def test_clearsky_limits_csi_max(times):
"""Increasing `csi_max` passes larger values."""
measured = pd.Series(np.linspace(800, 1000, len(times)), index=times)
measured.iloc[0] = 1300
measured.iloc[1] = 1200
measured.iloc[2] = 1100
clearsky = pd.Series(1000, index=times)
expected = pd.Series(True, index=times)
expected.iloc[0:3] = False
assert_series_equal(
irradiance.clearsky_limits(measured, clearsky, csi_max=1.0), expected)
expected.iloc[2] = True
assert_series_equal(
irradiance.clearsky_limits(measured, clearsky, csi_max=1.1), expected)
expected.iloc[1] = True
assert_series_equal(
irradiance.clearsky_limits(measured, clearsky, csi_max=1.2), expected)
def test_clearsky_limits(times):
"""Values greater than clearsky values are flagged False.
Notes
-----
Copyright (c) 2019 SolarArbiter. See the file
LICENSES/SOLARFORECASTARBITER_LICENSE at the top level directory
of this distribution and at `<https://github.com/pvlib/
pvanalytics/blob/master/LICENSES/SOLARFORECASTARBITER_LICENSE>`_
for more information.
"""
clearsky = pd.Series(np.linspace(50, 55, len(times)), index=times)
measured = clearsky.copy()
measured.iloc[0] *= 0.5
measured.iloc[-1] *= 2.0
clear_times = np.tile(True, len(times))
clear_times[-1] = False
assert_series_equal(irradiance.clearsky_limits(measured, clearsky),
pd.Series(index=times, data=clear_times))
def test_clearsky_limits_negative_and_nan():
"""Irradiance values greater than clearsky valuse are flagged False
along with NaNs.
Notes
-----
Copyright (c) 2019 SolarArbiter. See the file
LICENSES/SOLARFORECASTARBITER_LICENSE at the top level directory
of this distribution and at `<https://github.com/pvlib/
pvanalytics/blob/master/LICENSES/SOLARFORECASTARBITER_LICENSE>`_
for more information.
"""
index = pd.date_range(start=datetime(2019, 6, 15, 12, 0, 0),
freq='15T',
periods=5)
measured = pd.Series(index=index, data=[800, 1000, 1200, -200, np.nan])
clearsky = pd.Series(index=index, data=1000)
assert_series_equal(
irradiance.clearsky_limits(measured, clearsky),
pd.Series(index=index, data=[True, True, False, True, False]))
# :py:meth:`pvlib.location.Location.get_clearsky`, using the lat-long
# coordinates associated the RMIS NREL system.
location = pvlib.location.Location(39.7407, -105.1686)
clearsky = location.get_clearsky(data.index)
# %%
# Use :py:func:`pvanalytics.quality.irradiance.clearsky_limits`.
# Here, we check GHI data in field 'irradiance_ghi__7981'.
# :py:func:`pvanalytics.quality.irradiance.clearsky_limits`
# returns a mask that identifies data that falls between
# lower and upper limits. The defaults (used here)
# are upper bound of 110% of clear-sky GHI, and
# no lower bound.
clearsky_limit_mask = clearsky_limits(data['irradiance_ghi__7981'],
clearsky['ghi'])
# %%
# Mask nighttime values in the GHI time series using the
# :py:func:`pvanalytics.features.daytime.power_or_irradiance` function.
# We will then remove nighttime values from the GHI time series.
day_night_mask = power_or_irradiance(series=data['irradiance_ghi__7981'],
freq=freq)
# %%
# Plot the 'irradiance_ghi__7981' data stream and its associated clearsky GHI
# data stream. Mask the GHI time series by its clearsky_limit_mask for daytime
# periods.
# Please note that a simple Ineichen model with static monthly turbidities
# isn't always accurate, as in this case. Other models that may provide better
