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modularize style and add propagating widgets

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This commit is contained in:
matei jordache
2026-03-26 01:01:06 -07:00
parent 8e16f9f0b4
commit 2c3256fffc
17 changed files with 670 additions and 358 deletions
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28
backend/data_types.py
View File

@@ -39,6 +39,34 @@ class MeasureTable(list):
"""Named scalar measurements, typically rows of quantity/value/unit."""
@dataclass
class LineData:
data: np.ndarray
x_axis: np.ndarray | None = None
x_unit: str = ""
y_unit: str = ""
def __post_init__(self) -> None:
self.data = np.asarray(self.data, dtype=np.float64).ravel()
if self.x_axis is not None:
axis = np.asarray(self.x_axis, dtype=np.float64).ravel()
self.x_axis = axis[: len(self.data)]
else:
self.x_axis = None
def __array__(self, dtype=None):
return np.asarray(self.data, dtype=dtype) if dtype is not None else self.data
def __len__(self) -> int:
return len(self.data)
def __iter__(self):
return iter(self.data)
def __getitem__(self, item):
return self.data[item]
def _normalize_hex_color(color: Any, default: str = "#000000") -> str:
if isinstance(color, str):
text = color.strip()

8
backend/execution.py
View File

@@ -272,7 +272,7 @@ class ExecutionEngine:
"""
import numpy as np
from backend.data_types import (
DataField, image_to_uint8, encode_preview, render_datafield_preview,
DataField, LineData, image_to_uint8, encode_preview, render_datafield_preview,
)
from backend.nodes.io import Image, ImageDemo
@@ -302,7 +302,7 @@ class ExecutionEngine:
on_preview(node_id, encode_preview(arr))
return
if type_name == "LINE" and isinstance(value, np.ndarray) and on_preview:
if type_name == "LINE" and isinstance(value, (np.ndarray, LineData)) and on_preview:
preview = self._render_line_preview(cls, slot, result)
if preview:
on_preview(node_id, preview)
@@ -354,6 +354,7 @@ class ExecutionEngine:
) -> dict | None:
"""Return structured LINE preview data for responsive frontend rendering."""
import numpy as np
from backend.data_types import LineData
return_types = getattr(cls, "RETURN_TYPES", ())
@@ -374,7 +375,10 @@ class ExecutionEngine:
matplotlib.use("Agg")
import matplotlib.pyplot as plt
y_meta = y if isinstance(y, LineData) else None
y = np.asarray(y, dtype=np.float64).ravel()
if x is None and y_meta is not None and y_meta.x_axis is not None:
x = y_meta.x_axis
if x is None:
x = np.arange(len(y), dtype=np.float64)
else:

5
backend/node_menu.py
View File

@@ -20,6 +20,7 @@ MENU_LAYOUT: dict[str, list[str]] = {
"Number",
"RangeSlider",
"Coordinate",
"CoordinatePair",
"Font",
],
"Output": [
@@ -55,10 +56,10 @@ MENU_LAYOUT: dict[str, list[str]] = {
"FixZero",
],
"Measure": [
"Statistics",
"Histogram",
"CrossSection",
"Histogram",
"Cursors",
"Statistics",
"Stats",
],
"Mask": [

110
backend/nodes/analysis.py
View File

@@ -12,7 +12,8 @@ from __future__ import annotations
import numpy as np
from typing import Callable
from backend.node_registry import register_node
from backend.data_types import DataField, MeasureTable, RecordTable, datafield_to_uint8, encode_preview, render_datafield_preview
from backend.data_types import DataField, LineData, MeasureTable, RecordTable, datafield_to_uint8, encode_preview, render_datafield_preview
from backend.nodes.io import Coordinate, CoordinatePair
# ---------------------------------------------------------------------------
@@ -62,7 +63,7 @@ class Statistics:
# Histogram
# ---------------------------------------------------------------------------
@register_node(display_name="Height Histogram")
@register_node(display_name="Histogram")
class Histogram:
@classmethod
def INPUT_TYPES(cls):
@@ -78,8 +79,8 @@ class Histogram:
}
}
RETURN_TYPES = ("MEASURE_TABLE",)
RETURN_NAMES = ("measurements",)
RETURN_TYPES = ("MEASURE_TABLE", "COORDPAIR",)
RETURN_NAMES = ("measurements", "marker pair",)
FUNCTION = "process"
CATEGORY = "analysis"
DESCRIPTION = (
@@ -155,7 +156,7 @@ class Histogram:
{"quantity": "delta X", "value": xb - xa, "unit": field.si_unit_z},
{"quantity": "delta Y", "value": yb - ya, "unit": count_unit},
])
return (table,)
return (table, ((x1, y1), (x2, y2)))
# ---------------------------------------------------------------------------
@@ -177,12 +178,12 @@ class Cursors:
"y2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
},
"optional": {
"x_axis": ("LINE",),
"coord_pair": ("COORDPAIR", {"label": "coord pair"}),
},
}
RETURN_TYPES = ("MEASURE_TABLE",)
RETURN_NAMES = ("measurement",)
RETURN_TYPES = ("MEASURE_TABLE","COORDPAIR",)
RETURN_NAMES = ("measurement","coord pair",)
FUNCTION = "process"
CATEGORY = "analysis"
DESCRIPTION = (
@@ -196,12 +197,17 @@ class Cursors:
def process(
self, line, x1: float, y1: float, x2: float, y2: float,
x_axis=None,
coord_pair=None,
) -> tuple:
if isinstance(line, DataField):
return self._process_field(line, x1=x1, y1=y1, x2=x2, y2=y2)
if coord_pair is not None:
(x1, y1), (x2, y2) = coord_pair
return self._process_line(line, x1=x1, y1=y1, x2=x2, y2=y2, x_axis=x_axis)
locked = coord_pair is not None
if isinstance(line, DataField):
return self._process_field(line, x1=x1, y1=y1, x2=x2, y2=y2, locked=locked)
return self._process_line(line, x1=x1, y1=y1, x2=x2, y2=y2, locked=locked)
def _process_line(
self,
@@ -210,12 +216,14 @@ class Cursors:
y1: float,
x2: float,
y2: float,
x_axis=None,
locked: bool = False,
) -> tuple:
y = np.asarray(line, dtype=np.float64).ravel()
x_unit = line.x_unit if isinstance(line, LineData) else ""
y_unit = line.y_unit if isinstance(line, LineData) else ""
n = len(y)
if x_axis is not None:
x = np.asarray(x_axis, dtype=np.float64).ravel()[:n]
if isinstance(line, LineData) and line.x_axis is not None:
x = np.asarray(line.x_axis, dtype=np.float64).ravel()[:n]
else:
x = np.arange(n, dtype=np.float64)
x1 = float(np.clip(x1, 0.0, 1.0))
@@ -251,21 +259,21 @@ class Cursors:
"x2": x2,
"y1": float(y1),
"y2": float(y2),
"a_locked": False,
"b_locked": False,
"a_locked": locked,
"b_locked": locked,
},
)
# --- Output table ---
table = MeasureTable([
{"quantity": "A x", "value": xa, "unit": ""},
{"quantity": "A y", "value": ya, "unit": ""},
{"quantity": "B x", "value": xb, "unit": ""},
{"quantity": "B y", "value": yb, "unit": ""},
{"quantity": "dx", "value": xb - xa, "unit": ""},
{"quantity": "dy", "value": yb - ya, "unit": ""},
{"quantity": "A x", "value": xa, "unit": x_unit},
{"quantity": "A y", "value": ya, "unit": y_unit},
{"quantity": "B x", "value": xb, "unit": x_unit},
{"quantity": "B y", "value": yb, "unit": y_unit},
{"quantity": "dx", "value": xb - xa, "unit": x_unit},
{"quantity": "dy", "value": yb - ya, "unit": y_unit},
])
return (table,)
return (table, ((x1, y1), (x2, y2)))
def _process_field(
self,
@@ -274,6 +282,7 @@ class Cursors:
y1: float,
x2: float,
y2: float,
locked: bool = False,
) -> tuple:
from scipy.ndimage import map_coordinates
@@ -306,8 +315,8 @@ class Cursors:
"y1": y1,
"x2": x2,
"y2": y2,
"a_locked": False,
"b_locked": False,
"a_locked": locked,
"b_locked": locked,
},
)
@@ -322,7 +331,7 @@ class Cursors:
{"quantity": "dy", "value": by - ay, "unit": field.si_unit_xy},
{"quantity": "dz", "value": z2 - z1, "unit": field.si_unit_z},
])
return (table,)
return (table, ((x1, y1), (x2, y2)))
# ---------------------------------------------------------------------------
@@ -642,13 +651,12 @@ class CrossSection:
"n_samples": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
},
"optional": {
"marker_A": ("COORD",),
"marker_B": ("COORD",),
"marker_pair": ("COORDPAIR", {"label": "marker pair"}),
},
}
RETURN_TYPES = ("LINE",)
RETURN_NAMES = ("profile",)
RETURN_TYPES = ("LINE", "COORDPAIR",)
RETURN_NAMES = ("profile", "marker pair",)
FUNCTION = "process"
CATEGORY = "analysis"
DESCRIPTION = (
@@ -664,15 +672,13 @@ class CrossSection:
self, field: DataField,
x1: float, y1: float, x2: float, y2: float,
extend: str, n_samples: int,
marker_A=None, marker_B=None,
marker_pair=None,
) -> tuple:
from scipy.ndimage import map_coordinates
# COORD inputs override widget values
if marker_A is not None:
x1, y1 = float(marker_A[0]), float(marker_A[1])
if marker_B is not None:
x2, y2 = float(marker_B[0]), float(marker_B[1])
# COORDPAIR input overrides widget values
if marker_pair is not None:
(x1, y1), (x2, y2) = marker_pair
# Remember marker positions (before extend)
marker_x1, marker_y1 = float(x1), float(y1)
@@ -714,12 +720,24 @@ class CrossSection:
"image": image_uri,
"x1": marker_x1, "y1": marker_y1,
"x2": marker_x2, "y2": marker_y2,
"a_locked": marker_A is not None,
"b_locked": marker_B is not None,
"a_locked": marker_pair is not None,
"b_locked": marker_pair is not None,
},
)
return (profile.astype(np.float64),)
dx_real = (x2 - x1) * field.xreal
dy_real = (y2 - y1) * field.yreal
distance_axis = np.linspace(0.0, float(np.hypot(dx_real, dy_real)), n_samples, dtype=np.float64)
return (
LineData(
data=profile.astype(np.float64),
x_axis=distance_axis,
x_unit=field.si_unit_xy,
y_unit=field.si_unit_z,
),
((marker_x1, marker_y1), (marker_x2, marker_y2)),
)
# ---------------------------------------------------------------------------
@@ -1028,7 +1046,11 @@ class Stats:
if source_type == "LINE":
line_entry = LINE_OPS.get(operation)
explicit_unit = line_entry[1] if isinstance(line_entry, tuple) and len(line_entry) > 1 else ""
return _apply_scalar_unit(explicit_unit, operation)
if explicit_unit:
return _apply_scalar_unit(explicit_unit, operation)
if isinstance(input_value, LineData):
return _apply_scalar_unit(input_value.y_unit, operation)
return ""
if source_type == "RECORD_TABLE" and isinstance(input_value, list) and column:
return _apply_scalar_unit(_common_table_unit(input_value, column), operation)
@@ -1052,6 +1074,12 @@ class Stats:
raise ValueError(f"Column '{column_name}' has no numeric values.")
return ("RECORD_TABLE", np.asarray(values, dtype=np.float64), column_name)
if isinstance(input_value, LineData):
values = np.asarray(input_value.data, dtype=np.float64)
if values.size == 0:
raise ValueError("Stats requires a non-empty input.")
return ("LINE", values.ravel(), None)
if isinstance(input_value, np.ndarray):
values = np.asarray(input_value, dtype=np.float64)
if values.size == 0:

11
backend/nodes/filters.py
View File

@@ -13,7 +13,7 @@ from __future__ import annotations
from functools import lru_cache
import numpy as np
from backend.node_registry import register_node
from backend.data_types import DataField
from backend.data_types import DataField, LineData
# ---------------------------------------------------------------------------
@@ -251,6 +251,15 @@ class FFTFilter1D:
# Inverse FFT
filtered = np.fft.irfft(Z, n=n)
if isinstance(line, LineData):
return (
LineData(
data=filtered,
x_axis=line.x_axis.copy() if line.x_axis is not None else None,
x_unit=line.x_unit,
y_unit=line.y_unit,
),
)
return (filtered,)

23
backend/nodes/io.py
View File

@@ -446,7 +446,30 @@ class Coordinate:
def process(self, x: float, y: float) -> tuple:
return ((float(x), float(y)),)
@register_node(display_name="Coordinate Pair")
class CoordinatePair:
"""Provide a pair of Coordinates, for drawing lines between markers, etc."""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"a": ("COORD",),
"b": ("COORD",),
}
}
RETURN_TYPES = ("COORDPAIR",)
RETURN_NAMES = ("coord pair",)
FUNCTION = "process"
CATEGORY = "io"
DESCRIPTION = "Output a pair of coordinates."
def process(self, a: tuple, b: tuple) -> tuple:
return ((a, b),)
# ---------------------------------------------------------------------------
# Number