100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
from __future__ import annotations
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import numpy as np
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from backend.node_registry import register_node
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from backend.execution_context import emit_overlay
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from backend.data_types import DataField, MeasureTable
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@register_node(display_name="Histogram")
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class Histogram:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"field": ("DATA_FIELD",),
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"n_bins": ("INT", {"default": 256, "min": 10, "max": 1000, "step": 1}),
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"y_scale": (["linear", "log"],),
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"x1": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
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"y1": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
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"x2": ("FLOAT", {"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
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"y2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
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}
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}
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OUTPUTS = (
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('MEASURE_TABLE', 'measurements'),
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('COORDPAIR', 'marker_pair'),
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)
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FUNCTION = "process"
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DESCRIPTION = (
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"Compute the height distribution histogram (DH). "
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"Use log scale to reveal small peaks next to a dominant background. "
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"Outputs marker measurements while showing the histogram interactively in-node. "
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"Equivalent to gwy_data_field_dh."
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)
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_broadcast_overlay_fn = None
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_current_node_id: str = ""
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def process(
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self,
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field: DataField,
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n_bins: int,
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y_scale: str = "linear",
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x1: float = 0.25,
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y1: float = 0.5,
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x2: float = 0.75,
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y2: float = 0.5,
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) -> tuple:
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raw_counts, bin_edges = np.histogram(field.data.ravel(), bins=int(n_bins))
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bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
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counts = raw_counts.astype(np.float64)
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if y_scale == "log":
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counts = np.log10(1.0 + counts)
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x1 = float(np.clip(x1, 0.0, 1.0))
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x2 = float(np.clip(x2, 0.0, 0.0 + 1.0))
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xmin = float(np.min(bin_centers)) if len(bin_centers) else 0.0
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xmax = float(np.max(bin_centers)) if len(bin_centers) else 1.0
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def x_frac_to_idx(frac):
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if len(bin_centers) <= 1:
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return 0
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if xmax == xmin:
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return 0
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target_x = xmin + frac * (xmax - xmin)
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return int(np.argmin(np.abs(bin_centers - target_x)))
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idx_a = x_frac_to_idx(x1)
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idx_b = x_frac_to_idx(x2)
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xa = float(bin_centers[idx_a]) if len(bin_centers) else 0.0
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xb = float(bin_centers[idx_b]) if len(bin_centers) else 0.0
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ya = float(counts[idx_a]) if len(counts) else 0.0
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yb = float(counts[idx_b]) if len(counts) else 0.0
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count_unit = "count" if y_scale == "linear" else "log10(1+count)"
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emit_overlay({
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"kind": "line_plot",
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"section_title": "Histogram",
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"line": counts.tolist(),
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"x_axis": bin_centers.astype(np.float64).tolist(),
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"x1": float(np.clip(x1, 0.0, 1.0)),
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"x2": float(np.clip(x2, 0.0, 1.0)),
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"y1": float(y1),
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"y2": float(y2),
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"a_locked": False,
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"b_locked": False,
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})
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table = MeasureTable([
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{"quantity": "delta X", "value": xb - xa, "unit": field.si_unit_z},
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{"quantity": "delta Y", "value": yb - ya, "unit": count_unit},
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{"quantity": "A position", "value": xa, "unit": field.si_unit_z},
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{"quantity": "A count", "value": ya, "unit": count_unit},
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{"quantity": "B position", "value": xb, "unit": field.si_unit_z},
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{"quantity": "B count", "value": yb, "unit": count_unit},
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])
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return (table, ((x1, y1), (x2, y2)))
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