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"""
Preprocessing utilities for polymer classification app.
Adapted from the original scripts/preprocess_dataset.py for Hugging Face Spaces deployment.
"""
from __future__ import annotations
import numpy as np
from numpy.typing import DTypeLike
from scipy.interpolate import interp1d
from scipy.signal import savgol_filter
from scipy.interpolate import interp1d
TARGET_LENGTH = 500 # Frozen default per PREPROCESSING_BASELINE
def _ensure_1d_equal(x: np.ndarray, y: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
x = np.asarray(x, dtype=float)
y = np.asarray(y, dtype=float)
if x.ndim != 1 or y.ndim != 1 or x.size != y.size or x.size < 2:
raise ValueError("x and y must be 1D arrays of equal length >= 2")
return x, y
def resample_spectrum(x: np.ndarray, y: np.ndarray, target_len: int = TARGET_LENGTH) -> tuple[np.ndarray, np.ndarray]:
"""Linear re-sampling onto a uniform grid of length target_len."""
x, y = _ensure_1d_equal(x, y)
order = np.argsort(x)
x_sorted, y_sorted = x[order], y[order]
x_new = np.linspace(x_sorted[0], x_sorted[-1], int(target_len))
f = interp1d(x_sorted, y_sorted, kind="linear", assume_sorted=True)
y_new = f(x_new)
return x_new, y_new
def remove_baseline(y: np.ndarray, degree: int = 2) -> np.ndarray:
"""Polynomial baseline subtraction (degree=2 default)"""
y = np.asarray(y, dtype=float)
x_idx = np.arange(y.size, dtype=float)
coeffs = np.polyfit(x_idx, y, deg=int(degree))
baseline = np.polyval(coeffs, x_idx)
return y - baseline
def smooth_spectrum(y: np.ndarray, window_length: int = 11, polyorder: int = 2) -> np.ndarray:
"""Savitzky-Golay smoothing with safe/odd window enforcement"""
y = np.asarray(y, dtype=float)
window_length = int(window_length)
polyorder = int(polyorder)
# === window must be odd and >= polyorder+1 ===
if window_length % 2 == 0:
window_length += 1
min_win = polyorder + 1
if min_win % 2 == 0:
min_win += 1
window_length = max(window_length, min_win)
return savgol_filter(y, window_length=window_length, polyorder=polyorder, mode="interp")
def normalize_spectrum(y: np.ndarray) -> np.ndarray:
"""Min-max normalization to [0, 1] with constant-signal guard."""
y = np.asarray(y, dtype=float)
y_min = float(np.min(y))
y_max = float(np.max(y))
if np.isclose(y_max - y_min, 0.0):
return np.zeros_like(y)
return (y - y_min) / (y_max - y_min)
def preprocess_spectrum(
x: np.ndarray,
y: np.ndarray,
*,
target_len: int = TARGET_LENGTH,
do_baseline: bool = True,
degree: int = 2,
do_smooth: bool = True,
window_length: int = 11,
polyorder: int = 2,
do_normalize: bool = True,
out_dtype: DTypeLike = np.float32,
) -> tuple[np.ndarray, np.ndarray]:
"""Exact CLI baseline: resample -> baseline -> smooth -> normalize"""
x_rs, y_rs = resample_spectrum(x, y, target_len=target_len)
if do_baseline:
y_rs = remove_baseline(y_rs, degree=degree)
if do_smooth:
y_rs = smooth_spectrum(y_rs, window_length=window_length, polyorder=polyorder)
if do_normalize:
y_rs = normalize_spectrum(y_rs)
# === Coerce to a real dtype to satisfy static checkers & runtime ===
out_dt = np.dtype(out_dtype)
return x_rs.astype(out_dt, copy=False), y_rs.astype(out_dt, copy=False) |