#! /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
from numpy import nan # to fill nan values
import param # type: ignore
class samir_parameters_LC:
"""
This class allows to store all the SAMIR parameters for one land cover class
"""
def __init__(self, csvLine, defaultClass, mode_init = 1):
# List of parameters that will be optimised (and hence that are not read in the param csv file)
self.optimList = []
if defaultClass:
#print(csvLine)
for v in csvLine.values():
if v == '':
raise ValueError("All fields must be filled for the default value line")
self.name = csvLine['ClassName']
self.number = int(csvLine['ClassNumber'])
# Parameters for the NDVI - Fraction Cover relation
if (csvLine['FminFC'] != "optim"):
self.ndviFCminFC = param.Number(float(csvLine['FminFC']), bounds=(0., 1.)).default
else:
self.optimList.append("FminFC")
if (csvLine['FmaxFC'] != "optim"):
self.ndviFCmaxFC = param.Number(float(csvLine['FmaxFC']), bounds=(0., 1.)).default
else:
self.optimList.append("FmaxFC")
if (csvLine['Fslope'] != "optim"):
self.ndviFCslope = param.Number(float(csvLine['Fslope']), bounds=(0., 10)).default
else:
self.optimList.append("Fslope")
if (csvLine['Foffset'] != "optim"):
self.ndviFCoffset = param.Number(float(csvLine['Foffset']), bounds=(-1, 1)).default
else:
self.optimList.append("Foffset")
if (csvLine['Plateau'] != "optim"):
self.ndviPlateau = param.Number(int(float(csvLine['Plateau'])), bounds=(0, 365)).default
else:
self.optimList.append("Plateau")
# Parameters for the NDVI -Kcb relation
if (csvLine['KminKcb'] != "optim"):
self.ndviKcbminKcb = param.Number(float(csvLine['KminKcb']), bounds=(0, 0.5)).default
else:
self.optimList.append("KminKcb")
if (csvLine['KmaxKcb'] != "optim"):
self.ndviKcbmaxKcb = param.Number(float(csvLine['KmaxKcb']), bounds=(0.5, 2)).default
else:
self.optimList.append("KmaxKcb")
if (csvLine['Kslope'] != "optim"):
self.ndviKcbslope = param.Number(float(csvLine['Kslope'])).default
else:
self.optimList.append("Kslope")
if (csvLine['Koffset'] != "optim"):
self.ndviKcboffset = param.Number(float(csvLine['Koffset'])).default
else:
self.optimList.append("Koffset")
# Soil parameters
if (csvLine['Zsoil'] != "optim"):
self.Zsoil = param.Number(float(csvLine['Zsoil']), bounds=(100, 10000), doc = "Soil depth (in mm)").default
else:
self.optimList.append("Zsoil")
if (csvLine['Ze'] != "optim"):
self.Ze = param.Number(float(csvLine['Ze']), bounds=(1, self.Zsoil), doc = "Evaporative layer depth (in mm)").default
else:
self.optimList.append("Ze")
if mode_init == 1 or mode_init == 3:
if (csvLine['Init_RU'] != "optim"):
self.Init_RU = param.Number(float(csvLine['Init_RU']), doc = "Filling rate of the available water").default
else:
self.optimList.append("Init_RU")
else :
self.Dei = param.Number(float(csvLine['Init_Dei']), bounds=(0, None), doc = "Initial Depletion of the evaporative layer (irrigation + precipitation) (in mm)").default
self.Dep = param.Number(float(csvLine['Init_Dep']), bounds=(0, None), doc = "Initial Depletion of the evaporative layer (precipitation only) (in mm)").default
self.Dr = param.Number(float(csvLine['Init_Dr']), bounds=(0, None), doc = "Initial Depletion of the root layer (in mm)").default
self.Dd = param.Number(float(csvLine['Init_Dd']), bounds=(0, None), doc = "Initial Depletion of the deep layer (in mm)").default
if (csvLine['DiffE'] != "optim"):
self.DiffE = param.Number(float(csvLine['DiffE']), bounds=(0, 1000), doc = "Diffusion coefficient between evaporative and root layers (unitless)").default
else:
self.optimList.append("DiffE")
if (csvLine['DiffR'] != "optim"):
self.DiffR = param.Number(float(csvLine['DiffR']), bounds=(0, 1000), doc = "Diffusion coefficient between root and deep layers (unitless)").default
else:
self.optimList.append("DiffR")
if (csvLine['REW'] != "optim"):
self.REW = param.Number(float(csvLine['REW']), bounds=(-1000, 1000), doc = "Readily Evaporable Water (in mm)").default
else:
self.optimList.append("REW")
if (csvLine['m'] != "optim"):
self.m = param.Number(float(csvLine['m']), bounds=(0, 1), doc = "").default ## si utilise, REW minimum doit etre à 0 ?
else:
self.optimList.append("m")
# Crop parameters
if (csvLine['minZr'] != "optim"):
if (csvLine['Ze'] != "optim") & (csvLine['Zsoil'] != "optim"):
self.minZr = param.Number(float(csvLine['minZr']), bounds=(self.Ze, self.Zsoil), doc = "Minimum root depth (mm)").default
else:
self.minZr = param.Number(float(csvLine['minZr']), bounds=(1, 10000), doc = "Minimum root depth (mm)").default
else:
self.optimList.append("minZr")
if (csvLine['maxZr'] != "optim"):
if (csvLine['Zsoil'] != "optim"):
self.maxZr = param.Number(float(csvLine['maxZr']), bounds=(0, self.Zsoil-1), doc = "Maximum root depth (mm)").default
else:
self.maxZr = param.Number(float(csvLine['maxZr']), bounds=(0, 10000-1), doc = "Maximum root depth (mm)").default
else:
self.optimList.append("maxZr")
if (csvLine['p'] != "optim"):
self.p = param.Number(float(csvLine['p']), bounds=(0, 1), doc = "Fraction of readily available water").default
else:
self.optimList.append("p")
# Irrigation parameters
self.irrigFW = param.Number(float(csvLine['FW']), bounds=(0, 100), doc = "% of soil wetted by irrigation").default
self.Irrig_auto = param.Integer(int(float(csvLine['Irrig_auto'])), doc = "1 if the automatic irrigation mode is activated").default
self.Irrig_man = param.Integer(int(float(csvLine['Irrig_man'])), doc = "1 if the manual irrigation mode is activated").default
if (csvLine['Lame_max'] != "optim"):
self.Lame_max = param.Number(float(csvLine['Lame_max']), doc = "Maximum of irrigation height for each irrigation event (in mm)").default
else:
self.optimList.append("Lame_max")
if (csvLine['minDays'] != "optim"):
self.irrigMinDays = param.Integer(int(float(csvLine['minDays'])), bounds=(0, None), doc = "Minimum number of days between two irrigation events").default
else:
self.optimList.append("minDays")
if (csvLine['Kcbmin_start'] != "optim"):
self.Kcbmin_start = param.Number(float(csvLine['Kcbmin_start']), bounds=(0, 1), doc = "Minimum Kcb value above which irrigation may start").default
else:
self.optimList.append("Kcbmin_start")
if (csvLine['Kcbmax_stop'] != "optim"):
self.Kcbmax_stop = param.Number(float(csvLine['Kcbmax_stop']), bounds=(0, 1), doc = "Fraction of peak Kcb value below which irrigation stops").default
else:
self.optimList.append("Kcbmax_stop")
if (csvLine['Kcmax'] != "optim"):
self.Kcmax = param.Number(float(csvLine['Kcmax']), bounds=(0, None), doc = "pas d'info").default
else:
self.optimList.append("Kcmax")
if (csvLine['Fc_stop'] != "optim"):
self.Fc_stop = param.Number(float(csvLine['Fc_stop']), bounds=(0, None), doc = "pas d'info").default
else:
self.optimList.append("Fc_stop")
if (csvLine['Start_date_Irr'] != "optim"):
self.Start_date_Irr = param.Number(int(float(csvLine['Start_date_Irr'])), bounds=(0, None), doc = "pas d'info").default
else:
self.optimList.append("Start_date_Irr")
if (csvLine["p_trigger"] != "optim"):
self.p_trigger = param.Number(float(csvLine['p_trigger']), bounds=(-1, 1), doc = "Fraction of water storage capacity below which irrigation is triggered").default
else:
self.optimList.append("Fc_stop")
def setParam(self, paramName, value):
# Soil parameters
if paramName == "REW":
self.REW = value
elif paramName == "Init_RU":
self.Init_RU = value
elif paramName == "minZr":
self.minZr = value
elif paramName == "maxZr":
self.maxZr = value
elif paramName == "Ze":
self.Ze = value
elif paramName == "Zsoil":
self.Zsoil = value
elif paramName == "DiffR":
self.DiffR = value
elif paramName == "DiffE":
self.DiffE = value
# Irrigation parameters
elif paramName == "Lame_max":
self.Lame_max = value
elif paramName == "minDays":
self.irrigMinDays = value
# Vegetation parameters
elif paramName == "FminFC":
self.ndviFCminFC = value
elif paramName == "FmaxFC":
self.ndviFCmaxFC = value
elif paramName == "Fc_stop":
self.Fc_stop = value
elif paramName == "Kcmax":
self.Kcmax = value
elif paramName == "Kcbmin_start" :
self.Kcbmin_start = value
elif paramName == "Kcbmax_stop" :
self.Kcbmax_stop = value
elif paramName == "Plateau" :
self.ndviPlateau = value
elif paramName == "Fslope" :
self.ndviFCslope = value
elif paramName == "Foffset" :
self.ndviFCoffset = value
elif paramName == "KmaxKcb" :
self.ndviKcbmaxKcb = value
elif paramName == "KminKcb" :
self.ndviKcbminKcb = value
elif paramName == "Kslope" :
self.ndviKcbslope = value
elif paramName == "Koffset" :
self.ndviKcboffset = value
elif paramName == "m" :
self.m = value
elif paramName == "p" :
self.p = value
elif paramName == "p_trigger":
self.p_trigger = value
class samir_parameters:
"""
Load all parameters for multiples classes in one object.
"""
def __init__(self, paramFile, mode_init = 1):
self.classes = {}
# Read csv file with Pandas
csvFile = read_csv(paramFile, header = None)
# Index file for correct conversion to dictionnary
csvFile.index = csvFile.iloc[:,0]
csvFile.replace(nan, '', inplace = True)
defaultClass = True
# Loop on columns
for column in csvFile.columns[1:]:
# Convert pandas column to dictionnary
line = csvFile[column].to_dict()
#TODO : @VR+@CO Intoduire ici une verification des valeurs
#!! notamment si min=max alors error_rel=0
#!! Ajouter la possibilité de configurer plusieurs land cover
if defaultClass:
defaultLine = line.copy()
elif line['ClassName'] in ['error_rel','error_abs','min','max']:
self.classes[line['ClassName']] ={}
for k in line.keys():
if k != 'ClassName':
if line[k] == '':
line[k] = 0
self.classes[line['ClassName']][k] = float(line[k])
continue
else:
for k in line.keys():
if line[k] == '':
line[k] = defaultLine[k]
self.classes[line['ClassName']] = samir_parameters_LC(line, defaultClass, mode_init)
defaultClass = False