Add new Excel report BEA25P077_RP.xlsx with multiple worksheets and styles

2026-06-08 14:42:29 +02:00
parent 4e27fd2011
commit 045050f6bf
12 changed files with 1365 additions and 0 deletions
@@ -0,0 +1,62 @@
+import pandas as pd
+import numpy as np
+
+QC_FILE = "BEA25P077_RP.xlsx"
+PATIENTS_FILE = "patients.xlsx"
+THRESHOLDS = [0, 0.1, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 5.0]
+
+# Load patient data
+patients = pd.read_excel(PATIENTS_FILE, usecols=["Number", "Exf"])
+patients = patients.dropna(subset=["Number"])
+patients["Number"] = patients["Number"].astype(int)
+patients["Exf"] = patients["Exf"].astype(int)
+
+def parse_conc(val):
+    """Return float concentration, or NaN for 'Too low' / missing."""
+    if isinstance(val, str):
+        return np.nan
+    try:
+        return float(val)
+    except (TypeError, ValueError):
+        return np.nan
+
+def analyse_sheet(sheet_name, conc_col):
+    df = pd.read_excel(QC_FILE, sheet_name=sheet_name, usecols=["ID", conc_col])
+    df["conc_val"] = df[conc_col].apply(parse_conc)
+    # Extract patient number from ID like "4A_G2" or "4B_G2"
+    extracted = df["ID"].str.extract(r"^(\d+)[AB]")[0]
+    df["Number"] = pd.to_numeric(extracted, errors="coerce")
+    df = df.dropna(subset=["Number"])
+    df["Number"] = df["Number"].astype(int)
+    # Merge with patient diagnosis
+    df = df.merge(patients, on="Number", how="left")
+    return df
+
+sheets = {
+    "A (Schirmer strips)": ("A-samples", "conc (ng/ul)"),
+    "B (Lens tissues)":    ("B-samples", "Conc [ng/ul]"),
+}
+
+print(f"{'Threshold':>10}  {'Sheet':<22}  {'Total':>6}  {'Healthy (Exf=0)':>15}  {'Diseased (Exf=1)':>16}")
+print("-" * 80)
+
+for label, (sheet_name, conc_col) in sheets.items():
+    df = analyse_sheet(sheet_name, conc_col)
+    total_samples = len(df)
+    n_too_low = df["conc_val"].isna().sum()
+    print(f"\n  {label}  ({total_samples} samples total, {n_too_low} 'Too low')")
+    print(f"  {'Threshold':>10}  {'Passing':>7}  {'Healthy':>8}  {'Diseased':>9}  {'Unknown dx':>10}")
+    print(f"  {'-'*55}")
+    for t in THRESHOLDS:
+        if t == 0:
+            # threshold=0: include all samples (Too low counts as passing)
+            passing = df.copy()
+        else:
+            # Only samples with a numeric conc >= threshold pass
+            passing = df[df["conc_val"] >= t]
+        healthy  = (passing["Exf"] == 0).sum()
+        diseased = (passing["Exf"] == 1).sum()
+        unknown  = passing["Exf"].isna().sum()
+        print(f"  {t:>10.2f}  {len(passing):>7}  {healthy:>8}  {diseased:>9}  {unknown:>10}")
+
+print()