matei/tono
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

deduplication pass

Browse Source
This commit is contained in:
matei jordache
2026-04-03 18:19:08 -07:00
parent f6b47e6d79
commit c8d766677b
42 changed files with 484 additions and 689 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
backend/nodes/line_correction.py
View File

@@ -4,16 +4,7 @@ import numpy as np
from backend.data_types import DataField, LineData
from backend.node_registry import register_node
def _normalize_mask(mask: np.ndarray | None, shape: tuple[int, int]) -> np.ndarray | None:
if mask is None:
return None
mask_array = np.asarray(mask)
if mask_array.shape[:2] != shape:
raise ValueError(f"Mask shape {mask_array.shape} does not match field shape {shape}.")
return mask_array > 127
from backend.nodes.helpers import normalize_mask, apply_masking, masked_values
def _trimmed_mean_or_median(values: np.ndarray, trim_fraction: float) -> float:
@@ -33,18 +24,8 @@ def _trimmed_mean_or_median(values: np.ndarray, trim_fraction: float) -> float:
return float(trimmed.mean()) if trimmed.size else float(np.median(sorted_values))
def _masked_values(data: np.ndarray, mask: np.ndarray | None, masking: str) -> np.ndarray:
if mask is None or masking == "ignore":
return data
if masking == "include":
return data[mask]
if masking == "exclude":
return data[~mask]
raise ValueError(f"Unknown masking mode: {masking}")
def _global_masked_median(data: np.ndarray, mask: np.ndarray | None, masking: str) -> float:
selected = _masked_values(data, mask, masking)
selected = masked_values(data, mask, masking)
if selected.size == 0:
selected = np.asarray(data, dtype=np.float64).ravel()
return float(np.median(selected))
@@ -75,7 +56,7 @@ def _find_row_shifts_trimmed_mean(
shifts[i] = _trimmed_mean_or_median(row, trim_fraction)
continue
values = _masked_values(row, row_mask, masking)
values = masked_values(row, row_mask, masking)
if values.size >= mincount:
shifts[i] = _trimmed_mean_or_median(values, trim_fraction)
else:
@@ -162,12 +143,7 @@ def _row_level_poly(
row = data[i]
row_mask = None if mask is None else mask[i]
if row_mask is None or masking == "ignore":
valid = np.ones(xres, dtype=bool)
elif masking == "include":
valid = row_mask
else:
valid = ~row_mask
valid = apply_masking(row, row_mask, masking)
coeffs = np.zeros(degree + 1, dtype=np.float64)
if np.count_nonzero(valid) > degree:
@@ -331,7 +307,7 @@ class LineCorrection:
mask: np.ndarray | None = None,
) -> tuple:
data = np.asarray(field.data, dtype=np.float64)
mask_array = _normalize_mask(mask, data.shape)
mask_array = normalize_mask(mask, data.shape)
if direction not in {"horizontal", "vertical"}:
raise ValueError(f"Unknown direction: {direction}")