generate_measures.py

import sys

import pandas as pd
import numpy as np
from study_utils import (
    calculate_rate,
    round_values,
    drop_irrelevant_practices,
    redact_events_table,
)
from variables import low_count_threshold, rounding_base

if len(sys.argv) > 1:
    output_dir = sys.argv[1]
else:
    output_dir = "output"


counts_table = pd.read_csv(
    f"{output_dir}/counts_per_week_per_practice.csv", parse_dates=["date"]
)
list_sizes = pd.read_csv(f"{output_dir}/list_sizes.csv")

counts_table = counts_table.merge(list_sizes, on=["practice"], how="inner")
counts_table["value"] = counts_table["num"] / counts_table["list_size"]
counts_table["value"] = calculate_rate(counts_table, "value", round_rate=True)

practice_count_total = len(np.unique(counts_table["practice"]))
# drop practices with no events for entire period
counts_table = drop_irrelevant_practices(counts_table)

practice_count_subset = len(np.unique(counts_table["practice"]))

practice_count = pd.DataFrame(
    {"total": practice_count_total, "with_at_least_1_event": practice_count_subset},
    index=["count"],
)
practice_count.T.to_csv(f"{output_dir}/practice_count.csv")


counts_table.to_csv(f"{output_dir}/measure_counts_per_week_per_practice.csv", index=False)

# count total number of events

total_events = int(counts_table["num"].sum())


# total events in last week/month
latest_time_period = counts_table["date"].max()
events_in_latest_period = int(
    counts_table.loc[counts_table["date"] == latest_time_period, "num"].sum()
)

events_counts = pd.DataFrame(
    {
        "total_events": total_events,
        "events_in_latest_period": events_in_latest_period,
        "unique_patients": np.nan,
    },
    index=["count"],
)


events_counts = events_counts.T

redact_events_table(events_counts, low_count_threshold, rounding_base).to_csv(
    f"{output_dir}/event_counts.csv"
)