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Revision of degradation_timeseries_plot #1

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20b7f29
Revision of degradation_timeseries_plot
mdeceglie Jun 2, 2026
b8c155c
Bug fixes
mdeceglie Jun 4, 2026
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131 changes: 59 additions & 72 deletions rdtools/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -436,20 +436,23 @@ def availability_summary_plots(power_system, power_subsystem, loss_total,


def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True,
fig=None, plot_color=None, ci_color=None, **kwargs):
fig=None, plot_color=None, ci_color=None,
center=None, **kwargs):
'''
Plot resampled time series of degradation trend with time

Parameters
----------
yoy_info : dict
a dictionary with keys:
* YoY_values - pandas series of year on year slopes
* YoY_times - pandas series of corresponding timestamps

* YoY_values - pandas series of right-labeled year on year slopes
* YoY_times - dict containing a ``dt_center`` key with a
pandas DatetimeIndex of center timestamps for each YoY
window (required when ``center=True``)
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
the rolling plot, and the rolling window is centered.
Number of days for rolling window. Note that the window must contain
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
fig : matplotlib, optional
Expand All @@ -458,6 +461,12 @@ def degradation_timeseries_plot(yoy_info, rolling_days=365, include_ci=True,
color of the timeseries trendline
ci_color : str, optional
color of the confidence interval 'fuzz'
center : bool, default False
If ``True``, the rolling window is centered and ``results_values`` is
reindexed using ``yoy_info['YoY_times']['dt_center']`` before any calculations are
performed. The recommended value is ``True``; the default of ``False``
is retained only for backward compatibility. A warning is raised when
this argument is not explicitly supplied.
kwargs :
Extra parameters passed to matplotlib.pyplot.axis.plot()

Expand All @@ -466,98 +475,76 @@ 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
'''

if center is None:
warnings.warn(
"The default value of 'center' will remain False for backward "
"compatibility, but center=True is recommended. Pass "
"center=True to silence this warning.",
UserWarning,
stacklevel=2,
)
center = False

def _bootstrap(x, percentile, reps):
# stolen from degradation_year_on_year
n1 = len(x)
xb1 = np.random.choice(x, (n1, reps), replace=True)
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 = yoy_info['YoY_times'].join(yoy_info['YoY_values'].rename('yoy'))
results_values = yoy_info['YoY_values'].copy()

except KeyError:
raise KeyError("yoy_info input dictionary does not contain keys"
" `YoY_times` and `YoY_values`.")
raise KeyError("yoy_info input dictionary does not contain key `YoY_values`.")

# filter to only 2 years + 1 day length slopes to avoid over-smoothing in the multi-yoy case
# (applied before index reassignment while integer index still aligns with YoY_times)
yoy_durations = yoy_info['YoY_times']['dt_right'] - yoy_info['YoY_times']['dt_left']
results_values = results_values[
results_values.index.map(yoy_durations) <= pd.Timedelta(days=365 * 2 + 1)
]

if center:
try:
results_values.index = results_values.index.map(yoy_info['YoY_times']['dt_center'])
except KeyError:
raise KeyError("yoy_info input dictionary does not contain key `YoY_times['dt_center']`, "
"which is required when center=True.")
else:
results_values.index = results_values.index.map(yoy_info['YoY_times']['dt_right'])

results_values = results_values.sort_index()

if plot_color is None:
plot_color = 'tab:orange'
if ci_color is None:
ci_color = 'C0'

# 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.
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:
# 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)

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)
ci_lower = ci_lower[~ci_lower.index.duplicated(keep='last')]
ci_upper = ci_upper[~ci_upper.index.duplicated(keep='last')]
rolling_median = roller.median()
rolling_median = rolling_median[~rolling_median.index.duplicated(keep='last')]
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 = results_median.sort_index()
ax.plot(median.index,
median, color=plot_color, **kwargs)
ax.axhline(results_median.median(), c='k', ls='--')
ax.fill_between(ci_lower.index, ci_lower, ci_upper, color=ci_color)
ax.plot(rolling_median, color=plot_color, **kwargs)
ax.axhline(results_values.median(), c='k', ls='--')
plt.ylabel('Degradation trend (%/yr)')
fig.autofmt_xdate()

Expand Down