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3ecb175 affeea4 3ecb175 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | """QC filters applied to the per-tuber morphometrics dataframe.
Three steps:
- Drop ADC plot images whose ROI cropping removed substantial tuber area.
- Derive the PCA-based axis ratio from eccentricity.
- Filter out tubers with axis_ratio_pca above the pooled 99.5th percentile.
"""
from __future__ import annotations
import numpy as np
import pandas as pd
# ADC plot images dropped because the cropping step removed substantial
# tuber area. Values are the raw "Plot" column (image_id[-8:]).
ADC_QC_DROP_PLOTS: tuple[str, ...] = (
"5_ADC116",
"5_ADC131",
"5_ADC141",
"5_ADC129",
"5_ADC105",
"5_ADC205",
)
AR_PCA_FILTER_QUANTILE: float = 0.995
def drop_qc_plots(
df: pd.DataFrame,
drop_plots: tuple[str, ...] = ADC_QC_DROP_PLOTS,
plot_col: str = "Plot",
) -> pd.DataFrame:
"""Drop plots listed in drop_plots from the per-tuber dataframe."""
return df[~df[plot_col].isin(drop_plots)].copy()
def derive_axis_ratio_pca(df: pd.DataFrame) -> pd.DataFrame:
"""Add axis_ratio_pca = 1 / sqrt(1 - eccentricity^2)."""
df = df.copy()
ecc = np.clip(df["eccentricity"], 0, 0.999999)
df["axis_ratio_pca"] = 1.0 / np.sqrt(1.0 - ecc**2)
return df
def filter_axis_ratio(
df: pd.DataFrame,
quantile: float = AR_PCA_FILTER_QUANTILE,
) -> pd.DataFrame:
"""Keep tubers with axis_ratio_pca at or below the pooled quantile threshold."""
upper = df["axis_ratio_pca"].quantile(quantile)
return df[df["axis_ratio_pca"] <= upper].copy()
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