def test_geometric_clipping_missing_data(freq, power_pvwatts):
power = power_pvwatts.resample(freq).asfreq()
power.loc[power.between_time('09:00', '10:30').index] = np.nan
power.loc[power.between_time('12:15', '12:45').index] = np.nan
power.dropna(inplace=True)
with pytest.raises(ValueError,
match="Cannot infer frequency of `ac_power`. "
"Please resample or pass `freq`."):
clipping.geometric(power)
assert not clipping.geometric(power, freq=freq).any()
def test_geometric_index_not_sorted():
power = pd.Series(
[1, 2, 3],
index=pd.DatetimeIndex(
['20200201 0700', '20200201 0630', '20200201 0730']
)
)
with pytest.raises(ValueError,
match=r"Index must be monotonically increasing\."):
clipping.geometric(power, freq='30T')
def test_geometric_clipping_correct(power_pvwatts, freq):
power = power_pvwatts.resample(freq).asfreq()
clipped = clipping.geometric(power)
expected = power == power.max()
if freq == '5T':
assert np.allclose(power[clipped], power.max(), atol=0.5)
else:
assert_series_equal(clipped, expected)
def test_geometric_clipping_midday_clouds(power_pvwatts):
power = power_pvwatts.resample('15T').asfreq()
power.loc[power.between_time(
start_time='17:30', end_time='19:30',
include_start=True, include_end=True
).index] = list(range(30, 39)) * 31
clipped = clipping.geometric(power)
expected = power == power.max()
assert_series_equal(clipped, expected)
def find_clipped_times_pva(self):
# Make normalized power column.
self.df['power_dc'] = self.df[self.voltage_dc_key] * self.df[
self.
current_dc_key] / self.modules_per_string / self.parallel_strings
# Find clipped times.
self.df['clipped_times'] = clipping.geometric(
ac_power=self.df['power_dc'], freq=self.freq)
def classify_operating_mode(voltage,
current,
power_clip=np.inf,
method='fraction',
clipped_times=None,
freq='15min'):
"""
Parameters
----------
voltage
currente
method
Returns
-------
operating_cls : array
Array of classifications of each time stamp.
0: System at maximum power point.
1: System at open circuit conditions.
2: Clipped or curtailed. DC operating point is not necessarily at MPP.
-1: No power/inverter off
-2: Other
"""
cls = np.zeros(np.shape(voltage)) - 1
# Inverter on
cls[np.logical_and(
voltage > voltage.max() * 0.01,
current > current.max() * 0.01,
)] = 0
# Nighttime, low voltage and irradiance (inverter off)
cls[voltage < voltage.max() * 0.01] = -1
# Open circuit condition
cls[np.logical_and(current < current.max() * 0.01,
voltage > voltage.max() * 0.01)] = 1
if clipped_times is None:
clipped_times = clipping.geometric(ac_power=voltage * current,
freq=freq)
# Clipped data.
cls[clipped_times] = 2
return cls
def test_geometric_clipping_window_overrides_tracking(power_pvwatts):
power = power_pvwatts.resample('15T').asfreq()
clipped = clipping.geometric(power, tracking=True)
assert clipped.any()
clipped_override = clipping.geometric(power, tracking=True, window=24)
assert not clipped_override.any()
def test_geometric_clipping_tracking(power_pvwatts):
power = power_pvwatts.resample('15T').asfreq()
clipped = clipping.geometric(power)
assert clipped.any()
clipped = clipping.geometric(power, tracking=True)
assert not clipped.any()
def test_geometric_clipping_window(power_pvwatts):
power = power_pvwatts.resample('15T').asfreq()
clipped = clipping.geometric(power)
assert clipped.any()
clipped_window = clipping.geometric(power, window=24)
assert not clipped_window.any()
def test_geometric_clipping(power_pvwatts, freq):
clipped = clipping.geometric(power_pvwatts.resample(freq).asfreq())
assert clipped.any()
freq = "15T"
data['value_normalized'].plot()
data.loc[data['label'], 'value_normalized'].plot(ls='', marker='o')
plt.legend(labels=["AC Power", "Labeled Clipping"],
title="Clipped")
plt.xticks(rotation=20)
plt.xlabel("Date")
plt.ylabel("Normalized AC Power")
plt.tight_layout()
plt.show()
# %%
# Now, use :py:func:`pvanalytics.features.clipping.geometric` to identify
# clipping periods in the time series. Re-plot the data subset with this mask.
predicted_clipping_mask = geometric(ac_power=data['value_normalized'],
freq=freq)
data['value_normalized'].plot()
data.loc[predicted_clipping_mask, 'value_normalized'].plot(ls='', marker='o')
plt.legend(labels=["AC Power", "Detected Clipping"],
title="Clipped")
plt.xticks(rotation=20)
plt.xlabel("Date")
plt.ylabel("Normalized AC Power")
plt.tight_layout()
plt.show()
# %%
# Compare the filter results to the ground-truth labeled data side-by-side,
# and generate an accuracy metric.
acc = 100 * np.sum(np.equal(data.label,