Fix pandas 1.3.0 error and deprecations (#284)

* fix pandas 1.3.0 error with np.float32 vs python float * Index.astype -> Index.view to suppress deprecation warnings * bump nbsphinx version (see #275) * Update changelog * typo fix * another typo... * fix pr numbers * delete stray colons * remove extra . Co-authored-by: Michael Deceglie <[email protected]>
NREL · Jul 16, 2021 · b6dcdd2 · b6dcdd2
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diff --git a/docs/sphinx/source/changelog.rst b/docs/sphinx/source/changelog.rst
@@ -1,7 +1,7 @@
 RdTools Change Log
 ==================
 
-.. include:: changelog/pending.rst
+.. include:: changelog/v2.0.6.rst
 .. include:: changelog/v2.0.5.rst
 .. include:: changelog/v2.0.4.rst
 .. include:: changelog/v2.0.3.rst

diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst
diff --git a/docs/sphinx/source/changelog/v2.0.6.rst b/docs/sphinx/source/changelog/v2.0.6.rst
@@ -0,0 +1,20 @@
+**********************
+v2.0.6 (July 16, 2021)
+**********************
+
+Bug Fixes
+---------
+* Fix a failure of :py:func:`~rdtools.clearsky_temperature.get_clearsky_tamb` with pandas 1.3.0 (:pull:`284`)
+* Change internal casting of timestamps into integers to use ``.view()`` instead of pandas deprecated ``.astype()`` method (:pull:`284`)
+
+Requirements
+------------
+* Update specified versions of bleach in
+  ``docs/notebook_requirements.txt`` and matplotlib
+  in ``requirements.txt`` (:pull:`261`)
+
+
+Contributors
+------------
+* Kevin Anderson (:ghuser:`kanderso-nrel`)
+* Michael Deceglie (:ghuser:`mdeceglie`)
diff --git a/rdtools/clearsky_temperature.py b/rdtools/clearsky_temperature.py
@@ -139,7 +139,7 @@ def _get_pixel_value(data, i, j, k, radius):
     if len(list) == 0:
         return np.nan
 
-    return pd.Series(list).median()
+    return float(pd.Series(list).median())
 
 
 def _get_temperature(hour_of_day, night_temp, day_temp, solar_noon_offset):

diff --git a/rdtools/normalization.py b/rdtools/normalization.py
@@ -320,17 +320,17 @@ def _delta_index(series):
         # If there is no frequency information, explicitly calculate interval
         # sizes. Length of each interval calculated by using 'int64' to convert
         # to nanoseconds.
-        hours = pd.Series(series.index.astype('int64') / (10.0**9 * 3600.0))
+        hours = pd.Series(series.index.view('int64') / (10.0**9 * 3600.0))
         hours.index = series.index
         deltas = hours.diff()
     else:
         # If there is frequency information, pandas shift can be used to gain
         # a meaningful interval for the first element of the timeseries
         # Length of each interval calculated by using 'int64' to convert to
         # nanoseconds.
-        deltas = (series.index - series.index.shift(-1)).astype('int64') / \
+        deltas = (series.index - series.index.shift(-1)).view('int64') / \
                  (10.0**9 * 3600.0)
-    return deltas, np.mean(deltas.dropna())
+    return deltas, np.mean(deltas[~np.isnan(deltas)])
 
 
 delta_index = deprecated('2.0.0', removal='3.0.0')(_delta_index)
@@ -467,7 +467,7 @@ def _t_step_nanoseconds(time_series):
     return a series of right labeled differences in the index of time_series
     in nanoseconds
     '''
-    t_steps = np.diff(time_series.index.astype('int64')).astype('float')
+    t_steps = np.diff(time_series.index.view('int64')).astype('float')
     t_steps = np.insert(t_steps, 0, np.nan)
     t_steps = pd.Series(index=time_series.index, data=t_steps)
     return t_steps
@@ -613,7 +613,7 @@ def _aggregate(time_series, target_frequency, max_timedelta, series_type):
     values = time_series.values
 
     # Identify gaps (including from nans) bigger than max_time_delta
-    timestamps = time_series.index.astype('int64').values
+    timestamps = time_series.index.view('int64')
     timestamps = pd.Series(timestamps, index=time_series.index)
     t_diffs = timestamps.diff()
     # Keep track of the gap size but with refilled NaNs and new
@@ -628,7 +628,7 @@ def _aggregate(time_series, target_frequency, max_timedelta, series_type):
         gap_mask[:time_series.index[0]] = True
 
     time_series = time_series.reindex(union_index)
-    t_diffs = np.diff(time_series.index.astype('int64').values)
+    t_diffs = np.diff(time_series.index.view('int64'))
     t_diffs_hours = t_diffs / 10**9 / 3600.0
     if series_type == 'instantaneous':
         # interpolate with trapz sum
@@ -700,7 +700,7 @@ def _interpolate_series(time_series, target_index, max_timedelta=None,
     df = df.dropna()
 
     # convert to integer index and calculate the size of gaps in input
-    timestamps = df.index.astype('int64')
+    timestamps = df.index.view('int64')
     df['timestamp'] = timestamps
     df['gapsize_ns'] = df['timestamp'].diff()
     df.index = timestamps
@@ -720,7 +720,7 @@ def _interpolate_series(time_series, target_index, max_timedelta=None,
                       UserWarning)
 
     # put data on index that includes both original and target indicies
-    target_timestamps = target_index.astype('int64')
+    target_timestamps = pd.Index(target_index.view('int64'))
     union_index = df.index.append(target_timestamps)
     union_index = union_index.drop_duplicates(keep='first')
     df = df.reindex(union_index)

diff --git a/rdtools/test/normalization_pvwatts_test.py b/rdtools/test/normalization_pvwatts_test.py
@@ -41,7 +41,7 @@ def setUp(self):
                                      periods=12,
                                      freq='MS')
         power_meas = 19.75  # power in dummy conditions
-        hours = (energy_index - energy_index.shift(-1)).astype('int64') / (10.0**9 * 3600.0)
+        hours = (energy_index - energy_index.shift(-1)).view('int64') / (10.0**9 * 3600.0)
         dummy_energy = hours * power_meas
         self.energy = pd.Series(dummy_energy, index=energy_index)
 

diff --git a/setup.py b/setup.py
@@ -49,7 +49,7 @@
 EXTRAS_REQUIRE = {
     'doc': [
         'sphinx==1.8.5',
-        'nbsphinx==0.6.0',
+        'nbsphinx==0.8.5',
         'nbsphinx-link==1.3.0',
         'pandas==0.23.0',
         'pvlib==0.7.1',