def _validate_stale_interpolated(observation, values):
window = validator.stale_interpolated_window(observation.interval_length)
stale_flag = validator.detect_stale_values(values, window=window,
_return_mask=True)
interpolation_flag = validator.detect_interpolation(values, window=window,
_return_mask=True)
return stale_flag, interpolation_flag
def validate_daily_ac_power(observation, values):
"""
Run a number of validation checks on a daily timeseries of AC power.
Parameters
----------
observation : solarforecastarbiter.datamodel.Observation
Observation object that the data is associated with
values : pandas.Series
Series of observation values
Returns
-------
tuple
Tuple of integer bitmask series of flags from the following tests, in
order,
`validator.check_timestamp_spacing`
`validator.detect_stale_values`
`validator.detect_interpolation`
`validator.detect_clipping`
"""
timestamp_flag = _validate_timestamp(observation, values)
stale_flag = validator.detect_stale_values(values, _return_mask=True)
interpolation_flag = validator.detect_interpolation(values,
_return_mask=True)
clipping_flag = validator.detect_clipping(values, _return_mask=True)
return (timestamp_flag, stale_flag, interpolation_flag, clipping_flag)
def validate_daily_ghi(observation, values):
"""
Run validation on a daily timeseries of GHI. First,
all checks of `validate_ghi` are run in addition to
detecting stale values and interpolation
Parameters
----------
observation : solarforecastarbiter.datamodel.Observation
Observation object that the data is associated with
values : pandas.Series
Series of observation values
Returns
-------
tuple
Tuple of integer bitmask series of flags from the following tests, in
order,
tests from `validate_ghi`
`validator.detect_stale_values`
`validator.detect_interpolation`
"""
ghi_flags = validate_ghi(observation, values)
stale_flag = validator.detect_stale_values(values, _return_mask=True)
interpolation_flag = validator.detect_interpolation(values,
_return_mask=True)
return (*ghi_flags, stale_flag, interpolation_flag)
def test_detect_interpolation():
data = [1.0, 1.001, 1.002001, 1.003, 1.004, 1.001001, 1.001001, 1.001001,
1.2, 1.3, 1.5, 1.4, 1.5, 1.6, 1.7, 1.8, 2.0]
x = pd.Series(data=data)
res1 = validator.detect_interpolation(x, window=3)
assert_series_equal(res1, pd.Series([False, False, False, False, False,
False, False, True, False, False,
False, False, False, True, True, True,
False]))
res2 = validator.detect_interpolation(x, window=3, rtol=1e-2)
assert_series_equal(res2, pd.Series([False, False, True, True, True,
False, False, True, False, False,
False, False, False, True, True, True,
False]))
res3 = validator.detect_interpolation(x, window=5)
assert_series_equal(res3, pd.Series([False, False, False, False, False,
False, False, False, False, False,
False, False, False, False, False,
True, False]))
res4 = validator.detect_interpolation(x, window=3, atol=1e-2)
assert_series_equal(res4, pd.Series([False, False, True, True, True,
True, True, True, False, False,
False, False, False, True, True, True,
False]))
data = [0.0, 0.0, 0.0, -0.0, 0.00001, 0.000010001, -0.00000001]
y = pd.Series(data=data)
res = validator.detect_interpolation(y, window=3, atol=1e-5)
assert_series_equal(res, pd.Series([False, False, True, True, True, True,
False]))
res = validator.detect_stale_values(y, window=3, atol=1e-4)
assert_series_equal(res, pd.Series([False, False, True, True, True, True,
True]))
with pytest.raises(ValueError):
validator.detect_interpolation(x, window=2)