estimation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2278c778",
   "metadata": {},
   "source": [
    "### Settings & imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "52249722",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6af4ae0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import masserstein "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "55f0baff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from masserstein import Spectrum, NMRSpectrum\n",
    "from masserstein import estimate_proportions\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pulp\n",
    "\n",
    "import pickle\n",
    "\n",
    "import seaborn as sns\n",
    "from textwrap import wrap\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87bb9eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "nr_of_experiment = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e01aea71",
   "metadata": {},
   "outputs": [],
   "source": [
    "if nr_of_experiment == 10:\n",
    "    variant = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c66ece",
   "metadata": {},
   "source": [
    "### Experiments data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ac155e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "components_dictionary = {'experiment_1': ['Pinene', 'Benzyl benzoate'],\n",
    "                        'experiment_2': ['Pinene', 'Limonene'],\n",
    "                        'experiment_7': ['Benzyl benzoate', 'm Anisaldehyde'],\n",
    "                        'experiment_3': ['Isopropyl myristate', 'Benzyl benzoate', 'Alpha pinene', 'Limonene'],\n",
    "                        'experiment_6': ['Pinene', 'Benzyl benzoate'],\n",
    "                        'experiment_5': ['Lactate', 'Alanine', 'Creatine', 'Creatinine', 'Choline chloride'],\n",
    "                        'experiment_9': ['Lactate', 'Alanine', 'Creatine', 'Creatinine', 'Choline chloride'],\n",
    "                        'experiment_4': ['Lactate', 'Alanine', 'Creatine', 'Creatinine', 'Choline chloride'],\n",
    "                        'experiment_8': ['Benzyl benzoate', 'm Anisaldehyde'],\n",
    "                         'experiment_10': ['Leucine', 'Isoleucine', 'Valine'],\n",
    "                        'experiment_11': ['Leucine', 'Isoleucine', 'Valine']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dbafb3dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "protons_dictionary = {'experiment_1': [16, 12],\n",
    "                        'experiment_2': [16, 16],\n",
    "                        'experiment_7': [12, 8],\n",
    "                        'experiment_3': [34, 12, 16, 16],\n",
    "                        'experiment_6': [16, 12],\n",
    "                        'experiment_5': [4, 4, 5, 5, 13],\n",
    "                        'experiment_9': [4, 4, 5, 5, 13],\n",
    "                        'experiment_4': [4, 4, 5, 5, 13],\n",
    "                        'experiment_8': [12, 8],\n",
    "                        'experiment_10': [10, 10, 8],\n",
    "                        'experiment_11': [10, 10, 8]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ce66613",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_10_molar_proportions = [\n",
    "                            [0.3401, 0.3299, 0.3299],\n",
    "                            [0.2525, 0.2475, 0.5],\n",
    "                            [0.2538, 0.4949, 0.2513],\n",
    "                            [0.2075, 0.3942, 0.3983]\n",
    "                            ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "703c5272",
   "metadata": {},
   "outputs": [],
   "source": [
    "if nr_of_experiment == 10:\n",
    "    ground_truth_molar_proportions = {'experiment_1': [0.09088457406472417, 0.9091154259352758],\n",
    "                                        'experiment_2': [0.505, 0.495],\n",
    "                                        'experiment_7': [0.8403875207510383, 0.1596124792489616],\n",
    "                                        'experiment_3': [0.7264578344443725, 0.10578603326645526, 0.081968804608116, 0.08578732768105625],\n",
    "                                        'experiment_6': [0.3865, 0.6135],\n",
    "                                        'experiment_5': [0.3022, 0.2240, 0.1253, 0.2028, 0.1457],\n",
    "                                        'experiment_9': [0.3022, 0.2240, 0.1253, 0.2028, 0.1457],\n",
    "                                        'experiment_4': [0.3022, 0.2240, 0.1253, 0.2028, 0.1457],\n",
    "                                        'experiment_8': [0.3702, 0.6298],\n",
    "                                        'experiment_10': exp_10_molar_proportions[variant],\n",
    "                                        'experiment_11': [0.4855, 0.2427, 0.2718]}\n",
    "else:\n",
    "    ground_truth_molar_proportions = {'experiment_1': [0.09088457406472417, 0.9091154259352758],\n",
    "                                        'experiment_2': [0.505, 0.495],\n",
    "                                        'experiment_7': [0.8403875207510383, 0.1596124792489616],\n",
    "                                        'experiment_3': [0.7264578344443725, 0.10578603326645526, 0.081968804608116, 0.08578732768105625],\n",
    "                                        'experiment_6': [0.3865, 0.6135],\n",
    "                                        'experiment_5': [0.3022, 0.2240, 0.1253, 0.2028, 0.1457],\n",
    "                                        'experiment_9': [0.3022, 0.2240, 0.1253, 0.2028, 0.1457],\n",
    "                                        'experiment_4': [0.3022, 0.2240, 0.1253, 0.2028, 0.1457],\n",
    "                                        'experiment_8': [0.3702, 0.6298],\n",
    "                                        'experiment_10': [],\n",
    "                                        'experiment_11': [0.4855, 0.2427, 0.2718]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f07ffaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments_folders = {'experiment_1': 'experiment_1_intensity_difference',\n",
    "                        'experiment_6': 'experiment_6_miniperfumes',\n",
    "                        'experiment_5': 'experiment_5_metabolites',\n",
    "                         'experiment_7': 'experiment_7_overlapping_and_intensity_difference',\n",
    "                         'experiment_9': 'experiment_9_and_4_shim',\n",
    "                       'experiment_4': 'experiment_9_and_4_shim',\n",
    "                        'experiment_8': 'experiment_8_different_solvents',\n",
    "                        'experiment_3': 'experiment_3_perfumes_and_absent_components',\n",
    "                         'experiment_2': 'experiment_2_overlapping',\n",
    "                       'experiment_10': 'experiment_10_bcaa',\n",
    "                        'experiment_11': 'experiment_11_real_food_product'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c15336b",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_kappa_mix = [0.09, 0.05, 0.37, 0.11, 0.03, 0.06, 0.30, 0.2, 0.05, [0.23, 0.14, 0.09, 0.12], 0.27]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53eb8b54",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_kappa_model = [0.08, 0.01, 0.24, 0.02, 0.09, 0.2, 0.04, 0.29, 0.02, [0.09, 0.06, 0.05, 0.06], 0.11]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84eddc3",
   "metadata": {},
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a16bd1b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "if (nr_of_experiment != 9 and nr_of_experiment != 4 and nr_of_experiment != 10):\n",
    "    filename = experiments_folders['experiment_' + str(nr_of_experiment)] + '/preprocessed_mix.csv'\n",
    "    mix = np.loadtxt(filename, delimiter=',')\n",
    "elif nr_of_experiment == 10:\n",
    "    filename = experiments_folders['experiment_' + str(nr_of_experiment)] + '/preprocessed_mix_variant_'+str(variant+1)+'.csv'\n",
    "    mix = np.loadtxt(filename, delimiter=',')\n",
    "else:\n",
    "    filename = experiments_folders['experiment_' + str(nr_of_experiment)] + '/preprocessed_exp'+str(nr_of_experiment)+'_mix.csv'\n",
    "    mix = np.loadtxt(filename, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67c8aeb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "how_many_components = len(components_dictionary['experiment_'+str(nr_of_experiment)])\n",
    "names = ['comp' + str(i) for i in range(how_many_components)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d402de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "spectra = []\n",
    "for i in range(how_many_components):\n",
    "    if (nr_of_experiment != 9 and nr_of_experiment != 4 and nr_of_experiment != 10):\n",
    "        filename = experiments_folders['experiment_' + str(nr_of_experiment)] + \\\n",
    "                    '/preprocessed_comp' + str(i) + '.csv'\n",
    "        spectra.append(np.loadtxt(filename, delimiter=','))\n",
    "    elif nr_of_experiment == 10:\n",
    "        filename = experiments_folders['experiment_' + str(nr_of_experiment)] + '/preprocessed_variant_'+str(variant+1)+'_comp'+str(i)+'.csv'\n",
    "        spectra.append(np.loadtxt(filename, delimiter=','))\n",
    "    else:\n",
    "        filename = experiments_folders['experiment_' + str(nr_of_experiment)] + \\\n",
    "                    '/preprocessed_exp'+str(nr_of_experiment)+ '_comp'+ str(i) + '.csv'\n",
    "        spectra.append(np.loadtxt(filename, delimiter=','))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f68d15b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "spectra2 = []\n",
    "names = []\n",
    "for i in range(len(spectra)):\n",
    "    spectra2.append(NMRSpectrum(confs=list(zip(spectra[i][:,0], spectra[i][:,1])), \n",
    "                                protons=protons_dictionary['experiment_'+str(nr_of_experiment)][i]))\n",
    "    names.append('comp'+str(i))\n",
    "\n",
    "spectra = spectra2\n",
    "del(spectra2)\n",
    "mix = NMRSpectrum(confs=list(zip(mix[:,0], mix[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "066513a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, name in enumerate(names):\n",
    "    globals()[name] = spectra[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac6d46ac",
   "metadata": {},
   "outputs": [
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       "        if(isWebKit) {\n",
       "            return function (event) {\n",
       "                /* This prevents the web browser from automatically changing to\n",
       "                 * the text insertion cursor when the button is pressed. We\n",
       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        }\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    canvas_div.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\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.canvas_div.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",
       "\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",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\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"
    }
   ],
   "source": [
    "mix.plot(profile=True)\n",
    "for sp in spectra:\n",
    "    sp.plot(profile=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9cc65b",
   "metadata": {},
   "source": [
    "### Estimation with default kappas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fa502e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "default_MTD = 0.25\n",
    "default_MTD_th = 0.22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b555024a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "\n",
      "--------------------------------------------\n",
      "Warning: your license will expire in 3 days\n",
      "--------------------------------------------\n",
      "\n",
      "Academic license - for non-commercial use only - expires 2023-11-10\n",
      "Removed components' spectra due to no matching peaks in mixture's spectrum: []\n",
      "Envelope bounds: [(0.457009, 9.01304, 0), (0.457009, 9.01304, 1)]\n",
      "Number of chunks: 1\n",
      "ChunkIDs: [0, 0]\n",
      "Chunk bounds: [(0.207009, 9.26304)]\n",
      "Ion currents in chunks: [0.9999999999999998]\n",
      "Deconvolving chunk 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/basia/.local/lib/python3.10/site-packages/pulp/pulp.py:1352: UserWarning: Spaces are not permitted in the name. Converted to '_'\n",
      "  warnings.warn(\"Spaces are not permitted in the name. Converted to '_'\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chunk %i deconvolution status: Optimal\n",
      "Signal proportion in mixture's spectrum: 0.997286032762\n",
      "Noise proportion in mixture's spectrum: 0.002713967238\n",
      "Total explanation: 1.0\n",
      "Noise proportion in combination of components' spectra: 0.009471922476\n"
     ]
    }
   ],
   "source": [
    "start_vis = time.time()\n",
    "estimation_vis = estimate_proportions(mix, spectra,\n",
    "                                        MTD=default_MTD, \n",
    "                                        MTD_th=default_MTD_th,\n",
    "                                        verbose=True, solver=pulp.GUROBI(msg=False), \n",
    "                                        what_to_compare='area')\n",
    "stop_vis = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aaa68541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4621238178699999, 0.5351622148919999]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_vis['proportions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ffacb9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009471922475999999"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_vis['proportion_of_noise_in_components']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "33b21a28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0027139672380002144"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - sum(estimation_vis['proportions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "405bd6cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimation took 53.416203022003174 seconds.\n"
     ]
    }
   ],
   "source": [
    "print('Estimation took '+ str(stop_vis-start_vis) + ' seconds.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3114bfae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed components' spectra due to no matching peaks in mixture's spectrum: []\n",
      "Envelope bounds: [(0.457009, 9.01304, 0), (0.457009, 9.01304, 1)]\n",
      "Number of chunks: 1\n",
      "ChunkIDs: [0, 0]\n",
      "Chunk bounds: [(0.207009, 9.26304)]\n",
      "Ion currents in chunks: [0.9999999999999998]\n",
      "Deconvolving chunk 0\n",
      "Chunk %i deconvolution status: Optimal\n",
      "Signal proportion in mixture's spectrum: 0.997286032762\n",
      "Noise proportion in mixture's spectrum: 0.002713967238\n",
      "Total explanation: 1.0\n",
      "Noise proportion in combination of components' spectra: 0.009471922476\n"
     ]
    }
   ],
   "source": [
    "start_con = time.time()\n",
    "estimation_molar = estimate_proportions(mix, spectra,\n",
    "                                        MTD=default_MTD, \n",
    "                                        MTD_th=default_MTD_th,\n",
    "                                        verbose=True, solver=pulp.GUROBI(msg=False), \n",
    "                                        what_to_compare='concentration')\n",
    "stop_con = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6395dac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'proportions': [0.39307158181939095, 0.6069284181806092],\n",
       " 'Wasserstein distance': 0.014795729188074841}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_molar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "463a640c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimation took 52.69059228897095 seconds.\n"
     ]
    }
   ],
   "source": [
    "print('Estimation took '+ str(stop_con-start_con) + ' seconds.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2e48d69e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #MTD = 0.25, MTD_th = 0.22\n",
    "\n",
    "# #1: 'Pinene', 'Benzyl benzoate'\n",
    "# 0.0937, 0.9063\n",
    "\n",
    "# #2: 'Pinene', 'Limonene'\n",
    "# 0.5240, 0.4760\n",
    "\n",
    "# #3: 'Isopropyl myristate', 'Benzyl benzoate', 'Alpha pinene', 'Limonene'\n",
    "# 0.7511, 0.1059, 0.0809, 0.0621\n",
    "\n",
    "# #4: 'Lactate', 'Alanine', 'Creatine', 'Creatinine', 'Choline chloride'\n",
    "# 0.3165, 0.2137, 0.1341, 0.1960, 0.1397\n",
    "\n",
    "# #5: 'Lactate', 'Alanine', 'Creatine', 'Creatinine', 'Choline chloride'\n",
    "# 0.3180, 0.2113, 0.1293, 0.2018, 0.1397\n",
    "\n",
    "# #6: 'Pinene', 'Benzyl benzoate'\n",
    "# 0.3931, 0.6069\n",
    "\n",
    "# #7: 'Benzyl benzoate', 'm Anisaldehyde'\n",
    "# 0.8428, 0.1572\n",
    "\n",
    "# #8: 'Benzyl benzoate', 'm Anisaldehyde'\n",
    "# 0.3762, 0.6238\n",
    "\n",
    "# #9: Lactate', 'Alanine', 'Creatine', 'Creatinine', 'Choline chloride'\n",
    "# 0.3180, 0.2125, 0.1333, 0.1967, 0.1395\n",
    "\n",
    "# #10: 'Leucine', 'Isoleucine', 'Valine'\n",
    "# # variant 1\n",
    "# 0.3552, 0.3418, 0.3029\n",
    "\n",
    "# #10: 'Leucine', 'Isoleucine', 'Valine'\n",
    "# # variant 2\n",
    "# 0.2793, 0.2173, 0.5034\n",
    "\n",
    "# #10: 'Leucine', 'Isoleucine', 'Valine'\n",
    "# # variant 3\n",
    "# 0.2761, 0.4778, 0.2461\n",
    "\n",
    "# #10: 'Leucine', 'Isoleucine', 'Valine'\n",
    "# # variant 4\n",
    "# 0.2494, 0.3357, 0.4149\n",
    "\n",
    "# #11: 'Leucine', 'Isoleucine', 'Valine'\n",
    "# 0.4960, 0.2519, 0.2521"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81953cc",
   "metadata": {},
   "source": [
    "#### Saving results of estimation with default parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b013e53e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pinene</th>\n",
       "      <th>Benzyl benzoate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.462124</td>\n",
       "      <td>0.535162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pinene  Benzyl benzoate\n",
       "0  0.462124         0.535162"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_vis = pd.DataFrame(np.array(estimation_vis['proportions']).reshape(1,-1), \n",
    "             columns = components_dictionary['experiment_'+str(nr_of_experiment)])\n",
    "res_vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6335e47a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pinene</th>\n",
       "      <th>Benzyl benzoate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.393072</td>\n",
       "      <td>0.606928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pinene  Benzyl benzoate\n",
       "0  0.393072         0.606928"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_con = pd.DataFrame(np.array(estimation_molar['proportions']).reshape(1,-1), \n",
    "             columns = components_dictionary['experiment_'+str(nr_of_experiment)])\n",
    "res_con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "86e782c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if (nr_of_experiment != 9 and nr_of_experiment != 4 and nr_of_experiment != 10):\n",
    "#     res_vis.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                    '/results_area_default_parameters.csv')\n",
    "# elif nr_of_experiment ==10:\n",
    "#     res_vis.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                    '/results_area__default_parameters_variant_'+str(variant+1)+'.csv')\n",
    "# else:\n",
    "#     res_vis.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                    '/results_area__default_parameters_exp'+str(nr_of_experiment)+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61b26555",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if (nr_of_experiment != 9 and nr_of_experiment != 4 and nr_of_experiment != 10):\n",
    "#     res_con.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                '/results_concentration_default_parameters.csv')\n",
    "# elif nr_of_experiment == 10:\n",
    "#     res_con.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                '/results_concentration_default_parameters_variant_'+str(variant+1)+'.csv')\n",
    "# else:\n",
    "#     res_con.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                '/results_concentration_default_parameters_exp'+str(nr_of_experiment)+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f136050f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# comp_noise = np.array(estimation_vis['proportion_of_noise_in_components']).reshape(-1)\n",
    "# if (nr_of_experiment != 9 and nr_of_experiment != 4 and nr_of_experiment != 10):\n",
    "#     np.savetxt(experiments_folders['experiment_' + \\\n",
    "#                                     str(nr_of_experiment)] + \\\n",
    "#                                     '/proportion_of_noise_in_comp'+ \\\n",
    "#                                     '_default_parameters.csv',\n",
    "#                                       comp_noise)\n",
    "# elif nr_of_experiment == 10:\n",
    "#     np.savetxt(experiments_folders['experiment_' + \\\n",
    "#                                     str(nr_of_experiment)] + \\\n",
    "#                                     '/proportion_of_noise_in_comp'+ \\\n",
    "#                                     '_variant' + str(variant+1) + \\\n",
    "#                                     '_default_parameters.csv',\n",
    "#                                       comp_noise)\n",
    "# else:\n",
    "#     np.savetxt(experiments_folders['experiment_' + \\\n",
    "#                                     str(nr_of_experiment)] + \\\n",
    "#                                     '/proportion_of_noise_in_comp'+ \\\n",
    "#                                     '_exp' + str(nr_of_experiment) + \\\n",
    "#                                     '_default_parameters.csv',\n",
    "#                                       comp_noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "053f2e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mix_noise = np.array(1 - sum(estimation_vis['proportions'])).reshape(-1)\n",
    "# if (nr_of_experiment != 9 and nr_of_experiment != 4 and nr_of_experiment != 10):\n",
    "#     np.savetxt(experiments_folders['experiment_' + \\\n",
    "#                                     str(nr_of_experiment)] + \\\n",
    "#                                     '/proportion_of_noise_in_mix'+ \\\n",
    "#                                     '_default_parameters.csv',\n",
    "#                                       mix_noise)\n",
    "# elif nr_of_experiment == 10:\n",
    "#     np.savetxt(experiments_folders['experiment_' + \\\n",
    "#                                     str(nr_of_experiment)] + \\\n",
    "#                                     '/proportion_of_noise_in_mix'+ \\\n",
    "#                                     '_variant' + str(variant+1) + \\\n",
    "#                                     '_default_parameters.csv',\n",
    "#                                       mix_noise)\n",
    "# else:\n",
    "#     np.savetxt(experiments_folders['experiment_' + \\\n",
    "#                                     str(nr_of_experiment)] + \\\n",
    "#                                     '/proportion_of_noise_in_mix'+ \\\n",
    "#                                     '_exp' + str(nr_of_experiment) + \\\n",
    "#                                     '_default_parameters.csv',\n",
    "#                                       mix_noise)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31df385c",
   "metadata": {},
   "source": [
    "### Estimation with optimal kappas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d44ebf2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed components' spectra due to no matching peaks in mixture's spectrum: []\n",
      "Envelope bounds: [(0.457009, 9.01304, 0), (0.457009, 9.01304, 1)]\n",
      "Number of chunks: 1\n",
      "ChunkIDs: [0, 0]\n",
      "Chunk bounds: [(0.257009, 9.21304)]\n",
      "Ion currents in chunks: [0.9999999999999998]\n",
      "Deconvolving chunk 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/basia/.local/lib/python3.10/site-packages/pulp/pulp.py:1352: UserWarning: Spaces are not permitted in the name. Converted to '_'\n",
      "  warnings.warn(\"Spaces are not permitted in the name. Converted to '_'\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chunk %i deconvolution status: Optimal\n",
      "Signal proportion in mixture's spectrum: 0.9897877955100001\n",
      "Noise proportion in mixture's spectrum: 0.010212204484999989\n",
      "Total explanation: 0.9999999999950001\n",
      "Noise proportion in combination of components' spectra: 0.00593012087\n"
     ]
    }
   ],
   "source": [
    "start_vis_opt = time.time()\n",
    "if nr_of_experiment != 10:\n",
    "    estimation_vis_opt = estimate_proportions(mix, spectra,\n",
    "                                            MTD=best_kappa_mix[nr_of_experiment-1],\n",
    "                                            MTD_th=best_kappa_model[nr_of_experiment-1],\n",
    "                                            verbose=True, solver=pulp.GUROBI(msg=False),\n",
    "                                            what_to_compare='area')\n",
    "else:\n",
    "    estimation_vis_opt = estimate_proportions(mix, spectra,\n",
    "                                            MTD=best_kappa_mix[nr_of_experiment-1][variant],\n",
    "                                            MTD_th=best_kappa_model[nr_of_experiment-1][variant],\n",
    "                                            verbose=True, solver=pulp.GUROBI(msg=False),\n",
    "                                            what_to_compare='area')\n",
    "stop_vis_opt = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7c07657a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimation took 53.67149615287781 seconds.\n"
     ]
    }
   ],
   "source": [
    "print('Estimation took '+ str(stop_vis_opt-start_vis_opt) + ' seconds.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c58f7242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.4543065378329999, 0.5354812576769999]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_vis_opt['proportions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f463c3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005930120869999998"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_vis_opt['proportion_of_noise_in_components']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ca73a54f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed components' spectra due to no matching peaks in mixture's spectrum: []\n",
      "Envelope bounds: [(0.457009, 9.01304, 0), (0.457009, 9.01304, 1)]\n",
      "Number of chunks: 1\n",
      "ChunkIDs: [0, 0]\n",
      "Chunk bounds: [(0.257009, 9.21304)]\n",
      "Ion currents in chunks: [0.9999999999999998]\n",
      "Deconvolving chunk 0\n",
      "Chunk %i deconvolution status: Optimal\n",
      "Signal proportion in mixture's spectrum: 0.9897877955100001\n",
      "Noise proportion in mixture's spectrum: 0.010212204484999989\n",
      "Total explanation: 0.9999999999950001\n",
      "Noise proportion in combination of components' spectra: 0.00593012087\n"
     ]
    }
   ],
   "source": [
    "start_con_opt = time.time()\n",
    "if nr_of_experiment != 10:\n",
    "    estimation_molar_opt = estimate_proportions(mix, spectra,\n",
    "                                            MTD=best_kappa_mix[nr_of_experiment-1], \n",
    "                                            MTD_th=best_kappa_model[nr_of_experiment-1],\n",
    "                                            verbose=True, solver=pulp.GUROBI(msg=False), \n",
    "                                            what_to_compare='concentration')\n",
    "else:\n",
    "    estimation_molar_opt = estimate_proportions(mix, spectra,\n",
    "                                            MTD=best_kappa_mix[nr_of_experiment-1][variant], \n",
    "                                            MTD_th=best_kappa_model[nr_of_experiment-1][variant],\n",
    "                                            verbose=True, solver=pulp.GUROBI(msg=False), \n",
    "                                            what_to_compare='concentration')\n",
    "stop_con_opt = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "655ff1b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimation took 53.25800919532776 seconds.\n"
     ]
    }
   ],
   "source": [
    "print('Estimation took '+ str(stop_con_opt-start_con_opt) + ' seconds.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "354693e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.38886733988386857, 0.6111326601161314]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_molar_opt['proportions']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304c1464",
   "metadata": {},
   "source": [
    "### Saving estimation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "54ac2c19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pinene</th>\n",
       "      <th>Benzyl benzoate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.454307</td>\n",
       "      <td>0.535481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pinene  Benzyl benzoate\n",
       "0  0.454307         0.535481"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_vis_opt = pd.DataFrame(np.array(estimation_vis_opt['proportions']).reshape(1,-1), \n",
    "             columns = components_dictionary['experiment_'+str(nr_of_experiment)])\n",
    "res_vis_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f7dbc5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pinene</th>\n",
       "      <th>Benzyl benzoate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.388867</td>\n",
       "      <td>0.611133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pinene  Benzyl benzoate\n",
       "0  0.388867         0.611133"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_con_opt = pd.DataFrame(np.array(estimation_molar_opt['proportions']).reshape(1,-1), \n",
    "             columns = components_dictionary['experiment_'+str(nr_of_experiment)])\n",
    "res_con_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a2cfc1d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if (nr_of_experiment != 9 and nr_of_experiment != 4 and nr_of_experiment != 10):\n",
    "#     res_vis_opt.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                    '/results_area.csv')\n",
    "# elif nr_of_experiment == 10:\n",
    "#     res_vis_opt.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                    '/results_area_variant'+str(variant+1)+'.csv')\n",
    "# else:\n",
    "#     res_vis_opt.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                    '/results_area_exp'+str(nr_of_experiment)+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "15c18f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if (nr_of_experiment != 9 and nr_of_experiment != 4 and nr_of_experiment != 10):\n",
    "#     res_con_opt.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                '/results_concentration.csv')\n",
    "# elif nr_of_experiment == 10:\n",
    "#     res_con_opt.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                '/results_concentration_variant'+str(variant+1)+'.csv')\n",
    "# else:\n",
    "#     res_con_opt.to_csv(experiments_folders['experiment_' + str(nr_of_experiment)]+\n",
    "#                '/results_concentration_exp'+str(nr_of_experiment)+'.csv')"
   ]
  }
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