From 7c312ad86a9702e098e0e7c5da67eab415eb0eb3 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 28 May 2025 14:31:32 -0600
Subject: [PATCH 01/72] add keyword 'label' to degradation_timeseries_plot,
 enabling 'left' and 'center' labeling options.

---
 rdtools/plotting.py | 35 ++++++++++++++++++++++++++++-------
 1 file changed, 28 insertions(+), 7 deletions(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 93a07bac..108e704b 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -431,7 +431,7 @@ def availability_summary_plots(power_system, power_subsystem, loss_total,
     return fig
 
 
-def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True,
+def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, label= 'right',
                                 fig=None, plot_color=None, ci_color=None, **kwargs):
     '''
     Plot resampled time series of degradation trend with time
@@ -447,6 +447,11 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True,
         at least 50% of datapoints to be included in rolling plot.
     include_ci : bool, default True
         calculate and plot 2-sigma confidence intervals along with rolling median
+    label    : {'right', 'left', 'center'}, default 'right'
+        A combination of 1) which Year-on-Year slope edge to label, and 2) which rolling median edge to label. 
+        'right' : label right edge of YoY slope and right edge of rolling median interval.
+        'center': label center of YoY slope interval and center of rolling median interval.
+        'left'  : label left edge of YoY slope and center of rolling median interval.
     fig     : matplotlib, optional
         fig object to add new plot to (first set of axes only)
     plot_color : str, optional
@@ -465,7 +470,7 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True,
     -------
     matplotlib.figure.Figure
     '''
-
+    import datetime
     def _bootstrap(x, percentile, reps):
         # stolen from degradation_year_on_year
         n1 = len(x)
@@ -475,7 +480,6 @@ def _bootstrap(x, percentile, reps):
 
     try:
         results_values = yoy_info['YoY_values']
-
     except KeyError:
         raise KeyError("yoy_info input dictionary does not contain key `YoY_values`.")
 
@@ -483,8 +487,23 @@ def _bootstrap(x, percentile, reps):
         plot_color = 'tab:orange'
     if ci_color is None:
         ci_color = 'C0'
-
-    roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2)
+    
+    if label not in {None, "left", "right", "center"}:
+        raise ValueError(f"Unsupported value {label} for `label`")
+    if label is None:
+        label = "right"
+
+    if label == "right":
+        center = False
+        offset_days = 0
+    elif label == "center":
+        center = True
+        offset_days = 182
+    elif label == "left":
+        center = True
+        offset_days = 365
+
+    roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2, center=center)
     # unfortunately it seems that you can't return multiple values in the rolling.apply() kernel.
     # TODO: figure out some workaround to return both percentiles in a single pass
     if include_ci:
@@ -495,8 +514,10 @@ def _bootstrap(x, percentile, reps):
     else:
         ax = fig.axes[0]
     if include_ci:
-        ax.fill_between(ci_lower.index, ci_lower, ci_upper, color=ci_color)
-    ax.plot(roller.median(), color=plot_color, **kwargs)
+        ax.fill_between(ci_lower.index - datetime.timedelta(days=offset_days), 
+                        ci_lower, ci_upper, color=ci_color)
+    ax.plot(roller.median().index - datetime.timedelta(days=offset_days),
+            roller.median(), color=plot_color, **kwargs)
     ax.axhline(results_values.median(), c='k', ls='--')
     plt.ylabel('Degradation trend (%/yr)')
     fig.autofmt_xdate()

From d6a898e3d0424feb6a2c3ab2346e14aeddd2bd1d Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 28 May 2025 15:29:53 -0600
Subject: [PATCH 02/72] Update changelog, add pytests, update sphinx
 documentation

---
 docs/sphinx/source/changelog.rst         |  1 +
 docs/sphinx/source/changelog/pending.rst | 16 ++++++++++++++++
 rdtools/plotting.py                      |  9 +++++----
 rdtools/test/plotting_test.py            | 13 +++++++++++++
 4 files changed, 35 insertions(+), 4 deletions(-)
 create mode 100644 docs/sphinx/source/changelog/pending.rst

diff --git a/docs/sphinx/source/changelog.rst b/docs/sphinx/source/changelog.rst
index fc3d805a..341cb307 100644
--- a/docs/sphinx/source/changelog.rst
+++ b/docs/sphinx/source/changelog.rst
@@ -1,5 +1,6 @@
 RdTools Change Log
 ==================
+.. include:: changelog/pending.rst
 .. include:: changelog/v3.0.0.rst
 .. include:: changelog/v2.1.8.rst
 .. include:: changelog/v2.1.7.rst
diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
new file mode 100644
index 00000000..59ee736a
--- /dev/null
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -0,0 +1,16 @@
+*************************
+v3.0.x (X, X, 2025)
+*************************
+
+Enhancements
+------------
+* :py:func:`~rdtools.plotting.degradation_timeseries_plot` has new parameter ``label=`` 
+  to allow the timeseries plot to have right labeling (default), center or left labeling. 
+  (:issue:`455`)
+
+
+
+Contributors
+------------
+* Chris Deline (:ghuser:`cdeline`)
+
diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 108e704b..f9f814e8 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -448,10 +448,11 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, lab
     include_ci : bool, default True
         calculate and plot 2-sigma confidence intervals along with rolling median
     label    : {'right', 'left', 'center'}, default 'right'
-        A combination of 1) which Year-on-Year slope edge to label, and 2) which rolling median edge to label. 
-        'right' : label right edge of YoY slope and right edge of rolling median interval.
-        'center': label center of YoY slope interval and center of rolling median interval.
-        'left'  : label left edge of YoY slope and center of rolling median interval.
+        A combination of 1) which Year-on-Year slope edge to label, and 2) which rolling median edge to label.
+        
+        * ``right`` : label right edge of YoY slope and right edge of rolling median interval.
+        * ``center``: label center of YoY slope interval and center of rolling median interval.
+        * ``left``  : label left edge of YoY slope and center of rolling median interval.
     fig     : matplotlib, optional
         fig object to add new plot to (first set of axes only)
     plot_color : str, optional
diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index cb4639cb..20ff372e 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -255,4 +255,17 @@ def test_degradation_timeseries_plot(degradation_info):
     # test defaults
     result = degradation_timeseries_plot(yoy_info)
     assert_isinstance(result, plt.Figure)
+    # test other label options
+    result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label='center', fig=result)
+    assert_isinstance(result, plt.Figure)
+    result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label='left')
+    assert_isinstance(result, plt.Figure)
+    result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label=None)
+    assert_isinstance(result, plt.Figure)
+
+    with pytest.raises(KeyError):
+        degradation_timeseries_plot({'a':1}, include_ci=False)
+    with pytest.raises(ValueError):
+        degradation_timeseries_plot(yoy_info, include_ci=False, label='CENTER')
+
     plt.close('all')

From df2effcf405b1871915b79e07e45f7c432c2f56d Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 28 May 2025 15:39:50 -0600
Subject: [PATCH 03/72] fix flake8 grumbles

---
 rdtools/plotting.py           | 13 ++++++++-----
 rdtools/test/plotting_test.py |  5 +++--
 2 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index f9f814e8..11ecde8c 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -431,7 +431,7 @@ def availability_summary_plots(power_system, power_subsystem, loss_total,
     return fig
 
 
-def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, label= 'right',
+def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, label='right',
                                 fig=None, plot_color=None, ci_color=None, **kwargs):
     '''
     Plot resampled time series of degradation trend with time
@@ -448,8 +448,9 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, lab
     include_ci : bool, default True
         calculate and plot 2-sigma confidence intervals along with rolling median
     label    : {'right', 'left', 'center'}, default 'right'
-        A combination of 1) which Year-on-Year slope edge to label, and 2) which rolling median edge to label.
-        
+        A combination of 1) which Year-on-Year slope edge to label,
+        and 2) which rolling median edge to label.
+
         * ``right`` : label right edge of YoY slope and right edge of rolling median interval.
         * ``center``: label center of YoY slope interval and center of rolling median interval.
         * ``left``  : label left edge of YoY slope and center of rolling median interval.
@@ -471,7 +472,9 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, lab
     -------
     matplotlib.figure.Figure
     '''
+
     import datetime
+
     def _bootstrap(x, percentile, reps):
         # stolen from degradation_year_on_year
         n1 = len(x)
@@ -488,7 +491,7 @@ def _bootstrap(x, percentile, reps):
         plot_color = 'tab:orange'
     if ci_color is None:
         ci_color = 'C0'
-    
+
     if label not in {None, "left", "right", "center"}:
         raise ValueError(f"Unsupported value {label} for `label`")
     if label is None:
@@ -515,7 +518,7 @@ def _bootstrap(x, percentile, reps):
     else:
         ax = fig.axes[0]
     if include_ci:
-        ax.fill_between(ci_lower.index - datetime.timedelta(days=offset_days), 
+        ax.fill_between(ci_lower.index - datetime.timedelta(days=offset_days),
                         ci_lower, ci_upper, color=ci_color)
     ax.plot(roller.median().index - datetime.timedelta(days=offset_days),
             roller.median(), color=plot_color, **kwargs)
diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index 20ff372e..f1ebb0ae 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -256,7 +256,8 @@ def test_degradation_timeseries_plot(degradation_info):
     result = degradation_timeseries_plot(yoy_info)
     assert_isinstance(result, plt.Figure)
     # test other label options
-    result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label='center', fig=result)
+    result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False,
+                                         label='center', fig=result)
     assert_isinstance(result, plt.Figure)
     result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label='left')
     assert_isinstance(result, plt.Figure)
@@ -264,7 +265,7 @@ def test_degradation_timeseries_plot(degradation_info):
     assert_isinstance(result, plt.Figure)
 
     with pytest.raises(KeyError):
-        degradation_timeseries_plot({'a':1}, include_ci=False)
+        degradation_timeseries_plot({'a': 1}, include_ci=False)
     with pytest.raises(ValueError):
         degradation_timeseries_plot(yoy_info, include_ci=False, label='CENTER')
 

From 3099497aaf1d1f6b494a971170ff5fdd05846c4c Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 18 Jun 2025 14:06:48 -0600
Subject: [PATCH 04/72] update pytests to include axes limits

---
 rdtools/test/plotting_test.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index f1ebb0ae..453423a0 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -255,12 +255,15 @@ def test_degradation_timeseries_plot(degradation_info):
     # test defaults
     result = degradation_timeseries_plot(yoy_info)
     assert_isinstance(result, plt.Figure)
+    assert(result.get_axes()[0].get_xlim()[0] == 17685.55)
     # test other label options
     result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False,
                                          label='center', fig=result)
     assert_isinstance(result, plt.Figure)
+    assert(result.get_axes()[0].get_xlim()[0] == 17304.4)
     result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label='left')
     assert_isinstance(result, plt.Figure)
+    assert(result.get_axes()[0].get_xlim()[0] == 17130.5)
     result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label=None)
     assert_isinstance(result, plt.Figure)
 

From 1d140e6ed1feca9691c20aaa5a728569a953e85a Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 18 Jun 2025 14:27:06 -0600
Subject: [PATCH 05/72] fix flake8 grumbles

---
 rdtools/test/plotting_test.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index 453423a0..029514ef 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -255,15 +255,15 @@ def test_degradation_timeseries_plot(degradation_info):
     # test defaults
     result = degradation_timeseries_plot(yoy_info)
     assert_isinstance(result, plt.Figure)
-    assert(result.get_axes()[0].get_xlim()[0] == 17685.55)
+    assert (result.get_axes()[0].get_xlim()[0] == 17685.55)
     # test other label options
     result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False,
                                          label='center', fig=result)
     assert_isinstance(result, plt.Figure)
-    assert(result.get_axes()[0].get_xlim()[0] == 17304.4)
+    assert (result.get_axes()[0].get_xlim()[0] == 17304.4)
     result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label='left')
     assert_isinstance(result, plt.Figure)
-    assert(result.get_axes()[0].get_xlim()[0] == 17130.5)
+    assert (result.get_axes()[0].get_xlim()[0] == 17130.5)
     result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label=None)
     assert_isinstance(result, plt.Figure)
 

From 0548eab705452989e84164682fa9e341f21c874f Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Sun, 22 Jun 2025 14:37:42 -0600
Subject: [PATCH 06/72] add 'label' input option to `degradation_year_on_year`.
 Fixes #459

---
 rdtools/degradation.py | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 1698b368..4c0689c0 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -179,7 +179,8 @@ def degradation_classical_decomposition(energy_normalized,
 
 def degradation_year_on_year(energy_normalized, recenter=True,
                              exceedance_prob=95, confidence_level=68.2,
-                             uncertainty_method='simple', block_length=30):
+                             uncertainty_method='simple', block_length=30,
+                             label='right'):
     '''
     Estimate the trend of a timeseries using the year-on-year decomposition
     approach and calculate a Monte Carlo-derived confidence interval of slope.
@@ -208,6 +209,8 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         If `uncertainty_method` is 'circular_block', `block_length`
         determines the length of the blocks used in the circular block bootstrapping
         in number of days. Must be shorter than a third of the time series.
+    label    : {'right', 'center'}, default 'right'
+        Which Year-on-Year slope edge to label.
 
     Returns
     -------
@@ -218,7 +221,8 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         degradation rate estimate
     calc_info : dict
 
-        * `YoY_values` - pandas series of right-labeled year on year slopes
+        * `YoY_values` - pandas series of year on year slopes, either right
+            or center labeled, depending on the `label` parameter.
         * `renormalizing_factor` - float of value used to recenter data
         * `exceedance_level` - the degradation rate that was outperformed with
           probability of `exceedance_prob`
@@ -233,6 +237,12 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     energy_normalized.name = 'energy'
     energy_normalized.index.name = 'dt'
 
+    if label not in {None, "right", "center"}:
+        raise ValueError(f"Unsupported value {label} for `label`."
+                         " Must be 'right' or 'center'.")
+    if label is None:
+        label = "right"
+
     # Detect less than 2 years of data. This is complicated by two things:
     #   - leap days muddle the precise meaning of "two years of data".
     #   - can't just check the number of days between the first and last
@@ -284,11 +294,15 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     df['yoy'] = 100.0 * (df.energy - df.energy_right) / (df.time_diff_years)
     df.index = df.dt
 
-    yoy_result = df.yoy.dropna()
-
     df_right = df.set_index(df.dt_right).drop_duplicates('dt_right')
     df['usage_of_points'] = df.yoy.notnull().astype(int).add(
                 df_right.yoy.notnull().astype(int), fill_value=0)
+    df['dt_center'] = df[['dt', 'dt_right']].mean(axis=1)
+    if label == 'center':
+        df = df.set_index(df.dt_center)
+        df.index.name = 'dt'
+
+    yoy_result = df.yoy.dropna()
 
     if not len(yoy_result):
         raise ValueError('no year-over-year aggregated data pairs found')

From 9af563568c0facef64cce1da97842e0c4fb2a639 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Sun, 22 Jun 2025 15:55:03 -0600
Subject: [PATCH 07/72] add pytests and update changelog.

---
 docs/sphinx/source/changelog.rst         |  1 +
 docs/sphinx/source/changelog/pending.rst | 16 +++++++++++++++
 rdtools/test/degradation_test.py         | 25 ++++++++++++++++++++++++
 3 files changed, 42 insertions(+)
 create mode 100644 docs/sphinx/source/changelog/pending.rst

diff --git a/docs/sphinx/source/changelog.rst b/docs/sphinx/source/changelog.rst
index fc3d805a..341cb307 100644
--- a/docs/sphinx/source/changelog.rst
+++ b/docs/sphinx/source/changelog.rst
@@ -1,5 +1,6 @@
 RdTools Change Log
 ==================
+.. include:: changelog/pending.rst
 .. include:: changelog/v3.0.0.rst
 .. include:: changelog/v2.1.8.rst
 .. include:: changelog/v2.1.7.rst
diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
new file mode 100644
index 00000000..815f82e5
--- /dev/null
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -0,0 +1,16 @@
+*************************
+v3.0.x (X, X, 2025)
+*************************
+
+Enhancements
+------------
+* :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``label=`` 
+  to return the calc_info['YoY_values'] as either right labeled (default), or center labeled. 
+  (:issue:`459`)
+
+
+
+Contributors
+------------
+* Chris Deline (:ghuser:`cdeline`)
+
diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 4e92a1f1..8c1f5881 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -202,6 +202,31 @@ def test_usage_of_points(self):
             self.test_corr_energy[input_freq])
         self.assertTrue((np.sum(rd_result[2]['usage_of_points'])) == 1462)
 
+    def test_degradation_year_on_year_label_center(self):
+        ''' Test degradation_year_on_year with label="center". '''
+
+        funcName = sys._getframe().f_code.co_name
+        logging.debug('Running {}'.format(funcName))
+
+        # test YOY degradation calc with label='center'
+        input_freq = 'D'
+        rd_result = degradation_year_on_year(
+            self.test_corr_energy[input_freq], label='center')
+        self.assertAlmostEqual(rd_result[0], 100 * self.rd, places=1)
+        rd_result1 = degradation_year_on_year(
+            self.test_corr_energy[input_freq], label=None)
+        rd_result2 = degradation_year_on_year(
+            self.test_corr_energy[input_freq], label='right')
+        pd.testing.assert_index_equal(rd_result1[2]['YoY_values'].index,
+                         rd_result2[2]['YoY_values'].index)
+        # 365/2 days difference between center and right label
+        self.assertAlmostEqual((rd_result2[2]['YoY_values'].index -
+                               rd_result[2]['YoY_values'].index).mean(),
+                               pd.Timedelta('183d'),
+                               delta=pd.Timedelta('1d'))
+        with pytest.raises(ValueError):
+            degradation_year_on_year(self.test_corr_energy[input_freq], 
+                                     label='LEFT')
 
 @pytest.mark.parametrize(
     "start,end,freq",

From 89ccbabe468315ead750f6ce009e8ad9570440b8 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Sun, 22 Jun 2025 15:56:36 -0600
Subject: [PATCH 08/72] flake8 grumbles

---
 rdtools/test/degradation_test.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 8c1f5881..96a216c6 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -218,16 +218,17 @@ def test_degradation_year_on_year_label_center(self):
         rd_result2 = degradation_year_on_year(
             self.test_corr_energy[input_freq], label='right')
         pd.testing.assert_index_equal(rd_result1[2]['YoY_values'].index,
-                         rd_result2[2]['YoY_values'].index)
+                                      rd_result2[2]['YoY_values'].index)
         # 365/2 days difference between center and right label
         self.assertAlmostEqual((rd_result2[2]['YoY_values'].index -
                                rd_result[2]['YoY_values'].index).mean(),
                                pd.Timedelta('183d'),
                                delta=pd.Timedelta('1d'))
         with pytest.raises(ValueError):
-            degradation_year_on_year(self.test_corr_energy[input_freq], 
+            degradation_year_on_year(self.test_corr_energy[input_freq],
                                      label='LEFT')
 
+
 @pytest.mark.parametrize(
     "start,end,freq",
     [

From b32211f72fdd55fbca30e81af909f475ea5e4101 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Sun, 22 Jun 2025 18:48:55 -0600
Subject: [PATCH 09/72] Minor updates to setup.py (constrain scipy<1.16) and
 refactor degradation_test

---
 rdtools/test/degradation_test.py | 8 ++++----
 setup.py                         | 3 ++-
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 96a216c6..2f2a6ee3 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -220,10 +220,10 @@ def test_degradation_year_on_year_label_center(self):
         pd.testing.assert_index_equal(rd_result1[2]['YoY_values'].index,
                                       rd_result2[2]['YoY_values'].index)
         # 365/2 days difference between center and right label
-        self.assertAlmostEqual((rd_result2[2]['YoY_values'].index -
-                               rd_result[2]['YoY_values'].index).mean(),
-                               pd.Timedelta('183d'),
-                               delta=pd.Timedelta('1d'))
+        assert (rd_result2[2]['YoY_values'].index -
+                 rd_result[2]['YoY_values'].index).mean().days == \
+                    pytest.approx(183, abs=1)
+
         with pytest.raises(ValueError):
             degradation_year_on_year(self.test_corr_energy[input_freq],
                                      label='LEFT')
diff --git a/setup.py b/setup.py
index 441b16c0..75549f6a 100755
--- a/setup.py
+++ b/setup.py
@@ -47,7 +47,8 @@
     "numpy >= 1.22.4",
     "pandas >= 1.4.4",
     "statsmodels >= 0.13.5",
-    "scipy >= 1.8.1",
+    # statsmodels 0.14.4 is not able to handle the latest scipy
+    "scipy >= 1.8.1, <1.16.0", 
     "h5py >= 3.7.0",
     "plotly>=4.0.0",
     "xgboost >= 1.6.0",

From 8ad5dac505cc879814abb3df76cedb13cca76eb2 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 23 Jun 2025 14:35:36 -0600
Subject: [PATCH 10/72] Custom fix for Pandas < 2.0.0 which can't average two
 columns of timestamps.

---
 rdtools/degradation.py | 52 +++++++++++++++++++++++++++++++++++++++++-
 setup.py               |  2 +-
 2 files changed, 52 insertions(+), 2 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 4c0689c0..1a4ede15 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -297,7 +297,12 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     df_right = df.set_index(df.dt_right).drop_duplicates('dt_right')
     df['usage_of_points'] = df.yoy.notnull().astype(int).add(
                 df_right.yoy.notnull().astype(int), fill_value=0)
-    df['dt_center'] = df[['dt', 'dt_right']].mean(axis=1)
+    if pd.__version__ < '2.0.0':
+        # For old Pandas versions < 2.0.0, time columns cannot be averaged
+        # with each other, so we use a custom function to calculate center label
+        df['dt_center'] = _avg_timestamp_old_Pandas(df.dt, df.dt_right)
+    else:
+        df['dt_center'] = pd.to_datetime(df[['dt', 'dt_right']].mean(axis=1))
     if label == 'center':
         df = df.set_index(df.dt_center)
         df.index.name = 'dt'
@@ -370,6 +375,51 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         return Rd_pct
 
 
+def _avg_timestamp_old_Pandas(dt, dt_right):
+    '''
+    For old Pandas versions < 2.0.0, time columns cannot be averaged
+    together.  From https://stackoverflow.com/questions/57812300/
+    python-pandas-to-calculate-mean-of-datetime-of-multiple-columns
+
+    Parameters
+    ----------
+    dt : pandas.Series
+        First series with datetime values
+    dt_right : pandas.Series
+        Second series with datetime values.
+
+    Returns
+    -------
+    pandas.Series
+        Series with the average timestamp of df1 and df2.
+    '''
+    import time
+    import datetime
+
+    temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
+                            'dt_right' : dt_right.dt.tz_localize(None)
+                            }).tz_localize(None)
+
+    # conversion from dates to seconds since epoch (unix time)
+    def to_unix(s):
+        if type(s) is pd.Timestamp:
+            return time.mktime(s.date().timetuple())
+        else:
+            return pd.NaT
+
+    # sum the seconds since epoch, calculate average, and convert back to readable date
+    averages = []
+    for index, row in temp_df.iterrows():
+        unix = [to_unix(i) for i in row]
+        try:
+            average = sum(unix) / len(unix)
+            averages.append(datetime.datetime.utcfromtimestamp(average).strftime('%Y-%m-%d'))
+        except TypeError:
+            averages.append(pd.NaT)
+    temp_df['averages'] = averages
+    return temp_df['averages']
+
+
 def _mk_test(x, alpha=0.05):
     '''
     Mann-Kendall test of significance for trend (used in classical
diff --git a/setup.py b/setup.py
index 75549f6a..ed56d3cc 100755
--- a/setup.py
+++ b/setup.py
@@ -48,7 +48,7 @@
     "pandas >= 1.4.4",
     "statsmodels >= 0.13.5",
     # statsmodels 0.14.4 is not able to handle the latest scipy
-    "scipy >= 1.8.1, <1.16.0", 
+    "scipy >= 1.8.1, <1.16.0",
     "h5py >= 3.7.0",
     "plotly>=4.0.0",
     "xgboost >= 1.6.0",

From cf5ff77201ed0501f04b3f6dede27746734d7534 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 23 Jun 2025 14:39:45 -0600
Subject: [PATCH 11/72] flake8 grumbles

---
 rdtools/test/degradation_test.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 2f2a6ee3..9c00fd80 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -221,8 +221,8 @@ def test_degradation_year_on_year_label_center(self):
                                       rd_result2[2]['YoY_values'].index)
         # 365/2 days difference between center and right label
         assert (rd_result2[2]['YoY_values'].index -
-                 rd_result[2]['YoY_values'].index).mean().days == \
-                    pytest.approx(183, abs=1)
+                rd_result[2]['YoY_values'].index).mean().days == \
+            pytest.approx(183, abs=1)
 
         with pytest.raises(ValueError):
             degradation_year_on_year(self.test_corr_energy[input_freq],

From 1ff743e7d6d065aaedab61b0e28dec4d2303c443 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 25 Jun 2025 14:29:58 -0600
Subject: [PATCH 12/72] keep TZ-aware timestamps.  Update pytests to
 specifically test _avg_timestamp_old_Pandas

---
 rdtools/degradation.py           |  7 ++++---
 rdtools/test/degradation_test.py | 22 ++++++++++++++++++++++
 2 files changed, 26 insertions(+), 3 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 1a4ede15..485c0465 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -398,7 +398,7 @@ def _avg_timestamp_old_Pandas(dt, dt_right):
 
     temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
                             'dt_right' : dt_right.dt.tz_localize(None)
-                            }).tz_localize(None)
+                            })
 
     # conversion from dates to seconds since epoch (unix time)
     def to_unix(s):
@@ -413,11 +413,12 @@ def to_unix(s):
         unix = [to_unix(i) for i in row]
         try:
             average = sum(unix) / len(unix)
-            averages.append(datetime.datetime.utcfromtimestamp(average).strftime('%Y-%m-%d'))
+            #averages.append(datetime.datetime.utcfromtimestamp(average).strftime('%Y-%m-%d'))
+            averages.append(pd.to_datetime(average, unit='s'))
         except TypeError:
             averages.append(pd.NaT)
     temp_df['averages'] = averages
-    return temp_df['averages']
+    return temp_df['averages'].dt.tz_localize(dt.dt.tz)
 
 
 def _mk_test(x, alpha=0.05):
diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 9c00fd80..0a1291b2 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -227,6 +227,28 @@ def test_degradation_year_on_year_label_center(self):
         with pytest.raises(ValueError):
             degradation_year_on_year(self.test_corr_energy[input_freq],
                                      label='LEFT')
+    
+    def test_avg_timestamp_old_Pandas(self):
+        """Test the _avg_timestamp_old_Pandas function for correct averaging."""
+        from rdtools.degradation import _avg_timestamp_old_Pandas
+        funcName = sys._getframe().f_code.co_name
+        logging.debug('Running {}'.format(funcName))
+        dt = pd.Series(self.test_corr_energy['D'].index[-3:], index = self.test_corr_energy['D'].index[-3:])
+        dt_right = pd.Series(self.test_corr_energy['D'].index[-3:]+ 
+                             pd.Timedelta(days=365), index = self.test_corr_energy['D'].index[-3:])
+        # Expected result is the midpoint between each pair
+        expected = pd.Series([
+            pd.Timestamp("2015-06-30 19:00:00"),
+            pd.Timestamp("2015-07-01 19:00:00"),
+            pd.Timestamp("2015-07-02 19:00:00")],
+            index = self.test_corr_energy['D'].index[-3:],
+            name = 'averages'
+        )
+
+        result = _avg_timestamp_old_Pandas(dt, dt_right)
+        print(result)
+        print(expected)
+        pd.testing.assert_series_equal(result, expected)
 
 
 @pytest.mark.parametrize(

From bc86af61341e1099bc588759362bad21fbdc0ad8 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 25 Jun 2025 15:07:00 -0600
Subject: [PATCH 13/72] flake8 grumbles

---
 rdtools/degradation.py           |  3 +--
 rdtools/test/degradation_test.py | 13 +++++++------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 485c0465..ea3ec9e3 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -394,7 +394,6 @@ def _avg_timestamp_old_Pandas(dt, dt_right):
         Series with the average timestamp of df1 and df2.
     '''
     import time
-    import datetime
 
     temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
                             'dt_right' : dt_right.dt.tz_localize(None)
@@ -413,7 +412,7 @@ def to_unix(s):
         unix = [to_unix(i) for i in row]
         try:
             average = sum(unix) / len(unix)
-            #averages.append(datetime.datetime.utcfromtimestamp(average).strftime('%Y-%m-%d'))
+            # averages.append(datetime.datetime.utcfromtimestamp(average).strftime('%Y-%m-%d'))
             averages.append(pd.to_datetime(average, unit='s'))
         except TypeError:
             averages.append(pd.NaT)
diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 0a1291b2..4d437c72 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -227,22 +227,23 @@ def test_degradation_year_on_year_label_center(self):
         with pytest.raises(ValueError):
             degradation_year_on_year(self.test_corr_energy[input_freq],
                                      label='LEFT')
-    
+
     def test_avg_timestamp_old_Pandas(self):
         """Test the _avg_timestamp_old_Pandas function for correct averaging."""
         from rdtools.degradation import _avg_timestamp_old_Pandas
         funcName = sys._getframe().f_code.co_name
         logging.debug('Running {}'.format(funcName))
-        dt = pd.Series(self.test_corr_energy['D'].index[-3:], index = self.test_corr_energy['D'].index[-3:])
-        dt_right = pd.Series(self.test_corr_energy['D'].index[-3:]+ 
-                             pd.Timedelta(days=365), index = self.test_corr_energy['D'].index[-3:])
+        dt = pd.Series(self.test_corr_energy['D'].index[-3:],
+                       index=self.test_corr_energy['D'].index[-3:])
+        dt_right = pd.Series(self.test_corr_energy['D'].index[-3:] +
+                             pd.Timedelta(days=365), index=self.test_corr_energy['D'].index[-3:])
         # Expected result is the midpoint between each pair
         expected = pd.Series([
             pd.Timestamp("2015-06-30 19:00:00"),
             pd.Timestamp("2015-07-01 19:00:00"),
             pd.Timestamp("2015-07-02 19:00:00")],
-            index = self.test_corr_energy['D'].index[-3:],
-            name = 'averages'
+            index=self.test_corr_energy['D'].index[-3:],
+            name='averages'
         )
 
         result = _avg_timestamp_old_Pandas(dt, dt_right)

From ae080fd8dbee3c2aa16b504ceede00965ed76eec Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 25 Jun 2025 15:41:10 -0600
Subject: [PATCH 14/72] try to UTC localize the pytest...

---
 rdtools/degradation.py           |  4 ++--
 rdtools/test/degradation_test.py | 20 ++++++++++----------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index ea3ec9e3..aba83681 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -397,7 +397,7 @@ def _avg_timestamp_old_Pandas(dt, dt_right):
 
     temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
                             'dt_right' : dt_right.dt.tz_localize(None)
-                            })
+                            }).tz_localize(None)
 
     # conversion from dates to seconds since epoch (unix time)
     def to_unix(s):
@@ -417,7 +417,7 @@ def to_unix(s):
         except TypeError:
             averages.append(pd.NaT)
     temp_df['averages'] = averages
-    return temp_df['averages'].dt.tz_localize(dt.dt.tz)
+    return (temp_df['averages'].tz_localize(dt.dt.tz)).dt.tz_localize(dt.dt.tz)
 
 
 def _mk_test(x, alpha=0.05):
diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 4d437c72..20cf4103 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -233,18 +233,18 @@ def test_avg_timestamp_old_Pandas(self):
         from rdtools.degradation import _avg_timestamp_old_Pandas
         funcName = sys._getframe().f_code.co_name
         logging.debug('Running {}'.format(funcName))
-        dt = pd.Series(self.test_corr_energy['D'].index[-3:],
-                       index=self.test_corr_energy['D'].index[-3:])
-        dt_right = pd.Series(self.test_corr_energy['D'].index[-3:] +
-                             pd.Timedelta(days=365), index=self.test_corr_energy['D'].index[-3:])
+        dt = pd.Series(self.get_corr_energy(0,'D').index[-3:].tz_localize('UTC'),
+                       index=self.get_corr_energy(0,'D').index[-3:].tz_localize('UTC'))
+        dt_right = pd.Series(self.get_corr_energy(0,'D').index[-3:].tz_localize('UTC') +
+                             pd.Timedelta(days=365), index=self.get_corr_energy(0,'D').index[-3:].tz_localize('UTC'))
         # Expected result is the midpoint between each pair
         expected = pd.Series([
-            pd.Timestamp("2015-06-30 19:00:00"),
-            pd.Timestamp("2015-07-01 19:00:00"),
-            pd.Timestamp("2015-07-02 19:00:00")],
-            index=self.test_corr_energy['D'].index[-3:],
-            name='averages'
-        )
+            pd.Timestamp("2015-06-30 12:00:00"),
+            pd.Timestamp("2015-07-01 12:00:00"),
+            pd.Timestamp("2015-07-02 12:00:00")],
+            index=self.get_corr_energy(0,'D').index[-3:],
+            name='averages', dtype='datetime64[ns, UTC]'
+        ).tz_localize('UTC')
 
         result = _avg_timestamp_old_Pandas(dt, dt_right)
         print(result)

From e448560015d9b39faef973777c1c7baa3eb734d1 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 25 Jun 2025 15:57:19 -0600
Subject: [PATCH 15/72] Add .asfreq() to get pytests to agree

---
 rdtools/degradation.py           |  3 ++-
 rdtools/test/degradation_test.py | 13 +++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index aba83681..741a16de 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -409,7 +409,8 @@ def to_unix(s):
     # sum the seconds since epoch, calculate average, and convert back to readable date
     averages = []
     for index, row in temp_df.iterrows():
-        unix = [to_unix(i) for i in row]
+        # unix = [to_unix(i) for i in row]
+        unix = [pd.Timestamp(i).timestamp() for i in row]
         try:
             average = sum(unix) / len(unix)
             # averages.append(datetime.datetime.utcfromtimestamp(average).strftime('%Y-%m-%d'))
diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 20cf4103..b2f8df31 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -233,20 +233,21 @@ def test_avg_timestamp_old_Pandas(self):
         from rdtools.degradation import _avg_timestamp_old_Pandas
         funcName = sys._getframe().f_code.co_name
         logging.debug('Running {}'.format(funcName))
-        dt = pd.Series(self.get_corr_energy(0,'D').index[-3:].tz_localize('UTC'),
-                       index=self.get_corr_energy(0,'D').index[-3:].tz_localize('UTC'))
-        dt_right = pd.Series(self.get_corr_energy(0,'D').index[-3:].tz_localize('UTC') +
-                             pd.Timedelta(days=365), index=self.get_corr_energy(0,'D').index[-3:].tz_localize('UTC'))
+        dt = pd.Series(self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC'),
+                       index=self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC'))
+        dt_right = pd.Series(self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC') +
+                             pd.Timedelta(days=365),
+                             index=self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC'))
         # Expected result is the midpoint between each pair
         expected = pd.Series([
             pd.Timestamp("2015-06-30 12:00:00"),
             pd.Timestamp("2015-07-01 12:00:00"),
             pd.Timestamp("2015-07-02 12:00:00")],
-            index=self.get_corr_energy(0,'D').index[-3:],
+            index=self.get_corr_energy(0, 'D').index[-3:],
             name='averages', dtype='datetime64[ns, UTC]'
         ).tz_localize('UTC')
 
-        result = _avg_timestamp_old_Pandas(dt, dt_right)
+        result = _avg_timestamp_old_Pandas(dt, dt_right).asfreq(freq='D')
         print(result)
         print(expected)
         pd.testing.assert_series_equal(result, expected)

From fd62ea57fdda115b7c472c6bc7837925f02ee28b Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 25 Jun 2025 16:45:56 -0600
Subject: [PATCH 16/72] switch to calendar.timegm to hopefully remove TZ
 issues..

---
 rdtools/degradation.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 741a16de..058d7ee4 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -393,7 +393,7 @@ def _avg_timestamp_old_Pandas(dt, dt_right):
     pandas.Series
         Series with the average timestamp of df1 and df2.
     '''
-    import time
+    import calendar
 
     temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
                             'dt_right' : dt_right.dt.tz_localize(None)
@@ -402,15 +402,15 @@ def _avg_timestamp_old_Pandas(dt, dt_right):
     # conversion from dates to seconds since epoch (unix time)
     def to_unix(s):
         if type(s) is pd.Timestamp:
-            return time.mktime(s.date().timetuple())
+            return calendar.timegm(s.timetuple())
         else:
             return pd.NaT
 
     # sum the seconds since epoch, calculate average, and convert back to readable date
     averages = []
     for index, row in temp_df.iterrows():
-        # unix = [to_unix(i) for i in row]
-        unix = [pd.Timestamp(i).timestamp() for i in row]
+        unix = [to_unix(i) for i in row]
+        # unix = [pd.Timestamp(i).timestamp() for i in row]
         try:
             average = sum(unix) / len(unix)
             # averages.append(datetime.datetime.utcfromtimestamp(average).strftime('%Y-%m-%d'))

From dc83d529503827ff4426f84802d6922c328f597b Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 4 Aug 2025 16:37:48 -0600
Subject: [PATCH 17/72] regardless of uncertainty_method, return
 calc_info{'YoY_values')

---
 rdtools/degradation.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 058d7ee4..4a3ea7cf 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -364,6 +364,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
 
         # Save calculation information
         calc_info = {
+            'YoY_values': yoy_result,
             'renormalizing_factor': renorm,
             'exceedance_level': exceedance_level,
             'usage_of_points': df['usage_of_points'],
@@ -372,7 +373,8 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         return (Rd_pct, Rd_CI, calc_info)
 
     else:  # If we do not need confidence intervals and exceedance level
-        return Rd_pct
+        calc_info = {'YoY_values': yoy_result}
+        return (Rd_pct, None, calc_info)
 
 
 def _avg_timestamp_old_Pandas(dt, dt_right):

From c220fadba9622eb97be5bd781a142b67082f6b83 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Tue, 5 Aug 2025 10:45:02 -0600
Subject: [PATCH 18/72] update _right dt labels to correct _left labels in
 degradation_year_on_year

---
 rdtools/degradation.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 058d7ee4..e0299649 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -286,23 +286,23 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     df = pd.merge_asof(energy_normalized[['dt', 'energy']],
                        energy_normalized.sort_values('dt_shifted'),
                        left_on='dt', right_on='dt_shifted',
-                       suffixes=['', '_right'],
+                       suffixes=['', '_left'],
                        tolerance=pd.Timedelta('8D')
                        )
 
-    df['time_diff_years'] = (df.dt - df.dt_right) / pd.Timedelta('365d')
-    df['yoy'] = 100.0 * (df.energy - df.energy_right) / (df.time_diff_years)
+    df['time_diff_years'] = (df.dt - df.dt_left) / pd.Timedelta('365d')
+    df['yoy'] = 100.0 * (df.energy - df.energy_left) / (df.time_diff_years)
     df.index = df.dt
 
-    df_right = df.set_index(df.dt_right).drop_duplicates('dt_right')
+    df_left = df.set_index(df.dt_left).drop_duplicates('dt_left')
     df['usage_of_points'] = df.yoy.notnull().astype(int).add(
-                df_right.yoy.notnull().astype(int), fill_value=0)
+                df_left.yoy.notnull().astype(int), fill_value=0)
     if pd.__version__ < '2.0.0':
         # For old Pandas versions < 2.0.0, time columns cannot be averaged
         # with each other, so we use a custom function to calculate center label
-        df['dt_center'] = _avg_timestamp_old_Pandas(df.dt, df.dt_right)
+        df['dt_center'] = _avg_timestamp_old_Pandas(df.dt, df.dt_left)
     else:
-        df['dt_center'] = pd.to_datetime(df[['dt', 'dt_right']].mean(axis=1))
+        df['dt_center'] = pd.to_datetime(df[['dt', 'dt_left']].mean(axis=1))
     if label == 'center':
         df = df.set_index(df.dt_center)
         df.index.name = 'dt'
@@ -375,7 +375,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         return Rd_pct
 
 
-def _avg_timestamp_old_Pandas(dt, dt_right):
+def _avg_timestamp_old_Pandas(dt, dt_left):
     '''
     For old Pandas versions < 2.0.0, time columns cannot be averaged
     together.  From https://stackoverflow.com/questions/57812300/
@@ -385,7 +385,7 @@ def _avg_timestamp_old_Pandas(dt, dt_right):
     ----------
     dt : pandas.Series
         First series with datetime values
-    dt_right : pandas.Series
+    dt_left : pandas.Series
         Second series with datetime values.
 
     Returns
@@ -396,7 +396,7 @@ def _avg_timestamp_old_Pandas(dt, dt_right):
     import calendar
 
     temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
-                            'dt_right' : dt_right.dt.tz_localize(None)
+                            'dt_left' : dt_left.dt.tz_localize(None)
                             }).tz_localize(None)
 
     # conversion from dates to seconds since epoch (unix time)

From 0464c256a552adac096988744fea0881e0cc1650 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 6 Aug 2025 15:13:48 -0600
Subject: [PATCH 19/72] update _avg_timestamp_old_Pandas to allow for numeric
 index instead of timestamp

---
 rdtools/degradation.py | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index e0299649..0339f7b3 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -395,9 +395,15 @@ def _avg_timestamp_old_Pandas(dt, dt_left):
     '''
     import calendar
 
-    temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
+    # allow for numeric index
+    try:
+        temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
+                                'dt_left' : dt_left.dt.tz_localize(None)
+                                }).tz_localize(None)
+    except TypeError: # in case numeric index passed
+        temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
                             'dt_left' : dt_left.dt.tz_localize(None)
-                            }).tz_localize(None)
+                            })
 
     # conversion from dates to seconds since epoch (unix time)
     def to_unix(s):
@@ -418,7 +424,13 @@ def to_unix(s):
         except TypeError:
             averages.append(pd.NaT)
     temp_df['averages'] = averages
-    return (temp_df['averages'].tz_localize(dt.dt.tz)).dt.tz_localize(dt.dt.tz)
+    
+    try:
+        dt_center = (temp_df['averages'].tz_localize(dt.dt.tz)).dt.tz_localize(dt.dt.tz)
+    except TypeError:  # not a timeseries index
+        dt_center = (temp_df['averages']).dt.tz_localize(dt.dt.tz)
+    
+    return dt_center
 
 
 def _mk_test(x, alpha=0.05):

From 644f4a8170bbd1908b964faa134d739bb767bfcc Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 6 Aug 2025 15:16:32 -0600
Subject: [PATCH 20/72] add left label option to degradation_year_on_year

---
 rdtools/degradation.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 0339f7b3..9c47b25f 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -237,9 +237,9 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     energy_normalized.name = 'energy'
     energy_normalized.index.name = 'dt'
 
-    if label not in {None, "right", "center"}:
+    if label not in {None, "right", "left", "center"}:
         raise ValueError(f"Unsupported value {label} for `label`."
-                         " Must be 'right' or 'center'.")
+                         " Must be 'right', 'left' or 'center'.")
     if label is None:
         label = "right"
 
@@ -424,7 +424,7 @@ def to_unix(s):
         except TypeError:
             averages.append(pd.NaT)
     temp_df['averages'] = averages
-    
+
     try:
         dt_center = (temp_df['averages'].tz_localize(dt.dt.tz)).dt.tz_localize(dt.dt.tz)
     except TypeError:  # not a timeseries index

From 0957ade6986aaf2ea54ec8318a2f2bdbc08b34b9 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 6 Aug 2025 16:15:25 -0600
Subject: [PATCH 21/72] update degradation_year_on_year, index set to either
 left, center or right. Consistent with #394 - multi_yoy

---
 rdtools/degradation.py | 59 ++++++++++++++++++++++++++++--------------
 1 file changed, 39 insertions(+), 20 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 9c47b25f..9382b32c 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -292,33 +292,52 @@ def degradation_year_on_year(energy_normalized, recenter=True,
 
     df['time_diff_years'] = (df.dt - df.dt_left) / pd.Timedelta('365d')
     df['yoy'] = 100.0 * (df.energy - df.energy_left) / (df.time_diff_years)
-    df.index = df.dt
+    # df.index = df.dt
+
+    yoy_result = df.yoy.dropna()
+
+    if not len(yoy_result):
+        raise ValueError('no year-over-year aggregated data pairs found')
+
+    Rd_pct = yoy_result.median()
+
+    YoY_times = df.dropna(subset=['yoy'], inplace=False)
+
+    # calculate usage of points.
+    df_left = YoY_times.set_index(YoY_times.dt_left)  # .drop_duplicates('dt_left')
+    df_right = YoY_times.set_index(YoY_times.dt)  # .drop_duplicates('dt')
+    usage_of_points = df_right.yoy.notnull().astype(int).add(
+                df_left.yoy.notnull().astype(int),
+                fill_value=0).groupby(level=0).sum()
+    usage_of_points.name = 'usage_of_points'
 
-    df_left = df.set_index(df.dt_left).drop_duplicates('dt_left')
-    df['usage_of_points'] = df.yoy.notnull().astype(int).add(
-                df_left.yoy.notnull().astype(int), fill_value=0)
     if pd.__version__ < '2.0.0':
         # For old Pandas versions < 2.0.0, time columns cannot be averaged
         # with each other, so we use a custom function to calculate center label
-        df['dt_center'] = _avg_timestamp_old_Pandas(df.dt, df.dt_left)
+        YoY_times['dt_center'] = _avg_timestamp_old_Pandas(YoY_times['dt'], YoY_times['dt_left'])
     else:
-        df['dt_center'] = pd.to_datetime(df[['dt', 'dt_left']].mean(axis=1))
-    if label == 'center':
-        df = df.set_index(df.dt_center)
-        df.index.name = 'dt'
+        YoY_times['dt_center'] = pd.to_datetime(YoY_times[['dt', 'dt_left']].mean(axis=1))
+    # if label == 'center':
+    #    df = df.set_index(df.dt_center)
+    #    df.index.name = 'dt'
 
-    yoy_result = df.yoy.dropna()
+    YoY_times = YoY_times[['dt', 'dt_center', 'dt_left']]
+    YoY_times = YoY_times.rename(columns={'dt': 'dt_right'})
 
-    if not len(yoy_result):
-        raise ValueError('no year-over-year aggregated data pairs found')
+    YoY_times.set_index(YoY_times[f'dt_{label}'], inplace=True)
+    # YoY_times = YoY_times.rename_axis(None, axis=1)
+    YoY_times.index.name = None
+    yoy_result.index = YoY_times[f'dt_{label}']
+    yoy_result.index.name = None
 
-    Rd_pct = yoy_result.median()
+    energy_normalized = energy_normalized.merge(usage_of_points, how='left', left_on='dt',
+                                                right_index=True, left_index=False).fillna(0.0)
 
     if uncertainty_method == 'simple':  # If we need the full results
         calc_info = {
             'YoY_values': yoy_result,
             'renormalizing_factor': renorm,
-            'usage_of_points': df['usage_of_points']
+            'usage_of_points': energy_normalized.set_index('dt')['usage_of_points']
         }
 
         # bootstrap to determine 68% CI and exceedance probability
@@ -366,13 +385,13 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         calc_info = {
             'renormalizing_factor': renorm,
             'exceedance_level': exceedance_level,
-            'usage_of_points': df['usage_of_points'],
+            'usage_of_points': energy_normalized.set_index('dt')['usage_of_points'],
             'bootstrap_rates': bootstrap_rates}
 
         return (Rd_pct, Rd_CI, calc_info)
 
     else:  # If we do not need confidence intervals and exceedance level
-        return Rd_pct
+        return (Rd_pct, None, calc_info)
 
 
 def _avg_timestamp_old_Pandas(dt, dt_left):
@@ -400,10 +419,10 @@ def _avg_timestamp_old_Pandas(dt, dt_left):
         temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
                                 'dt_left' : dt_left.dt.tz_localize(None)
                                 }).tz_localize(None)
-    except TypeError: # in case numeric index passed
+    except TypeError:  # in case numeric index passed
         temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
-                            'dt_left' : dt_left.dt.tz_localize(None)
-                            })
+                                'dt_left' : dt_left.dt.tz_localize(None)
+                                })
 
     # conversion from dates to seconds since epoch (unix time)
     def to_unix(s):
@@ -429,7 +448,7 @@ def to_unix(s):
         dt_center = (temp_df['averages'].tz_localize(dt.dt.tz)).dt.tz_localize(dt.dt.tz)
     except TypeError:  # not a timeseries index
         dt_center = (temp_df['averages']).dt.tz_localize(dt.dt.tz)
-    
+
     return dt_center
 
 

From c624a8c9c04f228a52404eb7901ee579cfe12851 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 6 Aug 2025 16:28:00 -0600
Subject: [PATCH 22/72] update return for default = none uncertainty option

---
 rdtools/degradation.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 9382b32c..3260a007 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -391,7 +391,10 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         return (Rd_pct, Rd_CI, calc_info)
 
     else:  # If we do not need confidence intervals and exceedance level
-        return (Rd_pct, None, calc_info)
+        return (Rd_pct, None, {
+            'YoY_values': yoy_result,
+            'usage_of_points': energy_normalized.set_index('dt')['usage_of_points']
+        })
 
 
 def _avg_timestamp_old_Pandas(dt, dt_left):

From 3623edfd0ba4da8fcfd6ead28d3577ee554fc782 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Fri, 8 Aug 2025 16:11:34 -0600
Subject: [PATCH 23/72] degradation_year_on_year - go back to single return
 when uncertainty_value = None to avoid breaking pytests.

---
 rdtools/degradation.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 3260a007..85d1ceb9 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -301,7 +301,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
 
     Rd_pct = yoy_result.median()
 
-    YoY_times = df.dropna(subset=['yoy'], inplace=False)
+    YoY_times = df.dropna(subset=['yoy'], inplace=False).copy()
 
     # calculate usage of points.
     df_left = YoY_times.set_index(YoY_times.dt_left)  # .drop_duplicates('dt_left')
@@ -391,10 +391,13 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         return (Rd_pct, Rd_CI, calc_info)
 
     else:  # If we do not need confidence intervals and exceedance level
+        """ # TODO: return tuple just like all other cases. Issue: test_bootstrap_module
         return (Rd_pct, None, {
             'YoY_values': yoy_result,
             'usage_of_points': energy_normalized.set_index('dt')['usage_of_points']
         })
+        """
+        return Rd_pct
 
 
 def _avg_timestamp_old_Pandas(dt, dt_left):

From 49aa300c1b177329204e3a584d8fc3cd5c030140 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Fri, 15 Aug 2025 16:48:26 -0600
Subject: [PATCH 24/72] add multi-year aggregation of slopes in
 degradation_year_on_year

---
 rdtools/degradation.py | 34 +++++++++++++++++++---------------
 1 file changed, 19 insertions(+), 15 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 5725b4b1..04f2e078 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -279,16 +279,20 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     energy_normalized = energy_normalized.reset_index()
     energy_normalized['energy'] = energy_normalized['energy'] / renorm
 
-    energy_normalized['dt_shifted'] = energy_normalized.dt + pd.DateOffset(years=1)
-
-    # Merge with what happened one year ago, use tolerance of 8 days to allow
-    # for weekly aggregated data
-    df = pd.merge_asof(energy_normalized[['dt', 'energy']],
-                       energy_normalized.sort_values('dt_shifted'),
-                       left_on='dt', right_on='dt_shifted',
-                       suffixes=['', '_left'],
-                       tolerance=pd.Timedelta('8D')
-                       )
+    # dataframe container for combined year-over-year changes
+    df = pd.DataFrame()
+    for y in range(1, int((energy_normalized.iloc[-1]['dt'] -
+                           energy_normalized.iloc[0]['dt']).days/365)+1):
+        energy_normalized['dt_shifted'] = energy_normalized.dt + pd.DateOffset(years=y)
+        # Merge with what happened one year ago, use tolerance of 8 days to allow
+        # for weekly aggregated data
+        df_temp = pd.merge_asof(energy_normalized[['dt', 'energy']],
+                                energy_normalized.sort_values('dt_shifted'),
+                                left_on='dt', right_on='dt_shifted',
+                                suffixes=['', '_left'],
+                                tolerance=pd.Timedelta('8D')
+                                )
+        df = pd.concat([df, df_temp], ignore_index=True)
 
     df['time_diff_years'] = (df.dt - df.dt_left) / pd.Timedelta('365d')
     df['yoy'] = 100.0 * (df.energy - df.energy_left) / (df.time_diff_years)
@@ -317,9 +321,6 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         YoY_times['dt_center'] = _avg_timestamp_old_Pandas(YoY_times['dt'], YoY_times['dt_left'])
     else:
         YoY_times['dt_center'] = pd.to_datetime(YoY_times[['dt', 'dt_left']].mean(axis=1))
-    # if label == 'center':
-    #    df = df.set_index(df.dt_center)
-    #    df.index.name = 'dt'
 
     YoY_times = YoY_times[['dt', 'dt_center', 'dt_left']]
     YoY_times = YoY_times.rename(columns={'dt': 'dt_right'})
@@ -337,7 +338,8 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         calc_info = {
             'YoY_values': yoy_result,
             'renormalizing_factor': renorm,
-            'usage_of_points': energy_normalized.set_index('dt')['usage_of_points']
+            'usage_of_points': energy_normalized.set_index('dt')['usage_of_points'],
+            'YoY_times': YoY_times[['dt_right', 'dt_center', 'dt_left']]
         }
 
         # bootstrap to determine 68% CI and exceedance probability
@@ -387,6 +389,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
             'renormalizing_factor': renorm,
             'exceedance_level': exceedance_level,
             'usage_of_points': energy_normalized.set_index('dt')['usage_of_points'],
+            'YoY_times': YoY_times[['dt_right', 'dt_center', 'dt_left']],
             'bootstrap_rates': bootstrap_rates}
 
         return (Rd_pct, Rd_CI, calc_info)
@@ -395,7 +398,8 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         """ # TODO: return tuple just like all other cases. Issue: test_bootstrap_module
         return (Rd_pct, None, {
             'YoY_values': yoy_result,
-            'usage_of_points': energy_normalized.set_index('dt')['usage_of_points']
+            'usage_of_points': energy_normalized.set_index('dt')['usage_of_points'],
+            'YoY_times': YoY_times[['dt_right', 'dt_center', 'dt_left']]}
         })
         """
         return Rd_pct

From 8cc7562da9eea8765ae9e542368e9daa57adebe0 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Fri, 15 Aug 2025 17:17:02 -0600
Subject: [PATCH 25/72] add multi_yoy kwarg in degradation_year_on_year to
 toggle the multi-YoY function.

---
 rdtools/degradation.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 04f2e078..3f6660ee 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -180,7 +180,7 @@ def degradation_classical_decomposition(energy_normalized,
 def degradation_year_on_year(energy_normalized, recenter=True,
                              exceedance_prob=95, confidence_level=68.2,
                              uncertainty_method='simple', block_length=30,
-                             label='right'):
+                             label='right', multi_yoy=False):
     '''
     Estimate the trend of a timeseries using the year-on-year decomposition
     approach and calculate a Monte Carlo-derived confidence interval of slope.
@@ -209,8 +209,13 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         If `uncertainty_method` is 'circular_block', `block_length`
         determines the length of the blocks used in the circular block bootstrapping
         in number of days. Must be shorter than a third of the time series.
-    label    : {'right', 'center'}, default 'right'
+    label    : {'right', 'center', 'left'}, default 'right'
         Which Year-on-Year slope edge to label.
+    multi_yoy : bool, default False
+        Whether to return the standard Year-on-Year slopes where each slope
+        is calculated over points separated by 365 days (default) or 
+        multi_year-on-year where points can be separated by N * 365 days
+        where N is an integer from 1 to the length of the dataset in years.
 
     Returns
     -------
@@ -281,8 +286,12 @@ def degradation_year_on_year(energy_normalized, recenter=True,
 
     # dataframe container for combined year-over-year changes
     df = pd.DataFrame()
-    for y in range(1, int((energy_normalized.iloc[-1]['dt'] -
-                           energy_normalized.iloc[0]['dt']).days/365)+1):
+    if multi_yoy:
+        year_range = range(1, int((energy_normalized.iloc[-1]['dt'] -
+                                   energy_normalized.iloc[0]['dt']).days/365)+1)
+    else:
+        year_range = [1]
+    for y in year_range:
         energy_normalized['dt_shifted'] = energy_normalized.dt + pd.DateOffset(years=y)
         # Merge with what happened one year ago, use tolerance of 8 days to allow
         # for weekly aggregated data

From 8a5c9351d75ce5e02274f29f3de44e8814f5c2c3 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 18 Aug 2025 10:26:24 -0600
Subject: [PATCH 26/72] update plotting for detailed=True, allow
 usage_of_points > 2

---
 rdtools/plotting.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 93a07bac..ecc1b69b 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -109,7 +109,8 @@ def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield,
 
     renormalized_yield = normalized_yield / yoy_info['renormalizing_factor']
     if detailed:
-        colors = yoy_info['usage_of_points'].map({0: 'red', 1: 'green', 2: plot_color})
+        colors = yoy_info['usage_of_points'].map({0: 'red', 1: 'green', 2: plot_color},
+                                                 na_action='ignore').fillna(plot_color)
     else:
         colors = plot_color
     ax1.scatter(

From 15babe24abb6025196f9da1ccc3ae756b9b1f76f Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 18 Aug 2025 10:41:11 -0600
Subject: [PATCH 27/72] flake8 grumbles

---
 rdtools/degradation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 3f6660ee..5a959661 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -213,7 +213,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         Which Year-on-Year slope edge to label.
     multi_yoy : bool, default False
         Whether to return the standard Year-on-Year slopes where each slope
-        is calculated over points separated by 365 days (default) or 
+        is calculated over points separated by 365 days (default) or
         multi_year-on-year where points can be separated by N * 365 days
         where N is an integer from 1 to the length of the dataset in years.
 

From d6670b9ca7c84edc6e42a4e9851491de910945f0 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 18 Aug 2025 11:00:02 -0600
Subject: [PATCH 28/72] update plotting detailed=True for  (even) and (odd)
 number of points coloring

---
 rdtools/plotting.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index ecc1b69b..30762cae 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -54,8 +54,8 @@ def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield,
         Include extra information in the returned figure:
 
         * Color code points by the number of times they get used in calculating
-          Rd slopes.  Default color: 2 times (as a start and endpoint). Green:
-          1 time. Red: 0 times.
+          Rd slopes.  Default color: even times (as a start and endpoint). Green:
+          odd times. Red: 0 times.
         * The number of year-on-year slopes contributing to the histogram.
 
     Note
@@ -109,8 +109,9 @@ def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield,
 
     renormalized_yield = normalized_yield / yoy_info['renormalizing_factor']
     if detailed:
-        colors = yoy_info['usage_of_points'].map({0: 'red', 1: 'green', 2: plot_color},
-                                                 na_action='ignore').fillna(plot_color)
+        colors = yoy_info['usage_of_points'].map({0: 'red', 1: 'green', 3: 'green', 5: 'green',
+                                                  7: 'green', 9: 'green', 11: 'green'
+                                                  }, na_action='ignore').fillna(plot_color)
     else:
         colors = plot_color
     ax1.scatter(

From 13ac2c3dd96a658b7440b087404806686e630b34 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Wed, 20 Aug 2025 13:58:40 -0600
Subject: [PATCH 29/72] To allow multi_yoy=True in
 plotting.degradation_timeseries_plot, resample.mean() the YoY_values.

---
 rdtools/plotting.py              | 6 +++++-
 rdtools/test/degradation_test.py | 8 +++++---
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 30762cae..94af0134 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -485,8 +485,12 @@ def _bootstrap(x, percentile, reps):
         plot_color = 'tab:orange'
     if ci_color is None:
         ci_color = 'C0'
+    try:
+        roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2)
+    except ValueError:  # this occurs with degradation_yoy(multi_yoy=True). resample to daily mean
+        roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
+                                                             min_periods=rolling_days//2)
 
-    roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2)
     # unfortunately it seems that you can't return multiple values in the rolling.apply() kernel.
     # TODO: figure out some workaround to return both percentiles in a single pass
     if include_ci:
diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index b2f8df31..cd5ebbfe 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -233,23 +233,25 @@ def test_avg_timestamp_old_Pandas(self):
         from rdtools.degradation import _avg_timestamp_old_Pandas
         funcName = sys._getframe().f_code.co_name
         logging.debug('Running {}'.format(funcName))
-        dt = pd.Series(self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC'),
-                       index=self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC'))
+        dt = pd.Series(self.get_corr_energy(0, 'D').index[-4:].tz_localize('UTC'),
+                       index=self.get_corr_energy(0, 'D').index[-4:].tz_localize('UTC'))
         dt_right = pd.Series(self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC') +
                              pd.Timedelta(days=365),
                              index=self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC'))
         # Expected result is the midpoint between each pair
         expected = pd.Series([
+            pd.NaT,
             pd.Timestamp("2015-06-30 12:00:00"),
             pd.Timestamp("2015-07-01 12:00:00"),
             pd.Timestamp("2015-07-02 12:00:00")],
-            index=self.get_corr_energy(0, 'D').index[-3:],
+            index=self.get_corr_energy(0, 'D').index[-4:],
             name='averages', dtype='datetime64[ns, UTC]'
         ).tz_localize('UTC')
 
         result = _avg_timestamp_old_Pandas(dt, dt_right).asfreq(freq='D')
         print(result)
         print(expected)
+
         pd.testing.assert_series_equal(result, expected)
 
 

From 5d76c7efe0602deefb42b8a82526646ede8d3a3c Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Tue, 26 Aug 2025 11:45:59 -0600
Subject: [PATCH 30/72] flake8 grumbles

---
 rdtools/plotting.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 7eec04fb..27be112f 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -510,7 +510,8 @@ def _bootstrap(x, percentile, reps):
         offset_days = 365
 
     try:
-        roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2, center=center)
+        roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2,
+                                        center=center)
     except ValueError:  # this occurs with degradation_yoy(multi_yoy=True). resample to daily mean
         roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
                                                              min_periods=rolling_days//2,

From de2ceeec64e80d3cd5de4053cbe945038ef721fd Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Tue, 16 Sep 2025 10:22:13 -0600
Subject: [PATCH 31/72] Add warning to degradation_timeseries_plot when
 multi_YoY=True

---
 rdtools/plotting.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 27be112f..dbd91925 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -513,6 +513,10 @@ def _bootstrap(x, percentile, reps):
         roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2,
                                         center=center)
     except ValueError:  # this occurs with degradation_yoy(multi_yoy=True). resample to daily mean
+        warnings.warn("Input `yoy_info['YoY_values']` appears to have multiple "
+                       "annual slopes per day, which is the case if degradation_yoy(multi_yoy=True). "
+                       "  Resampling to daily mean which will average out the time-series trend.  "
+                       "Recommend re-running with degradation_yoy(multi_yoy=False).")
         roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
                                                              min_periods=rolling_days//2,
                                                              center=center)

From 4566413b340fb6dfed4b00c3ab2c4fbaa0bd0054 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Tue, 16 Sep 2025 10:22:59 -0600
Subject: [PATCH 32/72] update to warning message in
 plotting.degradation_timeseries_plot

---
 rdtools/plotting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index dbd91925..fd73378b 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -515,7 +515,7 @@ def _bootstrap(x, percentile, reps):
     except ValueError:  # this occurs with degradation_yoy(multi_yoy=True). resample to daily mean
         warnings.warn("Input `yoy_info['YoY_values']` appears to have multiple "
                        "annual slopes per day, which is the case if degradation_yoy(multi_yoy=True). "
-                       "  Resampling to daily mean which will average out the time-series trend.  "
+                       "Proceeding to plot with a daily mean which will average out the time-series trend.  "
                        "Recommend re-running with degradation_yoy(multi_yoy=False).")
         roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
                                                              min_periods=rolling_days//2,

From a31792665708c5f4bc88b8be59c5309be3806677 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Tue, 16 Sep 2025 10:38:12 -0600
Subject: [PATCH 33/72] fix flake8 grumbles

---
 rdtools/plotting.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index fd73378b..fa5847d8 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -513,10 +513,10 @@ def _bootstrap(x, percentile, reps):
         roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2,
                                         center=center)
     except ValueError:  # this occurs with degradation_yoy(multi_yoy=True). resample to daily mean
-        warnings.warn("Input `yoy_info['YoY_values']` appears to have multiple "
-                       "annual slopes per day, which is the case if degradation_yoy(multi_yoy=True). "
-                       "Proceeding to plot with a daily mean which will average out the time-series trend.  "
-                       "Recommend re-running with degradation_yoy(multi_yoy=False).")
+        warnings.warn("Input `yoy_info['YoY_values']` appears to have multiple annual "
+                      "slopes per day, which is the case if degradation_yoy(multi_yoy=True). "
+                      "Proceeding to plot with a daily mean which will average out the time-series"
+                      " trend. Recommend re-running with degradation_yoy(multi_yoy=False).")
         roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
                                                              min_periods=rolling_days//2,
                                                              center=center)

From 1646f165112042b1e6071909fe1f12544b56fb6e Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Tue, 16 Sep 2025 16:54:35 -0600
Subject: [PATCH 34/72] nbval fixes from qnguyen345-bare_except_error

---
 .github/workflows/nbval.yaml           | 2 +-
 docs/TrendAnalysis_example.ipynb       | 4 ++--
 docs/TrendAnalysis_example_NSRDB.ipynb | 2 +-
 setup.py                               | 4 +---
 4 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/nbval.yaml b/.github/workflows/nbval.yaml
index e014b494..84d99b25 100644
--- a/.github/workflows/nbval.yaml
+++ b/.github/workflows/nbval.yaml
@@ -29,7 +29,7 @@ jobs:
     - name: Run notebook and check output
       run: |
         # --sanitize-with:  pre-process text to remove irrelevant differences (e.g. warning filepaths)
-        pytest --nbval docs/${{ matrix.notebook-file }} --sanitize-with docs/nbval_sanitization_rules.cfg
+        pytest --nbval --nbval-sanitize-with docs/nbval_sanitization_rules.cfg docs/${{ matrix.notebook-file }}
     - name: Run notebooks again, save files
       run: |
         pip install nbconvert[webpdf]
diff --git a/docs/TrendAnalysis_example.ipynb b/docs/TrendAnalysis_example.ipynb
index 45744e99..14eb7955 100644
--- a/docs/TrendAnalysis_example.ipynb
+++ b/docs/TrendAnalysis_example.ipynb
@@ -62160,7 +62160,7 @@
     "# Visualize the results\n",
     "ta_new_filter.plot_degradation_summary('sensor', summary_title='Sensor-based degradation results',\n",
     "                                   scatter_ymin=0.5, scatter_ymax=1.1,\n",
-    "                                   hist_xmin=-30, hist_xmax=45);\n",
+    "                                   hist_xmin=-30, hist_xmax=45)\n",
     "plt.show()"
    ]
   },
@@ -62247,7 +62247,7 @@
     "# Visualize the results\n",
     "ta_stuck_filter.plot_degradation_summary('sensor', summary_title='Sensor-based degradation results',\n",
     "                                         scatter_ymin=0.5, scatter_ymax=1.1,\n",
-    "                                         hist_xmin=-30, hist_xmax=45);\n",
+    "                                         hist_xmin=-30, hist_xmax=45)\n",
     "plt.show()"
    ]
   },
diff --git a/docs/TrendAnalysis_example_NSRDB.ipynb b/docs/TrendAnalysis_example_NSRDB.ipynb
index 6c9f6b7d..fce1fa92 100644
--- a/docs/TrendAnalysis_example_NSRDB.ipynb
+++ b/docs/TrendAnalysis_example_NSRDB.ipynb
@@ -158,7 +158,7 @@
     "ax.plot(df.index, df.soiling, 'o', alpha=0.01)\n",
     "#ax.set_ylim(0,1500)\n",
     "fig.autofmt_xdate()\n",
-    "ax.set_ylabel('soiling signal');\n",
+    "ax.set_ylabel('soiling signal')\n",
     "df['power'] = df['power_ac'] * df['soiling']\n",
     "\n",
     "plt.show()"
diff --git a/setup.py b/setup.py
index 441b16c0..f38a2bec 100755
--- a/setup.py
+++ b/setup.py
@@ -36,9 +36,7 @@
     "pytest-cov",
     "coverage",
     "flake8",
-    # nbval greater than 0.9.6 has a bug with semicolon
-    # https://github.com/computationalmodelling/nbval/issues/194
-    "nbval<=0.9.6",
+    "nbval",
     "pytest-mock",
 ]
 

From 963527bc0c09f6f55b7e63bc0f5be67fb93ec25f Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Thu, 18 Sep 2025 10:47:17 -0600
Subject: [PATCH 35/72] Add pandas 3.0 futurewarning handling

---
 rdtools/degradation.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 85d1ceb9..64100cd6 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -330,8 +330,11 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     yoy_result.index = YoY_times[f'dt_{label}']
     yoy_result.index.name = None
 
-    energy_normalized = energy_normalized.merge(usage_of_points, how='left', left_on='dt',
-                                                right_index=True, left_index=False).fillna(0.0)
+    with pd.option_context('future.no_silent_downcasting', True):
+        # the following is throwing a warning without the above context manager.
+        # see https://github.com/pandas-dev/pandas/issues/57734
+        energy_normalized = energy_normalized.merge(usage_of_points, how='left', left_on='dt',
+                                                    right_index=True, left_index=False).fillna(0.0)
 
     if uncertainty_method == 'simple':  # If we need the full results
         calc_info = {

From 74357346359d98e3dfaa46015ac41660a56cce99 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Thu, 18 Sep 2025 10:55:26 -0600
Subject: [PATCH 36/72] Try again to solve pandas3.0 futurewarning

---
 rdtools/degradation.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 64100cd6..0be8db97 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -330,11 +330,12 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     yoy_result.index = YoY_times[f'dt_{label}']
     yoy_result.index.name = None
 
-    with pd.option_context('future.no_silent_downcasting', True):
-        # the following is throwing a warning without the above context manager.
-        # see https://github.com/pandas-dev/pandas/issues/57734
-        energy_normalized = energy_normalized.merge(usage_of_points, how='left', left_on='dt',
-                                                    right_index=True, left_index=False).fillna(0.0)
+    # with pd.option_context('future.no_silent_downcasting', True):
+    # the following is throwing a warning without the above context manager.
+    # see https://github.com/pandas-dev/pandas/issues/57734
+    energy_normalized = energy_normalized.merge(usage_of_points, how='left', left_on='dt',
+                                                right_index=True, left_index=False
+                                                ).fillna(0.0).infer_objects(copy=False)
 
     if uncertainty_method == 'simple':  # If we need the full results
         calc_info = {

From 3810fd57a95f2a6e54eeffe11cd02cd4d0e3c853 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Thu, 18 Sep 2025 16:21:00 -0600
Subject: [PATCH 37/72] attempt 3 to fix nbval

---
 docs/system_availability_example.ipynb | 2 +-
 rdtools/degradation.py                 | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/system_availability_example.ipynb b/docs/system_availability_example.ipynb
index 7a44ee09..0b9925d9 100644
--- a/docs/system_availability_example.ipynb
+++ b/docs/system_availability_example.ipynb
@@ -618,7 +618,7 @@
     }
    ],
    "source": [
-    "aa2.plot();"
+    "plt.show(aa2.plot())"
    ]
   },
   {
diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 0be8db97..ea1dcfb4 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -335,7 +335,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     # see https://github.com/pandas-dev/pandas/issues/57734
     energy_normalized = energy_normalized.merge(usage_of_points, how='left', left_on='dt',
                                                 right_index=True, left_index=False
-                                                ).fillna(0.0).infer_objects(copy=False)
+                                                ).fillna(0.0).infer_objects()
 
     if uncertainty_method == 'simple':  # If we need the full results
         calc_info = {

From fecbd2ecf400104cc443c01e4b680b3ba400f96b Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Thu, 18 Sep 2025 18:50:02 -0600
Subject: [PATCH 38/72] Add infer_objects to remove futurewarning

---
 rdtools/degradation.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index ea1dcfb4..b98d07db 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -331,11 +331,11 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     yoy_result.index.name = None
 
     # with pd.option_context('future.no_silent_downcasting', True):
-    # the following is throwing a warning without the above context manager.
+    # the following is throwing a futurewarning if infer_objects() isn't included here.
     # see https://github.com/pandas-dev/pandas/issues/57734
     energy_normalized = energy_normalized.merge(usage_of_points, how='left', left_on='dt',
                                                 right_index=True, left_index=False
-                                                ).fillna(0.0).infer_objects()
+                                                ).infer_objects().fillna(0.0)
 
     if uncertainty_method == 'simple':  # If we need the full results
         calc_info = {

From 4adc42efb787faf969252197ad46751265aa6920 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Fri, 19 Sep 2025 09:12:10 -0600
Subject: [PATCH 39/72] minor inline comment update

---
 rdtools/degradation.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index ece219df..80ce0ec2 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -340,7 +340,6 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     yoy_result.index = YoY_times[f'dt_{label}']
     yoy_result.index.name = None
 
-    # with pd.option_context('future.no_silent_downcasting', True):
     # the following is throwing a futurewarning if infer_objects() isn't included here.
     # see https://github.com/pandas-dev/pandas/issues/57734
     energy_normalized = energy_normalized.merge(usage_of_points, how='left', left_on='dt',

From 8451499c5fd68748b357ff24f190a23a19245151 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Fri, 19 Sep 2025 14:01:15 -0600
Subject: [PATCH 40/72] update plotting tests to be relative value, update
 ordering of module import in plotting.py, per Copilot review.

---
 rdtools/plotting.py           |  3 +-
 rdtools/test/plotting_test.py | 53 ++++++++++++++++++++++++++---------
 2 files changed, 40 insertions(+), 16 deletions(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 11ecde8c..b59e2b8c 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -5,6 +5,7 @@
 import plotly.express as px
 import numpy as np
 import warnings
+import datetime
 
 
 def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield,
@@ -473,8 +474,6 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, lab
     matplotlib.figure.Figure
     '''
 
-    import datetime
-
     def _bootstrap(x, percentile, reps):
         # stolen from degradation_year_on_year
         n1 = len(x)
diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index 029514ef..f4eb83f6 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -252,20 +252,45 @@ def test_availability_summary_plots_empty(availability_analysis_object):
 def test_degradation_timeseries_plot(degradation_info):
     power, yoy_rd, yoy_ci, yoy_info = degradation_info
 
-    # test defaults
-    result = degradation_timeseries_plot(yoy_info)
-    assert_isinstance(result, plt.Figure)
-    assert (result.get_axes()[0].get_xlim()[0] == 17685.55)
-    # test other label options
-    result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False,
-                                         label='center', fig=result)
-    assert_isinstance(result, plt.Figure)
-    assert (result.get_axes()[0].get_xlim()[0] == 17304.4)
-    result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label='left')
-    assert_isinstance(result, plt.Figure)
-    assert (result.get_axes()[0].get_xlim()[0] == 17130.5)
-    result = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label=None)
-    assert_isinstance(result, plt.Figure)
+    # test defaults (label='right')
+    result_right = degradation_timeseries_plot(yoy_info)
+    assert_isinstance(result_right, plt.Figure)
+    xlim_right = result_right.get_axes()[0].get_xlim()[0]
+
+    # test label='center'
+    result_center = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False,
+                                                label='center', fig=result_right)
+    assert_isinstance(result_center, plt.Figure)
+    xlim_center = result_center.get_axes()[0].get_xlim()[0]
+
+    # test label='left'
+    result_left = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label='left')
+    assert_isinstance(result_left, plt.Figure)
+    xlim_left = result_left.get_axes()[0].get_xlim()[0]
+
+    # test label=None (should default to 'right')
+    result_none = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label=None)
+    assert_isinstance(result_none, plt.Figure)
+    xlim_none = result_none.get_axes()[0].get_xlim()[0]
+
+    # Check that the xlim values are offset as expected
+    # right > center > left (since offset_days increases)
+    assert xlim_right > xlim_center > xlim_left
+    assert xlim_right == xlim_none  # label=None defaults to 'right'
+
+    # The expected difference from right to left is 548 days (1.5 yrs), allow 5% tolerance
+    expected_diff = 548
+    actual_diff = (xlim_right - xlim_left)
+    tolerance = expected_diff * 0.05
+    assert abs(actual_diff - expected_diff) <= tolerance, \
+        f"difference of right-left xlim {actual_diff} not within 5% of 1.5 yrs."
+
+    # The expected difference from right to center is 365 days, allow 5% tolerance
+    expected_diff2 = 365
+    actual_diff2 = (xlim_right - xlim_center)
+    tolerance2 = expected_diff2 * 0.05
+    assert abs(actual_diff2 - expected_diff2) <= tolerance2, \
+        f"difference of right-center xlim {actual_diff2} not within 5% of 1 yr."
 
     with pytest.raises(KeyError):
         degradation_timeseries_plot({'a': 1}, include_ci=False)

From 67795f43d2ebbee4ec63c834e5bc812aba551b99 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Fri, 19 Sep 2025 14:11:58 -0600
Subject: [PATCH 41/72] update inline comments and whatsnew docs

---
 docs/sphinx/source/changelog/pending.rst | 4 ++--
 rdtools/degradation.py                   | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
index 815f82e5..ddca3ae2 100644
--- a/docs/sphinx/source/changelog/pending.rst
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -5,8 +5,8 @@ v3.0.x (X, X, 2025)
 Enhancements
 ------------
 * :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``label=`` 
-  to return the calc_info['YoY_values'] as either right labeled (default), or center labeled. 
-  (:issue:`459`)
+  to return the calc_info['YoY_values'] as either right labeled (default), left or center
+  labeled. (:issue:`459`)
 
 
 
diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index b98d07db..92923efd 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -209,7 +209,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         If `uncertainty_method` is 'circular_block', `block_length`
         determines the length of the blocks used in the circular block bootstrapping
         in number of days. Must be shorter than a third of the time series.
-    label    : {'right', 'center'}, default 'right'
+    label    : {'right', 'center', 'left'}, default 'right'
         Which Year-on-Year slope edge to label.
 
     Returns
@@ -222,7 +222,7 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     calc_info : dict
 
         * `YoY_values` - pandas series of year on year slopes, either right
-            or center labeled, depending on the `label` parameter.
+           left or center labeled, depending on the `label` parameter.
         * `renormalizing_factor` - float of value used to recenter data
         * `exceedance_level` - the degradation rate that was outperformed with
           probability of `exceedance_prob`

From a0f4a1e1491eb031e2794b0251a3ffa9d5e9c49a Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Fri, 19 Sep 2025 14:35:36 -0600
Subject: [PATCH 42/72] Clean up inline comments per Copilot review

---
 rdtools/degradation.py           | 7 ++-----
 rdtools/test/degradation_test.py | 2 --
 2 files changed, 2 insertions(+), 7 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 92923efd..b7d28435 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -317,20 +317,17 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         YoY_times['dt_center'] = _avg_timestamp_old_Pandas(YoY_times['dt'], YoY_times['dt_left'])
     else:
         YoY_times['dt_center'] = pd.to_datetime(YoY_times[['dt', 'dt_left']].mean(axis=1))
-    # if label == 'center':
-    #    df = df.set_index(df.dt_center)
-    #    df.index.name = 'dt'
 
     YoY_times = YoY_times[['dt', 'dt_center', 'dt_left']]
     YoY_times = YoY_times.rename(columns={'dt': 'dt_right'})
 
     YoY_times.set_index(YoY_times[f'dt_{label}'], inplace=True)
-    # YoY_times = YoY_times.rename_axis(None, axis=1)
     YoY_times.index.name = None
+
+    # now apply either right, left, or center label index to the yoy_result
     yoy_result.index = YoY_times[f'dt_{label}']
     yoy_result.index.name = None
 
-    # with pd.option_context('future.no_silent_downcasting', True):
     # the following is throwing a futurewarning if infer_objects() isn't included here.
     # see https://github.com/pandas-dev/pandas/issues/57734
     energy_normalized = energy_normalized.merge(usage_of_points, how='left', left_on='dt',
diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index cd5ebbfe..ada944c4 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -249,8 +249,6 @@ def test_avg_timestamp_old_Pandas(self):
         ).tz_localize('UTC')
 
         result = _avg_timestamp_old_Pandas(dt, dt_right).asfreq(freq='D')
-        print(result)
-        print(expected)
 
         pd.testing.assert_series_equal(result, expected)
 

From 4393644a5c0edb836fd5df128a0c182553d2b5dc Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Fri, 19 Sep 2025 17:09:41 -0600
Subject: [PATCH 43/72] added multi-YoY pytest - still need to catch warnings

---
 rdtools/test/plotting_test.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index 029514ef..79f07395 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -17,6 +17,7 @@
 import plotly
 import pytest
 import re
+import copy
 
 from conftest import assert_isinstance
 
@@ -272,4 +273,13 @@ def test_degradation_timeseries_plot(degradation_info):
     with pytest.raises(ValueError):
         degradation_timeseries_plot(yoy_info, include_ci=False, label='CENTER')
 
+    # Add multi-YoY test by duplication idx=100
+    yoy_multi = copy.deepcopy(yoy_info)
+    new_idx = yoy_multi['YoY_values'].index[100]
+    new_val = yoy_multi['YoY_values'].iloc[100]
+    yoy_values_multi = pd.concat([yoy_multi['YoY_values'], pd.Series([new_val], index=[new_idx])])
+    yoy_multi['YoY_values'] = yoy_values_multi
+    result = degradation_timeseries_plot(yoy_info=yoy_multi, include_ci=False)
+    assert_isinstance(result, plt.Figure)
+
     plt.close('all')

From 728bb059241804b83604734e7268ea2e6dba1b4f Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 22 Sep 2025 09:47:40 -0600
Subject: [PATCH 44/72] Add a warnings.catch_warnings to the plotting pytest

---
 rdtools/test/plotting_test.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index 79f07395..eefe94a7 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -15,7 +15,7 @@
 import matplotlib.pyplot as plt
 import matplotlib
 import plotly
-import pytest
+import pytest, warnings
 import re
 import copy
 
@@ -273,13 +273,15 @@ def test_degradation_timeseries_plot(degradation_info):
     with pytest.raises(ValueError):
         degradation_timeseries_plot(yoy_info, include_ci=False, label='CENTER')
 
-    # Add multi-YoY test by duplication idx=100
+    # Add multi-YoY test by duplication idx=100.  TODO: catch warning
     yoy_multi = copy.deepcopy(yoy_info)
     new_idx = yoy_multi['YoY_values'].index[100]
     new_val = yoy_multi['YoY_values'].iloc[100]
     yoy_values_multi = pd.concat([yoy_multi['YoY_values'], pd.Series([new_val], index=[new_idx])])
     yoy_multi['YoY_values'] = yoy_values_multi
-    result = degradation_timeseries_plot(yoy_info=yoy_multi, include_ci=False)
-    assert_isinstance(result, plt.Figure)
+    with warnings.catch_warnings(record=True) as w:
+        result = degradation_timeseries_plot(yoy_info=yoy_multi, include_ci=False)
+        assert_isinstance(result, plt.Figure)
+        assert any("warning" in str(warn).lower() for warn in w), "Expected a warning to be raised"
 
     plt.close('all')

From 38989406a02276548843fee5ff51da33c71bfd96 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 22 Sep 2025 10:48:59 -0600
Subject: [PATCH 45/72] flake8 grumbles

---
 rdtools/test/plotting_test.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index eefe94a7..428be8ef 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -15,7 +15,8 @@
 import matplotlib.pyplot as plt
 import matplotlib
 import plotly
-import pytest, warnings
+import pytest
+import warnings
 import re
 import copy
 

From e28b1cf339602e846ce20083bb9c3c905c0d7bd3 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 22 Sep 2025 11:21:29 -0600
Subject: [PATCH 46/72] add multi-YoY=True pytest

---
 rdtools/test/degradation_test.py | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index cd5ebbfe..b999e643 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -290,6 +290,33 @@ def test_yoy_two_years_error(start, end, freq):
         _ = degradation_year_on_year(series.iloc[1:])
 
 
+def test_degradation_year_on_year_multi():
+    """Test degradation_year_on_year with multi_yoy=True. Thanks GPT!"""
+    rd = -0.005
+    # Generate a daily time series with 3 years of data
+    idx = pd.date_range('2017-01-01', '2020-01-01', freq='D', tz='UTC')
+    daily_rd = 1 - (1 + rd)**(1/365)
+    day_count = np.arange(len(idx))
+    degradation_derate = (1 + daily_rd) ** day_count
+    power = 1 - 0.1 * np.cos(day_count / 365 * 2 * np.pi)
+    power *= degradation_derate
+    power = pd.Series(power, index=idx)
+    # Standard yoy baseline
+    (rd0, rd_ci0, calc_info0) = degradation_year_on_year(power, multi_yoy=False)
+    # Run multi_yoy test
+    rd_result = degradation_year_on_year(power, multi_yoy=True)
+    # Should return a tuple (Rd_pct, Rd_CI, calc_info)
+    assert isinstance(rd_result, tuple)
+    assert len(rd_result) == 3
+    Rd_pct, Rd_CI, calc_info = rd_result
+    # Check that the result is close to expected degradation
+    assert np.isclose(Rd_pct * -1, 100 * rd, atol=0.5)
+    # Check that YoY_values exists and is a Series
+    assert isinstance(calc_info['YoY_values'], pd.Series)
+    # Should have more YoY value for multi_yoy than standard
+    assert len(calc_info['YoY_values']) > len(calc_info0['YoY_values'])
+
+
 if __name__ == '__main__':
     # Initialize logger when run as a module:
     #     python -m tests.degradation_test

From 49dd74026df32e71a140bd7b37260c33932cfdf9 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 22 Sep 2025 14:22:23 -0600
Subject: [PATCH 47/72] updated changelog

---
 docs/sphinx/source/changelog/pending.rst | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
index ed186a09..2359197c 100644
--- a/docs/sphinx/source/changelog/pending.rst
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -5,12 +5,16 @@ v3.0.x (X, X, 2025)
 Enhancements
 ------------
 * :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``label=`` 
-  to return the calc_info['YoY_values'] as either right labeled (default), or center labeled. 
-  (:issue:`459`)
-
+  to return the calc_info['YoY_values'] as either right labeled (default), left or
+  center labeled. (:issue:`459`)
 * :py:func:`~rdtools.plotting.degradation_timeseries_plot` has new parameter ``label=`` 
   to allow the timeseries plot to have right labeling (default), center or left labeling. 
   (:issue:`455`)
+* :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``multi_yoy`` 
+  (default False) to trigger multiple YoY degradation calculations similar to Hugo Quest et
+  al 2023. In this mode, instead of a series of 1-year duration slopes, 2-year, 3-year etc
+  slopes are also included.  calc_info['YoY_values'] returns a non-monotonic index
+  in this mode due to multiple overlapping annual slopes.  (:issue:`394`)  
 
 
 

From aec14b7c4b380ee3f5a8348b426fea30c779da2c Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 10:00:09 -0500
Subject: [PATCH 48/72] update changelog

---
 docs/sphinx/source/changelog/pending.rst | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
index 2359197c..eebe688a 100644
--- a/docs/sphinx/source/changelog/pending.rst
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -1,21 +1,26 @@
 *************************
-v3.0.x (X, X, 2025)
+v3.2.0 (X, X, 2026)
 *************************
 
 Enhancements
 ------------
-* :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``label=`` 
+* :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``label=``
   to return the calc_info['YoY_values'] as either right labeled (default), left or
   center labeled. (:issue:`459`)
-* :py:func:`~rdtools.plotting.degradation_timeseries_plot` has new parameter ``label=`` 
-  to allow the timeseries plot to have right labeling (default), center or left labeling. 
+* :py:func:`~rdtools.plotting.degradation_timeseries_plot` has new parameter ``label=``
+  to allow the timeseries plot to have right labeling (default), center or left labeling.
   (:issue:`455`)
-* :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``multi_yoy`` 
+* :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``multi_yoy``
   (default False) to trigger multiple YoY degradation calculations similar to Hugo Quest et
   al 2023. In this mode, instead of a series of 1-year duration slopes, 2-year, 3-year etc
   slopes are also included.  calc_info['YoY_values'] returns a non-monotonic index
-  in this mode due to multiple overlapping annual slopes.  (:issue:`394`)  
-
+  in this mode due to multiple overlapping annual slopes.  (:issue:`394`)
+* :py:func:`~rdtools.plotting.degradation_timeseries_plot` now supports ``multi_yoy=True``
+  data by resampling overlapping YoY values to their mean. A warning is issued when this
+  resampling occurs.  (:issue:`394`)
+* :py:func:`~rdtools.plotting.degradation_summary_plots` ``detailed=True`` mode now
+  properly handles points used odd vs even number of times (not just 0, 1, 2).
+  (:issue:`394`)
 
 
 Contributors

From aa6a7ffca83dfe383dba49d7c54d7859a0bd83f8 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 10:07:50 -0500
Subject: [PATCH 49/72] implement copilot suggestions

---
 .github/workflows/nbval.yaml  |  2 +-
 rdtools/degradation.py        | 15 ++++++++-------
 rdtools/plotting.py           | 14 ++++++++------
 rdtools/test/plotting_test.py |  7 +++++--
 4 files changed, 22 insertions(+), 16 deletions(-)

diff --git a/.github/workflows/nbval.yaml b/.github/workflows/nbval.yaml
index 84d99b25..53037e4b 100644
--- a/.github/workflows/nbval.yaml
+++ b/.github/workflows/nbval.yaml
@@ -28,7 +28,7 @@ jobs:
         pip install --timeout=300 -r requirements.txt -r docs/notebook_requirements.txt .[test]
     - name: Run notebook and check output
       run: |
-        # --sanitize-with:  pre-process text to remove irrelevant differences (e.g. warning filepaths)
+        # --nbval-sanitize-with:  pre-process text to remove irrelevant differences (e.g. warning filepaths)
         pytest --nbval --nbval-sanitize-with docs/nbval_sanitization_rules.cfg docs/${{ matrix.notebook-file }}
     - name: Run notebooks again, save files
       run: |
diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index cfaf6756..2c948dfb 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -407,13 +407,14 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         return (Rd_pct, Rd_CI, calc_info)
 
     else:  # If we do not need confidence intervals and exceedance level
-        """ # TODO: return tuple just like all other cases. Issue: test_bootstrap_module
-        return (Rd_pct, None, {
-            'YoY_values': yoy_result,
-            'usage_of_points': energy_normalized.set_index('dt')['usage_of_points'],
-            'YoY_times': YoY_times[['dt_right', 'dt_center', 'dt_left']]}
-        })
-        """
+        # TODO: Consider returning a tuple for consistency with other branches, e.g.:
+        # return (Rd_pct, None, {
+        #     'YoY_values': yoy_result,
+        #     'usage_of_points': energy_normalized.set_index('dt')['usage_of_points'],
+        #     'YoY_times': YoY_times[['dt_right', 'dt_center', 'dt_left']]}
+        # )
+        # Note: Current behavior intentionally returns only the scalar Rd_pct
+        # to maintain compatibility (see test_bootstrap_module).
         return Rd_pct
 
 
diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 9c65aac1..fcd42b30 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -110,9 +110,11 @@ def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield,
 
     renormalized_yield = normalized_yield / yoy_info['renormalizing_factor']
     if detailed:
-        colors = yoy_info['usage_of_points'].map({0: 'red', 1: 'green', 3: 'green', 5: 'green',
-                                                  7: 'green', 9: 'green', 11: 'green'
-                                                  }, na_action='ignore').fillna(plot_color)
+        # Color by usage parity: 0 -> red, odd -> green, even/non-zero or NaN -> plot_color
+        usage = yoy_info['usage_of_points']
+        colors = pd.Series(plot_color, index=usage.index)
+        colors[usage == 0] = 'red'
+        colors[usage % 2 == 1] = 'green'
     else:
         colors = plot_color
     ax1.scatter(
@@ -511,11 +513,11 @@ def _bootstrap(x, percentile, reps):
     try:
         roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2,
                                         center=center)
-    except ValueError:  # this occurs with degradation_yoy(multi_yoy=True). resample to daily mean
+    except ValueError:  # this occurs with degradation_year_on_year(multi_yoy=True). resample to daily mean
         warnings.warn("Input `yoy_info['YoY_values']` appears to have multiple annual "
-                      "slopes per day, which is the case if degradation_yoy(multi_yoy=True). "
+                      "slopes per day, which is the case if degradation_year_on_year(multi_yoy=True). "
                       "Proceeding to plot with a daily mean which will average out the time-series"
-                      " trend. Recommend re-running with degradation_yoy(multi_yoy=False).")
+                      " trend. Recommend re-running with degradation_year_on_year(multi_yoy=False).")
         roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
                                                              min_periods=rolling_days//2,
                                                              center=center)
diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index bbe0249f..715c57cd 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -299,15 +299,18 @@ def test_degradation_timeseries_plot(degradation_info):
     with pytest.raises(ValueError):
         degradation_timeseries_plot(yoy_info, include_ci=False, label='CENTER')
 
-    # Add multi-YoY test by duplication idx=100.  TODO: catch warning
+    # Add multi-YoY test by duplication idx=100.
     yoy_multi = copy.deepcopy(yoy_info)
     new_idx = yoy_multi['YoY_values'].index[100]
     new_val = yoy_multi['YoY_values'].iloc[100]
     yoy_values_multi = pd.concat([yoy_multi['YoY_values'], pd.Series([new_val], index=[new_idx])])
     yoy_multi['YoY_values'] = yoy_values_multi
     with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter('always')
         result = degradation_timeseries_plot(yoy_info=yoy_multi, include_ci=False)
         assert_isinstance(result, plt.Figure)
-        assert any("warning" in str(warn).lower() for warn in w), "Expected a warning to be raised"
+        assert len(w) > 0, "Expected at least one warning to be raised"
+        assert any(issubclass(warn.category, UserWarning) for warn in w), \
+            "Expected a UserWarning to be raised for multi-YoY values"
 
     plt.close('all')

From cd591c53646e2f3f658e05eaf635163f90fb7cef Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 10:09:27 -0500
Subject: [PATCH 50/72] linting

---
 rdtools/plotting.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index fcd42b30..538d7a6b 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -513,11 +513,16 @@ def _bootstrap(x, percentile, reps):
     try:
         roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2,
                                         center=center)
-    except ValueError:  # this occurs with degradation_year_on_year(multi_yoy=True). resample to daily mean
-        warnings.warn("Input `yoy_info['YoY_values']` appears to have multiple annual "
-                      "slopes per day, which is the case if degradation_year_on_year(multi_yoy=True). "
-                      "Proceeding to plot with a daily mean which will average out the time-series"
-                      " trend. Recommend re-running with degradation_year_on_year(multi_yoy=False).")
+    except ValueError:
+        # this occurs with degradation_year_on_year(multi_yoy=True). resample to daily mean
+        warnings.warn(
+            "Input `yoy_info['YoY_values']` appears to have multiple annual "
+            "slopes per day, which is the case if "
+            "degradation_year_on_year(multi_yoy=True). "
+            "Proceeding to plot with a daily mean which will average out the "
+            "time-series trend. Recommend re-running with "
+            "degradation_year_on_year(multi_yoy=False)."
+        )
         roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
                                                              min_periods=rolling_days//2,
                                                              center=center)

From 6c5c624743d9a019238448e0b539644faaec31d6 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 10:49:17 -0500
Subject: [PATCH 51/72] use s instead of ns for pandas 3 compatibility

---
 rdtools/test/degradation_test.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 62c4494f..a23898c7 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -245,7 +245,7 @@ def test_avg_timestamp_old_Pandas(self):
             pd.Timestamp("2015-07-01 12:00:00"),
             pd.Timestamp("2015-07-02 12:00:00")],
             index=self.get_corr_energy(0, 'D').index[-4:],
-            name='averages', dtype='datetime64[ns, UTC]'
+            name='averages', dtype='datetime64[s, UTC]'
         ).tz_localize('UTC')
 
         result = _avg_timestamp_old_Pandas(dt, dt_right).asfreq(freq='D')
@@ -293,7 +293,7 @@ def test_degradation_year_on_year_multi():
     rd = -0.005
     # Generate a daily time series with 3 years of data
     idx = pd.date_range('2017-01-01', '2020-01-01', freq='D', tz='UTC')
-    daily_rd = 1 - (1 + rd)**(1/365)
+    daily_rd = (1 + rd)**(1/365) - 1
     day_count = np.arange(len(idx))
     degradation_derate = (1 + daily_rd) ** day_count
     power = 1 - 0.1 * np.cos(day_count / 365 * 2 * np.pi)
@@ -308,7 +308,7 @@ def test_degradation_year_on_year_multi():
     assert len(rd_result) == 3
     Rd_pct, Rd_CI, calc_info = rd_result
     # Check that the result is close to expected degradation
-    assert np.isclose(Rd_pct * -1, 100 * rd, atol=0.5)
+    assert np.isclose(Rd_pct, 100 * rd, atol=0.5)
     # Check that YoY_values exists and is a Series
     assert isinstance(calc_info['YoY_values'], pd.Series)
     # Should have more YoY value for multi_yoy than standard

From 26e5893cadfa816e3785a0891fd0e3e5afc4ee28 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 10:59:54 -0500
Subject: [PATCH 52/72] update pandas version comparison

---
 rdtools/degradation.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 2c948dfb..9334d75b 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -323,7 +323,8 @@ def degradation_year_on_year(energy_normalized, recenter=True,
                 fill_value=0).groupby(level=0).sum()
     usage_of_points.name = 'usage_of_points'
 
-    if pd.__version__ < '2.0.0':
+    pandas_version = pd.__version__.split(".")
+    if int(pandas_version[0]) < 2:
         # For old Pandas versions < 2.0.0, time columns cannot be averaged
         # with each other, so we use a custom function to calculate center label
         YoY_times['dt_center'] = _avg_timestamp_old_Pandas(YoY_times['dt'], YoY_times['dt_left'])

From dc2d092ed787331469effbd1d7b5bd2f5a3fb818 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 11:10:30 -0500
Subject: [PATCH 53/72] set matplotlib non-gui backend for tests

---
 rdtools/test/conftest.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py
index b940f2df..c51d39dc 100644
--- a/rdtools/test/conftest.py
+++ b/rdtools/test/conftest.py
@@ -5,6 +5,10 @@
 import pvlib
 import re
 
+# Use non-GUI backend for matplotlib to avoid tkinter issues in tests
+import matplotlib
+matplotlib.use('Agg')
+
 import rdtools
 
 

From 640e03f129a1c6ba3e4be5406fb8dc29638afec3 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 11:11:42 -0500
Subject: [PATCH 54/72] set dtype resolution based on pandas version

---
 rdtools/test/degradation_test.py | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index a23898c7..e7deef42 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -238,15 +238,24 @@ def test_avg_timestamp_old_Pandas(self):
         dt_right = pd.Series(self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC') +
                              pd.Timedelta(days=365),
                              index=self.get_corr_energy(0, 'D').index[-3:].tz_localize('UTC'))
+        # Expected dtype depends on pandas version (ns for <3.0, s for >=3.0)
+        pandas_version = pd.__version__.split(".")
+        if int(pandas_version[0]) < 3:
+            expected_dtype = "datetime64[ns, UTC]"
+        else:
+            expected_dtype = "datetime64[s, UTC]"
         # Expected result is the midpoint between each pair
-        expected = pd.Series([
-            pd.NaT,
-            pd.Timestamp("2015-06-30 12:00:00"),
-            pd.Timestamp("2015-07-01 12:00:00"),
-            pd.Timestamp("2015-07-02 12:00:00")],
-            index=self.get_corr_energy(0, 'D').index[-4:],
-            name='averages', dtype='datetime64[s, UTC]'
-        ).tz_localize('UTC')
+        expected = pd.Series(
+            [
+                pd.NaT,
+                pd.Timestamp("2015-06-30 12:00:00"),
+                pd.Timestamp("2015-07-01 12:00:00"),
+                pd.Timestamp("2015-07-02 12:00:00"),
+            ],
+            index=self.get_corr_energy(0, "D").index[-4:],
+            name="averages",
+            dtype=expected_dtype,
+        ).tz_localize("UTC")
 
         result = _avg_timestamp_old_Pandas(dt, dt_right).asfreq(freq='D')
 

From 27d578f6ddd58ec5557079753587289b00c4ccdd Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 11:20:34 -0500
Subject: [PATCH 55/72] linting import order

---
 rdtools/test/conftest.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/rdtools/test/conftest.py b/rdtools/test/conftest.py
index c51d39dc..2dbbd5d0 100644
--- a/rdtools/test/conftest.py
+++ b/rdtools/test/conftest.py
@@ -1,15 +1,15 @@
-import pytest
-import numpy as np
-import pandas as pd
 import itertools
-import pvlib
 import re
 
-# Use non-GUI backend for matplotlib to avoid tkinter issues in tests
 import matplotlib
-matplotlib.use('Agg')
+import numpy as np
+import pandas as pd
+import pvlib
+import pytest
+
+matplotlib.use("Agg")  # Use non-GUI backend; must be set before importing rdtools
 
-import rdtools
+import rdtools  # noqa: E402
 
 
 def assert_isinstance(obj, klass):

From 4e6a4b65eefdce0647363ac3942b19b59b0cbbe2 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 11:28:21 -0500
Subject: [PATCH 56/72] boost degradation.py test coverage

---
 rdtools/test/degradation_test.py | 96 ++++++++++++++++++++++++++++++++
 1 file changed, 96 insertions(+)

diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index e7deef42..06be266d 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -324,6 +324,102 @@ def test_degradation_year_on_year_multi():
     assert len(calc_info['YoY_values']) > len(calc_info0['YoY_values'])
 
 
+def test_classical_decomposition_missing_data():
+    """Test that classical decomposition raises error for missing data."""
+    # Create a regular time series with missing values (NaN)
+    idx = pd.date_range("2012-01-01", "2015-01-01", freq="D")
+    series = pd.Series(1.0, index=idx)
+    series.iloc[100:105] = np.nan  # introduce missing data
+
+    with pytest.raises(ValueError, match="regular time series"):
+        degradation_classical_decomposition(series)
+
+
+def test_classical_decomposition_irregular_frequency():
+    """Test that classical decomposition raises error for irregular frequency."""
+    # Create an irregular time series by sampling randomly
+    idx = pd.date_range("2012-01-01", "2015-01-01", freq="D")
+    series = pd.Series(1.0, index=idx)
+    series = series.sample(frac=0.8, replace=False).sort_index()
+
+    with pytest.raises(ValueError, match="regular time series"):
+        degradation_classical_decomposition(series)
+
+
+def test_yoy_circular_block_no_frequency():
+    """Test circular_block raises error when frequency cannot be inferred."""
+    # Create an irregular time series
+    idx = pd.date_range("2012-01-01", "2015-01-01", freq="D")
+    series = pd.Series(1.0, index=idx)
+    series = series.sample(frac=0.8, replace=False).sort_index()
+
+    with pytest.raises(ValueError, match="fixed frequency"):
+        degradation_year_on_year(series, uncertainty_method="circular_block")
+
+
+def test_yoy_circular_block_too_long():
+    """Test circular_block raises error when block_length is too long."""
+    idx = pd.date_range("2012-01-01", "2015-01-01", freq="D")
+    series = pd.Series(1.0, index=idx)
+
+    # block_length must be less than 1/3 of the series length
+    too_long = len(series) // 2
+
+    with pytest.raises(ValueError, match="shorter than a third"):
+        degradation_year_on_year(
+            series, uncertainty_method="circular_block", block_length=too_long
+        )
+
+
+def test_yoy_no_pairs_found():
+    """Test year_on_year raises error when no valid pairs can be formed."""
+    # Create a series that's just over 1 year but with NaN in positions
+    # that prevent any valid year-over-year pairs
+    idx = pd.date_range("2012-01-01", "2014-06-01", freq="D")
+    series = pd.Series(1.0, index=idx)
+    # Make all values NaN except first few and last few (too far apart for pairs)
+    series.iloc[10:-10] = np.nan
+
+    with pytest.raises(ValueError, match="no year-over-year"):
+        degradation_year_on_year(series)
+
+
+def test_mk_test_no_trend():
+    """Test Mann-Kendall test with no trend (z == 0 case)."""
+    from rdtools.degradation import _mk_test
+
+    # Constant series should have no trend
+    x = np.array([1.0, 1.0, 1.0, 1.0, 1.0])
+    trend, h, p, z = _mk_test(x)
+
+    assert trend == "no trend"
+    assert z == 0
+    assert not h
+
+
+def test_mk_test_with_ties():
+    """Test Mann-Kendall test with tied values."""
+    from rdtools.degradation import _mk_test
+
+    # Series with ties (repeated values)
+    x = np.array([1, 2, 2, 3, 3, 3, 4, 5])
+    trend, h, p, z = _mk_test(x)
+
+    # Should still detect increasing trend
+    assert trend == "increasing"
+
+
+def test_mk_test_decreasing():
+    """Test Mann-Kendall test with clear decreasing trend."""
+    from rdtools.degradation import _mk_test
+
+    x = np.array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
+    trend, h, p, z = _mk_test(x)
+
+    assert trend == "decreasing"
+    assert z < 0
+
+
 if __name__ == '__main__':
     # Initialize logger when run as a module:
     #     python -m tests.degradation_test

From 09bc1fd6d3110da0e886e91938a986ae37816c0c Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 11:48:56 -0500
Subject: [PATCH 57/72] update changelog

---
 docs/sphinx/source/changelog/pending.rst | 25 ++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
index eebe688a..d503268b 100644
--- a/docs/sphinx/source/changelog/pending.rst
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -21,9 +21,34 @@ Enhancements
 * :py:func:`~rdtools.plotting.degradation_summary_plots` ``detailed=True`` mode now
   properly handles points used odd vs even number of times (not just 0, 1, 2).
   (:issue:`394`)
+* :py:func:`~rdtools.degradation.degradation_year_on_year` now returns
+  ``calc_info['YoY_times']`` DataFrame with ``dt_right``, ``dt_center``, and ``dt_left``
+  columns for each YoY slope.  (:issue:`459`)
+
+Bug Fixes
+---------
+* Fixed ``usage_of_points`` calculation in :py:func:`~rdtools.degradation.degradation_year_on_year`
+  to properly handle ``multi_yoy=True`` mode with overlapping slopes.  (:issue:`394`)
+
+Maintenance
+-----------
+* Added ``_avg_timestamp_old_Pandas`` helper function for pandas <2.0 compatibility
+  when calculating center labels.
+* Fixed nbval workflow command syntax (``--sanitize-with`` to ``--nbval-sanitize-with``).
+* Improved pandas 3.0 compatibility with datetime resolution handling.
+
+Testing
+-------
+* Added tests for error handling paths in :py:mod:`~rdtools.degradation`:
+  ``classical_decomposition`` missing/irregular data, ``year_on_year`` circular block
+  validation, no valid pairs error, and ``_mk_test`` edge cases (no trend, ties,
+  decreasing).
+* Added test for ``multi_yoy=True`` parameter in ``degradation_year_on_year``.
+* Set matplotlib backend to ``Agg`` in test ``conftest.py`` to avoid tkinter issues.
 
 
 Contributors
 ------------
 * Chris Deline (:ghuser:`cdeline`)
+* Martin Springer (:ghuser:`martin-springer`)
 

From 5280aaaf6b3032d70a99ed0349fe8612aa7eaab5 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 11:49:11 -0500
Subject: [PATCH 58/72] exclude coverage reports with suffixes

---
 .gitignore | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 95b56ca8..72111dd4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,9 +7,10 @@
 # ignore byte compiled files
 *.py[co]
 
-# ignore coveralls yaml, coverge dir
+# ignore coveralls yaml, coverage dir
 .coveralls.yml
 .coverage
+.coverage.*
 htmlcov/
 
 # ignore test cache

From 77d045cfb40819481ab36911a75d78707bc4b844 Mon Sep 17 00:00:00 2001
From: Martin Springer <97482055+martin-springer@users.noreply.github.com>
Date: Thu, 12 Feb 2026 12:13:13 -0500
Subject: [PATCH 59/72] Update rdtools/degradation.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 rdtools/degradation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 9334d75b..dc61dc85 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -451,7 +451,7 @@ def _avg_timestamp_old_Pandas(dt, dt_left):
 
     # conversion from dates to seconds since epoch (unix time)
     def to_unix(s):
-        if type(s) is pd.Timestamp:
+        if isinstance(s, pd.Timestamp):
             return calendar.timegm(s.timetuple())
         else:
             return pd.NaT

From 0fe8ebdab20bdc2bc907b3d775dc81dd2bfdc1c6 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 12:15:11 -0500
Subject: [PATCH 60/72] remove label=None handling, rely on default 'right'
 behavior

---
 rdtools/degradation.py           | 4 +---
 rdtools/plotting.py              | 4 +---
 rdtools/test/degradation_test.py | 5 ++++-
 rdtools/test/plotting_test.py    | 9 ++++-----
 4 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 9334d75b..a1647334 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -242,11 +242,9 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     energy_normalized.name = 'energy'
     energy_normalized.index.name = 'dt'
 
-    if label not in {None, "right", "left", "center"}:
+    if label not in {"right", "left", "center"}:
         raise ValueError(f"Unsupported value {label} for `label`."
                          " Must be 'right', 'left' or 'center'.")
-    if label is None:
-        label = "right"
 
     # Detect less than 2 years of data. This is complicated by two things:
     #   - leap days muddle the precise meaning of "two years of data".
diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 538d7a6b..6da66d1f 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -495,10 +495,8 @@ def _bootstrap(x, percentile, reps):
     if ci_color is None:
         ci_color = 'C0'
 
-    if label not in {None, "left", "right", "center"}:
+    if label not in {"left", "right", "center"}:
         raise ValueError(f"Unsupported value {label} for `label`")
-    if label is None:
-        label = "right"
 
     if label == "right":
         center = False
diff --git a/rdtools/test/degradation_test.py b/rdtools/test/degradation_test.py
index 06be266d..752a3035 100644
--- a/rdtools/test/degradation_test.py
+++ b/rdtools/test/degradation_test.py
@@ -214,7 +214,7 @@ def test_degradation_year_on_year_label_center(self):
             self.test_corr_energy[input_freq], label='center')
         self.assertAlmostEqual(rd_result[0], 100 * self.rd, places=1)
         rd_result1 = degradation_year_on_year(
-            self.test_corr_energy[input_freq], label=None)
+            self.test_corr_energy[input_freq])
         rd_result2 = degradation_year_on_year(
             self.test_corr_energy[input_freq], label='right')
         pd.testing.assert_index_equal(rd_result1[2]['YoY_values'].index,
@@ -227,6 +227,9 @@ def test_degradation_year_on_year_label_center(self):
         with pytest.raises(ValueError):
             degradation_year_on_year(self.test_corr_energy[input_freq],
                                      label='LEFT')
+        with pytest.raises(ValueError):
+            degradation_year_on_year(self.test_corr_energy[input_freq],
+                                     label=None)
 
     def test_avg_timestamp_old_Pandas(self):
         """Test the _avg_timestamp_old_Pandas function for correct averaging."""
diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index 715c57cd..988bab1e 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -270,15 +270,14 @@ def test_degradation_timeseries_plot(degradation_info):
     assert_isinstance(result_left, plt.Figure)
     xlim_left = result_left.get_axes()[0].get_xlim()[0]
 
-    # test label=None (should default to 'right')
-    result_none = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label=None)
-    assert_isinstance(result_none, plt.Figure)
-    xlim_none = result_none.get_axes()[0].get_xlim()[0]
+    # test default label matches label='right'
+    result_default = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False)
+    xlim_default = result_default.get_axes()[0].get_xlim()[0]
+    assert xlim_default == xlim_right
 
     # Check that the xlim values are offset as expected
     # right > center > left (since offset_days increases)
     assert xlim_right > xlim_center > xlim_left
-    assert xlim_right == xlim_none  # label=None defaults to 'right'
 
     # The expected difference from right to left is 548 days (1.5 yrs), allow 5% tolerance
     expected_diff = 548

From c545a5cfb9fa5eb8c026103215457dd65f45308e Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 12:17:03 -0500
Subject: [PATCH 61/72] refactor dt_center  tz handling for old pandas

---
 rdtools/degradation.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index a1647334..d00bc341 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -467,10 +467,9 @@ def to_unix(s):
             averages.append(pd.NaT)
     temp_df['averages'] = averages
 
-    try:
-        dt_center = (temp_df['averages'].tz_localize(dt.dt.tz)).dt.tz_localize(dt.dt.tz)
-    except TypeError:  # not a timeseries index
-        dt_center = (temp_df['averages']).dt.tz_localize(dt.dt.tz)
+    dt_center = temp_df["averages"].dt.tz_localize(dt.dt.tz)
+    dt_center.index = dt.index
+    dt_center.name = "averages"
 
     return dt_center
 

From 81b9b56f6c3853b0b98901a05264408ef3320fcd Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 12 Feb 2026 12:27:38 -0500
Subject: [PATCH 62/72] simplify _avg_timestamp_old_Pandas

---
 rdtools/degradation.py | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 80a6631d..5607092c 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -437,15 +437,10 @@ def _avg_timestamp_old_Pandas(dt, dt_left):
     '''
     import calendar
 
-    # allow for numeric index
-    try:
-        temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
-                                'dt_left' : dt_left.dt.tz_localize(None)
-                                }).tz_localize(None)
-    except TypeError:  # in case numeric index passed
-        temp_df = pd.DataFrame({'dt' : dt.dt.tz_localize(None),
-                                'dt_left' : dt_left.dt.tz_localize(None)
-                                })
+    # Remove timezone from datetime values for averaging
+    temp_df = pd.DataFrame(
+        {"dt": dt.dt.tz_localize(None), "dt_left": dt_left.dt.tz_localize(None)}
+    )
 
     # conversion from dates to seconds since epoch (unix time)
     def to_unix(s):

From 0d73d2e1144ddf575592d42f2b7b5a1afd449a25 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 16 Feb 2026 16:05:49 -0700
Subject: [PATCH 63/72] degradation_timeseries_plot: change rolling median
 min_periods to rolling_days / 4.

---
 rdtools/plotting.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 6da66d1f..f104688d 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -509,7 +509,7 @@ def _bootstrap(x, percentile, reps):
         offset_days = 365
 
     try:
-        roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//2,
+        roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//4,
                                         center=center)
     except ValueError:
         # this occurs with degradation_year_on_year(multi_yoy=True). resample to daily mean
@@ -522,7 +522,7 @@ def _bootstrap(x, percentile, reps):
             "degradation_year_on_year(multi_yoy=False)."
         )
         roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
-                                                             min_periods=rolling_days//2,
+                                                             min_periods=rolling_days//4,
                                                              center=center)
     # unfortunately it seems that you can't return multiple values in the rolling.apply() kernel.
     # TODO: figure out some workaround to return both percentiles in a single pass

From 3bd8c8d19b1319bef928e2fed44c8b754002abef Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 16 Feb 2026 17:13:34 -0700
Subject: [PATCH 64/72] remove degradation_timeseries_plot(label=) and just
 default to center=True

---
 docs/sphinx/source/changelog/pending.rst |  5 ++--
 rdtools/plotting.py                      | 29 ++++---------------
 rdtools/test/plotting_test.py            | 36 ++++++++++++++++--------
 3 files changed, 32 insertions(+), 38 deletions(-)

diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
index d503268b..5e4e1af5 100644
--- a/docs/sphinx/source/changelog/pending.rst
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -7,8 +7,8 @@ Enhancements
 * :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``label=``
   to return the calc_info['YoY_values'] as either right labeled (default), left or
   center labeled. (:issue:`459`)
-* :py:func:`~rdtools.plotting.degradation_timeseries_plot` has new parameter ``label=``
-  to allow the timeseries plot to have right labeling (default), center or left labeling.
+* :py:func:`~rdtools.plotting.degradation_timeseries_plot` now defaults to rolling
+  median, centered on the timestamp (pd.rolling(center=True)).
   (:issue:`455`)
 * :py:func:`~rdtools.degradation.degradation_year_on_year` has new parameter ``multi_yoy``
   (default False) to trigger multiple YoY degradation calculations similar to Hugo Quest et
@@ -30,6 +30,7 @@ Bug Fixes
 * Fixed ``usage_of_points`` calculation in :py:func:`~rdtools.degradation.degradation_year_on_year`
   to properly handle ``multi_yoy=True`` mode with overlapping slopes.  (:issue:`394`)
 
+
 Maintenance
 -----------
 * Added ``_avg_timestamp_old_Pandas`` helper function for pandas <2.0 compatibility
diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index f104688d..42bf716e 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -436,7 +436,7 @@ def availability_summary_plots(power_system, power_subsystem, loss_total,
     return fig
 
 
-def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, label='right',
+def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, 
                                 fig=None, plot_color=None, ci_color=None, **kwargs):
     '''
     Plot resampled time series of degradation trend with time
@@ -452,13 +452,6 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, lab
         at least 50% of datapoints to be included in rolling plot.
     include_ci : bool, default True
         calculate and plot 2-sigma confidence intervals along with rolling median
-    label    : {'right', 'left', 'center'}, default 'right'
-        A combination of 1) which Year-on-Year slope edge to label,
-        and 2) which rolling median edge to label.
-
-        * ``right`` : label right edge of YoY slope and right edge of rolling median interval.
-        * ``center``: label center of YoY slope interval and center of rolling median interval.
-        * ``left``  : label left edge of YoY slope and center of rolling median interval.
     fig     : matplotlib, optional
         fig object to add new plot to (first set of axes only)
     plot_color : str, optional
@@ -495,22 +488,10 @@ def _bootstrap(x, percentile, reps):
     if ci_color is None:
         ci_color = 'C0'
 
-    if label not in {"left", "right", "center"}:
-        raise ValueError(f"Unsupported value {label} for `label`")
-
-    if label == "right":
-        center = False
-        offset_days = 0
-    elif label == "center":
-        center = True
-        offset_days = 182
-    elif label == "left":
-        center = True
-        offset_days = 365
 
     try:
         roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//4,
-                                        center=center)
+                                        center=True)
     except ValueError:
         # this occurs with degradation_year_on_year(multi_yoy=True). resample to daily mean
         warnings.warn(
@@ -523,7 +504,7 @@ def _bootstrap(x, percentile, reps):
         )
         roller = results_values.resample('D').mean().rolling(f'{rolling_days}d',
                                                              min_periods=rolling_days//4,
-                                                             center=center)
+                                                             center=True)
     # unfortunately it seems that you can't return multiple values in the rolling.apply() kernel.
     # TODO: figure out some workaround to return both percentiles in a single pass
     if include_ci:
@@ -534,9 +515,9 @@ def _bootstrap(x, percentile, reps):
     else:
         ax = fig.axes[0]
     if include_ci:
-        ax.fill_between(ci_lower.index - datetime.timedelta(days=offset_days),
+        ax.fill_between(ci_lower.index,
                         ci_lower, ci_upper, color=ci_color)
-    ax.plot(roller.median().index - datetime.timedelta(days=offset_days),
+    ax.plot(roller.median().index,
             roller.median(), color=plot_color, **kwargs)
     ax.axhline(results_values.median(), c='k', ls='--')
     plt.ylabel('Degradation trend (%/yr)')
diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index 988bab1e..f605553d 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -58,6 +58,17 @@ def degradation_info(degradation_power_signal):
     rd, rd_ci, calc_info = degradation_year_on_year(degradation_power_signal)
     return degradation_power_signal, rd, rd_ci, calc_info
 
+@pytest.fixture()
+def degradation_info_center(degradation_power_signal):
+    # center-labeled YOY output for time-series degradation plot
+    rd, rd_ci, calc_info = degradation_year_on_year(degradation_power_signal, label='center')
+    return degradation_power_signal, rd, rd_ci, calc_info
+
+@pytest.fixture()
+def degradation_info_left(degradation_power_signal):
+    # left-labeled YOY output for time-series degradation plot
+    rd, rd_ci, calc_info = degradation_year_on_year(degradation_power_signal, label='left')
+    return degradation_power_signal, rd, rd_ci, calc_info
 
 def test_degradation_summary_plots(degradation_info):
     power, yoy_rd, yoy_ci, yoy_info = degradation_info
@@ -251,24 +262,27 @@ def test_availability_summary_plots_empty(availability_analysis_object):
     plt.close('all')
 
 
-def test_degradation_timeseries_plot(degradation_info):
+def test_degradation_timeseries_plot(degradation_info, degradation_info_center, degradation_info_left):
     power, yoy_rd, yoy_ci, yoy_info = degradation_info
 
     # test defaults (label='right')
     result_right = degradation_timeseries_plot(yoy_info)
     assert_isinstance(result_right, plt.Figure)
     xlim_right = result_right.get_axes()[0].get_xlim()[0]
+    #print(f'yoy_right: {yoy_info['YoY_values'].index[0]}')
 
     # test label='center'
-    result_center = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False,
-                                                label='center', fig=result_right)
+    result_center = degradation_timeseries_plot(yoy_info=degradation_info_center[3], include_ci=False)
     assert_isinstance(result_center, plt.Figure)
     xlim_center = result_center.get_axes()[0].get_xlim()[0]
+    #print(f'yoy_center: {degradation_info_center[3]['YoY_values'].index[0]}')
 
     # test label='left'
-    result_left = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False, label='left')
+    result_left = degradation_timeseries_plot(yoy_info=degradation_info_left[3], 
+                                              include_ci=False)
     assert_isinstance(result_left, plt.Figure)
     xlim_left = result_left.get_axes()[0].get_xlim()[0]
+    #print(f'yoy_left: {degradation_info_left[3]['YoY_values'].index[0]}')
 
     # test default label matches label='right'
     result_default = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False)
@@ -279,24 +293,22 @@ def test_degradation_timeseries_plot(degradation_info):
     # right > center > left (since offset_days increases)
     assert xlim_right > xlim_center > xlim_left
 
-    # The expected difference from right to left is 548 days (1.5 yrs), allow 5% tolerance
-    expected_diff = 548
+    # The expected difference from right to left is 365 days (1 yrs), allow 5% tolerance
+    expected_diff = 365
     actual_diff = (xlim_right - xlim_left)
     tolerance = expected_diff * 0.05
     assert abs(actual_diff - expected_diff) <= tolerance, \
-        f"difference of right-left xlim {actual_diff} not within 5% of 1.5 yrs."
+        f"difference of right-left xlim {actual_diff} not within 5% of 1 yr."
 
-    # The expected difference from right to center is 365 days, allow 5% tolerance
-    expected_diff2 = 365
+    # The expected difference from right to center is 182 days, allow 5% tolerance
+    expected_diff2 = 182
     actual_diff2 = (xlim_right - xlim_center)
     tolerance2 = expected_diff2 * 0.05
     assert abs(actual_diff2 - expected_diff2) <= tolerance2, \
-        f"difference of right-center xlim {actual_diff2} not within 5% of 1 yr."
+        f"difference of right-center xlim {actual_diff2} not within 5% of 1/2 year."
 
     with pytest.raises(KeyError):
         degradation_timeseries_plot({'a': 1}, include_ci=False)
-    with pytest.raises(ValueError):
-        degradation_timeseries_plot(yoy_info, include_ci=False, label='CENTER')
 
     # Add multi-YoY test by duplication idx=100.
     yoy_multi = copy.deepcopy(yoy_info)

From c55c207c6e2da49738618d44bc5d517ad2f559f0 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 16 Feb 2026 17:46:06 -0700
Subject: [PATCH 65/72] update sensor_analysis() and clearsky_analysis()
 docstrings to discuss passing `label=right` kwargs

---
 rdtools/analysis_chains.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py
index d2c58022..51db8092 100644
--- a/rdtools/analysis_chains.py
+++ b/rdtools/analysis_chains.py
@@ -1001,7 +1001,7 @@ def _clearsky_preprocess(self):
         )
 
     def sensor_analysis(
-        self, analyses=["yoy_degradation"], yoy_kwargs={}, srr_kwargs={}
+        self, analyses=["yoy_degradation"], yoy_kwargs={"label": "right"}, srr_kwargs={}
     ):
         """
         Perform entire sensor-based analysis workflow.
@@ -1014,6 +1014,7 @@ def sensor_analysis(
             and 'srr_soiling'
         yoy_kwargs : dict
             kwargs to pass to :py:func:`rdtools.degradation.degradation_year_on_year`
+            default is {"label": "right"}, which will right-label the YoY slope values.
         srr_kwargs : dict
             kwargs to pass to :py:func:`rdtools.soiling.soiling_srr`
 
@@ -1041,7 +1042,7 @@ def sensor_analysis(
         self.results["sensor"] = sensor_results
 
     def clearsky_analysis(
-        self, analyses=["yoy_degradation"], yoy_kwargs={}, srr_kwargs={}
+        self, analyses=["yoy_degradation"], yoy_kwargs={"label": "right"}, srr_kwargs={}
     ):
         """
         Perform entire clear-sky-based analysis workflow. Results are stored
@@ -1053,7 +1054,8 @@ def clearsky_analysis(
             Analyses to perform as a list of strings. Valid entries are 'yoy_degradation'
             and 'srr_soiling'
         yoy_kwargs : dict
-            kwargs to pass to :py:func:`rdtools.degradation.degradation_year_on_year`
+            kwargs to pass to :py:func:`rdtools.degradation.degradation_year_on_year`.
+            default is {"label": "right"}, which will right-label the YoY slope values.
         srr_kwargs : dict
             kwargs to pass to :py:func:`rdtools.soiling.soiling_srr`
 

From fb0da2b675fc2f699adeac62841a27df9735bd27 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Mon, 16 Feb 2026 18:48:20 -0700
Subject: [PATCH 66/72] flake8 updates

---
 rdtools/plotting.py           |  4 +---
 rdtools/test/plotting_test.py | 14 ++++++++------
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/rdtools/plotting.py b/rdtools/plotting.py
index 42bf716e..2191ac04 100644
--- a/rdtools/plotting.py
+++ b/rdtools/plotting.py
@@ -5,7 +5,6 @@
 import plotly.express as px
 import numpy as np
 import warnings
-import datetime
 
 
 def degradation_summary_plots(yoy_rd, yoy_ci, yoy_info, normalized_yield,
@@ -436,7 +435,7 @@ def availability_summary_plots(power_system, power_subsystem, loss_total,
     return fig
 
 
-def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True, 
+def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True,
                                 fig=None, plot_color=None, ci_color=None, **kwargs):
     '''
     Plot resampled time series of degradation trend with time
@@ -488,7 +487,6 @@ def _bootstrap(x, percentile, reps):
     if ci_color is None:
         ci_color = 'C0'
 
-
     try:
         roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//4,
                                         center=True)
diff --git a/rdtools/test/plotting_test.py b/rdtools/test/plotting_test.py
index f605553d..58cbe435 100644
--- a/rdtools/test/plotting_test.py
+++ b/rdtools/test/plotting_test.py
@@ -58,18 +58,21 @@ def degradation_info(degradation_power_signal):
     rd, rd_ci, calc_info = degradation_year_on_year(degradation_power_signal)
     return degradation_power_signal, rd, rd_ci, calc_info
 
+
 @pytest.fixture()
 def degradation_info_center(degradation_power_signal):
     # center-labeled YOY output for time-series degradation plot
     rd, rd_ci, calc_info = degradation_year_on_year(degradation_power_signal, label='center')
     return degradation_power_signal, rd, rd_ci, calc_info
 
+
 @pytest.fixture()
 def degradation_info_left(degradation_power_signal):
     # left-labeled YOY output for time-series degradation plot
     rd, rd_ci, calc_info = degradation_year_on_year(degradation_power_signal, label='left')
     return degradation_power_signal, rd, rd_ci, calc_info
 
+
 def test_degradation_summary_plots(degradation_info):
     power, yoy_rd, yoy_ci, yoy_info = degradation_info
 
@@ -262,27 +265,26 @@ def test_availability_summary_plots_empty(availability_analysis_object):
     plt.close('all')
 
 
-def test_degradation_timeseries_plot(degradation_info, degradation_info_center, degradation_info_left):
+def test_degradation_timeseries_plot(degradation_info, degradation_info_center,
+                                     degradation_info_left):
     power, yoy_rd, yoy_ci, yoy_info = degradation_info
 
     # test defaults (label='right')
     result_right = degradation_timeseries_plot(yoy_info)
     assert_isinstance(result_right, plt.Figure)
     xlim_right = result_right.get_axes()[0].get_xlim()[0]
-    #print(f'yoy_right: {yoy_info['YoY_values'].index[0]}')
 
     # test label='center'
-    result_center = degradation_timeseries_plot(yoy_info=degradation_info_center[3], include_ci=False)
+    result_center = degradation_timeseries_plot(yoy_info=degradation_info_center[3],
+                                                include_ci=False)
     assert_isinstance(result_center, plt.Figure)
     xlim_center = result_center.get_axes()[0].get_xlim()[0]
-    #print(f'yoy_center: {degradation_info_center[3]['YoY_values'].index[0]}')
 
     # test label='left'
-    result_left = degradation_timeseries_plot(yoy_info=degradation_info_left[3], 
+    result_left = degradation_timeseries_plot(yoy_info=degradation_info_left[3],
                                               include_ci=False)
     assert_isinstance(result_left, plt.Figure)
     xlim_left = result_left.get_axes()[0].get_xlim()[0]
-    #print(f'yoy_left: {degradation_info_left[3]['YoY_values'].index[0]}')
 
     # test default label matches label='right'
     result_default = degradation_timeseries_plot(yoy_info=yoy_info, include_ci=False)

From e53167867d1f156e9a18d3c6deea2e196c033328 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Tue, 17 Feb 2026 15:22:45 -0700
Subject: [PATCH 67/72] Initial commit - multi-YoY notebook

---
 docs/Multi-year_on_year_example.ipynb | 494 ++++++++++++++++++++++++++
 1 file changed, 494 insertions(+)
 create mode 100644 docs/Multi-year_on_year_example.ipynb

diff --git a/docs/Multi-year_on_year_example.ipynb b/docs/Multi-year_on_year_example.ipynb
new file mode 100644
index 00000000..2cfd9b2f
--- /dev/null
+++ b/docs/Multi-year_on_year_example.ipynb
@@ -0,0 +1,494 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multi- Year-on-year Example\n",
+    "\n",
+    "\n",
+    "This juypter notebook follows on from the existing TrendAnalysis_example.ipynb and assumes familiarity with that example.  This\n",
+    "example investigates two additional pieces of functinality introduced in RdTools 3.2.0: multi-Year on Year analysis, and \n",
+    "changing the right- or left- labeling of YoY outputs in `rdtools.degradation.degradation_year_on_year` function.\n",
+    "\n",
+    "Because we are implementing this with the `rdtools.TrendAnalysis` object-oriented API, it requires a bit of additional code to access `degradation_year_on_year` kwargs.  These are passed as a dictionary in the `TrendAnalysis.sensor_analysis(yoy_kwargs={})` input.\n",
+    "\n",
+    "Otherwise the initial steps follow the original notebooks.\n",
+    "\n",
+    "For a consistent experience, we recommend installing the packages and versions documented in `docs/notebook_requirements.txt`. This can be achieved in your environment by running `pip install -r docs/notebook_requirements.txt` from the base directory. (RdTools must also be separately installed.) This notebook was tested in python 3.12.\n",
+    "\n",
+    "This notebook works with data from the NREL PVDAQ `[4] NREL x-Si #1` system.   This notebook automatically downloads and locally caches the dataset used in this example. The data can also be found on the DuraMAT Datahub (https://datahub.duramat.org/dataset/pvdaq-time-series-with-soiling-signal).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-11T21:04:22.284754Z",
+     "iopub.status.busy": "2026-02-11T21:04:22.283768Z",
+     "iopub.status.idle": "2026-02-11T21:04:25.589287Z",
+     "shell.execute_reply": "2026-02-11T21:04:25.589287Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pvlib\n",
+    "import rdtools\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-11T21:04:25.592292Z",
+     "iopub.status.busy": "2026-02-11T21:04:25.592292Z",
+     "iopub.status.idle": "2026-02-11T21:04:25.603222Z",
+     "shell.execute_reply": "2026-02-11T21:04:25.603222Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#Update the style of plots\n",
+    "import matplotlib\n",
+    "matplotlib.rcParams.update({'font.size': 12,\n",
+    "                           'figure.figsize': [4.5, 3],\n",
+    "                           'lines.markeredgewidth': 0,\n",
+    "                           'lines.markersize': 2\n",
+    "                           })\n",
+    "# Register time series plotting in pandas > 1.0\n",
+    "from pandas.plotting import register_matplotlib_converters\n",
+    "register_matplotlib_converters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-11T21:04:25.606226Z",
+     "iopub.status.busy": "2026-02-11T21:04:25.605234Z",
+     "iopub.status.idle": "2026-02-11T21:04:25.608284Z",
+     "shell.execute_reply": "2026-02-11T21:04:25.608284Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Set the random seed for numpy to ensure consistent results\n",
+    "np.random.seed(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import and preliminary calculations\n",
+    "\n",
+    "\n",
+    "This section prepares the data necessary for an `rdtools` calculation.\n",
+    "\n",
+    "A common challenge is handling datasets with and without daylight savings time. Make sure to specify a `pytz` timezone that does or does not include daylight savings time as appropriate for your dataset.\n",
+    "\n",
+    "**The steps of this section may change depending on your data source or the system being considered. Note that nothing in this first section utilizes the `rdtools` library.** Transposition of irradiance and modeling of cell temperature are generally outside the scope of `rdtools`. A variety of tools for these calculations are available in [pvlib](https://github.com/pvlib/pvlib-python)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-11T21:04:25.610288Z",
+     "iopub.status.busy": "2026-02-11T21:04:25.610288Z",
+     "iopub.status.idle": "2026-02-11T21:04:25.664306Z",
+     "shell.execute_reply": "2026-02-11T21:04:25.663802Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Import the example data\n",
+    "file_url = ('https://datahub.duramat.org/dataset/'\n",
+    "            'a49bb656-7b36-437a-8089-1870a40c2a7d/'\n",
+    "            'resource/d2c3fcf4-4f5f-47ad-8743-fc29'\n",
+    "            'f1356835/download/pvdaq_system_4_2010'\n",
+    "            '-2016_subset_soil_signal.csv')\n",
+    "cache_file = 'PVDAQ_system_4_2010-2016_subset_soilsignal.pickle'\n",
+    "\n",
+    "try:\n",
+    "    df = pd.read_pickle(cache_file)\n",
+    "except FileNotFoundError:\n",
+    "    df = pd.read_csv(file_url, index_col=0, parse_dates=True)\n",
+    "    df.to_pickle(cache_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-11T21:04:25.667312Z",
+     "iopub.status.busy": "2026-02-11T21:04:25.667312Z",
+     "iopub.status.idle": "2026-02-11T21:04:25.839426Z",
+     "shell.execute_reply": "2026-02-11T21:04:25.839426Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df = df.rename(columns = {\n",
+    "    'ac_power':'power_ac',\n",
+    "    'wind_speed': 'wind_speed',\n",
+    "    'ambient_temp': 'Tamb',\n",
+    "    'poa_irradiance': 'poa',\n",
+    "})\n",
+    "\n",
+    "# Specify the Metadata\n",
+    "meta = {\"latitude\": 39.7406,\n",
+    "        \"longitude\": -105.1774,\n",
+    "        \"timezone\": 'Etc/GMT+7',\n",
+    "        \"gamma_pdc\": -0.0034,  # Temperature coefficient for modern silicon PV modules (1/K)\n",
+    "        \"azimuth\": 180,\n",
+    "        \"tilt\": 40,\n",
+    "        \"power_dc_rated\": 1000.0,\n",
+    "        \"temp_model_params\":'open_rack_glass_polymer'}\n",
+    "\n",
+    "df.index = df.index.tz_localize(meta['timezone'])\n",
+    "\n",
+    "# Set the pvlib location\n",
+    "loc = pvlib.location.Location(meta['latitude'], meta['longitude'], tz = meta['timezone'])\n",
+    "\n",
+    "# There is some missing data, but we can infer the frequency from\n",
+    "# the first several data points\n",
+    "freq = pd.infer_freq(df.index[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-11T21:04:25.842760Z",
+     "iopub.status.busy": "2026-02-11T21:04:25.841760Z",
+     "iopub.status.idle": "2026-02-11T21:04:47.653193Z",
+     "shell.execute_reply": "2026-02-11T21:04:47.653193Z"
+    }
+   },
+   "source": [
+    "Note: Unlike the original notebook, we are not incorporating soiling into this analysis and are focusing on year-on-year degradation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use of the object oriented system analysis API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As with the original example we first create a `TrendAnalysis` instance containing data to be analyzed and information about the system. The new functionality is accessed in the next step, setting up either the `sensor_analysis` or `clearsky_analysis`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-11T21:04:47.683740Z",
+     "iopub.status.busy": "2026-02-11T21:04:47.683740Z",
+     "iopub.status.idle": "2026-02-11T21:05:04.841064Z",
+     "shell.execute_reply": "2026-02-11T21:05:04.840432Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ta = rdtools.TrendAnalysis(df['power_ac'], df['poa'],\n",
+    "                           temperature_ambient=df['Tamb'],\n",
+    "                           gamma_pdc=meta['gamma_pdc'],\n",
+    "                           interp_freq=freq,\n",
+    "                           windspeed=df['wind_speed'],\n",
+    "                           power_dc_rated=meta['power_dc_rated'],\n",
+    "                           temperature_model=meta['temp_model_params'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the `TrendAnalysis` object is ready, the `sensor_analysis()` method can be used to deploy the full chain of analysis steps. Results are stored in nested dict, `TrendAnalysis.results`.\n",
+    "\n",
+    "New in RdTools 3.2, we have two new arguments in `rdtools.degradation_year_on_year()`:  `label='center'` (or 'left') and `multi_yoy=True`.  \n",
+    "To pass these arguments into the TrendAnalysis object, these are passed as a dictionary in the `TrendAnalysis.sensor_analysis(yoy_kwargs={})` input.\n",
+    "In the first new argument, we change the label index of our year-on-year degradation output.  By default this index is right-labeled.  For instance, if we have calculated a slope between '2021-01-01' and '2022-01-01', this slope value is labeled with the right index of '2022-01-01'.  By passing `label='center'` the output will be labeled as '2021-07-02' instead, which is the midpoint of the 365-day period."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-11T21:05:04.844914Z",
+     "iopub.status.busy": "2026-02-11T21:05:04.844351Z",
+     "iopub.status.idle": "2026-02-11T21:05:59.907315Z",
+     "shell.execute_reply": "2026-02-11T21:05:59.907315Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ta.sensor_analysis(analyses=['yoy_degradation'], yoy_kwargs={'label': 'center'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results of the calculations are stored in a nested dict, `TrendAnalysis.results`.\n",
+    "New keys are stored in the 'calc_info' dictionary: `YoY_times` which provides the left, right and center label for each calculated slope.\n",
+    "This provides important context for the next step which is multi-YoY calculation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-02-11T21:05:59.912324Z",
+     "iopub.status.busy": "2026-02-11T21:05:59.911334Z",
+     "iopub.status.idle": "2026-02-11T21:05:59.915394Z",
+     "shell.execute_reply": "2026-02-11T21:05:59.915394Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2010-08-27 12:00:00-07:00    31.724178\n",
+      "2010-08-28 12:00:00-07:00    21.474645\n",
+      "2010-08-29 12:00:00-07:00    20.989792\n",
+      "2010-08-30 12:00:00-07:00    25.881095\n",
+      "2010-09-01 12:00:00-07:00    21.321786\n",
+      "Name: yoy, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the YoY slopes. Timestamps are center-labeled because we passed label='center' in the yoy_kwargs.\n",
+    "calc_info = ta.results['sensor']['yoy_degradation']['calc_info']\n",
+    "print(calc_info['YoY_values'].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                           dt_right                 dt_center  \\\n",
+      "2010-08-27 12:00:00-07:00 2011-02-26 00:00:00-07:00 2010-08-27 12:00:00-07:00   \n",
+      "2010-08-28 12:00:00-07:00 2011-02-27 00:00:00-07:00 2010-08-28 12:00:00-07:00   \n",
+      "2010-08-29 12:00:00-07:00 2011-02-28 00:00:00-07:00 2010-08-29 12:00:00-07:00   \n",
+      "2010-08-30 12:00:00-07:00 2011-03-01 00:00:00-07:00 2010-08-30 12:00:00-07:00   \n",
+      "2010-09-01 12:00:00-07:00 2011-03-03 00:00:00-07:00 2010-09-01 12:00:00-07:00   \n",
+      "\n",
+      "                                            dt_left  \n",
+      "2010-08-27 12:00:00-07:00 2010-02-26 00:00:00-07:00  \n",
+      "2010-08-28 12:00:00-07:00 2010-02-27 00:00:00-07:00  \n",
+      "2010-08-29 12:00:00-07:00 2010-02-28 00:00:00-07:00  \n",
+      "2010-08-30 12:00:00-07:00 2010-03-01 00:00:00-07:00  \n",
+      "2010-09-01 12:00:00-07:00 2010-03-03 00:00:00-07:00  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Provide additional timestamp information for the YoY slopes including the left and right timestamps that were used to calculate the slope.\n",
+    "# The index is set to  equal dt_center because we passed label='center' in the yoy_kwargs. \n",
+    "print(calc_info['YoY_times'].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the standard YoY degradation plot to compare with the multi-YoY plot later\n",
+    "ta.plot_degradation_summary('sensor', summary_title='Standard YoY degradation results',\n",
+    "                                  scatter_ymin=0.8, scatter_ymax=1.1, detailed=True,\n",
+    "                                  hist_xmin=-15, hist_xmax=20, bins=500)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above plot with `detailed=True`, note that points in red are not included in the analysis because there is no matching start or endpoint 365 days away.  Also, points in green are used only once, either as a start-point or end-point, but not both.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 450x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the standard YoY time-series plot to compare with the multi-YoY plot later\n",
+    "ta.plot_degradation_timeseries(case='sensor')\n",
+    "plt.title('Standard YoY degradation time-series plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ### Multi-YoY analysis\n",
+    " By passing `multi_yoy=True` instead of the default 'False', we now trigger multi-YoY slope measurements. This follows the method of Quest et al, 2023.<sup>1</sup>  Instead of the standard Year-on-Year slopes where each slope is calculated over points separated by 365 days, the multi_year-on-year approach includes multiple slopes where points can be separated by N * 365 days where N is an integer from 1 to the length of the dataset in years.  Continuing our above example, if our dataset extends from 2020 to 2024, slopes beginning on '2020-01-01' will include slopes calculated to the following endpoints: '2021-01-01', '2022-01-01', '2023-01-01' and '2024-01-01'.  Slopes are still calculated in equivalent units of % / year, but now multiple values will occupy the same index.  \n",
+    "\n",
+    "<sup>1</sup>H. Quest, C. Ballif, A. Virtuani, \"Multi‐Annual Year‐on‐Year: Minimising the Uncertainty in Photovoltaic System Performance Loss Rates,\" Progress in Photovoltaics: Research and Applications. vol. 33, no. 3, pp. 411-424, Oct. 2024. doi: 10.1002/pip.3855"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "ta.sensor_analysis(analyses=['yoy_degradation'], yoy_kwargs={'label': 'center', 'multi_yoy': True})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calc_info_multi = ta.results['sensor']['yoy_degradation']['calc_info']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the multi-YoY degradation plot to compare with the multi-YoY plot later\n",
+    "ta.plot_degradation_summary('sensor', summary_title='Multi-YoY degradation results',\n",
+    "                                  scatter_ymin=0.8, scatter_ymax=1.1, detailed=True,\n",
+    "                                  hist_xmin=-15, hist_xmax=20, bins=500)\n",
+    "plt.show()\n",
+    "print(f'Multi-YoY number of slopes: {calc_info_multi[\"YoY_values\"].__len__()}')\n",
+    "print(f'Standard method number of slopes: {calc_info[\"YoY_values\"].__len__()}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the multi-YoY approach, we generate a larger number of slopes and datapoints from the same dataset. Note that n=5050 instead of n=1527 from the conventional approach. This tightens our confidence interval for overall degradation assessment.\n",
+    "\n",
+    "Also, with `detailed=True` showing which points are included in the analysis, none of the datapoints are red, indicating that even if there is a data outage one year removed from the point, there is some other datapoint within an integer number N * years to be used for a slope.\n",
+    "\n",
+    "Next we look at the time-dependent degradation plot, which does not work nearly so well with `multi_yoy = True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\cdeline\\Documents\\Python Scripts\\rdtools_dev\\rdtools\\plotting.py:495: UserWarning: Input `yoy_info['YoY_values']` appears to have multiple annual slopes per day, which is the case if degradation_year_on_year(multi_yoy=True). Proceeding to plot with a daily mean which will average out the time-series trend. Recommend re-running with degradation_year_on_year(multi_yoy=False).\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 450x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the multi- YoY time-series plot.  This will result in a warning because there are multiple YoY slopes for each timestamp.\n",
+    "# The method will automatically average all datapoints sharing a timestamp.  The result is a more filtered annual trend.\n",
+    "# Note that the choice of label='center' vs 'left' or 'right' will affect the timestamps that are averaged together and hence\n",
+    "# will change the appearance of the multi-YoY time-series plot.  \n",
+    "#   \n",
+    "ta.plot_degradation_timeseries(case='sensor')\n",
+    "plt.title('Multi- YoY degradation time-series plot')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:rdtools_dev]",
+   "language": "python",
+   "name": "conda-env-rdtools_dev-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 098da412cea0f04959dd764269d0ea3e98cc4132 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Tue, 17 Feb 2026 15:48:04 -0700
Subject: [PATCH 68/72] pretty-print notebook dataframes with tabulate.

---
 docs/Multi-year_on_year_example.ipynb | 199 ++++++++++----------------
 docs/notebook_requirements.txt        |   2 +
 2 files changed, 74 insertions(+), 127 deletions(-)

diff --git a/docs/Multi-year_on_year_example.ipynb b/docs/Multi-year_on_year_example.ipynb
index 2cfd9b2f..cbf6eee5 100644
--- a/docs/Multi-year_on_year_example.ipynb
+++ b/docs/Multi-year_on_year_example.ipynb
@@ -4,33 +4,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multi- Year-on-year Example\n",
+    "# Multi-Year-on-Year Example\n",
     "\n",
     "\n",
-    "This juypter notebook follows on from the existing TrendAnalysis_example.ipynb and assumes familiarity with that example.  This\n",
-    "example investigates two additional pieces of functinality introduced in RdTools 3.2.0: multi-Year on Year analysis, and \n",
-    "changing the right- or left- labeling of YoY outputs in `rdtools.degradation.degradation_year_on_year` function.\n",
+    "This Jupyter notebook follows on from the existing TrendAnalysis_example.ipynb and assumes familiarity with that example. This\n",
+    "example investigates two additional pieces of functionality introduced in RdTools 3.2.0: multi-year-on-year analysis, and \n",
+    "changing the right- or left-labeling of YoY outputs in the `rdtools.degradation.degradation_year_on_year` function.\n",
     "\n",
-    "Because we are implementing this with the `rdtools.TrendAnalysis` object-oriented API, it requires a bit of additional code to access `degradation_year_on_year` kwargs.  These are passed as a dictionary in the `TrendAnalysis.sensor_analysis(yoy_kwargs={})` input.\n",
+    "The calculations consist of:\n",
     "\n",
-    "Otherwise the initial steps follow the original notebooks.\n",
+    "1. Import and preliminary calculations: In this step data is imported and augmented to enable analysis with RdTools. **No RdTools functions are used in this step. It will vary depending on the particulars of your dataset.**  \n",
+    "2. Analysis with RdTools: This notebook illustrates the use of the TrendAnalysis API with two new features: `label='center'` and `multi_yoy=True`. Because we are implementing this with the `rdtools.TrendAnalysis` object-oriented API, it requires passing kwargs to `degradation_year_on_year` as a dictionary in the `TrendAnalysis.sensor_analysis(yoy_kwargs={})` input.\n",
     "\n",
     "For a consistent experience, we recommend installing the packages and versions documented in `docs/notebook_requirements.txt`. This can be achieved in your environment by running `pip install -r docs/notebook_requirements.txt` from the base directory. (RdTools must also be separately installed.) This notebook was tested in python 3.12.\n",
     "\n",
-    "This notebook works with data from the NREL PVDAQ `[4] NREL x-Si #1` system.   This notebook automatically downloads and locally caches the dataset used in this example. The data can also be found on the DuraMAT Datahub (https://datahub.duramat.org/dataset/pvdaq-time-series-with-soiling-signal).\n"
+    "This notebook works with data from the NREL PVDAQ `[4] NREL x-Si #1` system. This notebook automatically downloads and locally caches the dataset used in this example. The data can also be found on the DuraMAT Datahub (https://datahub.duramat.org/dataset/pvdaq-time-series-with-soiling-signal).\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:22.284754Z",
-     "iopub.status.busy": "2026-02-11T21:04:22.283768Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.589287Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.589287Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -44,14 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:25.592292Z",
-     "iopub.status.busy": "2026-02-11T21:04:25.592292Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.603222Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.603222Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#Update the style of plots\n",
@@ -69,14 +56,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:25.606226Z",
-     "iopub.status.busy": "2026-02-11T21:04:25.605234Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.608284Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.608284Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Set the random seed for numpy to ensure consistent results\n",
@@ -90,7 +70,7 @@
     "## Import and preliminary calculations\n",
     "\n",
     "\n",
-    "This section prepares the data necessary for an `rdtools` calculation.\n",
+    "This section prepares the data necessary for an `rdtools.TrendAnalysis` calculation.\n",
     "\n",
     "A common challenge is handling datasets with and without daylight savings time. Make sure to specify a `pytz` timezone that does or does not include daylight savings time as appropriate for your dataset.\n",
     "\n",
@@ -99,15 +79,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:25.610288Z",
-     "iopub.status.busy": "2026-02-11T21:04:25.610288Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.664306Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.663802Z"
-    }
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Import the example data\n",
@@ -127,15 +100,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:25.667312Z",
-     "iopub.status.busy": "2026-02-11T21:04:25.667312Z",
-     "iopub.status.idle": "2026-02-11T21:04:25.839426Z",
-     "shell.execute_reply": "2026-02-11T21:04:25.839426Z"
-    }
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = df.rename(columns = {\n",
@@ -190,20 +156,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As with the original example we first create a `TrendAnalysis` instance containing data to be analyzed and information about the system. The new functionality is accessed in the next step, setting up either the `sensor_analysis` or `clearsky_analysis`."
+    "The first step is to create a `TrendAnalysis` instance containing data to be analyzed and information about the system. The new functionality is accessed in a following step when calling the `sensor_analysis()` method."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:04:47.683740Z",
-     "iopub.status.busy": "2026-02-11T21:04:47.683740Z",
-     "iopub.status.idle": "2026-02-11T21:05:04.841064Z",
-     "shell.execute_reply": "2026-02-11T21:05:04.840432Z"
-    }
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "ta = rdtools.TrendAnalysis(df['power_ac'], df['poa'],\n",
@@ -219,24 +178,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once the `TrendAnalysis` object is ready, the `sensor_analysis()` method can be used to deploy the full chain of analysis steps. Results are stored in nested dict, `TrendAnalysis.results`.\n",
+    "Once the `TrendAnalysis` object is ready, the `sensor_analysis()` method can be used to deploy the full chain of analysis steps. Results are stored in a nested dict, `TrendAnalysis.results`.\n",
+    "\n",
+    "New in RdTools 3.2, there are two new arguments in `rdtools.degradation_year_on_year()`: `label='center'` (or 'left') and `multi_yoy=True`. To pass these arguments into the TrendAnalysis object, they are passed as a dictionary in the `TrendAnalysis.sensor_analysis(yoy_kwargs={})` input.\n",
     "\n",
-    "New in RdTools 3.2, we have two new arguments in `rdtools.degradation_year_on_year()`:  `label='center'` (or 'left') and `multi_yoy=True`.  \n",
-    "To pass these arguments into the TrendAnalysis object, these are passed as a dictionary in the `TrendAnalysis.sensor_analysis(yoy_kwargs={})` input.\n",
-    "In the first new argument, we change the label index of our year-on-year degradation output.  By default this index is right-labeled.  For instance, if we have calculated a slope between '2021-01-01' and '2022-01-01', this slope value is labeled with the right index of '2022-01-01'.  By passing `label='center'` the output will be labeled as '2021-07-02' instead, which is the midpoint of the 365-day period."
+    "The first new argument changes the label index of the year-on-year degradation output. By default this index is right-labeled. For instance, if we have calculated a slope between '2021-01-01' and '2022-01-01', this slope value is labeled with the right index of '2022-01-01'. By passing `label='center'`, the output will be labeled as '2021-07-02' instead, which is the midpoint of the 365-day period."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:05:04.844914Z",
-     "iopub.status.busy": "2026-02-11T21:05:04.844351Z",
-     "iopub.status.idle": "2026-02-11T21:05:59.907315Z",
-     "shell.execute_reply": "2026-02-11T21:05:59.907315Z"
-    }
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "ta.sensor_analysis(analyses=['yoy_degradation'], yoy_kwargs={'label': 'center'})"
@@ -246,76 +198,62 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The results of the calculations are stored in a nested dict, `TrendAnalysis.results`.\n",
-    "New keys are stored in the 'calc_info' dictionary: `YoY_times` which provides the left, right and center label for each calculated slope.\n",
-    "This provides important context for the next step which is multi-YoY calculation. "
+    "The results of the calculations are stored in a nested dict, `TrendAnalysis.results`. New keys are stored in the `calc_info` dictionary: `YoY_times` provides the left, right, and center label for each calculated slope. This provides important context for the next step, which is multi-YoY calculation. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-11T21:05:59.912324Z",
-     "iopub.status.busy": "2026-02-11T21:05:59.911334Z",
-     "iopub.status.idle": "2026-02-11T21:05:59.915394Z",
-     "shell.execute_reply": "2026-02-11T21:05:59.915394Z"
-    }
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2010-08-27 12:00:00-07:00    31.724178\n",
-      "2010-08-28 12:00:00-07:00    21.474645\n",
-      "2010-08-29 12:00:00-07:00    20.989792\n",
-      "2010-08-30 12:00:00-07:00    25.881095\n",
-      "2010-09-01 12:00:00-07:00    21.321786\n",
-      "Name: yoy, dtype: float64\n"
+      "|                           |     yoy |\n",
+      "|:--------------------------|--------:|\n",
+      "| 2010-08-27 12:00:00-07:00 | 31.7242 |\n",
+      "| 2010-08-28 12:00:00-07:00 | 21.4746 |\n",
+      "| 2010-08-29 12:00:00-07:00 | 20.9898 |\n",
+      "| 2010-08-30 12:00:00-07:00 | 25.8811 |\n",
+      "| 2010-09-01 12:00:00-07:00 | 21.3218 |\n"
      ]
     }
    ],
    "source": [
     "# Print the YoY slopes. Timestamps are center-labeled because we passed label='center' in the yoy_kwargs.\n",
     "calc_info = ta.results['sensor']['yoy_degradation']['calc_info']\n",
-    "print(calc_info['YoY_values'].head())"
+    "print(calc_info['YoY_values'].head().to_markdown())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                           dt_right                 dt_center  \\\n",
-      "2010-08-27 12:00:00-07:00 2011-02-26 00:00:00-07:00 2010-08-27 12:00:00-07:00   \n",
-      "2010-08-28 12:00:00-07:00 2011-02-27 00:00:00-07:00 2010-08-28 12:00:00-07:00   \n",
-      "2010-08-29 12:00:00-07:00 2011-02-28 00:00:00-07:00 2010-08-29 12:00:00-07:00   \n",
-      "2010-08-30 12:00:00-07:00 2011-03-01 00:00:00-07:00 2010-08-30 12:00:00-07:00   \n",
-      "2010-09-01 12:00:00-07:00 2011-03-03 00:00:00-07:00 2010-09-01 12:00:00-07:00   \n",
-      "\n",
-      "                                            dt_left  \n",
-      "2010-08-27 12:00:00-07:00 2010-02-26 00:00:00-07:00  \n",
-      "2010-08-28 12:00:00-07:00 2010-02-27 00:00:00-07:00  \n",
-      "2010-08-29 12:00:00-07:00 2010-02-28 00:00:00-07:00  \n",
-      "2010-08-30 12:00:00-07:00 2010-03-01 00:00:00-07:00  \n",
-      "2010-09-01 12:00:00-07:00 2010-03-03 00:00:00-07:00  \n"
+      "|                           | dt_right                  | dt_center                 | dt_left                   |\n",
+      "|:--------------------------|:--------------------------|:--------------------------|:--------------------------|\n",
+      "| 2010-08-27 12:00:00-07:00 | 2011-02-26 00:00:00-07:00 | 2010-08-27 12:00:00-07:00 | 2010-02-26 00:00:00-07:00 |\n",
+      "| 2010-08-28 12:00:00-07:00 | 2011-02-27 00:00:00-07:00 | 2010-08-28 12:00:00-07:00 | 2010-02-27 00:00:00-07:00 |\n",
+      "| 2010-08-29 12:00:00-07:00 | 2011-02-28 00:00:00-07:00 | 2010-08-29 12:00:00-07:00 | 2010-02-28 00:00:00-07:00 |\n",
+      "| 2010-08-30 12:00:00-07:00 | 2011-03-01 00:00:00-07:00 | 2010-08-30 12:00:00-07:00 | 2010-03-01 00:00:00-07:00 |\n",
+      "| 2010-09-01 12:00:00-07:00 | 2011-03-03 00:00:00-07:00 | 2010-09-01 12:00:00-07:00 | 2010-03-03 00:00:00-07:00 |\n"
      ]
     }
    ],
    "source": [
     "# Provide additional timestamp information for the YoY slopes including the left and right timestamps that were used to calculate the slope.\n",
     "# The index is set to  equal dt_center because we passed label='center' in the yoy_kwargs. \n",
-    "print(calc_info['YoY_times'].head())"
+    "print(calc_info['YoY_times'].head().to_markdown())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -341,17 +279,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the above plot with `detailed=True`, note that points in red are not included in the analysis because there is no matching start or endpoint 365 days away.  Also, points in green are used only once, either as a start-point or end-point, but not both.  "
+    "In the above plot with `detailed=True`, note that points in red are not included in the analysis because there is no matching start or endpoint 365 days away. Points in green are used only once, either as a start-point or end-point, but not both.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 450x300 with 1 Axes>"
       ]
@@ -371,25 +309,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " ### Multi-YoY analysis\n",
-    " By passing `multi_yoy=True` instead of the default 'False', we now trigger multi-YoY slope measurements. This follows the method of Quest et al, 2023.<sup>1</sup>  Instead of the standard Year-on-Year slopes where each slope is calculated over points separated by 365 days, the multi_year-on-year approach includes multiple slopes where points can be separated by N * 365 days where N is an integer from 1 to the length of the dataset in years.  Continuing our above example, if our dataset extends from 2020 to 2024, slopes beginning on '2020-01-01' will include slopes calculated to the following endpoints: '2021-01-01', '2022-01-01', '2023-01-01' and '2024-01-01'.  Slopes are still calculated in equivalent units of % / year, but now multiple values will occupy the same index.  \n",
+    "### Multi-YoY analysis\n",
+    "\n",
+    "By passing `multi_yoy=True` instead of the default `False`, we now trigger multi-YoY slope measurements. This follows the method of Quest et al., 2023.<sup>1</sup> Instead of the standard year-on-year slopes where each slope is calculated over points separated by 365 days, the multi-year-on-year approach includes multiple slopes where points can be separated by N × 365 days where N is an integer from 1 to the length of the dataset in years. Continuing our above example, if our dataset extends from 2020 to 2024, slopes beginning on '2020-01-01' will include slopes calculated to the following endpoints: '2021-01-01', '2022-01-01', '2023-01-01', and '2024-01-01'. Slopes are still calculated in equivalent units of % / year, but now multiple values will occupy the same index.\n",
     "\n",
-    "<sup>1</sup>H. Quest, C. Ballif, A. Virtuani, \"Multi‐Annual Year‐on‐Year: Minimising the Uncertainty in Photovoltaic System Performance Loss Rates,\" Progress in Photovoltaics: Research and Applications. vol. 33, no. 3, pp. 411-424, Oct. 2024. doi: 10.1002/pip.3855"
+    "<sup>1</sup>H. Quest, C. Ballif, A. Virtuani, \"Multi‐Annual Year‐on‐Year: Minimising the Uncertainty in Photovoltaic System Performance Loss Rates,\" Progress in Photovoltaics: Research and Applications, vol. 33, no. 3, pp. 411-424, Oct. 2024. doi: 10.1002/pip.3855"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "ta.sensor_analysis(analyses=['yoy_degradation'], yoy_kwargs={'label': 'center', 'multi_yoy': True})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -410,6 +348,14 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multi-YoY number of slopes: 5050\n",
+      "Standard method number of slopes: 1527\n"
+     ]
     }
    ],
    "source": [
@@ -428,14 +374,14 @@
    "source": [
     "In the multi-YoY approach, we generate a larger number of slopes and datapoints from the same dataset. Note that n=5050 instead of n=1527 from the conventional approach. This tightens our confidence interval for overall degradation assessment.\n",
     "\n",
-    "Also, with `detailed=True` showing which points are included in the analysis, none of the datapoints are red, indicating that even if there is a data outage one year removed from the point, there is some other datapoint within an integer number N * years to be used for a slope.\n",
+    "Also, with `detailed=True` showing which points are included in the analysis, none of the datapoints are red, indicating that even if there is a data outage one year removed from the point, there is some other datapoint within an integer number N × years to be used for a slope.\n",
     "\n",
-    "Next we look at the time-dependent degradation plot, which does not work nearly so well with `multi_yoy = True`."
+    "Next we look at the time-dependent degradation plot, which does not work as well with `multi_yoy=True`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -448,7 +394,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 450x300 with 1 Axes>"
       ]
@@ -458,13 +404,12 @@
     }
    ],
    "source": [
-    "# plot the multi- YoY time-series plot.  This will result in a warning because there are multiple YoY slopes for each timestamp.\n",
-    "# The method will automatically average all datapoints sharing a timestamp.  The result is a more filtered annual trend.\n",
+    "# Plot the multi-YoY time-series plot. This will result in a warning because there are multiple YoY slopes for each timestamp.\n",
+    "# The method will automatically average all datapoints sharing a timestamp. The result is a more filtered annual trend.\n",
     "# Note that the choice of label='center' vs 'left' or 'right' will affect the timestamps that are averaged together and hence\n",
-    "# will change the appearance of the multi-YoY time-series plot.  \n",
-    "#   \n",
+    "# will change the appearance of the multi-YoY time-series plot.\n",
     "ta.plot_degradation_timeseries(case='sensor')\n",
-    "plt.title('Multi- YoY degradation time-series plot')\n",
+    "plt.title('Multi-YoY degradation time-series plot')\n",
     "plt.show()"
    ]
   }
diff --git a/docs/notebook_requirements.txt b/docs/notebook_requirements.txt
index fc5b1b29..33fe7917 100644
--- a/docs/notebook_requirements.txt
+++ b/docs/notebook_requirements.txt
@@ -45,6 +45,7 @@ qtconsole==5.5.2
 Send2Trash==1.8.3
 simplegeneric==0.8.1
 soupsieve==2.6
+tabulate==0.8.8
 terminado==0.18.1
 testpath==0.6.0
 tinycss2==1.2.1
@@ -53,3 +54,4 @@ traitlets==5.14.3
 wcwidth==0.2.13
 webencodings==0.5.1
 widgetsnbextension==4.0.11
+

From 6880c0566c51f3742479e116929a5a018a6e200c Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 19 Feb 2026 14:43:44 -0500
Subject: [PATCH 69/72] update notebook requirements to silence pandas warnings

---
 docs/notebook_requirements.txt           | 4 ++--
 docs/sphinx/source/changelog/pending.rst | 3 +++
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/docs/notebook_requirements.txt b/docs/notebook_requirements.txt
index 33fe7917..b44d257d 100644
--- a/docs/notebook_requirements.txt
+++ b/docs/notebook_requirements.txt
@@ -30,7 +30,7 @@ nbconvert==7.17.0
 nbformat==5.10.4
 nest-asyncio==1.6.0
 notebook==7.2.2
-numexpr==2.10.1
+numexpr==2.10.2
 pandocfilters==1.5.1
 parso==0.8.4
 pexpect==4.9.0
@@ -45,7 +45,7 @@ qtconsole==5.5.2
 Send2Trash==1.8.3
 simplegeneric==0.8.1
 soupsieve==2.6
-tabulate==0.8.8
+tabulate==0.9.0
 terminado==0.18.1
 testpath==0.6.0
 tinycss2==1.2.1
diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
index 5e4e1af5..c4579162 100644
--- a/docs/sphinx/source/changelog/pending.rst
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -37,6 +37,9 @@ Maintenance
   when calculating center labels.
 * Fixed nbval workflow command syntax (``--sanitize-with`` to ``--nbval-sanitize-with``).
 * Improved pandas 3.0 compatibility with datetime resolution handling.
+* Updated ``docs/notebook_requirements.txt`` to require ``numexpr>=2.10.2`` and
+  ``tabulate>=0.9.0`` to satisfy pandas' optional dependency minimum versions and
+  avoid related warnings/errors.
 
 Testing
 -------

From 4c14f797d469677d1bb9abb44f87112a505ab0c5 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 19 Feb 2026 14:46:32 -0500
Subject: [PATCH 70/72] add multi-yoy nb to tutorials

---
 docs/sphinx/source/changelog/pending.rst                      | 3 +++
 docs/sphinx/source/examples.rst                               | 1 +
 docs/sphinx/source/examples/Multi-year_on_year_example.nblink | 3 +++
 3 files changed, 7 insertions(+)
 create mode 100644 docs/sphinx/source/examples/Multi-year_on_year_example.nblink

diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
index c4579162..818e078c 100644
--- a/docs/sphinx/source/changelog/pending.rst
+++ b/docs/sphinx/source/changelog/pending.rst
@@ -24,6 +24,9 @@ Enhancements
 * :py:func:`~rdtools.degradation.degradation_year_on_year` now returns
   ``calc_info['YoY_times']`` DataFrame with ``dt_right``, ``dt_center``, and ``dt_left``
   columns for each YoY slope.  (:issue:`459`)
+* Added new example notebook ``docs/Multi-year_on_year_example.ipynb`` demonstrating the
+  ``label='center'`` and ``multi_yoy=True`` features of
+  :py:func:`~rdtools.degradation.degradation_year_on_year`.  (:issue:`394`)
 
 Bug Fixes
 ---------
diff --git a/docs/sphinx/source/examples.rst b/docs/sphinx/source/examples.rst
index 57c5b6ae..748d8179 100644
--- a/docs/sphinx/source/examples.rst
+++ b/docs/sphinx/source/examples.rst
@@ -26,3 +26,4 @@ This page shows example usage of the RdTools analysis functions.
     examples/TrendAnalysis_example
     examples/TrendAnalysis_example_NSRDB
     examples/system_availability_example
+    examples/Multi-year_on_year_example
diff --git a/docs/sphinx/source/examples/Multi-year_on_year_example.nblink b/docs/sphinx/source/examples/Multi-year_on_year_example.nblink
new file mode 100644
index 00000000..40d78b6a
--- /dev/null
+++ b/docs/sphinx/source/examples/Multi-year_on_year_example.nblink
@@ -0,0 +1,3 @@
+{
+    "path": "../../../../Multi-year_on_year_example.ipynb"
+}

From 27fccf6101c389f677908cf4f961cbdf670c4d97 Mon Sep 17 00:00:00 2001
From: martin-springer <martinspringer.ms@gmail.com>
Date: Thu, 19 Feb 2026 15:12:14 -0500
Subject: [PATCH 71/72] fix nblink path

---
 docs/sphinx/source/examples/Multi-year_on_year_example.nblink | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sphinx/source/examples/Multi-year_on_year_example.nblink b/docs/sphinx/source/examples/Multi-year_on_year_example.nblink
index 40d78b6a..d53bdb6d 100644
--- a/docs/sphinx/source/examples/Multi-year_on_year_example.nblink
+++ b/docs/sphinx/source/examples/Multi-year_on_year_example.nblink
@@ -1,3 +1,3 @@
 {
-    "path": "../../../../Multi-year_on_year_example.ipynb"
+    "path": "../../../Multi-year_on_year_example.ipynb"
 }

From f6575ebdb3715ff03922cdbebd851189ff6b8e88 Mon Sep 17 00:00:00 2001
From: cdeline <chris.deline@nrel.gov>
Date: Thu, 19 Feb 2026 14:30:49 -0700
Subject: [PATCH 72/72] Change the yoy_values index to be named 'dt'.  Add new
 illustrations at the end of the multi-YoY notebook.

---
 docs/Multi-year_on_year_example.ipynb | 66 +++++++++++++++++++++------
 docs/notebook_requirements.txt        |  2 +-
 rdtools/degradation.py                |  4 +-
 3 files changed, 56 insertions(+), 16 deletions(-)

diff --git a/docs/Multi-year_on_year_example.ipynb b/docs/Multi-year_on_year_example.ipynb
index cbf6eee5..6d77058b 100644
--- a/docs/Multi-year_on_year_example.ipynb
+++ b/docs/Multi-year_on_year_example.ipynb
@@ -203,14 +203,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|                           |     yoy |\n",
+      "| dt                        |     yoy |\n",
       "|:--------------------------|--------:|\n",
       "| 2010-08-27 12:00:00-07:00 | 31.7242 |\n",
       "| 2010-08-28 12:00:00-07:00 | 21.4746 |\n",
@@ -228,14 +228,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|                           | dt_right                  | dt_center                 | dt_left                   |\n",
+      "| dt                        | dt_right                  | dt_center                 | dt_left                   |\n",
       "|:--------------------------|:--------------------------|:--------------------------|:--------------------------|\n",
       "| 2010-08-27 12:00:00-07:00 | 2011-02-26 00:00:00-07:00 | 2010-08-27 12:00:00-07:00 | 2010-02-26 00:00:00-07:00 |\n",
       "| 2010-08-28 12:00:00-07:00 | 2011-02-27 00:00:00-07:00 | 2010-08-28 12:00:00-07:00 | 2010-02-27 00:00:00-07:00 |\n",
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -318,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -374,15 +374,17 @@
    "source": [
     "In the multi-YoY approach, we generate a larger number of slopes and datapoints from the same dataset. Note that n=5050 instead of n=1527 from the conventional approach. This tightens our confidence interval for overall degradation assessment.\n",
     "\n",
-    "Also, with `detailed=True` showing which points are included in the analysis, none of the datapoints are red, indicating that even if there is a data outage one year removed from the point, there is some other datapoint within an integer number N × years to be used for a slope.\n",
+    "Also, with `detailed=True` showing which points are included in the analysis, none of the datapoints are red, indicating that even if there is a data outage one year removed from the point, there is some other datapoint within an integer number N × years to be included in the distribution.\n",
     "\n",
-    "Next we look at the time-dependent degradation plot, which does not work as well with `multi_yoy=True`."
+    "Next we look at the time-dependent degradation plot, which will average the multi-year slopes if `multi_yoy=True`.  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -412,6 +414,44 @@
     "plt.title('Multi-YoY degradation time-series plot')\n",
     "plt.show()"
    ]
+  },
+  {
+   "attachments": {
+    "554309ee-33e2-418a-b578-c5bfdcb1194f.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Further illustration\n",
+    "To further illustrate the details of multi-YoY operation, and how using `center` vs `right` labeling changes the process,\n",
+    "we consider the below multi-YoY example.   In the first case, we look at five slopes, each of which end at the same\n",
+    "right-labeled datapoint in 2015.  These consist of 1,2,3,4 and 5-year segment lengths.\n",
+    "![image.png](attachment:554309ee-33e2-418a-b578-c5bfdcb1194f.png)"
+   ]
+  },
+  {
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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, if we use the `center` labeling, a similar timestamp in 2013 will also include five slopes, \n",
+    "each of which has a midpoint of the segment coinciding with the same timestamp. \n",
+    "![image.png](attachment:8d50a6f5-7bca-4cf8-ba11-121df7a9fe8a.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the degradation summary plot, each of the multi-year slopes will be included in the histogram.  \n",
+    "For the time-series degradation plot, multiple slopes sharing the same timestamp will be averaged."
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/notebook_requirements.txt b/docs/notebook_requirements.txt
index 33fe7917..da893265 100644
--- a/docs/notebook_requirements.txt
+++ b/docs/notebook_requirements.txt
@@ -45,7 +45,7 @@ qtconsole==5.5.2
 Send2Trash==1.8.3
 simplegeneric==0.8.1
 soupsieve==2.6
-tabulate==0.8.8
+tabulate==0.9.0
 terminado==0.18.1
 testpath==0.6.0
 tinycss2==1.2.1
diff --git a/rdtools/degradation.py b/rdtools/degradation.py
index 5607092c..2cc611cc 100644
--- a/rdtools/degradation.py
+++ b/rdtools/degradation.py
@@ -333,11 +333,11 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     YoY_times = YoY_times.rename(columns={'dt': 'dt_right'})
 
     YoY_times.set_index(YoY_times[f'dt_{label}'], inplace=True)
-    YoY_times.index.name = None
+    YoY_times.index.name = 'dt'
 
     # now apply either right, left, or center label index to the yoy_result
     yoy_result.index = YoY_times[f'dt_{label}']
-    yoy_result.index.name = None
+    yoy_result.index.name = 'dt'
 
     # the following is throwing a futurewarning if infer_objects() isn't included here.
     # see https://github.com/pandas-dev/pandas/issues/57734