Orientation sphinx documentation (#148)

* Push up dummy files for the two orientation functions.

* add example for nrel mesa 1axis tracker

* update the routines for tracking and fixed tilt sunny days mask.

* updated the tracking routine to output plot

* added data files.

* fixed pep8 errors in docs.

* added to whatsnew file, docstring cleanup

* removed duplicate in documentation

* recommit bc jobs didn't run

* remove redundant data processing for tracking example

* update docs before branch switch

* Update docs/examples/fixed-nrel.py

Co-authored-by: Kevin Anderson <[email protected]>

* updated with OEDI system ID

* update the name of the serf east file

* Update the routine with the csv name

* pushed up reduced size file + changed wording per @kanderso-nrel's request

* fixed missing index on serf 15-min data file

* update the file to remove nan rows

Co-authored-by: Perry <[email protected]>
Co-authored-by: Kevin Anderson <[email protected]>
Co-authored-by: Perry <kperry>

docs/examples/fixed-nrel.py

@@ -0,0 +1,55 @@
+"""
+Flag Sunny Days for a Fixed-Tilt System
+=======================================
+
+Flag sunny days for a fixed-tilt PV system.
+"""
+
+# %%
+# Identifying and masking sunny days for a fixed-tilt system is important
+# when performing future analyses that require filtered sunny day data.
+# For this example we will use data from the fixed-tilt NREL SERF East system
+# located on the NREL campus in Colorado, USA, and generate a sunny day mask.
+# This data set is publicly available via the PVDAQ database in the
+# DOE Open Energy Data Initiative (OEDI)
+# (https://data.openei.org/submissions/4568), as system ID 50.
+# This data is timezone-localized.
+
+import pvanalytics
+from pvanalytics.features import daytime as day
+from pvanalytics.features.orientation import fixed_nrel
+import matplotlib.pyplot as plt
+import pandas as pd
+import pathlib
+
+# %%
+# First, read in data from the NREL SERF East fixed-tilt system. This data
+# set contains 15-minute interval AC power data.
+
+pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
+file = pvanalytics_dir / 'data' / 'serf_east_15min_ac_power.csv'
+data = pd.read_csv(file, index_col=0, parse_dates=True)
+
+
+# %%
+# Mask day-night periods using the
+# :py:func:`pvanalytics.features.daytime.power_or_irradiance` function.
+# Then apply :py:func:`pvanalytics.features.orientation.fixed_nrel`
+# to the AC power stream and mask the sunny days in the time series.
+
+daytime_mask = day.power_or_irradiance(data['ac_power'])
+
+fixed_sunny_days = fixed_nrel(data['ac_power'],
+                              daytime_mask)
+
+# %%
+# Plot the AC power stream with the sunny day mask applied to it.
+
+data['ac_power'].plot()
+data.loc[fixed_sunny_days, 'ac_power'].plot(ls='', marker='.')
+plt.legend(labels=["AC Power", "Sunny Day"],
+           loc="upper left")
+plt.xlabel("Date")
+plt.ylabel("AC Power (kW)")
+plt.tight_layout()
+plt.show()

docs/examples/tracking-nrel.py

@@ -0,0 +1,55 @@
+"""
+Flag Sunny Days for a Tracking System
+=====================================
+
+Flag sunny days for a single-axis tracking PV system.
+"""
+
+# %%
+# Identifying and masking sunny days for a single-axis tracking system is
+# important when performing future analyses that require filtered sunny day
+# data. For this example we will use data from the single-axis tracking
+# NREL Mesa system located on the NREL campus in Colorado, USA, and generate
+# a sunny day mask.
+# This data set is publicly available via the PVDAQ database in the
+# DOE Open Energy Data Initiative (OEDI)
+# (https://data.openei.org/submissions/4568), as system ID 50.
+# This data is timezone-localized.
+
+import pvanalytics
+from pvanalytics.features import daytime as day
+from pvanalytics.features.orientation import tracking_nrel
+import matplotlib.pyplot as plt
+import pandas as pd
+import pathlib
+
+# %%
+# First, read in data from the NREL Mesa 1-axis tracking system. This data
+# set contains 15-minute interval AC power data.
+
+pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
+file = pvanalytics_dir / 'data' / 'nrel_1axis_tracker_mesa_ac_power.csv'
+data = pd.read_csv(file, index_col=0, parse_dates=True)
+
+# %%
+# Mask day-night periods using the
+# :py:func:`pvanalytics.features.daytime.power_or_irradiance` function.
+# Then apply :py:func:`pvanalytics.features.orientation.tracking_nrel`
+# to the AC power stream and mask the sunny days in the time series.
+
+daytime_mask = day.power_or_irradiance(data['ac_power'])
+
+tracking_sunny_days = tracking_nrel(data['ac_power'],
+                                    daytime_mask)
+
+# %%
+# Plot the AC power stream with the sunny day mask applied to it.
+
+data['ac_power'].plot()
+data.loc[tracking_sunny_days, 'ac_power'].plot(ls='', marker='.')
+plt.legend(labels=["AC Power", "Sunny Day"],
+           loc="upper left")
+plt.xlabel("Date")
+plt.ylabel("AC Power (kW)")
+plt.tight_layout()
+plt.show()

docs/whatsnew/0.1.2.rst

@@ -46,10 +46,13 @@ Documentation
   :py:func:`pvanalytics.quality.irradiance.check_irradiance_consistency_qcrad`,
   and :py:func:`pvanalytics.quality.irradiance.check_irradiance_limits_qcrad`
   (:issue:`133`, :pull:`140`)
+* Added examples for the pvanalytics.features.orientation module,
+  including :py:func:`pvanalytics.features.orientation.fixed_nrel`,
+  and :py:func:`pvanalytics.quality.features.orientation.tracking_nrel`
+  (:issue:`133`, :pull:`148`)
 * Added an example for
   :py:func:`pvanalytics.quality.data_shifts` routine  (:pull:`131`)
 
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 Contributors
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