Merge remote-tracking branch 'upstream/main' into sdm-split

Author: kandersolar

docs/sphinx/source/whatsnew/v0.11.2.rst

@@ -12,6 +12,8 @@ Enhancements
 ~~~~~~~~~~~~
 * ``pvlib.ivtools.sdm`` is now a subpackage. (:issue:`2252`, :pull:`2256`)
 
+* :py:func:`~pvlib.ivtools.sdm.fit_desoto` now allows input of initial
+  parameter guesses. (:issue:`1014`, :pull:`2291`)
 
 Bug Fixes
 ~~~~~~~~~
@@ -31,6 +33,8 @@ Documentation
   page. (:issue:`2202`, :pull:`2226`)
 * Updated :py:func:`~pvlib.irradiance.reindl` to include definitions of terms
   and a new "notes" section (:issue:`2183`, :pull:`2193`)
+* Clarified the error message in :py:func:`~pvlib.clearsky.detect_clearsky` when
+  windows contain fewer than three data points (:issue:`2005`, :pull:`2281`)
 * Added a new :ref:`nomenclature` page, in place of the Variables and Symbols
   page, using the sphinx glossary directive. (:issue:`1421`, :pull:`2234`)
 * Explained how to write docstrings for new functions in :ref:`example-docstring`

pvlib/clearsky.py

@@ -683,22 +683,26 @@ def detect_clearsky(measured, clearsky, times=None, infer_limits=False,
                     var_diff=0.005, slope_dev=8, max_iterations=20,
                     return_components=False):
     """
-    Detects clear sky times according to the algorithm developed by Reno
-    and Hansen for GHI measurements. The algorithm [1]_ was designed and
-    validated for analyzing GHI time series only. Users may attempt to
-    apply it to other types of time series data using different filter
-    settings, but should be skeptical of the results.
+    Detects clear sky times using the algorithm developed by Reno
+    and Hansen.
 
-    The algorithm detects clear sky times by comparing statistics for a
+    The algorithm [1]_ was designed and
+    validated for analyzing GHI time series. Jordan and Hansen [2]_ extended
+    the algorithm to plane-of-array (POA) irradiance measurements.
+
+    The algorithm [1]_ detects clear sky times by comparing statistics for a
     measured time series and an expected clearsky time series.
     Statistics are calculated using a sliding time window (e.g., 10
     minutes). An iterative algorithm identifies clear periods, uses the
     identified periods to estimate bias in the clearsky data, scales the
     clearsky data and repeats.
 
-    Clear times are identified by meeting 5 criteria. Default values for
+    Clear times are identified by meeting five criteria. Default values for
     these thresholds are appropriate for 10 minute windows of 1 minute
-    GHI data.
+    GHI data. For data at longer intervals, it is recommended
+    to set ``infer_limits=True`` to use the thresholds from [2]_.
+
+    For POA data, ``clearsky`` must be on the same plane as ``measured``.
 
     Parameters
     ----------
@@ -713,8 +717,8 @@ def detect_clearsky(measured, clearsky, times=None, infer_limits=False,
         If True, does not use passed in kwargs (or defaults), but instead
         interpolates these values from Table 1 in [2]_.
     window_length : int, default 10
-        Length of sliding time window in minutes. Must be greater than 2
-        periods.
+        Length of sliding time window in minutes. Each window must contain at
+        least three data points.
     mean_diff : float, default 75
         Threshold value for agreement between mean values of measured
         and clearsky in each interval, see Eq. 6 in [1]. [W/m2]
@@ -723,8 +727,6 @@ def detect_clearsky(measured, clearsky, times=None, infer_limits=False,
         clearsky values in each interval, see Eq. 7 in [1]. [W/m2]
     lower_line_length : float, default -5
         Lower limit of line length criterion from Eq. 8 in [1].
-        Criterion satisfied when lower_line_length < line length difference
-        < upper_line_length.
     upper_line_length : float, default 10
         Upper limit of line length criterion from Eq. 8 in [1].
     var_diff : float, default 0.005
@@ -736,7 +738,7 @@ def detect_clearsky(measured, clearsky, times=None, infer_limits=False,
         change in successive values, see Eqs. 12 through 14 in [1].
     max_iterations : int, default 20
         Maximum number of times to apply a different scaling factor to
-        the clearsky and redetermine clear_samples. Must be 1 or larger.
+        the clearsky and redetermine ``clear_samples``. Must be 1 or larger.
     return_components : bool, default False
         Controls if additional output should be returned. See below.
 
@@ -748,19 +750,23 @@ def detect_clearsky(measured, clearsky, times=None, infer_limits=False,
 
     components : OrderedDict, optional
         Dict of arrays of whether or not the given time window is clear
-        for each condition. Only provided if return_components is True.
+        for each condition. Only provided if ``return_components`` is True.
 
     alpha : scalar, optional
-        Scaling factor applied to the clearsky_ghi to obtain the
-        detected clear_samples. Only provided if return_components is
+        Scaling factor applied to ``clearsky`` to obtain the
+        detected ``clear_samples``. Only provided if ``return_components`` is
         True.
 
     Raises
     ------
     ValueError
-        If measured is not a Series and times is not provided
+        If ``measured`` is not a Series and times is not provided.
+    ValueError
+        If a window contains less than three data points.
+    ValueError
+        If the measured data is not sufficient to fill a window.
     NotImplementedError
-        If timestamps are not equally spaced
+        If timestamps are not equally spaced.
 
     References
     ----------
@@ -812,6 +818,13 @@ def detect_clearsky(measured, clearsky, times=None, infer_limits=False,
     sample_interval, samples_per_window = \
         tools._get_sample_intervals(times, window_length)
 
+    if samples_per_window < 3:
+        raise ValueError(f"Samples per window of {samples_per_window}"
+                         " found. Each window must contain at least 3 data"
+                         " points."
+                         f" Window length of {window_length} found; increase"
+                         f" window length to {3*sample_interval} or longer.")
+
     # if infer_limits, find threshold values using the sample interval
     if infer_limits:
         window_length, mean_diff, max_diff, lower_line_length, \

pvlib/tests/test_clearsky.py

@@ -674,10 +674,16 @@ def test_detect_clearsky_missing_index(detect_clearsky_data):
 
 def test_detect_clearsky_not_enough_data(detect_clearsky_data):
     expected, cs = detect_clearsky_data
-    with pytest.raises(ValueError, match='have at least'):
+    with pytest.raises(ValueError, match='times has only'):
         clearsky.detect_clearsky(expected['GHI'], cs['ghi'], window_length=60)
 
 
+def test_detect_clearsky_window_too_short(detect_clearsky_data):
+    expected, cs = detect_clearsky_data
+    with pytest.raises(ValueError, match="Samples per window of "):
+        clearsky.detect_clearsky(expected['GHI'], cs['ghi'], window_length=2)
+
+
 @pytest.mark.parametrize("window_length", [5, 10, 15, 20, 25])
 def test_detect_clearsky_optimizer_not_failed(
     detect_clearsky_data, window_length