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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.0.min.js\": \"YobFyzPeVUsFQydHkJGsJL1kyfHnWxOlPc3EwaV22TmBaeGoXHLWx5aRRVPS9xlE\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.0.min.js\": \"NuAg9+TcTQQqvQCTtkCneRrpkTiMhhfiq0KHiBzx8ECiKiLWXHN6i6ia3q7b3eHu\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.0.min.js\": \"uMVqQc8JqHitD67bXTn9a06Mrk3EiHRaZ18EJENQenAKJ/KL71SakdXYomZQpGRr\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.0.min.js\": \"u+eGuEXC8aw0VSCm2mH+b/tQEAitUOYiR1H6SuIVEdUmXsf4vN8m/SmXpmjb7U/X\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.0.min.js\": \"YobFyzPeVUsFQydHkJGsJL1kyfHnWxOlPc3EwaV22TmBaeGoXHLWx5aRRVPS9xlE\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.0.min.js\": \"NuAg9+TcTQQqvQCTtkCneRrpkTiMhhfiq0KHiBzx8ECiKiLWXHN6i6ia3q7b3eHu\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.0.min.js\": \"uMVqQc8JqHitD67bXTn9a06Mrk3EiHRaZ18EJENQenAKJ/KL71SakdXYomZQpGRr\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.0.min.js\": \"u+eGuEXC8aw0VSCm2mH+b/tQEAitUOYiR1H6SuIVEdUmXsf4vN8m/SmXpmjb7U/X\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.1.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0918431019994a40a8dbac95e9df9ce6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Choose File:', options=('2017-12-31-pCdk2_2dDEER.DTA', '2018-04-18-Cdk2_2dDEER.DTA', 'ex…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy import optimize\n",
    "import scipy.integrate as integrate\n",
    "import glob\n",
    "import os\n",
    "from multiprocessing import Pool, cpu_count\n",
    "import multiprocessing as mp\n",
    "\n",
    "\n",
    "from ipywidgets import VBox,HBox, Layout, interact, interactive, fixed, interact_manual, FloatSlider, IntSlider\n",
    "import ipywidgets as widgets\n",
    "\n",
    "from bokeh.io import push_notebook, show, output_notebook\n",
    "from bokeh.plotting import figure, ColumnDataSource\n",
    "from bokeh.layouts import column, row, gridplot\n",
    "from bokeh.models import BoxAnnotation, DataTable, TableColumn, PointDrawTool, HTMLTemplateFormatter\n",
    "\n",
    "#from bokeh.models.widgets import DataTable, TableColumn, HTMLTemplateFormatter\n",
    "\n",
    "from src.spectrum import spectrum\n",
    "from src.spec_plot import spec_plot\n",
    "from src.lcurve_plot import lcurve_plot\n",
    "from src.mycolors import mycolors\n",
    "from src.background_models import bg3D, bgFractal\n",
    "from src.gradient_descent import gradient_descent\n",
    "from src.monte_carlo_error import monte_carlo_error\n",
    "from src.error_plot import error_plot\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "filelist = [os.path.basename(file) for file in glob.glob(os.path.join('data','raw','*.DTA'))]\n",
    "file_picker=widgets.Dropdown(description= 'Choose File:', options= filelist)\n",
    "file_picker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slice Averaging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"62727d99-2dcf-4541-b8ec-5bccea16585f\" data-root-id=\"38385\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[754]},\"y\":{\"__ndarray__\":\"ri9MkFhk7z/tLWfE34PvP3IPRFz3lu8/WMgjrhCq7z/aX1KP2MTvP3dXpyVV1u8/ZHiTQl3s7z87x/VQ3PbvP3Em7XiK+e8/6VSZMwD+7z8AAAAAAADwP2druNYr++8/JQRrTK3n7z/Wp22go9nvP59cxwUove8/Zpwis5u17z/2iAA8UJ/vP6kVKT9piu8/K5cSYrhn7z9e4r0eJFTvPyi3ProsRO8/HQfhhOAo7z8OYVeHlgbvP3b1gIMV7O4/QNjqzf/W7j9nWBj3YMLuP75pPHuzpe4/Dpy5lZqW7j8i7dq2/HnuP2U8d83ncO4/Bn3iL8BQ7j/NxIPQikzuP5fZdAV1M+4/X7qymW4b7j+Q7UxEExLuPxJhvCGUBe4/p+lpRrjg7T8T3+qzYujtP6ehFxXz1+0/+DZv1xDB7T/FSTrgArztP+vCrvJgru0/6M0/1lCe7T+8S3+ls5btPxFRiu1piO0/uYmtzhuE7T+bg9n+vHPtP2vKsFcqeO0/y2HUDjdw7T+4FQG0bV7tP7RqPbqMV+0/GEtZzVtY7T/2DdqRBVDtP2rIuMtbQe0/ta0coFBQ7T88cAnDzUXtP1kl1mMNPu0/LTCvpIs37T9Mu+jv9zPtP/5hafn4Ju0/MRXCqYct7T+bURCEayvtP/Rj3u/6Ju0/y7RlYwot7T+ElzMsNiTtP0AMJwAoIu0/nOP6sfcl7T/iNI/y+RrtP0mIi6mpEu0/ZkHSRVsn7T837jHLoxHtPw4v2/naDe0/+JNDCYsW7T9MGCQ3LBTtP9KFTJzNCe0/s4AqzHMJ7T8YPLdrXQ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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[754]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[754]}},\"selected\":{\"id\":\"39576\"},\"selection_policy\":{\"id\":\"39575\"}},\"id\":\"38380\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"38326\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"data_source\":{\"id\":\"38380\"},\"glyph\":{\"id\":\"38381\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"38382\"},\"selection_glyph\":null,\"view\":{\"id\":\"38384\"}},\"id\":\"38383\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis\":{\"id\":\"38357\"},\"dimension\":1,\"grid_line_alpha\":0.5,\"grid_line_dash\":[6,4],\"ticker\":null},\"id\":\"38360\",\"type\":\"Grid\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#ff7f0e\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"38382\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"38362\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"end\":46347816.72,\"start\":527200.0},\"id\":\"38306\",\"type\":\"Range1d\"},{\"attributes\":{\"formatter\":{\"id\":\"39562\"},\"major_label_text_font_size\":\"16pt\",\"ticker\":{\"id\":\"38317\"}},\"id\":\"38316\",\"type\":\"LinearAxis\"},{\"attributes\":{\"line_color\":\"#ff7f0e\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"38340\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"38361\",\"type\":\"PanTool\"},{\"attributes\":{\"source\":{\"id\":\"38380\"}},\"id\":\"38384\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"39569\",\"type\":\"Selection\"}],\"root_ids\":[\"38385\"]},\"title\":\"Bokeh Application\",\"version\":\"2.1.0\"}};\n",
       "  var render_items = [{\"docid\":\"205560ef-8553-4902-a677-4481b6f4341c\",\"notebook_comms_target\":\"39578\",\"root_ids\":[\"38385\"],\"roots\":{\"38385\":\"62727d99-2dcf-4541-b8ec-5bccea16585f\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "38385"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c80f8604d5a74d82a9e814e1560254aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(interactive(children=(SelectionSlider(description='value', index=3, options=(0, 1, 2, 3), value…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spec = spectrum(os.path.join('data','raw',file_picker.value))\n",
    "\n",
    "\n",
    "#Plot definitions\n",
    "xs= [spec.raw_time]*2\n",
    "ys = [spec.real2D[0],spec.imaginary2D[0]]\n",
    "\n",
    "spec2D = spec_plot(xs,ys,y_range = (spec.imaginary2D.min(),spec.real2D.max()*1.02), plot_height=300)\n",
    "\n",
    "ys = [spec.real,spec.imaginary]\n",
    "    \n",
    "specavg = spec_plot(xs,ys, y_range = (spec.imaginary.min(),spec.real.max()*1.02), plot_height=300) \n",
    "\n",
    "\n",
    "#Widget Defintions\n",
    "\n",
    "def on_button_clicked(val):\n",
    "    if np.sum(spec.slice_mask)>1: #prevent deleting of all slices\n",
    "        spec.delete_slice(myslider.value)\n",
    "        myslider.options=valuelist[spec.slice_mask]\n",
    "        myslider.value = myslider.options[-1]\n",
    "        spec.avg_slices()\n",
    "        update(myslider.value)\n",
    "    \n",
    "def update(index):\n",
    "    for idx,line in enumerate(spec2D.renderers):\n",
    "        line.data_source.data['y'] = [spec.real2D,spec.imaginary2D][idx][index]\n",
    "        \n",
    "    for idx,line in enumerate(specavg.renderers):\n",
    "        line.data_source.data['y'] = [spec.real,spec.imaginary][idx]\n",
    "        \n",
    "    push_notebook(handle=sliceplots)\n",
    "    return index\n",
    "\n",
    "valuelist=np.arange(spec.real2D.shape[0])\n",
    "\n",
    "myslider=widgets.SelectionSlider(description='value', value=valuelist[-1],options=valuelist)\n",
    "deletebutton=widgets.Button(description='Delete Slice')\n",
    "deletebutton.on_click(on_button_clicked)\n",
    "\n",
    "\n",
    "\n",
    "#Render plots, widgets\n",
    "sliceplots=show(row(spec2D,specavg),notebook_handle=True)\n",
    "HBox([interactive(update,index=myslider),deletebutton])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"31e938bb-1564-473b-8e0d-7dd00fddfb02\" data-root-id=\"39731\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"014ca6c7-8729-437d-a702-04435521db3c\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"39740\"}],\"center\":[{\"id\":\"39743\"},{\"id\":\"39747\"}],\"left\":[{\"id\":\"39744\"}],\"outline_line_alpha\":0,\"plot_height\":400,\"plot_width\":647,\"renderers\":[{\"id\":\"39765\"},{\"id\":\"39770\"},{\"id\":\"39775\"}],\"title\":{\"id\":\"40969\"},\"toolbar\":{\"id\":\"39755\"},\"x_range\":{\"id\":\"39732\"},\"x_scale\":{\"id\":\"39736\"},\"y_range\":{\"id\":\"39734\"},\"y_scale\":{\"id\":\"39738\"}},\"id\":\"39731\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"source\":{\"id\":\"39762\"}},\"id\":\"39766\",\"type\":\"CDSView\"},{\"attributes\":{\"axis_label\":\"Evolution Time 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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[754]}},\"selected\":{\"id\":\"40978\"},\"selection_policy\":{\"id\":\"40977\"}},\"id\":\"39767\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"39738\",\"type\":\"LinearScale\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"39754\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"39748\"},{\"id\":\"39749\"},{\"id\":\"39750\"},{\"id\":\"39751\"},{\"id\":\"39752\"},{\"id\":\"39753\"}]},\"id\":\"39755\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"40979\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"39748\",\"type\":\"PanTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"39764\",\"type\":\"Line\"},{\"attributes\":{\"text\":\"\"},\"id\":\"40969\",\"type\":\"Title\"}],\"root_ids\":[\"39731\"]},\"title\":\"Bokeh Application\",\"version\":\"2.1.0\"}};\n",
       "  var render_items = [{\"docid\":\"014ca6c7-8729-437d-a702-04435521db3c\",\"notebook_comms_target\":\"40982\",\"root_ids\":[\"39731\"],\"roots\":{\"39731\":\"31e938bb-1564-473b-8e0d-7dd00fddfb02\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "39731"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90702b6317134bedb96417cc096ef958",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(interactive(children=(SelectionSlider(description='Cutoff', index=753, options=(-0.068, -0.0600…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoffx=[spec.raw_time.max(),spec.raw_time.max()]\n",
    "cutoffy=[-.1,1]\n",
    "\n",
    "cutoffslider=widgets.SelectionSlider(description='Cutoff', value=spec.raw_time[-1], options=spec.raw_time)\n",
    "signal=spec.real+1j*spec.imaginary\n",
    "forphasing=signal[int(len(signal)/8):list(spec.raw_time).index(cutoffslider.value)]\n",
    "spec.phase=optimize.fmin(lambda x:abs((forphasing*np.e**(1j*x)).imag.mean()),1,disp=False)[0]\n",
    "phaseslider=widgets.FloatSlider(description='Phase', value=spec.phase, min=-np.pi, max=np.pi, step=0.001)\n",
    "\n",
    "\n",
    "xs = [spec.raw_time]*2\n",
    "ys = [np.real(signal*np.e**(1j*spec.phase))/np.max(np.real(signal*np.e**(1j*spec.phase))),\n",
    "      np.imag(signal*np.e**(1j*spec.phase))/np.max(np.real(signal*np.e**(1j*spec.phase)))]\n",
    "\n",
    "\n",
    "phaseplot=spec_plot(xs,ys)\n",
    "phaseplot.line(cutoffx,cutoffy, color=(255,0,0), line_width=1) #decorate plot with cutoff line\n",
    "phaser=show(phaseplot, notebook_handle=True);\n",
    "\n",
    "\n",
    "phasebutton=widgets.Button(description='Phase')\n",
    "# kernelbutton=widgets.Button(description='Build Kernel')\n",
    "\n",
    "def phase_button_clicked(val):\n",
    "    forphasing=signal[int(len(signal)/8):list(spec.raw_time).index(cutoffslider.value)]\n",
    "    spec.phase=optimize.fmin(lambda x:abs((forphasing*np.e**(1j*x)).imag.mean()),1,disp=False)[0]\n",
    "    phaseslider.value=spec.phase\n",
    "    \n",
    "# def kernel_button_clicked(val):\n",
    "    \n",
    "#     spec.tmax = cutoffslider.value\n",
    "#     spec.cutoff=cutoffslider.value # is cutoff needed (and not just as spec.tmax) ??\n",
    "#     spec.build_kernel()\n",
    "#     spec.real=np.real(signal*np.e**(1j*spec.phase))/np.max(np.real(signal*np.e**(1j*spec.phase)))\n",
    "#     spec.imag=np.imag(signal*np.e**(1j*spec.phase))/np.max(np.real(signal*np.e**(1j*spec.phase)))\n",
    "    \n",
    "   \n",
    "\n",
    "    \n",
    "def updatePhase(value):\n",
    "    phaseplot.renderers[-3].data_source.data['y'] = np.real(signal*np.e**(1j*value))/np.max(np.real(signal*np.e**(1j*value)))\n",
    "    phaseplot.renderers[-2].data_source.data['y'] = np.imag(signal*np.e**(1j*value))/np.max(np.real(signal*np.e**(1j*value)))\n",
    "    phaseplot.renderers[-1].data_source.data['y'] = [np.min(np.imag(signal*np.e**(1j*value))/np.max(np.real(signal*np.e**(1j*value)))),1]\n",
    "    push_notebook(handle=phaser)\n",
    "    return value\n",
    "\n",
    "def updateCutoff(value):\n",
    "    phaseplot.renderers[-1].data_source.data['x'] = [value,value]\n",
    "    push_notebook(handle=phaser)\n",
    "    return value\n",
    "\n",
    "#kernel,spec.t,spec.r,Lmatrix,spec.cutoff = \n",
    "\n",
    "phasebutton.on_click(phase_button_clicked)\n",
    "# kernelbutton.on_click(kernel_button_clicked)\n",
    "# HBox([interactive(updateCutoff,value=cutoffslider),interactive(updatePhase,value=phaseslider),phasebutton,kernelbutton])\n",
    "HBox([interactive(updateCutoff,value=cutoffslider),interactive(updatePhase,value=phaseslider),phasebutton])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Kernel, Set t=0 and Fit Initial Background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"1e84418b-fb01-4a6c-96e2-9d93eb52fd05\" data-root-id=\"41270\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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0)\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"41127\",\"type\":\"Circle\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#ff7f0e\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"41169\",\"type\":\"Line\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"41113\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"41251\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"41097\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"41204\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"41252\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"41098\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"41199\"},{\"id\":\"41200\"},{\"id\":\"41201\"},{\"id\":\"41202\"},{\"id\":\"41203\"},{\"id\":\"41204\"}]},\"id\":\"41206\",\"type\":\"Toolbar\"}],\"root_ids\":[\"41270\"]},\"title\":\"Bokeh Application\",\"version\":\"2.1.0\"}};\n",
       "  var render_items = [{\"docid\":\"593ea8ab-b2b9-4c80-aab8-ffc18b6e95e4\",\"notebook_comms_target\":\"42517\",\"root_ids\":[\"41270\"],\"roots\":{\"41270\":\"1e84418b-fb01-4a6c-96e2-9d93eb52fd05\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "41270"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71716e20acad402099f111def168ab25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(interactive(children=(IntSlider(value=18, description='Fit window', max=25, min=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#initial fit, finding spectrum start\n",
    "#need to reimplement different types of background models\n",
    "\n",
    "###originally in a build kernel button, but easy to forget to click\n",
    "spec.tmax = cutoffslider.value\n",
    "spec.cutoff=cutoffslider.value # is cutoff needed (and not just as spec.tmax) ??\n",
    "spec.build_kernel()\n",
    "spec.real=np.real(signal*np.e**(1j*spec.phase))/np.max(np.real(signal*np.e**(1j*spec.phase)))\n",
    "spec.imag=np.imag(signal*np.e**(1j*spec.phase))/np.max(np.real(signal*np.e**(1j*spec.phase)))\n",
    "######\n",
    "\n",
    "\n",
    "def gaussian(x,r,s,a):\n",
    "    return a*np.exp(-((x-r)**2/(2*s**2)))\n",
    "\n",
    "symmetry_point=np.argmax(spec.raw_time>=-spec.raw_time[0])+1 #find index of symmetry point about t=0,+1 for non inclusive\n",
    "spec.zeropoint,__,spec.zeroamp = gaussfit = optimize.curve_fit(gaussian,\n",
    "                                                               spec.raw_time[1:symmetry_point],\n",
    "                                                               spec.real[1:symmetry_point])[0]\n",
    "spec.bgmodel = bg3D\n",
    "spec.update_ranges()\n",
    "spec.fit_background(spec.bgmodel,spec.background_range)\n",
    "spec.background_correct()\n",
    "\n",
    "#zeropeak plot\n",
    "\n",
    "xs = [spec.raw_time[1:symmetry_point]]*2\n",
    "ys = [spec.real[1:symmetry_point],\n",
    "      gaussian(spec.raw_time,*gaussfit)[1:symmetry_point]]\n",
    "\n",
    "zeropointx = [spec.zeropoint]*2\n",
    "zeropointy = [spec.real[1:symmetry_point].min(),1]\n",
    "\n",
    "zeropeak = spec_plot(xs,ys, plot_height=300)\n",
    "zeropeak.line(zeropointx, zeropointy, color=(0,255,0))\n",
    "zeropeak.circle([spec.zeropoint], [spec.zeroamp], color=(255,0,0))\n",
    "\n",
    "#bg fit plot\n",
    "\n",
    "xs = [spec.raw_time]*2\n",
    "ys = [spec.real,spec.background]\n",
    "\n",
    "cutoffx=[spec.cutoff]*2\n",
    "cutoffy=[spec.real.min(),1]\n",
    "\n",
    "backgroundx=[spec.background_start]*2\n",
    "backgroundy=[spec.real.min(),1]\n",
    "\n",
    "bgsub=spec_plot(xs,ys,plot_height=300)\n",
    "bgsub.line(cutoffx,cutoffy,color=(255,0,0))\n",
    "bgsub.line(backgroundx,backgroundy,color=(0,0,255))\n",
    "\n",
    "#waveform plot \n",
    "waveformplot=spec_plot(spec.t,spec.waveform,plot_height=300,y_range = (-.2,1.1))\n",
    "waveformplot.line(spec.t,0,line_color = 'black', line_dash = 'dashed', line_width = 1)\n",
    "\n",
    "\n",
    "processingplots = show(gridplot([[zeropeak,bgsub],[None,waveformplot]]),notebook_handle=True)\n",
    "\n",
    "windowslider=widgets.IntSlider(description='Fit window', \n",
    "                               value=symmetry_point, min=4+np.argmax(spec.real==spec.real.max()), max=round(len(spec.raw_time)/30))\n",
    "bgslider=widgets.SelectionSlider(description='Background Start', \n",
    "                                 value=spec.background_start, \n",
    "                                 options=spec.raw_time[4+np.argmax(spec.real==spec.real.max()):np.argmax(spec.raw_time==spec.cutoff)-20])\n",
    "normalizer=widgets.Dropdown(description= 'V(t=0)', options=[\"Gaussian Fit\",\n",
    "                                   \"Gaussian Center, Data Maximum Amp\", \n",
    "                                   \"Data Max Center, Data Maximum Amp\"])\n",
    "\n",
    "bgmodel_select = widgets.Dropdown(description= 'BG Model', options=[\"3D\",\"Fractal\"])\n",
    "\n",
    "\n",
    "\n",
    "def updateBg(value): \n",
    "    spec.background_start = value\n",
    "    spec.update_ranges()\n",
    "\n",
    "    spec.fit_background(spec.bgmodel,spec.background_range)\n",
    "    bgsub.renderers[-3].data_source.data['y'] = spec.background\n",
    "    bgsub.renderers[-1].data_source.data['x']=[value]*2\n",
    "    \n",
    "    spec.background_correct()\n",
    "    \n",
    "    waveformplot.renderers[-2].data_source.data={'x':spec.t,'y':spec.waveform}\n",
    "    \n",
    "    push_notebook(handle=processingplots)\n",
    "\n",
    "\n",
    "def updateWindow(value):\n",
    "    spec.zeropoint,__,spec.zeroamp = gaussfit = optimize.curve_fit(gaussian,\n",
    "                                                                   spec.raw_time[1:value],\n",
    "                                                                   spec.real[1:value])[0]\n",
    "\n",
    "    if normalizer.value == \"Gaussian Center, Data Maximum Amp\":\n",
    "        spec.zeroamp = spec.real[abs(spec.raw_time-spec.zeropoint).argmin()]\n",
    "    elif normalizer.value == \"Data Max Center, Data Maximum Amp\":\n",
    "        spec.zeropoint = spec.raw_time[np.argmax(spec.real==spec.real.max())]\n",
    "        spec.zeroamp = spec.real.max()\n",
    "        \n",
    "    spec.update_ranges()\n",
    "    updateBg(bgslider.value)\n",
    "    \n",
    "    newx=spec.raw_time[1:value]\n",
    "    newy=spec.real[1:value]\n",
    "    fity=gaussian(newx,*gaussfit)\n",
    "    plot_order = [[newx,newy],\n",
    "                  [newx,fity],\n",
    "                  [[spec.zeropoint]*2,[newy.min(),1]],\n",
    "                  [[spec.zeropoint],[spec.zeroamp]]]\n",
    "    \n",
    "    for idx, line in enumerate(zeropeak.renderers):\n",
    "        line.data_source.data = {'x': plot_order[idx][0], 'y': plot_order[idx][1]}\n",
    "\n",
    "    \n",
    "    push_notebook(handle=processingplots)\n",
    "\n",
    "def updatebgModel(value):\n",
    "    if bgmodel_select.value == '3D':\n",
    "        spec.bgmodel = bg3D\n",
    "    elif bgmodel_select.value == 'Fractal':\n",
    "        spec.bgmodel = bgFractal\n",
    "        \n",
    "    updateBg(bgslider.value)\n",
    "    \n",
    "\n",
    "def updateNorm(value):\n",
    "    normMethod = value\n",
    "    updateWindow(windowslider.value)\n",
    "    updateBg(bgslider.value)\n",
    "\n",
    "VBox([HBox([interactive(updateWindow,value=windowslider),interactive(updateNorm,value=normalizer),interactive(updatebgModel,value=bgmodel_select)]),\n",
    "HBox([interactive(updateBg,value=bgslider)])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# L-Curve Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"9a98df62-0345-413f-918e-36502cfe067d\" data-root-id=\"43120\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
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Application\",\"version\":\"2.1.0\"}};\n",
       "  var render_items = [{\"docid\":\"a7a9a79d-59bf-4a2f-b960-8cd392343a3e\",\"notebook_comms_target\":\"44413\",\"root_ids\":[\"43120\"],\"roots\":{\"43120\":\"9a98df62-0345-413f-918e-36502cfe067d\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "43120"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b319e020a0b74ddb8485a967a487830b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(interactive(children=(IntSlider(value=32, description='Alpha Value', max=50), Output()), _dom_c…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spec.generate_lcurve()\n",
    "\n",
    "#l-curve compoenents, initialize choice of alpha with LOOCV\n",
    "residualnorm=np.linalg.norm(spec.tikhonovfits-spec.waveform,axis=1)**2\n",
    "solutionnorm=np.linalg.norm(np.dot(spec.Lmatrix,spec.solutions.T).T,axis=1)**2\n",
    "alpha_LOOCV_index=(residualnorm/(np.sum(1-spec.hMats,axis=1))**2).argmin()\n",
    "\n",
    "#NOT SURE IM COMPUTING LOOCV CORRECTLY ABOVE, NEED TO GO RE-WORK\n",
    "\n",
    "\n",
    "#l-curve\n",
    "lcurve = lcurve_plot([residualnorm,solutionnorm],plot_height=300)\n",
    "lcurve.circle([residualnorm[alpha_LOOCV_index]], [solutionnorm[alpha_LOOCV_index]],color=(255,0,0)) #LOOCV choice for alpha\n",
    "lcurve.circle([residualnorm[alpha_LOOCV_index]], [solutionnorm[alpha_LOOCV_index]],color=(0,0,0)) #user defined choice for alpha\n",
    "\n",
    "#waveform fit\n",
    "xs = [spec.t]*2\n",
    "ys = [spec.waveform,spec.tikhonovfits[alpha_LOOCV_index]]\n",
    "\n",
    "tikresults=spec_plot(xs,ys,plot_height=300)\n",
    "tikresults.line(spec.t,0,line_color = 'black', line_dash = 'dashed', line_width = 1)\n",
    "\n",
    "#fit residual\n",
    "tikresidual=spec_plot(spec.t,spec.waveform-spec.tikhonovfits[alpha_LOOCV_index],\n",
    "                      plot_height=300,\n",
    "                      y_range=([(spec.waveform-spec.tikhonovfits[alpha_LOOCV_index]).min(),\n",
    "                                (spec.waveform-spec.tikhonovfits[alpha_LOOCV_index]).max()]))\n",
    "\n",
    "#Distance distributions\n",
    "\n",
    "xs = [spec.r]*2\n",
    "ys = [spec.solutions[alpha_LOOCV_index]]*2\n",
    "distances = spec_plot(xs,ys,plot_height = 300,colorlist = [(255,0,0),mycolors(0)])\n",
    "distances.xaxis.axis_label=\"Distance (nm)\"\n",
    "# show the results\n",
    "\n",
    "\n",
    "lcurveplots = show(gridplot([[lcurve,tikresidual],[tikresults,distances]]),notebook_handle=True)\n",
    "\n",
    "def updateAlpha(value):\n",
    "    spec.alpha=spec.alphas[value]\n",
    "    tikresidual.renderers[-1].data_source.data['y']=spec.waveform-spec.tikhonovfits[value]\n",
    "    tikresults.renderers[-2].data_source.data['y']=spec.tikhonovfits[value]\n",
    "    lcurve.renderers[-1].data_source.data={'x':[residualnorm[value]],'y':[solutionnorm[value]]}\n",
    "    distances.renderers[-1].data_source.data['y'] = spec.solutions[value]\n",
    "    spec.solution = spec.solutions[value]\n",
    "    spec.tikhonovfit = spec.tikhonovfits[value]\n",
    "\n",
    "    push_notebook(handle=lcurveplots)\n",
    "\n",
    "#slider for index of list of alphas, actual alpha value can be found with spec.alpha\n",
    "alphaslider=widgets.IntSlider(description='Alpha Value', value=alpha_LOOCV_index, min=0, max=len(spec.alphas)-1)\n",
    "HBox([interactive(updateAlpha,value=alphaslider)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n",
      "Had to use 3D for this set of conditions\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"45a500ea-2394-4602-a40b-c88b4aeb64c8\" data-root-id=\"44951\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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KUyBKXtP0p5DXJeo+0/uFyhDOGh7T9rQat4O6DtP0z4UJ+Cnu0/THaAg76c7T9Ej0tV8prtP8k5IdYfme0/m+PQJEiX7T+xPRIJbJXtPwUHURaMk+0/xLcGvqiR7T8KQVpawo/tP5bWtjTZje0/qg8biu2L7T8wcQmO/4ntPy7Nm2wPiO0/hmIFTB2G7T9rVbJNKYTtPwU1Io8zgu0/wB2RKjyA7T97t3w3Q37tP6BADstIfO0/jCpw+Ex67T/c/BPRT3jtP9r162RRdu0/jv2awlF07T8L4Jz3UHLtP8NOaBBPcO0/StKLGExu7T/BlcYaSGztPy7EHSFDau0/GgvvND1o7T/guABfNmbtP8/Wj6cuZO0/qI1cFiZi7T8WFbWyHGDtP3Fjf4MSXu0/hcpBjwdc7T9fpirc+1ntP6s9F3DvV+0//+2ZUOJV7T+tuv+C1FPtP05RVQzGUe0/dpRr8bZP7T/Auts2p03tP18OC+GWS+0/6lcu9IVJ7T9r/Ux0dEftP+fdQ2ViRe0/R/DHyk9D7T/Vq2ioPEHtP8k/kgEpP+0/lJ6P2RQ97T9AYYwzADvtP5uGlhLrOO0/exGgedU27T8wiYBrvzTtP6te9uqoMu0/0jio+pEw7T8gKiadei7tP2/R6tRiLO0/uWdcpEoq7T9FvM0NMijtP8ogfxMZJu0/pEaft/8j7T9tDkz85SHtP+ZKk+PLH+0/KXhzb7Ed7T8eaNyhlhvtP9Dkr3x7Ge0/gEnCAWAX7T8OE9syRBXtP1lotREoE+0/G5sAoAsR7T/UoWDf7g7tPyeLbtHRDO0/L+u4d7QK7T8WQ8TTlgjtP2pjC+d4Bu0/acn/sloE7T+v9wk5PALtP3zKiXodAO0/38fWeP797D8GbEA13/vsP+5xDrG/+ew/pBiB7Z/37D9VZdHrf/XsP1JiMa1f8+w/NlvMMj/x7D9jFsd9Hu/sP+MLQI/97Ow/9ZlPaNzq7D9CNwgKu+jsP/WidnWZ5uw/wBKiq3fk7D8DX4ytVeLsPwotMnwz4Ow/mheLGBHe7D/g1YmD7tvsP7hgHL7L2ew/kRYsyajX7D/a3Z2lhdXsPxRGUlRi0+w/q6cl1j7R7D+IQvArG8/sP4dbhlb3zOw/yVi4VtPK7D/83FItr8jsP6nhHtuKxuw/etDhYGbE7D+xm12/QcLsP6XVUPccwOw/ecd2Cfi97D8Hh4f20rvsP/0LOL+tuew/R0Q6ZIi37D/NJz3mYrXsP3PL7EU9s+w/inPygxex7D+RpfSg8a7sP3A5l53LrOw/Hmp7eqWq7D+s5T84f6jsP+zcgNdYpuw/dxLYWDKk7D9P6dy8C6LsPwJzJATln+w/YX1BL76d7D/An8Q+l5vsP+BHPDNwmew/XsY0DUmX7D/ZWjjNIZXsP6c/z3P6kuw/PbV/AdOQ7D88Dc52q47sPyy1PNSDjOw/5kBMGlyK7D+1dHtJNIjsPylPR2IMhuw/nxIrZeSD7D+RTqBSvIHsP5PoHiuUf+w/GiUd72t97D8FsA+fQ3vsP+SkaTsbeew/DJecxPJ27D9tmRg7ynTsPzZGTJ+hcuw/SMak8Xhw7D9o2I0yUG7sP1fYcWInbOw/p8W5gf5p7D9sSs2Q1WfsP73BEpCsZew/ED7vf4Nj7D9lj8ZgWmHsP05J+zIxX+w/z8ju9gdd7D8bOgGt3lrsPyKekVW1WOw/D9D98ItW7D+RiqJ/YlTsPwxt2wE5Uuw/qQADeA9Q7D9JvXLi5U3sP1EOg0G8S+w/ZleLlZJJ7D/++OHeaEfsP+NU3B0/Rew/ldLOUhVD7D+N4wx+60DsP24H6Z/BPuw/H9C0uJc87D/F5cDIbTrsP60KXdBDOOw/Gx/Yzxk27D8GJYDH7zPsP8BDorfFMew/g8uKoJsv7D/xOIWCcS3sP3w43F1HK+w/wKnZMh0p7D/EosYB8ybsPypz68rIJOw/V6ePjp4i7D97C/pMdCDsP5aucAZKHuw/Y+U4ux8c7D86TZdr9RnsP97OzxfLF+w/QKElwKAV7D8vTNtkdhPsPwGrMgZMEew/Je9spCEP7D+uoso/9wzsP9Gqi9jMCuw/UUrvbqII7D/oIzQDeAbsP5Y8mJVNBOw/8v1YJiMC7D9oOLO1+P/rP20l40PO/es/qWkk0aP76z8TF7JdefnrPwyvxulO9+s/YiScdST16z9S3WsB+vLrP4K1bo3P8Os/6v/cGaXu6z+4iO6meuzrPy+X2jRQ6us/cu/XwyXo6z9S1BxU++XrPxAJ3+XQ4+s/EdNTeabh6z+R+68OfN/rP03RJ6ZR3es/ISrvPyfb6z+mZDnc/NjrP75pOXvS1us/J64hHajU6z/2MyTCfdLrPx+McmpT0Os/39c9FinO6z83yrbF/svrP1KpDXnUyes/4k9yMKrH6z+GLhTsf8XrPyJNIqxVw+s/JUzLcCvB6z/gZT06Ab/rP8hvpgjXvOs/tNsz3Ky66z8duRK1grjrP062b5NYtus/nSF3dy606z+M6lRhBLLrP/qiNFHar+s/PYBBR7Ct6z9DXKZDhqvrP6u2jUZcqes/1rUhUDKn6z/5J4xgCKXrPyaE9nfeous/VeuJlrSg6z9lKW+8ip7rPx62zulgnOs/IbbQHjea6z/w+5xbDZjrP84IW6Djles/vQ0y7bmT6z9f7EhCkJHrP+E3xp9mj+s/5DXQBT2N6z9T34x0E4vrP0rhIezpiOs/5520bMCG6z8qLWr2loTrP75dZ4ltgus/z7XQJUSA6z/Xc8rLGn7rP2aPeHvxe+s/5bn+NMh56z9jX4D4nnfrP02nIMZ1des/MXUCnkxz6z93aUiAI3HrPxriFG36bus/YPuJZNFs6z+JkMlmqGrrP4I89XN/aOs/lloujFZm6z8UB5avLWTrP/ofTd4EYus/n0V0GNxf6z9S2ytes13rPwAIlK+KW+s/07bMDGJZ6z/Pl/V1OVfrP20gLusQVes/M4yVbOhS6z9Q3Ur6v1DrPyndbJSXTus/9RwaO29M6z9F9nDuRkrrP5yLj64eSOs/8ciTe/ZF6z9GZJtVzkPrPyfewzymQes/N4IqMX4/6z+3Z+wyVj3rPwJyJkIuO+s/F1H1XgY56z8UgnWJ3jbrP7lPw8G2NOs/4NL6B48y6z/98jdcZzDrP5Jmlr4/Lus/rbMxLxgs6z9cMCWu8CnrPyADjDvJJ+s/YiOB16El6z/nWR+CeiPrPzxBgTtTIes/KkbBAywf6z8gqPnaBB3rP595RMHdGus/qKC7trYY6z8k13i7jxbrP0qrlc9oFOs/CoAr80ES6z9ujVMmGxDrPwLhJmn0Des/N16+u80L6z/CvjIepwnrP/6SnJCAB+s/TEIUE1oF6z9xC7KlMwPrP/QEjkgNAes/eh3A++b+6j8cHGC/wPzqP8mghZOa+uo/mSRIeHT46j8l+r5tTvbqP+FNAXQo9Oo/bSYmiwLy6j/uZESz3O/qP17Fcuy27eo/4d7HNpHr6j8UJFqSa+nqP2DjP/9F5+o/SUePfSDl6j+8Vl4N++LqP171wq7V4Oo/2ePSYbDe6j8lwKMmi9zqP9YFS/1l2uo/Zw7e5UDY6j+DEXLgG9bqP0klHO320+o/mz7xC9LR6j9iMQY9rc/qP9Cwb4CIzeo/rE9C1mPL6j+RgJI+P8nqPziWdLkax+o/tsP8RvbE6j+8HD/n0cLqP+KVT5qtwOo/3wRCYIm+6j/NICo5ZbzqP2iCGyVBuuo/SqQpJB246j8u42c2+bXqPyp+6VvVs+o/7ZbBlLGx6j/4MQPhja/qP982wUBqreo/fnAOtEar6j84jf06I6nqPyofodX/puo/bZwLhNyk6j9GX09GuaLqP2CmfhyWoOo/B5WrBnOe6j9ZM+gEUJzqP39uRhctmuo/4BjYPQqY6j9b6q5455XqP3OA3MfEk+o/i15yK6KR6j8T7oGjf4/qP71+HDBdjeo/sEZT0TqL6j+3YjeHGInqP3fW2VH2huo/mIxLMdSE6j/7Vp0lsoLqP+fu3y6QgOo/O/UjTW5+6j+Y8nmATHzqP5NX8sgqeuo/33ydJgl46j9/o4uZ53XqP+70zCHGc+o/T4Nxv6Rx6j+VSYlyg2/qP7QrJDtibeo/xPZRGUFr6j81YSINIGnqP/EKpRb/Zuo/in3pNd5k6j9jLP9qvWLqP9Z09bWcYOo/YZ7bFnxe6j/L2sCNW1zqP0tGtBo7Wuo/tOfEvRpY6j+VsAF3+lXqP2Z9eUbaU+o/rBU7LLpR6j8bLFUomk/qP8Fe1jp6Teo/KjfNY1pL6j+CKkijOknqP7qZVfkaR+o/sdEDZvtE6j9PC2Hp20LqP7Fre4O8QOo/RQRhNJ0+6j/00h/8fTzqPz/CxdpeOuo/Xalg0D846j9rTP7cIDbqP4BcrAACNOo/0Xd4O+Mx6j/XKXCNxC/qP2vroPalLeo/5iIYd4cr6j9DJOMOaSnqP0AxD75KJ+o/eHmphCwl6j+GGr9iDiPqPyUgXVjwIOo/SYSQZdIe6j9GL2aKtBzqP+D36saWGuo/faMrG3kY6j8r5jSHWxbqP9FiEws+FOo/QavTpiAS6j9WQIJaAxDqPxWSKybmDeo/wv/bCckL6j8E2J8FrAnqP/lYgxmPB+o/VbCSRXIF6j+A+9mJVQPqP6lHZeY4Aeo/6JFAWxz/6T9Xx3fo//zpPyrFFo7j+uk/ylgpTMf46T/sP7siq/bpP7Mo2BGP9Ok/v7GLGXPy6T9NauE5V/DpP0vS5HI77uk/eFqhxB/s6T9zZCIvBOrpP9hCc7Lo5+k/XDmfTs3l6T/efLEDsuPpP4AztdGW4ek/w3S1uHvf6T+ZSb24YN3pP36s19FF2+k/kYkPBCvZ6T+kvm9PENfpP14bA7T11Ok/RGHUMdvS6T/ZQ+7IwNDpP61oW3mmzuk/emcmQ4zM6T8xylkmcsrpPxkNACNYyOk/3J4jOT7G6T+f4M5oJMTpP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       "  var render_items = [{\"docid\":\"d5130950-2598-4f2e-b1f8-5ab8d157b306\",\"notebook_comms_target\":\"46300\",\"root_ids\":[\"44951\"],\"roots\":{\"44951\":\"45a500ea-2394-4602-a40b-c88b4aeb64c8\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "44951"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9f6d137193843f0bc3069e931ee7587",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(interactive(children=(IntRangeSlider(value=(1, 71), description='Backgrounds:', max=107), Outpu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##BG VALIDATION\n",
    "spec.validate_background(end_offset = 100)\n",
    "\n",
    "from scipy.signal import argrelextrema\n",
    "from scipy.stats import f as ftest\n",
    "\n",
    "# Measure residual of fits rescaled to uncorrected bg spectra to normalize noise\n",
    "\n",
    "amplitudes = [(1.0 - spec.backgrounds[i,spec.waveform_range][0])/(spec.backgrounds[i,spec.waveform_range][0]) \n",
    "              for i in range(len(spec.backgrounds))]\n",
    "\n",
    "reconstructed = [(np.dot(spec.kernel,spec.validation_solutions[i])/np.dot(spec.kernel,spec.validation_solutions[i]).max())\n",
    "                 *amplitudes[i]*spec.backgrounds[i,spec.waveform_range]+spec.backgrounds[i,spec.waveform_range] \n",
    "                 for i in range(len(spec.backgrounds))]\n",
    "\n",
    "validation_RMSD = np.sqrt(np.mean((spec.real[spec.waveform_range] - reconstructed)**2, axis = 1))\n",
    "\n",
    "# find local maxima, wellR and find wellL at the same RMSD value\n",
    "maxima_index = argrelextrema(validation_RMSD, np.greater)[0][0]\n",
    "\n",
    "#BUG HERE, this may fail!!\n",
    "try:\n",
    "    wellL = np.where(validation_RMSD[:maxima_index]>=validation_RMSD[maxima_index])[0][-1]\n",
    "except IndexError:\n",
    "    wellL = 0\n",
    "wellR = maxima_index\n",
    "RMSD_min_index = validation_RMSD[wellL:wellR].argmin()\n",
    "\n",
    "\n",
    "xs = spec.raw_time[[np.argwhere(spec.raw_time == point)[0][0] for point in spec.background_fit_points]]\n",
    "ys = validation_RMSD\n",
    "rmsd_plot = spec_plot(xs,ys,plot_height = 300, y_range = (min(validation_RMSD[wellL:wellR])*.98,validation_RMSD[wellL]))\n",
    "\n",
    "#my_DOF this is clearly incorrect, need to figure out DOF based on LOOCV but is good enought\n",
    "#since the purpose is to determine the bottoms of the background validation well only.\n",
    "my_DOF = len(spec.waveform) - 3 \n",
    "# f_test = np.where(0.6 >= np.round(ftest.cdf(validation_RMSD[wellL:wellR]/np.median(validation_RMSD[wellL:wellR]),my_DOF,my_DOF),decimals = 2))[0]\n",
    "f_test = np.where(0.6 >= np.round(ftest.cdf(validation_RMSD/validation_RMSD[RMSD_min_index],my_DOF,my_DOF),decimals = 2))[0]\n",
    "try:\n",
    "    f_test = f_test[:np.where(1<(f_test[1:]-f_test[:-1]))[0][0]+1] #find first discontinuity in ftest (edge of well)\n",
    "except IndexError:\n",
    "    f_test = f_test\n",
    "    \n",
    "\n",
    "\n",
    "rmsd_plot.circle(xs,ys)\n",
    "\n",
    "rmsd_plot.line(xs[f_test],validation_RMSD[f_test],color = (0,0,0), line_width=3)\n",
    "rmsd_plot.circle([xs[RMSD_min_index]],[validation_RMSD[RMSD_min_index]], color = (255,0,0))\n",
    "rmsd_plot.circle([xs[RMSD_min_index]],[validation_RMSD[RMSD_min_index]], color = (0,255,0))\n",
    "\n",
    "rmsd_plot.xaxis.axis_label=\"Background Fit Start (µs)\"\n",
    "rmsd_plot.yaxis.axis_label_text_font='helvetica'\n",
    "rmsd_plot.yaxis.axis_label_text_font_size='14pt'\n",
    "\n",
    "\n",
    "rmsd_plot.yaxis.axis_label=\"Spectrum Fit RMSD\"\n",
    "\n",
    "backgrounds_plot=spec_plot([spec.raw_time]*3, #x-values\n",
    "                      [spec.real,spec.background,spec.backgrounds[RMSD_min_index]], #y-values\n",
    "                      plot_height=300)\n",
    "\n",
    "spectrums_plot = spec_plot([spec.t]*2,\n",
    "                                 [spec.waveforms[RMSD_min_index],spec.validation_tikhonovfits[RMSD_min_index]],\n",
    "                                 plot_height = 300, y_range = (np.min(spec.waveforms),1.1)) #This is a DDT Lab joke\n",
    "spectrums_plot.line(spec.t,0,line_color = 'black', line_dash = 'dashed', line_width = 1)\n",
    "\n",
    "distances_plot = spec_plot([spec.r]*2,\n",
    "                                   [spec.validation_solutions[RMSD_min_index],spec.validation_solutions[RMSD_min_index]],\n",
    "                                   y_range = (0,np.max(spec.validation_solutions)), \n",
    "                                   plot_height = 300)\n",
    "distances_plot.varea(x=spec.r,\n",
    "        y1=np.quantile(spec.validation_solutions,0, axis=0),\n",
    "        y2=np.quantile(spec.validation_solutions,1, axis=0), fill_alpha = 0.2, level = 'underlay')\n",
    "\n",
    "distances_plot.xaxis.axis_label=\"Distance (nm)\"\n",
    "\n",
    "\n",
    "validation_plots = show(gridplot([[rmsd_plot,backgrounds_plot],[spectrums_plot,distances_plot]]), notebook_handle = True)\n",
    "\n",
    "def updateRegion(end_points):\n",
    "    new_range = range(*end_points)\n",
    "    \n",
    "    if not new_range:\n",
    "        new_range = [end_points[0]] #happens if the range slider handles are on top of eachother\n",
    "        \n",
    "    RMSD_min_index = validation_RMSD[new_range].argmin()\n",
    "    rmsd_plot.renderers[-2].data_source.data={'x':[xs[RMSD_min_index]],'y': [validation_RMSD[RMSD_min_index]]}\n",
    "    rmsd_plot.renderers[-3].data_source.data={'x':xs[new_range],'y': validation_RMSD[new_range]}\n",
    "    rmsd_plot.y_range.end = validation_RMSD[new_range].max()*1.02\n",
    "    spectrums_plot.y_range.start = np.min(spec.waveforms[new_range]) #this throws an error if the range is 0\n",
    "    #try:\n",
    "    distances_plot.renderers[-1].data_source.data = {'x': spec.r,\n",
    "                                                     'y1': np.quantile(spec.validation_solutions[new_range], 0, axis=0),\n",
    "                                                     'y2': np.quantile(spec.validation_solutions[new_range], 1, axis=0)}\n",
    "    distances_plot.renderers[0].data_source.data['y'] = spec.validation_solutions[RMSD_min_index]\n",
    "    \n",
    "    bgslider.options = new_range\n",
    "#     except IndexError:\n",
    "#         print('Zero width range, distances envelope not updated')\n",
    "#         bgslider.options = [end_points[0]]\n",
    "#         #dont update if range is 0 width\n",
    "    \n",
    "    push_notebook(handle = validation_plots)\n",
    "    \n",
    "def updateSelection(value):\n",
    "    rmsd_plot.renderers[-1].data_source.data={'x':[xs[value]],'y': [validation_RMSD[value]]}\n",
    "    backgrounds_plot.renderers[-1].data_source.data['y'] = spec.backgrounds[value]\n",
    "    spectrums_plot.renderers[-3].data_source.data['y'] = spec.waveforms[value]\n",
    "    spectrums_plot.renderers[-2].data_source.data['y'] = spec.validation_tikhonovfits[value]\n",
    "    distances_plot.renderers[-2].data_source.data['y'] = spec.validation_solutions[value]\n",
    "    \n",
    "    spec.background_start = spec.background_fit_points[bgslider.value]\n",
    "    spec.waveform = spec.waveforms[bgslider.value]\n",
    "    spec.tikhonovfit = spec.validation_tikhonovfits[bgslider.value]\n",
    "    spec.solution = spec.validation_solutions[bgslider.value]\n",
    "    \n",
    "    push_notebook(handle = validation_plots)\n",
    "\n",
    "rmsdSlider = widgets.IntRangeSlider(description = 'Backgrounds:', value =list(f_test[[0,-1]]), min = 0, max = validation_RMSD.shape[0])\n",
    "bgslider = widgets.SelectionSlider(description = 'Selection:', value = RMSD_min_index, options = range(rmsdSlider.value[0],rmsdSlider.value[1]+1))\n",
    "\n",
    "VBox([interactive(updateRegion,end_points=rmsdSlider),interactive(updateSelection,value=bgslider)])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background Supplementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"3ac81ff6-a0d1-4e6c-ba50-943f2515ca16\" data-root-id=\"46965\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"bcf0228c-e7c3-4e50-b844-062133f559d3\":{\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"46875\"},{\"id\":\"46924\"}]},\"id\":\"46965\",\"type\":\"Row\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"rgb(0, 0, 0)\",\"line_dash\":[6],\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"46913\",\"type\":\"Line\"},{\"attributes\":{\"axis_label\":\"Distance 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       "  var render_items = [{\"docid\":\"bcf0228c-e7c3-4e50-b844-062133f559d3\",\"notebook_comms_target\":\"48338\",\"root_ids\":[\"46965\"],\"roots\":{\"46965\":\"3ac81ff6-a0d1-4e6c-ba50-943f2515ca16\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
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       "id": "46965"
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     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c71cd0cfc1640ee8b76d793c3cdd65d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(interactive(children=(SelectionSlider(description='Instability Point:', index=295, options=(1.0…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width_cutoff = 4*(spec.t[-1]/2)**(1/3)\n",
    "distance_cutoff = 5*(spec.t[-1]/2)**(1/3)\n",
    "#Jeschke, G. (2012). DEER distance measurements on proteins. Annual Review of Physical Chemistry, 63, 419–446.\n",
    "instability_point_index = abs(spec.r-(width_cutoff+(distance_cutoff-width_cutoff)/2)).argmin()\n",
    "instability_point_start = spec.r[instability_point_index]\n",
    "distance_mask = spec.r*0\n",
    "distance_mask[instability_point_index:] = 1\n",
    "\n",
    "spec.unstable_waveform = np.dot(spec.kernel, spec.solution*distance_mask)\n",
    "spec.stable_waveform = (spec.waveform - spec.unstable_waveform)/(1.0 - spec.unstable_waveform.max())\n",
    "spec.stable_solution = spec.waveform_regularize(spec.stable_waveform)\n",
    "#below needs to be revisited, seems like solution shouldnt be modified after the fact\n",
    "spec.stable_solution = spec.stable_solution/integrate.trapz(spec.stable_solution)\n",
    "spec.stable_fit = np.dot(spec.kernel,spec.stable_solution)\n",
    "spec.stable_fit = spec.stable_fit/spec.stable_fit[0]\n",
    "\n",
    "\n",
    "instability_plot = spec_plot(spec.r,spec.solution,y_range = (0,spec.solution.max()*1.1), plot_height = 300)\n",
    "instability_plot.line([instability_point_start]*2,[0,spec.solution.max()*1.1],color = (0,0,0), line_dash = 'dashed')\n",
    "instability_plot.varea(x=spec.r,\n",
    "                       y1=np.quantile(spec.validation_solutions[range(*rmsdSlider.value)],0, axis=0),\n",
    "                       y2=np.quantile(spec.validation_solutions[range(*rmsdSlider.value)],1, axis=0), \n",
    "                       fill_alpha = 0.2, level = 'underlay')\n",
    "\n",
    "green_box = BoxAnnotation(left=0, right=width_cutoff, fill_color='green', fill_alpha=0.1)\n",
    "yellow_box = BoxAnnotation(left=width_cutoff, right=distance_cutoff, fill_color='yellow', fill_alpha=0.1)\n",
    "red_box = BoxAnnotation(left=width_cutoff, right=10, fill_color='red', fill_alpha=0.1)\n",
    "instability_plot.add_layout(green_box)\n",
    "instability_plot.add_layout(yellow_box)\n",
    "instability_plot.add_layout(red_box)\n",
    "instability_plot.xaxis.axis_label = \"Distance (nm)\"\n",
    "\n",
    "filtered_distances = spec_plot([spec.r,spec.r],\n",
    "                               [spec.solution/integrate.trapz(spec.solution-spec.solution*distance_mask),spec.stable_solution],y_range = (-.001,spec.solution.max()*1.1), plot_height = 300)\n",
    "filtered_distances.xaxis.axis_label = \"Distance (nm)\"\n",
    "\n",
    "instability_cutoff = show(row(instability_plot,filtered_distances), notebook_handle = True)\n",
    "\n",
    "def setCutoff(distance):\n",
    "    instability_plot.renderers[-2].data_source.data['x'] = [distance,distance]\n",
    "    \n",
    "    cutoff_index = np.where(spec.r == distance)[0][0]\n",
    "    distance_mask = spec.r*0\n",
    "    distance_mask[cutoff_index:] = 1\n",
    "\n",
    "\n",
    "    spec.unstable_waveform = np.dot(spec.kernel, spec.solution*distance_mask)\n",
    "    spec.stable_waveform = (spec.waveform - spec.unstable_waveform)/(1.0 - spec.unstable_waveform.max())\n",
    "    #below needs to be revisited, seems like solution shouldnt be modified after the fact\n",
    "    spec.stable_solution = spec.waveform_regularize(spec.stable_waveform)\n",
    "    spec.stable_solution = spec.stable_solution/integrate.trapz(spec.stable_solution)\n",
    "    spec.stable_fit = np.dot(spec.kernel,spec.stable_solution)\n",
    "    spec.stable_fit = spec.stable_fit/spec.stable_fit[0]\n",
    "    \n",
    "    filtered_distances.renderers[-1].data_source.data['y'] = spec.stable_solution\n",
    "    filtered_distances.renderers[-2].data_source.data['y'] = spec.solution/integrate.trapz(spec.solution-spec.solution*distance_mask)\n",
    "    \n",
    "    push_notebook(handle = instability_cutoff)\n",
    "\n",
    "\n",
    "#Does not currently update in real time, need to find a way to decouple with calculation of new distance distribution\n",
    "instability_slider = widgets.SelectionSlider(description = 'Instability Point:', value = instability_point_start, options = spec.r,continuous_update=True)\n",
    "\n",
    "HBox([interactive(setCutoff,distance = instability_slider)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"d2de3e93-dcce-478b-8064-248d154b1b0a\" data-root-id=\"48797\"></div>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  function embed_document(root) {\n",
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       "  var render_items = [{\"docid\":\"96bcd026-6730-4899-a3d0-ad2ac0baab59\",\"notebook_comms_target\":\"50238\",\"root_ids\":[\"48797\"],\"roots\":{\"48797\":\"d2de3e93-dcce-478b-8064-248d154b1b0a\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "48797"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61d30f575cb5449194f5c19e653562e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Tab(children=(VBox(children=(interactive(children=(SelectionSlider(description='1G', index=100,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.palettes import Category10_10 as Cat10\n",
    "\n",
    "#Gaussian modeling utility\n",
    "#create spec.gaussian_models\n",
    "ngauss = 5\n",
    "spec.gaussian_model_init(ngauss)\n",
    "#flags for fitting and optimization\n",
    "fitted = np.zeros(ngauss,dtype=int)\n",
    "optimized = np.zeros(ngauss,dtype=int)\n",
    "bics = np.ones(ngauss)\n",
    "model_rmsds = np.zeros(ngauss)\n",
    "\n",
    "def bic(data,fit,n_model_params):\n",
    "    k = n_model_params+1\n",
    "    n = len(data)\n",
    "    return n*np.log(np.linalg.norm(data-fit)**2/n)+k*np.log(n)\n",
    "\n",
    "\n",
    "\n",
    "model_plot = spec_plot(spec.r,spec.stable_solution, x_range = (1,10.1),plot_height = 300)\n",
    "model_plot.xaxis.axis_label=\"Distance (nm)\"\n",
    "#initialize points on which to seed gaussian fits\n",
    "for model in range(ngauss):\n",
    "    centers = spec.gaussian_model(model)[:,0]\n",
    "    amps = spec.stable_solution[np.argwhere([spec.r == item for item in centers])[:,1]]\n",
    "    model_plot.circle(centers,amps,visible = False)\n",
    "model_plot.renderers[1].visible = True \n",
    "\n",
    "#initialize eventual gaussian fit plots\n",
    "for model in range(ngauss):\n",
    "    model_plot.line(spec.r,spec.r*0, color = 'orange',visible = False)\n",
    "\n",
    "stable_rmsd = np.sqrt(np.mean((spec.stable_waveform-spec.stable_fit)**2))*10**2\n",
    "\n",
    "model_rmsd_plot = spec_plot(np.linspace(0,ngauss+1),np.array([stable_rmsd]*len(np.linspace(0,ngauss+1))),y_range = (0,1.5*stable_rmsd),plot_height = 300)\n",
    "model_rmsd_plot.xaxis.axis_label=\"Number of Gaussians\"\n",
    "model_rmsd_plot.renderers[0].glyph.line_dash = 'dashed'\n",
    "model_rmsd_plot.renderers[0].glyph.line_color = 'black'\n",
    "model_rmsd_plot.line(np.arange(ngauss)[fitted==1]+1,model_rmsds[fitted==1], line_color = 'black', visible = False)\n",
    "model_rmsd_plot.circle(np.arange(ngauss)[fitted==1]+1,model_rmsds[fitted==1], visible = True)\n",
    "model_rmsd_plot.circle([0],[0], fill_color=\"white\", size = 10, color = 'red', fill_alpha = 0,visible = False)\n",
    "\n",
    "model_waveform_plot = spec_plot([spec.t]*3,\n",
    "                                 [spec.stable_waveform,spec.stable_fit, spec.stable_fit*0],\n",
    "                                 plot_height = 300, y_range = (np.min(spec.stable_waveform),1.1))\n",
    "model_waveform_plot.renderers[0].glyph.line_color = 'gray'\n",
    "model_waveform_plot.renderers[0].glyph.line_alpha = 0.7\n",
    "model_waveform_plot.renderers[1].glyph.line_color = Cat10[0]\n",
    "model_waveform_plot.renderers[2].glyph.line_color = Cat10[1]\n",
    "\n",
    "model_residual_plot = spec_plot([spec.t]*2,[spec.stable_waveform-spec.stable_fit]*2,plot_height = 300)\n",
    "    \n",
    "gaussian_modeling = show(gridplot([[model_plot,model_rmsd_plot],[model_waveform_plot,model_residual_plot]]), notebook_handle=True)\n",
    "\n",
    "def update_gaussians(model,ith_gaussian, value):\n",
    "    spec.gaussian_model(model)[ith_gaussian,0] = value\n",
    "    move_points(model)\n",
    "\n",
    "def move_points(model, update_sliders = False):\n",
    "    centers = spec.gaussian_model(model)[:,0]\n",
    "    #centers may be due to fit, so finde nearest for spec.r to satisfy slider\n",
    "    nearest_centers = spec.r[np.argmin([abs(spec.r-item) for item in centers], axis =1)]\n",
    "    amps = spec.stable_solution[np.argwhere([spec.r == item for item in nearest_centers])[:,1]]\n",
    "    model_plot.renderers[model+1].data_source.data = {'x': centers, 'y': amps}\n",
    "    \n",
    "    if update_sliders:\n",
    "        #update sliders\n",
    "        for slider_no, center in enumerate(nearest_centers):\n",
    "            tabs.children[model].children[slider_no].children[0].value = center\n",
    "    \n",
    "    push_notebook(handle = gaussian_modeling)\n",
    "    \n",
    "def update_figure(widget):\n",
    "    #Show only points corresponding to the selected tab, only gets called once tab is clicked\n",
    "    tab_idx = widget['new']\n",
    "    for idx in range(1,len(model_plot.renderers)):\n",
    "        model_plot.renderers[idx].visible = False\n",
    "    model_plot.renderers[tab_idx+1].visible = True\n",
    "    \n",
    "    if fitted[tab_idx]:\n",
    "        model_plot.renderers[tab_idx+1+ngauss].visible = True\n",
    "\n",
    "        gaussian_waveform = spec.model_waveform(gaussians,spec.gaussian_model(tab_idx))\n",
    "        model_waveform_plot.renderers[2].data_source.data['y'] = gaussian_waveform\n",
    "        model_residual_plot.renderers[1].data_source.data['y'] = spec.stable_waveform-gaussian_waveform\n",
    "    \n",
    "    push_notebook(handle = gaussian_modeling)\n",
    "\n",
    "\n",
    "    \n",
    "def gaussians(x,coeffs):\n",
    "    #coeffs in form of [[ngauss,3]] and component order r,s,a\n",
    "    peaks = (coeffs[:,2]*np.exp(-((x[:,np.newaxis]-coeffs[:,0])**2/(2*coeffs[:,1]**2)))).T\n",
    "    return np.sum(peaks,axis = 0)\n",
    "\n",
    "def curve_fit_gaussians(x,*coeffs):\n",
    "    this_is_dumb = np.reshape(coeffs, (len(coeffs)//3,3))\n",
    "    return gaussians(x,this_is_dumb)\n",
    "\n",
    "def fit_gaussians(val):\n",
    "    model = tabs.selected_index\n",
    "    fitted[model] = 1\n",
    "    optimized[model] = 0\n",
    "    \n",
    "    initial_con = spec.gaussian_model(model).ravel()\n",
    "    #reset widths,amps so not trapped in bad fit\n",
    "    initial_con[1::3] = np.zeros(model+1)+.1\n",
    "    initial_con[2::3] = np.zeros(model+1)+.1\n",
    "    \n",
    "    low_bounds = np.array([[-1.2*value,-np.inf,-np.inf]for value in spec.gaussian_model(model)[:,0]]).flatten()\n",
    "    high_bounds = low_bounds*-1\n",
    "    \n",
    "    fit_coeff = optimize.curve_fit(curve_fit_gaussians,\n",
    "                                   spec.r,\n",
    "                                   spec.stable_solution, \n",
    "                                   p0 = initial_con,\n",
    "                                   bounds = (low_bounds,high_bounds),\n",
    "                                   method = 'trf')[0].reshape(model+1,3)\n",
    "    #update gaussian_models\n",
    "    spec.gaussian_model(model)[:] = abs(fit_coeff)\n",
    "    move_points(model, update_sliders = True)\n",
    "    draw_gaussians(model)\n",
    "    \n",
    "def draw_gaussians(model):\n",
    "    fit_curve = gaussians(spec.r,spec.gaussian_model(model))\n",
    "    fit_curve = fit_curve/integrate.trapz(fit_curve)\n",
    "   \n",
    "    model_plot.renderers[model+1+ngauss].data_source.data = {'x': spec.r,'y': fit_curve}\n",
    "    model_plot.renderers[model+1+ngauss].visible = True\n",
    "    \n",
    "    gaussian_waveform = spec.model_waveform(gaussians,spec.gaussian_model(model))\n",
    "    \n",
    "    bics[model] = bic(spec.stable_waveform,gaussian_waveform,(model+1)*3)\n",
    "    \n",
    "    bics_min = np.argmin(bics)\n",
    "    spec.best_model_id = bics_min\n",
    "    \n",
    "    model_rmsds[model] = np.sqrt(np.mean((spec.stable_waveform-gaussian_waveform)**2))*10**2\n",
    "    \n",
    "    model_rmsd_plot.renderers[1].data_source.data = {'x': np.arange(ngauss)[fitted==1]+1 ,'y': model_rmsds[fitted==1]}\n",
    "    model_rmsd_plot.renderers[2].data_source.data = {'x': np.arange(ngauss)[fitted==1]+1 ,'y': model_rmsds[fitted==1]}\n",
    "    model_rmsd_plot.renderers[3].data_source.data = {'x': [np.arange(ngauss)[bics_min]+1] , 'y': [model_rmsds[bics_min]]}\n",
    "    model_rmsd_plot.renderers[3].visible = True\n",
    "    model_rmsd_plot.y_range.end = model_rmsds.max()*1.1\n",
    "    \n",
    "    model_waveform_plot.renderers[2].data_source.data['y'] = gaussian_waveform\n",
    "    model_residual_plot.renderers[1].data_source.data['y'] = spec.stable_waveform-gaussian_waveform\n",
    "    \n",
    "    \n",
    "    if np.sum(fitted)>=2:\n",
    "        model_rmsd_plot.renderers[1].visible = True\n",
    "\n",
    "    push_notebook(handle = gaussian_modeling)\n",
    "\n",
    "\n",
    "\n",
    "def optimize_gaussians(val):\n",
    "    \n",
    "    model_func = lambda x: spec.model_waveform(gaussians,x)\n",
    "    \n",
    "    for model in np.where(optimized ^ fitted)[0]:\n",
    "        tabs.selected_index = model\n",
    "        spec.gaussian_model(model)[:] = gradient_descent(spec.stable_waveform,model_func,spec.gaussian_model(model))\n",
    "        move_points(model, update_sliders = True)\n",
    "        draw_gaussians(model)\n",
    "\n",
    "        optimized[model] = 1\n",
    "\n",
    "       \n",
    "    \n",
    "### Widget Initialization###\n",
    "children = [VBox([interactive(update_gaussians, model = fixed(model), \n",
    "                              ith_gaussian = fixed(ith_gaussian), \n",
    "                              value = widgets.SelectionSlider(value = spec.gaussian_model(model)[ith_gaussian,0], \n",
    "                                                              description = str(ith_gaussian+1)+'G', \n",
    "                                                              options = spec.r)) \n",
    "                  for ith_gaussian in range(model+1)]) \n",
    "            for model in range(ngauss)]\n",
    "\n",
    "tabs = widgets.Tab()\n",
    "tabs.children = children\n",
    "for model in range(ngauss):\n",
    "    tabs.set_title(model, str(model+1)+'G')\n",
    "    \n",
    "tabs.observe(update_figure, names = 'selected_index')\n",
    "\n",
    "fit_gauss_button=widgets.Button(description='Initialize Gaussian(s)')\n",
    "optimize_gauss_button=widgets.Button(description='Fit Waveform')\n",
    "\n",
    "fit_gauss_button.on_click(fit_gaussians)\n",
    "optimize_gauss_button.on_click(optimize_gaussians)\n",
    "\n",
    "HBox([tabs,VBox([fit_gauss_button,optimize_gauss_button])])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monte Carlo Error Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
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FNlKuc/8BZvMBHv7D/ao6iqN0/tP6LNxOg+h+0/BAYAAAAA4D+bh1lcGFnoP0nNCjLO+O0/8SbAd/Za7T9IyP05iNPjP4LL/Eo8Mew/Ab+4RaTk5j+Vfi1oHRHsP8QPFqtA5+g/CaJBlNzR7D96qlzoHS/qP8vl6Js2su0/KQU78Iie5z9NaFEPhmPgP3yDGfGUiu0/YIvmxDBV6z9MqYAox93qPwM0cbODPek/F1OsHjK+6T8s1W068znqP7ZV4DRBtes/BB/mn9E26z9Cqk2Q2z3kP/kaVfg0E+0/6nojT9pc7j+ukjZRXWToPy3rZ9xne+s/ceYd4P5U6z9SSdegm6PqPxPRoK/ZFuU/yweEzewM6D/UZoRmOrDrP+HAY37IRe0/FHY4O6306z94H315C/HpP8Sg0rgco+s/pYlCh94c6D8GhPL4gUruP5HqgTXxnew/tNWZTR9I7D8IE4qWzALjP6JtNW0P6eg/ewTScGZh6D/8AiQAP5vlP44BNaonIu4/nsycwjcR7T/Dcs28XBHoP2Qip9DSS+A/sO/jxzcQ4T9t5/8PYiDtP0zGCmz2eug/rBBQIuyv4D9dW3MA+mjqP+fw8fg42uo/H+KPWlcH5D8qGW/zJcLnP/wrR51ayeM/5l+nPrjy4z+Z0G2DHGLpPxbO59WE++o/aOR0Kv145z9IxEXF78LrP+TWcigq3eU/eKtA29cH6T8uN1YsoyXnP5h6b0S7LOs/LmNOjpRs7D+VTDsQXpLsP26icGPXaug/TPVfttAi5z8H+ax4MgXnP5pO1ZLem+s/ZYFCUAlp7D/mUEtQ4GPpP9iA+lMheuA/rLbqX7L37T9k+KcTXDfsPwKMBuvL2u0/VYfyI53p4z/CTrxB7gHrP2rHVjNO4eg/VlhLWupf7T89QfufXvTlP1qgJ82BuOo/B/vIil6l6T8a0XEnRDDqP+16TK/zJ+0/75ZnmR+X7D/+VbDhtsHiP6CmHEj6gus/h7yU616c7T8ongQTLCjtP950nkv6seQ/1kGSe0Wk7T9L7zo8XyzuP23hT+GI0us/iwKHAcdO4T+TSmZV7iHuP6NRkmoV6uo/mqAEYsVW7j/hGIFkmgPsPxwzZ+Vz8+w/jZumEEF27T/JFJHqClnrP7cj3ioGX+w/d1Dq4oZf7j8Uj/N1WvHqP/6lMIukY+M/1T4jJl8a7T/GOg7eiezrP8yxy0/xheo/pKlVyrzp7T+Ke7rvsFvmP8SyD7kdLO4/UmElpC285T+TaoFcFT/oP00LB8P8oe0/GGlwuZXL7T98wktwCunoP/oPqwfUQeU/wOf9H31N6j+y7NOH6Y3lP89g2Yn4NeU/UoiNqMf06T9yDF1LSxXtPwj6ITl9yuc/8AjPzRey7T+PdHXnxDfuPwUNaPp5ru0/kdmjz8lX6j85u2ttohjrP4YwXFyvmeQ/6ombvQ9K6T9oWKb9InHoP0qaBwDJ/Ow/z7FtVLGc6D/XU7ngdcnsPzBaiHd+XOE/5W+u0n4J7T8mn9PcGXbtP4bvNV4igOk/taHcb5IY5z+qKgrSHjnpP1f3TbqKXuw/1P9qPezm6D/ifFlp5mDoP2yfCOU9iuw/MiLRtFyp7T/WeVqKxcfoP2Z0n4VK0uM/gqJ28Lc05z9pZr51EbztP4xauh8G+e0/WUBcqZyd7T9seY71gazrPwqJTVoW0+s/BN3k0m8B7T8K6wQ+FXjlP4aXyaHz+O0/FP1nvHIq6z/nu9mgM0/mP0AWBFZWPe0/NkHmskN77T9bHHguRJ3qP/ydPpJcyew/KRrtHU5M6z/V+EiJ56LsP2IUEnglSug/wIZewlBt5j/9KDXOjhDmPz6WAs8ry+I/WDHArXM76j+Dif/k1QLuP4CrALJcuuY/jMdo1qw57j9qF8HnnUjmP71IjoKAO+g/IqG97+sJ6z8yFxXMjQDrP/fZDp30Duw/nHEbU8zG4j/Q/UIW2eTnP+Dz/VqmSuw/hmoTB5lF7T8eHZV8xyXsP4ybva3toew/5vxSEUHj7T/mWyr9FbbsPzgWh1kFweU/BtmxMoOJ7T/2cQc9jTvqP4BEevDiYeY/MjCrGtmT7T+NJ2kAjhXkP3epSgEgv+s/WDKeLtqR6z8CZXqmkB7sP65RijmqHuc/MQhBRfva3z/ZDeTpyRrlP+KpWsbqGuQ/88tWGFSc7D8diQ+F4wLhPxccbGihte0/rGeT8Ja25T+4lLJ0mijrP5uY2Xc/H+Y/8bM2nK5h7T8MZej+ju/rPx/cAYZ0Sew/YdRr6Nak5z8EBgAAAADgP54mm9n8ae0/Zf2S4/LN6T+7Qbj0zvLlP41M6oHVp+Y/zC9wkAoJ7T80uM58GzDjP6l2XWhujOQ/UdIT7dgM7j9/6xZL5EviP2sI2TCt3+w/aEXjtTpr7T8R++oua0zoP7KApDubV+0/u9A3atsN7T8R9MhG0Z/tP1PsFAkLk+o/YFcYkOmZ7T8mYLJcC9DtPzL7x2AXCuw/2Va86H3z6z/0KQ28G4fjP5KLJFn7jus/19q4Z0536z8I3wHOC0XqPzLpDG55H+E/urXqNAXR7D98VHCqPkDqP6/QrdWHfOk/HjHINRE66z9+WuxnKsHkP1YrwZ7eVeo/lcIQgkSu6D8bXe+o/cvrP4tT5c+WZOc/R2GqFAB/6T9zjw/qmK7mP/hdK7+jmOU/sfNEenF07T8qLumZvGbtP36AJUVzv+Y/x4TKBsYT7j9rt68F2RrkPw1D+KTcAu4/TFqM/n897D8Hb0DrkuPoP7+oCxQcgd0/3A2WHgY67j9dx3dS49nqPyoLc9pM5+0/BPy12wff5D8GH483DUDtP9hswoxlTuQ/Nu4XFbE36j85/edo1ynsP6Rg9Exya+k/6r5Dp4w94z+qnx/PLEjtP6xR6QGEiew/corVuMld6D9IK3YCtGjhP/uDgms4h+0/ozPO4Git6j+ROvqXnx7tP76K68zvSuw/EtVoNliP5D/SpV0Ddb7mPyy5ZoSs1Os/45IjzfWN6T+M1haWrBntP+Vkvp4h5Og/grtwOUrD7T/22Q4K4U/rP5h9xCnRRes/yRtJXTsR5T+K7+r/9ZnjPyZ31Uk8Juo/KrVwHqR/6j+8IzVHfxjjP+7GMIIb4uU/9PbdAXdl4j/45X19NbjoP+n/0RwInOs/U8z0XFz36z/0OUKNM3riP5kqCG/+bug/DIWrD8SB6z83canhbMXlP/jB2xysp+o/r7bHocG+6z+8ywc13NXpP+lxr+sBleQ/yuJKfupb6T8kpTmUFFjoP9MR8YRFPe4/0v8O5MsN7D+cHr+1J5rmPwnqSzVWVek/J8wJLuPg6z+LxEjQPdjsP30xWYv1xew/zd7+Xh9Q4z9dYhBC2kXrP4BagdJrBe4/3BjaeK2l4D+Q3X9XKynmPxww9DV1++s/EP3O2owz5z/SQF2xRSjsP1nO6dlUf+Y/lmwuv4xW6D+WqugmKRnpP90bbVox0Ow/9aZrK/lW5T/6bZVnHkjiP9agntIiD+E/BK0nYyle6j+ijRIg9m3oP/VO+X2BI+0/0DmkBw/55z9E5cwMj5jrPz5DzbZ5cOU/Y2uCEY+R4z8BDmjzq33oPzTbrn5uSe0/AF3Wx/Iv7D86MzpchMHtP3id09IwKOk/SD3XrvPe7T96EysaA8PtP2gn6YJddO0/9GJaqaT07T9laPNQoLnsP6Asu59kK+Y/XDUQ+ZNX5T/JcwHcONLtP/Xc0yrqT+0/zfJCnTBS6T/m9sL54CfiPyqgzQit6Ow/tWQU08b77D/E84KocSfuP9HUXlopYe4/LnGgM0Ob5j8A1D14P+LlP2cGBBF5AOk/OV8z1Rom6T/R5wEQ49XtP1TafDZ7uec/BILZezUY6j9QfSJV7dnsP/QdDIWdNuc/HYT10Wc06j+w7QvcYP3qPzzWgCpUF+k/e023TSYk7T+AgsKV87XpP+FytYsbUuc/Tjag51fN6j8yqv1U85/qPyGgDMvg5e0/7JRoNwsg6z/6VX833IzrP8C/zLn+Wu0/XL3ppwHA6z/CBc0cu07tP2QSBek08uU/50Sjzdra6z/AMcsL7vfrP2w2q2caLeI/aZ6/mLKL6T8+PxXHJGjnP3d6lUux2e0/eBTG/Q0L5z99jAHeBObrP/UViu29n+w/artc14ho6T93rCmb/vbtP7pPOpubKe0/bEV9gCNo6z/aWGFp4iHsP0/wPtdP1es/Dj27h0bp4j+Dwb+MWkbmP+FWGkBhbuo/gCbLYdcx5T8vplgviebtP7fAl56Km+M/WH+9+QXI6j/twGT/LFPlP8j3stL3LeI/hxTdPZ9a7j9YZajeFd3sP0/6UyUar+U/RIaJueo37T+YCA91lWrpP8ZjjTSDJus/eF79kuDJ6z8CAWyg2xHpP6WVJgYYo+w/ZdSpJK3F6T+wjvWzF8DkPwiIQySs+ec/5UIrzSSB5T+ESzXLk1XtPwUdBjq/7es/BAYAAAAA4D/K4/4qDS3sP3agl/ZwHuk/uBv7WB1H6T8GEDzQJBbuP9JrHn3Cm+c/jLdYJpgT6j8wDB5L7qXnPwP4Vig2FOw/+49Qz+lR6D+LPLSZpMbqP2GZwMKhSOo/+oeTH79A7T9VOHJYOB3rP472pAb/jOE/ukRp6bGD6z/CmUJtCQXmP5BHXq9Awew/EvgzSi/r6D+0nRaZo7/iP17mjnw2++w/yWauYtod5D+SSwmz2xDuPwtXFV6pIeo/qCnmELLY5T8usymv1drtP5dW11cj7ew/ybgeNTXX6j9+lts/rC/qPzQc+ohbK+I/v+rCGCaj5D809ifdqkzsP1mYOg+1vOk/NH69jDi17T84OEE93dHtP5YRRSbemuY/SZAS3i3N7T/eF/Xf+HTkPzE6C47Gxe0/kGmNv5PM4z/VgDYxRBzuP+ho62DmyuM/N7lPWKp36z9WV5ACmRHsP75yILdiVe0/MBLiU6697T/2J3zLUWjlP2V9RfK61Og/JJRwMdGF5T9SwSo9ZfblP8oNah5XEuw/jFIHXsqd5z/QNLnv2sfnP+fQj1IOKe0/3vCNqKAw5T99a80S7JnqP0INTeHoC+Y/lRopJN0q4j8ZmJpNd0DtP8VBIjojFO4/kTZx+s1X7T9ly2iHatvgP0EyLDgF6Ow/IAA2B/8Q6z8phOJby33iP6RVwxHSWO0/1Cd6lC0d6D9nEOPsitTrPzyEQpAd3ew/zHpFQnCp6j9Gt1VBaG7lPw5gT8Sbw+s/oNHz2Q0g7D9KRoT5A5nnP8BmsjJUcuo/dqEraVID7j/i4+V6isHtP1pOxckxLe0/ZroJYdh26T8LAAMfndnrP9Q5ZoP6Quo/E+l4H0wo6z/QxmZZdTzsP+gazCSqguY/sofj2Xqz6j8HytkAx0vsP1JLfborFes/ALmaY11K7j8KRYWMVFrtP65zbw1hg+s/zqTAd3CA6T8KKlaIqlfuP4Rav7sIOu4/dvVOHVsY6j93D7BLWybuP07K35iG6us/8c8PXgme5z9DBvjG/2XrPwvDzagWtew/EhJI14RI7T82Ld94dA/hP90tdm52dew/ennaKReW5z+T8PciXpLsPx/JlpZODO4/X/kZVQVZ7j9aDjdnQknuPxc+BT5Rc+s/wy1M7jFR7j8TUWKKHurpPyq9Z46Tfu0/2gl/CCP27D8d07hX5vzjP/C94wYhUeI/G1LwaHEU7j9nVFNI7R3tP4ZLsb2KLe4/8mc2aDAc6D+lscPcN4jsP6vqdBaUSu4/6lMYQ3ON5D/Hxzn9pkrpP0aTbpa+zOA/aLnVjlrI6T9b3SelP+vrP6/Z0r+Rkuo/ZOdTI2VE7T/IrddN8+TrP6JMgCAMJOo/Sz++bakc7D+3JQxxRCvuPwIpwBa78eo/w6TV53ep7T988/UEmfPqP3FLaKFMjeU/qmLNwa+16z947ywLppfsP6CEHPXL+Og/gAZsHtMg7j8qppfhCYXpP6+5W1Sfkuo/pgdY9ffy4D+MXb2OlizrP6Df//EbGOw/EXLodltI6z8MOv6TlzjtPzJHNi+zZes/YEjOp4mU5T/ptDAISuvtP823qWgwzu0/KK8ay4746z82/QNWOMrlP5SK1Gk5iew/Vpyfc4iC7D+3EdVx7xrnP7dx1I8vM+4/uNs3oHmk7T/kBF0PcqHpP0AYK5N2yOA/drAQS3FD7j+1d5I+uK7mP7a/o2b17e0/75VKvTi/5j87Sjw2QEvuP+Myz5ewXus/bMu8yEdj3j9xYTwa7ZvnP9sxh8muouE/R0YLJCDy6T962QoJsR/uP/IeNdgdGO0/fFvLZxwf7T8C9M3nKkfrP6aLdGAP++c/owP91Hyy5T8Twk8K2y3uP/z68okytOQ/KfxkC4O76T/bMAqbXlXsP9n3d2gKbuY/e+yEEy2F5D/PcZd+mqXpP2wH1ivUL+E/zdpQoajb6D8yxdOnp5jpP2zh9+jzVOs/b2WYOnHg6z+XHDhECFLqP6W6g1mIjug/Csx3eHa46z+UttiySQfpP6gv0i2Zqek/0P/PKpLJ7D/urakTGi7pP0HQ0jC4cuc/IwYBSE146D90bdMUE3PrPwdJRc7E1OE/iLP5Cp5L6D/SJwnSMjTrP3RhTxUxnuE/Hmpcq79i6T92X0gBMaLmP7Y/i4m+i+Y/BAYAAAAA4D9ykh7Ny6bnPzATLBgu2+c/i0/i+AcO7D9ixzJJ2q7pP3/YrbadQu0/mIgAasYQ7D/vjsIv4OPnP8fzizpFo+0/ZqqRoQsg7T/zjUCnZWbmP60igug8uu0/2EWYzHCB4j+SMDjkMpfhP//YFTtGQOo/vPQjE5DK7T9AjxQC3TnnPwt6HdQ73Ow/8Kom5egM5j+GGKS51LHmP/kkqxwbEe4/HzhmSH6j6j8nXwX18gLtP+KrZmMM+uU/GOSu9PQm6z+Y6ssQVtvpP8o2v0E/2uI/tfXGoV+r6T9PZY2IlB7sP7vD3ea3EO4/SlEF9oJC7D/C0Tb9n23rP3sUW5K1a+U/1xKg0Uhw6j+O/blZRg3tPyK8CrEYWOQ/VgDr83D65D9eudx3E0vpP/H6iPRT/Os/EHLVLz3l7T+1VCoQ2v3tP0rTgqtmee0/ktvYVtOP7D/S9GSNzbbjP8CBfDLPC+E/nQ5ioVST7T+iYQBsTmjrPxdyBBD+z+s/U+CtcS7g5T8srQb76A/iP4Lo582swOo/jzPGSUVU5z9gGdjjyhDkPwBbwBvXfek/p90ZnALL4D/MmMDG4+DpP1zRF1OKZu0/huXajCzu6D8OEpNwN8njP/PaUvimauE/Lj37CN4m6D88+iYALa3tP9Mo9EPPiO0/dE0MIHFI6z88OPFzTV3rPww39jap7O0/zC7aafLt5T+YZtdryqrpP3Ao1420meI/XCozbRPj6j8S8RlZcSPuP2/IT7p52+c/sdBO+K556T+uQnR01CHrP8NnINdaDu4/xBoQ70Om6j+KbxBMnDjlP1bGgTAM5uw/OfWXYCcB7D8spenPLK7mP0Gn3tvyn+g/DjL2ynuK6z+kLK2ASKbmP+0rDfaoveY/mPp17JWO7D+BUEjzh9/tP5NItqGece0/orf0Ye7U6j+Dr5INlzjtPz8PjhKw5ew/TNv7yCiS6D8kAorG3a7rP9ZqsMn3Nuw/nLQgK1b97T+MDDygnoHiP/aUyiXttOQ/zt9O3eBO7D9xN6PmqsLpP/C4oFmhl+o/Pf9ACaKU7D8aZi0GywjjPxG/GPPWAew/7z4ek+NP6j9S8eV69XvpP05lz9AxSO4/Kwc8l0wI7j+0soWu2l/iP92GWUehuec/CMYwqqgS6j/GzbSDlXvqPz0d0L6gFOk/NT3QDH3A4z/IOHWBskjtP4jqKzA9geg/ufohbxko5T8YCGzNcHzqP/1UUrhcu+k/1BsSz2N16T/dmFVvi3bqP5/NVPgtO+4/enDN2EN54T9mFtA+ALLtP3tDsp0Fwew/2wxcGrH05j9/EEESrDjkP0HOmAdeh+g/uftzo/Dk4z80zvKf0rjpP0NvrpYp/+M/pNI2S+Rw6z90u/SLfJDfP6SsHZiV5ek/gtvSUa+Q5D+H5hKQY9DpP9DjvYccVO4/W6zjWOSn6T8Pi3RHl//tPz4eCU7Cuu0/dYPgktyU7D9Ej9wOVzTsP9/7pd8TRuo/FCJBZcsK5D9egGUFmpXtP9e9DmzGjOw/nBxWRGC44j8JQhciBgfpP4eP3MlXJO4/IsYR9NGi7T/TYzTDDuvpPxQoNec9T+c/9sms1QZe5D+Een0066/tP46BkruK7uw/DImMqwPB6z/QM7/TsWLnP8iOC87u7+w/gjEqG3p95j900l1EBBXuP0RKB/g82uQ/Y6JquEso6D9Z7/dtdPDsP5FlG097Me0/H+qX4lQY4D8d92C322TuP2If+F/DnuY/PLnjNZrb5D/lPj+F+OLtP8+s0om50e0/Wp8FxBMp7j+Ub6jbFEruPzphyp/sX+U/4sHJnj/l6T/N1/ktiVrmPxRQGV1ve+Y/Dlx19MNQ6D8LZRiTWqTlP0AeApPPC+4/2tH5z7IH6z8U9uyaNsnkPxxQjbcoe+M/BAYAAAAA4D9yuzUQGEblP/8lx05+Bu4/q+1VdfAi4j8bVW0+88nsP4oIYp6GYe4/MEW6cjrf5T/Y+DZOf1XuP1VAEOXkHug/Zyb5aZIu6z/tTrvkqNftP1gOX9lp/OI/FQBkPX4b7j8+1csDz67mP7t+UVhByOo/g2uKMAwI4j9isb65SzzsP2j37DzIAew/IOwGFCoq6j/rD7fy8rvjP7Q0JpBI+OU/o+CClVYv7D99zBcY5lTlP/VXsU3oHe4/hf/FqycE6j/Sa+w6ZMHoPyZIDL0hN+g/vCc3vU1r5j8AjKYbR1ztPwrm9JA5E+w/EqbIig7R7T/gjvnmzNvsP2d5LlI1W+Q/m+pO2QIS7j8dDwxHOT7tP5ccyskY7Ok/5hyA8Bju7D9F0HXGvU/sPyfT94HVtek/y8bncUf36j+A2D6nRmbsP6q8MMojz+Y/HF7VNjeT4T+UJvxFjbXoP6CI4hCiouY/qNiO+JNy4T/5zqCqYMTnP3I07J6mbuM/lLJs+G9o5j+7oVx5forrP+0Mf59oQ+4/2gTGNV085D96izS7TfTkP/bokfxYE+A/SL5JtLnw5z++ut8nNEjuP1qIm+mqF+I/sRzKtIN/4j+vJwLs3VjuP5Jv6Z8FwOY/hkNLJOAh4T8TjibiEJjsP62oelLG7eI/Ov9wl8975D/qdHaJTC/nP26pe++ZKew/eEqkDwwE5D9vzPsGAUnqP9SY3gfcsug/whV84cwd7T9M4SFhfuLpP4KU1AkM++k/MtI4usbU4j9OKq+rX2nnP+BaLrSVSeQ/kOIxdM1s6z8hRaoYhLzkP38dvLpw8Oo/9Pyjr0Ae6j+0GAVw7jPpP4RVoBWhOeQ/rtVeybxV6z967grGVSXpP3Dz50UEReg/AQvyxueG7T+KT0txXd7mP48/xygQD+4/OM2/YM/r5T+sXwM8navhP5Glsq0iAe0/U/QDpH2S7D+/6va/MzntP2AtaKlrHu4/zNk1wI436j+8iGN2xCvtP7LHpQeUFu0/5mFi/azS5T/6TlJ1TxPtP47D/V/MuO0/plb4S/pe5D/M/epdgIzsP1huZyIdD+k/CsU7GSCW7D+xLhOPu27pPzrr9lBk/uk/fs1Mw/cX5T9t4ubLEHnsPxyUlldb8OE/khTwsPdK4T+VIBX0pYjsP0D73Uh6dOs/FOlHy/9S4z8D6BpD02jpP+V98QGE/eo/MKy8rlDw5T+0ffAnBs/sP89qpP4vh+w/Gl+pyYRF5D9K0GUJyjPoP0LEcJX+dOw/WJrTzzp94j86TrvYV4jqP9MtyPhFF+4//U30wD1Q7D98zw4+E1XuP+G0FSLjCug/QWP+5P5e6j8+DTYoLlDnP1mtKrPNduw/sSc/zr9w7T8aHJHopfDkP4IK/JWOd+I/QCfPaKMH6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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1583]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1583]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1583]},\"y\":{\"__ndarray__\":\"BAYAAAAA4D+Wx97o9sbrPz6Sd8GTSew/UEoes75+6z95iwQUFXvpP2kj/Gio7+c/1+7jBjpB7D+OsO596WLjP8uq0cPe/uE/jUWhrTWx5D+jw8+s6+fnP1KCJYM+pek/Bo5hhnne5j/wCNO5GxbqPwCEthdVp+A/ArNKKcJy5D99U8n52PjkP9bEppoqrOE/1TMrSE1H6z/V8Q+dXiLuP3iBPAyfReM/E/f/8fLy6z8WijSpbWnlPxroVmGUa+w/fJhCTLmT4z9vBJe+z+PtP8d9wCvjxuQ/ALjPXrPA5z+9dc2af8XoPzmkOiCpeu0/DBrHRdbl7T8kMkfqtr3sP3rsFYtouOE/AvNQGQzX6z9Dz3bTdNboP3/bf4eN8+o/E4Q3EXej6z8gUKRE0SPmPzwda/V//uY/ws2tqv3A6D+xoGkcj2XtP0Tt0yf41ug/4l+Aj9ki6T9iaMa7Ig/rPxBtm2GO6Ok/FjCWk40z5D9uXvqH/T7pP5Quk9ynQ+Q/v7pf+gYd7j+QyKDeM7znP2jKtdfUl+c/D88NP8kV6j+fku+B1PjqP5rHLLYbPe4/2PR0Abdl6j+4ZRBg1zzpPxK4TJvuh+w/l4No90D97T+ksxZv26voP3swuI9HF+4/T1R1ghoi7j+GWcu/lejsPxVOeMMMZu4/xcgE7UHw6D9SBIZ08kHqPwJbdmNpxOk/QPTfNa6c7D8lLdOCZeTtP/zA84/5huU/9iTGjlza6D+Cr4aus4jpPzsbUU513+0/uCqeuFHH5D9Q/ckkXfzsP+mJLKUY4uQ/JEiFfgV66z+OvI8rXV/nP+k7dKQOjus/4DROahgn7j9437lcTZfsP/TqbjGPYus/oKP+isUG4j/5JVW4lMbsP6g/dlLuFeQ/dysKSSKm6j/GN7YKKZvrP0MNnhJBX+o/t/4pfeLY6j8gzb2ZWSriP4B7ZSEtE+o/Zaz7zd5U7j/BEwBNKiDuPxJZFjtFiOk/a1qyc/u/6T/jsMcOO9jmP+CGee2aZuc/DCF1hhCO7D+cR8Ic7O7qP9QCUTelreM/oEpcWrMv6D9zt9AG8nrtP1Ci1OhIquU/Yrd2S3Rf7j8bBoiaGrPsP2dNGwgxCOw/ssDkmWJ15z/yJzcnX43gP+TsxV1JO+U/WFljIAIX5D+bO5cqjwrtP7m+JtB9N+o/RGGAVdEu6D847r5kfiTfP79mm4eaXO0/wilt2YTF6D+evQTrWE3tP9SmsTklq+s/pOP8v8Tl6D9Wd2WQapTsP2OLuyGMwuw/SFtD/4xA6D8hCKchcJ7qPwLyEwYqK+0/rMJWCg0a5j/IwIS2vBHrP3zKKwDpRe0/8zocLGP07T8XXGtnf83jP2DVjh1/yOU/ptw6b8Pa7D8OdH4XQV/nP1Upt0ByRuo/JfcjSdG+7D/4OMBBbBfjPwTCxFwNBuo/Z58K/hma5z8L4Fr8Ph/sP48fH+coBe0/FbtihRUw7D8QwXedE+3tPwvKyPoMBug/b6FBE1ZD6z8EU3BSTV7pP/bnD7dXUOk/dDkbaiL67T/bAgltbUrsP1430BIvIuQ/0rMmBS8L5z8fFUvs5UnkP51YDnp0Jug/94O6Nyfh7D/HcSur+kXtP22x6RJSfeo/pJYPFEWF7T8QfyDEenrtP52ca17qWus/tfU3in4y7T87d8QbH/btPzI2PWOfreo/OsxoOGp27T8hZiJ5BOnqP2HDzbk2De4/xTgVuPg34j/wqrYqCMzlP3xAfjmt1Os/TjOAfExr6T/CstVQNYXlP0JJYfTZh+0/BFl6o4Ao7T9ciQ9lC2DlPw+reHegW+Y/DnE1DxdP5T/fEvjzeCvrPzzW/oT2AuI/AeXe8EKw7T/gUMaPDQrtP6B3K7gXr+A/OJtmJ4f66T+OPFzkLVLhP04VnmpaMuw/b1ECKuNV6z+t74s7PE3oP3wQyd7MWuo/lmXynLHX7T8bTN0hODXsP1xuP4u1KeI/ciKM/ZO76z/vMu+UeaDmPxwxkb8+VuQ/Z/VCYeQN4j8emsYJQWzqP7tLszrMCeY/D6LHDuNL7D98FmsfyGTlP+H5N5PT2OU/hlcmhNK97T80W4IREf/tP1L6XQDPTek/mcobAf9d7D/O996S4NLoP6V28IU5cew/BoxUi1p45z8LIbXQenXkPwQGAAAAAOA/Oqb6IHwg6T8K9NTKAMrqP0GqXTsdCOk/O6ZUa8eI4D+TKkVs3qDgP9BrdzgFnec/TqJwNLC+6j/R3SrgvbvkP2dSIJ15Qu4/ULDuB4lN7D8fc5yiX3HsP1p5Ptzjl+M/2G5yK0eB4z+7RaznXNvmP2JOoBEFr+o/nLMIDRaz7T+JCQaUacTtP9iMap3c6Os/bBAMKPWD6T/sZbuXFALpP9wF3A9meu0/vz5q+n5f7j95G/GvcibuPzmV7fbPh+g/pm2Kq3i45T82Fp6BvenoP/QPvJ7mzeE/+N2n7sg37T/aOHkUNEDuP5Dh4zhy9Og/Lmt8K61V7T/GOz04slrkP+/tTQFAOOw/U8vQiAqg7D8qcc99crLjP60L1rSokuk/1KS+Mm/h7D9nDBT/BEPuP0uaQGqZCOo/SqGpIf5O7j/IKA5eBynpP+445c/aqOw/RvsVg/Zz5D/P+Z3LcffkP0kPcJ9yAe0/Mh54xzVk7D8pmGTBbSPnPxdoi+8v9O0/Pn9pdyyC4T+H15JhrLDrP7pukTSug+s/sngVPwwN7D+0J4WP6lDrP/Ovhhmgu+E/3F1EQ9gc4z+zOPqGMi7uP8weZUNLsOk/DJtf8+wl4j+UnNupBU3nP+iGaJ/67uY/cF1JJo/87T9mkBhWHt7sP/iQa0tUguo/5EF2cHA/6T/wiG2nsEvsP3F5OzrTHO0/oQSqRDUL7j8jSZd/klzjPzBXosyqCOQ/FFBSDK6V6j86jL2++HntP51DuNuJ/+w/41kStDXk7T8kggT5FwruP2RxmT9yN+I/yP+x+8OB5j/BHYt+XjrjP2FlJGjeqew/Ufeilboh5D81cdTnuqroP8/t2YOdG+U/E7Ya8IDQ5T+AU02/rGfpP2j+7nKLVeE/vty2SrJ65D9h4TlEGh7uP/QHtdqgy+w/iWrSWr9h6j+NfdZfxhvtP15t4xSXteU/4NnXx6v16z+U+ZIUPijrP3h/B/H2Rew/Zk+4iRZK7j+cLaPBfN/oP7zsA20/uOw/y1lqGdkO5j/oV7GwJZTrP5caMlPTZu0/JwbNEzYc7T+YR9xLwa7sP6xMbWhoguY/9Hk7RtPt6D/n5Y/5kt7sP0a2F89i5O0/0OtnnTZg6j+/N27z1lTuPyptvRxG5+M/+YF+1WSv6j/6c5A04kLuP4qDIENmkO0/8vzZne+T5D/KHmE60mjjPwxIFrydbeE/4YkysT817j+yyU2Etn3sPwoOY68rpec/dRgz9/O06D/4vPpiV0/tP/Y5xF0bEuc/dKArj6R46j8M8SsWZqPtP0C6ZfPY0eo/JuJhhEmA6j+pfDKVqN3tP96ApU23ROs/cAH6irw16z/i04iWEunsP455kwh0OOU/d5pELH5s6z81zYYhdb3pPyI+YMIGoOw/UiufW1qN4z8kXlKD09nsP26sdh2rJ+0/XR13D/Ez7j9WPfLHMFbrPwD2wP7CweQ/YJ0QKDnN7T+1ek0XucXoP0LSI1WwWu4/ENvCy42y6D+YWyWVHEbpPwkqC1M5wu0/ToPNowV75j8YSxRIgojlP/WMctOKCOk/CHYxPZAD5z+zx7KHwpzlP82pBjYga+w/zq9hvhfO5z/45G7oTELrPzB9MRnVG+Y/P8Z3RgEo4j9yvnDtLr7nPxRWIl87Oew/82ZdjExS7D9prMB7+xPuP6bUvI/OiOY/jrutxX/z6T/2fXkkJdTpPyBuU8yiH+k/5E27IJYe4T8ZRhi0mF/sP7KLF/bnKe4/SQNjs1jA6z89IRChu/PqP9Jpk3s5quI/gHVqa20K6T/KwYQMTHTpP4VYIu7aIOc/QAy4oIBy6z9kOwJ/iGrlP8keSJvaXek/jjEzVhoL5T8Glfbdp3bpPxIRyKsq7+0/6wQT48Ow6T8zEdPb5tfoP/6HrROrtOI/A/4kMZ2r5j8bfWtVuq7tP1AolLdkKuw/1FfInYlp6z8IAiP6T9HqPy7zf4y9n+w/KABa07md4T8BLycaZDzeP6rT7LI8Y+g/KfqPxG/d6z8dc4nBnTHtP5Zkjh2WKOo/fXif4BgY4z8eFhgERRPtP2kPMpGL4uo/KPNFTjVy4T9Wre5oPh/sP9STGk8MuOc/6pfFI28+4T/87CMCfwHlP7gVcMzaqO0/XuCD3FU67D/NUbVK6FDuPzZmDcsOFO0//3q5KTrl7T96uTLIteHrP6ltbFNlKuc/8BZvMBHv7D/ao6iqN0/tP6LNxOg+h+0/BAYAAAAA4D+bh1lcGFnoP0nNCjLO+O0/8SbAd/Za7T9IyP05iNPjP4LL/Eo8Mew/Ab+4RaTk5j+Vfi1oHRHsP8QPFqtA5+g/CaJBlNzR7D96qlzoHS/qP8vl6Js2su0/KQU78Iie5z9NaFEPhmPgP3yDGfGUiu0/YIvmxDBV6z9MqYAox93qPwM0cbODPek/F1OsHjK+6T8s1W068znqP7ZV4DRBtes/BB/mn9E26z9Cqk2Q2z3kP/kaVfg0E+0/6nojT9pc7j+ukjZRXWToPy3rZ9xne+s/ceYd4P5U6z9SSdegm6PqPxPRoK/ZFuU/yweEzewM6D/UZoRmOrDrP+HAY37IRe0/FHY4O6306z94H315C/HpP8Sg0rgco+s/pYlCh94c6D8GhPL4gUruP5HqgTXxnew/tNWZTR9I7D8IE4qWzALjP6JtNW0P6eg/ewTScGZh6D/8AiQAP5vlP44BNaonIu4/nsycwjcR7T/Dcs28XBHoP2Qip9DSS+A/sO/jxzcQ4T9t5/8PYiDtP0zGCmz2eug/rBBQIuyv4D9dW3MA+mjqP+fw8fg42uo/H+KPWlcH5D8qGW/zJcLnP/wrR51ayeM/5l+nPrjy4z+Z0G2DHGLpPxbO59WE++o/aOR0Kv145z9IxEXF78LrP+TWcigq3eU/eKtA29cH6T8uN1YsoyXnP5h6b0S7LOs/LmNOjpRs7D+VTDsQXpLsP26icGPXaug/TPVfttAi5z8H+ax4MgXnP5pO1ZLem+s/ZYFCUAlp7D/mUEtQ4GPpP9iA+lMheuA/rLbqX7L37T9k+KcTXDfsPwKMBuvL2u0/VYfyI53p4z/CTrxB7gHrP2rHVjNO4eg/VlhLWupf7T89QfufXvTlP1qgJ82BuOo/B/vIil6l6T8a0XEnRDDqP+16TK/zJ+0/75ZnmR+X7D/+VbDhtsHiP6CmHEj6gus/h7yU616c7T8ongQTLCjtP950nkv6seQ/1kGSe0Wk7T9L7zo8XyzuP23hT+GI0us/iwKHAcdO4T+TSmZV7iHuP6NRkmoV6uo/mqAEYsVW7j/hGIFkmgPsPxwzZ+Vz8+w/jZumEEF27T/JFJHqClnrP7cj3ioGX+w/d1Dq4oZf7j8Uj/N1WvHqP/6lMIukY+M/1T4jJl8a7T/GOg7eiezrP8yxy0/xheo/pKlVyrzp7T+Ke7rvsFvmP8SyD7kdLO4/UmElpC285T+TaoFcFT/oP00LB8P8oe0/GGlwuZXL7T98wktwCunoP/oPqwfUQeU/wOf9H31N6j+y7NOH6Y3lP89g2Yn4NeU/UoiNqMf06T9yDF1LSxXtPwj6ITl9yuc/8AjPzRey7T+PdHXnxDfuPwUNaPp5ru0/kdmjz8lX6j85u2ttohjrP4YwXFyvmeQ/6ombvQ9K6T9oWKb9InHoP0qaBwDJ/Ow/z7FtVLGc6D/XU7ngdcnsPzBaiHd+XOE/5W+u0n4J7T8mn9PcGXbtP4bvNV4igOk/taHcb5IY5z+qKgrSHjnpP1f3TbqKXuw/1P9qPezm6D/ifFlp5mDoP2yfCOU9iuw/MiLRtFyp7T/WeVqKxcfoP2Z0n4VK0uM/gqJ28Lc05z9pZr51EbztP4xauh8G+e0/WUBcqZyd7T9seY71gazrPwqJTVoW0+s/BN3k0m8B7T8K6wQ+FXjlP4aXyaHz+O0/FP1nvHIq6z/nu9mgM0/mP0AWBFZWPe0/NkHmskN77T9bHHguRJ3qP/ydPpJcyew/KRrtHU5M6z/V+EiJ56LsP2IUEnglSug/wIZewlBt5j/9KDXOjhDmPz6WAs8ry+I/WDHArXM76j+Dif/k1QLuP4CrALJcuuY/jMdo1qw57j9qF8HnnUjmP71IjoKAO+g/IqG97+sJ6z8yFxXMjQDrP/fZDp30Duw/nHEbU8zG4j/Q/UIW2eTnP+Dz/VqmSuw/hmoTB5lF7T8eHZV8xyXsP4ybva3toew/5vxSEUHj7T/mWyr9FbbsPzgWh1kFweU/BtmxMoOJ7T/2cQc9jTvqP4BEevDiYeY/MjCrGtmT7T+NJ2kAjhXkP3epSgEgv+s/WDKeLtqR6z8CZXqmkB7sP65RijmqHuc/MQhBRfva3z/ZDeTpyRrlP+KpWsbqGuQ/88tWGFSc7D8diQ+F4wLhPxccbGihte0/rGeT8Ja25T+4lLJ0mijrP5uY2Xc/H+Y/8bM2nK5h7T8MZej+ju/rPx/cAYZ0Sew/YdRr6Nak5z8EBgAAAADgP54mm9n8ae0/Zf2S4/LN6T+7Qbj0zvLlP41M6oHVp+Y/zC9wkAoJ7T80uM58GzDjP6l2XWhujOQ/UdIT7dgM7j9/6xZL5EviP2sI2TCt3+w/aEXjtTpr7T8R++oua0zoP7KApDubV+0/u9A3atsN7T8R9MhG0Z/tP1PsFAkLk+o/YFcYkOmZ7T8mYLJcC9DtPzL7x2AXCuw/2Va86H3z6z/0KQ28G4fjP5KLJFn7jus/19q4Z0536z8I3wHOC0XqPzLpDG55H+E/urXqNAXR7D98VHCqPkDqP6/QrdWHfOk/HjHINRE66z9+WuxnKsHkP1YrwZ7eVeo/lcIQgkSu6D8bXe+o/cvrP4tT5c+WZOc/R2GqFAB/6T9zjw/qmK7mP/hdK7+jmOU/sfNEenF07T8qLumZvGbtP36AJUVzv+Y/x4TKBsYT7j9rt68F2RrkPw1D+KTcAu4/TFqM/n897D8Hb0DrkuPoP7+oCxQcgd0/3A2WHgY67j9dx3dS49nqPyoLc9pM5+0/BPy12wff5D8GH483DUDtP9hswoxlTuQ/Nu4XFbE36j85/edo1ynsP6Rg9Exya+k/6r5Dp4w94z+qnx/PLEjtP6xR6QGEiew/corVuMld6D9IK3YCtGjhP/uDgms4h+0/ozPO4Git6j+ROvqXnx7tP76K68zvSuw/EtVoNliP5D/SpV0Ddb7mPyy5ZoSs1Os/45IjzfWN6T+M1haWrBntP+Vkvp4h5Og/grtwOUrD7T/22Q4K4U/rP5h9xCnRRes/yRtJXTsR5T+K7+r/9ZnjPyZ31Uk8Juo/KrVwHqR/6j+8IzVHfxjjP+7GMIIb4uU/9PbdAXdl4j/45X19NbjoP+n/0RwInOs/U8z0XFz36z/0OUKNM3riP5kqCG/+bug/DIWrD8SB6z83canhbMXlP/jB2xysp+o/r7bHocG+6z+8ywc13NXpP+lxr+sBleQ/yuJKfupb6T8kpTmUFFjoP9MR8YRFPe4/0v8O5MsN7D+cHr+1J5rmPwnqSzVWVek/J8wJLuPg6z+LxEjQPdjsP30xWYv1xew/zd7+Xh9Q4z9dYhBC2kXrP4BagdJrBe4/3BjaeK2l4D+Q3X9XKynmPxww9DV1++s/EP3O2owz5z/SQF2xRSjsP1nO6dlUf+Y/lmwuv4xW6D+WqugmKRnpP90bbVox0Ow/9aZrK/lW5T/6bZVnHkjiP9agntIiD+E/BK0nYyle6j+ijRIg9m3oP/VO+X2BI+0/0DmkBw/55z9E5cwMj5jrPz5DzbZ5cOU/Y2uCEY+R4z8BDmjzq33oPzTbrn5uSe0/AF3Wx/Iv7D86MzpchMHtP3id09IwKOk/SD3XrvPe7T96EysaA8PtP2gn6YJddO0/9GJaqaT07T9laPNQoLnsP6Asu59kK+Y/XDUQ+ZNX5T/JcwHcONLtP/Xc0yrqT+0/zfJCnTBS6T/m9sL54CfiPyqgzQit6Ow/tWQU08b77D/E84KocSfuP9HUXlopYe4/LnGgM0Ob5j8A1D14P+LlP2cGBBF5AOk/OV8z1Rom6T/R5wEQ49XtP1TafDZ7uec/BILZezUY6j9QfSJV7dnsP/QdDIWdNuc/HYT10Wc06j+w7QvcYP3qPzzWgCpUF+k/e023TSYk7T+AgsKV87XpP+FytYsbUuc/Tjag51fN6j8yqv1U85/qPyGgDMvg5e0/7JRoNwsg6z/6VX833IzrP8C/zLn+Wu0/XL3ppwHA6z/CBc0cu07tP2QSBek08uU/50Sjzdra6z/AMcsL7vfrP2w2q2caLeI/aZ6/mLKL6T8+PxXHJGjnP3d6lUux2e0/eBTG/Q0L5z99jAHeBObrP/UViu29n+w/artc14ho6T93rCmb/vbtP7pPOpubKe0/bEV9gCNo6z/aWGFp4iHsP0/wPtdP1es/Dj27h0bp4j+Dwb+MWkbmP+FWGkBhbuo/gCbLYdcx5T8vplgviebtP7fAl56Km+M/WH+9+QXI6j/twGT/LFPlP8j3stL3LeI/hxTdPZ9a7j9YZajeFd3sP0/6UyUar+U/RIaJueo37T+YCA91lWrpP8ZjjTSDJus/eF79kuDJ6z8CAWyg2xHpP6WVJgYYo+w/ZdSpJK3F6T+wjvWzF8DkPwiIQySs+ec/5UIrzSSB5T+ESzXLk1XtPwUdBjq/7es/BAYAAAAA4D/K4/4qDS3sP3agl/ZwHuk/uBv7WB1H6T8GEDzQJBbuP9JrHn3Cm+c/jLdYJpgT6j8wDB5L7qXnPwP4Vig2FOw/+49Qz+lR6D+LPLSZpMbqP2GZwMKhSOo/+oeTH79A7T9VOHJYOB3rP472pAb/jOE/ukRp6bGD6z/CmUJtCQXmP5BHXq9Awew/EvgzSi/r6D+0nRaZo7/iP17mjnw2++w/yWauYtod5D+SSwmz2xDuPwtXFV6pIeo/qCnmELLY5T8usymv1drtP5dW11cj7ew/ybgeNTXX6j9+lts/rC/qPzQc+ohbK+I/v+rCGCaj5D809ifdqkzsP1mYOg+1vOk/NH69jDi17T84OEE93dHtP5YRRSbemuY/SZAS3i3N7T/eF/Xf+HTkPzE6C47Gxe0/kGmNv5PM4z/VgDYxRBzuP+ho62DmyuM/N7lPWKp36z9WV5ACmRHsP75yILdiVe0/MBLiU6697T/2J3zLUWjlP2V9RfK61Og/JJRwMdGF5T9SwSo9ZfblP8oNah5XEuw/jFIHXsqd5z/QNLnv2sfnP+fQj1IOKe0/3vCNqKAw5T99a80S7JnqP0INTeHoC+Y/lRopJN0q4j8ZmJpNd0DtP8VBIjojFO4/kTZx+s1X7T9ly2iHatvgP0EyLDgF6Ow/IAA2B/8Q6z8phOJby33iP6RVwxHSWO0/1Cd6lC0d6D9nEOPsitTrPzyEQpAd3ew/zHpFQnCp6j9Gt1VBaG7lPw5gT8Sbw+s/oNHz2Q0g7D9KRoT5A5nnP8BmsjJUcuo/dqEraVID7j/i4+V6isHtP1pOxckxLe0/ZroJYdh26T8LAAMfndnrP9Q5ZoP6Quo/E+l4H0wo6z/QxmZZdTzsP+gazCSqguY/sofj2Xqz6j8HytkAx0vsP1JLfborFes/ALmaY11K7j8KRYWMVFrtP65zbw1hg+s/zqTAd3CA6T8KKlaIqlfuP4Rav7sIOu4/dvVOHVsY6j93D7BLWybuP07K35iG6us/8c8PXgme5z9DBvjG/2XrPwvDzagWtew/EhJI14RI7T82Ld94dA/hP90tdm52dew/ennaKReW5z+T8PciXpLsPx/JlpZODO4/X/kZVQVZ7j9aDjdnQknuPxc+BT5Rc+s/wy1M7jFR7j8TUWKKHurpPyq9Z46Tfu0/2gl/CCP27D8d07hX5vzjP/C94wYhUeI/G1LwaHEU7j9nVFNI7R3tP4ZLsb2KLe4/8mc2aDAc6D+lscPcN4jsP6vqdBaUSu4/6lMYQ3ON5D/Hxzn9pkrpP0aTbpa+zOA/aLnVjlrI6T9b3SelP+vrP6/Z0r+Rkuo/ZOdTI2VE7T/IrddN8+TrP6JMgCAMJOo/Sz++bakc7D+3JQxxRCvuPwIpwBa78eo/w6TV53ep7T988/UEmfPqP3FLaKFMjeU/qmLNwa+16z947ywLppfsP6CEHPXL+Og/gAZsHtMg7j8qppfhCYXpP6+5W1Sfkuo/pgdY9ffy4D+MXb2OlizrP6Df//EbGOw/EXLodltI6z8MOv6TlzjtPzJHNi+zZes/YEjOp4mU5T/ptDAISuvtP823qWgwzu0/KK8ay4746z82/QNWOMrlP5SK1Gk5iew/Vpyfc4iC7D+3EdVx7xrnP7dx1I8vM+4/uNs3oHmk7T/kBF0PcqHpP0AYK5N2yOA/drAQS3FD7j+1d5I+uK7mP7a/o2b17e0/75VKvTi/5j87Sjw2QEvuP+Myz5ewXus/bMu8yEdj3j9xYTwa7ZvnP9sxh8muouE/R0YLJCDy6T962QoJsR/uP/IeNdgdGO0/fFvLZxwf7T8C9M3nKkfrP6aLdGAP++c/owP91Hyy5T8Twk8K2y3uP/z68okytOQ/KfxkC4O76T/bMAqbXlXsP9n3d2gKbuY/e+yEEy2F5D/PcZd+mqXpP2wH1ivUL+E/zdpQoajb6D8yxdOnp5jpP2zh9+jzVOs/b2WYOnHg6z+XHDhECFLqP6W6g1mIjug/Csx3eHa46z+UttiySQfpP6gv0i2Zqek/0P/PKpLJ7D/urakTGi7pP0HQ0jC4cuc/IwYBSE146D90bdMUE3PrPwdJRc7E1OE/iLP5Cp5L6D/SJwnSMjTrP3RhTxUxnuE/Hmpcq79i6T92X0gBMaLmP7Y/i4m+i+Y/BAYAAAAA4D9ykh7Ny6bnPzATLBgu2+c/i0/i+AcO7D9ixzJJ2q7pP3/YrbadQu0/mIgAasYQ7D/vjsIv4OPnP8fzizpFo+0/ZqqRoQsg7T/zjUCnZWbmP60igug8uu0/2EWYzHCB4j+SMDjkMpfhP//YFTtGQOo/vPQjE5DK7T9AjxQC3TnnPwt6HdQ73Ow/8Kom5egM5j+GGKS51LHmP/kkqxwbEe4/HzhmSH6j6j8nXwX18gLtP+KrZmMM+uU/GOSu9PQm6z+Y6ssQVtvpP8o2v0E/2uI/tfXGoV+r6T9PZY2IlB7sP7vD3ea3EO4/SlEF9oJC7D/C0Tb9n23rP3sUW5K1a+U/1xKg0Uhw6j+O/blZRg3tPyK8CrEYWOQ/VgDr83D65D9eudx3E0vpP/H6iPRT/Os/EHLVLz3l7T+1VCoQ2v3tP0rTgqtmee0/ktvYVtOP7D/S9GSNzbbjP8CBfDLPC+E/nQ5ioVST7T+iYQBsTmjrPxdyBBD+z+s/U+CtcS7g5T8srQb76A/iP4Lo582swOo/jzPGSUVU5z9gGdjjyhDkPwBbwBvXfek/p90ZnALL4D/MmMDG4+DpP1zRF1OKZu0/huXajCzu6D8OEpNwN8njP/PaUvimauE/Lj37CN4m6D88+iYALa3tP9Mo9EPPiO0/dE0MIHFI6z88OPFzTV3rPww39jap7O0/zC7aafLt5T+YZtdryqrpP3Ao1420meI/XCozbRPj6j8S8RlZcSPuP2/IT7p52+c/sdBO+K556T+uQnR01CHrP8NnINdaDu4/xBoQ70Om6j+KbxBMnDjlP1bGgTAM5uw/OfWXYCcB7D8spenPLK7mP0Gn3tvyn+g/DjL2ynuK6z+kLK2ASKbmP+0rDfaoveY/mPp17JWO7D+BUEjzh9/tP5NItqGece0/orf0Ye7U6j+Dr5INlzjtPz8PjhKw5ew/TNv7yCiS6D8kAorG3a7rP9ZqsMn3Nuw/nLQgK1b97T+MDDygnoHiP/aUyiX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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1583]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1583]}},\"selected\":{\"id\":\"52090\"},\"selection_policy\":{\"id\":\"52089\"}},\"id\":\"51719\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"51900\",\"type\":\"LinearScale\"},{\"attributes\":{\"data\":{\"x\":{\"__ndarray__\":\"mJRST/CnEUBkESFMY0MRQOpkas5mwBFAb+165UTREUBrF+5zmrkRQD3mNl+pRhFAWj2pdCX0EUDCPwsrAekRQA5paU6lexFA2bEu31x+EUAHtcJZKagRQKZGaHJ0zBFA2z8NbkXMEUApNK7FYMsRQLgvQ2Ls4xFA1PWlPfn/EUAJ7Wvo4WARQDh23Az7HxFAbsfRvoSvEUD9LZe1YXsRQOS/eJNXoxFAgPqOaseCEUAPB7nNoFcRQClq4La/tBFAIa9OMoupEUCJI62yIEkRQNjf0YuYmxFABIt/hQ2YEUCVBeDLllASQAEal4e5yxFA7lrN5AehEUCjAQiwlgERQFob/QOgGhFAX/p79GC+EUAM5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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1583]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1583]}},\"selected\":{\"id\":\"51562\"},\"selection_policy\":{\"id\":\"51561\"}},\"id\":\"51513\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"51533\"}},\"id\":\"51537\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"52109\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"axis\":{\"id\":\"51908\"},\"dimension\":1,\"grid_line_alpha\":0.5,\"ticker\":null},\"id\":\"51911\",\"type\":\"Grid\"},{\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i0Wcg/KFhOJeoyxT+dqD+VNVLLP/rBBANro8k/EY7FjTDyzD92Av+cYpLPP3A/8BZoSsk/a38pT4KMyT8FV/29wNjKP9bmvMf/tsg/PwRBa4O/zD/hnbDYRJPEPyDL9BIJFcg/NuQ/kMQYyz8Oqgiuog7KP93QVBvOFMs/zLv1L4qHyz+iBWs7J/vKPzt22f3dbss/3hwxmeMqwz97J3uXRsrMPw9G73ekAcg/4eDTPm8Uyj+PS06O84HJPy6wQZYH88o/6eLnLXxryz9yLXGx2l3IPxcgmOCUn8o/49LLOLkVyj/2X+f1webKP1S5VnP1tcc/WaFryJhtyD/gjV5IoJLHPxq60WC2Pcg/ftBuibidzD+JmYSRtSvIP+x7ggNP0Mc/NIvu0OMWyj/LxtZbZw3IP+Ox9sDrAcg/qHMmf382xz95Sht4qF3GP10N9zPOWsQ/a0LU6MVG0D8KAJe19rvQP7e29SW6Gsg/0Vg/+MEpyz81Raklmf3GP5AYFj00Bsk/dydqr9Wiyj+OociOTDrMP8vmndaHmck/kXOdbHX4zD+dpjUqAZvDP24e4saujcM/JMpYQn9LyD8vLMQ+gyLLP8FgySNdIc4/mhEc3dfUyz/Om9LecDPIPwTY11n9gs0/oY68a3RLzz8ztjqqbFLNP0s3pOclWc8/XIuDQuZ8yj9d63k4jGfJP1Jt8K/xhMs/WhtC6mq1yz/rWWAkIJTLPz8S9spJ0cw/GQ2zxARWyj/sr3/V2XjFP1k+IsA+qs4/OCzqW5t5yT9Xj46F6zPEP/v5EBJd1sc/N0Y1EuILxT+YsT/XwuXGP3Dc6PmxLso/o6MN5QTrwj/99lwjM2DIP1sgAARwjsc/1azYsdXyxT+cXoQvhqzIP64hQ+xxScU/YaR/Q2cfxT8JzE1xiTHJP2kdz1z6UM8/+Yr9bC2Pxz+POrVtzbnGP+H5uo1tGcw/4XKCR+AmzD/OCcHRhQLKP9h0KuVCNMg/7DHMvOl4xj81kvQNeY7NP+cjAWrfWMg/5Rx+r+IMyD88QHEr07THPz0I/6TcoMY/EwR88IqwyT8CvSsz9pjMP3QBX1/5FM0/mQe6vaOdxT+Yj5oSTa3PP0k8Brnm+NI/q5gnAzOWyz/UhAE3HGDHP9EkpkWwN8s/ZtrDABlS0T+9jEzgRvDSP17GxksrGsc/t7Fm1TXZyj+Bi36DQMTJPz5jnzWfGck/XzGBfnQ6yT+/xk2gFRfJPxh4p7NNj84/nsg0Fohtzj8N4wEa6cTPP1TGZcjabss//EiblzvByD/NVp3nC4nGP0bMjktQ7MY/n1lfQtkGzD+7573wkA7LPxArU3bJYco/TZ9kbeKjyj+EGVRVGzLGP2ylP5kKwcU/0asE/zAHyj+GkBZ9ldLGP+WbGK1Smcs/7ApivTSOyD/9Ytx285HKPxg5dNtNgck/Wi5uP+8MyD8BjYvhGubJP9mGDIZJ48U/I1fhqHX+yD/NQbAHGPjKP8LjRlKMY8k/tf/rqVXqzj8E919bdPDMP8NH4+uOT9A/aOV6J1b7yD8I4gjYuADJP/++exzIH8g/64dVoD4Ixz9JMy05M4zMPyydlViIvsg/vlgwc3shyz9CdS8DAcvOP4oMK3FiMck/jeVGFB26yD8hidqgKdDLP4eb0C/ykco/HKRljpQGxj8mi2gz5BHJP5vGJf+wDsw//8jivxPPyT9zSVHdFojHP+1WD2tB48k/gyByskpyyz+VGtpXqi7FPxbAXCYIr8Q/OCHJk6z/yT9GgM4yRnzKP5xvS1cZzck/BEfi+/xOzD98zwsQTEPPP9lGCLbQlcw/lhOcpNwAzT/wm3hKQyHMPw1jnT/O3cM/3IZaHeka0D9Oep33xqjKP4TBfpI+o8k/ZuM+wdLgxj8X497BTITJP5BPoZ5k6cc/iq7rJktFyD/zjs6ARdLKP5AwJtTkKc8/j5D//9ExzT/VuzJ6t1rPP6BryPsV1Mk/CWB0edd/xz+blTOEaK/MP9h3o77ALtI/EOA8BYiVyD+4dxp16mjIP0fSQnxhcco/diyETYMTyD9Fv2agWEPJP57TBmbxptA/ic/WocznyT+3Ign0ajvGP0zS3WUwB9E/KwxsaeLWzD9nPoORxczFPwm44KEjicc/1mZGNp4dyD+2+FC/5+7HP4zRGPA5cs8/M2+XMUeLzz+2o/xsPmHNPxP3qMGCrck/OoEIaF83xj8OsuSYevXHP6Uj0D3Hi8k/jMTdieriyz9gdqvCwpTDPzRgO2+g58w/xTvb4MgsxT+k/pvU9w3HPzsnrSypx8s/N92iZcYLzj8Vr0fzkczKPwo5EzTlI8s/JIf4wwD+yT8ogNjUMLDLPyMOYlnniMs/rOE6EhqMyD+alX+1aVjIP9N6hb7e/8c/TK28Ni34yz9XXhTJxTrMP+Q8UZ2kDcU/V/RgVr2gyj8HKlQ4KoDHP4wHxE/h+sk/6PnrjyyOyj+hiYxdpO3DP0AYCQNKFcg/l+agsAE5zT967ruaoJfKP6+V2kGzPMk/oOY6J+HjzD+cvCdW7ebMP5n31Ux8BMc/LT25wkOjyD8LLLLXkkzLP0WAELI6Yc0/gM5HNtrWyj+fgQqVPHnIP6SMa7kphsQ/Qu0XDcGQyT+b506g3FDKPyiTKU2mkMc/I0ELwDhpyD/ub9bHmb/KP7ahNykUtcc/LjEk5FFWxz8bJH2LdfbIP7WrdQpD+8g/2/n4CLcHyz8n+i8lMx3JP/wajGtY2MY/eh+pfj3ezD/tZ1tqtffHPy50I1TnhMU/AKCA8u4ixj99rOBJ7kDGP20bud/vess/uArMaskVzD+mk91/WrHHPxmXYDpaoMk/+9LVmDvgwj+X9G8zeN7DP/o47rnL/cY/M/x3sPSByD9GsAg15OPHP3Co5+Ah98c/7J9ysYW8xj+FVw+2n+PGP3rPbbRPGMk/9xTwu/wnzD/tsDkKUP7GP4VcmLKyYMI/EPUAd/+fyj/m6ropBi3MP1NGOrKX9Mw/ictqPejBxj87Fq0AOVDMP3xEg0FDw8I/MDqCXf6mwj8lxDJGrkvIP9tFIATM78w/i4qnSjqcxD+kcYcYzAnMP6cjFZNeEcY/N8nDZEeAyz/jTQd0V0LFP7P5yXMNu8Y/N1qxNUvVzD+WgzTEtT3PP1NyqeZZHsc/Xdx3QMWSyD+NqHW6f0nGP/A9dUL4tMw/jfIb8zEiyT+HUwAAhDLGP2Q45JADK8Y/OLitIj8hzD80ni5gvQHMP/odwBMNY8k/vBvuCRuLyj9F1YJzDdPJPzO8wV8D+tA/jcaUX0JUyj+enyuiT37MP7AwLpNDXsg/78Fju9WQxj+NlOKv173NP+V84rA8+cc/KAYY1TrNxz8Iw4uOsO7JP+xD5mZSecw/TKoy3s6ZyT8DerpOrHzKP42vvlavpcg/E22XAfKH0T9GVRMue4HKPzDL5xjigsg/I4acu1nKyj+gGwhThL3LP3t1QZWfXs4/tAOxUzL9yD8Sz/oL1yHJP09QvKCrjMw/MajP6KRx0j+7FYk6svrHP1Gy3UKYPck/0asE/zAHyj8x+LcxK5nPP4KqM5HAhsg/791o+599xz8UTnDDDuLGPytxJCXSA8U/rH1rw1ZKwz8vgVy1n2rHPwwEM0bGucc/2c8F/8XKxz/J7Sykjp7LP9BI1r42DNA/Tgtigwp1xz/hFBak1x7JP2ee6eyT+co/k9XI79mDzT/JqWJ7qM7JP0R8tMKCAsc/p28ngBfmyD9eY5qU9ZrIP8VlHAk23MQ/Yr1Xejxzxz90E9tB60DOPy5/386Ulco/ioTPy5x5zT9D+NlCeUrGPwAcPkYn2c0/yWTekxfgxT/6naG74gPMP5+dZkd2OtA/wyJEFd6Qxj8nwznDYKTKP0srorJY5sc/XgrA3BJnzD+Eb1K0fUzNPw4nFNLNtsc/gaJlovvoxj8siyEu8sfEP0VOhQCSM8Y/wtm6YiZ2zz9G3colzhjNPyIIg131Gc8/HpBf6ePpzT/Q2kLunrDHP4Clmbwx0cw/PxOEwe8Vxz/lfEaBVyXIP/Q8HgIHicw/X/C3gwjnxz/wuMZPjmHJP3NVU/lKLsk/yFCIOe+3yT/2KcFEfvDLPzt7wwtA5cs/uLl/dANcyT9a2ewNRWjLPzJFJ2fwls4/i7qgv6Cpyj9NEQLKxrzKPypkoRolSMw//dTOfSG3yT8muzcFEnfQP5AEtSBMA8o/X+QFxr4Ayj88LuK2l9nMP7nL8trFKcg/29T0ecowyj88VJGdh4nQPzGLWBotG80/eqli+kD5xD/geGcgbLvHP5prpuVWmMc/Yr38Bb3/wj+iJWlbK5jMP2a9QiHPJMo/5aBcd+uuyj+gcjvAc1TKP6Wv6U7Mjs0/kGwXhnAWxj8yPl72i0rGP6Ld3TuQtc0/VFdh/4APyz/Mz81QD0PQP85FcUxzJtE/DWGEsHmPxD/8iggJukPCP2oNor/u188/iURs7wQTxz+/dHemtkPMP4WYWPdkvc4/zHKi9cnZxj/PQ84dNObIPyKcZE1AgMg/3SmlvgjNxj9tBTQsfYfLPzUOk96IGck/PCc4HfLhxj8mYhWpSJPJP57i0dG56cw/EyTCmlZVzD9M9OEGF7bGP+7fZYKEWcY/W7VnMewByz9DhzeVWmzHP6CABmTw0MQ/WPq7ugINzT8E2hhpFtTJP9+HA/Oh9ss/h0wlT63pyj8wY4iN42LPP4j3kxTJcMk/jXn+3JJAyz/9EASIJVPKP5YbaNW4M88/kYYPWKzUxz8+h0H7brLKP/JCst5afMY/i+MJPEz/yT+MTstTpc3JP9y6UqsEPMY/nLUBJZdhzj/aXKt0zvDKP2Z8DXV0TcY/deQkHSyCxT8bKy25ZJjMPyC9N9C2o84/M3DzbqhmzT/p56jMTG3FP3Nw7Mziocc/K/NQxlINyT/1R1R8GD7HP7lHkWz+Zck/U3h+HMHZyj99oJAyDRzIP0LFGrKD8ck/iNu58yyfxz/Qyg0BgyjOPwb/J+tk+cg/Rdl8cOxozD+y+HiYrDDKP30C4ZjfV8Y/mw9lCI4Syj9vx7HdD2rIP3UKW8P4G8g/6O4l7/NSyD9E+G7MJ3PIP+KmHeXLFcY/G6pQwNzfwz/mnXvxlrfGP7jcIRILb8g/0eGTTRehyT91nAQ4Uq/IPxM4RmsQ6MY/Nzk/wh5mzD8B0zjIHQnHP10LSdtgTso/JkgBow1iyT+jiCs0ZVnJP7kv0LbtCco/2j2xIQukxz9L2EHLu2TKP62mi39wsM8/iYKzTGv4yT+EpS8lASrIP9HZs5rwIMc/eHJeXczvzT8G61qsodDOP84LuGuapMg/Dcp0nUDdyz/hlHWNkvfOP+PXVXxY2sY/XEP1q74myD+P3sW5M7vJP7pdu59NCMs/YoCDuQXRzD/1FjfMjtzGP+aJ1T+4Xsg/LR9ewWNFyj+RYjz1avnGP47DM7Jzncc/0asE/zAHyj8hstA64FzLP93k/qbyOsc/8u7mgpGlyT8Z51L/Z1zMPy3QShxQlcw/vm4feu4lyj/NMxvu5OHNP8LdcRBUFsg/ZBczaozbyz/VReGvvEnHP2TdRpBRo84/1S0gIh1YzD9IVcvP0X3MP3Fx8B/2hMg/z7Vxv8J3yz/XprqaZofEPy0hqP7nRMk/G7ukZEmayz8YznkDXuXKP9Spx4jwN8o/U+dpiMfdxT8pIYihwH/FP/lynFHtt8Y/kJ2vIyl9yD+bxOn4cS/JPyUFyTsaO8k/Pb/hEvh5zz8osp1weUnMP/Hml/LGbcw/JWldKngSxT+hghp1PabKP9idbkYWfc0/w8PxyNQjyT+sbOxUCcTLPzWrKau4ocs/mHjwhnQUzT9KWuhbzlPFP2VCX/YTwsk/eaX0V/Cpxz9qxjrSYMnQP6YDtyUoPMg/7AIjV1tDzD9gsygQjQjJPya3lY3ea8c/TvuLSJNkyz/T7BxvawXPP7HyxC4Cwcw/fLnRMoAayT/eVoCpLajPPwMS4Qm//8c/i9RPXYudyj9j0V51IJPJPzzqpRP1Wcs/EfEVtkcXxD98NehHvGHLP7HNVbcoicw/uzxsF1mUyj9OyJFrriDGPxOXSClICsc/1XeqLns3yT8xSr7U3abGPxsQZbb9/8g/bbqA4IKxzT8ByHlwMMbJPzuQYaaxQsk/zIvrumsWxj9L2PG34BjKP1qBloiU+Mc/cjjnENQxzD/Yz30L1IXJPw6zHefavMs/+I0YrDJfzT/n29XSbETIP5dnbFO76sw/5fEbKXm/xD9NyX5X+AfJP10iznvVXcU/TWa3hf2VxT+8XLUAtGLNP3hlQPZ9D8s/JDQBpil3yT/e7otDI6/FPydDChd23cs/Cyb5F6lVzT9UmgFQvKHIP82zCahxBdA/Da3uGsFVyj897JWm1SHKPzvW6xxWfMw/HBAZf2lnyT8KdGFF0AnMP7azwQyvpsg/If/+tBhmzT/iCCL8l7jGP3wkUwGwqsk/tffRmez5yz/02s+Ri/rLP1O5FsH68Mo/IqRVfcSuyD9Z2tR611fLP57X6OZdrsk/zl1y6JiVxj+TgBarJf7JP1YE665k6sc/90ahLF5Eyz/VSJppUzvJP3a1iX8qSMo/8O9dq1AT0T89QVSttyXHP2ikOSFW28s/DsPHH6b8yD8eDDTePMXKPzaalK8EV88/Dbwpdkn1xj+gDjwNqazNP6grZtCHFsc/UmWNIafiyj8vzBBEp5vGP67GVfDslc0/9naJjat6yj8KOlFdagDIP1HrP+elmc4/D+AGg4YN0T+4B+JOR9fUP86P0+hdTdI/dKmN8/n21T+MmqSV2PLQPxiNRgVZKMs/L4SVt/j3yj9jKXf2VNTKP9Ia/N0SWM4/ygqfX4t8zT/umo65o8bPP4jXECEdD80/8PFfApqDzj9uAvk6crnMP856WKbkXc4/EhyhakRlyj8SKdqn4ojKPz2o2cCtz8w/qN1OO9Fdyj80a9ocN4TQP8oulwKRMc0/GJ8ZPmFeyz/VlkM3jinGPzmiLXpmBsk/o0OMFoE5xT8GKz7tNbbJP1wdxzuRvME/Cg3bXsuzwz/LwYon7H7LP+k0Q0cw/MY/RJ+J/7Axxj/oSqB2JGvJP4XO+FdY4c4/yFNqPDV5yz8qOGH5LADIP5ELe0i6Ls4/xlEtsCjRzz/Kvjb0cZnRPypU8Xs77Mw/neS7jFyayD8e5fZm1YHHP6X4jzsx08Q/02KBzEPsyD9LjAYx5KPJP4Fz3zVG0cc/wBi3mCdPwj9u7Pf+2eTIP49fMhFNXtI/EEpBf+BBxz9ymeYwVHDEPwdKSPObncs/To/MUHyByj/RqwT/MAfKP99mkoGvRMo/LlzP+7bSzD8pfdFog3TLP4oGUi8Os8o/IA1IWJhMxD/AvVDwR0fMP9xXh6s8Ccw/aJb7Nl38yz/Uv30jwCbHP7do1KdE0ss/wE7e+SKgxj/tuOSESWfIPyzUUZleiso/3iyJhbdDyT/eVMyN4DTGP3dNUKOWwM0/mp3j/7L1xz8KFL2+mNjPPxHdtpVk4sY/IBXaexs1yj9DRQdX9m/NP2h2AGiQisw/rIp9FVvuxD/T3GcqQ6PNPwXpgSVQH8Y/6hiSAkkszD9OSG8IlXHGP2TI3bRbLcM/sRo9QuJixz87XnftCK3HPyVXwIPLq8g/+AePrH/ryz87v648hp7EP5EwolB+v8Y/LsaMyc3Jxj/rMvy2HQjHPwDW5hAkvMY/1lwvOa8uxz/UHqsh1lLHPxiM6AYpM8c/dWzqr12czj99k6180nzOPycQ6uq86Mw/EIQ/RGYwyz8PXDaBnj7PP5PFXGVIWsY/E7NAvxpk0D+BTi78MvnQPxLBVLeIx80/OSjaRmwv0T/Bi8Xy2PrLP23FMuGO3cg/9EyGSA6qzT8ri4aNv43KP66mVN7lDsk/ZT9AiUphyz/38+tU4S3JP5tV+4EsAMg/lnCevV1SyD8TeYvZQpjHP2FOXFySms0/GPo4iGx4yD+Aq+SMJCzKP6FifaVJKsU/mzWtIlXxzT/cCxy1+T7LPwwOZ9SqDsg/1pyVMPuazz/bK9k1RZPMP4wkDY4r08c/4QcZUP1zyj9iXXqS3Q7KP/9cAAqdwsc/c1FOl0O6xz9QIHqsv23GP2sXPyj2i8I/s5Po21fkyT/CIAhgj9zKP8DgpQCcFcs/M6G/Nr0L0D+f/QVDBafKP/O5JZioAcw/yTYnotZ1yT/Xj4y2O87OP1qUx1kpHMY/MFK6DwHyyT9NB9X7tEjNP0ajFXi74sg/Ztpl5gYjyD8Vz9IGMCzIP4wp9ekEDcU/2TwXOoerxz9NLv3R2OjOP0CUBYKcQ8o/bt5cx699xj9JsNQqaAPJP9Juv+nD68g/NRkb4kvbxz+PykRBGTPIP/3Dlv4ADck/PaP1Y0Zlxz+fKXr3/czKP8e/ZAQyjsk/73aUA65XyD/J88lDcsLGPyKKW/Q0R84/sbssSExqzj9oo8u/IITGP2QWLMkBZ8w/xasdun6zzD+ARDb3M1vKP3yEMFuedMc/htcAUm/PyT/tICYrVHPLPx8PN4pA0Mk/VLXMJFN9yT/6HdhCgT3JP9YRuU9+WMo/+4F0QPqLyz8FE1cwmCHOP9TKK3lGAsg/cjg4MIWaxT8zG0K+oITHP2xHgC08vM8/qkJuBZT9xj928I7KZm7LPwn0gxcCe8I/+v/zspQnyT/JmYoCGIHIP+BVRKNDcMs/3fnik81Tzj9HtMRvsALMP8rGeUDjbcw/zsMEsksqzj/5u9QIhtHKP6yGGVXtlcU/iUMdDp9vwz+nabT7irbGP8cNL5vv6Mk/boMijEVlzj8WLCz+UC3GP00/K0eUssY/1anGPn3gxj+xDx2NqjjFPxNZXeLW5sw/ZEt0UPQtyz+BL9s5pNPGP9/RlJy4A8c/Sem0ouSKyT/TDUiJ507MP9l2P+Y0E8w/QQNvZMLMyT+fB0SET+zFP/KexxCUEMY/2yb30O2gyj+7nFcWw2HIP26Z7t7Zmcg/nk9NSsMjxz8BBmi8W+7JP4vuYmX0xck/nphtUylgyz9gAXnUiYXKP27hpczT18Y/kVTzMlZ+yD/2VP9PTD/NP+Z5uLpLJs8/dOXddsBpzT+iHiST4gzLPx6dRn//L8o/qqJJFhPMyj8ug8mWCWbKP6X/V4ixeMY/MFZ02wQaxj/Syt22eLnIPzt8DNGAJsg/9seWh/Kbyj+gDD4OLH7IP3Q5C4W77sc/12PQiT2Nxz93sOQh9e3MP1VcZYNlNcU/qosMGfHlxz/Tf1q8cQLHP1ngXhSZM8k/j9lR8Holzj9M7S/4CY/KP9meF8Mhasw/3Cp7tQBSxj9dZTZHXB7OP5EgDm/CBM0/XBsXSUaVyj8FdEDmnlTMPw==\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1583]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1583]}},\"selected\":{\"id\":\"52186\"},\"selection_policy\":{\"id\":\"52185\"}},\"id\":\"52031\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"below\":[{\"id\":\"51696\"}],\"center\":[{\"id\":\"51699\"},{\"id\":\"51703\"}],\"left\":[{\"id\":\"51700\"}],\"plot_height\":200,\"plot_width\":200,\"renderers\":[{\"id\":\"51722\"},{\"id\":\"51727\"},{\"id\":\"51732\"},{\"id\":\"51737\"}],\"title\":{\"id\":\"51718\"},\"toolbar\":{\"id\":\"51711\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"51688\"},\"x_scale\":{\"id\":\"51692\"},\"y_range\":{\"id\":\"51690\"},\"y_scale\":{\"id\":\"51694\"}},\"id\":\"51687\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"51759\",\"type\":\"SaveTool\"},{\"attributes\":{\"overlay\":{\"id\":\"52022\"}},\"id\":\"52018\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"52151\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"52021\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"51798\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"52019\",\"type\":\"SaveTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"51756\"},{\"id\":\"51757\"},{\"id\":\"51758\"},{\"id\":\"51759\"},{\"id\":\"51760\"},{\"id\":\"51761\"}]},\"id\":\"51763\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"51760\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"52144\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"52009\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"52017\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"overlay\":{\"id\":\"51710\"}},\"id\":\"51706\",\"type\":\"BoxZoomTool\"}],\"root_ids\":[\"52199\"]},\"title\":\"Bokeh Application\",\"version\":\"2.1.0\"}};\n",
       "  var render_items = [{\"docid\":\"9e937a0f-8fad-4894-a10b-426c6670b3b7\",\"notebook_comms_target\":\"53800\",\"root_ids\":[\"52199\"],\"roots\":{\"52199\":\"248b0aab-bbed-473c-b52a-65eab9b4bebd\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "52199"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c62e8037ee74e24b0696f8d177cdd12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Tab(children=(VBox(children=(IntText(value=1, description='Pop 1'),)), VBox(children=(IntText(v…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#initial plot is determined by BIC minimum (red circle in plots above)\n",
    "initial_iterations = 5000\n",
    "optimized_models = np.argwhere(optimized).flatten()\n",
    "deltar = spec.r[1] - spec.r[0]\n",
    "\n",
    "#weight search space of r and s higher for small populations, giving larger step size\n",
    "rs_weights = np.array([np.round(spec.gaussian_model(optimized_model)[:,2].max()/spec.gaussian_model(optimized_model)[:,2]) \n",
    "                       for optimized_model in optimized_models])\n",
    "step_weights = np.array([np.hstack((np.tile(rs_weights[optimized_model][:,np.newaxis],(1,2))*deltar,np.ones((optimized_model+1,1))*.01))\n",
    "                         for optimized_model in optimized_models])\n",
    "\n",
    "def model_func(x):\n",
    "    #to get around pickling issues with lambda functions\n",
    "    return spec.model_waveform(gaussians,x)\n",
    "\n",
    "\n",
    "spec.model_landscape, spec.landscape_statistics = monte_carlo_error(model_func, \n",
    "                                                             data = spec.stable_waveform, \n",
    "                                                             origin = spec.gaussian_model(spec.best_model_id), \n",
    "                                                             weights = step_weights[spec.best_model_id],\n",
    "                                                             iterations = initial_iterations)\n",
    "\n",
    "#Properly normalize, convert to mole fractions\n",
    "spec.norm_constants = np.array([integrate.trapz(gaussians(spec.r,coeff),spec.r) for coeff in spec.model_landscape])\n",
    "#spec.model_landscape[:,:,2] = np.sqrt(2*np.pi)*spec.model_landscape[:,:,1]*spec.model_landscape[:,:,2]/norm_constants[:,None]\n",
    "\n",
    "def reset_weights(val):\n",
    "    \n",
    "    for i,kids in enumerate(weights_tabs.children):\n",
    "        for j,kid in enumerate(kids.children):\n",
    "            kid.value = rs_weights[i][j]\n",
    "    \n",
    "\n",
    "\n",
    "def update_error_plot(val):\n",
    "    \n",
    "    #weight search space of r and s higher for small populations, giving larger step sized\n",
    "    new_rs_weights = np.array([child.value for child in weights_tabs.children[weights_tabs.selected_index].children])\n",
    "    new_step_weights = np.hstack((np.tile(new_rs_weights[:,np.newaxis],(1,2))*deltar,np.ones((weights_tabs.selected_index+1,1))*.01))\n",
    "\n",
    "\n",
    "\n",
    "    spec.model_landscape, spec.landscape_statistics = monte_carlo_error(model_func, \n",
    "                                                             data = spec.stable_waveform, \n",
    "                                                             origin = spec.gaussian_model(weights_tabs.selected_index), \n",
    "                                                             weights = new_step_weights,\n",
    "                                                             iterations = iteration_widget.value)\n",
    "    \n",
    "    #Properly normalize, convert to mole fractions\n",
    "    spec.norm_constants = np.array([integrate.trapz(gaussians(spec.r,coeff),spec.r) for coeff in spec.model_landscape])\n",
    "    #spec.model_landscape[:,:,2] = np.sqrt(2*np.pi)*spec.model_landscape[:,:,1]*spec.model_landscape[:,:,2]/norm_constants[:,None]\n",
    "\n",
    "    surface.children, slicegrid.children = error_plot(spec.model_landscape.copy(),spec.landscape_statistics,refresh = True, norm_constants = spec.norm_constants)\n",
    "    push_notebook(handle = handle)\n",
    "    \n",
    "    \n",
    "\n",
    "plotbutton=widgets.Button(description='Update Plot')\n",
    "plotbutton.on_click(update_error_plot)\n",
    "resetbutton = widgets.Button(description = 'Reset Weights')\n",
    "resetbutton.on_click(reset_weights)\n",
    "    \n",
    "handle, surface, slicegrid = error_plot(spec.model_landscape.copy(),spec.landscape_statistics, norm_constants = spec.norm_constants)\n",
    "\n",
    "### Widget Initialization###\n",
    "iteration_widget = widgets.IntText(value = initial_iterations, description='Iterations:')\n",
    "children = [VBox([widgets.IntText(value = value, description='Pop '+str(idx+1))\n",
    "                  for idx,value in enumerate(rs_weights[model])]) \n",
    "            for model in optimized_models]\n",
    "\n",
    "weights_tabs = widgets.Tab()\n",
    "weights_tabs.children = children\n",
    "for model in optimized_models:\n",
    "    weights_tabs.set_title(model, str(model+1)+'G')\n",
    "weights_tabs.selected_index = spec.best_model_id\n",
    "    \n",
    "HBox([weights_tabs,VBox([plotbutton,resetbutton,iteration_widget])])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit Menagerie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
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       "  function embed_document(root) {\n",
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[656]}},\"selected\":{\"id\":\"64096\"},\"selection_policy\":{\"id\":\"64095\"}},\"id\":\"62470\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"62465\"}},\"id\":\"62469\",\"type\":\"CDSView\"},{\"attributes\":{\"line_color\":\"#0072B2\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"62471\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"62451\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"62452\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"overlay\":{\"id\":\"62457\"}},\"id\":\"62453\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"62454\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"62455\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"62456\",\"type\":\"HelpTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"62451\"},{\"id\":\"62452\"},{\"id\":\"62453\"},{\"id\":\"62454\"},{\"id\":\"62455\"},{\"id\":\"62456\"}]},\"id\":\"62458\",\"type\":\"Toolbar\"},{\"attributes\":{\"line_color\":\"gray\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"62466\",\"type\":\"Line\"}],\"root_ids\":[\"62531\"]},\"title\":\"Bokeh Application\",\"version\":\"2.1.0\"}};\n",
       "  var render_items = [{\"docid\":\"309df4e0-3473-47ac-9f08-4b37a3c95764\",\"root_ids\":[\"62531\"],\"roots\":{\"62531\":\"a2df288c-08aa-4f61-a910-edc4c607b6d2\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "62531"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Show best fit from montecarlo search and 95% confidence range\n",
    "\n",
    "from bokeh.palettes import Colorblind6 as palette\n",
    "\n",
    "spec.best_model = spec.model_landscape[np.argmin(spec.landscape_statistics)]\n",
    "spec.best_model_fit = spec.model_waveform(gaussians,spec.best_model)\n",
    "spec.best_model_solution = gaussians(spec.r,spec.best_model)\n",
    "\n",
    "norm_constant = integrate.trapz(spec.best_model_solution,spec.r)\n",
    "spec.best_model_solution = spec.best_model_solution/norm_constant\n",
    "\n",
    "model_variability = np.array([spec.model_waveform(gaussians,model) \n",
    "                              for model in  spec.model_landscape[spec.landscape_statistics<.95]])\n",
    "\n",
    "\n",
    "\n",
    "#Show best model and 95% bands\n",
    "monte_spectra_plot = spec_plot(plot_height = 300)\n",
    "monte_spectra_plot.line(spec.t,spec.stable_waveform,line_color='gray')\n",
    "monte_spectra_plot.line(spec.t,spec.best_model_fit,line_color = palette[0])\n",
    "monte_spectra_plot.varea(x=spec.t,\n",
    "                       y1=np.quantile(model_variability,0, axis=0),\n",
    "                       y2=np.quantile(model_variability,1, axis=0), \n",
    "                       fill_alpha = 0.5, level = 'underlay', fill_color = palette[5])\n",
    "\n",
    "#Show gaussian model and comprising populations\n",
    "sub_gauss_plot = spec_plot(plot_height = 300)\n",
    "sub_gauss_plot.xaxis.axis_label = 'Distance (nm)'\n",
    "sub_gauss_plot.line(spec.r,spec.stable_solution/integrate.trapz(spec.stable_solution,spec.r),line_color = 'gray')\n",
    "\n",
    "for pop in range(spec.best_model.shape[0]):\n",
    "    \n",
    "    sub_gauss_plot.line(spec.r,gaussians(spec.r,spec.best_model[pop,None])/norm_constant)\n",
    "sub_gauss_plot.line(spec.r,gaussians(spec.r,spec.best_model)/norm_constant,line_color = 'orange')\n",
    "\n",
    "show(row(monte_spectra_plot,sub_gauss_plot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_save = ['full_t','raw_time', 'real', 'imag', 'background', 't','waveform','tikhonovfit',\n",
    "           'stable_waveform','unstable_waveform', 'stable_fit','best_model_fit',\n",
    "           'r','solution', 'stable_solution', 'best_model_solution']\n",
    "           #'zeroamp', 'zeropoint','normamp','tmax','phase','alpha','background_start']\n",
    "\n",
    "filename = os.path.basename(spec.path).split('.')[0]\n",
    "output = pd.DataFrame(columns = to_save)\n",
    "\n",
    "specvars = vars(spec)\n",
    "\n",
    "output = pd.DataFrame(columns = to_save)\n",
    "for item in to_save:\n",
    "    output[item] = pd.Series(specvars[item])\n",
    "#Add renormalized Tikhonov\n",
    "output['Rescaled Tik'] = pd.Series(spec.stable_solution/integrate.trapz(spec.stable_solution,spec.r))\n",
    "    \n",
    "#Add sub populations to the end\n",
    "for pop in range(spec.best_model.shape[0]):\n",
    "    output['Population: ' + str(pop+1)] = pd.Series(gaussians(spec.r,spec.best_model[pop,None])/norm_constant)\n",
    "    \n",
    "\n",
    "#convert to mole fractions\n",
    "spec.model_landscape_scaled = spec.model_landscape.copy()\n",
    "spec.model_landscape_scaled[:,:,2] = np.sqrt(2*np.pi)*spec.model_landscape_scaled[:,:,1]*spec.model_landscape_scaled[:,:,2]/spec.norm_constants[:,None]\n",
    "spec.best_model_scaled = spec.model_landscape_scaled[np.argmin(spec.landscape_statistics)]\n",
    "\n",
    "thresholds = [.6,.75,.95]\n",
    "for thresh in thresholds:\n",
    "    gaussian_output = pd.DataFrame(index = list(range(1,spec.best_model.shape[0]+1)),columns = ['[r (nm), lb, ub]', '[σ (nm), lb, ub]', '[Mol Frac, lb, ub]'])\n",
    "    lower_bound = spec.model_landscape_scaled[spec.landscape_statistics<thresh]\n",
    "    upper_bound = spec.model_landscape_scaled[spec.landscape_statistics<thresh]\n",
    "    for i in range(spec.best_model.shape[0]):\n",
    "        for j in range(3):   \n",
    "            gaussian_output.iloc[i,j] =np.round([spec.best_model_scaled[i,j],lower_bound[:,i,j].min(),upper_bound[:,i,j].max()],decimals = 3)\n",
    "    gaussian_output.to_csv(os.path.join('results',filename+'_'+str(int(thresh*100))+'cutoff_'+'model.csv'))\n",
    "\n",
    "        \n",
    "output.to_csv(os.path.join('results',filename+'_results.csv'))\n"
   ]
  }
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