Multi yoy no label

docs/sphinx/source/changelog/v3.2.0.rst

@@ -4,40 +4,33 @@ v3.2.0 (X, X, 2026)
 
 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` now defaults to rolling
-  median, centered on the timestamp (pd.rolling(center=True)), and reduces
-  ``min_periods`` from ``rolling_days//2`` to ``rolling_days//4``.
-  (: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`)
+* :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`)
 * :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`)
+  data. To avoid over-filtering, only slopes on the order of 2 years (731 days) or
+  shorter are included in the center-labeled median.  (:issue:`394`) (:pull:`498`)
+* :py:func:`~rdtools.plotting.degradation_timeseries_plot` refactored to do a rolling
+  median, for all slopes whose center timestamp is inside the window.
+  ``min_periods`` reduced to ``rolling_days//4``.
+  (:issue:`455`) (:pull:`498`)  
 * :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`)
 * Added new example notebook ``docs/Multi-year_on_year_example.ipynb`` demonstrating the
-  ``label='center'`` and ``multi_yoy=True`` features of
+  ``multi_yoy=True`` features of
   :py:func:`~rdtools.degradation.degradation_year_on_year`.  (:issue:`394`)
-* :py:meth:`~rdtools.analysis_chains.TrendAnalysis.sensor_analysis` and
-  :py:meth:`~rdtools.analysis_chains.TrendAnalysis.clearsky_analysis` now
-  explicitly default ``yoy_kwargs={"label": "right"}``.
 
 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
@@ -63,8 +56,7 @@ Testing
   decreasing).
 * Added test for ``multi_yoy=True`` parameter in ``degradation_year_on_year``.
 * Added tests for :py:func:`~rdtools.plotting.degradation_timeseries_plot`
-  covering ``label='center'``, ``label='left'``, multi-YoY duplicate index
-  handling, and ``KeyError`` path.
+  covering multi-YoY duplicate index handling, and ``KeyError`` path.
 * Set matplotlib backend to ``Agg`` in test ``conftest.py`` to avoid tkinter issues.
 
 

rdtools/analysis_chains.py

@@ -1001,7 +1001,7 @@ def _clearsky_preprocess(self):
         )
 
     def sensor_analysis(
-        self, analyses=["yoy_degradation"], yoy_kwargs={"label": "right"}, srr_kwargs={}
+        self, analyses=["yoy_degradation"], yoy_kwargs={}, srr_kwargs={}
     ):
         """
         Perform entire sensor-based analysis workflow.
@@ -1014,7 +1014,6 @@ 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`
 
@@ -1042,7 +1041,7 @@ def sensor_analysis(
         self.results["sensor"] = sensor_results
 
     def clearsky_analysis(
-        self, analyses=["yoy_degradation"], yoy_kwargs={"label": "right"}, srr_kwargs={}
+        self, analyses=["yoy_degradation"], yoy_kwargs={}, srr_kwargs={}
     ):
         """
         Perform entire clear-sky-based analysis workflow. Results are stored
@@ -1055,7 +1054,6 @@ def clearsky_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`
 

rdtools/degradation.py

@@ -209,8 +209,6 @@ 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', '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
@@ -226,8 +224,8 @@ def degradation_year_on_year(energy_normalized, recenter=True,
         degradation rate estimate
     calc_info : dict
 
-        * `YoY_values` - pandas series of year on year slopes, either right
-           left or center labeled, depending on the `label` parameter.
+        * `YoY_values` - pandas series of year on year slopes, right
+           labeled.
         * `renormalizing_factor` - float of value used to recenter data
         * `exceedance_level` - the degradation rate that was outperformed with
           probability of `exceedance_prob`
@@ -242,10 +240,6 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     energy_normalized.name = 'energy'
     energy_normalized.index.name = 'dt'
 
-    if label not in {"right", "left", "center"}:
-        raise ValueError(f"Unsupported value {label} for `label`."
-                         " Must be 'right', 'left' or 'center'.")
-
     # 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
@@ -332,11 +326,8 @@ def degradation_year_on_year(energy_normalized, recenter=True,
     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.index.name = 'dt'
-
-    # now apply either right, left, or center label index to the yoy_result
-    yoy_result.index = YoY_times[f'dt_{label}']
+    # now apply right label index to the yoy_result
+    yoy_result.index = YoY_times.index
     yoy_result.index.name = 'dt'
 
     # the following is throwing a futurewarning if infer_objects() isn't included here.

rdtools/plotting.py

@@ -445,6 +445,7 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True,
     yoy_info : dict
         a dictionary with keys:
         * YoY_values - pandas series of year on year slopes
+        * YoY_times - pandas series of corresponding timestamps
     rolling_days: int, default 365
         Number of days for rolling window. Note that
         the window must contain at least 25% of datapoints to be included in
@@ -465,6 +466,9 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True,
     It should be noted that ``yoy_info`` is an output
     from :py:func:`rdtools.degradation.degradation_year_on_year`.
 
+    Also, if a multi-YoY analysis is passed in, only slopes of length <=
+    731 days are considered in this time-series plot to avoid over-smoothing
+
     Returns
     -------
     matplotlib.figure.Figure
@@ -477,49 +481,83 @@ def _bootstrap(x, percentile, reps):
         mb1 = np.nanmedian(xb1, axis=0)
         return np.percentile(mb1, percentile)
 
+    def _roll_median(df, win_right, rolling_days, min_periods):
+        """
+        rolling median
+        df includes following columns: dt_center, yoy
+        win_right: Datetime of the right end of the rolling window
+        rolling_days: number of days in the rolling window
+        min_periods: minimum number of points in the rolling window to return a value
+
+        returns: median of yoy values if the center of the slope is in the rolling window,
+            or NaN if there are fewer than min_periods points. Time index of the returned
+            value is centered on the rolling window.
+        """
+        win_left = win_right - pd.Timedelta(days=rolling_days)
+        in_window = (df['dt_center'] <= win_right) & (df['dt_center'] >= win_left)
+        if in_window.sum() < min_periods:
+            return np.nan
+        else:
+            return df.loc[in_window, 'yoy'].median()
+
     try:
-        results_values = yoy_info['YoY_values']
+        results = yoy_info['YoY_times'].join(yoy_info['YoY_values'].rename('yoy'))
     except KeyError:
-        raise KeyError("yoy_info input dictionary does not contain key `YoY_values`.")
+        raise KeyError("yoy_info input dictionary does not contain keys"
+                       " `YoY_times` and `YoY_values`.")
 
     if plot_color is None:
         plot_color = 'tab:orange'
     if ci_color is None:
         ci_color = 'C0'
 
-    results_values = results_values.sort_index()
-    if results_values.index.has_duplicates:
-        # 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//4,
-                                                             center=True)
-    else:
-        roller = results_values.rolling(f'{rolling_days}d', min_periods=rolling_days//4,
-                                        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
+    # filter to only 2 years + 1 day length slopes to avoid over-smoothing in the multi-yoy case
+    results = results[(results['dt_right'] - results['dt_left']) <= pd.Timedelta(days=365 * 2 + 1)]
+
+    # loop through results in a daily timeindex from min(dt_left) to max(dt_right)
+    # Apply rolling median and bootstrap confidence intervals
+
+    timeindex = pd.date_range(start=results['dt_left'].min(),
+                              end=results['dt_right'].max(), freq='D')
+    results_median = pd.Series(index=timeindex, dtype=float)
+    for win_center in timeindex:
+        win_right = win_center + pd.Timedelta(days=rolling_days/2)
+        results_median.loc[win_center] = _roll_median(df=results,
+                                                      win_right=win_right,
+                                                      rolling_days=rolling_days,
+                                                      min_periods=rolling_days//4)
+
+    # calculate confidence intervals for each point in the rolling median.
     if include_ci:
-        ci_lower = roller.apply(_bootstrap, kwargs={'percentile': 2.5, 'reps': 100}, raw=True)
-        ci_upper = roller.apply(_bootstrap, kwargs={'percentile': 97.5, 'reps': 100}, raw=True)
+        # downsample the timeindex to every 2 days to speed up the bootstrap calculation,
+        # since it is very slow.
+        timeindex = timeindex[::2]
+        ci_lower = pd.Series(index=timeindex, dtype=float)
+        ci_upper = pd.Series(index=timeindex, dtype=float)
+        for win_center in timeindex:
+            win_right = win_center + pd.Timedelta(days=rolling_days/2)
+            win_left = win_center - pd.Timedelta(days=rolling_days/2)
+            in_window = (results['dt_center'] <= win_right) & (results['dt_center'] >= win_left)
+            if in_window.sum() < rolling_days//4:
+                ci_lower.loc[win_center] = np.nan
+                ci_upper.loc[win_center] = np.nan
+            else:
+                ci_lower.loc[win_center] = _bootstrap(results.loc[in_window, 'yoy'],
+                                                      percentile=2.5, reps=50)
+                ci_upper.loc[win_center] = _bootstrap(results.loc[in_window, 'yoy'],
+                                                      percentile=97.5, reps=50)
+
     if fig is None:
         fig, ax = plt.subplots()
     else:
         ax = fig.axes[0]
     if include_ci:
         ax.fill_between(ci_lower.index,
                         ci_lower, ci_upper, color=ci_color)
-    median = roller.median()
+    median = results_median.sort_index()
     ax.plot(median.index,
             median, color=plot_color, **kwargs)
-    ax.axhline(results_values.median(), c='k', ls='--')
+    ax.axhline(results_median.median(), c='k', ls='--')
     plt.ylabel('Degradation trend (%/yr)')
     fig.autofmt_xdate()
 

rdtools/test/degradation_test.py

@@ -202,35 +202,6 @@ 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])
-        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
-        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')
-        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."""
         from rdtools.degradation import _avg_timestamp_old_Pandas

rdtools/test/plotting_test.py

@@ -16,7 +16,6 @@
 import matplotlib
 import plotly
 import pytest
-import warnings
 import re
 import copy
 
@@ -59,20 +58,6 @@ def degradation_info(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
 
@@ -265,49 +250,13 @@ 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):
     power, yoy_rd, yoy_ci, yoy_info = degradation_info
 
-    # test defaults (label='right')
+    # test defaults
     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=degradation_info_center[3],
-                                                include_ci=False)
-    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=degradation_info_left[3],
-                                              include_ci=False)
-    assert_isinstance(result_left, plt.Figure)
-    xlim_left = result_left.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
-
-    # 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 yr."
-
-    # 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 1/2 year."
+    result_right.get_axes()[0].get_xlim()[0]
 
     with pytest.raises(KeyError):
         degradation_timeseries_plot({'a': 1}, include_ci=False)
@@ -318,12 +267,7 @@ def test_degradation_timeseries_plot(degradation_info, degradation_info_center,
     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 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"
+    result = degradation_timeseries_plot(yoy_info=yoy_multi, include_ci=False)
+    assert_isinstance(result, plt.Figure)
 
     plt.close('all')