csiborgtools/notebooks/knn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5a38ed25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:25:46.519408Z",
     "start_time": "2023-04-12T14:25:03.003304Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import joblib\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import yaml\n",
    "import scienceplots\n",
    "\n",
    "sys.path.append(\"../\")\n",
    "import csiborgtools\n",
    "\n",
    "%matplotlib widget \n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d39187a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:25:46.554959Z",
     "start_time": "2023-04-12T14:25:46.521474Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('../scripts/knn_auto.yml', 'r') as file:\n",
    "    config = yaml.safe_load(file)\n",
    "\n",
    "paths = csiborgtools.read.CSiBORGPaths(**csiborgtools.paths_glamdring)\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": 28,
   "id": "430044b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T22:38:52.737953Z",
     "start_time": "2023-04-09T22:38:51.453058Z"
    }
   },
   "outputs": [],
   "source": [
    "rp, wp = reader.read(\"mass002_spinlow\", folder)\n",
    "wp = reader.mean_wp(wp)\n",
    "\n",
    "rp2, wp2 = reader.read(\"mass002_spinhigh\", folder)\n",
    "wp2 = reader.mean_wp(wp2)\n",
    "\n",
    "rp3, wp3 = reader.read(\"mass002_spinmedian_perm\", folder)\n",
    "wp3 = reader.mean_wp(wp3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c05b4db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "connect ECONNRFUSED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9a07acc5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-09T22:40:07.043102Z",
     "start_time": "2023-04-09T22:40:06.448027Z"
    }
   },
   "outputs": [
    {
     "data": {
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' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.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  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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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.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\nmpl.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\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.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\nmpl.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\nmpl.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\nvar _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\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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\nmpl.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\nmpl.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\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
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       "                </div>\n",
       "                <img 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' width=800.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
    "\n",
    "plt.figure()\n",
    "# for i in range(4):\n",
    "plt.plot(rp, wp[:, 0], c=cols[0], label=\"Below median spin\")\n",
    "plt.fill_between(rp, wp[:, 0] - wp[:, 1], wp[:, 0] + wp[:, 1], color=cols[0], alpha=0.3)\n",
    "\n",
    "plt.plot(rp2, wp2[:, 0], c=cols[1], label=\"Above median spin\")\n",
    "plt.fill_between(rp2, wp2[:, 0] - wp2[:, 1], wp2[:, 0] + wp2[:, 1], color=cols[1], alpha=0.3)\n",
    "\n",
    "plt.plot(rp3, wp3[:, 0], c=cols[2], label=\"Randomised spin\")\n",
    "plt.fill_between(rp3, wp3[:, 0] - wp3[:, 1], wp3[:, 0] + wp3[:, 1], color=cols[2], alpha=0.3)\n",
    "\n",
    "plt.legend()\n",
    "plt.xscale(\"log\")\n",
    "plt.xlabel(r\"$r~[\\mathrm{Mpc}]$\")\n",
    "plt.ylabel(r\"$\\xi(r)$\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"../plots/tpcf_spin.png\", dpi=450)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d39187a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:25:46.878788Z",
     "start_time": "2023-04-12T14:25:46.556378Z"
    }
   },
   "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": 37,
   "id": "d46e176f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:51:22.432093Z",
     "start_time": "2023-04-12T14:49:45.843753Z"
    }
   },
   "outputs": [],
   "source": [
    "rs, cdf = knnreader.read(\"mass001_spinlow\", auto_folder, rmin=0.5, rmax=50)\n",
    "pk = knnreader.mean_prob_k(cdf)\n",
    "\n",
    "\n",
    "rs, cdf = knnreader.read(\"mass001_spinhigh\", auto_folder, rmin=0.5, rmax=50)\n",
    "pk2 = knnreader.mean_prob_k(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "09015847",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:51:47.023893Z",
     "start_time": "2023-04-12T14:51:46.404156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.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\nmpl.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\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.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\nmpl.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\nmpl.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\nvar _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\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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\nmpl.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 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setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.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\nmpl.figure.prototype._key_event_extra = function (event, _name) 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= 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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
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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.title(r\"Solid: high $\\lambda$, dashed: low $\\lambda$\")\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, 5):\n",
    "    plt.plot(rs, pk[k, :, 0], c=cols[k], label=r\"$k = {}$\".format(k))\n",
    "    plt.plot(rs, pk2[k, :, 0], c=cols[k], ls=\"--\")\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.ylabel(r\"$P(k | r)$\")\n",
    "plt.xlabel(r\"$r~[\\mathrm{Mpc}]$\")\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig(\"../plots/autoknn.png\", dpi=450)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1279a8cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:39:14.994393Z",
     "start_time": "2023-04-12T14:38:38.593354Z"
    }
   },
   "outputs": [],
   "source": [
    "rs, cross = knnreader.read(\"mass001\", cross_folder, rmin=1, rmax=50)\n",
    "# pk = knnreader.mean_prob_k(cdf)\n",
    "\n",
    "rs, cdf = knnreader.read(\"mass001_random\", auto_folder, rmin=1, rmax=50)\n",
    "pk = knnreader.mean_prob_k(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9e0185ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-12T14:48:35.806517Z",
     "start_time": "2023-04-12T14:48:35.106183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute('tabindex', '0');\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            'z-index: 2;'\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(\n        'style',\n        'box-sizing: content-box;' +\n            'pointer-events: none;' +\n            'position: relative;' +\n            'z-index: 0;'\n    );\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;' +\n            'left: 0;' +\n            'pointer-events: none;' +\n            'position: absolute;' +\n            'top: 0;' +\n            '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            /* This rescales the canvas back to display pixels, so that it\n             * appears correct on HiDPI screens. */\n            canvas.style.width = width + 'px';\n            canvas.style.height = height + 'px';\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        /* User Agent sniffing is bad, but WebKit is busted:\n         * https://bugs.webkit.org/show_bug.cgi?id=144526\n         * https://bugs.webkit.org/show_bug.cgi?id=181818\n         * The worst that happens here is that they get an extra browser\n         * selection when dragging, if this check fails to catch them.\n         */\n        var UA = navigator.userAgent;\n        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n        if(isWebKit) {\n            return function (event) {\n                /* This prevents the web browser from automatically changing to\n                 * the text insertion cursor when the button is pressed. We\n                 * want to control all of the cursor setting manually through\n                 * the 'cursor' event from matplotlib */\n                event.preventDefault()\n                return fig.mouse_event(event, name);\n            };\n        } else {\n            return function (event) {\n                return fig.mouse_event(event, name);\n            };\n        }\n    }\n\n    canvas_div.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    canvas_div.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    canvas_div.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    canvas_div.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    canvas_div.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    canvas_div.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    canvas_div.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.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\nmpl.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\nmpl.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.\nmpl.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\nfunction getModifiers(event) {\n    var mods = [];\n    if (event.ctrlKey) {\n        mods.push('ctrl');\n    }\n    if (event.altKey) {\n        mods.push('alt');\n    }\n    if (event.shiftKey) {\n        mods.push('shift');\n    }\n    if (event.metaKey) {\n        mods.push('meta');\n    }\n    return mods;\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 */\nfunction 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\nmpl.figure.prototype.mouse_event = function (event, name) {\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    // from https://stackoverflow.com/q/1114465\n    var boundingRect = this.canvas.getBoundingClientRect();\n    var x = (event.clientX - boundingRect.left) * this.ratio;\n    var y = (event.clientY - boundingRect.top) * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        modifiers: getModifiers(event),\n        guiEvent: simpleKeys(event),\n    });\n\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.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\nmpl.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\nmpl.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\nvar _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\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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\nmpl.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\nmpl.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\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.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\nmpl.figure.prototype._key_event_extra = function (event, _name) 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= 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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
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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",
    "plt.figure()\n",
    "for i in range(1, 6):\n",
    "    plt.plot(rs, cross[0, i, :], c=cols[i], label=r\"k = {}\".format(i))\n",
    "    \n",
    "    plt.plot(rs, pk[i, :, 0], c=cols[i], ls=\"--\")\n",
    "\n",
    "plt.axvline(2.65 / 0.705, c=\"red\", ls=\"--\")\n",
    "plt.xlabel(r\"$r~[\\mathrm{Mpc}]$\")\n",
    "plt.ylabel(r\"Cross-correlation\")\n",
    "plt.xscale(\"log\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"../plots/knncross.png\", dpi=450)\n",
    "plt.show()"
   ]
  },
  {
   "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.ClumpsCatalogue(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.ClumpsCatalogue(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.ClumpsCatalogue(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.ClumpsCatalogue(7444, min_mass=1e13, max_dist=155 / 0.705)\n",
    "cat2 = csiborgtools.read.ClumpsCatalogue(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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