Day night masking sphinx documentation

docs/examples/day-night-masking.py

@@ -0,0 +1,125 @@
+"""
+Day-Night Masking
+=================
+
+Masking day-night periods using the PVAnalytics daytime module.
+"""
+
+# %%
+# Identifying and masking day-night periods in an AC power time series or
+# irradiance time series can aid in future data analysis, such as detecting
+# if a time series has daylight savings time or time shifts. Here, we use
+# :py:func:`pvanalytics.features.daytime.power_or_irradiance` to mask day/night
+# periods, as well as estimate sunrise and sunset times in the data set. This
+# function is particularly useful for cases where the time zone of a data
+# stream is unknown or incorrect.
+
+import pvanalytics
+from pvanalytics.features.daytime import power_or_irradiance
+import matplotlib.pyplot as plt
+import pandas as pd
+import pathlib
+import pvlib
+import numpy as np
+
+# %%
+# First, read in the 1-minute sampled AC power time series data, taken
+# from the SERF East installation on the NREL campus.
+# This sample is provided from the NREL PVDAQ database, and contains
+# a column representing an AC power data stream.
+
+pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
+ac_power_file = pvanalytics_dir / 'data' / 'serf_east_1min_ac_power.csv'
+data = pd.read_csv(ac_power_file, index_col=0, parse_dates=True)
+data = data.sort_index()
+# 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"
+# These are the latitude-longitude coordinates associated with the
+# SERF East system.
+latitude = 39.742
+longitude = -105.173
+# Plot the time series.
+data['ac_power__752'].plot()
+plt.xlabel("Date")
+plt.ylabel("AC Power (kW)")
+plt.tight_layout()
+plt.show()
+
+# %%
+# It is critical to set all negative values in the AC power time series to 0
+# for :py:func:`pvanalytics.features.daytime.power_or_irradiance` to work
+# properly. Negative erroneous data may affect daytime mask assignments.
+data.loc[data['ac_power__752'] < 0, 'ac_power__752'] = 0
+
+# %%
+# Now, use :py:func:`pvanalytics.features.daytime.power_or_irradiance`
+# to identify day periods in the time series.
+predicted_day_night_mask = power_or_irradiance(series=data['ac_power__752'],
+                                               freq=freq)
+
+# %%
+# Function :py:func:`pvlib.solarposition.sun_rise_set_transit_spa` is
+# used to get ground-truth sunrise and sunset times for each day at the site
+# location, and a SPA-daytime mask is calculated based on these times. Data
+# associated with SPA daytime periods is labeled as True, and data associated
+# with SPA nighttime periods is labeled as False.
+# SPA sunrise and sunset times are used here as a point of comparison to the
+# sensor-based AC power data; SPA-based sunrise and sunset values are not
+# needed to run :py:func:`pvanalytics.features.daytime.power_or_irradiance`.
+
+sunrise_sunset_df = pvlib.solarposition.sun_rise_set_transit_spa(data.index,
+                                                                 latitude,
+                                                                 longitude,
+                                                                 how='numpy',
+                                                                 delta_t=67.0)
+data['sunrise_time'] = sunrise_sunset_df['sunrise']
+data['sunset_time'] = sunrise_sunset_df['sunset']
+
+data['daytime_mask'] = True
+data.loc[(data.index < data.sunrise_time) |
+         (data.index > data.sunset_time), "daytime_mask"] = False
+
+
+# %%
+# Plot the Ac power data stream with the mask output from
+# :py:func:`pvanalytics.features.daytime.power_or_irradiance`,
+# as well as the SPA-calculate sunrise and sunset
+
+data['ac_power__752'].plot()
+data.loc[predicted_day_night_mask, 'ac_power__752'].plot(ls='', marker='o')
+data.loc[~predicted_day_night_mask, 'ac_power__752'].plot(ls='', marker='o')
+sunrise_sunset_times = sunrise_sunset_df[['sunrise',
+                                          'sunset']].drop_duplicates()
+for sunrise, sunset in sunrise_sunset_times.itertuples(index=False):
+    plt.axvline(x=sunrise, c="blue")
+    plt.axvline(x=sunset, c="red")
+plt.legend(labels=["AC Power", "Daytime", "Nighttime",
+                   "SPA Sunrise", "SPA Sunset"])
+plt.xlabel("Date")
+plt.ylabel("AC Power (kW)")
+plt.tight_layout()
+plt.show()
+
+# %%
+# Compare the predicted mask to the ground-truth SPA mask, to get the model
+# accuracy. Also, compare sunrise and sunset times for the predicted mask
+# compared to the ground truth sunrise and sunset times.
+acc = 100 * np.sum(np.equal(data.daytime_mask,
+                            predicted_day_night_mask))/len(data.daytime_mask)
+print("Overall model prediction accuracy: " + str(round(acc, 2)) + "%")
+
+# Generate predicted + SPA sunrise times for each day
+print("Sunrise Comparison:")
+print(pd.DataFrame({'predicted_sunrise': predicted_day_night_mask
+                    .index[predicted_day_night_mask]
+                    .to_series().resample("d").first(),
+                    'pvlib_spa_sunrise': sunrise_sunset_df["sunrise"]
+                    .resample("d").first()}))
+# Generate predicted + SPA sunset times for each day
+print("Sunset Comparison:")
+print(pd.DataFrame({'predicted_sunset': predicted_day_night_mask
+                    .index[predicted_day_night_mask]
+                    .to_series().resample("d").last(),
+                    'pvlib_spa_sunset': sunrise_sunset_df["sunrise"]
+                    .resample("d").last()}))

docs/whatsnew/0.1.2.rst

@@ -33,8 +33,12 @@ Documentation
 * Added examples for the quality.outliers module, including
   :py:func:`pvanalytics.quality.outliers.tukey`,
   :py:func:`pvanalytics.quality.outliers.zscore`,
-  :py:func:`pvanalytics.quality.outliers.hampel`,
+  :py:func:`pvanalytics.quality.outliers.hampel`
   (:issue:`133`, :pull:`138`)
+* Added examples for the pvanalytics.features.daytime module,
+  including :py:func:`pvanalytics.features.daytime.power_or_irradiance`
+  (:issue:`133`, :pull:`139`)
+
 
 Contributors
 ~~~~~~~~~~~~