@ class CODSAnalysis():
     ----------
     .. [1] Skomedal, Å. and Deceglie, M. G., IEEE Journal of
        Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219
-    '''
+    """
 
     def __init__(self, energy_normalized_daily):
         self.pm = energy_normalized_daily  # daily performance metric
@@ -1178,18 +1556,17 @@ def __init__(self, energy_normalized_daily):
             first_keeper = self.pm.isna().idxmin()
             self.pm = self.pm.loc[first_keeper:]
 
-        if self.pm.index.freq != 'D':
-            raise ValueError('Daily performance metric series must have '
-                             'daily frequency (missing dates should be '
-                             'represented by NaNs)')
+        if self.pm.index.freq != "D":
+            raise ValueError("Daily performance metric series must have "
+                             "daily frequency (missing dates should be "
+                             "represented by NaNs)")
 
     def iterative_signal_decomposition(
-            self, order=('SR', 'SC', 'Rd'), degradation_method='YoY',
-            max_iterations=18, cleaning_sensitivity=.5, convergence_criterion=5e-3,
-            pruning_iterations=1, clean_pruning_sensitivity=.6, soiling_significance=.75,
-            process_noise=1e-4, renormalize_SR=None, ffill=True, clip_soiling=True,
-            verbose=False):
-        '''
+            self, order=("SR", "SC", "Rd"), degradation_method="YoY", max_iterations=18,
+            cleaning_sensitivity=0.5, convergence_criterion=5e-3, pruning_iterations=1,
+            clean_pruning_sensitivity=0.6, soiling_significance=0.75, process_noise=1e-4,
+            renormalize_SR=None, ffill=True, clip_soiling=True, verbose=False):
+        """
         Estimates the soiling losses and the degradation rate of a PV system
         based on its daily normalized energy, or daily Performance Index (PI).
         The underlying assumption is that the PI
@@ -1328,14 +1705,13 @@ def iterative_signal_decomposition(
 
         .. [3] Skomedal, Å. and Deceglie, M. G., IEEE Journal of
            Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219
-        '''
+        """
         pi = self.pm.copy()
-        if degradation_method == 'STL' and 'Rd' in order:
-            order = tuple([c for c in order if c != 'Rd'])
+        if degradation_method == "STL" and "Rd" in order:
+            order = tuple([c for c in order if c != "Rd"])
 
-        if 'SR' not in order:
-            raise ValueError('\'SR\' must be in argument \'order\' ' +
-                             '(e.g. order=[\'SR\', \'SC\', \'Rd\']')
+        if "SR" not in order:
+            raise ValueError("'SR' must be in argument 'order' (e.g. order=['SR', 'SC', 'Rd']")
         n_steps = len(order)
         day = np.arange(len(pi))
         degradation_trend = [1]
@@ -1348,148 +1724,133 @@ def iterative_signal_decomposition(
         convergence_metric = [_RMSE(pi, np.ones((len(pi),)))]
 
         # Find possible cleaning events based on the performance index
-        ce, rm9 = _rolling_median_ce_detection(pi.index, pi, ffill=ffill,
-                                               tuner=cleaning_sensitivity)
+        ce, rm9 = _rolling_median_ce_detection(
+            pi.index, pi, ffill=ffill, tuner=cleaning_sensitivity)
         pce = _collapse_cleaning_events(ce, rm9.diff().values, 5)
 
         small_soiling_signal, perfect_cleaning = False, True
         ic = 0  # iteration counter
 
         if verbose:
-            print('It. nr\tstep\tRMSE\ttimer')
+            print("It. nr\tstep\tRMSE\ttimer")
         if verbose:
-            print('{:}\t- \t{:.5f}'.format(ic, convergence_metric[ic]))
+            print("{:}\t- \t{:.5f}".format(ic, convergence_metric[ic]))
         while ic < max_iterations:
             t0 = time.time()
             ic += 1
 
             # Find soiling component
-            if order[(ic-1) % n_steps] == 'SR':
+            if order[(ic - 1) % n_steps] == "SR":
                 if ic > 2:  # Add possible cleaning events found by considering
                     # the residuals
                     pce = soiling_dfs[-1].cleaning_events.copy()
                     cleaning_sensitivity *= 1.2  # decrease sensitivity
                     ce, rm9 = _rolling_median_ce_detection(
-                        pi.index, residuals, ffill=ffill,
-                        tuner=cleaning_sensitivity)
+                        pi.index, residuals, ffill=ffill, tuner=cleaning_sensitivity)
                     ce = _collapse_cleaning_events(ce, rm9.diff().values, 5)
                     pce[ce] = True
                     clean_pruning_sensitivity /= 1.1  # increase pruning sensitivity
 
                 # Decompose input signal
-                soiling_dummy = (pi /
-                                 degradation_trend[-1] /
-                                 seasonal_component[-1] /
-                                 residual_shift)
+                soiling_dummy = (
+                    pi / degradation_trend[-1] / seasonal_component[-1] / residual_shift)
 
                 # Run Kalman Filter for obtaining soiling component
                 kdf, Ps = self._Kalman_filter_for_SR(
-                    zs_series=soiling_dummy,
-                    clip_soiling=clip_soiling,
-                    prescient_cleaning_events=pce,
-                    pruning_iterations=pruning_iterations,
+                    zs_series=soiling_dummy, clip_soiling=clip_soiling,
+                    prescient_cleaning_events=pce, pruning_iterations=pruning_iterations,
                     clean_pruning_sensitivity=clean_pruning_sensitivity,
-                    perfect_cleaning=perfect_cleaning,
-                    process_noise=process_noise,
+                    perfect_cleaning=perfect_cleaning, process_noise=process_noise,
                     renormalize_SR=renormalize_SR)
                 soiling_ratio.append(kdf.soiling_ratio)
                 soiling_dfs.append(kdf)
 
             # Find seasonal component
-            if order[(ic-1) % n_steps] == 'SC':
+            if order[(ic - 1) % n_steps] == "SC":
                 season_dummy = pi / soiling_ratio[-1]  # Decompose signal
                 if season_dummy.isna().sum() > 0:
-                    season_dummy.interpolate('linear', inplace=True)
+                    season_dummy.interpolate("linear", inplace=True)
                 season_dummy = season_dummy.apply(np.log)  # Log transform
                 # Run STL model
-                STL_res = STL(season_dummy, period=365, seasonal=999999,
-                              seasonal_deg=0, trend_deg=0,
-                              robust=True, low_pass_jump=30, seasonal_jump=30,
-                              trend_jump=365).fit()
+                STL_res = STL(
+                    season_dummy, period=365, seasonal=999999, seasonal_deg=0, trend_deg=0,
+                    robust=True, low_pass_jump=30, seasonal_jump=30, trend_jump=365).fit()
                 # Smooth result
-                smooth_season = lowess(STL_res.seasonal.apply(np.exp),
-                                       pi.index, is_sorted=True, delta=30,
-                                       frac=180/len(pi), return_sorted=False)
+                smooth_season = lowess(
+                    STL_res.seasonal.apply(np.exp), pi.index, is_sorted=True, delta=30,
+                    frac=180 / len(pi), return_sorted=False)
                 # Ensure periodic seaonal component
-                seasonal_comp = _force_periodicity(smooth_season,
-                                                   season_dummy.index,
-                                                   pi.index)
+                seasonal_comp = _force_periodicity(smooth_season, season_dummy.index, pi.index)
                 seasonal_component.append(seasonal_comp)
-                if degradation_method == 'STL':  # If not YoY
-                    deg_trend = pd.Series(index=pi.index,
-                                          data=STL_res.trend.apply(np.exp))
+                if degradation_method == "STL":  # If not YoY
+                    deg_trend = pd.Series(index=pi.index, data=STL_res.trend.apply(np.exp))
                     degradation_trend.append(deg_trend / deg_trend.iloc[0])
-                    yoy_save.append(RdToolsDeg.degradation_year_on_year(
-                        degradation_trend[-1], uncertainty_method=None))
+                    yoy_save.append(
+                        degradation.degradation_year_on_year(
+                            degradation_trend[-1], uncertainty_method=None
+                        )
+                    )
 
             # Find degradation component
-            if order[(ic-1) % n_steps] == 'Rd':
+            if order[(ic - 1) % n_steps] == "Rd":
                 # Decompose signal
-                trend_dummy = (pi /
-                               seasonal_component[-1] /
-                               soiling_ratio[-1])
+                trend_dummy = pi / seasonal_component[-1] / soiling_ratio[-1]
                 # Run YoY
-                yoy = RdToolsDeg.degradation_year_on_year(
-                    trend_dummy, uncertainty_method=None)
+                yoy = degradation.degradation_year_on_year(trend_dummy, uncertainty_method=None)
                 # Convert degradation rate to trend
-                degradation_trend.append(pd.Series(
-                    index=pi.index, data=(1 + day * yoy / 100 / 365.0)))
+                degradation_trend.append(
+                    pd.Series(index=pi.index, data=(1 + day * yoy / 100 / 365.0)))
                 yoy_save.append(yoy)
 
             # Combine and calculate residual flatness
-            total_model = (degradation_trend[-1] *
-                           seasonal_component[-1] *
-                           soiling_ratio[-1])
+            total_model = degradation_trend[-1] * seasonal_component[-1] * soiling_ratio[-1]
             residuals = pi / total_model
             residual_shift = residuals.mean()
             total_model *= residual_shift
             convergence_metric.append(_RMSE(pi, total_model))
 
             if verbose:
-                print('{:}\t{:}\t{:.5f}\t\t\t{:.1f} s'.format(
-                    ic, order[(ic-1) % n_steps], convergence_metric[-1],
-                    time.time()-t0))
+                print("{:}\t{:}\t{:.5f}\t\t\t{:.1f} s".format(
+                    ic, order[(ic - 1) % n_steps], convergence_metric[-1], time.time() - t0))
 
             # Convergence happens if there is no improvement in RMSE from one
             # step to the next
             if ic >= n_steps:
-                relative_improvement = ((convergence_metric[-n_steps-1] -
-                                         convergence_metric[-1]) /
-                                        convergence_metric[-n_steps-1])
-                if perfect_cleaning and (
-                        ic >= max_iterations / 2 or
-                        relative_improvement < convergence_criterion):
+                relative_improvement = (convergence_metric[-n_steps - 1] - convergence_metric[-1]
+                                        ) / convergence_metric[-n_steps - 1]
+                if perfect_cleaning and (ic >= max_iterations / 2 or
+                                         relative_improvement < convergence_criterion):
                     # From now on, do not assume perfect cleaning
                     perfect_cleaning = False
                     # Reorder to ensure SR first
-                    order = tuple([order[(i+n_steps-1-(ic-1) % n_steps) % n_steps]
-                                   for i in range(n_steps)])
+                    order = tuple(
+                        [order[(i + n_steps - 1 - (ic - 1) % n_steps) % n_steps]
+                         for i in range(n_steps)])
                     change_point = ic
                     if verbose:
-                        print('Now not assuming perfect cleaning')
-                elif (not perfect_cleaning and
-                      (ic >= max_iterations or
-                        (ic >= change_point + n_steps and
-                         relative_improvement <
-                         convergence_criterion))):
+                        print("Now not assuming perfect cleaning")
+                elif not perfect_cleaning and (
+                    ic >= max_iterations
+                    or (ic >= change_point + n_steps
+                        and relative_improvement < convergence_criterion)):
                     if verbose:
                         if relative_improvement < convergence_criterion:
-                            print('Convergence reached.')
+                            print("Convergence reached.")
                         else:
-                            print('Max iterations reached.')
+                            print("Max iterations reached.")
                     ic = max_iterations
 
         # Initialize output DataFrame
-        df_out = pd.DataFrame(index=pi.index,
-                              columns=['soiling_ratio', 'soiling_rates',
-                                       'cleaning_events', 'seasonal_component',
-                                       'degradation_trend', 'total_model',
-                                       'residuals'])
+        df_out = pd.DataFrame(
+            index=pi.index,
+            columns=[
+                "soiling_ratio", "soiling_rates", "cleaning_events", "seasonal_component",
+                "degradation_trend", "total_model", "residuals"])
 
         # Save values
         df_out.seasonal_component = seasonal_component[-1]
         df_out.degradation_trend = degradation_trend[-1]
-        degradation = yoy_save[-1]
+        degradation_value = yoy_save[-1]
         final_kdf = soiling_dfs[-1]
         df_out.soiling_ratio = final_kdf.soiling_ratio
         df_out.soiling_rates = final_kdf.soiling_rates
@@ -1499,18 +1860,17 @@ def iterative_signal_decomposition(
         soiling_loss = (1 - df_out.soiling_ratio).mean() * 100
 
         # Total model
-        df_out.total_model = (df_out.soiling_ratio *
-                              df_out.seasonal_component *
-                              df_out.degradation_trend)
+        df_out.total_model = (
+            df_out.soiling_ratio * df_out.seasonal_component * df_out.degradation_trend)
         df_out.residuals = pi / df_out.total_model
         residual_shift = df_out.residuals.mean()
         df_out.total_model *= residual_shift
         RMSE = _RMSE(pi, df_out.total_model)
-        adf_res = adfuller(df_out.residuals.dropna(), regression='ctt', autolag=None)
+        adf_res = adfuller(df_out.residuals.dropna(), regression="ctt", autolag=None)
         if verbose:
-            print('p-value for the H0 that there is a unit root in the' +
-                  'residuals (using the Augmented Dickey-fuller test):' +
-                  '{:.3e}'.format(adf_res[1]))
+            print("p-value for the H0 that there is a unit root in the"
+                  + "residuals (using the Augmented Dickey-fuller test):"
+                  + "{:.3e}".format(adf_res[1]))
 
         # Check size of soiling signal vs residuals
         SR_amp = float(np.diff(df_out.soiling_ratio.quantile([.1, .9]))[0])
@@ -1518,20 +1878,15 @@ def iterative_signal_decomposition(
         soiling_signal_strength = SR_amp / residuals_amp
         if soiling_signal_strength < soiling_significance:
             if verbose:
-                print('Soiling signal is small relative to the noise')
+                print("Soiling signal is small relative to the noise")
             small_soiling_signal = True
             df_out.SR_high = 1.0
             df_out.SR_low = 1.0 - SR_amp
 
         # Set up results dictionary
-        results_dict = dict(
-            degradation=degradation,
-            soiling_loss=soiling_loss,
-            residual_shift=residual_shift,
-            RMSE=RMSE,
-            small_soiling_signal=small_soiling_signal,
-            adf_res=adf_res
-        )
+        results_dict = dict(degradation=degradation_value, soiling_loss=soiling_loss,
+                            residual_shift=residual_shift, RMSE=RMSE,
+                            small_soiling_signal=small_soiling_signal, adf_res=adf_res)
 
         return df_out, results_dict
 
@@ -1548,7 +1903,7 @@ def run_bootstrap(self,
                       verbose=False,
                       bootstrap_seed=None,
                       **kwargs):
-        '''
+        """
         Bootstrapping of CODS algorithm for uncertainty analysis, inherently accounting
         for model and parameter choices.
 
@@ -1671,7 +2026,7 @@ def run_bootstrap(self,
         ----------
         .. [1] Skomedal, Å. and Deceglie, M. G., IEEE Journal of
            Photovoltaics, Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219
-        '''
+        """
         pi = self.pm.copy()
 
         # ###################### #
@@ -1679,14 +2034,13 @@ def run_bootstrap(self,
         # ###################### #
 
         # Generate combinations of model parameter alternatives
-        parameter_alternatives = [order_alternatives,
-                                  cleaning_sensitivity_alternatives,
-                                  clean_pruning_sensitivity_alternatives,
-                                  forward_fill_alternatives]
+        parameter_alternatives = [
+            order_alternatives, cleaning_sensitivity_alternatives,
+            clean_pruning_sensitivity_alternatives, forward_fill_alternatives]
         index_list = list(itertools.product([0, 1], repeat=len(parameter_alternatives)))
-        combination_of_parameters = [[parameter_alternatives[j][indexes[j]]
-                                     for j in range(len(parameter_alternatives))]
-                                     for indexes in index_list]
+        combination_of_parameters = [
+            [parameter_alternatives[j][indexes[j]] for j in range(len(parameter_alternatives))]
+            for indexes in index_list]
         nr_models = len(index_list)
         bootstrap_samples_list, list_of_df_out, results = [], [], []
 
@@ -1695,24 +2049,24 @@ def run_bootstrap(self,
             reps += nr_models - reps % nr_models
 
         if verbose:
-            print('Initially fitting {:} models'.format(nr_models))
+            print("Initially fitting {:} models".format(nr_models))
         t00 = time.time()
         # For each combination of model parameter alternatives, fit one model:
         for c, (order, dt, pt, ff) in enumerate(combination_of_parameters):
             try:
                 df_out, result_dict = self.iterative_signal_decomposition(
-                    max_iterations=18, order=order, clip_soiling=True,
-                    cleaning_sensitivity=dt, pruning_iterations=1,
-                    clean_pruning_sensitivity=pt, process_noise=process_noise, ffill=ff,
-                    degradation_method=degradation_method, **kwargs)
+                    max_iterations=18, order=order, clip_soiling=True, cleaning_sensitivity=dt,
+                    pruning_iterations=1, clean_pruning_sensitivity=pt,
+                    process_noise=process_noise, ffill=ff, degradation_method=degradation_method,
+                    **kwargs)
 
                 # Save results
                 list_of_df_out.append(df_out)
                 results.append(result_dict)
-                adf = result_dict['adf_res']
+                adf = result_dict["adf_res"]
                 # If we can reject the null-hypothesis that there is a unit
                 # root in the residuals:
-                if adf[1] < .05:
+                if adf[1] < 0.05:
                     # ... generate bootstrap samples based on the fit:
                     bootstrap_samples_list.append(
                         _make_time_series_bootstrap_samples(
@@ -1722,42 +2076,38 @@ def run_bootstrap(self,
 
                 # Print progress
                 if verbose:
-                    _progressBarWithETA(c+1, nr_models, time.time()-t00,
-                                        bar_length=30)
+                    _progressBarWithETA(c + 1, nr_models, time.time() - t00, bar_length=30)
             except ValueError as ex:
                 print(ex)
 
         # Revive results
-        adfs = np.array([(r['adf_res'][0] if r['adf_res'][1] < 0.05 else 0) for r in results])
-        RMSEs = np.array([r['RMSE'] for r in results])
-        SR_is_one_fraction = np.array(
-            [(df.soiling_ratio == 1).mean() for df in list_of_df_out])
-        small_soiling_signal = [r['small_soiling_signal'] for r in results]
+        adfs = np.array([(r["adf_res"][0] if r["adf_res"][1] < 0.05 else 0) for r in results])
+        RMSEs = np.array([r["RMSE"] for r in results])
+        SR_is_one_fraction = np.array([(df.soiling_ratio == 1).mean() for df in list_of_df_out])
+        small_soiling_signal = [r["small_soiling_signal"] for r in results]
 
         # Calculate weights
         weights = 1 / RMSEs / (1 + SR_is_one_fraction)
         weights /= np.sum(weights)
 
         # Save sensitivities and weights for initial model fits
-        _parameters_n_weights = pd.concat([pd.DataFrame(combination_of_parameters),
-                                           pd.Series(RMSEs),
-                                           pd.Series(SR_is_one_fraction),
-                                           pd.Series(weights),
-                                           pd.Series(small_soiling_signal)],
-                                          axis=1, ignore_index=True)
+        _parameters_n_weights = pd.concat(
+            [pd.DataFrame(combination_of_parameters), pd.Series(RMSEs),
+             pd.Series(SR_is_one_fraction), pd.Series(weights), pd.Series(small_soiling_signal)],
+            axis=1, ignore_index=True)
 
         if verbose:  # Print summary
-            _parameters_n_weights.columns = ['order', 'dt', 'pt', 'ff', 'RMSE',
-                                             'SR==1', 'weights', 'small_soiling_signal']
+            _parameters_n_weights.columns = [
+                "order", "dt", "pt", "ff", "RMSE", "SR==1", "weights", "small_soiling_signal"]
             if verbose:
-                print('\n', _parameters_n_weights)
+                print("\n", _parameters_n_weights)
 
         # Check if data is decomposable
         if np.sum(adfs == 0) > nr_models / 2:
             raise RuntimeError(
-                'Test for stationary residuals (Augmented Dickey-Fuller'
-                + ' test) not passed in half  of the instances:\nData not'
-                + ' decomposable.')
+                "Test for stationary residuals (Augmented Dickey-Fuller"
+                + " test) not passed in half  of the instances:\nData not"
+                + " decomposable.")
 
         # Save best model
         self.initial_fits = [df for df in list_of_df_out]
@@ -1767,83 +2117,76 @@ def run_bootstrap(self,
         # don't do bootstrapping
         if np.sum(small_soiling_signal) > nr_models / 2:
             self.result_df = result_df
-            self.residual_shift = results[np.argmax(weights)]['residual_shift']
-            YOY = RdToolsDeg.degradation_year_on_year(pi)
+            self.residual_shift = results[np.argmax(weights)]["residual_shift"]
+            YOY = degradation.degradation_year_on_year(pi)
             self.degradation = [YOY[0], YOY[1][0], YOY[1][1]]
             self.soiling_loss = [0, 0, (1 - result_df.soiling_ratio).mean()]
             self.small_soiling_signal = True
             self.errors = (
-                'Soiling signal is small relative to the noise. '
-                'Iterative decomposition not possible. '
-                'Degradation found by RdTools YoY.')
+                "Soiling signal is small relative to the noise. "
+                "Iterative decomposition not possible. "
+                "Degradation found by RdTools YoY."
+            )
             warnings.warn(self.errors)
             return self.result_df, self.degradation, self.soiling_loss
         self.small_soiling_signal = False
 
         # Aggregate all bootstrap samples
-        all_bootstrap_samples = pd.concat(bootstrap_samples_list, axis=1,
-                                          ignore_index=True)
+        all_bootstrap_samples = pd.concat(bootstrap_samples_list, axis=1, ignore_index=True)
 
         # Seasonal samples are generated from previously fitted seasonal
         # components, by perturbing amplitude and phase shift
         # Number of samples per fit:
         sample_nr = int(reps / nr_models)
-        list_of_SCs = [list_of_df_out[m].seasonal_component
-                       for m in range(nr_models) if weights[m] > 0]
-        seasonal_samples = _make_seasonal_samples(list_of_SCs,
-                                                  sample_nr=sample_nr,
-                                                  min_multiplier=.8,
-                                                  max_multiplier=1.75,
-                                                  max_shift=30)
+        list_of_SCs = [
+            list_of_df_out[m].seasonal_component for m in range(nr_models) if weights[m] > 0]
+        seasonal_samples = _make_seasonal_samples(
+            list_of_SCs, sample_nr=sample_nr, min_multiplier=0.8, max_multiplier=1.75,
+            max_shift=30)
 
         # ###################### #
         # ###### STAGE 2 ####### #
         # ###################### #
 
         if verbose and reps > 0:
-            print('\nBootstrapping for uncertainty analysis',
-                  '({:} realizations):'.format(reps))
-        order = ('SR', 'SC' if degradation_method == 'STL' else 'Rd')
+            print("\nBootstrapping for uncertainty analysis",
+                  "({:} realizations):".format(reps))
+        order = ("SR", "SC" if degradation_method == "STL" else "Rd")
         t0 = time.time()
-        bt_kdfs, bt_SL, bt_deg, parameters, adfs, RMSEs, SR_is_1, rss, errors = \
-            [], [], [], [], [], [], [], [], ['Bootstrapping errors']
+        bt_kdfs, bt_SL, bt_deg, parameters, adfs, RMSEs, SR_is_1, rss, errors = (
+            [], [], [], [], [], [], [], [], ["Bootstrapping errors"])
         for b in range(reps):
             try:
                 # randomly choose model sensitivities
-                dt = np.random.uniform(parameter_alternatives[1][0],
-                                       parameter_alternatives[1][-1])
-                pt = np.random.uniform(parameter_alternatives[2][0],
-                                       parameter_alternatives[2][-1])
+                dt = np.random.uniform(parameter_alternatives[1][0], parameter_alternatives[1][-1])
+                pt = np.random.uniform(parameter_alternatives[2][0], parameter_alternatives[2][-1])
                 pn = np.random.uniform(process_noise / 1.5, process_noise * 1.5)
-                renormalize_SR = np.random.choice([None,
-                                                   np.random.uniform(.5, .95)])
+                renormalize_SR = np.random.choice([None, np.random.uniform(0.5, 0.95)])
                 ffill = np.random.choice([True, False])
                 parameters.append([dt, pt, pn, renormalize_SR, ffill])
 
                 # Sample to infer soiling from
-                bootstrap_sample = \
-                    all_bootstrap_samples[b] / seasonal_samples[b]
+                bootstrap_sample = all_bootstrap_samples[b] / seasonal_samples[b]
 
                 # Set up a temprary instance of the CODSAnalysis object
                 temporary_cods_instance = CODSAnalysis(bootstrap_sample)
 
                 # Do Signal decomposition for soiling and degradation component
                 kdf, results_dict = temporary_cods_instance.iterative_signal_decomposition(
-                        max_iterations=4, order=order, clip_soiling=True,
-                        cleaning_sensitivity=dt, pruning_iterations=1,
-                        clean_pruning_sensitivity=pt, process_noise=pn,
-                        renormalize_SR=renormalize_SR, ffill=ffill,
-                        degradation_method=degradation_method, **kwargs)
+                    max_iterations=4, order=order, clip_soiling=True, cleaning_sensitivity=dt,
+                    pruning_iterations=1, clean_pruning_sensitivity=pt, process_noise=pn,
+                    renormalize_SR=renormalize_SR, ffill=ffill,
+                    degradation_method=degradation_method, **kwargs)
 
                 # If we can reject the null-hypothesis that there is a unit
                 # root in the residuals:
-                if results_dict['adf_res'][1] < .05:  # Save the results
+                if results_dict["adf_res"][1] < 0.05:  # Save the results
                     bt_kdfs.append(kdf)
-                    adfs.append(results_dict['adf_res'][0])
-                    RMSEs.append(results_dict['RMSE'])
-                    bt_deg.append(results_dict['degradation'])
-                    bt_SL.append(results_dict['soiling_loss'])
-                    rss.append(results_dict['residual_shift'])
+                    adfs.append(results_dict["adf_res"][0])
+                    RMSEs.append(results_dict["RMSE"])
+                    bt_deg.append(results_dict["degradation"])
+                    bt_SL.append(results_dict["soiling_loss"])
+                    rss.append(results_dict["residual_shift"])
                     SR_is_1.append((kdf.soiling_ratio == 1).mean())
                 else:
                     seasonal_samples.drop(columns=[b], inplace=True)
@@ -1854,20 +2197,16 @@ def run_bootstrap(self,
 
             # Print progress
             if verbose:
-                _progressBarWithETA(b+1, reps, time.time()-t0, bar_length=30)
+                _progressBarWithETA(b + 1, reps, time.time() - t0, bar_length=30)
 
         # Reweight and save weights
         weights = 1 / np.array(RMSEs) / (1 + np.array(SR_is_1))
         weights /= np.sum(weights)
         self._parameters_n_weights = pd.concat(
-            [pd.DataFrame(parameters),
-             pd.Series(RMSEs),
-             pd.Series(adfs),
-             pd.Series(SR_is_1),
-             pd.Series(weights)],
-            axis=1, ignore_index=True)
-        self._parameters_n_weights.columns = ['dt', 'pt', 'pn', 'RSR', 'ffill',
-                                              'RMSE', 'ADF', 'SR==1', 'weights']
+            [pd.DataFrame(parameters), pd.Series(RMSEs), pd.Series(adfs),
+             pd.Series(SR_is_1), pd.Series(weights)], axis=1, ignore_index=True)
+        self._parameters_n_weights.columns = [
+            "dt", "pt", "pn", "RSR", "ffill", "RMSE", "ADF", "SR==1", "weights"]
 
         # ###################### #
         # ###### STAGE 3 ####### #
@@ -1884,10 +2223,10 @@ def run_bootstrap(self,
         concat_ce = pd.concat([kdf.cleaning_events for kdf in bt_kdfs], axis=1)
 
         # Find confidence intervals for SR and soiling rates
-        df_out['SR_low'] = concat_SR.quantile(ci_low_edge, 1)
-        df_out['SR_high'] = concat_SR.quantile(ci_high_edge, 1)
-        df_out['rates_low'] = concat_r_s.quantile(ci_low_edge, 1)
-        df_out['rates_high'] = concat_r_s.quantile(ci_high_edge, 1)
+        df_out["SR_low"] = concat_SR.quantile(ci_low_edge, 1)
+        df_out["SR_high"] = concat_SR.quantile(ci_high_edge, 1)
+        df_out["rates_low"] = concat_r_s.quantile(ci_low_edge, 1)
+        df_out["rates_high"] = concat_r_s.quantile(ci_high_edge, 1)
 
         # Save best estimate and bootstrapped estimates of SR and soiling rates
         df_out.soiling_ratio = df_out.soiling_ratio.clip(lower=0, upper=1)
@@ -1899,16 +2238,15 @@ def run_bootstrap(self,
         df_out.cleaning_events = (concat_ce * weights).sum(axis=1)
 
         # Find degradation rates
-        self.degradation = [np.dot(bt_deg, weights),
-                            np.quantile(bt_deg, ci_low_edge),
-                            np.quantile(bt_deg, ci_high_edge)]
-        df_out.degradation_trend = 1 + np.arange(len(pi)) * \
-            self.degradation[0] / 100 / 365.0
+        self.degradation = [
+            np.dot(bt_deg, weights), np.quantile(bt_deg, ci_low_edge),
+            np.quantile(bt_deg, ci_high_edge)]
+        df_out.degradation_trend = 1 + np.arange(len(pi)) * self.degradation[0] / 100 / 365.0
 
         # Soiling losses
-        self.soiling_loss = [np.dot(bt_SL, weights),
-                             np.quantile(bt_SL, ci_low_edge),
-                             np.quantile(bt_SL, ci_high_edge)]
+        self.soiling_loss = [
+            np.dot(bt_SL, weights), np.quantile(bt_SL, ci_low_edge),
+            np.quantile(bt_SL, ci_high_edge)]
 
         # Save "confidence intervals" for seasonal component
         df_out.seasonal_component = (seasonal_samples * weights).sum(axis=1)
@@ -1916,36 +2254,33 @@ def run_bootstrap(self,
         df_out['seasonal_high'] = seasonal_samples.quantile(ci_high_edge, 1)
 
         # Total model with confidence intervals
-        df_out.total_model = (df_out.degradation_trend *
-                              df_out.seasonal_component *
-                              df_out.soiling_ratio)
-        df_out['model_low'] = concat_tot_mod.quantile(ci_low_edge, 1)
-        df_out['model_high'] = concat_tot_mod.quantile(ci_high_edge, 1)
+        df_out.total_model = (
+            df_out.degradation_trend * df_out.seasonal_component * df_out.soiling_ratio)
+        df_out["model_low"] = concat_tot_mod.quantile(ci_low_edge, 1)
+        df_out["model_high"] = concat_tot_mod.quantile(ci_high_edge, 1)
 
         # Residuals and residual shift
         df_out.residuals = pi / df_out.total_model
         self.residual_shift = df_out.residuals.mean()
         df_out.total_model *= self.residual_shift
         self.RMSE = _RMSE(pi, df_out.total_model)
-        self.adf_results = adfuller(df_out.residuals.dropna(),
-                                    regression='ctt', autolag=None)
+        self.adf_results = adfuller(df_out.residuals.dropna(), regression="ctt", autolag=None)
         self.result_df = df_out
         self.errors = errors
 
         if verbose:
-            print('\nFinal RMSE: {:.5f}'.format(self.RMSE))
+            print("\nFinal RMSE: {:.5f}".format(self.RMSE))
             if len(self.errors) > 1:
                 print(self.errors)
 
         return self.result_df, self.degradation, self.soiling_loss
 
-    def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05,
-                              rate_std=.005, max_soiling_rates=.0005,
-                              pruning_iterations=1, clean_pruning_sensitivity=.6,
-                              renormalize_SR=None, perfect_cleaning=False,
-                              prescient_cleaning_events=None,
-                              clip_soiling=True, ffill=True):
-        '''
+    def _Kalman_filter_for_SR(
+            self, zs_series, process_noise=1e-4, zs_std=0.05, rate_std=0.005,
+            max_soiling_rates=0.0005, pruning_iterations=1, clean_pruning_sensitivity=0.6,
+            renormalize_SR=None, perfect_cleaning=False, prescient_cleaning_events=None,
+            clip_soiling=True, ffill=True):
+        """
         A function for estimating the underlying Soiling Ratio (SR) and the
         rate of change of the SR (the soiling rate), based on a noisy time series
         of daily (corrected) normalized energy using a Kalman Filter (KF). See
@@ -2016,39 +2351,36 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05,
         References
         ----------
         .. [1] R. R. Labbe, Kalman and Bayesian Filters in Python. 2016.
-        '''
+        """
 
         # Ensure numeric index
         zs_series = zs_series.copy()  # Make copy, so as not to change input
         zs_series = zs_series.astype(float)
         original_index = zs_series.index.copy()
-        if (original_index.dtype not in [int, 'int64']):
+        if original_index.dtype not in [int, "int64"]:
             zs_series.index = range(len(zs_series))
 
         # Check prescient_cleaning_events. If not present, find cleaning events
         if isinstance(prescient_cleaning_events, list):
             cleaning_events = prescient_cleaning_events
         else:
-            if (isinstance(prescient_cleaning_events, type(zs_series)) and
-                    (prescient_cleaning_events.sum() > 4)):
+            if isinstance(prescient_cleaning_events, type(zs_series)) and (
+                    prescient_cleaning_events.sum() > 4):
                 if len(prescient_cleaning_events) == len(zs_series):
                     prescient_cleaning_events = prescient_cleaning_events.copy()
                     prescient_cleaning_events.index = zs_series.index
                 else:
                     raise ValueError(
-                        "The indices of prescient_cleaning_events must correspond to the" +
-                        " indices of zs_series; they must be of the same length")
+                        "The indices of prescient_cleaning_events must correspond to the"
+                        + " indices of zs_series; they must be of the same length")
             else:  # If no prescient cleaning events, detect cleaning events
-                ce, rm9 = _rolling_median_ce_detection(
-                    zs_series.index, zs_series, tuner=0.5)
-                prescient_cleaning_events = \
-                    _collapse_cleaning_events(ce, rm9.diff().values, 5)
+                ce, rm9 = _rolling_median_ce_detection(zs_series.index, zs_series, tuner=0.5)
+                prescient_cleaning_events = _collapse_cleaning_events(ce, rm9.diff().values, 5)
 
             cleaning_events = prescient_cleaning_events[prescient_cleaning_events].index.tolist()
 
         # Find soiling events (e.g. dust storms)
-        soiling_events = _soiling_event_detection(
-            zs_series.index, zs_series, ffill=ffill, tuner=5)
+        soiling_events = _soiling_event_detection(zs_series.index, zs_series, ffill=ffill, tuner=5)
         soiling_events = soiling_events[soiling_events].index.tolist()
 
         # Initialize various parameters
@@ -2069,17 +2401,17 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05,
         dt = 1  # All time stemps are one day
 
         # Initialize Kalman filter
-        f = self._initialize_univariate_model(zs_series, dt, process_noise,
-                                              measurement_noise, rate_std,
-                                              zs_std, initial_slope)
+        f = self._initialize_univariate_model(
+            zs_series, dt, process_noise, measurement_noise, rate_std, zs_std, initial_slope)
 
         # Initialize miscallenous variables
-        dfk = pd.DataFrame(index=zs_series.index, dtype=float,
-                           columns=['raw_pi', 'raw_rates', 'smooth_pi',
-                                    'smooth_rates', 'soiling_ratio',
-                                    'soiling_rates', 'cleaning_events',
-                                    'days_since_ce'])
-        dfk['cleaning_events'] = False
+        dfk = pd.DataFrame(
+            index=zs_series.index,
+            dtype=float,
+            columns=[
+                "raw_pi", "raw_rates", "smooth_pi", "smooth_rates", "soiling_ratio",
+                "soiling_rates", "cleaning_events", "days_since_ce"])
+        dfk["cleaning_events"] = False
 
         # Kalman Filter part:
         #######################################################################
@@ -2089,8 +2421,7 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05,
 
         # Save results and smooth with rts smoother
         dfk, Xs, Ps = self._smooth_results(
-            dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events,
-            perfect_cleaning)
+            dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning)
         #######################################################################
 
         # Some steps to clean up the soiling data:
@@ -2103,34 +2434,28 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05,
                 rm_smooth_pi = dfk.smooth_pi.rolling(7).median().shift(-6)
                 pi_after_cleaning = rm_smooth_pi.loc[cleaning_events]
                 # Detect outiers/false positives
-                false_positives = _find_numeric_outliers(pi_after_cleaning,
-                                                         clean_pruning_sensitivity, 'lower')
-                cleaning_events = \
-                    false_positives[~false_positives].index.tolist()
+                false_positives = _find_numeric_outliers(
+                    pi_after_cleaning, clean_pruning_sensitivity, "lower")
+                cleaning_events = false_positives[~false_positives].index.tolist()
 
             # 2: Remove longer periods with positive (soiling) rates
             if (dfk.smooth_rates > max_soiling_rates).sum() > 1:
                 exceeding_rates = dfk.smooth_rates > max_soiling_rates
                 new_cleaning_events = _collapse_cleaning_events(
                     exceeding_rates, dfk.smooth_rates, 4)
-                cleaning_events.extend(
-                    new_cleaning_events[new_cleaning_events].index)
+                cleaning_events.extend(new_cleaning_events[new_cleaning_events].index)
                 cleaning_events.sort()
 
             # 3: If the list of cleaning events has changed, run the Kalman
             #    Filter and smoother again
             if not ce_0 == cleaning_events:
-                f = self._initialize_univariate_model(zs_series, dt,
-                                                      process_noise,
-                                                      measurement_noise,
-                                                      rate_std, zs_std,
-                                                      initial_slope)
+                f = self._initialize_univariate_model(
+                    zs_series, dt, process_noise, measurement_noise, rate_std, zs_std,
+                    initial_slope)
                 Xs, Ps, rate_std, zs_std = self._forward_pass(
-                    f, zs_series, rolling_median_7, cleaning_events,
-                    soiling_events)
+                    f, zs_series, rolling_median_7, cleaning_events, soiling_events)
                 dfk, Xs, Ps = self._smooth_results(
-                    dfk, f, Xs, Ps, zs_series, cleaning_events,
-                    soiling_events, perfect_cleaning)
+                    dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning)
 
             else:
                 counter = 100  # Make sure the while loop stops
@@ -2139,14 +2464,13 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05,
             if perfect_cleaning:  # SR = 1 after cleaning events
                 if len(cleaning_events) > 0:
                     pi_dummy = pd.Series(index=dfk.index, data=np.nan)
-                    pi_dummy.loc[cleaning_events] = \
-                        dfk.smooth_pi.loc[cleaning_events]
+                    pi_dummy.loc[cleaning_events] = dfk.smooth_pi.loc[cleaning_events]
                     dfk.soiling_ratio = 1 / pi_dummy.ffill() * dfk.smooth_pi
                     # Set the SR in the first soiling period based on the mean
                     # ratio of the Kalman estimate (smooth_pi) and the SR
-                    dfk.loc[:cleaning_events[0], 'soiling_ratio'] = \
-                        dfk.loc[:cleaning_events[0], 'smooth_pi'] \
-                        * (dfk.soiling_ratio / dfk.smooth_pi).mean()
+                    dfk.loc[: cleaning_events[0], "soiling_ratio"] = (
+                        dfk.loc[: cleaning_events[0], "smooth_pi"]
+                        * (dfk.soiling_ratio / dfk.smooth_pi).mean())
                 else:  # If no cleaning events
                     dfk.soiling_ratio = 1
             else:  # Otherwise, if the inut signal has been decomposed, and
@@ -2154,40 +2478,37 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05,
                 dfk.soiling_ratio = dfk.smooth_pi
             # 5: Renormalize Soiling Ratio
             if renormalize_SR is not None:
-                dfk.soiling_ratio /= dfk.loc[cleaning_events, 'soiling_ratio'
-                                             ].quantile(renormalize_SR)
+                dfk.soiling_ratio /= dfk.loc[cleaning_events, "soiling_ratio"].quantile(
+                    renormalize_SR)
 
             # 6: Force soiling ratio to not exceed 1:
             if clip_soiling:
                 dfk['soiling_ratio'] = dfk['soiling_ratio'].clip(upper=1)
                 dfk.soiling_rates = dfk.smooth_rates
-                dfk.loc[dfk.soiling_ratio.diff() == 0, 'soiling_rates'] = 0
+                dfk.loc[dfk.soiling_ratio.diff() == 0, "soiling_rates"] = 0
 
         # Set number of days since cleaning event
         nr_days_dummy = pd.Series(index=dfk.index, data=np.nan)
-        nr_days_dummy.loc[cleaning_events] = [int(date-dfk.index[0])
-                                              for date in cleaning_events]
+        nr_days_dummy.loc[cleaning_events] = [int(date - dfk.index[0]) for date in cleaning_events]
         nr_days_dummy.iloc[0] = 0
         dfk.days_since_ce = range(len(zs_series)) - nr_days_dummy.ffill()
 
         # Save cleaning events and soiling events
-        dfk.loc[cleaning_events, 'cleaning_events'] = True
+        dfk.loc[cleaning_events, "cleaning_events"] = True
         dfk.index = original_index  # Set index back to orignial index
 
         return dfk, Ps
 
-    def _forward_pass(self, f, zs_series, rolling_median_7, cleaning_events,
-                      soiling_events):
-        ''' Run the forward pass of the Kalman Filter algortihm '''
+    def _forward_pass(self, f, zs_series, rolling_median_7, cleaning_events, soiling_events):
+        """Run the forward pass of the Kalman Filter algortihm"""
         zs = zs_series.values
         N = len(zs)
         Xs, Ps = np.zeros((N, 2)), np.zeros((N, 2, 2))
         # Enter forward pass of filtering algorithm
         for i, z in enumerate(zs):
-            if 7 < i < N-7 and (i in cleaning_events or i in soiling_events):
-                rolling_median_local = rolling_median_7.loc[i-5:i+5].values
-                u = self._set_control_input(f, rolling_median_local, i,
-                                            cleaning_events)
+            if 7 < i < N - 7 and (i in cleaning_events or i in soiling_events):
+                rolling_median_local = rolling_median_7.loc[i - 5 : i + 5].values
+                u = self._set_control_input(f, rolling_median_local, i, cleaning_events)
                 f.predict(u=u)  # Predict wth control input u
             else:  # If no cleaning detection, predict without control input
                 f.predict()
@@ -2199,49 +2520,47 @@ def _forward_pass(self, f, zs_series, rolling_median_7, cleaning_events,
             rate_std, zs_std = Ps[-1, 1, 1], Ps[-1, 0, 0]
         return Xs, Ps, rate_std, zs_std  # Convert to numpy and return
 
-    def _set_control_input(self, f, rolling_median_local, index,
-                           cleaning_events):
-        '''
+    def _set_control_input(self, f, rolling_median_local, index, cleaning_events):
+        """
         For each cleaning event, sets control input u based on current
         Kalman Filter state estimate (f.x), and the median value for the
         following week. If the cleaning event seems to be misplaced, moves
         the cleaning event to a more sensible location. If the cleaning
         event seems to be correct, removes other cleaning events in the 10
         days surrounding this day
-        '''
+        """
         u = np.zeros(f.x.shape)  # u is the control input
         window_size = 11  # len of rolling_median_local
         HW = 5  # Half window
         moving_diff = np.diff(rolling_median_local)
         # Index of maximum change in rolling median
         max_diff_index = moving_diff.argmax()
-        if max_diff_index == HW-1 or index not in cleaning_events:
+        if max_diff_index == HW - 1 or index not in cleaning_events:
             # The median zs of the week after the cleaning event
-            z_med = rolling_median_local[HW+3]
+            z_med = rolling_median_local[HW + 3]
             # Set control input this future median
             u[0] = z_med - np.dot(f.H, np.dot(f.F, f.x)).item()
             # If the change is bigger than the measurement noise:
-            if np.abs(u[0]) > np.sqrt(f.R)/2:
-                index_dummy = [n+3 for n in range(window_size-HW-1)
-                               if n+3 != HW]
-                cleaning_events = [ce for ce in cleaning_events
-                                   if ce-index+HW not in index_dummy]
+            if np.abs(u[0]) > np.sqrt(f.R) / 2:
+                index_dummy = [n + 3 for n in range(window_size - HW - 1) if n + 3 != HW]
+                cleaning_events = [
+                    ce for ce in cleaning_events if ce - index + HW not in index_dummy]
             else:  # If the cleaning event is insignificant
                 u[0] = 0
                 if index in cleaning_events:
                     cleaning_events.remove(index)
         else:  # If the index with the maximum difference is not today...
             cleaning_events.remove(index)  # ...remove today from the list
-            if moving_diff[max_diff_index] > 0 \
-                    and index+max_diff_index-HW+1 not in cleaning_events:
+            if (moving_diff[max_diff_index] > 0
+                    and index + max_diff_index - HW + 1 not in cleaning_events):
                 # ...and add the missing day
-                bisect.insort(cleaning_events, index+max_diff_index-HW+1)
+                bisect.insort(cleaning_events, index + max_diff_index - HW + 1)
         return u
 
-    def _smooth_results(self, dfk, f, Xs, Ps, zs_series, cleaning_events,
-                        soiling_events, perfect_cleaning):
-        ''' Smoother for Kalman Filter estimates. Smooths the Kalaman estimate
-            between given cleaning events and saves all in DataFrame dfk'''
+    def _smooth_results(
+            self, dfk, f, Xs, Ps, zs_series, cleaning_events, soiling_events, perfect_cleaning):
+        """Smoother for Kalman Filter estimates. Smooths the Kalaman estimate
+        between given cleaning events and saves all in DataFrame dfk"""
         # Save unsmoothed estimates
         dfk.raw_pi = Xs[:, 0]
         dfk.raw_rates = Xs[:, 1]
@@ -2256,8 +2575,7 @@ def _smooth_results(self, dfk, f, Xs, Ps, zs_series, cleaning_events,
         # Smooth between cleaning events
         for start, end in zip(ce_dummy[:-1], ce_dummy[1:]):
             num_ind = df_num_ind.loc[start:end].iloc[:-1]
-            Xs[num_ind], Ps[num_ind], _, _ = f.rts_smoother(Xs[num_ind],
-                                                            Ps[num_ind])
+            Xs[num_ind], Ps[num_ind], _, _ = f.rts_smoother(Xs[num_ind], Ps[num_ind])
 
         # Save smoothed estimates
         dfk.smooth_pi = Xs[:, 0]
@@ -2265,17 +2583,15 @@ def _smooth_results(self, dfk, f, Xs, Ps, zs_series, cleaning_events,
 
         return dfk, Xs, Ps
 
-    def _initialize_univariate_model(self, zs_series, dt, process_noise,
-                                     measurement_noise, rate_std, zs_std,
-                                     initial_slope):
-        ''' Initializes the univariate Kalman Filter model, using the filterpy
-            package '''
+    def _initialize_univariate_model(
+            self, zs_series, dt, process_noise, measurement_noise,
+            rate_std, zs_std, initial_slope):
+        """Initializes the univariate Kalman Filter model, using the filterpy
+        package"""
         f = KalmanFilter(dim_x=2, dim_z=1)
-        f.F = np.array([[1., dt],
-                        [0., 1.]])
-        f.H = np.array([[1., 0.]])
-        f.P = np.array([[zs_std**2, 0],
-                        [0, rate_std**2]])
+        f.F = np.array([[1.0, dt], [0.0, 1.0]])
+        f.H = np.array([[1.0, 0.0]])
+        f.P = np.array([[zs_std**2, 0], [0, rate_std**2]])
         f.Q = Q_discrete_white_noise(dim=2, dt=dt, var=process_noise**2)
         f.x = np.array([initial_slope[1], initial_slope[0]])
         f.B = np.zeros(f.F.shape)
@@ -2284,19 +2600,14 @@ def _initialize_univariate_model(self, zs_series, dt, process_noise,
         return f
 
 
-def soiling_cods(energy_normalized_daily,
-                 reps=512,
-                 confidence_level=68.2,
-                 degradation_method='YoY',
-                 process_noise=1e-4,
-                 order_alternatives=(('SR', 'SC', 'Rd'),
-                                     ('SC', 'SR', 'Rd')),
-                 cleaning_sensitivity_alternatives=(.25, .75),
-                 clean_pruning_sensitivity_alternatives=(1/1.5, 1.5),
-                 forward_fill_alternatives=(True, False),
-                 verbose=False,
-                 **kwargs):
-    '''
+def soiling_cods(
+    energy_normalized_daily, reps=512, confidence_level=68.2, degradation_method="YoY",
+    process_noise=1e-4, order_alternatives=(
+        ("SR", "SC", "Rd"), ("SC", "SR", "Rd")),
+        cleaning_sensitivity_alternatives=(0.25, 0.75),
+        clean_pruning_sensitivity_alternatives=(1 / 1.5, 1.5),
+        forward_fill_alternatives=(True, False), verbose=False, **kwargs):
+    """
     Functional wrapper for :py:class:`~rdtools.soiling.CODSAnalysis` and its
     subroutine :py:func:`~rdtools.soiling.CODSAnalysis.run_bootstrap`. Runs
     the combined degradation and soiling (CODS) algorithm with bootstrapping.
@@ -2409,31 +2720,26 @@ def soiling_cods(energy_normalized_daily,
     ----------
     .. [1] Skomedal, Å. and Deceglie, M. G., IEEE Journal of Photovoltaics,
        Sept. 2020. https://doi.org/10.1109/JPHOTOV.2020.3018219
-    '''
+    """
 
     CODS = CODSAnalysis(energy_normalized_daily)
 
     CODS.run_bootstrap(
-        reps=reps,
-        confidence_level=confidence_level,
-        verbose=verbose,
-        degradation_method=degradation_method,
-        process_noise=process_noise,
+        reps=reps, confidence_level=confidence_level, verbose=verbose,
+        degradation_method=degradation_method, process_noise=process_noise,
         order_alternatives=order_alternatives,
         cleaning_sensitivity_alternatives=cleaning_sensitivity_alternatives,
         clean_pruning_sensitivity_alternatives=clean_pruning_sensitivity_alternatives,
-        forward_fill_alternatives=forward_fill_alternatives,
-        **kwargs)
+        forward_fill_alternatives=forward_fill_alternatives, **kwargs)
 
     sr = 1 - CODS.soiling_loss[0] / 100
     sr_ci = 1 - np.array(CODS.soiling_loss[1:3]) / 100
 
-    return sr, sr_ci, CODS.degradation[0], np.array(CODS.degradation[1:3]), \
-        CODS.result_df
+    return (sr, sr_ci, CODS.degradation[0], np.array(CODS.degradation[1:3]), CODS.result_df)
 
 
 def _collapse_cleaning_events(inferred_ce_in, metric, f=4):
-    ''' A function for replacing quick successive cleaning events with one
+    """A function for replacing quick successive cleaning events with one
         (most probable) cleaning event.
 
     Parameters
@@ -2450,10 +2756,9 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4):
     -------
     inferred_ce : pandas.Series
         boolean values for cleaning events
-    '''
+    """
     # Ensure numeric index
-    if isinstance(inferred_ce_in.index,
-                  pd.core.indexes.datetimes.DatetimeIndex):
+    if isinstance(inferred_ce_in.index, pd.core.indexes.datetimes.DatetimeIndex):
         saveindex = inferred_ce_in.copy().index
         inferred_ce_in.index = range(len(saveindex))
     else:
@@ -2473,11 +2778,10 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4):
         end_true_vals = collapsed_ce_dummy.loc[start_true_vals:].idxmin() - 1
         if end_true_vals >= start_true_vals:  # If the island ends
             # Find the day with mac probability of being a cleaning event
-            max_diff_day = \
-                metric.loc[start_true_vals-f:end_true_vals+f].idxmax()
+            max_diff_day = metric.loc[start_true_vals - f : end_true_vals + f].idxmax()
             # Set all days in this period as false
-            collapsed_ce.loc[start_true_vals-f:end_true_vals+f] = False
-            collapsed_ce_dummy.loc[start_true_vals-f:end_true_vals+f] = False
+            collapsed_ce.loc[start_true_vals - f : end_true_vals + f] = False
+            collapsed_ce_dummy.loc[start_true_vals - f : end_true_vals + f] = False
             # Set the max probability day as True (cleaning event)
             collapsed_ce.loc[max_diff_day] = True
             # Find the next island of true values
@@ -2491,51 +2795,52 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4):
 
 
 def _rolling_median_ce_detection(x, y, ffill=True, rolling_window=9, tuner=1.5):
-    ''' Finds cleaning events in a time series of performance index (y) '''
+    """Finds cleaning events in a time series of performance index (y)"""
     y = pd.Series(index=x, data=y)
     y = y.astype(float)
     if ffill:  # forward fill NaNs in y before running mean
         rm = y.ffill().rolling(rolling_window, center=True).median()
     else:  # ... or backfill instead
         rm = y.bfill().rolling(rolling_window, center=True).median()
-    Q3 = rm.diff().abs().quantile(.75)
-    Q1 = rm.diff().abs().quantile(.25)
+    Q3 = rm.diff().abs().quantile(0.75)
+    Q1 = rm.diff().abs().quantile(0.25)
     limit = Q3 + tuner * (Q3 - Q1)
     cleaning_events = rm.diff() > limit
     return cleaning_events, rm
 
 
 def _soiling_event_detection(x, y, ffill=True, tuner=5):
-    ''' Finds cleaning events in a time series of performance index (y) '''
+    """Finds cleaning events in a time series of performance index (y)"""
     y = pd.Series(index=x, data=y)
     y = y.astype(float)
     if ffill:  # forward fill NaNs in y before running mean
         rm = y.ffill().rolling(9, center=True).median()
     else:  # ... or backfill instead
         rm = y.bfill().rolling(9, center=True).median()
-    Q3 = rm.diff().abs().quantile(.99)
-    Q1 = rm.diff().abs().quantile(.01)
+    Q3 = rm.diff().abs().quantile(0.99)
+    Q1 = rm.diff().abs().quantile(0.01)
     limit = Q1 - tuner * (Q3 - Q1)
     soiling_events = rm.diff() < limit
     return soiling_events
 
 
-def _make_seasonal_samples(list_of_SCs, sample_nr=10, min_multiplier=0.5,
-                           max_multiplier=2, max_shift=20):
-    ''' Generate seasonal samples by perturbing the amplitude and the phase of
-        a seasonal components found with the fitted CODS model '''
-    samples = pd.DataFrame(index=list_of_SCs[0].index,
-                           columns=range(int(sample_nr*len(list_of_SCs))),
-                           dtype=float)
+def _make_seasonal_samples(
+        list_of_SCs, sample_nr=10, min_multiplier=0.5, max_multiplier=2, max_shift=20):
+    """Generate seasonal samples by perturbing the amplitude and the phase of
+    a seasonal components found with the fitted CODS model"""
+    samples = pd.DataFrame(
+        index=list_of_SCs[0].index,
+        columns=range(int(sample_nr * len(list_of_SCs))),
+        dtype=float)
     # From each fitted signal, we will generate new seaonal components
     for i, signal in enumerate(list_of_SCs):
         # Remove beginning and end of signal
         signal_mean = signal.mean()
         # Make a signal matrix where each column is a year and each row a date
-        year_matrix = signal.rename('values').to_frame().assign(
-                doy=signal.index.dayofyear,
-                year=signal.index.year
-            ).pivot(index='doy', columns='year', values='values')
+        year_matrix = (
+            signal.rename("values").to_frame()
+            .assign(doy=signal.index.dayofyear, year=signal.index.year)
+            .pivot(index="doy", columns="year", values="values"))
         # We will use the median signal through all the years...
         median_signal = year_matrix.median(axis=1)
         for j in range(sample_nr):
@@ -2546,25 +2851,21 @@ def _make_seasonal_samples(list_of_SCs, sample_nr=10, min_multiplier=0.5,
             # constructing the new signal based on median_signal
             shifted_signal = pd.Series(
                 index=signal.index,
-                data=median_signal.reindex(
-                    (signal.index.dayofyear-shift) % 365 + 1).values)
+                data=median_signal.reindex((signal.index.dayofyear - shift) % 365 + 1).values)
             # Perturb amplitude by recentering to 0 multiplying by multiplier
-            samples.loc[:, i*sample_nr + j] = \
-                multiplier * (shifted_signal - signal_mean) + 1
+            samples.loc[:, i * sample_nr + j] = multiplier * (shifted_signal - signal_mean) + 1
     return samples
 
 
 def _force_periodicity(in_signal, signal_index, out_index):
-    ''' Function for forcing periodicity in a seasonal component signal '''
+    """Function for forcing periodicity in a seasonal component signal"""
     # Make sure the in_signal is a Series
     if isinstance(in_signal, np.ndarray):
-        signal = pd.Series(index=pd.DatetimeIndex(signal_index.date),
-                           data=in_signal)
+        signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), data=in_signal)
     elif isinstance(in_signal, pd.Series):
-        signal = pd.Series(index=pd.DatetimeIndex(signal_index.date),
-                           data=in_signal.values)
+        signal = pd.Series(index=pd.DatetimeIndex(signal_index.date), data=in_signal.values)
     else:
-        raise ValueError('in_signal must be numpy array or pandas Series')
+        raise ValueError("in_signal must be numpy array or pandas Series")
 
     # Make sure that we don't remove too much of the data:
     remove_length = np.min([180, int((len(signal) - 365) / 2)])
@@ -2576,66 +2877,196 @@ def _force_periodicity(in_signal, signal_index, out_index):
     # Make a signal matrix where each column is a year and each row is a date
     year_matrix = pd.DataFrame(index=np.arange(0, 365), columns=unique_years)
     for year in unique_years:
-        dates_in_year = pd.date_range(str(year)+'-01-01', str(year)+'-12-31')
+        dates_in_year = pd.date_range(str(year) + "-01-01", str(year) + "-12-31")
         # We cut off the extra day(s) of leap years
-        year_matrix[year] = \
-            signal.loc[str(year)].reindex(dates_in_year).values[:365]
+        year_matrix[year] = signal.loc[str(year)].reindex(dates_in_year).values[:365]
     # We will use the median signal through all the years...
     median_signal = year_matrix.median(axis=1)
     # The output is the median signal broadcasted to the whole time series
-    output = pd.Series(
-        index=out_index,
-        data=median_signal.reindex(out_index.dayofyear - 1).values)
+    output = pd.Series(index=out_index, data=median_signal.reindex(out_index.dayofyear - 1).values)
     return output
 
 
-def _find_numeric_outliers(x, multiplier=1.5, where='both', verbose=False):
-    ''' Function for finding numeric outliers '''
+def _find_numeric_outliers(x, multiplier=1.5, where="both", verbose=False):
+    """Function for finding numeric outliers"""
     try:  # Calulate third and first quartile
-        Q3 = np.quantile(x, .75)
-        Q1 = np.quantile(x, .25)
+        Q3 = np.quantile(x, 0.75)
+        Q1 = np.quantile(x, 0.25)
     except IndexError as ie:
         print(ie, x)
     except RuntimeWarning as rw:
         print(rw, x)
     IQR = Q3 - Q1  # Interquartile range
-    if where == 'upper':  # If detecting upper outliers
+    if where == "upper":  # If detecting upper outliers
         if verbose:
-            print('Upper limit', Q3 + multiplier * IQR)
-        return (x > Q3 + multiplier * IQR)
-    elif where == 'lower':  # If detecting lower outliers
+            print("Upper limit", Q3 + multiplier * IQR)
+        return x > Q3 + multiplier * IQR
+    elif where == "lower":  # If detecting lower outliers
         if verbose:
-            print('Lower limit', Q1 - multiplier * IQR)
-        return (x < Q1 - multiplier * IQR)
-    elif where == 'both':  # If detecting both lower and upper outliers
+            print("Lower limit", Q1 - multiplier * IQR)
+        return x < Q1 - multiplier * IQR
+    elif where == "both":  # If detecting both lower and upper outliers
         if verbose:
-            print('Upper, lower limit',
-                  Q3 + multiplier * IQR,
-                  Q1 - multiplier * IQR)
+            print("Upper, lower limit", Q3 + multiplier * IQR, Q1 - multiplier * IQR)
         return (x > Q3 + multiplier * IQR), (x < Q1 - multiplier * IQR)
 
 
 def _RMSE(y_true, y_pred):
-    '''Calculates the Root Mean Squared Error for y_true and y_pred, where
-        y_pred is the "prediction", and y_true is the truth.'''
-    mask = ~np.isnan(y_pred)
-    return np.sqrt(np.mean((y_pred[mask]-y_true[mask])**2))
+    """Calculates the Root Mean Squared Error for y_true and y_pred, where
+    y_pred is the "prediction", and y_true is the truth."""
+    mask = ~pd.isnull(y_pred)
+    return np.sqrt(np.mean((y_pred[mask] - y_true[mask]) ** 2))
 
 
 def _MSD(y_true, y_pred):
-    '''Calculates the Mean Signed Deviation for y_true and y_pred, where y_pred
-        is the "prediction", and y_true is the truth.'''
+    """Calculates the Mean Signed Deviation for y_true and y_pred, where y_pred
+    is the "prediction", and y_true is the truth."""
     return np.mean(y_pred - y_true)
 
 
 def _progressBarWithETA(value, endvalue, time, bar_length=20):
-    ''' Prints a progressbar with an estimated time of "arrival" '''
+    """Prints a progressbar with an estimated time of "arrival" """
     percent = float(value) / endvalue * 100
-    arrow = '-' * int(round(percent/100 * bar_length)-1) + '>'
-    spaces = ' ' * (bar_length - len(arrow))
+    arrow = "-" * int(round(percent / 100 * bar_length) - 1) + ">"
+    spaces = " " * (bar_length - len(arrow))
     used = time / 60  # Time Used
-    left = used / percent*(100-percent)  # Estimated time left
+    left = used / percent * (100 - percent)  # Estimated time left
     sys.stdout.write(
-        "\r# {:} | Used: {:.1f} min | Left: {:.1f}".format(value, used, left) +
-        " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent))
+        "\r# {:} | Used: {:.1f} min | Left: {:.1f}".format(value, used, left)
+        + " min | Progress: [{:}] {:.0f} %".format(arrow + spaces, percent))
     sys.stdout.flush()
+
+
+def _piecewise_linear(x, x0, b, k1, k2):
+    """
+    Piecewise linear function with a single breakpoint.
+
+    Parameters
+    ----------
+    x : array-like
+        Independent variable
+    x0 : float
+        Breakpoint location where the slope changes
+    b : float
+        y-intercept of the first segment
+    k1 : float
+        Slope of the first segment (x < x0)
+    k2 : float
+        Change in slope for the second segment (x >= x0).
+        The slope of the second segment is k1 + k2.
+
+    Returns
+    -------
+    array-like
+        Piecewise linear values:
+        - For x < x0: k1 * x + b
+        - For x >= x0: k1 * x + b + k2 * (x - x0)
+    """
+    cond_list = [x < x0, x >= x0]
+    func_list = [lambda x: k1 * x + b, lambda x: k1 * x + b + k2 * (x - x0)]
+    return np.piecewise(x, cond_list, func_list)
+
+
+def segmented_soiling_period(
+    pr,
+    days_clean_vs_cp=13,
+    initial_guesses=None,
+    bounds=None,
+    min_r2=0.15,
+):
+    """
+    Applies segmented regression to a single deposition period
+    (data points in between two cleaning events).
+    Segmentation is neglected if change point occurs within a number of days
+    (days_clean_vs_cp) of the cleanings.
+
+    Parameters
+    ----------
+    pr :
+        Series of daily performance ratios measured during the given deposition period.
+    days_clean_vs_cp : numeric (default=13)
+        Minimum number of days accepted between cleanings and change points.
+    bounds : list (default=None)
+        List of bounds for fitting function. If not specified, they are
+        defined in the function.
+    initial_guesses : list (default=None)
+        List of initial guesses for fitting function. If None, uses [13, 1, 0, 0].
+    min_r2 : numeric (default=0.15)
+        Minimum R2 to consider valid the extracted soiling profile.
+
+    Returns
+    -------
+    sr: numeric
+        Series containing the daily soiling ratio values after segmentation.
+        List of nan if segmentation was not possible.
+    cp_index: int
+        Integer index at which continuous change point occurred.
+        None if segmentation was not possible.
+    """
+    # Check if PR dataframe has datetime index
+    if not isinstance(pr.index, pd.DatetimeIndex):
+        raise ValueError("The time series does not have DatetimeIndex")
+
+    # Initialize default for initial_guesses to avoid mutable default argument
+    if initial_guesses is None:
+        initial_guesses = [13, 1, 0, 0]
+
+    # Define bounds if not provided
+    if bounds is None:
+        # bounds are neg in first 4 and pos in second 4
+        # ordered as x0,b,k1,k2 where x0 is the breakpoint k1 and k2 are slopes
+        bounds = [(13, -5, -np.inf, -np.inf), ((len(pr) - 13), 5, +np.inf, +np.inf)]
+    y = pr.values
+    x = np.arange(0.0, len(y))
+
+    # Check for constant or near-constant y values (cannot fit piecewise regression)
+    if np.nanstd(y) < 1e-10 or np.all(np.isnan(y)):
+        z = [np.nan] * len(x)
+        cp_index = None
+        sr = pd.Series(z, index=pr.index)
+        return sr, cp_index
+
+    try:
+        # Fit soiling profile with segmentation
+        p, e = curve_fit(_piecewise_linear, x, y, p0=initial_guesses, bounds=bounds)
+
+        # Ignore change point if too close to a cleaning
+        # Change point p[0] converted to integer to use as an index.
+        # None if no change point is found.
+        if p[0] > days_clean_vs_cp and p[0] < len(y) - days_clean_vs_cp:
+            z = _piecewise_linear(x, *p)
+            cp_index = int(p[0])
+
+            # Calculate R² improvement to decide if piecewise fit is worthwhile
+            R2_original = st.linregress(y, x)[2] ** 2
+            R2_piecewise = st.linregress(y, z)[2] ** 2
+
+            R2_improve = R2_piecewise - R2_original
+            R2_percent_improve = (R2_piecewise / R2_original) - 1 if R2_original > 0 else 0
+            if R2_original < 1:
+                R2_percent_of_possible_improve = R2_improve / (1 - R2_original)
+            else:
+                R2_percent_of_possible_improve = 0
+
+            # Apply stricter requirements for shorter soiling periods
+            if len(y) < 45:
+                if (R2_piecewise < min_r2) or (
+                        (R2_percent_of_possible_improve < 0.5) and (R2_percent_improve < 0.5)):
+                    z = [np.nan] * len(x)
+                    cp_index = None
+            else:
+                if (R2_percent_improve < 0.01) or (R2_piecewise < 0.4):
+                    z = [np.nan] * len(x)
+                    cp_index = None
+        else:
+            z = [np.nan] * len(x)
+            cp_index = None
+    except (ValueError, RuntimeError):
+        # curve_fit can fail for various reasons (constant values, convergence issues)
+        # This is expected for some intervals; silently return no change point
+        z = [np.nan] * len(x)
+        cp_index = None
+    # Create Series from modelled profile
+    sr = pd.Series(z, index=pr.index)
+
+    return sr, cp_index
diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py
index b940f2df..4085b997 100644
--- a/rdtools/test/conftest.py
+++ b/rdtools/test/conftest.py
@@ -54,6 +54,41 @@ def soiling_normalized_daily(soiling_times):
     return normalized_daily
 
 
+@pytest.fixture()
+def soiling_normalized_daily_with_neg_shifts(soiling_times):
+    interval_1_v1 = 1 - 0.005 * np.arange(0, 15, 1)
+    interval_1_v2 = (0.9 - 0.005 * 15) - 0.005 * np.arange(0, 10, 1)
+    interval_2 = 1 - 0.002 * np.arange(0, 25, 1)
+    interval_3_v1 = 1 - 0.001 * np.arange(0, 10, 1)
+    interval_3_v2 = (0.95 - 0.001 * 10) - 0.001 * np.arange(0, 15, 1)
+    profile = np.concatenate(
+        (interval_1_v1, interval_1_v2, interval_2, interval_3_v1, interval_3_v2)
+    )
+    np.random.seed(1977)
+    noise = 0.01 * np.random.rand(75)
+    normalized_daily = pd.Series(data=profile, index=soiling_times)
+    normalized_daily = normalized_daily + noise
+
+    return normalized_daily
+
+
+@pytest.fixture()
+def soiling_normalized_daily_with_piecewise_slope(soiling_times):
+    interval_1_v1 = 1 - 0.002 * np.arange(0, 20, 1)
+    interval_1_v2 = (1 - 0.002 * 20) - 0.007 * np.arange(0, 20, 1)
+    interval_2_v1 = 1 - 0.01 * np.arange(0, 20, 1)
+    interval_2_v2 = (1 - 0.01 * 20) - 0.001 * np.arange(0, 15, 1)
+    profile = np.concatenate(
+        (interval_1_v1, interval_1_v2, interval_2_v1, interval_2_v2)
+    )
+    np.random.seed(1977)
+    noise = 0.01 * np.random.rand(75)
+    normalized_daily = pd.Series(data=profile, index=soiling_times)
+    normalized_daily = normalized_daily + noise
+
+    return normalized_daily
+
+
 @pytest.fixture()
 def soiling_insolation(soiling_times):
     insolation = np.empty((75,))
diff --git a/rdtools/test/soiling_cods_test.py b/rdtools/test/soiling_cods_test.py
index 62046fd8..8c7879c4 100644
--- a/rdtools/test/soiling_cods_test.py
+++ b/rdtools/test/soiling_cods_test.py
@@ -6,6 +6,55 @@
 # from rdtools.test.conftest import cods_normalized_daily
 
 
+def test_cods_with_nan_prefix(cods_normalized_daily):
+    '''Test CODSAnalysis with data starting with NaN values'''
+    # Add NaN values at the beginning
+    data_with_nan_prefix = cods_normalized_daily.copy()
+    data_with_nan_prefix.iloc[:5] = np.nan
+
+    # Should work - the NaN prefix should be trimmed
+    cods = soiling.CODSAnalysis(data_with_nan_prefix)
+    assert len(cods.pm) == len(cods_normalized_daily) - 5, \
+        "NaN prefix was not trimmed correctly"
+    assert not np.isnan(cods.pm.iloc[0]), \
+        "First value should not be NaN after trimming"
+
+
+def test_cods_non_daily_frequency_raises():
+    '''Test CODSAnalysis raises ValueError for non-daily frequency data'''
+    # Create hourly data (non-daily frequency)
+    hourly_index = pd.date_range('2019/01/01', periods=100, freq='h', tz='UTC')
+    hourly_data = pd.Series(data=np.random.rand(100), index=hourly_index)
+
+    match = "Daily performance metric series must have daily frequency"
+    with pytest.raises(ValueError, match=match):
+        soiling.CODSAnalysis(hourly_data)
+
+
+def test_cods_invalid_order_raises(cods_normalized_daily):
+    '''Test iterative_signal_decomposition raises ValueError when SR not in order'''
+    cods = soiling.CODSAnalysis(cods_normalized_daily)
+
+    # order without 'SR' should raise ValueError
+    with pytest.raises(ValueError, match="'SR' must be in argument 'order'"):
+        cods.iterative_signal_decomposition(order=('SC', 'Rd'))
+
+
+def test_kalman_filter_prescient_cleaning_events_mismatch(cods_normalized_daily):
+    '''Test Kalman filter raises ValueError for mismatched prescient_cleaning_events length'''
+    cods = soiling.CODSAnalysis(cods_normalized_daily)
+
+    # Create prescient_cleaning_events with wrong length but sum > 4 to trigger check
+    events_data = [True, True, True, True, True, False, False, False, False, False]
+    wrong_length_events = pd.Series(
+        events_data, index=pd.date_range('2019/01/01', periods=10, freq='D'))
+
+    match = "The indices of prescient_cleaning_events must correspond"
+    with pytest.raises(ValueError, match=match):
+        cods._Kalman_filter_for_SR(cods_normalized_daily,
+                                   prescient_cleaning_events=wrong_length_events)
+
+
 def test_iterative_signal_decomposition(cods_normalized_daily):
     ''' Test iterative_signal_decomposition with fixed test case '''
     np.random.seed(1977)
diff --git a/rdtools/test/soiling_test.py b/rdtools/test/soiling_test.py
index a1a67837..3d342081 100644
--- a/rdtools/test/soiling_test.py
+++ b/rdtools/test/soiling_test.py
@@ -5,6 +5,7 @@
 from rdtools.soiling import annual_soiling_ratios
 from rdtools.soiling import monthly_soiling_rates
 from rdtools.soiling import NoValidIntervalError
+from rdtools.soiling import segmented_soiling_period
 import pytest
 
 
@@ -13,86 +14,93 @@ def test_soiling_srr(soiling_normalized_daily, soiling_insolation, soiling_times
     reps = 10
     np.random.seed(1977)
     sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=reps)
+
     assert 0.964369 == pytest.approx(sr, abs=1e-6), \
-        'Soiling ratio different from expected value'
+        "Soiling ratio different from expected value"
     assert np.array([0.962540, 0.965295]) == pytest.approx(sr_ci, abs=1e-6), \
-        'Confidence interval different from expected value'
-    assert 0.960205 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6), \
-        'Exceedance level different from expected value'
-    assert 0.984079 == pytest.approx(soiling_info['renormalizing_factor'], abs=1e-6), \
-        'Renormalizing factor different from expected value'
-    assert len(soiling_info['stochastic_soiling_profiles']) == reps, \
+        "Confidence interval different from expected value"
+    assert 0.960205 == pytest.approx(soiling_info["exceedance_level"], abs=1e-6), \
+        "Exceedance level different from expected value"
+    assert 0.984079 == pytest.approx(soiling_info["renormalizing_factor"], abs=1e-6), \
+        "Renormalizing factor different from expected value"
+    assert (len(soiling_info["stochastic_soiling_profiles"]) == reps), \
         'Length of soiling_info["stochastic_soiling_profiles"] different than expected'
-    assert isinstance(soiling_info['stochastic_soiling_profiles'], list), \
+    assert isinstance(soiling_info["stochastic_soiling_profiles"], list), \
         'soiling_info["stochastic_soiling_profiles"] is not a list'
 
     # Check soiling_info['soiling_interval_summary']
-    expected_summary_columns = ['start', 'end', 'soiling_rate', 'soiling_rate_low',
-                                'soiling_rate_high', 'inferred_start_loss', 'inferred_end_loss',
-                                'length', 'valid']
-    actual_summary_columns = soiling_info['soiling_interval_summary'].columns.values
+    expected_summary_columns = [
+        "start", "end", "soiling_rate", "soiling_rate_low", "soiling_rate_high",
+        "inferred_start_loss", "inferred_end_loss", "inferred_recovery",
+        "inferred_begin_shift", "length", "valid"]
+    actual_summary_columns = soiling_info["soiling_interval_summary"].columns.values
 
     for x in actual_summary_columns:
-        assert x in expected_summary_columns, \
+        assert (x in expected_summary_columns), \
             f"'{x}' not an expected column in soiling_info['soiling_interval_summary']"
     for x in expected_summary_columns:
-        assert x in actual_summary_columns, \
+        assert (x in actual_summary_columns), \
             f"'{x}' was expected as a column, but not in soiling_info['soiling_interval_summary']"
-    assert isinstance(soiling_info['soiling_interval_summary'], pd.DataFrame), \
+
+    assert isinstance(soiling_info["soiling_interval_summary"], pd.DataFrame), \
         'soiling_info["soiling_interval_summary"] not a dataframe'
-    expected_means = pd.Series({'soiling_rate': -0.002644544,
-                                'soiling_rate_low': -0.002847504,
-                                'soiling_rate_high': -0.002455915,
-                                'inferred_start_loss': 1.020124,
-                                'inferred_end_loss': 0.9566552,
-                                'length': 24.0,
-                                'valid': 1.0})
-    expected_means = expected_means[['soiling_rate', 'soiling_rate_low', 'soiling_rate_high',
-                                     'inferred_start_loss', 'inferred_end_loss',
-                                     'length', 'valid']]
-    actual_means = soiling_info['soiling_interval_summary'][expected_means.index].mean()
+
+    expected_means = pd.Series(
+        {"soiling_rate": -0.002644544, "soiling_rate_low": -0.002847504,
+         "soiling_rate_high": -0.002455915, "inferred_start_loss": 1.020124,
+         "inferred_end_loss": 0.9566552, "inferred_recovery": 0.065416,
+         "inferred_begin_shift": 0.084814, "length": 24.0, "valid": 1.0})
+    expected_means = expected_means[
+        ["soiling_rate", "soiling_rate_low", "soiling_rate_high", "inferred_start_loss",
+         "inferred_end_loss", "inferred_recovery", "inferred_begin_shift", "length", "valid"]]
+    actual_means = soiling_info["soiling_interval_summary"][expected_means.index].mean()
     pd.testing.assert_series_equal(expected_means, actual_means, check_exact=False)
 
     # Check soiling_info['soiling_ratio_perfect_clean']
-    pd.testing.assert_index_equal(soiling_info['soiling_ratio_perfect_clean'].index, soiling_times,
-                                  check_names=False)
-    sr_mean = soiling_info['soiling_ratio_perfect_clean'].mean()
-    assert 0.968265 == pytest.approx(sr_mean, abs=1e-6), \
-        "The mean of soiling_info['soiling_ratio_perfect_clean'] differs from expected"
-    assert isinstance(soiling_info['soiling_ratio_perfect_clean'], pd.Series), \
-        'soiling_info["soiling_ratio_perfect_clean"] not a pandas series'
+    pd.testing.assert_index_equal(
+        soiling_info["soiling_ratio_perfect_clean"].index, soiling_times, check_names=False)
+    sr_mean = soiling_info["soiling_ratio_perfect_clean"].mean()
+    assert 0.968265 == pytest.approx(
+        sr_mean, abs=1e-6
+    ), "The mean of soiling_info['soiling_ratio_perfect_clean'] differs from expected"
+    assert isinstance(
+        soiling_info["soiling_ratio_perfect_clean"], pd.Series
+    ), 'soiling_info["soiling_ratio_perfect_clean"] not a pandas series'
 
 
 @pytest.mark.filterwarnings("ignore:.*20% or more of the daily data.*:UserWarning")
-@pytest.mark.parametrize('method,expected_sr',
-                         [('random_clean', 0.936177),
-                          ('half_norm_clean', 0.915093),
-                          ('perfect_clean', 0.977116)])
-def test_soiling_srr_consecutive_invalid(soiling_normalized_daily, soiling_insolation,
-                                         soiling_times, method, expected_sr):
+@pytest.mark.parametrize(
+    "method,detect_neg_shifts,expected_sr",
+    [("random_clean", False, 0.936177),
+     ("half_norm_clean", False, 0.915093),
+     ("perfect_clean", False, 0.977116),
+     ("perfect_clean", True, 0.977116),
+     ("inferred_clean", True, 0.975805)])
+def test_soiling_srr_consecutive_invalid(
+        soiling_normalized_daily, soiling_insolation, soiling_times,
+        method, detect_neg_shifts, expected_sr):
     reps = 10
     np.random.seed(1977)
-    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=reps,
-                                          max_relative_slope_error=20.0, method=method)
+    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation,
+                                          reps=reps, max_relative_slope_error=20.0,
+                                          method=method, detect_neg_shifts=detect_neg_shifts)
     assert expected_sr == pytest.approx(sr, abs=1e-6), \
-        f'Soiling ratio different from expected value for {method} with consecutive invalid intervals'  # noqa: E501
+        f"Soiling ratio different from expected value for {method} \
+        with consecutive invalid intervals"
 
 
-@pytest.mark.parametrize('clean_criterion,expected_sr',
-                         [('precip_and_shift', 0.982546),
-                          ('precip_or_shift', 0.973433),
-                          ('precip', 0.976196),
-                          ('shift', 0.964369)])
-def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation, soiling_times,
-                                 clean_criterion, expected_sr):
+@pytest.mark.parametrize("clean_criterion,expected_sr",
+                         [("precip_and_shift", 0.982546),
+                          ("precip_or_shift", 0.973433),
+                          ("precip", 0.976196),
+                          ("shift", 0.964369)])
+def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation,
+                                 soiling_times, clean_criterion, expected_sr):
     precip = pd.Series(index=soiling_times, data=0)
-    precip['2019-01-18 00:00:00-07:00'] = 1
-    precip['2019-02-20 00:00:00-07:00'] = 1
+    precip["2019-01-18 00:00:00-07:00"] = 1
+    precip["2019-02-20 00:00:00-07:00"] = 1
 
-    kwargs = {
-        'reps': 10,
-        'precipitation_daily': precip
-    }
+    kwargs = {"reps": 10, "precipitation_daily": precip}
     np.random.seed(1977)
     sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation,
                                           clean_criterion=clean_criterion, **kwargs)
@@ -101,18 +109,18 @@ def test_soiling_srr_with_precip(soiling_normalized_daily, soiling_insolation, s
 
 
 def test_soiling_srr_confidence_levels(soiling_normalized_daily, soiling_insolation):
-    'Tests SRR with different confidence level settingsf from above'
+    """Tests SRR with different confidence level settings."""
     np.random.seed(1977)
     sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation,
                                           confidence_level=95, reps=10, exceedance_prob=80.0)
     assert np.array([0.959322, 0.966066]) == pytest.approx(sr_ci, abs=1e-6), \
-        'Confidence interval with confidence_level=95 different than expected'
-    assert 0.962691 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6), \
+        "Confidence interval with confidence_level=95 different than expected"
+    assert 0.962691 == pytest.approx(soiling_info["exceedance_level"], abs=1e-6), \
         'soiling_info["exceedance_level"] different than expected when exceedance_prob=80'
 
 
 def test_soiling_srr_dayscale(soiling_normalized_daily, soiling_insolation):
-    'Test that a long dayscale can prevent valid intervals from being found'
+    "Test that a long dayscale can prevent valid intervals from being found"
     with pytest.raises(NoValidIntervalError):
         np.random.seed(1977)
         sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation,
@@ -120,64 +128,70 @@ def test_soiling_srr_dayscale(soiling_normalized_daily, soiling_insolation):
 
 
 def test_soiling_srr_clean_threshold(soiling_normalized_daily, soiling_insolation):
-    '''Test that clean test_soiling_srr_clean_threshold works with a float and
-    can cause no soiling intervals to be found'''
+    """Test that clean_threshold works with a float and
+    can cause no soiling intervals to be found."""
     np.random.seed(1977)
-    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10,
-                                          clean_threshold=0.01)
+    sr, sr_ci, soiling_info = soiling_srr(
+        soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.01)
     assert 0.964369 == pytest.approx(sr, abs=1e-6), \
-        'Soiling ratio with specified clean_threshold different from expected value'
+        "Soiling ratio with specified clean_threshold different from expected value"
 
     with pytest.raises(NoValidIntervalError):
         np.random.seed(1977)
-        sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation,
-                                              reps=10, clean_threshold=0.1)
+        sr, sr_ci, soiling_info = soiling_srr(
+            soiling_normalized_daily, soiling_insolation, reps=10, clean_threshold=0.1)
 
 
 def test_soiling_srr_trim(soiling_normalized_daily, soiling_insolation):
     np.random.seed(1977)
-    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10,
-                                          trim=True)
+    sr, sr_ci, soiling_info = soiling_srr(
+        soiling_normalized_daily, soiling_insolation, reps=10, trim=True)
 
     assert 0.978093 == pytest.approx(sr, abs=1e-6), \
-        'Soiling ratio with trim=True different from expected value'
-    assert len(soiling_info['soiling_interval_summary']) == 1, \
-        'Wrong number of soiling intervals found with trim=True'
-
-
-@pytest.mark.parametrize('method,expected_sr',
-                         [('random_clean', 0.920444),
-                          ('perfect_clean', 0.966912)
-                          ])
-def test_soiling_srr_method(soiling_normalized_daily, soiling_insolation, method, expected_sr):
+        "Soiling ratio with trim=True different from expected value"
+    assert (len(soiling_info["soiling_interval_summary"]) == 1), \
+        "Wrong number of soiling intervals found with trim=True"
+
+
+@pytest.mark.parametrize(
+    "method,detect_neg_shifts,expected_sr",
+    [("random_clean", False, 0.920444),
+     ("perfect_clean", False, 0.966912),
+     ("perfect_clean", True, 0.966912),
+     ("inferred_clean", True, 0.965565)])
+def test_soiling_srr_method(
+    soiling_normalized_daily, soiling_insolation, method, detect_neg_shifts, expected_sr
+):
     np.random.seed(1977)
-    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10,
-                                          method=method)
+    sr, sr_ci, soiling_info = soiling_srr(
+        soiling_normalized_daily, soiling_insolation, reps=10, method=method,
+        detect_neg_shifts=detect_neg_shifts
+    )
     assert expected_sr == pytest.approx(sr, abs=1e-6), \
         f'Soiling ratio with method="{method}" different from expected value'
 
 
-def test_soiling_srr_min_interval_length(soiling_normalized_daily, soiling_insolation):
-    'Test that a long minimum interval length prevents finding shorter intervals'
+def test_soiling_srr_min_interval_days(soiling_normalized_daily, soiling_insolation):
+    "Test that a long minimum interval length prevents finding shorter intervals"
     with pytest.raises(NoValidIntervalError):
         np.random.seed(1977)
         # normalized_daily intervals are 25 days long, so min=26 should fail:
         _ = soiling_srr(soiling_normalized_daily, soiling_insolation, confidence_level=68.2,
-                        reps=10, min_interval_length=26)
+                        reps=10, min_interval_days=26)
 
     # but min=24 should be fine:
     _ = soiling_srr(soiling_normalized_daily, soiling_insolation, confidence_level=68.2,
-                    reps=10, min_interval_length=24)
+                    reps=10, min_interval_days=24)
 
 
 def test_soiling_srr_recenter_false(soiling_normalized_daily, soiling_insolation):
     np.random.seed(1977)
-    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10,
-                                          recenter=False)
-    assert 1 == soiling_info['renormalizing_factor'], \
-        'Renormalizing factor != 1 with recenter=False'
+    sr, sr_ci, soiling_info = soiling_srr(
+        soiling_normalized_daily, soiling_insolation, reps=10, recenter=False)
+    assert (1 == soiling_info["renormalizing_factor"]), \
+        "Renormalizing factor != 1 with recenter=False"
     assert 0.966387 == pytest.approx(sr, abs=1e-6), \
-        'Soiling ratio different than expected when recenter=False'
+        "Soiling ratio different than expected when recenter=False"
 
 
 def test_soiling_srr_negative_step(soiling_normalized_daily, soiling_insolation):
@@ -185,97 +199,108 @@ def test_soiling_srr_negative_step(soiling_normalized_daily, soiling_insolation)
     stepped_daily.iloc[37:] = stepped_daily.iloc[37:] - 0.1
 
     np.random.seed(1977)
-    with pytest.warns(UserWarning, match='20% or more of the daily data'):
+    with pytest.warns(UserWarning, match="20% or more of the daily data"):
         sr, sr_ci, soiling_info = soiling_srr(stepped_daily, soiling_insolation, reps=10)
 
-    assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, False, True], \
-        'Soiling interval validity differs from expected when a large negative step\
-        is incorporated into the data'
+    assert list(soiling_info["soiling_interval_summary"]["valid"].values) == [
+        True, False, True], \
+        "Soiling interval validity differs from expected when a large negative step\
+        is incorporated into the data"
 
     assert 0.936932 == pytest.approx(sr, abs=1e-6), \
-        'Soiling ratio different from expected when a large negative step is incorporated into the data'  # noqa: E501
+        "Soiling ratio different from expected when a large negative step is\
+        incorporated into the data"
 
 
 def test_soiling_srr_max_negative_slope_error(soiling_normalized_daily, soiling_insolation):
     np.random.seed(1977)
-    with pytest.warns(UserWarning, match='20% or more of the daily data'):
+    with pytest.warns(UserWarning, match="20% or more of the daily data"):
         sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily, soiling_insolation,
                                               reps=10, max_relative_slope_error=45.0)
 
-    assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, True, False], \
-        'Soiling interval validity differs from expected when max_relative_slope_error=45.0'
+    assert list(soiling_info["soiling_interval_summary"]["valid"].values) == [
+        True, True, False], \
+        "Soiling interval validity differs from expected when max_relative_slope_error=45.0"
 
     assert 0.958761 == pytest.approx(sr, abs=1e-6), \
-        'Soiling ratio different from expected when max_relative_slope_error=45.0'
+        "Soiling ratio different from expected when max_relative_slope_error=45.0"
 
 
 def test_soiling_srr_with_nan_interval(soiling_normalized_daily, soiling_insolation):
-    '''
+    """
     Previous versions had a bug which would have raised an error when an entire interval
     was NaN. See https://github.com/NREL/rdtools/issues/129
-    '''
+    """
     reps = 10
     normalized_corrupt = soiling_normalized_daily.copy()
     normalized_corrupt[26:50] = np.nan
     np.random.seed(1977)
-    with pytest.warns(UserWarning, match='20% or more of the daily data'):
+    with pytest.warns(UserWarning, match="20% or more of the daily data"):
         sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt, soiling_insolation, reps=reps)
     assert 0.948792 == pytest.approx(sr, abs=1e-6), \
-        'Soiling ratio different from expected value when an entire interval was NaN'
+        "Soiling ratio different from expected value when an entire interval was NaN"
+
+    # With perfect_clean method, detect_neg_shifts, and piecewise_fit the warning is not raised
+    np.random.seed(1977)
+    sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt, soiling_insolation,
+                                          reps=reps, method="perfect_clean",
+                                          detect_neg_shifts=True, piecewise_fit=True)
+    assert 0.974103 == pytest.approx(sr, abs=1e-6), \
+        "Soiling ratio different from expected value when an entire interval was NaN"
 
 
 def test_soiling_srr_outlier_factor(soiling_normalized_daily, soiling_insolation):
-    _, _, info = soiling_srr(soiling_normalized_daily, soiling_insolation,
-                             reps=1, outlier_factor=8)
-    assert len(info['soiling_interval_summary']) == 2, \
-        'Increasing the outlier_factor did not result in the expected number of soiling intervals'
+    _, _, info = soiling_srr(
+        soiling_normalized_daily, soiling_insolation, reps=1, outlier_factor=8
+    )
+    assert (len(info["soiling_interval_summary"]) == 2), \
+        "Increasing the outlier_factor did not result in the expected number of soiling intervals"
 
 
 def test_soiling_srr_kwargs(monkeypatch, soiling_normalized_daily, soiling_insolation):
-    '''
+    """
     Make sure that all soiling_srr parameters get passed on to SRRAnalysis and
     SRRAnalysis.run(), i.e. all necessary inputs to SRRAnalysis are provided by
     soiling_srr.  Done by removing the SRRAnalysis default param values
     and making sure everything still runs.
-    '''
+    """
     # the __defaults__ attr is the tuple of default values in py3
     monkeypatch.delattr(SRRAnalysis.__init__, "__defaults__")
     monkeypatch.delattr(SRRAnalysis.run, "__defaults__")
     _ = soiling_srr(soiling_normalized_daily, soiling_insolation, reps=10)
 
 
-@pytest.mark.parametrize(('start,expected_sr'),
-                         [(18, 0.984779), (17, 0.981258)])
-def test_soiling_srr_min_interval_length_default(soiling_normalized_daily, soiling_insolation,
-                                                 start, expected_sr):
-    '''
-    Make sure that the default value of min_interval_length is 7 days by testing
+@pytest.mark.parametrize(("start,expected_sr"), [(18, 0.984779), (17, 0.981258)])
+def test_soiling_srr_min_interval_days_default(
+        soiling_normalized_daily, soiling_insolation, start, expected_sr):
+    """
+    Make sure that the default value of min_interval_days is 7 days by testing
     on a cropped version of the example data
-    '''
+    """
     reps = 10
     np.random.seed(1977)
-    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily[start:],
-                                          soiling_insolation[start:], reps=reps)
+    sr, sr_ci, soiling_info = soiling_srr(
+        soiling_normalized_daily[start:], soiling_insolation[start:], reps=reps
+    )
     assert expected_sr == pytest.approx(sr, abs=1e-6), \
-        'Soiling ratio different from expected value'
+        "Soiling ratio different from expected value"
 
 
-@pytest.mark.parametrize('test_param', ['energy_normalized_daily',
-                                        'insolation_daily',
-                                        'precipitation_daily'])
+@pytest.mark.parametrize(
+    "test_param", ["energy_normalized_daily", "insolation_daily", "precipitation_daily"])
 def test_soiling_srr_non_daily_inputs(test_param):
-    '''
+    """
     Validate the frequency check for input time series
-    '''
-    dummy_daily_explicit = pd.Series(0, index=pd.date_range('2019-01-01', periods=10, freq='d'))
-    dummy_daily_implicit = pd.Series(0, index=pd.date_range('2019-01-01', periods=10, freq='d'))
+    """
+    dummy_daily_explicit = pd.Series(0, index=pd.date_range("2019-01-01", periods=10, freq="d"))
+    dummy_daily_implicit = pd.Series(0, index=pd.date_range("2019-01-01", periods=10, freq="d"))
     dummy_daily_implicit.index.freq = None
     dummy_nondaily = pd.Series(0, index=dummy_daily_explicit.index[::2])
 
     kwargs = {
-        'energy_normalized_daily': dummy_daily_explicit,
-        'insolation_daily': dummy_daily_explicit,
-        'precipitation_daily': dummy_daily_explicit,
+        "energy_normalized_daily": dummy_daily_explicit,
+        "insolation_daily": dummy_daily_explicit,
+        "precipitation_daily": dummy_daily_explicit,
     }
     # no error for implicit daily inputs
     kwargs[test_param] = dummy_daily_implicit
@@ -283,27 +308,111 @@ def test_soiling_srr_non_daily_inputs(test_param):
 
     # yes error for non-daily inputs
     kwargs[test_param] = dummy_nondaily
-    with pytest.raises(ValueError, match='must have daily frequency'):
+    with pytest.raises(ValueError, match="must have daily frequency"):
         _ = SRRAnalysis(**kwargs)
 
 
 def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolation):
-    '''
+    """
     Make sure various argument validation warnings and errors are raised
-    '''
+    """
     kwargs = {
-        'energy_normalized_daily': soiling_normalized_daily,
-        'insolation_daily': soiling_insolation,
-        'reps': 10
+        "energy_normalized_daily": soiling_normalized_daily,
+        "insolation_daily": soiling_insolation,
+        "reps": 10,
     }
-    with pytest.warns(UserWarning, match='An even value of day_scale was passed'):
+    with pytest.warns(UserWarning, match="An even value of day_scale was passed"):
         _ = soiling_srr(day_scale=12, **kwargs)
 
-    with pytest.raises(ValueError, match='clean_criterion must be one of'):
-        _ = soiling_srr(clean_criterion='bad', **kwargs)
+    with pytest.raises(ValueError, match="clean_criterion must be one of"):
+        _ = soiling_srr(clean_criterion="bad", **kwargs)
 
-    with pytest.raises(ValueError, match='Invalid method specification'):
-        _ = soiling_srr(method='bad', **kwargs)
+    with pytest.raises(ValueError, match="Invalid method specification"):
+        _ = soiling_srr(method="bad", **kwargs)
+
+    match = "method='inferred_clean' requires detect_neg_shifts=True"
+    with pytest.raises(ValueError, match=match):
+        _ = soiling_srr(method="inferred_clean", detect_neg_shifts=False, **kwargs)
+
+
+# ###########################
+# detect_neg_shifts and piecewise_fit tests
+# ###########################
+@pytest.mark.parametrize(
+    "method,detect_neg_shifts,expected_sr",
+    [("half_norm_clean", False, 0.980143),
+     ("half_norm_clean", True, 0.975057),
+     ("perfect_clean", True, 0.964117),
+     ("inferred_clean", True, 0.963585)])
+def test_negative_shifts(
+    soiling_normalized_daily_with_neg_shifts, soiling_insolation, soiling_times,
+        method, detect_neg_shifts, expected_sr):
+    reps = 10
+    np.random.seed(1977)
+    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts,
+                                          soiling_insolation, reps=reps,
+                                          method=method, detect_neg_shifts=detect_neg_shifts)
+    assert expected_sr == pytest.approx(sr, abs=1e-6), \
+        f'Soiling ratio with method="{method}" and detect_neg_shifts="{detect_neg_shifts}" \
+        different from expected value'
+
+
+@pytest.mark.parametrize(
+    "method,piecewise_fit,expected_sr",
+    [("half_norm_clean", False, 0.8670264),
+     ("half_norm_clean", True, 0.927017),
+     ("perfect_clean", True, 0.896936),
+     ("inferred_clean", True, 0.896214)])
+def test_piecewise(soiling_normalized_daily_with_piecewise_slope, soiling_insolation,
+                   soiling_times, method, piecewise_fit, expected_sr):
+    reps = 10
+    np.random.seed(1977)
+    # Note: piecewise methods require detect_neg_shifts=True for perfect_clean/inferred_clean
+    detect_neg_shifts = method in ("perfect_clean", "inferred_clean")
+    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope,
+                                          soiling_insolation, reps=reps, method=method,
+                                          detect_neg_shifts=detect_neg_shifts,
+                                          piecewise_fit=piecewise_fit)
+    assert expected_sr == pytest.approx(sr, abs=1e-6), \
+        f'Soiling ratio with method="{method}" and piecewise_fit="{piecewise_fit}" \
+        different from expected value'
+
+
+def test_piecewise_and_neg_shifts(soiling_normalized_daily_with_piecewise_slope,
+                                  soiling_normalized_daily_with_neg_shifts,
+                                  soiling_insolation, soiling_times):
+    reps = 10
+    np.random.seed(1977)
+    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_piecewise_slope,
+                                          soiling_insolation, reps=reps,
+                                          method="perfect_clean", detect_neg_shifts=True,
+                                          piecewise_fit=True)
+    assert 0.896936 == pytest.approx(sr, abs=1e-6), \
+        "Soiling ratio different from expected value for data with piecewise slopes"
+    np.random.seed(1977)
+    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts,
+                                          soiling_insolation, reps=reps,
+                                          method="perfect_clean", detect_neg_shifts=True)
+    assert 0.964117 == pytest.approx(sr, abs=1e-6), \
+        "Soiling ratio different from expected value for data with negative shifts"
+
+
+def test_detect_neg_shifts_clean_threshold(soiling_normalized_daily_with_neg_shifts,
+                                           soiling_insolation):
+    """Test that clean_threshold works with detect_neg_shifts=True and
+    can cause no soiling intervals to be found"""
+    np.random.seed(1977)
+    sr, sr_ci, soiling_info = soiling_srr(soiling_normalized_daily_with_neg_shifts,
+                                          soiling_insolation, reps=10, clean_threshold=0.1,
+                                          method="perfect_clean", detect_neg_shifts=True,
+                                          piecewise_fit=True)
+    assert 0.934926 == pytest.approx(sr, abs=1e-6), \
+        "Soiling ratio with specified clean_threshold different from expected value"
+
+    with pytest.raises(NoValidIntervalError):
+        np.random.seed(1977)
+        soiling_srr(soiling_normalized_daily_with_neg_shifts,
+                    soiling_insolation, reps=10, clean_threshold=1)
 
 
 # ###########################
@@ -313,25 +422,24 @@ def test_soiling_srr_argument_checks(soiling_normalized_daily, soiling_insolatio
 
 @pytest.fixture()
 def multi_year_profiles():
-    times = pd.date_range('01-01-2018', '11-30-2019', freq='D')
-    data = np.array([0]*365 + [10]*334)
+    times = pd.date_range("01-01-2018", "11-30-2019", freq="D")
+    data = np.array([0] * 365 + [10] * 334)
     profiles = [pd.Series(x + data, times) for x in range(10)]
 
     # make insolation slighly longer to test for proper normalization
-    times = pd.date_range('01-01-2018', '12-31-2019', freq='D')
-    insolation = 350*[0.8] + (len(times)-350)*[1]
+    times = pd.date_range("01-01-2018", "12-31-2019", freq="D")
+    insolation = 350 * [0.8] + (len(times) - 350) * [1]
     insolation = pd.Series(insolation, index=times)
 
     return profiles, insolation
 
 
 def test_annual_soiling_ratios(multi_year_profiles):
-    expected_data = np.array([[2018, 4.5, 1.431, 7.569],
-                              [2019, 14.5, 11.431, 17.569]])
-    expected = pd.DataFrame(data=expected_data,
-                            columns=['year', 'soiling_ratio_median', 'soiling_ratio_low',
-                                     'soiling_ratio_high'])
-    expected['year'] = expected['year'].astype(int)
+    expected_data = np.array([[2018, 4.5, 1.431, 7.569], [2019, 14.5, 11.431, 17.569]])
+    expected = pd.DataFrame(
+        data=expected_data,
+        columns=["year", "soiling_ratio_median", "soiling_ratio_low", "soiling_ratio_high"])
+    expected["year"] = expected["year"].astype(int)
 
     srr_profiles, insolation = multi_year_profiles
     result = annual_soiling_ratios(srr_profiles, insolation)
@@ -340,12 +448,11 @@ def test_annual_soiling_ratios(multi_year_profiles):
 
 
 def test_annual_soiling_ratios_confidence_interval(multi_year_profiles):
-    expected_data = np.array([[2018, 4.5, 0.225, 8.775],
-                              [2019, 14.5, 10.225, 18.775]])
-    expected = pd.DataFrame(data=expected_data,
-                            columns=['year', 'soiling_ratio_median', 'soiling_ratio_low',
-                                     'soiling_ratio_high'])
-    expected['year'] = expected['year'].astype(int)
+    expected_data = np.array([[2018, 4.5, 0.225, 8.775], [2019, 14.5, 10.225, 18.775]])
+    expected = pd.DataFrame(
+        data=expected_data,
+        columns=["year", "soiling_ratio_median", "soiling_ratio_low", "soiling_ratio_high"])
+    expected["year"] = expected["year"].astype(int)
 
     srr_profiles, insolation = multi_year_profiles
     result = annual_soiling_ratios(srr_profiles, insolation, confidence_level=95)
@@ -356,9 +463,10 @@ def test_annual_soiling_ratios_confidence_interval(multi_year_profiles):
 def test_annual_soiling_ratios_warning(multi_year_profiles):
     srr_profiles, insolation = multi_year_profiles
     insolation = insolation.iloc[:-200]
-    match = ('The indexes of stochastic_soiling_profiles are not entirely contained '
-             'within the index of insolation_daily. Every day in stochastic_soiling_profiles '
-             'should be represented in insolation_daily. This may cause erroneous results.')
+    match = (
+        "The indexes of stochastic_soiling_profiles are not entirely contained "
+        "within the index of insolation_daily. Every day in stochastic_soiling_profiles "
+        "should be represented in insolation_daily. This may cause erroneous results.")
     with pytest.warns(UserWarning, match=match):
         _ = annual_soiling_ratios(srr_profiles, insolation)
 
@@ -370,41 +478,42 @@ def test_annual_soiling_ratios_warning(multi_year_profiles):
 
 @pytest.fixture()
 def soiling_interval_summary():
-    starts = ['2019/01/01', '2019/01/16', '2019/02/08', '2019/03/06']
-    starts = pd.to_datetime(starts).tz_localize('America/Denver')
-    ends = ['2019/01/15', '2019/02/07', '2019/03/05', '2019/04/07']
-    ends = pd.to_datetime(ends).tz_localize('America/Denver')
+    starts = ["2019/01/01", "2019/01/16", "2019/02/08", "2019/03/06"]
+    starts = pd.to_datetime(starts).tz_localize("America/Denver")
+    ends = ["2019/01/15", "2019/02/07", "2019/03/05", "2019/04/07"]
+    ends = pd.to_datetime(ends).tz_localize("America/Denver")
     slopes = [-0.005, -0.002, -0.001, -0.002]
     slopes_low = [-0.0055, -0.0025, -0.0015, -0.003]
     slopes_high = [-0.004, 0, 0, -0.001]
     valids = [True, True, False, True]
 
     soiling_interval_summary = pd.DataFrame()
-    soiling_interval_summary['start'] = starts
-    soiling_interval_summary['end'] = ends
-    soiling_interval_summary['soiling_rate'] = slopes
-    soiling_interval_summary['soiling_rate_low'] = slopes_low
-    soiling_interval_summary['soiling_rate_high'] = slopes_high
-    soiling_interval_summary['inferred_start_loss'] = np.nan
-    soiling_interval_summary['inferred_end_loss'] = np.nan
-    soiling_interval_summary['length'] = (ends - starts).days
-    soiling_interval_summary['valid'] = valids
+    soiling_interval_summary["start"] = starts
+    soiling_interval_summary["end"] = ends
+    soiling_interval_summary["soiling_rate"] = slopes
+    soiling_interval_summary["soiling_rate_low"] = slopes_low
+    soiling_interval_summary["soiling_rate_high"] = slopes_high
+    soiling_interval_summary["inferred_start_loss"] = np.nan
+    soiling_interval_summary["inferred_end_loss"] = np.nan
+    soiling_interval_summary["length"] = (ends - starts).days
+    soiling_interval_summary["valid"] = valids
 
     return soiling_interval_summary
 
 
 def _build_monthly_summary(top_rows):
-    '''
+    """
     Convienience function to build a full monthly soiling summary
     dataframe from the expected_top_rows which summarize Jan-April
-    '''
+    """
 
-    all_rows = np.vstack((top_rows, [[1, np.nan, np.nan, np.nan, 0]]*8))
+    all_rows = np.vstack((top_rows, [[1, np.nan, np.nan, np.nan, 0]] * 8))
 
-    df = pd.DataFrame(data=all_rows,
-                      columns=['month', 'soiling_rate_median', 'soiling_rate_low',
-                               'soiling_rate_high', 'interval_count'])
-    df['month'] = range(1, 13)
+    df = pd.DataFrame(
+        data=all_rows,
+        columns=["month", "soiling_rate_median", "soiling_rate_low",
+                 "soiling_rate_high", "interval_count"])
+    df["month"] = range(1, 13)
 
     return df
 
@@ -413,25 +522,25 @@ def test_monthly_soiling_rates(soiling_interval_summary):
     np.random.seed(1977)
     result = monthly_soiling_rates(soiling_interval_summary)
 
-    expected = np.array([
-        [1.00000000e+00, -2.42103810e-03, -5.00912766e-03, -7.68551806e-04, 2.00000000e+00],
-        [2.00000000e+00, -1.25092837e-03, -2.10091842e-03, -3.97354321e-04, 1.00000000e+00],
-        [3.00000000e+00, -2.00313359e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00],
-        [4.00000000e+00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e+00]])
+    expected = np.array(
+        [[1.00000000e00, -2.42103810e-03, -5.00912766e-03, -7.68551806e-04, 2.00000000e00],
+         [2.00000000e00, -1.25092837e-03, -2.10091842e-03, -3.97354321e-04, 1.00000000e00],
+         [3.00000000e00, -2.00313359e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e00],
+         [4.00000000e00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e00]])
     expected = _build_monthly_summary(expected)
 
     pd.testing.assert_frame_equal(result, expected, check_dtype=False)
 
 
-def test_monthly_soiling_rates_min_interval_length(soiling_interval_summary):
+def test_monthly_soiling_rates_min_interval_days(soiling_interval_summary):
     np.random.seed(1977)
-    result = monthly_soiling_rates(soiling_interval_summary, min_interval_length=20)
+    result = monthly_soiling_rates(soiling_interval_summary, min_interval_days=20)
 
-    expected = np.array([
-        [1.00000000e+00, -1.24851539e-03, -2.10394564e-03, -3.98358211e-04, 1.00000000e+00],
-        [2.00000000e+00, -1.25092837e-03, -2.10091842e-03, -3.97330424e-04, 1.00000000e+00],
-        [3.00000000e+00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00],
-        [4.00000000e+00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e+00]])
+    expected = np.array(
+        [[1.00000000e00, -1.24851539e-03, -2.10394564e-03, -3.98358211e-04, 1.00000000e00],
+         [2.00000000e00, -1.25092837e-03, -2.10091842e-03, -3.97330424e-04, 1.00000000e00],
+         [3.00000000e00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e00],
+         [4.00000000e00, -1.99729563e-03, -2.68067699e-03, -1.31667446e-03, 1.00000000e00]])
     expected = _build_monthly_summary(expected)
 
     pd.testing.assert_frame_equal(result, expected, check_dtype=False)
@@ -439,13 +548,15 @@ def test_monthly_soiling_rates_min_interval_length(soiling_interval_summary):
 
 def test_monthly_soiling_rates_max_slope_err(soiling_interval_summary):
     np.random.seed(1977)
-    result = monthly_soiling_rates(soiling_interval_summary, max_relative_slope_error=120)
-
-    expected = np.array([
-        [1.00000000e+00, -4.74910923e-03, -5.26236739e-03, -4.23901493e-03, 1.00000000e+00],
-        [2.00000000e+00, np.nan, np.nan, np.nan, 0.00000000e+00],
-        [3.00000000e+00, -2.00074270e-03, -2.68073474e-03, -1.31786434e-03, 1.00000000e+00],
-        [4.00000000e+00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e+00]])
+    result = monthly_soiling_rates(
+        soiling_interval_summary, max_relative_slope_error=120
+    )
+
+    expected = np.array(
+        [[1.00000000e00, -4.74910923e-03, -5.26236739e-03, -4.23901493e-03, 1.00000000e00],
+         [2.00000000e00, np.nan, np.nan, np.nan, 0.00000000e00],
+         [3.00000000e00, -2.00074270e-03, -2.68073474e-03, -1.31786434e-03, 1.00000000e00],
+         [4.00000000e00, -2.00309454e-03, -2.68359541e-03, -1.31927678e-03, 1.00000000e00]])
     expected = _build_monthly_summary(expected)
 
     pd.testing.assert_frame_equal(result, expected, check_dtype=False)
@@ -455,11 +566,11 @@ def test_monthly_soiling_rates_confidence_level(soiling_interval_summary):
     np.random.seed(1977)
     result = monthly_soiling_rates(soiling_interval_summary, confidence_level=95)
 
-    expected = np.array([
-        [1.00000000e+00, -2.42103810e-03, -5.42313113e-03, -1.21156562e-04, 2.00000000e+00],
-        [2.00000000e+00, -1.25092837e-03, -2.43731574e-03, -6.23842627e-05, 1.00000000e+00],
-        [3.00000000e+00, -2.00313359e-03, -2.94998476e-03, -1.04988760e-03, 1.00000000e+00],
-        [4.00000000e+00, -1.99729563e-03, -2.95063841e-03, -1.04869949e-03, 1.00000000e+00]])
+    expected = np.array(
+        [[1.00000000e00, -2.42103810e-03, -5.42313113e-03, -1.21156562e-04, 2.00000000e00],
+         [2.00000000e00, -1.25092837e-03, -2.43731574e-03, -6.23842627e-05, 1.00000000e00],
+         [3.00000000e00, -2.00313359e-03, -2.94998476e-03, -1.04988760e-03, 1.00000000e00],
+         [4.00000000e00, -1.99729563e-03, -2.95063841e-03, -1.04869949e-03, 1.00000000e00]])
 
     expected = _build_monthly_summary(expected)
 
@@ -470,12 +581,95 @@ def test_monthly_soiling_rates_reps(soiling_interval_summary):
     np.random.seed(1977)
     result = monthly_soiling_rates(soiling_interval_summary, reps=3)
 
-    expected = np.array([
-        [1.00000000e+00, -2.88594088e-03, -5.03736679e-03, -6.47391131e-04, 2.00000000e+00],
-        [2.00000000e+00, -1.67359565e-03, -2.00504171e-03, -1.33240044e-03, 1.00000000e+00],
-        [3.00000000e+00, -1.22306993e-03, -2.19274892e-03, -1.11793240e-03, 1.00000000e+00],
-        [4.00000000e+00, -1.94675549e-03, -2.42574164e-03, -1.54850795e-03, 1.00000000e+00]])
+    expected = np.array(
+        [[1.00000000e00, -2.88594088e-03, -5.03736679e-03, -6.47391131e-04, 2.00000000e00],
+         [2.00000000e00, -1.67359565e-03, -2.00504171e-03, -1.33240044e-03, 1.00000000e00],
+         [3.00000000e00, -1.22306993e-03, -2.19274892e-03, -1.11793240e-03, 1.00000000e00],
+         [4.00000000e00, -1.94675549e-03, -2.42574164e-03, -1.54850795e-03, 1.00000000e00]])
 
     expected = _build_monthly_summary(expected)
 
     pd.testing.assert_frame_equal(result, expected, check_dtype=False)
+
+
+# ######################################
+# invalid segmented_soiling_period tests
+# ######################################
+
+
+@pytest.fixture
+def pr_series():
+    """
+    Panda series of daily performance ratios measured during the given deposition period
+    with datetime index and is length 10.
+    """
+    pr_idx = pd.date_range(start="2022-01-01", periods=10, freq="D")
+    pr_series = pd.Series(np.random.rand(10), index=pr_idx)
+    return pr_series
+
+
+def test_no_datetime_index_pr(pr_series):
+    """
+    Tests if ValueError is raised when pr_series does not have datetime index.
+    """
+    pr = pr_series.reset_index()
+    with pytest.raises(ValueError, match="The time series does not have DatetimeIndex"):
+        _ = segmented_soiling_period(pr)
+
+
+def test_no_change_point(pr_series):
+    """
+    Tests if no change point was found when fitting soiling profile with segmentation.
+    """
+    days_clean_vs_cp = 7
+    result_sr, result_cp_date = segmented_soiling_period(pr_series,
+                                                         days_clean_vs_cp=days_clean_vs_cp)
+    expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index)
+    expected_cp_date = None
+
+    pd.testing.assert_series_equal(result_sr, expected_sr)
+    assert result_cp_date == expected_cp_date
+
+
+def test_except_block():
+    """
+    Tests except block for when all segmentation methods did not work.
+    """
+    pr_idx = pd.date_range(start="2022-01-01", periods=5, freq="D")
+    pr_series = pd.Series(np.array([1, 2, 3, 4, 5]), index=pr_idx)
+    result_sr, result_cp_date = segmented_soiling_period(pr_series)
+
+    expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index)
+    expected_cp_date = None
+
+    pd.testing.assert_series_equal(result_sr, expected_sr)
+    assert result_cp_date == expected_cp_date
+
+
+def test_short_segmentation_periods():
+    """
+    Tests if segmentation fails for short soiling periods.
+    """
+    pr_idx = pd.date_range(start="2022-01-01", periods=35, freq="D")
+    pr_series = pd.Series(np.random.normal(loc=5, scale=2, size=35), index=pr_idx)
+    result_sr, result_cp_date = segmented_soiling_period(pr_series)
+
+    expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index)
+    expected_cp_date = None
+
+    pd.testing.assert_series_equal(result_sr, expected_sr)
+    assert result_cp_date == expected_cp_date
+
+
+def test_long_segmentation_periods():
+    "Tests if segmentation fails for longer soiling periods."
+    pr_idx = pd.date_range(start="2022-01-01", periods=47, freq="D")
+    testing_list = list(np.arange(46)) + [50]
+    pr_series = pd.Series(testing_list, index=pr_idx)
+    result_sr, result_cp_date = segmented_soiling_period(pr_series)
+
+    expected_sr = pd.Series([np.nan]*len(pr_series), index=pr_series.index)
+    expected_cp_date = None
+
+    pd.testing.assert_series_equal(result_sr, expected_sr)
+    assert result_cp_date == expected_cp_date