params_samir_class.py

#! /usr/bin/env python
#-*- coding: utf-8 -*-
"""
11-07-2023 adapted from modspa-parcel code
@author: jeremy auclair

Classes to load and store SAMIR parameters.
"""

from pandas import read_csv  # to read csv parameter files


class samir_parameters:
    """
    Load all parameters for multiples classes in one object. 
    
    Attributes
    ----------
    
    1. .table: ``pd.DataFrame``
        ``pandas DataFrame`` with all paramters (rows) for all classes (columns)
 
    
        It also contains :
        
        - a ``scale_factor`` column (first column) that allows to convert all parameters to integer values for reduced memory usage
        
        - a ``Default`` column (second column) that contains default values to fill in missing values
        
        Example of the parameter table:
    
    
        .. code-block:: python
            
            ClassName       scale_factor   Default   no_sim  straw_cereal  oilseed        soy  sunflower      corn
            ClassNumber                1     0.000     1.000        2.000     3.000     4.000      5.000     6.000
            NDVIsol                    1     0.150     0.150        0.150     0.150     0.150      0.150     0.150
            NDVImax                    1     0.850     0.850        0.850     0.850     0.850      0.850     0.850
            FCmax                   1000     1.000     0.000        1.000     1.000     1.000      1.000     1.000
            Fslope                  1000     1.400     0.000        1.180     1.220     1.400      1.400     1.400
            Foffset                 1000    -0.075     0.000       -0.165    -0.170    -0.075     -0.075    -0.075
            Kcmax                   1000     1.150     0.000        1.150     0.700     1.150      1.150     1.150
            Kslope                  1000     1.200     0.000        1.470     1.350     1.200      1.200     1.200
            Koffset                 1000    -0.240     0.000       -0.170    -0.160    -0.240     -0.240    -0.240
            Zsoil                      1  2000.000  2000.000     2000.000  2000.000  2000.000   2000.000  2000.000
            Ze                         1   300.000   300.000      125.000   125.000   300.000    300.000   300.000
            Init_RU                 1000     0.870     0.870        0.500     0.500     0.870      0.870     0.870
            DiffE                      1     1.000     0.000        0.000     0.000     1.000      1.000     1.000
            DiffR                      1     5.000     0.000        0.000     0.000     5.000      5.000     5.000
            REW                        1     0.000     0.000        9.000     9.000     0.000      0.000     0.000
            minZr                      1   150.000     0.000      125.000   125.000   150.000    150.000   150.000
            maxZr                      1   600.000     0.000     1250.000  1450.000   600.000    600.000   600.000
            p                       1000     0.550     0.000        0.550     0.650     0.550      0.550     0.550
            FW                      1000     1.000     0.000        1.000     1.000     1.000      1.000     1.000
            Irrig_auto                 1     1.000     0.000        1.000     1.000     1.000      1.000     1.000
            Irrig_man                  1     0.000     0.000        0.000     0.000     0.000      0.000     0.000
            Kcb_stop_irrig             1     0.500     0.000        0.500     0.500     0.500      0.500     0.500
            
    
    Methods
    -------
    
    """

    
    def __init__(self, paramFile: str) -> None:
        """    
        Create pandas table from the csv parameter file.

        Arguments
        =========
        
        1. paramFile: ``str``
            path to csv parameter file
        
        """

        # Read csv file with Pandas
        csvFile = read_csv(paramFile)
        
        # Index file for correct conversion to dictionnary
        csvFile.index = csvFile.iloc[:,0]
        csvFile.drop(columns = ['ClassName'], inplace = True)
        
        # Replace missing parameters by their default values
        csvFile[csvFile.columns[1:]] = csvFile[csvFile.columns[1:]].astype('float32')
        for col in csvFile.columns[2:]:
            mask = csvFile[col].isna()
            csvFile.loc[mask, col] = csvFile.loc[mask, 'Default']

        # Store dataframe in attribute table
        self.table = csvFile