solarpy.plotting.plot_time_drift_correlation

solarpy.plotting.plot_time_drift_correlation#

solarpy.plotting.plot_time_drift_correlation(times, ghi, ghi_clear, is_clearsky, window='5D', min_periods=240, plot_colorbar=True, colorbar_label='Correlation [-]', cmap='viridis_r', ax=None)#

Plot time-drift correlation between measured and clear-sky GHI.

For each time lag between −30 and +30 minutes, the Pearson correlation between measured GHI and the time-shifted clear-sky GHI is computed using a rolling window over clear-sky periods only. The result is resampled to daily means and displayed as a heatmap with date on the x-axis and lag on the y-axis. A trend in the correlation peak at a non-zero lag indicates a systematic timing offset in the measured data.

Parameters:

  • times (array-like of datetime-like) – Timestamps corresponding to each observation.

  • ghi (array-like of float) – Measured global horizontal irradiance [W/m²].

  • ghi_clear (array-like of float) – Modelled clear-sky global horizontal irradiance [W/m²].

  • is_clearsky (array-like of bool) – Boolean mask that is True for clear-sky periods. Only flagged periods are used to calculate correlation.

  • window (str, optional) – Rolling window size passed to pandas.Series.rolling(). Default is '5D' (five days).

  • min_periods (int, optional) – Minimum number of observations required within the rolling window, passed to pandas.Series.rolling(). Windows with fewer observations produce NaN. Default is 240 (4 hours of 1-min data).

  • plot_colorbar (bool, optional) – Whether to add an inset colorbar. Default is True.

  • colorbar_label (str, optional) – Label displayed alongside the colorbar. Default is "Correlation (-)".

  • cmap (str, optional) – Matplotlib colormap name used for the correlation heatmap. Default is 'viridis_r'.

  • ax (matplotlib.axes.Axes, optional) – Axes to draw on. If None, a new figure and axes are created.

Returns:

  • fig (matplotlib.figure.Figure) – The figure containing the plot.

  • ax (matplotlib.axes.Axes) – The axes containing the plot.

Return type:

tuple[plt.Figure, plt.Axes]

Examples

>>> import pvlib
>>> import solarpy
>>> data, meta = solarpy.iotools.read_t16("data/LYN_2023.csv", map_variables=True)
>>> location = pvlib.location.Location(meta["latitude"], meta["longitude"])
>>> cs = location.get_clearsky(data.index)
>>> is_clearsky = pvlib.clearsky.detect_clearsky(data["ghi"], cs["ghi"], data.index)
>>> fig, ax = solarpy.plotting.plot_time_drift_correlation(
...     times=data.index,
...     ghi=data["ghi"],
...     ghi_clear=cs["ghi"],
...     is_clearsky=is_clearsky,
... )
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