Code and data to reproduce the temperature anomaly animation.

Tdata.csv

TemperatureAnomalyFigs.py

# Copyright (c) 2020 Antti Lipponen
# 
#  Permission is hereby granted, free of charge, to any person obtaining
#  a copy of this software and associated documentation files (the
#  "Software"), to deal in the Software without restriction, including
#  without limitation the rights to use, copy, modify, merge, publish,
#  distribute, sublicense, and/or sell copies of the Software, and to
#  permit persons to whom the Software is furnished to do so, subject to
#  the following conditions:
# 
#  The above copyright notice and this permission notice shall be included
#  in all copies or substantial portions of the Software.
# 
#  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
#  EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
#  MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
#  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
#  CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
#  TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
#  SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

# Code to reproduce the gif-animation "Temperature change by country years 1900–2019"
#   https://twitter.com/anttilip/status/1259906496940433408
# #################################################################################
# You need the following data / files to run this as planned:
# * Google Fonts - Lato: https://fonts.google.com/specimen/Lato
# * Flag png files (png100px folder) from a git repository
#     by Hampus Joakim Nilsson: https://github.com/hjnilsson/country-flags
# * Imagemagick to construct the final gif animation (not required)
# * Tdata.csv - The temperature data, distributed together with this source code file
# #################################################################################

import numpy as np
import pandas as pd
from imageio import imread
import matplotlib.pyplot as plt
from matplotlib import rc
import os
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF

# Set location of directory containing the flag png files
png100pxFLAGDIR = './png100px/'

# Set Lato as font for Matplotlib
rc('font', **{'family': 'serif', 'serif': ['Lato']})

# Dictionary to convert A3 to A2
isoa3toa2 = {'MDV': 'MV', 'AFG': 'AF', 'ALA': 'AX', 'ALB': 'AL', 'DZA': 'DZ', 'ASM': 'AS', 'AND': 'AD', 'AGO': 'AO', 'AIA': 'AI', 'ATA': 'AQ', 'ATG': 'AG', 'ARG': 'AR', 'ARM': 'AM', 'ABW': 'AW', 'AUS': 'AU', 'AUT': 'AT', 'AZE': 'AZ', 'BHS': 'BS', 'BHR': 'BH', 'BGD': 'BD', 'BRB': 'BB', 'BLR': 'BY', 'BEL': 'BE', 'BLZ': 'BZ', 'BEN': 'BJ', 'BMU': 'BM', 'BTN': 'BT', 'BOL': 'BO', 'BIH': 'BA', 'BWA': 'BW', 'BVT': 'BV', 'BRA': 'BR', 'VGB': 'VG', 'IOT': 'IO', 'BRN': 'BN', 'BGR': 'BG', 'BFA': 'BF', 'BDI': 'BI', 'KHM': 'KH', 'CMR': 'CM', 'CAN': 'CA', 'CPV': 'CV', 'CYM': 'KY', 'CAF': 'CF', 'TCD': 'TD', 'CHL': 'CL', 'CHN': 'CN', 'HKG': 'HK', 'MAC': 'MO', 'CXR': 'CX', 'CCK': 'CC', 'COL': 'CO', 'COM': 'KM', 'COG': 'CG', 'COD': 'CD', 'COK': 'CK', 'CRI': 'CR', 'CIV': 'CI', 'HRV': 'HR', 'CUB': 'CU', 'CYP': 'CY', 'CZE': 'CZ', 'DNK': 'DK', 'DJI': 'DJ', 'DMA': 'DM', 'DOM': 'DO', 'ECU': 'EC', 'EGY': 'EG', 'SLV': 'SV', 'GNQ': 'GQ', 'ERI': 'ER', 'EST': 'EE', 'ETH': 'ET', 'FLK': 'FK', 'FRO': 'FO', 'FJI': 'FJ', 'FIN': 'FI', 'FRA': 'FR', 'GUF': 'GF', 'PYF': 'PF', 'ATF': 'TF', 'GAB': 'GA', 'GMB': 'GM', 'GEO': 'GE', 'DEU': 'DE', 'GHA': 'GH', 'GIB': 'GI', 'GRC': 'GR', 'GRL': 'GL', 'GRD': 'GD', 'GLP': 'GP', 'GUM': 'GU', 'GTM': 'GT', 'GGY': 'GG', 'GIN': 'GN', 'GNB': 'GW', 'GUY': 'GY', 'HTI': 'HT', 'HMD': 'HM', 'VAT': 'VA', 'HND': 'HN', 'HUN': 'HU', 'ISL': 'IS', 'IND': 'IN', 'IDN': 'ID', 'IRN': 'IR', 'IRQ': 'IQ', 'IRL': 'IE', 'IMN': 'IM', 'ISR': 'IL', 'ITA': 'IT', 'JAM': 'JM', 'JPN': 'JP', 'JEY': 'JE', 'JOR': 'JO', 'KAZ': 'KZ', 'KEN': 'KE', 'KIR': 'KI', 'PRK': 'KP', 'KOR': 'KR', 'KWT': 'KW', 'KGZ': 'KG', 'LAO': 'LA', 'LVA': 'LV', 'LBN': 'LB', 'LSO': 'LS', 'LBR': 'LR', 'LBY': 'LY', 'LIE': 'LI', 'LTU': 'LT', 'LUX': 'LU', 'MKD': 'MK', 'MDG': 'MG', 'MWI': 'MW', 'MYS': 'MY', 'MDV': 'MV', 'MLI': 'ML', 'MLT': 'MT', 'MHL': 'MH', 'MTQ': 'MQ', 'MRT': 'MR', 'MUS': 'MU', 'MYT': 'YT', 'MEX': 'MX', 'FSM': 'FM', 'MDA': 'MD', 'MCO': 'MC', 'MNG': 'MN', 'MNE': 'ME', 'MSR': 'MS', 'MAR': 'MA', 'MOZ': 'MZ', 'MMR': 'MM', 'NAM': 'NA', 'NRU': 'NR', 'NPL': 'NP', 'NLD': 'NL', 'ANT': 'AN', 'NCL': 'NC', 'NZL': 'NZ', 'NIC': 'NI', 'NER': 'NE', 'NGA': 'NG', 'NIU': 'NU', 'NFK': 'NF', 'MNP': 'MP', 'NOR': 'NO', 'OMN': 'OM', 'PAK': 'PK', 'PLW': 'PW', 'PSE': 'PS', 'PAN': 'PA', 'PNG': 'PG', 'PRY': 'PY', 'PER': 'PE', 'PHL': 'PH', 'PCN': 'PN', 'POL': 'PL', 'PRT': 'PT', 'PRI': 'PR', 'QAT': 'QA', 'REU': 'RE', 'ROU': 'RO', 'RUS': 'RU', 'RWA': 'RW', 'BLM': 'BL', 'SHN': 'SH', 'KNA': 'KN', 'LCA': 'LC', 'MAF': 'MF', 'SPM': 'PM', 'VCT': 'VC', 'WSM': 'WS', 'SMR': 'SM', 'STP': 'ST', 'SAU': 'SA', 'SEN': 'SN', 'SRB': 'RS', 'SYC': 'SC', 'SLE': 'SL', 'SGP': 'SG', 'SVK': 'SK', 'SVN': 'SI', 'SLB': 'SB', 'SOM': 'SO', 'ZAF': 'ZA', 'SGS': 'GS', 'SSD': 'SS', 'ESP': 'ES', 'LKA': 'LK', 'SDN': 'SD', 'SUR': 'SR', 'SJM': 'SJ', 'SWZ': 'SZ', 'SWE': 'SE', 'CHE': 'CH', 'SYR': 'SY', 'TWN': 'TW', 'TJK': 'TJ', 'TZA': 'TZ', 'THA': 'TH', 'TLS': 'TL', 'TGO': 'TG', 'TKL': 'TK', 'TON': 'TO', 'TTO': 'TT', 'TUN': 'TN', 'TUR': 'TR', 'TKM': 'TM', 'TCA': 'TC', 'TUV': 'TV', 'UGA': 'UG', 'UKR': 'UA', 'ARE': 'AE', 'GBR': 'GB', 'USA': 'US', 'UMI': 'UM', 'URY': 'UY', 'UZB': 'UZ', 'VUT': 'VU', 'VEN': 'VE', 'VNM': 'VN', 'VIR': 'VI', 'WLF': 'WF', 'ESH': 'EH', 'YEM': 'YE', 'ZMB': 'ZM', 'ZWE': 'ZW'}

# Create fig folder if it does not exist
os.makedirs('figs', exist_ok=True)

# Read the data (GISTEMP data, processed (country average computations) by A. Lipponen)
d = pd.read_csv('Tdata.csv')

# Loop countries
for ii, country in d.iterrows():
    isoa3, isoa2 = country['ISOA3'], isoa3toa2[country['ISOA3']]
    countryname = country['Country']
    print(countryname, isoa2, isoa3)

    # Get temperature anomaly data & fit a smooth curve with Gaussian Process
    years, temp_anomalies = [], []
    for yy in np.arange(1900, 2019 + 1):
        T = country[str(yy)]
        if np.isnan(T):
            continue
        temp_anomalies.append(T)
        years.append(yy)
    temp_anomalies = np.array(temp_anomalies)
    years = np.array(years)
    clf = GaussianProcessRegressor(kernel=RBF(length_scale=50.0), alpha=0.1, optimizer=None)
    clf = clf.fit(years[:, None], temp_anomalies)
    smoothX = np.linspace(years.min(), years.max())
    smoothY = clf.predict(smoothX[:, None])

    # Plot figure!
    fig = plt.figure(figsize=(8, 8), facecolor='#fbf2eb')

    # Add flag
    flagimg = imread(os.path.join(png100pxFLAGDIR, '{}.png'.format(isoa2.lower())))
    axFLAG = fig.add_axes([0.99 - 0.125, 0.99 - 0.12 / flagimg.shape[1] * flagimg.shape[0], 0.12, 0.12 / flagimg.shape[1] * flagimg.shape[0]])
    axFLAG.imshow(flagimg[::-1, :, :])
    axFLAG.set_xlim([0, flagimg.shape[1]])
    axFLAG.set_ylim([0, flagimg.shape[0]])
    axFLAG.axis('off')

    # Add some texts
    axTEXT = fig.add_axes([0, 0, 1, 1], facecolor=None)
    axTEXT.patch.set_alpha(0.0)
    axTEXT.text(0.0125 + 0.001, 0.95 - 0.0025, countryname, color='#808080', fontsize=30, fontweight='bold', horizontalalignment='left', verticalalignment='center', transform=fig.transFigure)
    axTEXT.text(0.0125, 0.95, countryname, color='#000000', fontsize=30, fontweight='bold', horizontalalignment='left', verticalalignment='center', transform=fig.transFigure)
    axTEXT.text(0.01, 0.02, 'Data source: NASA GISTEMP.', color='#909090', fontsize=12, horizontalalignment='left', verticalalignment='center', transform=fig.transFigure)
    axTEXT.text(0.99, 0.039, 'CC-BY-4.0', color='#909090', fontsize=12, horizontalalignment='right', verticalalignment='center', transform=fig.transFigure)
    axTEXT.text(0.99, 0.02, 'Antti Lipponen (@anttilip)', color='#909090', fontsize=12, horizontalalignment='right', verticalalignment='center', transform=fig.transFigure)
    axTEXT.text(0.185, 0.1875, 'Temperature', color='#202020', fontsize=24, fontweight='bold', horizontalalignment='center', verticalalignment='center', clip_on=False)
    axTEXT.text(0.185, 0.1500, 'Change', color='#202020', fontsize=24, fontweight='bold', horizontalalignment='center', verticalalignment='center', clip_on=False)
    axTEXT.text(0.185, 0.1150, 'Base Period', color='#202020', fontsize=16, horizontalalignment='center', verticalalignment='center', clip_on=False)
    axTEXT.text(0.185, 0.0875, '1951-1980.', color='#202020', fontsize=16, horizontalalignment='center', verticalalignment='center', clip_on=False)

    # Plot data and set some ticks and other axis properties
    axDATA = fig.add_axes([0.185, 0.125, 0.75, 0.74])
    axDATA.patch.set_alpha(0.0)
    axDATA.spines['top'].set_visible(False)
    axDATA.spines['left'].set_visible(False)
    axDATA.spines['bottom'].set_visible(False)
    axDATA.spines['right'].set_visible(False)

    axDATA.scatter(years, temp_anomalies, c='#f42080', marker='o', lw=1.5, alpha=0.5, clip_on=False)
    axDATA.plot(smoothX, smoothY, linewidth=3, linestyle='-', c='#303030', alpha=0.9, clip_on=False)

    axDATA.xaxis.grid(True, linewidth=1.0, linestyle='--', color='k', alpha=0.2)
    axDATA.set_xlim([1900, 2025.5])
    axDATA.set_xlabel('Year', color='#202020', fontsize=25, fontweight='bold')
    axDATA.set_xticks([1925, 1950, 1975, 2000, 2025])
    axDATA.set_xticklabels(['1925', '1950', '1975', '2000', '2025'], fontdict={'fontsize': 18, 'fontweight': 'bold'})
    axDATA.tick_params(axis='x', length=10, width=2)

    axDATA.yaxis.grid(True, linewidth=1.0, linestyle='-', color='k', alpha=0.25)
    axDATA.set_ylim([-3.25, 2.5])
    axDATA.set_yticks([-2, -1, 0, 1, 2])
    axDATA.set_yticklabels(['-2 °C', '-1 °C', '0 °C', '+1 °C', '+2 °C'], fontdict={'fontsize': 24})
    axDATA.tick_params(axis='y', pad=12.0, length=0)

    # save file & close figure
    plt.savefig(os.path.join('figs', '{}.png'.format(isoa3)), dpi=80, facecolor=fig.get_facecolor(), edgecolor='none')
    plt.close()

print('\nAll done!\n\nTo make the animated gif type:\nconvert -delay 6 figs/*.png -layers Optimize animation.gif\n\n')