refactor(code): Split validity calc and matrix extract
Validity calculation belongs to the modelling, so we put it into the validity module. Extracting our matrix is a processing step so we made its own matrix module and put it in their. Should hopefully provide better separation of concerns going forward.
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5 changed files with 103 additions and 89 deletions
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@ -41,7 +41,7 @@ cmd = "nvim"
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[tool.poe.tasks.extract]
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help = "Extract the csv data from raw yaml files"
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shell = """
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python src/prep_data.py > 02-data/processed/extracted.csv
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python src/matrix.py > 02-data/processed/extracted.csv
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"""
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[tool.poe.tasks.milestone]
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help = "Extract, render, commit and version a finished artifact"
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@ -613,9 +613,9 @@ to better identify areas of strong analytical lenses or areas of more limited an
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```{python}
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#| label: fig-validity
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from src import prep_data
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from src.model import validity
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validities = prep_data.calculate_validities(by_intervention)
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validities = validity.calculate(by_intervention)
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validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
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g = sns.PairGrid(validities[["internal_validity", "external_validity", "identifier"]].drop_duplicates(subset="identifier"),
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38
src/matrix.py
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38
src/matrix.py
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@ -0,0 +1,38 @@
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from io import StringIO
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from pathlib import Path
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from pandas import DataFrame
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try:
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from src.model import validity # for quarto document scripts
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except ModuleNotFoundError:
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from model import validity # for directly running the package
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def extract(df: DataFrame, file: Path | StringIO) -> None:
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(
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validity.calculate(df)
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.drop(labels=["observation"], axis="columns")
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.to_csv(file, index=False, encoding="utf-8")
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)
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if __name__ == "__main__":
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import os
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import sys
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import load_data
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if len(sys.argv) == 2:
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df = load_data.from_yml(Path(sys.argv[1]))
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else:
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df = load_data.from_yml()
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output = StringIO()
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extract(df, output)
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output.seek(0)
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try:
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print(output.read())
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except BrokenPipeError:
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devnull = os.open(os.devnull, os.O_WRONLY)
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os.dup2(devnull, sys.stdout.fileno())
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@ -1,9 +1,8 @@
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import math
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from typing import cast
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from pandas import DataFrame
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from src import prep_data
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def _binned_strength(strength: float) -> str:
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if strength < 3.0:
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@ -28,11 +27,70 @@ def _combined_validities(
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return r"\-"
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def calculate(
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df: DataFrame, repr_col: str = "representativeness", method_col: str = "method"
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) -> DataFrame:
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EXT_COL_NAME: str = "external_validity"
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INT_COL_NAME: str = "internal_validity"
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cols = {EXT_COL_NAME: 0.0, INT_COL_NAME: 0.0}
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vd = df[
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(df["design"] == "quasi-experimental") | (df["design"] == "experimental")
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].copy()
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vd.assign(**cols)
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vd = cast(DataFrame, vd)
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vd[repr_col] = vd[repr_col].fillna("")
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vd[method_col] = vd[method_col].fillna("")
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# needs to check national before subnational and census, subnational before local
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vd.loc[
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vd[repr_col].str.contains("|".join(["national", "regional"])), EXT_COL_NAME
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] = 4.0
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vd.loc[vd[repr_col].str.contains("census"), EXT_COL_NAME] = 5.0
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vd.loc[vd[repr_col].str.contains("subnational"), EXT_COL_NAME] = 3.0
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vd.loc[vd[repr_col].str.contains("local"), EXT_COL_NAME] = 2.0
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# needs to go lowest to highest in case of multiple mentioned approaches
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vd.loc[
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vd[method_col].str.contains(
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"|".join(["OLS", "ordinary.least.square", "logistic.regression"])
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),
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INT_COL_NAME,
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] = 2.0
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vd.loc[
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vd[method_col].str.contains("|".join(["DM", "discontinuity.matching"])),
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INT_COL_NAME,
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] = 3.0
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vd.loc[
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vd[method_col].str.contains(
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"|".join(["DID", "difference.in.diff", "diff.in.diff", "triple.diff"])
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),
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INT_COL_NAME,
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] = 3.0
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vd.loc[
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vd[method_col].str.contains(
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"|".join(["PSM", "propensity.score.matching", "score.matching"])
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),
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INT_COL_NAME,
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] = 3.5
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vd.loc[
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vd[method_col].str.contains("|".join(["IV", "instrumental.variable"])),
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INT_COL_NAME,
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] = 4.0
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vd.loc[
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vd[method_col].str.contains("|".join(["RD", "regression.discontinuity"])),
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INT_COL_NAME,
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] = 4.5
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vd.loc[vd[method_col].str.contains("RCT"), INT_COL_NAME] = 5.0
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return vd
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def add_to_findings(
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findings_df: DataFrame, studies_by_intervention: DataFrame
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) -> DataFrame:
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valid_subset = (
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prep_data.calculate_validities(studies_by_intervention)[
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calculate(studies_by_intervention)[
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["internal_validity", "external_validity", "citation"]
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]
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.fillna(1.0)
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@ -1,82 +0,0 @@
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from typing import cast
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from pandas import DataFrame
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def calculate_validities(
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df: DataFrame, repr_col: str = "representativeness", method_col: str = "method"
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) -> DataFrame:
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EXT_COL_NAME: str = "external_validity"
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INT_COL_NAME: str = "internal_validity"
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cols = {EXT_COL_NAME: 0.0, INT_COL_NAME: 0.0}
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vd = df[
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(df["design"] == "quasi-experimental") | (df["design"] == "experimental")
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].copy()
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vd.assign(**cols)
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vd = cast(DataFrame, vd)
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vd[repr_col] = vd[repr_col].fillna("")
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vd[method_col] = vd[method_col].fillna("")
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# needs to check national before subnational and census, subnational before local
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vd.loc[vd[repr_col].str.contains("|".join(["national", "regional"])), EXT_COL_NAME] = 4.0
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vd.loc[vd[repr_col].str.contains("census"), EXT_COL_NAME] = 5.0
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vd.loc[vd[repr_col].str.contains("subnational"), EXT_COL_NAME] = 3.0
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vd.loc[vd[repr_col].str.contains("local"), EXT_COL_NAME] = 2.0
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# needs to go lowest to highest in case of multiple mentioned approaches
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vd.loc[
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vd[method_col].str.contains("|".join(["OLS", "ordinary.least.square", "logistic.regression"])),
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INT_COL_NAME,
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] = 2.0
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vd.loc[
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vd[method_col].str.contains("|".join(["DM", "discontinuity.matching"])),
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INT_COL_NAME,
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] = 3.0
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vd.loc[
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vd[method_col].str.contains(
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"|".join(["DID", "difference.in.diff", "diff.in.diff", "triple.diff"])
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),
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INT_COL_NAME,
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] = 3.0
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vd.loc[
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vd[method_col].str.contains(
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"|".join(["PSM", "propensity.score.matching", "score.matching"])
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),
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INT_COL_NAME,
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] = 3.5
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vd.loc[
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vd[method_col].str.contains("|".join(["IV", "instrumental.variable"])),
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INT_COL_NAME,
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] = 4.0
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vd.loc[
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vd[method_col].str.contains("|".join(["RD", "regression.discontinuity"])),
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INT_COL_NAME,
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] = 4.5
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vd.loc[vd[method_col].str.contains("RCT"), INT_COL_NAME] = 5.0
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return vd
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if __name__ == "__main__":
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import os
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import sys
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from io import StringIO
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from pathlib import Path
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import load_data
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if len(sys.argv) == 2:
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df = load_data.from_yml(Path(sys.argv[1]))
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else:
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df = load_data.from_yml()
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df = calculate_validities(df)
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output = StringIO()
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df.to_csv(output)
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output.seek(0)
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try:
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print(output.read())
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except BrokenPipeError:
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devnull = os.open(os.devnull, os.O_WRONLY)
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os.dup2(devnull, sys.stdout.fileno())
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