Metadata-Version: 2.1
Name: mva
Version: 0.0.1
Summary: A package to correct the bias of forecasts/hindcasts. Read the documentation at https://github.com/garovent/mva
Home-page: https://github.com/garovent/mva
Author: Naveen Goutham
Author-email: naveen.goutham@outlook.com
License: Apache License 2.0
Description: # Documentation  
        
        A python package to adjust the bias of probabilistic forecasts/hindcasts using "Mean and Variance Adjustment" method.
        
        Read documentation at [https://github.com/garovent/mva](https://github.com/garovent/mva)
        
        _References_:
        
        [1] Torralba, V., Doblas-Reyes, F. J., MacLeod, D., Christel, I. & Davis, M. Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources. Journal of Applied Meteorology and Climatology 56, 1231–1247 (2017).
        
        [2] Manzanas, R. et al. Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset. Clim Dyn 53, 1287–1305 (2019).
        
        ## _Installation:_
        
        ```sh
        pip install mva
        ```
        
        ## _Parameters:_
        
        **hindcast**: numpy.ndarray
        
        The hindcast (or training) data. Kindly maintain the shape of the array as (years/samples,time,ensemble-members,grid-points).
        
        **observation**: numpy.ndarray
        
        The truth or observations corresponding to the hindcast. Kindly maintain the shape of the array as (years/samples,time,grid-points).
            
        **forecast**: numpy.ndarray, optional
        
        The forecast (or test) data. Kindly maintain the shape of the array as (time,ensemble-members,grid-points).
        
        _Note_: Kindly respect the array shapes even if the computation is done for one time/grid point/ensemble member.
        
        ## _Methods:_
        
        **adjust_hindcast()**:
        
        This method corrects the bias of the hindcast using hindcast of the remaining years in the set (i.e., leave-one-out approach) and the corresponding observations.
        
        _Returns_:
        
        bias_adjusted_hindcast (Note: It has the same shape as the hindcast)
        
        **adjust_forecast()**:
        
        This method corrects the bias of the forecast using hindcast and the corresponding observations. This method works only when the forecast parameter is given.
        
        _Returns_:
        
        bias_adjusted_forecast (Note: It has the same shape as the forecast)
        
        ## _Demonstration:_
        
        ```sh
        import numpy as np
        import mva.mva as mva
        ```
        Let's imagine that we have loaded the data of hindcast, forecast, and observation.
        
        Example - 1
        ```sh
        In [1]: hcast.shape
        Out[1]: (20,46,10,6)
        In [2]: fcast.shape
        Out[2]: (46,50,6)
        In [3]: obs.shape
        Out[3]: (20,46,6)
        In [4]: bc = mva(hcast,obs,fcast)
        In [5]: ad_hcast = bc.adjust_hindcast()
        In [6]: ad_hcast.shape
        Out[6]: (20,46,10,6)
        In [7]: ad_fcast = bc.adjust_forecast()
        In [8]: ad_fcast.shape
        Out[8]: (46,50,6)
        ```
        
        Example - 2
        ```sh
        In [1]: hcast.shape
        Out[1]: (20,46,10,6)
        In [2]: fcast.shape
        Out[2]: (48,50,6)
        In [3]: obs.shape
        Out[3]: (20,46,6)
        In [4]: ad_hcast = mva(hcast,obs,fcast).adjust_hindcast()
        In [5]: ad_hcast.shape
        Out[5]: (20,46,10,6)
        In [6]: ad_fcast = mva(hcast,obs,fcast).adjust_forecast()
        Out[6]: Please respect the array shapes and try again!
        ```
        
Keywords: mva,mean,variance,bias correction,mean and variance adjustment,bias adjustment,calibration,python
Platform: UNKNOWN
Description-Content-Type: text/markdown
