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azmp_climate_index.py
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azmp_climate_index.py
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# -*- coding: utf-8 -*-
'''
To generate Colbourne's and STACFIS composite anomalies
Uses this pickled DataFrame:
/home/cyrf0006/AZMP/state_reports/SSTs/SSTs_merged_monthly.pkl
generated by from azmp_sst_scorecards.py
'''
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import unicodedata
from matplotlib.colors import from_levels_and_colors
import cmocean as cmo
import seaborn as sn
# Adjust fontsize/weight
font = {'family' : 'sans-serif',
'weight' : 'normal',
'size' : 14}
plt.rc('font', **font)
YEAR_MIN = 1950
YEAR_MAX = 2024
#clim_year = [1981, 2010]
clim_year = [1991, 2020]
width = 0.7
#Determine the name of the working directory
work_name = './'
'''
work_name = input('What is the working directory (ex: "~/AZMP")? [default: "./"]: ')
if work_name == '':
work_name='./'
print(' -> '+work_name+' used as working directory!')
'''
#### ---- LOAD THE DATA (and prepare) ---- ####
# 1. NAO
nao = pd.read_pickle(os.path.join(work_name, 'operation_files/NAO_winter.pkl'))
nao.name = 'Wint. NAO'
nao = nao[nao.index<YEAR_MAX+1]
# normalize NAO with std.
#nao_clim = nao[(nao.index>=clim_year[0]) & (nao.index<=clim_year[1])]
#nao = nao / nao_clim.std()
nao.index.name = 'Year'
nao_natural = nao.copy()
nao = nao*-1
# 2. Air temp
if clim_year[0] == 1981:
air = pd.read_pickle(os.path.join(work_name, 'operation_files/airT_std_81anom.pkl'))
else:
air = pd.read_pickle(os.path.join(work_name, 'operation_files/airT_std_anom.pkl'))
air.index.name='Year'
air = air.mean(axis=1)
air.name = 'Air Temp'
# 3. Sea Ice (And icebergs)
if clim_year[0] == 1981:
ice = pd.read_csv('~/github/AZMP-NL/external_data/Galbraith_data/ice-area-thick.NL.ClimateIndex.1981-2010.dat', header=None, sep=' ')
else:
ice = pd.read_csv('~/github/AZMP-NL/external_data/Galbraith_data/ice-area-thick.NL.ClimateIndex.1991-2020.dat', header=None, sep=' ')
ice.set_index(0, inplace=True)
ice.index.name='Year'
ice.rename(columns={ice.columns[0]: "Sea Ice" }, inplace = True)
ice = ice[['Sea Ice']]
ice_natural = ice.copy()
ice = ice*-1
# previous version:
#ice = pd.read_pickle('/home/cyrf0006/AZMP/state_reports/ice/ice_index.pkl')
#ice_natural = ice.copy()
#ice = ice*-1
# 4. Icebergs
if clim_year[0] == 1981:
bergs = pd.read_pickle(os.path.join(work_name, 'operation_files/bergs_std_anom_1981clim.pkl'))
else:
bergs = pd.read_pickle(os.path.join(work_name, 'operation_files/bergs_std_anom.pkl'))
bergs.index.name='Year'
bergs.name = 'Icebergs'
bergs_natural = bergs.copy()
bergs_natural.name = 'Icebergs'
bergs = bergs*-1
# 5. SSTs
if clim_year[0] == 1981:
sst = pd.read_csv('~/github/AZMP-NL/external_data/Galbraith_data/NL_ClimateIndex_SST.1981-2010.dat', header=None, sep=' ')
else:
sst = pd.read_csv('~/github/AZMP-NL/external_data/Galbraith_data/NL_ClimateIndex_SST.1991-2020.dat', header=None, sep=' ')
sst.set_index(0, inplace=True)
sst.index.name='Year'
sst.rename(columns={sst.columns[0]: "SST" }, inplace = True)
sst = sst[['SST']]
# previous version
#sst = pd.read_pickle('/home/cyrf0006/AZMP/state_reports/SSTs/SST_anom.pkl')
# 6. Stn27 (0-176m, 0-50m, 150-176m)
if clim_year[0] == 1981:
s27_temp = pd.read_pickle(os.path.join(work_name, 'operation_files/s27_temp_std_anom.pkl'))
s27_sal = pd.read_pickle(os.path.join(work_name, 'operation_files/s27_sal_std_anom.pkl'))
else:
s27_temp = pd.read_pickle(os.path.join(work_name, 'operation_files/s27_temp_std_anom.pkl'))
s27_sal = pd.read_pickle(os.path.join(work_name, 'operation_files/s27_sal_std_anom.pkl'))
# Flag years with less than 8 months (!!!Should be in pkl object!!!)
s27_temp[s27_temp.index.year==1950] = np.nan
s27_temp[s27_temp.index.year==1980] = np.nan
s27_temp[s27_temp.index.year==1981] = np.nan
s27_temp[s27_temp.index.year==2020] = np.nan
s27_sal[s27_sal.index.year==1950] = np.nan
s27_sal[s27_sal.index.year==1980] = np.nan
s27_sal[s27_sal.index.year==1981] = np.nan
s27_sal[s27_sal.index.year==2020] = np.nan
# Set index and rename
s27_temp.index = s27_temp.index.year
#s27_temp.to_csv('S27_stn_anom_3fields.csv', float_format='%.2f')
s27_sal.index = s27_sal.index.year
# Take only 0-176m average (correlation is 0.94, see below)
#s27_temp = s27_temp.mean(axis=1)
#s27_sal = s27_sal.mean(axis=1)
s27_temp = s27_temp['Temp 0-176m']
s27_sal = s27_sal['Sal 0-176m']
s27_temp.name = 'S27 T'
s27_sal.name = 'S27 S'
# rename index
s27_temp.index.name='Year'
s27_sal.index.name='Year'
# change salinity sign
s27_sal_natural = s27_sal.copy()
s27_sal = s27_sal*-1 # assume fresh = warm
# compa previous and new T index
## s27_temp1 = s27_temp.mean(axis=1)
## s27_temp2 = s27_temp['Temp 0-176m']
## A = pd.concat([s27_temp1, s27_temp2], axis=1)
## s27_sal1 = s27_sal.mean(axis=1)
## s27_sal2 = s27_sal['Sal 0-176m']
## B = pd.concat([s27_sal1, s27_sal2], axis=1)
## A.plot()
## plt.grid()
## plt.legend(['prelim (all 3 depths)', 'new (0-176m)'])
## B.plot()
## plt.grid()
## plt.legend(['prelim (all 3 depths)', 'new (0-176m)'])
# s27_cil not std_anom yet
s27_cil = pd.read_pickle(os.path.join(work_name, 'operation_files/S27_CIL_summer_stats.pkl'))
s27_cil.index = s27_cil.index.year
s27_cil_clim = s27_cil[(s27_cil.index>=clim_year[0]) & (s27_cil.index<=clim_year[1])]
s27_cil = (s27_cil-s27_cil_clim.mean(axis=0))/s27_cil_clim.std(axis=0)
# Flag years with less than 8 months
s27_cil[s27_cil.index==1950] = np.nan
s27_cil[s27_cil.index==1980] = np.nan
s27_cil[s27_cil.index==1981] = np.nan
s27_cil[s27_cil.index==2020] = np.nan
# compa previous and new CIL index
## C = pd.concat([s27_cil[['CIL temp', 'CIL core T']].mean(axis=1), s27_cil[['CIL core T']].mean(axis=1)], axis=1)
## C.plot()
## plt.grid()
## plt.legend(['prelim (coreT, meanT)', 'coreT only'])
#s27_cil = s27_cil[['CIL temp', 'CIL core T']].mean(axis=1) # previous version
s27_cil = s27_cil[['CIL core T']].mean(axis=1) # new version
s27_cil.name = 'S27 CIL'
s27_cil.index.name='Year'
# 7. Section CIL (only area) [.pkl files form azmp_CIL_mean_anomaly.py]
if clim_year[0] == 1981:
section_cil = pd.read_pickle(os.path.join(work_name, 'operation_files/section_cil_index_1981clim.pkl'))
else:
section_cil = pd.read_pickle(os.path.join(work_name, 'operation_files/section_cil_index.pkl'))
# Select volume and/or coreT
section_cil = section_cil['volume'] # volume and/or core
section_cil.name = 'CIL area'
section_cil.index.name='Year'
section_cil_natural = section_cil.copy()
section_cil = section_cil*-1
#section_cil[section_cil.index==2022] = np.nan
# 8. bottomT [.pkl files from azmp_bottomT_mean_anomaly.py]
if clim_year[0] == 1981:
bottomT_spring = pd.read_pickle(os.path.join(work_name, 'operation_files/bottomT_index_spring_1981clim.pkl'))
bottomT_fall = pd.read_pickle(os.path.join(work_name, 'operation_files/bottomT_index_fall_1981clim.pkl'))
else:
bottomT_spring = pd.read_pickle(os.path.join(work_name, 'operation_files/bottomT_index_spring.pkl'))
bottomT_fall = pd.read_pickle(os.path.join(work_name, 'operation_files/bottomT_index_fall.pkl'))
bottomT = pd.concat([bottomT_spring, bottomT_fall], axis=1).mean(axis=1)
bottomT.name = 'Bottom T'
bottomT.index.name = 'Year'
#### ----- Merge the data ---- ####
climate_index = pd.concat([nao, air, ice, bergs, sst, s27_temp, s27_sal, s27_cil, section_cil, bottomT], axis=1)
#climate_index = pd.concat([nao, s27_temp, s27_sal, s27_cil, section_cil, bottomT], axis=1)
climate_index.sort_index(inplace=True)
climate_index = climate_index[climate_index.index>=YEAR_MIN]
climate_index_sc = climate_index.copy() # for scorecards at top
climate_index.loc[1950] = climate_index.loc[1950]*np.nan
# keep a copy with Natural signs
climate_index_natural = pd.concat([nao_natural, air, ice_natural, bergs_natural, sst, s27_temp, s27_sal_natural, s27_cil, section_cil_natural, bottomT], axis=1)
#climate_index_natural = pd.concat([nao_natural, s27_temp, s27_sal_natural, s27_cil, section_cil_natural, bottomT], axis=1)
climate_index_natural.sort_index(inplace=True)
climate_index_natural = climate_index_natural[climate_index_natural.index>=YEAR_MIN]
## Save index (in English)
# 1. All fields.
climate_index = climate_index[(climate_index.index>=YEAR_MIN) & (climate_index.index<=YEAR_MAX)]
climate_index.index.name = 'Year'
climate_index_sc.to_csv('NL_climate_index_all_fields.csv', float_format='%.2f', index_label='Year')
# 2. All fields natural signs
climate_index_natural = climate_index_natural[(climate_index_natural.index>=YEAR_MIN) & (climate_index_natural.index<=YEAR_MAX)]
climate_index_natural.index.name = 'Year'
climate_index_natural.to_csv('NL_climate_index_all_fields_natural_signs.csv', float_format='%.2f', index_label='Year')
# 3. Mean index.
climate_index_mean = climate_index.mean(axis=1)
climate_index_mean.index.name = 'Year'
climate_index_mean = climate_index_mean.rename('Climate index').to_frame()
climate_index_mean.to_csv('NL_climate_index.csv', float_format='%.2f', index_label='Year')
## restrict time series and normalize for plots.
climate_index = climate_index[climate_index.index<YEAR_MAX+1]
climate_index_norm = climate_index.divide((10 - climate_index.isna().sum(axis=1)).values, axis=0)
climate_index_norm_ns = climate_index_sc.divide((10 - climate_index_sc.isna().sum(axis=1)).values, axis=0)
#### ----- Plot climate index [1] ---- ####
## n = 5 # xtick every n years
## #ax = climate_index.plot(kind='bar', stacked=True, cmap='gist_rainbow')
## ax = climate_index_norm.plot(kind='bar', stacked=True, cmap='nipy_spectral', zorder=10)
## ticks = ax.xaxis.get_ticklocs()
## ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
## ax.xaxis.set_ticks(ticks[::n])
## ax.xaxis.set_ticklabels(ticklabels[::n])
## plt.fill_between([ticks[0]-1, ticks[-1]+1], [-.5, -.5], [.5, .5], facecolor='gray', alpha=.2)
## plt.grid('on')
## ax.set_ylabel(r'Normalized anomaly')
## ax.set_title('NL Climate Index')
## #ax.legend(loc=2, bbox_to_anchor=(0.45, 1), fontsize=12)
## ax.legend(bbox_to_anchor=(1.0, 1), loc='upper left', fontsize=12)
## #ax.legend(fontsize=12)
## fig = ax.get_figure()
## fig.set_size_inches(w=15.5,h=8)
## fig_name = 'NL_climate_index.png'
## fig.savefig(fig_name, dpi=300)
## os.system('convert -trim -bordercolor White -border 10x10 ' + fig_name + ' ' + fig_name)
#### ----- Plot climate index (French) ---- ####
## climate_index_normFR = climate_index_norm.copy()
## climate_index_normFR.rename(columns={
## 'Winter NAO':'ONA hiver',
## 'Air Temp':'Temp Air',
## 'Sea Ice':'Glace',
## 'SST':'SST',
## 'S27 CIL':'S27 CIF',
## 'CIL area':'Aire CIF',
## 'Bottom T.': 'Temp Fond'
## }, inplace=True)
## ax = climate_index_normFR.plot(kind='bar', stacked=True, cmap='nipy_spectral', zorder=10)
## ticks = ax.xaxis.get_ticklocs()
## ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
## ax.xaxis.set_ticks(ticks[::n])
## ax.xaxis.set_ticklabels(ticklabels[::n])
## plt.fill_between([ticks[0]-1, ticks[-1]+1], [-.5, -.5], [.5, .5], facecolor='gray', alpha=.2)
## plt.grid('on')
## ax.set_ylabel(r'Anomalie normalisée cummulée')
## ax.set_title('Indice climatique pour TNL')
## fig = ax.get_figure()
## fig.set_size_inches(w=12,h=8)
## fig_name = 'NL_climate_index_FR.png'
## fig.savefig(fig_name, dpi=300)
## os.system('convert -trim -bordercolor White -border 10x10 ' + fig_name + ' ' + fig_name)
#### ----- Climate index with simple Scorecards [1] ---- ####
# Need this from above:
#climate_index = climate_index[climate_index.index<YEAR_MAX+1]
#climate_index_norm = climate_index.divide((10 - climate_index.isna().sum(axis=1)).values, axis=0)
# Build the colormap - Stack
from matplotlib.colors import from_levels_and_colors
YlGn = plt.cm.YlGn(np.linspace(0,1, num=12))
YlGn = YlGn[4:,]
cmap_stack, norm_stack = from_levels_and_colors(np.arange(0,7), YlGn, extend='both')
# Build the colormap - Scorecard
vmin = -3.49
vmax = 3.49
midpoint = 0
levels = np.linspace(vmin, vmax, 15)
midp = np.mean(np.c_[levels[:-1], levels[1:]], axis=1)
colvals = np.interp(midp, [vmin, midpoint, vmax], [-1, 0., 1])
normal = plt.Normalize(-3.49, 3.49)
reds = plt.cm.Reds(np.linspace(0,1, num=7))
blues = plt.cm.Blues_r(np.linspace(0,1, num=7))
whites = [(1,1,1,1)]*2
colors = np.vstack((blues[0:-1,:], whites, reds[1:,:]))
colors = np.concatenate([[colors[0,:]], colors, [colors[-1,:]]], 0)
cmap, norm = from_levels_and_colors(levels, colors, extend='both')
cmap_r, norm_r = from_levels_and_colors(levels, np.flipud(colors), extend='both')
# Plot
fig, ax = plt.subplots(nrows=1, ncols=1)
n = 5 # xtick every n years
ax = climate_index_norm.plot(kind='bar', stacked=True, cmap='nipy_spectral', zorder=10)
ticks = ax.xaxis.get_ticklocs()
ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
ax.xaxis.set_ticks(ticks[::n])
ax.xaxis.set_ticklabels(ticklabels[::n])
plt.fill_between([ticks[0]-1, ticks[-1]+1], [-.5, -.5], [.5, .5], facecolor='gray', alpha=.2)
plt.grid('on')
ax.set_xlabel(r'')
ax.set_ylabel(r'Normalized anomaly')
ax.set_title('NL Climate Index')
ax.legend(bbox_to_anchor=(1.0, 1), loc='upper left', fontsize=12)
colors = cmap(normal(np.nansum(climate_index_norm.values, axis=1)))
nlci_text = np.nansum(climate_index_norm.values, axis=1).round(1).astype('str')
if nlci_text[0] == '0.0':
nlci_text[0] = 'nan'
the_table = ax.table(cellText=[nlci_text],
rowLabels=['NL climate index'],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='bottom', bbox=[0, -0.14, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell.set_fontsize(0)
else:
cell._text.set_rotation(90)
fig = ax.get_figure()
fig.set_size_inches(w=15.5,h=12)
fig_name = 'NL_climate_index_ms.png'
fig.savefig(fig_name, dpi=300)
os.system('convert -trim -bordercolor White -border 10x10 ' + fig_name + ' ' + fig_name)
### ----- Plot climate index (French) ---- ####
climate_index_normFR = climate_index_norm.copy()
climate_index_normFR.rename(columns={
'Wint. NAO':'ONA hiver',
'Air Temp':'Temp Air',
'Sea Ice':'Glace',
'SST':'SST',
'S27 CIL':'S27 CIF',
'CIL area':'Aire CIF',
'Bottom T': 'Temp Fond'
}, inplace=True)
ax = climate_index_norm.plot(kind='bar', stacked=True, cmap='nipy_spectral', zorder=10)
ticks = ax.xaxis.get_ticklocs()
ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
ax.xaxis.set_ticks(ticks[::n])
ax.xaxis.set_ticklabels(ticklabels[::n])
plt.fill_between([ticks[0]-1, ticks[-1]+1], [-.5, -.5], [.5, .5], facecolor='gray', alpha=.2)
plt.grid('on')
ax.set_xlabel(r'')
ax.set_ylabel(r'Anomalie normalisée cummulée')
ax.set_title('Indice climatique pour TNL')
ax.legend(bbox_to_anchor=(1.0, 1), loc='upper left', fontsize=12)
colors = cmap(normal(np.nansum(climate_index_norm.values, axis=1)))
nlci_text = np.nansum(climate_index_norm.values, axis=1).round(1).astype('str')
if nlci_text[0] == '0.0':
nlci_text[0] = 'nan'
the_table = ax.table(cellText=[nlci_text],
rowLabels=['Indice climatique'],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='bottom', bbox=[0, -0.14, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell.set_fontsize(0)
else:
cell._text.set_rotation(90)
fig = ax.get_figure()
fig.set_size_inches(w=15.5,h=12)
fig_name = 'NL_climate_index_ms_FR.png'
fig.savefig(fig_name, dpi=300)
os.system('convert -trim -bordercolor White -border 10x10 ' + fig_name + ' ' + fig_name)
#### ----- Climate index with extended Scorecards [2] ---- ####
# Build the colormap - Stack
from matplotlib.colors import from_levels_and_colors
YlGn = plt.cm.YlGn(np.linspace(0,1, num=12))
YlGn = YlGn[4:,]
cmap_stack, norm_stack = from_levels_and_colors(np.arange(0,7), YlGn, extend='both')
# Build the colormap - Scorecard
vmin = -3.49
vmax = 3.49
midpoint = 0
levels = np.linspace(vmin, vmax, 15)
midp = np.mean(np.c_[levels[:-1], levels[1:]], axis=1)
colvals = np.interp(midp, [vmin, midpoint, vmax], [-1, 0., 1])
normal = plt.Normalize(-3.49, 3.49)
reds = plt.cm.Reds(np.linspace(0,1, num=7))
blues = plt.cm.Blues_r(np.linspace(0,1, num=7))
whites = [(1,1,1,1)]*2
colors = np.vstack((blues[0:-1,:], whites, reds[1:,:]))
colors = np.concatenate([[colors[0,:]], colors, [colors[-1,:]]], 0)
cmap, norm = from_levels_and_colors(levels, colors, extend='both')
cmap_r, norm_r = from_levels_and_colors(levels, np.flipud(colors), extend='both')
fig, ax = plt.subplots(nrows=1, ncols=1)
n = 5 # xtick every n years
ax = climate_index_norm.plot(kind='bar', stacked=True, cmap='nipy_spectral', zorder=10)
ticks = ax.xaxis.get_ticklocs()
ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
ax.xaxis.set_ticks(ticks[::n])
ax.xaxis.set_ticklabels(ticklabels[::n])
plt.fill_between([ticks[0]-1, ticks[-1]+1], [-.5, -.5], [.5, .5], facecolor='gray', alpha=.2)
plt.grid('on')
ax.set_ylim([-1.6, 1.6])
ax.set_xlabel(r'')
ax.set_ylabel(r'Normalized anomaly')
#ax.set_title('NL Climate Index')
#leg = ax.legend(bbox_to_anchor=(1.0, 1), loc='upper left', fontsize=12)
leg = ax.legend(fontsize=12)
# Climate Index
colors = cmap(normal(np.nansum(climate_index_norm.values, axis=1)))
# nansum leads to 0.0 when only NaNs; correcting it here:
nlci_text = np.nansum(climate_index_norm.values, axis=1).round(1).astype('str')
if nlci_text[0] == '0.0':
nlci_text[0] = 'nan'
nlci_table = ax.table(cellText=[nlci_text],
rowLabels=['NL climate index'],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='bottom', bbox=[0, -0.14, 1, 0.05])
nlci_table.auto_set_font_size (False)
nlci_table.set_fontsize(11)
for key, cell in nlci_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell.set_fontsize(0)
else:
cell._text.set_rotation(90)
# CIL area
colors = cmap(normal(climate_index_sc['CIL area'].values))
the_table = ax.table(cellText=[climate_index_sc['CIL area'].values.round(1)],
rowLabels=['CIL area '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.01, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# Bottom T
colors = cmap(normal(climate_index_sc['Bottom T'].values))
the_table = ax.table(cellText=[climate_index_sc['Bottom T'].values.round(1)],
rowLabels=['Bottom T '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.06, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# S27 CIL
colors = cmap(normal(climate_index_sc['S27 CIL'].values))
the_table = ax.table(cellText=[climate_index_sc['S27 CIL'].values.round(1)],
rowLabels=['S27 CIL '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.11, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# S27 S
colors = cmap(normal(climate_index_sc['S27 S'].values))
the_table = ax.table(cellText=[climate_index_sc['S27 S'].values.round(1)],
rowLabels=['S27 S '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.16, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# S27 T
colors = cmap(normal(climate_index_sc['S27 T'].values))
the_table = ax.table(cellText=[climate_index_sc['S27 T'].values.round(1)],
rowLabels=['S27 T '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.21, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# SST
colors = cmap(normal(climate_index_sc['SST'].values))
the_table = ax.table(cellText=[climate_index_sc['SST'].values.round(1)],
rowLabels=['SST '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.26, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# Icebergs
colors = cmap(normal(climate_index_sc['Icebergs'].values))
the_table = ax.table(cellText=[climate_index_sc['Icebergs'].values.round(1)],
rowLabels=['Icebergs '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.31, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# Sea Ice
colors = cmap(normal(climate_index_sc['Sea Ice'].values))
the_table = ax.table(cellText=[climate_index_sc['Sea Ice'].values.round(1)],
rowLabels=['Sea Ice '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.36, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_text('')
cell._text.set_rotation(90)
# Air Temp
colors = cmap(normal(climate_index_sc['Air Temp'].values))
the_table = ax.table(cellText=[climate_index_sc['Air Temp'].values.round(1)],
rowLabels=['Air Temp '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.41, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# Wint. NAO
colors = cmap(normal(climate_index_sc['Wint. NAO'].values))
nao_table = ax.table(cellText=[climate_index_sc['Wint. NAO'].values.round(1)],
rowLabels=['Wint. NAO '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.46, 1, 0.05])
nao_table.auto_set_font_size (False)
nao_table.set_fontsize(11)
for key, cell in nao_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_text('')
cell._text.set_rotation(90)
fig = ax.get_figure()
fig.set_size_inches(w=15.5,h=12)
fig_name = 'NL_climate_index_ms_scorecards.png'
fig.savefig(fig_name, dpi=300, bbox_extra_artists=(nao_table,nlci_table,leg), bbox_inches='tight')
#fig.savefig(fig_name, dpi=300, bbox_extra_artists=(nlci_table,leg), bbox_inches='tight')
os.system('convert -trim -bordercolor White -border 10x10 ' + fig_name + ' ' + fig_name)
#### ----- Climate index with extended Scorecards [2] (French) ---- ####
fig, ax = plt.subplots(nrows=1, ncols=1)
n = 5 # xtick every n years
ax = climate_index_normFR.plot(kind='bar', stacked=True, cmap='nipy_spectral', zorder=10)
ticks = ax.xaxis.get_ticklocs()
ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
ax.xaxis.set_ticks(ticks[::n])
ax.xaxis.set_ticklabels(ticklabels[::n])
plt.fill_between([ticks[0]-1, ticks[-1]+1], [-.5, -.5], [.5, .5], facecolor='gray', alpha=.2)
plt.grid('on')
ax.set_ylim([-1.6, 1.6])
ax.set_xlabel(r'')
ax.set_ylabel(r'Anomalie normalisée')
#ax.set_title('NL Climate Index')
#leg = ax.legend(bbox_to_anchor=(1.0, 1), loc='upper left', fontsize=12)
leg = ax.legend(fontsize=12)
# Climate Index
colors = cmap(normal(np.nansum(climate_index_normFR.values, axis=1)))
# nansum leads to 0.0 when only NaNs; correcting it here:
nlci_text = np.nansum(climate_index_normFR.values, axis=1).round(1).astype('str')
if nlci_text[0] == '0.0':
nlci_text[0] = 'nan'
nlci_table = ax.table(cellText=[nlci_text],
rowLabels=['Indice climatique TNL'],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='bottom', bbox=[0, -0.14, 1, 0.05])
nlci_table.auto_set_font_size (False)
nlci_table.set_fontsize(11)
for key, cell in nlci_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell.set_fontsize(0)
else:
cell._text.set_rotation(90)
# CIL area
colors = cmap(normal(climate_index_sc['CIL area'].values))
the_table = ax.table(cellText=[climate_index_sc['CIL area'].values.round(1)],
rowLabels=['Aire CIF '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.01, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# Bottom T
colors = cmap(normal(climate_index_sc['Bottom T'].values))
the_table = ax.table(cellText=[climate_index_sc['Bottom T'].values.round(1)],
rowLabels=['Temp fond '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.06, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# S27 CIL
colors = cmap(normal(climate_index_sc['S27 CIL'].values))
the_table = ax.table(cellText=[climate_index_sc['S27 CIL'].values.round(1)],
rowLabels=['S27 CIF '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.11, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# S27 S
colors = cmap(normal(climate_index_sc['S27 S'].values))
the_table = ax.table(cellText=[climate_index_sc['S27 S'].values.round(1)],
rowLabels=['S27 S '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.16, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# S27 T
colors = cmap(normal(climate_index_sc['S27 T'].values))
the_table = ax.table(cellText=[climate_index_sc['S27 T'].values.round(1)],
rowLabels=['S27 T '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.21, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# SST
colors = cmap(normal(climate_index_sc['SST'].values))
the_table = ax.table(cellText=[climate_index_sc['SST'].values.round(1)],
rowLabels=['SST '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.26, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# Icebergs
colors = cmap(normal(climate_index_sc['Icebergs'].values))
the_table = ax.table(cellText=[climate_index_sc['Icebergs'].values.round(1)],
rowLabels=['Icebergs '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.31, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# Sea Ice
colors = cmap(normal(climate_index_sc['Sea Ice'].values))
the_table = ax.table(cellText=[climate_index_sc['Sea Ice'].values.round(1)],
rowLabels=['Glace '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.36, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_text('')
cell._text.set_rotation(90)
# Air Temp
colors = cmap(normal(climate_index_sc['Air Temp'].values))
the_table = ax.table(cellText=[climate_index_sc['Air Temp'].values.round(1)],
rowLabels=['Temp air '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.41, 1, 0.05])
the_table.auto_set_font_size (False)
the_table.set_fontsize(11)
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1:
cell.set_linewidth(0)
cell.set_fontsize(10)
elif cell_text=='nan':
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cell._text.set_text('')
else:
cell._text.set_rotation(90)
cell._text.set_text('')
# Wint. NAO
colors = cmap(normal(climate_index_sc['Wint. NAO'].values))
nao_table = ax.table(cellText=[climate_index_sc['Wint. NAO'].values.round(1)],
rowLabels=['ONA hiver '],
colLabels=None,
cellColours = [colors],
cellLoc = 'center', rowLoc = 'center',
loc='top', bbox=[0, +1.46, 1, 0.05])
nao_table.auto_set_font_size (False)
nao_table.set_fontsize(11)
for key, cell in nao_table.get_celld().items():
cell_text = cell.get_text().get_text()
if key[1] == -1: