def __init__(self,
ref_spd: List,
target_spd,
averaging_prd='1H',
coverage_threshold=0.9,
preprocess=True):
self.ref_spd = pd.concat(ref_spd, axis=1, join='inner')
self.target_spd = target_spd
self.averaging_prd = averaging_prd
self.coverage_threshold = coverage_threshold
self.preprocess = preprocess
if preprocess:
self.data = pd.concat(list(
tf._preprocess_data_for_correlations(self.ref_spd,
self.target_spd,
averaging_prd,
coverage_threshold)),
axis=1,
join='inner')
else:
self.data = pd.concat(list(self.ref_spd, self.target_spd),
axis=1,
join='inner')
self.data.columns = [
'ref_spd_' + str(i + 1)
for i in range(0, len(self.ref_spd.columns))
] + ['target_spd']
self.data = self.data.dropna()
self.params = 'not run yet'
def _averager(ref_spd, target_spd, averaging_prd, coverage_threshold, ref_dir, target_dir):
data = pd.concat(list(tf._preprocess_data_for_correlations(
ref_spd, target_spd, averaging_prd, coverage_threshold)),
axis=1, join='inner')
if ref_dir is not None and target_dir is not None:
data = pd.concat([data] + list(tf._preprocess_dir_data_for_correlations(
ref_spd, ref_dir, target_spd, target_dir, averaging_prd, coverage_threshold)),
axis=1, join='inner')
return data
def concurrent_coverage(ref,
target,
averaging_prd,
aggregation_method_target='mean'):
"""
Accepts ref and target data and returns the coverage of concurrent data.
:param ref: Reference data
:type ref: pandas.Series
:param target: Target data
:type target: pandas.Series
:param averaging_prd: Groups data by the period specified by period.
* 2T, 2 min for minutely average
* Set period to 1D for a daily average, 3D for three hourly average, similarly 5D, 7D, 15D etc.
* Set period to 1H for hourly average, 3H for three hourly average and so on for 5H, 6H etc.
* Set period to 1M for monthly average
* Set period to 1AS fo annual average
:type averaging_prd: str
:param aggregation_method_target: (Optional) Calculates mean of the data for the given averaging_prd by default.
Can be changed to 'sum', 'std', 'max', 'min', etc. or a user defined function
:return: A DataFrame with concurrent coverage and resolution of the new data. The columns with coverage are named as
<column name>_Coverage
"""
coverage_df = tf._preprocess_data_for_correlations(
ref=ref,
target=target,
averaging_prd=averaging_prd,
coverage_threshold=0,
aggregation_method_target=aggregation_method_target,
get_coverage=True)
coverage_df.columns = [
"Coverage" if "_Coverage" in col else col
for col in coverage_df.columns
]
return coverage_df