Merge pull request #34 from CSSFrancis/feat/hyperspy-parity-0.3.0

Update documentation for 0.3.0 release

Author: CSSFrancis

Examples/Interactive/plot_figure_annotations.py

@@ -0,0 +1,87 @@
+"""
+Interactive figure annotations — double-click to add, drag to place
+===================================================================
+
+anyplotlib has a *figure-level* annotation layer that floats above the panels,
+positioned in **figure fractions** (0…1, origin top-left) rather than any
+panel's data coordinates.  Set it with
+:meth:`~anyplotlib.Figure.set_figure_markers`; the supported kinds are
+``"text"``, ``"circle"``, ``"rect"`` and ``"arrow"``.
+
+Turn on ``fig.edit_chrome`` and every annotation becomes draggable in the
+browser.  Combine that with a ``double_click`` handler and you get a simple
+annotate-by-clicking workflow: double-click a feature to drop a labelled
+arrow, then drag it to line it up.  Positions round-trip back to Python via
+:attr:`~anyplotlib.Figure.figure_markers`.
+"""
+import numpy as np
+import anyplotlib as apl
+
+rng  = np.random.default_rng(7)
+data = rng.standard_normal((160, 160)).cumsum(0).cumsum(1)
+data = (data - data.min()) / (data.max() - data.min())
+
+fig, ax = apl.subplots(1, 1, figsize=(520, 520))
+v = ax.imshow(data, cmap="magma", units="px")
+
+# Enable the editable-annotation ("report builder") mode so figure markers are
+# hit-testable and draggable, and the figure emits background/marker events.
+fig.edit_chrome = True
+
+# %%
+# Seed a couple of annotations
+# ----------------------------
+# Each marker is a dict with a ``kind`` and fraction-space geometry.  A text
+# label and an arrow pointing into the image to start with.
+
+fig.set_figure_markers([
+    {"kind": "text", "x": 0.5, "y": 0.06,
+     "text": "Double-click a feature to annotate it",
+     "color": "#ffffff", "fontsize": 14},
+    {"kind": "arrow", "x": 0.20, "y": 0.30, "u": 0.12, "v": 0.12,
+     "color": "#ffd54f", "linewidth": 2},
+])
+
+fig
+
+# %%
+# Double-click to drop a new annotation
+# -------------------------------------
+# On a single-panel figure the panel nearly fills the canvas, so we turn the
+# click's device pixels into a figure fraction with the panel's
+# ``display_width`` / ``display_height`` and append an arrow + label there.
+# Because ``edit_chrome`` is on, the new marker is immediately draggable.
+
+
+def _on_double_click(event):
+    if event.x is None or event.display_width is None:
+        return
+    fx = float(np.clip(event.x / event.display_width, 0.02, 0.98))
+    fy = float(np.clip(event.y / event.display_height, 0.02, 0.98))
+    markers = fig.figure_markers            # current list (a copy)
+    n = sum(1 for m in markers if m["kind"] == "text")
+    markers.append({"kind": "arrow", "x": fx, "y": fy,
+                    "u": 0.08, "v": -0.08, "color": "#40c4ff", "linewidth": 2})
+    markers.append({"kind": "text", "x": fx + 0.08, "y": fy - 0.10,
+                    "text": f"mark {n}", "color": "#40c4ff", "fontsize": 13})
+    fig.set_figure_markers(markers)
+
+
+v.add_event_handler(_on_double_click, "double_click")
+
+fig.set_help(
+    "Double-click on the image to drop a labelled arrow.\n"
+    "Drag any annotation to reposition it (edit mode is on)."
+)
+
+fig  # Interactive
+
+# %%
+# Read the placements back
+# ------------------------
+# After the user drags things around, ``fig.figure_markers`` reflects the
+# current fraction positions — persist them, export them, or feed them into a
+# report.
+
+for m in fig.figure_markers:
+    print(m["kind"], round(m["x"], 3), round(m["y"], 3))

Examples/Interactive/plot_voxel_grain_explorer.py

@@ -29,7 +29,7 @@
 rng = np.random.default_rng(11)
 
 # ── 1. Synthetic 3-D polycrystal: nearest-seed voxel grain map ──────────────
-N = 48                       # volume is N³ voxels, indexed V[z, y, x]
+N = 24                       # volume is N³ voxels, indexed V[z, y, x]
 N_GRAINS = 40
 
 seeds = rng.uniform(0, N, size=(N_GRAINS, 3))            # (z, y, x)
@@ -76,7 +76,8 @@ def random_rotations(n):
 # planes.  This anchors the highlight exactly where the slices intersect,
 # shows real slice contents in 3-D, and scales: the on-plane count is
 # ~3·(N/step)² regardless of N, so it stays fast even for a 256³ volume.
-VSTEP = max(1, N // 48)        # in-plane downsample → ~48² cubes per plane
+VSTEP = max(1, N // 48)        # in-plane downsample, capping at ~48² cubes/plane
+                               # for large N (no downsampling at this N=24)
 
 # Voxel cube size in data units.  A touch larger than VSTEP so the three
 # slabs read as solid sheets rather than a dotted grid.
@@ -140,6 +141,8 @@ def slice_voxels(ix, iy, iz):
     size=VOXSIZE, alpha=0.55,
     x_label="x", y_label="y", z_label="z",
     bounds=((0, N - 1),) * 3, zoom=1.1,
+    # gpu="auto" (default): ~1.7k cubes is over the ~1k voxel threshold, so the
+    # WebGPU instanced path handles each drag re-slice when WebGPU is present.
 )
 v_vol.set_title("Grain volume — drag a plane to re-slice")
 

Examples/Markers/plot_transform_markers.py

@@ -0,0 +1,49 @@
+"""
+Coordinate transforms — pin markers to the axes or the screen
+=============================================================
+
+Every ``add_*`` marker method takes a ``transform`` that decides which
+coordinate system its positions live in:
+
+- ``"data"`` (default) — data coordinates; the marker moves and scales with
+  zoom / pan, staying glued to the underlying data.
+- ``"axes"`` — axes-normalised ``(0, 0)`` bottom-left … ``(1, 1)`` top-right;
+  the marker stays in the same corner of the panel no matter how you zoom.
+- ``"display"`` — raw CSS pixels within the panel; a fixed-size decoration.
+
+The ``"axes"`` and ``"display"`` transforms are how you build overlays that
+should *not* track the data — a navigation index in the corner, a scale bar, a
+persistent legend chip.
+"""
+import numpy as np
+import anyplotlib as apl
+
+rng  = np.random.default_rng(1)
+data = rng.standard_normal((128, 128)).cumsum(0).cumsum(1)
+data = (data - data.min()) / (data.max() - data.min())
+xy   = np.linspace(0, 10, 128)
+
+fig, ax = apl.subplots(1, 1, figsize=(480, 480))
+v = ax.imshow(data, axes=[xy, xy], units="nm")
+
+# Data-anchored label — sits at (5, 5) in nm and rides along on zoom/pan.
+v.add_texts(offsets=[(5, 5)], texts=["feature @ (5, 5)"],
+            color="#ffffff", fontsize=12, name="data_label")
+
+# Axes-anchored index — stays pinned to the top-left corner of the panel
+# regardless of zoom (0, 1 = top-left in axes fractions).
+v.add_texts(offsets=[(0.04, 0.96)], texts=["frame 3 / 20"],
+            transform="axes", color="#ffd54f", fontsize=13, name="nav_index")
+
+fig
+
+# %%
+# Try it
+# ------
+# Zoom into the image: the white ``feature`` label moves with the data, while
+# the yellow ``frame 3 / 20`` index stays locked to the corner — because it is
+# positioned in ``"axes"`` coordinates.
+
+fig.set_help("Zoom in: the corner index stays put; the data label moves.")
+
+fig  # Interactive

Examples/PlotTypes/plot_gpu_voxels.py

@@ -0,0 +1,82 @@
+"""
+GPU-accelerated voxels
+======================
+
+:meth:`~anyplotlib.Axes.voxels` renders shaded translucent cubes for a
+volumetric field.  With ``gpu="auto"`` (the default) anyplotlib uses WebGPU
+instancing when a GPU is available and the cube count is large, so hundreds of
+thousands of voxels stay interactive; otherwise it falls back to the Canvas2D
+path.  Read :attr:`~anyplotlib.Plot3D.gpu_active` after the first frame to see
+which path was chosen.
+
+Here we build a dense spherical shell — enough cubes that the WebGPU path
+kicks in — and drop a draggable :class:`~anyplotlib.PlaneWidget` through it as
+a slice selector.
+"""
+import numpy as np
+import anyplotlib as apl
+
+# %%
+# Build a volumetric field
+# ------------------------
+# Voxel *centres* are passed as three flat coordinate arrays (not a dense 3-D
+# grid), so you only send the cubes you actually want drawn.  We keep the
+# voxels inside a spherical shell and colour them by radius.
+
+N = 64
+g = np.arange(N)
+Z, Y, X = np.meshgrid(g, g, g, indexing="ij")
+r = np.sqrt((X - N / 2) ** 2 + (Y - N / 2) ** 2 + (Z - N / 2) ** 2)
+shell = (r > N * 0.30) & (r < N * 0.42)   # a hollow sphere
+
+xs, ys, zs = X[shell], Y[shell], Z[shell]
+print(f"{xs.size:,} voxels")  # tens of thousands → GPU path under gpu='auto'
+
+# Colour by radius with the viridis-ish default cycle mapped through intensity.
+t = (r[shell] - r[shell].min()) / (np.ptp(r[shell]) + 1e-9)
+colors = np.stack([0.2 + 0.8 * t, 0.4 * np.ones_like(t), 1.0 - 0.8 * t], axis=1)
+
+fig, ax = apl.subplots(1, 1, figsize=(560, 520))
+vol = ax.voxels(
+    xs, ys, zs, colors=colors,
+    size=1.0, alpha=0.35,
+    bounds=((0, N - 1),) * 3,
+    azimuth=-55, elevation=28, zoom=1.1,
+    gpu="auto",                 # WebGPU when available, Canvas2D otherwise
+)
+vol.set_title("Spherical shell — drag to rotate, scroll to zoom")
+
+# %%
+# Add a slice-selector plane
+# --------------------------
+# A :class:`~anyplotlib.PlaneWidget` is a draggable axis-aligned plane.  Voxels
+# lying on it render more opaque, so the current slice pops out of the
+# translucent volume.  Drag it along z in the browser, or move it from Python.
+
+plane = vol.add_widget("plane", axis="z", position=N // 2,
+                       color="#40c4ff", alpha=0.18)
+
+
+@plane.add_event_handler("pointer_move")
+def _on_slice(event):
+    # Fires while the plane is dragged; pw.position holds the live slice index.
+    print("slice at z =", round(plane.position, 1))
+
+
+fig.set_help(
+    "Drag: rotate · Scroll: zoom · R: reset view\n"
+    "Drag the blue plane to slide the z-slice through the shell."
+)
+
+fig  # Interactive
+
+# %%
+# Which render path ran?
+# ----------------------
+# ``gpu_active`` is populated once the browser reports back after the first
+# frame.  It is ``True`` when the WebGPU instanced path is live, ``False`` on
+# the Canvas2D fallback, and ``None`` before the first frame (as when this
+# gallery page is built headlessly).  Force a path with ``gpu=True`` /
+# ``gpu=False`` if you need determinism.
+
+print("gpu_active:", vol.gpu_active)

Examples/PlotTypes/plot_step_and_log.py

@@ -0,0 +1,43 @@
+"""
+Step lines and log-scale spectra
+================================
+
+Two 1-D options that suit spectral data: a **mid-riser step** line (constant
+within each bin, jumping at bin midpoints) via ``linestyle="step-mid"``, and a
+**logarithmic y-axis** via :meth:`~anyplotlib.Axes.semilogy` (or
+``ax.plot(..., yscale="log")``).
+"""
+import numpy as np
+import anyplotlib as apl
+
+# A noisy binned spectrum.
+rng = np.random.default_rng(0)
+energy = np.linspace(0, 20, 60)
+counts = (np.exp(-(energy - 6) ** 2 / 4) * 1000
+          + np.exp(-(energy - 13) ** 2 / 8) * 400
+          + rng.uniform(0, 20, energy.size))
+
+# %%
+# Step line
+# ---------
+# ``linestyle="step-mid"`` draws a horizontal segment centred on each x value
+# with vertical risers between them — the standard way to show histogram-like
+# spectra without implying interpolation between channels.
+
+fig, ax = apl.subplots(1, 1, figsize=(560, 340))
+ax.plot(counts, axes=[energy], color="#4fc3f7",
+        linestyle="step-mid", label="counts")
+
+fig
+
+# %%
+# Log y-axis
+# ----------
+# ``semilogy`` is shorthand for a log y-scale, which brings out the small
+# secondary peak that the linear plot flattens.  Combine it with the step line
+# for a classic spectroscopy view.
+
+fig2, ax2 = apl.subplots(1, 1, figsize=(560, 340))
+ax2.semilogy(counts, axes=[energy], color="#ff7043", linestyle="step-mid")
+
+fig2