Demonstrates the importance of perceptually uniform colormaps using temperature in Central England over the past 200 years

temperature.py

import requests
import pandas as pd
from io import BytesIO
import matplotlib as mpl
import matplotlib.pyplot as plt

url = 'https://www.metoffice.gov.uk/hadobs/hadcet/cetdl1772on.dat'

raw = pd.read_csv(BytesIO(requests.get(url).content), sep='\s+', header=None)
raw.columns = ['year', 'day'] + list(range(1, 13))

stacked = raw.set_index(['year', 'day']).stack().loc[lambda s: s != -999]/10
stacked.index = pd.to_datetime([f'{y:4d}-{m:02d}-{d:02d}' for y, d, m in stacked.index])
stacked = stacked.sort_index()

cmaps = ['gnuplot', 'inferno', 'viridis']
fig, axes = plt.subplots(len(cmaps), 1, sharex=True)
for cmap, ax in zip(cmaps, axes):
    extent = (mpl.dates.date2num(stacked.index[0]), mpl.dates.date2num(stacked.index[-1]), 0, 1)
    ax.imshow(stacked.resample('A').mean().values[None, :], extent=extent, aspect=2e4, cmap=cmap)
    ax.set_yticks([])
    ax.xaxis_date()
    ax.set_title(cmap, size='medium')

    ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%Y'))