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plots.py
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plots.py
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import pandas as pd
from pathlib import Path
from ebmdatalab import charts
from utilities import OUTPUT_DIR, ANALYSIS_DIR, plot_measures, redact_small_numbers, convert_binary
Path("output/monthly/redacted").mkdir(parents=True, exist_ok=True)
Path("output/weekly/redacted").mkdir(parents=True, exist_ok=True)
for frequency in ["monthly", "weekly"]:
for test in ["alt", "ast", "bilirubin", "gi_illness"]:
# plot rates
df = pd.read_csv(
OUTPUT_DIR / f"{frequency}/measure_{test}_rate.csv", parse_dates=["date"]
)
df["rate"] = df[f"value"] * 1000
df = redact_small_numbers(
df, 5, test, "population", "rate", "date"
)
df.to_csv(
OUTPUT_DIR / f"{frequency}/redacted/measure_{test}_rate.csv", index=False
)
plot_measures(
df=df,
filename=f"{frequency}/plot_{test}",
column_to_plot="rate",
title="",
y_label="Rate per 1000",
as_bar=False,
)
# plot count
plot_measures(
df=df,
filename=f"{frequency}/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}/measure_{test}_practice_rate.csv",
parse_dates=["date"],
)
df_practice["rate"] = df_practice[f"value"] * 1000
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}/deciles_chart_{test}.png", bbox_inches="tight"
)
# plot out of range rates
if test in ["alt", "ast", "bilirubin"]:
if test == "bilirubin":
df_oor = pd.read_csv(
OUTPUT_DIR / f"{frequency}/measure_{test}_oor_ref_rate.csv",
parse_dates=["date"],
)
df_oor = redact_small_numbers(
df_oor, 5, f"{test}_numeric_value_out_of_ref_range", test, "value", "date"
)
df_oor.to_csv(
OUTPUT_DIR / f"{frequency}/redacted/measure_{test}_oor_ref_rate.csv", index=False
)
else:
df_oor = pd.read_csv(
OUTPUT_DIR / f"{frequency}/measure_{test}_oor_rate.csv",
parse_dates=["date"],
)
df_oor = redact_small_numbers(
df_oor, 5, f"{test}_numeric_value_out_of_range", test, "value", "date"
)
df_oor.to_csv(
OUTPUT_DIR / f"{frequency}/redacted/measure_{test}_oor_rate.csv", index=False
)
df_oor["rate"] = df_oor[f"value"] * 1000
plot_measures(
df=df_oor,
filename=f"{frequency}/plot_{test}_oor",
column_to_plot="rate",
title="",
y_label="Rate per 1000",
as_bar=False,
)
# plot counts
if test == "bilirubin":
plot_measures(
df=df_oor,
filename=f"{frequency}/plot_{test}_oor_count",
column_to_plot=f"{test}_numeric_value_out_of_ref_range",
title="",
y_label="Count",
as_bar=False,
)
else:
plot_measures(
df=df_oor,
filename=f"{frequency}/plot_{test}_oor_count",
column_to_plot=f"{test}_numeric_value_out_of_range",
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}/measure_{test}_oor_recent_cov_rate.csv",
parse_dates=["date"],
)
if test == "bilirubin":
df_oor_cov = redact_small_numbers(
df_oor_cov, 5, f"{test}_numeric_value_out_of_ref_range", test, "value", "date"
)
else:
df_oor_cov = redact_small_numbers(
df_oor_cov, 5, f"{test}_numeric_value_out_of_range", test, "value", "date"
)
df_oor_cov.to_csv(
OUTPUT_DIR / f"{frequency}/redacted/measure_{test}_oor_recent_cov_rate.csv",index=False
)
df_oor_cov["rate"] = df_oor_cov[f"value"] * 1000
convert_binary(df_oor_cov, "recent_positive_covid_test", "Yes", "No")
plot_measures(
df=df_oor_cov,
filename=f"{frequency}/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
if test == "bilirubin":
plot_measures(
df=df_oor_cov,
filename=f"{frequency}/plot_{test}_oor_recent_cov_count",
column_to_plot=f"{test}_numeric_value_out_of_ref_range",
title="",
y_label="Count",
as_bar=False,
category="recent_positive_covid_test",
)
else:
plot_measures(
df=df_oor_cov,
filename=f"{frequency}/plot_{test}_oor_recent_cov_count",
column_to_plot=f"{test}_numeric_value_out_of_range",
title="",
y_label="Count",
as_bar=False,
category="recent_positive_covid_test",
)
for d in ["age_band", "region"]:
demographic_df = pd.read_csv(
OUTPUT_DIR / f"{frequency}/measure_{test}_{d}_rate.csv",
parse_dates=["date"],
)
if d == "age_band":
demographic_df = demographic_df[
demographic_df["age_band"] != "missing"
]
elif d == "region":
demographic_df = demographic_df[demographic_df["region"].notnull()]
demographic_df["rate"] = demographic_df[f"value"] * 1000
demographic_df = redact_small_numbers(
demographic_df, 5, test, "population", "rate", "date"
)
demographic_df.to_csv(
OUTPUT_DIR / f"{frequency}/redacted/measure_{test}_{d}_rate.csv",index=False
)
plot_measures(
df=demographic_df,
filename=f"{frequency}/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}/plot_{test}_{d}_count",
column_to_plot=test,
title="",
y_label="Count",
as_bar=False,
category=d,
)