Changed the timestamp calls for the documentation

Author: kperrynrel

docs/examples/clipping/clipping.py

@@ -35,7 +35,7 @@
 data['label'] = data['label'].astype(bool)
 # This is the known frequency of the time series. You may need to infer
 # the frequency or set the frequency with your AC power time series.
-freq = "15T"
+freq = "15min"
 
 data['value_normalized'].plot()
 data.loc[data['label'], 'value_normalized'].plot(ls='', marker='o')

docs/examples/day-night-masking/day-night-masking.py

@@ -36,7 +36,7 @@
 
 # This is the known frequency of the time series. You may need to infer
 # the frequency or set the frequency with your AC power time series.
-freq = "1T"
+freq = "1min"
 # These are the latitude-longitude coordinates associated with the
 # SERF East system.
 latitude = 39.742

docs/examples/gaps/data-completeness.py

@@ -34,7 +34,7 @@
 pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
 file = pvanalytics_dir / 'data' / 'ac_power_inv_2173.csv'
 data = pd.read_csv(file, index_col=0, parse_dates=True)
-data = data.asfreq("15T")
+data = data.asfreq("15min")
 
 # %%
 # Now, we use :py:func:`pvanalytics.quality.gaps.completeness_score` to get the

docs/examples/gaps/stale-data.py

@@ -34,7 +34,7 @@
 pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
 file = pvanalytics_dir / 'data' / 'ac_power_inv_2173_stale_data.csv'
 data = pd.read_csv(file, index_col=0, parse_dates=True)
-data = data.asfreq("15T")
+data = data.asfreq("15min")
 data['value_normalized'].plot()
 data.loc[data["stale_data_mask"], "value_normalized"].plot(ls='', marker='.')
 plt.legend(labels=["AC Power", "Inserted Stale Data"])

docs/examples/irradiance-quality/clearsky-limits-irradiance.py

@@ -29,7 +29,7 @@
 pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
 rmis_file = pvanalytics_dir / 'data' / 'irradiance_RMIS_NREL.csv'
 data = pd.read_csv(rmis_file, index_col=0, parse_dates=True)
-freq = '5T'
+freq = '5min'
 # Make the datetime index tz-aware.
 data.index = data.index.tz_localize("Etc/GMT+7")
 

docs/examples/metrics/variability-index.py

@@ -52,7 +52,7 @@
 # an hourly frequency.
 variability_index_series = variability_index(data['irradiance_ghi__7981'],
                                              clearsky['ghi'],
-                                             freq='1H')
+                                             freq='1h')
 
 # %%
 # Plot the calculated VI against the underlying GHI measurements, for the

docs/examples/system/infer-orientation-fit-pvwatts.py

@@ -32,7 +32,7 @@
 data = pd.read_csv(ac_power_file, index_col=0, parse_dates=True)
 data = data.sort_index()
 time_series = data['ac_power']
-time_series = time_series.asfreq('15T')
+time_series = time_series.asfreq('15min')
 
 # Plot the first few days of the time series to visualize it
 time_series[:pd.to_datetime("2016-07-06 00:00:00-07:00")].plot()

docs/examples/system/system-tracking.py

@@ -36,7 +36,7 @@
 data = pd.read_csv(ac_power_file, index_col=0, parse_dates=True)
 data = data.sort_index()
 time_series = data['ac_power']
-time_series = time_series.asfreq('15T')
+time_series = time_series.asfreq('15min')
 
 # Plot the first few days of the time series to visualize it
 time_series[:pd.to_datetime("2016-07-06 00:00:00-07:00")].plot()

docs/examples/weather/module-temperature-check.py

@@ -61,7 +61,7 @@
 # We mask the irradiance time series into day-night periods, and remove
 # any nighttime data to clean up the future regression.
 predicted_day_night_mask = power_or_irradiance(
-    series=data['poa_irradiance__771'], freq='15T')
+    series=data['poa_irradiance__771'], freq='15min')
 # Filter out nighttime periods
 data = data[predicted_day_night_mask]