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 +1,88 @@
+"""
+Generate sample_sequencing_plan.xlsx
+Labels every sample as Already sequenced / To be sequenced / Skip
+at the chosen concentration threshold, with colour-coded rows.
+"""
+
+import numpy as np
+import pandas as pd
+from openpyxl import load_workbook
+from openpyxl.styles import PatternFill
+
+THRESHOLD  = 0.25
+OUT_FILE   = "sample_sequencing_plan.xlsx"
+
+PILOT_IDS  = {("A", n) for n in [1, 2, 3, 4, 5, 6]} | \
+             {("B", n) for n in [1, 2, 15, 16, 17, 18]}
+
+FILLS = {
+    "Already sequenced": PatternFill("solid", fgColor="C6EFCE"),  # green
+    "To be sequenced":   PatternFill("solid", fgColor="FFEB9C"),  # yellow
+    "Skip":              PatternFill("solid", fgColor="FFC7CE"),  # red/pink
+}
+
+# ── load data ─────────────────────────────────────────────────────────────────
+patients = pd.read_excel("patients.xlsx", usecols=["Number", "Exf"])
+patients = patients.dropna(subset=["Number"])
+patients["Number"] = patients["Number"].astype(int)
+patients["Exf"]    = patients["Exf"].astype(int)
+
+def parse_conc(v):
+    if isinstance(v, str): return np.nan
+    try: return float(v)
+    except: return np.nan
+
+qa = pd.read_excel("BEA25P077_RP.xlsx", sheet_name="A-samples")
+qb = pd.read_excel("BEA25P077_RP.xlsx", sheet_name="B-samples")
+qa["conc_val"] = qa["conc (ng/ul)"].apply(parse_conc)
+qb["conc_val"] = qb["Conc [ng/ul]"].apply(parse_conc)
+qa["Number"] = pd.to_numeric(qa["ID"].str.extract(r"^(\d+)A")[0], errors="coerce")
+qb["Number"] = pd.to_numeric(qb["ID"].str.extract(r"^(\d+)B")[0], errors="coerce")
+qa = qa.dropna(subset=["Number"]); qa["Number"] = qa["Number"].astype(int); qa["tissue"] = "A"
+qb = qb.dropna(subset=["Number"]); qb["Number"] = qb["Number"].astype(int); qb["tissue"] = "B"
+qa = qa.merge(patients, on="Number", how="left")
+qb = qb.merge(patients, on="Number", how="left")
+
+# ── build plan ────────────────────────────────────────────────────────────────
+rows = []
+for _, r in pd.concat([qa, qb], ignore_index=True).iterrows():
+    tissue, num, conc = r["tissue"], r["Number"], r["conc_val"]
+    if (tissue, num) in PILOT_IDS:
+        status = "Already sequenced"
+    elif pd.isna(conc) or conc < THRESHOLD:
+        status = "Skip"
+    else:
+        status = "To be sequenced"
+    rows.append({
+        "Sample":       f"{num}{tissue}_G2",
+        "Patient":      num,
+        "Tissue":       tissue,
+        "Conc (ng/ul)": conc,
+        "Status":       status,
+    })
+
+df = pd.DataFrame(rows).sort_values(["Tissue", "Patient"])
+
+# ── write Excel ───────────────────────────────────────────────────────────────
+df.to_excel(OUT_FILE, index=False)
+
+wb = load_workbook(OUT_FILE)
+ws = wb.active
+
+# auto-width columns
+for col_cells in ws.columns:
+    width = max(len(str(c.value)) if c.value is not None else 0 for c in col_cells)
+    ws.column_dimensions[col_cells[0].column_letter].width = width + 4
+
+# colour every row based on Status (Status is the last column)
+status_col = df.columns.get_loc("Status") + 1   # 1-indexed
+for row in ws.iter_rows(min_row=2):
+    status = row[status_col - 1].value
+    fill = FILLS.get(status)
+    if fill:
+        for cell in row:
+            cell.fill = fill
+
+wb.save(OUT_FILE)
+print(f"Saved {OUT_FILE}  ({len(df)} samples)")
+print(df["Status"].value_counts().to_string())