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August 26, 2018 18:23
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Demonstrates the importance of perceptually uniform colormaps using temperature in Central England over the past 200 years
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| 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')) |
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