hospital_data.py

   1 #!/usr/bin/env python
   2 # coding: utf-8
   3 # %%
   4 import itertools
   5 import collections
   6 import json
   7 import pandas as pd
   8 import numpy as np
   9 from scipy.stats import gmean
  10 import datetime
  11
  12 import sqlalchemy
  13
  14 import matplotlib as mpl
  15 import matplotlib.pyplot as plt
  16 plt.ioff()
  17 # # %matplotlib inline
  18
  19
  20 # %%
  21 connection_string = 'postgresql://covid:3NbjJTkT63@localhost/covid'
  22
  23
  24 # %%
  25 engine = sqlalchemy.create_engine(connection_string)
  26
  27
  28 # %%
  29 normalisation_date = '2020-08-01' # '2020-05-15'
  30
  31 hospital_normalisation_date = {
  32     'hospital_normalisation_date': normalisation_date
  33 }
  34
  35 with open('hospital_normalisation_date.json', 'w') as f:
  36     json.dump(hospital_normalisation_date, f)
  37
  38 hnd = pd.to_datetime(normalisation_date).strftime("%d %B %Y")
  39 with open('hospital_normalisation_date.js', 'w') as f:
  40     f.write(f"document.write('{hnd}');")
  41
  42
  43 # %%
  44 qstr = '''select uk_data.date
  45     , uk_data.new_admissions, uk_data_7.new_admissions as new_admissions_7
  46     , uk_data.new_cases, uk_data_7.new_cases as new_cases_7
  47     , uk_data.new_deaths, uk_data_7.new_deaths as new_deaths_7
  48     , uk_data.hospital_cases, uk_data_7.hospital_cases as hospital_cases_7
  49     , uk_data.ventilator_beds, uk_data_7.ventilator_beds as ventilator_beds_7
  50     from uk_data left outer join uk_data_7 using (date)
  51     order by uk_data.date'''
  52 hospital_data = pd.read_sql_query(qstr, engine,
  53     index_col='date',
  54     parse_dates = ['date'])
  55
  56
  57 # %%
  58 fig = plt.figure(figsize=(10, 8))
  59 ax = hospital_data.loc['2020-03-20':, ['hospital_cases', 'ventilator_beds']].plot(
  60     secondary_y='ventilator_beds',
  61     figsize=(10, 8),
  62     title="People in hospital, and on mechanical ventilation")
  63 # axl.legend(['Number in hospital', 'Number on ventilator'])
  64 plt.savefig('people_in_hospital.png')
  65
  66
  67 # %%
  68 hospital_data['cases_m7nd'] = hospital_data.new_cases_7 / hospital_data.new_cases_7.loc[normalisation_date]
  69 hospital_data['deaths_m7nd'] = hospital_data.new_deaths_7 / hospital_data.new_deaths_7.loc[normalisation_date]
  70 hospital_data['admissions_m7nd'] = hospital_data.new_admissions_7 / hospital_data.new_admissions_7.loc[normalisation_date]
  71
  72
  73 # %%
  74 ymax = (int(hospital_data.loc[normalisation_date:, ['cases_m7nd', 'admissions_m7nd', 'deaths_m7nd']].max().max() * 10) + 1) / 10.0
  75 ax = hospital_data.loc[normalisation_date:, ['cases_m7nd', 'admissions_m7nd', 'deaths_m7nd']].plot(
  76     ylim=(0, ymax),
  77     figsize=(10, 8),
  78     title="Cases, hospital admissions, and deaths\n(7-day moving averages)")
  79
  80 lvi = hospital_data.cases_m7nd.last_valid_index()
  81 ax.text(x = lvi + pd.Timedelta(days=1), y = hospital_data.cases_m7nd[lvi], s = f'{hospital_data.cases_m7nd[lvi]:.2f}')
  82
  83 lvi = hospital_data.admissions_m7nd.last_valid_index()
  84 ax.text(x = lvi + pd.Timedelta(days=1), y = hospital_data.admissions_m7nd[lvi], s = f'{hospital_data.admissions_m7nd[lvi]:.2f}')
  85
  86 lvi = hospital_data.deaths_m7nd.last_valid_index()
  87 ax.text(x = lvi + pd.Timedelta(days=1), y = hospital_data.deaths_m7nd[lvi], s = f'{hospital_data.deaths_m7nd[lvi]:.2f}')
  88
  89
  90 ax.set_xlabel("Date")
  91 ax.set_ylabel(f'Normalised units, scaled from {normalisation_date}')
  92 ax.legend(['Cases', 'Admissions', 'Deaths'])
  93
  94 plt.savefig('cases_admissions_deaths.png')
  95
  96
  97 # %%
  98
  99
 100
 101
 102
 103 # %% [markdown]
 104 #