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loadMinimizerResult_checkCandidates.py
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import pandas as pd
import numpy as np
from lmfit import report_fit, fit_report
# from example_brute import plot_results_brute
# import matplotlib.pyplot as plt
import pickle
from gradeparamsbrute import plot_results_brute
# UGRHI SP ##
# tagname = '58220000'
# -------------------------
# tagname = '3D-001'
# -------------------------
# tagname = '4C-007'
# -------------------------
tagname = '4B-015'
# -------------------------
# tagname = '5B-011'
# -------------------------
############
# diresults = '/vol0/evandro/resultados/'
# diresults = '/home/evandro/lcbiag/ProcessoOtimizacaoModelos/resultados/'
diresults = '/dados/ProcessoOtimizacaoModelos/calibracaoBalagua/resultados/'
# diresults = '/vol0/evandro/lcbiag/' \
# 'ProcessoOtimizacaoModelos/calibracaoBalagua/resultados/'
# dirInput = '/vol0/evandro/lcbiag/ProcessoOtimizacaoModelos/' \
# 'calibracaoBalagua/dados/inputs/ugrhi_sp/'
dirInput = '/dados/ProcessoOtimizacaoModelos/' \
'calibracaoBalagua/dados/inputs/ugrhi_sp/'
# CRU
# arqvminres = tagname+'_ugrhi_bruteMinimizerResult.pkl'
# pngfigplot = '/vol0/evandro/lcbiag/' \
# 'ProcessoOtimizacaoModelos/calibracaoBalagua/' \
# 'plots/'+tagname+'_ugrhi_pltGridParams.png'
# input_df = pd.read_pickle(dirInput+tagname+'_ugrhi_sp.pkl')
# Xavier
arqvminres = tagname+'_xavier_ugrhi_bruteMinimizerResult.pkl'
pngfigplot = '/dados/' \
'ProcessoOtimizacaoModelos/calibracaoBalagua/' \
'plots/'+tagname+'_xavier_ugrhi_pltGridParams.png'
input_df = pd.read_pickle(dirInput+tagname+'_xavier_ugrhi_sp.pkl')
# pngfigplot = '/dados/ProcessoOtimizacaoModelos/' \
# 'calibracaoBalagua/plots/plotaBruteParams.png'
# pngfigplot = '/home/evandro/lcbiag/' \
# 'ProcessoOtimizacaoModelos/calibracaoBalagua/' \
# 'plots/'+tagname+'_pltGridParams.png'
# pngfigplot = '/dados/' \
# 'ProcessoOtimizacaoModelos/calibracaoBalagua/' \
# 'plots/'+tagname+'_pltGridParams.png'
out = pickle.load(open(diresults+arqvminres, "rb"))
"""
Verificacao dos parametros candidatos caculados
"""
# dirInput = '/vol0/evandro/ProcessoOtimizacaoModelos/' \
# 'calibracaoBalagua/dados/inputs/ugrhi_sp/'
# dirInput = '/home/evandro/lcbiag/ProcessoOtimizacaoModelos/' \
# 'calibracaoBalagua/dados/inputs/ugrhi_sp/'
# dirInput = '/dados/ProcessoOtimizacaoModelos/' \
# 'calibracaoBalagua/dados/inputs/ugrhi_sp/'
etp = input_df.etp
p2 = input_df.p
q2 = input_df.q
posval = np.asarray(~np.isnan(etp) &
~np.isnan(p2) &
~np.isnan(q2)).nonzero()
posval = posval[0]
etp = etp[posval]
p2 = p2[posval]
q2 = q2[posval]
# arqinput = 'input.txt'
# entradados = dirdados+arqinput
ncand = len(out.candidates)
for ck in range(ncand):
a1 = out.candidates[ck].params['a1']
a2 = out.candidates[ck].params['a2']
a22 = out.candidates[ck].params['a22']
a3 = out.candidates[ck].params['a3']
print(out.show_candidates(ck+1))
# dadosobs = pd.read_table(
# entradados, header=None, delim_whitespace=True, names=[
# 'ano', 'mes', 'dia', 'hora', 'minuto', 'segundo',
# 'etp', 'p2', 'q2', 'escb'])
m_func = len(etp)
modeloerro = np.zeros(m_func, dtype='float64')
#
ts_mt = np.zeros(m_func, dtype='float64')
ts_dt = np.zeros(m_func, dtype='float64')
ts_u = np.zeros(m_func, dtype='float64')
#
# etp = np.float64(dadosobs['etp'])
# p2 = np.float64(dadosobs['p2'])
# q2 = np.float64(dadosobs['q2'])
# escb = np.float64(dadosobs['escb'])
#
m1 = 500.
r2 = np.float64(0)
s2 = np.float64(0)
n2 = np.float64(0)
d2 = np.float64(0)
m2 = np.float64(0)
for kount in range(0, m_func):
"""
Modelo de balanco de agua
r2 => evapotranpiracao real
s2 => escoamento lento
n2 => precipitacao ativa
f2 => escoamento rapido
d2 => vazao com filtro
"""
r2 = min(
etp[kount]*(1.-a1**((p2[kount]+max(m1, 0.))/etp[kount])),
(p2[kount]+max(m1, 0.))
)
s2 = a2*(max(m1, 0.)**a22)
n2 = p2[kount]-etp[kount]*(1-np.exp(-p2[kount]/etp[kount]))
f2 = a3*max(m1, 0.)*n2
d2 = s2+f2
m2 = m1 + p2[kount] - r2 - d2
# print(d2, s2, f2, m2)
ts_mt[kount] = m2
ts_dt[kount] = d2
ts_u[kount] = (np.sqrt(q2[kount]) - np.sqrt(d2))
m1 = m2
# print(kount)
print('Media mt ---------------> ', np.average(ts_mt))
print('Media u ----------------> ', np.average(ts_u),
np.average(out.residual))
report_fit(out)
"""
Plota a grade de parametros
"""
plot_results_brute(out, best_vals=True, varlabels=None,
output=pngfigplot)
# plot_results_brute(out, best_vals=True, varlabels=None, output=None)