rate_calculations.py

from utilities import *
from pathlib import Path
import pandas as pd
import os
from cohortextractor import Measure
from config import demographics, codelist_path, vertical_lines
from ebmdatalab import charts

BASE_DIR = Path(__file__).parents[1]
OUTPUT_DIR = BASE_DIR / "output"

#import measures
from study_definition import measures



measures_dict = {}

for m in measures:
    measures_dict[m.id] = m
 
print(measures_dict)


for key, value in measures_dict.items():
    
    df = pd.read_csv(os.path.join(OUTPUT_DIR, f'measure_{value.id}.csv'), parse_dates=['date']).sort_values(by='date')
    df = drop_missing_demographics(df, value.group_by[0])

    # if key == "ethnicity_rate":
    #     df = convert_ethnicity(df)
        
    df = calculate_rate(df, numerator=value.numerator, denominator=value.denominator, rate_per=100)
    
    if key == "imd_rate":
        df = redact_small_numbers(df, 5, value.numerator, value.denominator, 'rate')
    
    elif key == "care_home_status_rate":
        df = convert_binary(df, 'care_home_status', 'Record of positive care home status', 'No record of positive care home status')

    elif key =='learning_disability_rate':
        df = convert_binary(df, 'learning_disability', 'Record of learning disability', 'No record of learning disability')


    # get total population rate
    if value.id=='practice_rate':
        
        df = drop_irrelevant_practices(df, 'practice')
        df.to_csv(os.path.join(OUTPUT_DIR, f'rate_table_{value.group_by[0]}.csv'), index=False)

        ast_decile = charts.deciles_chart(
            df,
            period_column='date',
            column='value',
            title=None,
            ylabel=None,
            show_outer_percentiles=False,
            show_legend=True,
        )
        
        add_date_lines(ast_decile, vertical_lines)    
        ast_decile.gcf().set_size_inches(15, 8)
        ast_decile.gca().set_yticklabels(
            ["{:.0f}%".format(x * 100) for x in ast_decile.gca().get_yticks()]
        )
   
        ast_decile.savefig("output/decile_chart.png", bbox_inches="tight")

        df_total = df.groupby(by='date')[[value.numerator, value.denominator]].sum().reset_index()

        df_total = calculate_rate(df_total, numerator=value.numerator, denominator=value.denominator, rate_per=100)

        plot_measures(
            df_total, 
            filename='plot_total.png', 
            title=None, 
            column_to_plot='rate', 
            category=None, 
            y_label=None,
            vlines=vertical_lines
            )
       
        df_total.to_csv(os.path.join(OUTPUT_DIR, 'rate_table_total.csv'), index=False)

    # elif value.id=='event_code_rate':
    #     df.to_csv(os.path.join(OUTPUT_DIR, f'rate_table_{value.group_by[0]}.csv'), index=False)
    #     codelist = pd.read_csv(codelist_path)
    #     child_code_table = create_child_table(df=df, code_df=codelist, code_column='code', term_column='term')
    #     child_code_table.to_csv('output/child_code_table.csv', index=False)

    else:
        plot_measures(
                df, 
                filename=f'plot_{value.group_by[0]}.png', 
                title=None, 
                column_to_plot='rate', 
                category=value.group_by[0], 
                y_label=None,
                vlines=vertical_lines
        )
        
        df.to_csv(os.path.join(OUTPUT_DIR, f'rate_table_{value.group_by[0]}.csv'), index=False)