chore(repo): Expose dataframes directly from source

src/__init__.py

@@ -0,0 +1,13 @@
+from src.process.generate_dataframes import bib_sample, bib_sample_db_raw, df_by_intervention, df_main, df_validities
+
+# each observation in a single dataframe
+df = df_main()
+
+# all observations but split per individual intervention
+df_by_intervention = df_by_intervention()
+
+# Calc study validities (internal & external separated)
+validities = df_validities()
+
+bib_sample = bib_sample()
+bib_sample_db_raw = bib_sample_db_raw()

src/model/prisma.py

@@ -1,8 +1,8 @@
-from src.process.generate_dataframes import bib_sample, bib_sample_raw_db
+from src import bib_sample, bib_sample_db_raw
 
 
 class PrismaNumbers:
-    raw_db = len(bib_sample_raw_db.entries)
+    raw_db = len(bib_sample_db_raw.entries)
     raw_snowball = 2240
 
     # list of all keywords (semicolon-delimited string) for each entry in sample
@@ -43,7 +43,7 @@ class PrismaNumbers:
     final_extracted = len([1 for kw in all_kw if "done::extracted" in kw])
 
 
-del bib_sample, bib_sample_raw_db
+del bib_sample, bib_sample_db_raw
 
 if __name__ == "__main__":
     prisma = PrismaNumbers()

src/process/generate_dataframes.py

@@ -1,34 +1,57 @@
-from pathlib import Path
 import re
+
+import pandas as pd
+from bibtexparser import Library
+
 import src.globals as g
+from src.extract import load_data as load
+from src.model import validity
 
 ## Creates 3 important data structures:
 # df: The main dataframe containing all final sample studies
 # df_by_intervention: The same dataframe but split up by individual interventions per study
 # validities: The studies with their validities, containing only quasi-/experimental studies
 
-from src.process import add_metadata as meta
+
+# the complete library of sampled (and working) literature
+def bib_sample() -> Library:
+    return meta.bib_library_from_file(g.REFERENCE_DATA.joinpath("zotero-library.bib"))
+
 
 # raw database-search results
-bib_sample_raw_db = meta.bib_library_from_dir(g.REFERENCE_DATA.joinpath("db"))
-# the complete library of sampled (and working) literature
-bib_sample = meta.bib_library_from_file(g.REFERENCE_DATA.joinpath("zotero-library.bib"))
+def bib_sample_db_raw() -> Library:
+    return meta.bib_library_from_dir(g.REFERENCE_DATA.joinpath("db"))
 
-# load relevant studies
-from src.extract import load_data as load
 
-# each observation in a single dataframe
-df = meta.observations_with_metadata_df(
-    raw_observations = load.from_yml(g.EXTRACTED_DATA),
-    study_metadata = meta.bib_metadata_df(bib_sample),
-    country_groups = meta.country_groups_df(Path(f"{g.SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx")),
-)
+def df_main() -> pd.DataFrame:
+    df = meta.observations_with_metadata_df(
+        raw_observations=load.from_yml(g.EXTRACTED_DATA),
+        study_metadata=meta.bib_metadata_df(bib_sample()),
+        country_groups=meta.country_groups_df(
+            g.SUPPLEMENTARY_DATA.joinpath("wb-country-groupings.xlsx")
+        ),
+    )
+    return df
 
-# all observations but split per individual intervention
-df_by_intervention = (
-    df
+
+from src.process import add_metadata as meta
+
+
+def df_by_intervention() -> pd.DataFrame:
+    df_by_intervention = (
+        df_main()
         .fillna("")
-    .groupby(["author", "year", "title", "design", "method", "representativeness", "citation"])
+        .groupby(
+            [
+                "author",
+                "year",
+                "title",
+                "design",
+                "method",
+                "representativeness",
+                "citation",
+            ]
+        )
         .agg(
             {
                 "intervention": lambda _col: "; ".join(_col),
@@ -38,19 +61,30 @@ df_by_intervention = (
         .drop_duplicates()
         .assign(
             intervention=lambda _df: _df["intervention"].apply(
-            lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
+                lambda _cell: set(
+                    [x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")]
+                )
             ),
         )
         .explode("intervention")
-)
+    )
+    return df_by_intervention
 
-# Calc study validities (internal & external separated)
-from src.model import validity
 
-validities = validity.calculate(df_by_intervention)
-validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
-validities = validities.loc[(validities["design"] == "quasi-experimental") | (validities["design"] == "experimental")]
-#validities["external_validity"] = validities["external_validity"].astype('category')
-validities["internal_validity"] = validities["internal_validity"].astype('category')
-validities["External Validity"] = validities["external_validity"]
-validities["Internal Validity"] = validities["internal_validity"]
+def df_validities() -> pd.DataFrame:
+    validities = validity.calculate(df_by_intervention())
+    validities["identifier"] = (
+        validities["author"].str.replace(r",.*$", "", regex=True)
+        + " ("
+        + validities["year"].astype(str)
+        + ")"
+    )
+    validities = validities.loc[
+        (validities["design"] == "quasi-experimental")
+        | (validities["design"] == "experimental")
+    ]
+    # validities["external_validity"] = validities["external_validity"].astype('category')
+    validities["internal_validity"] = validities["internal_validity"].astype("category")
+    validities["External Validity"] = validities["external_validity"]
+    validities["Internal Validity"] = validities["internal_validity"]
+    return validities