csiborgtools/notebooks/knn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5a38ed25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T17:12:51.060443Z",
     "start_time": "2023-04-09T17:12:47.288759Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not found\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import joblib\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "try:\n",
    "    import csiborgtools\n",
    "except ModuleNotFoundError:\n",
    "    print(\"not found\")\n",
    "    import sys\n",
    "    sys.path.append(\"../\")\n",
    "    import csiborgtools\n",
    "import yaml\n",
    "\n",
    "\n",
    "%matplotlib notebook\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3d39187a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T19:51:15.144264Z",
     "start_time": "2023-04-09T19:51:14.672337Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('../scripts/knn_auto.yml', 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "paths = csiborgtools.read.CSiBORGPaths()\n",
    "knnreader = csiborgtools.read.kNNCDFReader()\n",
    "\n",
    "auto_folder = \"/mnt/extraspace/rstiskalek/csiborg/knn/auto/\"\n",
    "cross_folder = \"/mnt/extraspace/rstiskalek/csiborg/knn/cross/\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "202cb57b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T19:53:58.915750Z",
     "start_time": "2023-04-09T19:53:55.595006Z"
    }
   },
   "outputs": [],
   "source": [
    "rs, corr = knnreader.read(\"mass001\", cross_folder, rmin=1, rmax=50)\n",
    "rs, corr_rand = knnreader.read(\"mass001_random\", auto_folder, rmin=1, rmax=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9b3adce7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T19:55:25.114871Z",
     "start_time": "2023-04-09T19:55:24.983841Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "                'This browser does not support binary websocket messages. ' +\n",
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       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
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       "    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",
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       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
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       "        '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",
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       "\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",
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       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
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       "            // 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",
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       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
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       "            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",
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       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
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       "            return fig.mouse_event(event, name);\n",
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       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
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       "        on_mouse_event_closure('figure_enter')\n",
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       "        } 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",
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       "\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",
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       "\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",
       "    fig.rubberband_canvas.style.cursor = msg['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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "                img\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 https://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",
       " * https://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.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\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\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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",
       "    // 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": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "plt.figure()\n",
    "for k in range(64):\n",
    "    plt.plot(rs, corr[0, k, :], c=cols[0], lw=0.5)\n",
    "#     plt.plot(rs, np.abs(corr_rand[0, k, :]), c=cols[1], lw=0.5)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.axvline(2.65 / 0.705, c=\"red\", ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ec93a98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "952cc374",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e38b7cf9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d46e176f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T16:12:21.080837Z",
     "start_time": "2023-04-09T16:12:19.137559Z"
    }
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "No files found for run `mass001_spinloww`.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [57], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m rs, cdf \u001b[38;5;241m=\u001b[39m knnreader\u001b[38;5;241m.\u001b[39mread_auto(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmass001_spinloww\u001b[39m\u001b[38;5;124m\"\u001b[39m, folder, rmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, rmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m      2\u001b[0m pk \u001b[38;5;241m=\u001b[39m knnreader\u001b[38;5;241m.\u001b[39mmean_prob_k(cdf)\n",
      "File \u001b[0;32m~/csiborgtools/csiborgtools/read/summaries.py:215\u001b[0m, in \u001b[0;36mkNNCDFReader.read_auto\u001b[0;34m(self, run, folder, rmin, rmax, to_clip)\u001b[0m\n\u001b[1;32m    213\u001b[0m files \u001b[38;5;241m=\u001b[39m [f \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m glob(join(folder, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*\u001b[39m\u001b[38;5;124m\"\u001b[39m)) \u001b[38;5;28;01mif\u001b[39;00m run \u001b[38;5;129;01min\u001b[39;00m f]\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(files) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 215\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo files found for run `\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m`.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(run[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m]))\n\u001b[1;32m    216\u001b[0m kind \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcorr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcross\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m run \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcdf\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, file \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(files):\n",
      "\u001b[0;31mRuntimeError\u001b[0m: No files found for run `mass001_spinloww`."
     ]
    }
   ],
   "source": [
    "rs, cdf = knnreader.read_auto(\"mass001_spinloww\", folder, rmin=1, rmax=50)\n",
    "pk = knnreader.mean_prob_k(cdf)\n",
    "\n",
    "\n",
    "# rs, cdf = knnreader.read_auto(\"mass001_spinhigh\", folder, rmin=1, rmax=50)\n",
    "# pk_perm = knnreader.mean_prob_k(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ad36cab2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T16:09:55.420866Z",
     "start_time": "2023-04-09T16:09:55.391296Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 5663, 2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "09015847",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T16:13:34.120967Z",
     "start_time": "2023-04-09T16:13:31.837869Z"
    }
   },
   "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_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\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",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\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",
       "    fig.rubberband_canvas.style.cursor = msg['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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "                img\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 https://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",
       " * https://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.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\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\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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",
       "    // 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": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "# plt.plot(rs, np.nansum(pk, axis=0)[:, 0], c=cols[k])\n",
    "# plt.plot(rs, np.nansum(pk_perm, axis=0)[:, 0], c=cols[k], ls=\"--\")\n",
    "for k in range(1, 2):\n",
    "    plt.plot(rs, pk[k, :, 0], c=cols[k])\n",
    "#     plt.plot(rs, pk_perm[k, :, 0], c=cols[k], ls=\"--\")\n",
    "# plt.fill_between(rs, pk[k, :, 0] - pk[k, :, 1], pk[k, :, 0] + pk[k, :, 1])\n",
    "\n",
    "# plt.plot(rs, np.sum(pk, axis=0)[:, 0])\n",
    "\n",
    "# plt.xscale(\"log\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1279a8cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e0185ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df973ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T15:08:50.076207Z",
     "start_time": "2023-04-08T15:08:46.683983Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "\n",
    "k = 2\n",
    "\n",
    "rs, mu, std = mean_auto(k, \"mass_003_spinhigh\")\n",
    "rs, mu_perm, std_perm = mean_auto(k, \"mass003_spinlow\")\n",
    "z = mu / mu_perm\n",
    "deltaz = z * np.sqrt((std / mu)**2 + (std_perm / mu_perm)**2)\n",
    "plt.plot(rs, z, c=cols[0])\n",
    "plt.fill_between(rs, z - deltaz, z + deltaz, color=cols[0], alpha=0.3)\n",
    "\n",
    "\n",
    "# rs, mu, std = mean_auto(k, \"mass001_spinhigh\")\n",
    "# rs, mu_perm, std_perm = mean_auto(k, \"mass001_spinhigh_perm\")\n",
    "# z = mu / mu_perm\n",
    "# deltaz = z * np.sqrt((std / mu)**2 + (std_perm / mu_perm)**2)\n",
    "# plt.plot(rs, z, c=cols[1])\n",
    "# plt.fill_between(rs, z - deltaz, z + deltaz, color=cols[1], alpha=0.3)\n",
    "\n",
    "\n",
    "plt.axhline(1, c=\"black\", ls=\"--\")\n",
    "# plt.fill_between(rs, mu - std, mu + std, color=cols[2], alpha=0.3)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f500800",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:11:02.568517Z",
     "start_time": "2023-04-08T14:11:00.406477Z"
    }
   },
   "outputs": [],
   "source": [
    "# rs, corr = knnreader.read_auto(\"mass001_spinmedian_cross\", folder, rmin=0.5)\n",
    "# rs, corr_perm = knnreader.read_auto(\"mass001_spinmedian_cross_perm\", folder, rmin=0.5)\n",
    "\n",
    "# rs, cdf_low = knnreader.read_auto(\"mass001_spinmedian_cross_perm\", folder, rmin=0.5)\n",
    "rs, cdf_high = knnreader.read_auto(\"mass001_spinmedian_cross_perm\", folder, rmin=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6565101a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:04:17.514165Z",
     "start_time": "2023-04-08T14:04:15.555281Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84729726",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T15:04:01.025738Z",
     "start_time": "2023-04-08T15:03:56.792105Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "rs, mu, std = mean_auto(0, \"mass001_spinlow\")\n",
    "plt.plot(rs, mu, c=cols[0])\n",
    "plt.fill_between(rs, mu - std, mu + std, color=cols[0], alpha=0.5)\n",
    "\n",
    "rs, mu, std = mean_auto(0, \"mass001_spinlow_perm\")\n",
    "plt.plot(rs, mu, c=cols[1])\n",
    "plt.fill_between(rs, mu - std, mu + std, color=cols[1], alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f85f943",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:11:05.066794Z",
     "start_time": "2023-04-08T14:11:04.362662Z"
    }
   },
   "outputs": [],
   "source": [
    "prk_low = knnreader.prk(rs, cdf_low)\n",
    "prk_high = knnreader.prk(rs, cdf_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c862e8b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:11:07.074775Z",
     "start_time": "2023-04-08T14:11:05.148559Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 0\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "for i in range(101):\n",
    "    plt.plot(rs, prk_low[i, k, :], lw=0.1, c=cols[0])\n",
    "    plt.plot(rs, prk_high[i, k, :], lw=0.1, c=cols[1])\n",
    "\n",
    "    \n",
    "rs, mu, std = mean_auto(0, \"mass001\")\n",
    "plt.plot(rs, mu, c=cols[2])\n",
    "plt.fill_between(rs, mu - std, mu + std, color=cols[2], alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6682fa88",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:59:29.041395Z",
     "start_time": "2023-04-08T14:59:24.875903Z"
    }
   },
   "outputs": [],
   "source": [
    "rs, corr = knnreader.read_auto(\"mass001_spinmedian_cross\", folder, rmin=0.5)\n",
    "rs, corrperm = knnreader.read_auto(\"mass001_spinmedian_cross_perm\", folder, rmin=0.5)\n",
    "\n",
    "# rs, corr_low = knnreader.read_auto(\"mass001_spinlow_cross_perm\", folder, rmin=0.5)\n",
    "# rs, corr_high = knnreader.read_auto(\"mass001_spinhigh_cross_perm\", folder, rmin=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "298f28b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T14:59:47.556944Z",
     "start_time": "2023-04-08T14:59:47.103398Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 2\n",
    "plt.figure()\n",
    "for i in range(101):\n",
    "    plt.plot(rs, corr[i, k, :], lw=0.1, c=cols[0])\n",
    "    plt.plot(rs, corrperm[i, k, :], lw=0.1, c=cols[1])\n",
    "#     plt.plot(rs, corr[i, k, :] - corrperm[i, k, :], lw=0.1, c=cols[0])\n",
    "#     plt.plot(rs, , lw=0.1, c=cols[1])\n",
    "    \n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a28d0290",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T13:59:25.401233Z",
     "start_time": "2023-04-08T13:59:25.157683Z"
    }
   },
   "outputs": [],
   "source": [
    "k = 0\n",
    "\n",
    "plt.figure()\n",
    "for i in range(101):\n",
    "    plt.plot(rs, corr[i, k, :], c=cols[0], lw=0.1)\n",
    "    \n",
    "    \n",
    "for i in range(101):\n",
    "    plt.plot(rs, corr_perm[i, k, :], c=cols[1], lw=0.1)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e86ce611",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-08T09:30:44.330451Z",
     "start_time": "2023-04-08T09:30:44.295713Z"
    }
   },
   "outputs": [],
   "source": [
    "mean_prk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe9a0ef3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de17207d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4cbad0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe11b032",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "491be45e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f19accd3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T16:51:43.224223Z",
     "start_time": "2023-04-07T16:51:35.597574Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "rs, cdf = knnreader.read_auto(\"mass001_spinlow\", folder, rmin=0.5)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 5, :], lw=0.1, c=cols[0])\n",
    "    \n",
    "    \n",
    "rs, cdf = knnreader.read_auto(\"mass001_spinhigh\", folder, rmin=0.5)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 5, :], lw=0.1, c=cols[1])    \n",
    "    \n",
    "    \n",
    "rs, cdf = knnreader.read_auto(\"mass001_spinhigh_perm\", folder, rmin=0.5)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 5, :], lw=0.1, c=cols[2])\n",
    "\n",
    "# plt.xscale(\"log\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f735b44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a273df53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "900fd4f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01974708",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T12:20:08.521290Z",
     "start_time": "2023-04-07T12:20:05.258954Z"
    }
   },
   "outputs": [],
   "source": [
    "cat = csiborgtools.read.HaloCatalogue(7444, paths, min_mass=1e12, max_dist=155/0.705)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47c494f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f02695",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T11:51:11.124583Z",
     "start_time": "2023-04-07T11:51:11.094094Z"
    }
   },
   "outputs": [],
   "source": [
    "x = \"\"\n",
    "for key in auto_config.keys():\n",
    "    x += key + \" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62d9e837",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T11:51:12.533128Z",
     "start_time": "2023-04-07T11:51:12.499981Z"
    }
   },
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db6fc6e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T10:28:20.801576Z",
     "start_time": "2023-04-07T10:28:20.766160Z"
    }
   },
   "outputs": [],
   "source": [
    "auto_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fdcfb01",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T10:21:14.858309Z",
     "start_time": "2023-04-07T10:21:14.826448Z"
    }
   },
   "outputs": [],
   "source": [
    "auto_folder = \"/mnt/extraspace/rstiskalek/csiborg/knn/auto/\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b5f37af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T12:20:38.019809Z",
     "start_time": "2023-04-07T12:20:37.987281Z"
    }
   },
   "outputs": [],
   "source": [
    "np.log10(cat[\"totpartmass\"].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5b2df40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T11:13:18.959385Z",
     "start_time": "2023-04-07T11:13:13.072536Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "rs, cdf = knnreader.read_auto(\"004\", auto_folder)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, normalise=True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 0, :], c=cols[0], lw=0.1)\n",
    "\n",
    "\n",
    "rs, cdf = knnreader.read_auto(\"005\", auto_folder)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, normalise=True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 0, :], c=cols[1], lw=0.1)\n",
    "\n",
    "    \n",
    "rs, cdf = knnreader.read_auto(\"001\", auto_folder)\n",
    "pk = knnreader.prob_kvolume(cdf, rs, normalise=True)\n",
    "for i in range(101):\n",
    "    plt.plot(rs, pk[i, 0, :], c=cols[2], lw=0.1)\n",
    "\n",
    "# plt.xscale(\"log\")\n",
    "# plt.yscale(\"log\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85c65eef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20ecf551",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ac70147",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T09:47:03.873611Z",
     "start_time": "2023-04-07T09:47:01.794363Z"
    }
   },
   "outputs": [],
   "source": [
    "cat = csiborgtools.read.HaloCatalogue(7444, paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b77b1377",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T07:49:23.539504Z",
     "start_time": "2023-04-07T07:45:50.514216Z"
    }
   },
   "outputs": [],
   "source": [
    "from tqdm import trange\n",
    "x = np.full((len(ics), 3), np.nan)\n",
    "for i in trange(len(ics)):\n",
    "    cat = csiborgtools.read.HaloCatalogue(ics[i], paths, max_dist=155 / 0.705)\n",
    "    for j, th in enumerate([1e12, 1e13, 1e14]):\n",
    "        mask = cat[\"totpartmass\"] > th\n",
    "        x[i, j] = np.nanmedian(cat[\"lambda200c\"][mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e55d821d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T07:51:55.598449Z",
     "start_time": "2023-04-07T07:51:55.567861Z"
    }
   },
   "outputs": [],
   "source": [
    "np.mean(x[:, 2]), np.std(x[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bae7373",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-07T11:12:14.971055Z",
     "start_time": "2023-04-07T11:12:14.938117Z"
    }
   },
   "outputs": [],
   "source": [
    "cdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "127cb78e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8368d476",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.nanmedian()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b5a68c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-06T17:59:09.028319Z",
     "start_time": "2023-04-06T17:59:08.953783Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.scatter(cat[\"m200\"], cat[\"lambda200c\"], s=1)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eb1c2e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a79dde6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T13:42:26.370674Z",
     "start_time": "2023-04-05T13:42:22.862756Z"
    }
   },
   "outputs": [],
   "source": [
    "files = glob(\"/mnt/extraspace/rstiskalek/csiborg/knn/auto/*\")\n",
    "\n",
    "ks = [0, 1, 2, 3, 4, 5, 6, 7]\n",
    "rs, cdf, thresholds = knnreader.read(files, ks, rmin=0.01, rmax=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88de6882",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T13:42:26.943147Z",
     "start_time": "2023-04-05T13:42:26.372382Z"
    }
   },
   "outputs": [],
   "source": [
    "pk = knnreader.prob_kvolume(cdf, rs, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbb11ba2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T13:43:45.754506Z",
     "start_time": "2023-04-05T13:43:38.953908Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "plt.figure()\n",
    "n = 1\n",
    "for k in range(7):\n",
    "    plt.plot(rs, np.mean(pk[:, n, k, :], axis=0), c=cols[k], label=r\"$k = {}$\".format(k))\n",
    "    for i in range(101):\n",
    "        plt.plot(rs, pk[i, n, k, :], c=cols[k], lw=0.05)\n",
    "\n",
    "plt.legend(frameon=False)\n",
    "plt.xlabel(r\"$r~\\left[\\mathrm{Mpc}\\right]$\")\n",
    "plt.ylabel(r\"$P\\left(k | V = 4 \\pi r^3 / 3\\right)$\")\n",
    "# plt.savefig(\"../plots/test.png\", dpi=450)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6999c90d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20bbeb54",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T17:10:30.078450Z",
     "start_time": "2023-04-03T17:10:29.171089Z"
    }
   },
   "outputs": [],
   "source": [
    "n = 2\n",
    "k = 1\n",
    "\n",
    "x = cdf[:, n, k - 1, :] - cdf[:, n, k, :]\n",
    "\n",
    "plt.figure()\n",
    "for i in range(101):\n",
    "    plt.plot(rs, x[i, :])\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86091fcc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T17:13:07.495144Z",
     "start_time": "2023-04-03T17:13:06.635811Z"
    }
   },
   "outputs": [],
   "source": [
    "files = knnreader.cross_files(7444, \"/mnt/extraspace/rstiskalek/csiborg/knn/cross/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0917bd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T17:13:11.011391Z",
     "start_time": "2023-04-03T17:13:07.496523Z"
    }
   },
   "outputs": [],
   "source": [
    "ks = [0, 1, 2, 3, 4, 5, 6, 7]\n",
    "rs, cross, threshold = knnreader.read(files, ks, rmin=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beba86db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T17:13:11.152680Z",
     "start_time": "2023-04-03T17:13:11.013209Z"
    }
   },
   "outputs": [],
   "source": [
    "n = 0\n",
    "k = 1\n",
    "\n",
    "plt.figure()\n",
    "for i in range(100):\n",
    "    plt.plot(rs, cross[i, n, k - 1, :] - cross[i, n, k, :])\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.axvline(2.65 / 0.705)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adf96c3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-03T14:25:45.275844Z",
     "start_time": "2023-04-03T14:25:44.855207Z"
    }
   },
   "outputs": [],
   "source": [
    "\"/mnt/extraspace/hdesmond/ramses_out_7444/output_00950/clump_00950.dat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8179c3e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f803105a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55544ddd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c7add55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75a5e6f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b6d813b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19115f4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4218b673",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:07.868868Z",
     "start_time": "2023-04-01T08:27:04.088778Z"
    }
   },
   "outputs": [],
   "source": [
    "cat1 = csiborgtools.read.HaloCatalogue(7444, min_mass=1e13, max_dist=155 / 0.705)\n",
    "cat2 = csiborgtools.read.HaloCatalogue(7468, min_mass=1e13, max_dist=155 / 0.705)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ff7a1b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:07.923418Z",
     "start_time": "2023-04-01T08:27:07.870519Z"
    }
   },
   "outputs": [],
   "source": [
    "knncdf = csiborgtools.match.kNN_CDF()\n",
    "\n",
    "\n",
    "knn1 = NearestNeighbors()\n",
    "knn1.fit(cat1.positions)\n",
    "\n",
    "knn2 = NearestNeighbors()\n",
    "knn2.fit(cat2.positions)\n",
    "\n",
    "# rs, cdf = knncdf(knn, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,\n",
    "#                   nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88a31951",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:46:56.273595Z",
     "start_time": "2023-04-01T08:46:56.088408Z"
    }
   },
   "outputs": [],
   "source": [
    "!ls /mnt/extraspace/rstiskalek/csiborg/knn/cross/knncdf_7444_7468.p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c2d465",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T10:05:26.294670Z",
     "start_time": "2023-04-01T10:05:25.954223Z"
    }
   },
   "outputs": [],
   "source": [
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6083fcbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T10:05:52.758709Z",
     "start_time": "2023-04-01T10:05:52.705627Z"
    }
   },
   "outputs": [],
   "source": [
    "files = glob(\"/mnt/extraspace/rstiskalek/csiborg/knn/cross/*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d6b9d57",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T10:06:00.023427Z",
     "start_time": "2023-04-01T10:05:59.645149Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "980f74df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T10:12:49.924557Z",
     "start_time": "2023-04-01T10:12:49.714545Z"
    }
   },
   "outputs": [],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "plt.figure()\n",
    "for file in files:\n",
    "    d = joblib.load(file)\n",
    "    mask = d[\"rs\"] > 0.1\n",
    "    plt.plot(d[\"rs\"][mask], d[\"corr_0\"][0, mask], c=cols[0], lw=0.4)\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.axvline(2.65 / 0.705, lw=0.8, c=\"red\", ls=\"--\")\n",
    "# plt.yscale(\"log\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "997e8f91",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25936419",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:51:11.896378Z",
     "start_time": "2023-04-01T08:51:11.865150Z"
    }
   },
   "outputs": [],
   "source": [
    "5500 / comb(5, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "043a93ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:50:37.181588Z",
     "start_time": "2023-04-01T08:50:36.718438Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(d[\"rs\"], d[\"corr_0\"][1, :])\n",
    "plt.plot(d[\"rs\"], d[\"corr_1\"][1, :])\n",
    "plt.plot(d[\"rs\"], d[\"corr_2\"][1, :])\n",
    "\n",
    "# plt.yscale(\"log\")\n",
    "# plt.xscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "279d8e58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d08b0dc3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:19.271922Z",
     "start_time": "2023-04-01T08:27:07.925222Z"
    }
   },
   "outputs": [],
   "source": [
    "# rs, cdf = knncdf(knn1, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,\n",
    "#                  nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))\n",
    "\n",
    "rs, cdf0, cdf1, joint_cdf = knncdf.joint(knn1, knn2, nneighbours=8, Rmax=155 / 0.705,\n",
    "                                         rmin=0.01, rmax=100, nsamples=int(1e6), neval=int(1e4),\n",
    "                                         random_state=42, batch_size=int(1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0866fe23",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:19.436097Z",
     "start_time": "2023-04-01T08:27:19.397998Z"
    }
   },
   "outputs": [],
   "source": [
    "cdf0 = knncdf.clipped_cdf(cdf0)\n",
    "cdf1 = knncdf.clipped_cdf(cdf1)\n",
    "joint_cdf = knncdf.clipped_cdf(joint_cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0649b7e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:27:59.413449Z",
     "start_time": "2023-04-01T08:27:59.244281Z"
    }
   },
   "outputs": [],
   "source": [
    "corr = knncdf.joint_to_corr(cdf0, cdf1, joint_cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4d28785",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:33:12.811065Z",
     "start_time": "2023-04-01T08:33:12.386639Z"
    }
   },
   "outputs": [],
   "source": [
    "ics = [7444, 7468, 7492, 7516, 7540, 7564, 7588, 7612, 7636, 7660, 7684,\n",
    "       7708, 7732, 7756, 7780, 7804, 7828, 7852, 7876, 7900, 7924, 7948,\n",
    "       7972, 7996, 8020, 8044, 8068, 8092, 8116, 8140, 8164, 8188, 8212,\n",
    "       8236, 8260, 8284, 8308, 8332, 8356, 8380, 8404, 8428, 8452, 8476,\n",
    "       8500, 8524, 8548, 8572, 8596, 8620, 8644, 8668, 8692, 8716, 8740,\n",
    "       8764, 8788, 8812, 8836, 8860, 8884, 8908, 8932, 8956, 8980, 9004,\n",
    "       9028, 9052, 9076, 9100, 9124, 9148, 9172, 9196, 9220, 9244, 9268,\n",
    "       9292, 9316, 9340, 9364, 9388, 9412, 9436, 9460, 9484, 9508, 9532,\n",
    "       9556, 9580, 9604, 9628, 9652, 9676, 9700, 9724, 9748, 9772, 9796,\n",
    "       9820, 9844]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaac3eff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:34:00.873304Z",
     "start_time": "2023-04-01T08:34:00.842613Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.special import comb\n",
    "\n",
    "from itertools import combinations\n",
    "# for subset in itertools.combinations(stuff, L):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "042fe713",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:34:21.387439Z",
     "start_time": "2023-04-01T08:34:21.325627Z"
    }
   },
   "outputs": [],
   "source": [
    "list(combinations(ics, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "762ec153",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c43f012",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:33:18.308219Z",
     "start_time": "2023-04-01T08:33:18.275965Z"
    }
   },
   "outputs": [],
   "source": [
    "comb()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaca64f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fb9e08e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T08:28:35.326570Z",
     "start_time": "2023-04-01T08:28:35.158664Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "\n",
    "# plt.plot(rs, knncdf.peaked_cdf(cdf0[0, :]))\n",
    "# plt.plot(rs, knncdf.peaked_cdf(cdf1[0, :]))\n",
    "# plt.plot(rs, knncdf.peaked_cdf(joint_cdf[0, :]))\n",
    "for i in range(8):\n",
    "    plt.plot(rs, corr[i, :])\n",
    "\n",
    "\n",
    "# plt.yscale(\"log\")\n",
    "# plt.xscale(\"log\")\n",
    "plt.axvline(2.65 / 0.705, c=\"red\", ls=\"--\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f295a0f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d5f3d02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T07:20:34.202874Z",
     "start_time": "2023-04-01T07:20:28.353637Z"
    }
   },
   "outputs": [],
   "source": [
    "dist1, dist2 = knncdf.joint(knn1, knn2, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,\n",
    "             nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a76c8124",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T07:20:41.074933Z",
     "start_time": "2023-04-01T07:20:41.041448Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "127b91a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b9a8cf0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1825f00",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-01T06:01:29.388586Z",
     "start_time": "2023-04-01T06:01:29.321025Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(rs, knncdf.peaked_cdf(cdf[0, :]))\n",
    "\n",
    "plt.yscale(\"log\" )\n",
    "plt.xscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289549a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T22:55:20.690887Z",
     "start_time": "2023-03-31T22:55:20.656550Z"
    }
   },
   "outputs": [],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a8c5202",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T22:54:52.330633Z",
     "start_time": "2023-03-31T22:54:52.299548Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46f54897",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T22:54:25.138813Z",
     "start_time": "2023-03-31T22:54:25.105044Z"
    }
   },
   "outputs": [],
   "source": [
    "dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58806ab9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c59b3a19",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e345945c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T09:35:49.059172Z",
     "start_time": "2023-03-31T09:35:42.817291Z"
    }
   },
   "outputs": [],
   "source": [
    "m1 = (rs > 1) & (rs < 35)\n",
    "\n",
    "fig, axs = plt.subplots(ncols=3, figsize=(6.4 * 1.5, 4.8), sharey=True)\n",
    "fig.subplots_adjust(wspace=0)\n",
    "for k in range(3):\n",
    "    for n in range(len(ics)):\n",
    "        m = m1 & (cdfs[n, k, :] > 1e-3)\n",
    "        axs[k].plot(rs[m], cdfs[n, k, m], c=\"black\", lw=0.05)\n",
    "\n",
    "    axs[k].set_xscale(\"log\")\n",
    "    axs[k].set_yscale(\"log\")\n",
    "    axs[k].set_title(r\"$k = {}$\".format(k))\n",
    "    axs[k].set_xlabel(r\"$r~\\left[\\mathrm{Mpc}\\right]$\")\n",
    "\n",
    "axs[0].set_ylabel(r\"Peaked CDF\")\n",
    "\n",
    "plt.tight_layout(w_pad=0)\n",
    "fig.savefig(\"../plots/peaked_cdf.png\", dpi=450)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f8786c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-31T09:50:10.103650Z",
     "start_time": "2023-03-31T09:50:02.221741Z"
    }
   },
   "outputs": [],
   "source": [
    "m = (rs > 0.5) & (rs < 35)\n",
    "\n",
    "fig, axs = plt.subplots(ncols=3, figsize=(6.4 * 1.5, 4.8), sharey=True)\n",
    "fig.subplots_adjust(wspace=0)\n",
    "for k in range(3):\n",
    "    mu = np.nanmean(cdfs[:, k, :], axis=0)\n",
    "\n",
    "    for n in range(len(ics)):\n",
    "        axs[k].plot(rs[m], (cdfs[n, k, :] / mu)[m], c=\"black\", lw=0.1)\n",
    "\n",
    "    axs[k].set_ylim(0.5, 1.5)\n",
    "    axs[k].axhline(1, ls=\"--\", c=\"red\", zorder=0)\n",
    "    axs[k].axvline(2.65 / 0.705, ls=\"--\", c=\"red\", zorder=0)\n",
    "    axs[k].set_xscale(\"log\")\n",
    "    axs[k].set_xlabel(r\"$r~\\left[\\mathrm{Mpc}\\right]$\")\n",
    "    axs[k].set_title(r\"$k = {}$\".format(k))\n",
    "    \n",
    "axs[0].set_ylabel(r\"Relative peaked CDF\")\n",
    "plt.tight_layout(w_pad=0)\n",
    "fig.savefig(\"../plots/peaked_cdf_ratios.png\", dpi=450)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f64cec1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T15:46:31.532259Z",
     "start_time": "2023-03-30T15:46:30.977449Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "k = 2\n",
    "mu = np.nanmean(cdfs[:, k, :], axis=0)\n",
    "# plt.plot(rs, mu, c=\"black\")\n",
    "for i in range(len(ics)):\n",
    "    plt.plot(rs, cdfs[i, k, :] / mu)\n",
    "\n",
    "\n",
    "plt.ylim(0.75, 1.25)\n",
    "plt.axhline(1, ls=\"--\", c=\"black\")\n",
    "plt.xscale(\"log\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6784766",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b416efb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e650fe2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1311187d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03e49a11",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:58:29.937514Z",
     "start_time": "2023-03-30T14:58:29.530552Z"
    }
   },
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24578cba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0024bbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dc55410",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:41:24.290602Z",
     "start_time": "2023-03-30T14:41:16.204679Z"
    }
   },
   "outputs": [],
   "source": [
    "dist0, __ = knn0.kneighbors(X, 3)\n",
    "distx, __ = knnx.kneighbors(X, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11508c3c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:41:24.560538Z",
     "start_time": "2023-03-30T14:41:24.292674Z"
    }
   },
   "outputs": [],
   "source": [
    "x0, y0 = knncdf.peaked_cdf_from_samples(dist0[:, 0], 0.5, 20, neval=10000)\n",
    "xx, yx = knncdf.peaked_cdf_from_samples(distx[:, 0], 0.5, 20, neval=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "404501ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:41:24.598933Z",
     "start_time": "2023-03-30T14:41:24.562062Z"
    }
   },
   "outputs": [],
   "source": [
    "distx[:, 0].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43e08969",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T14:46:10.262865Z",
     "start_time": "2023-03-30T14:46:09.486658Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(x0, y0)\n",
    "plt.plot(xx, yx)\n",
    "\n",
    "plt.yscale(\"log\")\n",
    "plt.xscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39547a75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e160b38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T13:02:02.033125Z",
     "start_time": "2023-03-30T13:02:00.674878Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "\n",
    "for i in range(3):\n",
    "    plt.plot(*knncdf.cdf_from_samples(dist0[:, i], 1, 25))\n",
    "    plt.plot(*knncdf.cdf_from_samples(distx[:, i], 1, 25))\n",
    "\n",
    "# plt.xlim(0.5, 25)\n",
    "\n",
    "plt.yscale(\"log\")\n",
    "plt.xscale(\"log\")\n",
    "plt.xlabel(r\"$r~\\left[\\mathrm{Mpc}\\right]$\")\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bfb65d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4703d81c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T12:13:35.958444Z",
     "start_time": "2023-03-30T12:13:35.924241Z"
    }
   },
   "outputs": [],
   "source": [
    "x = dist[:, 0]\n",
    "q = np.linspace(0, 100, int(x.size / 5))\n",
    "\n",
    "p = np.percentile(x, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b054c6df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T12:16:50.052225Z",
     "start_time": "2023-03-30T12:16:50.020395Z"
    }
   },
   "outputs": [],
   "source": [
    "y = np.sort(x)\n",
    "\n",
    "yy = np.arange(y.size) / y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5445c964",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T12:16:53.599925Z",
     "start_time": "2023-03-30T12:16:53.521266Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(p, q / 100)\n",
    "\n",
    "plt.plot(y, yy)\n",
    "\n",
    "# plt.yscale(\"log\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87fe5874",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb0ad6b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T12:03:34.387625Z",
     "start_time": "2023-03-30T12:03:34.290961Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(dist[:, 0], bins=\"auto\", histtype=\"step\")\n",
    "plt.hist(dist[:, 1], bins=\"auto\", histtype=\"step\")\n",
    "plt.hist(dist[:, 2], bins=\"auto\", histtype=\"step\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2aba833",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f70f238",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03bcb191",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:38:04.906150Z",
     "start_time": "2023-03-30T11:38:04.758107Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(cat0[\"dec\"], bins=\"auto\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5ad4722",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:53:23.004853Z",
     "start_time": "2023-03-30T11:53:22.971967Z"
    }
   },
   "outputs": [],
   "source": [
    "gen = np.random.default_rng(22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "785b530a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:53:23.330397Z",
     "start_time": "2023-03-30T11:53:23.296612Z"
    }
   },
   "outputs": [],
   "source": [
    "gen.normal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3d3b5e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "464b606d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:36:13.649124Z",
     "start_time": "2023-03-30T11:36:12.995693Z"
    }
   },
   "outputs": [],
   "source": [
    "theta = np.linspace( t, np.pi, 100)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(theta, np.sin(theta))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c29049f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd2a3295",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af9abf04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:10:11.104389Z",
     "start_time": "2023-03-30T11:10:11.070499Z"
    }
   },
   "outputs": [],
   "source": [
    "X = np.array([-3.9514747, -0.6966991,  2.97158]).reshape(1, -1)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e181b3c3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:32:17.840355Z",
     "start_time": "2023-03-30T11:32:17.351883Z"
    }
   },
   "outputs": [],
   "source": [
    "dist, indxs = knn0.kneighbors(X, n_neighbors=1)\n",
    "\n",
    "dist, indxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d38fd960",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-03-30T11:10:18.182326Z",
     "start_time": "2023-03-30T11:10:18.145629Z"
    }
   },
   "outputs": [],
   "source": [
    "cat0.positions[indxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a16ddc2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbbe8fb6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "759a0149",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "312c96c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b097637b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ced23cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be26cbcc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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