Real Time Branch - Implementation of 2020/21 Summer Research #5
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Earthquake Filter & uncertainty analysis
To prevent immediate errors with current forecasters I added back Ncl. - Once everyone hardcodes or works around this can remove from train.
The exclude dates in the ForecastModel call is only used to calculate 'zsc_X' - It is solely used to hide TEST data when calculatig the z score, - otherwise the eruption that was supposed to be "hidden" would skew the z score - ensure that if 'zsc_X' is in datastream MUST have necessary exclude_dates in ForecastModel() call - The same exclude_dates should be used in .train() USAGE: fm = ForecastModel(..., data_streams=['zsc_rsam'], exclude_dates=[[tei,tef],], ... ) fm.train(..., exclude_dates=[[tei,tef],], ...) NOTE: This is part of Annie Li's summer research 2020/21
By changing the __init__() remember to make changes to .hires_forecast() - a ForecastModel is created using positional arguments NOTE: Consider updating .hires_forecast() with name based arguments
- sig_params in forecast, hires_forecast to represent a and b - sigmoid() function - Return the calibrated probability by applying the sigmoid function to the model consensus. - plot_forecast_calibrated for probability version of plot_forecast - changes to y limits and threshold - similarly plot_hires_forecast_calibrated for plot_hires_forecast - important changes to compute_CI implementation
- same paradigm as forecast_dec2019() - sig_params calculated in s_calibration branch
* allows for stacking of ensemble mean and probability
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Key developments to whakaari code