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add snapshot tool, masks, and build for mac

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matei jordache
2026-03-23 21:52:17 -07:00
parent 080eefbef6
commit a34b1c980d
29 changed files with 2016 additions and 170 deletions
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146
backend/nodes/analysis.py
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@@ -69,6 +69,7 @@ class HeightHistogram:
"required": {
"field": ("DATA_FIELD",),
"n_bins": ("INT", {"default": 256, "min": 10, "max": 1000, "step": 1}),
"y_scale": (["linear", "log"],),
}
}
@@ -78,13 +79,150 @@ class HeightHistogram:
CATEGORY = "analysis"
DESCRIPTION = (
"Compute the height distribution histogram (DH). "
"Use log scale to reveal small peaks next to a dominant background. "
"Equivalent to gwy_data_field_dh."
)
def process(self, field: DataField, n_bins: int) -> tuple:
def process(self, field: DataField, n_bins: int, y_scale: str = "linear") -> tuple:
counts, bin_edges = np.histogram(field.data.ravel(), bins=int(n_bins))
bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
return (counts.astype(np.float64), bin_centers)
counts = counts.astype(np.float64)
if y_scale == "log":
counts = np.log10(1.0 + counts)
return (counts, bin_centers)
# ---------------------------------------------------------------------------
# LineCursors — interactive measurement cursors on any LINE plot
# ---------------------------------------------------------------------------
@register_node(display_name="Line Cursors")
class LineCursors:
"""Place two draggable cursors on any LINE plot to measure values and deltas."""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"line": ("LINE",),
"x1": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
"y1": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
"x2": ("FLOAT", {"default": 0.75, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
"y2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
},
"optional": {
"x_axis": ("LINE",),
},
}
RETURN_TYPES = ("TABLE",)
RETURN_NAMES = ("measurement",)
FUNCTION = "process"
CATEGORY = "analysis"
DESCRIPTION = (
"Place two cursors on any line plot (histogram, cross section, profile) "
"to measure positions, values, and deltas. Drag the markers to reposition."
)
_broadcast_overlay_fn = None
_current_node_id: str = ""
def process(
self, line, x1: float, y1: float, x2: float, y2: float,
x_axis=None,
) -> tuple:
import io as _io
import base64
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
y = np.asarray(line, dtype=np.float64).ravel()
n = len(y)
if x_axis is not None:
x = np.asarray(x_axis, dtype=np.float64).ravel()[:n]
else:
x = np.arange(n, dtype=np.float64)
# --- Render the base plot first to determine axes bounds ---
fig, ax = plt.subplots(figsize=(3.2, 2.2), dpi=100)
fig.patch.set_facecolor("#1e293b")
ax.set_facecolor("#0f172a")
ax.plot(x, y, color="#ff9800", linewidth=1.2)
ax.tick_params(colors="#94a3b8", labelsize=7)
for spine in ax.spines.values():
spine.set_color("#334155")
ax.grid(True, color="#334155", linewidth=0.3, alpha=0.5)
fig.tight_layout(pad=0.4)
# Force a draw so transforms are valid
fig.canvas.draw()
# Get axes position in figure-fraction coordinates
ax_pos = ax.get_position()
ax_l, ax_b = ax_pos.x0, ax_pos.y0
ax_w, ax_h = ax_pos.width, ax_pos.height
# x1/y1 arrive as image-fraction from the frontend drag.
# Convert image-fraction x → axes-fraction → nearest data index.
def img_x_to_idx(ix):
axes_frac = np.clip((ix - ax_l) / ax_w, 0, 1)
return int(np.clip(round(axes_frac * (n - 1)), 0, n - 1))
idx_a = img_x_to_idx(x1)
idx_b = img_x_to_idx(x2)
xa, ya = float(x[idx_a]), float(y[idx_a])
xb, yb = float(x[idx_b]), float(y[idx_b])
# --- Draw cursor lines and markers on the plot ---
ax.axvline(xa, color="#ffd700", linewidth=1.5, linestyle="--", alpha=0.9)
ax.axvline(xb, color="#ffd700", linewidth=1.5, linestyle="--", alpha=0.9)
ax.plot(xa, ya, "o", color="#ffd700", markersize=6, zorder=5)
ax.plot(xb, yb, "o", color="#ffd700", markersize=6, zorder=5)
ax.annotate(
"", xy=(xb, yb), xytext=(xa, ya),
arrowprops=dict(arrowstyle="<->", color="#90caf9", lw=1.5),
)
# --- Broadcast overlay ---
if LineCursors._broadcast_overlay_fn is not None:
# Convert data-space positions back to image-fraction for markers
fig.canvas.draw()
inv = fig.transFigure.inverted()
fig_a = inv.transform(ax.transData.transform([xa, ya]))
fig_b = inv.transform(ax.transData.transform([xb, yb]))
buf = _io.BytesIO()
fig.savefig(buf, format="png", facecolor=fig.get_facecolor())
buf.seek(0)
image_uri = "data:image/png;base64," + base64.b64encode(buf.read()).decode()
LineCursors._broadcast_overlay_fn(
LineCursors._current_node_id,
{
"image": image_uri,
"x1": float(fig_a[0]),
"y1": float(1.0 - fig_a[1]), # flip: image y=0 is top
"x2": float(fig_b[0]),
"y2": float(1.0 - fig_b[1]),
"a_locked": False,
"b_locked": False,
},
)
plt.close(fig)
# --- Output table ---
table = [
{"quantity": "A position", "value": xa, "unit": ""},
{"quantity": "A value", "value": ya, "unit": ""},
{"quantity": "B position", "value": xb, "unit": ""},
{"quantity": "B value", "value": yb, "unit": ""},
{"quantity": "delta X", "value": xb - xa, "unit": ""},
{"quantity": "delta Y", "value": yb - ya, "unit": ""},
]
return (table,)
# ---------------------------------------------------------------------------
@@ -242,9 +380,9 @@ class CrossSection:
return {
"required": {
"field": ("DATA_FIELD",),
"x1": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
"x1": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
"y1": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
"x2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
"x2": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
"y2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01, "hidden": True}),
"extend": (["none", "to_edges"],),
"n_samples": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),