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plots.py
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plots.py
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import pandas as pd
import json
from pathlib import Path
from ebmdatalab import charts
from utilities import (
OUTPUT_DIR,
ANALYSIS_DIR,
plot_measures,
redact_small_numbers,
convert_binary,
calculate_rate,
drop_irrelevant_practices,
)
Path("output/monthly/joined/redacted").mkdir(parents=True, exist_ok=True)
Path("output/weekly/joined/redacted").mkdir(parents=True, exist_ok=True)
for frequency in ["monthly", "weekly"]:
mean_values = pd.read_csv(OUTPUT_DIR / f"{frequency}/joined/redacted/mean_test_value_by_age.csv")
for test in ["alt", "ast", "bilirubin", "gi_illness", "hepatitis"]:
# plot rates
df = pd.read_csv(
OUTPUT_DIR / f"{frequency}/joined/measure_{test}_rate.csv",
parse_dates=["date"],
)
df["rate"] = calculate_rate(df, "value")
df = redact_small_numbers(df, 5, test, "population", "rate", "date", None)
df.to_csv(
OUTPUT_DIR / f"{frequency}/joined/redacted/measure_{test}_rate.csv",
index=False,
)
plot_measures(
df=df,
filename=f"{frequency}/joined/plot_{test}",
column_to_plot="rate",
title="",
y_label="Rate per 1000",
as_bar=False,
)
# plot count
plot_measures(
df=df,
filename=f"{frequency}/joined/plot_{test}_count",
column_to_plot=test,
title="",
y_label="Count",
as_bar=False,
)
# deciles chart
df_practice = pd.read_csv(
OUTPUT_DIR / f"{frequency}/joined/measure_{test}_practice_rate.csv",
parse_dates=["date"],
)
df_practice["rate"] = calculate_rate(df_practice, "value")
df_practice, practice_summary = drop_irrelevant_practices(df_practice)
with open(
OUTPUT_DIR / f"{frequency}/joined/practice_count_{test}.json", "w"
) as f:
json.dump({"num_practices": practice_summary}, f)
decile_chart = charts.deciles_chart(
df_practice,
period_column="date",
column="rate",
show_outer_percentiles=False,
ylabel="Rate per 1000",
)
decile_chart.savefig(
OUTPUT_DIR / f"{frequency}/joined/deciles_chart_{test}.png",
bbox_inches="tight",
)
# plot age band rates for hepatitis and gi illness
if test in ["hepatitis", "gi_illness"]:
df = pd.read_csv(
OUTPUT_DIR / f"{frequency}/joined/measure_{test}_age_rate.csv",
parse_dates=["date"],
)
df["rate"] = calculate_rate(df, "value")
df = redact_small_numbers(df, 5, test, "population", "rate", "date", "age_band_months")
df.to_csv(
OUTPUT_DIR / f"{frequency}/joined/redacted/measure_{test}_age_rate.csv",
index=False,
)
plot_measures(
df=df,
filename=f"{frequency}/joined/plot_{test}_age",
column_to_plot="rate",
title="",
y_label="Rate per 1000",
as_bar=False,
category="age_band_months",
)
# plot count
plot_measures(
df=df,
filename=f"{frequency}/joined/plot_{test}_count_age",
column_to_plot=test,
title="",
y_label="Count",
as_bar=False,
category="age_band_months",
)
# plot out of range rates
if test in ["alt", "ast", "bilirubin"]:
#plot mean value
plot_measures(
df=mean_values,
filename=f"{frequency}/joined/plot_{test}_mean_value",
column_to_plot=f"{test}_numeric_value",
title="",
y_label="Mean test value",
as_bar=False,
category="age_band_months"
)
if test == "bilirubin":
input_file = f"{frequency}/joined/measure_{test}_oor_ref_rate.csv"
numerator = f"{test}_numeric_value_out_of_ref_range"
output_file = f"measure_{test}_oor_ref_rate.csv"
else:
input_file = f"{frequency}/joined/measure_{test}_oor_rate.csv"
numerator = f"{test}_numeric_value_out_of_range"
output_file = f"measure_{test}_oor_rate.csv"
df_oor = pd.read_csv(
OUTPUT_DIR / input_file,
parse_dates=["date"],
)
df_oor = redact_small_numbers(df_oor, 5, numerator, test, "value", "date", None)
df_oor.to_csv(
OUTPUT_DIR / f"{frequency}/joined/redacted/{output_file}", index=False
)
# if ast plot quarterly numbers instead
if test == "ast":
df_oor = df_oor.groupby(pd.PeriodIndex(df["date"], freq="q"), axis=0)[[numerator, test]].sum().reset_index()
df_oor["value"] = df_oor[numerator] / df_oor[test]
df_oor["date"] = df_oor["date"].astype(str)
df_oor["rate"] = calculate_rate(df_oor, "value")
plot_measures(
df=df_oor,
filename=f"{frequency}/joined/plot_{test}_oor",
column_to_plot="rate",
title="",
y_label="Rate per 1000",
as_bar=False,
)
# plot counts
plot_measures(
df=df_oor,
filename=f"{frequency}/joined/plot_{test}_oor_count",
column_to_plot=numerator,
title="",
y_label="Count",
as_bar=False,
)
# chart for those with recent test and out of range
df_oor_cov = pd.read_csv(
OUTPUT_DIR
/ f"{frequency}/joined/measure_{test}_oor_recent_cov_rate.csv",
parse_dates=["date"],
)
df_oor_cov = redact_small_numbers(
df_oor_cov, 5, numerator, test, "value", "date", "recent_positive_covid_test"
)
df_oor_cov.to_csv(
OUTPUT_DIR
/ f"{frequency}/joined/redacted/measure_{test}_oor_recent_cov_rate.csv",
index=False,
)
df_oor_cov["rate"] = calculate_rate(df_oor_cov, "value")
convert_binary(df_oor_cov, "recent_positive_covid_test", "Yes", "No")
plot_measures(
df=df_oor_cov,
filename=f"{frequency}/joined/plot_{test}_oor_recent_cov",
column_to_plot="rate",
title="",
y_label="Rate per 1000",
as_bar=False,
category="recent_positive_covid_test",
)
# plot count
plot_measures(
df=df_oor_cov,
filename=f"{frequency}/joined/plot_{test}_oor_recent_cov_count",
column_to_plot=numerator,
title="",
y_label="Count",
as_bar=False,
category="recent_positive_covid_test",
)
# chart for those with out of range broken down by age band
df_oor_age = pd.read_csv(
OUTPUT_DIR
/ f"{frequency}/joined/measure_{test}_oor_age_rate.csv",
parse_dates=["date"],
)
df_oor_age = redact_small_numbers(
df_oor_age, 5, numerator, test, "value", "date", "age_band_months"
)
df_oor_age.to_csv(
OUTPUT_DIR
/ f"{frequency}/joined/redacted/measure_{test}_oor_age_rate.csv",
index=False,
)
df_oor_age["rate"] = calculate_rate(df_oor_age, "value")
plot_measures(
df=df_oor_age,
filename=f"{frequency}/joined/plot_{test}_oor_age",
column_to_plot="rate",
title="",
y_label="Rate per 1000",
as_bar=False,
category="age_band_months",
)
# plot count
plot_measures(
df=df_oor_age,
filename=f"{frequency}/joined/plot_{test}_oor_age_count",
column_to_plot=numerator,
title="",
y_label="Count",
as_bar=False,
category="age_band_months",
)
for d in ["age_band_months", "region"]:
demographic_df = pd.read_csv(
OUTPUT_DIR / f"{frequency}/joined/measure_{test}_{d}_rate.csv",
parse_dates=["date"],
)
if d == "age_band_months":
demographic_df = demographic_df[
demographic_df["age_band_months"] != "missing"
]
elif d == "region":
demographic_df = demographic_df[demographic_df["region"].notnull()]
demographic_df["rate"] = calculate_rate(demographic_df, "value")
demographic_df = redact_small_numbers(
demographic_df, 5, test, "population", "rate", "date", d
)
demographic_df.to_csv(
OUTPUT_DIR
/ f"{frequency}/joined/redacted/measure_{test}_{d}_rate.csv",
index=False,
)
plot_measures(
df=demographic_df,
filename=f"{frequency}/joined/plot_{test}_{d}",
column_to_plot="rate",
title="",
y_label="Rate per 1000",
as_bar=False,
category=d,
)
# count
plot_measures(
df=demographic_df,
filename=f"{frequency}/joined/plot_{test}_{d}_count",
column_to_plot=test,
title="",
y_label="Count",
as_bar=False,
category=d,
)