Clean up

Examples/PlotTypes/README.rst

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+Plot Types
+----------
+A collection of short examples showing different plot types.

Examples/PlotTypes/plot_3d.py

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+"""
+3D Plotting
+===========
+
+Demonstrate the three 3-D geometry types supported by
+:meth:`~anyplotlib.Axes.plot_surface`,
+:meth:`~anyplotlib.Axes.scatter3d`, and
+:meth:`~anyplotlib.Axes.plot3d`.
+Drag to rotate, scroll to zoom, press **R** to reset the view.
+"""
+import numpy as np
+import anyplotlib as vw
+
+# ── Surface ───────────────────────────────────────────────────────────────────
+x = np.linspace(-3, 3, 60)
+y = np.linspace(-3, 3, 60)
+XX, YY = np.meshgrid(x, y)
+ZZ = np.sin(np.sqrt(XX ** 2 + YY ** 2))
+
+fig, ax = vw.subplots(1, 1, figsize=(520, 480))
+surf = ax.plot_surface(XX, YY, ZZ,
+                       colormap="viridis",
+                       x_label="x", y_label="y", z_label="sin(r)")
+
+fig
+
+# %%
+# Scatter plot
+# ------------
+
+rng = np.random.default_rng(1)
+n = 300
+theta = rng.uniform(0, 2 * np.pi, n)
+phi   = rng.uniform(0, np.pi, n)
+r     = rng.uniform(0.6, 1.0, n)
+xs = r * np.sin(phi) * np.cos(theta)
+ys = r * np.sin(phi) * np.sin(theta)
+zs = r * np.cos(phi)
+
+fig2, ax2 = vw.subplots(1, 1, figsize=(480, 480))
+sc = ax2.scatter3d(xs, ys, zs,
+                   color="#4fc3f7", point_size=3,
+                   x_label="x", y_label="y", z_label="z")
+
+fig2
+
+# %%
+# 3-D line — parametric helix
+# ----------------------------
+
+t  = np.linspace(0, 4 * np.pi, 300)
+hx = np.cos(t)
+hy = np.sin(t)
+hz = t / (4 * np.pi)
+
+fig3, ax3 = vw.subplots(1, 1, figsize=(480, 480))
+ln = ax3.plot3d(hx, hy, hz,
+                color="#ff7043", linewidth=2,
+                x_label="cos t", y_label="sin t", z_label="t")
+
+fig3
+
+# %%
+# Update the surface data live
+# ----------------------------
+# Call :meth:`~anyplotlib.Plot3D.set_data` to replace the geometry
+# without recreating the panel.
+
+ZZ2 = np.cos(np.sqrt(XX ** 2 + YY ** 2))
+surf.set_data(XX, YY, ZZ2)
+surf.set_colormap("plasma")
+surf.set_view(azimuth=30, elevation=40)
+
+fig

Examples/PlotTypes/plot_bar.py

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+"""
+Bar Chart
+=========
+
+Demonstrate :meth:`~anyplotlib.Axes.bar` with:
+
+* **Matplotlib-aligned API** — ``ax.bar(x, height, width, bottom, …)``
+* Vertical and horizontal orientations, per-bar colours, category labels
+* **Grouped bars** — pass a 2-D *height* array ``(N, G)``
+* **Log-scale value axis** — ``log_scale=True``
+* Live data updates via :meth:`~anyplotlib.PlotBar.set_data`
+"""
+import numpy as np
+import anyplotlib as vw
+
+rng = np.random.default_rng(7)
+
+# ── 1. Vertical bar chart — monthly sales ────────────────────────────────────
+# The first positional argument is now *x* (positions or labels), matching
+# ``matplotlib.pyplot.bar(x, height, width=0.8, bottom=0.0, ...)``.
+months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
+          "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+sales = np.array([42, 55, 48, 63, 71, 68, 74, 81, 66, 59, 52, 78],
+                 dtype=float)
+
+fig1, ax1 = vw.subplots(1, 1, figsize=(640, 340))
+bar1 = ax1.bar(
+    months,           # x  — category strings become x_labels automatically
+    sales,            # height
+    width=0.6,
+    color="#4fc3f7",
+    show_values=True,
+    units="Month",
+    y_units="Units sold",
+)
+fig1
+
+# %%
+# Horizontal bar chart — ranked items
+# -------------------------------------
+# Set ``orient="h"`` for a horizontal layout.  Pass a list of CSS colours
+# to ``colors`` to give each bar its own colour.
+
+categories = ["NumPy", "SciPy", "Matplotlib", "Pandas", "Scikit-learn",
+              "PyTorch", "TensorFlow", "JAX", "Polars", "Dask"]
+scores = np.array([95, 88, 91, 87, 83, 79, 76, 72, 68, 65], dtype=float)
+
+palette = [
+    "#ef5350", "#ec407a", "#ab47bc", "#7e57c2", "#42a5f5",
+    "#26c6da", "#26a69a", "#66bb6a", "#d4e157", "#ffa726",
+]
+
+fig2, ax2 = vw.subplots(1, 1, figsize=(540, 400))
+bar2 = ax2.bar(
+    categories,
+    scores,
+    orient="h",
+    colors=palette,
+    width=0.65,
+    show_values=True,
+    y_units="Popularity score",
+)
+fig2
+
+# %%
+# Grouped bar chart — quarterly comparison
+# -----------------------------------------
+# Pass a 2-D *height* array of shape ``(N, G)`` to draw *G* bars side by
+# side for each category.  Provide ``group_labels`` to show a legend and
+# ``group_colors`` to customise each group's colour.
+
+quarters = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
+q_data = np.array([
+    [42, 58, 51],   # Jan — Q1, Q2, Q3
+    [55, 61, 59],   # Feb
+    [48, 70, 65],   # Mar
+    [63, 75, 71],   # Apr
+    [71, 69, 80],   # May
+    [68, 83, 77],   # Jun
+], dtype=float)     # shape (6, 3) → 6 categories, 3 groups
+
+fig3, ax3 = vw.subplots(1, 1, figsize=(680, 340))
+bar3 = ax3.bar(
+    quarters,
+    q_data,
+    width=0.8,
+    group_labels=["Q1", "Q2", "Q3"],
+    group_colors=["#4fc3f7", "#ff7043", "#66bb6a"],
+    show_values=False,
+    y_units="Sales",
+)
+fig3
+
+# %%
+# Log-scale value axis
+# ---------------------
+# Set ``log_scale=True`` for a logarithmic value axis.  Non-positive values
+# are clamped to ``1e-10`` — no error is raised.  Tick marks are placed at
+# each decade (10⁰, 10¹, 10², …) with faint minor gridlines at 2×, 3×, 5×
+# multiples.
+
+log_labels = ["A", "B", "C", "D", "E"]
+log_vals   = np.array([1, 10, 100, 1_000, 10_000], dtype=float)
+
+fig4, ax4 = vw.subplots(1, 1, figsize=(500, 340))
+bar4 = ax4.bar(
+    log_labels,
+    log_vals,
+    log_scale=True,
+    color="#ab47bc",
+    show_values=True,
+    y_units="Count (log scale)",
+)
+fig4
+
+# %%
+# Side-by-side comparison — update data live
+# -------------------------------------------
+# Place two :class:`~anyplotlib.PlotBar` panels in one figure.
+# Call :meth:`~anyplotlib.PlotBar.set_data` to swap in Q2 data —
+# the value-axis range recalculates automatically.
+
+q1 = np.array([42, 55, 48, 63, 71, 68, 74, 81, 66, 59, 52, 78], dtype=float)
+q2 = np.array([58, 61, 70, 75, 69, 83, 90, 88, 77, 64, 71, 95], dtype=float)
+all_months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
+              "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+
+fig5, (ax_left, ax_right) = vw.subplots(1, 2, figsize=(820, 320))
+bar_left = ax_left.bar(
+    all_months, q1, width=0.6,
+    color="#4fc3f7", show_values=False, y_units="Q1 sales",
+)
+bar_right = ax_right.bar(
+    all_months, q1, width=0.6,
+    color="#ff7043", show_values=False, y_units="Q2 sales",
+)
+bar_right.set_data(q2)    # swap in Q2 — axis range recalculates automatically
+
+fig5
+
+# %%
+# Mutate colours, annotations, and scale at runtime
+# --------------------------------------------------
+# :meth:`~anyplotlib.PlotBar.set_color` repaints all bars,
+# :meth:`~anyplotlib.PlotBar.set_show_values` toggles labels,
+# :meth:`~anyplotlib.PlotBar.set_log_scale` switches the
+# value-axis between linear and logarithmic.
+
+bar1.set_color("#ff7043")
+bar1.set_show_values(False)
+fig1

Examples/PlotTypes/plot_image2d.py

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+"""
+2D Image with Histogram
+=======================
+
+Display a 2-D image with physical axes, a colourmap, and an interactive
+histogram below — all wired together with draggable threshold widgets.
+
+Layout
+------
+A :class:`~anyplotlib.GridSpec` with two rows puts the image
+on top and a bar-chart histogram below.  Two
+:class:`~anyplotlib.widgets.VLineWidget` handles on the histogram mark the
+``display_min`` / ``display_max`` thresholds; dragging them updates the
+image colour scale in real time.
+
+Key bindings on the image panel: **R** reset view · **C** toggle colorbar ·
+**L** / **S** cycle colour-scale modes.
+
+New ``imshow`` parameters
+-------------------------
+``cmap``
+    Colormap name passed directly to :meth:`~anyplotlib.Axes.imshow`
+    (e.g. ``"viridis"``, ``"inferno"``).  Defaults to ``"gray"``.
+``vmin`` / ``vmax``
+    Colormap clipping limits in data units.  Values outside the range are
+    clamped to the colormap endpoints.  Defaults to the data min/max.
+``origin``
+    ``"upper"`` (default) places row 0 at the top (image convention).
+    ``"lower"`` places row 0 at the bottom (scientific / matrix convention)
+    and automatically reverses the y-axis so tick values increase upward.
+"""
+import numpy as np
+import anyplotlib as apl
+
+
+rng = np.random.default_rng(1)
+
+# ── Synthetic diffraction pattern ─────────────────────────────────────────────
+N = 256
+x = np.linspace(-5, 5, N)   # physical axis in nm
+y = np.linspace(-5, 5, N)
+XX, YY = np.meshgrid(x, y)
+R = np.sqrt(XX ** 2 + YY ** 2)
+
+
+def _ring(r, r0, width, amp):
+    return amp * np.exp(-0.5 * ((r - r0) / width) ** 2)
+
+
+image = (
+    _ring(R, 0.0, 0.30, 1.00)    # central spot
+    + _ring(R, 2.1, 0.15, 0.55)  # first-order ring
+    + _ring(R, 4.2, 0.15, 0.25)  # second-order ring
+    + rng.normal(scale=0.04, size=(N, N))
+)
+
+# ── Layout: image (top, 3×) + histogram bar chart (bottom, 1×) ────────────────
+gs = apl.GridSpec(2, 1, height_ratios=[3, 1])
+fig = apl.Figure(figsize=(500, 640))
+ax_img  = fig.add_subplot(gs[0, 0])
+ax_hist = fig.add_subplot(gs[1, 0])
+
+# ── Image panel — cmap, vmin, vmax supplied directly to imshow ────────────────
+vmin_init = float(image.min())
+vmax_init = float(image.max())
+
+# Pass cmap, vmin, and vmax directly — no separate set_colormap / set_clim call
+# needed for the initial display.
+v = ax_img.imshow(image, axes=[x, y], units="nm",
+                  cmap="inferno", vmin=vmin_init, vmax=vmax_init)
+
+# First-order spot markers in the same physical coordinates used by imshow
+spot_nm = np.array([[ 2.1,  0.0], [-2.1,  0.0],
+                    [ 0.0,  2.1], [ 0.0, -2.1]])
+v.add_circles(spot_nm, name="spots", radius=7,
+              edgecolors="#00e5ff", facecolors="#00e5ff22",
+              labels=["g1", "g1_bar", "g2", "g2_bar"])
+
+# ── Histogram bar chart ────────────────────────────────────────────────────────
+counts, edges = np.histogram(image.ravel(), bins=64)
+bin_centers   = 0.5 * (edges[:-1] + edges[1:])
+
+h = ax_hist.bar(counts, x_centers=bin_centers, orient="v",
+                color="#4fc3f7", y_units="count")
+
+# ── Draggable threshold handles on the histogram ──────────────────────────────
+wlo = h.add_vline_widget(vmin_init, color="#ff6e40")   # low-threshold handle
+whi = h.add_vline_widget(vmax_init, color="#ffffff")   # high-threshold handle
+
+
+@wlo.on_release
+def _apply_low(event):
+    """Update image display_min when the low handle is released."""
+    v.set_clim(vmin=event.x)
+
+
+@whi.on_release
+def _apply_high(event):
+    """Update image display_max when the high handle is released."""
+    v.set_clim(vmax=event.x)
+
+
+fig # Interactive
+
+# %%
+# Adjust colour map and display range
+# ------------------------------------
+# :meth:`~anyplotlib.Plot2D.set_colormap` switches the palette;
+# :meth:`~anyplotlib.Plot2D.set_clim` adjusts the display range.
+# Both are equivalent to passing ``cmap`` / ``vmin`` / ``vmax`` at construction.
+
+v.set_colormap("viridis")
+v.set_clim(vmin=0.0, vmax=0.8)
+
+fig
+
+# %%
+# origin='lower' — scientific / matrix convention
+# ------------------------------------------------
+# Passing ``origin='lower'`` places row 0 of the data at the *bottom* of the
+# image, matching the matplotlib / scientific convention.  The y-axis is
+# automatically reversed so tick values still increase upward.
+
+mat = np.arange(64, dtype=float).reshape(8, 8)   # row 0 = small values
+
+fig2, ax2 = apl.subplots()
+v2 = ax2.imshow(mat, cmap="plasma", origin="lower")
+
+fig2 # Interactive
+