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 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Tutorial 14 – Axis and layout controls\n",
    "\n",
    "This tutorial demonstrates the latest Matplotlib-style wrappers: filled polygons, logarithmic shortcuts, axis limits and autoscaling, secondary axes, box aspect, and explicit tick labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from maxplotlib import Canvas\n",
    "\n",
    "x = np.linspace(0.1, 10, 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "## Filled polygons\n",
    "\n",
    "`fill()` forwards polygon coordinates to Matplotlib and becomes a filled Plotly scatter trace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3",
   "metadata": {},
   "outputs": [],
   "source": [
    "canvas = Canvas()\n",
    "canvas.fill([0, 1, 2, 1], [0, 2, 0, -1], color=\"tab:orange\", alpha=0.5)\n",
    "canvas.set_title(\"A filled polygon\")\n",
    "canvas.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {},
   "source": [
    "## Logarithmic plotting shortcuts\n",
    "\n",
    "Use `semilogx()`, `semilogy()`, or `loglog()` when the data and axis scale should be configured together. The three-panel example below shows the optional `fig`/`axs` style for users familiar with Matplotlib; ordinary one-panel examples use `Canvas` directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "canvas, (ax1, ax2, ax3) = Canvas.subplots(nrows=1, ncols=3)\n",
    "ax1.semilogx(x, np.sin(x) + 2)\n",
    "ax2.semilogy(x, np.exp(x / 4))\n",
    "ax3.loglog(x, x**2)\n",
    "ax1.set_title(\"semilogx\")\n",
    "ax2.set_title(\"semilogy\")\n",
    "ax3.set_title(\"loglog\")\n",
    "canvas.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "## Axis limits, modes, and autoscaling\n",
    "\n",
    "`axis()` accepts Matplotlib-style modes such as `\"equal\"`, `\"tight\"`, and `\"off\"`, or a four-value `[xmin, xmax, ymin, ymax]` limit list. `relim()` and `autoscale_view()` recompute the visible limits from plotted data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "canvas = Canvas()\n",
    "canvas.plot([0, 1, 2], [0, 4, 1])\n",
    "canvas.axis([0, 2, -1, 5])\n",
    "canvas.relim()\n",
    "canvas.autoscale_view(tight=True)\n",
    "canvas.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {},
   "source": [
    "## Secondary x- and y-axes\n",
    "\n",
    "Secondary axes use forward and inverse functions. They are currently rendered by the Matplotlib backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9",
   "metadata": {},
   "outputs": [],
   "source": [
    "canvas = Canvas()\n",
    "canvas.plot(x, x**2)\n",
    "canvas.set_xlabel(\"distance (m)\")\n",
    "canvas.set_ylabel(\"area (m²)\")\n",
    "canvas.secondary_xaxis(\n",
    "    \"top\",\n",
    "    functions=(lambda value: value / 1000, lambda value: value * 1000),\n",
    "    label=\"distance (km)\",\n",
    ")\n",
    "canvas.secondary_yaxis(\n",
    "    \"right\", functions=(np.sqrt, lambda value: value**2), label=\"length (m)\"\n",
    ")\n",
    "canvas.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "## Box aspect and explicit tick labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11",
   "metadata": {},
   "outputs": [],
   "source": [
    "canvas = Canvas()\n",
    "canvas.plot([0, 1, 2], [0, 1, 0], marker=\"o\")\n",
    "canvas.set_box_aspect(1)\n",
    "canvas.set_xticks([0, 1, 2])\n",
    "canvas.set_xticklabels([\"start\", \"middle\", \"end\"], rotation=25, color=\"navy\")\n",
    "canvas.set_yticks([0, 1])\n",
    "canvas.set_yticklabels([\"low\", \"high\"], fontweight=\"bold\")\n",
    "canvas.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "## Plotly rendering\n",
    "\n",
    "The filled polygon, logarithmic scales, axis modes, box aspect, and tick labels also work with Plotly. Secondary axes currently require Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13",
   "metadata": {},
   "outputs": [],
   "source": [
    "plotly_canvas = Canvas()\n",
    "plotly_canvas.fill(x, np.sin(x) + 2, color=\"purple\", alpha=0.25)\n",
    "plotly_canvas.loglog(x, x**2, color=\"black\")\n",
    "plotly_canvas.set_xticklabels([\"small\", \"medium\", \"large\"], color=\"darkgreen\")\n",
    "plotly_canvas.show(backend=\"plotly\")"
   ]
  }
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