rpotter6298
100 lines
3.3 KiB
Python
Executable File
100 lines
3.3 KiB
Python
Executable File
# papila_builders.py
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from typing import List, Dict
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import numpy as np
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import pandas as pd
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from classes import ClinicalData # adjust import path if needed
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# ---- Pachymetry → IOP correction (per PAPILA Table 3) ----
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_PACHY_TABLE: Dict[int, int] = {
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475:+5, 485:+4, 495:+4, 505:+3, 515:+2, 525:+1, 535:+1,
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545: 0, 555:-1, 565:-1, 575:-2, 585:-3, 595:-4, 605:-4, 615:-5,
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}
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_PACHY_KEYS = np.array(sorted(_PACHY_TABLE.keys()))
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def _nearest_pachy_key(x: float) -> int:
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idx = int(np.argmin(np.abs(_PACHY_KEYS - float(x))))
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return int(_PACHY_KEYS[idx])
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def _pick_iop(row: pd.Series) -> float:
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"""Prefer Pneumatic, else Perkins; may return NaN."""
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raw = row["Pneumatic"] if not pd.isna(row.get("Pneumatic", np.nan)) else row.get("Perkins", np.nan)
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return float(raw) if not pd.isna(raw) else np.nan
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def _correct_iop(raw_iop: float, pachy: float) -> float:
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"""Return corrected IOP using nearest pachymetry bin; if pachy missing, return raw."""
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if pd.isna(raw_iop):
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return np.nan
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if pd.isna(pachy):
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return float(raw_iop)
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key = _nearest_pachy_key(float(pachy))
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return float(raw_iop) + float(_PACHY_TABLE[key])
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def _apply_iop_and_drop_md(df: pd.DataFrame) -> pd.DataFrame:
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"""Add IOP_raw/IOP_corr and drop VF_MD if present (in-place safe)."""
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# IOP_raw
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df["IOP_raw"] = df.apply(_pick_iop, axis=1)
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# IOP_corr
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pachy = df.get("Pachymetry", pd.Series(np.nan, index=df.index))
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df["IOP_corr"] = [
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_correct_iop(r, p) for r, p in zip(df["IOP_raw"].values, pachy.values)
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]
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# Drop VF_MD if present
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if "VF_MD" in df.columns:
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df.drop(columns=["VF_MD"], inplace=True)
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return df
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def build_papila_clinical(
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image_dir: str,
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clinical_dir: str,
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label_col: str,
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cat_cols: List[str],
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n_splits: int = 5,
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random_seed: int = 42,
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) -> ClinicalData:
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"""
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Build ClinicalData exactly like the user's original build_clinical:
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- add_df(OD), set eyeID='OD'
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- add_df(OS), set eyeID='OS'
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- normalize 'Patient ID' on frames
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THEN:
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- compute IOP_raw / IOP_corr on each frame
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- drop VF_MD
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- refresh master df + kfold indices
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"""
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clinical = ClinicalData(
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image_dir=image_dir,
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clinical_dir=clinical_dir,
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label_col=label_col,
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cat_cols=cat_cols,
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n_splits=n_splits,
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random_seed=random_seed,
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)
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# --- Load exactly like original build_clinical ---
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clinical.add_df(pd.read_excel(f"{clinical_dir}/patient_data_od.xlsx", header=1), id_column="ID")
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clinical.frames[0]["eyeID"] = "OD"
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clinical.add_df(pd.read_excel(f"{clinical_dir}/patient_data_os.xlsx", header=1), id_column="ID")
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clinical.frames[1]["eyeID"] = "OS"
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# Normalize 'Patient ID' on the per-eye frames (string → int)
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for frame in clinical.frames:
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frame["Patient ID"] = frame["Patient ID"].astype(str).str.extract(r"(\d+)")[0].astype(int)
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# Build initial master as in original
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clinical._refresh_master_df()
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# --- Post-processing ON THE FRAMES (so everything stays consistent) ---
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for i in range(len(clinical.frames)):
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clinical.frames[i] = _apply_iop_and_drop_md(clinical.frames[i])
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# Refresh master again so IOP_raw/IOP_corr & MD removal propagate
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clinical._refresh_master_df()
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clinical._build_kfold_indices()
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return clinical
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