{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec2b290e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "petitRADTRANS is not installed on this system\n",
      "petitRADTRANS is not installed on this system\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# from multiprocessing import Pool\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import starships.planet_obs as pl_obs\n",
    "from astropy import units as u\n",
    "from starships import correlation as corr\n",
    "from starships.orbite import rv_theo_t\n",
    "from starships.planet_obs import Observations\n",
    "\n",
    "from logl_analysis import get_logl\n",
    "\n",
    "# seed the random number generator\n",
    "rstate = np.random.default_rng(736109)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "014acfab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "retrieval_input_3-pc_mask_wings90_tr1_data_info.npz\r\n",
      "retrieval_input_3-pc_mask_wings90_tr1_data_trs_0.npz\r\n"
     ]
    }
   ],
   "source": [
    "!ls ~/scratch/DataAnalysis/SPIRou/Reductions/WASP-127b_genest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f338a41a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use scratch if available, use home if not.\n",
    "try:\n",
    "    base_dir = Path(os.environ['SCRATCH'])\n",
    "except KeyError:\n",
    "    base_dir = Path.home()\n",
    "\n",
    "# Where the reduced data is saved\n",
    "high_res_path = base_dir / Path(f'DataAnalysis/SPIRou/Reductions/WASP-127b_genest')\n",
    "\n",
    "# The stem of the files where the infos are saved (retrieval ready files)\n",
    "high_res_file_stem = f'retrieval_input_3-pc_mask_wings90_tr1'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a95baf2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_model_file = Path('.')\n",
    "specfile = Path('WASP-127b_best_fit_H2O_no_convolution.npz')\n",
    "\n",
    "\n",
    "# Read the model file.\n",
    "model_file = np.load(path_model_file / specfile)\n",
    "wv_high, model_high = model_file['wl'], model_file['dppm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e30f6fb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03241dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15037f57bdc0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QUick check\n",
    "plt.plot(wv_high[10000:10100], model_high[10000:10100], \".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65c3f81c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting WASP-127 b from ExoFile\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/adb/.venvs/starships_env_noPRT/lib/python3.9/site-packages/exofile/archive.py:351: GetLocalFileWarning: DID NOT READ CUSTOM FILE. FileNotFoundError has occur when trying to query/read custom file.\n",
      "  warn(GetLocalFileWarning(file=\"custom file\", err=e))\n"
     ]
    }
   ],
   "source": [
    "# %%\n",
    "pl_name = 'WASP-127 b'\n",
    "\n",
    "# --- Data parameters\n",
    "pl_kwargs = {}\n",
    "obs = Observations(name=pl_name, pl_kwargs=pl_kwargs)\n",
    "planet = obs.planet\n",
    "Kp_scale = (planet.M_pl / planet.M_star).decompose().value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9a8979bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading: /scratch/adb/DataAnalysis/SPIRou/Reductions/WASP-127b_genest/retrieval_input_3-pc_mask_wings90_tr1_data_trs_0.npz\n"
     ]
    }
   ],
   "source": [
    "kind_trans = 'transmission'\n",
    "\n",
    "# - Which sequences are taken (always take only 1)\n",
    "do_tr = [1]\n",
    "\n",
    "# - Selecting bad exposures if wanted/needed\n",
    "bad_indexs = None\n",
    "\n",
    "## --- Additionnal global variables\n",
    "inj_alpha = 'ones'\n",
    "idx_orders = slice(None)\n",
    "nolog = True\n",
    "\n",
    "# Choose over which axis the logl is summed.\n",
    "# -1 (or equivalently 2) should always be present (sum over spectral axis)\n",
    "# It is possible to sum over multiple axis, like orders and spectra\n",
    "# with (-2, -1) or equivalently, (1, 2).\n",
    "axis_sum = -1  # axis along which the logl is summed.\n",
    "\n",
    "# -----------------------------------------------------------\n",
    "# LOAD HIGHRES DATA\n",
    "data_info, data_trs = pl_obs.load_sequences(high_res_file_stem, do_tr, path=high_res_path)\n",
    "data_info['bad_indexs'] = bad_indexs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2032f03f",
   "metadata": {},
   "outputs": [],
   "source": [
    "(in_transit, ) = np.nonzero(data_info['trall_alpha_frac'] > 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a85eebc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-compute all values for the logl that are independent of the model, for all orders.\n",
    "uncert_sum = np.sum(np.ma.log(data_trs['0']['noise'][:, idx_orders]), axis=axis_sum)\n",
    "s2f= np.sum(data_trs['0']['flux'][:, idx_orders]**2, axis=axis_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9bb1325e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save (big) arrays that will be shared between processes.\n",
    "# NOTE: Their values must not be changed by the processes.\n",
    "out = [(obj, key) for key, obj in data_trs['0'].items()\n",
    "       if isinstance(obj, np.ndarray)]\n",
    "shared_arrays, shared_keys = zip(*out)  # transpose result\n",
    "# Convert to object array\n",
    "shared_arrays = np.array(shared_arrays, dtype=object)\n",
    "\n",
    "# If non array objects are needed, it is better to define them separately (below)\n",
    "# as global variables.\n",
    "pca = data_trs['0']['pca']\n",
    "\n",
    "# Define util functions to get the index of a key in the shared arrays.\n",
    "def get_shared_array_index(*args):\n",
    "    \"\"\"Get the index of a key in the shared arrays\"\"\"\n",
    "    idx_list = [shared_keys.index(key) for key in args]\n",
    "    return idx_list\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb965409",
   "metadata": {},
   "source": [
    "# Play with transit  rotation kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7ebb4d5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from starships.spectrum import RotKerTransitCloudy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c7fc9aee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[4.178062] \\; \\mathrm{d}$"
      ],
      "text/plain": [
       "<Quantity [4.17806203] d>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.period.to('d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2a0a90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "omega = 1. / planet.period\n",
    "rot_ker_obj = RotKerTransitCloudy(planet.R_pl.to(u.R_jup), planet.M_pl, planet.Tp, omega,\n",
    "                                  100000., right_val=0.5, step_smooth=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d8b764a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rot_ker_obj.show(norm=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0562b4b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "R_sampling = 400000.  # Sampling resolution of nirps\n",
    "profil = rot_ker_obj.resample(R_sampling, n_os=1000, pad=7, norm=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "87090f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_conv = np.convolve(model_high, profil, mode='same')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c648acfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15037abb6460>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "plt.plot(wv_high, model_high)\n",
    "plt.plot(wv_high[15:-15], model_conv[15:-15])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7df943",
   "metadata": {},
   "source": [
    "# Fit the rotation kernel\n",
    "We want to fit the omega (rotation frequency) and the fraction of clouds on the right hemisphere"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cea768",
   "metadata": {},
   "source": [
    "## Use the same chi2 computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "acf85dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_chi2_terms(model, idx_ord=None, axis=None):\n",
    "    \"\"\"Compute the model dependent terms of the chi2.\"\"\"\n",
    "    \n",
    "    # Get values from global variables if not provided.\n",
    "    if idx_ord is None:\n",
    "        idx_ord = idx_orders\n",
    "        \n",
    "    if axis is None:\n",
    "        axis = axis_sum\n",
    "        \n",
    "    # Get the index of the shared arrays and the arrays themselves.\n",
    "    idx_list = get_shared_array_index('flux', 'noise')\n",
    "    flux, noise = shared_arrays[idx_list]\n",
    "    \n",
    "    # Divide model by the uncertainty\n",
    "    # NOTE: flux is already divided by the uncertainty.\n",
    "    model = model[:, idx_ord] / noise[:, idx_ord]\n",
    "\n",
    "    # Compute each terms of the chi2\n",
    "    f_x_g = np.ma.sum(model * flux[:, idx_ord], axis=axis) \n",
    "    s2g = np.ma.sum(model**2, axis=axis)\n",
    "\n",
    "    return f_x_g, s2g\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799d7426",
   "metadata": {},
   "source": [
    "## Prepare for dynesty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "dc470036",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dynesty\n",
    "from dynesty import plotting as dyplot\n",
    "import dynesty.pool as dypool\n",
    "\n",
    "# seed the random number generator\n",
    "rstate = np.random.default_rng(736109)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8d73999c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters included in the fit: ['v_sys', 'Kp', 'omega', 'clouds_R']\n"
     ]
    }
   ],
   "source": [
    "####################################################\n",
    "# --- Prepare for retrieval\n",
    "####################################################\n",
    "\n",
    "# --- Define the prior functions.\n",
    "# NOTE: the first argument of the prior function must be the value of the parameter.\n",
    "\n",
    "# Define our uniform prior.\n",
    "def uniform_prior(val, low, high):\n",
    "\n",
    "    out = (high - low) * val + low\n",
    "\n",
    "    return out\n",
    "\n",
    "def log_uniform_prior(val, low, high):\n",
    "\n",
    "    out = np.exp((high - low) * val + low)\n",
    "\n",
    "    return out\n",
    "\n",
    "# --- Define a dictionary of model prior parameters.\n",
    "# NOTE: the prior function must be defined or imported above.\n",
    "# NOTE: the first argument of the prior function must be the value of the parameter.\n",
    "params_prior = dict()\n",
    "# Structure:\n",
    "# params_prior['param_name'] = (prior_function, tuple_of_arguments, dictionnary_of_kwargs)\n",
    "# NOTE: the tuple of arguments is passed after the first argument, which is the value of the parameter.\n",
    "# NOTE: if there are no kwargs, pass None or an empty dictionnary.\n",
    "params_prior['v_sys'] = (uniform_prior, (-30.0, 10.0), None)\n",
    "params_prior['Kp'] = (uniform_prior, (100., 175.0), None)\n",
    "# params_prior['alpha'] = (uniform_prior, (0., 2.), None)\n",
    "# params_prior['beta'] = (log_uniform_prior, (-2, 2), None)\n",
    "params_prior['omega'] = (uniform_prior, (0.5 * 1 / 4, 10. * 1 / 4), None)  # In day^-1\n",
    "params_prior['clouds_R'] = (uniform_prior, (0., 1.), None)\n",
    "\n",
    "print(f\"Parameters included in the fit: {list(params_prior.keys())}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bb7f662a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define prior transform using the params_prior dictionary.\n",
    "def ptform(unif_samples):\n",
    "    \"\"\"Transforms samples from the unit cube to the parameter space using the params_prior dictionary.\"\"\"\n",
    "    \n",
    "    # Initialize the output array.\n",
    "    theta = np.zeros(len(params_prior))\n",
    "\n",
    "    # Loop over the parameters.\n",
    "    for i, val in enumerate(params_prior.values()):\n",
    "        # Get the prior function and its arguments.\n",
    "        prior_func, args, kwargs = val\n",
    "        if kwargs is None:\n",
    "            kwargs = dict()\n",
    "        # Transform the uniform samples to the parameter space.\n",
    "        theta[i] = prior_func(unif_samples[i], *args, **kwargs)\n",
    "\n",
    "    return theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ddf2f4a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "R_nirps = 100000.\n",
    "R_sampling = 400000.\n",
    "\n",
    "# Define the log likelihood function.\n",
    "def log_like(theta):\n",
    "    \n",
    "    # Get the parameters and unpack them in a dictionnary for clarity.\n",
    "    theta_dict = {key: val for key, val in zip(params_prior.keys(), theta)}\n",
    "    \n",
    "    # Apply rotation kernel\n",
    "    rot_ker_obj = RotKerTransitCloudy(planet.R_pl.to(u.R_jup), planet.M_pl, planet.Tp,\n",
    "                                      theta_dict['omega'] * u.Unit('1/d'),\n",
    "                                      R_nirps,\n",
    "                                      right_val=theta_dict['clouds_R'],\n",
    "                                      step_smooth=250)\n",
    "    profil = rot_ker_obj.resample(R_sampling, n_os=1000, pad=7, norm=True)\n",
    "    model_conv = np.convolve(model_high, profil, mode='same')\n",
    "    \n",
    "    # --- Computing the logL for all sequences\n",
    "    # We need from data_tr: RV_const, t_start, wave, sep, pca, params.\n",
    "    idx_list = get_shared_array_index('RV_const', 't_start', 'wave', 'sep', 'params')\n",
    "    rv_const, t_start, wave, sep, params = shared_arrays[idx_list]\n",
    "    \n",
    "    vrp_orb = rv_theo_t(theta_dict['Kp'], t_start * u.d, planet.mid_tr,\n",
    "                        planet.period, plnt=True).value\n",
    "    \n",
    "    \n",
    "    # Get the model sequence.\n",
    "    n_pc = int(params[5])\n",
    "    velocities = theta_dict['v_sys'] + vrp_orb - vrp_orb * Kp_scale + rv_const\n",
    "    model_seq = corr.gen_model_sequence_noinj(velocities,\n",
    "                                              data_wave=wave,\n",
    "                                              data_sep=sep,\n",
    "                                              data_pca=pca,\n",
    "                                              data_npc=n_pc,\n",
    "                                              planet=planet,\n",
    "                                              model_wave=wv_high[20:-20],\n",
    "                                              model_spec=model_conv[20:-20],\n",
    "                                              kind_trans=kind_trans,\n",
    "                                              alpha=data_info['trall_alpha_frac']\n",
    "                                              )\n",
    "\n",
    "    # Calculate the log likelihood.\n",
    "    f_x_g, s2g = calc_chi2_terms(model_seq)\n",
    "    \n",
    "    # Get the log likelihood map (function of Kp and vsys).\n",
    "    alpha = theta_dict.get('alpha', 1.)  # If alpha is not fitted, set to 1.\n",
    "    logl = get_logl(f_x_g=f_x_g, s2g=s2g, s2f=s2f, N=data_trs['0']['N'], uncert_sum=uncert_sum,\n",
    "                    idx_orders=None, idx_exposure=in_transit,\n",
    "                    alpha=alpha, kind='BL', sum_axis=(-2, -1))\n",
    "    \n",
    "    return logl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024f6f83",
   "metadata": {},
   "source": [
    "### Test the logl function\n",
    "Note that it takes some time to run, so the retrieval will take some time too..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "6f8943c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.06 s, sys: 535 ms, total: 4.6 s\n",
      "Wall time: 4.74 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-244214.4364072235"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "log_like([0., 130., 1./4., 0.9])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b3aee3d",
   "metadata": {},
   "source": [
    "### Run dynesty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff558788",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running dynesty...\n"
     ]
    }
   ],
   "source": [
    "# Run dynesty.\n",
    "print(\"Running dynesty...\")\n",
    "n_cpu = 1\n",
    "n_process = 1 * n_cpu  # Could vary. In this  case it seems more efficient.\n",
    "n_param = len(params_prior)\n",
    "with dypool.Pool(n_process, loglike=log_like, prior_transform=ptform) as pool:\n",
    "    dsampler = dynesty.DynamicNestedSampler(pool.loglike, pool.prior_transform, ndim=n_param,\n",
    "                                            bound='balls', sample='unif', rstate=rstate, pool=pool)\n",
    "\n",
    "    dsampler.run_nested(dlogz_init=0.01, nlive_init=500, nlive_batch=500,\n",
    "                        wt_kwargs={'pfrac': 1.0}, stop_kwargs={'pfrac': 1.0},\n",
    "                        print_progress=True, maxiter=3000)\n",
    "    \n",
    "    dres_p = dsampler.results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af23788b",
   "metadata": {},
   "source": [
    "It takes forever with only one core, so may be better to run in a dedicated python code... \n",
    "Or with more cpus, but I'm not sure how to make it work with notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57946218",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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