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visuals.py
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visuals.py
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'''
PLANS - Planning Nature-based Solutions
Visual routines
Copyright (C) 2022 Iporã Brito Possantti
************ GNU GENERAL PUBLIC LICENSE ************
https://www.gnu.org/licenses/gpl-3.0.en.html
Permissions:
- Commercial use
- Distribution
- Modification
- Patent use
- Private use
Conditions:
- Disclose source
- License and copyright notice
- Same license
- State changes
Limitations:
- Liability
- Warranty
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
'''
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import warnings
warnings.filterwarnings("ignore")
def _custom_cmaps():
from matplotlib import cm
from matplotlib.colors import ListedColormap
#
earth_big = cm.get_cmap('gist_earth_r', 512)
earthcm = ListedColormap(earth_big(np.linspace(0.10, 0.95, 256)))
#
jet_big = cm.get_cmap('jet_r', 512)
jetcm = ListedColormap(jet_big(np.linspace(0.3, 0.75, 256)))
#
jet_big2 = cm.get_cmap('jet', 512)
jetcm2 = ListedColormap(jet_big2(np.linspace(0.1, 0.9, 256)))
#
viridis_big = cm.get_cmap('viridis_r', 512)
viridiscm = ListedColormap(viridis_big(np.linspace(0.05, 0.9)))
return {'flow_v':jetcm, 'D':jetcm2, 'flow':earthcm, 'stk':viridiscm, 'sed':'hot_r'}
def pannel_obs_sim_analyst(series, freq, params, fld_obs='Obs', fld_sim='Sim', fld_date='Date', filename='analyst', suff='',
folder='C:/bin', show=False, log=True, units='flow', title='Obs/Sim Analysis'):
"""
Pannel of Obs vs. Sim Analyst
:param series: Series Analyst dataframe (from obs_sim_analyst() function in tools.py)
:param freq: Frequency Analyst dataframe (from obs_sim_analyst() function in tools.py)
:param params: Parameters Analyst dataframe
:param fld_obs: string of field of observed series data
:param fld_sim: string of field of simulated series data
:param fld_date: string of date field
:param filename: string of file name
:param suff: optional string for suffix
:param folder: string of export directory
:param show: boolean to control showing figure
:return: string of file
"""
#
fig = plt.figure(figsize=(18, 9)) # Width, Height
gs = mpl.gridspec.GridSpec(5, 13, wspace=0.9, hspace=0.9, left=0.05, bottom=0.1, top=0.9, right=0.95)
fig.suptitle(title)
if units == 'flow':
units = 'mm/d'
elif units == 'stock':
units = 'mm'
#
# min max setup
vmax = np.max((np.max(series[fld_obs]), np.max(series[fld_sim])))
vmin = np.min((np.min(series[fld_obs]), np.min(series[fld_sim])))
#
# plot of CFCs
plt.subplot(gs[0:2, 0:2])
plt.title('CFCs', loc='left')
plt.plot(freq['Exceedance'], freq['ValuesObs'], 'tab:grey', label='Obs')
plt.plot(freq['Exceedance'], freq['ValuesSim'], 'tab:blue', label='Sim')
plt.ylim((vmin, 1.2 * vmax))
if log:
plt.yscale('log')
plt.ylabel(units)
plt.xlabel('Exeed. %')
plt.grid(True)
plt.legend(loc='upper right')
#
# plot of series
plt.subplot(gs[0:2, 3:10])
plt.title('Series', loc='left')
plt.plot(series[fld_date], series[fld_obs], 'tab:grey', linewidth=2, label='Observed')
plt.plot(series[fld_date], series[fld_sim], 'tab:blue', label='Simulated')
plt.ylim((vmin, 1.2 * vmax))
if log:
plt.yscale('log')
plt.ylabel(units)
plt.grid(True)
plt.legend(loc='upper right', ncol=2)
#
# plot of Scatter
plt.subplot(gs[0:2, 11:])
plt.title('Obs vs. Sim (R={:.2f})'.format(float(params[params['Parameter'] == 'R']['Value'])), loc='left')
plt.scatter(series[fld_obs], series[fld_sim], c='tab:grey', s=15, alpha=0.3, edgecolors='none')
plt.xlabel('Obs ({})'.format(units))
plt.ylabel('Sim ({})'.format(units))
if log:
plt.xscale('log')
plt.yscale('log')
plt.plot([0, vmax], [0, vmax], 'tab:grey', linestyle='--', label='1:1')
plt.ylim((vmin, 1.2 * vmax))
plt.xlim((vmin, 1.2 * vmax))
plt.grid(True)
plt.legend(loc='upper left')
#
# plot of CFC Erros
plt.subplot(gs[2, 0:2])
plt.title('CFC Error', loc='left')
plt.plot(freq['Exceedance'], freq['E'], 'tab:red')
plt.ylabel(units)
plt.xlabel('Exceed. %')
plt.grid(True)
#
# plot Error
plt.subplot(gs[2, 3:10])
plt.title('Series - Error', loc='left')
plt.plot(series[fld_date], series['E'], 'tab:red')
plt.ylabel(units)
plt.grid(True)
#
# plot
plt.subplot(gs[3, 0:2])
plt.title('CFC - Squared Error', loc='left')
plt.plot(freq['Exceedance'], freq['SE'], 'tab:red')
plt.xlabel('Exceed. %')
plt.grid(True)
#
# plot
plt.subplot(gs[3, 3:10])
plt.title('Series - Sq. Error', loc='left')
plt.plot(series[fld_date], series['SE'], 'tab:red')
plt.grid(True)
#
plt.subplot(gs[3, 11:])
plt.title('Analyst parameters', loc='left')
plt.text(x=0, y=0.8, s='Pbias : {:.2f}%'.format(float(params[params['Parameter'] == 'PBias']['Value'])))
plt.text(x=0, y=0.6, s='R : {:.2f}'.format(float(params[params['Parameter'] == 'R']['Value'])))
plt.text(x=0, y=0.4, s='RMSE : {:.2f} mm'.format(float(params[params['Parameter'] == 'RMSE']['Value'])))
plt.text(x=0, y=0.2, s='NSE : {:.2f}'.format(float(params[params['Parameter'] == 'NSE']['Value'])))
plt.text(x=0, y=0.0, s='KGE : {:.2f}'.format(float(params[params['Parameter'] == 'KGE']['Value'])))
if log:
plt.text(x=0, y=-0.2, s='KGElog : {:.2f}'.format(float(params[params['Parameter'] == 'KGElog']['Value'])))
plt.text(x=0, y=-0.4, s='RMSElog : {:.2f}'.format(float(params[params['Parameter'] == 'RMSElog']['Value'])))
plt.text(x=0, y=-0.6, s='NSElog : {:.2f}'.format(float(params[params['Parameter'] == 'NSElog']['Value'])))
plt.text(x=0, y=-1.0, s='CFC-R : {:.2f}'.format(float(params[params['Parameter'] == 'R-CFC']['Value'])))
plt.text(x=0, y=-1.2, s='CFC-RMSE : {:.2f}'.format(float(params[params['Parameter'] == 'RMSE-CFC']['Value'])))
if log:
plt.text(x=0, y=-1.4, s='CFC-RMSElog : {:.2f}'.format(float(params[params['Parameter'] == 'RMSElog-CFC']['Value'])))
plt.axis('off')
#
if log:
# plot
plt.subplot(gs[4, 0:2])
plt.title('CFC - Sq. Error of Log', loc='left')
plt.plot(freq['Exceedance'], freq['SElog'], 'tab:red')
plt.xlabel('Exceed. %')
plt.grid(True)
# plot
plt.subplot(gs[4, 3:10])
plt.title('Series - Sq. Error of Log', loc='left')
plt.plot(series[fld_date], series['SElog'], 'tab:red')
plt.grid(True)
#
if show:
plt.show()
plt.close(fig)
else:
# export file
if suff != '':
filepath = folder + '/' + filename + '_' + suff + '.png'
else:
filepath = folder + '/' + filename + '.png'
plt.savefig(filepath)
plt.close(fig)
return filepath
def pannel_calib_valid(series_full, series_calib, series_valid, freq_full, params_full, params_calib, params_valid,
fld_obs='Obs', fld_sim='Sim', fld_date='Date',
filename='analyst_CVF', suff='', folder='C:/bin',
show=False, log=True, units='flow', title='Obs/Sim Analysis'):
"""
Plot Calibration/Validation/Full Pannel - CVF Analyst
:param series_full: pandas dataframe
:param series_calib: pandas dataframe
:param series_valid: pandas dataframe
:param freq_full: pandas dataframe
:param params_full: pandas dataframe
:param params_calib: pandas dataframe
:param params_valid: pandas dataframe
:param fld_obs: string of field of OBS
:param fld_sim: string of field of SIM
:param fld_date: string of field of Date or X axis
:param filename: string of filename
:param suff: string suffix
:param folder: string to foldepath
:param show: boolean to show instead of save
:param log: boolean to log scale
:param units: string of units type. Options: 'flow' or 'stock'
:param title: string of superior title
:return: string of filepath
"""
#
fig = plt.figure(figsize=(18, 9)) # Width, Height
gs = mpl.gridspec.GridSpec(5, 13, wspace=0.9, hspace=0.9, left=0.05, bottom=0.1, top=0.9, right=0.95)
fig.suptitle(title)
if units == 'flow':
units = 'mm/d'
elif units == 'stock':
units = 'mm'
#
# min max setup
vmax = np.max((np.max(series_full[fld_obs]), np.max(series_full[fld_sim])))
vmin = np.min((np.min(series_full[fld_obs]), np.min(series_full[fld_sim])))
#
# plot of CFCs
plt.subplot(gs[0:2, 0:2])
plt.title('CFCs - Full', loc='left')
plt.plot(freq_full['Exceedance'], freq_full['ValuesObs'], 'tab:grey', label='Obs.')
plt.plot(freq_full['Exceedance'], freq_full['ValuesSim'], 'blue', label='Sim.')
plt.ylim((vmin, 1.2 * vmax))
if log:
plt.yscale('log')
plt.ylabel(units)
plt.xlabel('Exeed. %')
plt.grid(True)
plt.legend(loc='upper right')
#
# plot of series
plt.subplot(gs[0:2, 3:10])
plt.title('Series', loc='left')
plt.plot(series_full[fld_date], series_full[fld_obs], 'tab:grey', linewidth=2, label='Observed')
plt.plot(series_calib[fld_date], series_calib[fld_sim], 'tab:blue', label='Calibration')
plt.plot(series_valid[fld_date], series_valid[fld_sim], 'navy', label='Validation')
plt.ylim((vmin, 1.5 * vmax))
if log:
plt.yscale('log')
plt.ylabel(units)
plt.grid(True)
plt.legend(loc='upper right', ncol=3)
#
# plot of Scatter
plt.subplot(gs[0:2, 11:])
params = params_full
plt.title('Obs vs. Sim (R={:.2f})'.format(float(params[params['Parameter'] == 'R']['Value'])), loc='left')
plt.scatter(series_calib[fld_obs], series_calib[fld_sim], c='tab:blue', s=15, alpha=0.3, edgecolors='none', label='Calib.')
plt.scatter(series_valid[fld_obs], series_valid[fld_sim], c='navy', s=15, alpha=0.6, edgecolors='none', label='Valid.')
plt.xlabel('Obs ({})'.format(units))
plt.ylabel('Sim ({})'.format(units))
if log:
plt.xscale('log')
plt.yscale('log')
plt.plot([0, vmax], [0, vmax], 'tab:grey', linestyle='--', label='1:1')
plt.ylim((vmin, 1.2 * vmax))
plt.xlim((vmin, 1.2 * vmax))
plt.grid(True)
plt.legend(loc='upper left')
#
# plot of CFC Erros
plt.subplot(gs[2, 0:2])
plt.title('CFC Error', loc='left')
plt.plot(freq_full['Exceedance'], freq_full['E'], 'tab:red')
plt.ylabel(units)
plt.xlabel('Exceed. %')
plt.grid(True)
#
# plot Error
plt.subplot(gs[2, 3:10])
plt.title('Series - Error', loc='left')
plt.plot(series_calib[fld_date], series_calib['E'], 'tab:red')
plt.plot(series_valid[fld_date], series_valid['E'], 'maroon')
plt.ylabel(units)
plt.grid(True)
#
# plot
plt.subplot(gs[3, 0:2])
plt.title('CFC - Squared Error', loc='left')
plt.plot(freq_full['Exceedance'], freq_full['SE'], 'tab:red')
plt.xlabel('Exceed. %')
plt.grid(True)
#
# plot
plt.subplot(gs[3, 3:10])
plt.title('Series - Sq. Error', loc='left')
plt.plot(series_calib[fld_date], series_calib['SE'], 'tab:red')
plt.plot(series_valid[fld_date], series_valid['SE'], 'maroon')
plt.grid(True)
#
plt.subplot(gs[3, 11:])
plt.title('Analyst parameters', loc='left')
subtls = ['Full', 'Calib.', 'Valid.']
all_params = [params_full, params_calib, params_valid]
xs = [-0.8, 0.15, 1.1]
for i in range(len(subtls)):
params = all_params[i]
plt.text(x=xs[i], y=0.8, s='{}'.format(subtls[i]))
plt.text(x=xs[i], y=0.6, s='Pbias : {:.2f}%'.format(float(params[params['Parameter'] == 'PBias']['Value'])))
plt.text(x=xs[i], y=0.4, s='R : {:.2f}'.format(float(params[params['Parameter'] == 'R']['Value'])))
plt.text(x=xs[i], y=0.2, s='RMSE : {:.2f} mm'.format(float(params[params['Parameter'] == 'RMSE']['Value'])))
plt.text(x=xs[i], y=0.0, s='NSE : {:.2f}'.format(float(params[params['Parameter'] == 'NSE']['Value'])))
plt.text(x=xs[i], y=-0.2, s='KGE : {:.2f}'.format(float(params[params['Parameter'] == 'KGE']['Value'])))
if log:
plt.text(x=xs[i], y=-0.4, s='KGElog : {:.2f}'.format(float(params[params['Parameter'] == 'KGElog']['Value'])))
plt.text(x=xs[i], y=-0.6, s='RMSElog : {:.2f}'.format(float(params[params['Parameter'] == 'RMSElog']['Value'])))
plt.text(x=xs[i], y=-0.8, s='NSElog : {:.2f}'.format(float(params[params['Parameter'] == 'NSElog']['Value'])))
if subtls[i] == 'Full':
plt.text(x=xs[i], y=-1.2, s='CFC-R : {:.2f}'.format(float(params[params['Parameter'] == 'R-CFC']['Value'])))
plt.text(x=xs[i], y=-1.4, s='CFC-RMSE : {:.2f}'.format(float(params[params['Parameter'] == 'RMSE-CFC']['Value'])))
if log:
plt.text(x=xs[i], y=-1.6,
s='CFC-RMSElog : {:.2f}'.format(float(params[params['Parameter'] == 'RMSElog-CFC']['Value'])))
plt.axis('off')
#
if log:
# plot
plt.subplot(gs[4, 0:2])
plt.title('CFC - Sq. Error of Log', loc='left')
plt.plot(freq_full['Exceedance'], freq_full['SElog'], 'tab:red')
plt.xlabel('Exceed. %')
plt.grid(True)
# plot
plt.subplot(gs[4, 3:10])
plt.title('Series - Sq. Error of Log', loc='left')
plt.plot(series_calib[fld_date], series_calib['SElog'], 'tab:red')
plt.plot(series_valid[fld_date], series_valid['SElog'], 'maroon')
plt.grid(True)
#
if show:
plt.show()
plt.close(fig)
else:
# export file
if suff != '':
filepath = folder + '/' + filename + '_' + suff + '.png'
else:
filepath = folder + '/' + filename + '.png'
plt.savefig(filepath)
plt.close(fig)
return filepath
def pannel_local(series, star, deficit, sups, mids, star_rng, deficit_rng,
sup1_rng, sup2_rng, sup3_rng, sup4_rng,
mid1_rng, mid2_rng, mid3_rng, mid4_rng,
t, offset_back=10, offset_front=10, offset=False, type='ET', filename='frame', folder='C:/bin',
show=False, suff='', dpi=300, png=True):
"""
Plot the local pannel frame
:param series: pandas dataframe
:param star: 2d numpy array of Star map
:param deficit: 2d numpy array of Deficit
:param sups: 3d numpy array of superior 2d numpy arrays maps
:param mids: 3d numpy array of median 2d numpy arrays maps
:param star_rng: iterable of star range
:param deficit_rng: iterable of map range
:param sup1_rng: iterable of map range
:param sup2_rng: iterable of map range
:param sup3_rng: iterable of map range
:param sup4_rng: iterable of map range
:param mid1_rng: iterable of map range
:param mid2_rng: iterable of map range
:param mid3_rng: iterable of map range
:param mid4_rng: iterable of map range
:param t: integer time step
:param offset_back: int offset to back window
:param offset_front: int offset to front window
:param type: string - type of pannel - ET, Qv, R.
:param filename: string filename
:param folder: string folder path
:param show: boolean to show instead of save
:param suff: string suffix
:param dpi: boolean
:param png: boolean
:return: string filepath
"""
from matplotlib import cm
from matplotlib.colors import ListedColormap
from pandas import to_datetime
#
cmaps = _custom_cmaps()
#
dates_labels = to_datetime(series['Date'], format='%Y%m%d')
dates_labels = dates_labels.astype('str')
#
#
if type == 'ET':
cmaps = (cmaps['flow_v'], cmaps['D'], 'Blues', 'BuPu', 'BuPu', 'BuPu', cmaps['flow_v'], cmaps['flow_v'], cmaps['flow_v'], cmaps['flow_v'])
titles = ('ET - Evapotranspiration (mm/d)',
'Groundwater\ndeficit (mm)',
'Precipitation\n(mm/d)',
'Irrigation by\naspersion (mm/d)',
'Irrigation by\ninundation (mm/d)',
'Total irrigation\ninput (mm/d)',
'Evaporation\nfrom canopy (mm/d)', 'Evaporation\nfrom surface (mm/d)',
'Transpiration\nfrom soil (mm/d)', 'Transpiration from\ngroundwater (mm/d)')
suptitle = 'Evapotranspiration (ET) Pannel | {}'.format(dates_labels.values[t])
series_label = 'ET & PET.\nmm/d'
lengend_lbl = 'ET'
star_color = 'tab:red'
elif type == 'Qv':
cmaps = (cmaps['flow'], cmaps['D'], 'Blues', 'BuPu', 'BuPu', 'BuPu', cmaps['flow'], cmaps['stk'], cmaps['stk'], cmaps['stk'])
titles = ('Recharge (mm/d)',
'Groundwater\ndeficit (mm)',
'Precipitation\n(mm/d)',
'Irrigation by\naspersion (mm/d)',
'Irrigation by\ninundation (mm/d)',
'Total irrigation\ninput (mm/d)',
'Infiltration\n(mm/d)', 'Canopy\nwater stock (mm)',
'Surface\nwater stock (mm)', 'Vadoze zone\nwater stock (mm)')
suptitle = 'Recharge to groundwater Pannel | {}'.format(dates_labels.values[t])
series_label = 'Recharge\nmm/d'
star_color = 'teal'
lengend_lbl = 'Recharge'
elif type == 'R':
cmaps = (cmaps['flow'], cmaps['D'], 'Blues', 'BuPu', 'BuPu', 'BuPu', cmaps['flow'], cmaps['flow'], cmaps['flow'], 'Blues')
titles = ('Runoff (mm/d)',
'Groundwater\ndeficit (mm)',
'Precipitation\n(mm/d)',
'Irrigation by\naspersion (mm/d)',
'Irrigation by\ninundation (mm/d)',
'Total irrigation\ninput (mm/d)',
'Throughfall\n(mm/d)', 'Infiltration excess\nrunoff (mm/d)',
'Saturation excess\nrunoff (mm/d)', 'Variable\nSource Area')
suptitle = 'Runoff Pannel | {}'.format(dates_labels.values[t])
series_label = 'Runoff\nmm/d'
lengend_lbl = 'Runoff'
star_color = 'blue'
#
# Star plot
fig = plt.figure(figsize=(17, 8)) # Width, Height
gs = mpl.gridspec.GridSpec(8, 17, wspace=0.8, hspace=0.6, left=0.05, bottom=0.1, top=0.9, right=0.95)
fig.suptitle(suptitle)
#
# STAR
plt.subplot(gs[:5, :5])
im = plt.imshow(star, cmap=cmaps[0], vmin=star_rng[0], vmax=star_rng[1])
plt.title(titles[0])
plt.colorbar(im, shrink=0.4)
plt.axis('off')
#
# DEFICIT
plt.subplot(gs[5:, :3])
im = plt.imshow(deficit, cmap=cmaps[1], vmin=deficit_rng[0], vmax=deficit_rng[1])
plt.title(titles[1], fontsize=10)
plt.colorbar(im, shrink=0.4)
plt.axis('off')
#
# SUP 1 - Prec
lcl_rng = sup1_rng
prec_map = (sups[0] * 0.0) + series['Prec'].values[t]
plt.subplot(gs[:2, 5:7])
im = plt.imshow(prec_map, cmap=cmaps[2], vmin=lcl_rng[0], vmax=lcl_rng[1])
plt.title(titles[2], fontsize=10)
plt.colorbar(im, shrink=0.4)
plt.axis('off')
# SUP 2 - IRA
lcl_rng = sup2_rng
plt.subplot(gs[:2, 8:10])
im = plt.imshow(sups[0], cmap=cmaps[3], vmin=lcl_rng[0], vmax=lcl_rng[1])
plt.title(titles[3], fontsize=10)
plt.colorbar(im, shrink=0.4)
plt.axis('off')
# SUP 3
lcl_rng = sup3_rng
plt.subplot(gs[:2, 11:13])
im = plt.imshow(sups[1], cmap=cmaps[4], vmin=lcl_rng[0], vmax=lcl_rng[1])
plt.title(titles[4], fontsize=10)
plt.colorbar(im, shrink=0.4)
plt.axis('off')
# SUP 4
lcl_rng = sup4_rng
plt.subplot(gs[:2, 14:16])
im = plt.imshow(sups[0] + sups[1], cmap=cmaps[5], vmin=lcl_rng[0], vmax=lcl_rng[1])
plt.title(titles[5], fontsize=10)
plt.colorbar(im, shrink=0.4)
plt.axis('off')
#
#
# MID 1
lcl_rng = mid1_rng
plt.subplot(gs[2:5, 5:8])
im = plt.imshow(mids[0], cmap=cmaps[6], vmin=lcl_rng[0], vmax=lcl_rng[1])
plt.title(titles[6], fontsize=10)
plt.colorbar(im, shrink=0.4)
plt.axis('off')
# MID 2
lcl_rng = mid2_rng
plt.subplot(gs[2:5, 8:11])
im = plt.imshow(mids[1], cmap=cmaps[7], vmin=lcl_rng[0], vmax=lcl_rng[1])
plt.title(titles[7], fontsize=10)
plt.colorbar(im, shrink=0.4)
plt.axis('off')
# MID 3
lcl_rng = mid3_rng
plt.subplot(gs[2:5, 11:14])
im = plt.imshow(mids[2], cmap=cmaps[8], vmin=lcl_rng[0], vmax=lcl_rng[1])
plt.title(titles[8], fontsize=10)
plt.colorbar(im, shrink=0.4)
plt.axis('off')
# MID 4
lcl_rng = mid4_rng
plt.subplot(gs[2:5, 14:])
im = plt.imshow(mids[3], cmap=cmaps[9], vmin=lcl_rng[0], vmax=lcl_rng[1])
plt.title(titles[9], fontsize=10)
plt.colorbar(im, shrink=0.4)
plt.axis('off')
#
# SERIES
#
low = 0
hi = len(series)
if offset:
if t < offset_back:
low = 0
hi = offset_front + offset_back + 1
elif t >= len(series) - offset_front - 1:
low = len(series) - offset_front - offset_back - 1
hi = len(series)
else:
low = t - offset_back
hi = t + offset_front + 1
#
#
#
# Input water
ax = fig.add_subplot(gs[5, 5:])
plt.title('Date: {}'.format(dates_labels.values[t]), loc='left', fontsize=10)
plt.vlines(series['Date'].values[t], ymax=1.5 * np.max(series['Prec'].values), ymin=0, colors=['k'])
plt.plot(series['Date'].values[low:hi],
series['Prec'].values[low:hi], 'tab:blue', label='Precip.')
plt.legend(loc='upper left', ncol=1, framealpha=1, fancybox=False)
plt.ylim((0, 1.5 * np.max(series['Prec'].values)))
# markers
plt.plot(series['Date'].values[t], series['Prec'].values[t], 'o', color='tab:blue')
# IRA and IRI
ax2 = ax.twinx()
plt.plot(series['Date'].values[low:hi],
series['IRA'].values[low:hi], 'green', label='IRA')
plt.plot(series['Date'].values[low:hi],
series['IRI'].values[low:hi], 'orange', label='IRI')
plt.ylim((0, 1.5 * np.max((series['IRA'].values, series['IRI'].values))))
# markers
plt.plot(series['Date'].values[t], series['IRA'].values[t], 'o', color='green')
plt.plot(series['Date'].values[t], series['IRI'].values[t], 'o', color='orange')
#
# legend
plt.legend(loc='upper right', ncol=2, framealpha=1, fancybox=False)
fig.text(x=0.28, y=0.33, s='Precip.\nmm/d')
fig.text(x=0.93, y=0.33, s='Irrigation\nmm/d')
#
#
#
# star plot
ax = fig.add_subplot(gs[6, 5:])
if type == 'ET':
plt.plot(series['Date'].values[low:hi],
series['PET'].values[low:hi], 'tab:grey', label='PET')
plt.plot(series['Date'].values[low:hi],
series[type].values[low:hi], color=star_color, label=lengend_lbl)
plt.vlines(series['Date'].values[t], ymax=1.5 * np.max(series[type].values), ymin=0, colors=['k'])
plt.plot(series['Date'].values[t], series[type].values[t], 'o', color=star_color)
plt.ylim((0, 1.5 * np.max(series[type])))
plt.legend(loc='upper right', ncol=2, framealpha=1, fancybox=False)
fig.text(x=0.28, y=0.23, s=series_label)
#
#
#
# Flow
ax = fig.add_subplot(gs[7, 5:])
plt.plot(series['Date'].values[low:hi],
series['Q'].values[low:hi], 'tab:blue', label='Flow')
plt.plot(series['Date'].values[low:hi],
series['Qb'].values[low:hi], 'navy', label='Baseflow')
plt.vlines(series['Date'].values[t], ymax= 10 * np.max(series['Q'].values), ymin=np.min(series['Q'].values),
colors=['k'])
if series['Q'].values[t] == series['Qb'].values[t]:
plt.plot(series['Date'].values[t], series['Q'].values[t], 'o', color='navy')
else:
plt.plot(series['Date'].values[t], series['Q'].values[t], 'o', color='tab:blue')
plt.plot(series['Date'].values[t], series['Qb'].values[t], 'o', color='navy')
plt.ylim((np.min(series['Q']), 10 * np.max(series['Q'])))
plt.legend(loc='upper right', ncol=2, framealpha=1, fancybox=False)
plt.yscale('log')
fig.text(x=0.28, y=0.13, s='Flow\nmm/d')
#
#
if show:
plt.show()
plt.close(fig)
else:
# export file
if suff != '':
filepath = '{}/{}_{}_{}_{}'.format(folder, suff, filename, type, dates_labels.values[t])
else:
filepath = '{}/{}_{}_{}'.format(folder, filename, type, dates_labels.values[t])
if png:
filepath = filepath + '.png'
else:
filepath = filepath + '.jpg'
plt.savefig(filepath, dpi=dpi)
plt.close(fig)
plt.clf()
return filepath
def pannel_prec_q(t, prec, q, grid=True, folder='C:/bin', filename='pannel_prec_q', suff='', show=False):
"""
A simple Prec/Q plot
:param t: iterable of dates/timestep
:param prec: iterable of Prec
:param q: iterable of Q
:param grid: boolean to grid
:param folder: string to folder path
:param filename: string of filename
:param suff: string of suffix
:param show: boolean to show instead of saving
:return: string filepath
"""
#
fig = plt.figure(figsize=(16, 8)) # Width, Height
gs = mpl.gridspec.GridSpec(2, 1, wspace=0.8, hspace=0.6, left=0.05, bottom=0.1, top=0.9, right=0.95)
# plot prec
y = prec
ymax = np.max(y)
ax1 = fig.add_subplot(gs[0, 0])
plt.title('Precipitation', loc='left')
plt.ylabel('mm/d')
plt.plot(t, y)
plt.ylim(0, 1.1 * ymax)
plt.grid(grid)
#plt.xticks(locs, labels)
# plot q
y = q
ymax = np.max(y)
ax2 = fig.add_subplot(gs[1, 0], sharex=ax1)
plt.title('Flow', loc='left')
plt.ylabel('mm/d')
plt.plot(t, y)
plt.ylim(0, 1.1 * ymax)
plt.grid(grid)
#plt.xticks(locs, labels)
#
if show:
plt.show()
plt.close(fig)
else:
# export file
filepath = folder + '/' + filename + '_' + suff + '.png'
plt.savefig(filepath)
plt.close(fig)
return filepath
def pannel_prec_q_logq(t, prec, q, grid=True, folder='C:/bin', filename='pannel_prec_q_logq', suff='', show=False):
"""
A simple Prec/Q plot but with also the Log Q
:param t: iterable of dates/timestep
:param prec: iterable of Prec
:param q: iterable of Q
:param grid: boolean to grid
:param folder: string to folder path
:param filename: string of filename
:param suff: string of suffix
:param show: boolean to show instead of saving
:return: string filepath
"""
#
fig = plt.figure(figsize=(16, 8)) # Width, Height
gs = mpl.gridspec.GridSpec(3, 1, wspace=0.8, hspace=0.6)
# plot prec
y = prec
ymax = np.max(y)
ax1 = fig.add_subplot(gs[0, 0])
plt.title('Precipitation', loc='left')
plt.ylabel('mm/d')
plt.plot(t, y)
plt.ylim(0, 1.1 * ymax)
plt.grid(grid)
# plot q
y = q
ymax = np.max(y)
ax2 = fig.add_subplot(gs[1, 0], sharex=ax1)
plt.title('Flow', loc='left')
plt.ylabel('mm/d')
plt.plot(t, y)
plt.ylim(0, 1.1 * ymax)
plt.grid(grid)
# plot log q
y = q
ymax = np.max(y)
ymin = np.min(y)
ax3 = fig.add_subplot(gs[2, 0], sharex=ax1)
plt.title('Flow (log)', loc='left')
plt.ylabel('mm/d')
plt.plot(t, y)
plt.ylim(0.9 * ymin, 1.1 * ymax)
plt.yscale('log')
plt.grid(grid)
#
if show:
plt.show()
plt.close(fig)
else:
# export file
if suff == '':
filepath = folder + '/' + filename + '.png'
else:
filepath = folder + '/' + filename + '_' + suff + '.png'
plt.savefig(filepath)
plt.close(fig)
return filepath
def pannel_calib_series(dataframe, grid=True, folder='C:/bin', filename='calib_series', suff='', show=False):
"""
The calib series pannel
:param dataframe: pandas dataframe of the calib_serie.txt file
:param grid: boolean
:param folder: string - output folder path
:param filename: string - filename
:param suff: string - suffix
:param show: boolean - to show figure
:return: string - filepath
"""
#
fig = plt.figure(figsize=(16, 10)) # Width, Height
fig.suptitle('Calibration basin series')
gs = mpl.gridspec.GridSpec(5, 1, wspace=0.8, hspace=0.6)
#
# plot prec
var = 'Prec'
ax1 = fig.add_subplot(gs[0, 0])
plt.title('Precipitation', loc='left')
plt.ylabel('mm/d')
plt.plot(dataframe['Date'], dataframe[var])
plt.ylim(0, 1.1 * np.max(dataframe[var]))
plt.grid(grid)
#
# plot temp
var = 'Temp'
ax2 = fig.add_subplot(gs[1, 0], sharex=ax1)
plt.title('Temperature', loc='left')
plt.ylabel('°C')
plt.plot(dataframe['Date'], dataframe[var], 'tab:orange')
plt.ylim(0, 1.1 * np.max(dataframe[var]))
plt.grid(grid)
#
# plot IRI and IRA
ax3 = fig.add_subplot(gs[2, 0], sharex=ax1)
plt.title('Irrigation', loc='left')
plt.ylabel('mm/d')
plt.plot(dataframe['Date'], dataframe['IRA'], 'orange', label='Irrigation by aspersion')
plt.plot(dataframe['Date'], dataframe['IRI'], 'green', label='Irrigation by inundation')
plt.ylabel('mm/d')
plt.ylim(0, 1.1 * np.max([dataframe['IRI'].values, dataframe['IRA'].values]))
plt.legend(loc='upper right', ncol=2, framealpha=1, fancybox=False)
plt.grid(grid)
#
# plot q
var = 'Q'
ax4 = fig.add_subplot(gs[3, 0], sharex=ax1)
plt.title('Flow', loc='left')
plt.ylabel('mm/d')
plt.plot(dataframe['Date'], dataframe[var], 'tab:blue')
plt.ylim(0, 1.1 * np.max(dataframe[var]))
plt.grid(grid)
#
# plot log q
var = 'Q'
ax5 = fig.add_subplot(gs[4, 0], sharex=ax1)
plt.title('Flow (log)', loc='left')
plt.ylabel('mm/d')
plt.plot(dataframe['Date'], dataframe[var], 'tab:blue')
plt.ylim(0.5 * np.min(dataframe[var]), 1.1 * np.max(dataframe[var]))
plt.grid(grid)
plt.yscale('log')
#
if show:
plt.show()
plt.close(fig)
else:
# export file
if suff == '':
filepath = folder + '/' + filename + '.png'
else:
filepath = folder + '/' + filename + '_' + suff + '.png'
plt.savefig(filepath)
plt.close(fig)
return filepath
def pannel_aoi_series(dataframe, grid=True, folder='C:/bin', filename='aoi_series', suff='', show=False):
"""
The aoi series pannel
:param dataframe: pandas dataframe of the calib_serie.txt file
:param grid: boolean
:param folder: string - output folder path
:param filename: string - filename
:param suff: string - suffix
:param show: boolean - to show figure
:return: string - filepath
"""
#
fig = plt.figure(figsize=(16, 7)) # Width, Height
fig.suptitle('AOI basin series')
gs = mpl.gridspec.GridSpec(3, 1, wspace=0.8, hspace=0.6)
#
# plot prec
var = 'Prec'
ax1 = fig.add_subplot(gs[0, 0])
plt.title('Precipitation', loc='left')
plt.ylabel('mm/d')
plt.plot(dataframe['Date'], dataframe[var])
plt.ylim(0, 1.1 * np.max(dataframe[var]))
plt.grid(grid)
#
# plot temp
var = 'Temp'
ax2 = fig.add_subplot(gs[1, 0], sharex=ax1)
plt.title('Temperature', loc='left')
plt.ylabel('°C')
plt.plot(dataframe['Date'], dataframe[var], 'tab:orange')
plt.ylim(0, 1.1 * np.max(dataframe[var]))
plt.grid(grid)
#
# plot IRI and IRA
ax3 = fig.add_subplot(gs[2, 0], sharex=ax1)
plt.title('Irrigation', loc='left')
plt.ylabel('mm/d')
plt.plot(dataframe['Date'], dataframe['IRA'], 'orange', label='Irrigation by aspersion')
plt.plot(dataframe['Date'], dataframe['IRI'], 'green', label='Irrigation by inundation')
plt.ylabel('mm/d')
plt.ylim(0, 1.1 * np.max([dataframe['IRI'].values, dataframe['IRA'].values]))
plt.legend(loc='upper right', ncol=2, framealpha=1, fancybox=False)
plt.grid(grid)
#
if show:
plt.show()
plt.close(fig)
else:
# export file
if suff == '':
filepath = folder + '/' + filename + '.png'
else:
filepath = folder + '/' + filename + '_' + suff + '.png'
plt.savefig(filepath)
plt.close(fig)
return filepath
def pannel_sim_prec_q_logq(t, prec, qobs, qsim,
grid=True,
folder='C:/bin',
filename='pannel_sim_prec_q_logq',
suff='',
show=False):
"""
Plot a Pannel of Qsim and Qobs in linear and log scale
:param t: iterable of dates/timestep
:param prec: iterable of Prec
:param qobs: iterable of QOBS
:param qsim: iterable of QSIM
:param grid: boolean to grid
:param folder: string to folder path
:param filename: string of filename
:param suff: string of suffix
:param show: boolean to show instead of saving
:return:
"""
fig = plt.figure(figsize=(16, 8)) # Width, Height
gs = mpl.gridspec.GridSpec(3, 1, wspace=0.8, hspace=0.6)
# plot prec
y = prec
ymax = np.max(y)
ax1 = fig.add_subplot(gs[0, 0])
plt.title('Precipitation', loc='left')
plt.ylabel('mm/d')
plt.plot(t, y)
plt.ylim(0, 1.1 * ymax)
plt.grid(grid)
# plot q
y1 = qobs
y2 = qsim
ymax = np.max((y1, y2))
ax2 = fig.add_subplot(gs[1, 0], sharex=ax1)
plt.title('Flow', loc='left')
plt.ylabel('mm/d')
plt.plot(t, y1)
plt.plot(t, y2)
plt.ylim(0, 1.1 * ymax)
plt.grid(grid)
# plot log q
ymin = np.min((y1, y2))
ax3 = fig.add_subplot(gs[2, 0], sharex=ax1)
plt.title('Flow (log)', loc='left')
plt.ylabel('mm/d')
plt.plot(t, y1)
plt.plot(t, y2)
plt.ylim(0.9 * ymin, 1.1 * ymax)
plt.yscale('log')
plt.grid(grid)
#
if show:
plt.show()
plt.close(fig)
else:
# export file
filepath = folder + '/' + filename + '_' + suff + '.png'
plt.savefig(filepath)
plt.close(fig)
return filepath
def pannel_global(series_df,
qobs=False,
etobs=False,
grid=True,
show=False,
folder='C:/bin',
filename='pannel',
suff=''):
"""
visualize the model global variables in a single pannel
:param series_df: pandas dataframe from hydrology.topmodel_sim()
:param qobs: boolean for Qobs
:param etobs: boolean for ETobs
:param grid: boolean for grid
:param folder: string to destination directory
:param filename: string file name
:param suff: string suffix in file name
:param show: boolean control to show figure instead of saving it
:return: string file path
"""
#
fig = plt.figure(figsize=(20, 12)) # Width, Height
fig.suptitle('Pannel of simulated hydrological processes')
gs = mpl.gridspec.GridSpec(5, 18, wspace=0.0, hspace=0.2, left=0.05, bottom=0.05, top=0.95, right=0.95) # nrows, ncols
col1 = 8
col2 = 10
max_prec = 1.2 * np.max(series_df['Prec'].values)
max_et = 1.5 * np.max(series_df['PET'].values)
max_irr = 1.5 * np.max((series_df['IRI'].values, series_df['IRA'].values))
max_stocks = 1.2 * np.max((series_df['Unz'].values, series_df['Sfs'].values, series_df['Cpy'].values))
max_int_flow = 1.2 * np.max((series_df['Inf'].values, series_df['Qv'].values))
if qobs:
qmin = 0.8 * np.min((series_df['Q'].values, series_df['Qobs'].values))
qmax = 1.5 * np.max((series_df['Q'].values, series_df['Qobs'].values))
else:
qmin = 0.8 * np.min(series_df['Q'].values)
qmax = 1.5 * np.max(series_df['Q'].values)
#
# Prec
ax = fig.add_subplot(gs[0, 0:col1])
plt.grid(grid)
plt.plot(series_df['Date'], series_df['Prec'], 'tab:grey', label='Precipitation')
plt.ylabel('mm/d (Prec)')
plt.ylim(0, max_prec)
plt.legend(loc='upper left', ncol=1, framealpha=1, fancybox=False)
if max_irr == 0:
pass