brightwind.analyse.analyse.coverage

brightwind.analyse.analyse.coverage(data, period='1M', aggregation_method='mean')

Get the data coverage over the period specified.

Coverage is defined as the ratio of number of data points present in the period and the maximum number of data points that a period should have. Example, for 10 minute data resolution and a period of 1 hour the maximum number of data points in one period is 6. But if the number if data points available is only 3 for that hour the coverage is 3/6=0.5 . For more details see average_data_by_period as this function is a wrapper around it.

Parameters

• data (pandas.Series or pandas.DataFrame) – Data to find average or aggregate of

• period (str or pandas.DateOffset) –

  Groups data by the period specified here. The following formats are supported

  • Set period to 10min for 10 minute average, 20min for 20 minute average and so on for 4min, 15min, etc.

  • Set period to 1H for hourly average, 3H for three hourly average and so on for 5H, 6H etc.

  • Set period to 1D for a daily average, 3D for three day average, similarly 5D, 7D, 15D etc.

  • Set period to 1W for a weekly average, 3W for three week average, similarly 2W, 4W etc.

  • Set period to 1M for monthly average

  • Set period to 1AS fo annual average

  • Can be a DateOffset object too

• aggregation_method (str) – Default mean, returns the mean of the data for the specified period. Can also use median, prod, sum, std,`var`, max, min which are shorthands for median, product, summation, standard deviation, variance, maximum and minimum respectively.

Returns

A DataFrame with data aggregated with the specified aggregation_method (mean by default) and coverage. The columns with coverage are named as <column name>_Coverage

Example usage

import brightwind as bw
data = bw.load_campbell_scientific(bw.datasets.demo_campbell_scientific_site_data)

#To find hourly coverage
data_hourly = bw.coverage(data.Spd80mN, period='1H')

#To find hourly coverage for multiple columns
data_hourly_multiple = bw.coverage(data[['Spd80mS','Spd60mN']], period='1H')

#To find monthly_coverage
data_monthly = bw.coverage(data.Spd80mN, period='1M')

#To find monthly_coverage of variance
data_monthly_var = bw.coverage(data.Spd80mN, period='1M', aggregation_method='var')

bw.average_data_by_period