plot_measures.py

from utilities import *
import pandas as pd
import os
from config import indicators_list
from calculate_measures import demographics
import re
from collections import OrderedDict

from utilities import *
import numpy as np
import matplotlib.pyplot as plt

additional_indicators = ["e", "f", "li"]
indicators_list.extend(additional_indicators)

# SELECT DATES FOR AGGREGATE DEMOGRAPHIC VALUES
pre_q1 = ["2020-01-01", "2020-02-01", "2020-03-01"]
post_q1 = ["2021-01-01", "2021-02-01", "2021-03-01"]

medians_dict = {}

time_period_mapping = {
    "ac": "2021-05-01",
    "me_no_fbc": "2020-06-01",
    "me_no_lft": "2020-06-01",
    "li": "2020-06-01",
    "am": "2020-09-01",
}


if not (OUTPUT_DIR / "figures").exists():
    os.mkdir(OUTPUT_DIR / "figures")


# set up plots for combined decile charts
gi_bleed_x = np.arange(0, 2, 1)
gi_bleed_y = np.arange(0, 3, 1)
gi_bleed_axs_list = [(i, j) for i in gi_bleed_x for j in gi_bleed_y]
gi_bleed_fig, gi_bleed_axs = plt.subplots(2, 3, figsize=(30, 20), sharex="col")


gi_bleed_indicators = ["a", "b", "c", "d", "e", "f"]


prescribing_y = np.arange(0, 3, 1)
prescribing_axs_list = [i for i in prescribing_y]

prescribing_fig, prescribing_axs = plt.subplots(1, 3, figsize=(30, 10), sharex="col")


prescribing_indicators = ["g", "i", "k"]

monitoring_x = np.arange(0, 2, 1)
monitoring_y = np.arange(0, 3, 1)
monitoring_axs_list = [(i, j) for i in monitoring_x for j in monitoring_y]
monitoring_axs_list.remove((0, 2))
monitoring_fig, monitoring_axs = plt.subplots(2, 3, figsize=(30, 20), sharex="col")
monitoring_fig.delaxes(monitoring_axs[0, 2])


monitoring_indicators = ["ac", "me_no_fbc", "me_no_lft", "li", "am"]

title_mapping = {
    "a": "Age >= 65 & NSAID",  # "NSAID without gastroprotection, age >=65",
    "b": "PU & NSAID",  # "NSAID without gastroprotection, H/O peptic ulcer",
    "c": "PU & antiplatelet",  # "Antiplatelet without gastroprotection, H/O peptic ulcer",
    "d": "Warfarin/DOAC & NSAID",  # "DOAC with warfarin",
    # "Anticoagulation and antiplatelet, no gastroprotection",
    "e": "Warfarin/DOAC & antiplatelet",
    # "Aspirin and antiplatelet, no gastroprotection",
    "f": "Aspirin & other antiplatelet",
    "g": "Asthma & beta-blocker",  # "Asthma and non-selective beta-blocker",
    "i": "HF & NSAID",  # "Heart failure and NSAID",
    "k": "CRF & NSAID",  # "Chronic renal impairment and NSAID",
    # "ACE inhibitor or loop diuretic without renal function/electrolyte test",
    "ac": "ACEI or loop diuretic, no blood tests",
    "me_no_fbc": "Methotrexate and no FBC", #"Methotrexate without full blood count",
    "me_no_lft": "Methotrexate and no LFT", #"Methotrexate without liver function test",
    # "Lithium without lithium concentration test",
    "li": "Lithium and no level recording",
    "am": "Amiodarone and no TFT",  # "Amiodarone without thyroid function test",
}


# Dataframe for demographic aggregates

demographic_aggregate_df = pd.DataFrame(
    columns=["indicator", "demographic", "group", "pre_mean", "post_mean"]
)


for i in indicators_list:
    # indicator plots
    df = pd.read_csv(
        OUTPUT_DIR / f"measure_indicator_{i}_rate.csv", parse_dates=["date"]
    )
    df = drop_irrelevant_practices(df)

    if i in ["me_no_fbc", "me_no_lft"]:
        denominator = "indicator_me_denominator"

    else:
        denominator = f"indicator_{i}_denominator"

    df["rate"] = df[f"value"] * 100

    df = df.drop(["value"], axis=1)

    # Need this for dummy data
    df = df.replace(np.inf, np.nan)
    
    rate_df_pre = df.loc[df["date"].isin(pre_q1),"rate"].mean()
    rate_df_post = df.loc[df["date"].isin(post_q1),"rate"].mean()
    medians_dict[i] = {"pre": rate_df_pre, "post": rate_df_post}


    deciles_chart(
        df,
        filename=f"output/figures/plot_{i}.jpeg",
        period_column="date",
        column="rate",
        title=title_mapping[i],
        ylabel="Percentage",
        time_window=time_period_mapping.get(i, ""),
    )

    # gi bleed
    if i in gi_bleed_indicators:
        ind = gi_bleed_indicators.index(i)

        deciles_chart_subplots(
            df,
            period_column="date",
            column="rate",
            title=title_mapping[i],
            ylabel="Percentage",
            show_outer_percentiles=False,
            show_legend=False,
            ax=gi_bleed_axs[gi_bleed_axs_list[ind]],
            time_window=time_period_mapping.get(i, ""),
        )

    # prescribing

    if i in prescribing_indicators:
        ind = prescribing_indicators.index(i)

        deciles_chart_subplots(
            df,
            period_column="date",
            column="rate",
            title=title_mapping[i],
            ylabel="Percentage",
            show_outer_percentiles=False,
            show_legend=False,
            ax=prescribing_axs[prescribing_axs_list[ind]],
            time_window=time_period_mapping.get(i, ""),
        )

    # monitoring

    if i in monitoring_indicators:
        ind = monitoring_indicators.index(i)

        deciles_chart_subplots(
            df,
            period_column="date",
            column="rate",
            title=title_mapping[i],
            ylabel="Percentage",
            show_outer_percentiles=False,
            show_legend=False,
            ax=monitoring_axs[monitoring_axs_list[ind]],
            time_window=time_period_mapping.get(i, ""),
        )

    # demographic plots
    for d in demographics:
        df = pd.read_csv(
            OUTPUT_DIR / f"indicator_measure_{i}_{d}.csv", parse_dates=["date"]
        )

        if d == "sex":
            df = df[df["sex"].isin(["M", "F"])]

        elif d == "imd":
            df = df[df["imd"] != 0]

        elif d == "age_band":
            df = df[df["age_band"] != "missing"]

            if i == "a":
                # remove bands < 65
                df = df[df["age_band"].isin(["60-69", "70-79", "80+"])]

            elif i == "ac":
                # remove bands < 75
                df = df[df["age_band"].isin(["70-79", "80+"])]

        df = redact_small_numbers(
            df, 10, f"indicator_{i}_numerator", denominator, "rate", "date"
        )

        

        pre_df = df.loc[df["date"].isin(pre_q1), :]
        mean_pre = pre_df.groupby(by=[d])["rate"].mean().rename("pre")

        post_df = df.loc[df["date"].isin(post_q1), :]
        mean_post = post_df.groupby(by=[d])["rate"].mean().rename("post")

        mean_values = pd.concat([mean_pre, mean_post], axis=1)

        for index, row in mean_values.iterrows():

            demographic_aggregate_row = OrderedDict()
            demographic_aggregate_row["indicator"] = i
            demographic_aggregate_row["demographic"] = d
            demographic_aggregate_row["group"] = index
            demographic_aggregate_row["pre_mean"] = row["pre"]
            demographic_aggregate_row["post_mean"] = row.loc["post"]

            
            demographic_aggregate_df = pd.concat(
                [demographic_aggregate_df, pd.DataFrame(demographic_aggregate_row, index=[0])]
            )

        plot_measures(
            df=df,
            filename=f"plot_{i}_{d}",
            title=f"Indicator {i} by {d}",
            column_to_plot="rate",
            y_label="Proportion",
            as_bar=False,
            category=d,
        )


# plot composite measures

composite_indicators = ["gi_bleed", "monitoring", "other_prescribing", "all"]

for i in composite_indicators:
    df = pd.read_csv(OUTPUT_DIR / f"{i}_composite_measure.csv", parse_dates=["date"])

    # group those with 7+ indicators if all-composite
    if i == "all":

        num_indicators = list(df["num_indicators"].unique())
        if "Other" in num_indicators:
            num_indicators.remove("Other")
        max_indicator = min([int(max(num_indicators)), 6])

        above_nums = [f"{i}" for i in range(max_indicator, 14)]
        above_nums.extend(["Other"])
        below_nums = [f"{i}" for i in range(0, max_indicator)]

        df_7_plus_count = (
            df.loc[df["num_indicators"].isin(above_nums), :]
            .groupby(["date"])[["count"]]
            .sum()
            .reset_index()
        )
        df_7_plus_population = (
            df.loc[df["num_indicators"].isin(above_nums), :]
            .groupby(["date"])[["denominator"]]
            .mean()
            .reset_index()
        )

        df_7_plus = df_7_plus_count.merge(df_7_plus_population, on=["date"])

        if max_indicator < 7:
            df_7_plus["num_indicators"] = f"{max_indicator}+"
        else:
            df_7_plus["num_indicators"] = "7+"

        # drop combined columns from original df
        df = df.loc[df["num_indicators"].isin(below_nums), :]

        # concatenate
        df = pd.concat([df, df_7_plus])

        df["num_indicators"] = df["num_indicators"].astype("str")

    df["rate"] = df["count"] / df["denominator"]
    plot_measures(
        df=df,
        filename=f"plot_{i}_composite",
        title=f"{i} composite indicator",
        column_to_plot="rate",
        y_label="Proportion",
        as_bar=False,
        category="num_indicators",
    )


demographic_aggregate_df.to_csv("output/demographic_aggregates.csv")

gi_bleed_fig.subplots_adjust(bottom=0.15)
gi_bleed_fig.savefig("output/figures/combined_plot_gi_bleed.png")
plt.clf()
prescribing_fig.subplots_adjust(bottom=0.3)
prescribing_fig.savefig("output/figures/combined_plot_prescribing.png")
plt.clf()
monitoring_fig.subplots_adjust(bottom=0.15)
monitoring_fig.savefig("output/figures/combined_plot_monitoring.png")

with open(f"output/medians.json", "w") as f:
    json.dump({"summary": medians_dict}, f)