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azmp_SSTs_scorecards.py
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# -*- coding: utf-8 -*-
'''
To generate AZMP SST scorecards
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
clim_year = [1981, 2010]
years = [1980, 2019]
def is_number(s):
#https://www.pythoncentral.io/how-to-check-if-a-string-is-a-number-in-python-including-unicode/
try:
float(s)
return True
except ValueError:
pass
try:
unicodedata.numeric(s)
return True
except (TypeError, ValueError):
pass
return False
#### ---- Load the data and compute anomalies ---- ####
df_month = pd.read_pickle('/home/cyrf0006/AZMP/state_reports/SSTs/SSTs_merged_monthly.pkl')
df_year = df_month.resample('As').mean()
df_clim_month = df_month[(df_month.index.year>=clim_year[0]) & (df_month.index.year<=clim_year[1])]
df_clim_year = df_clim_month.resample('As').mean()
#### ------------- Scorecard 1 - Monthly ---------------- ####
# anomalies
df_clim_month['month'] = df_clim_month.index.month
monthly_clim = df_clim_month.groupby('month').mean()
monthly_std = df_clim_month.groupby('month').std()
this_year = df_month[(df_month.index.year==years[1])]
this_year.index = this_year.index.month
this_year.index.name = 'month'
monthly_std_anom = (this_year - monthly_clim) / monthly_std
# Reorder columns
cols = [
'Greenland_Shelf',
'North_Central_Labrador_Sea',
'Hudson_Strait',
'Central_Labrador_Sea',
'Bravo',
'Hamilton_Bank',
'St.Anthony_Basin',
'Northeast_Nfld_Shelf',
'Orphan_Knoll',
'Avalon_Channel',
'Hybernia',
'Flemish_Pass',
'Flemish_Cap',
'Green-St._Pierre_Bank'
]
monthly_std_anom = monthly_std_anom[cols]
# Rename columns
monthly_std_anom_FR = monthly_std_anom.copy()
monthly_std_anom.rename(columns={
'Avalon_Channel' : 'Avalon Channel (AC)',
'Bravo':'Bravo (BRA)',
'Central_Labrador_Sea':'Cent. Lab. Sea (CLS)',
'Flemish_Cap' : 'Flemish Cap (FC)',
'Flemish_Pass' : 'Flemish Pass (FP)',
'Greenland_Shelf' : 'Greenland Shelf (GS)',
'Green-St._Pierre_Bank':'St.Pierre Bank (SPB)' ,
'Hamilton_Bank':'Hamilton Bank (HB)',
'Hudson_Strait': 'Hudson Strait (HS)',
'Hybernia' : 'Hibernia (HIB)',
'North_Central_Labrador_Sea':'NC Lab. Sea (NCLS)',
'Northeast_Nfld_Shelf':'NE NF shelf (NENL)',
'Orphan_Knoll':'Orphan Knoll (OK)',
'St.Anthony_Basin':'St. Anthony B. (SAB)'
}, inplace=True)
monthly_std_anom_FR.rename(columns={
'Avalon_Channel' : 'Chenal Avalon (AC)',
'Bravo':'Bravo (BRA)',
'Central_Labrador_Sea':'Mer Lab. Centrale (CLS)',
'Flemish_Cap' : 'Bonnet Flammand (FC)',
'Flemish_Pass' : 'Passe Flamande (FP)',
'Greenland_Shelf' : 'Plateau Groenland (GS)',
'Green-St._Pierre_Bank':'Banc St.Pierre (SPB)' ,
'Hamilton_Bank':'Banc Hamilton (HB)',
'Hudson_Strait': 'Detroit Hudson (HS)',
'Hybernia' : 'Hibernia (HIB)',
'North_Central_Labrador_Sea':'Mer Lab. Nord Cent. (NCLS)',
'Northeast_Nfld_Shelf':'Plateau T-N NE (NENL)',
'Orphan_Knoll':'Orphan Knoll (OK)',
'St.Anthony_Basin':'Basin St. Anthony (SAB)'
}, inplace=True)
# preamble
month_list = ['J','F','M','A','M','J','J','A','S','O','N','D']
# Build the colormap
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')
# Common parameters
hcell, wcell = 0.6, 0.6
hpad, wpad = 1, 10
my_df = monthly_std_anom.T
# Get text values + cell color
vals = np.around(my_df.values,1)
vals[vals==-0.] = 0.
vals_color = vals.copy()
#vals_colors[np.isnan(vals_colors)] = 0.
nrows, ncols = my_df.index.size+1, my_df.columns.size
fig=plt.figure(figsize=(ncols*wcell+wpad, nrows*hcell+hpad))
ax = fig.add_subplot(111)
ax.axis('off')
#do the table
header = ax.table(cellText=[['']],
colLabels=['-- 2019 Monthly Sea Surface Temperature anomalies --'],
loc='center'
)
header.set_fontsize(13)
the_table=ax.table(cellText=vals, rowLabels=my_df.index, colLabels=month_list,
loc='center', cellColours=cmap(norm(vals_color)), cellLoc='center',
bbox=[0, 0, 1, 0.5]
)
# change font color to white where needed:
the_table.auto_set_font_size(False)
the_table.set_fontsize(13)
table_props = the_table.properties()
table_cells = table_props['child_artists']
last_columns = np.arange(vals.shape[1]-2, vals.shape[1]) # last columns
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if is_number(cell_text) == False:
cell._text.set_font_properties('normal') # Not really bold, but smaller thus fits better.
elif key[0] == 0: #year's row = no color
pass
## elif key[1] in last_columns:
## cell._text.set_color('darkslategray')
elif (np.float(cell_text) <= -2.) | (np.float(cell_text) >= 2.) :
cell._text.set_color('white')
elif (cell_text=='nan'):
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cellDict = the_table.get_celld()
for x in range(1, len(my_df.index)+1): # right-align row label
cellDict[(x,-1)]._loc = 'right'
plt.savefig("scorecards_sst_monthly.png", dpi=300)
os.system('convert -trim scorecards_sst_monthly.png scorecards_sst_monthly.png')
## French table
my_df = monthly_std_anom_FR.T
fig=plt.figure(figsize=(ncols*wcell+wpad, nrows*hcell+hpad))
ax = fig.add_subplot(111)
ax.axis('off')
#do the table
header = ax.table(cellText=[['']],
colLabels=['-- Anomalies de température de surface mensuelle en 2019 --'],
loc='center'
)
header.set_fontsize(13)
the_table=ax.table(cellText=vals, rowLabels=my_df.index, colLabels=month_list,
loc='center', cellColours=cmap(norm(vals_color)), cellLoc='center',
bbox=[0, 0, 1, 0.5]
)
# change font color to white where needed:
the_table.auto_set_font_size(False)
the_table.set_fontsize(13)
table_props = the_table.properties()
table_cells = table_props['child_artists']
last_columns = np.arange(vals.shape[1]-2, vals.shape[1]) # last columns
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if is_number(cell_text) == False:
cell._text.set_font_properties('normal') # Not really bold, but smaller thus fits better.
elif key[0] == 0: #year's row = no color
pass
## elif key[1] in last_columns:
## cell._text.set_color('darkslategray')
elif (np.float(cell_text) <= -2.) | (np.float(cell_text) >= 2.) :
cell._text.set_color('white')
elif (cell_text=='nan'):
cell._set_facecolor('darkgray')
cell._text.set_color('darkgray')
cellDict = the_table.get_celld()
for x in range(1, len(my_df.index)+1): # right-align row label
cellDict[(x,-1)]._loc = 'right'
plt.savefig("scorecards_sst_monthly_FR.png", dpi=300)
os.system('convert -trim scorecards_sst_monthly_FR.png scorecards_sst_monthly_FR.png')
#### ------------- Scorecard 2 - Annual ---------------- ####
## Yearly anomalies
yearly_std_anom = (df_year - df_clim_year.mean()) / df_clim_year.std()
yearly_std_anom.index = yearly_std_anom.index.year
# Reorder columns
yearly_std_anom = yearly_std_anom[cols]
yearly_std_anom_FR = yearly_std_anom.copy()
my_df = yearly_std_anom.T
my_df['MEAN'] = df_clim_year.mean()
my_df['SD'] = df_clim_year.std()
my_df.rename(index={
'Avalon_Channel' : 'Avalon Channel (AC)',
'Bravo':'Bravo (BRA)',
'Central_Labrador_Sea':'Cent. Lab. Sea (CLS)',
'Flemish_Cap' : 'Flemish Cap (FC)',
'Flemish_Pass' : 'Flemish Pass (FP)',
'Greenland_Shelf' : 'Greenland Shelf (GS)',
'Green-St._Pierre_Bank':'St.Pierre Bank (SPB)' ,
'Hamilton_Bank':'Hamilton Bank (HB)',
'Hudson_Strait': 'Hudson Strait (HS)',
'Hybernia' : 'Hibernia (HIB)',
'North_Central_Labrador_Sea':'NC Lab. Sea (NCLS)',
'Northeast_Nfld_Shelf':'NE NF shelf (NENL)',
'Orphan_Knoll':'Orphan Knoll (OK)',
'St.Anthony_Basin':'St. Anthony B. (SAB)'
}, inplace=True)
my_df.rename(columns={
'MEAN' : r'$\rm \overline{x}$',
'SD':u'sd'
}, inplace=True)
year_list = yearly_std_anom.index.astype('str')
year_list = [i[2:4] for i in year_list] # 2-digit year
year_list.append(r'$\rm \overline{x}$') # add 2 extra columns
year_list.append(r'sd')
# Cell parameters
hcell, wcell = 0.6, 0.6
hpad, wpad = 1, 0
# Get text values + cell color
vals = np.around(my_df.values,1)
vals[vals==-0.] = 0.
vals_color = vals.copy()
vals_color[:,-1] = 0 # No color to last two columns (mean and STD)
vals_color[:,-2] = 0
nrows, ncols = my_df.index.size+1, my_df.columns.size
fig=plt.figure(figsize=(ncols*wcell+wpad, nrows*hcell+hpad))
ax = fig.add_subplot(111)
ax.axis('off')
#do the table
header = ax.table(cellText=[['']],
colLabels=['-- Sea Surface temperature anomalies --'],
loc='center'
)
header.set_fontsize(13)
the_table=ax.table(cellText=vals, rowLabels=my_df.index, colLabels=year_list,
loc='center', cellColours=cmap(norm(vals_color)), cellLoc='center',
bbox=[0, 0, 1, 0.5]
)
# change font color to white where needed:
the_table.auto_set_font_size(False)
the_table.set_fontsize(13)
table_props = the_table.properties()
table_cells = table_props['child_artists']
last_columns = np.arange(vals.shape[1]-2, vals.shape[1]) # last columns
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if is_number(cell_text) == False:
pass
elif key[0] == 0: # Years are white
pass
elif key[1] in last_columns:
cell._text.set_color('darkslategray')
elif (np.float(cell_text) <= -2) | (np.float(cell_text) >= 2) :
cell._text.set_color('white')
plt.savefig("scorecards_sst_yearly.png", dpi=300)
os.system('convert -trim scorecards_sst_yearly.png scorecards_sst_yearly.png')
# French table
my_df = yearly_std_anom_FR.T
my_df['MEAN'] = df_clim_year.mean()
my_df['SD'] = df_clim_year.std()
my_df.rename(index={
'Avalon_Channel' : 'Chenal Avalon (AC)',
'Bravo':'Bravo (BRA)',
'Central_Labrador_Sea':'Mer Lab. Centrale (CLS)',
'Flemish_Cap' : 'Bonnet Flammand (FC)',
'Flemish_Pass' : 'Passe Flamande (FP)',
'Greenland_Shelf' : 'Plateau Groenland (GS)',
'Green-St._Pierre_Bank':'Banc St.Pierre (SPB)' ,
'Hamilton_Bank':'Banc Hamilton (HB)',
'Hudson_Strait': 'Detroit Hudson (HS)',
'Hybernia' : 'Hibernia (HIB)',
'North_Central_Labrador_Sea':'Mer Lab. Nord Cent. (NCLS)',
'Northeast_Nfld_Shelf':'Plateau T-N NE (NENL)',
'Orphan_Knoll':'Orphan Knoll (OK)',
'St.Anthony_Basin':'Basin St. Anthony (SAB)'
}, inplace=True)
year_list = yearly_std_anom.index.astype('str')
year_list = [i[2:4] for i in year_list] # 2-digit year
year_list.append(r'$\rm \overline{x}$') # add 2 extra columns
year_list.append(r'ET')
header = ax.table(cellText=[['']],
colLabels=['-- Anomalies de température de surface --'],
loc='center'
)
header.set_fontsize(13)
the_table=ax.table(cellText=vals, rowLabels=my_df.index, colLabels=year_list,
loc='center', cellColours=cmap(norm(vals_color)), cellLoc='center',
bbox=[0, 0, 1, 0.5]
)
# change font color to white where needed:
the_table.auto_set_font_size(False)
the_table.set_fontsize(13)
table_props = the_table.properties()
table_cells = table_props['child_artists']
last_columns = np.arange(vals.shape[1]-2, vals.shape[1]) # last columns
for key, cell in the_table.get_celld().items():
cell_text = cell.get_text().get_text()
if is_number(cell_text) == False:
pass
elif key[0] == 0: #year's row = no color
pass
elif key[1] in last_columns:
cell._text.set_color('darkslategray')
elif (np.float(cell_text) <= -2) | (np.float(cell_text) >= 2) :
cell._text.set_color('white')
plt.savefig("scorecards_sst_yearly_FR.png", dpi=300)
os.system('convert -trim scorecards_sst_yearly_FR.png scorecards_sst_yearly_FR.png')