Python warming stripes and trend

Software requirements:

• Python 3

• numpy

• pandas

• matplotlib

Example script

warming_stripes_with_timeseries_and_trend.py

#!/usr/bin/env python
# coding: utf-8
'''
DKRZ example

Warming stripes plot II

Most of us have already come across Ed Hawkins' depiction of the
'Warming Stripes'. In this example we show how to create this plot with
Python using the annual mean temperature data from the DWD.

The goal is to generate the Warming Stripes for the German states Hamburg,
Bremen, and Nidersachsen and additionally to draw the time series and
trend data.

Content

- read CSV data file
- use Rectangle and PatchCollection to generate the warmin stripes plot
- add the time series data
- add the trend data

Data
- from Deutscher Wetterdienst (German Weather Service), Climate Data Center (CDC)
https://www.dwd.de/DE/leistungen/zeitreihen/zeitreihen.html

Based on
- Ed Hawkins' 'Temperature changes around the world (1901-2018)'
               https://showyourstripes.info/s/globe

-------------------------------------------------------------------------------
2023 copyright DKRZ licensed under CC BY-NC-SA 4.0
               (https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
-------------------------------------------------------------------------------
'''
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection

def main():

    # Input data
    input_file = '../../data/Hamburg_Bremen_Niedersachsen_Temperature_Anomaly_1881-2018_ym.txt'
    
    state = 'Hamburg,Bremen,Niedersachsen'
    
    df = pd.read_csv(input_file, skiprows=range(0,23), usecols=['#', 'Gebietsmittel'], sep=' ')
    df.columns = ['Jahr', 'temperature']
    
    # Define x-axis limits
    start_year = df.Jahr[0]
    end_year = df.Jahr[df.Jahr.count()-1]
    
    # Create the plot
    plt.switch_backend('agg')

    fig,ax = plt.subplots(figsize=(14, 4))
    
    cmap = 'RdBu_r'
    
    rect_coll = PatchCollection([Rectangle((y, 0), 1, 1) for y in range(start_year, end_year + 1)], zorder=2)
    rect_coll.set_array(df.temperature)
    rect_coll.set_cmap(cmap)
    ax.add_collection(rect_coll)
    
    ax.set_ylim(0, 1)
    ax.set_xlim(start_year, end_year + 1)
    ax.yaxis.set_visible(False)
    ax.set_title(state, fontsize=20, loc='left', y=1.03)
    
    ax2 = ax.twinx()
    ax2.plot(df.Jahr, df.temperature, color='black', linewidth=1.5, )
    ax2.yaxis.tick_left()
    ax2.yaxis.set_label_position('left')
    ax2.set_ylabel('temperature')
    
    ax3 = ax2.twinx()
    ax3.set_ylim(ax2.get_ylim())
    ax3.yaxis.set_visible(False)
    
    coef = np.polyfit(df.Jahr, df.temperature, 1)
    trend = np.poly1d(coef)
    
    ax3.plot(df.Jahr, trend(df.Jahr), linestyle='--', color='black', linewidth=1, )
    
    plt.figtext(0.856, 0.087, '© 2023 DKRZ', fontsize=7)
    plt.figtext(0.907, 0.15, 'Data source: Deutscher Wetterdienst',
                rotation=270, fontsize=7)
    
    plotfile = 'plot_warming_stripes_plus_timeseries_and_trend.png'
    fig.savefig(plotfile, bbox_inches='tight', facecolor='white')



if __name__ == '__main__':
    main()

Plot result