azmp_bottomT_scorecards.py

'''
To generate AZMP score cards for bottom temperature

Uses pickled object generated by azmp_bottom_stats.py

'''

import numpy as  np
import matplotlib.pyplot as plt
import pandas as pd
import os
import unicodedata

clim_year = [1981, 2010]

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

#### ------------- For fall ---------------- ####
# 1.
infile = 'stats_2J_fall.pkl'
df = pd.read_pickle(infile)
year_list = df.index # save former index
year_list = [i[2:4] for i in year_list] # 2-digit year
df.index = pd.to_datetime(df.index) # update index to datetime
df['area_colder0'] = df['area_colder0']/1000 # In 1000km
df['area_colder1'] = df['area_colder1']/1000 # In 1000km
df['area_warmer2'] = df['area_warmer2']/1000
df_clim = df[(df.index.year>=clim_year[0]) & (df.index.year<=clim_year[1])]
std_anom = (df-df_clim.mean(axis=0))/df_clim.std(axis=0)
std_anom = std_anom.T
std_anom['MEAN'] = df_clim.mean(axis=0)
std_anom['SD'] = df_clim.std(axis=0)
std_anom = std_anom.reindex(['Tmean', 'Tmean_sha200', 'area_warmer2', 'area_colder1'])
std_anom = std_anom.rename({'Tmean': r'$\rm T_{bot}$', 'Tmean_sha200': r'$\rm T_{bot_{<200m}}$', 'area_warmer2': r'$\rm Area_{>2^{\circ}C}$', 'area_colder1': r'$\rm Area_{<1^{\circ}C}$'})
std_anom.rename(columns={'MEAN': r'$\rm \overline{x}$', 'SD': r'sd'}, inplace=True)

# Get text values +  cell color
year_list.append(r'$\rm \overline{x}$') # add 2 extra columns
year_list.append(r'sd')   
vals = np.around(std_anom.values,1)
vals[vals==-0.] = 0.
vals_color = vals.copy()
vals_color[-1,] = vals_color[-1,]*-1 # Reverse last row colorscale
vals_color[:,-1] = 0 # No color to last two columns (mean and STD)
vals_color[:,-2] = 0
#vals_color[(vals_color<0.5) & (vals_color>-.5)] = 0.

normal = plt.Normalize(-4.49, 4.49)
cmap = plt.cm.get_cmap('seismic', 9) 
#cmap = plt.cm.get_cmap('seismic', 15) 
nrows, ncols = std_anom.index.size+1, std_anom.columns.size
hcell, wcell = 0.5, 0.5
hpad, wpad = 0, 0    
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=['-- NAFO divison 2J --'],
                      loc='center'
                      )
header.set_fontsize(13)
#the_table=ax.table(cellText=vals, rowLabels=std_anom.index, colLabels=std_anom.columns, 
the_table=ax.table(cellText=vals, rowLabels=std_anom.index, colLabels=year_list,
                    loc='center', cellColours=cmap(normal(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(12.5)
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) <= -1.5) | (np.float(cell_text) >= 1.5) :
        cell._text.set_color('white')
plt.savefig("scorecards_fall_2J.png", dpi=300)
os.system('convert -trim scorecards_fall_2J.png scorecards_fall_2J.png')


# 2.
infile = 'stats_3K_fall.pkl'
df = pd.read_pickle(infile)
df.index = pd.to_datetime(df.index) # update index to datetime
df['area_colder0'] = df['area_colder0']/1000 # In 1000km
df['area_colder1'] = df['area_colder1']/1000 # In 1000km
df['area_warmer2'] = df['area_warmer2']/1000
df_clim = df[(df.index.year>=clim_year[0]) & (df.index.year<=clim_year[1])]
std_anom = (df-df_clim.mean(axis=0))/df_clim.std(axis=0)
std_anom = std_anom.T
std_anom['MEAN'] = df_clim.mean(axis=0)
std_anom['SD'] = df_clim.std(axis=0)
std_anom = std_anom.reindex(['Tmean', 'Tmean_sha200', 'area_warmer2', 'area_colder1'])
std_anom = std_anom.rename({'Tmean': r'$\rm T_{bot}$', 'Tmean_sha200': r'$\rm T_{bot_{<200m}}$', 'area_warmer2': r'$\rm Area_{>2^{\circ}C}$', 'area_colder1': r'$\rm Area_{<1^{\circ}C}$'})
std_anom.rename(columns={'MEAN': r'$\rm \overline{x}$', 'SD': r'sd'}, inplace=True)
## df = pd.read_pickle(infile)
## # Pass in 2 digit-index:
## new_index = [i[2:4] for i in df.index]
## df = df.set_index(pd.Series(new_index))
## df['area_colder0'] = df['area_colder0']/1000 # In 1000km
## df['area_colder1'] = df['area_colder1']/1000 # In 1000km
## df['area_warmer2'] = df['area_warmer2']/1000
## std_anom = (df-df.mean(axis=0))/df.std(axis=0)
## std_anom = std_anom.T
## std_anom['MEAN'] = df.mean(axis=0)
## std_anom['SD'] = df.std(axis=0)
## std_anom = std_anom.reindex(['Tmean', 'Tmean_sha300', 'area_warmer2', 'area_colder1'])
## std_anom = std_anom.rename({'Tmean': r'$\rm T_{bot}$', 'Tmean_sha300': r'$\rm T_{bot_{<300m}}$', 'area_warmer2': r'$\rm Area_{>2^{\circ}C}$', 'area_colder1': r'$\rm Area_{<1^{\circ}C}$'})
## std_anom.rename(columns={'MEAN': r'$\rm \overline{x}$', 'SD': r'sd'}, inplace=True)

vals = np.around(std_anom.values,1)
vals[vals==-0.] = 0.
vals_color = vals.copy()
vals_color[-1,] = vals_color[-1,]*-1
vals_color[:,-1] = 0 # No color to last two columns (mean and STD)
vals_color[:,-2] = 0 
normal = plt.Normalize(-4.49, 4.49)
cmap = plt.cm.get_cmap('seismic', 9) 
nrows, ncols = std_anom.index.size+1, std_anom.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=['-- NAFO divison 3K --'],
                      loc='center'
                      )
#the_table=ax.table(cellText=vals, rowLabels=std_anom.index, colLabels=std_anom.columns, 
header.set_fontsize(12.5)
the_table=ax.table(cellText=vals, rowLabels=std_anom.index, colLabels=None, 
                    loc='center', cellColours=cmap(normal(vals_color)), cellLoc='center',
                    bbox=[0, 0, 1.0, 0.50]
                    )
the_table.auto_set_font_size(False)
the_table.set_fontsize(12.5)
# change font color to white where needed:
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:# <--- remove when no years
    #    pass
    elif key[1] in last_columns:
         cell._text.set_color('darkslategray')
    elif (np.float(cell_text) <= -1.5) | (np.float(cell_text) >= 1.5) :
        cell._text.set_color('white')

plt.savefig("scorecards_fall_3K.png", dpi=300)
os.system('convert -trim scorecards_fall_3K.png scorecards_fall_3K.png')


# 3.
infile = 'stats_3LNO_fall.pkl'
df = pd.read_pickle(infile)
df.index = pd.to_datetime(df.index) # update index to datetime
df['area_colder0'] = df['area_colder0']/1000 # In 1000km
df['area_colder1'] = df['area_colder1']/1000 # In 1000km
df['area_warmer2'] = df['area_warmer2']/1000
df_clim = df[(df.index.year>=clim_year[0]) & (df.index.year<=clim_year[1])]
std_anom = (df-df_clim.mean(axis=0))/df_clim.std(axis=0)
std_anom = std_anom.T
std_anom['MEAN'] = df_clim.mean(axis=0)
std_anom['SD'] = df_clim.std(axis=0)
std_anom = std_anom.reindex(['Tmean', 'Tmean_sha200', 'area_warmer2', 'area_colder1'])
std_anom = std_anom.rename({'Tmean': r'$\rm T_{bot}$', 'Tmean_sha200': r'$\rm T_{bot_{<200m}}$', 'area_warmer2': r'$\rm Area_{>2^{\circ}C}$', 'area_colder1': r'$\rm Area_{<1^{\circ}C}$'})
std_anom.rename(columns={'MEAN': r'$\rm \overline{x}$', 'SD': r'sd'}, inplace=True)

vals = np.around(std_anom.values,1)
vals[vals==-0.] = 0.
vals_color = vals.copy()
vals_color[-1,] = vals_color[-1,]*-1
vals_color[:,-1] = 0 # No color to last two columns (mean and STD)
vals_color[:,-2] = 0
normal = plt.Normalize(-4.49, 4.49)
cmap = plt.cm.get_cmap('seismic', 9) 
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=['-- NAFO divison 3LNO --'],
                      loc='center'
                      )

header.set_fontsize(12.5)

the_table=ax.table(cellText=vals, rowLabels=std_anom.index, colLabels=None, 
                    loc='center', cellColours=cmap(normal(vals_color)), cellLoc='center',
                    bbox=[0, 0, 1.0, 0.50]
                    )
# change font color to white where needed:
the_table.auto_set_font_size(False)
the_table.set_fontsize(12.5)
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:# <--- remove when no years
    #    pass
    elif key[1] in last_columns:
         cell._text.set_color('darkslategray')
    elif (np.float(cell_text) <= -1.5) | (np.float(cell_text) >= 1.5) :
        cell._text.set_color('white')
plt.savefig("scorecards_fall_3LNO.png", dpi=300)
os.system('convert -trim scorecards_fall_3LNO.png scorecards_fall_3LNO.png')

os.system('montage  scorecards_fall_2J.png scorecards_fall_3K.png scorecards_fall_3LNO.png -tile 1x3 -geometry +1+1  -background white  scorecards_botT_fall.png') 



#### ------------- For Spring ---------------- ####
# 1.
infile = 'stats_3LNO_spring.pkl'
df = pd.read_pickle(infile)
year_list = df.index # save former index
year_list = [i[2:4] for i in year_list] # 2-digit year
df.index = pd.to_datetime(df.index) # update index to datetime
df['area_colder0'] = df['area_colder0']/1000 # In 1000km
df['area_colder1'] = df['area_colder1']/1000 # In 1000km
df['area_warmer2'] = df['area_warmer2']/1000
df_clim = df[(df.index.year>=clim_year[0]) & (df.index.year<=clim_year[1])]
std_anom = (df-df_clim.mean(axis=0))/df_clim.std(axis=0)
std_anom = std_anom.T
std_anom['MEAN'] = df_clim.mean(axis=0)
std_anom['SD'] = df_clim.std(axis=0)
std_anom = std_anom.reindex(['Tmean', 'Tmean_sha200', 'area_warmer2', 'area_colder1'])
std_anom = std_anom.rename({'Tmean': r'$\rm T_{bot}$', 'Tmean_sha200': r'$\rm T_{bot_{<200m}}$', 'area_warmer2': r'$\rm Area_{>2^{\circ}C}$', 'area_colder1': r'$\rm Area_{<1^{\circ}C}$'})
std_anom.rename(columns={'MEAN': r'$\rm \overline{x}$', 'SD': r'sd'}, inplace=True)

year_list.append(r'$\rm \overline{x}$') # add 2 extra columns
year_list.append(r'sd')
vals = np.around(std_anom.values,1)
vals[vals==-0.] = 0.
vals_color = vals.copy()
vals_color[-1,] = vals_color[-1,]*-1
vals_color[:,-1] = 0 # No color to last two columns (mean and STD)
vals_color[:,-2] = 0
normal = plt.Normalize(-4.49, 4.49)
cmap = plt.cm.get_cmap('seismic', 9) 
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=['-- NAFO divison 3LNO --'],
                      loc='center'
                      )

header.set_fontsize(12.5)
#the_table=ax.table(cellText=vals, rowLabels=std_anom.index, colLabels=std_anom.columns, 
the_table=ax.table(cellText=vals, rowLabels=std_anom.index, colLabels=year_list, 
                    loc='center', cellColours=cmap(normal(vals_color)), cellLoc='center',
                    bbox=[0, 0, 1.0, 0.50]
                    )
# change font color to white where needed:
the_table.auto_set_font_size(False)
the_table.set_fontsize(12.5)
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:
        pass
    elif key[1] in last_columns:
         cell._text.set_color('darkslategray')
    elif (np.float(cell_text) <= -1.5) | (np.float(cell_text) >= 1.5) :
        cell._text.set_color('white')
plt.savefig("scorecards_spring_3LNO.png", dpi=300)
os.system('convert -trim scorecards_spring_3LNO.png scorecards_spring_3LNO.png')

# 2.
infile = 'stats_3Ps_spring.pkl'
df = pd.read_pickle(infile)
df.index = pd.to_datetime(df.index) # update index to datetime
df['area_colder0'] = df['area_colder0']/1000 # In 1000km
df['area_colder1'] = df['area_colder1']/1000 # In 1000km
df['area_warmer2'] = df['area_warmer2']/1000
df_clim = df[(df.index.year>=clim_year[0]) & (df.index.year<=clim_year[1])]
std_anom = (df-df_clim.mean(axis=0))/df_clim.std(axis=0)
std_anom = std_anom.T
std_anom['MEAN'] = df_clim.mean(axis=0)
std_anom['SD'] = df_clim.std(axis=0)
std_anom = std_anom.reindex(['Tmean', 'Tmean_sha200', 'area_warmer2', 'area_colder1'])
std_anom = std_anom.rename({'Tmean': r'$\rm T_{bot}$', 'Tmean_sha200': r'$\rm T_{bot_{<200m}}$', 'area_warmer2': r'$\rm Area_{>2^{\circ}C}$', 'area_colder1': r'$\rm Area_{<1^{\circ}C}$'})
std_anom.rename(columns={'MEAN': r'$\rm \overline{x}$', 'SD': r'sd'}, inplace=True)

vals = np.around(std_anom.values,1)
vals[vals==-0.] = 0.
vals_color = vals.copy()
vals_color[-1,] = vals_color[-1,]*-1
vals_color[:,-1] = 0 # No color to last two columns (mean and STD)
vals_color[:,-2] = 0 
normal = plt.Normalize(-4.49, 4.49)
cmap = plt.cm.get_cmap('seismic', 9) 
nrows, ncols = std_anom.index.size+1, std_anom.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=['-- NAFO divison 3Ps --'],
                      loc='center'
                      )
header.set_fontsize(12.5)
#the_table=ax.table(cellText=vals, rowLabels=std_anom.index, colLabels=std_anom.columns, 
the_table=ax.table(cellText=vals, rowLabels=std_anom.index, colLabels=None, 
                    loc='center', cellColours=cmap(normal(vals_color)), cellLoc='center',
                    bbox=[0, 0, 1.0, 0.50]
                    )
# change font color to white where needed:
the_table.auto_set_font_size(False)
the_table.set_fontsize(12.5)
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: # <--- remove when no years
    #    pass
    elif key[1] in last_columns:
         cell._text.set_color('darkslategray')
    elif (np.float(cell_text) <= -1.5) | (np.float(cell_text) >= 1.5) :
        cell._text.set_color('white')

plt.savefig("scorecards_spring_3Ps.png", dpi=300)
os.system('convert -trim scorecards_spring_3Ps.png scorecards_spring_3Ps.png')

os.system('montage  scorecards_spring_3LNO.png scorecards_spring_3Ps.png -tile 1x3 -geometry +1+1  -background white  scorecards_botT_spring.png')