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Merge pull request #200 from djay/plot_split5
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| Original file line number | Diff line number | Diff line change |
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| import matplotlib.cm | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
| import pandas as pd | ||
| import numpy as np | ||
| import re | ||
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| from covid_data import get_ifr, scrape_and_combine | ||
| from utils_pandas import cum2daily, cut_ages, cut_ages_labels, decreasing, get_cycle, human_format, perc_format, \ | ||
| import_csv, increasing, normalise_to_total, rearrange, set_time_series_labels_2, topprov, pred_vac, fix_gaps | ||
| from utils_scraping import remove_prefix, remove_suffix, any_in, logger | ||
| from utils_thai import DISTRICT_RANGE, DISTRICT_RANGE_SIMPLE, AREA_LEGEND, AREA_LEGEND_SIMPLE, \ | ||
| AREA_LEGEND_ORDERED, FIRST_AREAS, area_crosstab, get_provinces, join_provinces, thaipop, thaipop2, trend_table | ||
| import utils_thai | ||
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| from covid_plot_utils import plot_area, source | ||
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| def save_active_plots(df: pd.DataFrame) -> None: | ||
| logger.info('======== Generating Tests Plots ==========') | ||
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| ####################### | ||
| # Hospital plots | ||
| ####################### | ||
| cols_delayed = ["Hospitalized", "Recovered", "Hospitalized Severe", "Hospitalized Respirator", "Hospitalized Field"] | ||
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| # TODO: we are missing some severe, ventilator mid april. why? | ||
| df[cols_delayed] = df[cols_delayed].interpolate(limit_area="inside") | ||
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| # TODO: use unknowns to show this plot earlier? | ||
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| # because severe includes those on respirators | ||
| df["Hospitalized Severe excl vent"] = df["Hospitalized Severe"].sub(df["Hospitalized Respirator"], fill_value=None) | ||
| non_split = df[["Hospitalized Severe excl vent", "Hospitalized Respirator", "Hospitalized Field"]].sum(skipna=True, | ||
| axis=1) | ||
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| df["Hospitalized Hospital"] = df["Hospitalized"].sub(non_split, fill_value=None) | ||
| cols = [ | ||
| 'Hospitalized Respirator', | ||
| 'Hospitalized Severe excl vent', | ||
| 'Hospitalized Hospital', | ||
| 'Hospitalized Field', | ||
| ] | ||
| legends = [ | ||
| 'In Serious Condition on Ventilator', | ||
| 'In Serious Condition without Ventilator', | ||
| 'In Isolation/Hospital', | ||
| 'In Field Hospital', | ||
| ] | ||
| # plot_area(df=df, png_prefix='cases_active', cols_subset=cols, | ||
| # title='Thailand Active Covid Cases\n(Severe, Field, and Respirator only available from ' | ||
| # '2021-04-24 onwards)', | ||
| # legends=legends, | ||
| # kind='area', stacked=True, percent_fig=False, ma_days=None, cmap='tab10') | ||
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| cols = [ | ||
| 'Hospitalized Severe', | ||
| 'Hospitalized Severe excl vent', | ||
| 'Hospitalized Respirator', | ||
| ] | ||
| legends = [ | ||
| 'In Serious Condition', | ||
| 'In Serious Condition without Ventilator', | ||
| 'In Serious Condition on Ventilator', | ||
| ] | ||
| plot_area(df=df, | ||
| title='Active Covid Cases in Serious Condition - Thailand', | ||
| legends=legends, | ||
| png_prefix='active_severe', cols_subset=cols, | ||
| actuals=False, | ||
| ma_days=7, | ||
| kind='line', stacked=False, percent_fig=False, | ||
| cmap='tab10', | ||
| footnote_left=f'{source}Data Source: CCSA Daily Briefing') | ||
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| # show cumulative deaths, recoveries and hospitalisations (which should all add up to cases) | ||
| df['Recovered since 2021-04-01'] = df['2021-04-14':]['Recovered'].cumsum() | ||
| df['Died since 2021-04-01'] = df['2021-04-01':]['Deaths'].cumsum() | ||
| df['Cases since 2021-04-01'] = df['2021-04-01':]['Cases'].cumsum() | ||
| # This is those in hospital but we make this the catch all | ||
| exits = df[['Recovered since 2021-04-01', 'Died since 2021-04-01']].sum(axis=1, skipna=True) | ||
| df['Other Active Cases'] = \ | ||
| df['Cases since 2021-04-01'].sub(non_split, fill_value=0).sub(exits, fill_value=0).clip(0) | ||
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| cols = [ | ||
| 'Died since 2021-04-01', | ||
| 'Hospitalized Respirator', | ||
| 'Hospitalized Severe', | ||
| 'Other Active Cases', | ||
| 'Hospitalized Field', | ||
| 'Recovered since 2021-04-01', | ||
| ] | ||
| legends = [ | ||
| 'Deaths from Cases since 1st April', | ||
| 'In Serious Condition on Ventilator', | ||
| 'In Serious Condition without Ventilator', | ||
| 'In Hospital/Mild Condition', | ||
| 'In Field Hospital', | ||
| 'Recovered from Cases Confirmed since 1st April', | ||
| ] | ||
| plot_area(df=df, | ||
| title='Covid Cases by Current Outcome since 1st April 2021 - Thailand', | ||
| legends=legends, | ||
| png_prefix='cases_cumulative', cols_subset=cols, | ||
| ma_days=None, | ||
| kind='area', stacked=True, percent_fig=False, | ||
| cmap='tab10', | ||
| footnote_left=f'{source}Data Source: CCSA Daily Briefing') | ||
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| # TODO: I think we can replace the recovered since april with plot showing just hospitalisations? | ||
| df["Hospitalized Field Unknown"] = df["Hospitalized Field"].sub(df[["Hospitalized Field Hospitel", "Hospitalized Field HICI"]].sum(axis=1, skipna=True), fill_value=0) | ||
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| cols = [ | ||
| 'Hospitalized Respirator', | ||
| 'Hospitalized Severe', | ||
| 'Hospitalized Field Unknown', | ||
| 'Hospitalized Field Hospitel', | ||
| 'Hospitalized Field HICI', | ||
| ] | ||
| df["Hospitalized Mild"] = df["Hospitalized"].sub(df[cols].sum(axis=1, skipna=True), fill_value=0) | ||
| cols = [ | ||
| 'Hospitalized Respirator', | ||
| 'Hospitalized Severe', | ||
| 'Hospitalized Mild', | ||
| 'Hospitalized Field Unknown', | ||
| 'Hospitalized Field Hospitel', | ||
| 'Hospitalized Field HICI', | ||
| ] | ||
| legends = [ | ||
| 'In Serious Condition on Ventilator', | ||
| 'In Serious Condition without Ventilator', | ||
| 'In Mild Condition in Hospital', | ||
| 'In Mild Condition in Field Hospital/Other', | ||
| 'In Mild Condition in Hotel Field Hospital (Hospitel)', | ||
| 'In Mild Condition in Home/Community Isolation (HICI)', | ||
| ] | ||
| plot_area(df=df, | ||
| title='Active Cases by Condition - Thailand', | ||
| png_prefix='active_hospital', cols_subset=cols, legends=legends, | ||
| # unknown_name='Hospitalized Other', unknown_total='Hospitalized', unknown_percent=True, | ||
| ma_days=7, | ||
| kind='area', stacked=True, percent_fig=True, clean_end=True, | ||
| cmap='tab10', | ||
| footnote_left='Data Source: MOPH Covid-19 Dashboard, CCSA Daily Briefing') | ||
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| df["Hospitalized All Mild"] = df["Hospitalized Mild"] + df["Hospitalized Field"] | ||
| cols = [ | ||
| 'Hospitalized Respirator', | ||
| 'Hospitalized Severe', | ||
| 'Hospitalized All Mild', | ||
| ] | ||
| legends = [ | ||
| 'In Serious Condition on Ventilator', | ||
| 'In Serious Condition without Ventilator', | ||
| 'In Mild Condition', | ||
| ] | ||
| peaks = df[cols] / df.rolling(7).mean().max(axis=0) * 100 | ||
| plot_area(df=peaks, | ||
| title='Active Covid Cases by Condition as % of Peak - Thailand', | ||
| png_prefix='active_peak', cols_subset=cols, | ||
| legends=legends, | ||
| ma_days=7, | ||
| kind='line', stacked=False, percent_fig=False, clean_end=True, | ||
| cmap='tab10', | ||
| y_formatter=perc_format, | ||
| footnote_left='Data Source: MOPH Covid-19 Dashboard') | ||
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