"""
Author: HECE - University of Liege, Pierre Archambeau, Christophe Dessers
Date: 2024
Copyright (c) 2024 University of Liege. All rights reserved.
This script and its content are protected by copyright law. Unauthorized
copying or distribution of this file, via any medium, is strictly prohibited.
"""
from os import path
import matplotlib.pyplot as plt
import numpy.ma as ma
import numpy as np
from ..wolf_array import WolfArray
from ..PyTranslate import _
[docs]
class slope_stats():
def __init__(self,dir_charact, *args, **kwargs):
self.mydir=path.normpath(dir_charact)
self.reaches= WolfArray(self.mydir+'\\Drainage_basin.reachs')
self.slopes= WolfArray(self.mydir+'\\Drainage_basin.slope')
self.subs= WolfArray(self.mydir+'\\Drainage_basin.sub')
self.times= WolfArray(self.mydir+'\\Drainage_basin.time')
return super().__init__(*args, **kwargs)
[docs]
def init_subs(self):
self.nbsubs = int(np.max(self.subs.array))
self.mysubs={}
#Initialisation de la matrice de mask (d'une extension et d'une résolution similaire aux données radar)
for i in range(1,self.nbsubs+1):
self.mysubs[i]={}
self.mysubs[i]['name'] = 'sub n'+str(i)
self.mysubs[i]['mask'] = WolfArray(mold=self.subs)
self.mysubs[i]['mask'].mask_allexceptdata(float(i))
self.mysubs[i]['surface'] = self.mysubs[i]['mask'].nbnotnull * self.mysubs[i]['mask'].dx * self.mysubs[i]['mask'].dy
[docs]
def compute_stats(self):
self.mystats={}
self.mystats['slopemin'] = ma.min(self.slopes.array)
self.mystats['slopemax'] = ma.max(self.slopes.array)
self.mystats['slopemedian'] = ma.median(self.slopes.array)
self.mystats['slopemean'] = ma.mean(self.slopes.array)
self.mystats['hist'] = self.slopes.array.data[self.slopes.array.mask == False]
self.slopes.array.mask = np.logical_or(self.slopes.array.mask,np.logical_not(self.reaches.array.mask))
self.mystats['hist_watershed'] = self.slopes.array.data[self.slopes.array.mask == False]
self.slopes.array.mask = self.reaches.array.mask
self.mystats['hist_reaches'] = self.slopes.array.data[self.slopes.array.mask == False]
for i in range(1,self.nbsubs+1):
self.mysubs[i]['stats']={}
self.slopes.array.mask = self.mysubs[i]['mask'].array.mask
self.mysubs[i]['stats']['slopemin'] = ma.min(self.slopes.array)
self.mysubs[i]['stats']['slopemax'] = ma.max(self.slopes.array)
self.mysubs[i]['stats']['slopemedian'] = ma.median(self.slopes.array)
self.mysubs[i]['stats']['slopemean'] = ma.mean(self.slopes.array)
self.mysubs[i]['stats']['hist'] = self.slopes.array.data[self.slopes.array.mask == False]
self.slopes.array.mask = np.logical_or(self.slopes.array.mask,self.reaches.array.mask)
self.mysubs[i]['stats']['hist_reaches'] = self.slopes.array.data[self.slopes.array.mask == False]
self.slopes.array.mask = self.mysubs[i]['mask'].array.mask
self.slopes.array.mask = np.logical_or(self.slopes.array.mask,np.logical_not(self.reaches.array.mask))
self.mysubs[i]['stats']['hist_watershed'] = self.slopes.array.data[self.slopes.array.mask == False]
self.slopes.mask_data(0.)
[docs]
def plot_stats(self):
bins=[0,1e-8,1e-7,1e-5,1e-2,1e-1,2e-1,3e-1,4e-1,5e-1,6e-1,7e-1,8e-1,9e-1,1]
fig,ax = plt.subplots(3)
ax[0].hist(self.mystats['hist'],bins,cumulative=True,density=True)
ax[0].set_xscale('log')
#ax[0].set_yscale('log')
ax[0].set_xlabel('All meshes')
ax[1].hist(self.mystats['hist_watershed'],bins,cumulative=True,density=True)
ax[1].set_xscale('log')
#ax[1].set_yscale('log')
ax[1].set_xlabel('Watershed')
ax[2].hist(self.mystats['hist_reaches'],bins,cumulative=True,density=True)
ax[2].set_xscale('log')
#ax[2].set_yscale('log')
ax[2].set_xlabel('River')
nblines = int(np.ceil(np.sqrt(self.nbsubs+1)))
fig,ax=plt.subplots(nblines,nblines)
fig.suptitle('All meshes')
for i in range(1,self.nbsubs+1):
curax = ax[int(np.floor((i-1)/nblines)),int(np.mod((i-1),nblines))]
curax.hist(self.mysubs[i]['stats']['hist'],bins,cumulative=True,density=True)
curax.set_xscale('log')
#curax.set_yscale('log')
fig,ax=plt.subplots(nblines,nblines)
fig.suptitle('River reaches')
for i in range(1,self.nbsubs+1):
curax = ax[int(np.floor((i-1)/nblines)),int(np.mod((i-1),nblines))]
curax.hist(self.mysubs[i]['stats']['hist_reaches'],bins,cumulative=True,density=True)
curax.set_xscale('log')
#curax.set_yscale('log')
fig,ax=plt.subplots(nblines,nblines)
fig.suptitle('Watershed')
for i in range(1,self.nbsubs+1):
curax = ax[int(np.floor((i-1)/nblines)),int(np.mod((i-1),nblines))]
curax.hist(self.mysubs[i]['stats']['hist_watershed'],bins,cumulative=True,density=True)
curax.set_xscale('log')
#curax.set_yscale('log')
plt.show()