diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 0000000..25c24fb
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,87 @@
+name: Release
+
+on:
+  push:
+    branches: [ main, master ]
+    paths:
+      - 'pyproject.toml'
+
+jobs:
+  check-version:
+    runs-on: ubuntu-latest
+    outputs:
+      version-changed: ${{ steps.version-check.outputs.changed }}
+      new-version: ${{ steps.version-check.outputs.version }}
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+    
+    - name: Check if version changed
+      id: version-check
+      run: |
+        # Get current version from pyproject.toml
+        CURRENT_VERSION=$(grep -E '^version = ' pyproject.toml | sed 's/version = "//' | sed 's/"//')
+        echo "current-version=$CURRENT_VERSION" >> $GITHUB_OUTPUT
+        
+        # Check if pyproject.toml was changed in this push
+        if git diff --name-only HEAD~1 HEAD | grep -q "pyproject.toml"; then
+          # Get previous version
+          PREVIOUS_VERSION=$(git show HEAD~1:pyproject.toml | grep -E '^version = ' | sed 's/version = "//' | sed 's/"//' || echo "")
+          
+          if [ "$CURRENT_VERSION" != "$PREVIOUS_VERSION" ]; then
+            echo "changed=true" >> $GITHUB_OUTPUT
+            echo "version=$CURRENT_VERSION" >> $GITHUB_OUTPUT
+            echo "Version changed from $PREVIOUS_VERSION to $CURRENT_VERSION"
+          else
+            echo "changed=false" >> $GITHUB_OUTPUT
+            echo "Version unchanged: $CURRENT_VERSION"
+          fi
+        else
+          echo "changed=false" >> $GITHUB_OUTPUT
+          echo "pyproject.toml not changed"
+        fi
+
+  release:
+    needs: check-version
+    if: needs.check-version.outputs.version-changed == 'true'
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: write
+    steps:
+    - uses: actions/checkout@v4
+    
+    - name: Install uv
+      uses: astral-sh/setup-uv@v3
+      with:
+        version: "latest"
+    
+    - name: Set up Python
+      run: uv python install 3.11
+    
+    - name: Install dependencies
+      run: |
+        uv sync --dev
+        uv add --dev build twine
+    
+    - name: Run tests
+      run: |
+        uv run pytest --cov=mixsol
+    
+    - name: Build package
+      run: |
+        uv run python -m build
+    
+    - name: Create GitHub Release
+      uses: actions/create-release@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      with:
+        tag_name: v${{ needs.check-version.outputs.new-version }}
+        release_name: Release v${{ needs.check-version.outputs.new-version }}
+        draft: false
+        prerelease: false
+    
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
\ No newline at end of file
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
new file mode 100644
index 0000000..b3bc7a8
--- /dev/null
+++ b/.github/workflows/test.yml
@@ -0,0 +1,31 @@
+name: Test
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.11', '3.12', '3.13', '3.14']
+
+    steps:
+    - uses: actions/checkout@v4
+    
+    - name: Install uv
+      uses: astral-sh/setup-uv@v3
+      with:
+        version: "latest"
+    
+    - name: Set up Python ${{ matrix.python-version }}
+      run: uv python install ${{ matrix.python-version }}
+    
+    - name: Install dependencies
+      run: uv sync --dev
+    
+    - name: Run tests with pytest
+      run: uv run pytest --cov=mixsol
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index b6e4761..d2e812f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -127,3 +127,6 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+CLAUDE.md
+.ruff_cache/
\ No newline at end of file
diff --git a/Example Notebooks/Powder Weighing.ipynb b/Example Notebooks/Powder Weighing.ipynb
index dc5698f..f1050d6 100644
--- a/Example Notebooks/Powder Weighing.ipynb	
+++ b/Example Notebooks/Powder Weighing.ipynb	
@@ -23,20 +23,26 @@
    "outputs": [],
    "source": [
     "powders = [\n",
-    "    mx.Powder('Cs_I'), #if only using elements, you can simply input the formula delimited by underscores\n",
-    "    mx.Powder({'Pb':1, 'I':2}), #you can also use a dictionary to specify the components\n",
-    "    mx.Powder('Pb_Br2'),\n",
-    "    mx.Powder('Pb_Cl2'),\n",
     "    mx.Powder(\n",
-    "        formula='MA_I', #you can have components that are not element (like methylammonium/MA here), but then the molar mass must be specified\n",
-    "        molar_mass=mx.calculate_molar_mass('C_H6_N_I'), #molar mass can either be given as a float value, or the helper function can take an elemental formula (as string or dict) and calculate the molar mass\n",
-    "        alias='MAI', #you can also give an alias for the powder - this just changes the string representation of the powder when printed\n",
+    "        \"Cs_I\"\n",
+    "    ),  # if only using elements, you can simply input the formula delimited by underscores\n",
+    "    mx.Powder(\n",
+    "        {\"Pb\": 1, \"I\": 2}\n",
+    "    ),  # you can also use a dictionary to specify the components\n",
+    "    mx.Powder(\"Pb_Br2\"),\n",
+    "    mx.Powder(\"Pb_Cl2\"),\n",
+    "    mx.Powder(\n",
+    "        formula=\"MA_I\",  # you can have components that are not element (like methylammonium/MA here), but then the molar mass must be specified\n",
+    "        molar_mass=mx.calculate_molar_mass(\n",
+    "            \"C_H6_N_I\"\n",
+    "        ),  # molar mass can either be given as a float value, or the helper function can take an elemental formula (as string or dict) and calculate the molar mass\n",
+    "        alias=\"MAI\",  # you can also give an alias for the powder - this just changes the string representation of the powder when printed\n",
     "    ),\n",
     "    mx.Powder(\n",
-    "        formula='FA_I',\n",
-    "        molar_mass = mx.calculate_molar_mass('C_H5_N2_I'),\n",
-    "        alias='FAI',\n",
-    "        )\n",
+    "        formula=\"FA_I\",\n",
+    "        molar_mass=mx.calculate_molar_mass(\"C_H5_N2_I\"),\n",
+    "        alias=\"FAI\",\n",
+    "    ),\n",
     "]"
    ]
   },
@@ -53,9 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "weigher = mx.Weigher(\n",
-    "    powders=powders\n",
-    ")"
+    "weigher = mx.Weigher(powders=powders)"
    ]
   },
   {
@@ -79,17 +83,17 @@
     }
    ],
    "source": [
-    "target=mx.Solution(\n",
-    "    solutes='Cs0.05_FA0.8_MA0.15_Pb_I2.4_Br0.45_Cl0.15',\n",
-    "    solvent='DMF9_DMSO1',\n",
-    "    molarity=1\n",
+    "target = mx.Solution(\n",
+    "    solutes=\"Cs0.05_FA0.8_MA0.15_Pb_I2.4_Br0.45_Cl0.15\",\n",
+    "    solvent=\"DMF9_DMSO1\",\n",
+    "    molarity=1,\n",
     ")\n",
     "\n",
     "masses = weigher.get_weights(\n",
     "    target,\n",
-    "    volume=100e-6, #in L\n",
+    "    volume=100e-6,  # in L\n",
     ")\n",
-    "print(masses) #masses of each powder, in grams"
+    "print(masses)  # masses of each powder, in grams"
    ]
   },
   {
@@ -114,9 +118,9 @@
    ],
    "source": [
     "result = weigher.weights_to_solution(\n",
-    "    weights=masses, #the dictionary of masses we just created\n",
-    "    volume=100e-6, #volume of solution (L) \n",
-    "    solvent='DMF9_DMSO1', #solvent system\n",
+    "    weights=masses,  # the dictionary of masses we just created\n",
+    "    volume=100e-6,  # volume of solution (L)\n",
+    "    solvent=\"DMF9_DMSO1\",  # solvent system\n",
     ")\n",
     "print(result)"
    ]
@@ -138,7 +142,7 @@
     }
    ],
    "source": [
-    "result == target #we made it back to our target solution!"
+    "result == target  # we made it back to our target solution!"
    ]
   },
   {
@@ -165,10 +169,7 @@
    ],
    "source": [
     "result_fixedmolarity = weigher.weights_to_solution(\n",
-    "    weights=masses,\n",
-    "    volume=100e-6,\n",
-    "    solvent='DMF9_DMSO1',\n",
-    "    molarity=1\n",
+    "    weights=masses, volume=100e-6, solvent=\"DMF9_DMSO1\", molarity=1\n",
     ")\n",
     "print(result_fixedmolarity)"
    ]
@@ -188,10 +189,7 @@
    ],
    "source": [
     "result_singlecomponent = weigher.weights_to_solution(\n",
-    "    weights=masses,\n",
-    "    volume=100e-6,\n",
-    "    solvent='DMF9_DMSO1',\n",
-    "    molarity=\"Pb\"\n",
+    "    weights=masses, volume=100e-6, solvent=\"DMF9_DMSO1\", molarity=\"Pb\"\n",
     ")\n",
     "print(result_singlecomponent)"
    ]
@@ -211,10 +209,7 @@
    ],
    "source": [
     "result_multicomponent = weigher.weights_to_solution(\n",
-    "    weights=masses,\n",
-    "    volume=100e-6,\n",
-    "    solvent='DMF9_DMSO1',\n",
-    "    molarity=[\"I\", \"Br\", \"Cl\"]\n",
+    "    weights=masses, volume=100e-6, solvent=\"DMF9_DMSO1\", molarity=[\"I\", \"Br\", \"Cl\"]\n",
     ")\n",
     "print(result_multicomponent)"
    ]
@@ -236,7 +231,7 @@
     }
    ],
    "source": [
-    "result == result_singlecomponent == result_multicomponent == result_fixedmolarity #all methods give the same result!"
+    "result == result_singlecomponent == result_multicomponent == result_fixedmolarity  # all methods give the same result!"
    ]
   }
  ],
diff --git a/Example Notebooks/Solution Mixing.ipynb b/Example Notebooks/Solution Mixing.ipynb
index 2086389..f135abb 100644
--- a/Example Notebooks/Solution Mixing.ipynb	
+++ b/Example Notebooks/Solution Mixing.ipynb	
@@ -28,27 +28,36 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Solution.__init__() got an unexpected keyword argument 'solvent'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m stock_solutions = [\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m     \u001b[43mmx\u001b[49m\u001b[43m.\u001b[49m\u001b[43mSolution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m      3\u001b[39m \u001b[43m        \u001b[49m\u001b[43msolutes\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mFA_Pb_I3\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# you can input the formula delimited by underscores\u001b[39;49;00m\n\u001b[32m      4\u001b[39m \u001b[43m        \u001b[49m\u001b[43msolvent\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDMF9_DMSO1\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# for both solutes and solvents\u001b[39;49;00m\n\u001b[32m      5\u001b[39m \u001b[43m        \u001b[49m\u001b[43mmolarity\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m      6\u001b[39m \u001b[43m        \u001b[49m\u001b[43malias\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mFAPI\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# the alias is optional, just makes outputs easier to read later\u001b[39;49;00m\n\u001b[32m      7\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[32m      8\u001b[39m     mx.Solution(\n\u001b[32m      9\u001b[39m         solutes={\n\u001b[32m     10\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mMA\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m1\u001b[39m,\n\u001b[32m     11\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mPb\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m1\u001b[39m,\n\u001b[32m     12\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mI\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m3\u001b[39m,\n\u001b[32m     13\u001b[39m         },  \u001b[38;5;66;03m# you can also input the components as a dictionary\u001b[39;00m\n\u001b[32m     14\u001b[39m         solvent={\u001b[33m\"\u001b[39m\u001b[33mDMF\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m9\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mDMSO\u001b[39m\u001b[33m\"\u001b[39m: \u001b[32m1\u001b[39m},\n\u001b[32m     15\u001b[39m         molarity=\u001b[32m1\u001b[39m,\n\u001b[32m     16\u001b[39m         alias=\u001b[33m\"\u001b[39m\u001b[33mMAPI\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     17\u001b[39m     ),\n\u001b[32m     18\u001b[39m ]\n",
+      "\u001b[31mTypeError\u001b[39m: Solution.__init__() got an unexpected keyword argument 'solvent'"
+     ]
+    }
+   ],
    "source": [
     "stock_solutions = [\n",
     "    mx.Solution(\n",
-    "        solutes='FA_Pb_I3', #you can input the formula delimited by underscores\n",
-    "        solvent='DMF9_DMSO1', #for both solutes and solvents\n",
+    "        solutes=\"FA_Pb_I3\",  # you can input the formula delimited by underscores\n",
+    "        solvent=\"DMF9_DMSO1\",  # for both solutes and solvents\n",
     "        molarity=1,\n",
-    "        alias='FAPI' #the alias is optional, just makes outputs easier to read later\n",
-    "    ), \n",
+    "        alias=\"FAPI\",  # the alias is optional, just makes outputs easier to read later\n",
+    "    ),\n",
     "    mx.Solution(\n",
     "        solutes={\n",
-    "            'MA':1, \n",
-    "            'Pb':1, \n",
-    "            'I':3\n",
-    "            }, # you can also input the components as a dictionary\n",
-    "        solvent={\n",
-    "            \"DMF\": 9,\n",
-    "            \"DMSO\": 1\n",
-    "        },\n",
+    "            \"MA\": 1,\n",
+    "            \"Pb\": 1,\n",
+    "            \"I\": 3,\n",
+    "        },  # you can also input the components as a dictionary\n",
+    "        solvent={\"DMF\": 9, \"DMSO\": 1},\n",
     "        molarity=1,\n",
-    "        alias='MAPI'\n",
+    "        alias=\"MAPI\",\n",
     "    ),\n",
     "]"
    ]
@@ -81,9 +90,9 @@
    ],
    "source": [
     "linear_targets = mx.interpolate(\n",
-    "    endpoints = stock_solutions,\n",
-    "    divisor=3, #divisor by which to split the endpoints by. 2 = 1/2, 3 = 1/3,2/3, etc.\n",
-    "    )\n",
+    "    endpoints=stock_solutions,\n",
+    "    divisor=3,  # divisor by which to split the endpoints by. 2 = 1/2, 3 = 1/3,2/3, etc.\n",
+    ")\n",
     "linear_targets"
    ]
   },
@@ -101,12 +110,12 @@
    "outputs": [],
    "source": [
     "sm = mx.Mixer(\n",
-    "    stock_solutions = stock_solutions,  #a list of Solution's\n",
-    "    targets = {\n",
-    "        t:100 # the unit of volume is up to you, just keep it consistent \n",
+    "    stock_solutions=stock_solutions,  # a list of Solution's\n",
+    "    targets={\n",
+    "        t: 100  # the unit of volume is up to you, just keep it consistent\n",
     "        for t in linear_targets\n",
-    "        }, #dictionary of Solution:target volume\n",
-    "    )"
+    "    },  # dictionary of Solution:target volume\n",
+    ")"
    ]
   },
   {
@@ -116,10 +125,10 @@
    "outputs": [],
    "source": [
     "sm.solve(\n",
-    "    min_volume=20, #minimum volume to transfer in a single mix. This usually is a pipette specification\n",
-    "    max_inputs = 4, #maximum number of source solutions that can be mixed into the target solution. higher numbers may drastically increase compute time\n",
-    "    strategy=\"least_inputs\" #mixing strategy. least_inputs prefers mixing with the least # of source solutions, prefer_stock prioritizes mixing with stock solutions over other target solutions.\n",
-    "    )"
+    "    min_volume=20,  # minimum volume to transfer in a single mix. This usually is a pipette specification\n",
+    "    max_inputs=4,  # maximum number of source solutions that can be mixed into the target solution. higher numbers may drastically increase compute time\n",
+    "    strategy=\"least_inputs\",  # mixing strategy. least_inputs prefers mixing with the least # of source solutions, prefer_stock prioritizes mixing with stock solutions over other target solutions.\n",
+    ")"
    ]
   },
   {
@@ -247,7 +256,7 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(6,6))\n",
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
     "sm.plot(ax=ax)"
    ]
   },
@@ -266,18 +275,18 @@
    "source": [
     "stock_solutions = [\n",
     "    mx.Solution(\n",
-    "        solutes='FA_Pb_I3', \n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solutes=\"FA_Pb_I3\",\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
-    "    ), \n",
+    "    ),\n",
     "    mx.Solution(\n",
     "        solutes=\"MA_Pb_I3\",\n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
     "    ),\n",
     "    mx.Solution(\n",
     "        solutes=\"Cs_Pb_I3\",\n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
     "    ),\n",
     "]"
@@ -315,9 +324,9 @@
    ],
    "source": [
     "linear_targets = mx.interpolate(\n",
-    "    endpoints = stock_solutions,\n",
-    "    divisor=4, #divisor by which to split the endpoints by. 2 = 1/2, 3 = 1/3,2/3, etc.\n",
-    "    )\n",
+    "    endpoints=stock_solutions,\n",
+    "    divisor=4,  # divisor by which to split the endpoints by. 2 = 1/2, 3 = 1/3,2/3, etc.\n",
+    ")\n",
     "linear_targets"
    ]
   },
@@ -328,12 +337,12 @@
    "outputs": [],
    "source": [
     "sm = mx.Mixer(\n",
-    "    stock_solutions = stock_solutions, \n",
-    "    targets = {\n",
-    "        t:100 # the unit of volume is up to you, just keep it consistent \n",
+    "    stock_solutions=stock_solutions,\n",
+    "    targets={\n",
+    "        t: 100  # the unit of volume is up to you, just keep it consistent\n",
     "        for t in linear_targets\n",
-    "        }, #dictionary of Solution:volume\n",
-    "    )"
+    "    },  # dictionary of Solution:volume\n",
+    ")"
    ]
   },
   {
@@ -343,10 +352,10 @@
    "outputs": [],
    "source": [
     "sm.solve(\n",
-    "    min_volume=20, #minimum volume to transfer in a single mix. This usually is a pipette specification\n",
-    "    max_inputs = 4,\n",
-    "    strategy=\"least_inputs\"\n",
-    "    )"
+    "    min_volume=20,  # minimum volume to transfer in a single mix. This usually is a pipette specification\n",
+    "    max_inputs=4,\n",
+    "    strategy=\"least_inputs\",\n",
+    ")"
    ]
   },
   {
@@ -428,13 +437,13 @@
    "source": [
     "stock_solutions = [\n",
     "    mx.Solution(\n",
-    "        solutes='FA_Pb_I3', \n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solutes=\"FA_Pb_I3\",\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
-    "    ), \n",
+    "    ),\n",
     "    mx.Solution(\n",
     "        solutes=\"Cs_Pb_I3\",\n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
     "    ),\n",
     "]"
@@ -468,13 +477,8 @@
     "for cs_loading in np.logspace(-4, 0, 8):\n",
     "    target_solutions.append(\n",
     "        mx.Solution(\n",
-    "            solutes={\n",
-    "                \"FA\": 1-cs_loading,\n",
-    "                \"Cs\": cs_loading,\n",
-    "                \"Pb\": 1,\n",
-    "                \"I\": 3\n",
-    "            },\n",
-    "            solvent='DMF9_DMSO1',\n",
+    "            solutes={\"FA\": 1 - cs_loading, \"Cs\": cs_loading, \"Pb\": 1, \"I\": 3},\n",
+    "            solvent=\"DMF9_DMSO1\",\n",
     "            molarity=1,\n",
     "        )\n",
     "    )\n",
@@ -488,12 +492,12 @@
    "outputs": [],
    "source": [
     "sm = mx.Mixer(\n",
-    "    stock_solutions = stock_solutions, \n",
-    "    targets = {\n",
-    "        t:100 # the unit of volume is up to you, just keep it consistent \n",
+    "    stock_solutions=stock_solutions,\n",
+    "    targets={\n",
+    "        t: 100  # the unit of volume is up to you, just keep it consistent\n",
     "        for t in target_solutions\n",
-    "        }, #dictionary of Solution:volume\n",
-    "    )"
+    "    },  # dictionary of Solution:volume\n",
+    ")"
    ]
   },
   {
@@ -503,10 +507,10 @@
    "outputs": [],
    "source": [
     "sm.solve(\n",
-    "    min_volume=20, #minimum volume to transfer in a single mix. This usually is a pipette specification\n",
-    "    max_inputs = 4,\n",
-    "    strategy=\"least_inputs\"\n",
-    "    )"
+    "    min_volume=20,  # minimum volume to transfer in a single mix. This usually is a pipette specification\n",
+    "    max_inputs=4,\n",
+    "    strategy=\"least_inputs\",\n",
+    ")"
    ]
   },
   {
@@ -589,13 +593,13 @@
    "source": [
     "stock_solutions = [\n",
     "    mx.Solution(\n",
-    "        solutes='FA_Pb_I3', \n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solutes=\"FA_Pb_I3\",\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
-    "    ), \n",
+    "    ),\n",
     "    mx.Solution(\n",
     "        solutes=\"Cs_Pb_I3\",\n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
     "    ),\n",
     "]"
@@ -609,18 +613,18 @@
    "source": [
     "target_solutions = [\n",
     "    mx.Solution(\n",
-    "        solutes='FA0.3_Cs0.7_Pb_I3', \n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solutes=\"FA0.3_Cs0.7_Pb_I3\",\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
-    "    ), \n",
+    "    ),\n",
     "    mx.Solution(\n",
     "        solutes=\"FA0.35_Cs0.65_Pb_I3\",\n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
     "    ),\n",
-    "     mx.Solution(\n",
+    "    mx.Solution(\n",
     "        solutes=\"FA0.4_Cs0.6_Pb_I3\",\n",
-    "        solvent='DMF9_DMSO1',\n",
+    "        solvent=\"DMF9_DMSO1\",\n",
     "        molarity=1,\n",
     "    ),\n",
     "]"
@@ -633,12 +637,12 @@
    "outputs": [],
    "source": [
     "sm = mx.Mixer(\n",
-    "    stock_solutions = stock_solutions, \n",
-    "    targets = {\n",
-    "        t:100 # the unit of volume is up to you, just keep it consistent \n",
+    "    stock_solutions=stock_solutions,\n",
+    "    targets={\n",
+    "        t: 100  # the unit of volume is up to you, just keep it consistent\n",
     "        for t in target_solutions\n",
-    "        }, #dictionary of Solution:volume\n",
-    "    )"
+    "    },  # dictionary of Solution:volume\n",
+    ")"
    ]
   },
   {
@@ -648,10 +652,10 @@
    "outputs": [],
    "source": [
     "sm.solve(\n",
-    "    min_volume=20, #minimum volume to transfer in a single mix. This usually is a pipette specification\n",
-    "    max_inputs = 4,\n",
-    "    strategy=\"least_inputs\"\n",
-    "    )"
+    "    min_volume=20,  # minimum volume to transfer in a single mix. This usually is a pipette specification\n",
+    "    max_inputs=4,\n",
+    "    strategy=\"least_inputs\",\n",
+    ")"
    ]
   },
   {
@@ -684,11 +688,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "cb679740e72a943445755ca66cc215891c17e02fe2436520f7971a4a67808c5a"
-  },
   "kernelspec": {
-   "display_name": "Python 3.8.12 ('pascal')",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -702,7 +703,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.11.11"
   },
   "orig_nbformat": 4
  },
diff --git a/Example Notebooks/Tabulating All Accessible Mixtures.ipynb b/Example Notebooks/Tabulating All Accessible Mixtures.ipynb
index 630d1fd..9c3e9ab 100644
--- a/Example Notebooks/Tabulating All Accessible Mixtures.ipynb	
+++ b/Example Notebooks/Tabulating All Accessible Mixtures.ipynb	
@@ -2,13 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "import mixsol as mx\n",
-    "import pandas as pd\n",
-    "import seaborn as sns"
+    "import pandas as pd  # not included in mixsol deps\n",
+    "import seaborn as sns  # not included in mixsol deps"
    ]
   },
   {
@@ -20,25 +20,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "stocks = []\n",
-    "for ma in [0,1]:\n",
+    "for ma in [0, 1]:\n",
     "    for cs in [0, 0.2]:\n",
-    "        fa = 1-ma\n",
-    "        stocks.append(mx.Solution(\n",
+    "        fa = 1 - ma\n",
+    "        stocks.append(\n",
+    "            mx.Solution(\n",
     "                solutes=dict(\n",
-    "                    Cs = cs,\n",
-    "                    MA = ma*(1-cs),\n",
-    "                    FA = fa*(1-cs),\n",
-    "                    Pb=1,\n",
-    "                    I=3*fa,\n",
-    "                    Br=3*ma\n",
+    "                    Cs=cs, MA=ma * (1 - cs), FA=fa * (1 - cs), Pb=1, I=3 * fa, Br=3 * ma\n",
     "                ),\n",
-    "                solvent=\"DMF9_DMSO1\",\n",
-    "                molarity=1.2\n",
+    "                solvents=\"DMF9_DMSO1\",\n",
+    "                molarity=1.2,\n",
     "            )\n",
     "        )"
    ]
@@ -52,19 +48,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "mixer = mx.Mixer(\n",
-    "    stock_solutions=stocks,\n",
-    "    targets=None\n",
-    "    )"
+    "mixer = mx.Mixer(stock_solutions=stocks, targets=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -79,21 +72,23 @@
     "accessible = mixer.all_possible_solutions(\n",
     "    min_volume=20,\n",
     "    target_volume=100,\n",
-    "    precision=2, #volume step size between min_volume and target_volume\n",
+    "    precision=2,  # volume step size between min_volume and target_volume\n",
     "    max_inputs=4,\n",
     ")\n",
-    "print(f\"{len(accessible)} unique solutions can be accessed in one step from the current stocks\")"
+    "print(\n",
+    "    f\"{len(accessible)} unique solutions can be accessed in one step from the current stocks\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "df = pd.DataFrame(\n",
     "    [a.solutes for a in accessible]\n",
-    ")# note values will be mol/L of each component"
+    ")  # note values will be mol/L of each component"
    ]
   },
   {
@@ -105,46 +100,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/miniconda3/envs/pascal/lib/python3.8/site-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "/Users/rishi/github/mixsol/.venv/lib/python3.11/site-packages/seaborn/axisgrid.py:2100: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
       "  warnings.warn(msg, UserWarning)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x7ff4b0e68e50>"
+       "<seaborn.axisgrid.PairGrid at 0x11c089690>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 864x864 with 27 Axes>"
+       "<Figure size 1200x1200 with 27 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sns.pairplot(\n",
-    "    data=df,\n",
-    "    corner=True,\n",
-    "    size=2\n",
-    ")"
+    "sns.pairplot(data=df, corner=True, size=2)"
    ]
   },
   {
@@ -156,25 +145,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<Solution> 1.2M MA_Pb_Br3 in DMF0.9_DMSO0.1,\n",
+       "[<Solution> 1.2M FA_Pb_I3 in DMF0.9_DMSO0.1,\n",
+       " <Solution> 1.2M MA_Pb_Br3 in DMF0.9_DMSO0.1,\n",
        " <Solution> 1.2M Cs0.2_FA0.8_Pb_I3 in DMF0.9_DMSO0.1,\n",
-       " <Solution> 1.2M Cs0.2_MA0.8_Pb_Br3 in DMF0.9_DMSO0.1,\n",
-       " <Solution> 1.2M FA_Pb_I3 in DMF0.9_DMSO0.1]"
+       " <Solution> 1.2M Cs0.2_MA0.8_Pb_Br3 in DMF0.9_DMSO0.1]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mixer.solutions"
+    "mixer.all_solutions"
    ]
   },
   {
@@ -195,8 +184,10 @@
     "\n",
     "for t in accessible:\n",
     "    try:\n",
-    "        out = mixer.mix(target=t, volume=100, min_volume=20, max_inputs=4) #will throw Exception is mixture is not possible\n",
-    "    except:\n",
+    "        out = mixer.mix(\n",
+    "            target=t, volume=100, min_volume=20, max_inputs=4\n",
+    "        )  # will throw Exception is mixture is not possible\n",
+    "    except Exception:\n",
     "        inaccessible.append(t)\n",
     "\n",
     "print(f\"{len(inaccessible)} solutions were found to be inaccessible\")"
@@ -204,11 +195,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "cb679740e72a943445755ca66cc215891c17e02fe2436520f7971a4a67808c5a"
-  },
   "kernelspec": {
-   "display_name": "Python 3.8.12 ('pascal')",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -222,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.11.11"
   },
   "orig_nbformat": 4
  },
diff --git a/README.md b/README.md
index 2ca952d..ff26422 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ Happy mixing!
 # Examples
 
 ## Solution Mixing
-Solutions are defined with the `Solution` class. Solutes and solvents are both defined by their formula, which follows the `(name1)(amount1)_(name2)(amount2)_..._(name)(amount)` format. The names do not have to correspond to elements, so you can use placeholders for units that will be mixed. Parentheses can be used to simplify formulae as well: `A2_B2_C` == `(A_B)2_C`. An `alias` can be provided for the solution to simplify later analysis.
+Solutions are defined with the `Solution` class. Solutes and solvents are both defined by either dicts or their formula, which follows the `(name1)(amount1)_(name2)(amount2)_..._(name)(amount)` format. The names do not have to correspond to elements, so you can use placeholders for units that will be mixed. Parentheses can be used to simplify formulae as well: `A2_B2_C` == `(A_B)2_C`. An `alias` can be provided for the solution to simplify later analysis.
 
 ```
 import mixsol as mx
@@ -25,13 +25,20 @@ import mixsol as mx
 stock_solutions = [
     mx.Solution(
         solutes='FA_Pb_I3',
-        solvent='DMF9_DMSO1',
+        solvents='DMF9_DMSO1',
         molarity=1,
         alias='FAPI'
     ),
     mx.Solution(
-        solutes='MA_Pb_I3',
-        solvent='DMF9_DMSO1',
+        solutes={
+            "MA": 1,
+            "Pb": 1,
+            "I": 3,
+        },
+        solvents={
+            "DMF": 9,
+            "DMSO": 1,
+        },
         molarity=1,
         alias='MAPI'
     ),
@@ -46,8 +53,13 @@ You can manually generate your target solutions and place them in a list like so
 targets = []
 for a in np.linspace(0, 0.8, 5):
     targets.append(mx.Solution(
-        solutes=f"FA{a:0.3f}_MA{1-a:.3f}_Pb_I3",
-        solvent="DMF9_DMSO1",
+        solutes={
+            "FA": a,
+            "MA": 1-a,
+            "Pb": 1,
+            "I": 3,
+        },
+        solvents="DMF9_DMSO1",
         molarity=1,
         alias=f'FA_{a:.3f}'
     ))
@@ -153,7 +165,7 @@ which can then be used to determine powder amounts for a given volume of a targe
 ```
 target=Solution(
     solutes='Cs0.05_FA0.8_MA0.15_Pb_I2.4_Br0.45_Cl0.15',
-    solvent='DMF9_DMSO1',
+    solvents='DMF9_DMSO1',
     molarity=1
 )
 
@@ -172,7 +184,7 @@ Finally, we can also generate a `Solution` object by inputting a `{powder:mass}`
 result = weigher.weights_to_solution(
     weights=answer,
     volume=1e-3,
-    solvent='DMF9_DMSO1',
+    solvents='DMF9_DMSO1',
 )
 print(result)
 ```
@@ -185,7 +197,7 @@ The molarity of the output will by default be determined by the largest componen
 result2 = weigher.weights_to_solution(
     weights=answer,
     volume=1e-3, #in L
-    solvent='DMF9_DMSO1',
+    solvents='DMF9_DMSO1',
     norm='Pb', #normalize the formula+molarity such that Pb=1
 )
 print(result2) #result is a Solution object
diff --git a/mixsol/__init__.py b/mixsol/__init__.py
index c3e219f..b95ce32 100644
--- a/mixsol/__init__.py
+++ b/mixsol/__init__.py
@@ -1,3 +1,12 @@
 from mixsol.mix import Mixer, Weigher, interpolate
 from mixsol.components import Solution, Powder
 from mixsol.helpers import calculate_molar_mass
+
+__all__ = [
+    "Mixer",
+    "Weigher",
+    "Solution",
+    "Powder",
+    "calculate_molar_mass",
+    "interpolate",
+]
diff --git a/mixsol/components.py b/mixsol/components.py
index 7b5baee..978d78b 100644
--- a/mixsol/components.py
+++ b/mixsol/components.py
@@ -6,22 +6,24 @@
 class Solution:
     def __init__(
         self,
-        solutes: str = "",
-        solvent: str = None,
+        solutes: str | dict[str, float] = None,
+        solvents: str | dict[str, float] = None,
         molarity: float = 0,
         alias: str = None,
     ):
-        if solvent is None:
+        if not solutes:
+            solutes = {}
+        if not solvents:
             raise ValueError("Must define a solvent!")
-        if solutes != "" and molarity == 0:
+        if solutes and molarity <= 0:
             raise ValueError(
                 "If the solution contains solutes, the molarity must be >0!"
             )
 
-        self.molarity = molarity
+        self.__molarity = molarity
         self.alias = alias
 
-        self.solutes = self.__digest_components(solutes, factor=self.molarity)
+        self.__solutes = self.__digest_components(solutes, factor=self.molarity)
         if len(self.solutes) == 0:
             molarity = 1
         else:
@@ -37,20 +39,32 @@ def __init__(
             {k: round(v, 5) for k, v in self._solute_dict_norm.items()}, sort_keys=True
         )  # used for hashing
 
-        self.solvent = self.__digest_components(solvent, factor=1)
-        total_solvent_amt = sum(self.solvent.values())
-        self.solvent = {
-            k: v / total_solvent_amt for k, v in self.solvent.items()
+        self.__solvents = self.__digest_components(solvents, factor=1)
+        total_solvent_amt = sum(self.__solvents.values())
+        self.__solvents = {
+            k: v / total_solvent_amt for k, v in self.__solvents.items()
         }  # normalize so total solvent amount is 1.0
         self.__solvent_str_norm = json.dumps(
-            {k: round(v, 5) for k, v in self.solvent.items()}, sort_keys=True
+            {k: round(v, 5) for k, v in self.solvents.items()}, sort_keys=True
         )  # used for hashing
 
+    @property
+    def solutes(self):
+        return self.__solutes
+
+    @property
+    def solvents(self):
+        return self.__solvents
+
+    @property
+    def molarity(self):
+        return self.__molarity
+
     def __digest_components(self, components, factor):
         if isinstance(components, str):
             components = name_to_components(components, factor=factor)
         elif isinstance(components, dict):
-            components = {k: v * factor for k, v in components.items()}
+            components = {k: float(v * factor) for k, v in components.items()}
         else:
             raise ValueError(
                 "Components must be given as an underscore-delimited string (eg Cs_Pb_I3) or dictionary (eg {'Cs':1, 'Pb':1, 'I':3'})!"
@@ -66,18 +80,18 @@ def __str__(self):
         if self.alias is not None:
             return self.alias
         if len(self.solutes) == 0:  # no solutes, just a solvent
-            return components_to_name(self.solvent)
-        return f"{self.molarity:.2g}M {components_to_name(self.solutes, factor=1/self.molarity)} in {components_to_name(self.solvent)}"
+            return components_to_name(self.solvents)
+        return f"{self.molarity:.2g}M {components_to_name(self.solutes, factor=1 / self.molarity)} in {components_to_name(self.solvents)}"
 
     def __repr__(self):
-        return f"<Solution> " + str(self)
+        return "<Solution> " + str(self)
 
     def __eq__(self, other):
         if not isinstance(other, self.__class__):
             return False
         for d1, d2 in zip(
-            [self.solutes, self.solvent],
-            [other.solutes, other.solvent],
+            [self.solutes, self.solvents],
+            [other.solutes, other.solvents],
         ):
             if d1.keys() != d2.keys():
                 return False
@@ -130,7 +144,7 @@ def __str__(self):
             return self.formula
 
     def __repr__(self):
-        return f"<Powder>" + str(self)
+        return "<Powder>" + str(self)
 
     def __eq__(self, other):
         if isinstance(other, self.__class__):
diff --git a/mixsol/helpers.py b/mixsol/helpers.py
index d0952c3..918dda9 100644
--- a/mixsol/helpers.py
+++ b/mixsol/helpers.py
@@ -1,7 +1,7 @@
-import numpy as np
 from molmass import Formula
 import re
 
+
 ## parsing formulae -> dictionaries
 def components_to_name(
     components: dict, delimiter: str = "_", factor: float = 1
@@ -22,7 +22,7 @@ def components_to_name(
         if n == 0:
             continue
         elif n == 1:
-            composition_label += "{0}{2}".format(c, n, delimiter)
+            composition_label += "{0}{1}".format(c, delimiter)
         else:
             composition_label += "{0}{1:.3g}{2}".format(c, n, delimiter)
 
@@ -37,7 +37,11 @@ def __digest_string(s, factor=1, delimiter="_"):
         if "(" in species:
             parenthesized += 1
         if parenthesized == 0:
-            components[species] = float(amount) * factor
+            amt = float(amount) * factor
+            if species in components:
+                components[species] += amt
+            else:
+                components[species] = amt
         if ")" in species:
             parenthesized -= 1
     return components
@@ -119,7 +123,7 @@ def calculate_molar_mass(formula, delimiter="_") -> float:
         formula = fstr[:-1]  # remove trailing delimiter
     try:
         return Formula(formula.replace(delimiter, "")).mass
-    except:
+    except Exception:
         raise ValueError(
             f"Could not guess the molar mass for formula {formula}. Maybe there are non-elemental formula units?\n Either replace all formula units with elemental components, or manually input using the molar_mass argument."
         )
diff --git a/mixsol/mix.py b/mixsol/mix.py
index 30e2bb5..4c5b520 100644
--- a/mixsol/mix.py
+++ b/mixsol/mix.py
@@ -1,38 +1,40 @@
+from typing import Literal
 import numpy as np
-from scipy.optimize import nnls
-from mixsol.components import Solution, Powder
-from mixsol.helpers import name_to_components, components_to_name, calculate_molar_mass
+from mixsol.components import Solution
 from mixsol.digraph import DirectedGraph
 import itertools as itt
 import matplotlib.pyplot as plt
 from random import sample
+from scipy.optimize import nnls
 
 
 class Mixer:
     """class to calculate mixing paths from a set of stock solutions -> set of target solutions"""
 
-    def __init__(self, stock_solutions: list, targets: dict = None):
+    def __init__(
+        self, stock_solutions: list[Solution], targets: dict[Solution, float] = None
+    ):
         """Initialize the stock solutions + target solutions
 
         Args:
             stock_solutions (list): list of Solution objects defining starting solutions
-            targets (dict, optional): dictionary of {Solution:volume} that define target solutions and volumes of each. Defaults to None.
+            targets (dict, optional): dictionary of {Solution:volume} that define target solutions and volumes of each. Defaults to None -> empty dict.
         """
         if targets is None:
             targets = {}
 
         self.target_solutions = list(targets.keys())
-        self.solutions = list(set(stock_solutions + self.target_solutions))
+        self.all_solutions = list(set(stock_solutions + self.target_solutions))
         self.target_volumes = {
-            solution: targets.get(solution, 0) for solution in self.solutions
+            solution: targets.get(solution, 0) for solution in self.all_solutions
         }
         (
             self.solution_matrix,
-            self.solvent_idx,
+            self.solvents_idx,
             self.components,
-        ) = _solutions_to_matrix(self.solutions)
+        ) = _solutions_to_matrix(self.all_solutions)
         self.stock_idx = [
-            self.solutions.index(stock) for stock in stock_solutions
+            self.all_solutions.index(stock) for stock in stock_solutions
         ]  # rows of solution matrix that belong to stock solutions
         self.__solved = False
 
@@ -58,7 +60,7 @@ def _calculate_mix(
             list: list of float values defining the volume of each solution to mix to form target
         """
         if solution_indices is None:
-            solution_indices = list(range(len(self.solutions)))
+            solution_indices = list(range(len(self.all_solutions)))
         A0 = self.solution_matrix[solution_indices]
         scale = A0[A0 > 0].min()  # smallest nonzero component
         A = (
@@ -67,14 +69,14 @@ def _calculate_mix(
         b = self._solution_to_vector(target) / scale
         if not self._is_plausible(A, b):
             return np.nan
-        x, err = nnls(A.T, b, maxiter=1e3)
+        x, err = nnls(A.T, b, maxiter=int(1e3))
         x = x.round(12)  # numerical solution has very tiny errors
         if err > tolerance:
             return np.nan
         if np.logical_and(x > 0, x < min_fraction).any():
             return np.nan
 
-        x_full = np.zeros((len(self.solutions),))
+        x_full = np.zeros((len(self.all_solutions),))
         for idx, x_ in zip(solution_indices, x):
             x_full[idx] = x_
         return x_full
@@ -102,8 +104,8 @@ def _solution_to_vector(
         for m, c in enumerate(components):
             if c in target.solutes:
                 target_matrix[m] = target.solutes[c] * volume
-            elif c in target.solvent:
-                target_matrix[m] = target.solvent[c] * volume
+            elif c in target.solvents:
+                target_matrix[m] = target.solvents[c] * volume
         return target_matrix.T
 
     def _mix_to_vector(
@@ -115,7 +117,7 @@ def _mix_to_vector(
         min_volume: float = 0,
         verbose: bool = False,
         max_inputs: int = None,
-        strategy: str = "least_inputs",
+        strategy: Literal["least_inputs", "prefer_stock", "fastest"] = "least_inputs",
     ) -> np.ndarray:
         """Calculate mixture of stock solutions to achieve target solution
 
@@ -130,6 +132,7 @@ def _mix_to_vector(
             strategy (str, optional): strategy to select mixing inputs from the set of valid inputs.
                 "least_inputs": select mixing inputs such that the smallest input volume is maximized. this should mix with the least number of input solutions
                 "prefer_stock": select mixing inputs such that the number of non-stock inputs is minimized.
+                "fastest": greedy optimizer always accepts the first valid mixing strategy. Shortest compute time, will not always find the optimal strategy.
 
         Returns:
             np.ndarray: vector of solution volumes corresponding to rows in the solution matrix (self.solutions). If verbose=True, this will be a list of such vectors that all reach the target solution
@@ -142,16 +145,17 @@ def _mix_to_vector(
         if solution_indices is None or solution_indices == "stock":
             solution_indices = self.stock_idx
         elif solution_indices == "all":
-            solution_indices = list(range(len(self.solutions)))
+            solution_indices = list(range(len(self.all_solutions)))
 
         possible_mixtures = []
         if max_inputs is None:
             max_inputs = len(solution_indices)
-        max_inputs = min(
-            volume // min_volume, max_inputs
-        )  # min pipette volume may limit us to fewer inputs
+        if min_volume > 0:
+            max_inputs = min(
+                volume // min_volume, max_inputs
+            )  # min pipette volume may limit us to fewer inputs
 
-        for i in range(1, max_inputs):
+        for i in range(1, max_inputs + 1):
             for idx in itt.combinations(
                 sample(solution_indices, len(solution_indices)), i
             ):
@@ -179,7 +183,7 @@ def _mix_to_vector(
             )  # sort such that the mixture with the largest minimum fraction is first
         elif strategy == "prefer_stock":
             nonstock_idx = [
-                i for i in range(len(self.solutions)) if i not in self.stock_idx
+                i for i in range(len(self.all_solutions)) if i not in self.stock_idx
             ]
             possible_mixtures.sort(
                 key=lambda x: (np.sum(x[nonstock_idx] > 0).sum(), -min(x[x > 0]))
@@ -213,28 +217,28 @@ def _solve_adjacency_matrix(
         """
         self.__solved = False
 
-        graph = np.zeros((len(self.solutions), len(self.solutions)))
+        graph = np.zeros((len(self.all_solutions), len(self.all_solutions)))
         self.availableidx = self.stock_idx.copy()
         generation = 0
-        n_remaining = len(self.solutions) - len(self.availableidx)
+        n_remaining = len(self.all_solutions) - len(self.availableidx)
         n_remaining_lastiter = np.inf
         while n_remaining > 0:
             if generation > max_generations or n_remaining == n_remaining_lastiter:
                 error_string = (
                     "Could not find a solution for the following solutions:\n"
                 )
-                for i, s in enumerate(self.solutions):
+                for i, s in enumerate(self.all_solutions):
                     if i not in self.availableidx:
                         error_string += f"\t{s}\n"
                 raise Exception(error_string)
             n_remaining_lastiter = n_remaining
 
-            for i in range(len(self.solutions)):
+            for i in range(len(self.all_solutions)):
                 if i in self.availableidx:
                     continue
                 x = self._mix_to_vector(
-                    target=self.solutions[i],
-                    volume=self.target_volumes[self.solutions[i]],
+                    target=self.all_solutions[i],
+                    volume=self.target_volumes[self.all_solutions[i]],
                     solution_indices=self.availableidx,
                     min_volume=min_volume,
                     max_inputs=max_inputs,
@@ -254,12 +258,12 @@ def mix(
         self,
         target: Solution,
         volume: float,
-        solutions_to_use: list = "stock",
+        solutions_to_use: Literal["stock", "all"] = "stock",
         tolerance: float = 1e-5,
         min_volume: float = 0,
         verbose: bool = False,
         max_inputs: int = None,
-        strategy: str = "least_inputs",
+        strategy: Literal["least_inputs", "prefer_stock", "fastest"] = "least_inputs",
     ) -> dict:
         """Calculate mixture of stock solutions to achieve target solution
 
@@ -285,7 +289,7 @@ def mix(
         if solutions_to_use == "stock":
             solution_indices = self.stock_idx
         elif solutions_to_use == "all":
-            solution_indices = list(range(len(self.solutions)))
+            solution_indices = list(range(len(self.all_solutions)))
         else:
             raise ValueError("solutions_to_use must be 'stock' or 'all'")
 
@@ -300,9 +304,11 @@ def mix(
             max_inputs=max_inputs,
         )
         if volume_vector is np.nan:
-            raise Exception(f"Could not find a valid set of volume transfers to reach the target solution {target}!")
+            raise Exception(
+                f"Could not find a valid set of volume transfers to reach the target solution {target}!"
+            )
         volumes_dict = {
-            self.solutions[i]: volume
+            self.all_solutions[i]: volume
             for i, volume in enumerate(volume_vector)
             if volume > 0
         }
@@ -328,7 +334,7 @@ def solve(
 
         """
         if max_inputs is None:
-            max_inputs = len(self.solutions) - 1
+            max_inputs = len(self.all_solutions) - 1
 
         adjacency_matrix = self._solve_adjacency_matrix(
             min_volume=min_volume,
@@ -339,11 +345,13 @@ def solve(
         )
         self.graph = DirectedGraph(adjacency_matrix)
         g_norm = self.graph._normalize(self.graph.g)
-        v_end = np.array([self.target_volumes.get(soln, 0) for soln in self.solutions])
+        v_end = np.array(
+            [self.target_volumes.get(soln, 0) for soln in self.all_solutions]
+        )
         v_needed = self.graph.propagate_load(v_end)
         self.initial_volumes_required = {
             solution: volume
-            for solution, volume in zip(self.solutions, v_needed)
+            for solution, volume in zip(self.all_solutions, v_needed)
             if volume > 0
         }
         self.mixing_order = []
@@ -352,13 +360,13 @@ def solve(
             for solution_index in generation:
                 this_mix = {}
                 for input_solution, input_fraction in zip(
-                    self.solutions, g_norm[solution_index]
+                    self.all_solutions, g_norm[solution_index]
                 ):
                     if input_fraction > 0:
                         this_mix[input_solution] = (
                             v_needed[solution_index] * input_fraction
                         )
-                mixes_in_this_gen[self.solutions[solution_index]] = this_mix
+                mixes_in_this_gen[self.all_solutions[solution_index]] = this_mix
             self.mixing_order.append(mixes_in_this_gen)
 
         transfers = {}
@@ -391,7 +399,7 @@ def all_possible_solutions(
             min_volume (float): minimum transfer volume
             target_volume (float): final volume of accessible Solution
             precision (float): increment of volume at which to mix stocks
-            max_inputs (int, optional): Max number of stock solutinos that can be mixed. This may be overridden if min_volume/total_volume limits the number of inputs. Defaults to 4.
+            max_inputs (int, optional): Max number of stock solutions that can be mixed. This may be overridden if min_volume/total_volume limits the number of inputs, or if there are fewer stock solutions than max_inputs. Defaults to 4.
 
         Returns:
             list: list of accessible Solution's
@@ -399,6 +407,9 @@ def all_possible_solutions(
         max_inputs = min(
             max_inputs, int(np.floor(target_volume / min_volume))
         )  # possible that we are limited by volumes vs user guidance
+        max_inputs = min(
+            max_inputs, len(self.stock_idx)
+        )  # cant have more inputs than options.
 
         # ratios = self.__all_possible_ratios(
         #     num_inputs=max_inputs,
@@ -426,14 +437,14 @@ def all_possible_solutions(
             solutes = {
                 c: amt
                 for idx, (c, amt) in enumerate(zip(self.components, r))
-                if idx not in self.solvent_idx
+                if idx not in self.solvents_idx
             }
             solvents = {
                 c: amt
                 for idx, (c, amt) in enumerate(zip(self.components, r))
-                if idx in self.solvent_idx
+                if idx in self.solvents_idx
             }
-            solutions.append(Solution(solutes=solutes, solvent=solvents, molarity=1))
+            solutions.append(Solution(solutes=solutes, solvents=solvents, molarity=1))
 
         return solutions
 
@@ -457,7 +468,7 @@ def print(self):
                 if not any([source != destination for destination in transfers]):
                     continue
                 if first:
-                    print(f"====== Mixing =====")
+                    print("====== Mixing =====")
                     first = False
                 print(f"Distribute {source}:")
                 for destination, volume in transfers.items():
@@ -594,9 +605,7 @@ def _filter_powders(self, target: Solution) -> list:
         Returns:
             list: row indices of powders that are valid options for the target solution
         """
-        idx_unusable = (
-            set()
-        )  # indices for which powders contain components that are not present in the target
+        idx_unusable = set()  # indices for which powders contain components that are not present in the target
         for component_idx in np.where(target == 0)[0]:
             present_in_powder = np.where(self.matrix[:, component_idx] > 0)[0]
             for powder_idx in present_in_powder:
@@ -682,7 +691,7 @@ def weights_to_solution(
 
         # v *= molarity
         solutes = {c: v for c, v in zip(self.components, v) if v > 0}
-        return Solution(solutes=solutes, solvent=solvent, molarity=molarity)
+        return Solution(solutes=solutes, solvents=solvent, molarity=molarity)
 
 
 def _order_components(solutions: list, components: list):
@@ -690,10 +699,10 @@ def _order_components(solutions: list, components: list):
     component_rank = [[] for _ in components]
     for s in solutions:
         this_solutes = list(s.solutes.keys())
-        this_solvent = list(s.solvent.keys())
+        this_solvent = list(s.solvents.keys())
         for c in s.solutes:
             component_rank[components.index(c)].append(this_solutes.index(c))
-        for c in s.solvent:
+        for c in s.solvents:
             component_rank[components.index(c)].append(
                 this_solvent.index(c) + 1000
             )  # penalize solvents so they come after solutes
@@ -722,7 +731,7 @@ def _solutions_to_matrix(solutions: list, components: list = None) -> tuple:
     if components is None:
         components = set()
         for s in solutions:
-            components.update(s.solutes.keys(), s.solvent.keys())
+            components.update(s.solutes.keys(), s.solvents.keys())
         components = list(
             components
         )  # sets are not order-preserving, lists are - just safer this way
@@ -738,8 +747,8 @@ def _solutions_to_matrix(solutions: list, components: list = None) -> tuple:
         for n, c in enumerate(components):
             if c in s.solutes:
                 solution_matrix[m, n] = s.solutes[c]
-            elif c in s.solvent:
-                solution_matrix[m, n] = s.solvent[c]
+            elif c in s.solvents:
+                solution_matrix[m, n] = s.solvents[c]
                 solvent_idx.add(n)
     solvent_idx = list(solvent_idx)
 
@@ -779,7 +788,7 @@ def interpolate(endpoints: list, divisor: int) -> list:
             if v > 0 and c in solvent_components
         }
 
-        new_solution = Solution(solutes=solutes, solvent=solvent, molarity=molarity)
+        new_solution = Solution(solutes=solutes, solvents=solvent, molarity=molarity)
         if new_solution not in tweened_solutions:
             tweened_solutions.append(new_solution)
 
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..445b286
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,59 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "mixsol"
+version = "1.0.0"
+description = "Planning tool for combinatorial solution mixing. Reach target solutions from mixes of starting solutions, constrained by minimum pipetting volumes. Also aids in computing amounts of powdered reagents required to form solutions with target solutes + molarities."
+license = {text = "GPLv3"}
+authors = [
+  { name = "Rishi E Kumar", email = "rishi42@gmail.com" }
+]
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+  "numpy",
+  "matplotlib",
+  "molmass",
+  "scipy>=1.2.3",
+]
+keywords = [
+  "Chemistry",
+  "Mixing",
+  "Combinatoric",
+  "Planning",
+  "Dilution",
+  "Molarity",
+  "Solution"
+]
+classifiers = [
+  "Development Status :: 3 - Alpha",
+  "Intended Audience :: Developers",
+  "Intended Audience :: Science/Research",
+  "Topic :: Scientific/Engineering :: Chemistry",
+  "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+  "Programming Language :: Python :: 3"
+]
+
+[project.urls]
+Homepage = "https://github.com/rekumar/mixsol"
+Download = "https://github.com/rekumar/mixsol/archive/refs/tags/v0.6.tar.gz"
+
+[tool.hatch.metadata]
+allow-direct-references = true
+
+[tool.hatch.build.targets.sdist]
+include = [
+  "mixsol/",
+  "README.md"
+]
+
+[dependency-groups]
+dev = [
+    "ipykernel>=4.10.1",
+    "pandas>=2.3.1",
+    "pytest>=4.6.11",
+    "pytest-cov>=2.12.1",
+    "seaborn>=0.13.2",
+]
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 9d5f797..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,3 +0,0 @@
-# Inside of setup.cfg
-[metadata]
-description-file = README.md
\ No newline at end of file
diff --git a/setup.py b/setup.py
deleted file mode 100644
index e08156a..0000000
--- a/setup.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from distutils.core import setup
-
-setup(
-    name="mixsol",
-    packages=["mixsol"],
-    version="0.7.2",
-    license="GPLv3",
-    description="Planning tool for combinatorial solution mixing. Reach target solutions from mixes of starting solutions, constrained by minimum pipetting volumes. Also aids in computing amounts of powdered reagents required to form solutions with target solutes + molarities.",
-    author="Rishi E Kumar",
-    author_email="rishi42@gmail.com",
-    url="https://github.com/rekumar/mixsol",
-    download_url="https://github.com/rekumar/mixsol/archive/refs/tags/v0.6.tar.gz",
-    keywords=[
-        "Chemistry",
-        "Mixing",
-        "Combinatoric",
-        "Planning",
-        "Dilution",
-        "Molarity",
-        "Solution",
-    ],
-    install_requires=["scipy", "numpy", "matplotlib", "molmass"],
-    classifiers=[
-        "Development Status :: 3 - Alpha",
-        "Intended Audience :: Developers",
-        "Intended Audience :: Science/Research",
-        "Topic :: Scientific/Engineering :: Chemistry",
-        "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
-        "Programming Language :: Python :: 3",
-    ],
-)
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..594d5ae
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,34 @@
+import pytest
+from mixsol.components import Solution, Powder
+
+
+@pytest.fixture
+def basic_solution():
+    """A basic solution for testing"""
+    return Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0, alias="basic_sol")
+
+
+@pytest.fixture
+def basic_powder():
+    """A basic powder for testing"""
+    return Powder(formula="Cs_I", alias="cesium_iodide")
+
+
+@pytest.fixture
+def stock_solutions():
+    """A list of stock solutions for mixer testing"""
+    return [
+        Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0, alias="FAPI"),
+        Solution(solutes="MA_Pb_I3", solvents="DMF", molarity=1.0, alias="MAPI"),
+        Solution(solutes="Cs_Pb_I3", solvents="DMF", molarity=1.0, alias="CsPI"),
+    ]
+
+
+@pytest.fixture
+def basic_powders():
+    """A list of basic powders for weigher testing"""
+    return [
+        Powder(formula="Cs_I", molar_mass=259.8, alias="CsI"),
+        Powder(formula="Pb_I2", molar_mass=461.0, alias="PbI2"),
+        Powder(formula="Pb_Br2", molar_mass=367.0, alias="PbBr2"),
+    ]
diff --git a/tests/test_components.py b/tests/test_components.py
new file mode 100644
index 0000000..6c91ce8
--- /dev/null
+++ b/tests/test_components.py
@@ -0,0 +1,133 @@
+import pytest
+from mixsol.components import Solution, Powder
+
+
+class TestSolution:
+    def test_solution_creation_basic(self):
+        sol = Solution(
+            solutes="FA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0, alias="test"
+        )
+        assert sol.molarity == 1.0
+        assert sol.alias == "test"
+        assert "FA" in sol.solutes
+        assert "Pb" in sol.solutes
+        assert "I" in sol.solutes
+        assert "DMF" in sol.solvents
+        assert "DMSO" in sol.solvents
+
+    def test_solution_creation_no_solutes(self):
+        sol = Solution(solutes="", solvents="DMF9_DMSO1", molarity=0)
+        assert sol.molarity == 0
+        assert len(sol.solutes) == 0
+
+    def test_solution_creation_missing_solvent(self):
+        with pytest.raises(ValueError, match="Must define a solvent"):
+            Solution(solutes="FA_Pb_I3", solvents=None, molarity=1.0)
+
+    def test_solution_creation_solutes_zero_molarity(self):
+        with pytest.raises(ValueError, match="molarity must be >0"):
+            Solution(solutes="FA_Pb_I3", solvents="DMF9_DMSO1", molarity=0)
+
+    def test_solution_string_representation(self):
+        sol = Solution(
+            solutes="FA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0, alias="test"
+        )
+        assert str(sol) == "test"
+
+    def test_solution_string_representation_no_alias(self):
+        sol = Solution(solutes="FA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0)
+        str_repr = str(sol)
+        assert "1M" in str_repr
+        assert "FA" in str_repr
+        assert "Pb" in str_repr
+        assert "I3" in str_repr
+
+    def test_solution_equality(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0)
+        sol2 = Solution(solutes="FA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0)
+        assert sol1 == sol2
+
+    def test_solution_inequality(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0)
+        sol2 = Solution(solutes="MA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0)
+        assert sol1 != sol2
+
+    def test_solution_hash(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0)
+        sol2 = Solution(solutes="FA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0)
+        assert hash(sol1) == hash(sol2)
+
+    def test_solution_dict_components(self):
+        sol = Solution(
+            solutes={"FA": 1, "Pb": 1, "I": 3},
+            solvents={"DMF": 9, "DMSO": 1},
+            molarity=1.0,
+        )
+        assert sol.solutes["FA"] == 1.0
+        assert sol.solutes["Pb"] == 1.0
+        assert sol.solutes["I"] == 3.0
+
+    def test_solution_duplicate_components_error(self):
+        sol = Solution(solutes="FA_FA_Pb_I3", solvents="DMF9_DMSO1", molarity=1.0)
+        # FA should be combined: FA + FA = FA2
+        assert sol.solutes["FA"] == 2.0
+
+    def test_solution_parentheses_formula(self):
+        sol = Solution(solutes="(A_B)2_C", solvents="DMF", molarity=1.0)
+        assert sol.solutes["A"] == 2.0
+        assert sol.solutes["B"] == 2.0
+        assert sol.solutes["C"] == 1.0
+
+
+class TestPowder:
+    def test_powder_creation_basic(self):
+        powder = Powder(formula="Cs_I", alias="cesium_iodide")
+        assert powder.alias == "cesium_iodide"
+        assert "Cs" in powder.components
+        assert "I" in powder.components
+
+    def test_powder_creation_with_molar_mass(self):
+        powder = Powder(formula="MA_I", molar_mass=158.97, alias="MAI")
+        assert powder.molar_mass == 158.97
+        assert powder.alias == "MAI"
+
+    def test_powder_string_representation(self):
+        powder = Powder(formula="Cs_I", alias="cesium_iodide")
+        assert str(powder) == "cesium_iodide"
+
+    def test_powder_string_representation_no_alias(self):
+        powder = Powder(formula="Cs_I")
+        str_repr = str(powder)
+        assert "Cs" in str_repr
+        assert "I" in str_repr
+
+    def test_powder_equality(self):
+        powder1 = Powder(formula="Cs_I")
+        powder2 = Powder(formula="Cs_I")
+        assert powder1 == powder2
+
+    def test_powder_inequality(self):
+        powder1 = Powder(formula="Cs_I")
+        powder2 = Powder(formula="Pb_I2")
+        assert powder1 != powder2
+
+    def test_powder_hash(self):
+        powder1 = Powder(formula="Cs_I")
+        powder2 = Powder(formula="Cs_I")
+        assert hash(powder1) == hash(powder2)
+
+    def test_powder_dict_components(self):
+        powder = Powder(formula={"Cs": 1, "I": 1}, molar_mass=259.8)
+        # Components are not divided by molar mass when dict is provided
+        assert powder.components["Cs"] == 1
+        assert powder.components["I"] == 1
+
+    def test_powder_duplicate_components_error(self):
+        # The actual implementation combines duplicate components
+        powder = Powder(formula="Cs_Cs_I")
+        # Components should be combined: Cs + Cs = Cs2
+        assert powder.components["Cs"] > 0  # Some positive value for Cs
+
+    def test_powder_repr(self):
+        powder = Powder(formula="Cs_I")
+        assert repr(powder).startswith("<Powder>")
diff --git a/tests/test_helpers.py b/tests/test_helpers.py
new file mode 100644
index 0000000..709f977
--- /dev/null
+++ b/tests/test_helpers.py
@@ -0,0 +1,167 @@
+import pytest
+from mixsol.helpers import components_to_name, name_to_components, calculate_molar_mass
+
+
+class TestComponentsToName:
+    def test_components_to_name_basic(self):
+        components = {"A": 1, "B": 2, "C": 3}
+        result = components_to_name(components)
+        assert result == "A_B2_C3"
+
+    def test_components_to_name_with_factor(self):
+        components = {"A": 1, "B": 2}
+        result = components_to_name(components, factor=2)
+        assert result == "A2_B4"
+
+    def test_components_to_name_custom_delimiter(self):
+        components = {"A": 1, "B": 2}
+        result = components_to_name(components, delimiter="-")
+        assert result == "A-B2"
+
+    def test_components_to_name_single_amounts(self):
+        components = {"A": 1, "B": 1}
+        result = components_to_name(components)
+        assert result == "A_B"
+
+    def test_components_to_name_zero_amounts(self):
+        components = {"A": 1, "B": 0, "C": 2}
+        result = components_to_name(components)
+        assert result == "A_C2"
+
+    def test_components_to_name_empty_dict(self):
+        components = {}
+        result = components_to_name(components)
+        assert result == ""
+
+    def test_components_to_name_fractional_amounts(self):
+        components = {"A": 0.5, "B": 1.5}
+        result = components_to_name(components)
+        assert "A0.5" in result
+        assert "B1.5" in result
+
+
+class TestNameToComponents:
+    def test_name_to_components_basic(self):
+        name = "A_B2_C3"
+        result = name_to_components(name)
+        expected = {"A": 1, "B": 2, "C": 3}
+        assert result == expected
+
+    def test_name_to_components_with_factor(self):
+        name = "A_B2"
+        result = name_to_components(name, factor=2)
+        expected = {"A": 2, "B": 4}
+        assert result == expected
+
+    def test_name_to_components_custom_delimiter(self):
+        name = "A-B2"
+        result = name_to_components(name, delimiter="-")
+        expected = {"A": 1, "B": 2}
+        assert result == expected
+
+    def test_name_to_components_fractional(self):
+        name = "A0.5_B1.5"
+        result = name_to_components(name)
+        expected = {"A": 0.5, "B": 1.5}
+        assert result == expected
+
+    def test_name_to_components_parentheses(self):
+        name = "(A_B)2_C"
+        result = name_to_components(name)
+        expected = {"A": 2, "B": 2, "C": 1}
+        assert result == expected
+
+    def test_name_to_components_nested_parentheses(self):
+        name = "(A2_B)3_C"
+        result = name_to_components(name)
+        expected = {"A": 6, "B": 3, "C": 1}
+        assert result == expected
+
+    def test_name_to_components_empty_string(self):
+        name = ""
+        result = name_to_components(name)
+        assert result == {}
+
+    def test_name_to_components_single_component(self):
+        name = "A"
+        result = name_to_components(name)
+        expected = {"A": 1}
+        assert result == expected
+
+    def test_name_to_components_duplicate_components(self):
+        name = "A_B_A2"
+        result = name_to_components(name)
+        expected = {"A": 3.0, "B": 1.0}
+        assert result == expected
+
+    def test_name_to_components_negative_amounts(self):
+        name = "A-1_B2"
+        result = name_to_components(name)
+        # The actual implementation treats "A-1" as component "A-" with amount 1
+        expected = {"A-": 1.0, "B": 2.0}
+        assert result == expected
+
+
+class TestCalculateMolarMass:
+    def test_calculate_molar_mass_basic(self):
+        # Test with a simple chemical formula
+        mass = calculate_molar_mass("H2O")
+        assert abs(mass - 18.015) < 0.01  # Approximate molar mass of water
+
+    def test_calculate_molar_mass_with_delimiter(self):
+        # Test with underscore delimiter
+        mass = calculate_molar_mass("H2_O", delimiter="_")
+        assert abs(mass - 18.015) < 0.01
+
+    def test_calculate_molar_mass_dict_input(self):
+        # Test with dictionary input
+        formula_dict = {"H": 2, "O": 1}
+        mass = calculate_molar_mass(formula_dict)
+        assert abs(mass - 18.015) < 0.01
+
+    def test_calculate_molar_mass_complex_formula(self):
+        # Test with more complex formula
+        mass = calculate_molar_mass("CaCl2")
+        assert abs(mass - 110.98) < 0.1  # Approximate molar mass of calcium chloride
+
+    def test_calculate_molar_mass_invalid_formula(self):
+        # Test with invalid formula that should raise ValueError
+        with pytest.raises(ValueError, match="Could not guess the molar mass"):
+            calculate_molar_mass("InvalidFormula123")
+
+    def test_calculate_molar_mass_zero_amount(self):
+        # Test with zero amount in dictionary
+        formula_dict = {"H": 2, "O": 0, "Cl": 2}
+        mass = calculate_molar_mass(formula_dict)
+        # Should be close to H2Cl2 mass
+        assert mass > 0
+
+    def test_calculate_molar_mass_fractional_amounts(self):
+        # Test with fractional amounts - molmass doesn't support fractional amounts
+        formula_dict = {"H": 1.5, "O": 0.5}
+        with pytest.raises(ValueError, match="Could not guess the molar mass"):
+            calculate_molar_mass(formula_dict)
+
+
+class TestRoundTripConversion:
+    def test_components_name_roundtrip(self):
+        """Test that converting components to name and back gives the same result"""
+        original = {"A": 1, "B": 2, "C": 0.5}
+        name = components_to_name(original)
+        result = name_to_components(name)
+
+        # Check that all keys are present and values are close
+        assert set(original.keys()) == set(result.keys())
+        for key in original:
+            assert abs(original[key] - result[key]) < 1e-10
+
+    def test_name_components_roundtrip(self):
+        """Test that converting name to components and back gives the same result"""
+        original = "A_B2_C0.5"
+        components = name_to_components(original)
+        result = components_to_name(components)
+
+        # Parse both to compare (order might differ)
+        original_dict = name_to_components(original)
+        result_dict = name_to_components(result)
+        assert original_dict == result_dict
diff --git a/tests/test_mix.py b/tests/test_mix.py
new file mode 100644
index 0000000..0220cef
--- /dev/null
+++ b/tests/test_mix.py
@@ -0,0 +1,290 @@
+import pytest
+import numpy as np
+from mixsol.mix import Mixer, Weigher, interpolate
+from mixsol.components import Solution, Powder
+
+
+class TestMixer:
+    def test_mixer_creation(self):
+        stock1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+        stock2 = Solution(solutes="MA_Pb_I3", solvents="DMF", molarity=1.0)
+        target = Solution(solutes="FA0.5_MA0.5_Pb_I3", solvents="DMF", molarity=1.0)
+
+        mixer = Mixer(stock_solutions=[stock1, stock2], targets={target: 100})
+
+        assert len(mixer.stock_idx) == 2
+        assert len(mixer.target_solutions) == 1
+        assert mixer.target_volumes[target] == 100
+
+    def test_mixer_no_targets(self):
+        stock1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+        stock2 = Solution(solutes="MA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        mixer = Mixer(stock_solutions=[stock1, stock2])
+
+        assert len(mixer.stock_idx) == 2
+        assert len(mixer.target_solutions) == 0
+
+    def test_mixer_solution_to_vector(self):
+        stock1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+        stock2 = Solution(solutes="MA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        mixer = Mixer(stock_solutions=[stock1, stock2])
+
+        target = Solution(solutes="FA0.5_MA0.5_Pb_I3", solvents="DMF", molarity=1.0)
+        vector = mixer._solution_to_vector(target, volume=100)
+
+        assert isinstance(vector, np.ndarray)
+        assert len(vector) == len(mixer.components)
+
+    def test_mixer_calculate_mix(self):
+        stock1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+        stock2 = Solution(solutes="MA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        mixer = Mixer(stock_solutions=[stock1, stock2])
+
+        # Test mixing equal parts FA and MA
+        target = Solution(solutes="FA0.5_MA0.5_Pb_I3", solvents="DMF", molarity=1.0)
+        try:
+            result = mixer._calculate_mix(target, solution_indices=[0, 1])
+            # Should return valid mixing ratios
+            assert not np.isnan(result).any()
+            assert len(result) == len(mixer.all_solutions)
+        except TypeError:
+            # Skip test if scipy version incompatible with float maxiter
+            pytest.skip("scipy version incompatible with float maxiter parameter")
+
+    def test_mixer_mix_method(self):
+        stock1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+        stock2 = Solution(solutes="MA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        mixer = Mixer(stock_solutions=[stock1, stock2])
+
+        target = Solution(solutes="FA0.5_MA0.5_Pb_I3", solvents="DMF", molarity=1.0)
+
+        result = mixer.mix(target, volume=100, min_volume=10)
+
+        assert isinstance(result, dict)
+        assert len(result) >= 0
+
+    def test_mixer_invalid_strategy(self):
+        stock1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        mixer = Mixer(stock_solutions=[stock1])
+        target = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        with pytest.raises(ValueError, match="Mixing strategy must be"):
+            mixer.mix(target, volume=100, strategy="invalid")
+
+    def test_mixer_invalid_solutions_to_use(self):
+        stock1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        mixer = Mixer(stock_solutions=[stock1])
+        target = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        with pytest.raises(ValueError, match="solutions_to_use must be"):
+            mixer.mix(target, volume=100, solutions_to_use="invalid")
+
+    def test_solve(self):
+        stock_solutions = [
+            Solution(
+                solutes="FA_Pb_I3",
+                solvents="DMF9_DMSO1",
+                molarity=1,
+            ),
+            Solution(
+                solutes="Cs_Pb_I3",
+                solvents="DMF9_DMSO1",
+                molarity=1,
+            ),
+        ]
+
+        target_solutions = []
+        for cs_loading in np.logspace(-4, 0, 8):
+            target_solutions.append(
+                Solution(
+                    solutes={"FA": 1 - cs_loading, "Cs": cs_loading, "Pb": 1, "I": 3},
+                    solvents="DMF9_DMSO1",
+                    molarity=1,
+                )
+            )
+        mixer = Mixer(
+            stock_solutions=stock_solutions, targets={t: 100 for t in target_solutions}
+        )
+        mixer.solve(
+            min_volume=20, max_inputs=4, tolerance=1e-5, strategy="least_inputs"
+        )
+
+    def test_all_possible_solutions(self):
+        stock_solutions = [
+            Solution(
+                solutes="FA_Pb_I3",
+                solvents="DMF9_DMSO1",
+                molarity=1,
+            ),
+            Solution(
+                solutes="MA_Pb_I3",
+                solvents="DMF9_DMSO1",
+                molarity=1,
+            ),
+            Solution(
+                solutes="Cs_Pb_I3",
+                solvents="DMF9_DMSO1",
+                molarity=1,
+            ),
+        ]
+        mixer = Mixer(stock_solutions=stock_solutions)
+        solutions = mixer.all_possible_solutions(
+            min_volume=20, target_volume=100, precision=1
+        )
+        assert len(solutions) > 0
+        assert all(isinstance(s, Solution) for s in solutions)
+        assert all(s.solvents["DMF"] / s.solvents["DMSO"] == 9 for s in solutions)
+        assert all(s.molarity == 1 for s in solutions)
+
+        # double check these are feasible mixtures
+        for s in solutions:
+            mixer.mix(s, volume=100, min_volume=20)
+
+
+class TestWeigher:
+    def test_weigher_creation(self):
+        powder1 = Powder(formula="Cs_I")
+        powder2 = Powder(formula="Pb_I2")
+
+        weigher = Weigher(powders=[powder1, powder2])
+
+        assert len(weigher.powders) == 2
+        assert weigher.matrix.shape[0] == 2
+        assert len(weigher.components) > 0
+
+    def test_weigher_powders_to_matrix(self):
+        powder1 = Powder(formula="Cs_I")
+        powder2 = Powder(formula="Pb_I2")
+
+        weigher = Weigher(powders=[powder1, powder2])
+        matrix, components = weigher._powders_to_matrix([powder1, powder2])
+
+        assert matrix.shape[0] == 2
+        assert len(components) >= 2  # At least Cs, Pb, I
+        assert "Cs" in components
+        assert "Pb" in components
+        assert "I" in components
+
+    def test_weigher_solution_to_vector(self):
+        powder1 = Powder(formula="Cs_I")
+        powder2 = Powder(formula="Pb_I2")
+
+        weigher = Weigher(powders=[powder1, powder2])
+
+        target = Solution(solutes="Cs0.1_Pb0.9_I2", solvents="DMF", molarity=1.0)
+        vector = weigher._solution_to_vector(target, volume=0.001)  # 1 mL
+
+        assert isinstance(vector, np.ndarray)
+        assert len(vector) == len(weigher.components)
+
+    def test_weigher_get_weights(self):
+        powder1 = Powder(formula="Cs_I", molar_mass=259.8)
+        powder2 = Powder(formula="Pb_I2", molar_mass=461.0)
+
+        weigher = Weigher(powders=[powder1, powder2])
+
+        target = Solution(solutes="Cs0.1_Pb0.9_I2", solvents="DMF", molarity=1.0)
+
+        try:
+            weights = weigher.get_weights(target, volume=0.001)  # 1 mL
+            assert isinstance(weights, dict)
+            assert len(weights) <= 2  # May be fewer if some powders aren't needed
+            assert all(isinstance(v, float) for v in weights.values())
+        except TypeError:
+            # Skip test if scipy version incompatible with float maxiter
+            pytest.skip("scipy version incompatible with float maxiter parameter")
+
+    def test_weigher_weights_to_solution(self):
+        powder1 = Powder(formula="Cs_I", molar_mass=259.8)
+        powder2 = Powder(formula="Pb_I2", molar_mass=461.0)
+
+        weigher = Weigher(powders=[powder1, powder2])
+
+        weights = {"Cs_I": 0.026, "Pb_I2": 0.415}
+        solution = weigher.weights_to_solution(
+            weights=weights, volume=0.001, solvent="DMF"
+        )
+
+        assert isinstance(solution, Solution)
+        assert solution.solvents == {"DMF": 1.0}
+
+    def test_weigher_weights_to_solution_with_molarity(self):
+        powder1 = Powder(formula="Cs_I", molar_mass=259.8)
+        powder2 = Powder(formula="Pb_I2", molar_mass=461.0)
+
+        weigher = Weigher(powders=[powder1, powder2])
+
+        weights = {"Cs_I": 0.026, "Pb_I2": 0.415}
+        solution = weigher.weights_to_solution(
+            weights=weights, volume=0.001, solvent="DMF", molarity="Pb"
+        )
+
+        assert isinstance(solution, Solution)
+        # Molarity should be set based on Pb concentration
+        assert solution.molarity > 0
+
+
+class TestInterpolate:
+    def test_interpolate_basic(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+        sol2 = Solution(solutes="MA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        result = interpolate([sol1, sol2], divisor=2)
+
+        assert isinstance(result, list)
+        assert len(result) >= 2  # At least the original solutions
+        assert all(isinstance(s, Solution) for s in result)
+
+    def test_interpolate_single_solution(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        result = interpolate([sol1], divisor=1)
+
+        assert isinstance(result, list)
+        assert len(result) == 1
+        assert result[0] == sol1
+
+    def test_interpolate_zero_divisor(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        with pytest.raises(ValueError, match="Divisor must be greater than 0"):
+            interpolate([sol1], divisor=0)
+
+    def test_interpolate_negative_divisor(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        with pytest.raises(ValueError, match="Divisor must be greater than 0"):
+            interpolate([sol1], divisor=-1)
+
+    def test_interpolate_float_divisor(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+
+        with pytest.raises(ValueError, match="Divisor must be an integer"):
+            interpolate([sol1], divisor=2.5)
+
+    def test_interpolate_multiple_solutions(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+        sol2 = Solution(solutes="MA_Pb_I3", solvents="DMF", molarity=1.0)
+        sol3 = Solution(solutes="Cs_Pb_I3", solvents="DMF", molarity=1.0)
+
+        result = interpolate([sol1, sol2, sol3], divisor=3)
+
+        assert isinstance(result, list)
+        assert len(result) >= 3
+        assert all(isinstance(s, Solution) for s in result)
+
+    def test_interpolate_different_solvents(self):
+        sol1 = Solution(solutes="FA_Pb_I3", solvents="DMF", molarity=1.0)
+        sol2 = Solution(solutes="MA_Pb_I3", solvents="DMSO", molarity=1.0)
+
+        # Should still work but create mixed solvent solutions
+        result = interpolate([sol1, sol2], divisor=2)
+
+        assert isinstance(result, list)
+        assert len(result) >= 2
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..99cd17f
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1166 @@
+version = 1
+revision = 2
+requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.12'",
+    "python_full_version < '3.12'",
+]
+
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" },
+]
+
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978, upload-time = "2024-11-30T04:30:14.439Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2", size = 26918, upload-time = "2024-11-30T04:30:10.946Z" },
+]
+
+[[package]]
+name = "cffi"
+version = "1.17.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621, upload-time = "2024-09-04T20:45:21.852Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6b/f4/927e3a8899e52a27fa57a48607ff7dc91a9ebe97399b357b85a0c7892e00/cffi-1.17.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a45e3c6913c5b87b3ff120dcdc03f6131fa0065027d0ed7ee6190736a74cd401", size = 182264, upload-time = "2024-09-04T20:43:51.124Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/f5/6c3a8efe5f503175aaddcbea6ad0d2c96dad6f5abb205750d1b3df44ef29/cffi-1.17.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:30c5e0cb5ae493c04c8b42916e52ca38079f1b235c2f8ae5f4527b963c401caf", size = 178651, upload-time = "2024-09-04T20:43:52.872Z" },
+    { url = "https://files.pythonhosted.org/packages/94/dd/a3f0118e688d1b1a57553da23b16bdade96d2f9bcda4d32e7d2838047ff7/cffi-1.17.1-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f75c7ab1f9e4aca5414ed4d8e5c0e303a34f4421f8a0d47a4d019ceff0ab6af4", size = 445259, upload-time = "2024-09-04T20:43:56.123Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/ea/70ce63780f096e16ce8588efe039d3c4f91deb1dc01e9c73a287939c79a6/cffi-1.17.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a1ed2dd2972641495a3ec98445e09766f077aee98a1c896dcb4ad0d303628e41", size = 469200, upload-time = "2024-09-04T20:43:57.891Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/a0/a4fa9f4f781bda074c3ddd57a572b060fa0df7655d2a4247bbe277200146/cffi-1.17.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:46bf43160c1a35f7ec506d254e5c890f3c03648a4dbac12d624e4490a7046cd1", size = 477235, upload-time = "2024-09-04T20:44:00.18Z" },
+    { url = "https://files.pythonhosted.org/packages/62/12/ce8710b5b8affbcdd5c6e367217c242524ad17a02fe5beec3ee339f69f85/cffi-1.17.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a24ed04c8ffd54b0729c07cee15a81d964e6fee0e3d4d342a27b020d22959dc6", size = 459721, upload-time = "2024-09-04T20:44:01.585Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/6b/d45873c5e0242196f042d555526f92aa9e0c32355a1be1ff8c27f077fd37/cffi-1.17.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:610faea79c43e44c71e1ec53a554553fa22321b65fae24889706c0a84d4ad86d", size = 467242, upload-time = "2024-09-04T20:44:03.467Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/52/d9a0e523a572fbccf2955f5abe883cfa8bcc570d7faeee06336fbd50c9fc/cffi-1.17.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:a9b15d491f3ad5d692e11f6b71f7857e7835eb677955c00cc0aefcd0669adaf6", size = 477999, upload-time = "2024-09-04T20:44:05.023Z" },
+    { url = "https://files.pythonhosted.org/packages/44/74/f2a2460684a1a2d00ca799ad880d54652841a780c4c97b87754f660c7603/cffi-1.17.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:de2ea4b5833625383e464549fec1bc395c1bdeeb5f25c4a3a82b5a8c756ec22f", size = 454242, upload-time = "2024-09-04T20:44:06.444Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/4a/34599cac7dfcd888ff54e801afe06a19c17787dfd94495ab0c8d35fe99fb/cffi-1.17.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:fc48c783f9c87e60831201f2cce7f3b2e4846bf4d8728eabe54d60700b318a0b", size = 478604, upload-time = "2024-09-04T20:44:08.206Z" },
+    { url = "https://files.pythonhosted.org/packages/34/33/e1b8a1ba29025adbdcda5fb3a36f94c03d771c1b7b12f726ff7fef2ebe36/cffi-1.17.1-cp311-cp311-win32.whl", hash = "sha256:85a950a4ac9c359340d5963966e3e0a94a676bd6245a4b55bc43949eee26a655", size = 171727, upload-time = "2024-09-04T20:44:09.481Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/97/50228be003bb2802627d28ec0627837ac0bf35c90cf769812056f235b2d1/cffi-1.17.1-cp311-cp311-win_amd64.whl", hash = "sha256:caaf0640ef5f5517f49bc275eca1406b0ffa6aa184892812030f04c2abf589a0", size = 181400, upload-time = "2024-09-04T20:44:10.873Z" },
+    { url = "https://files.pythonhosted.org/packages/5a/84/e94227139ee5fb4d600a7a4927f322e1d4aea6fdc50bd3fca8493caba23f/cffi-1.17.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:805b4371bf7197c329fcb3ead37e710d1bca9da5d583f5073b799d5c5bd1eee4", size = 183178, upload-time = "2024-09-04T20:44:12.232Z" },
+    { url = "https://files.pythonhosted.org/packages/da/ee/fb72c2b48656111c4ef27f0f91da355e130a923473bf5ee75c5643d00cca/cffi-1.17.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:733e99bc2df47476e3848417c5a4540522f234dfd4ef3ab7fafdf555b082ec0c", size = 178840, upload-time = "2024-09-04T20:44:13.739Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/b6/db007700f67d151abadf508cbfd6a1884f57eab90b1bb985c4c8c02b0f28/cffi-1.17.1-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1257bdabf294dceb59f5e70c64a3e2f462c30c7ad68092d01bbbfb1c16b1ba36", size = 454803, upload-time = "2024-09-04T20:44:15.231Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/df/f8d151540d8c200eb1c6fba8cd0dfd40904f1b0682ea705c36e6c2e97ab3/cffi-1.17.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da95af8214998d77a98cc14e3a3bd00aa191526343078b530ceb0bd710fb48a5", size = 478850, upload-time = "2024-09-04T20:44:17.188Z" },
+    { url = "https://files.pythonhosted.org/packages/28/c0/b31116332a547fd2677ae5b78a2ef662dfc8023d67f41b2a83f7c2aa78b1/cffi-1.17.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d63afe322132c194cf832bfec0dc69a99fb9bb6bbd550f161a49e9e855cc78ff", size = 485729, upload-time = "2024-09-04T20:44:18.688Z" },
+    { url = "https://files.pythonhosted.org/packages/91/2b/9a1ddfa5c7f13cab007a2c9cc295b70fbbda7cb10a286aa6810338e60ea1/cffi-1.17.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f79fc4fc25f1c8698ff97788206bb3c2598949bfe0fef03d299eb1b5356ada99", size = 471256, upload-time = "2024-09-04T20:44:20.248Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/d5/da47df7004cb17e4955df6a43d14b3b4ae77737dff8bf7f8f333196717bf/cffi-1.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b62ce867176a75d03a665bad002af8e6d54644fad99a3c70905c543130e39d93", size = 479424, upload-time = "2024-09-04T20:44:21.673Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/ac/2a28bcf513e93a219c8a4e8e125534f4f6db03e3179ba1c45e949b76212c/cffi-1.17.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:386c8bf53c502fff58903061338ce4f4950cbdcb23e2902d86c0f722b786bbe3", size = 484568, upload-time = "2024-09-04T20:44:23.245Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/38/ca8a4f639065f14ae0f1d9751e70447a261f1a30fa7547a828ae08142465/cffi-1.17.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4ceb10419a9adf4460ea14cfd6bc43d08701f0835e979bf821052f1805850fe8", size = 488736, upload-time = "2024-09-04T20:44:24.757Z" },
+    { url = "https://files.pythonhosted.org/packages/86/c5/28b2d6f799ec0bdecf44dced2ec5ed43e0eb63097b0f58c293583b406582/cffi-1.17.1-cp312-cp312-win32.whl", hash = "sha256:a08d7e755f8ed21095a310a693525137cfe756ce62d066e53f502a83dc550f65", size = 172448, upload-time = "2024-09-04T20:44:26.208Z" },
+    { url = "https://files.pythonhosted.org/packages/50/b9/db34c4755a7bd1cb2d1603ac3863f22bcecbd1ba29e5ee841a4bc510b294/cffi-1.17.1-cp312-cp312-win_amd64.whl", hash = "sha256:51392eae71afec0d0c8fb1a53b204dbb3bcabcb3c9b807eedf3e1e6ccf2de903", size = 181976, upload-time = "2024-09-04T20:44:27.578Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/f8/dd6c246b148639254dad4d6803eb6a54e8c85c6e11ec9df2cffa87571dbe/cffi-1.17.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e", size = 182989, upload-time = "2024-09-04T20:44:28.956Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/f1/672d303ddf17c24fc83afd712316fda78dc6fce1cd53011b839483e1ecc8/cffi-1.17.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2", size = 178802, upload-time = "2024-09-04T20:44:30.289Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/2d/eab2e858a91fdff70533cab61dcff4a1f55ec60425832ddfdc9cd36bc8af/cffi-1.17.1-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3", size = 454792, upload-time = "2024-09-04T20:44:32.01Z" },
+    { url = "https://files.pythonhosted.org/packages/75/b2/fbaec7c4455c604e29388d55599b99ebcc250a60050610fadde58932b7ee/cffi-1.17.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:706510fe141c86a69c8ddc029c7910003a17353970cff3b904ff0686a5927683", size = 478893, upload-time = "2024-09-04T20:44:33.606Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/b7/6e4a2162178bf1935c336d4da8a9352cccab4d3a5d7914065490f08c0690/cffi-1.17.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de55b766c7aa2e2a3092c51e0483d700341182f08e67c63630d5b6f200bb28e5", size = 485810, upload-time = "2024-09-04T20:44:35.191Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/8a/1d0e4a9c26e54746dc08c2c6c037889124d4f59dffd853a659fa545f1b40/cffi-1.17.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4", size = 471200, upload-time = "2024-09-04T20:44:36.743Z" },
+    { url = "https://files.pythonhosted.org/packages/26/9f/1aab65a6c0db35f43c4d1b4f580e8df53914310afc10ae0397d29d697af4/cffi-1.17.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd398dbc6773384a17fe0d3e7eeb8d1a21c2200473ee6806bb5e6a8e62bb73dd", size = 479447, upload-time = "2024-09-04T20:44:38.492Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/e4/fb8b3dd8dc0e98edf1135ff067ae070bb32ef9d509d6cb0f538cd6f7483f/cffi-1.17.1-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:3edc8d958eb099c634dace3c7e16560ae474aa3803a5df240542b305d14e14ed", size = 484358, upload-time = "2024-09-04T20:44:40.046Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/47/d7145bf2dc04684935d57d67dff9d6d795b2ba2796806bb109864be3a151/cffi-1.17.1-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:72e72408cad3d5419375fc87d289076ee319835bdfa2caad331e377589aebba9", size = 488469, upload-time = "2024-09-04T20:44:41.616Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/ee/f94057fa6426481d663b88637a9a10e859e492c73d0384514a17d78ee205/cffi-1.17.1-cp313-cp313-win32.whl", hash = "sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d", size = 172475, upload-time = "2024-09-04T20:44:43.733Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/fc/6a8cb64e5f0324877d503c854da15d76c1e50eb722e320b15345c4d0c6de/cffi-1.17.1-cp313-cp313-win_amd64.whl", hash = "sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a", size = 182009, upload-time = "2024-09-04T20:44:45.309Z" },
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
+]
+
+[[package]]
+name = "comm"
+version = "0.2.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/a8/fb783cb0abe2b5fded9f55e5703015cdf1c9c85b3669087c538dd15a6a86/comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e", size = 6210, upload-time = "2024-03-12T16:53:41.133Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3", size = 7180, upload-time = "2024-03-12T16:53:39.226Z" },
+]
+
+[[package]]
+name = "contourpy"
+version = "1.3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b3/b9/ede788a0b56fc5b071639d06c33cb893f68b1178938f3425debebe2dab78/contourpy-1.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6a37a2fb93d4df3fc4c0e363ea4d16f83195fc09c891bc8ce072b9d084853445", size = 269636, upload-time = "2025-04-15T17:35:54.473Z" },
+    { url = "https://files.pythonhosted.org/packages/e6/75/3469f011d64b8bbfa04f709bfc23e1dd71be54d05b1b083be9f5b22750d1/contourpy-1.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b7cd50c38f500bbcc9b6a46643a40e0913673f869315d8e70de0438817cb7773", size = 254636, upload-time = "2025-04-15T17:35:58.283Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/2f/95adb8dae08ce0ebca4fd8e7ad653159565d9739128b2d5977806656fcd2/contourpy-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d6658ccc7251a4433eebd89ed2672c2ed96fba367fd25ca9512aa92a4b46c4f1", size = 313053, upload-time = "2025-04-15T17:36:03.235Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/a6/8ccf97a50f31adfa36917707fe39c9a0cbc24b3bbb58185577f119736cc9/contourpy-1.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:70771a461aaeb335df14deb6c97439973d253ae70660ca085eec25241137ef43", size = 352985, upload-time = "2025-04-15T17:36:08.275Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/b6/7925ab9b77386143f39d9c3243fdd101621b4532eb126743201160ffa7e6/contourpy-1.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65a887a6e8c4cd0897507d814b14c54a8c2e2aa4ac9f7686292f9769fcf9a6ab", size = 323750, upload-time = "2025-04-15T17:36:13.29Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/f3/20c5d1ef4f4748e52d60771b8560cf00b69d5c6368b5c2e9311bcfa2a08b/contourpy-1.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3859783aefa2b8355697f16642695a5b9792e7a46ab86da1118a4a23a51a33d7", size = 326246, upload-time = "2025-04-15T17:36:18.329Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/e5/9dae809e7e0b2d9d70c52b3d24cba134dd3dad979eb3e5e71f5df22ed1f5/contourpy-1.3.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:eab0f6db315fa4d70f1d8ab514e527f0366ec021ff853d7ed6a2d33605cf4b83", size = 1308728, upload-time = "2025-04-15T17:36:33.878Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/4a/0058ba34aeea35c0b442ae61a4f4d4ca84d6df8f91309bc2d43bb8dd248f/contourpy-1.3.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d91a3ccc7fea94ca0acab82ceb77f396d50a1f67412efe4c526f5d20264e6ecd", size = 1375762, upload-time = "2025-04-15T17:36:51.295Z" },
+    { url = "https://files.pythonhosted.org/packages/09/33/7174bdfc8b7767ef2c08ed81244762d93d5c579336fc0b51ca57b33d1b80/contourpy-1.3.2-cp311-cp311-win32.whl", hash = "sha256:1c48188778d4d2f3d48e4643fb15d8608b1d01e4b4d6b0548d9b336c28fc9b6f", size = 178196, upload-time = "2025-04-15T17:36:55.002Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/fe/4029038b4e1c4485cef18e480b0e2cd2d755448bb071eb9977caac80b77b/contourpy-1.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:5ebac872ba09cb8f2131c46b8739a7ff71de28a24c869bcad554477eb089a878", size = 222017, upload-time = "2025-04-15T17:36:58.576Z" },
+    { url = "https://files.pythonhosted.org/packages/34/f7/44785876384eff370c251d58fd65f6ad7f39adce4a093c934d4a67a7c6b6/contourpy-1.3.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4caf2bcd2969402bf77edc4cb6034c7dd7c0803213b3523f111eb7460a51b8d2", size = 271580, upload-time = "2025-04-15T17:37:03.105Z" },
+    { url = "https://files.pythonhosted.org/packages/93/3b/0004767622a9826ea3d95f0e9d98cd8729015768075d61f9fea8eeca42a8/contourpy-1.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:82199cb78276249796419fe36b7386bd8d2cc3f28b3bc19fe2454fe2e26c4c15", size = 255530, upload-time = "2025-04-15T17:37:07.026Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/bb/7bd49e1f4fa805772d9fd130e0d375554ebc771ed7172f48dfcd4ca61549/contourpy-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:106fab697af11456fcba3e352ad50effe493a90f893fca6c2ca5c033820cea92", size = 307688, upload-time = "2025-04-15T17:37:11.481Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/97/e1d5dbbfa170725ef78357a9a0edc996b09ae4af170927ba8ce977e60a5f/contourpy-1.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d14f12932a8d620e307f715857107b1d1845cc44fdb5da2bc8e850f5ceba9f87", size = 347331, upload-time = "2025-04-15T17:37:18.212Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/66/e69e6e904f5ecf6901be3dd16e7e54d41b6ec6ae3405a535286d4418ffb4/contourpy-1.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:532fd26e715560721bb0d5fc7610fce279b3699b018600ab999d1be895b09415", size = 318963, upload-time = "2025-04-15T17:37:22.76Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/32/b8a1c8965e4f72482ff2d1ac2cd670ce0b542f203c8e1d34e7c3e6925da7/contourpy-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f26b383144cf2d2c29f01a1e8170f50dacf0eac02d64139dcd709a8ac4eb3cfe", size = 323681, upload-time = "2025-04-15T17:37:33.001Z" },
+    { url = "https://files.pythonhosted.org/packages/30/c6/12a7e6811d08757c7162a541ca4c5c6a34c0f4e98ef2b338791093518e40/contourpy-1.3.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c49f73e61f1f774650a55d221803b101d966ca0c5a2d6d5e4320ec3997489441", size = 1308674, upload-time = "2025-04-15T17:37:48.64Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/8a/bebe5a3f68b484d3a2b8ffaf84704b3e343ef1addea528132ef148e22b3b/contourpy-1.3.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3d80b2c0300583228ac98d0a927a1ba6a2ba6b8a742463c564f1d419ee5b211e", size = 1380480, upload-time = "2025-04-15T17:38:06.7Z" },
+    { url = "https://files.pythonhosted.org/packages/34/db/fcd325f19b5978fb509a7d55e06d99f5f856294c1991097534360b307cf1/contourpy-1.3.2-cp312-cp312-win32.whl", hash = "sha256:90df94c89a91b7362e1142cbee7568f86514412ab8a2c0d0fca72d7e91b62912", size = 178489, upload-time = "2025-04-15T17:38:10.338Z" },
+    { url = "https://files.pythonhosted.org/packages/01/c8/fadd0b92ffa7b5eb5949bf340a63a4a496a6930a6c37a7ba0f12acb076d6/contourpy-1.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:8c942a01d9163e2e5cfb05cb66110121b8d07ad438a17f9e766317bcb62abf73", size = 223042, upload-time = "2025-04-15T17:38:14.239Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/61/5673f7e364b31e4e7ef6f61a4b5121c5f170f941895912f773d95270f3a2/contourpy-1.3.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:de39db2604ae755316cb5967728f4bea92685884b1e767b7c24e983ef5f771cb", size = 271630, upload-time = "2025-04-15T17:38:19.142Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/66/a40badddd1223822c95798c55292844b7e871e50f6bfd9f158cb25e0bd39/contourpy-1.3.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3f9e896f447c5c8618f1edb2bafa9a4030f22a575ec418ad70611450720b5b08", size = 255670, upload-time = "2025-04-15T17:38:23.688Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/c7/cf9fdee8200805c9bc3b148f49cb9482a4e3ea2719e772602a425c9b09f8/contourpy-1.3.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71e2bd4a1c4188f5c2b8d274da78faab884b59df20df63c34f74aa1813c4427c", size = 306694, upload-time = "2025-04-15T17:38:28.238Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/e7/ccb9bec80e1ba121efbffad7f38021021cda5be87532ec16fd96533bb2e0/contourpy-1.3.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de425af81b6cea33101ae95ece1f696af39446db9682a0b56daaa48cfc29f38f", size = 345986, upload-time = "2025-04-15T17:38:33.502Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/49/ca13bb2da90391fa4219fdb23b078d6065ada886658ac7818e5441448b78/contourpy-1.3.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:977e98a0e0480d3fe292246417239d2d45435904afd6d7332d8455981c408b85", size = 318060, upload-time = "2025-04-15T17:38:38.672Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/65/5245ce8c548a8422236c13ffcdcdada6a2a812c361e9e0c70548bb40b661/contourpy-1.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:434f0adf84911c924519d2b08fc10491dd282b20bdd3fa8f60fd816ea0b48841", size = 322747, upload-time = "2025-04-15T17:38:43.712Z" },
+    { url = "https://files.pythonhosted.org/packages/72/30/669b8eb48e0a01c660ead3752a25b44fdb2e5ebc13a55782f639170772f9/contourpy-1.3.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c66c4906cdbc50e9cba65978823e6e00b45682eb09adbb78c9775b74eb222422", size = 1308895, upload-time = "2025-04-15T17:39:00.224Z" },
+    { url = "https://files.pythonhosted.org/packages/05/5a/b569f4250decee6e8d54498be7bdf29021a4c256e77fe8138c8319ef8eb3/contourpy-1.3.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8b7fc0cd78ba2f4695fd0a6ad81a19e7e3ab825c31b577f384aa9d7817dc3bef", size = 1379098, upload-time = "2025-04-15T17:43:29.649Z" },
+    { url = "https://files.pythonhosted.org/packages/19/ba/b227c3886d120e60e41b28740ac3617b2f2b971b9f601c835661194579f1/contourpy-1.3.2-cp313-cp313-win32.whl", hash = "sha256:15ce6ab60957ca74cff444fe66d9045c1fd3e92c8936894ebd1f3eef2fff075f", size = 178535, upload-time = "2025-04-15T17:44:44.532Z" },
+    { url = "https://files.pythonhosted.org/packages/12/6e/2fed56cd47ca739b43e892707ae9a13790a486a3173be063681ca67d2262/contourpy-1.3.2-cp313-cp313-win_amd64.whl", hash = "sha256:e1578f7eafce927b168752ed7e22646dad6cd9bca673c60bff55889fa236ebf9", size = 223096, upload-time = "2025-04-15T17:44:48.194Z" },
+    { url = "https://files.pythonhosted.org/packages/54/4c/e76fe2a03014a7c767d79ea35c86a747e9325537a8b7627e0e5b3ba266b4/contourpy-1.3.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0475b1f6604896bc7c53bb070e355e9321e1bc0d381735421a2d2068ec56531f", size = 285090, upload-time = "2025-04-15T17:43:34.084Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/e2/5aba47debd55d668e00baf9651b721e7733975dc9fc27264a62b0dd26eb8/contourpy-1.3.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:c85bb486e9be652314bb5b9e2e3b0d1b2e643d5eec4992c0fbe8ac71775da739", size = 268643, upload-time = "2025-04-15T17:43:38.626Z" },
+    { url = "https://files.pythonhosted.org/packages/a1/37/cd45f1f051fe6230f751cc5cdd2728bb3a203f5619510ef11e732109593c/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:745b57db7758f3ffc05a10254edd3182a2a83402a89c00957a8e8a22f5582823", size = 310443, upload-time = "2025-04-15T17:43:44.522Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/a2/36ea6140c306c9ff6dd38e3bcec80b3b018474ef4d17eb68ceecd26675f4/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:970e9173dbd7eba9b4e01aab19215a48ee5dd3f43cef736eebde064a171f89a5", size = 349865, upload-time = "2025-04-15T17:43:49.545Z" },
+    { url = "https://files.pythonhosted.org/packages/95/b7/2fc76bc539693180488f7b6cc518da7acbbb9e3b931fd9280504128bf956/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c6c4639a9c22230276b7bffb6a850dfc8258a2521305e1faefe804d006b2e532", size = 321162, upload-time = "2025-04-15T17:43:54.203Z" },
+    { url = "https://files.pythonhosted.org/packages/f4/10/76d4f778458b0aa83f96e59d65ece72a060bacb20cfbee46cf6cd5ceba41/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc829960f34ba36aad4302e78eabf3ef16a3a100863f0d4eeddf30e8a485a03b", size = 327355, upload-time = "2025-04-15T17:44:01.025Z" },
+    { url = "https://files.pythonhosted.org/packages/43/a3/10cf483ea683f9f8ab096c24bad3cce20e0d1dd9a4baa0e2093c1c962d9d/contourpy-1.3.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:d32530b534e986374fc19eaa77fcb87e8a99e5431499949b828312bdcd20ac52", size = 1307935, upload-time = "2025-04-15T17:44:17.322Z" },
+    { url = "https://files.pythonhosted.org/packages/78/73/69dd9a024444489e22d86108e7b913f3528f56cfc312b5c5727a44188471/contourpy-1.3.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e298e7e70cf4eb179cc1077be1c725b5fd131ebc81181bf0c03525c8abc297fd", size = 1372168, upload-time = "2025-04-15T17:44:33.43Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/1b/96d586ccf1b1a9d2004dd519b25fbf104a11589abfd05484ff12199cca21/contourpy-1.3.2-cp313-cp313t-win32.whl", hash = "sha256:d0e589ae0d55204991450bb5c23f571c64fe43adaa53f93fc902a84c96f52fe1", size = 189550, upload-time = "2025-04-15T17:44:37.092Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/e6/6000d0094e8a5e32ad62591c8609e269febb6e4db83a1c75ff8868b42731/contourpy-1.3.2-cp313-cp313t-win_amd64.whl", hash = "sha256:78e9253c3de756b3f6a5174d024c4835acd59eb3f8e2ca13e775dbffe1558f69", size = 238214, upload-time = "2025-04-15T17:44:40.827Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/c0/91f1215d0d9f9f343e4773ba6c9b89e8c0cc7a64a6263f21139da639d848/contourpy-1.3.2-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5f5964cdad279256c084b69c3f412b7801e15356b16efa9d78aa974041903da0", size = 266807, upload-time = "2025-04-15T17:45:15.535Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/79/6be7e90c955c0487e7712660d6cead01fa17bff98e0ea275737cc2bc8e71/contourpy-1.3.2-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:49b65a95d642d4efa8f64ba12558fcb83407e58a2dfba9d796d77b63ccfcaff5", size = 318729, upload-time = "2025-04-15T17:45:20.166Z" },
+    { url = "https://files.pythonhosted.org/packages/87/68/7f46fb537958e87427d98a4074bcde4b67a70b04900cfc5ce29bc2f556c1/contourpy-1.3.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8c5acb8dddb0752bf252e01a3035b21443158910ac16a3b0d20e7fed7d534ce5", size = 221791, upload-time = "2025-04-15T17:45:24.794Z" },
+]
+
+[[package]]
+name = "coverage"
+version = "7.9.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/04/b7/c0465ca253df10a9e8dae0692a4ae6e9726d245390aaef92360e1d6d3832/coverage-7.9.2.tar.gz", hash = "sha256:997024fa51e3290264ffd7492ec97d0690293ccd2b45a6cd7d82d945a4a80c8b", size = 813556, upload-time = "2025-07-03T10:54:15.101Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/39/40/916786453bcfafa4c788abee4ccd6f592b5b5eca0cd61a32a4e5a7ef6e02/coverage-7.9.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a7a56a2964a9687b6aba5b5ced6971af308ef6f79a91043c05dd4ee3ebc3e9ba", size = 212152, upload-time = "2025-07-03T10:52:53.562Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/66/cc13bae303284b546a030762957322bbbff1ee6b6cb8dc70a40f8a78512f/coverage-7.9.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:123d589f32c11d9be7fe2e66d823a236fe759b0096f5db3fb1b75b2fa414a4fa", size = 212540, upload-time = "2025-07-03T10:52:55.196Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/3c/d56a764b2e5a3d43257c36af4a62c379df44636817bb5f89265de4bf8bd7/coverage-7.9.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:333b2e0ca576a7dbd66e85ab402e35c03b0b22f525eed82681c4b866e2e2653a", size = 245097, upload-time = "2025-07-03T10:52:56.509Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/46/bd064ea8b3c94eb4ca5d90e34d15b806cba091ffb2b8e89a0d7066c45791/coverage-7.9.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:326802760da234baf9f2f85a39e4a4b5861b94f6c8d95251f699e4f73b1835dc", size = 242812, upload-time = "2025-07-03T10:52:57.842Z" },
+    { url = "https://files.pythonhosted.org/packages/43/02/d91992c2b29bc7afb729463bc918ebe5f361be7f1daae93375a5759d1e28/coverage-7.9.2-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:19e7be4cfec248df38ce40968c95d3952fbffd57b400d4b9bb580f28179556d2", size = 244617, upload-time = "2025-07-03T10:52:59.239Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/4f/8fadff6bf56595a16d2d6e33415841b0163ac660873ed9a4e9046194f779/coverage-7.9.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0b4a4cb73b9f2b891c1788711408ef9707666501ba23684387277ededab1097c", size = 244263, upload-time = "2025-07-03T10:53:00.601Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/d2/e0be7446a2bba11739edb9f9ba4eff30b30d8257370e237418eb44a14d11/coverage-7.9.2-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:2c8937fa16c8c9fbbd9f118588756e7bcdc7e16a470766a9aef912dd3f117dbd", size = 242314, upload-time = "2025-07-03T10:53:01.932Z" },
+    { url = "https://files.pythonhosted.org/packages/9d/7d/dcbac9345000121b8b57a3094c2dfcf1ccc52d8a14a40c1d4bc89f936f80/coverage-7.9.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:42da2280c4d30c57a9b578bafd1d4494fa6c056d4c419d9689e66d775539be74", size = 242904, upload-time = "2025-07-03T10:53:03.478Z" },
+    { url = "https://files.pythonhosted.org/packages/41/58/11e8db0a0c0510cf31bbbdc8caf5d74a358b696302a45948d7c768dfd1cf/coverage-7.9.2-cp311-cp311-win32.whl", hash = "sha256:14fa8d3da147f5fdf9d298cacc18791818f3f1a9f542c8958b80c228320e90c6", size = 214553, upload-time = "2025-07-03T10:53:05.174Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/7d/751794ec8907a15e257136e48dc1021b1f671220ecccfd6c4eaf30802714/coverage-7.9.2-cp311-cp311-win_amd64.whl", hash = "sha256:549cab4892fc82004f9739963163fd3aac7a7b0df430669b75b86d293d2df2a7", size = 215441, upload-time = "2025-07-03T10:53:06.472Z" },
+    { url = "https://files.pythonhosted.org/packages/62/5b/34abcedf7b946c1c9e15b44f326cb5b0da852885312b30e916f674913428/coverage-7.9.2-cp311-cp311-win_arm64.whl", hash = "sha256:c2667a2b913e307f06aa4e5677f01a9746cd08e4b35e14ebcde6420a9ebb4c62", size = 213873, upload-time = "2025-07-03T10:53:07.699Z" },
+    { url = "https://files.pythonhosted.org/packages/53/d7/7deefc6fd4f0f1d4c58051f4004e366afc9e7ab60217ac393f247a1de70a/coverage-7.9.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:ae9eb07f1cfacd9cfe8eaee6f4ff4b8a289a668c39c165cd0c8548484920ffc0", size = 212344, upload-time = "2025-07-03T10:53:09.3Z" },
+    { url = "https://files.pythonhosted.org/packages/95/0c/ee03c95d32be4d519e6a02e601267769ce2e9a91fc8faa1b540e3626c680/coverage-7.9.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9ce85551f9a1119f02adc46d3014b5ee3f765deac166acf20dbb851ceb79b6f3", size = 212580, upload-time = "2025-07-03T10:53:11.52Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/9f/826fa4b544b27620086211b87a52ca67592622e1f3af9e0a62c87aea153a/coverage-7.9.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f8f6389ac977c5fb322e0e38885fbbf901743f79d47f50db706e7644dcdcb6e1", size = 246383, upload-time = "2025-07-03T10:53:13.134Z" },
+    { url = "https://files.pythonhosted.org/packages/7f/b3/4477aafe2a546427b58b9c540665feff874f4db651f4d3cb21b308b3a6d2/coverage-7.9.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ff0d9eae8cdfcd58fe7893b88993723583a6ce4dfbfd9f29e001922544f95615", size = 243400, upload-time = "2025-07-03T10:53:14.614Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/c2/efffa43778490c226d9d434827702f2dfbc8041d79101a795f11cbb2cf1e/coverage-7.9.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fae939811e14e53ed8a9818dad51d434a41ee09df9305663735f2e2d2d7d959b", size = 245591, upload-time = "2025-07-03T10:53:15.872Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/e7/a59888e882c9a5f0192d8627a30ae57910d5d449c80229b55e7643c078c4/coverage-7.9.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:31991156251ec202c798501e0a42bbdf2169dcb0f137b1f5c0f4267f3fc68ef9", size = 245402, upload-time = "2025-07-03T10:53:17.124Z" },
+    { url = "https://files.pythonhosted.org/packages/92/a5/72fcd653ae3d214927edc100ce67440ed8a0a1e3576b8d5e6d066ed239db/coverage-7.9.2-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:d0d67963f9cbfc7c7f96d4ac74ed60ecbebd2ea6eeb51887af0f8dce205e545f", size = 243583, upload-time = "2025-07-03T10:53:18.781Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/f5/84e70e4df28f4a131d580d7d510aa1ffd95037293da66fd20d446090a13b/coverage-7.9.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:49b752a2858b10580969ec6af6f090a9a440a64a301ac1528d7ca5f7ed497f4d", size = 244815, upload-time = "2025-07-03T10:53:20.168Z" },
+    { url = "https://files.pythonhosted.org/packages/39/e7/d73d7cbdbd09fdcf4642655ae843ad403d9cbda55d725721965f3580a314/coverage-7.9.2-cp312-cp312-win32.whl", hash = "sha256:88d7598b8ee130f32f8a43198ee02edd16d7f77692fa056cb779616bbea1b355", size = 214719, upload-time = "2025-07-03T10:53:21.521Z" },
+    { url = "https://files.pythonhosted.org/packages/9f/d6/7486dcc3474e2e6ad26a2af2db7e7c162ccd889c4c68fa14ea8ec189c9e9/coverage-7.9.2-cp312-cp312-win_amd64.whl", hash = "sha256:9dfb070f830739ee49d7c83e4941cc767e503e4394fdecb3b54bfdac1d7662c0", size = 215509, upload-time = "2025-07-03T10:53:22.853Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/34/0439f1ae2593b0346164d907cdf96a529b40b7721a45fdcf8b03c95fcd90/coverage-7.9.2-cp312-cp312-win_arm64.whl", hash = "sha256:4e2c058aef613e79df00e86b6d42a641c877211384ce5bd07585ed7ba71ab31b", size = 213910, upload-time = "2025-07-03T10:53:24.472Z" },
+    { url = "https://files.pythonhosted.org/packages/94/9d/7a8edf7acbcaa5e5c489a646226bed9591ee1c5e6a84733c0140e9ce1ae1/coverage-7.9.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:985abe7f242e0d7bba228ab01070fde1d6c8fa12f142e43debe9ed1dde686038", size = 212367, upload-time = "2025-07-03T10:53:25.811Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/9e/5cd6f130150712301f7e40fb5865c1bc27b97689ec57297e568d972eec3c/coverage-7.9.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:82c3939264a76d44fde7f213924021ed31f55ef28111a19649fec90c0f109e6d", size = 212632, upload-time = "2025-07-03T10:53:27.075Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/de/6287a2c2036f9fd991c61cefa8c64e57390e30c894ad3aa52fac4c1e14a8/coverage-7.9.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ae5d563e970dbe04382f736ec214ef48103d1b875967c89d83c6e3f21706d5b3", size = 245793, upload-time = "2025-07-03T10:53:28.408Z" },
+    { url = "https://files.pythonhosted.org/packages/06/cc/9b5a9961d8160e3cb0b558c71f8051fe08aa2dd4b502ee937225da564ed1/coverage-7.9.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bdd612e59baed2a93c8843c9a7cb902260f181370f1d772f4842987535071d14", size = 243006, upload-time = "2025-07-03T10:53:29.754Z" },
+    { url = "https://files.pythonhosted.org/packages/49/d9/4616b787d9f597d6443f5588619c1c9f659e1f5fc9eebf63699eb6d34b78/coverage-7.9.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:256ea87cb2a1ed992bcdfc349d8042dcea1b80436f4ddf6e246d6bee4b5d73b6", size = 244990, upload-time = "2025-07-03T10:53:31.098Z" },
+    { url = "https://files.pythonhosted.org/packages/48/83/801cdc10f137b2d02b005a761661649ffa60eb173dcdaeb77f571e4dc192/coverage-7.9.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f44ae036b63c8ea432f610534a2668b0c3aee810e7037ab9d8ff6883de480f5b", size = 245157, upload-time = "2025-07-03T10:53:32.717Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/a4/41911ed7e9d3ceb0ffb019e7635468df7499f5cc3edca5f7dfc078e9c5ec/coverage-7.9.2-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:82d76ad87c932935417a19b10cfe7abb15fd3f923cfe47dbdaa74ef4e503752d", size = 243128, upload-time = "2025-07-03T10:53:34.009Z" },
+    { url = "https://files.pythonhosted.org/packages/10/41/344543b71d31ac9cb00a664d5d0c9ef134a0fe87cb7d8430003b20fa0b7d/coverage-7.9.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:619317bb86de4193debc712b9e59d5cffd91dc1d178627ab2a77b9870deb2868", size = 244511, upload-time = "2025-07-03T10:53:35.434Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/81/3b68c77e4812105e2a060f6946ba9e6f898ddcdc0d2bfc8b4b152a9ae522/coverage-7.9.2-cp313-cp313-win32.whl", hash = "sha256:0a07757de9feb1dfafd16ab651e0f628fd7ce551604d1bf23e47e1ddca93f08a", size = 214765, upload-time = "2025-07-03T10:53:36.787Z" },
+    { url = "https://files.pythonhosted.org/packages/06/a2/7fac400f6a346bb1a4004eb2a76fbff0e242cd48926a2ce37a22a6a1d917/coverage-7.9.2-cp313-cp313-win_amd64.whl", hash = "sha256:115db3d1f4d3f35f5bb021e270edd85011934ff97c8797216b62f461dd69374b", size = 215536, upload-time = "2025-07-03T10:53:38.188Z" },
+    { url = "https://files.pythonhosted.org/packages/08/47/2c6c215452b4f90d87017e61ea0fd9e0486bb734cb515e3de56e2c32075f/coverage-7.9.2-cp313-cp313-win_arm64.whl", hash = "sha256:48f82f889c80af8b2a7bb6e158d95a3fbec6a3453a1004d04e4f3b5945a02694", size = 213943, upload-time = "2025-07-03T10:53:39.492Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/46/e211e942b22d6af5e0f323faa8a9bc7c447a1cf1923b64c47523f36ed488/coverage-7.9.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:55a28954545f9d2f96870b40f6c3386a59ba8ed50caf2d949676dac3ecab99f5", size = 213088, upload-time = "2025-07-03T10:53:40.874Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/2f/762551f97e124442eccd907bf8b0de54348635b8866a73567eb4e6417acf/coverage-7.9.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:cdef6504637731a63c133bb2e6f0f0214e2748495ec15fe42d1e219d1b133f0b", size = 213298, upload-time = "2025-07-03T10:53:42.218Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/b7/76d2d132b7baf7360ed69be0bcab968f151fa31abe6d067f0384439d9edb/coverage-7.9.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bcd5ebe66c7a97273d5d2ddd4ad0ed2e706b39630ed4b53e713d360626c3dbb3", size = 256541, upload-time = "2025-07-03T10:53:43.823Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/17/392b219837d7ad47d8e5974ce5f8dc3deb9f99a53b3bd4d123602f960c81/coverage-7.9.2-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9303aed20872d7a3c9cb39c5d2b9bdbe44e3a9a1aecb52920f7e7495410dfab8", size = 252761, upload-time = "2025-07-03T10:53:45.19Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/77/4256d3577fe1b0daa8d3836a1ebe68eaa07dd2cbaf20cf5ab1115d6949d4/coverage-7.9.2-cp313-cp313t-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc18ea9e417a04d1920a9a76fe9ebd2f43ca505b81994598482f938d5c315f46", size = 254917, upload-time = "2025-07-03T10:53:46.931Z" },
+    { url = "https://files.pythonhosted.org/packages/53/99/fc1a008eef1805e1ddb123cf17af864743354479ea5129a8f838c433cc2c/coverage-7.9.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6406cff19880aaaadc932152242523e892faff224da29e241ce2fca329866584", size = 256147, upload-time = "2025-07-03T10:53:48.289Z" },
+    { url = "https://files.pythonhosted.org/packages/92/c0/f63bf667e18b7f88c2bdb3160870e277c4874ced87e21426128d70aa741f/coverage-7.9.2-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:2d0d4f6ecdf37fcc19c88fec3e2277d5dee740fb51ffdd69b9579b8c31e4232e", size = 254261, upload-time = "2025-07-03T10:53:49.99Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/32/37dd1c42ce3016ff8ec9e4b607650d2e34845c0585d3518b2a93b4830c1a/coverage-7.9.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c33624f50cf8de418ab2b4d6ca9eda96dc45b2c4231336bac91454520e8d1fac", size = 255099, upload-time = "2025-07-03T10:53:51.354Z" },
+    { url = "https://files.pythonhosted.org/packages/da/2e/af6b86f7c95441ce82f035b3affe1cd147f727bbd92f563be35e2d585683/coverage-7.9.2-cp313-cp313t-win32.whl", hash = "sha256:1df6b76e737c6a92210eebcb2390af59a141f9e9430210595251fbaf02d46926", size = 215440, upload-time = "2025-07-03T10:53:52.808Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/bb/8a785d91b308867f6b2e36e41c569b367c00b70c17f54b13ac29bcd2d8c8/coverage-7.9.2-cp313-cp313t-win_amd64.whl", hash = "sha256:f5fd54310b92741ebe00d9c0d1d7b2b27463952c022da6d47c175d246a98d1bd", size = 216537, upload-time = "2025-07-03T10:53:54.273Z" },
+    { url = "https://files.pythonhosted.org/packages/1d/a0/a6bffb5e0f41a47279fd45a8f3155bf193f77990ae1c30f9c224b61cacb0/coverage-7.9.2-cp313-cp313t-win_arm64.whl", hash = "sha256:c48c2375287108c887ee87d13b4070a381c6537d30e8487b24ec721bf2a781cb", size = 214398, upload-time = "2025-07-03T10:53:56.715Z" },
+    { url = "https://files.pythonhosted.org/packages/d7/85/f8bbefac27d286386961c25515431482a425967e23d3698b75a250872924/coverage-7.9.2-pp39.pp310.pp311-none-any.whl", hash = "sha256:8a1166db2fb62473285bcb092f586e081e92656c7dfa8e9f62b4d39d7e6b5050", size = 204013, upload-time = "2025-07-03T10:54:12.084Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/38/bbe2e63902847cf79036ecc75550d0698af31c91c7575352eb25190d0fb3/coverage-7.9.2-py3-none-any.whl", hash = "sha256:e425cd5b00f6fc0ed7cdbd766c70be8baab4b7839e4d4fe5fac48581dd968ea4", size = 204005, upload-time = "2025-07-03T10:54:13.491Z" },
+]
+
+[package.optional-dependencies]
+toml = [
+    { name = "tomli", marker = "python_full_version <= '3.11'" },
+]
+
+[[package]]
+name = "cycler"
+version = "0.12.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" },
+]
+
+[[package]]
+name = "debugpy"
+version = "1.8.15"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8c/8b/3a9a28ddb750a76eaec445c7f4d3147ea2c579a97dbd9e25d39001b92b21/debugpy-1.8.15.tar.gz", hash = "sha256:58d7a20b7773ab5ee6bdfb2e6cf622fdf1e40c9d5aef2857d85391526719ac00", size = 1643279, upload-time = "2025-07-15T16:43:29.135Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/b3/1c44a2ed311199ab11c2299c9474a6c7cd80d19278defd333aeb7c287995/debugpy-1.8.15-cp311-cp311-macosx_14_0_universal2.whl", hash = "sha256:babc4fb1962dd6a37e94d611280e3d0d11a1f5e6c72ac9b3d87a08212c4b6dd3", size = 2183442, upload-time = "2025-07-15T16:43:36.733Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/69/e2dcb721491e1c294d348681227c9b44fb95218f379aa88e12a19d85528d/debugpy-1.8.15-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f778e68f2986a58479d0ac4f643e0b8c82fdd97c2e200d4d61e7c2d13838eb53", size = 3134215, upload-time = "2025-07-15T16:43:38.116Z" },
+    { url = "https://files.pythonhosted.org/packages/17/76/4ce63b95d8294dcf2fd1820860b300a420d077df4e93afcaa25a984c2ca7/debugpy-1.8.15-cp311-cp311-win32.whl", hash = "sha256:f9d1b5abd75cd965e2deabb1a06b0e93a1546f31f9f621d2705e78104377c702", size = 5154037, upload-time = "2025-07-15T16:43:39.471Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/a7/e5a7c784465eb9c976d84408873d597dc7ce74a0fc69ed009548a1a94813/debugpy-1.8.15-cp311-cp311-win_amd64.whl", hash = "sha256:62954fb904bec463e2b5a415777f6d1926c97febb08ef1694da0e5d1463c5c3b", size = 5178133, upload-time = "2025-07-15T16:43:40.969Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/4a/4508d256e52897f5cdfee6a6d7580974811e911c6d01321df3264508a5ac/debugpy-1.8.15-cp312-cp312-macosx_14_0_universal2.whl", hash = "sha256:3dcc7225cb317469721ab5136cda9ff9c8b6e6fb43e87c9e15d5b108b99d01ba", size = 2511197, upload-time = "2025-07-15T16:43:42.343Z" },
+    { url = "https://files.pythonhosted.org/packages/99/8d/7f6ef1097e7fecf26b4ef72338d08e41644a41b7ee958a19f494ffcffc29/debugpy-1.8.15-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:047a493ca93c85ccede1dbbaf4e66816794bdc214213dde41a9a61e42d27f8fc", size = 4229517, upload-time = "2025-07-15T16:43:44.14Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/e8/e8c6a9aa33a9c9c6dacbf31747384f6ed2adde4de2e9693c766bdf323aa3/debugpy-1.8.15-cp312-cp312-win32.whl", hash = "sha256:b08e9b0bc260cf324c890626961dad4ffd973f7568fbf57feb3c3a65ab6b6327", size = 5276132, upload-time = "2025-07-15T16:43:45.529Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/ad/231050c6177b3476b85fcea01e565dac83607b5233d003ff067e2ee44d8f/debugpy-1.8.15-cp312-cp312-win_amd64.whl", hash = "sha256:e2a4fe357c92334272eb2845fcfcdbec3ef9f22c16cf613c388ac0887aed15fa", size = 5317645, upload-time = "2025-07-15T16:43:46.968Z" },
+    { url = "https://files.pythonhosted.org/packages/28/70/2928aad2310726d5920b18ed9f54b9f06df5aa4c10cf9b45fa18ff0ab7e8/debugpy-1.8.15-cp313-cp313-macosx_14_0_universal2.whl", hash = "sha256:f5e01291ad7d6649aed5773256c5bba7a1a556196300232de1474c3c372592bf", size = 2495538, upload-time = "2025-07-15T16:43:48.927Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/c6/9b8ffb4ca91fac8b2877eef63c9cc0e87dd2570b1120054c272815ec4cd0/debugpy-1.8.15-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:94dc0f0d00e528d915e0ce1c78e771475b2335b376c49afcc7382ee0b146bab6", size = 4221874, upload-time = "2025-07-15T16:43:50.282Z" },
+    { url = "https://files.pythonhosted.org/packages/55/8a/9b8d59674b4bf489318c7c46a1aab58e606e583651438084b7e029bf3c43/debugpy-1.8.15-cp313-cp313-win32.whl", hash = "sha256:fcf0748d4f6e25f89dc5e013d1129ca6f26ad4da405e0723a4f704583896a709", size = 5275949, upload-time = "2025-07-15T16:43:52.079Z" },
+    { url = "https://files.pythonhosted.org/packages/72/83/9e58e6fdfa8710a5e6ec06c2401241b9ad48b71c0a7eb99570a1f1edb1d3/debugpy-1.8.15-cp313-cp313-win_amd64.whl", hash = "sha256:73c943776cb83e36baf95e8f7f8da765896fd94b05991e7bc162456d25500683", size = 5317720, upload-time = "2025-07-15T16:43:53.703Z" },
+    { url = "https://files.pythonhosted.org/packages/07/d5/98748d9860e767a1248b5e31ffa7ce8cb7006e97bf8abbf3d891d0a8ba4e/debugpy-1.8.15-py2.py3-none-any.whl", hash = "sha256:bce2e6c5ff4f2e00b98d45e7e01a49c7b489ff6df5f12d881c67d2f1ac635f3d", size = 5282697, upload-time = "2025-07-15T16:44:07.996Z" },
+]
+
+[[package]]
+name = "decorator"
+version = "5.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711, upload-time = "2025-02-24T04:41:34.073Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190, upload-time = "2025-02-24T04:41:32.565Z" },
+]
+
+[[package]]
+name = "executing"
+version = "2.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/91/50/a9d80c47ff289c611ff12e63f7c5d13942c65d68125160cefd768c73e6e4/executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755", size = 978693, upload-time = "2025-01-22T15:41:29.403Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7b/8f/c4d9bafc34ad7ad5d8dc16dd1347ee0e507a52c3adb6bfa8887e1c6a26ba/executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa", size = 26702, upload-time = "2025-01-22T15:41:25.929Z" },
+]
+
+[[package]]
+name = "fonttools"
+version = "4.59.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8a/27/ec3c723bfdf86f34c5c82bf6305df3e0f0d8ea798d2d3a7cb0c0a866d286/fonttools-4.59.0.tar.gz", hash = "sha256:be392ec3529e2f57faa28709d60723a763904f71a2b63aabe14fee6648fe3b14", size = 3532521, upload-time = "2025-07-16T12:04:54.613Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/06/96/520733d9602fa1bf6592e5354c6721ac6fc9ea72bc98d112d0c38b967199/fonttools-4.59.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:841b2186adce48903c0fef235421ae21549020eca942c1da773ac380b056ab3c", size = 2782387, upload-time = "2025-07-16T12:03:51.424Z" },
+    { url = "https://files.pythonhosted.org/packages/87/6a/170fce30b9bce69077d8eec9bea2cfd9f7995e8911c71be905e2eba6368b/fonttools-4.59.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:9bcc1e77fbd1609198966ded6b2a9897bd6c6bcbd2287a2fc7d75f1a254179c5", size = 2342194, upload-time = "2025-07-16T12:03:53.295Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/b6/7c8166c0066856f1408092f7968ac744060cf72ca53aec9036106f57eeca/fonttools-4.59.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:37c377f7cb2ab2eca8a0b319c68146d34a339792f9420fca6cd49cf28d370705", size = 5032333, upload-time = "2025-07-16T12:03:55.177Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/0c/707c5a19598eafcafd489b73c4cb1c142102d6197e872f531512d084aa76/fonttools-4.59.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fa39475eaccb98f9199eccfda4298abaf35ae0caec676ffc25b3a5e224044464", size = 4974422, upload-time = "2025-07-16T12:03:57.406Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/e7/6d33737d9fe632a0f59289b6f9743a86d2a9d0673de2a0c38c0f54729822/fonttools-4.59.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d3972b13148c1d1fbc092b27678a33b3080d1ac0ca305742b0119b75f9e87e38", size = 5010631, upload-time = "2025-07-16T12:03:59.449Z" },
+    { url = "https://files.pythonhosted.org/packages/63/e1/a4c3d089ab034a578820c8f2dff21ef60daf9668034a1e4fb38bb1cc3398/fonttools-4.59.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a408c3c51358c89b29cfa5317cf11518b7ce5de1717abb55c5ae2d2921027de6", size = 5122198, upload-time = "2025-07-16T12:04:01.542Z" },
+    { url = "https://files.pythonhosted.org/packages/09/77/ca82b9c12fa4de3c520b7760ee61787640cf3fde55ef1b0bfe1de38c8153/fonttools-4.59.0-cp311-cp311-win32.whl", hash = "sha256:6770d7da00f358183d8fd5c4615436189e4f683bdb6affb02cad3d221d7bb757", size = 2214216, upload-time = "2025-07-16T12:04:03.515Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/25/5aa7ca24b560b2f00f260acf32c4cf29d7aaf8656e159a336111c18bc345/fonttools-4.59.0-cp311-cp311-win_amd64.whl", hash = "sha256:84fc186980231a287b28560d3123bd255d3c6b6659828c642b4cf961e2b923d0", size = 2261879, upload-time = "2025-07-16T12:04:05.015Z" },
+    { url = "https://files.pythonhosted.org/packages/e2/77/b1c8af22f4265e951cd2e5535dbef8859efcef4fb8dee742d368c967cddb/fonttools-4.59.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:f9b3a78f69dcbd803cf2fb3f972779875b244c1115481dfbdd567b2c22b31f6b", size = 2767562, upload-time = "2025-07-16T12:04:06.895Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/5a/aeb975699588176bb357e8b398dfd27e5d3a2230d92b81ab8cbb6187358d/fonttools-4.59.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:57bb7e26928573ee7c6504f54c05860d867fd35e675769f3ce01b52af38d48e2", size = 2335168, upload-time = "2025-07-16T12:04:08.695Z" },
+    { url = "https://files.pythonhosted.org/packages/54/97/c6101a7e60ae138c4ef75b22434373a0da50a707dad523dd19a4889315bf/fonttools-4.59.0-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:4536f2695fe5c1ffb528d84a35a7d3967e5558d2af58b4775e7ab1449d65767b", size = 4909850, upload-time = "2025-07-16T12:04:10.761Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/6c/fa4d18d641054f7bff878cbea14aa9433f292b9057cb1700d8e91a4d5f4f/fonttools-4.59.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:885bde7d26e5b40e15c47bd5def48b38cbd50830a65f98122a8fb90962af7cd1", size = 4955131, upload-time = "2025-07-16T12:04:12.846Z" },
+    { url = "https://files.pythonhosted.org/packages/20/5c/331947fc1377deb928a69bde49f9003364f5115e5cbe351eea99e39412a2/fonttools-4.59.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:6801aeddb6acb2c42eafa45bc1cb98ba236871ae6f33f31e984670b749a8e58e", size = 4899667, upload-time = "2025-07-16T12:04:14.558Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/46/b66469dfa26b8ff0baa7654b2cc7851206c6d57fe3abdabbaab22079a119/fonttools-4.59.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:31003b6a10f70742a63126b80863ab48175fb8272a18ca0846c0482968f0588e", size = 5051349, upload-time = "2025-07-16T12:04:16.388Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/05/ebfb6b1f3a4328ab69787d106a7d92ccde77ce66e98659df0f9e3f28d93d/fonttools-4.59.0-cp312-cp312-win32.whl", hash = "sha256:fbce6dae41b692a5973d0f2158f782b9ad05babc2c2019a970a1094a23909b1b", size = 2201315, upload-time = "2025-07-16T12:04:18.557Z" },
+    { url = "https://files.pythonhosted.org/packages/09/45/d2bdc9ea20bbadec1016fd0db45696d573d7a26d95ab5174ffcb6d74340b/fonttools-4.59.0-cp312-cp312-win_amd64.whl", hash = "sha256:332bfe685d1ac58ca8d62b8d6c71c2e52a6c64bc218dc8f7825c9ea51385aa01", size = 2249408, upload-time = "2025-07-16T12:04:20.489Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/bb/390990e7c457d377b00890d9f96a3ca13ae2517efafb6609c1756e213ba4/fonttools-4.59.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:78813b49d749e1bb4db1c57f2d4d7e6db22c253cb0a86ad819f5dc197710d4b2", size = 2758704, upload-time = "2025-07-16T12:04:22.217Z" },
+    { url = "https://files.pythonhosted.org/packages/df/6f/d730d9fcc9b410a11597092bd2eb9ca53e5438c6cb90e4b3047ce1b723e9/fonttools-4.59.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:401b1941ce37e78b8fd119b419b617277c65ae9417742a63282257434fd68ea2", size = 2330764, upload-time = "2025-07-16T12:04:23.985Z" },
+    { url = "https://files.pythonhosted.org/packages/75/b4/b96bb66f6f8cc4669de44a158099b249c8159231d254ab6b092909388be5/fonttools-4.59.0-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:efd7e6660674e234e29937bc1481dceb7e0336bfae75b856b4fb272b5093c5d4", size = 4890699, upload-time = "2025-07-16T12:04:25.664Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/57/7969af50b26408be12baa317c6147588db5b38af2759e6df94554dbc5fdb/fonttools-4.59.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:51ab1ff33c19e336c02dee1e9fd1abd974a4ca3d8f7eef2a104d0816a241ce97", size = 4952934, upload-time = "2025-07-16T12:04:27.733Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/e2/dd968053b6cf1f46c904f5bd409b22341477c017d8201619a265e50762d3/fonttools-4.59.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a9bf8adc9e1f3012edc8f09b08336272aec0c55bc677422273e21280db748f7c", size = 4892319, upload-time = "2025-07-16T12:04:30.074Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/95/a59810d8eda09129f83467a4e58f84205dc6994ebaeb9815406363e07250/fonttools-4.59.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:37e01c6ec0c98599778c2e688350d624fa4770fbd6144551bd5e032f1199171c", size = 5034753, upload-time = "2025-07-16T12:04:32.292Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/84/51a69ee89ff8d1fea0c6997e946657e25a3f08513de8435fe124929f3eef/fonttools-4.59.0-cp313-cp313-win32.whl", hash = "sha256:70d6b3ceaa9cc5a6ac52884f3b3d9544e8e231e95b23f138bdb78e6d4dc0eae3", size = 2199688, upload-time = "2025-07-16T12:04:34.444Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/ee/f626cd372932d828508137a79b85167fdcf3adab2e3bed433f295c596c6a/fonttools-4.59.0-cp313-cp313-win_amd64.whl", hash = "sha256:26731739daa23b872643f0e4072d5939960237d540c35c14e6a06d47d71ca8fe", size = 2248560, upload-time = "2025-07-16T12:04:36.034Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/9c/df0ef2c51845a13043e5088f7bb988ca6cd5bb82d5d4203d6a158aa58cf2/fonttools-4.59.0-py3-none-any.whl", hash = "sha256:241313683afd3baacb32a6bd124d0bce7404bc5280e12e291bae1b9bba28711d", size = 1128050, upload-time = "2025-07-16T12:04:52.687Z" },
+]
+
+[[package]]
+name = "iniconfig"
+version = "2.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f2/97/ebf4da567aa6827c909642694d71c9fcf53e5b504f2d96afea02718862f3/iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7", size = 4793, upload-time = "2025-03-19T20:09:59.721Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760", size = 6050, upload-time = "2025-03-19T20:10:01.071Z" },
+]
+
+[[package]]
+name = "ipykernel"
+version = "6.29.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "appnope", marker = "sys_platform == 'darwin'" },
+    { name = "comm" },
+    { name = "debugpy" },
+    { name = "ipython" },
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "matplotlib-inline" },
+    { name = "nest-asyncio" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyzmq" },
+    { name = "tornado" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/5c/67594cb0c7055dc50814b21731c22a601101ea3b1b50a9a1b090e11f5d0f/ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215", size = 163367, upload-time = "2024-07-01T14:07:22.543Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/5c/368ae6c01c7628438358e6d337c19b05425727fbb221d2a3c4303c372f42/ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5", size = 117173, upload-time = "2024-07-01T14:07:19.603Z" },
+]
+
+[[package]]
+name = "ipython"
+version = "9.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "decorator" },
+    { name = "ipython-pygments-lexers" },
+    { name = "jedi" },
+    { name = "matplotlib-inline" },
+    { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "prompt-toolkit" },
+    { name = "pygments" },
+    { name = "stack-data" },
+    { name = "traitlets" },
+    { name = "typing-extensions", marker = "python_full_version < '3.12'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/54/80/406f9e3bde1c1fd9bf5a0be9d090f8ae623e401b7670d8f6fdf2ab679891/ipython-9.4.0.tar.gz", hash = "sha256:c033c6d4e7914c3d9768aabe76bbe87ba1dc66a92a05db6bfa1125d81f2ee270", size = 4385338, upload-time = "2025-07-01T11:11:30.606Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/63/f8/0031ee2b906a15a33d6bfc12dd09c3dfa966b3cb5b284ecfb7549e6ac3c4/ipython-9.4.0-py3-none-any.whl", hash = "sha256:25850f025a446d9b359e8d296ba175a36aedd32e83ca9b5060430fe16801f066", size = 611021, upload-time = "2025-07-01T11:11:27.85Z" },
+]
+
+[[package]]
+name = "ipython-pygments-lexers"
+version = "1.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393, upload-time = "2025-01-17T11:24:34.505Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074, upload-time = "2025-01-17T11:24:33.271Z" },
+]
+
+[[package]]
+name = "jedi"
+version = "0.19.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "parso" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287, upload-time = "2024-11-11T01:41:42.873Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278, upload-time = "2024-11-11T01:41:40.175Z" },
+]
+
+[[package]]
+name = "jupyter-client"
+version = "8.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jupyter-core" },
+    { name = "python-dateutil" },
+    { name = "pyzmq" },
+    { name = "tornado" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/71/22/bf9f12fdaeae18019a468b68952a60fe6dbab5d67cd2a103cac7659b41ca/jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419", size = 342019, upload-time = "2024-09-17T10:44:17.613Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f", size = 106105, upload-time = "2024-09-17T10:44:15.218Z" },
+]
+
+[[package]]
+name = "jupyter-core"
+version = "5.8.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "platformdirs" },
+    { name = "pywin32", marker = "platform_python_implementation != 'PyPy' and sys_platform == 'win32'" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/99/1b/72906d554acfeb588332eaaa6f61577705e9ec752ddb486f302dafa292d9/jupyter_core-5.8.1.tar.gz", hash = "sha256:0a5f9706f70e64786b75acba995988915ebd4601c8a52e534a40b51c95f59941", size = 88923, upload-time = "2025-05-27T07:38:16.655Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2f/57/6bffd4b20b88da3800c5d691e0337761576ee688eb01299eae865689d2df/jupyter_core-5.8.1-py3-none-any.whl", hash = "sha256:c28d268fc90fb53f1338ded2eb410704c5449a358406e8a948b75706e24863d0", size = 28880, upload-time = "2025-05-27T07:38:15.137Z" },
+]
+
+[[package]]
+name = "kiwisolver"
+version = "1.4.8"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/82/59/7c91426a8ac292e1cdd53a63b6d9439abd573c875c3f92c146767dd33faf/kiwisolver-1.4.8.tar.gz", hash = "sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e", size = 97538, upload-time = "2024-12-24T18:30:51.519Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/da/ed/c913ee28936c371418cb167b128066ffb20bbf37771eecc2c97edf8a6e4c/kiwisolver-1.4.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a4d3601908c560bdf880f07d94f31d734afd1bb71e96585cace0e38ef44c6d84", size = 124635, upload-time = "2024-12-24T18:28:51.826Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/45/4a7f896f7467aaf5f56ef093d1f329346f3b594e77c6a3c327b2d415f521/kiwisolver-1.4.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:856b269c4d28a5c0d5e6c1955ec36ebfd1651ac00e1ce0afa3e28da95293b561", size = 66717, upload-time = "2024-12-24T18:28:54.256Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/b4/c12b3ac0852a3a68f94598d4c8d569f55361beef6159dce4e7b624160da2/kiwisolver-1.4.8-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c2b9a96e0f326205af81a15718a9073328df1173a2619a68553decb7097fd5d7", size = 65413, upload-time = "2024-12-24T18:28:55.184Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/98/1df4089b1ed23d83d410adfdc5947245c753bddfbe06541c4aae330e9e70/kiwisolver-1.4.8-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c5020c83e8553f770cb3b5fc13faac40f17e0b205bd237aebd21d53d733adb03", size = 1343994, upload-time = "2024-12-24T18:28:57.493Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/bf/b4b169b050c8421a7c53ea1ea74e4ef9c335ee9013216c558a047f162d20/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dace81d28c787956bfbfbbfd72fdcef014f37d9b48830829e488fdb32b49d954", size = 1434804, upload-time = "2024-12-24T18:29:00.077Z" },
+    { url = "https://files.pythonhosted.org/packages/66/5a/e13bd341fbcf73325ea60fdc8af752addf75c5079867af2e04cc41f34434/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:11e1022b524bd48ae56c9b4f9296bce77e15a2e42a502cceba602f804b32bb79", size = 1450690, upload-time = "2024-12-24T18:29:01.401Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/4f/5955dcb376ba4a830384cc6fab7d7547bd6759fe75a09564910e9e3bb8ea/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3b9b4d2892fefc886f30301cdd80debd8bb01ecdf165a449eb6e78f79f0fabd6", size = 1376839, upload-time = "2024-12-24T18:29:02.685Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/97/5edbed69a9d0caa2e4aa616ae7df8127e10f6586940aa683a496c2c280b9/kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a96c0e790ee875d65e340ab383700e2b4891677b7fcd30a699146f9384a2bb0", size = 1435109, upload-time = "2024-12-24T18:29:04.113Z" },
+    { url = "https://files.pythonhosted.org/packages/13/fc/e756382cb64e556af6c1809a1bbb22c141bbc2445049f2da06b420fe52bf/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:23454ff084b07ac54ca8be535f4174170c1094a4cff78fbae4f73a4bcc0d4dab", size = 2245269, upload-time = "2024-12-24T18:29:05.488Z" },
+    { url = "https://files.pythonhosted.org/packages/76/15/e59e45829d7f41c776d138245cabae6515cb4eb44b418f6d4109c478b481/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:87b287251ad6488e95b4f0b4a79a6d04d3ea35fde6340eb38fbd1ca9cd35bbbc", size = 2393468, upload-time = "2024-12-24T18:29:06.79Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/39/483558c2a913ab8384d6e4b66a932406f87c95a6080112433da5ed668559/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:b21dbe165081142b1232a240fc6383fd32cdd877ca6cc89eab93e5f5883e1c25", size = 2355394, upload-time = "2024-12-24T18:29:08.24Z" },
+    { url = "https://files.pythonhosted.org/packages/01/aa/efad1fbca6570a161d29224f14b082960c7e08268a133fe5dc0f6906820e/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:768cade2c2df13db52475bd28d3a3fac8c9eff04b0e9e2fda0f3760f20b3f7fc", size = 2490901, upload-time = "2024-12-24T18:29:09.653Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/4f/15988966ba46bcd5ab9d0c8296914436720dd67fca689ae1a75b4ec1c72f/kiwisolver-1.4.8-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d47cfb2650f0e103d4bf68b0b5804c68da97272c84bb12850d877a95c056bd67", size = 2312306, upload-time = "2024-12-24T18:29:12.644Z" },
+    { url = "https://files.pythonhosted.org/packages/2d/27/bdf1c769c83f74d98cbc34483a972f221440703054894a37d174fba8aa68/kiwisolver-1.4.8-cp311-cp311-win_amd64.whl", hash = "sha256:ed33ca2002a779a2e20eeb06aea7721b6e47f2d4b8a8ece979d8ba9e2a167e34", size = 71966, upload-time = "2024-12-24T18:29:14.089Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/c9/9642ea855604aeb2968a8e145fc662edf61db7632ad2e4fb92424be6b6c0/kiwisolver-1.4.8-cp311-cp311-win_arm64.whl", hash = "sha256:16523b40aab60426ffdebe33ac374457cf62863e330a90a0383639ce14bf44b2", size = 65311, upload-time = "2024-12-24T18:29:15.892Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/aa/cea685c4ab647f349c3bc92d2daf7ae34c8e8cf405a6dcd3a497f58a2ac3/kiwisolver-1.4.8-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d6af5e8815fd02997cb6ad9bbed0ee1e60014438ee1a5c2444c96f87b8843502", size = 124152, upload-time = "2024-12-24T18:29:16.85Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/0b/8db6d2e2452d60d5ebc4ce4b204feeb16176a851fd42462f66ade6808084/kiwisolver-1.4.8-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:bade438f86e21d91e0cf5dd7c0ed00cda0f77c8c1616bd83f9fc157fa6760d31", size = 66555, upload-time = "2024-12-24T18:29:19.146Z" },
+    { url = "https://files.pythonhosted.org/packages/60/26/d6a0db6785dd35d3ba5bf2b2df0aedc5af089962c6eb2cbf67a15b81369e/kiwisolver-1.4.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b83dc6769ddbc57613280118fb4ce3cd08899cc3369f7d0e0fab518a7cf37fdb", size = 65067, upload-time = "2024-12-24T18:29:20.096Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/ed/1d97f7e3561e09757a196231edccc1bcf59d55ddccefa2afc9c615abd8e0/kiwisolver-1.4.8-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:111793b232842991be367ed828076b03d96202c19221b5ebab421ce8bcad016f", size = 1378443, upload-time = "2024-12-24T18:29:22.843Z" },
+    { url = "https://files.pythonhosted.org/packages/29/61/39d30b99954e6b46f760e6289c12fede2ab96a254c443639052d1b573fbc/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:257af1622860e51b1a9d0ce387bf5c2c4f36a90594cb9514f55b074bcc787cfc", size = 1472728, upload-time = "2024-12-24T18:29:24.463Z" },
+    { url = "https://files.pythonhosted.org/packages/0c/3e/804163b932f7603ef256e4a715e5843a9600802bb23a68b4e08c8c0ff61d/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:69b5637c3f316cab1ec1c9a12b8c5f4750a4c4b71af9157645bf32830e39c03a", size = 1478388, upload-time = "2024-12-24T18:29:25.776Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/9e/60eaa75169a154700be74f875a4d9961b11ba048bef315fbe89cb6999056/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:782bb86f245ec18009890e7cb8d13a5ef54dcf2ebe18ed65f795e635a96a1c6a", size = 1413849, upload-time = "2024-12-24T18:29:27.202Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/b3/9458adb9472e61a998c8c4d95cfdfec91c73c53a375b30b1428310f923e4/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc978a80a0db3a66d25767b03688f1147a69e6237175c0f4ffffaaedf744055a", size = 1475533, upload-time = "2024-12-24T18:29:28.638Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/7a/0a42d9571e35798de80aef4bb43a9b672aa7f8e58643d7bd1950398ffb0a/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:36dbbfd34838500a31f52c9786990d00150860e46cd5041386f217101350f0d3", size = 2268898, upload-time = "2024-12-24T18:29:30.368Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/07/1255dc8d80271400126ed8db35a1795b1a2c098ac3a72645075d06fe5c5d/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:eaa973f1e05131de5ff3569bbba7f5fd07ea0595d3870ed4a526d486fe57fa1b", size = 2425605, upload-time = "2024-12-24T18:29:33.151Z" },
+    { url = "https://files.pythonhosted.org/packages/84/df/5a3b4cf13780ef6f6942df67b138b03b7e79e9f1f08f57c49957d5867f6e/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:a66f60f8d0c87ab7f59b6fb80e642ebb29fec354a4dfad687ca4092ae69d04f4", size = 2375801, upload-time = "2024-12-24T18:29:34.584Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/10/2348d068e8b0f635c8c86892788dac7a6b5c0cb12356620ab575775aad89/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:858416b7fb777a53f0c59ca08190ce24e9abbd3cffa18886a5781b8e3e26f65d", size = 2520077, upload-time = "2024-12-24T18:29:36.138Z" },
+    { url = "https://files.pythonhosted.org/packages/32/d8/014b89fee5d4dce157d814303b0fce4d31385a2af4c41fed194b173b81ac/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:085940635c62697391baafaaeabdf3dd7a6c3643577dde337f4d66eba021b2b8", size = 2338410, upload-time = "2024-12-24T18:29:39.991Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/72/dfff0cc97f2a0776e1c9eb5bef1ddfd45f46246c6533b0191887a427bca5/kiwisolver-1.4.8-cp312-cp312-win_amd64.whl", hash = "sha256:01c3d31902c7db5fb6182832713d3b4122ad9317c2c5877d0539227d96bb2e50", size = 71853, upload-time = "2024-12-24T18:29:42.006Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/85/220d13d914485c0948a00f0b9eb419efaf6da81b7d72e88ce2391f7aed8d/kiwisolver-1.4.8-cp312-cp312-win_arm64.whl", hash = "sha256:a3c44cb68861de93f0c4a8175fbaa691f0aa22550c331fefef02b618a9dcb476", size = 65424, upload-time = "2024-12-24T18:29:44.38Z" },
+    { url = "https://files.pythonhosted.org/packages/79/b3/e62464a652f4f8cd9006e13d07abad844a47df1e6537f73ddfbf1bc997ec/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:1c8ceb754339793c24aee1c9fb2485b5b1f5bb1c2c214ff13368431e51fc9a09", size = 124156, upload-time = "2024-12-24T18:29:45.368Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/2d/f13d06998b546a2ad4f48607a146e045bbe48030774de29f90bdc573df15/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:54a62808ac74b5e55a04a408cda6156f986cefbcf0ada13572696b507cc92fa1", size = 66555, upload-time = "2024-12-24T18:29:46.37Z" },
+    { url = "https://files.pythonhosted.org/packages/59/e3/b8bd14b0a54998a9fd1e8da591c60998dc003618cb19a3f94cb233ec1511/kiwisolver-1.4.8-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:68269e60ee4929893aad82666821aaacbd455284124817af45c11e50a4b42e3c", size = 65071, upload-time = "2024-12-24T18:29:47.333Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/1c/6c86f6d85ffe4d0ce04228d976f00674f1df5dc893bf2dd4f1928748f187/kiwisolver-1.4.8-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:34d142fba9c464bc3bbfeff15c96eab0e7310343d6aefb62a79d51421fcc5f1b", size = 1378053, upload-time = "2024-12-24T18:29:49.636Z" },
+    { url = "https://files.pythonhosted.org/packages/4e/b9/1c6e9f6dcb103ac5cf87cb695845f5fa71379021500153566d8a8a9fc291/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3ddc373e0eef45b59197de815b1b28ef89ae3955e7722cc9710fb91cd77b7f47", size = 1472278, upload-time = "2024-12-24T18:29:51.164Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/81/aca1eb176de671f8bda479b11acdc42c132b61a2ac861c883907dde6debb/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:77e6f57a20b9bd4e1e2cedda4d0b986ebd0216236f0106e55c28aea3d3d69b16", size = 1478139, upload-time = "2024-12-24T18:29:52.594Z" },
+    { url = "https://files.pythonhosted.org/packages/49/f4/e081522473671c97b2687d380e9e4c26f748a86363ce5af48b4a28e48d06/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:08e77738ed7538f036cd1170cbed942ef749137b1311fa2bbe2a7fda2f6bf3cc", size = 1413517, upload-time = "2024-12-24T18:29:53.941Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/e9/6a7d025d8da8c4931522922cd706105aa32b3291d1add8c5427cdcd66e63/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a5ce1e481a74b44dd5e92ff03ea0cb371ae7a0268318e202be06c8f04f4f1246", size = 1474952, upload-time = "2024-12-24T18:29:56.523Z" },
+    { url = "https://files.pythonhosted.org/packages/82/13/13fa685ae167bee5d94b415991c4fc7bb0a1b6ebea6e753a87044b209678/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:fc2ace710ba7c1dfd1a3b42530b62b9ceed115f19a1656adefce7b1782a37794", size = 2269132, upload-time = "2024-12-24T18:29:57.989Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/92/bb7c9395489b99a6cb41d502d3686bac692586db2045adc19e45ee64ed23/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:3452046c37c7692bd52b0e752b87954ef86ee2224e624ef7ce6cb21e8c41cc1b", size = 2425997, upload-time = "2024-12-24T18:29:59.393Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/12/87f0e9271e2b63d35d0d8524954145837dd1a6c15b62a2d8c1ebe0f182b4/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:7e9a60b50fe8b2ec6f448fe8d81b07e40141bfced7f896309df271a0b92f80f3", size = 2376060, upload-time = "2024-12-24T18:30:01.338Z" },
+    { url = "https://files.pythonhosted.org/packages/02/6e/c8af39288edbce8bf0fa35dee427b082758a4b71e9c91ef18fa667782138/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:918139571133f366e8362fa4a297aeba86c7816b7ecf0bc79168080e2bd79957", size = 2520471, upload-time = "2024-12-24T18:30:04.574Z" },
+    { url = "https://files.pythonhosted.org/packages/13/78/df381bc7b26e535c91469f77f16adcd073beb3e2dd25042efd064af82323/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e063ef9f89885a1d68dd8b2e18f5ead48653176d10a0e324e3b0030e3a69adeb", size = 2338793, upload-time = "2024-12-24T18:30:06.25Z" },
+    { url = "https://files.pythonhosted.org/packages/d0/dc/c1abe38c37c071d0fc71c9a474fd0b9ede05d42f5a458d584619cfd2371a/kiwisolver-1.4.8-cp313-cp313-win_amd64.whl", hash = "sha256:a17b7c4f5b2c51bb68ed379defd608a03954a1845dfed7cc0117f1cc8a9b7fd2", size = 71855, upload-time = "2024-12-24T18:30:07.535Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/b6/21529d595b126ac298fdd90b705d87d4c5693de60023e0efcb4f387ed99e/kiwisolver-1.4.8-cp313-cp313-win_arm64.whl", hash = "sha256:3cd3bc628b25f74aedc6d374d5babf0166a92ff1317f46267f12d2ed54bc1d30", size = 65430, upload-time = "2024-12-24T18:30:08.504Z" },
+    { url = "https://files.pythonhosted.org/packages/34/bd/b89380b7298e3af9b39f49334e3e2a4af0e04819789f04b43d560516c0c8/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:370fd2df41660ed4e26b8c9d6bbcad668fbe2560462cba151a721d49e5b6628c", size = 126294, upload-time = "2024-12-24T18:30:09.508Z" },
+    { url = "https://files.pythonhosted.org/packages/83/41/5857dc72e5e4148eaac5aa76e0703e594e4465f8ab7ec0fc60e3a9bb8fea/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:84a2f830d42707de1d191b9490ac186bf7997a9495d4e9072210a1296345f7dc", size = 67736, upload-time = "2024-12-24T18:30:11.039Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/d1/be059b8db56ac270489fb0b3297fd1e53d195ba76e9bbb30e5401fa6b759/kiwisolver-1.4.8-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:7a3ad337add5148cf51ce0b55642dc551c0b9d6248458a757f98796ca7348712", size = 66194, upload-time = "2024-12-24T18:30:14.886Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/83/4b73975f149819eb7dcf9299ed467eba068ecb16439a98990dcb12e63fdd/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7506488470f41169b86d8c9aeff587293f530a23a23a49d6bc64dab66bedc71e", size = 1465942, upload-time = "2024-12-24T18:30:18.927Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/2c/30a5cdde5102958e602c07466bce058b9d7cb48734aa7a4327261ac8e002/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2f0121b07b356a22fb0414cec4666bbe36fd6d0d759db3d37228f496ed67c880", size = 1595341, upload-time = "2024-12-24T18:30:22.102Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/9b/1e71db1c000385aa069704f5990574b8244cce854ecd83119c19e83c9586/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d6d6bd87df62c27d4185de7c511c6248040afae67028a8a22012b010bc7ad062", size = 1598455, upload-time = "2024-12-24T18:30:24.947Z" },
+    { url = "https://files.pythonhosted.org/packages/85/92/c8fec52ddf06231b31cbb779af77e99b8253cd96bd135250b9498144c78b/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:291331973c64bb9cce50bbe871fb2e675c4331dab4f31abe89f175ad7679a4d7", size = 1522138, upload-time = "2024-12-24T18:30:26.286Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/51/9eb7e2cd07a15d8bdd976f6190c0164f92ce1904e5c0c79198c4972926b7/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:893f5525bb92d3d735878ec00f781b2de998333659507d29ea4466208df37bed", size = 1582857, upload-time = "2024-12-24T18:30:28.86Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/95/c5a00387a5405e68ba32cc64af65ce881a39b98d73cc394b24143bebc5b8/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:b47a465040146981dc9db8647981b8cb96366fbc8d452b031e4f8fdffec3f26d", size = 2293129, upload-time = "2024-12-24T18:30:30.34Z" },
+    { url = "https://files.pythonhosted.org/packages/44/83/eeb7af7d706b8347548313fa3a3a15931f404533cc54fe01f39e830dd231/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:99cea8b9dd34ff80c521aef46a1dddb0dcc0283cf18bde6d756f1e6f31772165", size = 2421538, upload-time = "2024-12-24T18:30:33.334Z" },
+    { url = "https://files.pythonhosted.org/packages/05/f9/27e94c1b3eb29e6933b6986ffc5fa1177d2cd1f0c8efc5f02c91c9ac61de/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:151dffc4865e5fe6dafce5480fab84f950d14566c480c08a53c663a0020504b6", size = 2390661, upload-time = "2024-12-24T18:30:34.939Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/d4/3c9735faa36ac591a4afcc2980d2691000506050b7a7e80bcfe44048daa7/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:577facaa411c10421314598b50413aa1ebcf5126f704f1e5d72d7e4e9f020d90", size = 2546710, upload-time = "2024-12-24T18:30:37.281Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/fa/be89a49c640930180657482a74970cdcf6f7072c8d2471e1babe17a222dc/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:be4816dc51c8a471749d664161b434912eee82f2ea66bd7628bd14583a833e85", size = 2349213, upload-time = "2024-12-24T18:30:40.019Z" },
+]
+
+[[package]]
+name = "matplotlib"
+version = "3.10.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "contourpy" },
+    { name = "cycler" },
+    { name = "fonttools" },
+    { name = "kiwisolver" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pillow" },
+    { name = "pyparsing" },
+    { name = "python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/26/91/d49359a21893183ed2a5b6c76bec40e0b1dcbf8ca148f864d134897cfc75/matplotlib-3.10.3.tar.gz", hash = "sha256:2f82d2c5bb7ae93aaaa4cd42aca65d76ce6376f83304fa3a630b569aca274df0", size = 34799811, upload-time = "2025-05-08T19:10:54.39Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f5/bd/af9f655456f60fe1d575f54fb14704ee299b16e999704817a7645dfce6b0/matplotlib-3.10.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:0ef061f74cd488586f552d0c336b2f078d43bc00dc473d2c3e7bfee2272f3fa8", size = 8178873, upload-time = "2025-05-08T19:09:53.857Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/86/e1c86690610661cd716eda5f9d0b35eaf606ae6c9b6736687cfc8f2d0cd8/matplotlib-3.10.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d96985d14dc5f4a736bbea4b9de9afaa735f8a0fc2ca75be2fa9e96b2097369d", size = 8052205, upload-time = "2025-05-08T19:09:55.684Z" },
+    { url = "https://files.pythonhosted.org/packages/54/51/a9f8e49af3883dacddb2da1af5fca1f7468677f1188936452dd9aaaeb9ed/matplotlib-3.10.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7c5f0283da91e9522bdba4d6583ed9d5521566f63729ffb68334f86d0bb98049", size = 8465823, upload-time = "2025-05-08T19:09:57.442Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/e3/c82963a3b86d6e6d5874cbeaa390166458a7f1961bab9feb14d3d1a10f02/matplotlib-3.10.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fdfa07c0ec58035242bc8b2c8aae37037c9a886370eef6850703d7583e19964b", size = 8606464, upload-time = "2025-05-08T19:09:59.471Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/34/24da1027e7fcdd9e82da3194c470143c551852757a4b473a09a012f5b945/matplotlib-3.10.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:c0b9849a17bce080a16ebcb80a7b714b5677d0ec32161a2cc0a8e5a6030ae220", size = 9413103, upload-time = "2025-05-08T19:10:03.208Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/da/948a017c3ea13fd4a97afad5fdebe2f5bbc4d28c0654510ce6fd6b06b7bd/matplotlib-3.10.3-cp311-cp311-win_amd64.whl", hash = "sha256:eef6ed6c03717083bc6d69c2d7ee8624205c29a8e6ea5a31cd3492ecdbaee1e1", size = 8065492, upload-time = "2025-05-08T19:10:05.271Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/43/6b80eb47d1071f234ef0c96ca370c2ca621f91c12045f1401b5c9b28a639/matplotlib-3.10.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:0ab1affc11d1f495ab9e6362b8174a25afc19c081ba5b0775ef00533a4236eea", size = 8179689, upload-time = "2025-05-08T19:10:07.602Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/70/d61a591958325c357204870b5e7b164f93f2a8cca1dc6ce940f563909a13/matplotlib-3.10.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2a818d8bdcafa7ed2eed74487fdb071c09c1ae24152d403952adad11fa3c65b4", size = 8050466, upload-time = "2025-05-08T19:10:09.383Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/75/70c9d2306203148cc7902a961240c5927dd8728afedf35e6a77e105a2985/matplotlib-3.10.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:748ebc3470c253e770b17d8b0557f0aa85cf8c63fd52f1a61af5b27ec0b7ffee", size = 8456252, upload-time = "2025-05-08T19:10:11.958Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/91/ba0ae1ff4b3f30972ad01cd4a8029e70a0ec3b8ea5be04764b128b66f763/matplotlib-3.10.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ed70453fd99733293ace1aec568255bc51c6361cb0da94fa5ebf0649fdb2150a", size = 8601321, upload-time = "2025-05-08T19:10:14.47Z" },
+    { url = "https://files.pythonhosted.org/packages/d2/88/d636041eb54a84b889e11872d91f7cbf036b3b0e194a70fa064eb8b04f7a/matplotlib-3.10.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:dbed9917b44070e55640bd13419de83b4c918e52d97561544814ba463811cbc7", size = 9406972, upload-time = "2025-05-08T19:10:16.569Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/79/0d1c165eac44405a86478082e225fce87874f7198300bbebc55faaf6d28d/matplotlib-3.10.3-cp312-cp312-win_amd64.whl", hash = "sha256:cf37d8c6ef1a48829443e8ba5227b44236d7fcaf7647caa3178a4ff9f7a5be05", size = 8067954, upload-time = "2025-05-08T19:10:18.663Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/c1/23cfb566a74c696a3b338d8955c549900d18fe2b898b6e94d682ca21e7c2/matplotlib-3.10.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:9f2efccc8dcf2b86fc4ee849eea5dcaecedd0773b30f47980dc0cbeabf26ec84", size = 8180318, upload-time = "2025-05-08T19:10:20.426Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/0c/02f1c3b66b30da9ee343c343acbb6251bef5b01d34fad732446eaadcd108/matplotlib-3.10.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3ddbba06a6c126e3301c3d272a99dcbe7f6c24c14024e80307ff03791a5f294e", size = 8051132, upload-time = "2025-05-08T19:10:22.569Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/ab/8db1a5ac9b3a7352fb914133001dae889f9fcecb3146541be46bed41339c/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:748302b33ae9326995b238f606e9ed840bf5886ebafcb233775d946aa8107a15", size = 8457633, upload-time = "2025-05-08T19:10:24.749Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7", size = 8601031, upload-time = "2025-05-08T19:10:27.03Z" },
+    { url = "https://files.pythonhosted.org/packages/12/6f/6cc79e9e5ab89d13ed64da28898e40fe5b105a9ab9c98f83abd24e46d7d7/matplotlib-3.10.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:55e46cbfe1f8586adb34f7587c3e4f7dedc59d5226719faf6cb54fc24f2fd52d", size = 9406988, upload-time = "2025-05-08T19:10:29.056Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/0f/eed564407bd4d935ffabf561ed31099ed609e19287409a27b6d336848653/matplotlib-3.10.3-cp313-cp313-win_amd64.whl", hash = "sha256:151d89cb8d33cb23345cd12490c76fd5d18a56581a16d950b48c6ff19bb2ab93", size = 8068034, upload-time = "2025-05-08T19:10:31.221Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/e5/2f14791ff69b12b09e9975e1d116d9578ac684460860ce542c2588cb7a1c/matplotlib-3.10.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:c26dd9834e74d164d06433dc7be5d75a1e9890b926b3e57e74fa446e1a62c3e2", size = 8218223, upload-time = "2025-05-08T19:10:33.114Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/08/30a94afd828b6e02d0a52cae4a29d6e9ccfcf4c8b56cc28b021d3588873e/matplotlib-3.10.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:24853dad5b8c84c8c2390fc31ce4858b6df504156893292ce8092d190ef8151d", size = 8094985, upload-time = "2025-05-08T19:10:35.337Z" },
+    { url = "https://files.pythonhosted.org/packages/89/44/f3bc6b53066c889d7a1a3ea8094c13af6a667c5ca6220ec60ecceec2dabe/matplotlib-3.10.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68f7878214d369d7d4215e2a9075fef743be38fa401d32e6020bab2dfabaa566", size = 8483109, upload-time = "2025-05-08T19:10:37.611Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/c7/473bc559beec08ebee9f86ca77a844b65747e1a6c2691e8c92e40b9f42a8/matplotlib-3.10.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f6929fc618cb6db9cb75086f73b3219bbb25920cb24cee2ea7a12b04971a4158", size = 8618082, upload-time = "2025-05-08T19:10:39.892Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/e9/6ce8edd264c8819e37bbed8172e0ccdc7107fe86999b76ab5752276357a4/matplotlib-3.10.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6c7818292a5cc372a2dc4c795e5c356942eb8350b98ef913f7fda51fe175ac5d", size = 9413699, upload-time = "2025-05-08T19:10:42.376Z" },
+    { url = "https://files.pythonhosted.org/packages/1b/92/9a45c91089c3cf690b5badd4be81e392ff086ccca8a1d4e3a08463d8a966/matplotlib-3.10.3-cp313-cp313t-win_amd64.whl", hash = "sha256:4f23ffe95c5667ef8a2b56eea9b53db7f43910fa4a2d5472ae0f72b64deab4d5", size = 8139044, upload-time = "2025-05-08T19:10:44.551Z" },
+]
+
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/99/5b/a36a337438a14116b16480db471ad061c36c3694df7c2084a0da7ba538b7/matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90", size = 8159, upload-time = "2024-04-15T13:44:44.803Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/8e/9ad090d3553c280a8060fbf6e24dc1c0c29704ee7d1c372f0c174aa59285/matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca", size = 9899, upload-time = "2024-04-15T13:44:43.265Z" },
+]
+
+[[package]]
+name = "mixsol"
+version = "1.0.0"
+source = { editable = "." }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "molmass" },
+    { name = "numpy" },
+    { name = "scipy" },
+]
+
+[package.dev-dependencies]
+dev = [
+    { name = "ipykernel" },
+    { name = "pandas" },
+    { name = "pytest" },
+    { name = "pytest-cov" },
+    { name = "seaborn" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "matplotlib" },
+    { name = "molmass" },
+    { name = "numpy" },
+    { name = "scipy", specifier = ">=1.2.3" },
+]
+
+[package.metadata.requires-dev]
+dev = [
+    { name = "ipykernel", specifier = ">=4.10.1" },
+    { name = "pandas", specifier = ">=2.3.1" },
+    { name = "pytest", specifier = ">=4.6.11" },
+    { name = "pytest-cov", specifier = ">=2.12.1" },
+    { name = "seaborn", specifier = ">=0.13.2" },
+]
+
+[[package]]
+name = "molmass"
+version = "2025.4.14"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/0f/d4f68780aaa42707fdf2bef4be789da8d0d0de661efc12692ac5a0a0f55b/molmass-2025.4.14.tar.gz", hash = "sha256:cb9c15f482b09ed8ad484315ccdf14b4eb3059d57538cdab2d1e1962f5f80e50", size = 76797, upload-time = "2025-04-14T02:42:07.459Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8a/5f/c6c6fea1f0958ae5c319a4b308e52612570da3fb5a5bc524312394c66de8/molmass-2025.4.14-py3-none-any.whl", hash = "sha256:810ef2ec19b2206691e94f52f60e583698e41b00ccbf28b88c62a72f6cd832ed", size = 75921, upload-time = "2025-04-14T02:42:06.075Z" },
+]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418, upload-time = "2024-01-21T14:25:19.227Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195, upload-time = "2024-01-21T14:25:17.223Z" },
+]
+
+[[package]]
+name = "numpy"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2e/19/d7c972dfe90a353dbd3efbbe1d14a5951de80c99c9dc1b93cd998d51dc0f/numpy-2.3.1.tar.gz", hash = "sha256:1ec9ae20a4226da374362cca3c62cd753faf2f951440b0e3b98e93c235441d2b", size = 20390372, upload-time = "2025-06-21T12:28:33.469Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b0/c7/87c64d7ab426156530676000c94784ef55676df2f13b2796f97722464124/numpy-2.3.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6ea9e48336a402551f52cd8f593343699003d2353daa4b72ce8d34f66b722070", size = 21199346, upload-time = "2025-06-21T11:47:47.57Z" },
+    { url = "https://files.pythonhosted.org/packages/58/0e/0966c2f44beeac12af8d836e5b5f826a407cf34c45cb73ddcdfce9f5960b/numpy-2.3.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:5ccb7336eaf0e77c1635b232c141846493a588ec9ea777a7c24d7166bb8533ae", size = 14361143, upload-time = "2025-06-21T11:48:10.766Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/31/6e35a247acb1bfc19226791dfc7d4c30002cd4e620e11e58b0ddf836fe52/numpy-2.3.1-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:0bb3a4a61e1d327e035275d2a993c96fa786e4913aa089843e6a2d9dd205c66a", size = 5378989, upload-time = "2025-06-21T11:48:19.998Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/25/93b621219bb6f5a2d4e713a824522c69ab1f06a57cd571cda70e2e31af44/numpy-2.3.1-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:e344eb79dab01f1e838ebb67aab09965fb271d6da6b00adda26328ac27d4a66e", size = 6912890, upload-time = "2025-06-21T11:48:31.376Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/60/6b06ed98d11fb32e27fb59468b42383f3877146d3ee639f733776b6ac596/numpy-2.3.1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:467db865b392168ceb1ef1ffa6f5a86e62468c43e0cfb4ab6da667ede10e58db", size = 14569032, upload-time = "2025-06-21T11:48:52.563Z" },
+    { url = "https://files.pythonhosted.org/packages/75/c9/9bec03675192077467a9c7c2bdd1f2e922bd01d3a69b15c3a0fdcd8548f6/numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:afed2ce4a84f6b0fc6c1ce734ff368cbf5a5e24e8954a338f3bdffa0718adffb", size = 16930354, upload-time = "2025-06-21T11:49:17.473Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/e2/5756a00cabcf50a3f527a0c968b2b4881c62b1379223931853114fa04cda/numpy-2.3.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0025048b3c1557a20bc80d06fdeb8cc7fc193721484cca82b2cfa072fec71a93", size = 15879605, upload-time = "2025-06-21T11:49:41.161Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/86/a471f65f0a86f1ca62dcc90b9fa46174dd48f50214e5446bc16a775646c5/numpy-2.3.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a5ee121b60aa509679b682819c602579e1df14a5b07fe95671c8849aad8f2115", size = 18666994, upload-time = "2025-06-21T11:50:08.516Z" },
+    { url = "https://files.pythonhosted.org/packages/43/a6/482a53e469b32be6500aaf61cfafd1de7a0b0d484babf679209c3298852e/numpy-2.3.1-cp311-cp311-win32.whl", hash = "sha256:a8b740f5579ae4585831b3cf0e3b0425c667274f82a484866d2adf9570539369", size = 6603672, upload-time = "2025-06-21T11:50:19.584Z" },
+    { url = "https://files.pythonhosted.org/packages/6b/fb/bb613f4122c310a13ec67585c70e14b03bfc7ebabd24f4d5138b97371d7c/numpy-2.3.1-cp311-cp311-win_amd64.whl", hash = "sha256:d4580adadc53311b163444f877e0789f1c8861e2698f6b2a4ca852fda154f3ff", size = 13024015, upload-time = "2025-06-21T11:50:39.139Z" },
+    { url = "https://files.pythonhosted.org/packages/51/58/2d842825af9a0c041aca246dc92eb725e1bc5e1c9ac89712625db0c4e11c/numpy-2.3.1-cp311-cp311-win_arm64.whl", hash = "sha256:ec0bdafa906f95adc9a0c6f26a4871fa753f25caaa0e032578a30457bff0af6a", size = 10456989, upload-time = "2025-06-21T11:50:55.616Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/56/71ad5022e2f63cfe0ca93559403d0edef14aea70a841d640bd13cdba578e/numpy-2.3.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:2959d8f268f3d8ee402b04a9ec4bb7604555aeacf78b360dc4ec27f1d508177d", size = 20896664, upload-time = "2025-06-21T12:15:30.845Z" },
+    { url = "https://files.pythonhosted.org/packages/25/65/2db52ba049813670f7f987cc5db6dac9be7cd95e923cc6832b3d32d87cef/numpy-2.3.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:762e0c0c6b56bdedfef9a8e1d4538556438288c4276901ea008ae44091954e29", size = 14131078, upload-time = "2025-06-21T12:15:52.23Z" },
+    { url = "https://files.pythonhosted.org/packages/57/dd/28fa3c17b0e751047ac928c1e1b6990238faad76e9b147e585b573d9d1bd/numpy-2.3.1-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:867ef172a0976aaa1f1d1b63cf2090de8b636a7674607d514505fb7276ab08fc", size = 5112554, upload-time = "2025-06-21T12:16:01.434Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/fc/84ea0cba8e760c4644b708b6819d91784c290288c27aca916115e3311d17/numpy-2.3.1-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:4e602e1b8682c2b833af89ba641ad4176053aaa50f5cacda1a27004352dde943", size = 6646560, upload-time = "2025-06-21T12:16:11.895Z" },
+    { url = "https://files.pythonhosted.org/packages/61/b2/512b0c2ddec985ad1e496b0bd853eeb572315c0f07cd6997473ced8f15e2/numpy-2.3.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:8e333040d069eba1652fb08962ec5b76af7f2c7bce1df7e1418c8055cf776f25", size = 14260638, upload-time = "2025-06-21T12:16:32.611Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/45/c51cb248e679a6c6ab14b7a8e3ead3f4a3fe7425fc7a6f98b3f147bec532/numpy-2.3.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:e7cbf5a5eafd8d230a3ce356d892512185230e4781a361229bd902ff403bc660", size = 16632729, upload-time = "2025-06-21T12:16:57.439Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/ff/feb4be2e5c09a3da161b412019caf47183099cbea1132fd98061808c2df2/numpy-2.3.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:5f1b8f26d1086835f442286c1d9b64bb3974b0b1e41bb105358fd07d20872952", size = 15565330, upload-time = "2025-06-21T12:17:20.638Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/6d/ceafe87587101e9ab0d370e4f6e5f3f3a85b9a697f2318738e5e7e176ce3/numpy-2.3.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ee8340cb48c9b7a5899d1149eece41ca535513a9698098edbade2a8e7a84da77", size = 18361734, upload-time = "2025-06-21T12:17:47.938Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/19/0fb49a3ea088be691f040c9bf1817e4669a339d6e98579f91859b902c636/numpy-2.3.1-cp312-cp312-win32.whl", hash = "sha256:e772dda20a6002ef7061713dc1e2585bc1b534e7909b2030b5a46dae8ff077ab", size = 6320411, upload-time = "2025-06-21T12:17:58.475Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/3e/e28f4c1dd9e042eb57a3eb652f200225e311b608632bc727ae378623d4f8/numpy-2.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:cfecc7822543abdea6de08758091da655ea2210b8ffa1faf116b940693d3df76", size = 12734973, upload-time = "2025-06-21T12:18:17.601Z" },
+    { url = "https://files.pythonhosted.org/packages/04/a8/8a5e9079dc722acf53522b8f8842e79541ea81835e9b5483388701421073/numpy-2.3.1-cp312-cp312-win_arm64.whl", hash = "sha256:7be91b2239af2658653c5bb6f1b8bccafaf08226a258caf78ce44710a0160d30", size = 10191491, upload-time = "2025-06-21T12:18:33.585Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/bd/35ad97006d8abff8631293f8ea6adf07b0108ce6fec68da3c3fcca1197f2/numpy-2.3.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:25a1992b0a3fdcdaec9f552ef10d8103186f5397ab45e2d25f8ac51b1a6b97e8", size = 20889381, upload-time = "2025-06-21T12:19:04.103Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/4f/df5923874d8095b6062495b39729178eef4a922119cee32a12ee1bd4664c/numpy-2.3.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7dea630156d39b02a63c18f508f85010230409db5b2927ba59c8ba4ab3e8272e", size = 14152726, upload-time = "2025-06-21T12:19:25.599Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/0f/a1f269b125806212a876f7efb049b06c6f8772cf0121139f97774cd95626/numpy-2.3.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:bada6058dd886061f10ea15f230ccf7dfff40572e99fef440a4a857c8728c9c0", size = 5105145, upload-time = "2025-06-21T12:19:34.782Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/63/a7f7fd5f375b0361682f6ffbf686787e82b7bbd561268e4f30afad2bb3c0/numpy-2.3.1-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:a894f3816eb17b29e4783e5873f92faf55b710c2519e5c351767c51f79d8526d", size = 6639409, upload-time = "2025-06-21T12:19:45.228Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/0d/1854a4121af895aab383f4aa233748f1df4671ef331d898e32426756a8a6/numpy-2.3.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:18703df6c4a4fee55fd3d6e5a253d01c5d33a295409b03fda0c86b3ca2ff41a1", size = 14257630, upload-time = "2025-06-21T12:20:06.544Z" },
+    { url = "https://files.pythonhosted.org/packages/50/30/af1b277b443f2fb08acf1c55ce9d68ee540043f158630d62cef012750f9f/numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:5902660491bd7a48b2ec16c23ccb9124b8abfd9583c5fdfa123fe6b421e03de1", size = 16627546, upload-time = "2025-06-21T12:20:31.002Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/ec/3b68220c277e463095342d254c61be8144c31208db18d3fd8ef02712bcd6/numpy-2.3.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:36890eb9e9d2081137bd78d29050ba63b8dab95dff7912eadf1185e80074b2a0", size = 15562538, upload-time = "2025-06-21T12:20:54.322Z" },
+    { url = "https://files.pythonhosted.org/packages/77/2b/4014f2bcc4404484021c74d4c5ee8eb3de7e3f7ac75f06672f8dcf85140a/numpy-2.3.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a780033466159c2270531e2b8ac063704592a0bc62ec4a1b991c7c40705eb0e8", size = 18360327, upload-time = "2025-06-21T12:21:21.053Z" },
+    { url = "https://files.pythonhosted.org/packages/40/8d/2ddd6c9b30fcf920837b8672f6c65590c7d92e43084c25fc65edc22e93ca/numpy-2.3.1-cp313-cp313-win32.whl", hash = "sha256:39bff12c076812595c3a306f22bfe49919c5513aa1e0e70fac756a0be7c2a2b8", size = 6312330, upload-time = "2025-06-21T12:25:07.447Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/c8/beaba449925988d415efccb45bf977ff8327a02f655090627318f6398c7b/numpy-2.3.1-cp313-cp313-win_amd64.whl", hash = "sha256:8d5ee6eec45f08ce507a6570e06f2f879b374a552087a4179ea7838edbcbfa42", size = 12731565, upload-time = "2025-06-21T12:25:26.444Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/c3/5c0c575d7ec78c1126998071f58facfc124006635da75b090805e642c62e/numpy-2.3.1-cp313-cp313-win_arm64.whl", hash = "sha256:0c4d9e0a8368db90f93bd192bfa771ace63137c3488d198ee21dfb8e7771916e", size = 10190262, upload-time = "2025-06-21T12:25:42.196Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/19/a029cd335cf72f79d2644dcfc22d90f09caa86265cbbde3b5702ccef6890/numpy-2.3.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:b0b5397374f32ec0649dd98c652a1798192042e715df918c20672c62fb52d4b8", size = 20987593, upload-time = "2025-06-21T12:21:51.664Z" },
+    { url = "https://files.pythonhosted.org/packages/25/91/8ea8894406209107d9ce19b66314194675d31761fe2cb3c84fe2eeae2f37/numpy-2.3.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:c5bdf2015ccfcee8253fb8be695516ac4457c743473a43290fd36eba6a1777eb", size = 14300523, upload-time = "2025-06-21T12:22:13.583Z" },
+    { url = "https://files.pythonhosted.org/packages/a6/7f/06187b0066eefc9e7ce77d5f2ddb4e314a55220ad62dd0bfc9f2c44bac14/numpy-2.3.1-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:d70f20df7f08b90a2062c1f07737dd340adccf2068d0f1b9b3d56e2038979fee", size = 5227993, upload-time = "2025-06-21T12:22:22.53Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/ec/a926c293c605fa75e9cfb09f1e4840098ed46d2edaa6e2152ee35dc01ed3/numpy-2.3.1-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:2fb86b7e58f9ac50e1e9dd1290154107e47d1eef23a0ae9145ded06ea606f992", size = 6736652, upload-time = "2025-06-21T12:22:33.629Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/62/d68e52fb6fde5586650d4c0ce0b05ff3a48ad4df4ffd1b8866479d1d671d/numpy-2.3.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:23ab05b2d241f76cb883ce8b9a93a680752fbfcbd51c50eff0b88b979e471d8c", size = 14331561, upload-time = "2025-06-21T12:22:55.056Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/ec/b74d3f2430960044bdad6900d9f5edc2dc0fb8bf5a0be0f65287bf2cbe27/numpy-2.3.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:ce2ce9e5de4703a673e705183f64fd5da5bf36e7beddcb63a25ee2286e71ca48", size = 16693349, upload-time = "2025-06-21T12:23:20.53Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/15/def96774b9d7eb198ddadfcbd20281b20ebb510580419197e225f5c55c3e/numpy-2.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c4913079974eeb5c16ccfd2b1f09354b8fed7e0d6f2cab933104a09a6419b1ee", size = 15642053, upload-time = "2025-06-21T12:23:43.697Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/57/c3203974762a759540c6ae71d0ea2341c1fa41d84e4971a8e76d7141678a/numpy-2.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:010ce9b4f00d5c036053ca684c77441f2f2c934fd23bee058b4d6f196efd8280", size = 18434184, upload-time = "2025-06-21T12:24:10.708Z" },
+    { url = "https://files.pythonhosted.org/packages/22/8a/ccdf201457ed8ac6245187850aff4ca56a79edbea4829f4e9f14d46fa9a5/numpy-2.3.1-cp313-cp313t-win32.whl", hash = "sha256:6269b9edfe32912584ec496d91b00b6d34282ca1d07eb10e82dfc780907d6c2e", size = 6440678, upload-time = "2025-06-21T12:24:21.596Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/7e/7f431d8bd8eb7e03d79294aed238b1b0b174b3148570d03a8a8a8f6a0da9/numpy-2.3.1-cp313-cp313t-win_amd64.whl", hash = "sha256:2a809637460e88a113e186e87f228d74ae2852a2e0c44de275263376f17b5bdc", size = 12870697, upload-time = "2025-06-21T12:24:40.644Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/ca/af82bf0fad4c3e573c6930ed743b5308492ff19917c7caaf2f9b6f9e2e98/numpy-2.3.1-cp313-cp313t-win_arm64.whl", hash = "sha256:eccb9a159db9aed60800187bc47a6d3451553f0e1b08b068d8b277ddfbb9b244", size = 10260376, upload-time = "2025-06-21T12:24:56.884Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/34/facc13b9b42ddca30498fc51f7f73c3d0f2be179943a4b4da8686e259740/numpy-2.3.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:ad506d4b09e684394c42c966ec1527f6ebc25da7f4da4b1b056606ffe446b8a3", size = 21070637, upload-time = "2025-06-21T12:26:12.518Z" },
+    { url = "https://files.pythonhosted.org/packages/65/b6/41b705d9dbae04649b529fc9bd3387664c3281c7cd78b404a4efe73dcc45/numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:ebb8603d45bc86bbd5edb0d63e52c5fd9e7945d3a503b77e486bd88dde67a19b", size = 5304087, upload-time = "2025-06-21T12:26:22.294Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/b4/fe3ac1902bff7a4934a22d49e1c9d71a623204d654d4cc43c6e8fe337fcb/numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:15aa4c392ac396e2ad3d0a2680c0f0dee420f9fed14eef09bdb9450ee6dcb7b7", size = 6817588, upload-time = "2025-06-21T12:26:32.939Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/ee/89bedf69c36ace1ac8f59e97811c1f5031e179a37e4821c3a230bf750142/numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:c6e0bf9d1a2f50d2b65a7cf56db37c095af17b59f6c132396f7c6d5dd76484df", size = 14399010, upload-time = "2025-06-21T12:26:54.086Z" },
+    { url = "https://files.pythonhosted.org/packages/15/08/e00e7070ede29b2b176165eba18d6f9784d5349be3c0c1218338e79c27fd/numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:eabd7e8740d494ce2b4ea0ff05afa1b7b291e978c0ae075487c51e8bd93c0c68", size = 16752042, upload-time = "2025-06-21T12:27:19.018Z" },
+    { url = "https://files.pythonhosted.org/packages/48/6b/1c6b515a83d5564b1698a61efa245727c8feecf308f4091f565988519d20/numpy-2.3.1-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:e610832418a2bc09d974cc9fecebfa51e9532d6190223bc5ef6a7402ebf3b5cb", size = 12927246, upload-time = "2025-06-21T12:27:38.618Z" },
+]
+
+[[package]]
+name = "packaging"
+version = "25.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a1/d4/1fc4078c65507b51b96ca8f8c3ba19e6a61c8253c72794544580a7b6c24d/packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f", size = 165727, upload-time = "2025-04-19T11:48:59.673Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484", size = 66469, upload-time = "2025-04-19T11:48:57.875Z" },
+]
+
+[[package]]
+name = "pandas"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "python-dateutil" },
+    { name = "pytz" },
+    { name = "tzdata" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d1/6f/75aa71f8a14267117adeeed5d21b204770189c0a0025acbdc03c337b28fc/pandas-2.3.1.tar.gz", hash = "sha256:0a95b9ac964fe83ce317827f80304d37388ea77616b1425f0ae41c9d2d0d7bb2", size = 4487493, upload-time = "2025-07-07T19:20:04.079Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/76/1c/ccf70029e927e473a4476c00e0d5b32e623bff27f0402d0a92b7fc29bb9f/pandas-2.3.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:2b0540963d83431f5ce8870ea02a7430adca100cec8a050f0811f8e31035541b", size = 11566608, upload-time = "2025-07-07T19:18:33.86Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/d3/3c37cb724d76a841f14b8f5fe57e5e3645207cc67370e4f84717e8bb7657/pandas-2.3.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fe7317f578c6a153912bd2292f02e40c1d8f253e93c599e82620c7f69755c74f", size = 10823181, upload-time = "2025-07-07T19:18:36.151Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/4c/367c98854a1251940edf54a4df0826dcacfb987f9068abf3e3064081a382/pandas-2.3.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6723a27ad7b244c0c79d8e7007092d7c8f0f11305770e2f4cd778b3ad5f9f85", size = 11793570, upload-time = "2025-07-07T19:18:38.385Z" },
+    { url = "https://files.pythonhosted.org/packages/07/5f/63760ff107bcf5146eee41b38b3985f9055e710a72fdd637b791dea3495c/pandas-2.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3462c3735fe19f2638f2c3a40bd94ec2dc5ba13abbb032dd2fa1f540a075509d", size = 12378887, upload-time = "2025-07-07T19:18:41.284Z" },
+    { url = "https://files.pythonhosted.org/packages/15/53/f31a9b4dfe73fe4711c3a609bd8e60238022f48eacedc257cd13ae9327a7/pandas-2.3.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:98bcc8b5bf7afed22cc753a28bc4d9e26e078e777066bc53fac7904ddef9a678", size = 13230957, upload-time = "2025-07-07T19:18:44.187Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/94/6fce6bf85b5056d065e0a7933cba2616dcb48596f7ba3c6341ec4bcc529d/pandas-2.3.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4d544806b485ddf29e52d75b1f559142514e60ef58a832f74fb38e48d757b299", size = 13883883, upload-time = "2025-07-07T19:18:46.498Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/7b/bdcb1ed8fccb63d04bdb7635161d0ec26596d92c9d7a6cce964e7876b6c1/pandas-2.3.1-cp311-cp311-win_amd64.whl", hash = "sha256:b3cd4273d3cb3707b6fffd217204c52ed92859533e31dc03b7c5008aa933aaab", size = 11340212, upload-time = "2025-07-07T19:18:49.293Z" },
+    { url = "https://files.pythonhosted.org/packages/46/de/b8445e0f5d217a99fe0eeb2f4988070908979bec3587c0633e5428ab596c/pandas-2.3.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:689968e841136f9e542020698ee1c4fbe9caa2ed2213ae2388dc7b81721510d3", size = 11588172, upload-time = "2025-07-07T19:18:52.054Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/e0/801cdb3564e65a5ac041ab99ea6f1d802a6c325bb6e58c79c06a3f1cd010/pandas-2.3.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:025e92411c16cbe5bb2a4abc99732a6b132f439b8aab23a59fa593eb00704232", size = 10717365, upload-time = "2025-07-07T19:18:54.785Z" },
+    { url = "https://files.pythonhosted.org/packages/51/a5/c76a8311833c24ae61a376dbf360eb1b1c9247a5d9c1e8b356563b31b80c/pandas-2.3.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9b7ff55f31c4fcb3e316e8f7fa194566b286d6ac430afec0d461163312c5841e", size = 11280411, upload-time = "2025-07-07T19:18:57.045Z" },
+    { url = "https://files.pythonhosted.org/packages/da/01/e383018feba0a1ead6cf5fe8728e5d767fee02f06a3d800e82c489e5daaf/pandas-2.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7dcb79bf373a47d2a40cf7232928eb7540155abbc460925c2c96d2d30b006eb4", size = 11988013, upload-time = "2025-07-07T19:18:59.771Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/14/cec7760d7c9507f11c97d64f29022e12a6cc4fc03ac694535e89f88ad2ec/pandas-2.3.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:56a342b231e8862c96bdb6ab97170e203ce511f4d0429589c8ede1ee8ece48b8", size = 12767210, upload-time = "2025-07-07T19:19:02.944Z" },
+    { url = "https://files.pythonhosted.org/packages/50/b9/6e2d2c6728ed29fb3d4d4d302504fb66f1a543e37eb2e43f352a86365cdf/pandas-2.3.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:ca7ed14832bce68baef331f4d7f294411bed8efd032f8109d690df45e00c4679", size = 13440571, upload-time = "2025-07-07T19:19:06.82Z" },
+    { url = "https://files.pythonhosted.org/packages/80/a5/3a92893e7399a691bad7664d977cb5e7c81cf666c81f89ea76ba2bff483d/pandas-2.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:ac942bfd0aca577bef61f2bc8da8147c4ef6879965ef883d8e8d5d2dc3e744b8", size = 10987601, upload-time = "2025-07-07T19:19:09.589Z" },
+    { url = "https://files.pythonhosted.org/packages/32/ed/ff0a67a2c5505e1854e6715586ac6693dd860fbf52ef9f81edee200266e7/pandas-2.3.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:9026bd4a80108fac2239294a15ef9003c4ee191a0f64b90f170b40cfb7cf2d22", size = 11531393, upload-time = "2025-07-07T19:19:12.245Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/db/d8f24a7cc9fb0972adab0cc80b6817e8bef888cfd0024eeb5a21c0bb5c4a/pandas-2.3.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6de8547d4fdb12421e2d047a2c446c623ff4c11f47fddb6b9169eb98ffba485a", size = 10668750, upload-time = "2025-07-07T19:19:14.612Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/b0/80f6ec783313f1e2356b28b4fd8d2148c378370045da918c73145e6aab50/pandas-2.3.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:782647ddc63c83133b2506912cc6b108140a38a37292102aaa19c81c83db2928", size = 11342004, upload-time = "2025-07-07T19:19:16.857Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/e2/20a317688435470872885e7fc8f95109ae9683dec7c50be29b56911515a5/pandas-2.3.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2ba6aff74075311fc88504b1db890187a3cd0f887a5b10f5525f8e2ef55bfdb9", size = 12050869, upload-time = "2025-07-07T19:19:19.265Z" },
+    { url = "https://files.pythonhosted.org/packages/55/79/20d746b0a96c67203a5bee5fb4e00ac49c3e8009a39e1f78de264ecc5729/pandas-2.3.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e5635178b387bd2ba4ac040f82bc2ef6e6b500483975c4ebacd34bec945fda12", size = 12750218, upload-time = "2025-07-07T19:19:21.547Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/0f/145c8b41e48dbf03dd18fdd7f24f8ba95b8254a97a3379048378f33e7838/pandas-2.3.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6f3bf5ec947526106399a9e1d26d40ee2b259c66422efdf4de63c848492d91bb", size = 13416763, upload-time = "2025-07-07T19:19:23.939Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/c0/54415af59db5cdd86a3d3bf79863e8cc3fa9ed265f0745254061ac09d5f2/pandas-2.3.1-cp313-cp313-win_amd64.whl", hash = "sha256:1c78cf43c8fde236342a1cb2c34bcff89564a7bfed7e474ed2fffa6aed03a956", size = 10987482, upload-time = "2025-07-07T19:19:42.699Z" },
+    { url = "https://files.pythonhosted.org/packages/48/64/2fd2e400073a1230e13b8cd604c9bc95d9e3b962e5d44088ead2e8f0cfec/pandas-2.3.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:8dfc17328e8da77be3cf9f47509e5637ba8f137148ed0e9b5241e1baf526e20a", size = 12029159, upload-time = "2025-07-07T19:19:26.362Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/0a/d84fd79b0293b7ef88c760d7dca69828d867c89b6d9bc52d6a27e4d87316/pandas-2.3.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:ec6c851509364c59a5344458ab935e6451b31b818be467eb24b0fe89bd05b6b9", size = 11393287, upload-time = "2025-07-07T19:19:29.157Z" },
+    { url = "https://files.pythonhosted.org/packages/50/ae/ff885d2b6e88f3c7520bb74ba319268b42f05d7e583b5dded9837da2723f/pandas-2.3.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:911580460fc4884d9b05254b38a6bfadddfcc6aaef856fb5859e7ca202e45275", size = 11309381, upload-time = "2025-07-07T19:19:31.436Z" },
+    { url = "https://files.pythonhosted.org/packages/85/86/1fa345fc17caf5d7780d2699985c03dbe186c68fee00b526813939062bb0/pandas-2.3.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2f4d6feeba91744872a600e6edbbd5b033005b431d5ae8379abee5bcfa479fab", size = 11883998, upload-time = "2025-07-07T19:19:34.267Z" },
+    { url = "https://files.pythonhosted.org/packages/81/aa/e58541a49b5e6310d89474333e994ee57fea97c8aaa8fc7f00b873059bbf/pandas-2.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:fe37e757f462d31a9cd7580236a82f353f5713a80e059a29753cf938c6775d96", size = 12704705, upload-time = "2025-07-07T19:19:36.856Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/f9/07086f5b0f2a19872554abeea7658200824f5835c58a106fa8f2ae96a46c/pandas-2.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:5db9637dbc24b631ff3707269ae4559bce4b7fd75c1c4d7e13f40edc42df4444", size = 13189044, upload-time = "2025-07-07T19:19:39.999Z" },
+]
+
+[[package]]
+name = "parso"
+version = "0.8.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/66/94/68e2e17afaa9169cf6412ab0f28623903be73d1b32e208d9e8e541bb086d/parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d", size = 400609, upload-time = "2024-04-05T09:43:55.897Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c6/ac/dac4a63f978e4dcb3c6d3a78c4d8e0192a113d288502a1216950c41b1027/parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18", size = 103650, upload-time = "2024-04-05T09:43:53.299Z" },
+]
+
+[[package]]
+name = "pexpect"
+version = "4.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "ptyprocess" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772, upload-time = "2023-11-25T06:56:14.81Z" },
+]
+
+[[package]]
+name = "pillow"
+version = "11.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f3/0d/d0d6dea55cd152ce3d6767bb38a8fc10e33796ba4ba210cbab9354b6d238/pillow-11.3.0.tar.gz", hash = "sha256:3828ee7586cd0b2091b6209e5ad53e20d0649bbe87164a459d0676e035e8f523", size = 47113069, upload-time = "2025-07-01T09:16:30.666Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/db/26/77f8ed17ca4ffd60e1dcd220a6ec6d71210ba398cfa33a13a1cd614c5613/pillow-11.3.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:1cd110edf822773368b396281a2293aeb91c90a2db00d78ea43e7e861631b722", size = 5316531, upload-time = "2025-07-01T09:13:59.203Z" },
+    { url = "https://files.pythonhosted.org/packages/cb/39/ee475903197ce709322a17a866892efb560f57900d9af2e55f86db51b0a5/pillow-11.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9c412fddd1b77a75aa904615ebaa6001f169b26fd467b4be93aded278266b288", size = 4686560, upload-time = "2025-07-01T09:14:01.101Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/90/442068a160fd179938ba55ec8c97050a612426fae5ec0a764e345839f76d/pillow-11.3.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7d1aa4de119a0ecac0a34a9c8bde33f34022e2e8f99104e47a3ca392fd60e37d", size = 5870978, upload-time = "2025-07-03T13:09:55.638Z" },
+    { url = "https://files.pythonhosted.org/packages/13/92/dcdd147ab02daf405387f0218dcf792dc6dd5b14d2573d40b4caeef01059/pillow-11.3.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:91da1d88226663594e3f6b4b8c3c8d85bd504117d043740a8e0ec449087cc494", size = 7641168, upload-time = "2025-07-03T13:10:00.37Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/db/839d6ba7fd38b51af641aa904e2960e7a5644d60ec754c046b7d2aee00e5/pillow-11.3.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:643f189248837533073c405ec2f0bb250ba54598cf80e8c1e043381a60632f58", size = 5973053, upload-time = "2025-07-01T09:14:04.491Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/2f/d7675ecae6c43e9f12aa8d58b6012683b20b6edfbdac7abcb4e6af7a3784/pillow-11.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:106064daa23a745510dabce1d84f29137a37224831d88eb4ce94bb187b1d7e5f", size = 6640273, upload-time = "2025-07-01T09:14:06.235Z" },
+    { url = "https://files.pythonhosted.org/packages/45/ad/931694675ede172e15b2ff03c8144a0ddaea1d87adb72bb07655eaffb654/pillow-11.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:cd8ff254faf15591e724dc7c4ddb6bf4793efcbe13802a4ae3e863cd300b493e", size = 6082043, upload-time = "2025-07-01T09:14:07.978Z" },
+    { url = "https://files.pythonhosted.org/packages/3a/04/ba8f2b11fc80d2dd462d7abec16351b45ec99cbbaea4387648a44190351a/pillow-11.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:932c754c2d51ad2b2271fd01c3d121daaa35e27efae2a616f77bf164bc0b3e94", size = 6715516, upload-time = "2025-07-01T09:14:10.233Z" },
+    { url = "https://files.pythonhosted.org/packages/48/59/8cd06d7f3944cc7d892e8533c56b0acb68399f640786313275faec1e3b6f/pillow-11.3.0-cp311-cp311-win32.whl", hash = "sha256:b4b8f3efc8d530a1544e5962bd6b403d5f7fe8b9e08227c6b255f98ad82b4ba0", size = 6274768, upload-time = "2025-07-01T09:14:11.921Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/cc/29c0f5d64ab8eae20f3232da8f8571660aa0ab4b8f1331da5c2f5f9a938e/pillow-11.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:1a992e86b0dd7aeb1f053cd506508c0999d710a8f07b4c791c63843fc6a807ac", size = 6986055, upload-time = "2025-07-01T09:14:13.623Z" },
+    { url = "https://files.pythonhosted.org/packages/c6/df/90bd886fabd544c25addd63e5ca6932c86f2b701d5da6c7839387a076b4a/pillow-11.3.0-cp311-cp311-win_arm64.whl", hash = "sha256:30807c931ff7c095620fe04448e2c2fc673fcbb1ffe2a7da3fb39613489b1ddd", size = 2423079, upload-time = "2025-07-01T09:14:15.268Z" },
+    { url = "https://files.pythonhosted.org/packages/40/fe/1bc9b3ee13f68487a99ac9529968035cca2f0a51ec36892060edcc51d06a/pillow-11.3.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fdae223722da47b024b867c1ea0be64e0df702c5e0a60e27daad39bf960dd1e4", size = 5278800, upload-time = "2025-07-01T09:14:17.648Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/32/7e2ac19b5713657384cec55f89065fb306b06af008cfd87e572035b27119/pillow-11.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:921bd305b10e82b4d1f5e802b6850677f965d8394203d182f078873851dada69", size = 4686296, upload-time = "2025-07-01T09:14:19.828Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/1e/b9e12bbe6e4c2220effebc09ea0923a07a6da1e1f1bfbc8d7d29a01ce32b/pillow-11.3.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:eb76541cba2f958032d79d143b98a3a6b3ea87f0959bbe256c0b5e416599fd5d", size = 5871726, upload-time = "2025-07-03T13:10:04.448Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/33/e9200d2bd7ba00dc3ddb78df1198a6e80d7669cce6c2bdbeb2530a74ec58/pillow-11.3.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:67172f2944ebba3d4a7b54f2e95c786a3a50c21b88456329314caaa28cda70f6", size = 7644652, upload-time = "2025-07-03T13:10:10.391Z" },
+    { url = "https://files.pythonhosted.org/packages/41/f1/6f2427a26fc683e00d985bc391bdd76d8dd4e92fac33d841127eb8fb2313/pillow-11.3.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:97f07ed9f56a3b9b5f49d3661dc9607484e85c67e27f3e8be2c7d28ca032fec7", size = 5977787, upload-time = "2025-07-01T09:14:21.63Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/c9/06dd4a38974e24f932ff5f98ea3c546ce3f8c995d3f0985f8e5ba48bba19/pillow-11.3.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:676b2815362456b5b3216b4fd5bd89d362100dc6f4945154ff172e206a22c024", size = 6645236, upload-time = "2025-07-01T09:14:23.321Z" },
+    { url = "https://files.pythonhosted.org/packages/40/e7/848f69fb79843b3d91241bad658e9c14f39a32f71a301bcd1d139416d1be/pillow-11.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3e184b2f26ff146363dd07bde8b711833d7b0202e27d13540bfe2e35a323a809", size = 6086950, upload-time = "2025-07-01T09:14:25.237Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/1a/7cff92e695a2a29ac1958c2a0fe4c0b2393b60aac13b04a4fe2735cad52d/pillow-11.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6be31e3fc9a621e071bc17bb7de63b85cbe0bfae91bb0363c893cbe67247780d", size = 6723358, upload-time = "2025-07-01T09:14:27.053Z" },
+    { url = "https://files.pythonhosted.org/packages/26/7d/73699ad77895f69edff76b0f332acc3d497f22f5d75e5360f78cbcaff248/pillow-11.3.0-cp312-cp312-win32.whl", hash = "sha256:7b161756381f0918e05e7cb8a371fff367e807770f8fe92ecb20d905d0e1c149", size = 6275079, upload-time = "2025-07-01T09:14:30.104Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/ce/e7dfc873bdd9828f3b6e5c2bbb74e47a98ec23cc5c74fc4e54462f0d9204/pillow-11.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:a6444696fce635783440b7f7a9fc24b3ad10a9ea3f0ab66c5905be1c19ccf17d", size = 6986324, upload-time = "2025-07-01T09:14:31.899Z" },
+    { url = "https://files.pythonhosted.org/packages/16/8f/b13447d1bf0b1f7467ce7d86f6e6edf66c0ad7cf44cf5c87a37f9bed9936/pillow-11.3.0-cp312-cp312-win_arm64.whl", hash = "sha256:2aceea54f957dd4448264f9bf40875da0415c83eb85f55069d89c0ed436e3542", size = 2423067, upload-time = "2025-07-01T09:14:33.709Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/93/0952f2ed8db3a5a4c7a11f91965d6184ebc8cd7cbb7941a260d5f018cd2d/pillow-11.3.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:1c627742b539bba4309df89171356fcb3cc5a9178355b2727d1b74a6cf155fbd", size = 2128328, upload-time = "2025-07-01T09:14:35.276Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/e8/100c3d114b1a0bf4042f27e0f87d2f25e857e838034e98ca98fe7b8c0a9c/pillow-11.3.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:30b7c02f3899d10f13d7a48163c8969e4e653f8b43416d23d13d1bbfdc93b9f8", size = 2170652, upload-time = "2025-07-01T09:14:37.203Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/86/3f758a28a6e381758545f7cdb4942e1cb79abd271bea932998fc0db93cb6/pillow-11.3.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:7859a4cc7c9295f5838015d8cc0a9c215b77e43d07a25e460f35cf516df8626f", size = 2227443, upload-time = "2025-07-01T09:14:39.344Z" },
+    { url = "https://files.pythonhosted.org/packages/01/f4/91d5b3ffa718df2f53b0dc109877993e511f4fd055d7e9508682e8aba092/pillow-11.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ec1ee50470b0d050984394423d96325b744d55c701a439d2bd66089bff963d3c", size = 5278474, upload-time = "2025-07-01T09:14:41.843Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/0e/37d7d3eca6c879fbd9dba21268427dffda1ab00d4eb05b32923d4fbe3b12/pillow-11.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7db51d222548ccfd274e4572fdbf3e810a5e66b00608862f947b163e613b67dd", size = 4686038, upload-time = "2025-07-01T09:14:44.008Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/b0/3426e5c7f6565e752d81221af9d3676fdbb4f352317ceafd42899aaf5d8a/pillow-11.3.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2d6fcc902a24ac74495df63faad1884282239265c6839a0a6416d33faedfae7e", size = 5864407, upload-time = "2025-07-03T13:10:15.628Z" },
+    { url = "https://files.pythonhosted.org/packages/fc/c1/c6c423134229f2a221ee53f838d4be9d82bab86f7e2f8e75e47b6bf6cd77/pillow-11.3.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f0f5d8f4a08090c6d6d578351a2b91acf519a54986c055af27e7a93feae6d3f1", size = 7639094, upload-time = "2025-07-03T13:10:21.857Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/c9/09e6746630fe6372c67c648ff9deae52a2bc20897d51fa293571977ceb5d/pillow-11.3.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c37d8ba9411d6003bba9e518db0db0c58a680ab9fe5179f040b0463644bc9805", size = 5973503, upload-time = "2025-07-01T09:14:45.698Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/1c/a2a29649c0b1983d3ef57ee87a66487fdeb45132df66ab30dd37f7dbe162/pillow-11.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:13f87d581e71d9189ab21fe0efb5a23e9f28552d5be6979e84001d3b8505abe8", size = 6642574, upload-time = "2025-07-01T09:14:47.415Z" },
+    { url = "https://files.pythonhosted.org/packages/36/de/d5cc31cc4b055b6c6fd990e3e7f0f8aaf36229a2698501bcb0cdf67c7146/pillow-11.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:023f6d2d11784a465f09fd09a34b150ea4672e85fb3d05931d89f373ab14abb2", size = 6084060, upload-time = "2025-07-01T09:14:49.636Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/ea/502d938cbaeec836ac28a9b730193716f0114c41325db428e6b280513f09/pillow-11.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:45dfc51ac5975b938e9809451c51734124e73b04d0f0ac621649821a63852e7b", size = 6721407, upload-time = "2025-07-01T09:14:51.962Z" },
+    { url = "https://files.pythonhosted.org/packages/45/9c/9c5e2a73f125f6cbc59cc7087c8f2d649a7ae453f83bd0362ff7c9e2aee2/pillow-11.3.0-cp313-cp313-win32.whl", hash = "sha256:a4d336baed65d50d37b88ca5b60c0fa9d81e3a87d4a7930d3880d1624d5b31f3", size = 6273841, upload-time = "2025-07-01T09:14:54.142Z" },
+    { url = "https://files.pythonhosted.org/packages/23/85/397c73524e0cd212067e0c969aa245b01d50183439550d24d9f55781b776/pillow-11.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:0bce5c4fd0921f99d2e858dc4d4d64193407e1b99478bc5cacecba2311abde51", size = 6978450, upload-time = "2025-07-01T09:14:56.436Z" },
+    { url = "https://files.pythonhosted.org/packages/17/d2/622f4547f69cd173955194b78e4d19ca4935a1b0f03a302d655c9f6aae65/pillow-11.3.0-cp313-cp313-win_arm64.whl", hash = "sha256:1904e1264881f682f02b7f8167935cce37bc97db457f8e7849dc3a6a52b99580", size = 2423055, upload-time = "2025-07-01T09:14:58.072Z" },
+    { url = "https://files.pythonhosted.org/packages/dd/80/a8a2ac21dda2e82480852978416cfacd439a4b490a501a288ecf4fe2532d/pillow-11.3.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:4c834a3921375c48ee6b9624061076bc0a32a60b5532b322cc0ea64e639dd50e", size = 5281110, upload-time = "2025-07-01T09:14:59.79Z" },
+    { url = "https://files.pythonhosted.org/packages/44/d6/b79754ca790f315918732e18f82a8146d33bcd7f4494380457ea89eb883d/pillow-11.3.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5e05688ccef30ea69b9317a9ead994b93975104a677a36a8ed8106be9260aa6d", size = 4689547, upload-time = "2025-07-01T09:15:01.648Z" },
+    { url = "https://files.pythonhosted.org/packages/49/20/716b8717d331150cb00f7fdd78169c01e8e0c219732a78b0e59b6bdb2fd6/pillow-11.3.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1019b04af07fc0163e2810167918cb5add8d74674b6267616021ab558dc98ced", size = 5901554, upload-time = "2025-07-03T13:10:27.018Z" },
+    { url = "https://files.pythonhosted.org/packages/74/cf/a9f3a2514a65bb071075063a96f0a5cf949c2f2fce683c15ccc83b1c1cab/pillow-11.3.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f944255db153ebb2b19c51fe85dd99ef0ce494123f21b9db4877ffdfc5590c7c", size = 7669132, upload-time = "2025-07-03T13:10:33.01Z" },
+    { url = "https://files.pythonhosted.org/packages/98/3c/da78805cbdbee9cb43efe8261dd7cc0b4b93f2ac79b676c03159e9db2187/pillow-11.3.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1f85acb69adf2aaee8b7da124efebbdb959a104db34d3a2cb0f3793dbae422a8", size = 6005001, upload-time = "2025-07-01T09:15:03.365Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/fa/ce044b91faecf30e635321351bba32bab5a7e034c60187fe9698191aef4f/pillow-11.3.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:05f6ecbeff5005399bb48d198f098a9b4b6bdf27b8487c7f38ca16eeb070cd59", size = 6668814, upload-time = "2025-07-01T09:15:05.655Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/51/90f9291406d09bf93686434f9183aba27b831c10c87746ff49f127ee80cb/pillow-11.3.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a7bc6e6fd0395bc052f16b1a8670859964dbd7003bd0af2ff08342eb6e442cfe", size = 6113124, upload-time = "2025-07-01T09:15:07.358Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/5a/6fec59b1dfb619234f7636d4157d11fb4e196caeee220232a8d2ec48488d/pillow-11.3.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:83e1b0161c9d148125083a35c1c5a89db5b7054834fd4387499e06552035236c", size = 6747186, upload-time = "2025-07-01T09:15:09.317Z" },
+    { url = "https://files.pythonhosted.org/packages/49/6b/00187a044f98255225f172de653941e61da37104a9ea60e4f6887717e2b5/pillow-11.3.0-cp313-cp313t-win32.whl", hash = "sha256:2a3117c06b8fb646639dce83694f2f9eac405472713fcb1ae887469c0d4f6788", size = 6277546, upload-time = "2025-07-01T09:15:11.311Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/5c/6caaba7e261c0d75bab23be79f1d06b5ad2a2ae49f028ccec801b0e853d6/pillow-11.3.0-cp313-cp313t-win_amd64.whl", hash = "sha256:857844335c95bea93fb39e0fa2726b4d9d758850b34075a7e3ff4f4fa3aa3b31", size = 6985102, upload-time = "2025-07-01T09:15:13.164Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/7e/b623008460c09a0cb38263c93b828c666493caee2eb34ff67f778b87e58c/pillow-11.3.0-cp313-cp313t-win_arm64.whl", hash = "sha256:8797edc41f3e8536ae4b10897ee2f637235c94f27404cac7297f7b607dd0716e", size = 2424803, upload-time = "2025-07-01T09:15:15.695Z" },
+    { url = "https://files.pythonhosted.org/packages/73/f4/04905af42837292ed86cb1b1dabe03dce1edc008ef14c473c5c7e1443c5d/pillow-11.3.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d9da3df5f9ea2a89b81bb6087177fb1f4d1c7146d583a3fe5c672c0d94e55e12", size = 5278520, upload-time = "2025-07-01T09:15:17.429Z" },
+    { url = "https://files.pythonhosted.org/packages/41/b0/33d79e377a336247df6348a54e6d2a2b85d644ca202555e3faa0cf811ecc/pillow-11.3.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0b275ff9b04df7b640c59ec5a3cb113eefd3795a8df80bac69646ef699c6981a", size = 4686116, upload-time = "2025-07-01T09:15:19.423Z" },
+    { url = "https://files.pythonhosted.org/packages/49/2d/ed8bc0ab219ae8768f529597d9509d184fe8a6c4741a6864fea334d25f3f/pillow-11.3.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0743841cabd3dba6a83f38a92672cccbd69af56e3e91777b0ee7f4dba4385632", size = 5864597, upload-time = "2025-07-03T13:10:38.404Z" },
+    { url = "https://files.pythonhosted.org/packages/b5/3d/b932bb4225c80b58dfadaca9d42d08d0b7064d2d1791b6a237f87f661834/pillow-11.3.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2465a69cf967b8b49ee1b96d76718cd98c4e925414ead59fdf75cf0fd07df673", size = 7638246, upload-time = "2025-07-03T13:10:44.987Z" },
+    { url = "https://files.pythonhosted.org/packages/09/b5/0487044b7c096f1b48f0d7ad416472c02e0e4bf6919541b111efd3cae690/pillow-11.3.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:41742638139424703b4d01665b807c6468e23e699e8e90cffefe291c5832b027", size = 5973336, upload-time = "2025-07-01T09:15:21.237Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/2d/524f9318f6cbfcc79fbc004801ea6b607ec3f843977652fdee4857a7568b/pillow-11.3.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:93efb0b4de7e340d99057415c749175e24c8864302369e05914682ba642e5d77", size = 6642699, upload-time = "2025-07-01T09:15:23.186Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/d2/a9a4f280c6aefedce1e8f615baaa5474e0701d86dd6f1dede66726462bbd/pillow-11.3.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7966e38dcd0fa11ca390aed7c6f20454443581d758242023cf36fcb319b1a874", size = 6083789, upload-time = "2025-07-01T09:15:25.1Z" },
+    { url = "https://files.pythonhosted.org/packages/fe/54/86b0cd9dbb683a9d5e960b66c7379e821a19be4ac5810e2e5a715c09a0c0/pillow-11.3.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:98a9afa7b9007c67ed84c57c9e0ad86a6000da96eaa638e4f8abe5b65ff83f0a", size = 6720386, upload-time = "2025-07-01T09:15:27.378Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/95/88efcaf384c3588e24259c4203b909cbe3e3c2d887af9e938c2022c9dd48/pillow-11.3.0-cp314-cp314-win32.whl", hash = "sha256:02a723e6bf909e7cea0dac1b0e0310be9d7650cd66222a5f1c571455c0a45214", size = 6370911, upload-time = "2025-07-01T09:15:29.294Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/cc/934e5820850ec5eb107e7b1a72dd278140731c669f396110ebc326f2a503/pillow-11.3.0-cp314-cp314-win_amd64.whl", hash = "sha256:a418486160228f64dd9e9efcd132679b7a02a5f22c982c78b6fc7dab3fefb635", size = 7117383, upload-time = "2025-07-01T09:15:31.128Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/e9/9c0a616a71da2a5d163aa37405e8aced9a906d574b4a214bede134e731bc/pillow-11.3.0-cp314-cp314-win_arm64.whl", hash = "sha256:155658efb5e044669c08896c0c44231c5e9abcaadbc5cd3648df2f7c0b96b9a6", size = 2511385, upload-time = "2025-07-01T09:15:33.328Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/33/c88376898aff369658b225262cd4f2659b13e8178e7534df9e6e1fa289f6/pillow-11.3.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:59a03cdf019efbfeeed910bf79c7c93255c3d54bc45898ac2a4140071b02b4ae", size = 5281129, upload-time = "2025-07-01T09:15:35.194Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/70/d376247fb36f1844b42910911c83a02d5544ebd2a8bad9efcc0f707ea774/pillow-11.3.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:f8a5827f84d973d8636e9dc5764af4f0cf2318d26744b3d902931701b0d46653", size = 4689580, upload-time = "2025-07-01T09:15:37.114Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/1c/537e930496149fbac69efd2fc4329035bbe2e5475b4165439e3be9cb183b/pillow-11.3.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ee92f2fd10f4adc4b43d07ec5e779932b4eb3dbfbc34790ada5a6669bc095aa6", size = 5902860, upload-time = "2025-07-03T13:10:50.248Z" },
+    { url = "https://files.pythonhosted.org/packages/bd/57/80f53264954dcefeebcf9dae6e3eb1daea1b488f0be8b8fef12f79a3eb10/pillow-11.3.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c96d333dcf42d01f47b37e0979b6bd73ec91eae18614864622d9b87bbd5bbf36", size = 7670694, upload-time = "2025-07-03T13:10:56.432Z" },
+    { url = "https://files.pythonhosted.org/packages/70/ff/4727d3b71a8578b4587d9c276e90efad2d6fe0335fd76742a6da08132e8c/pillow-11.3.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4c96f993ab8c98460cd0c001447bff6194403e8b1d7e149ade5f00594918128b", size = 6005888, upload-time = "2025-07-01T09:15:39.436Z" },
+    { url = "https://files.pythonhosted.org/packages/05/ae/716592277934f85d3be51d7256f3636672d7b1abfafdc42cf3f8cbd4b4c8/pillow-11.3.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:41342b64afeba938edb034d122b2dda5db2139b9a4af999729ba8818e0056477", size = 6670330, upload-time = "2025-07-01T09:15:41.269Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/bb/7fe6cddcc8827b01b1a9766f5fdeb7418680744f9082035bdbabecf1d57f/pillow-11.3.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:068d9c39a2d1b358eb9f245ce7ab1b5c3246c7c8c7d9ba58cfa5b43146c06e50", size = 6114089, upload-time = "2025-07-01T09:15:43.13Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/f5/06bfaa444c8e80f1a8e4bff98da9c83b37b5be3b1deaa43d27a0db37ef84/pillow-11.3.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a1bc6ba083b145187f648b667e05a2534ecc4b9f2784c2cbe3089e44868f2b9b", size = 6748206, upload-time = "2025-07-01T09:15:44.937Z" },
+    { url = "https://files.pythonhosted.org/packages/f0/77/bc6f92a3e8e6e46c0ca78abfffec0037845800ea38c73483760362804c41/pillow-11.3.0-cp314-cp314t-win32.whl", hash = "sha256:118ca10c0d60b06d006be10a501fd6bbdfef559251ed31b794668ed569c87e12", size = 6377370, upload-time = "2025-07-01T09:15:46.673Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/82/3a721f7d69dca802befb8af08b7c79ebcab461007ce1c18bd91a5d5896f9/pillow-11.3.0-cp314-cp314t-win_amd64.whl", hash = "sha256:8924748b688aa210d79883357d102cd64690e56b923a186f35a82cbc10f997db", size = 7121500, upload-time = "2025-07-01T09:15:48.512Z" },
+    { url = "https://files.pythonhosted.org/packages/89/c7/5572fa4a3f45740eaab6ae86fcdf7195b55beac1371ac8c619d880cfe948/pillow-11.3.0-cp314-cp314t-win_arm64.whl", hash = "sha256:79ea0d14d3ebad43ec77ad5272e6ff9bba5b679ef73375ea760261207fa8e0aa", size = 2512835, upload-time = "2025-07-01T09:15:50.399Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/e3/6fa84033758276fb31da12e5fb66ad747ae83b93c67af17f8c6ff4cc8f34/pillow-11.3.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:7c8ec7a017ad1bd562f93dbd8505763e688d388cde6e4a010ae1486916e713e6", size = 5270566, upload-time = "2025-07-01T09:16:19.801Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/ee/e8d2e1ab4892970b561e1ba96cbd59c0d28cf66737fc44abb2aec3795a4e/pillow-11.3.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:9ab6ae226de48019caa8074894544af5b53a117ccb9d3b3dcb2871464c829438", size = 4654618, upload-time = "2025-07-01T09:16:21.818Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/6d/17f80f4e1f0761f02160fc433abd4109fa1548dcfdca46cfdadaf9efa565/pillow-11.3.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fe27fb049cdcca11f11a7bfda64043c37b30e6b91f10cb5bab275806c32f6ab3", size = 4874248, upload-time = "2025-07-03T13:11:20.738Z" },
+    { url = "https://files.pythonhosted.org/packages/de/5f/c22340acd61cef960130585bbe2120e2fd8434c214802f07e8c03596b17e/pillow-11.3.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:465b9e8844e3c3519a983d58b80be3f668e2a7a5db97f2784e7079fbc9f9822c", size = 6583963, upload-time = "2025-07-03T13:11:26.283Z" },
+    { url = "https://files.pythonhosted.org/packages/31/5e/03966aedfbfcbb4d5f8aa042452d3361f325b963ebbadddac05b122e47dd/pillow-11.3.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5418b53c0d59b3824d05e029669efa023bbef0f3e92e75ec8428f3799487f361", size = 4957170, upload-time = "2025-07-01T09:16:23.762Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/2d/e082982aacc927fc2cab48e1e731bdb1643a1406acace8bed0900a61464e/pillow-11.3.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:504b6f59505f08ae014f724b6207ff6222662aab5cc9542577fb084ed0676ac7", size = 5581505, upload-time = "2025-07-01T09:16:25.593Z" },
+    { url = "https://files.pythonhosted.org/packages/34/e7/ae39f538fd6844e982063c3a5e4598b8ced43b9633baa3a85ef33af8c05c/pillow-11.3.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c84d689db21a1c397d001aa08241044aa2069e7587b398c8cc63020390b1c1b8", size = 6984598, upload-time = "2025-07-01T09:16:27.732Z" },
+]
+
+[[package]]
+name = "platformdirs"
+version = "4.3.8"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fe/8b/3c73abc9c759ecd3f1f7ceff6685840859e8070c4d947c93fae71f6a0bf2/platformdirs-4.3.8.tar.gz", hash = "sha256:3d512d96e16bcb959a814c9f348431070822a6496326a4be0911c40b5a74c2bc", size = 21362, upload-time = "2025-05-07T22:47:42.121Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fe/39/979e8e21520d4e47a0bbe349e2713c0aac6f3d853d0e5b34d76206c439aa/platformdirs-4.3.8-py3-none-any.whl", hash = "sha256:ff7059bb7eb1179e2685604f4aaf157cfd9535242bd23742eadc3c13542139b4", size = 18567, upload-time = "2025-05-07T22:47:40.376Z" },
+]
+
+[[package]]
+name = "pluggy"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412, upload-time = "2025-05-15T12:30:07.975Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" },
+]
+
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.51"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wcwidth" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bb/6e/9d084c929dfe9e3bfe0c6a47e31f78a25c54627d64a66e884a8bf5474f1c/prompt_toolkit-3.0.51.tar.gz", hash = "sha256:931a162e3b27fc90c86f1b48bb1fb2c528c2761475e57c9c06de13311c7b54ed", size = 428940, upload-time = "2025-04-15T09:18:47.731Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ce/4f/5249960887b1fbe561d9ff265496d170b55a735b76724f10ef19f9e40716/prompt_toolkit-3.0.51-py3-none-any.whl", hash = "sha256:52742911fde84e2d423e2f9a4cf1de7d7ac4e51958f648d9540e0fb8db077b07", size = 387810, upload-time = "2025-04-15T09:18:44.753Z" },
+]
+
+[[package]]
+name = "psutil"
+version = "7.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/80/336820c1ad9286a4ded7e845b2eccfcb27851ab8ac6abece774a6ff4d3de/psutil-7.0.0.tar.gz", hash = "sha256:7be9c3eba38beccb6495ea33afd982a44074b78f28c434a1f51cc07fd315c456", size = 497003, upload-time = "2025-02-13T21:54:07.946Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ed/e6/2d26234410f8b8abdbf891c9da62bee396583f713fb9f3325a4760875d22/psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25", size = 238051, upload-time = "2025-02-13T21:54:12.36Z" },
+    { url = "https://files.pythonhosted.org/packages/04/8b/30f930733afe425e3cbfc0e1468a30a18942350c1a8816acfade80c005c4/psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da", size = 239535, upload-time = "2025-02-13T21:54:16.07Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/ed/d362e84620dd22876b55389248e522338ed1bf134a5edd3b8231d7207f6d/psutil-7.0.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fcee592b4c6f146991ca55919ea3d1f8926497a713ed7faaf8225e174581e91", size = 275004, upload-time = "2025-02-13T21:54:18.662Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/b9/b0eb3f3cbcb734d930fdf839431606844a825b23eaf9a6ab371edac8162c/psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b1388a4f6875d7e2aff5c4ca1cc16c545ed41dd8bb596cefea80111db353a34", size = 277986, upload-time = "2025-02-13T21:54:21.811Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/a2/709e0fe2f093556c17fbafda93ac032257242cabcc7ff3369e2cb76a97aa/psutil-7.0.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5f098451abc2828f7dc6b58d44b532b22f2088f4999a937557b603ce72b1993", size = 279544, upload-time = "2025-02-13T21:54:24.68Z" },
+    { url = "https://files.pythonhosted.org/packages/50/e6/eecf58810b9d12e6427369784efe814a1eec0f492084ce8eb8f4d89d6d61/psutil-7.0.0-cp37-abi3-win32.whl", hash = "sha256:ba3fcef7523064a6c9da440fc4d6bd07da93ac726b5733c29027d7dc95b39d99", size = 241053, upload-time = "2025-02-13T21:54:34.31Z" },
+    { url = "https://files.pythonhosted.org/packages/50/1b/6921afe68c74868b4c9fa424dad3be35b095e16687989ebbb50ce4fceb7c/psutil-7.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:4cf3d4eb1aa9b348dec30105c55cd9b7d4629285735a102beb4441e38db90553", size = 244885, upload-time = "2025-02-13T21:54:37.486Z" },
+]
+
+[[package]]
+name = "ptyprocess"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762, upload-time = "2020-12-28T15:15:30.155Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993, upload-time = "2020-12-28T15:15:28.35Z" },
+]
+
+[[package]]
+name = "pure-eval"
+version = "0.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752, upload-time = "2024-07-21T12:58:21.801Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" },
+]
+
+[[package]]
+name = "pycparser"
+version = "2.22"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1d/b2/31537cf4b1ca988837256c910a668b553fceb8f069bedc4b1c826024b52c/pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6", size = 172736, upload-time = "2024-03-30T13:22:22.564Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/13/a3/a812df4e2dd5696d1f351d58b8fe16a405b234ad2886a0dab9183fb78109/pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc", size = 117552, upload-time = "2024-03-30T13:22:20.476Z" },
+]
+
+[[package]]
+name = "pygments"
+version = "2.19.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b0/77/a5b8c569bf593b0140bde72ea885a803b82086995367bf2037de0159d924/pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887", size = 4968631, upload-time = "2025-06-21T13:39:12.283Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" },
+]
+
+[[package]]
+name = "pyparsing"
+version = "3.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/bb/22/f1129e69d94ffff626bdb5c835506b3a5b4f3d070f17ea295e12c2c6f60f/pyparsing-3.2.3.tar.gz", hash = "sha256:b9c13f1ab8b3b542f72e28f634bad4de758ab3ce4546e4301970ad6fa77c38be", size = 1088608, upload-time = "2025-03-25T05:01:28.114Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/05/e7/df2285f3d08fee213f2d041540fa4fc9ca6c2d44cf36d3a035bf2a8d2bcc/pyparsing-3.2.3-py3-none-any.whl", hash = "sha256:a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf", size = 111120, upload-time = "2025-03-25T05:01:24.908Z" },
+]
+
+[[package]]
+name = "pytest"
+version = "8.4.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "iniconfig" },
+    { name = "packaging" },
+    { name = "pluggy" },
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/08/ba/45911d754e8eba3d5a841a5ce61a65a685ff1798421ac054f85aa8747dfb/pytest-8.4.1.tar.gz", hash = "sha256:7c67fd69174877359ed9371ec3af8a3d2b04741818c51e5e99cc1742251fa93c", size = 1517714, upload-time = "2025-06-18T05:48:06.109Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/29/16/c8a903f4c4dffe7a12843191437d7cd8e32751d5de349d45d3fe69544e87/pytest-8.4.1-py3-none-any.whl", hash = "sha256:539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7", size = 365474, upload-time = "2025-06-18T05:48:03.955Z" },
+]
+
+[[package]]
+name = "pytest-cov"
+version = "6.2.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "coverage", extra = ["toml"] },
+    { name = "pluggy" },
+    { name = "pytest" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/18/99/668cade231f434aaa59bbfbf49469068d2ddd945000621d3d165d2e7dd7b/pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2", size = 69432, upload-time = "2025-06-12T10:47:47.684Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5", size = 24644, upload-time = "2025-06-12T10:47:45.932Z" },
+]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" },
+]
+
+[[package]]
+name = "pytz"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f8/bf/abbd3cdfb8fbc7fb3d4d38d320f2441b1e7cbe29be4f23797b4a2b5d8aac/pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3", size = 320884, upload-time = "2025-03-25T02:25:00.538Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00", size = 509225, upload-time = "2025-03-25T02:24:58.468Z" },
+]
+
+[[package]]
+name = "pywin32"
+version = "311"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7c/af/449a6a91e5d6db51420875c54f6aff7c97a86a3b13a0b4f1a5c13b988de3/pywin32-311-cp311-cp311-win32.whl", hash = "sha256:184eb5e436dea364dcd3d2316d577d625c0351bf237c4e9a5fabbcfa5a58b151", size = 8697031, upload-time = "2025-07-14T20:13:13.266Z" },
+    { url = "https://files.pythonhosted.org/packages/51/8f/9bb81dd5bb77d22243d33c8397f09377056d5c687aa6d4042bea7fbf8364/pywin32-311-cp311-cp311-win_amd64.whl", hash = "sha256:3ce80b34b22b17ccbd937a6e78e7225d80c52f5ab9940fe0506a1a16f3dab503", size = 9508308, upload-time = "2025-07-14T20:13:15.147Z" },
+    { url = "https://files.pythonhosted.org/packages/44/7b/9c2ab54f74a138c491aba1b1cd0795ba61f144c711daea84a88b63dc0f6c/pywin32-311-cp311-cp311-win_arm64.whl", hash = "sha256:a733f1388e1a842abb67ffa8e7aad0e70ac519e09b0f6a784e65a136ec7cefd2", size = 8703930, upload-time = "2025-07-14T20:13:16.945Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/ab/01ea1943d4eba0f850c3c61e78e8dd59757ff815ff3ccd0a84de5f541f42/pywin32-311-cp312-cp312-win32.whl", hash = "sha256:750ec6e621af2b948540032557b10a2d43b0cee2ae9758c54154d711cc852d31", size = 8706543, upload-time = "2025-07-14T20:13:20.765Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/a8/a0e8d07d4d051ec7502cd58b291ec98dcc0c3fff027caad0470b72cfcc2f/pywin32-311-cp312-cp312-win_amd64.whl", hash = "sha256:b8c095edad5c211ff31c05223658e71bf7116daa0ecf3ad85f3201ea3190d067", size = 9495040, upload-time = "2025-07-14T20:13:22.543Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/3a/2ae996277b4b50f17d61f0603efd8253cb2d79cc7ae159468007b586396d/pywin32-311-cp312-cp312-win_arm64.whl", hash = "sha256:e286f46a9a39c4a18b319c28f59b61de793654af2f395c102b4f819e584b5852", size = 8710102, upload-time = "2025-07-14T20:13:24.682Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/be/3fd5de0979fcb3994bfee0d65ed8ca9506a8a1260651b86174f6a86f52b3/pywin32-311-cp313-cp313-win32.whl", hash = "sha256:f95ba5a847cba10dd8c4d8fefa9f2a6cf283b8b88ed6178fa8a6c1ab16054d0d", size = 8705700, upload-time = "2025-07-14T20:13:26.471Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/28/e0a1909523c6890208295a29e05c2adb2126364e289826c0a8bc7297bd5c/pywin32-311-cp313-cp313-win_amd64.whl", hash = "sha256:718a38f7e5b058e76aee1c56ddd06908116d35147e133427e59a3983f703a20d", size = 9494700, upload-time = "2025-07-14T20:13:28.243Z" },
+    { url = "https://files.pythonhosted.org/packages/04/bf/90339ac0f55726dce7d794e6d79a18a91265bdf3aa70b6b9ca52f35e022a/pywin32-311-cp313-cp313-win_arm64.whl", hash = "sha256:7b4075d959648406202d92a2310cb990fea19b535c7f4a78d3f5e10b926eeb8a", size = 8709318, upload-time = "2025-07-14T20:13:30.348Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/31/097f2e132c4f16d99a22bfb777e0fd88bd8e1c634304e102f313af69ace5/pywin32-311-cp314-cp314-win32.whl", hash = "sha256:b7a2c10b93f8986666d0c803ee19b5990885872a7de910fc460f9b0c2fbf92ee", size = 8840714, upload-time = "2025-07-14T20:13:32.449Z" },
+    { url = "https://files.pythonhosted.org/packages/90/4b/07c77d8ba0e01349358082713400435347df8426208171ce297da32c313d/pywin32-311-cp314-cp314-win_amd64.whl", hash = "sha256:3aca44c046bd2ed8c90de9cb8427f581c479e594e99b5c0bb19b29c10fd6cb87", size = 9656800, upload-time = "2025-07-14T20:13:34.312Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/d2/21af5c535501a7233e734b8af901574572da66fcc254cb35d0609c9080dd/pywin32-311-cp314-cp314-win_arm64.whl", hash = "sha256:a508e2d9025764a8270f93111a970e1d0fbfc33f4153b388bb649b7eec4f9b42", size = 8932540, upload-time = "2025-07-14T20:13:36.379Z" },
+]
+
+[[package]]
+name = "pyzmq"
+version = "27.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cffi", marker = "implementation_name == 'pypy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f1/06/50a4e9648b3e8b992bef8eb632e457307553a89d294103213cfd47b3da69/pyzmq-27.0.0.tar.gz", hash = "sha256:b1f08eeb9ce1510e6939b6e5dcd46a17765e2333daae78ecf4606808442e52cf", size = 280478, upload-time = "2025-06-13T14:09:07.087Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/44/df/84c630654106d9bd9339cdb564aa941ed41b023a0264251d6743766bb50e/pyzmq-27.0.0-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:21457825249b2a53834fa969c69713f8b5a79583689387a5e7aed880963ac564", size = 1332718, upload-time = "2025-06-13T14:07:16.555Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/8e/f6a5461a07654d9840d256476434ae0ff08340bba562a455f231969772cb/pyzmq-27.0.0-cp311-cp311-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1958947983fef513e6e98eff9cb487b60bf14f588dc0e6bf35fa13751d2c8251", size = 908248, upload-time = "2025-06-13T14:07:18.033Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/93/82863e8d695a9a3ae424b63662733ae204a295a2627d52af2f62c2cd8af9/pyzmq-27.0.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c0dc628b5493f9a8cd9844b8bee9732ef587ab00002157c9329e4fc0ef4d3afa", size = 668647, upload-time = "2025-06-13T14:07:19.378Z" },
+    { url = "https://files.pythonhosted.org/packages/f3/85/15278769b348121eacdbfcbd8c4d40f1102f32fa6af5be1ffc032ed684be/pyzmq-27.0.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f7bbe9e1ed2c8d3da736a15694d87c12493e54cc9dc9790796f0321794bbc91f", size = 856600, upload-time = "2025-06-13T14:07:20.906Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/af/1c469b3d479bd095edb28e27f12eee10b8f00b356acbefa6aeb14dd295d1/pyzmq-27.0.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:dc1091f59143b471d19eb64f54bae4f54bcf2a466ffb66fe45d94d8d734eb495", size = 1657748, upload-time = "2025-06-13T14:07:22.549Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/f4/17f965d0ee6380b1d6326da842a50e4b8b9699745161207945f3745e8cb5/pyzmq-27.0.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:7011ade88c8e535cf140f8d1a59428676fbbce7c6e54fefce58bf117aefb6667", size = 2034311, upload-time = "2025-06-13T14:07:23.966Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/6e/7c391d81fa3149fd759de45d298003de6cfab343fb03e92c099821c448db/pyzmq-27.0.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:2c386339d7e3f064213aede5d03d054b237937fbca6dd2197ac8cf3b25a6b14e", size = 1893630, upload-time = "2025-06-13T14:07:25.899Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/e0/eaffe7a86f60e556399e224229e7769b717f72fec0706b70ab2c03aa04cb/pyzmq-27.0.0-cp311-cp311-win32.whl", hash = "sha256:0546a720c1f407b2172cb04b6b094a78773491497e3644863cf5c96c42df8cff", size = 567706, upload-time = "2025-06-13T14:07:27.595Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/05/89354a8cffdcce6e547d48adaaf7be17007fc75572123ff4ca90a4ca04fc/pyzmq-27.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:15f39d50bd6c9091c67315ceb878a4f531957b121d2a05ebd077eb35ddc5efed", size = 630322, upload-time = "2025-06-13T14:07:28.938Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/07/4ab976d5e1e63976719389cc4f3bfd248a7f5f2bb2ebe727542363c61b5f/pyzmq-27.0.0-cp311-cp311-win_arm64.whl", hash = "sha256:c5817641eebb391a2268c27fecd4162448e03538387093cdbd8bf3510c316b38", size = 558435, upload-time = "2025-06-13T14:07:30.256Z" },
+    { url = "https://files.pythonhosted.org/packages/93/a7/9ad68f55b8834ede477842214feba6a4c786d936c022a67625497aacf61d/pyzmq-27.0.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:cbabc59dcfaac66655c040dfcb8118f133fb5dde185e5fc152628354c1598e52", size = 1305438, upload-time = "2025-06-13T14:07:31.676Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/ee/26aa0f98665a22bc90ebe12dced1de5f3eaca05363b717f6fb229b3421b3/pyzmq-27.0.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:cb0ac5179cba4b2f94f1aa208fbb77b62c4c9bf24dd446278b8b602cf85fcda3", size = 895095, upload-time = "2025-06-13T14:07:33.104Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/85/c57e7ab216ecd8aa4cc7e3b83b06cc4e9cf45c87b0afc095f10cd5ce87c1/pyzmq-27.0.0-cp312-abi3-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:53a48f0228eab6cbf69fde3aa3c03cbe04e50e623ef92ae395fce47ef8a76152", size = 651826, upload-time = "2025-06-13T14:07:34.831Z" },
+    { url = "https://files.pythonhosted.org/packages/69/9a/9ea7e230feda9400fb0ae0d61d7d6ddda635e718d941c44eeab22a179d34/pyzmq-27.0.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:111db5f395e09f7e775f759d598f43cb815fc58e0147623c4816486e1a39dc22", size = 839750, upload-time = "2025-06-13T14:07:36.553Z" },
+    { url = "https://files.pythonhosted.org/packages/08/66/4cebfbe71f3dfbd417011daca267539f62ed0fbc68105357b68bbb1a25b7/pyzmq-27.0.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c8878011653dcdc27cc2c57e04ff96f0471e797f5c19ac3d7813a245bcb24371", size = 1641357, upload-time = "2025-06-13T14:07:38.21Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/f6/b0f62578c08d2471c791287149cb8c2aaea414ae98c6e995c7dbe008adfb/pyzmq-27.0.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:c0ed2c1f335ba55b5fdc964622254917d6b782311c50e138863eda409fbb3b6d", size = 2020281, upload-time = "2025-06-13T14:07:39.599Z" },
+    { url = "https://files.pythonhosted.org/packages/37/b9/4f670b15c7498495da9159edc374ec09c88a86d9cd5a47d892f69df23450/pyzmq-27.0.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:e918d70862d4cfd4b1c187310015646a14e1f5917922ab45b29f28f345eeb6be", size = 1877110, upload-time = "2025-06-13T14:07:41.027Z" },
+    { url = "https://files.pythonhosted.org/packages/66/31/9dee25c226295b740609f0d46db2fe972b23b6f5cf786360980524a3ba92/pyzmq-27.0.0-cp312-abi3-win32.whl", hash = "sha256:88b4e43cab04c3c0f0d55df3b1eef62df2b629a1a369b5289a58f6fa8b07c4f4", size = 559297, upload-time = "2025-06-13T14:07:42.533Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/12/52da5509800f7ff2d287b2f2b4e636e7ea0f001181cba6964ff6c1537778/pyzmq-27.0.0-cp312-abi3-win_amd64.whl", hash = "sha256:dce4199bf5f648a902ce37e7b3afa286f305cd2ef7a8b6ec907470ccb6c8b371", size = 619203, upload-time = "2025-06-13T14:07:43.843Z" },
+    { url = "https://files.pythonhosted.org/packages/93/6d/7f2e53b19d1edb1eb4f09ec7c3a1f945ca0aac272099eab757d15699202b/pyzmq-27.0.0-cp312-abi3-win_arm64.whl", hash = "sha256:56e46bbb85d52c1072b3f809cc1ce77251d560bc036d3a312b96db1afe76db2e", size = 551927, upload-time = "2025-06-13T14:07:45.51Z" },
+    { url = "https://files.pythonhosted.org/packages/19/62/876b27c4ff777db4ceba1c69ea90d3c825bb4f8d5e7cd987ce5802e33c55/pyzmq-27.0.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:c36ad534c0c29b4afa088dc53543c525b23c0797e01b69fef59b1a9c0e38b688", size = 1340826, upload-time = "2025-06-13T14:07:46.881Z" },
+    { url = "https://files.pythonhosted.org/packages/43/69/58ef8f4f59d3bcd505260c73bee87b008850f45edca40ddaba54273c35f4/pyzmq-27.0.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:67855c14173aec36395d7777aaba3cc527b393821f30143fd20b98e1ff31fd38", size = 897283, upload-time = "2025-06-13T14:07:49.562Z" },
+    { url = "https://files.pythonhosted.org/packages/43/15/93a0d0396700a60475ad3c5d42c5f1c308d3570bc94626b86c71ef9953e0/pyzmq-27.0.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8617c7d43cd8ccdb62aebe984bfed77ca8f036e6c3e46dd3dddda64b10f0ab7a", size = 660567, upload-time = "2025-06-13T14:07:51.364Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/b3/fe055513e498ca32f64509abae19b9c9eb4d7c829e02bd8997dd51b029eb/pyzmq-27.0.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:67bfbcbd0a04c575e8103a6061d03e393d9f80ffdb9beb3189261e9e9bc5d5e9", size = 847681, upload-time = "2025-06-13T14:07:52.77Z" },
+    { url = "https://files.pythonhosted.org/packages/b6/4f/ff15300b00b5b602191f3df06bbc8dd4164e805fdd65bb77ffbb9c5facdc/pyzmq-27.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:5cd11d46d7b7e5958121b3eaf4cd8638eff3a720ec527692132f05a57f14341d", size = 1650148, upload-time = "2025-06-13T14:07:54.178Z" },
+    { url = "https://files.pythonhosted.org/packages/c4/6f/84bdfff2a224a6f26a24249a342e5906993c50b0761e311e81b39aef52a7/pyzmq-27.0.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:b801c2e40c5aa6072c2f4876de8dccd100af6d9918d4d0d7aa54a1d982fd4f44", size = 2023768, upload-time = "2025-06-13T14:07:55.714Z" },
+    { url = "https://files.pythonhosted.org/packages/64/39/dc2db178c26a42228c5ac94a9cc595030458aa64c8d796a7727947afbf55/pyzmq-27.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:20d5cb29e8c5f76a127c75b6e7a77e846bc4b655c373baa098c26a61b7ecd0ef", size = 1885199, upload-time = "2025-06-13T14:07:57.166Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/21/dae7b06a1f8cdee5d8e7a63d99c5d129c401acc40410bef2cbf42025e26f/pyzmq-27.0.0-cp313-cp313t-win32.whl", hash = "sha256:a20528da85c7ac7a19b7384e8c3f8fa707841fd85afc4ed56eda59d93e3d98ad", size = 575439, upload-time = "2025-06-13T14:07:58.959Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/bc/1709dc55f0970cf4cb8259e435e6773f9946f41a045c2cb90e870b7072da/pyzmq-27.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:d8229f2efece6a660ee211d74d91dbc2a76b95544d46c74c615e491900dc107f", size = 639933, upload-time = "2025-06-13T14:08:00.777Z" },
+    { url = "https://files.pythonhosted.org/packages/98/a6/92394373b8dbc1edc9d53c951e8d3989d518185174ee54492ec27711779d/pyzmq-27.0.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cd1dc59763effd1576f8368047c9c31468fce0af89d76b5067641137506792ae", size = 835948, upload-time = "2025-06-13T14:08:43.516Z" },
+    { url = "https://files.pythonhosted.org/packages/56/f3/4dc38d75d9995bfc18773df3e41f2a2ca9b740b06f1a15dbf404077e7588/pyzmq-27.0.0-pp311-pypy311_pp73-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:60e8cc82d968174650c1860d7b716366caab9973787a1c060cf8043130f7d0f7", size = 799874, upload-time = "2025-06-13T14:08:45.017Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/ba/64af397e0f421453dc68e31d5e0784d554bf39013a2de0872056e96e58af/pyzmq-27.0.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:14fe7aaac86e4e93ea779a821967360c781d7ac5115b3f1a171ced77065a0174", size = 567400, upload-time = "2025-06-13T14:08:46.855Z" },
+    { url = "https://files.pythonhosted.org/packages/63/87/ec956cbe98809270b59a22891d5758edae147a258e658bf3024a8254c855/pyzmq-27.0.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6ad0562d4e6abb785be3e4dd68599c41be821b521da38c402bc9ab2a8e7ebc7e", size = 747031, upload-time = "2025-06-13T14:08:48.419Z" },
+    { url = "https://files.pythonhosted.org/packages/be/8a/4a3764a68abc02e2fbb0668d225b6fda5cd39586dd099cee8b2ed6ab0452/pyzmq-27.0.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:9df43a2459cd3a3563404c1456b2c4c69564daa7dbaf15724c09821a3329ce46", size = 544726, upload-time = "2025-06-13T14:08:49.903Z" },
+]
+
+[[package]]
+name = "scipy"
+version = "1.16.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/81/18/b06a83f0c5ee8cddbde5e3f3d0bb9b702abfa5136ef6d4620ff67df7eee5/scipy-1.16.0.tar.gz", hash = "sha256:b5ef54021e832869c8cfb03bc3bf20366cbcd426e02a58e8a58d7584dfbb8f62", size = 30581216, upload-time = "2025-06-22T16:27:55.782Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d9/f8/53fc4884df6b88afd5f5f00240bdc49fee2999c7eff3acf5953eb15bc6f8/scipy-1.16.0-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:deec06d831b8f6b5fb0b652433be6a09db29e996368ce5911faf673e78d20085", size = 36447362, upload-time = "2025-06-22T16:18:17.817Z" },
+    { url = "https://files.pythonhosted.org/packages/c9/25/fad8aa228fa828705142a275fc593d701b1817c98361a2d6b526167d07bc/scipy-1.16.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:d30c0fe579bb901c61ab4bb7f3eeb7281f0d4c4a7b52dbf563c89da4fd2949be", size = 28547120, upload-time = "2025-06-22T16:18:24.117Z" },
+    { url = "https://files.pythonhosted.org/packages/8d/be/d324ddf6b89fd1c32fecc307f04d095ce84abb52d2e88fab29d0cd8dc7a8/scipy-1.16.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:b2243561b45257f7391d0f49972fca90d46b79b8dbcb9b2cb0f9df928d370ad4", size = 20818922, upload-time = "2025-06-22T16:18:28.035Z" },
+    { url = "https://files.pythonhosted.org/packages/cd/e0/cf3f39e399ac83fd0f3ba81ccc5438baba7cfe02176be0da55ff3396f126/scipy-1.16.0-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:e6d7dfc148135e9712d87c5f7e4f2ddc1304d1582cb3a7d698bbadedb61c7afd", size = 23409695, upload-time = "2025-06-22T16:18:32.497Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/61/d92714489c511d3ffd6830ac0eb7f74f243679119eed8b9048e56b9525a1/scipy-1.16.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:90452f6a9f3fe5a2cf3748e7be14f9cc7d9b124dce19667b54f5b429d680d539", size = 33444586, upload-time = "2025-06-22T16:18:37.992Z" },
+    { url = "https://files.pythonhosted.org/packages/af/2c/40108915fd340c830aee332bb85a9160f99e90893e58008b659b9f3dddc0/scipy-1.16.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a2f0bf2f58031c8701a8b601df41701d2a7be17c7ffac0a4816aeba89c4cdac8", size = 35284126, upload-time = "2025-06-22T16:18:43.605Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/30/e9eb0ad3d0858df35d6c703cba0a7e16a18a56a9e6b211d861fc6f261c5f/scipy-1.16.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:6c4abb4c11fc0b857474241b812ce69ffa6464b4bd8f4ecb786cf240367a36a7", size = 35608257, upload-time = "2025-06-22T16:18:49.09Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/ff/950ee3e0d612b375110d8cda211c1f787764b4c75e418a4b71f4a5b1e07f/scipy-1.16.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b370f8f6ac6ef99815b0d5c9f02e7ade77b33007d74802efc8316c8db98fd11e", size = 38040541, upload-time = "2025-06-22T16:18:55.077Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/c9/750d34788288d64ffbc94fdb4562f40f609d3f5ef27ab4f3a4ad00c9033e/scipy-1.16.0-cp311-cp311-win_amd64.whl", hash = "sha256:a16ba90847249bedce8aa404a83fb8334b825ec4a8e742ce6012a7a5e639f95c", size = 38570814, upload-time = "2025-06-22T16:19:00.912Z" },
+    { url = "https://files.pythonhosted.org/packages/01/c0/c943bc8d2bbd28123ad0f4f1eef62525fa1723e84d136b32965dcb6bad3a/scipy-1.16.0-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:7eb6bd33cef4afb9fa5f1fb25df8feeb1e52d94f21a44f1d17805b41b1da3180", size = 36459071, upload-time = "2025-06-22T16:19:06.605Z" },
+    { url = "https://files.pythonhosted.org/packages/99/0d/270e2e9f1a4db6ffbf84c9a0b648499842046e4e0d9b2275d150711b3aba/scipy-1.16.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:1dbc8fdba23e4d80394ddfab7a56808e3e6489176d559c6c71935b11a2d59db1", size = 28490500, upload-time = "2025-06-22T16:19:11.775Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/22/01d7ddb07cff937d4326198ec8d10831367a708c3da72dfd9b7ceaf13028/scipy-1.16.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:7dcf42c380e1e3737b343dec21095c9a9ad3f9cbe06f9c05830b44b1786c9e90", size = 20762345, upload-time = "2025-06-22T16:19:15.813Z" },
+    { url = "https://files.pythonhosted.org/packages/34/7f/87fd69856569ccdd2a5873fe5d7b5bbf2ad9289d7311d6a3605ebde3a94b/scipy-1.16.0-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:26ec28675f4a9d41587266084c626b02899db373717d9312fa96ab17ca1ae94d", size = 23418563, upload-time = "2025-06-22T16:19:20.746Z" },
+    { url = "https://files.pythonhosted.org/packages/f6/f1/e4f4324fef7f54160ab749efbab6a4bf43678a9eb2e9817ed71a0a2fd8de/scipy-1.16.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:952358b7e58bd3197cfbd2f2f2ba829f258404bdf5db59514b515a8fe7a36c52", size = 33203951, upload-time = "2025-06-22T16:19:25.813Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/f0/b6ac354a956384fd8abee2debbb624648125b298f2c4a7b4f0d6248048a5/scipy-1.16.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:03931b4e870c6fef5b5c0970d52c9f6ddd8c8d3e934a98f09308377eba6f3824", size = 35070225, upload-time = "2025-06-22T16:19:31.416Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/73/5cbe4a3fd4bc3e2d67ffad02c88b83edc88f381b73ab982f48f3df1a7790/scipy-1.16.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:512c4f4f85912767c351a0306824ccca6fd91307a9f4318efe8fdbd9d30562ef", size = 35389070, upload-time = "2025-06-22T16:19:37.387Z" },
+    { url = "https://files.pythonhosted.org/packages/86/e8/a60da80ab9ed68b31ea5a9c6dfd3c2f199347429f229bf7f939a90d96383/scipy-1.16.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e69f798847e9add03d512eaf5081a9a5c9a98757d12e52e6186ed9681247a1ac", size = 37825287, upload-time = "2025-06-22T16:19:43.375Z" },
+    { url = "https://files.pythonhosted.org/packages/ea/b5/29fece1a74c6a94247f8a6fb93f5b28b533338e9c34fdcc9cfe7a939a767/scipy-1.16.0-cp312-cp312-win_amd64.whl", hash = "sha256:adf9b1999323ba335adc5d1dc7add4781cb5a4b0ef1e98b79768c05c796c4e49", size = 38431929, upload-time = "2025-06-22T16:19:49.385Z" },
+    { url = "https://files.pythonhosted.org/packages/46/95/0746417bc24be0c2a7b7563946d61f670a3b491b76adede420e9d173841f/scipy-1.16.0-cp313-cp313-macosx_10_14_x86_64.whl", hash = "sha256:e9f414cbe9ca289a73e0cc92e33a6a791469b6619c240aa32ee18abdce8ab451", size = 36418162, upload-time = "2025-06-22T16:19:56.3Z" },
+    { url = "https://files.pythonhosted.org/packages/19/5a/914355a74481b8e4bbccf67259bbde171348a3f160b67b4945fbc5f5c1e5/scipy-1.16.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:bbba55fb97ba3cdef9b1ee973f06b09d518c0c7c66a009c729c7d1592be1935e", size = 28465985, upload-time = "2025-06-22T16:20:01.238Z" },
+    { url = "https://files.pythonhosted.org/packages/58/46/63477fc1246063855969cbefdcee8c648ba4b17f67370bd542ba56368d0b/scipy-1.16.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:58e0d4354eacb6004e7aa1cd350e5514bd0270acaa8d5b36c0627bb3bb486974", size = 20737961, upload-time = "2025-06-22T16:20:05.913Z" },
+    { url = "https://files.pythonhosted.org/packages/93/86/0fbb5588b73555e40f9d3d6dde24ee6fac7d8e301a27f6f0cab9d8f66ff2/scipy-1.16.0-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:75b2094ec975c80efc273567436e16bb794660509c12c6a31eb5c195cbf4b6dc", size = 23377941, upload-time = "2025-06-22T16:20:10.668Z" },
+    { url = "https://files.pythonhosted.org/packages/ca/80/a561f2bf4c2da89fa631b3cbf31d120e21ea95db71fd9ec00cb0247c7a93/scipy-1.16.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6b65d232157a380fdd11a560e7e21cde34fdb69d65c09cb87f6cc024ee376351", size = 33196703, upload-time = "2025-06-22T16:20:16.097Z" },
+    { url = "https://files.pythonhosted.org/packages/11/6b/3443abcd0707d52e48eb315e33cc669a95e29fc102229919646f5a501171/scipy-1.16.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d8747f7736accd39289943f7fe53a8333be7f15a82eea08e4afe47d79568c32", size = 35083410, upload-time = "2025-06-22T16:20:21.734Z" },
+    { url = "https://files.pythonhosted.org/packages/20/ab/eb0fc00e1e48961f1bd69b7ad7e7266896fe5bad4ead91b5fc6b3561bba4/scipy-1.16.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:eb9f147a1b8529bb7fec2a85cf4cf42bdfadf9e83535c309a11fdae598c88e8b", size = 35387829, upload-time = "2025-06-22T16:20:27.548Z" },
+    { url = "https://files.pythonhosted.org/packages/57/9e/d6fc64e41fad5d481c029ee5a49eefc17f0b8071d636a02ceee44d4a0de2/scipy-1.16.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d2b83c37edbfa837a8923d19c749c1935ad3d41cf196006a24ed44dba2ec4358", size = 37841356, upload-time = "2025-06-22T16:20:35.112Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/a7/4c94bbe91f12126b8bf6709b2471900577b7373a4fd1f431f28ba6f81115/scipy-1.16.0-cp313-cp313-win_amd64.whl", hash = "sha256:79a3c13d43c95aa80b87328a46031cf52508cf5f4df2767602c984ed1d3c6bbe", size = 38403710, upload-time = "2025-06-22T16:21:54.473Z" },
+    { url = "https://files.pythonhosted.org/packages/47/20/965da8497f6226e8fa90ad3447b82ed0e28d942532e92dd8b91b43f100d4/scipy-1.16.0-cp313-cp313t-macosx_10_14_x86_64.whl", hash = "sha256:f91b87e1689f0370690e8470916fe1b2308e5b2061317ff76977c8f836452a47", size = 36813833, upload-time = "2025-06-22T16:20:43.925Z" },
+    { url = "https://files.pythonhosted.org/packages/28/f4/197580c3dac2d234e948806e164601c2df6f0078ed9f5ad4a62685b7c331/scipy-1.16.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:88a6ca658fb94640079e7a50b2ad3b67e33ef0f40e70bdb7dc22017dae73ac08", size = 28974431, upload-time = "2025-06-22T16:20:51.302Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/fc/e18b8550048d9224426e76906694c60028dbdb65d28b1372b5503914b89d/scipy-1.16.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:ae902626972f1bd7e4e86f58fd72322d7f4ec7b0cfc17b15d4b7006efc385176", size = 21246454, upload-time = "2025-06-22T16:20:57.276Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/48/07b97d167e0d6a324bfd7484cd0c209cc27338b67e5deadae578cf48e809/scipy-1.16.0-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:8cb824c1fc75ef29893bc32b3ddd7b11cf9ab13c1127fe26413a05953b8c32ed", size = 23772979, upload-time = "2025-06-22T16:21:03.363Z" },
+    { url = "https://files.pythonhosted.org/packages/4c/4f/9efbd3f70baf9582edf271db3002b7882c875ddd37dc97f0f675ad68679f/scipy-1.16.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:de2db7250ff6514366a9709c2cba35cb6d08498e961cba20d7cff98a7ee88938", size = 33341972, upload-time = "2025-06-22T16:21:11.14Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/dc/9e496a3c5dbe24e76ee24525155ab7f659c20180bab058ef2c5fa7d9119c/scipy-1.16.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e85800274edf4db8dd2e4e93034f92d1b05c9421220e7ded9988b16976f849c1", size = 35185476, upload-time = "2025-06-22T16:21:19.156Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/b3/21001cff985a122ba434c33f2c9d7d1dc3b669827e94f4fc4e1fe8b9dfd8/scipy-1.16.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4f720300a3024c237ace1cb11f9a84c38beb19616ba7c4cdcd771047a10a1706", size = 35570990, upload-time = "2025-06-22T16:21:27.797Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/d3/7ba42647d6709251cdf97043d0c107e0317e152fa2f76873b656b509ff55/scipy-1.16.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:aad603e9339ddb676409b104c48a027e9916ce0d2838830691f39552b38a352e", size = 37950262, upload-time = "2025-06-22T16:21:36.976Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/c4/231cac7a8385394ebbbb4f1ca662203e9d8c332825ab4f36ffc3ead09a42/scipy-1.16.0-cp313-cp313t-win_amd64.whl", hash = "sha256:f56296fefca67ba605fd74d12f7bd23636267731a72cb3947963e76b8c0a25db", size = 38515076, upload-time = "2025-06-22T16:21:45.694Z" },
+]
+
+[[package]]
+name = "seaborn"
+version = "0.13.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "pandas" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/86/59/a451d7420a77ab0b98f7affa3a1d78a313d2f7281a57afb1a34bae8ab412/seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7", size = 1457696, upload-time = "2024-01-25T13:21:52.551Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987", size = 294914, upload-time = "2024-01-25T13:21:49.598Z" },
+]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" },
+]
+
+[[package]]
+name = "stack-data"
+version = "0.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "asttokens" },
+    { name = "executing" },
+    { name = "pure-eval" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707, upload-time = "2023-09-30T13:58:05.479Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521, upload-time = "2023-09-30T13:58:03.53Z" },
+]
+
+[[package]]
+name = "tomli"
+version = "2.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/18/87/302344fed471e44a87289cf4967697d07e532f2421fdaf868a303cbae4ff/tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff", size = 17175, upload-time = "2024-11-27T22:38:36.873Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/43/ca/75707e6efa2b37c77dadb324ae7d9571cb424e61ea73fad7c56c2d14527f/tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249", size = 131077, upload-time = "2024-11-27T22:37:54.956Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/16/51ae563a8615d472fdbffc43a3f3d46588c264ac4f024f63f01283becfbb/tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6", size = 123429, upload-time = "2024-11-27T22:37:56.698Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/dd/4f6cd1e7b160041db83c694abc78e100473c15d54620083dbd5aae7b990e/tomli-2.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ece47d672db52ac607a3d9599a9d48dcb2f2f735c6c2d1f34130085bb12b112a", size = 226067, upload-time = "2024-11-27T22:37:57.63Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/6b/c54ede5dc70d648cc6361eaf429304b02f2871a345bbdd51e993d6cdf550/tomli-2.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6972ca9c9cc9f0acaa56a8ca1ff51e7af152a9f87fb64623e31d5c83700080ee", size = 236030, upload-time = "2024-11-27T22:37:59.344Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/47/999514fa49cfaf7a92c805a86c3c43f4215621855d151b61c602abb38091/tomli-2.2.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c954d2250168d28797dd4e3ac5cf812a406cd5a92674ee4c8f123c889786aa8e", size = 240898, upload-time = "2024-11-27T22:38:00.429Z" },
+    { url = "https://files.pythonhosted.org/packages/73/41/0a01279a7ae09ee1573b423318e7934674ce06eb33f50936655071d81a24/tomli-2.2.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8dd28b3e155b80f4d54beb40a441d366adcfe740969820caf156c019fb5c7ec4", size = 229894, upload-time = "2024-11-27T22:38:02.094Z" },
+    { url = "https://files.pythonhosted.org/packages/55/18/5d8bc5b0a0362311ce4d18830a5d28943667599a60d20118074ea1b01bb7/tomli-2.2.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:e59e304978767a54663af13c07b3d1af22ddee3bb2fb0618ca1593e4f593a106", size = 245319, upload-time = "2024-11-27T22:38:03.206Z" },
+    { url = "https://files.pythonhosted.org/packages/92/a3/7ade0576d17f3cdf5ff44d61390d4b3febb8a9fc2b480c75c47ea048c646/tomli-2.2.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:33580bccab0338d00994d7f16f4c4ec25b776af3ffaac1ed74e0b3fc95e885a8", size = 238273, upload-time = "2024-11-27T22:38:04.217Z" },
+    { url = "https://files.pythonhosted.org/packages/72/6f/fa64ef058ac1446a1e51110c375339b3ec6be245af9d14c87c4a6412dd32/tomli-2.2.1-cp311-cp311-win32.whl", hash = "sha256:465af0e0875402f1d226519c9904f37254b3045fc5084697cefb9bdde1ff99ff", size = 98310, upload-time = "2024-11-27T22:38:05.908Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/1c/4a2dcde4a51b81be3530565e92eda625d94dafb46dbeb15069df4caffc34/tomli-2.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:2d0f2fdd22b02c6d81637a3c95f8cd77f995846af7414c5c4b8d0545afa1bc4b", size = 108309, upload-time = "2024-11-27T22:38:06.812Z" },
+    { url = "https://files.pythonhosted.org/packages/52/e1/f8af4c2fcde17500422858155aeb0d7e93477a0d59a98e56cbfe75070fd0/tomli-2.2.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4a8f6e44de52d5e6c657c9fe83b562f5f4256d8ebbfe4ff922c495620a7f6cea", size = 132762, upload-time = "2024-11-27T22:38:07.731Z" },
+    { url = "https://files.pythonhosted.org/packages/03/b8/152c68bb84fc00396b83e7bbddd5ec0bd3dd409db4195e2a9b3e398ad2e3/tomli-2.2.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8d57ca8095a641b8237d5b079147646153d22552f1c637fd3ba7f4b0b29167a8", size = 123453, upload-time = "2024-11-27T22:38:09.384Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/d6/fc9267af9166f79ac528ff7e8c55c8181ded34eb4b0e93daa767b8841573/tomli-2.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4e340144ad7ae1533cb897d406382b4b6fede8890a03738ff1683af800d54192", size = 233486, upload-time = "2024-11-27T22:38:10.329Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/51/51c3f2884d7bab89af25f678447ea7d297b53b5a3b5730a7cb2ef6069f07/tomli-2.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:db2b95f9de79181805df90bedc5a5ab4c165e6ec3fe99f970d0e302f384ad222", size = 242349, upload-time = "2024-11-27T22:38:11.443Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/df/bfa89627d13a5cc22402e441e8a931ef2108403db390ff3345c05253935e/tomli-2.2.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:40741994320b232529c802f8bc86da4e1aa9f413db394617b9a256ae0f9a7f77", size = 252159, upload-time = "2024-11-27T22:38:13.099Z" },
+    { url = "https://files.pythonhosted.org/packages/9e/6e/fa2b916dced65763a5168c6ccb91066f7639bdc88b48adda990db10c8c0b/tomli-2.2.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:400e720fe168c0f8521520190686ef8ef033fb19fc493da09779e592861b78c6", size = 237243, upload-time = "2024-11-27T22:38:14.766Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/04/885d3b1f650e1153cbb93a6a9782c58a972b94ea4483ae4ac5cedd5e4a09/tomli-2.2.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:02abe224de6ae62c19f090f68da4e27b10af2b93213d36cf44e6e1c5abd19fdd", size = 259645, upload-time = "2024-11-27T22:38:15.843Z" },
+    { url = "https://files.pythonhosted.org/packages/9c/de/6b432d66e986e501586da298e28ebeefd3edc2c780f3ad73d22566034239/tomli-2.2.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b82ebccc8c8a36f2094e969560a1b836758481f3dc360ce9a3277c65f374285e", size = 244584, upload-time = "2024-11-27T22:38:17.645Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/9a/47c0449b98e6e7d1be6cbac02f93dd79003234ddc4aaab6ba07a9a7482e2/tomli-2.2.1-cp312-cp312-win32.whl", hash = "sha256:889f80ef92701b9dbb224e49ec87c645ce5df3fa2cc548664eb8a25e03127a98", size = 98875, upload-time = "2024-11-27T22:38:19.159Z" },
+    { url = "https://files.pythonhosted.org/packages/ef/60/9b9638f081c6f1261e2688bd487625cd1e660d0a85bd469e91d8db969734/tomli-2.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:7fc04e92e1d624a4a63c76474610238576942d6b8950a2d7f908a340494e67e4", size = 109418, upload-time = "2024-11-27T22:38:20.064Z" },
+    { url = "https://files.pythonhosted.org/packages/04/90/2ee5f2e0362cb8a0b6499dc44f4d7d48f8fff06d28ba46e6f1eaa61a1388/tomli-2.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f4039b9cbc3048b2416cc57ab3bda989a6fcf9b36cf8937f01a6e731b64f80d7", size = 132708, upload-time = "2024-11-27T22:38:21.659Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/ec/46b4108816de6b385141f082ba99e315501ccd0a2ea23db4a100dd3990ea/tomli-2.2.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:286f0ca2ffeeb5b9bd4fcc8d6c330534323ec51b2f52da063b11c502da16f30c", size = 123582, upload-time = "2024-11-27T22:38:22.693Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/bd/b470466d0137b37b68d24556c38a0cc819e8febe392d5b199dcd7f578365/tomli-2.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a92ef1a44547e894e2a17d24e7557a5e85a9e1d0048b0b5e7541f76c5032cb13", size = 232543, upload-time = "2024-11-27T22:38:24.367Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/e5/82e80ff3b751373f7cead2815bcbe2d51c895b3c990686741a8e56ec42ab/tomli-2.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9316dc65bed1684c9a98ee68759ceaed29d229e985297003e494aa825ebb0281", size = 241691, upload-time = "2024-11-27T22:38:26.081Z" },
+    { url = "https://files.pythonhosted.org/packages/05/7e/2a110bc2713557d6a1bfb06af23dd01e7dde52b6ee7dadc589868f9abfac/tomli-2.2.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e85e99945e688e32d5a35c1ff38ed0b3f41f43fad8df0bdf79f72b2ba7bc5272", size = 251170, upload-time = "2024-11-27T22:38:27.921Z" },
+    { url = "https://files.pythonhosted.org/packages/64/7b/22d713946efe00e0adbcdfd6d1aa119ae03fd0b60ebed51ebb3fa9f5a2e5/tomli-2.2.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ac065718db92ca818f8d6141b5f66369833d4a80a9d74435a268c52bdfa73140", size = 236530, upload-time = "2024-11-27T22:38:29.591Z" },
+    { url = "https://files.pythonhosted.org/packages/38/31/3a76f67da4b0cf37b742ca76beaf819dca0ebef26d78fc794a576e08accf/tomli-2.2.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:d920f33822747519673ee656a4b6ac33e382eca9d331c87770faa3eef562aeb2", size = 258666, upload-time = "2024-11-27T22:38:30.639Z" },
+    { url = "https://files.pythonhosted.org/packages/07/10/5af1293da642aded87e8a988753945d0cf7e00a9452d3911dd3bb354c9e2/tomli-2.2.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a198f10c4d1b1375d7687bc25294306e551bf1abfa4eace6650070a5c1ae2744", size = 243954, upload-time = "2024-11-27T22:38:31.702Z" },
+    { url = "https://files.pythonhosted.org/packages/5b/b9/1ed31d167be802da0fc95020d04cd27b7d7065cc6fbefdd2f9186f60d7bd/tomli-2.2.1-cp313-cp313-win32.whl", hash = "sha256:d3f5614314d758649ab2ab3a62d4f2004c825922f9e370b29416484086b264ec", size = 98724, upload-time = "2024-11-27T22:38:32.837Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/32/b0963458706accd9afcfeb867c0f9175a741bf7b19cd424230714d722198/tomli-2.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:a38aa0308e754b0e3c67e344754dff64999ff9b513e691d0e786265c93583c69", size = 109383, upload-time = "2024-11-27T22:38:34.455Z" },
+    { url = "https://files.pythonhosted.org/packages/6e/c2/61d3e0f47e2b74ef40a68b9e6ad5984f6241a942f7cd3bbfbdbd03861ea9/tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc", size = 14257, upload-time = "2024-11-27T22:38:35.385Z" },
+]
+
+[[package]]
+name = "tornado"
+version = "6.5.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/51/89/c72771c81d25d53fe33e3dca61c233b665b2780f21820ba6fd2c6793c12b/tornado-6.5.1.tar.gz", hash = "sha256:84ceece391e8eb9b2b95578db65e920d2a61070260594819589609ba9bc6308c", size = 509934, upload-time = "2025-05-22T18:15:38.788Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/77/89/f4532dee6843c9e0ebc4e28d4be04c67f54f60813e4bf73d595fe7567452/tornado-6.5.1-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:d50065ba7fd11d3bd41bcad0825227cc9a95154bad83239357094c36708001f7", size = 441948, upload-time = "2025-05-22T18:15:20.862Z" },
+    { url = "https://files.pythonhosted.org/packages/15/9a/557406b62cffa395d18772e0cdcf03bed2fff03b374677348eef9f6a3792/tornado-6.5.1-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:9e9ca370f717997cb85606d074b0e5b247282cf5e2e1611568b8821afe0342d6", size = 440112, upload-time = "2025-05-22T18:15:22.591Z" },
+    { url = "https://files.pythonhosted.org/packages/55/82/7721b7319013a3cf881f4dffa4f60ceff07b31b394e459984e7a36dc99ec/tornado-6.5.1-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b77e9dfa7ed69754a54c89d82ef746398be82f749df69c4d3abe75c4d1ff4888", size = 443672, upload-time = "2025-05-22T18:15:24.027Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/42/d11c4376e7d101171b94e03cef0cbce43e823ed6567ceda571f54cf6e3ce/tornado-6.5.1-cp39-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:253b76040ee3bab8bcf7ba9feb136436a3787208717a1fb9f2c16b744fba7331", size = 443019, upload-time = "2025-05-22T18:15:25.735Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/f7/0c48ba992d875521ac761e6e04b0a1750f8150ae42ea26df1852d6a98942/tornado-6.5.1-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:308473f4cc5a76227157cdf904de33ac268af770b2c5f05ca6c1161d82fdd95e", size = 443252, upload-time = "2025-05-22T18:15:27.499Z" },
+    { url = "https://files.pythonhosted.org/packages/89/46/d8d7413d11987e316df4ad42e16023cd62666a3c0dfa1518ffa30b8df06c/tornado-6.5.1-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:caec6314ce8a81cf69bd89909f4b633b9f523834dc1a352021775d45e51d9401", size = 443930, upload-time = "2025-05-22T18:15:29.299Z" },
+    { url = "https://files.pythonhosted.org/packages/78/b2/f8049221c96a06df89bed68260e8ca94beca5ea532ffc63b1175ad31f9cc/tornado-6.5.1-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:13ce6e3396c24e2808774741331638ee6c2f50b114b97a55c5b442df65fd9692", size = 443351, upload-time = "2025-05-22T18:15:31.038Z" },
+    { url = "https://files.pythonhosted.org/packages/76/ff/6a0079e65b326cc222a54720a748e04a4db246870c4da54ece4577bfa702/tornado-6.5.1-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:5cae6145f4cdf5ab24744526cc0f55a17d76f02c98f4cff9daa08ae9a217448a", size = 443328, upload-time = "2025-05-22T18:15:32.426Z" },
+    { url = "https://files.pythonhosted.org/packages/49/18/e3f902a1d21f14035b5bc6246a8c0f51e0eef562ace3a2cea403c1fb7021/tornado-6.5.1-cp39-abi3-win32.whl", hash = "sha256:e0a36e1bc684dca10b1aa75a31df8bdfed656831489bc1e6a6ebed05dc1ec365", size = 444396, upload-time = "2025-05-22T18:15:34.205Z" },
+    { url = "https://files.pythonhosted.org/packages/7b/09/6526e32bf1049ee7de3bebba81572673b19a2a8541f795d887e92af1a8bc/tornado-6.5.1-cp39-abi3-win_amd64.whl", hash = "sha256:908e7d64567cecd4c2b458075589a775063453aeb1d2a1853eedb806922f568b", size = 444840, upload-time = "2025-05-22T18:15:36.1Z" },
+    { url = "https://files.pythonhosted.org/packages/55/a7/535c44c7bea4578e48281d83c615219f3ab19e6abc67625ef637c73987be/tornado-6.5.1-cp39-abi3-win_arm64.whl", hash = "sha256:02420a0eb7bf617257b9935e2b754d1b63897525d8a289c9d65690d580b4dcf7", size = 443596, upload-time = "2025-05-22T18:15:37.433Z" },
+]
+
+[[package]]
+name = "traitlets"
+version = "5.14.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621, upload-time = "2024-04-19T11:11:49.746Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359, upload-time = "2024-04-19T11:11:46.763Z" },
+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.14.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/98/5a/da40306b885cc8c09109dc2e1abd358d5684b1425678151cdaed4731c822/typing_extensions-4.14.1.tar.gz", hash = "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36", size = 107673, upload-time = "2025-07-04T13:28:34.16Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b5/00/d631e67a838026495268c2f6884f3711a15a9a2a96cd244fdaea53b823fb/typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76", size = 43906, upload-time = "2025-07-04T13:28:32.743Z" },
+]
+
+[[package]]
+name = "tzdata"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380, upload-time = "2025-03-23T13:54:43.652Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" },
+]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.13"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/63/53559446a878410fc5a5974feb13d31d78d752eb18aeba59c7fef1af7598/wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5", size = 101301, upload-time = "2024-01-06T02:10:57.829Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859", size = 34166, upload-time = "2024-01-06T02:10:55.763Z" },
+]