def interaction(river_name, path_to_scheme, path_to_observations,\
parBETA, parCET, parFC, parK0, parK1, parK2, parLP, parMAXBAS,\
parPERC, parUZL, parPCORR, parTT, parCFMAX, parSFCF, parCFR, parCWH):
# simulate our modeled hydrograph
data = dataframe_construction(path_to_scheme)
data['Qsim'] = simulation(data, [parBETA, parCET, parFC, parK0, parK1,\
parK2, parLP, parMAXBAS, parPERC, parUZL, parPCORR, parTT, parCFMAX,\
parSFCF, parCFR, parCWH])
# read observations
obs = pd.read_csv(path_to_observations,
index_col=0,
parse_dates=True,
squeeze=True,
header=None,
names=['Date', 'Qobs'])
# concatenate data
data = pd.concat([data, obs], axis=1)
# calculate efficiency criterion
# slice data only for observational period and drop NA values
data_for_obs = data.ix[obs.index, ['Qsim', 'Qobs']].dropna()
eff = NS(data_for_obs['Qobs'], data_for_obs['Qsim'])
# plot
ax = data.ix[obs.index, ['Qsim', 'Qobs']].plot(figsize=(10, 7),
style=['b-', 'k.'])
ax.set_title(river_name + ' daily runoff modelling, ' +
'Nash-Sutcliffe efficiency: {}'.format(np.round(eff, 2)))
def interaction(river_name, path_to_scheme, path_to_observations, INSC, COEFF,
SQ, SMSC, SUB, CRAK, K, etmul, DELAY, X_m, X5, X6):
# simulate our modeled hydrograph
data = dataframe_construction(path_to_scheme)
data['Qsim'] = simulation(
data, [INSC, COEFF, SQ, SMSC, SUB, CRAK, K, etmul, DELAY, X_m, X5, X6])
# read observations
obs = pd.read_csv(path_to_observations,
index_col=0,
parse_dates=True,
squeeze=True,
header=None,
names=['Date', 'Qobs'])
# concatenate data
data = pd.concat([data, obs], axis=1)
# calculate efficiency criterion
# slice data only for observational period and drop NA values
data_for_obs = data.ix[obs.index, ['Qsim', 'Qobs']].dropna()
eff = NS(data_for_obs['Qobs'], data_for_obs['Qsim'])
# plot
ax = data.ix[obs.index, ['Qsim', 'Qobs']].plot(figsize=(10, 7),
style=['b-', 'k.'])
ax.set_title(river_name + ' daily runoff modelling, ' +
'Nash-Sutcliffe efficiency: {}'.format(np.round(eff, 2)))
def interaction(river_name, path_to_scheme, path_to_observations,
X1, X2, X3, X4, X5, X6):
# import modules for interaction()
import pandas as pd
import sys
sys.path.append('../tools/')
from wfdei_to_lumped_dataframe import dataframe_construction
from metrics import NS
# simulate our modeled hydrograph
data = dataframe_construction(path_to_scheme)
data['Qsim'] = simulation(data, [X1, X2, X3, X4, X5, X6])
# read observations
obs = pd.read_csv(path_to_observations, index_col=0, parse_dates=True,
squeeze=True, header=None, names=['Date', 'Qobs'])
# concatenate data
data = pd.concat([data, obs], axis=1)
# calculate efficiency criterion
# slice data only for observational period and drop NA values
data_for_obs = data.ix[obs.index, ['Qsim', 'Qobs']].dropna()
eff = NS(data_for_obs['Qobs'], data_for_obs['Qsim'])
# plot
ax = data.ix[obs.index, ['Qsim', 'Qobs']].plot(figsize=(10, 7), style=['b-', 'k.'])
ax.set_title(river_name + ' daily runoff modelling, ' + 'Nash-Sutcliffe efficiency: {}'.format(np.round(eff, 2)))
def interaction(river_name, path_to_scheme, path_to_observations,\
parBETA, parCET, parFC, parK0, parK1, parK2, parLP, parMAXBAS,\
parPERC, parUZL, parPCORR, parTT, parCFMAX, parSFCF, parCFR, parCWH):
# simulate our modeled hydrograph
data = dataframe_construction(path_to_scheme)
data['Qsim'] = simulation(data, [parBETA, parCET, parFC, parK0, parK1,\
parK2, parLP, parMAXBAS, parPERC, parUZL, parPCORR, parTT, parCFMAX,\
parSFCF, parCFR, parCWH])
# read observations
obs = pd.read_csv(path_to_observations, index_col=0, parse_dates=True,
squeeze=True, header=None, names=['Date', 'Qobs'])
# concatenate data
data = pd.concat([data, obs], axis=1)
# calculate efficiency criterion
# slice data only for observational period and drop NA values
data_for_obs = data.ix[obs.index, ['Qsim', 'Qobs']].dropna()
eff = NS(data_for_obs['Qobs'], data_for_obs['Qsim'])
# plot
ax = data.ix[obs.index, ['Qsim', 'Qobs']].plot(figsize=(10, 7), style=['b-', 'k.'])
ax.set_title(river_name + ' daily runoff modelling, ' + 'Nash-Sutcliffe efficiency: {}'.format(np.round(eff, 2)))
def interaction(river_name, path_to_scheme, path_to_observations,\
INSC, COEFF, SQ, SMSC, SUB, CRAK, K, etmul, DELAY, X_m, X5, X6):
# simulate our modeled hydrograph
data = dataframe_construction(path_to_scheme)
data['Qsim'] = simulation(data, [INSC, COEFF, SQ, SMSC, SUB, CRAK, K,\
etmul, DELAY, X_m, X5, X6])
# read observations
obs = pd.read_csv(path_to_observations, index_col=0, parse_dates=True,
squeeze=True, header=None, names=['Date', 'Qobs'])
# concatenate data
data = pd.concat([data, obs], axis=1)
# calculate efficiency criterion
# slice data only for observational period and drop NA values
data_for_obs = data.ix[obs.index, ['Qsim', 'Qobs']].dropna()
eff = NS(data_for_obs['Qobs'], data_for_obs['Qsim'])
# plot
ax = data.ix[obs.index, ['Qsim', 'Qobs']].plot(figsize=(10, 7), style=['b-', 'k.'])
ax.set_title(river_name + ' daily runoff modelling, ' + 'Nash-Sutcliffe efficiency: {}'.format(np.round(eff, 2)))