quick n dirty hack to visualise covid19 data

c19.py

#!/usr/bin/env python3

import argparse
import traceback
import logging
import pprint as pp
import os
import tqdm
import time
import datetime
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from mpl_toolkits.mplot3d.axes3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
import seaborn as sns
from scipy.interpolate import griddata
from arcgis.gis import GIS
from arcgis.features import FeatureLayer
from arcgis.features import FeatureLayerCollection

################################################################################
#  Main script                                                                 #
################################################################################
if __name__ == "__main__":
    try:
        parser = argparse.ArgumentParser()
        logfilename, _ext = os.path.splitext(os.path.basename(__file__))
        parser.add_argument('-l', '--logfile', help="Log file", default="%s.log" % logfilename)
        parser.add_argument('-L', '--loglevel', help="Log level", default="INFO")
        args = parser.parse_args()

        tqdm_disable = None

        logging.basicConfig(filename=args.logfile,
                            filemode="w",
                            datefmt="%H:%M:%S",
                            format='[%(levelname)s] %(asctime)s (%(relativeCreated)d) {%(process)s|%(threadName)s} : %(message)s',
                            level=args.loglevel)
    except Exception as e:
        print("Setup exception!")
        pp.pprint(e)
        traceback.print_exc()
        traceback.print_stack()
        logging.exception("Setup exception!")
        logging.exception(pp.pformat(e))
        logging.exception(traceback.format_exc())
        logging.exception(traceback.format_stack())

    gis = GIS()

    content_cases = gis.content.search('title: CovidStatisticsProfileHPSCIrelandOpenData','Feature Layer')[0]
    content_countycases = gis.content.search('title: COVID-19 HPSC County Statistics Historic Data','Feature Layer')[0]
    content_lab= gis.content.search('title: COVID-19 Laboratory Testing Time Series','Feature Layer')[0]
    content_hospital= gis.content.search('title: COVID-19 SDU Acute Hospital Time Series Summary','Feature Layer')[0]

    fl_cases = FeatureLayerCollection(content_cases['url']).layers[0]
    fl_lab = FeatureLayerCollection(content_lab['url']).layers[0]
    fl_hospital = FeatureLayerCollection(content_hospital['url']).layers[0]

    query = fl_cases.query(out_fields='Date,HospitalisedAged5,HospitalisedAged5to14,HospitalisedAged15to24,HospitalisedAged25to34,HospitalisedAged35to44,HospitalisedAged45to54,HospitalisedAged55to64,HospitalisedAged65to74,HospitalisedAged75to84,HospitalisedAged85up,Aged1to4,Aged5to14,Aged15to24,Aged25to34,Aged35to44,Aged45to54,Aged55to64,Aged65to74,Aged75to84,Aged85up')

    data_date = []
    data_cases = []
    data = []
    for feature in query.features:
        data.append([pd.to_datetime(feature.attributes['Date'], unit='ms'),
#                       feature.attributes['HospitalisedAged5'],
#                       feature.attributes['HospitalisedAged5to14'],
#                       feature.attributes['HospitalisedAged15to24'],
#                       feature.attributes['HospitalisedAged25to34'],
#                       feature.attributes['HospitalisedAged35to44'],
#                       feature.attributes['HospitalisedAged45to54'],
#                       feature.attributes['HospitalisedAged55to64'],
#                       feature.attributes['HospitalisedAged65to74'],
#                       feature.attributes['HospitalisedAged75to84'],
#                       feature.attributes['HospitalisedAged85up'],
                     feature.attributes['Aged1to4'],
                     feature.attributes['Aged5to14'],
                     feature.attributes['Aged15to24'],
                     feature.attributes['Aged25to34'],
                     feature.attributes['Aged35to44'],
                     feature.attributes['Aged45to54'],
                     feature.attributes['Aged55to64'],
                     feature.attributes['Aged65to74'],
                     feature.attributes['Aged75to84'],
                     feature.attributes['Aged85up'],
                    ])
        #data_date.append(pd.to_datetime(feature.attributes['Date'], unit='ms'))
        #data_cases.append(feature.attributes['SUM_number_of_new_covid_19_cases_co'])

    
    df = pd.DataFrame(data, columns=['date', 
#                                       'HospitalisedAged5',
#                                       'HospitalisedAged5to14',
#                                       'HospitalisedAged15to24',
#                                       'HospitalisedAged25to34',
#                                       'HospitalisedAged35to44',
#                                       'HospitalisedAged45to54',
#                                       'HospitalisedAged55to64',
#                                       'HospitalisedAged65to74',
#                                       'HospitalisedAged75to84',
#                                       'HospitalisedAged85up',
                                     'Aged1to4',
                                     'Aged5to14',
                                     'Aged15to24',
                                     'Aged25to34',
                                     'Aged35to44',
                                     'Aged45to54',
                                     'Aged55to64',
                                     'Aged65to74',
                                     'Aged75to84',
                                     'Aged85up'])
    df.set_index('date', inplace=True)
    df = df.diff()

    x = np.arange(len(df.columns))
    y = df.index.astype(np.int64)
    y = y / 1000000000
    y = y / 86400
    X,Y = np.meshgrid(x,y,sparse=True)
    Z = df

    fig = plt.figure(tight_layout=True)
    ax = fig.add_subplot(111, projection='3d')
    fig.set_figheight(10)
    fig.set_figwidth(15)

    x_scale=1
    y_scale=2
    z_scale=1

    scale=np.diag([x_scale, y_scale, z_scale, 1.0])
    scale=scale*(1.0/scale.max())
    scale[3,3]=1.0

    def short_proj():
        return np.dot(Axes3D.get_proj(ax), scale)

    ax.get_proj=short_proj

    ax.set_zlabel('Cases')

    ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1))
    ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(1))

    rev_labels=np.flip(df.columns.values)
    ax.set_xticklabels(df.columns.values)
    ax.tick_params(axis='x', which='major', pad=15)

    ax.yaxis.set_major_locator(mdates.MonthLocator(bymonth=(1)))
    ax.yaxis.set_minor_locator(mdates.MonthLocator())
    ax.yaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
    ax.yaxis.set_minor_formatter(mdates.DateFormatter('%b'))
    ax.yaxis.set_tick_params(rotation=50)

    #fig.colorbar(surf, shrink=1, aspect=10)

    fig.tight_layout(pad=0.0) 
    fig.subplots_adjust(left=0, right=1, bottom=0, top=1)

    surf = ax.plot_surface(X,Y,Z,
                           cmap=plt.cm.plasma, 
                           linewidth=1,
                           rstride=1,
                           cstride=1,
#                             rcount=len(df.columns),
#                             ccount=len(df.index),
                           antialiased=False)

    ax.view_init(20, -20)
    ax.dist = 9
    #plt.show()

    plt.savefig("surface.png")