homothetic-scaling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62f0bb6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "from islpy import *\n",
    "import numpy as np\n",
    "from IPython.display import Image\n",
    "from simplify.plot import plot_3d_sets\n",
    "from islpy import BasicSet, BasicMap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b3a42da",
   "metadata": {},
   "source": [
    "## Homothetic Scaling\n",
    "\n",
    "We would like to be able to detect whether two ISL sets are homothetic. In other words, we would like to say whether one set a homothetic scaling of the other. A homothetic transformation of set $A$ is an affine transformation where every point in $A$ is scaled by the **same** factor relative to some homothetic center.\n",
    "\n",
    "For example, the following image illustrates homothetic scaling relative to the origin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26e71dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s0 = BasicSet('[N]->{[i,j,k] : N=4 and 0<=i,j,k and i+j+k<=N }').apply(BasicMap('[N]->{[i,j,k]->[i+2,j+2,k+2]}'))\n",
    "s1 = BasicSet('[N]->{[i,j,k] : N=4 and 0<=i,j,k and i+j+k<=3N }').apply(BasicMap('[N]->{[i,j,k]->[i+3,j+4,k+8]}'))\n",
    "\n",
    "plot_3d_sets([s0, s1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4469c8f8",
   "metadata": {},
   "source": [
    "## Helper functions\n",
    "\n",
    "The following funtions are used here. Everything below uses the python bindings to ISL$^2$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71b8b95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi_aff_to_vec(maff, num_indices, num_params):\n",
    "    # given maff: [((A1)N + (B1)M + ... + (C1)), ((A2)N + (B2)M + ... + (C2)), ... ]\n",
    "    # return vec: [A1, B1, ..., C1, A2, B2, ..., C2, ... ]\n",
    "    vec = []\n",
    "    for i in range(num_indices):\n",
    "        aff = maff.get_aff(i)\n",
    "        vec += [aff.get_coefficient_val(dim_type.param, j) for j in range(num_params)] + [aff.get_constant_val()]\n",
    "    return vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f38efe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def subtract_maffs_i(l, r, i):\n",
    "    if l and r:\n",
    "        return l.get_aff(i) - r.get_aff(i)\n",
    "    elif l and not r:\n",
    "        return l.get_aff(i)\n",
    "    elif not l and r:\n",
    "        return r.get_aff(i).neg()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90318739",
   "metadata": {},
   "source": [
    "### vertices(bset)\n",
    "\n",
    "Given an ISL basic set (i.e., a single convex set), get the vertices in each chamber."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f68dc9d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "non-parametric example: {[i,j] : 0<=i,j<=13 }\n",
      "{ [(0), (0)] }\n",
      "{ [(0), (13)] }\n",
      "{ [(13), (0)] }\n",
      "{ [(13), (13)] }\n",
      "\n",
      "parametric example: [N] -> {[i,j] : 0<=i,j and i+j<=N }\n",
      "[N] -> { [(0), (0)] }\n",
      "[N] -> { [(0), (N)] }\n",
      "[N] -> { [(N), (0)] }\n"
     ]
    }
   ],
   "source": [
    "def vertices(bset, num_indices, num_params):\n",
    "    chambers = []\n",
    "    bset.polyhedral_hull().compute_vertices().foreach_cell(chambers.append)\n",
    "    vertices = []\n",
    "    for chamber in chambers:\n",
    "        vertices.append([])\n",
    "        chamber.foreach_vertex(vertices[-1].append)\n",
    "        vertices[-1] = [v.get_expr() for v in vertices[-1]]\n",
    "        # vertices are not guaranteed to be in same relative order\n",
    "        # sort them in lexicographic order\n",
    "        vertices[-1].sort(key=lambda v: multi_aff_to_vec(v, num_indices, num_params))\n",
    "    return vertices\n",
    "\n",
    "\n",
    "s = '{[i,j] : 0<=i,j<=13 }'\n",
    "print('non-parametric example:', s)\n",
    "for v in vertices(BasicSet(s), 2, 0)[0]:\n",
    "    print(v)\n",
    "print()\n",
    "s = '[N] -> {[i,j] : 0<=i,j and i+j<=N }'\n",
    "print('parametric example:', s)\n",
    "for v in vertices(BasicSet(s), 2, 1)[0]:\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0f05144",
   "metadata": {},
   "source": [
    "### build_map(set_space, multi_aff_0, multi_aff_1, nparam, n_out)\n",
    "\n",
    "Given two vertices (as multi-affine expressions), compute the map that represents the translation from one vertex to the other.\n",
    "\n",
    "In other words, given 2D expressions like this:\n",
    "* `multi_aff_0` = `(0, 0)`\n",
    "* `multi_aff_1` = `(A, B)`\n",
    "\n",
    "Produce the map:\n",
    "* `{[i,j]->[i-A,j-B]}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4b8639d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[N] -> { [(0), (0)] }\n",
      "[N] -> { [(N), (2N)] }\n",
      "map:\n",
      "[N] -> { [i0, i1] -> [o0, o1] : o0 = -N + i0 and o1 = -2N + i1 }\n"
     ]
    }
   ],
   "source": [
    "def build_map(set_space, multi_aff_0, multi_aff_1, nparam, n_out, direction=0):\n",
    "    n_in = n_out\n",
    "    space = Space.alloc(set_space.get_ctx(), nparam, n_in, n_out)\n",
    "    for name, tuple in set_space.get_var_dict().items():\n",
    "        type, pos = tuple\n",
    "        space = space.set_dim_name(type, pos, name)\n",
    "        if type == dim_type.out:\n",
    "            space = space.set_dim_name(dim_type.in_, pos, name)\n",
    "\n",
    "    m = BasicMap.universe(space)\n",
    "\n",
    "    for i in range(n_in):\n",
    "        if direction == 0:\n",
    "            diff = subtract_maffs_i(multi_aff_1, multi_aff_0, i)\n",
    "        else:\n",
    "            diff = subtract_maffs_i(multi_aff_0, multi_aff_1, i)\n",
    "\n",
    "        # can only set integer values so scale all dims by lcm denominators\n",
    "        if nparam > 0:\n",
    "            max_den = np.lcm.reduce([diff.get_coefficient_val(dim_type.param, j).get_den_val().to_python() for j in range(nparam)])\n",
    "        else:\n",
    "            max_den = diff.get_constant_val().get_den_val()\n",
    "        constraint = Constraint.alloc_equality(space)\n",
    "        constraint = constraint.set_coefficient_val(dim_type.out, i, max_den)\n",
    "        constraint = constraint.set_coefficient_val(dim_type.in_, i, max_den * -1)\n",
    "        for j in range(nparam):\n",
    "            val = diff.get_coefficient_val(dim_type.param, j)\n",
    "            constraint = constraint.set_coefficient_val(dim_type.param, j, val * max_den)\n",
    "        constraint = constraint.set_constant_val(diff.get_constant_val() * max_den)\n",
    "        m = m.add_constraint(constraint)\n",
    "\n",
    "    return m\n",
    "\n",
    "\n",
    "chamber = 0\n",
    "\n",
    "bset_0 = BasicSet('[N] -> {[0,0]}')\n",
    "bset_1 = BasicSet('[N] -> {[N,2N]}')\n",
    "\n",
    "vertex_0 = vertices(bset_0, 2, 1)[chamber][0]\n",
    "vertex_1 = vertices(bset_1, 2, 1)[chamber][0]\n",
    "\n",
    "# ISL parametric vertices are multiple affine expressions\n",
    "\n",
    "print(vertex_0)\n",
    "print(vertex_1)\n",
    "\n",
    "print('map:')\n",
    "print(build_map(bset_0.get_space(), vertex_0, vertex_1, 1, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa20f63a",
   "metadata": {},
   "source": [
    "## make_integer_valued(vec)\n",
    "\n",
    "To avoid floating point representations, we can scale any vector by the least common multiple (LCM) of denominators present among all elements. In other words, given:\n",
    "\n",
    "vec = `[1/3, 0, 2/5, 1]`\n",
    "\n",
    "identify that 15 is the LCM and produce:\n",
    "\n",
    "`[5, 0, 6, 15]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58778318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 0, 6, 15]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_integer_valued(vec):\n",
    "    max_den = np.lcm.reduce([v.get_den_val().to_python() for v in vec])\n",
    "    return [(v * max_den).to_python() for v in vec]\n",
    "\n",
    "make_integer_valued([Val('1/3'), Val('0'), Val('2/5'), Val('1')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "84da53dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def homothetic(s0, s1, chamber=0, verbose=None):\n",
    "    def pprint(*args, **kwargs):\n",
    "        if verbose:\n",
    "            print(*args, **kwargs)\n",
    "    pprint('s0:', s0)\n",
    "    pprint('s1:', s1)\n",
    "    pprint()\n",
    "    return bsets_are_homothetic(BasicSet(s0), BasicSet(s1), chamber=chamber, verbose=verbose)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c08f81d5",
   "metadata": {},
   "source": [
    "## bsets_are_homothetic(bset_0, bset_1, ...)\n",
    "\n",
    "This is the main algorithm. Reports if the set `s1` is a homothetic scaling of set `s0`. ISL's parametric vertex computation reports vertices in lexicographically descreasing order. This means that we can iterate through vertices in lexicographic order.\n",
    "\n",
    "This only assumes that the two sets are not rotated in any way. The algorithm proceeds as follows:\n",
    "\n",
    "1. compute the parametric vertices for each set\n",
    "1. get the lexicographic minimum vertices for each set\n",
    "1. compute the map $M$ from `s1`'s lexmin vertex to `s0`'s lexmin vertex\n",
    "1. apply map $M$ to set `s1` to translate `s1` such that its lexmin vertex lands on `s0`'s lexmin vertex\n",
    "1. treat this (now common) lexmin vertex as the homothetic center\n",
    "1. for each pair of vertices between `s1` and `s0`, test if the amount by which the magnitudes of vertices differ is the same for all pairs.\n",
    "1. if this amount is the same for all vertex pairs, then conlude that `s1` is a homothetic scaling of `s0`, else report otherwise.\n",
    "\n",
    "This fails if the number of vertices differs between the two, as it should."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a80cb39",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bsets_are_homothetic(bset0, bset1, chamber=0, verbose=None):\n",
    "    def pprint(*args, **kwargs):\n",
    "        if verbose:\n",
    "            print(*args, **kwargs)\n",
    "\n",
    "    space = bset0.get_space()\n",
    "    num_indices = len([k for k, v in space.get_var_dict().items() if v[0] == dim_type.out])\n",
    "    num_params = len([k for k, v in space.get_var_dict().items() if v[0] == dim_type.param])\n",
    "\n",
    "    chambers_0 = vertices(bset0, num_indices, num_params)\n",
    "    chambers_1 = vertices(bset1, num_indices, num_params)\n",
    "\n",
    "    vertices_0 = chambers_0[chamber]\n",
    "    vertices_1 = chambers_1[chamber]\n",
    "\n",
    "    pprint('vertices_0:')\n",
    "    [pprint(v) for v in vertices_0]\n",
    "    pprint('vertices_1:')\n",
    "    [pprint(v) for v in vertices_1]\n",
    "    pprint()\n",
    "\n",
    "    if len(vertices_0) != len(vertices_1):\n",
    "        return False\n",
    "\n",
    "    # vertices are sorted in increasing lexicographic order\n",
    "    # translate bset1 so that it's min vertex lands on bset0's min vertex\n",
    "    # the min vertex is then treated as the homothetic center\n",
    "    lexmin_v0 = vertices_0[0]\n",
    "    lexmin_v1 = vertices_1[0]\n",
    "    m = build_map(space, lexmin_v0, lexmin_v1, num_params, num_indices)\n",
    "    pprint('map:', m)\n",
    "    pprint()\n",
    "    translated_bset1 = bset1.apply(m)\n",
    "    translated_chambers_1 = vertices(translated_bset1, num_indices, num_params)\n",
    "\n",
    "    vertices_1 = translated_chambers_1[chamber]\n",
    "\n",
    "    # now the min vertices are the same, take this as the homothetic center\n",
    "    homothetic_center = lexmin_v0\n",
    "    vertices_0 = vertices_0[1:]\n",
    "    vertices_1 = vertices_1[1:]\n",
    "\n",
    "    pprint('homothetic_center: ', homothetic_center)\n",
    "    pprint()\n",
    "    pprint('vertices_0:')\n",
    "    [pprint(v) for v in vertices_0]\n",
    "    pprint('translated_vertices_1:')\n",
    "    [pprint(v) for v in vertices_1]\n",
    "    pprint()\n",
    "\n",
    "    # hstack vertex vectors relative to homothetic center\n",
    "    def hstack_vertices(vertices):\n",
    "        all_vecs = []\n",
    "        for vertex in vertices:\n",
    "            for i in range(num_indices):\n",
    "                aff = vertex.get_aff(i) - homothetic_center.get_aff(i)\n",
    "                all_vecs += [aff.get_coefficient_val(dim_type.param, j) for j in range(num_params)] + [aff.get_constant_val()]\n",
    "        return all_vecs\n",
    "\n",
    "    all_vecs_0 = make_integer_valued(hstack_vertices(vertices_0))\n",
    "    all_vecs_1 = make_integer_valued(hstack_vertices(vertices_1))\n",
    "\n",
    "    # to be homothetic, vec0 and vec must be linearly dependent\n",
    "    mat = np.vstack((all_vecs_0, all_vecs_1))\n",
    "    is_homothetic = np.linalg.matrix_rank(mat) == 1\n",
    "    return is_homothetic\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435c6f48",
   "metadata": {},
   "source": [
    "## Example 1\n",
    "\n",
    "This is a homothetic scaling, and reports True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2597d248",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s0: [N,M]->{[i,j] : 0<=i<=N and 0<=j<=M and 3j>=i }\n",
      "s1: [N,M]->{[i,j] : 0<=i<=2N and 0<=j<=2M and 3j>=i }\n",
      "\n",
      "vertices_0:\n",
      "[N, M] -> { [(0), (0)] }\n",
      "[N, M] -> { [(0), (M)] }\n",
      "[N, M] -> { [(N), (M)] }\n",
      "[N, M] -> { [(N), (N)/3] }\n",
      "vertices_1:\n",
      "[N, M] -> { [(0), (0)] }\n",
      "[N, M] -> { [(0), (2M)] }\n",
      "[N, M] -> { [(2N), (2M)] }\n",
      "[N, M] -> { [(2N), (2N)/3] }\n",
      "\n",
      "map: [N, M] -> { [i, j] -> [i', j'] : i' = i and j' = j }\n",
      "\n",
      "homothetic_center:  [N, M] -> { [(0), (0)] }\n",
      "\n",
      "vertices_0:\n",
      "[N, M] -> { [(0), (M)] }\n",
      "[N, M] -> { [(N), (M)] }\n",
      "[N, M] -> { [(N), (N)/3] }\n",
      "translated_vertices_1:\n",
      "[N, M] -> { [(0), (2M)] }\n",
      "[N, M] -> { [(2N), (2M)] }\n",
      "[N, M] -> { [(2N), (2N)/3] }\n",
      "\n",
      "homothetic(s0, s1):  True\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('homothetic(s0, s1): ', homothetic(\n",
    "    '[N,M]->{[i,j] : 0<=i<=N and 0<=j<=M and 3j>=i }',\n",
    "    '[N,M]->{[i,j] : 0<=i<=2N and 0<=j<=2M and 3j>=i }',\n",
    "    verbose = True\n",
    "), end='\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aac6c48d",
   "metadata": {},
   "source": [
    "## Example 2\n",
    "\n",
    "This is identical to example 1, except the bottom boundary is shifted up by 1. This is not homothetic anymore, and reports False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "409e8e43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s0: [N,M]->{[i,j] : 0<=i<=N and 0<=j<=M and 3j>=i+3 }\n",
      "s1: [N,M]->{[i,j] : 0<=i<=2N and 0<=j<=2M and 3j>=i+3 }\n",
      "\n",
      "vertices_0:\n",
      "[N, M] -> { [(0), (1)] }\n",
      "[N, M] -> { [(0), (M)] }\n",
      "[N, M] -> { [(N), (M)] }\n",
      "[N, M] -> { [(N), (3 + N)/3] }\n",
      "vertices_1:\n",
      "[N, M] -> { [(0), (1)] }\n",
      "[N, M] -> { [(0), (2M)] }\n",
      "[N, M] -> { [(2N), (2M)] }\n",
      "[N, M] -> { [(2N), (3 + 2N)/3] }\n",
      "\n",
      "map: [N, M] -> { [i, j] -> [i', j'] : i' = i and j' = j }\n",
      "\n",
      "homothetic_center:  [N, M] -> { [(0), (1)] }\n",
      "\n",
      "vertices_0:\n",
      "[N, M] -> { [(0), (M)] }\n",
      "[N, M] -> { [(N), (M)] }\n",
      "[N, M] -> { [(N), (3 + N)/3] }\n",
      "translated_vertices_1:\n",
      "[N, M] -> { [(0), (2M)] }\n",
      "[N, M] -> { [(2N), (2M)] }\n",
      "[N, M] -> { [(2N), (3 + 2N)/3] }\n",
      "\n",
      "homothetic(s0, s1):  False\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('homothetic(s0, s1): ', homothetic(\n",
    "    '[N,M]->{[i,j] : 0<=i<=N and 0<=j<=M and 3j>=i+3 }',\n",
    "    '[N,M]->{[i,j] : 0<=i<=2N and 0<=j<=2M and 3j>=i+3 }',\n",
    "    verbose=True\n",
    "), end='\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0583e340",
   "metadata": {},
   "source": [
    "## Example 3\n",
    "\n",
    "This is identical to example 1, except **both** the bottom and top boundaries are shifted by 1. This is now homothetic and reports True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b1bc179",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s0: [N,M]->{[i,j] : 0<=i<=N and 0<=j<=M and 3j>=i+3 }\n",
      "s1: [N,M]->{[i,j] : 0<=i<=2N and 0<=j<=2M-1 and 3j>=i+3 }\n",
      "\n",
      "vertices_0:\n",
      "[N, M] -> { [(0), (1)] }\n",
      "[N, M] -> { [(0), (M)] }\n",
      "[N, M] -> { [(N), (M)] }\n",
      "[N, M] -> { [(N), (3 + N)/3] }\n",
      "vertices_1:\n",
      "[N, M] -> { [(0), (1)] }\n",
      "[N, M] -> { [(0), (-1 + 2M)] }\n",
      "[N, M] -> { [(2N), (-1 + 2M)] }\n",
      "[N, M] -> { [(2N), (3 + 2N)/3] }\n",
      "\n",
      "map: [N, M] -> { [i, j] -> [i', j'] : i' = i and j' = j }\n",
      "\n",
      "homothetic_center:  [N, M] -> { [(0), (1)] }\n",
      "\n",
      "vertices_0:\n",
      "[N, M] -> { [(0), (M)] }\n",
      "[N, M] -> { [(N), (M)] }\n",
      "[N, M] -> { [(N), (3 + N)/3] }\n",
      "translated_vertices_1:\n",
      "[N, M] -> { [(0), (-1 + 2M)] }\n",
      "[N, M] -> { [(2N), (-1 + 2M)] }\n",
      "[N, M] -> { [(2N), (3 + 2N)/3] }\n",
      "\n",
      "homothetic(s0, s1):  True\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('homothetic(s0, s1): ', homothetic(\n",
    "    '[N,M]->{[i,j] : 0<=i<=N and 0<=j<=M and 3j>=i+3 }',\n",
    "    '[N,M]->{[i,j] : 0<=i<=2N and 0<=j<=2M-1 and 3j>=i+3 }',\n",
    "    verbose = True\n",
    "), end='\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c71369",
   "metadata": {},
   "source": [
    "## Example 4\n",
    "\n",
    "A 3D input. These are homothetic, and it reports True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70dd3dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s0: [N]->{[i,j,k] : 0<=i,j,k and i+j+k<=N }\n",
      "s1: [N]->{[i,j,k] : 0<=i,j,k and i+j+k<=13N }\n",
      "\n",
      "vertices_0:\n",
      "[N] -> { [(0), (0), (0)] }\n",
      "[N] -> { [(0), (0), (N)] }\n",
      "[N] -> { [(0), (N), (0)] }\n",
      "[N] -> { [(N), (0), (0)] }\n",
      "vertices_1:\n",
      "[N] -> { [(0), (0), (0)] }\n",
      "[N] -> { [(0), (0), (13N)] }\n",
      "[N] -> { [(0), (13N), (0)] }\n",
      "[N] -> { [(13N), (0), (0)] }\n",
      "\n",
      "map: [N] -> { [i, j, k] -> [i', j', k'] : i' = i and j' = j and k' = k }\n",
      "\n",
      "homothetic_center:  [N] -> { [(0), (0), (0)] }\n",
      "\n",
      "vertices_0:\n",
      "[N] -> { [(0), (0), (N)] }\n",
      "[N] -> { [(0), (N), (0)] }\n",
      "[N] -> { [(N), (0), (0)] }\n",
      "translated_vertices_1:\n",
      "[N] -> { [(0), (0), (13N)] }\n",
      "[N] -> { [(0), (13N), (0)] }\n",
      "[N] -> { [(13N), (0), (0)] }\n",
      "\n",
      "homothetic(s0, s1):  True\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('homothetic(s0, s1): ', homothetic(\n",
    "    '[N]->{[i,j,k] : 0<=i,j,k and i+j+k<=N }',\n",
    "    '[N]->{[i,j,k] : 0<=i,j,k and i+j+k<=13N }',\n",
    "    verbose=True\n",
    "), end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4ccc7ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# negative cases\n",
    "assert not homothetic('{[i,j] : 0<=i,j<=10  }',\n",
    "                      '{[i,j] : 0<=i<j<=200 }')\n",
    "assert not homothetic('{[i,j] : 17<=i<=217 and 15<=j<=155 }',\n",
    "                      '{[i,j] : 0<=i<=10 and 0<=j<=8 }')\n",
    "assert not homothetic('[N,M]->{[i,j] : 0<=i<=N and 0<=j<=M }',\n",
    "                      '[N,M]->{[i,j] : 0<=i<=2N and 0<=j<=4M }')\n",
    "assert not homothetic('[N,M]->{[i,j] : 0<=i<=N and 0<=j<=M and 3j>=i+3 }',\n",
    "                      '[N,M]->{[i,j] : 0<=i<=2N and 0<=j<=2M and 3j>=i+3 }')\n",
    "assert not homothetic('[N]->{[i,j] : 0<=i,j and 3i-3N<=j<=i and 0<N }',\n",
    "                      '[N]->{[i,j] : 0<=i,j and 3i-3N<=j<=i and i<=N }')\n",
    "# positive cases\n",
    "assert homothetic('{[i,j] : 0<=i<=10 and 0<=j<=7 }',\n",
    "                  '{[i,j] : 0<=i<=200 and 0<=j<=140 }')\n",
    "assert homothetic('{[i,j] : 0<=i<=10 and 0<=j<=7 }',\n",
    "                  '{[i,j] : 17<=i<=217 and 15<=j<=155 }')\n",
    "assert homothetic('{[i,j] : 17<=i<=217 and 15<=j<=155 }',\n",
    "                  '{[i,j] : 0<=i<=10 and 0<=j<=7 }')\n",
    "assert homothetic('{[i,j,k,l] : 0<=i<=10 and 0<=j<=7 and -4<=k,l<=5 }',\n",
    "                  '{[i,j,k,l] : 0<=i<=200 and 0<=j<=140 and -80<=k,l<=100 }')\n",
    "assert homothetic('[N,M]->{[i,j] : 0<=i<=N and 0<=j<=M and 3j>=i }',\n",
    "                  '[N,M]->{[i,j] : 0<=i<=2N and 0<=j<=2M and 3j>=i }')\n",
    "assert homothetic('[N,M]->{[i,j] : 0<=i<=N and 0<=j<=M and 3j>=i+3 }',\n",
    "                  '[N,M]->{[i,j] : 0<=i<=2N and 0<=j<=2M-1 and 3j>=i+3 }')\n",
    "assert homothetic('[N]->{[i,j,k] : 0<=i,j,k and i+j+k<=N }',\n",
    "                  '[N]->{[i,j,k] : 0<=i,j,k and i+j+k<=13N }')\n",
    "assert homothetic('[N]->{[i,j] : 0<=i,j and 3i-3N<=j<=i and 0<N }',\n",
    "                  '[N]->{[i,j] : 0<=i,j and 3i-3N<=j<=i and 0<N<=j }')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcccf4cb",
   "metadata": {},
   "source": [
    "## Refs\n",
    "\n",
    "1. [https://documen.tician.de/islpy/](https://documen.tician.de/islpy/)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}