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@@ -0,0 +1,9511 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "\n",
+    "\n",
+    "<font size = \"5\"> **pyTEMlib : [EELS_tools](../4_EELS_Tools.ipynb)** </font>\n",
+    "\n",
+    "<hr style=\"height:1px;border-top:4px solid #FF8200\" />\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Analysing Low-Loss Spectra with Drude Theory\n",
+    "[<img src=https://www.coeuscreativegroup.com/wp-content/uploads/2020/04/download-button.png, width=125>](https://raw.githubusercontent.com/pycroscopy/pyTEMlib/main/notebooks/EELS/Analyse_Low_Loss.ipynb) \n",
+    "\n",
+    "[![OpenInColab](https://colab.research.google.com/assets/colab-badge.svg)](\n",
+    "    https://colab.research.google.com/github/pycroscopy/pyTEMlib/blob/main/notebooks/Spectroscopy/Analyse_Low_Loss.ipynb)\n",
+    "    \n",
+    "part of \n",
+    "\n",
+    "<font size = \"5\"> **[pyTEMlib](https://pycroscopy.github.io/pyTEMlib/about.html)**</font>\n",
+    "\n",
+    "a [pycroscopy](https://pycroscopy.github.io/pycroscopy/about.html) ecosystem package\n",
+    "Notebook by \n",
+    "\n",
+    "Gerd Duscher\n",
+    "\n",
+    "Microscopy Facilities\n",
+    "Materials Science & Engineering<br>\n",
+    "Institute of Advanced Materials & Manufacturing<br>\n",
+    "The University of Tennessee, Knoxville\n",
+    "\n",
+    "\n",
+    "Analyse EELS spectra and spectrum images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Content\n",
+    "The main feature in a low-loss EELS spectrum is the ``volume plasmon`` peak.\n",
+    "\n",
+    "This ``volume plasmon`` and all other features in the ``low-loss`` region of an EELS spectrum are described by Dielectric Theory of Electrodynamics.\n",
+    "\n",
+    "The simplest theory to interprete this energy range is the Drude theory. \n",
+    "\n",
+    "Another easy to observe component is the multiple scattering of this plasmon peak, which we can correct for or use for thickness determination.\n",
+    "\n",
+    ">See [Notebook: Analysing Low-Loss Spectra with Drude Theory](https://raw.githubusercontent.com/gduscher/MSE672-Introduction-to-TEM/main/Spectroscopy/CH4_03-Drude.ipynb) of the MSE672-Introduction-to-TEM Lecture in my Github account.\n",
+    "\n",
+    "\n",
+    "## Load important packages\n",
+    "\n",
+    "### Check Installed Packages\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-25T17:42:31.009870Z",
+     "start_time": "2024-09-25T17:41:39.294433Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import importlib.metadata\n",
+    "def test_package(package_name):\n",
+    "    \"\"\"Test if package exists and returns version or -1\"\"\"\n",
+    "    try:\n",
+    "        version = importlib.metadata.version(package_name)\n",
+    "    except importlib.metadata.PackageNotFoundError:\n",
+    "        version = '-1'\n",
+    "    return version\n",
+    "\n",
+    "# pyTEMlib setup ------------------\n",
+    "if test_package('pyTEMlib') < '0.2025.1.0':\n",
+    "    print('installing pyTEMlib')\n",
+    "    !{sys.executable} -m pip install --upgrade git+https://github.com/pycroscopy/pyTEMlib.git@main -q --upgrade\n",
+    "# ------------------------------\n",
+    "print('done')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting pint-xarray\n",
+      "  Downloading pint_xarray-0.4-py3-none-any.whl.metadata (3.2 kB)\n",
+      "Requirement already satisfied: numpy>=1.23 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from pint-xarray) (1.26.4)\n",
+      "Requirement already satisfied: xarray>=2022.06.0 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from pint-xarray) (2024.11.0)\n",
+      "Collecting pint>=0.21 (from pint-xarray)\n",
+      "  Downloading Pint-0.24.4-py3-none-any.whl.metadata (8.5 kB)\n",
+      "Requirement already satisfied: platformdirs>=2.1.0 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from pint>=0.21->pint-xarray) (4.3.6)\n",
+      "Requirement already satisfied: typing-extensions>=4.0.0 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from pint>=0.21->pint-xarray) (4.12.2)\n",
+      "Collecting flexcache>=0.3 (from pint>=0.21->pint-xarray)\n",
+      "  Downloading flexcache-0.3-py3-none-any.whl.metadata (7.0 kB)\n",
+      "Collecting flexparser>=0.4 (from pint>=0.21->pint-xarray)\n",
+      "  Downloading flexparser-0.4-py3-none-any.whl.metadata (18 kB)\n",
+      "Requirement already satisfied: packaging>=23.2 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from xarray>=2022.06.0->pint-xarray) (24.2)\n",
+      "Requirement already satisfied: pandas>=2.1 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from xarray>=2022.06.0->pint-xarray) (2.2.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas>=2.1->xarray>=2022.06.0->pint-xarray) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas>=2.1->xarray>=2022.06.0->pint-xarray) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas>=2.1->xarray>=2022.06.0->pint-xarray) (2024.2)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\gduscher\\appdata\\local\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=2.1->xarray>=2022.06.0->pint-xarray) (1.17.0)\n",
+      "Downloading pint_xarray-0.4-py3-none-any.whl (32 kB)\n",
+      "Downloading Pint-0.24.4-py3-none-any.whl (302 kB)\n",
+      "Downloading flexcache-0.3-py3-none-any.whl (13 kB)\n",
+      "Downloading flexparser-0.4-py3-none-any.whl (27 kB)\n",
+      "Installing collected packages: flexparser, flexcache, pint, pint-xarray\n",
+      "Successfully installed flexcache-0.3 flexparser-0.4 pint-0.24.4 pint-xarray-0.4\n"
+     ]
+    }
+   ],
+   "source": [
+    " !{sys.executable} -m pip install pint-xarray\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "### Import all relevant libraries\n",
+    "\n",
+    "Please note that the EELS_tools package from pyTEMlib is essential."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-25T17:48:41.105440Z",
+     "start_time": "2024-09-25T17:48:39.171687Z"
+    },
+    "hideCode": false,
+    "hidePrompt": false,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n",
+      "pyTEM version:  0.2024.09.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import sys\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    from google.colab import output\n",
+    "    output.enable_custom_widget_manager()\n",
+    "    from google.colab import drive\n",
+    "\n",
+    "sys.path.insert(0,'../../')\n",
+    "sys.path.insert(0,'../../../sidpy/')\n",
+    "import sidpy\n",
+    "\n",
+    "# Import libraries from pyTEMlib\n",
+    "import pyTEMlib\n",
+    "import pyTEMlib.file_tools as ft          # File input/ output library\n",
+    "from pyTEMlib import eels_tools  \n",
+    "import pyTEMlib.info_widget3\n",
+    "import pyTEMlib.eels_dialog\n",
+    "import pyTEMlib.peak_dialog\n",
+    "\n",
+    "import pyTEMlib.kinematic_scattering as ks         # Kinematic sCattering Library\n",
+    "                             # Atomic form factors from Kirklands book\n",
+    "\n",
+    "# For archiving reasons it is a good idea to print the version numbers out at this point\n",
+    "print('pyTEM version: ',pyTEMlib.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "561a0dbaa40b4b348fe5a2b267f1e8b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Dropdown(description='directory:', layout=Layout(width='90%'), options=('C:\\\\Users\\\\gduscher\\\\D…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d2cc9a74af445f89a3bd1e6e15034b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Button(description='Select Main', layout=Layout(grid_area='header', width='auto'), style=Button…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "file_widget = ft.FileWidget3()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
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+       " '__dask_optimize__',\n",
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+       " '__dir__',\n",
+       " '__div__',\n",
+       " '__divmod__',\n",
+       " '__doc__',\n",
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+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__index__',\n",
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+       " '__iter__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__long__',\n",
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+       " '__nonzero__',\n",
+       " '__or__',\n",
+       " '__pos__',\n",
+       " '__pow__',\n",
+       " '__radd__',\n",
+       " '__rand__',\n",
+       " '__rdiv__',\n",
+       " '__rdivmod__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rfloordiv__',\n",
+       " '__rlshift__',\n",
+       " '__rmatmul__',\n",
+       " '__rmod__',\n",
+       " '__rmul__',\n",
+       " '__ror__',\n",
+       " '__rpow__',\n",
+       " '__rrshift__',\n",
+       " '__rshift__',\n",
+       " '__rsub__',\n",
+       " '__rtruediv__',\n",
+       " '__rxor__',\n",
+       " '__setattr__',\n",
+       " '__setitem__',\n",
+       " '__sizeof__',\n",
+       " '__slots__',\n",
+       " '__str__',\n",
+       " '__sub__',\n",
+       " '__subclasshook__',\n",
+       " '__truediv__',\n",
+       " '__weakref__',\n",
+       " '__xor__',\n",
+       " '_axes',\n",
+       " '_cached_keys',\n",
+       " '_chunks',\n",
+       " '_closest_point',\n",
+       " '_data_type',\n",
+       " '_elemwise',\n",
+       " '_griddata_transform',\n",
+       " '_h5_dataset',\n",
+       " '_key_array',\n",
+       " '_meta',\n",
+       " '_metadata',\n",
+       " '_min_dist',\n",
+       " '_modality',\n",
+       " '_name',\n",
+       " '_original_metadata',\n",
+       " '_quantity',\n",
+       " '_rebuild',\n",
+       " '_repr_html_',\n",
+       " '_reset_cache',\n",
+       " '_scalarfunc',\n",
+       " '_source',\n",
+       " '_structures',\n",
+       " '_title',\n",
+       " '_units',\n",
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+       " '_vindex',\n",
+       " 'abs',\n",
+       " 'add_provenance',\n",
+       " 'add_structure',\n",
+       " 'adjust_axis',\n",
+       " 'all',\n",
+       " 'angle',\n",
+       " 'any',\n",
+       " 'argmax',\n",
+       " 'argmin',\n",
+       " 'argtopk',\n",
+       " 'astype',\n",
+       " 'blocks',\n",
+       " 'choose',\n",
+       " 'chunks',\n",
+       " 'chunksize',\n",
+       " 'clip',\n",
+       " 'compute',\n",
+       " 'compute_chunk_sizes',\n",
+       " 'conj',\n",
+       " 'copy',\n",
+       " 'cumprod',\n",
+       " 'cumsum',\n",
+       " 'dask',\n",
+       " 'data_descriptor',\n",
+       " 'data_type',\n",
+       " 'del_dimension',\n",
+       " 'dim_0',\n",
+       " 'dot',\n",
+       " 'dtype',\n",
+       " 'energy_loss',\n",
+       " 'fft',\n",
+       " 'flatten',\n",
+       " 'flatten_complex',\n",
+       " 'fold',\n",
+       " 'from_array',\n",
+       " 'get_dimension_by_number',\n",
+       " 'get_dimension_slope',\n",
+       " 'get_dimensions_by_type',\n",
+       " 'get_dimensions_types',\n",
+       " 'get_extent',\n",
+       " 'get_image_dims',\n",
+       " 'get_spectral_dims',\n",
+       " 'h5_dataset',\n",
+       " 'hdf_close',\n",
+       " 'imag',\n",
+       " 'itemsize',\n",
+       " 'labels',\n",
+       " 'like_data',\n",
+       " 'map_blocks',\n",
+       " 'map_overlap',\n",
+       " 'max',\n",
+       " 'mean',\n",
+       " 'metadata',\n",
+       " 'min',\n",
+       " 'modality',\n",
+       " 'moment',\n",
+       " 'name',\n",
+       " 'nbytes',\n",
+       " 'ndim',\n",
+       " 'nonzero',\n",
+       " 'npartitions',\n",
+       " 'numblocks',\n",
+       " 'original_metadata',\n",
+       " 'partitions',\n",
+       " 'persist',\n",
+       " 'plot',\n",
+       " 'point_cloud',\n",
+       " 'prod',\n",
+       " 'provenance',\n",
+       " 'quantity',\n",
+       " 'ravel',\n",
+       " 'real',\n",
+       " 'rechunk',\n",
+       " 'reduce_dims',\n",
+       " 'rename_dimension',\n",
+       " 'repeat',\n",
+       " 'reshape',\n",
+       " 'round',\n",
+       " 'set_dimension',\n",
+       " 'set_thumbnail',\n",
+       " 'shape',\n",
+       " 'shuffle',\n",
+       " 'size',\n",
+       " 'source',\n",
+       " 'squeeze',\n",
+       " 'std',\n",
+       " 'store',\n",
+       " 'structures',\n",
+       " 'sum',\n",
+       " 'swapaxes',\n",
+       " 'title',\n",
+       " 'to_backend',\n",
+       " 'to_dask_dataframe',\n",
+       " 'to_delayed',\n",
+       " 'to_hdf5',\n",
+       " 'to_svg',\n",
+       " 'to_tiledb',\n",
+       " 'to_zarr',\n",
+       " 'topk',\n",
+       " 'trace',\n",
+       " 'transpose',\n",
+       " 'unfold',\n",
+       " 'units',\n",
+       " 'var',\n",
+       " 'variance',\n",
+       " 'view',\n",
+       " 'view_metadata',\n",
+       " 'view_original_metadata',\n",
+       " 'vindex',\n",
+       " 'visualize']"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(file_widget.dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
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+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 33kB\n",
+       "Dimensions:       (energy_scale: 2048)\n",
+       "Coordinates:\n",
+       "  * energy_scale  (energy_scale) float64 16kB -6.126 -6.095 ... 56.06 56.1\n",
+       "Data variables:\n",
+       "    spectrum      (energy_scale) float32 8kB -3.419e+03 -4.697e+03 ... 1.886e+04\n",
+       "    spectrum2     (energy_scale) float32 8kB -34.19 -46.97 ... 195.7 188.6\n",
+       "Attributes:\n",
+       "    units:      counts\n",
+       "    data_type:  spectrum\n",
+       "    quantity:   intensity</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-994aee1d-92f5-4497-956d-52d1cb3cdadf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-994aee1d-92f5-4497-956d-52d1cb3cdadf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>energy_scale</span>: 2048</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-278ed5ee-732e-465e-93d9-ce6d5be1af60' class='xr-section-summary-in' type='checkbox'  checked><label for='section-278ed5ee-732e-465e-93d9-ce6d5be1af60' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>energy_scale</span></div><div class='xr-var-dims'>(energy_scale)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.126 -6.095 -6.065 ... 56.06 56.1</div><input id='attrs-1f728627-48db-4c22-9394-66aed695c279' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f728627-48db-4c22-9394-66aed695c279' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29f3a908-d9c1-4806-93de-aa0ab92980f7' class='xr-var-data-in' type='checkbox'><label for='data-29f3a908-d9c1-4806-93de-aa0ab92980f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>eV</dd><dt><span>dimension_type :</span></dt><dd>spectral</dd><dt><span>quantity :</span></dt><dd>energy_loss</dd></dl></div><div class='xr-var-data'><pre>array([-6.125551, -6.095155, -6.064759, ..., 56.034363, 56.064759, 56.095155])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd430030-40f5-4f4a-9541-1b46cd32f3de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bd430030-40f5-4f4a-9541-1b46cd32f3de' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spectrum</span></div><div class='xr-var-dims'>(energy_scale)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-3.419e+03 -4.697e+03 ... 1.886e+04</div><input id='attrs-ca381d03-88a4-40e5-a426-2a0c0de259ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca381d03-88a4-40e5-a426-2a0c0de259ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d4acb6b-fbdb-4800-b3df-a53b19f71da6' class='xr-var-data-in' type='checkbox'><label for='data-6d4acb6b-fbdb-4800-b3df-a53b19f71da6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-3418.7104, -4697.22  , -6225.6997, ..., 21864.629 , 19569.832 ,\n",
+       "       18860.043 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectrum2</span></div><div class='xr-var-dims'>(energy_scale)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-34.19 -46.97 ... 195.7 188.6</div><input id='attrs-c93d40f4-4ca8-40aa-bdaf-c87131e46531' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c93d40f4-4ca8-40aa-bdaf-c87131e46531' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4159121-70c9-40ee-8e3e-3ece31d9c969' class='xr-var-data-in' type='checkbox'><label for='data-b4159121-70c9-40ee-8e3e-3ece31d9c969' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-34.187103, -46.972202, -62.256996, ..., 218.64629 , 195.69832 ,\n",
+       "       188.60043 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-460b7cbc-2227-405c-b1b5-35999e428901' class='xr-section-summary-in' type='checkbox'  ><label for='section-460b7cbc-2227-405c-b1b5-35999e428901' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>energy_scale</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ab9d301e-6c7d-453b-a1ac-a2709b9638ed' class='xr-index-data-in' type='checkbox'/><label for='index-ab9d301e-6c7d-453b-a1ac-a2709b9638ed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -6.125551413023828,   -6.09515536690688,  -6.064759320789932,\n",
+       "        -6.034363274672984,  -6.003967228556036,  -5.973571182439088,\n",
+       "       -5.9431751363221395,  -5.912779090205191,  -5.882383044088243,\n",
+       "        -5.851986997971295,\n",
+       "       ...\n",
+       "        55.821590573316456,  55.851986619433404,   55.88238266555035,\n",
+       "          55.9127787116673,   55.94317475778425,    55.9735708039012,\n",
+       "        56.003966850018145,   56.03436289613509,   56.06475894225204,\n",
+       "         56.09515498836899],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;energy_scale&#x27;, length=2048))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1814f7ee-245c-476c-b935-a79db17d0896' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1814f7ee-245c-476c-b935-a79db17d0896' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>counts</dd><dt><span>data_type :</span></dt><dd>spectrum</dd><dt><span>quantity :</span></dt><dd>intensity</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 33kB\n",
+       "Dimensions:       (energy_scale: 2048)\n",
+       "Coordinates:\n",
+       "  * energy_scale  (energy_scale) float64 16kB -6.126 -6.095 ... 56.06 56.1\n",
+       "Data variables:\n",
+       "    spectrum      (energy_scale) float32 8kB -3.419e+03 -4.697e+03 ... 1.886e+04\n",
+       "    spectrum2     (energy_scale) float32 8kB -34.19 -46.97 ... 195.7 188.6\n",
+       "Attributes:\n",
+       "    units:      counts\n",
+       "    data_type:  spectrum\n",
+       "    quantity:   intensity"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset2 = xarray.Dataset( {\"spectrum\": ([ \"energy_scale\"], np.array(file_widget.dataset))},\n",
+    "                           coords={\"energy_scale\":  np.array(file_widget.dataset.energy_loss),}\n",
+    "                         )\n",
+    "spectrum_smaller = dataset2['spectrum']/100\n",
+    "\n",
+    "dataset2.attrs ={'units': 'counts', \n",
+    "                 'data_type': 'spectrum',\n",
+    "                 'quantity': 'intensity'}\n",
+    "\n",
+    "dataset2.energy_scale.attrs={'units': 'eV', \n",
+    "                             'dimension_type': 'spectral',\n",
+    "                             'quantity': 'energy_loss'}\n",
+    "dataset2['energy_scale'].attrs, dataset2.energy_scale.units, list(dataset2.coords.keys()), list(dataset2.keys())\n",
+    "dataset2['spectrum2'] = spectrum_smaller\n",
+    "\n",
+    "dataset2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataTree 'root'>\n",
+      "Group: /\n",
+      "│   Dimensions:  ()\n",
+      "│   Data variables:\n",
+      "│       file     <U79 316B 'C:\\\\Users\\\\gduscher\\\\Desktop\\\\drive-download-20241125...\n",
+      "│   Attributes:\n",
+      "│       relationship:  {'main': 'Channel_000'}\n",
+      "└── Group: /Channel_000\n",
+      "    │   Dimensions:       (energy_scale: 2048)\n",
+      "    │   Coordinates:\n",
+      "    │     * energy_scale  (energy_scale) float64 16kB -6.126 -6.095 ... 56.06 56.1\n",
+      "    │   Data variables:\n",
+      "    │       spectrum      (energy_scale) float64 16kB 3.205e+03 1.926e+03 ... 2.548e+04\n",
+      "    │       spectrum2     (energy_scale) float64 16kB 32.05 19.26 3.979 ... 261.9 254.8\n",
+      "    │   Attributes:\n",
+      "    │       units:       counts\n",
+      "    │       data_type:   spectrum\n",
+      "    │       quantity:    intensity\n",
+      "    │       provenance:  {'sidpy': 'init_0.12.3_2024-12-28-17:59:31.459046'}\n",
+      "    └── Group: /Channel_000/other\n",
+      "            Dimensions:       (energy_scale: 2048)\n",
+      "            Data variables:\n",
+      "                spectrum      (energy_scale) float64 16kB 666.0 666.0 666.0 ... 666.0 666.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'units': 'counts',\n",
+       " 'data_type': 'spectrum',\n",
+       " 'quantity': 'intensity',\n",
+       " 'provenance': {'sidpy': 'init_0.12.3_2024-12-28-17:59:31.459046'}}"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xarray\n",
+    "import datetime\n",
+    "ds1 = xarray.Dataset({\"file\": file_widget.file_name })\n",
+    "ds1.attrs = file_widget.dataset.metadata\n",
+    "sidpy_tree = xarray.DataTree(name=\"root\", dataset=ds1)\n",
+    "sidpy_tree.attrs = {'relationship': {'main': 'Channel_000'}}\n",
+    "\n",
+    "sidpy_tree.children = {\"Channel_000\":  xarray.DataTree(name=\"spectrum\", dataset=dataset2)} \n",
+    "sidpy_tree['Channel_000'] -=sidpy_tree['Channel_000'].min()\n",
+    "#sidpy_tree['Channel_000'].coords =   sidpy_tree['Channel_000'].coords['energy_scale']+1000\n",
+    "d = sidpy_tree['Channel_000']/sidpy_tree['Channel_000'].max()\n",
+    "d.name = 'sin'\n",
+    "sidpy_tree['Channel_000']['other'] = np.sin(d['spectrum'].to_dataset())+666\n",
+    "\n",
+    "sidpy_tree['Channel_000']['spectrum'].attrs = file_widget.dataset.original_metadata\n",
+    "\n",
+    "sidpy_tree['Channel_000'].attrs ={'units': 'counts', \n",
+    "                 'data_type': 'spectrum',\n",
+    "                 'quantity': 'intensity',\n",
+    "                 'provenance': {'sidpy': f\"init_{sidpy.__version__}_\"+str(datetime.datetime.now()).replace(' ','-')}}\n",
+    "\n",
+    "print(sidpy_tree)\n",
+    "sidpy_tree['Channel_000'].to_dataset().attrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 352,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_31260\\4049633596.py:31: AccessorRegistrationWarning: registration of accessor <class '__main__.SidpyAccessor'> under name 'sidpy' for type <class 'xarray.core.dataset.Dataset'> is overriding a preexisting attribute with the same name.\n",
+      "  @xarray.register_dataset_accessor(\"sidpy\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray\n",
+    "\n",
+    "import pint_xarray\n",
+    "\n",
+    "from enum import Enum\n",
+    "class DataType(Enum):\n",
+    "    UNKNOWN = -1\n",
+    "    SPECTRUM = 1\n",
+    "    LINE_PLOT = 2\n",
+    "    LINE_PLOT_FAMILY = 3\n",
+    "    IMAGE = 4\n",
+    "    IMAGE_MAP = 5\n",
+    "    IMAGE_STACK = 6  # 3d\n",
+    "    SPECTRAL_IMAGE = 7\n",
+    "    IMAGE_4D = 8\n",
+    "    POINT_CLOUD = 9\n",
+    "\n",
+    "class DimensionType(Enum):\n",
+    "    \"\"\"\n",
+    "    Physical type of Dimension object. This information will be used for\n",
+    "    visualization and processing purposes.\n",
+    "    \"\"\"\n",
+    "    UNKNOWN = -1\n",
+    "    SPATIAL = 1\n",
+    "    RECIPROCAL = 2\n",
+    "    SPECTRAL = 3\n",
+    "    TEMPORAL = 4\n",
+    "    CHANNEL = 5\n",
+    "    POINT_CLOUD = 6\n",
+    "    \n",
+    "@xarray.register_dataset_accessor(\"sidpy\")\n",
+    "class SidpyAccessor:\n",
+    "    def __init__(self, xarray_obj):\n",
+    "        self._obj = xarray_obj\n",
+    "        self._unit = None\n",
+    "        self._dimensions ={} \n",
+    "        self._data_type = ''\n",
+    "        self._units = 'counts'\n",
+    "        self._quantity ='intensity'\n",
+    "        self.provenance = {}\n",
+    "        print(list(self._obj.sizes.keys()))\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "    @property\n",
+    "    def units(self):\n",
+    "        return self._units\n",
+    "    \n",
+    "    @units.setter\n",
+    "    def units(self, value):\n",
+    "        if isinstance(value, str):\n",
+    "            self._units = value\n",
+    "        else:\n",
+    "            raise ValueError('units needs to be a string')\n",
+    "    @property\n",
+    "    def quantity(self):\n",
+    "        return self._quantity\n",
+    "\n",
+    "    @quantity.setter\n",
+    "    def quantity(self, value):\n",
+    "        if isinstance(value, str):\n",
+    "            self._quantity = value\n",
+    "        else:\n",
+    "            raise ValueError('quantity needs to be a string')\n",
+    "\n",
+    "    \n",
+    "    @property\n",
+    "    def data_type(self):\n",
+    "        return self._data_type\n",
+    "\n",
+    "    @data_type.setter\n",
+    "    def data_type(self, value):\n",
+    "        if isinstance(value, str):\n",
+    "            if value.upper() in DataType._member_names_:\n",
+    "                self._data_type = DataType[value.upper()]\n",
+    "            else:\n",
+    "                self._data_type = DataType.UNKNOWN\n",
+    "                raise Warning('Supported data_types for plotting are only: ', DataType._member_names_)\n",
+    "\n",
+    "        elif isinstance(value, DataType):\n",
+    "            self._data_type = value\n",
+    "        else:\n",
+    "            raise ValueError('data_type needs to be a string')\n",
+    "\n",
+    "    \n",
+    "    def plot(self):\n",
+    "        \"\"\"Plot data on a map.\"\"\"\n",
+    "        return \"plotting!\"\n",
+    "        \n",
+    "\n",
+    "\n",
+    "#print(sidpy_tree), d['spectrum']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['T',\n",
+       " '_HANDLED_TYPES',\n",
+       " '__abs__',\n",
+       " '__add__',\n",
+       " '__and__',\n",
+       " '__annotations__',\n",
+       " '__array__',\n",
+       " '__array_priority__',\n",
+       " '__array_ufunc__',\n",
+       " '__array_wrap__',\n",
+       " '__bool__',\n",
+       " '__class__',\n",
+       " '__complex__',\n",
+       " '__contains__',\n",
+       " '__copy__',\n",
+       " '__dask_graph__',\n",
+       " '__dask_keys__',\n",
+       " '__dask_layers__',\n",
+       " '__dask_optimize__',\n",
+       " '__dask_postcompute__',\n",
+       " '__dask_postpersist__',\n",
+       " '__dask_scheduler__',\n",
+       " '__dask_tokenize__',\n",
+       " '__deepcopy__',\n",
+       " '__delattr__',\n",
+       " '__delitem__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__enter__',\n",
+       " '__eq__',\n",
+       " '__exit__',\n",
+       " '__float__',\n",
+       " '__floordiv__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattr__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__iadd__',\n",
+       " '__iand__',\n",
+       " '__ifloordiv__',\n",
+       " '__ilshift__',\n",
+       " '__imod__',\n",
+       " '__imul__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__int__',\n",
+       " '__invert__',\n",
+       " '__ior__',\n",
+       " '__ipow__',\n",
+       " '__irshift__',\n",
+       " '__isub__',\n",
+       " '__iter__',\n",
+       " '__itruediv__',\n",
+       " '__ixor__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lshift__',\n",
+       " '__lt__',\n",
+       " '__matmul__',\n",
+       " '__mod__',\n",
+       " '__module__',\n",
+       " '__mul__',\n",
+       " '__ne__',\n",
+       " '__neg__',\n",
+       " '__new__',\n",
+       " '__or__',\n",
+       " '__pos__',\n",
+       " '__pow__',\n",
+       " '__radd__',\n",
+       " '__rand__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rfloordiv__',\n",
+       " '__rmatmul__',\n",
+       " '__rmod__',\n",
+       " '__rmul__',\n",
+       " '__ror__',\n",
+       " '__rpow__',\n",
+       " '__rshift__',\n",
+       " '__rsub__',\n",
+       " '__rtruediv__',\n",
+       " '__rxor__',\n",
+       " '__setattr__',\n",
+       " '__setitem__',\n",
+       " '__sizeof__',\n",
+       " '__slots__',\n",
+       " '__str__',\n",
+       " '__sub__',\n",
+       " '__subclasshook__',\n",
+       " '__truediv__',\n",
+       " '__weakref__',\n",
+       " '__xor__',\n",
+       " '_all_compat',\n",
+       " '_attr_sources',\n",
+       " '_binary_op',\n",
+       " '_cache',\n",
+       " '_calc_assign_results',\n",
+       " '_close',\n",
+       " '_construct_direct',\n",
+       " '_coords',\n",
+       " '_copy',\n",
+       " '_copy_attrs_from',\n",
+       " '_cum_extra_args_docstring',\n",
+       " '_dask_finalize',\n",
+       " '_from_temp_dataset',\n",
+       " '_get_axis_num',\n",
+       " '_getitem_coord',\n",
+       " '_in_memory',\n",
+       " '_indexes',\n",
+       " '_inplace_binary_op',\n",
+       " '_ipython_key_completions_',\n",
+       " '_item_key_to_dict',\n",
+       " '_item_sources',\n",
+       " '_iter',\n",
+       " '_name',\n",
+       " '_overwrite_indexes',\n",
+       " '_reduce_extra_args_docstring',\n",
+       " '_reduce_method',\n",
+       " '_reindex_callback',\n",
+       " '_replace',\n",
+       " '_replace_maybe_drop_dims',\n",
+       " '_repr_html_',\n",
+       " '_resample',\n",
+       " '_setattr_dict',\n",
+       " '_shuffle',\n",
+       " '_title_for_slice',\n",
+       " '_to_dataset_split',\n",
+       " '_to_dataset_whole',\n",
+       " '_to_index',\n",
+       " '_to_temp_dataset',\n",
+       " '_unary_op',\n",
+       " '_variable',\n",
+       " 'all',\n",
+       " 'any',\n",
+       " 'argmax',\n",
+       " 'argmin',\n",
+       " 'argsort',\n",
+       " 'as_numpy',\n",
+       " 'assign_attrs',\n",
+       " 'assign_coords',\n",
+       " 'astype',\n",
+       " 'attrs',\n",
+       " 'bfill',\n",
+       " 'broadcast_equals',\n",
+       " 'broadcast_like',\n",
+       " 'chunk',\n",
+       " 'chunks',\n",
+       " 'chunksizes',\n",
+       " 'clip',\n",
+       " 'close',\n",
+       " 'coarsen',\n",
+       " 'combine_first',\n",
+       " 'compute',\n",
+       " 'conj',\n",
+       " 'conjugate',\n",
+       " 'convert_calendar',\n",
+       " 'coords',\n",
+       " 'copy',\n",
+       " 'count',\n",
+       " 'cumprod',\n",
+       " 'cumsum',\n",
+       " 'cumulative',\n",
+       " 'cumulative_integrate',\n",
+       " 'curvefit',\n",
+       " 'data',\n",
+       " 'diff',\n",
+       " 'differentiate',\n",
+       " 'dims',\n",
+       " 'dot',\n",
+       " 'drop',\n",
+       " 'drop_attrs',\n",
+       " 'drop_duplicates',\n",
+       " 'drop_encoding',\n",
+       " 'drop_indexes',\n",
+       " 'drop_isel',\n",
+       " 'drop_sel',\n",
+       " 'drop_vars',\n",
+       " 'dropna',\n",
+       " 'dt',\n",
+       " 'dtype',\n",
+       " 'eV',\n",
+       " 'encoding',\n",
+       " 'energy_scale',\n",
+       " 'equals',\n",
+       " 'expand_dims',\n",
+       " 'ffill',\n",
+       " 'fillna',\n",
+       " 'from_dict',\n",
+       " 'from_iris',\n",
+       " 'from_series',\n",
+       " 'get_axis_num',\n",
+       " 'get_index',\n",
+       " 'groupby',\n",
+       " 'groupby_bins',\n",
+       " 'head',\n",
+       " 'identical',\n",
+       " 'idxmax',\n",
+       " 'idxmin',\n",
+       " 'imag',\n",
+       " 'indexes',\n",
+       " 'integrate',\n",
+       " 'interp',\n",
+       " 'interp_calendar',\n",
+       " 'interp_like',\n",
+       " 'interpolate_na',\n",
+       " 'isel',\n",
+       " 'isin',\n",
+       " 'isnull',\n",
+       " 'item',\n",
+       " 'load',\n",
+       " 'loc',\n",
+       " 'map_blocks',\n",
+       " 'max',\n",
+       " 'mean',\n",
+       " 'median',\n",
+       " 'min',\n",
+       " 'name',\n",
+       " 'nbytes',\n",
+       " 'ndim',\n",
+       " 'notnull',\n",
+       " 'pad',\n",
+       " 'persist',\n",
+       " 'pipe',\n",
+       " 'plot',\n",
+       " 'polyfit',\n",
+       " 'prod',\n",
+       " 'quantile',\n",
+       " 'query',\n",
+       " 'rank',\n",
+       " 'real',\n",
+       " 'reduce',\n",
+       " 'reindex',\n",
+       " 'reindex_like',\n",
+       " 'rename',\n",
+       " 'reorder_levels',\n",
+       " 'resample',\n",
+       " 'reset_coords',\n",
+       " 'reset_encoding',\n",
+       " 'reset_index',\n",
+       " 'roll',\n",
+       " 'rolling',\n",
+       " 'rolling_exp',\n",
+       " 'round',\n",
+       " 'searchsorted',\n",
+       " 'sel',\n",
+       " 'set_close',\n",
+       " 'set_index',\n",
+       " 'set_xindex',\n",
+       " 'shape',\n",
+       " 'shift',\n",
+       " 'size',\n",
+       " 'sizes',\n",
+       " 'sortby',\n",
+       " 'squeeze',\n",
+       " 'stack',\n",
+       " 'std',\n",
+       " 'str',\n",
+       " 'sum',\n",
+       " 'swap_dims',\n",
+       " 'tail',\n",
+       " 'thin',\n",
+       " 'to_dask_dataframe',\n",
+       " 'to_dataframe',\n",
+       " 'to_dataset',\n",
+       " 'to_dict',\n",
+       " 'to_index',\n",
+       " 'to_iris',\n",
+       " 'to_masked_array',\n",
+       " 'to_netcdf',\n",
+       " 'to_numpy',\n",
+       " 'to_pandas',\n",
+       " 'to_series',\n",
+       " 'to_unstacked_dataset',\n",
+       " 'to_zarr',\n",
+       " 'transpose',\n",
+       " 'unify_chunks',\n",
+       " 'unstack',\n",
+       " 'values',\n",
+       " 'var',\n",
+       " 'variable',\n",
+       " 'weighted',\n",
+       " 'where',\n",
+       " 'xindexes']"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = sidpy_tree['Channel_000'].to_dataset()/3\n",
+    "dir(d.energy_scale)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'DataArray' object has no attribute 'units'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[268], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43md\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menergy_scale\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munits\u001b[49m()\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\xarray\\core\\common.py:302\u001b[0m, in \u001b[0;36mAttrAccessMixin.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m    300\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mKeyError\u001b[39;00m):\n\u001b[0;32m    301\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m source[name]\n\u001b[1;32m--> 302\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m    303\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    304\u001b[0m )\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'DataArray' object has no attribute 'units'"
+     ]
+    }
+   ],
+   "source": [
+    "float(d['spectrum'].max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['energy_scale']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('counts', 'counts')"
+      ]
+     },
+     "execution_count": 246,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds2 = sidpy_tree['Channel_000']['other']\n",
+    "ds1.sidpy.units, ds2.sidpy.units# , list(ds2.sizes.keys()), \n",
+    "#ds2.sidpy.units, ds2.sidpy.dimensions={'energy_scale':  {'units': 'eV', 'quantity': 'energy_loss',  'dimension_type': 'SPECTRAL'}}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Load and plot a spectrum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x20847e01d00>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib.pylab as plt\n",
+    "plt.figure()\n",
+    "sidpy_tree['Channel_000'].to_dataset().plot.scatter(x ='energy_scale', y ='Channel_000')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-09-25T17:48:50.060660Z",
+     "start_time": "2024-09-25T17:48:47.136159Z"
+    },
+    "hideCode": false,
+    "hidePrompt": false,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "310afc4dc0384a80b25fb303674d94cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "AppLayout(children=(Tab(children=(GridspecLayout(children=(Dropdown(description='directory:', layout=Layout(gr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if 'google.colab' in sys.modules:\n",
+    "    drive.mount(\"/content/drive\")\n",
+    "import pyTEMlib.info_widget    \n",
+    "# filename = '../../example_data/AL-DFoffset0.00.dm3'\n",
+    "infoWidget= pyTEMlib.info_widget3.EELSWidget()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.low_loss._update()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sidpy.Dataset of type SPECTRAL_IMAGE with:\n",
+      " dask.array<array, shape=(25, 10, 2048), dtype=float32, chunksize=(25, 10, 2048), chunktype=numpy.ndarray>\n",
+      " data contains: intensity (counts)\n",
+      " and Dimensions: \n",
+      "x:  distance (µm) of size (25,)\n",
+      "y:  distance (µm) of size (10,)\n",
+      "energy_loss:  energy-loss (eV) of size (2048,)\n",
+      " with metadata: ['experiment', 'annotations']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print(infoWidget.datasets['shifted_low_loss'])\n",
+    "print(infoWidget.datasets['Channel_000'])\n",
+    "isinstance(infoWidget.datasets['Channel_000'], sidpy.Dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.added_spectra\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sidpy.Dataset of type SPECTRAL_IMAGE with:\n",
+      " dask.array<setitem, shape=(25, 10, 2048), dtype=float32, chunksize=(25, 10, 2048), chunktype=numpy.ndarray>\n",
+      " data contains: intensity (counts)\n",
+      " and Dimensions: \n",
+      "x:  distance (µm) of size (25,)\n",
+      "y:  distance (µm) of size (10,)\n",
+      "energy_loss:  energy-loss (eV) of size (2048,)\n",
+      " with metadata: ['experiment', 'annotations', 'zero_loss']\n"
+     ]
+    }
+   ],
+   "source": [
+    "low_loss = infoWidget.dataset\n",
+    "\n",
+    "sl = eels_tools.align_zero_loss(low_loss)\n",
+    "print(sl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Channel_000\n",
+      "_relationship\n",
+      "shifted_low_loss\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['None', 'Channel_000: 11-eels']"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "self = infoWidget.info\n",
+    "spectrum_list = ['None']\n",
+    "reference_list = ['None']\n",
+    "data_list = []\n",
+    "for key in self.parent.datasets.keys():\n",
+    "    print(key)\n",
+    "    if isinstance(self.parent.datasets[key], sidpy.Dataset):\n",
+    "        \n",
+    "        if key[0] != '_' :\n",
+    "            \n",
+    "            data_list.append(f'{key}: {self.parent.datasets[key].title}')\n",
+    "            if 'SPECTR' in self.parent.datasets[key].data_type.name:\n",
+    "                spectrum_data = True\n",
+    "                spectrum_list.append(f'{key}: {self.parent.datasets[key].title}')\n",
+    "                if self.info_key == key:\n",
+    "                    info_index = len(spectrum_list)-1\n",
+    "        reference_list.append(f'{key}: {self.parent.datasets[key].title}')\n",
+    "\n",
+    "\n",
+    "reference_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "self.parent.datasets['shifted_low_loss']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.low_loss.get_drude()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'go' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[22], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m infoWidget\u001b[38;5;241m.\u001b[39m_update()\n\u001b[0;32m      2\u001b[0m resolution_function \u001b[38;5;241m=\u001b[39m infoWidget\u001b[38;5;241m.\u001b[39mlow_loss\u001b[38;5;241m.\u001b[39mget_additional_spectrum(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzero_loss\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 3\u001b[0m infoWidget\u001b[38;5;241m.\u001b[39mspectrum_plot\u001b[38;5;241m.\u001b[39madd_trace(\u001b[43mgo\u001b[49m\u001b[38;5;241m.\u001b[39mScatter(x\u001b[38;5;241m=\u001b[39minfoWidget\u001b[38;5;241m.\u001b[39menergy_scale, y\u001b[38;5;241m=\u001b[39mresolution_function))\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'go' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "infoWidget._update()\n",
+    "resolution_function = infoWidget.low_loss.get_additional_spectrum('zero_loss')\n",
+    "infoWidget.spectrum_plot.add_trace(go.Scatter(x=infoWidget.energy_scale, y=resolution_function))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.energy_scale[:3].values\n",
+    "k = infoWidget.key\n",
+    "infoWidget.datasets['Channel_000'].energy_loss[:3].values\n",
+    "infoWidget.spectrum_plot.data[1].x[1]-infoWidget.spectrum_plot.data[1].x[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.canvas_plot.children = [infoWidget.image_plot]\n",
+    "infoWidget.canvas_plot.children"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.image_plot.data[0].x = infoWidget.datasset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce46460f48b9408183cba716aa5c6c35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FigureWidget({\n",
+       "    'data': [{'type': 'heatmap',\n",
+       "              'uid': '591bccf3-96de-4ed2-9089-c1b5eefd9fa1',\n",
+       "              'x': array([0.00000000e+00, 1.48742899e-01, 2.97485799e-01, ..., 1.51866500e+02,\n",
+       "                          1.52015243e+02, 1.52163986e+02]),\n",
+       "              'y': array([0.00000000e+00, 1.48742899e-01, 2.97485799e-01, ..., 1.51866500e+02,\n",
+       "                          1.52015243e+02, 1.52163986e+02]),\n",
+       "              'z': array([[  13136,   42313,   29516, ...,   21711,   60883,   13305],\n",
+       "                          [  59442,   43388,    9402, ...,   19257,   31239,   35096],\n",
+       "                          [  59924,   52782,   17703, ...,   16142,   25584,   31181],\n",
+       "                          ...,\n",
+       "                          [ 826035,  883774, 1075301, ...,  952118,  767503,  889465],\n",
+       "                          [ 884179,  949242, 1025625, ...,  724397,  829963,  864289],\n",
+       "                          [ 975021,  817816,  936112, ...,  850066,  878260,  819927]],\n",
+       "                         dtype=uint32)}],\n",
+       "    'layout': {'autosize': True,\n",
+       "               'height': 500,\n",
+       "               'plot_bgcolor': 'white',\n",
+       "               'template': '...',\n",
+       "               'width': 500,\n",
+       "               'xaxis': {'showgrid': False, 'title': {'text': 'distance (nm)'}},\n",
+       "               'yaxis': {'scaleanchor': 'x', 'showgrid': False, 'title': {'text': 'distance (nm)'}}}\n",
+       "})"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image_dims = infoWidget.dataset.get_image_dims(return_axis=True)\n",
+    "        \n",
+    "if len(infoWidget.image_plot.data) == 0:\n",
+    "    infoWidget.image_plot.add_trace(go.Heatmap(z=self.dataset))\n",
+    "else:\n",
+    "    infoWidget.image_plot.data[0].z=infoWidget.dataset\n",
+    "infoWidget.image_plot.data[0].x = image_dims[0].values\n",
+    "infoWidget.image_plot.data[0].y = image_dims[1].values\n",
+    "\n",
+    "infoWidget.image_plot.update_layout(xaxis_title = f\"{image_dims[0].quantity} ({image_dims[0].units})\", \n",
+    "                              yaxis_title = f\"{image_dims[0].quantity} ({image_dims[0].units})\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plasmon = eels_tools.fit_plasmon(infoWidget.dataset, 12, 20)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plasmon = np.array(plasmon)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.47891069e+01, 5.24804751e+00, 1.59869663e+04])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plasmon[23,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[126], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m res  \u001b[38;5;241m=\u001b[39m eels_tools\u001b[38;5;241m.\u001b[39mget_resolution_functions(infoWidget\u001b[38;5;241m.\u001b[39mdataset, startFitEnergy\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m.5\u001b[39m, endFitEnergy\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.5\u001b[39m, n_workers \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m8\u001b[39m, n_threads\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\eels_tools.py:494\u001b[0m, in \u001b[0;36mget_resolution_functions\u001b[1;34m(dataset, startFitEnergy, endFitEnergy, n_workers, n_threads)\u001b[0m\n\u001b[0;32m    490\u001b[0m \u001b[38;5;66;03m# apply to all spectra\u001b[39;00m\n\u001b[0;32m    491\u001b[0m zero_loss_fitter \u001b[38;5;241m=\u001b[39m SidFitter(fit_dset, zl_func, num_workers\u001b[38;5;241m=\u001b[39mn_workers, guess_fn\u001b[38;5;241m=\u001b[39mguess_function, threads\u001b[38;5;241m=\u001b[39mn_threads,\n\u001b[0;32m    492\u001b[0m                              return_cov\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, return_fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, return_std\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, km_guess\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, num_fit_parms\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m)\n\u001b[1;32m--> 494\u001b[0m [z_loss_params] \u001b[38;5;241m=\u001b[39m zero_loss_fitter\u001b[38;5;241m.\u001b[39mdo_fit()\n\u001b[0;32m    495\u001b[0m z_loss_dset \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m    496\u001b[0m z_loss_dset \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\proc\\fitter.py:279\u001b[0m, in \u001b[0;36mSidFitter.do_fit\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    273\u001b[0m         lazy_result \u001b[38;5;241m=\u001b[39m dask\u001b[38;5;241m.\u001b[39mdelayed(SidFitter\u001b[38;5;241m.\u001b[39mdefault_curve_fit)(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_fn, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdep_vec,\n\u001b[0;32m    274\u001b[0m                                                                 ydata, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_fit_parms,\n\u001b[0;32m    275\u001b[0m                                                                 return_cov\u001b[38;5;241m=\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_cov \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_std),\n\u001b[0;32m    276\u001b[0m                                                                 p0\u001b[38;5;241m=\u001b[39mp0, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    277\u001b[0m         fit_results\u001b[38;5;241m.\u001b[39mappend(lazy_result)\n\u001b[1;32m--> 279\u001b[0m     fit_results_comp \u001b[38;5;241m=\u001b[39m dask\u001b[38;5;241m.\u001b[39mcompute(\u001b[38;5;241m*\u001b[39mfit_results)\n\u001b[0;32m    280\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m    282\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:661\u001b[0m, in \u001b[0;36mcompute\u001b[1;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[0;32m    658\u001b[0m     postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[0;32m    660\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m shorten_traceback():\n\u001b[1;32m--> 661\u001b[0m     results \u001b[38;5;241m=\u001b[39m schedule(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    663\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\threading.py:655\u001b[0m, in \u001b[0;36mEvent.wait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    653\u001b[0m signaled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flag\n\u001b[0;32m    654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m signaled:\n\u001b[1;32m--> 655\u001b[0m     signaled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cond\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[0;32m    656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m signaled\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\threading.py:359\u001b[0m, in \u001b[0;36mCondition.wait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    358\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 359\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mTrue\u001b[39;00m, timeout)\n\u001b[0;32m    360\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    361\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "res  = eels_tools.get_resolution_functions(infoWidget.dataset, startFitEnergy=-.5, endFitEnergy=.5, n_workers = 8, n_threads=32)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73e8726e4ea64fc48f95af4652debd0b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Dropdown(description='Image', layout=Layout(height='50px', width='30%'), options=(('Pixel Wise'…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "005523bfdd4b440f895b8f045c99dca8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "v = res.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 6.67765731e-02  5.41174565e+03  2.09773567e-01 -5.03759089e-02\n",
+      "  4.60116290e+03  1.76471738e-01]\n",
+      "[ 6.40790854e-02  5.42690876e+03  1.95865661e-01 -5.43761627e-02\n",
+      "  4.89364258e+03  1.77493041e-01]\n",
+      "[ 6.81077719e-02  6.43605514e+03  1.95012100e-01 -5.30016379e-02\n",
+      "  4.43884015e+03  1.66523164e-01]\n",
+      "[ 6.64093078e-02  5.75427504e+03  1.97398452e-01 -5.44820718e-02\n",
+      "  4.76615072e+03  1.74423078e-01]\n",
+      "[ 6.60569720e-02  5.51845692e+03  1.96624862e-01 -5.43996081e-02\n",
+      "  4.90618330e+03  1.73954089e-01]\n",
+      "[ 6.88292998e-02  5.83932800e+03  2.00135178e-01 -5.42389948e-02\n",
+      "  4.77339641e+03  1.73471449e-01]\n",
+      "[ 6.88602553e-02  6.40214431e+03  1.98363423e-01 -5.58406094e-02\n",
+      "  4.40044152e+03  1.74087986e-01]\n",
+      "[ 6.85837537e-02  5.50451130e+03  2.04716106e-01 -5.38545064e-02\n",
+      "  4.91184340e+03  1.76830016e-01]\n",
+      "[ 6.80843999e-02  5.92034916e+03  1.98310795e-01 -5.36603097e-02\n",
+      "  4.72749111e+03  1.71696664e-01]\n",
+      "[ 7.02924129e-02  5.79923590e+03  2.03052815e-01 -6.00875377e-02\n",
+      "  4.77021463e+03  1.85617368e-01]\n",
+      "[ 7.59351953e-02  5.74719392e+03  2.17761905e-01 -4.90964002e-02\n",
+      "  4.57181160e+03  1.73439777e-01]\n",
+      "[ 7.14214173e-02  6.42868692e+03  2.00225951e-01 -5.61403256e-02\n",
+      "  4.45349710e+03  1.73052011e-01]\n",
+      "[ 6.85377605e-02  6.03738747e+03  1.97822842e-01 -5.54147849e-02\n",
+      "  4.65419643e+03  1.73201414e-01]\n",
+      "[ 6.73344556e-02  6.21211761e+03  1.93919859e-01 -5.67820114e-02\n",
+      "  4.59378752e+03  1.73964356e-01]\n",
+      "[ 8.27238627e-02  6.45369213e+03  2.18628990e-01 -5.20886642e-02\n",
+      "  4.36081813e+03  1.73897061e-01]\n",
+      "[ 7.06435960e-02  6.36686440e+03  1.96169775e-01 -5.63710159e-02\n",
+      "  4.59170667e+03  1.72175490e-01]\n",
+      "[ 7.27963085e-02  6.55894563e+03  1.97397768e-01 -6.05839363e-02\n",
+      "  4.55419840e+03  1.77460246e-01]\n",
+      "[ 6.83713072e-02  6.45395612e+03  1.91981356e-01 -5.61672744e-02\n",
+      "  4.57266329e+03  1.69070320e-01]\n",
+      "[ 7.54707228e-02  5.14341506e+03  2.46466476e-01 -4.61403460e-02\n",
+      "  4.52140072e+03  1.89674132e-01]\n",
+      "[ 7.26078887e-02  5.75959368e+03  2.12141579e-01 -5.14107506e-02\n",
+      "  4.75981161e+03  1.73648064e-01]\n",
+      "[ 6.84163221e-02  5.74088531e+03  2.02574067e-01 -5.28523555e-02\n",
+      "  4.65835166e+03  1.73869145e-01]\n",
+      "[ 6.65971194e-02  5.77069798e+03  1.99722139e-01 -5.26893130e-02\n",
+      "  4.67800248e+03  1.73498950e-01]\n",
+      "[ 6.72157208e-02  5.92408665e+03  1.96250631e-01 -5.53627606e-02\n",
+      "  4.71342603e+03  1.74468412e-01]\n",
+      "[ 6.61735212e-02  6.39596380e+03  1.89017846e-01 -5.70988566e-02\n",
+      "  4.53804164e+03  1.71331806e-01]\n",
+      "[ 6.68025761e-02  5.63468078e+03  2.02774223e-01 -5.14165922e-02\n",
+      "  4.67414362e+03  1.73111664e-01]\n",
+      "[ 6.75920132e-02  5.96000308e+03  1.96650576e-01 -5.61433858e-02\n",
+      "  4.70591085e+03  1.75041706e-01]\n",
+      "[ 7.02985918e-02  6.39022118e+03  1.95886368e-01 -5.66922117e-02\n",
+      "  4.62697084e+03  1.71689098e-01]\n",
+      "[ 7.01250351e-02  6.33569920e+03  1.96729667e-01 -5.53759978e-02\n",
+      "  4.62154202e+03  1.71107272e-01]\n",
+      "[ 6.64675874e-02  6.50765523e+03  1.89619124e-01 -5.49894099e-02\n",
+      "  4.47981522e+03  1.67988504e-01]\n",
+      "[ 7.20129273e-02  6.20841628e+03  2.01333628e-01 -5.59094321e-02\n",
+      "  4.64307700e+03  1.74518796e-01]\n",
+      "[ 6.74800030e-02  5.29962324e+03  2.14577044e-01 -5.05834397e-02\n",
+      "  4.62101331e+03  1.81137746e-01]\n",
+      "[ 7.27224927e-02  5.74897004e+03  2.07121011e-01 -5.61475091e-02\n",
+      "  4.77914010e+03  1.77795688e-01]\n",
+      "[ 7.24757611e-02  5.47407419e+03  2.15253862e-01 -5.27279509e-02\n",
+      "  4.82354314e+03  1.78170799e-01]\n",
+      "[ 7.13477161e-02  6.05142285e+03  2.04043003e-01 -5.59095898e-02\n",
+      "  4.65357328e+03  1.76093187e-01]\n",
+      "[ 7.11669155e-02  5.86250488e+03  2.06734879e-01 -5.02780506e-02\n",
+      "  4.61744850e+03  1.69751817e-01]\n",
+      "[ 6.89522783e-02  6.43281917e+03  1.93392595e-01 -5.57871904e-02\n",
+      "  4.55906778e+03  1.69946641e-01]\n",
+      "[ 6.78458237e-02  6.66039151e+03  1.86035878e-01 -5.94598297e-02\n",
+      "  4.57993305e+03  1.69507571e-01]\n",
+      "[ 6.77096956e-02  7.65977493e+03  1.85012212e-01 -5.73856381e-02\n",
+      "  4.03951871e+03  1.65608309e-01]\n",
+      "[ 7.08814555e-02  6.29021760e+03  1.93424048e-01 -5.98209165e-02\n",
+      "  4.77915795e+03  1.73819512e-01]\n",
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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01a276afbc2d4c8a852038d26a010308",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "par = np.array(res.metadata['zero_loss']['fit_parameter'])\n",
+    "\n",
+    "for x in range(par.shape[0]):\n",
+    "    for y in range(par.shape[1]):\n",
+    "        res[x,y] = eels_tools.zl_func(res.energy_loss, *par[x,y])\n",
+    "res.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for dset in infoWidget.datasets.values():\n",
+    "    if isinstance(dset, sidpy.Dataset):\n",
+    "        if 'Measurement' in dset.title:\n",
+    "            dset.title = dset.title.split('/')[-1]\n",
+    "\n",
+    "            print(dset.title)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.low_loss.get_drude()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['Channel_000', '_relationship', 'shifted_low_loss', 'zero_loss', 'plasmon', 'low_loss_model'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(infoWidget.datasets.keys())\n",
+    "dat ={ 'Channel_000': infoWidget.datasets['Channel_000'], \n",
+    "       '_relationship': infoWidget.datasets['_relationship'],\n",
+    "       'plasmon': infoWidget.datasets['plasmon']}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['shifted', 'parameter', 'startFitEnergy', 'endFitEnergy', 'fit_parameter', 'original_low_loss', (0, 0), (20, 6), '0,0', '0,1', '0,2', '0,3', '0,4', '0,5', '0,6', '0,7', '0,8', '0,9', '1,0', '1,1', '1,2', '1,3', '1,4', '1,5', '1,6', '1,7', '1,8', '1,9', '2,0', '2,1', '2,2', '2,3', '2,4', '2,5', '2,6', '2,7', '2,8', '2,9', '3,0', '3,1', '3,2', '3,3', '3,4', '3,5', '3,6', '3,7', '3,8', '3,9', '4,0', '4,1', '4,2', '4,3', '4,4', '4,5', '4,6', '4,7', '4,8', '4,9', '5,0', '5,1', '5,2', '5,3', '5,4', '5,5', '5,6', '5,7', '5,8', '5,9', '6,0', '6,1', '6,2', '6,3', '6,4', '6,5', '6,6', '6,7', '6,8', '6,9', '7,0', '7,1', '7,2', '7,3', '7,4', '7,5', '7,6', '7,7', '7,8', '7,9', '8,0', '8,1', '8,2', '8,3', '8,4', '8,5', '8,6', '8,7', '8,8', '8,9', '9,0', '9,1', '9,2', '9,3', '9,4', '9,5', '9,6', '9,7', '9,8', '9,9', '10,0', '10,1', '10,2', '10,3', '10,4', '10,5', '10,6', '10,7', '10,8', '10,9', '11,0', '11,1', '11,2', '11,3', '11,4', '11,5', '11,6', '11,7', '11,8', '11,9', '12,0', '12,1', '12,2', '12,3', '12,4', '12,5', '12,6', '12,7', '12,8', '12,9', '13,0', '13,1', '13,2', '13,3', '13,4', '13,5', '13,6', '13,7', '13,8', '13,9', '14,0', '14,1', '14,2', '14,3', '14,4', '14,5', '14,6', '14,7', '14,8', '14,9', '15,0', '15,1', '15,2', '15,3', '15,4', '15,5', '15,6', '15,7', '15,8', '15,9', '16,0', '16,1', '16,2', '16,3', '16,4', '16,5', '16,6', '16,7', '16,8', '16,9', '17,0', '17,1', '17,2', '17,3', '17,4', '17,5', '17,6', '17,7', '17,8', '17,9', '18,0', '18,1', '18,2', '18,3', '18,4', '18,5', '18,6', '18,7', '18,8', '18,9', '19,0', '19,1', '19,2', '19,3', '19,4', '19,5', '19,6', '19,7', '19,8', '19,9', '20,0', '20,1', '20,2', '20,3', '20,4', '20,5', '20,6', '20,7', '20,8', '20,9', '21,0', '21,1', '21,2', '21,3', '21,4', '21,5', '21,6', '21,7', '21,8', '21,9', '22,0', '22,1', '22,2', '22,3', '22,4', '22,5', '22,6', '22,7', '22,8', '22,9', '23,0', '23,1', '23,2', '23,3', '23,4', '23,5', '23,6', '23,7', '23,8', '23,9', '24,0', '24,1', '24,2', '24,3', '24,4', '24,5', '24,6', '24,7', '24,8', '24,9'])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['plasmon'].metadata['zero_loss'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cannot overwrite file. Using:  11_si-1.hf5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_io.py:111: UserWarning: main_data_name should not contain the \"-\" character. Reformatted name from:11-eels to 11_eels\n",
+      "  warn('main_data_name should not contain the \"-\" character. Reformatted'\n",
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n",
+      "  warn('validate_h5_dimension may be removed in a future version',\n",
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_io.py:111: UserWarning: main_data_name should not contain the \"-\" character. Reformatted name from:11-eels_new to 11_eels_new\n",
+      "  warn('main_data_name should not contain the \"-\" character. Reformatted'\n",
+      "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (0, 0) from type: <class 'tuple'> to str\n",
+      "  warn('Converted key: {} from type: {} to str'\n",
+      "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (19, 2) from type: <class 'tuple'> to str\n",
+      "  warn('Converted key: {} from type: {} to str'\n",
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n",
+      "  warn('validate_h5_dimension may be removed in a future version',\n",
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_io.py:111: UserWarning: main_data_name should not contain the \"-\" character. Reformatted name from:11-eels_new_new to 11_eels_new_new\n",
+      "  warn('main_data_name should not contain the \"-\" character. Reformatted'\n",
+      "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (19, 2) from type: <class 'tuple'> to str\n",
+      "  warn('Converted key: {} from type: {} to str'\n",
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n",
+      "  warn('validate_h5_dimension may be removed in a future version',\n",
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_io.py:111: UserWarning: main_data_name should not contain the \"-\" character. Reformatted name from:11-eels_new_new to 11_eels_new_new\n",
+      "  warn('main_data_name should not contain the \"-\" character. Reformatted'\n",
+      "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (0, 0) from type: <class 'tuple'> to str\n",
+      "  warn('Converted key: {} from type: {} to str'\n",
+      "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (19, 2) from type: <class 'tuple'> to str\n",
+      "  warn('Converted key: {} from type: {} to str'\n",
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n",
+      "  warn('validate_h5_dimension may be removed in a future version',\n",
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_io.py:111: UserWarning: main_data_name should not contain the \"-\" character. Reformatted name from:11-eels_new_new to 11_eels_new_new\n",
+      "  warn('main_data_name should not contain the \"-\" character. Reformatted'\n",
+      "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (0, 0) from type: <class 'tuple'> to str\n",
+      "  warn('Converted key: {} from type: {} to str'\n",
+      "c:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../../../sidpy\\sidpy\\hdf\\hdf_utils.py:387: UserWarning: Converted key: (19, 2) from type: <class 'tuple'> to str\n",
+      "  warn('Converted key: {} from type: {} to str'\n",
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n",
+      "  warn('validate_h5_dimension may be removed in a future version',\n"
+     ]
+    }
+   ],
+   "source": [
+    "g =pyTEMlib.file_tools.save_dataset(infoWidget.datasets, '11_si.hf5')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['_MutableMapping__marker',\n",
+       " '__abstractmethods__',\n",
+       " '__bool__',\n",
+       " '__class__',\n",
+       " '__class_getitem__',\n",
+       " '__contains__',\n",
+       " '__delattr__',\n",
+       " '__delitem__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getnewargs__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__iter__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__nonzero__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__reversed__',\n",
+       " '__setattr__',\n",
+       " '__setitem__',\n",
+       " '__sizeof__',\n",
+       " '__slots__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " '_abc_impl',\n",
+       " '_d',\n",
+       " '_e',\n",
+       " '_gcpl_crt_order',\n",
+       " '_id',\n",
+       " '_ipython_key_completions_',\n",
+       " '_lapl',\n",
+       " '_lcpl',\n",
+       " 'attrs',\n",
+       " 'build_virtual_dataset',\n",
+       " 'clear',\n",
+       " 'copy',\n",
+       " 'create_dataset',\n",
+       " 'create_dataset_like',\n",
+       " 'create_group',\n",
+       " 'create_virtual_dataset',\n",
+       " 'file',\n",
+       " 'get',\n",
+       " 'id',\n",
+       " 'items',\n",
+       " 'keys',\n",
+       " 'move',\n",
+       " 'name',\n",
+       " 'parent',\n",
+       " 'pop',\n",
+       " 'popitem',\n",
+       " 'ref',\n",
+       " 'regionref',\n",
+       " 'require_dataset',\n",
+       " 'require_group',\n",
+       " 'setdefault',\n",
+       " 'update',\n",
+       " 'values',\n",
+       " 'visit',\n",
+       " 'visit_links',\n",
+       " 'visititems',\n",
+       " 'visititems_links']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.file.close()\n",
+    "dir(g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'single_exposure_time': 0.1,\n",
+       "  'number_of_frames': 1,\n",
+       "  'collection_angle': 100.0,\n",
+       "  'convergence_angle': 0.0,\n",
+       "  'exposure_time': 0.1,\n",
+       "  'microscope': '',\n",
+       "  'acceleration_voltage': 199990.28125,\n",
+       "  'flux_ppm': 90.36209375,\n",
+       "  'count_conversion': 1,\n",
+       "  'beam_current': 0},\n",
+       " 'annotations': {'0': {'type': 'spectral_image',\n",
+       "   'label': '1',\n",
+       "   'width': 0.004483174532651901,\n",
+       "   'height': 0.04483174532651901,\n",
+       "   'position': [0.07173079252243042, 0.0],\n",
+       "   'Rectangle': array([ 0., 16., 10., 17.])}},\n",
+       " 'zero_loss': {'shifted': array([[-0.17133928, -0.20872347, -0.15697378, -0.22911345, -0.41304692,\n",
+       "          -0.27802566, -0.28852486, -0.19660906, -0.24778839, -0.29709778],\n",
+       "         [-0.17452395, -0.25264281, -0.19338031, -0.22475387, -0.3867537 ,\n",
+       "          -0.26830998, -0.30173948, -0.18973434, -0.24055065, -0.27581737],\n",
+       "         [-0.18777319, -0.21813711, -0.23541803, -0.25136158, -0.37223943,\n",
+       "          -0.28151478, -0.27689985, -0.20720506, -0.20844662, -0.26624501],\n",
+       "         [-0.19766428, -0.22570338, -0.2310333 , -0.25888585, -0.36432819,\n",
+       "          -0.26848219, -0.29230228, -0.23995783, -0.25165612, -0.29044124],\n",
+       "         [-0.16309471, -0.20200637, -0.22812651, -0.24042406, -0.39094313,\n",
+       "          -0.29055997, -0.27075889, -0.18784859, -0.2146395 , -0.332887  ],\n",
+       "         [-0.18236734, -0.18087611, -0.22866335, -0.21551541, -0.36639231,\n",
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+       "         [-0.1738958 , -0.1913216 , -0.21929859, -0.24915983, -0.3876799 ,\n",
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+       "          -0.31558961, -0.19452586, -0.21314073, -0.24035954, -0.2328248 ],\n",
+       "         [-0.2032972 , -0.14719534, -0.19907061, -0.2462344 , -0.31856248,\n",
+       "          -0.3517771 , -0.20909715, -0.23441361, -0.25507181, -0.25137647],\n",
+       "         [-0.17083077, -0.15719943, -0.2197162 , -0.28521577, -0.31527722,\n",
+       "          -0.33289726, -0.18606309, -0.19628289, -0.25623027, -0.23231349],\n",
+       "         [-0.12688726, -0.17655356, -0.19887589, -0.29611456, -0.37989474,\n",
+       "          -0.32507459, -0.22918826, -0.21149825, -0.21018903, -0.2608516 ],\n",
+       "         [-0.18858283, -0.17266317, -0.20583162, -0.29928345, -0.3205138 ,\n",
+       "          -0.34542016, -0.25208158, -0.26567733, -0.19913084, -0.2670854 ],\n",
+       "         [-0.20338365, -0.18022165, -0.22180157, -0.29435486, -0.35585983,\n",
+       "          -0.37953179, -0.17357063, -0.20438077, -0.22438605, -0.24467854],\n",
+       "         [-0.2061708 , -0.21012867, -0.24347788, -0.29475718, -0.34663862,\n",
+       "          -0.41550053, -0.18316705, -0.23659631, -0.24869219, -0.21216895],\n",
+       "         [-0.22316636, -0.21843133, -0.19459794, -0.30371964, -0.31803614,\n",
+       "          -0.36346566, -0.1827611 , -0.24550804, -0.26715442, -0.23049209],\n",
+       "         [-0.22549864, -0.20233576, -0.25172761, -0.32813819, -0.33494639,\n",
+       "          -0.41605422, -0.182917  , -0.18322456, -0.28218851, -0.22714036],\n",
+       "         [-0.22792804, -0.18826536, -0.25218583, -0.29268463, -0.32082594,\n",
+       "          -0.32751488, -0.16049068, -0.19076274, -0.2756469 , -0.23645988],\n",
+       "         [-0.17985229, -0.17842844, -0.20377253, -0.31785076, -0.34848329,\n",
+       "          -0.41367577, -0.17336046, -0.16275926, -0.30444214, -0.2085971 ]]),\n",
+       "  'startFitEnergy': -0.5,\n",
+       "  'endFitEnergy': 0.5,\n",
+       "  'fit_parameter': array([-1.51221768e-02,  2.67527012e+03,  1.67573156e-01,  4.36801062e-02,\n",
+       "          2.74369530e+03,  3.35819747e-01]),\n",
+       "  'original_low_loss': '11-eels_new_new',\n",
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+       "  '19,4': {...},\n",
+       "  '19,5': {...},\n",
+       "  '19,6': {...},\n",
+       "  '19,7': {...},\n",
+       "  '19,8': {...},\n",
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+       "  '20,0': {...},\n",
+       "  '20,1': {...},\n",
+       "  '20,2': {...},\n",
+       "  '20,3': {...},\n",
+       "  '20,4': {...},\n",
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+       "  '20,7': {...},\n",
+       "  '20,8': {...},\n",
+       "  '20,9': {...},\n",
+       "  '21,0': {...},\n",
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+       "  '21,2': {...},\n",
+       "  '21,3': {...},\n",
+       "  '21,4': {...},\n",
+       "  '21,5': {...},\n",
+       "  '21,6': {...},\n",
+       "  '21,7': {...},\n",
+       "  '21,8': {...},\n",
+       "  '21,9': {...},\n",
+       "  '22,0': {...},\n",
+       "  '22,1': {...},\n",
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+       "  '22,4': {...},\n",
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+       "  '22,6': {...},\n",
+       "  '22,7': {...},\n",
+       "  '22,8': {...},\n",
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+       "  '23,0': {...},\n",
+       "  '23,1': {...},\n",
+       "  '23,2': {...},\n",
+       "  '23,3': {...},\n",
+       "  '23,4': {...},\n",
+       "  '23,5': {...},\n",
+       "  '23,6': {...},\n",
+       "  '23,7': {...},\n",
+       "  '23,8': {...},\n",
+       "  '23,9': {...},\n",
+       "  '24,0': {...},\n",
+       "  '24,1': {...},\n",
+       "  '24,2': {...},\n",
+       "  '24,3': {...},\n",
+       "  '24,4': {...},\n",
+       "  '24,5': {...},\n",
+       "  '24,6': {...},\n",
+       "  '24,7': {...},\n",
+       "  '24,8': {...},\n",
+       "  '24,9': {...}}}"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets[infoWidget.datasets['_relationship']['low_loss']].metadata\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[20], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m infoWidget\u001b[38;5;241m.\u001b[39mlow_loss\u001b[38;5;241m.\u001b[39mget_multiple_scattering()\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\low_loss_widget.py:281\u001b[0m, in \u001b[0;36mget_multiple_scattering\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m      0\u001b[0m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:716\u001b[0m, in \u001b[0;36mTraitType.__set__\u001b[1;34m(self, obj, value)\u001b[0m\n\u001b[0;32m    714\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread_only:\n\u001b[0;32m    715\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m TraitError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m trait is read-only.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m--> 716\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset(obj, value)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:706\u001b[0m, in \u001b[0;36mTraitType.set\u001b[1;34m(self, obj, value)\u001b[0m\n\u001b[0;32m    702\u001b[0m     silent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    703\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m silent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m    704\u001b[0m     \u001b[38;5;66;03m# we explicitly compare silent to True just in case the equality\u001b[39;00m\n\u001b[0;32m    705\u001b[0m     \u001b[38;5;66;03m# comparison above returns something other than True/False\u001b[39;00m\n\u001b[1;32m--> 706\u001b[0m     obj\u001b[38;5;241m.\u001b[39m_notify_trait(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, old_value, new_value)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:1513\u001b[0m, in \u001b[0;36mHasTraits._notify_trait\u001b[1;34m(self, name, old_value, new_value)\u001b[0m\n\u001b[0;32m   1512\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_notify_trait\u001b[39m(\u001b[38;5;28mself\u001b[39m, name: \u001b[38;5;28mstr\u001b[39m, old_value: t\u001b[38;5;241m.\u001b[39mAny, new_value: t\u001b[38;5;241m.\u001b[39mAny) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1513\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnotify_change(\n\u001b[0;32m   1514\u001b[0m         Bunch(\n\u001b[0;32m   1515\u001b[0m             name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m   1516\u001b[0m             old\u001b[38;5;241m=\u001b[39mold_value,\n\u001b[0;32m   1517\u001b[0m             new\u001b[38;5;241m=\u001b[39mnew_value,\n\u001b[0;32m   1518\u001b[0m             owner\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1519\u001b[0m             \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchange\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1520\u001b[0m         )\n\u001b[0;32m   1521\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\ipywidgets\\widgets\\widget.py:687\u001b[0m, in \u001b[0;36mWidget.notify_change\u001b[1;34m(self, change)\u001b[0m\n\u001b[0;32m    684\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_send_property(name, \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)):\n\u001b[0;32m    685\u001b[0m         \u001b[38;5;66;03m# Send new state to front-end\u001b[39;00m\n\u001b[0;32m    686\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend_state(key\u001b[38;5;241m=\u001b[39mname)\n\u001b[1;32m--> 687\u001b[0m \u001b[38;5;28msuper\u001b[39m(Widget, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mnotify_change(change)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:1525\u001b[0m, in \u001b[0;36mHasTraits.notify_change\u001b[1;34m(self, change)\u001b[0m\n\u001b[0;32m   1523\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnotify_change\u001b[39m(\u001b[38;5;28mself\u001b[39m, change: Bunch) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1524\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Notify observers of a change event\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1525\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_notify_observers(change)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\traitlets\\traitlets.py:1568\u001b[0m, in \u001b[0;36mHasTraits._notify_observers\u001b[1;34m(self, event)\u001b[0m\n\u001b[0;32m   1565\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, EventHandler) \u001b[38;5;129;01mand\u001b[39;00m c\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1566\u001b[0m     c \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, c\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m-> 1568\u001b[0m c(event)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\info_widget.py:507\u001b[0m, in \u001b[0;36mEELSBaseWidget._update\u001b[1;34m(self, ev)\u001b[0m\n\u001b[0;32m    505\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mylabel)\n\u001b[0;32m    506\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchange_y_scale \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n\u001b[1;32m--> 507\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate_tab_spectra()\n\u001b[0;32m    508\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle()\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\info_widget.py:818\u001b[0m, in \u001b[0;36mEELSWidget.update_tab_spectra\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    816\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate_tab_spectra\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    817\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtabval \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m--> 818\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_loss\u001b[38;5;241m.\u001b[39m_update()\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\low_loss_widget.py:334\u001b[0m, in \u001b[0;36mLowLoss._update\u001b[1;34m(self, ev)\u001b[0m\n\u001b[0;32m    332\u001b[0m         difference \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m plasmon\n\u001b[0;32m    333\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_loss_tab[\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_loss_tab[\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_loss_tab[\u001b[38;5;241m15\u001b[39m, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 334\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39menergy_scale, difference, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifference\u001b[39m\u001b[38;5;124m'\u001b[39m)               \n\u001b[0;32m    335\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mlegend()\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1779\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1536\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1537\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[0;32m   1538\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1776\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[0;32m   1777\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1778\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[1;32m-> 1779\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[0;32m   1780\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[0;32m   1781\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_base.py:296\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[1;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    294\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m    295\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m--> 296\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_plot_args(\n\u001b[0;32m    297\u001b[0m     axes, this, kwargs, ambiguous_fmt_datakey\u001b[38;5;241m=\u001b[39mambiguous_fmt_datakey)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\axes\\_base.py:483\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[1;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[0;32m    481\u001b[0m     axes\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mupdate_units(x)\n\u001b[0;32m    482\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axes\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 483\u001b[0m     axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[0;32m    485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m    486\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    487\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\axis.py:1750\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate_units\u001b[39m(\u001b[38;5;28mself\u001b[39m, data):\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m \u001b[38;5;124;03m    Introspect *data* for units converter and update the\u001b[39;00m\n\u001b[0;32m   1747\u001b[0m \u001b[38;5;124;03m    ``axis.converter`` instance if necessary. Return *True*\u001b[39;00m\n\u001b[0;32m   1748\u001b[0m \u001b[38;5;124;03m    if *data* is registered for unit conversion.\u001b[39;00m\n\u001b[0;32m   1749\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1750\u001b[0m     converter \u001b[38;5;241m=\u001b[39m munits\u001b[38;5;241m.\u001b[39mregistry\u001b[38;5;241m.\u001b[39mget_converter(data)\n\u001b[0;32m   1751\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m converter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1752\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\units.py:183\u001b[0m, in \u001b[0;36mRegistry.get_converter\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    181\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m    182\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:  \u001b[38;5;66;03m# If cache lookup fails, look up based on first element...\u001b[39;00m\n\u001b[1;32m--> 183\u001b[0m     first \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39m_safe_first_finite(x)\n\u001b[0;32m    184\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mStopIteration\u001b[39;00m):\n\u001b[0;32m    185\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\cbook.py:1782\u001b[0m, in \u001b[0;36m_safe_first_finite\u001b[1;34m(obj)\u001b[0m\n\u001b[0;32m   1780\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1781\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m obj:\n\u001b[1;32m-> 1782\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m safe_isfinite(val):\n\u001b[0;32m   1783\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m val\n\u001b[0;32m   1784\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m safe_first_element(obj)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\matplotlib\\cbook.py:1762\u001b[0m, in \u001b[0;36m_safe_first_finite.<locals>.safe_isfinite\u001b[1;34m(val)\u001b[0m\n\u001b[0;32m   1760\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m   1761\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m math\u001b[38;5;241m.\u001b[39misfinite(val)\n\u001b[0;32m   1763\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n\u001b[0;32m   1764\u001b[0m     \u001b[38;5;66;03m# if the outer object is 2d, then val is a 1d array, and\u001b[39;00m\n\u001b[0;32m   1765\u001b[0m     \u001b[38;5;66;03m# - math.isfinite(numpy.zeros(3)) raises TypeError\u001b[39;00m\n\u001b[0;32m   1766\u001b[0m     \u001b[38;5;66;03m# - math.isfinite(torch.zeros(3)) raises ValueError\u001b[39;00m\n\u001b[0;32m   1767\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\array\\core.py:1883\u001b[0m, in \u001b[0;36mArray.__float__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1882\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__float__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m-> 1883\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_scalarfunc(\u001b[38;5;28mfloat\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\array\\core.py:1875\u001b[0m, in \u001b[0;36mArray._scalarfunc\u001b[1;34m(self, cast_type)\u001b[0m\n\u001b[0;32m   1873\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOnly length-1 arrays can be converted to Python scalars\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1874\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1875\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast_type(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute()\u001b[38;5;241m.\u001b[39mitem())\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:375\u001b[0m, in \u001b[0;36mDaskMethodsMixin.compute\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    351\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    352\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[0;32m    353\u001b[0m \n\u001b[0;32m    354\u001b[0m \u001b[38;5;124;03m    This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    373\u001b[0m \u001b[38;5;124;03m    dask.compute\u001b[39;00m\n\u001b[0;32m    374\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 375\u001b[0m     (result,) \u001b[38;5;241m=\u001b[39m compute(\u001b[38;5;28mself\u001b[39m, traverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    376\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:654\u001b[0m, in \u001b[0;36mcompute\u001b[1;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[0;32m    646\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m args\n\u001b[0;32m    648\u001b[0m schedule \u001b[38;5;241m=\u001b[39m get_scheduler(\n\u001b[0;32m    649\u001b[0m     scheduler\u001b[38;5;241m=\u001b[39mscheduler,\n\u001b[0;32m    650\u001b[0m     collections\u001b[38;5;241m=\u001b[39mcollections,\n\u001b[0;32m    651\u001b[0m     get\u001b[38;5;241m=\u001b[39mget,\n\u001b[0;32m    652\u001b[0m )\n\u001b[1;32m--> 654\u001b[0m dsk \u001b[38;5;241m=\u001b[39m collections_to_dsk(collections, optimize_graph, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    655\u001b[0m keys, postcomputes \u001b[38;5;241m=\u001b[39m [], []\n\u001b[0;32m    656\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m collections:\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:427\u001b[0m, in \u001b[0;36mcollections_to_dsk\u001b[1;34m(collections, optimize_graph, optimizations, **kwargs)\u001b[0m\n\u001b[0;32m    425\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m opt, val \u001b[38;5;129;01min\u001b[39;00m groups\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m    426\u001b[0m     dsk, keys \u001b[38;5;241m=\u001b[39m _extract_graph_and_keys(val)\n\u001b[1;32m--> 427\u001b[0m     dsk \u001b[38;5;241m=\u001b[39m opt(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    429\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m opt_inner \u001b[38;5;129;01min\u001b[39;00m optimizations:\n\u001b[0;32m    430\u001b[0m         dsk \u001b[38;5;241m=\u001b[39m opt_inner(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\array\\optimization.py:51\u001b[0m, in \u001b[0;36moptimize\u001b[1;34m(dsk, keys, fuse_keys, fast_functions, inline_functions_fast_functions, rename_fused_keys, **kwargs)\u001b[0m\n\u001b[0;32m     49\u001b[0m dsk \u001b[38;5;241m=\u001b[39m optimize_blockwise(dsk, keys\u001b[38;5;241m=\u001b[39mkeys)\n\u001b[0;32m     50\u001b[0m dsk \u001b[38;5;241m=\u001b[39m fuse_roots(dsk, keys\u001b[38;5;241m=\u001b[39mkeys)\n\u001b[1;32m---> 51\u001b[0m dsk \u001b[38;5;241m=\u001b[39m dsk\u001b[38;5;241m.\u001b[39mcull(\u001b[38;5;28mset\u001b[39m(keys))\n\u001b[0;32m     53\u001b[0m \u001b[38;5;66;03m# Perform low-level fusion unless the user has\u001b[39;00m\n\u001b[0;32m     54\u001b[0m \u001b[38;5;66;03m# specified False explicitly.\u001b[39;00m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptimization.fuse.active\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\highlevelgraph.py:738\u001b[0m, in \u001b[0;36mHighLevelGraph.cull\u001b[1;34m(self, keys)\u001b[0m\n\u001b[0;32m    736\u001b[0m output_keys \u001b[38;5;241m=\u001b[39m keys_set\u001b[38;5;241m.\u001b[39mintersection(layer\u001b[38;5;241m.\u001b[39mget_output_keys())\n\u001b[0;32m    737\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_keys:\n\u001b[1;32m--> 738\u001b[0m     culled_layer, culled_deps \u001b[38;5;241m=\u001b[39m layer\u001b[38;5;241m.\u001b[39mcull(output_keys, all_ext_keys)\n\u001b[0;32m    739\u001b[0m     \u001b[38;5;66;03m# Update `keys` with all layer's external key dependencies, which\u001b[39;00m\n\u001b[0;32m    740\u001b[0m     \u001b[38;5;66;03m# are all the layer's dependencies (`culled_deps`) excluding\u001b[39;00m\n\u001b[0;32m    741\u001b[0m     \u001b[38;5;66;03m# the layer's output keys.\u001b[39;00m\n\u001b[0;32m    742\u001b[0m     external_deps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\highlevelgraph.py:154\u001b[0m, in \u001b[0;36mLayer.cull\u001b[1;34m(self, keys, all_hlg_keys)\u001b[0m\n\u001b[0;32m    152\u001b[0m k \u001b[38;5;241m=\u001b[39m work\u001b[38;5;241m.\u001b[39mpop()\n\u001b[0;32m    153\u001b[0m out[k] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m[k]\n\u001b[1;32m--> 154\u001b[0m ret_deps[k] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_dependencies(k, all_hlg_keys)\n\u001b[0;32m    155\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m ret_deps[k]:\n\u001b[0;32m    156\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m d \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m seen:\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\highlevelgraph.py:178\u001b[0m, in \u001b[0;36mLayer.get_dependencies\u001b[1;34m(self, key, all_hlg_keys)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_dependencies\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: Key, all_hlg_keys: Collection[Key]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mset\u001b[39m:\n\u001b[0;32m    164\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Get dependencies of `key` in the layer\u001b[39;00m\n\u001b[0;32m    165\u001b[0m \n\u001b[0;32m    166\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    176\u001b[0m \u001b[38;5;124;03m        A set of dependencies\u001b[39;00m\n\u001b[0;32m    177\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 178\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m keys_in_tasks(all_hlg_keys, [\u001b[38;5;28mself\u001b[39m[key]])\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\core.py:199\u001b[0m, in \u001b[0;36mkeys_in_tasks\u001b[1;34m(keys, tasks, as_list)\u001b[0m\n\u001b[0;32m    197\u001b[0m                 \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m    198\u001b[0m     tasks \u001b[38;5;241m=\u001b[39m work\n\u001b[1;32m--> 199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret \u001b[38;5;28;01mif\u001b[39;00m as_list \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mset\u001b[39m(ret)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "infoWidget.low_loss.get_multiple_scattering()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    }
+   ],
+   "source": [
+    "infoWidget.low_loss.do_all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e29c5f2893d49c79893d5d23ee0a277",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Dropdown(description='Image', layout=Layout(height='50px', width='30%'), options=(('Pixel Wise'…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3444d7e8f5c4030ad1d983db1abe76d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    11-eels_new_new\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['low_loss_model'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048,) </td>\n",
+       "                        <td> (2048,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 256 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "sidpy.Dataset of type SPECTRAL_IMAGE with:\n",
+       " dask.array<mul, shape=(2048,), dtype=float32, chunksize=(2048,), chunktype=numpy.ndarray>\n",
+       " data contains: intensity (counts)\n",
+       " and Dimensions: \n",
+       "energy_loss:  energy-loss (eV) of size (2048,)\n",
+       " with metadata: ['experiment', 'annotations', 'zero_loss']"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13-without blue laser 10% (2048,)\n",
+      "EELS Acquire (2048,)\n",
+      "13-with laser (2048,)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33cc9a3199df4d4a953bbc3e3baa001b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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O/AcAQPJwTQ9gcXGxmpqadPDgQX3ve9/TypUr9bvf/W7U/S3LitoeWrpkeHuk6upq5eTkhB+FhYX2FA8AADCBXBMAMzIydNVVV6msrEzV1dWaP3++XnjhhZj75uXlqaOjI6qts7NTaWlpmj59+qi/o6qqSt3d3eFHa2urre8hmUUPAY8eogEAQOK5Zgh4OGNM1HBtJL/frzfffDOqbe/evSorK1N6evqoP9Pn88nn89lap3swCAwAQLJwRQ/gunXr9O677+qPf/yjjh49qieeeEJ1dXW67777JA303D3wwAPh/VetWqWTJ0+qsrJSx44d0yuvvKKtW7dq7dq1iXoLSS+6B5AwCACAk7miB/CTTz7R/fffr/b2duXk5GjevHl65513dMcdd0iS2tvb1dLSEt6/qKhIe/bs0eOPP66XXnpJBQUF2rhxI0vAxCF6IWgAAOBkrgiAW7duHfP1bdu2jWhbsmSJjhw5cpEqAgAAcC5XDAEj8bj9GwAAyYMACFuwEDQAAMmDAAhbhELcARgAgGRBAIQtLBEAAQBIFgRA2CJkCIAAACQLAiBs0p/oAgAAwDkiAMIeTAIBACBpEABhE4aAAQBIFgRA2MJErAPDkjAAADgbARD2MFwDCABAsiAAwhYmYgjYSmAdAABgfARA2INlYAAASBoEQNiEC/8AAEgWBEDYxMR4BgAAnIgACFsY7gUMAEDSIADCFoZ1AAEASBoEQNjCYtwXAICkQQCELegBBAAgeRAAYQtLfYkuAQAAnCMCIGzBCDAAAMmDAAhbGBaCBgAgaRAAYQvLRK4DSH8gAABORgCELYz6I54DAAAnIwDCHqQ+AACSBgEQtohcBoYsCACAsxEAYQ8TOQRMBAQAwMkIgLAd8Q8AAGcjAMImDAEDAJAsCICwB+sAAgCQNAiAsIWJek4fIAAATkYAhD0MQ8AAACQLAiDsQQAEACBpuCIAVldX68Ybb1RWVpZmzJihe+65R8ePHx/zmLq6OlmWNeLx4YcfTlDVbmNiPAMAAE7kigBYX1+v1atX6+DBg6qtrVVfX58qKirU09Mz7rHHjx9Xe3t7+HH11VdPQMXuE7kQNBEQAABnS0t0AXZ45513orZfffVVzZgxQ4cPH9bixYvHPHbGjBmaOnXqRazOKxgCBgAgWbiiB3C47u5uSdK0adPG3XfBggXKz89XeXm59u3bN+a+wWBQgUAg6oGRCIAAADib6wKgMUaVlZW6+eabNXfu3FH3y8/P15YtW1RTU6Ndu3apuLhY5eXl2r9//6jHVFdXKycnJ/woLCy8GG8hKZnISSBWAgsBAADjsowxruqwWb16td566y399re/1cyZM8/r2OXLl8uyLL3xxhsxXw8GgwoGg+HtQCCgwsJCdXd3Kzs7O666k91/+58v693MTZKkWX3SW989muCKAACILRAIKCcnx9Pf367qAXzkkUf0xhtvaN++fecd/iRp4cKFOnHixKiv+3w+ZWdnRz0wIPKfEa76FwUAAC7kikkgxhg98sgj2r17t+rq6lRUVHRBP6exsVH5+fk2V+cVzAIGACBZuCIArl69Wr/85S/1b//2b8rKylJHR4ckKScnR5MmTZIkVVVVqa2tTdu3b5ckbdiwQVdeeaVKSkrU29urHTt2qKamRjU1NQl7H8mNWcAAACQLVwTATZsGrj275ZZbotpfffVV/d3f/Z0kqb29XS0tLeHXent7tXbtWrW1tWnSpEkqKSnRW2+9pWXLlk1U2e5iWAgaAIBk4YoAeC7zWLZt2xa1/f3vf1/f//73L1JFXkQPIAAAycJVk0CQOIZbwQEAkDQIgLAJsQ8AgGRBAIQ9DEPAAAAkCwIgbHI29oXG2AsAACQeARC2iJ6HQx8gAABORgCELSz1h58T/wAAcDYCIGwSMQvYSmAZAABgXARA2IR+PwAAkgUBELYwzAIGACBpEABhO2YBAwDgbARA2ITYBwBAsiAAwh6GW8EBAJAsCICwhbEIgAAAJAsCIOzBJBAAAJIGARC2IwACAOBsBEDYworsAWQhaAAAHI0ACFsYcQ0gAADJggAImxD7AABIFgRA2INZwAAAJA0CIGxhItYBDImLAAEAcDICIGxhcScQAACSBgEQtjDcCQQAgKRBAIQ9Iq8BZAQYAABHIwDCJvQAAgCQLAiAsAdDwAAAJA0CIGxCAAQAIFkQAGEPegABAEgaBEDYg4WgAQBIGgRA2OTsOoDGYhowAABORgCELSLXAZQkE2JhaAAAnIoACJsMC4CmP0F1AACA8bgiAFZXV+vGG29UVlaWZsyYoXvuuUfHjx8f97j6+nqVlpYqMzNTs2fP1ubNmyegWreiBxAAgGThigBYX1+v1atX6+DBg6qtrVVfX58qKirU09Mz6jHNzc1atmyZFi1apMbGRq1bt06PPvqoampqJrByNxkWALk3MAAAjpWW6ALs8M4770Rtv/rqq5oxY4YOHz6sxYsXxzxm8+bNmjVrljZs2CBJmjNnjhoaGrR+/XqtWLHiYpfsQsOHgAmAAAA4lSt6AIfr7u6WJE2bNm3UfQ4cOKCKioqotjvvvFMNDQ06c+ZMzGOCwaACgUDUA6MwLAYDAIBTuS4AGmNUWVmpm2++WXPnzh11v46ODuXm5ka15ebmqq+vT11dXTGPqa6uVk5OTvhRWFhoa+1JbVjgC5m+BBUCAADG47oA+PDDD+v999/Xr371q3H3tYatVze0lMnw9iFVVVXq7u4OP1pbW+Mv2C2s6CFfJoEAAOBcrrgGcMgjjzyiN954Q/v379fMmTPH3DcvL08dHR1RbZ2dnUpLS9P06dNjHuPz+eTz+Wyr112GTwJhCBgAAKdyRQ+gMUYPP/ywdu3apd/85jcqKioa9xi/36/a2tqotr1796qsrEzp6ekXq1QXYxIIAADJwhUBcPXq1dqxY4d++ctfKisrSx0dHero6NAXX3wR3qeqqkoPPPBAeHvVqlU6efKkKisrdezYMb3yyivaunWr1q5dm4i3kPyGd/gRAAEAcCxXBMBNmzapu7tbt9xyi/Lz88OP1157LbxPe3u7WlpawttFRUXas2eP6urqdMMNN+jHP/6xNm7cyBIwF2j4un/0AAIA4FyuuAZw+H1oY9m2bduItiVLlujIkSMXoSKEQtwKDgAAp3JFDyCcgDuBAACQLAiAsMnwSSDMAgYAwKkIgLDJiFkgCakCAACMjwCIi4KFoAEAcC4CIGwxfOHnkGESCAAATkUAhC0sFoIGACBpEABhi+GTPpgEAgCAcxEAYQtrxLIv9AACAOBUBEDYwljDthkCBgDAsQiAsAdDwAAAJA0CIGwyfBZwX4LqAAAA4yEAwhYj+vvoAQQAwLEIgLCFZbEMDAAAyYIACJtwDSAAAMmCAAhbjFgHkGVgAABwLAIgbMIQMAAAyYIACHsMXwcwxL2AAQBwKgIgbDJ8CBgAADgVARA2YQgYAIBkQQCEPYZ1+TELGAAA5yIAwhaGHkAAAJIGARC2GLkQNJNAAABwKgIgbMJC0AAAJAsCIOwx4hpAhoABAHAqAiBsMeIaQBaCAQDAsQiAsAmTQAAASBYEQFwU9AACAOBcBEDYZFgPILeCAwDAsQiAsMeIZWAYAgYAwKkIgLAFk0AAAEgeBEBcFPQAAgDgXK4JgPv379fy5ctVUFAgy7L0+uuvj7l/XV2dLMsa8fjwww8npmDXGd7jRw8gAABOlZboAuzS09Oj+fPn68EHH9SKFSvO+bjjx48rOzs7vH355ZdfjPI8hzuBAADgXK4JgEuXLtXSpUvP+7gZM2Zo6tSp9hfkcSFmAQMA4FiuGQK+UAsWLFB+fr7Ky8u1b9++MfcNBoMKBAJRDwxhEggAAMnCswEwPz9fW7ZsUU1NjXbt2qXi4mKVl5dr//79ox5TXV2tnJyc8KOwsHACK3a64cvAEAABAHAq1wwBn6/i4mIVFxeHt/1+v1pbW7V+/XotXrw45jFVVVWqrKwMbwcCAUJg2PDAxyxgAACcyrM9gLEsXLhQJ06cGPV1n8+n7OzsqAcGjJgDTA8gAACORQCM0NjYqPz8/ESXkaSiAx+TQAAAcC7XDAGfPn1av//978Pbzc3Nampq0rRp0zRr1ixVVVWpra1N27dvlyRt2LBBV155pUpKStTb26sdO3aopqZGNTU1iXoLSc1iEggAAEnDNQGwoaFBt956a3h76Fq9lStXatu2bWpvb1dLS0v49d7eXq1du1ZtbW2aNGmSSkpK9NZbb2nZsmUTXrsbjBwC5hpAAACcyjJcrHXBAoGAcnJy1N3d7fnrAR/4H7epcdqfw9uvzHtMNy54KIEVAQAQG9/fXAMImxiLZWAAAEgWBEBcFIZlYAAAcCwCIOI20Ns3bBYw1wACAOBYBEDEbWC0lyFgAACSBQEQcYsZ9egBBADAsQiAiFus3j46AAEAcC4CIOIWMpKGzwJmEggAAI5FAETcYt31g2sAAQBwLgIg4hZ7Egg9gAAAOBUBEBcFQ8AAADgXARBxYxkYAACSCwEQcYt5DWDsxWEAAIADEAARt5izgOkBBADAsQiAiFvsdQD7E1AJAAA4FwRAxM1E/G+4jR5AAAAciwCIuMXKelwDCACAcxEAET8z8n7A9AACAOBcBEDEbaC3b/it4AiAAAA4FQEQcRuYBRzdxp1AAABwLgIg4jYw3MskEAAAkgUBEHGLFfUIgAAAOBcBEHEzsSaBcC9gAAAciwCIuMWcBEIPIAAAjkUARPxiTQJhFjAAAI5FAETcYt0JJObq0AAAwBEIgIhbKEbYC7EMDAAAjkUARNy4FRwAAMmFAIi4DUwBYRIIAADJggCIuMUOewRAAACcigCIuMUcAqYHEAAAxyIAwh5WdOALmf4EFQIAAMbjmgC4f/9+LV++XAUFBbIsS6+//vq4x9TX16u0tFSZmZmaPXu2Nm/efPELdaGQGTnlg0kgAAA4l2sCYE9Pj+bPn68XX3zxnPZvbm7WsmXLtGjRIjU2NmrdunV69NFHVVNTc5ErdR9mAQMAkFzSEl2AXZYuXaqlS5ee8/6bN2/WrFmztGHDBknSnDlz1NDQoPXr12vFihUXqUp3GnkjOLEQNAAADuaaHsDzdeDAAVVUVES13XnnnWpoaNCZM2diHhMMBhUIBKIeGJrwMWwZGHoAAQBwLM8GwI6ODuXm5ka15ebmqq+vT11dXTGPqa6uVk5OTvhRWFg4EaU6XsxFYMh/AAA4lmcDoCRZlhW1PbR0yfD2IVVVVeru7g4/WltbL3qNycAYScNOWUjcCg4AAKdyzTWA5ysvL08dHR1RbZ2dnUpLS9P06dNjHuPz+eTz+SaivCRjYtwJhAAIAIBTebYH0O/3q7a2Nqpt7969KisrU3p6eoKqSk6hGMO9IcaAAQBwLNcEwNOnT6upqUlNTU2SBpZ5aWpqUktLi6SB4dsHHnggvP+qVat08uRJVVZW6tixY3rllVe0detWrV27NhHlJ7XYd4IjAAIA4FSuGQJuaGjQrbfeGt6urKyUJK1cuVLbtm1Te3t7OAxKUlFRkfbs2aPHH39cL730kgoKCrRx40aWgLkAJsZCMCFmAQMA4FiuCYC33HLLmPef3bZt24i2JUuW6MiRIxexKm8wZuRMYJaBAQDAuVwzBIzEiZW7QyEmgQAA4FQEQMTNyISXgbHCaZAeQAAAnIoAiLhFDgEPfaBizQwGAADOQABE3AY6/QYX0R5qowcQAADHIgAibpHLQKeER4AJgAAAOBUBEHGLzHopg1Gw3/QnqBoAADAeAiDiFtnXxwcKAADn4/sacTPm7BDw0DWALAQNAIBzEQARt5CRZA1OAhnMfWMtyg0AABKLAAgbnO0BTB1qIQACAOBYBEDELTLrsQwMAADORwBE3ELmbOBLCQ8Bcys4AACcigCIuIUiugCtGG0AAMBZCICIW8icvRdwSmQbAABwJAIg4hZ5L+DUwSchFoIGAMCxCICIW2RvX4oZ6Ars5xpAAAAciwCIuIUiegCHPlAEQAAAnIsAiLiFjImYBWwNthEAAQBwKgIg4maM0WDuCy8ETQ8gAADORQBE3EIRWS+VHkAAAByPAIi4hYzRUNxLEZNAAABwOgIg4hYykrGirwHkVnAAADgXARBxM8ZEzAKmBxAAAKcjACJuoaiFoLkGEAAApyMAIm6R1wBagx+pfoaAAQBwLAIg4haKWAYmZfAjZegBBADAsQiAiNvAvYCjJ4H0iwAIAIBTEQARt1DUJJDUcBsAAHAmAiDiFjJSaHAI2DIDH6kQ1wACAOBYBEDELeYkEK4BBADAsQiAiFv0OoCDk0DoAQQAwLFcFQBffvllFRUVKTMzU6WlpXr33XdH3beurk6WZY14fPjhhxNYsTsM3Alk4DnXAAIA4HyuCYCvvfaa1qxZoyeeeEKNjY1atGiRli5dqpaWljGPO378uNrb28OPq6++eoIqdo/ISSDWYABkHUAAAJzLNQHw+eef13e/+1099NBDmjNnjjZs2KDCwkJt2rRpzONmzJihvLy88CM1NXWCKnaPkJH6B58PBUAmgQAA4FyuCIC9vb06fPiwKioqotorKir03nvvjXnsggULlJ+fr/Lycu3bt2/MfYPBoAKBQNQDg9cADs0CHgyAhiFgAAAcyxUBsKurS/39/crNzY1qz83NVUdHR8xj8vPztWXLFtXU1GjXrl0qLi5WeXm59u/fP+rvqa6uVk5OTvhRWFho6/tIVqFQjHUA6QEEAMCx0hJdgJ0sy4raNsaMaBtSXFys4uLi8Lbf71dra6vWr1+vxYsXxzymqqpKlZWV4e1AIEAI1OA6gBo4z5Y18JHiGkAAAJzLFT2Al112mVJTU0f09nV2do7oFRzLwoULdeLEiVFf9/l8ys7Ojnpg+DqAAwGQVQABAHAuVwTAjIwMlZaWqra2Nqq9trZWN9100zn/nMbGRuXn59tdnuuFjDl7J5DBHkDWAQQAwLlcMwRcWVmp+++/X2VlZfL7/dqyZYtaWlq0atUqSQPDt21tbdq+fbskacOGDbryyitVUlKi3t5e7dixQzU1NaqpqUnk20hK/SFFXAM41ANIAAQAwKlcEwDvvfdeffrpp3r66afV3t6uuXPnas+ePbriiiskSe3t7VFrAvb29mrt2rVqa2vTpEmTVFJSorfeekvLli1L1FtIWv2h0NkhX2toHUAAAOBUlmG9jgsWCASUk5Oj7u5uT18PuGHvcW1t/xtJ0h1f3KTaSe/pK32WXv/u+wmuDACAkfj+dsk1gEisfnMm/DzFypAkBoABAHAwAiDi1t/fG36eYqUPtBEBAQBwLAIg4hYZAJUy0APIMjAAADgXARBxM6GzAdCyMiUxCxgAACcjACJu/X3B8POhawDpAQQAwLkIgIhbKHR2EohSfANtCaoFAACMjwCIuPUPDgGnGhO+E0go9i2YAQCAAxAAETcz2AOYKillcCForgEEAMC5CICIWzgAGiMrZehWcAAAwKkIgIhb/2AATJEIgAAAJAECIOI2tAxMmpFSNTgEzDWAAAA4FgEQcQuZs9cApqUNzALuT2A9AABgbARAxM2E+iRJqbKUnn6JJOmMRRcgAABORQBE3CKvAUxLnyxpIACG+vsSWBUAABgNARBxC/UPrQMoZWRMCbf39p5KVEkAAGAMBEDE7ew1gJYyMrLC7b29pxNVEgAAGAMBEHHr7x+4F3C6LGVmTA63EwABAHAmAiDiZsyXkgYDYHqafKGBu4AEGQIGAMCRCICImzED1wCmK0UZaSlKNwMBsLe3J5FlAQCAURAAEbfQ0ELQliVfWorSB28D3HuGAAgAgBMRABE3o4FJIOlKHewBHGjvPfN5AqsCAACjIQAibuEhYCtFvrRUpZuBRaCD9AACAOBIBEDEzQwuA5NuDfQApg4GQHoAAQBwJgIg4ja0DmB6ykAATBsKgH1fJLIsAAAwCgIg4hbSwDqAvpQM+SICYJAACACAIxEAERdjjELWwDWAk1N9ykhLUYoZ+Fj19n2ZyNIAAMAoCICIS7AvJA0GwEvSMpWdma7UUKok6XSwO5GlAQCAURAAEZcvevtlUgauAZycfokuvSRd6f0+SdKfe7oSWRoAABgFARBxOR3sUyilT5J0SfolSktNUbrJkiT9+Yu/JLI0AAAwCgIg4vLZ52d0ZrAHMMs3VZKUbuVIkv7XGe4FDACAExEAEZe/fN6rL1MHegCnTcmVJPnSLht4LcQsYAAAnMhVAfDll19WUVGRMjMzVVpaqnfffXfM/evr61VaWqrMzEzNnj1bmzdvnqBK3ePPp4I6nRaSJE2d8leSpJzJsyVJf7J6Zfr7E1YbAACIzTUB8LXXXtOaNWv0xBNPqLGxUYsWLdLSpUvV0tISc//m5mYtW7ZMixYtUmNjo9atW6dHH31UNTU1E1x5cmvu7FBX6sC6f3+Vd4Mk6fo531BmyOgvqSl6/0POJwAATmMZY0yii7DD17/+dX31q1/Vpk2bwm1z5szRPffco+rq6hH7/+AHP9Abb7yhY8eOhdtWrVql//zP/9SBAwfO6XcGAgHl5OSou7tb2dnZ8b+JJPN5b58e37Ra7019T9P6Q6r7u6OyUlL0v3p69ci2m/X+lC90WV9I/1vqNbrm8ht0/Vdu0TWz5mnyJZcmunQAgId5/ftbktISXYAdent7dfjwYf3whz+Maq+oqNB7770X85gDBw6ooqIiqu3OO+/U1q1bdebMGaWnp484JhgMKhgMhrcDgYAN1Y+05fUndKjzN3H/nNGivZFkZGTCW2e3zbBtxWgzMvrC6tPplJA6pw50Ii+dfJWslIHn0yZnaMX8f9KfP1yj9vQU/Zt+L3X9Xur6V+k/JF/IKFVGqWagC3ro/60YtQ61WTHeixXj2ej7jO989j0fjqjhIv0zzzqPihN1fmO+biRr8AXLxP4kDT8u8r1a5uwOox0/4pgYVV1Q7aMx1qgHjPVzYp+fC/k7df5/wrZ/Js7jB57PZzdeE/ebRv62if3d9rMsaUlehVb+7z9OdCmu44oA2NXVpf7+fuXm5ka15+bmqqOjI+YxHR0dMffv6+tTV1eX8vPzRxxTXV2tp556yr7CR3HyL7/TQd/pi/574pciyxgtNtl6dPnWqFe+efPtumH223rtNz/RRz1H1ZFySm3pRmcsS8EUS8n/nyW4x7mmY1cMlgBJ56q/HE90Ca7kigA4xLKiQ4UxZkTbePvHah9SVVWlysrK8HYgEFBhYeGFljuqhV/5hiadrLflZ0W+lch3lWKlSJYlS5YsK0XW0HNZA8+tlKhtyVJKSoosDbTnTJ6uvKl/pa9es0iXTpsd83fPLihU1f/xP8PbZ/rOqL3rT+rubtOXZ4LqD/Wqr++MQuaM+vsHlpIJmYEJJUaSMaGBqk1ooPiILk0jIxkppNBAswm3ho+XMeH9wrtE/hgT+ZUeOudzOvQ5Oac4MEo3rIl19DlcjWFiPBu/hPPZ9zzOwwWcs1iG98REnpsx/voO/txQ5MaIXuuhz0X0zw9F1DPYq20iPiGDPzM0uG3JhP9ozr5nE/E5MCPbTNQnUcaEhn86R/0jHH5s9O8Ytu9o7aOe7mHnYsR7GHv/cEvM3U3sn3M+fwdG/7VjNY9p9GPsC/Rj/qRR37+Nv2Oc33VeP+scCxvzz+889jmXOspml1/4z8GoXBEAL7vsMqWmpo7o7evs7BzRyzckLy8v5v5paWmaPn16zGN8Pp98Pp89RY9h+eLvarm+e9F/z0RLT0vXrLwiKa8o0aUAAOBprpgFnJGRodLSUtXW1ka119bW6qabbop5jN/vH7H/3r17VVZWFvP6PwAAALdwRQCUpMrKSv3sZz/TK6+8omPHjunxxx9XS0uLVq1aJWlg+PaBBx4I779q1SqdPHlSlZWVOnbsmF555RVt3bpVa9euTdRbAAAAmBCuGAKWpHvvvVeffvqpnn76abW3t2vu3Lnas2ePrrjiCklSe3t71JqARUVF2rNnjx5//HG99NJLKigo0MaNG7VixYpEvQUAAIAJ4Zp1ABOBdYQAAEg+fH+7aAgYAAAA54YACAAA4DEEQAAAAI8hAAIAAHgMARAAAMBjCIAAAAAeQwAEAADwGAIgAACAxxAAAQAAPMY1t4JLhKGbqAQCgQRXAgAAztXQ97aXb4ZGAIzDqVOnJEmFhYUJrgQAAJyvU6dOKScnJ9FlJAT3Ao5DKBTSxx9/rKysLFmWlehyEioQCKiwsFCtra2eva/iWDg/4+McjY3zMz7O0dg4P2cZY3Tq1CkVFBQoJcWbV8PRAxiHlJQUzZw5M9FlOEp2drbn/8MyFs7P+DhHY+P8jI9zNDbOzwCv9vwN8WbsBQAA8DACIAAAgMcQAGELn8+nJ598Uj6fL9GlOBLnZ3yco7FxfsbHORob5weRmAQCAADgMfQAAgAAeAwBEAAAwGMIgAAAAB5DAAQAAPAYAiDi9vLLL6uoqEiZmZkqLS3Vu+++m+iSEmb//v1avny5CgoKZFmWXn/99ajXjTH60Y9+pIKCAk2aNEm33HKLPvjgg8QUmwDV1dW68cYblZWVpRkzZuiee+7R8ePHo/bx+jnatGmT5s2bF16s1+/36+233w6/7vXzM1x1dbUsy9KaNWvCbV4+Rz/60Y9kWVbUIy8vL/y6l88NohEAEZfXXntNa9as0RNPPKHGxkYtWrRIS5cuVUtLS6JLS4ienh7Nnz9fL774YszXn3vuOT3//PN68cUXdejQIeXl5emOO+4I31fa7err67V69WodPHhQtbW16uvrU0VFhXp6esL7eP0czZw5U88884waGhrU0NCg2267TXfffXf4S9rr5yfSoUOHtGXLFs2bNy+q3evnqKSkRO3t7eHH0aNHw695/dwgggHi8LWvfc2sWrUqqu3aa681P/zhDxNUkXNIMrt37w5vh0Ihk5eXZ5555plw25dffmlycnLM5s2bE1Bh4nV2dhpJpr6+3hjDORrNpZdean72s59xfiKcOnXKXH311aa2ttYsWbLEPPbYY8YYPkNPPvmkmT9/fszXvH5uEI0eQFyw3t5eHT58WBUVFVHtFRUVeu+99xJUlXM1Nzero6Mj6nz5fD4tWbLEs+eru7tbkjRt2jRJnKPh+vv7tXPnTvX09Mjv93N+IqxevVp33XWXbr/99qh2zpF04sQJFRQUqKioSH/7t3+rjz76SBLnBtHSEl0AkldXV5f6+/uVm5sb1Z6bm6uOjo4EVeVcQ+ck1vk6efJkIkpKKGOMKisrdfPNN2vu3LmSOEdDjh49Kr/fry+//FJTpkzR7t27dd1114W/pL1+fnbu3KkjR47o0KFDI17z+mfo61//urZv365rrrlGn3zyiX7yk5/opptu0gcffOD5c4NoBEDEzbKsqG1jzIg2nMX5GvDwww/r/fff129/+9sRr3n9HBUXF6upqUmfffaZampqtHLlStXX14df9/L5aW1t1WOPPaa9e/cqMzNz1P28eo6WLl0afn799dfL7/frK1/5in7+859r4cKFkrx7bhCNIWBcsMsuu0ypqakjevs6OztH/AsTCs/E43xJjzzyiN544w3t27dPM2fODLdzjgZkZGToqquuUllZmaqrqzV//ny98MILnB9Jhw8fVmdnp0pLS5WWlqa0tDTV19dr48aNSktLC58HL5+jSJMnT9b111+vEydO8PlBFAIgLlhGRoZKS0tVW1sb1V5bW6ubbropQVU5V1FRkfLy8qLOV29vr+rr6z1zvowxevjhh7Vr1y795je/UVFRUdTrnKPYjDEKBoOcH0nl5eU6evSompqawo+ysjLdd999ampq0uzZsz1/jiIFg0EdO3ZM+fn5fH4QLWHTT+AKO3fuNOnp6Wbr1q3md7/7nVmzZo2ZPHmy+eMf/5jo0hLi1KlTprGx0TQ2NhpJ5vnnnzeNjY3m5MmTxhhjnnnmGZOTk2N27dpljh49ar797W+b/Px8EwgEElz5xPje975ncnJyTF1dnWlvbw8/Pv/88/A+Xj9HVVVVZv/+/aa5udm8//77Zt26dSYlJcXs3bvXGMP5iSVyFrAx3j5H//AP/2Dq6urMRx99ZA4ePGi+8Y1vmKysrPB/k718bhCNAIi4vfTSS+aKK64wGRkZ5qtf/Wp4SQ8v2rdvn5E04rFy5UpjzMAyDE8++aTJy8szPp/PLF682Bw9ejSxRU+gWOdGknn11VfD+3j9HH3nO98J/326/PLLTXl5eTj8GcP5iWV4APTyObr33ntNfn6+SU9PNwUFBeab3/ym+eCDD8Kve/ncIJpljDGJ6XsEAABAInANIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHvP/A4Mg6Grfsdy8AAAAAElFTkSuQmCC",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "for key, dset in infoWidget.datasets.items():\n",
+    "    if key[0] != '_': \n",
+    "        print(dset.title, dset.shape)\n",
+    "        plt.plot(dset.energy_loss, dset)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.close('all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Channel_000 None 0\n",
+      "Channel_000 None 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "class EELSWidget2(pyTEMlib.info_widget.EELSBaseWidget):\n",
+    "    def __init__(self, datasets=None):\n",
+    "        \n",
+    "        super().__init__(datasets, sidebar=None)\n",
+    "        super().set_dataset()\n",
+    "infoWidget.low_loss.update_ll_sidebar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ImageData': {'Calibrations': {'Brightness': {'Origin': 0.0,\n",
+       "    'Scale': 1.0,\n",
+       "    'Units': ''},\n",
+       "   'Dimension': {'0': {'Origin': 0.0,\n",
+       "     'Scale': 0.1487438678741455,\n",
+       "     'Units': 'nm'},\n",
+       "    '1': {'Origin': 0.0, 'Scale': 0.1487438678741455, 'Units': 'nm'}},\n",
+       "   'DisplayCalibratedUnits': 1},\n",
+       "  'Data': 'read',\n",
+       "  'DataType': 11,\n",
+       "  'Dimensions': {'0': 1024, '1': 1024},\n",
+       "  'PixelDepth': 4},\n",
+       " 'ImageTags': {'DataBar': {'Acquisition Date': '3/16/2023',\n",
+       "   'Acquisition Time': '1:23:37 PM',\n",
+       "   'Acquisition Time (OS)': 1.3323461017764891e+17,\n",
+       "   'Custom elements': {},\n",
+       "   'Device Name': 'DigiScan',\n",
+       "   'Exposure Number': 3960478},\n",
+       "  'DigiScan': {'Bitshift': 0,\n",
+       "   'Channel': 0,\n",
+       "   'Flip': 0,\n",
+       "   'Flyback': 50.0,\n",
+       "   'Horizontal DS Offset': 0.0,\n",
+       "   'Horizontal Image Center': 512.0,\n",
+       "   'Horizontal Perpendicular Pixel Step': -16,\n",
+       "   'Horizontal Pixel Step': -16,\n",
+       "   'Horizontal Spacing': 22.4,\n",
+       "   'Integrate Frames': 0,\n",
+       "   'Integration Percentage': 80.0,\n",
+       "   'Number Summing Frames': 5.0,\n",
+       "   'Recorded': 1,\n",
+       "   'Rotation': 45.0,\n",
+       "   'Rotation Offset': 180.0,\n",
+       "   'Sample Time': 32.400001525878906,\n",
+       "   'SessionID': 1678985640,\n",
+       "   'Signal Index': 0,\n",
+       "   'Signals': {'0': {'Acquired': 1,\n",
+       "     'Datatype': 4,\n",
+       "     'Selected': 1,\n",
+       "     'Signal ID': 0},\n",
+       "    '1': {'Acquired': 0, 'Datatype': 4, 'Selected': 0, 'Signal ID': 1}},\n",
+       "   'Slot': 0,\n",
+       "   'Sum Frames': 0,\n",
+       "   'Synchronize Lines': 1,\n",
+       "   'TimeStamp': 'Thu Mar 16 13:23:03 2023\\u2028',\n",
+       "   'Vertical DS Offset': 0.0,\n",
+       "   'Vertical Image Center': 512.0,\n",
+       "   'Vertical Perpendicular Pixel Step': -16,\n",
+       "   'Vertical Pixel Step': 16,\n",
+       "   'Vertical Spacing': 22.4,\n",
+       "   'Zoom factor': 0.7},\n",
+       "  'Microscope Info': {'Cs(mm)': 2.2,\n",
+       "   'Emission Current (A)': 230.0,\n",
+       "   'Field of View (m)': 0.21540414060665797,\n",
+       "   'Formatted Indicated Mag': '249.0kx',\n",
+       "   'Formatted Voltage': '200.0kV',\n",
+       "   'HT Extrapolated': 0,\n",
+       "   'Illumination Mode': 'STEM',\n",
+       "   'Imaging Mode': 'Diffraction Mag',\n",
+       "   'Indicated Magnification': 248993.484375,\n",
+       "   'Items': {'0': {'Data Type': 20,\n",
+       "     'Label': 'Specimen',\n",
+       "     'Tag path': 'Microscope Info:Specimen',\n",
+       "     'Value': 'TEST-Helium bubble'},\n",
+       "    '1': {'Data Type': 20,\n",
+       "     'Label': 'Operator',\n",
+       "     'Tag path': 'Microscope Info:Operator',\n",
+       "     'Value': 'Shradha'},\n",
+       "    '2': {'Data Type': 20,\n",
+       "     'Label': 'Microscope',\n",
+       "     'Tag path': 'Microscope Info:Microscope',\n",
+       "     'Value': ''}},\n",
+       "   'Magnification Interpolated': 0,\n",
+       "   'Microscope': '',\n",
+       "   'Name': 'Libra COM',\n",
+       "   'Operation Mode': 'SCANNING',\n",
+       "   'Operator': 'Shradha',\n",
+       "   'Probe Current (nA)': 0.0,\n",
+       "   'Probe Size (nm)': 0.0,\n",
+       "   'Specimen': 'TEST-Helium bubble',\n",
+       "   'STEM Camera Length': 1200.0000476837158,\n",
+       "   'Voltage': 199990.28125},\n",
+       "  'Survey Image': {'UID': {'0': 1024205530,\n",
+       "    '1': 1461147407,\n",
+       "    '2': 52899482,\n",
+       "    '3': 519921575}}},\n",
+       " 'Name': '12-survey',\n",
+       " 'UniqueID': {'0': 1024205530, '1': 1461147407, '2': 52899482, '3': 519921575},\n",
+       " 'DM': {'dm_version': 3,\n",
+       "  'file_size': 4797467,\n",
+       "  'full_file_name': 'C:\\\\Users\\\\gduscher\\\\Desktop\\\\drive-download-20241125T130908Z-001\\\\12-survey.dm3'},\n",
+       " 'original_filename': 'C:\\\\Users\\\\gduscher\\\\Desktop\\\\drive-download-20241125T130908Z-001\\\\12-survey.dm3',\n",
+       " 'ApplicationBounds': [0, 0, 1343, 2152],\n",
+       " 'DocumentObjectList': {'0': {'AnnotationGroupList': {'0': {'AnnotationType': 27,\n",
+       "     'BackgroundColor': [-1, -1, -1],\n",
+       "     'BackgroundMode': 2,\n",
+       "     'Color': [-1, 0, 0],\n",
+       "     'FillMode': 2,\n",
+       "     'ForegroundColor': [0, -1, 0],\n",
+       "     'HasBackground': 0,\n",
+       "     'IsDeletable': 1,\n",
+       "     'IsMoveable': 1,\n",
+       "     'IsResizable': 1,\n",
+       "     'IsSelectable': 1,\n",
+       "     'IsTranslatable': 1,\n",
+       "     'IsVisible': 1,\n",
+       "     'IsVolatile': 0,\n",
+       "     'Label': 'Beam',\n",
+       "     'Name': 'DS_BEAM',\n",
+       "     'ObjectTags': {},\n",
+       "     'Rectangle': [547.0, 769.0, 547.0, 769.0],\n",
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+       "  'Win32_DevNamesW': b'\\x04\\x00*\\x00?\\x00\\x00\\x00S\\x00e\\x00n\\x00d\\x00 \\x00T\\x00o\\x00 \\x00M\\x00i\\x00c\\x00r\\x00o\\x00s\\x00o\\x00f\\x00t\\x00 \\x00O\\x00n\\x00e\\x00N\\x00o\\x00t\\x00e\\x00 \\x002\\x000\\x001\\x000\\x00 \\x00D\\x00r\\x00i\\x00v\\x00e\\x00r\\x00\\x00\\x00S\\x00e\\x00n\\x00d\\x00 \\x00T\\x00o\\x00 \\x00O\\x00n\\x00e\\x00N\\x00o\\x00t\\x00e\\x00 \\x002\\x000\\x001\\x000\\x00\\x00\\x00n\\x00u\\x00l\\x00:\\x00\\x00\\x00'},\n",
+       " 'SentinelList': {},\n",
+       " 'Thumbnails': {'0': {'ImageIndex': 0, 'SourceSize_Pixels': [628, 628]}},\n",
+       " 'WindowPosition': [38, 9, 666, 637]}"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['Channel_002'].original_metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[884.0, 64.0, 960.0, 544.0]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5bfd69fcea194a5cafc48275713e791f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "d = infoWidget.datasets['Channel_000'].original_metadata\n",
+    "\n",
+    "rect = d['DocumentObjectList']['0']['AnnotationGroupList']['2']['Rectangle']\n",
+    "import matplotlib.patches\n",
+    "plt.figure()\n",
+    "plt.imshow(infoWidget.datasets['Channel_000'].T)\n",
+    "kwargs={'edgecolor': 'red', 'facecolor': 'None'}\n",
+    "r = matplotlib.patches.Rectangle([rect[0], rect[2]], rect[1]-rect[0], rect[3]-rect[2], **kwargs)\n",
+    "plt.gca().add_artist(r)\n",
+    "rect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'image_type': 'survey image',\n",
+       " 'experiment': {'microscope': '', 'acceleration_voltage': 199990.28125},\n",
+       " 'annotations': {}}"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a0063153df364ce3bceef64164623036",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "def read_annotation(image):\n",
+    "    scale_x = np.abs(image.x[1]-image.x[0])\n",
+    "    scale_y = np.abs(image.y[1]-image.y[0])\n",
+    "    rec_scale = np.array([scale_x, scale_y,scale_x, scale_y])\n",
+    "    if 'DocumentObjectList' not in image.original_metadata:\n",
+    "        return {}\n",
+    "    if '0' not in image.original_metadata['DocumentObjectList']:\n",
+    "        return {}\n",
+    "    annotations = {}        \n",
+    "    tags = image.original_metadata['DocumentObjectList']['0']     \n",
+    "    for key in tags:\n",
+    "        if 'AnnotationGroupList' in key:\n",
+    "            an_tags = tags[key]\n",
+    "            for key2 in an_tags:\n",
+    "                if isinstance(an_tags[key2], dict):\n",
+    "                    if an_tags[key2]['AnnotationType'] == 13:  #type 'text'\n",
+    "                        annotations[key2] = {'type': 'text'}\n",
+    "                        if 'Label' in an_tags:\n",
+    "                            annotations[key2]['label'] = an_tags['Label']\n",
+    "                        rect = np.array(an_tags[key2]['Rectangle']) * rec_scale\n",
+    "                        annotations[key2]['position'] = [rect[1],rect[0]]\n",
+    "                        annotations[key2]['text'] = an_tags['Text'] \n",
+    "                        \n",
+    "                    elif an_tags[key2]['AnnotationType']==6:\n",
+    "                        annotations[key2] = {'type': 'circle'}\n",
+    "                        if 'Label' in an_tags:\n",
+    "                            annotations[key2]['label'] = an_tags['Label']\n",
+    "                        rect = np.array(an_tags[key2]['Rectangle']) * rec_scale\n",
+    "                    \n",
+    "                        annotations[key2]['radius'] =rect[3]-rect[1]\n",
+    "                        annotations[key2]['position'] = [rect[1],rect[0]]\n",
+    "        \n",
+    "                    elif an_tags[key2]['AnnotationType'] == 23:\n",
+    "                        print('1')\n",
+    "                        annotations[key2] = {'type':  'spectral_image'}\n",
+    "                        if 'Label' in an_tags[key2]:\n",
+    "                            annotations[key2]['label'] = an_tags[key2]['Label']\n",
+    "                        rect = np.array(an_tags[key2]['Rectangle']) * rec_scale\n",
+    "                        \n",
+    "                        annotations[key2]['width'] =rect[3]-rect[1]\n",
+    "                        annotations[key2]['height'] =rect[2]-rect[0]\n",
+    "                        annotations[key2]['position'] = [rect[1],rect[0]]\n",
+    "                        annotations[key2]['Rectangle'] = np.array(an_tags[key2]['Rectangle'])\n",
+    "            \n",
+    "    image.metadata['annotations'] = annotations                \n",
+    "    return annotations\n",
+    "\n",
+    "\n",
+    "dset = infoWidget.datasets['Channel_001']\n",
+    "read_annotation(dset)\n",
+    "\n",
+    "\n",
+    "dset.plot()\n",
+    "if 'annotations' in dset.metadata:\n",
+    "    annotations = dset.metadata['annotations']\n",
+    "    for key in annotations:\n",
+    "        if annotations[key]['type'] == 'spectral_image':\n",
+    "            kwargs={'edgecolor': 'red', 'facecolor': 'None'}\n",
+    "        \n",
+    "            r = matplotlib.patches.Rectangle(annotations[key]['position'], annotations[key]['width'], annotations[key]['height'], **kwargs)\n",
+    "            plt.gca().text(annotations[key]['position'][0], annotations[key]['position'][1], annotations[key]['label'], color='r')\n",
+    "            plt.gca().add_artist(r)\n",
+    "dset.metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'AnnotationType': 23,\n",
+       " 'BackgroundColor': [-1, -1, -1],\n",
+       " 'BackgroundMode': 2,\n",
+       " 'Color': [-258, 0, 0],\n",
+       " 'FillMode': 2,\n",
+       " 'ForegroundColor': [0, -1, 0],\n",
+       " 'HasBackground': 0,\n",
+       " 'IsDeletable': 1,\n",
+       " 'IsMoveable': 1,\n",
+       " 'IsResizable': 1,\n",
+       " 'IsSelectable': 1,\n",
+       " 'IsTranslatable': 1,\n",
+       " 'IsVisible': 1,\n",
+       " 'IsVolatile': 0,\n",
+       " 'Label': '1',\n",
+       " 'Name': 'SICursor',\n",
+       " 'ObjectTags': {},\n",
+       " 'Rectangle': [1.0, 26.0, 10.0, 27.0],\n",
+       " 'SelectionStyle': 1,\n",
+       " 'UniqueID': 13}"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndentationError",
+     "evalue": "expected an indented block after 'for' statement on line 4 (3538655079.py, line 5)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[41], line 5\u001b[1;36m\u001b[0m\n\u001b[1;33m    if annotations[key]['AnnotationType']==13:\u001b[0m\n\u001b[1;37m                                               ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block after 'for' statement on line 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "                \n",
+    "            if split_keys[5] in ['AnnotationType','Text','Rectangle','Name', 'Label']:\n",
+    "            \n",
+    "        tags['annotations'] = {}\n",
+    "        for key in annotations:\n",
+    "            if annotations[key]['AnnotationType']==13: \n",
+    "                if 'Label' in annotations[key]:\n",
+    "                    tags['annotations']['annotations_'+str(key)+'_label'] = annotations[key]['Label']\n",
+    "                tags['annotations']['annotations_'+str(key)+'_type'] = 'text'\n",
+    "                rect = np.array(annotations[key]['Rectangle'])* rec_scale\n",
+    "                tags['annotations']['annotations_'+str(key)+'_x'] = rect[1]\n",
+    "                tags['annotations']['annotations_'+str(key)+'_y'] = rect[0]\n",
+    "                tags['annotations']['annotations_'+str(key)+'_text'] = annotations[key]['Text']\n",
+    "                \n",
+    "            elif annotations[key]['AnnotationType']==6:\n",
+    "                #out_tags['annotations'][key] = {}\n",
+    "                if 'Label' in annotations[key]:\n",
+    "                    tags['annotations']['annotations_'+str(key)+'_label'] = annotations[key]['Label']\n",
+    "                tags['annotations']['annotations_'+str(key)+'_type'] = 'circle'\n",
+    "                rect = np.array(annotations[key]['Rectangle'])* rec_scale\n",
+    "                \n",
+    "                tags['annotations']['annotations_'+str(key)+'_radius'] =rect[3]-rect[1]\n",
+    "                tags['annotations']['annotations_'+str(key)+'_position'] = [rect[1],rect[0]]\n",
+    "                \n",
+    "            elif annotations[key]['AnnotationType']==6:\n",
+    "                #out_tags['annotations'][key] = {}\n",
+    "                if 'Label' in annotations[key]:\n",
+    "                    tags['annotations']['annotations_'+str(key)+'_label'] = annotations[key]['Label']\n",
+    "                tags['annotations']['annotations_'+str(key)+'_type'] = 'circle'\n",
+    "                rect = np.array(annotations[key]['Rectangle'])* rec_scale\n",
+    "                \n",
+    "                tags['annotations']['annotations_'+str(key)+'_radius'] =rect[3]-rect[1]\n",
+    "                tags['annotations']['annotations_'+str(key)+'_position'] = [rect[1],rect[0]]\n",
+    "\n",
+    "                \n",
+    "\n",
+    "            elif annotations[key]['AnnotationType']==23:\n",
+    "                if 'Name' in annotations[key]:\n",
+    "                    if annotations[key]['Name'] == 'Spectrum Image':\n",
+    "                        #tags['annotations'][key] = {}\n",
+    "                        if 'Label' in annotations[key]:\n",
+    "                            tags['annotations']['annotations_'+str(key)+'_label'] = annotations[key]['Label']\n",
+    "                        tags['annotations']['annotations_'+str(key)+'_type'] = 'spectrum image'\n",
+    "                        rect = np.array(annotations[key]['Rectangle'])* rec_scale\n",
+    "                        \n",
+    "                        tags['annotations']['annotations_'+str(key)+'_width'] =rect[3]-rect[1]\n",
+    "                        tags['annotations']['annotations_'+str(key)+'_height'] =rect[2]-rect[0]\n",
+    "                        tags['annotations']['annotations_'+str(key)+'_position'] = [rect[1],rect[0]]\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'EELSWidget' object has no attribute 'tab_buttons'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[20], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m infoWidget\u001b[38;5;241m.\u001b[39mtab_buttons\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m      3\u001b[0m infoWidget\u001b[38;5;241m.\u001b[39mlow_loss\u001b[38;5;241m.\u001b[39mupdate_ll_sidebar()\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'EELSWidget' object has no attribute 'tab_buttons'"
+     ]
+    }
+   ],
+   "source": [
+    "infoWidget.tab_buttons.index = 2\n",
+    "\n",
+    "infoWidget.low_loss.update_ll_sidebar()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.tabval = 2\n",
+    "infoWidget.tab_activated()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipywidgets\n",
+    "infoWidget.tab.children = [infoWidget.tab_buttons, infoWidget.children[infoWidget.tab_buttons.index]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.info.update_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.low_loss._update()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'single_exposure_time': 0.1,\n",
+       "  'exposure_time': 10.0,\n",
+       "  'number_of_frames': 100,\n",
+       "  'collection_angle': 100.0,\n",
+       "  'convergence_angle': 0.0,\n",
+       "  'microscope': 'Libra 200 MC',\n",
+       "  'acceleration_voltage': 199990.28125,\n",
+       "  'flux_ppm': 4875.3037109375,\n",
+       "  'count_conversion': 1,\n",
+       "  'beam_current': 0},\n",
+       " 'zero_loss': {'shifted': array([-0.14012023]),\n",
+       "  'startFitEnergy': -0.5,\n",
+       "  'endFitEnergy': 0.5,\n",
+       "  'fit_parameter': array([ 3.38795373e-02,  2.04002455e+04,  2.93662064e-01, -1.63207014e-02,\n",
+       "          2.28877644e+04,  1.92327898e-01]),\n",
+       "  'original_low_loss': 'Squeezed_EELS90muOAonaxis3_new_new'},\n",
+       " 'plasmon': {'parameter': array([1.50324046e+01, 7.46074698e-01, 7.41146395e+08]),\n",
+       "  'epsilon': array([0.00000000e+00       +0.j        ,\n",
+       "         0.00000000e+00       +0.j        ,\n",
+       "         0.00000000e+00       +0.j        , ...,\n",
+       "         6.23143817e+08+21041344.40887087j,\n",
+       "         6.23269871e+08+21023350.85181732j,\n",
+       "         6.23395723e+08+21005387.31173421j])}}"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['plasmon'].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x210e9b9a510>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ab93ce31fc3408f95552d6bb064788e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "an, p = eels_tools.angle_correction(infoWidget.dataset)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(infoWidget.dataset/(an/an.max()))\n",
+    "\n",
+    "plt.plot(infoWidget.dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.60512722e+01 1.76590538e+00 3.48812177e+05 1.70000000e-01]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'single_exposure_time': 0.1,\n",
+       "  'exposure_time': 10.0,\n",
+       "  'number_of_frames': 100,\n",
+       "  'collection_angle': 100.0,\n",
+       "  'convergence_angle': 0.0,\n",
+       "  'microscope': 'Libra 200 MC',\n",
+       "  'acceleration_voltage': 199990.28125,\n",
+       "  'flux_ppm': 4875.3037109375,\n",
+       "  'count_conversion': 1,\n",
+       "  'beam_current': 0},\n",
+       " 'zero_loss': {'shifted': array([-0.14012023]),\n",
+       "  'startFitEnergy': -0.5,\n",
+       "  'endFitEnergy': 0.5,\n",
+       "  'fit_parameter': array([ 3.38795373e-02,  2.04002455e+04,  2.93662064e-01, -1.63207014e-02,\n",
+       "          2.28877644e+04,  1.92327898e-01]),\n",
+       "  'original_low_loss': 'EELS90muOAonaxis3_new_new'},\n",
+       " 'multiple_scattering': {'parameter': array([1.50313404e+01, 7.54438111e-01, 3.50839049e+05, 2.38991855e-01])}}"
+      ]
+     },
+     "execution_count": 266,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d148e0f0e27d4a15b75960a50747e5c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pin = infoWidget.datasets['plasmon'].metadata['plasmon']['parameter']\n",
+    "pin = np.append(pin, [0.17])\n",
+    "print(pin)\n",
+    "\n",
+    "c = eels_tools.fit_multiple_scattering(infoWidget.dataset, 5, 40, pin)\n",
+    "c.plot()\n",
+    "c.metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d555771b457474dabb65ee42b41a289",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "v = infoWidget.datasets['plasmon'].plot()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def energy_loss_function(energy_scale: np.ndarray, p: np.ndarray) -> np.ndarray:\n",
+    "    eps = 1 - p[0]**2/(energy_scale**2+p[1]**2) + 1j * p[1] * p[0]**2/energy_scale/(energy_scale**2+p[1]**2)\n",
+    "    elf = (-1/eps).imag\n",
+    "    return elf*p[2]\n",
+    "\n",
+    "def multiple_scattering(energy_scale: np.ndarray, p: list, core_loss=False)-> np.ndarray:\n",
+    "    p = np.abs(p)\n",
+    "    tmfp = p[3]\n",
+    "    if core_loss:\n",
+    "        dif = 1\n",
+    "    else:\n",
+    "        dif = 16\n",
+    "    LLene = np.linspace(1, 2048-1,2048)/dif\n",
+    "    \n",
+    "    SSD = energy_loss_function(LLene, p)\n",
+    "    ssd  = np.fft.fft(SSD)\n",
+    "    ssd2 = ssd.copy()\n",
+    "    \n",
+    "    ### sum contribution from each order of scattering:\n",
+    "    PSD = np.zeros(len(LLene))\n",
+    "    for order in range(15):\n",
+    "        # This order convoluted spectum \n",
+    "        # convoluted SSD is SSD2\n",
+    "        SSD2 = np.fft.ifft(ssd).real\n",
+    "    \n",
+    "        # scale right (could be done better? GERD) \n",
+    "        # And add this order to final spectrum\n",
+    "        PSD += SSD2*abs(sum(SSD)/sum(SSD2)) / scipy.special.factorial(order+1)*np.power(tmfp, (order+1))*np.exp(-tmfp) #using equation 4.1 of egerton ed2\n",
+    "        \n",
+    "        # next order convolution\n",
+    "        ssd = ssd * ssd2\n",
+    "    \n",
+    "    PSD /=tmfp*np.exp(-tmfp)\n",
+    "    BGDcoef = scipy.interpolate.splrep(LLene, PSD, s=0)    \n",
+    "    return scipy.interpolate.splev(energy_scale, BGDcoef)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2106c17d8e0>]"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e146f1e22d84dadbb18b9cc9c6e70c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmfp = .17  #p[3]\n",
+    "p0 =[1.50312722e+01, 7.45905380e-01, 3.48811157e+05, tmfp]\n",
+    "x = infoWidget.dataset.energy_loss\n",
+    "\n",
+    "cts = multiple_scattering(x, p0, core_loss=False)\n",
+    "plt.figure()\n",
+    "plt.plot(x,cts)\n",
+    "plt.plot(x, infoWidget.dataset)\n",
+    "plt.plot(x, infoWidget.dataset-cts)\n",
+    "plt.plot(x, infoWidget.datasets['plasmon'])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "multiple_scattering(energy_scale, p)\n",
+    "\n",
+    "\n",
+    "def errf_multi(p, y, x):\n",
+    "    elf = multiple_scattering(x, p)\n",
+    "    err = y - elf\n",
+    "    #print (p,sum(np.abs(err)))\n",
+    "    return np.abs(err) # /np.sqrt(y)\n",
+    "\n",
+    "\n",
+    "pin2 = np.array([9,1,.7, 1.11])\n",
+    "E = energy_scale = infoWidget.dataset.energy_loss\n",
+    "startFit =np.argmin(abs(energy_scale-6))\n",
+    "endFit = np.argmin(abs(energy_scale-35))\n",
+    "    \n",
+    "p2, lsq = leastsq(errf_multi, p0, args=(infoWidget.dataset[startFit:endFit], energy_scale[startFit:endFit]), maxfev=2000)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.50313215e+01, 7.53493487e-01, 3.50578991e+05, 2.37203551e-01]),\n",
+       " [15.0312722, 0.74590538, 348811.157, 0.17])"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "53c7f551bd2f41c6b0ecfc79f6ba96ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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O3xoOABh+AiClLIh7KoAAkBwBkFIWLgStAggAqREAKcVOIACQPgGQUuZX/IwBBID0CICUsnAZmJmBtAMAWLrKBsAdO3bEO97xjpiYmIizzz47rrzyynjqqacG3ayht3ASiAogAKSmsgFw9+7dce2118bDDz8cu3btipmZmbjssstiampq0E0basYAAkD6RgbdgEG5//77ex7feeedcfbZZ8ejjz4a73vf+wbUquE3f8yfWcAAkJ7KVgDnm5ycjIiI1atXD7glw81ewACQvspWALsVRRHbt2+P9773vXHBBRe85HnT09MxPT3deXz48OFT0byhogsYANKnAhgR1113XTzxxBPx53/+5y973o4dO2LVqlWdrw0bNpyiFg6PBQEwbw6kHQDA0lU+AF5//fXxta99LR544IFYv379y5574403xuTkZOdr//79p6iVw2NhF7AxgACQmsp2ARdFEddff33cc8898eCDD8bGjRtf8TWNRiMajcYpaN3w0gUMAOmrbAC89tpr40tf+lJ89atfjYmJiThw4EBERKxatSqWLVs24NYNr9mKXzb3WAAEgORUtgv49ttvj8nJybjkkkvijW98Y+fr7rvvHnTThtrCCqAuYABITWUrgILL0iys96kAAkBqKlsB5OQQpAEgPQIgpcyPe7llYAAgOQIgpVgGBgDSJwBSSp71PjYLGADSIwDyKqkAAkBqBEBKWbgVnAogAKRGAKQUYwABIH0CIKXMr/cZAwgA6REAKWXBMjCFZWAAIDUCIK+OhaABIDkCIKUs3AtYFzAApEYApJQFYwBNAgGA5AiAlLKwAigAAkBqBEBelWJBTRAAGHYCIKUsXAjaLGAASI0ASCntvYCzVtevHmAASI8ASCntvFfrPNYFDACpEQAppR0As/ZjJUAASI4AyJKoAAJAugRASmnHvXq78KcCCADJEQApZcEYQAEQAJIjAFJKkc2O/mt/cPLCMjAAkBoBkL4V+dx4P5NAACBdAiB9K4q5AFhvHzMJBACSIwDSt+4AmM0dHEhbAIClEwDpW3cArLVyX7FgczgAYNgJgPSte9/fuVnAuoABIDUCIH3rHu/XHgOYC4AAkBwBkL71dAG3RgGqAAJAegRA+mYSCAC8PgiA9K170ee5vYAFQABIjQBI//LFuoAFQABIjQBI37ongagAAkC6BED61l3tq2UqgACQKgGQvuX5TOd+Zyu4rnGBAEAaBED61t3d2xkDqAsYAJIjANK/RZaB0QUMAOkRAOlbzxhAC0EDQLIEQPrWuw6gLmAASJUASN+K3E4gAPB6IADSt551AC0DAwDJEgDpXyvs1YqiUwHMLQMDAMkRAOlbns+GvazrmDGAAJAeAZC+tbuAs4jI7AUMAMkSAOlb95IvtUWOAQBpEADpX3sMYHRNAtEFDADJEQDpW3vCR1bMdQE3c5NAACA1AiB9a3f3ZhFRtxMIACRLAKRv3QGwXQG0DAwApEcApG/tGb9ZzI0BFAABID0CIH0rirl1AGudCqAuYABIjQBI3xavAAqAAJAaAZC+dY8BrJkEAgDJEgDpW3vNv+4KYNMYQABIjgBI3xYbA6gCCADpEQDpW5EvtgyMAAgAqREA6Vt3F3A9m/3oCIAAkB4BkL7l3V3AZgEDQLIEQPpWtPb9rRcRWagAAkCqBED61l0BrNsJBACSJQDStzyfiYjZD83cJJBigC0CAJZCAKRveWsWcD2yqJkEAgDJEgDpW7M4ERHtSSACIACkSgCkb0WnAji3EHQeAiAApEYApG/N1hjArKsL2E4gAJAeAZC+tbeCq8VcF3BTAASA5AiA9K295Esty+wFDAAJEwDpW7NrGZi5nUAsAwMAqal0AHzooYfi8ssvj3Xr1kWWZXHvvfcOuklDrV3tm10Gph4REXkIgACQmkoHwKmpqdi0aVPcdtttg25KEpq5vYAB4PVgZNANGKStW7fG1q1bB92MZMxNAskisw4gACSr0gGwrOnp6Zienu48Pnz48ABbc+rNjQHMot4qHusCBoD0VLoLuKwdO3bEqlWrOl8bNmwYdJNOqfYYwFoWKoAAkDABsIQbb7wxJicnO1/79+8fdJNOqXbYq9kLGACSpgu4hEajEY1GY9DNGJieLuD2TiC6gAEgOSqA9K3TBRxzXcBN6wACQHIqXQE8evRo/OAHP+g83rdvX+zduzdWr14d55xzzgBbNpyanZ1AanN7AYcuYABITaUD4COPPBLvf//7O4+3b98eERHbtm2LL37xiwNq1fDqrgB2FoJWAQSA5FQ6AF5yySVRCDB9mxsDWOssBN00CQQAkmMMIH2bWwYm6+oCFqABIDUCIH3rXQbGXsAAkCoBkL7NTQLpXgdQAASA1AiA9K3oqgB2dgJRAQSA5AiA9K27Alg3CxgAkiUA0rdika3gTAIBgPQIgPStXQHMurqA7QQCAOkRAOlbe83EelaLem12CUkVQABIjwBI3/KudQBNAgGAdAmA9K0zCSTm9gIWAAEgPQIgfevdCcQsYABIlQBI39r7/mZds4DtBAwA6REA6Vu7AljParaCA4CECYD0rd3dm2W1qNVmA6BlYAAgPQIgfWtPAuleBkYFEADSIwDSt+6dQEbqYxER0RQAASA5AiB9a7amfGRZLeq10YiIOCEAAkByBED61rMTSH02ABoDCADpEQDpW2cZmCyL0Xpj9pgKIAAkRwCkb51lYKIW9drsGMCZQTYIAFgSAZC+tWf8ZlktRkbaAVAFEABSIwDSt2ZnK7ha1DuzgAGA1AiA9K17J5D2MjAz2SBbBAAshQBI3+YmgdRipDMJBABIjQBI34rWeL9alnW6gE0CAYD0CID0baa1FdxINhojI+Ozx3QBA0ByBED6diJvBcD6iEkgAJAwAZC+tbeCG6mNdsYAFlkWzZnjg2wWAFCSAEjfZlqTQEaykU4XcEREszk9qCYBAEsgANK3TgCsj0a9VQGMiJg58eKgmgQALIEASN86k0BqY50u4IiIGRVAAEiKAEjfeiqAI3MBsNk0BhAAUiIA0rf2vr8jtdGo1UeiVsw+npk5NshmAQAlCYD0ba4COLsEzEjruAogAKRFAKRvJ9oVwNb4v3rROq4CCABJEQDpW7MTAEdnb9vHVQABICkCIH2baY35G21VAAVAAEiTAEjfZl6iC9gyMACQFgGQvs20buvzuoBnchVAAEiJAEjfFlQAI5s9bi9gAEiKAEjfZmbzXoy0FoFutALg8ZkXBtUkAGAJBED6dqJ1264ANrLZj8/xEwIgAKREAKRvzdbtaKsCONb6+EwLgACQFAGQvnW6gOvjETFXARQAASAtAiB9a88CHhmZDYBj2ew84OmZFwfUIgBgKQRA+pI3ZyLPZkuA7QDYqNUjIuK4reAAICkCIH2Z6aryLagANgVAAEiJAEhfZk50B8BlERHRqM0uCD1tJxAASIoASF+OHT/cud9orJy9bQXA4zMCIACkRACkL8eOHYqIiEZeRK0+2/U7Vh+LiIhpW8EBQFIEQPpybHoyIiLGu451KoBNARAAUiIA0pcXWxXA8WLu2FhrR5Dp/MQirwAAhpUASF9enD4SERHLuj4yDQEQAJIkANKXY8dbATDrCoAjAiAApEgApC8vtgLgeFbvHFs+uiIiIl4wCQQAkiIA0pdjJ6YiYl4AbC0HM6UCCABJEQDpy4utALisNfM3IuINjdMiIuKFYmaxlwAAQ0oApC/HZl6IiIjx7gA4fnpEREwV+UDaBAAsjQBIX15sbQW3rDbWObZ82WwAfCGKRV8DAAwnAZC+HDo+uxD0ytE3dI69YdkZERHxQjaQJgEASyQA0pdDrVnAp4+v7hxbvvzMiIh4sZZFc8ZMYABIhQBIX37SGgN4+rIzO8fesPyszv0XXvjXU94mAGBpBED68uN8OiIiVq9Y2zk2NjYRo8Xs+L8jR58dSLsAgPIEQPpyqLXUy2kr1nWOZbVarG5NAP7xoX2DaBYAsAQCIK9o5sSx+NfWJ+Ws09/U89wZ2UhERDx/eP+pbhYAsEQjg24Aw+9fntkTM1kW43kRa9Zs6nludW08Iqbi+aPPDKZxVN6h//5Q/Oie/xIjbzwnNv76b0VtdHzQTQIYegIgr+h/PLsnIiLOiXrU6r0fmTNGV0ScmIofmwTCADz+mf8Yy3Z+PYqZWhyPv4nv3fX/xMR/+r/jf3rbuwbdNIChpguYV/Tks38XERHnja1e8Nya1lqA/zxlEginzonp4/F3V10R43f91yhmalGsyCIbKaL41zyOXL0t9n7xPw+6iQBDrfIB8Atf+EJs3LgxxsfH46KLLopvf/vbg27S0PnOoaciIuKday5a8NxbzvzZiIj4x2MHT2mbqK59jz8R3/3wv42JPd+LiIjjF62PN3/n8Vj+n2+P7LSI4lgWjVv/z/jOx/+3ODFtfUqAxVQ6AN59991xww03xCc/+cl4/PHH4+KLL46tW7fG008/PeimDY29f/+l+PvaTIwURVx84dULnn/rxg9GRMRT2UwcOPD4qW4eFfHi0RfisT/fGf/tl7bGsY/9u2g8ezSykTyK/+Xi2HTXX8VIoxHn/Nv3xzlf/1bETy2PKLJY/c2/jqcufnv8t+3XxNN790ZR2LIQoC0rKvy34rve9a54+9vfHrfffnvn2E//9E/HlVdeGTt27HjF1x8+fDhWrVoVk5OTsXLlyteyqadcc+Z4PPz4HfF/PPkHcbCexRWja+Izv/r/LjivyPO46r9sjv9eOxETeRHvGT093rzy38Tp42fGymWrozG6PEbrjRgdGY/R+niMjjRipN6IWn006tlIZLV61LJ61Gr1qNVGZr+yetRqo1Gr1SPL6q/Q0pf++M5+tPOIoogiiiiKIooijyLy1svy1rGidax1Xp5HRBFFRBRF634+e07ne76krOd2/i55tdb77X7fWed2pOv5kajVap1zs6hFFhFZFhFFEVlkEdG+7fqJRRFZNnc0K2Z/R1mWRXT+qBftX9C8+6/muVj0uaIoIj8xEydmTsTMiRPRnJ6JmRMzMXP8RDSnX4x8+oXIj70YzamjMX3oUJw4ciSak0cijvwkih//OGrPH4qRyamo/eR4RHPuvY6uyWP8P/zvsf5//vcRETGTz8RIbXZ8atGciX/8D78ctW/9feTH5/6Pmy0vIj+9EflpK6I4fVVkp62KWLU6RlZOxOiKFTG2fHnUly+LrLEsasuXRdZoRL0xFmOjY1EfqUd9dCSy+mhktVpE1v2VzV3prPf69/84eq7lorKFz88eWuR1i5w791PmPfcy57afz9q1gu72L/peet9X1vn9zP95i/w+Sn7vVzxvwdspefwVzP7dEZEXReSt2/bjIlq3efv52XNqWcRIrRb1ehYjtSzqtdnbbIltYOlez/9+96uyAfD48eOxfPny+PKXvxw///M/3zn+8Y9/PPbu3Ru7d+9e8Jrp6emYnp7uPD58+HBs2LDhpH+Adt7w4Xjj3/5/C45nS7hSZV5TvMT9ehER+WlRFIsXjEfieOS1o93/Ps/9/H5+WB/nn8z3/rJ/1Q5ru07m9zqJ7/HlXlPLI0ZeLiuXdHRZEd/fWI/H3nFaHLzw30S9PhaTxyfjR1M/isPHD8eK0RVx1vKzYvX46pgYm4jaTw7Fxgf/MS548mi88UdF1IqT949sM4sosog8i8hrs/fbj4uYvT9f+1jPr7PrvGLeeYu9tuf1ix0r87Nfp14hzvZxrFj8nGKRY318w6X+zsu9rvcH9/Pasn8cBvnZefYd6+NX/tOuk/o9BcAKzwJ+7rnnotlsxpo1a3qOr1mzJg4cOLDoa3bs2BE33XTTa9+4I1Nx9k9e+x9TzqFBN4DXkWYW0axFNOuzt8dHIl5oRLw4FvFCI4sXGxGHl0f86LQsDp4Wsf+sLJ5ZHa1qzeGI555Y8D2PnjgaRyePxr7JrkXJN0fE5pEYny7ipw4UsfYnEWt/UsRpUxErXoxYcayIFS9GjM3Mfo005+6/nHpXoTOaJ+VXwtBQjRs2B6aODboJr0uVDYBt80vvRasLbTE33nhjbN++vfO4XQE82X76f/2P8T/e9ciC47O9G7VF/3oq2l0u3c8u0j3U6Zic13MSUYuReiPqI8uiXh+PWn08xpe/MWq1sbnX1hbpguo0bP7PmH+8u13ZYncXVcTsNVrsvCxbvCK56PfMFj7R+V31dMMt/s0WHn+pn7OwTYv9jl6+sbNd1XmRR5HPtLqnm61rXIt2128Rxdz/4tvvr5h9NqL1P/ws61Q0Wh3cs/c7L5w71kk03ZWl1p+HIoqI9m1RtDqgo/NzZs+b+x5FRES9HtlIPWqjIzEyNhL10ZGojY1EfaQeo633XXS3rdUF3yyaked5NIvm7Ffe7NzPizxm8pnZ22ImJkYnYs3yNXH6+OkxOT0Zz734XDx/7Pk4cvxI1LN61LJaNOqNaIw0Yrw+Ho16I8ZHxmOsPhajtdHOOZ3b2uxtLWpRnJiJmekT0TzRjJlmM07MNKNo5rPDCJp5RDOPPM+jyPPIZ5oRRR55sxlFXrS6A4tOF3met95jXvT8bufu5+07vZ+EYt6x+d3xMa9C2+qGLLqHKXR65F+mHLtIR9BLVXxmv//LmNfGoii6qmqzt3Mdtd3PtbuNZz8VWVFE1npz7feY9b7Z1p/hfPY7FK2hHVkeeTH3uZob6jH7jfLO72E2xecx97uf+xzOPtP+vLeuYnT/eWn/jrPOsWK2ozyLqEUtIiuiltUiy+Y63ovo7jqePdDuIp79uMx2Fc+/5O3Xdt+2/w7ovJX23yWd81u/n2KulVnXn9yi8zud/U49/3Z0Lknv339zj+ad32pVFr3nLybr/h12f1YWfLaLzqELzn/7y3xHlqqyAfDMM8+Mer2+oNp38ODBBVXBtkajEY1G4zVv26aLr4hNF1/xmv8ceD05Y9kZ8VOn/dTJ+4bjETFx8r4dwDCp7CzgsbGxuOiii2LXrt5xBbt27Yr3vOc9A2oVAMBrr7IVwIiI7du3x1VXXRWbN2+OLVu2xB133BFPP/10XHPNNYNuGgDAa6bSAfCXf/mX4/nnn49Pf/rT8eyzz8YFF1wQ9913X5x77rmDbhoAwGumssvAnAymkQNAevz7XeExgAAAVSUAAgBUjAAIAFAxAiAAQMUIgAAAFSMAAgBUjAAIAFAxAiAAQMUIgAAAFVPpreBerfYmKocPHx5wSwCAfrX/3a7yZmgC4Ktw5MiRiIjYsGHDgFsCAJR15MiRWLVq1aCbMRD2An4V8jyPZ555JiYmJiLLskE355Q5fPhwbNiwIfbv31/ZPRRT5LqlxzVLk+s2/IqiiCNHjsS6deuiVqvmaDgVwFehVqvF+vXrB92MgVm5cqW/3BLkuqXHNUuT6zbcqlr5a6tm7AUAqDABEACgYgRASms0GvGpT30qGo3GoJtCCa5belyzNLlupMAkEACAilEBBACoGAEQAKBiBEAAgIoRAAEAKkYApLQvfOELsXHjxhgfH4+LLroovv3tbw+6SbQ89NBDcfnll8e6desiy7K49957e54viiJ+93d/N9atWxfLli2LSy65JL773e8OprFERMSOHTviHe94R0xMTMTZZ58dV155ZTz11FM957huw+f222+PCy+8sLPY85YtW+Ib3/hG53nXjGEnAFLK3XffHTfccEN88pOfjMcffzwuvvji2Lp1azz99NODbhoRMTU1FZs2bYrbbrtt0ec/+9nPxu///u/HbbfdFnv27Im1a9fGhz70oc6+1px6u3fvjmuvvTYefvjh2LVrV8zMzMRll10WU1NTnXNct+Gzfv36uOWWW+KRRx6JRx55JC699NK44oorOiHPNWPoFVDCO9/5zuKaa67pOXb++ecXv/3bvz2gFvFSIqK45557Oo/zPC/Wrl1b3HLLLZ1jx44dK1atWlX8wR/8wQBayGIOHjxYRESxe/fuoihct5ScfvrpxR/90R+5ZiRBBZC+HT9+PB599NG47LLLeo5fdtll8Td/8zcDahX92rdvXxw4cKDn+jUajfi5n/s512+ITE5ORkTE6tWrI8J1S0Gz2YydO3fG1NRUbNmyxTUjCQIgfXvuueei2WzGmjVreo6vWbMmDhw4MKBW0a/2NXL9hldRFLF9+/Z473vfGxdccEFEuG7D7Mknn4wVK1ZEo9GIa665Ju65555461vf6pqRhJFBN4D0ZFnW87goigXHGF6u3/C67rrr4oknnoi//uu/XvCc6zZ83vKWt8TevXvj0KFD8Zd/+Zexbdu22L17d+d514xhpgJI384888yo1+sL/gd78ODBBf/TZfisXbs2IsL1G1LXX399fO1rX4sHHngg1q9f3znuug2vsbGxeNOb3hSbN2+OHTt2xKZNm+Jzn/uca0YSBED6NjY2FhdddFHs2rWr5/iuXbviPe95z4BaRb82btwYa9eu7bl+x48fj927d7t+A1QURVx33XXxla98Jb71rW/Fxo0be5533dJRFEVMT0+7ZiRBFzClbN++Pa666qrYvHlzbNmyJe644454+umn45prrhl004iIo0ePxg9+8IPO43379sXevXtj9erVcc4558QNN9wQN998c5x33nlx3nnnxc033xzLly+PX/3VXx1gq6vt2muvjS996Uvx1a9+NSYmJjpVo1WrVsWyZcsiyzLXbQh94hOfiK1bt8aGDRviyJEjsXPnznjwwQfj/vvvd81IwwBnIJOoz3/+88W5555bjI2NFW9/+9s7y1UweA888EAREQu+tm3bVhTF7JIin/rUp4q1a9cWjUajeN/73lc8+eSTg210xS12vSKiuPPOOzvnuG7D59d//dc7fw+eddZZxQc+8IHir/7qrzrPu2YMu6woimJA2RMAgAEwBhAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAq5v8HiZwCz95ZDc4AAAAASUVORK5CYII=",
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+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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AMAWDoBkFfFLGAASI8ASCnzxwDO3xoOABh+AiClLIh7KoAAkBwBkFIWLgStAggAqREAKcVOIACQPgGQUuZX/IwBBID0CICUsnAZmJmBtAMAWLrKBsAdO3bEO97xjpiYmIizzz47rrzyynjqqacG3ayht3ASiAogAKSmsgFw9+7dce2118bDDz8cu3btipmZmbjssstiampq0E0basYAAkD6RgbdgEG5//77ex7feeedcfbZZ8ejjz4a73vf+wbUquE3f8yfWcAAkJ7KVgDnm5ycjIiI1atXD7glw81ewACQvspWALsVRRHbt2+P9773vXHBBRe85HnT09MxPT3deXz48OFT0byhogsYANKnAhgR1113XTzxxBPx53/+5y973o4dO2LVqlWdrw0bNpyiFg6PBQEwbw6kHQDA0lU+AF5//fXxta99LR544IFYv379y5574403xuTkZOdr//79p6iVw2NhF7AxgACQmsp2ARdFEddff33cc8898eCDD8bGjRtf8TWNRiMajcYpaN3w0gUMAOmrbAC89tpr40tf+lJ89atfjYmJiThw4EBERKxatSqWLVs24NYNr9mKXzb3WAAEgORUtgv49ttvj8nJybjkkkvijW98Y+fr7rvvHnTThtrCCqAuYABITWUrgILL0iys96kAAkBqKlsB5OQQpAEgPQIgpcyPe7llYAAgOQIgpVgGBgDSJwBSSp71PjYLGADSIwDyKqkAAkBqBEBKWbgVnAogAKRGAKQUYwABIH0CIKXMr/cZAwgA6REAKWXBMjCFZWAAIDUCIK+OhaABIDkCIKUs3AtYFzAApEYApJQFYwBNAgGA5AiAlLKwAigAAkBqBEBelWJBTRAAGHYCIKUsXAjaLGAASI0ASCntvYCzVtevHmAASI8ASCntvFfrPNYFDACpEQAppR0As/ZjJUAASI4AyJKoAAJAugRASmnHvXq78KcCCADJEQApZcEYQAEQAJIjAFJKkc2O/mt/cPLCMjAAkBoBkL4V+dx4P5NAACBdAiB9K4q5AFhvHzMJBACSIwDSt+4AmM0dHEhbAIClEwDpW3cArLVyX7FgczgAYNgJgPSte9/fuVnAuoABIDUCIH3rHu/XHgOYC4AAkBwBkL71dAG3RgGqAAJAegRA+mYSCAC8PgiA9K170ee5vYAFQABIjQBI//LFuoAFQABIjQBI37ongagAAkC6BED61l3tq2UqgACQKgGQvuX5TOd+Zyu4rnGBAEAaBED61t3d2xkDqAsYAJIjANK/RZaB0QUMAOkRAOlbzxhAC0EDQLIEQPrWuw6gLmAASJUASN+K3E4gAPB6IADSt551AC0DAwDJEgDpXyvs1YqiUwHMLQMDAMkRAOlbns+GvazrmDGAAJAeAZC+tbuAs4jI7AUMAMkSAOlb95IvtUWOAQBpEADpX3sMYHRNAtEFDADJEQDpW3vCR1bMdQE3c5NAACA1AiB9a3f3ZhFRtxMIACRLAKRv3QGwXQG0DAwApEcApG/tGb9ZzI0BFAABID0CIH0rirl1AGudCqAuYABIjQBI3xavAAqAAJAaAZC+dY8BrJkEAgDJEgDpW3vNv+4KYNMYQABIjgBI3xYbA6gCCADpEQDpW5EvtgyMAAgAqREA6Vt3F3A9m/3oCIAAkB4BkL7l3V3AZgEDQLIEQPpWtPb9rRcRWagAAkCqBED61l0BrNsJBACSJQDStzyfiYjZD83cJJBigC0CAJZCAKRveWsWcD2yqJkEAgDJEgDpW7M4ERHtSSACIACkSgCkb0WnAji3EHQeAiAApEYApG/N1hjArKsL2E4gAJAeAZC+tbeCq8VcF3BTAASA5AiA9K295Esty+wFDAAJEwDpW7NrGZi5nUAsAwMAqal0AHzooYfi8ssvj3Xr1kWWZXHvvfcOuklDrV3tm10Gph4REXkIgACQmkoHwKmpqdi0aVPcdtttg25KEpq5vYAB4PVgZNANGKStW7fG1q1bB92MZMxNAskisw4gACSr0gGwrOnp6Zienu48Pnz48ABbc+rNjQHMot4qHusCBoD0VLoLuKwdO3bEqlWrOl8bNmwYdJNOqfYYwFoWKoAAkDABsIQbb7wxJicnO1/79+8fdJNOqXbYq9kLGACSpgu4hEajEY1GY9DNGJieLuD2TiC6gAEgOSqA9K3TBRxzXcBN6wACQHIqXQE8evRo/OAHP+g83rdvX+zduzdWr14d55xzzgBbNpyanZ1AanN7AYcuYABITaUD4COPPBLvf//7O4+3b98eERHbtm2LL37xiwNq1fDqrgB2FoJWAQSA5FQ6AF5yySVRCDB9mxsDWOssBN00CQQAkmMMIH2bWwYm6+oCFqABIDUCIH3rXQbGXsAAkCoBkL7NTQLpXgdQAASA1AiA9K3oqgB2dgJRAQSA5AiA9K27Alg3CxgAkiUA0rdika3gTAIBgPQIgPStXQHMurqA7QQCAOkRAOlbe83EelaLem12CUkVQABIjwBI3/KudQBNAgGAdAmA9K0zCSTm9gIWAAEgPQIgfevdCcQsYABIlQBI39r7/mZds4DtBAwA6REA6Vu7AljParaCA4CECYD0rd3dm2W1qNVmA6BlYAAgPQIgfWtPAuleBkYFEADSIwDSt+6dQEbqYxER0RQAASA5AiB9a7amfGRZLeq10YiIOCEAAkByBED61rMTSH02ABoDCADpEQDpW2cZmCyL0Xpj9pgKIAAkRwCkb51lYKIW9drsGMCZQTYIAFgSAZC+tWf8ZlktRkbaAVAFEABSIwDSt2ZnK7ha1DuzgAGA1AiA9K17J5D2MjAz2SBbBAAshQBI3+YmgdRipDMJBABIjQBI34rWeL9alnW6gE0CAYD0CID0baa1FdxINhojI+Ozx3QBA0ByBED6diJvBcD6iEkgAJAwAZC+tbeCG6mNdsYAFlkWzZnjg2wWAFCSAEjfZlqTQEaykU4XcEREszk9qCYBAEsgANK3TgCsj0a9VQGMiJg58eKgmgQALIEASN86k0BqY50u4IiIGRVAAEiKAEjfeiqAI3MBsNk0BhAAUiIA0rf2vr8jtdGo1UeiVsw+npk5NshmAQAlCYD0ba4COLsEzEjruAogAKRFAKRvJ9oVwNb4v3rROq4CCABJEQDpW7MTAEdnb9vHVQABICkCIH2baY35G21VAAVAAEiTAEjfZl6iC9gyMACQFgGQvs20buvzuoBnchVAAEiJAEjfFlQAI5s9bi9gAEiKAEjfZmbzXoy0FoFutALg8ZkXBtUkAGAJBED6dqJ1264ANrLZj8/xEwIgAKREAKRvzdbtaKsCONb6+EwLgACQFAGQvnW6gOvjETFXARQAASAtAiB9a88CHhmZDYBj2ew84OmZFwfUIgBgKQRA+pI3ZyLPZkuA7QDYqNUjIuK4reAAICkCIH2Z6aryLagANgVAAEiJAEhfZk50B8BlERHRqM0uCD1tJxAASIoASF+OHT/cud9orJy9bQXA4zMCIACkRACkL8eOHYqIiEZeRK0+2/U7Vh+LiIhpW8EBQFIEQPpybHoyIiLGu451KoBNARAAUiIA0pcXWxXA8WLu2FhrR5Dp/MQirwAAhpUASF9enD4SERHLuj4yDQEQAJIkANKXY8dbATDrCoAjAiAApEgApC8vtgLgeFbvHFs+uiIiIl4wCQQAkiIA0pdjJ6YiYl4AbC0HM6UCCABJEQDpy4utALisNfM3IuINjdMiIuKFYmaxlwAAQ0oApC/HZl6IiIjx7gA4fnpEREwV+UDaBAAsjQBIX15sbQW3rDbWObZ82WwAfCGKRV8DAAwnAZC+HDo+uxD0ytE3dI69YdkZERHxQjaQJgEASyQA0pdDrVnAp4+v7hxbvvzMiIh4sZZFc8ZMYABIhQBIX37SGgN4+rIzO8fesPyszv0XXvjXU94mAGBpBED68uN8OiIiVq9Y2zk2NjYRo8Xs+L8jR58dSLsAgPIEQPpyqLXUy2kr1nWOZbVarG5NAP7xoX2DaBYAsAQCIK9o5sSx+NfWJ+Ws09/U89wZ2UhERDx/eP+pbhYAsEQjg24Aw+9fntkTM1kW43kRa9Zs6nludW08Iqbi+aPPDKZxVN6h//5Q/Oie/xIjbzwnNv76b0VtdHzQTQIYegIgr+h/PLsnIiLOiXrU6r0fmTNGV0ScmIofmwTCADz+mf8Yy3Z+PYqZWhyPv4nv3fX/xMR/+r/jf3rbuwbdNIChpguYV/Tks38XERHnja1e8Nya1lqA/zxlEginzonp4/F3V10R43f91yhmalGsyCIbKaL41zyOXL0t9n7xPw+6iQBDrfIB8Atf+EJs3LgxxsfH46KLLopvf/vbg27S0PnOoaciIuKday5a8NxbzvzZiIj4x2MHT2mbqK59jz8R3/3wv42JPd+LiIjjF62PN3/n8Vj+n2+P7LSI4lgWjVv/z/jOx/+3ODFtfUqAxVQ6AN59991xww03xCc/+cl4/PHH4+KLL46tW7fG008/PeimDY29f/+l+PvaTIwURVx84dULnn/rxg9GRMRT2UwcOPD4qW4eFfHi0RfisT/fGf/tl7bGsY/9u2g8ezSykTyK/+Xi2HTXX8VIoxHn/Nv3xzlf/1bETy2PKLJY/c2/jqcufnv8t+3XxNN790ZR2LIQoC0rKvy34rve9a54+9vfHrfffnvn2E//9E/HlVdeGTt27HjF1x8+fDhWrVoVk5OTsXLlyteyqadcc+Z4PPz4HfF/PPkHcbCexRWja+Izv/r/LjivyPO46r9sjv9eOxETeRHvGT093rzy38Tp42fGymWrozG6PEbrjRgdGY/R+niMjjRipN6IWn006tlIZLV61LJ61Gr1qNVGZr+yetRqo1Gr1SPL6q/Q0pf++M5+tPOIoogiiiiKIooijyLy1svy1rGidax1Xp5HRBFFRBRF634+e07ne76krOd2/i55tdb77X7fWed2pOv5kajVap1zs6hFFhFZFhFFEVlkEdG+7fqJRRFZNnc0K2Z/R1mWRXT+qBftX9C8+6/muVj0uaIoIj8xEydmTsTMiRPRnJ6JmRMzMXP8RDSnX4x8+oXIj70YzamjMX3oUJw4ciSak0cijvwkih//OGrPH4qRyamo/eR4RHPuvY6uyWP8P/zvsf5//vcRETGTz8RIbXZ8atGciX/8D78ctW/9feTH5/6Pmy0vIj+9EflpK6I4fVVkp62KWLU6RlZOxOiKFTG2fHnUly+LrLEsasuXRdZoRL0xFmOjY1EfqUd9dCSy+mhktVpE1v2VzV3prPf69/84eq7lorKFz88eWuR1i5w791PmPfcy57afz9q1gu72L/peet9X1vn9zP95i/w+Sn7vVzxvwdspefwVzP7dEZEXReSt2/bjIlq3efv52XNqWcRIrRb1ehYjtSzqtdnbbIltYOlez/9+96uyAfD48eOxfPny+PKXvxw///M/3zn+8Y9/PPbu3Ru7d+9e8Jrp6emYnp7uPD58+HBs2LDhpH+Adt7w4Xjj3/5/C45nS7hSZV5TvMT9ehER+WlRFIsXjEfieOS1o93/Ps/9/H5+WB/nn8z3/rJ/1Q5ru07m9zqJ7/HlXlPLI0ZeLiuXdHRZEd/fWI/H3nFaHLzw30S9PhaTxyfjR1M/isPHD8eK0RVx1vKzYvX46pgYm4jaTw7Fxgf/MS548mi88UdF1IqT949sM4sosog8i8hrs/fbj4uYvT9f+1jPr7PrvGLeeYu9tuf1ix0r87Nfp14hzvZxrFj8nGKRY318w6X+zsu9rvcH9/Pasn8cBvnZefYd6+NX/tOuk/o9BcAKzwJ+7rnnotlsxpo1a3qOr1mzJg4cOLDoa3bs2BE33XTTa9+4I1Nx9k9e+x9TzqFBN4DXkWYW0axFNOuzt8dHIl5oRLw4FvFCI4sXGxGHl0f86LQsDp4Wsf+sLJ5ZHa1qzeGI555Y8D2PnjgaRyePxr7JrkXJN0fE5pEYny7ipw4UsfYnEWt/UsRpUxErXoxYcayIFS9GjM3Mfo005+6/nHpXoTOaJ+VXwtBQjRs2B6aODboJr0uVDYBt80vvRasLbTE33nhjbN++vfO4XQE82X76f/2P8T/e9ciC47O9G7VF/3oq2l0u3c8u0j3U6Zic13MSUYuReiPqI8uiXh+PWn08xpe/MWq1sbnX1hbpguo0bP7PmH+8u13ZYncXVcTsNVrsvCxbvCK56PfMFj7R+V31dMMt/s0WHn+pn7OwTYv9jl6+sbNd1XmRR5HPtLqnm61rXIt2128Rxdz/4tvvr5h9NqL1P/ws61Q0Wh3cs/c7L5w71kk03ZWl1p+HIoqI9m1RtDqgo/NzZs+b+x5FRES9HtlIPWqjIzEyNhL10ZGojY1EfaQeo633XXS3rdUF3yyaked5NIvm7Ffe7NzPizxm8pnZ22ImJkYnYs3yNXH6+OkxOT0Zz734XDx/7Pk4cvxI1LN61LJaNOqNaIw0Yrw+Ho16I8ZHxmOsPhajtdHOOZ3b2uxtLWpRnJiJmekT0TzRjJlmM07MNKNo5rPDCJp5RDOPPM+jyPPIZ5oRRR55sxlFXrS6A4tOF3met95jXvT8bufu5+07vZ+EYt6x+d3xMa9C2+qGLLqHKXR65F+mHLtIR9BLVXxmv//LmNfGoii6qmqzt3Mdtd3PtbuNZz8VWVFE1npz7feY9b7Z1p/hfPY7FK2hHVkeeTH3uZob6jH7jfLO72E2xecx97uf+xzOPtP+vLeuYnT/eWn/jrPOsWK2ozyLqEUtIiuiltUiy+Y63ovo7jqePdDuIp79uMx2Fc+/5O3Xdt+2/w7ovJX23yWd81u/n2KulVnXn9yi8zud/U49/3Z0Lknv339zj+ad32pVFr3nLybr/h12f1YWfLaLzqELzn/7y3xHlqqyAfDMM8+Mer2+oNp38ODBBVXBtkajEY1G4zVv26aLr4hNF1/xmv8ceD05Y9kZ8VOn/dTJ+4bjETFx8r4dwDCp7CzgsbGxuOiii2LXrt5xBbt27Yr3vOc9A2oVAMBrr7IVwIiI7du3x1VXXRWbN2+OLVu2xB133BFPP/10XHPNNYNuGgDAa6bSAfCXf/mX4/nnn49Pf/rT8eyzz8YFF1wQ9913X5x77rmDbhoAwGumssvAnAymkQNAevz7XeExgAAAVSUAAgBUjAAIAFAxAiAAQMUIgAAAFSMAAgBUjAAIAFAxAiAAQMUIgAAAFVPpreBerfYmKocPHx5wSwCAfrX/3a7yZmgC4Ktw5MiRiIjYsGHDgFsCAJR15MiRWLVq1aCbMRD2An4V8jyPZ555JiYmJiLLskE355Q5fPhwbNiwIfbv31/ZPRRT5LqlxzVLk+s2/IqiiCNHjsS6deuiVqvmaDgVwFehVqvF+vXrB92MgVm5cqW/3BLkuqXHNUuT6zbcqlr5a6tm7AUAqDABEACgYgRASms0GvGpT30qGo3GoJtCCa5belyzNLlupMAkEACAilEBBACoGAEQAKBiBEAAgIoRAAEAKkYApLQvfOELsXHjxhgfH4+LLroovv3tbw+6SbQ89NBDcfnll8e6desiy7K49957e54viiJ+93d/N9atWxfLli2LSy65JL773e8OprFERMSOHTviHe94R0xMTMTZZ58dV155ZTz11FM957huw+f222+PCy+8sLPY85YtW+Ib3/hG53nXjGEnAFLK3XffHTfccEN88pOfjMcffzwuvvji2Lp1azz99NODbhoRMTU1FZs2bYrbbrtt0ec/+9nPxu///u/HbbfdFnv27Im1a9fGhz70oc6+1px6u3fvjmuvvTYefvjh2LVrV8zMzMRll10WU1NTnXNct+Gzfv36uOWWW+KRRx6JRx55JC699NK44oorOiHPNWPoFVDCO9/5zuKaa67pOXb++ecXv/3bvz2gFvFSIqK45557Oo/zPC/Wrl1b3HLLLZ1jx44dK1atWlX8wR/8wQBayGIOHjxYRESxe/fuoihct5ScfvrpxR/90R+5ZiRBBZC+HT9+PB599NG47LLLeo5fdtll8Td/8zcDahX92rdvXxw4cKDn+jUajfi5n/s512+ITE5ORkTE6tWrI8J1S0Gz2YydO3fG1NRUbNmyxTUjCQIgfXvuueei2WzGmjVreo6vWbMmDhw4MKBW0a/2NXL9hldRFLF9+/Z473vfGxdccEFEuG7D7Mknn4wVK1ZEo9GIa665Ju65555461vf6pqRhJFBN4D0ZFnW87goigXHGF6u3/C67rrr4oknnoi//uu/XvCc6zZ83vKWt8TevXvj0KFD8Zd/+Zexbdu22L17d+d514xhpgJI384888yo1+sL/gd78ODBBf/TZfisXbs2IsL1G1LXX399fO1rX4sHHngg1q9f3znuug2vsbGxeNOb3hSbN2+OHTt2xKZNm+Jzn/uca0YSBED6NjY2FhdddFHs2rWr5/iuXbviPe95z4BaRb82btwYa9eu7bl+x48fj927d7t+A1QURVx33XXxla98Jb71rW/Fxo0be5533dJRFEVMT0+7ZiRBFzClbN++Pa666qrYvHlzbNmyJe644454+umn45prrhl004iIo0ePxg9+8IPO43379sXevXtj9erVcc4558QNN9wQN998c5x33nlx3nnnxc033xzLly+PX/3VXx1gq6vt2muvjS996Uvx1a9+NSYmJjpVo1WrVsWyZcsiyzLXbQh94hOfiK1bt8aGDRviyJEjsXPnznjwwQfj/vvvd81IwwBnIJOoz3/+88W5555bjI2NFW9/+9s7y1UweA888EAREQu+tm3bVhTF7JIin/rUp4q1a9cWjUajeN/73lc8+eSTg210xS12vSKiuPPOOzvnuG7D59d//dc7fw+eddZZxQc+8IHir/7qrzrPu2YMu6woimJA2RMAgAEwBhAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAqRgAEAKgYARAAoGIEQACAihEAAQAq5v8HiZwCz95ZDc4AAAAASUVORK5CYII=' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "cts = multiple_scattering(x, p2, core_loss=False)\n",
+    "plt.figure()\n",
+    "plt.plot(x,cts)\n",
+    "plt.plot(x, infoWidget.dataset)\n",
+    "plt.plot(x, infoWidget.dataset-cts)\n",
+    "plt.plot(x, infoWidget.datasets['plasmon'])\n",
+    "\n",
+    "p2, p0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'spec' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m tags \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m----> 2\u001b[0m tags[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdispersion\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m spec\u001b[38;5;241m.\u001b[39menergy_loss[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m spec\u001b[38;5;241m.\u001b[39menergy_loss[\u001b[38;5;241m10\u001b[39m]   \u001b[38;5;66;03m# input('ev per channel : ');\u001b[39;00m\n\u001b[0;32m      3\u001b[0m tags[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mE0\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m200\u001b[39m \u001b[38;5;66;03m# tags['E0']#input('incident energy E0(kev) : ');\u001b[39;00m\n\u001b[0;32m      4\u001b[0m tags[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcollAngle\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m30\u001b[39m \u001b[38;5;66;03m# tags['collAngle']\u001b[39;00m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'spec' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "tags = {}\n",
+    "tags['dispersion'] = spec.energy_loss[1] - spec.energy_loss[10]   # input('ev per channel : ');\n",
+    "tags['E0'] = 200 # tags['E0']#input('incident energy E0(kev) : ');\n",
+    "tags['collAngle'] = 30 # tags['collAngle']\n",
+    "\n",
+    "\n",
+    "def Drude( tags, e, p):\n",
+    "    return drude(tags,  e, p[0], p[1], p[2],  p[3])\n",
+    "def drude(tags, e, ep, ew, tnm, eb):\n",
+    "    pc = tags['dispersion']#input('ev per channel : ');\n",
+    "    e0 = tags['E0']#input('incident energy E0(kev) : ');\n",
+    "    beta = tags['collAngle']#input('collection semiangle beta(mrad) : ');\n",
+    "    #tnm = input('thickness(nm) : ');\n",
+    "\n",
+    "    b = beta/1000.0 # %rad\n",
+    "    T = 1000.0*e0*(1.+e0/1022.12)/(1.0+e0/511.06)**2;# %eV # equ.5.2a or Appendix E p 427 \n",
+    "    tgt = 1000*e0*(1022.12 + e0)/(511.06 + e0);# %eV  Appendix E p 427 \n",
+    "    rk0 = 2590*(1.0+e0/511.06)*np.sqrt(2.0*T/511060);\n",
+    "    os = e[0]\n",
+    "    ewMod = eb\n",
+    "\n",
+    "    eps = 1 - (ep**2-ewMod*e*1j)/(e**2+2*e*ew*1j) #Mod Drude term\n",
+    "    \n",
+    "    eps[np.nonzero(eps==0.0)]= 1e-19\n",
+    "    elf = np.imag(-1/eps)\n",
+    "\n",
+    "    the = e/tgt; #% varies with energy loss! # Appendix E p 427 \n",
+    "    \n",
+    "    srfelf=np.imag(-4./(1.0+eps))-elf; #% for 2 surfaces\n",
+    "    angdep = np.arctan(b/the)/the - b/(b*b+the*the);\n",
+    "    srfint = angdep*srfelf/(3.1416*0.05292*rk0*T); #% probability per eV\n",
+    "    anglog = np.log(1.0+ b*b/the/the);\n",
+    "    # I0 = tags['spec'].sum()#*self.tags['counts2e']\n",
+    "    #print('counts2e',1/self.tags['counts2e'])\n",
+    "    \n",
+    "\n",
+    "    # 2 * T = m_0 v**2 !!!  a_0 = 0.05292 nm\n",
+    "    volint = abs(tnm/(np.pi*0.05292*T*2.0)*elf*anglog); #S equ 4.26% probability per eV\n",
+    "    #volint = volint *I0/ epc #S probability per channel\n",
+    "    ssd = volint #+ srfint;\n",
+    "\n",
+    "    if os <-1.0:\n",
+    "        xs = int(abs(-os/epc))\n",
+    "   \n",
+    "        ssd[0:xs]=0.0\n",
+    "        volint[0:xs]=0.0\n",
+    "        srfint[0:xs]=0.0\n",
+    "        \n",
+    "        #if os <0:\n",
+    "        Ps = np.trapz(e,srfint); #% 2 surfaces but includes negative begrenzungs contribn.\n",
+    "        Pv = abs(np.trapz(e,abs(volint/self.tags['spec'].sum()))); #% integrated volume probability\n",
+    "        Pv = (volint/I0).sum() ## our data have he same epc and the trapz formula does not include \n",
+    "        lam = tnm/Pv; #% does NOT depend on free-electron approximation (no damping). \n",
+    "        lamfe = 4.0*0.05292*T/ep/np.log(1+(b* tgt / ep) **2); #% Eq.(3.44) approximation\n",
+    "    \n",
+    "        #print('Ps(2surfaces+begrenzung terms) =', Ps, 'Pv=t/lambda(beta)= ',Pv,'\\n');\n",
+    "        #print('Volume-plasmon MFP(nm) = ', lam,' Free-electron MFP(nm) = ',lamfe,'\\n');\n",
+    "        #print('--------------------------------\\n');\n",
+    "\n",
+    "        \"\"\"self.tags['eps'] = eps\n",
+    "        \n",
+    "        self.tags['lam'] = lam\n",
+    "        self.tags['lamfe'] = lamfe\n",
+    "        self.tags['Pv'] = Pv\n",
+    "        \"\"\"\n",
+    "        \n",
+    "    return ssd#/np.pi\n",
+    "    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def newDrudeBgd(energy_scale,p):\n",
+    "        tags = self.tags\n",
+    "\n",
+    "        startB = energy_scale[0]\n",
+    "        endB = energy_scale[-1]\n",
+    "        p = np.abs(p)\n",
+    "       \n",
+    "\n",
+    "        LLene = np.linspace(0, 2047,2048)\n",
+    "        SSD =  drude(LLene,p)\n",
+    "        ssd  = np.fft.fft(SSD)\n",
+    "\n",
+    "        ssd2 = ssd.copy()\n",
+    "        SSD2 = SSD.copy()\n",
+    "        \n",
+    "        ### sum contribution from each order of scattering:\n",
+    "        PSD = np.zeros(len(LLene))\n",
+    "        for order in range(15):\n",
+    "            # This order convoluted spectum \n",
+    "            PPSD = np.zeros(len(LLene))\n",
+    "            # convoluted SSD is SSD2\n",
+    "            SSD2 = np.fft.ifft(ssd).real\n",
+    "            \n",
+    "            # scale right (could be done better? GERD) \n",
+    "            mult = sum(SSD)/sum(SSD2)\n",
+    "            SSD2 *= abs(mult)\n",
+    "            \n",
+    "            PPSD = SSD2/factorial(order+1)*np.power(tmfp,(order+1))*np.exp(-tmfp) #using equation 4.1 of egerton ed2\n",
+    "           # Add this order to final spectrum\n",
+    "            PSD += PPSD\n",
+    "\n",
+    "            # next order convolution\n",
+    "            ssd = ssd * ssd2\n",
+    "            \n",
+    "\n",
+    "        cts = np.zeros(len(x))\n",
+    "        \n",
+    "        if startB < 0:\n",
+    "            startB = 0\n",
+    "        BGDcoef = splrep(LLene[int(startB):int(endB)],PSD[int(startB):int(endB)],s=0)\n",
+    "\n",
+    "\n",
+    "        lin = np.zeros(len(x))\n",
+    "\n",
+    "        cts =splev( x, BGDcoef)*p[1]\n",
+    "        \n",
+    "        self.cor =  self.EffectiveAngle(self.tags['E0'],x, self.tags['convAngle'], self.tags['collAngle'])  \n",
+    "        return cts#*self.cor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drude_simulation(dset, e, ep, ew, tnm, eb):\n",
+    "    \n",
+    "    energy_scale = dset.get_spectral_dims(return_axis=True)[0].values\n",
+    "    dispersion = energy_scale[1] - energy_scale[0]  # input('ev per channel : ');\n",
+    "    \n",
+    "    beta = dset.metadata['collection_angle'] / 1000.  # rad\n",
+    "    e0 = dset.metadata['acceleration_voltage'] / 1000.  # input('incident energy e0(kev) : ');\n",
+    "\n",
+    "    # effective kinetic energy: T = m_o v^2/2,\n",
+    "    t = 1000.0 * e0 * (1. + e0 / 1022.12) / (1.0 + e0 / 511.06) ** 2  # eV # equ.5.2a or Appendix E p 427\n",
+    "    \n",
+    "    # 2 gamma T\n",
+    "    tgt = 1000 * e0 * (1022.12 + e0) / (511.06 + e0)  # eV  Appendix E p 427\n",
+    "    rk0 = 2590 * (1.0 + e0 / 511.06) * np.sqrt(2.0 * t / 511060)\n",
+    "    \n",
+    "    os = e[0]\n",
+    "    ew_mod = eb\n",
+    "    tags = dset.metadata\n",
+    "   \n",
+    "    eps = 1 - (ep ** 2 - ew_mod * e * 1j) / (e ** 2 + 2 * e * ew * 1j)  # Mod drude term\n",
+    "    \n",
+    "    eps[np.nonzero(eps == 0.0)] = 1e-19\n",
+    "    elf = np.imag(-1 / eps)\n",
+    "\n",
+    "    the = e / tgt  # varies with energy loss! # Appendix E p 427\n",
+    "    # srfelf = 4..*eps2./((1+eps1).^2+eps2.^2) - elf; %equivalent\n",
+    "    srfelf = np.imag(-4. / (1.0 + eps)) - elf  # for 2 surfaces\n",
+    "    angdep = np.arctan(beta / the) / the -  beta/ (beta * beta + the * the)\n",
+    "    srfint = angdep * srfelf / (3.1416 * 0.05292 * rk0 * t)  # probability per eV\n",
+    "    anglog = np.log(1.0 + beta * beta / the / the)\n",
+    "    i0 = dset.sum()  # *tags['counts2e']\n",
+    "\n",
+    "    # 2 * t = m_0 v**2 !!!  a_0 = 0.05292 nm\n",
+    "    volint = abs(tnm / (np.pi * 0.05292 * t * 2.0) * elf * anglog)  # S equ 4.26% probability per eV\n",
+    "    volint = volint * i0 / dispersion  # S probability per channel\n",
+    "    ssd = volint  # + srfint;\n",
+    "\n",
+    "    if e[0] < -1.0:\n",
+    "        xs = int(abs(-e[0] / dispersion))\n",
+    "\n",
+    "        ssd[0:xs] = 0.0\n",
+    "        volint[0:xs] = 0.0\n",
+    "        srfint[0:xs] = 0.0\n",
+    "\n",
+    "        # if os <0:\n",
+    "        p_s = np.trapz(e, srfint)  # 2 surfaces but includes negative Begrenzung contribution.\n",
+    "        p_v = abs(np.trapz(e, abs(volint / tags['spec'].sum())))  # integrated volume probability\n",
+    "        p_v = (volint / i0).sum()  # our data have he same epc and the trapez formula does not include\n",
+    "        lam = tnm / p_v  # does NOT depend on free-electron approximation (no damping).\n",
+    "        lamfe = 4.0 * 0.05292 * t / ep / np.log(1 + (beta * tgt / ep) ** 2)  # Eq.(3.44) approximation\n",
+    "\n",
+    "        tags['eps'] = eps\n",
+    "        tags['lam'] = lam\n",
+    "        tags['lamfe'] = lamfe\n",
+    "        tags['p_v'] = p_v\n",
+    "\n",
+    "    return ssd  # /np.pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'spectrum1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m peak_model, p \u001b[38;5;241m=\u001b[39m pyTEMlib\u001b[38;5;241m.\u001b[39meels_tools\u001b[38;5;241m.\u001b[39mgaussian_mixture_model(spectrum1, p_in\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(p)\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m3\u001b[39m))\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'spectrum1' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "peak_model, p = pyTEMlib.eels_tools.gaussian_mixture_model(spectrum1, p_in=None)\n",
+    "\n",
+    "print(int(len(p)/3))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x183f06e74d0>]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68d997ac70384ccdb756bda8db862064",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "spectrum = spectrum1.view.get_spectrum()\n",
+    "plt.figure()\n",
+    "\n",
+    "plt.plot(spectrum, c='red')\n",
+    "plt.plot(peak_model, linewidth=2,c='blue')\n",
+    "plt.plot(spectrum-peak_model)\n",
+    "plt.plot((spectrum-peak_model)/np.sqrt(spectrum/20)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x183f1ec3020>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acc04cbbd6e149d7956e250a006d4ed9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "\n",
+    "plt.plot(spectrum.energy_loss, spectrum, c='r', label='experiment')\n",
+    "plt.plot(spectrum.energy_loss, peak_model, c='b',linewidth=2, label='model')\n",
+    "plt.plot(spectrum.energy_loss, (spectrum-peak_model)/np.sqrt(spectrum/20)*100, label='residuals')\n",
+    "for i in range(int(len(p)/3)):\n",
+    "    plt.plot(spectrum.energy_loss, pyTEMlib.eels_tools.gauss(spectrum.energy_loss, p[i*3:(i+1)*3]), label=f'Gauss {i}')\n",
+    "\n",
+    "plt.legend(loc='upper right')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## peakfit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4aa43febd2840e8b692560d768a6174",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "AppLayout(children=(GridspecLayout(children=(Button(description='Fit Area', layout=Layout(grid_area='widget001…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyTEMlib import peak_dialog\n",
+    "\n",
+    "peakFitWidget = peak_dialog.PeakFitWidget(infoWidget.datasets, infoWidget.datasets['_relationship']['low_loss'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'main_dataset': 'Channel_000',\n",
+       " 'low_loss': 'Channel_000',\n",
+       " 'resolution_functions': 'resolution_functions'}"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['_relationship']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3e48d3ff676489abd5b39ef0b3f2b46",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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+       "\n",
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+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "k = peakFitWidget.dataset-np.array(peakFitWidget.model)\n",
+    "v =k.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\eels_tools.py:1829: RuntimeWarning: Number of calls to function has reached maxfev = 10000.\n",
+      "  [p, _] = leastsq(residuals3, pin, args=(x, y),maxfev = 10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "peakFitWidget.smooth()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['Channel_000', 'Channel_001', '_relationship', 'resolution_functions'])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peakFitWidget.datasets.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\peak_dialog.py:357: RuntimeWarning: Number of calls to function has reached maxfev = 11000.\n",
+      "  #self.model[start_channel:end_channel] = model\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\jupyter_client\\session.py:721: UserWarning: Message serialization failed with:\n",
+      "Out of range float values are not JSON compliant: nan\n",
+      "Supporting this message is deprecated in jupyter-client 7, please make sure your message is JSON-compliant\n",
+      "  content = self.pack(content)\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\jupyter_client\\session.py:721: UserWarning: Message serialization failed with:\n",
+      "Out of range float values are not JSON compliant: nan\n",
+      "Supporting this message is deprecated in jupyter-client 7, please make sure your message is JSON-compliant\n",
+      "  content = self.pack(content)\n"
+     ]
+    }
+   ],
+   "source": [
+    "peakFitWidget.fit_peaks()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ff = eels_tools.gmm(energy_scale, np.array(peakFitWidget.peak_out_list).flatten())\n",
+    "plt.figure()\n",
+    "plt.plot(ff)\n",
+    "#peakFitWidget.dataset.plot()\n",
+    "\n",
+    "np.isnan(ff).sum(), np.isinf(ff).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peakFitWidget.peak_out_list = peak_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.close('all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.25, 5595090.5, 4.747764687329006]\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "can't unbox heterogeneous list: float64 != float32",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[12], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m p \u001b[38;5;241m=\u001b[39m [peak[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mposition\u001b[39m\u001b[38;5;124m'\u001b[39m], peak[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mamplitude\u001b[39m\u001b[38;5;124m'\u001b[39m], peak[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(p)\n\u001b[1;32m----> 4\u001b[0m additional_spectra[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpeak \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mindex\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39m eels_tools\u001b[38;5;241m.\u001b[39mgauss(np\u001b[38;5;241m.\u001b[39marray(peakFitWidget\u001b[38;5;241m.\u001b[39menergy_scale), p)\n",
+      "\u001b[1;31mTypeError\u001b[0m: can't unbox heterogeneous list: float64 != float32"
+     ]
+    }
+   ],
+   "source": [
+    "for index, peak in peakFitWidget.peaks['peaks'].items(): # ll\n",
+    "    p = [peak['position'], peak['amplitude'], peak['width']]\n",
+    "    print(p)\n",
+    "    additional_spectra[f'peak {index}']= eels_tools.gauss(np.array(peakFitWidget.energy_scale), p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(0.05)"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pyTEMlib.eels_tools.get_slope(peakFitWidget.dataset.energy_loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2048,)\n",
+      "[-4.83000000e+01  3.83246094e+03  2.80766823e+00 -2.13000000e+01\n",
+      "  3.34756055e+03  2.50309866e+00 -1.36424205e-11  6.00148960e+07\n",
+      "  1.69709218e+00  1.98000000e+01  2.50729328e+05  1.09172481e+01\n",
+      "  8.07000000e+01  1.41388223e+04  6.03046268e+00  9.21000000e+01\n",
+      "  1.34123496e+04  1.58681033e+00  1.15500000e+02  9.99801367e+03\n",
+      "  1.67314163e+00  1.28400000e+02  8.61218750e+03  6.05662983e+00\n",
+      "  1.63500000e+02  5.94440137e+03  1.55680767e+00  1.67100000e+02\n",
+      "  5.93256055e+03  1.67007282e+00  1.98600000e+02  4.87127148e+03\n",
+      "  1.65743531e+00  2.34600000e+02  3.18025830e+03  1.63774453e+00\n",
+      "  2.50500000e+02  3.69103735e+03  1.52271110e+00  2.55900000e+02\n",
+      "  3.20574658e+03  2.78972361e+00  2.67600000e+02  2.57806250e+03\n",
+      "  2.29663513e+00  2.81100000e+02  3.54892529e+03  1.66879714e+00\n",
+      "  3.07200000e+02  1.55079565e+03  1.95856399e+00  3.20400000e+02\n",
+      "  2.64509766e+03  1.79741244e+00  3.40800000e+02  2.07240796e+03\n",
+      "  2.33128935e+00  3.52500000e+02  2.45149805e+03  2.76355395e+00\n",
+      "  3.58500000e+02  1.76404834e+03  1.98714577e+00  4.08600000e+02\n",
+      "  1.12217920e+03  2.18032596e+00  4.11600000e+02  1.82581519e+03\n",
+      "  1.92283312e+00  4.75800000e+02  1.60657446e+03  2.27419354e+00\n",
+      "  4.78800000e+02  1.94508496e+03  1.82035011e+00  5.14200000e+02\n",
+      "  1.01093506e+03  1.92720941e+00  5.42400000e+02  1.51592969e+03\n",
+      "  2.27115801e+00  5.46300000e+02  1.11388831e+03  2.00918175e+00]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1847"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "635347d105c94bc5ab16221ab14bed82",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipy\n",
+    "spectrum = peakFitWidget.dataset\n",
+    "\n",
+    "energy_scale = peakFitWidget.dataset.get_spectral_dims(return_axis=True)[0].values\n",
+    "pin = np.array(peakFitWidget.peak_out_list[:6]).flatten()\n",
+    "x = np.array(spectrum)\n",
+    "y = energy_scale\n",
+    "\n",
+    "\n",
+    "print(x.shape)\n",
+    "#p = scipy.optimize.least_squares(eels_tools.residuals3, pin, args=(x,y), method='lm', verbose= 2)\n",
+    "print(peak_model)\n",
+    "energy_scale[1]-energy_scale[0]\n",
+    "\n",
+    "v = eels_tools.residuals3(pin, x, y)\n",
+    "plt.figure()\n",
+    "plt.plot(v)\n",
+    "np.isfinite(v).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65f395b24139446f941974e797c9cb4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "spectrum[spectrum<0]=10000\n",
+    "vv = spectrum.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'eels_tools'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[8], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01meels_tools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m find_peaks\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Assuming peakFitWidget.dataset is your spectrum data\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Example data, replace with your actual spectrum\u001b[39;00m\n\u001b[0;32m      7\u001b[0m spectrum \u001b[38;5;241m=\u001b[39m peakFitWidget\u001b[38;5;241m.\u001b[39mdataset\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'eels_tools'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from eels_tools import find_peaks\n",
+    "\n",
+    "# Assuming peakFitWidget.dataset is your spectrum data\n",
+    "# Example data, replace with your actual spectrum\n",
+    "spectrum = peakFitWidget.dataset\n",
+    "peaks = peakFitWidget.dataset.metadata['peak_fit']\n",
+    "\n",
+    "# Fit the peaks using the find_peaks function\n",
+    "peak_model, peak_out_list = find_peaks(spectrum, peaks['fit_start'], peaks['fit_end'])\n",
+    "\n",
+    "# Extract the peak positions and amplitudes\n",
+    "peak_positions = [peak['center'] for peak in peak_out_list]\n",
+    "peak_amplitudes = [peak['amplitude'] for peak in peak_out_list]\n",
+    "\n",
+    "# Plot the original spectrum\n",
+    "x = np.arange(len(spectrum))\n",
+    "plt.plot(x, spectrum, label='Original Spectrum', color='gray', alpha=0.5)\n",
+    "\n",
+    "# Plot the identified peaks\n",
+    "for pos, amp in zip(peak_positions, peak_amplitudes):\n",
+    "    plt.axvline(pos, color='red', linestyle=':', label=f'Peak at {pos:.2f}')\n",
+    "    plt.plot(pos, amp, 'ro')  # Mark the peak positions\n",
+    "\n",
+    "plt.xlabel('Channel')\n",
+    "plt.ylabel('Intensity')\n",
+    "plt.title('Identified Peaks in Spectrum')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print the peak positions for verification\n",
+    "print(\"Identified peak positions:\", peak_positions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
+      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:198: RuntimeWarning: invalid value encountered in divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    }
+   ],
+   "source": [
+    "peaks = peakFitWidget.dataset.metadata['peak_fit']\n",
+    "\n",
+    "peak_model, peak_out_list = eels_tools.find_peaks(peakFitWidget.dataset,  peaks['fit_start'],  peaks['fit_end'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
+      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:198: RuntimeWarning: invalid value encountered in divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:520: RuntimeWarning: Mean of empty slice.\n",
+      "  avg = a.mean(axis, **keepdims_kw)\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([], shape=(0, 3), dtype=float64)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peakFitWidget.smooth()\n",
+    "peakFitWidget.peak_out_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.datasets['_relationship']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:206: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:163: RuntimeWarning: invalid value encountered in divide\n",
+      "  arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\_methods.py:198: RuntimeWarning: invalid value encountered in divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    }
+   ],
+   "source": [
+    "da = infoWidget.datasets[infoWidget.datasets['_relationship']['low_loss']]\n",
+    "gg = pyTEMlib.eels_tools.find_peaks(da, -4, 40)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__bool__',\n",
+       " '__class__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__']"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(da.view)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sidpy.Dataset of type SPECTRAL_IMAGE with:\n",
+      " dask.array<setitem, shape=(23, 28, 2048), dtype=float32, chunksize=(23, 28, 2048), chunktype=numpy.ndarray>\n",
+      " data contains: intensity (counts)\n",
+      " and Dimensions: \n",
+      "x:  distance (µm) of size (23,)\n",
+      "y:  distance (µm) of size (28,)\n",
+      "energy_loss:  energy-loss (eV) of size (2048,)\n",
+      " with metadata: ['experiment', 'peak_fit', 'zero_loss']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'fit_start': -9.95,\n",
+       " 'fit_end': 92.30000000000001,\n",
+       " 'peaks': {'0': {'position': -9.95,\n",
+       "   'amplitude': 1000.0,\n",
+       "   'width': 1.0,\n",
+       "   'type': 'Gauss',\n",
+       "   'asymmetry': 0}}}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da = infoWidget.datasets[infoWidget.datasets['_relationship']['low_loss']]\n",
+    "\n",
+    "print(da)\n",
+    "da.metadata['peak_fit']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0., 0., 0., ..., 0., 0., 0.]),\n",
+       " array([0.00000000e+00, 5.60210700e+06, 0.00000000e+00, 3.38997724e+00,\n",
+       "        4.15490805e+04, 1.52867594e+00]))"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gg\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[29], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m shifts \u001b[38;5;241m=\u001b[39m pyTEMlib\u001b[38;5;241m.\u001b[39meels_tools\u001b[38;5;241m.\u001b[39mget_zero_loss_energy(infoWidget\u001b[38;5;241m.\u001b[39mdatasets[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mChannel_002\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
+      "File \u001b[1;32m~\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\eels_tools.py:340\u001b[0m, in \u001b[0;36mget_zero_loss_energy\u001b[1;34m(dataset)\u001b[0m\n\u001b[0;32m    338\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(dataset\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]):\n\u001b[0;32m    339\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m y \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(dataset\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]):\n\u001b[1;32m--> 340\u001b[0m             _, shifts[x, y] \u001b[38;5;241m=\u001b[39m get_channel_zero(dataset[x, y, :], energy, width)\n\u001b[0;32m    341\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m shifts\n",
+      "File \u001b[1;32m~\\Documents\\Github\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\eels_tools.py:298\u001b[0m, in \u001b[0;36mget_channel_zero\u001b[1;34m(spectrum, energy, width)\u001b[0m\n\u001b[0;32m    294\u001b[0m y \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(spectrum[\u001b[38;5;28mint\u001b[39m(zero\u001b[38;5;241m-\u001b[39mwidth):\u001b[38;5;28mint\u001b[39m(zero\u001b[38;5;241m+\u001b[39mwidth)])\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m    296\u001b[0m y[np\u001b[38;5;241m.\u001b[39mnonzero(y \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e-12\u001b[39m\n\u001b[1;32m--> 298\u001b[0m p0 \u001b[38;5;241m=\u001b[39m [energy[zero], spectrum\u001b[38;5;241m.\u001b[39mmax(), \u001b[38;5;241m.5\u001b[39m]  \u001b[38;5;66;03m# Initial guess is a normal distribution\u001b[39;00m\n\u001b[0;32m    300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21merrfunc\u001b[39m(pp, xx, yy):\n\u001b[0;32m    301\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (gauss(xx, pp) \u001b[38;5;241m-\u001b[39m yy) \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39msqrt(yy)  \u001b[38;5;66;03m# Distance to the target function\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\sidpy\\sid\\dataset.py:1220\u001b[0m, in \u001b[0;36mDataset.reduce_dims.<locals>.wrapper_method\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1218\u001b[0m axis, keepdims \u001b[38;5;241m=\u001b[39m arguments\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxis\u001b[39m\u001b[38;5;124m'\u001b[39m), arguments\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkeepdims\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m   1219\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m keepdims:\n\u001b[1;32m-> 1220\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mcompute()\n\u001b[0;32m   1221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1222\u001b[0m     axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim))\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:375\u001b[0m, in \u001b[0;36mDaskMethodsMixin.compute\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    351\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    352\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[0;32m    353\u001b[0m \n\u001b[0;32m    354\u001b[0m \u001b[38;5;124;03m    This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    373\u001b[0m \u001b[38;5;124;03m    dask.compute\u001b[39;00m\n\u001b[0;32m    374\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 375\u001b[0m     (result,) \u001b[38;5;241m=\u001b[39m compute(\u001b[38;5;28mself\u001b[39m, traverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    376\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\dask\\base.py:661\u001b[0m, in \u001b[0;36mcompute\u001b[1;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[0;32m    658\u001b[0m     postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[0;32m    660\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m shorten_traceback():\n\u001b[1;32m--> 661\u001b[0m     results \u001b[38;5;241m=\u001b[39m schedule(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    663\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\queue.py:180\u001b[0m, in \u001b[0;36mQueue.get\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m    178\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m remaining \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[0;32m    179\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[1;32m--> 180\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_empty\u001b[38;5;241m.\u001b[39mwait(remaining)\n\u001b[0;32m    181\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get()\n\u001b[0;32m    182\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_full\u001b[38;5;241m.\u001b[39mnotify()\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\threading.py:359\u001b[0m, in \u001b[0;36mCondition.wait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    358\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 359\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mTrue\u001b[39;00m, timeout)\n\u001b[0;32m    360\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    361\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "shifts = pyTEMlib.eels_tools.get_zero_loss_energy(infoWidget.datasets['Channel_002'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'infoWidget' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dataset \u001b[38;5;241m=\u001b[39m infoWidget\u001b[38;5;241m.\u001b[39mdatasets[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mChannel_002\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      3\u001b[0m spectrum \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39msum(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mrange\u001b[39m(dataset\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)))\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'infoWidget' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy\n",
+    "dataset = infoWidget.datasets['Channel_002']\n",
+    "spectrum = dataset.sum(axis=tuple(range(dataset.ndim - 1)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "210 202 8\n",
+      "4\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.27885956, 0.27191713, 0.28913556, 0.25769037, 0.2942762 ,\n",
+       "        0.28620583, 0.33639917, 0.29324584, 0.23019431, 0.24396385,\n",
+       "        0.28630434, 0.34782575, 0.29164501, 0.32428319, 0.31925456,\n",
+       "        0.33853458, 0.34647511, 0.31610986, 0.38146446, 0.31699432,\n",
+       "        0.34575115, 0.35986766, 0.33364593, 0.33229606, 0.34956855,\n",
+       "        0.33242329, 0.3915634 , 0.35407163],\n",
+       "       [0.29500225, 0.2373293 , 0.25763805, 0.28486225, 0.31641727,\n",
+       "        0.29900238, 0.28421314, 0.27215118, 0.25969281, 0.23715325,\n",
+       "        0.3184073 , 0.29690798, 0.27968236, 0.32664399, 0.27429016,\n",
+       "        0.30321827, 0.33636393, 0.33146419, 0.37165488, 0.31699942,\n",
+       "        0.32685554, 0.37155651, 0.31661427, 0.34338988, 0.33284669,\n",
+       "        0.31143029, 0.41046077, 0.35855505],\n",
+       "       [0.29311011, 0.22077191, 0.3060771 , 0.27921994, 0.28125945,\n",
+       "        0.3067318 , 0.26348024, 0.25086256, 0.25962378, 0.2529393 ,\n",
+       "        0.31787102, 0.29516299, 0.28017314, 0.3592165 , 0.267943  ,\n",
+       "        0.30651435, 0.33832316, 0.31661111, 0.34534481, 0.28676643,\n",
+       "        0.33204225, 0.35063015, 0.34971729, 0.36301199, 0.35443506,\n",
+       "        0.34508562, 0.41291565, 0.32720252],\n",
+       "       [0.27862603, 0.2395802 , 0.27097475, 0.29844929, 0.32480325,\n",
+       "        0.31210681, 0.30410157, 0.31895698, 0.25131819, 0.21974032,\n",
+       "        0.26824316, 0.3026477 , 0.33016488, 0.36298262, 0.27516956,\n",
+       "        0.32761501, 0.34910861, 0.30489061, 0.33654038, 0.27761667,\n",
+       "        0.34485607, 0.41076118, 0.35082191, 0.41209721, 0.36572742,\n",
+       "        0.35657333, 0.4043328 , 0.3366633 ],\n",
+       "       [0.28915177, 0.25477428, 0.25937191, 0.28322656, 0.33555691,\n",
+       "        0.31758257, 0.27861604, 0.25593468, 0.30145909, 0.21596642,\n",
+       "        0.27045277, 0.28342595, 0.33550093, 0.37912592, 0.27235156,\n",
+       "        0.29138667, 0.35342075, 0.3278171 , 0.33455263, 0.29913195,\n",
+       "        0.35593614, 0.37443954, 0.3313424 , 0.39522888, 0.34047513,\n",
+       "        0.34615577, 0.37257263, 0.35127896],\n",
+       "       [0.26825451, 0.22317499, 0.27205782, 0.28977687, 0.31335867,\n",
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+       "        0.32663139, 0.33336231, 0.35315843],\n",
+       "       [0.3171484 , 0.32838144, 0.26763272, 0.25482452, 0.24839447,\n",
+       "        0.22693682, 0.25846255, 0.23695833, 0.24745501, 0.28812388,\n",
+       "        0.30393928, 0.31321126, 0.2949203 , 0.30777781, 0.34019723,\n",
+       "        0.32201243, 0.3350535 , 0.36631547, 0.32803027, 0.35022398,\n",
+       "        0.33984022, 0.29018393, 0.35647662, 0.34727768, 0.30282655,\n",
+       "        0.34913906, 0.36157026, 0.38905264],\n",
+       "       [0.29772264, 0.31244   , 0.25824082, 0.2633365 , 0.29358153,\n",
+       "        0.2853858 , 0.31198789, 0.25574126, 0.23576208, 0.33307473,\n",
+       "        0.28249016, 0.30273328, 0.27431271, 0.31244071, 0.32128821,\n",
+       "        0.31963668, 0.34839687, 0.35969038, 0.34473335, 0.34859848,\n",
+       "        0.35485971, 0.3338992 , 0.35779789, 0.33342661, 0.32066506,\n",
+       "        0.38048255, 0.35227182, 0.37892143]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import scipy\n",
+    "dataset = infoWidget.datasets['Channel_002']\n",
+    "spectrum = dataset.sum(axis=tuple(range(dataset.ndim - 1)))\n",
+    "\n",
+    "startx = scipy.signal.find_peaks(spectrum/np.max(spectrum), height=0.98)[0][0]\n",
+    "\n",
+    "end = startx + 3\n",
+    "start = startx - 3\n",
+    "for i in range(10):\n",
+    "    if spectrum[startx - i] < 0.3 * spectrum[startx]:\n",
+    "        start = startx - i\n",
+    "    if spectrum[startx + i] < 0.3 * spectrum[startx]:\n",
+    "        end = startx + i\n",
+    "if end - start < 7:\n",
+    "    end = startx + 4\n",
+    "    start = startx - 4\n",
+    "\n",
+    "\n",
+    "energy = dataset.get_spectral_dims(return_axis=True)[0].values\n",
+    "\n",
+    "if dataset.ndim == 1:  # single spectrum\n",
+    "    _, shifts = pyTEMlib.eels_tools.get_channel_zero(np.array(dataset), energy, width)\n",
+    "    shifts = np.array([shifts])\n",
+    "elif dataset.ndim == 2:  # line scan\n",
+    "    shifts = np.zeros(dataset.shape[:1])\n",
+    "    for x in range(dataset.shape[0]):\n",
+    "        _, shifts[x] = pyTEMlib.eels_tools.get_channel_zero(dataset[x, :], energy, width)\n",
+    "elif dataset.ndim == 3:  # spectral image\n",
+    "    shifts = np.zeros(dataset.shape[:2])\n",
+    "    for x in range(dataset.shape[0]):\n",
+    "        for y in range(dataset.shape[1]):\n",
+    "            _, shifts[x, y] = pyTEMlib.eels_tools.get_channel_zero(dataset[x, y, :], energy, width)\n",
+    "shifts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__call__',\n",
+       " '__class__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__func__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__self__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(infoWidget.info.shift_low_loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8aef8a91-2b7f-4834-a33b-c2c0b88d3b77_1715976165623\n"
+     ]
+    }
+   ],
+   "source": [
+    "import uuid\n",
+    "import time\n",
+    "\n",
+    "# Combine UUID and timestamp\n",
+    "unique_id = f\"{uuid.uuid4()}_{int(time.time() * 1000)}\"\n",
+    "print(unique_id)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "infoWidget.core_loss.update_cl_dataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'single_exposure_time': 0.01,\n",
+       "  'exposure_time': 0.21,\n",
+       "  'number_of_frames': 21,\n",
+       "  'collection_angle': 33.0,\n",
+       "  'convergence_angle': 30.0,\n",
+       "  'acceleration_voltage': 0.0,\n",
+       "  'flux_ppm': 115.68867492675781,\n",
+       "  'count_conversion': 1,\n",
+       "  'beam_current': 0},\n",
+       " 'zero_loss': {'shifted': array([1.0174319]),\n",
+       "  'startFitEnergy': -1.0,\n",
+       "  'endFitEnergy': 1.0,\n",
+       "  'fit_parameter': array([-1.51964411e-01,  5.09698919e+03,  1.33700815e+00,  2.38917861e-01,\n",
+       "          5.06085447e+03,  1.72056710e+00]),\n",
+       "  'original_low_loss': '1EELS Acquire (low-loss)_new'}}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['resolution_functions'].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'single_exposure_time': 3.0,\n",
+       "  'exposure_time': 63.0,\n",
+       "  'number_of_frames': 21,\n",
+       "  'collection_angle': 50.0,\n",
+       "  'convergence_angle': 30.0,\n",
+       "  'acceleration_voltage': 60000.0,\n",
+       "  'flux_ppm': 34706.602478027344,\n",
+       "  'count_conversion': 1,\n",
+       "  'beam_current': 0},\n",
+       " 'zero_loss': {'shifted': array([1.0174319])},\n",
+       " 'edges': {'fit_area': {'fit_start': 120.0, 'fit_end': 600.0},\n",
+       "  '0': {'z': 5,\n",
+       "   'symmetry': 'K1',\n",
+       "   'element': 'B',\n",
+       "   'onset': 188.0,\n",
+       "   'end_exclude': 238.0,\n",
+       "   'start_exclude': 185.0,\n",
+       "   'all_edges': {'K1': {'onset': 188.0}},\n",
+       "   'chemical_shift': 0.0,\n",
+       "   'areal_density': 1745395038619.995,\n",
+       "   'original_onset': 188.0,\n",
+       "   'data': array([5.66032845e-09, 5.61175423e-09, 5.56318001e-09, ...,\n",
+       "          1.68366704e-10, 1.68110794e-10, 1.67854883e-10]),\n",
+       "   'X_section_type': 'XRPA',\n",
+       "   'X_section_source': 'pyTEMlib'},\n",
+       "  '1': {'z': 7,\n",
+       "   'symmetry': 'K1',\n",
+       "   'element': 'N',\n",
+       "   'onset': 401.6,\n",
+       "   'end_exclude': 451.6,\n",
+       "   'start_exclude': 396.6,\n",
+       "   'all_edges': {'K1': {'onset': 401.6}},\n",
+       "   'chemical_shift': 0.0,\n",
+       "   'areal_density': 5330240008575.439,\n",
+       "   'original_onset': 401.6,\n",
+       "   'data': array([2.25376447e-08, 2.23462509e-08, 2.21548571e-08, ...,\n",
+       "          5.61435758e-10, 5.60622520e-10, 5.59809282e-10]),\n",
+       "   'X_section_type': 'XRPA',\n",
+       "   'X_section_source': 'pyTEMlib'},\n",
+       "  'model': {'background': energy_loss:  energy-loss (eV) of size (2048,),\n",
+       "   'background-poly_0': -7789207450.593016,\n",
+       "   'background-poly_1': -92.67688006132725,\n",
+       "   'background-poly_2': 0.07927340377406612,\n",
+       "   'background-A': 7789231272.866521,\n",
+       "   'background-r': -13.743081547476294,\n",
+       "   'spectrum': energy_loss:  energy-loss (eV) of size (2048,),\n",
+       "   'blurred': array([139757.55   , 139617.84   , 139350.38   , ...,    658.92535,\n",
+       "             628.73956,    612.56323], dtype=float32),\n",
+       "   'mask': array([0., 0., 0., ..., 0., 0., 1.]),\n",
+       "   'fit_parameter': array([ 7.78923127e+09, -1.37430815e+01, -7.78920745e+09, -9.26768801e+01,\n",
+       "           7.92734038e-02,  1.74529850e+12,  5.33029998e+12]),\n",
+       "   'fit_area_start': 120.0,\n",
+       "   'fit_area_end': 600.0}}}"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['Channel_000'].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.close('all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2048,) </td>\n",
+       "                        <td> (2048,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2048</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "sidpy.Dataset of type SPECTRUM with:\n",
+       " dask.array<mul, shape=(2048,), dtype=float32, chunksize=(2048,), chunktype=numpy.ndarray>\n",
+       " data contains: intensity (counts)\n",
+       " and Dimensions: \n",
+       "energy_loss:  energy-loss (eV) of size (2048,)\n",
+       " with metadata: ['experiment', 'zero_loss']"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.spectrum\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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0J1O3bl0TFhZmqlatajp06GDmzp3rbvPee++Z6667zsTFxZnQ0FCTkJBgbrvtNrNlyxaP5/vxxx+NJDN79uwy1w34imXMWU7qAAAV4Omnn9aYMWPOep6Z88GDDz6oJUuWuE/Ah+KefPJJvf/++/rpp58UHMxOAlQs5twAgJeeeOIJ7dmzR7Nnz/Z3Keelw4cP680339Tzzz9PsIFfEG4AwEtxcXGaMWMGhziXIj09XSNHjjxvrj2GwMNuKQAAYCuM3AAAAFsh3AAAAFsh3AAAAFsJ6HCzYsUK9ezZUwkJCbIsS59++qlXj3/66adlWVaxW2RkZPkUDAAAziqgw01WVpaaNWumN95445weP2LECO3bt8/jdsUVV+jWW2/1caUAAKCsAjrcdOvWTc8995xuueWWEtfn5ubqkUceUc2aNRUZGanWrVtr+fLl7vVRUVGqUaOG+/brr79q27ZtZzydPQAAKF+cXekM+vXrp59//lkzZ85UQkKCPvnkE91www3aunWrLrvssmLt3333XdWvX1/t2rXzQ7UAAEAK8JGbM/npp5/04Ycf6l//+pfatWunpKQkjRgxQtdee62mTp1arH1OTo5mzJjBqA0AAH7GyE0pNm7cKGNMsav+5uTkqGrVqsXaz5kzR0ePHlWfPn0qqkQAAFACwk0pnE6ngoKCtGHDBgUFBXmsi4qKKtb+3XffVY8ePVSjRo2KKhEAAJSAcFOKFi1aqKCgQPv37z/rHJr09HQtW7ZMc+fOraDqAABAaQI63Bw7dkw//vij+356ero2b96sKlWqqH79+rrrrrvUp08fvfTSS2rRooUOHDigpUuXqkmTJurevbv7cVOmTFF8fLy6devmj5cBAACKCOgLZy5fvlzXXXddseV9+/bVtGnTlJeXp+eee07vv/++9uzZo6pVqyo5OVljxoxRkyZNJBXuvqpbt6769Omjv//97xX9EgAAwGkCOtwAAAD74VBwAABgKwE358bpdGrv3r2Kjo6WZVn+LgcAAJSBMUZHjx5VQkKCHI4zj80EXLjZu3evateu7e8yAADAOdi9e7dq1ap1xjYBF26io6MlFXZOpUqV/FwNAAAoi8zMTNWuXdv9OX4mARduXLuiKlWqRLgBAOACU5YpJUwoBgAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4CXAn8grkdBp/lwEAgM8QbgLY0RN5ajz6K/3prW/8XQoAAD5DuAlg3/x4UPlOo//8csTfpQAA4DOEmwBmWf6uAAAA3yPcAAAAWyHcAAAAWyHcBDD2SgEA7IhwAwAAbIVwAwAAbIVwE8AsDpcCANgQ4QYAANgK4QYAANgK4SaAsVMKAGBHhBsAAGArhBsAAGArhJsAxsFSAAA7ItwAAABbIdwAAABbIdwEMHZLAQDsiHADAABshXADAABshXATwKwip/EzxvixEgAAfIdwA0kS2QYAYBeEG0iSyDYAALsg3ASyIkdLsVsKAGAXhBtIYuQGAGAfhBtIYs4NAMA+CDcBrOg5/AxjNwAAmyDcQBIjNwAA+yDcQBLhBgBgH4SbAGYVubgUu6UAAHZBuIEkRm4AAPZBuIEkDgUHANgH4SaAeRwtxdANAMAmCDeQxMgNAMA+CDeQxJwbAIB9EG4CmOV5Fj8AAGyBcANJHAoOALAPwg0ksVsKAGAfhJsAZqnoSfwAALAHwg0kcSg4AMA+CDeQxMgNAMA+CDeQxJwbAIB9EG4CWNEjpDhaCgBgF4QbFCLbAABsgnATwIruiiLbAADsgnATwIoGGubcAADsgnATwIoe/s2cGwCAXRBuIImRGwCAffg13IwdO1ZXXXWVoqOjFRsbq5tvvlk7duw46+PS0tLUsmVLhYeHKzExURMnTqyAau3HlPIzAAAXMr+Gm7S0NA0ePFhr1qzRokWLlJ+fry5duigrK6vUx6Snp6t79+5q166dNm3apMcff1xDhw7V7NmzK7Bymyg6oZihGwCATQT788m//PJLj/tTp05VbGysNmzYoPbt25f4mIkTJ6pOnToaP368JOnyyy/X+vXr9eKLL6p3797lXbJtkW0AAHZxXs25OXLkiCSpSpUqpbZZvXq1unTp4rGsa9euWr9+vfLy8oq1z8nJUWZmpscNhZhEDACwo/Mm3BhjlJqaqmuvvVaNGzcutV1GRobi4uI8lsXFxSk/P18HDhwo1n7s2LGKiYlx32rXru3z2i9UHue5IecAAGzivAk3Q4YM0ZYtW/Thhx+eta1lWR73XfNFTl8uSSNHjtSRI0fct927d/umYJthFAcAYBd+nXPj8uCDD2ru3LlasWKFatWqdca2NWrUUEZGhsey/fv3Kzg4WFWrVi3WPiwsTGFhYT6t1y4YuQEA2JFfR26MMRoyZIjmzJmjpUuXql69emd9THJyshYtWuSxbOHChWrVqpVCQkLKq1RbKppnnKQbAIBN+DXcDB48WNOnT9cHH3yg6OhoZWRkKCMjQ9nZ2e42I0eOVJ8+fdz3Bw0apJ07dyo1NVXbt2/XlClTNHnyZI0YMcIfLwEAAJxn/BpuJkyYoCNHjqhjx46Kj49332bNmuVus2/fPu3atct9v169epo/f76WL1+u5s2b69lnn9Vrr73GYeDnwPPyCwAA2INf59yU5cRx06ZNK7asQ4cO2rhxYzlUFFgINAAAOzpvjpaCfzHlBgBgF4SbAOYZaEg3AAB7INwENAINAMB+CDeQxG4pAIB9EG4CGIEGAGBHhJsAZkr5GQCACxnhBpIYxQEA2AfhJoARaAAAdkS4CWBFrwTOVcEBAHZBuIEkRnEAAPZBuAlgBBoAgB0RbgKYx9FSBB0AgE0QbgAAgK0QbgJY0auyM6EYAGAXhBtIYrcUAMA+CDcAAMBWCDcBjNEaAIAdEW4CmMdJ/Ag6AACbINwAAABbIdwEsKKjNRwtBQCwC8JNAGNXFADAjgg3kETQAQDYB+EmgJlSfgYA4EJGuAlghuEaAIANEW4giaADALAPwk0AY7cUAMCOCDeBjEQDALAhwg0kcbQUAMA+CDcBjBP3AQDsiHATwDxHawg6AAB7INxAErulAAD2QbgJYOQZAIAdEW4CmOeFMwEAsAfCDQAAsBXCTQArerQUc24AAHZBuAlgHrulSDcAAJsg3AAAAFsh3AQwri0FALAjwk0gM8y5AQDYD+EmgJFnAAB2RLiBJK4zBQCwD8JNAGNXFADAjgg3AcxwimIAgA0RbiCJbAMAsA/CTQAj0AAA7IhwE8A8z1DsvzoAAPAlwg0kcbQUAMA+CDcBjDgDALAjwk0AM5yhGABgQ4QbAABgK4QbSGIXFQDAPoK9fUBOTo7Wrl2rn3/+WcePH1f16tXVokUL1atXrzzqQznyPFqKeAMAsIcyh5tVq1bp9ddf16effqrc3FxdfPHFioiI0O+//66cnBwlJibqL3/5iwYNGqTo6OjyrBkAAKBUZdot1atXL/2///f/VLNmTX311Vc6evSoDh48qF9++UXHjx/XDz/8oCeeeEJLlixR/fr1tWjRovKuGz5Q9PBvxm0AAHZRppGbLl266F//+pdCQ0NLXJ+YmKjExET17dtX3333nfbu3evTIlE+PPZEkW4AADZRpnAzePDgMm+wUaNGatSo0TkXBAAA8Ed4fbTU7t279csvv7jvr127VsOGDdOkSZN8WhjKn+fADUM3AAB78Drc3HnnnVq2bJkkKSMjQ9dff73Wrl2rxx9/XM8884zPC0T54QApAIAdeR1uvv32W1199dWSpI8++kiNGzfWqlWr9MEHH2jatGm+rg8VhKADALALr8NNXl6ewsLCJEmLFy/WTTfdJElq2LCh9u3b59vqUK48jpYi3AAAbMLrcNOoUSNNnDhRK1eu1KJFi3TDDTdIkvbu3auqVav6vECUHwINAMCOvA4348aN09tvv62OHTvqjjvuULNmzSRJc+fOde+uKqsVK1aoZ8+eSkhIkGVZ+vTTT8/Yfvny5bIsq9jtv//9r7cvA6ch5wAA7MLryy907NhRBw4cUGZmpipXruxe/pe//EWRkZFebSsrK0vNmjVTv3791Lt37zI/bseOHapUqZL7fvXq1b16XgAAYF9eh5tOnTppzpw5HsFGkqpUqaKbb75ZS5cuLfO2unXrpm7dunlbgmJjY3XxxReXqW1OTo5ycnLc9zMzM71+Prsqej0pri0FALALr3dLLV++XLm5ucWWnzhxQitXrvRJUWfTokULxcfHKyUlxX1YemnGjh2rmJgY96127doVUuOFhmgDALCLMo/cbNmyxf3ztm3blJGR4b5fUFCgL7/8UjVr1vRtdaeJj4/XpEmT1LJlS+Xk5Oif//ynUlJStHz5crVv377Ex4wcOVKpqanu+5mZmQSckxisAQDYUZnDTfPmzd0TeDt16lRsfUREhF5//XWfFne6Bg0aqEGDBu77ycnJ2r17t1588cVSw01YWJj70HV48jhDMUEHAGATZQ436enpMsYoMTFRa9eu9ZjEGxoaqtjYWAUFBZVLkWdyzTXXaPr06RX+vPZDugEA2EOZw03dunUlSU6ns9yKORebNm1SfHy8v8u4IDFaAwCwI6+PlpKk77//XsuXL9f+/fuLhZ2nnnqqzNs5duyYfvzxR/f99PR0bd68WVWqVFGdOnU0cuRI7dmzR++//74kafz48brkkkvUqFEj5ebmavr06Zo9e7Zmz559Li8j4HGGYgCAHXkdbt555x3df//9qlatmmrUqCHLstzrLMvyKtysX79e1113nfu+a+Jv3759NW3aNO3bt0+7du1yr8/NzdWIESO0Z88eRUREqFGjRpo3b566d+/u7csAAAA2ZRkvT3BSt25dPfDAA3r00UfLq6ZylZmZqZiYGB05csTjRICB6JVF3+vVJT9Ikt6660p1b8LuPQDA+cmbz2+vz3Nz6NAh3XrrredcHM4fHC0FALAjr8PNrbfeqoULF5ZHLQAAAH+Y13NuLr30Uj355JNas2aNmjRpopCQEI/1Q4cO9VlxKGdFL7/AoeAAAJvwOtxMmjRJUVFRSktLU1pamsc6y7IINxcQdksBAOzI63CTnp5eHnUAAAD4hNdzbmAfRUdrGLgBANiF1yM3/fv3P+P6KVOmnHMxqFjMswEA2JHX4ebQoUMe9/Py8vTtt9/q8OHDJV5QExcGL093BADAecvrcPPJJ58UW+Z0OvXAAw8oMTHRJ0WhYpBnAAB25JM5Nw6HQ8OHD9crr7zii82hgpBtAAB25LMJxT/99JPy8/N9tTlUMEZxAAB24fVuKdfFLV2MMdq3b5/mzZunvn37+qwwlD/Po6VINwAAe/A63GzatMnjvsPhUPXq1fXSSy+d9UgqnF8INAAAO/I63Cxbtqw86oCfsVsKAGAXXocbl99++007duyQZVmqX7++qlev7su6UBEINAAAG/J6QnFWVpb69++v+Ph4tW/fXu3atVNCQoIGDBig48ePl0eNKCdcWwoAYEdeh5vU1FSlpaXp888/1+HDh3X48GF99tlnSktL08MPP1weNaICkG0AAHbh9W6p2bNn6+OPP1bHjh3dy7p3766IiAjddtttmjBhgi/rQznirMQAADvyeuTm+PHjiouLK7Y8NjaW3VIXGI9DwQk6AACb8DrcJCcna/To0Tpx4oR7WXZ2tsaMGaPk5GSfFofyRZwBANiR17ulXn31Vd1www2qVauWmjVrJsuytHnzZoWHh+urr74qjxpRAQg6AAC78DrcNG7cWD/88IOmT5+u//73vzLG6M9//rPuuusuRURElEeNKCcee6JINwAAmzin89xERERo4MCBvq4FFYwzFAMA7MjrOTdjx47VlClTii2fMmWKxo0b55OiUPEIOgAAu/A63Lz99ttq2LBhseWNGjXSxIkTfVIUKobn0VL+qwMAAF/yOtxkZGQoPj6+2PLq1atr3759PikKAADgXHkdbmrXrq1vvvmm2PJvvvlGCQkJPikKFY+BGwCAXXg9ofi+++7TsGHDlJeXp06dOkmSlixZokceeYTLL1xgOHEfAMCOvA43jzzyiH7//Xc98MADys3NlSSFh4fr0Ucf1ciRI31eIMoPF84EANiR1+HGsiyNGzdOTz75pLZv366IiAhddtllCgsLK4/6UEE4WgoAYBfndJ4bSYqKitJVV13ly1pQwRitAQDYUZkmFA8aNEi7d+8u0wZnzZqlGTNm/KGiUDGKjtYQdAAAdlGmkZvq1aurcePGatOmjW666Sa1atVKCQkJCg8P16FDh7Rt2zZ9/fXXmjlzpmrWrKlJkyaVd93wMbINAMAuyhRunn32WT344IOaPHmyJk6cqG+//dZjfXR0tDp37qx3331XXbp0KZdC4XuM1gAA7KjMc25iY2M1cuRIjRw5UocPH9bOnTuVnZ2tatWqKSkpSZZllWedKAce2YakAwCwiXOaUHzxxRfr4osv9nEpAAAAf5zXZyiGfXhcW8p/ZQAA4FOEm4DG0VIAAPsh3AAAAFsh3AQwj91SDN0AAGzC63Dz9NNPa+fOneVRCyoYeQYAYEdeh5vPP/9cSUlJSklJ0QcffKATJ06UR12oYOQcAIBdeB1uNmzYoI0bN6pp06YaPny44uPjdf/992vdunXlUR/KEZdfAADY0TnNuWnatKleeeUV7dmzR1OmTNGePXvUtm1bNWnSRK+++qqOHDni6zpRDgg0AAA7+kMTip1Op3Jzc5WTkyNjjKpUqaIJEyaodu3amjVrlq9qRAUg5wAA7OKcws2GDRs0ZMgQxcfHa/jw4WrRooW2b9+utLQ0/fe//9Xo0aM1dOhQX9cKHysaaDhaCgBgF16Hm6ZNm+qaa65Renq6Jk+erN27d+uFF17QpZde6m7Tp08f/fbbbz4tFL5HngEA2JHX15a69dZb1b9/f9WsWbPUNtWrV5fT6fxDhQEAAJwLr0dujDGqXLlyseXZ2dl65plnfFIUKoZhpg0AwIa8DjdjxozRsWPHii0/fvy4xowZ45OiUEE8zlDsvzIAAPClcxq5sSyr2PL//Oc/qlKlik+KQsVjFAcAYBdlnnNTuXJlWZYly7JUv359j4BTUFCgY8eOadCgQeVSJMoHcQYAYEdlDjfjx4+XMUb9+/fXmDFjFBMT414XGhqqSy65RMnJyeVSJMpH0cO/2S0FALCLMoebvn37SpLq1aunNm3aKCQkpNyKQsUj2wAA7KJM4SYzM1OVKlWSJLVo0ULZ2dnKzs4usa2rHc5/BBoAgB2VKdxUrlxZ+/btU2xsrC6++OISJxS7JhoXFBT4vEiUD8PRUgAAGypTuFm6dKn7SKhly5aVa0EAAAB/RJnCTYcOHUr8GRc2j2tLsZMKAGATXp/n5ssvv9TXX3/tvv/mm2+qefPmuvPOO3Xo0CGfFofyxdFSAAA78jrc/O1vf1NmZqYkaevWrUpNTVX37t31v//9T6mpqT4vEAAAwBteh5v09HRdccUVkqTZs2erZ8+eev755/XWW29pwYIFXm1rxYoV6tmzpxISEmRZlj799NOzPiYtLU0tW7ZUeHi4EhMTNXHiRG9fAk5isAYAYEdeh5vQ0FAdP35ckrR48WJ16dJFklSlShX3iE5ZZWVlqVmzZnrjjTfK1D49PV3du3dXu3bttGnTJj3++OMaOnSoZs+e7d2LQCGPo6WIOgAAeyjzSfxcrr32WqWmpqpt27Zau3atZs2aJUn6/vvvVatWLa+21a1bN3Xr1q3M7SdOnKg6depo/PjxkqTLL79c69ev14svvqjevXuX+JicnBzl5OS473sbwAAAwIXF65GbN954Q8HBwfr44481YcIE1axZU5K0YMEC3XDDDT4vsKjVq1e7R4pcunbtqvXr1ysvL6/Ex4wdO1YxMTHuW+3atcu1xgtJ0SOkGLgBANiF1yM3derU0RdffFFs+SuvvOKTgs4kIyNDcXFxHsvi4uKUn5+vAwcOKD4+vthjRo4c6THROTMzk4BzEoEGAGBHXocbSXI6nfrxxx+1f/9+OZ1Oj3Xt27f3SWGlOf3syK65IiWdNVmSwsLCFBYWVq412QE5BwBgF16HmzVr1ujOO+/Uzp07i01CLe/LL9SoUUMZGRkey/bv36/g4GBVrVq13J7Xrrj8AgDAjrwON4MGDVKrVq00b948xcfHlzpiUh6Sk5P1+eefeyxbuHChWrVqxVXKzwFnJQYA2JHX4eaHH37Qxx9/rEsvvfQPP/mxY8f0448/uu+np6dr8+bNqlKliurUqaORI0dqz549ev/99yUVBqs33nhDqampGjhwoFavXq3Jkyfrww8//MO1BDqCDgDALrw+Wqp169YegeSPWL9+vVq0aKEWLVpIklJTU9WiRQs99dRTkqR9+/Zp165d7vb16tXT/PnztXz5cjVv3lzPPvusXnvttVIPA8eZsSsKAGBHXo/cPPjgg3r44YeVkZGhJk2aFNsd1LRp0zJvq2PHjmc8edy0adOKLevQoYM2btxY5udA6TwunEnQAQDYhNfhxjVK0r9/f/cyy7JkjCn3CcXwLY8Jxf4rAwAAn/I63KSnp5dHHQAAAD7hdbipW7duedQBv+BYcACA/Xg9oViS/vnPf6pt27ZKSEjQzp07JUnjx4/XZ5995tPiUL7YLQUAsCOvw82ECROUmpqq7t276/Dhw+45NhdffLH7gpYAAAD+4nW4ef311/XOO+9o1KhRCgoKci9v1aqVtm7d6tPiUL44WgoAYEdeh5v09HT3eWmKCgsLU1ZWlk+KQsU402H4AABcqLwON/Xq1dPmzZuLLV+wYIGuuOIKX9QEP+AMxQAAu/D6aKm//e1vGjx4sE6cOCFjjNauXasPP/xQY8eO1bvvvlseNaKcsFsKAGBHXoebfv36KT8/X4888oiOHz+uO++8UzVr1tSrr76qP//5z+VRI8oJgQYAYEdehxtJGjhwoAYOHKgDBw7I6XQqNjbW13WhgpFzAAB24fWcm06dOunw4cOSpGrVqrmDTWZmpjp16uTT4lC+2C0FALAjr8PN8uXLlZubW2z5iRMntHLlSp8UhYrB0VIAADsq826pLVu2uH/etm2bMjIy3PcLCgr05ZdfqmbNmr6tDhWGo6UAAHZR5nDTvHlzWZYly7JK3P0UERGh119/3afFAQAAeKvM4SY9PV3GGCUmJmrt2rWqXr26e11oaKhiY2M9zliM85/HXikGbgAANlHmcOO6GrjT6Sy3YuA/ZBsAgF2c06Hg33//vZYvX679+/cXCztPPfWUTwpD+WOeDQDAjrwON++8847uv/9+VatWTTVq1JBlWe51lmURbi4gRXdLceQUAMAuvA43zz33nP7+97/r0UcfLY964CdkGwCAXXh9nptDhw7p1ltvLY9aUMEINAAAO/I63Nx6661auHBhedSCClZ0zg05BwBgF17vlrr00kv15JNPas2aNWrSpIlCQkI81g8dOtRnxQEAAHjL63AzadIkRUVFKS0tTWlpaR7rLMsi3FxAPCcU+68OAAB8yetwk56eXh51wA88z+FHugEA2IPXc24AAADOZ2UauUlNTdWzzz6ryMhIpaamnrHtyy+/7JPCUAHYLQUAsKEyhZtNmzYpLy/P/XNpip7QD+c/dkUBAOyoTOFm2bJlJf4MAABwvmHOTQDj8gsAADsi3AQw4gwAwI4IN5BE0AEA2AfhJoAV3RXFXikAgF0QbgIYeQYAYEeEG0jisHAAgH0QbgIY15YCANgR4SaAFc0zTsINAMAmCDc4iXQDALAHwk0gK7Ivyun0Yx0AAPgQ4SaAGY+fGbkBANgD4QaSmHMDALAPwk0A42gpAIAdEW4CWNFdUVw4EwBgF4QbSJKchBsAgE0QbgKYx24p/5UBAIBPEW4CWNFww4RiAIBdEG4giTk3AAD7INwEMI/z3JBtAAA2QbgJYEVHaziJHwDALgg3kMTlFwAA9kG4gSRGbgAA9kG4CWBFz23D0VIAALsg3AQwLr8AALAjwk0AKzpyw6HgAAC7INwEMFPKzwAAXMgIN4HM4wzFxBsAgD0QbgKY524pPxYCAIAPEW4CWNE8w8gNAMAuCDcBjEADALAjwk0AM8y5AQDYkN/DzVtvvaV69eopPDxcLVu21MqVK0ttu3z5clmWVez23//+twIrtg+PcMPlFwAANuHXcDNr1iwNGzZMo0aN0qZNm9SuXTt169ZNu3btOuPjduzYoX379rlvl112WQVVbC9cOBMAYEd+DTcvv/yyBgwYoPvuu0+XX365xo8fr9q1a2vChAlnfFxsbKxq1KjhvgUFBVVQxfbiOaHYb2UAAOBTfgs3ubm52rBhg7p06eKxvEuXLlq1atUZH9uiRQvFx8crJSVFy5YtO2PbnJwcZWZmetxQyGOeDeEGAGATfgs3Bw4cUEFBgeLi4jyWx8XFKSMjo8THxMfHa9KkSZo9e7bmzJmjBg0aKCUlRStWrCj1ecaOHauYmBj3rXbt2j59HRcyJhQDAOwo2N8FWJblcd8YU2yZS4MGDdSgQQP3/eTkZO3evVsvvvii2rdvX+JjRo4cqdTUVPf9zMxMAs5JXH4BAGBHfhu5qVatmoKCgoqN0uzfv7/YaM6ZXHPNNfrhhx9KXR8WFqZKlSp53FCo6IRiRm4AAHbht3ATGhqqli1batGiRR7LFy1apDZt2pR5O5s2bVJ8fLyvywsIHlNuyDYAAJvw626p1NRU3XPPPWrVqpWSk5M1adIk7dq1S4MGDZJUuEtpz549ev/99yVJ48eP1yWXXKJGjRopNzdX06dP1+zZszV79mx/vowLlsduKdINAMAm/Bpubr/9dh08eFDPPPOM9u3bp8aNG2v+/PmqW7euJGnfvn0e57zJzc3ViBEjtGfPHkVERKhRo0aaN2+eunfv7q+XcEHzuHCmH+sAAMCXLBNgX9kzMzMVExOjI0eOBPz8m2ZjFupIdp4kqXHNSvriwXZ+rggAgJJ58/nt98svwH88Rm4CKuICAOyMcBPIPM5z478yAADwJcJNAGNCMQDAjgg3AYzdUgAAOyLcBDDPS0uRbgAA9kC4CWBOjzMU+7EQAAB8iHATwIrmGS6/AACwC8JNIOPKmQAAGyLcBDAnF84EANgQ4SaAMXADALAjwk0AY+QGAGBHhJsA5nEoONkGAGAThJsAdfoZiQk3AAC7INwEqNPDDJdfAADYBeEmQJ0eZTiJHwDALgg3AarYbimOlwIA2AThJkCdPlLDyA0AwC4INwHq9JEaptwAAOyCcBOgmFAMALArwk2AKhZu/FMGAAA+R7gJUKefkZgzFAMA7IJwE6BODzNkGwCAXRBuApTTedp90g0AwCYINwGKkRsAgF0RbgJUQbFwQ7oBANgD4SZAFZ9Q7KdCAADwMcJNgDp9zg2XXwAA2AXhJkAxcgMAsCvCTYAqOD3NEG4AADZBuAlQp88f5lBwAIBdEG4CVLGjpfxUBwAAvka4CVBcfgEAYFeEmwDldHISPwCAPRFuApQr21jWqWWcyA8AYAeEmwDlOloq2HEq3ZBtAAB2QLgJUK45NkFFwg3zbgAAdkC4CVCuIBPsOPUWINoAAOyAcBOgXHNuio7cFDuxHwAAFyDCTYByBZnQ4FNvAXZLAQDsgHAToFxHRoUGnXoL5DNyAwCwAcJNgCpp5KaggHADALjwEW4ClGuQJthhuc91w8gNAMAOCDcByjW/xmFZ7nPdMKEYAGAHhJsAlVfglCQFB1nuI6bynU5/lgQAgE8QbgJU/sn5NcFBDve5bvKZcwMAsAHCTYByjdKEOIqO3BBuAAAXPsJNgMpzj9ww5wYAYC+EmwDlGrkJdjiYcwMAsBXCTYDKLzJyE3LyRH6M3AAA7IBwE6Bc82s8R24INwCACx/hJkDlnzwUPIQ5NwAAmyHcBKi8ooeCBxWGm7x85twAAC58hJsAVfRQ8LDgIElSDuEGAGADhJsAVfRQ8PCQwrdBTn6BP0sCAMAnCDcByjVKExYc5B65OZHHyA0A4MJHuAlQJ/IKR2kiQoMUFszIDQDAPgg3ASo7tzDIhIcEKezkbilGbgAAdkC4CVDZrpGbkCCFu3dLMXIDALjwEW4ClHu3VIhDF4UVhpusXMINAODCR7gJUMdzT825iYkIkSRlZuf5syQAAHzC7+HmrbfeUr169RQeHq6WLVtq5cqVZ2yflpamli1bKjw8XImJiZo4cWIFVWovB7NyJUlVIsPc4eYI4QYAYAN+DTezZs3SsGHDNGrUKG3atEnt2rVTt27dtGvXrhLbp6enq3v37mrXrp02bdqkxx9/XEOHDtXs2bMruPIL34GjOZKkqlGhqhIZJknaf/SEP0sCAMAnLGOM3y4o1Lp1a1155ZWaMGGCe9nll1+um2++WWPHji3W/tFHH9XcuXO1fft297JBgwbpP//5j1avXl2m58zMzFRMTIyOHDmiSpUq/fEXcVJuvlM/7D+q/AKj4CBLuflOWZYlhyUZI1mW5LAsOSxLwUGWCpxGBU4jpzn1rzGF551xOAof4+L62ch43JckpzGyLEtBluVu4zSSMYWtLRVOHj52Il/HcvJ19ES+Dmbl6rUlP0iS1o5K0S+HsnXLW6sUGuzQiC719XtWnrb8cliVwkMUf3G4Kl8UqsoXhSgyLFhHT+Rr75FsHcrKVVZugYIdloIc1sl/HQoJsmRJiokIUVhI4WHmkWHBcliFF+kMCXYoNMiSZZ3qA9fjQ4IcsiwpJMghh2XJsgqvXn7ypSkyLFg5eQWFr09GwQ6HHJbc/ezqX8sq7O+8AiPrZB/l5Dvd19CSJIfD0om8AkWEBCkn3+k+HD4336ngoMKLibpaF5z83QQ7LLm63tW/p34fnr8by5JCgwp/l0eO5ykiNMjj/RLksJTvNApxOGRkVNr/QiMp6ORrysl3ul+vpcJ+MjJyOgtrDHYUvv6i7xVJcjolh6OwL8NDgnQ8N9/dZ66TOTpO9rvTGDmswt+p6z15oQkJcig336mQYEtOp9x95HpvncgrUHBQYV85zanfW4HTuPugpOusBTkK37PBQZbyC071sOt94nqfWrLcP6vIOlcNRZ8z3+lUREiwHI7CoxUtnXwfOwrrMebUfUuW8p1OWbLcfyNcvyeXwm0aOU57flddxZaV0O50BU6jfKdTwQ6Hux/O5NTfNCks2OFxQV5T5P3l6k9Xe1ff62QfFP2b6bAk58n+yj15ji7X/4PT67Gswtfqev+6tulaXvQ1F13m+jmvwKm8Aqf771DR/vV47Mn+dNV5PDff/f81NNjh+Tdcnu8nY059Lrj6zHGGX8axnHyFBjsUFuxwv6cLnIV/A11Huxb9HCn6mWGM3M9+6rQfTvdnk6v/XJfhcT/WFP/Mcb33LevUMtfv0fV7dr2KkCCHrkjw3Wes5N3nd7BPn9kLubm52rBhgx577DGP5V26dNGqVatKfMzq1avVpUsXj2Vdu3bV5MmTlZeXp5CQkGKPycnJUU5Ojvt+ZmamD6ov7vesXN342tflsu3y0rRWjGKjC8NL3aoXaefB43p+/n/9XRYA4AIXGx2mtaM6++35/RZuDhw4oIKCAsXFxXksj4uLU0ZGRomPycjIKLF9fn6+Dhw4oPj4+GKPGTt2rMaMGeO7wksRGuxQbHSYQoIcyitwKizEUZhwVfgNwxhz8puHKfLt+NQ3kyDHyW/neU53Wi76zeDUt8OS032B07jbWdapbyfGFE4ajgoLVnR44S0qLFhVo8LUJ7mupMKE/dFfkzXj37u062CWIkKD1LhmjLJzC3QwK1eHj+fqUFaejuXk66LQINWqfJGqRoXqotAg9+hLvuvfAqdyC4yOnshTfoHRifwCZeUUyGmM8gqcyi8wyi1wymkKRy0cjsJvtHnOwnWnj2jJknvE5diJfIWFBBV+Wz35LcGo8FuP0xR+OzfGuEdaQoJO7XUNC3EU9tHJx7i+FbvW5eY7T46cOZTndMpZ5JQ/QQ6r8JtSgfHof9e3Pdfv6tTPhd8yc/OdOpFfODpU9Nule5Qg2PJ4j5zOaYz7W7jrW7B7ndMot6BwXdDJEZu8As/zFLk26bAKR4mCHZZy8p0KD3bIsiwdy8lXpYhg97fTwveeJ4dV8jf+85WRUX6Bcf8/dPW769tlXoFTESFByisyaljY/4XfuPNO9rdrlKKogpPr8pxGoSdHGT1HWE+9J0//5izJ3c9FRwWDgxzKziuQ01k4qiadGvlw/V7d72/X6KEpHKkLsizlFhSOSBZ9+xQ45R5BLd4/pfTbGUboHFZhnfkn318lDGq55eQXKDTY4R6Fzc0vHPEp2leu/6MFTinIcaqv851OBVmW+/904ajLqVFS12haVNipjy3Xtoq+DteIddHRn9NHWk8f0XCNvrpGMkKCHO6/a64/I54jMUX7rvBx4SFBys4rUEiQpQKn599tqfgomWt0yfXe89ymZ9ugkyPNBcYoPDiocKTaYbn/nhb9ux9knTY6pVN/X3LyC9y1Oo1Rbr5TuSf/TxSOUJ36zDn12MKfHVbhHgnXCJvrd+r6PCv691YqnPLgT34LNy6n/1E3xpT6AV5a+5KWu4wcOVKpqanu+5mZmapdu/a5lluqKpGhfk2pf1RcpXClXl/f32UAAPCH+S3cVKtWTUFBQcVGafbv319sdMalRo0aJbYPDg5W1apVS3xMWFiYwsLCfFM0AAA47/ntaKnQ0FC1bNlSixYt8li+aNEitWnTpsTHJCcnF2u/cOFCtWrVqsT5NgAAIPD49VDw1NRUvfvuu5oyZYq2b9+u4cOHa9euXRo0aJCkwl1Kffr0cbcfNGiQdu7cqdTUVG3fvl1TpkzR5MmTNWLECH+9BAAAcJ7x65yb22+/XQcPHtQzzzyjffv2qXHjxpo/f77q1i2c6Lpv3z6Pc97Uq1dP8+fP1/Dhw/Xmm28qISFBr732mnr37u2vlwAAAM4zfj3PjT+U13luAABA+fHm89vvl18AAADwJcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFb9efsEfXCdkzszM9HMlAACgrFyf22W5sELAhZujR49KkmrXru3nSgAAgLeOHj2qmJiYM7YJuGtLOZ1O7d27V9HR0bIsy9/l+FVmZqZq166t3bt3c52tckD/li/6t3zRv+WL/vWeMUZHjx5VQkKCHI4zz6oJuJEbh8OhWrVq+buM80qlSpX4z1WO6N/yRf+WL/q3fNG/3jnbiI0LE4oBAICtEG4AAICtEG4CWFhYmEaPHq2wsDB/l2JL9G/5on/LF/1bvujf8hVwE4oBAIC9MXIDAABshXADAABshXADAABshXADAABshXAToN566y3Vq1dP4eHhatmypVauXOnvki4IK1asUM+ePZWQkCDLsvTpp596rDfG6Omnn1ZCQoIiIiLUsWNHfffddx5tcnJy9OCDD6patWqKjIzUTTfdpF9++aUCX8X5a+zYsbrqqqsUHR2t2NhY3XzzzdqxY4dHG/r43E2YMEFNmzZ1nzguOTlZCxYscK+nb31r7NixsixLw4YNcy+jjysG4SYAzZo1S8OGDdOoUaO0adMmtWvXTt26ddOuXbv8Xdp5LysrS82aNdMbb7xR4vp//OMfevnll/XGG29o3bp1qlGjhq6//nr3Nc0kadiwYfrkk080c+ZMff311zp27Jh69OihgoKCinoZ5620tDQNHjxYa9as0aJFi5Sfn68uXbooKyvL3YY+Pne1atXSCy+8oPXr12v9+vXq1KmTevXq5f5wpW99Z926dZo0aZKaNm3qsZw+riAGAefqq682gwYN8ljWsGFD89hjj/mpoguTJPPJJ5+47zudTlOjRg3zwgsvuJedOHHCxMTEmIkTJxpjjDl8+LAJCQkxM2fOdLfZs2ePcTgc5ssvv6yw2i8U+/fvN5JMWlqaMYY+Lg+VK1c27777Ln3rQ0ePHjWXXXaZWbRokenQoYN56KGHjDG8fysSIzcBJjc3Vxs2bFCXLl08lnfp0kWrVq3yU1X2kJ6eroyMDI++DQsLU4cOHdx9u2HDBuXl5Xm0SUhIUOPGjen/Ehw5ckSSVKVKFUn0sS8VFBRo5syZysrKUnJyMn3rQ4MHD9aNN96ozp07eyynjytOwF04M9AdOHBABQUFiouL81geFxenjIwMP1VlD67+K6lvd+7c6W4TGhqqypUrF2tD/3syxig1NVXXXnutGjduLIk+9oWtW7cqOTlZJ06cUFRUlD755BNdccUV7g9O+vaPmTlzpjZu3Kh169YVW8f7t+IQbgKUZVke940xxZbh3JxL39L/xQ0ZMkRbtmzR119/XWwdfXzuGjRooM2bN+vw4cOaPXu2+vbtq7S0NPd6+vbc7d69Ww899JAWLlyo8PDwUtvRx+WP3VIBplq1agoKCir2DWD//v3Fvk3AOzVq1JCkM/ZtjRo1lJubq0OHDpXaBtKDDz6ouXPnatmyZapVq5Z7OX38x4WGhurSSy9Vq1atNHbsWDVr1kyvvvoqfesDGzZs0P79+9WyZUsFBwcrODhYaWlpeu211xQcHOzuI/q4/BFuAkxoaKhatmypRYsWeSxftGiR2rRp46eq7KFevXqqUaOGR9/m5uYqLS3N3bctW7ZUSEiIR5t9+/bp22+/pf9V+O10yJAhmjNnjpYuXap69ep5rKePfc8Yo5ycHPrWB1JSUrR161Zt3rzZfWvVqpXuuusubd68WYmJifRxRfHPPGb408yZM01ISIiZPHmy2bZtmxk2bJiJjIw0P//8s79LO+8dPXrUbNq0yWzatMlIMi+//LLZtGmT2blzpzHGmBdeeMHExMSYOXPmmK1bt5o77rjDxMfHm8zMTPc2Bg0aZGrVqmUWL15sNm7caDp16mSaNWtm8vPz/fWyzhv333+/iYmJMcuXLzf79u1z344fP+5uQx+fu5EjR5oVK1aY9PR0s2XLFvP4448bh8NhFi5caIyhb8tD0aOljKGPKwrhJkC9+eabpm7duiY0NNRceeWV7kNtcWbLli0zkord+vbta4wpPNRz9OjRpkaNGiYsLMy0b9/ebN261WMb2dnZZsiQIaZKlSomIiLC9OjRw+zatcsPr+b8U1LfSjJTp051t6GPz13//v3d/++rV69uUlJS3MHGGPq2PJwebujjimEZY4x/xowAAAB8jzk3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AADAVgg3AGyhY8eOGjZsmN+e/+DBg4qNjdXPP//s0+1u3bpVtWrVUlZWlk+3C9gZ4QYAfGDs2LHq2bOnLrnkkjK179mzpzp37lziutWrV8uyLG3cuFFNmjTR1VdfrVdeecWH1QL2RrgBUGHy8vL8XUK5yM7O1uTJk3XfffeV+TEDBgzQ0qVLtXPnzmLrpkyZoubNm+vKK6+UJPXr108TJkxQQUGBz2oG7IxwAwQgY4z+8Y9/KDExUREREWrWrJk+/vhj9/rly5fLsiwtWbJErVq10kUXXaQ2bdpox44dHtv5/PPP1bJlS4WHhysxMVFjxoxRfn6+e71lWZo4caJ69eqlyMhIPffcc5Kk5557TrGxsYqOjtZ9992nxx57TM2bN5ckrVixQiEhIcrIyPB4rocffljt27cv82s8dOiQ+vTpo8qVK+uiiy5St27d9MMPP7jX79y5Uz179lTlypUVGRmpRo0aaf78+e7H3nXXXapevboiIiJ02WWXaerUqaU+14IFCxQcHKzk5GSP5du2bVP37t0VFRWluLg43XPPPTpw4IAkqUePHoqNjdW0adM8HnP8+HHNmjVLAwYMcC/r2rWrDh48qLS0tDK/fiCQEW6AAPTEE09o6tSpmjBhgr777jsNHz5cd999d7EPz1GjRumll17S+vXrFRwcrP79+7vXffXVV7r77rs1dOhQbdu2TW+//bamTZumv//97x7bGD16tHr16qWtW7eqf//+mjFjhv7+979r3Lhx2rBhg+rUqaMJEya427dv316JiYn65z//6V6Wn5+v6dOnq1+/fmV+jffee6/Wr1+vuXPnavXq1TLGqHv37u7Ro8GDBysnJ0crVqzQ1q1bNW7cOEVFRUmSnnzySW3btk0LFizQ9u3bNWHCBFWrVq3U51qxYoVatWrlsWzfvn3q0KGDmjdvrvXr1+vLL7/Ur7/+qttuu02SFBwcrD59+mjatGkqev3if/3rX8rNzdVdd93lXhYaGqpmzZpp5cqVZX79QEDz6zXJAVS4Y8eOmfDwcLNq1SqP5QMGDDB33HGHMcaYZcuWGUlm8eLF7vXz5s0zkkx2drYxxph27dqZ559/3mMb//znP018fLz7viQzbNgwjzatW7c2gwcP9ljWtm1b06xZM/f9cePGmcsvv9x9/9NPPzVRUVHm2LFjpb6uDh06mIceesgYY8z3339vJJlvvvnGvf7AgQMmIiLCfPTRR8YYY5o0aWKefvrpErfVs2dP069fv1Kf63S9evUy/fv391j25JNPmi5dungs2717t5FkduzYYYwxZvv27UaSWbp0qbtN+/bt3b+Hov70pz+Ze++9t8w1AYGMkRsgwGzbtk0nTpzQ9ddfr6ioKPft/fff108//eTRtmnTpu6f4+PjJUn79++XJG3YsEHPPPOMxzYGDhyoffv26fjx4+7HnT6isWPHDl199dUey06/f++99+rHH3/UmjVrJBXOQbntttsUGRmplStXejznjBkzir3G7du3Kzg4WK1bt3Yvq1q1qho0aKDt27dLkoYOHarnnntObdu21ejRo7VlyxZ32/vvv18zZ85U8+bN9cgjj2jVqlVn7NPs7GyFh4d7LNuwYYOWLVvmUWvDhg0lyd3PDRs2VJs2bTRlyhT38pUrV3qMkLlERER49CuA0gX7uwAAFcvpdEqS5s2bp5o1a3qsCwsL87gfEhLi/tmyLI/HO51OjRkzRrfcckux5yj6QR8ZGVlsvWtbLqbIbhlJio2NVc+ePTV16lQlJiZq/vz5Wr58uaTCsLR582Z327i4uGLbP317RZe7nvu+++5T165dNW/ePC1cuFBjx47VSy+9pAcffFDdunXTzp07NW/ePC1evFgpKSkaPHiwXnzxxRK3W61aNR06dMhjmdPpVM+ePTVu3Lhi7V1BUSqcWDxkyBC9+eabmjp1qurWrauUlJRij/n999+VlJRU4vMD8MTIDRBgrrjiCoWFhWnXrl269NJLPW61a9cu83auvPJK7dixo9g2Lr30Ujkcpf9padCggdauXeuxbP369cXa3XfffZo5c6befvttJSUlqW3btpIKRzCKPld0dHSJrzE/P1///ve/3csOHjyo77//Xpdffrl7We3atTVo0CDNmTNHDz/8sN555x33uurVq+vee+/V9OnTNX78eE2aNKnU19SiRQtt27atWP989913uuSSS4r1T9HAd9tttykoKEgffPCB3nvvPfXr169Y+JOkb7/9Vi1atCi1BgCnMHIDBJjo6GiNGDFCw4cPl9Pp1LXXXqvMzEytWrVKUVFR6tu3b5m289RTT6lHjx6qXbu2br31VjkcDm3ZskVbt251HxVVkgcffFADBw5Uq1at1KZNG82aNUtbtmxRYmKiR7uuXbsqJiZGzz33nJ555hmvXuNll12mXr16aeDAgXr77bcVHR2txx57TDVr1lSvXr0kScOGDVO3bt1Uv359HTp0SEuXLnUHn6eeekotW7ZUo0aNlJOToy+++MIjFJ2ua9euGjlypA4dOqTKlStLKpyw/M477+iOO+7Q3/72N1WrVk0//vijZs6cqXfeeUdBQUGSpKioKN1+++16/PHHdeTIEd17773Ftv/zzz9rz549pZ4XB4AnRm6AAPTss8/qqaee0tixY3X55Zera9eu+vzzz1WvXr0yb6Nr16764osvtGjRIl111VW65ppr9PLLL6tu3bpnfNxdd92lkSNHasSIEbryyiuVnp6ue++9t9icFYfDoXvvvVcFBQXq06eP169x6tSpatmypXr06KHk5GQZYzR//nz3rraCggINHjxYl19+uW644QY1aNBAb731lqTCo5NGjhyppk2bqn379goKCtLMmTNLfa4mTZqoVatW+uijj9zLEhIS9M0336igoEBdu3ZV48aN9dBDDykmJqbYyNaAAQN06NAhde7cWXXq1Cm2/Q8//FBdunQ5a98CKGSZ0nZOA0AFuf7661WjRg2Pw78laeDAgfr11181d+5cP1VWdvPnz9eIESP07bffnnG3nLdycnJ02WWX6cMPP3TvmgNwZuyWAlChjh8/rokTJ6pr164KCgrShx9+qMWLF2vRokXuNkeOHNG6des0Y8YMffbZZ36stuy6d++uH374QXv27PFq7tLZ7Ny5U6NGjSLYAF5g5AZAhcrOzlbPnj21ceNG5eTkqEGDBnriiSc8jrrq2LGj1q5dq7/+9a9cUwmA1wg3AADAVphQDAAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbIVwAwAAbOX/A1j2OUYYWxKDAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3828f82d23404eeb9cb0c9d8f9aede19",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ll = infoWidget.low_loss.dataset\n",
+    "zero_loss_fit_width = infoWidget.low_loss.low_loss_tab[5, 0].value\n",
+    "print(zero_loss_fit_width)\n",
+    "\n",
+    "ll.plot()\n",
+    "#vplt.plot(ll.energy_loss, ll)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'single_exposure_time': 0.01,\n",
+       "  'exposure_time': 0.21,\n",
+       "  'number_of_frames': 21,\n",
+       "  'collection_angle': 0.0,\n",
+       "  'convergence_angle': 30.0,\n",
+       "  'acceleration_voltage': 0.0,\n",
+       "  'flux_ppm': 115.68867492675781,\n",
+       "  'count_conversion': 1,\n",
+       "  'beam_current': 0},\n",
+       " 'zero_loss': {'shifted': array([1.0174319]),\n",
+       "  'startFitEnergy': -0.3,\n",
+       "  'endFitEnergy': 0.3,\n",
+       "  'fit_parameter': array([-1.49985841e-01,  2.33687317e+03,  1.33828265e+00,  2.36651224e-01,\n",
+       "          1.09947513e+04,  1.72584231e+00]),\n",
+       "  'original_low_loss': '1EELS Acquire (low-loss)_new'}}"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['resolution_functions'].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = infoWidget.low_loss.parent.dataset\n",
+    "energy_offset = s.get_spectral_dims(return_axis=True)[0][0]\n",
+    "energy_offset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'core_loss' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m view \u001b[38;5;241m=\u001b[39m core_loss\u001b[38;5;241m.\u001b[39mplot()\n\u001b[0;32m      3\u001b[0m core_loss\u001b[38;5;241m.\u001b[39mmetadata[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzero_loss\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mshifted\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'core_loss' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "view = core_loss.plot()\n",
+    "\n",
+    "core_loss.metadata['zero_loss']['shifted']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████| 2046/2046 [00:00<00:00, 130983.97it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfbae69297904a80b0bb0861acd19af4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm, trange, auto\n",
+    "\n",
+    "dataset = infoWidget.selected_dataset\n",
+    "dataset.original_metadata.keys()\n",
+    "dataset_np = np.array(dataset)\n",
+    "for i in trange(1,len(dataset.energy_loss)-1):\n",
+    "    if np.abs(dataset_np[i] - dataset_np[i+1]) > 1e3  and np.abs(dataset_np[i] - dataset_np[i-1]) > 1e3:\n",
+    "        dataset_np[i] = (dataset_np[i-1] + dataset_np[i+1])/2\n",
+    "        pass\n",
+    "core_loss = dataset.like_data(dataset_np)\n",
+    "\n",
+    "core_loss = eels_tools.shift_energy(core_loss, np.array([-0.2791726]))\n",
+    "view = core_loss.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1f6e27f3110>]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a51865a997c4c66a86d7b550cdec780",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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4NPvlWzgLmIhICQyARORVFpt93J82bA32a57Hz4d/VrhGRES+hwGQiLzKXNztq4taCgCYuGmiktUhIvJJDIBE5FVWG2f+EhEpjQGQiLzKcR1AIiJSBgMgEXkVWwCJiJTHAEhEXmXh0i9ERIpjACQir7KwBZCISHEMgETkVRZu/0FEpDgGQCLyKgsngRARKY4BkIi8il3ARETKYwAkIq9iACQiUh4DIBF5FWcBExEpjwGQiLyKYwCJiJTHAEhEXsUuYCIi5TEAEpFXuVsGxmqzKlATIiLfxQBIRF5l7wJ2DoEmm0mZyhAR+SgGQCLyKovNBgjOLX4mKwMgEZE3MQASkVfZxwA6twCabWZlKkNE5KNqTQBcu3Yt+vbti9jYWAiCgEWLFlX62g0bNkCtVqNt27Yeqx8R2VmsEiCUCYBWBkAiIm+qNQEwPz8fbdq0wbRp06p0XXZ2NoYOHYrbbrvNQzUjIkcWm2sA5BhAIiLvUitdgeqSlJSEpKSkKl/35JNPYsiQIVCpVFVqNSSiK2Ox2iCUnQTCMYBERF5Va1oAr8TXX3+NY8eO4c0331S6KkQ+w+qmBZBjAImIvKvWtABW1ZEjR/Dyyy9j3bp1UKsr92MwGo0wGo3y65ycHE9Vj6jWMrtZBoYBkIjIu3yyBdBqtWLIkCGYMGECmjRpUunrJk+ejODgYPmRkJDgwVoS1U42iZNAiIiU5pMBMDc3F9u2bcMzzzwDtVoNtVqNiRMnYvfu3VCr1Vi5cqXb68aNG4fs7Gz5kZqa6uWaE1373AVATgIhIvIun+wCDgoKwt69e52OTZ8+HStXrsTChQuRmJjo9jqdTgedTueNKhLVWlab5DIJxGKzKFQbIiLfVGsCYF5eHo4ePSq/TklJwa5duxAWFoa6deti3LhxOHPmDL799luIooiWLVs6XR8ZGQm9Xu9ynIiql00Cu4CJiBRWawLgtm3b0KNHD/n1mDFjAAAPP/ww5s6di7S0NJw6dUqp6hFRMRvXASQiUlytCYDdu3eHJEnlnp87d26F148fPx7jx4+v3koRkQubxFnARERK88lJIESkHHddwFwImojIuxgAicirbJIEAVanY2wBJCLyLgZAIvIqrgNIRKQ8BkAi8ip3W8FxEggRkXcxABKRV9nnajlP2GIXMBGRdzEAEpFX2buAy4wBZBcwEZFXMQASkVdZbRKEsmMA2QJIRORVDIBE5FU2CeA6gEREymIAJCKvktzMAuY6gERE3sUASEReZeVOIEREimMAJCKvcrcTCAMgEZF3MQASkVfZ3EwCYRcwEZF3MQASkVfZ2AVMRKQ4BkAi8ipuBUdEpDwGQCLyKpsNKGkBlCQBAFsAiYi8jQGQiLzKqQXQpgHAAEhE5G0MgETkVVapdBKIJNkDICeBEBF5FwMgEXmVfScQyf6iOABeLChEkdla7jVERFS9GACJyKskh1nAUnEX8OmsXExZelDBWhER+RYGQCLyKqtNAoSSFkA1AEAQrJi35aSCtSIi8i0MgETkVU5dwMUtgBAskCSlakRE5HsYAInIqySptAVQKm4BhGCFxcYESETkLQyARORVVpuEspNABMECwL5NHBEReR4DIBF5lc1pEkhpCyAAjJy3ozggEhGRJzEAEpFX2SRAEJxbAO0BUMKy/9Lx284zitWNiMhXMAASkVfZWwCLxwDaSrqAS1sFT2TmK1QzIiLfwQBIRF7l2AUstwACcjew2WbzfqWIiHwMAyAReZXNhtJZwDbHAGifCGK2cAwgEZGnMQASkVc5dgEDKvm4UNwCePR8nvcrRUTkYxgAicirnHcCESDZ7CFQW2cdRP0p5BSaFawdEZFvYAAkIq9y2gkEorwdnLbOWvgnTodZyFCqakREPoMBkIi8SnLoAr6lcSQkSeV0vkh9SIFaERH5lloTANeuXYu+ffsiNjYWgiBg0aJFFZb/9ddfcccddyAiIgJBQUHo2LEjli9f7p3KEvkwqyQBgn2m700NwhEdGOB0vgiZSlSLiMin1JoAmJ+fjzZt2mDatGmVKr927VrccccdWLJkCbZv344ePXqgb9++2Llzp4drSuTbbDYJQnELoAABGpXG6bwZOUpUi4jIp6iVrkB1SUpKQlJSUqXLT5061en1O++8g99//x2LFy9Gu3btqrl2RFRCchgDqBJU0IjOAdDCAEhE5HG1JgBeLZvNhtzcXISFhZVbxmg0wmg0yq9zcvgPFVFVOXYBC4IAg9rgdN6s+w/7L+zHdXWuU6J6REQ+odZ0AV+tDz/8EPn5+bjvvvvKLTN58mQEBwfLj4SEBC/WkKh2cFwHUBRExAfGu5R5YsWTXq4VEZFvYQAE8OOPP2L8+PFYsGABIiMjyy03btw4ZGdny4/U1FQv1pKodrDv9FYaAEN1oS5lsk1ZXq0TEZGv8fkAuGDBAgwbNgw//fQTbr/99grL6nQ6BAUFOT2IqGpsUulC0KIgolt8NwBAhCFCyWoREfkUnw6AP/74Ix555BH88MMP6NOnj9LVIfIJ9i5g+xhAEfYA+PWdX2Px3YvLlLMpUDsiIt9QawJgXl4edu3ahV27dgEAUlJSsGvXLpw6dQqAvft26NChcvkff/wRQ4cOxYcffoibb74Z6enpSE9PR3Z2thLVJ/IJkiTZdwIpaQEURQiCgA7RHeCv8UdI7nC5bL45X6FaEhHVfrUmAG7btg3t2rWTl3AZM2YM2rVrhzfeeAMAkJaWJodBAJg5cyYsFguefvppxMTEyI/nnntOkfoT+QJJkp8BsLcAOgqWrodksy9OkGPiLHsiIk+pNcvAdO/evXiLKffmzp3r9Hr16tWerRARubBKJQtAl44BdKRXqyDZDBDEXOSacr1ePyIiX1FrWgCJqOazlfySJrgPgAatCpLVvi5gjpEtgEREnsIASERe49IFXCYAhvhpgZIAyC5gIiKPYQAkIq+x2koSYOlOII7WHzlf2gLIAEhE5DEMgETkNWW7gFWCyun8LU0iINnYBUxE5GkMgETkNbYyXcBlWwDv65AAyaoHAOSaOQmEiMhTGACJyGtsJQlQKF0I2pFOI0KyFQdAzgImIvIYBkAi8hq5Cxjuu4A1KlEeA5hnyvNm1YiIfAoDIBF5Tdl1AMt2AWtUIsAWQCIij2MAJCKvkRsAy1kHUKsWOQaQiMgLGACJyGtKuoCF8gKgqnQMILuAiYg8hwGQiLymdB1A9wHQcQwg1wEkIvIcBkAi8hp5GcByZgFrVAJbAImIvIABkIi8xqUFUCwTANWivBVcrjkXVpvVm9UjIvIZDIBE5DUuYwDhZgxg8SQQAMgzsxWQiMgTGACJyGsutxOIRiUCUEOy2dcHLDAXeK9yREQ+hAGQiLzmcnsBq0QBKlFAyVeTVWIXMBGRJzAAEpHX2MosBF12FjBgnwgCiQGQiMiTGACJyGtstuInxbOAy3YBA8XdwJK9ZZABkIjIMxgAichrLrcXMFA8EaSkC5izgImIPIIBkIi8pmwALL8FkF3ARESexABIRF4jrwNYzjIwAKBRCwCE4vIMgEREnsAASEReU3YZGHeTQFSCII8BtEgWL9WMiMi3MAASkddIZZaBcRcABcFhFjBbAImIPIIBkIi8pnQruOK9gN0EwJTM/NJJIBwDSETkEQyAROQ1lekCtp+2HzdZ2AVMROQJDIBE5DVlZwGXGwCLv5ouFRZ5vlJERD6IAZCIvKZ0K7jiLuDyvoKKWwCNFrM3qkVE5HMYAInIa0rHAJa/DqD9dHEXsJVdwEREnsAASEReI5UZA+huJxD7WQZAIiJPUiv55kajEVu3bsWJEydQUFCAiIgItGvXDomJiUpWi4g8pKQLWEL5ewHbC9gDoJkBkIjIIxQJgBs3bsRnn32GRYsWwWQyISQkBAaDARcvXoTRaESDBg3wxBNPYMSIEQgMDFSiikTkAWW7gC83C9hs5TIwRESe4PUu4P79++Pee+9FXFwcli9fjtzcXFy4cAGnT59GQUEBjhw5gtdeew3//PMPmjRpguTk5Erdd+3atejbty9iY2MhCAIWLVp02WvWrFmD9u3bQ6/Xo0GDBvjiiy+u8tMRUUUqvQxM8VfT9P0TYbQaq/w+7y8/iPu+2IQiMwMkEZE7Xg+APXv2xIkTJ/DBBx+gW7du8PPzczrfoEEDPPzww1i2bBn+/vvvSt83Pz8fbdq0wbRp0ypVPiUlBb1790bXrl2xc+dOvPLKK3j22Wfxyy+/VOnzEFHl2buApQp3AgEgtwACQPLJyv0S6OjzVcew9cRFrNh/7kqqSURU63m9C/jpp5+udNkWLVqgRYsWlSqblJSEpKSkSt/7iy++QN26dTF16lQAQPPmzbFt2zZ88MEHGDhwYKXvQ0SVJwfAYuUuAyOWLv9S1e3gCk2l5U0WW5WuJSLyFYrOAk5NTcXp06fl11u3bsWoUaMwa9Ysj7/3pk2b0LNnT6djd955J7Zt2waz2f3aY0ajETk5OU4PIqo8+xhAhwAouv8KEsTSBaCr2gXc+d2V8nOpdNoxERE5UDQADhkyBKtWrQIApKen44477sDWrVvxyiuvYOLEiR597/T0dERFRTkdi4qKgsViQWZmpttrJk+ejODgYPmRkJDg0ToS1TaSBLn7F6igBVAonf17qehSpe9vttpwMd8kv/7vLH9JIyJyR9EAuG/fPtx4440AgJ9++gktW7bExo0b8cMPP2Du3Lkef/+yS1CUtBaUtzTFuHHjkJ2dLT9SU1M9Xkei2sTeBVzaLVveGEBBLA2AWcasSt+/7KSPuRtPILuAu4kQEZWlaAA0m83Q6XQAgL///hv9+vUDADRr1gxpaWkefe/o6Gikp6c7HcvIyIBarUadOnXcXqPT6RAUFOT0IKLKc+kCLm8SiEML4MWii5e9b26RGdNWHsF+Ny1+XRy6hImIyE7RANiiRQt88cUXWLduHZKTk9GrVy8AwNmzZ8sNYdWlY8eOLkvMrFixAh06dIBGo/HoexP5Kpcu4EoEwMp0AX+28ig+WHEYg2dtdjmXa7Tg4+TDyC1iSyARUQlFA+C7776LmTNnonv37njggQfQpk0bAMAff/whdw1XVl5eHnbt2oVdu3YBsC/zsmvXLpw6dQqAvft26NChcvkRI0bg5MmTGDNmDA4cOIA5c+Zg9uzZeOGFF6rnwxGRC2uZLuDyhlsIQtW6gN21/Dn65J8j+GD5oUrV0dGZrEIM+mIjlu3zbI8EEZG3KboVXPfu3ZGZmYmcnByEhobKx5944gn4+/tX6V7btm1Djx495NdjxowBADz88MOYO3cu0tLS5DAIAImJiViyZAlGjx6Nzz//HLGxsfj000+5BAyRB9kkyakFsLy9gCFWrQs4JljvcuyNu67DxD/3y6+/2XQS91wfjzYJIZWu78TF/+HfE5fw74lLODGlT6WvIyKq6RQNgLfeeit+/fVXp/AHAGFhYRgwYABWrqz82J3u3btXuOSDu0klt9xyC3bs2FHp9yCiq2OzSRAcxgAKKK8FsHQyx6WiS5Akqfx9gwFElwmAUUE6FGp3Qh+zCEXn+gI2+4Lz/T/fgEVPd0bbSobAzLzSGcWXqwMR0bVE0S7g1atXw2QyuRwvKirCunXrFKgREXmSfSu44tn2ECoVqEw2EwothRWWKXuXF3o2xRcH3oQmZCf0UYudzg34fEOl66sSS+/85HfbK30dEVFNp0gL4J49e+Tn+/fvd5qNa7VasWzZMsTFxSlRNSLyIKuttAu43O5fADZjOERd6XqcuaZc+Gn8yi1vsjq3/tcJ0MrPNSE7UZR2H1xj4uVtTSntfl6x/xyKzFboNeXXm4joWqFIAGzbti0Ewf7b/6233upy3mAw4LPPPlOgZkTkSY5bwVXU+ldw+mHo6qyCJsQ+RMNkde0pKHEgLQdfrDnmdEyvc57x61f/cxSceBqVCYF7Tmfhq3Up2H06y+VcdqGZAZCIagVFAmBKSgokSUKDBg2wdetWREREyOe0Wi0iIyOhUvFLlqi2sQ/Ttc8CLncJGACSKQJFafdBFXAIojq/wu3gkj4pHS4i6tIh2TTItOxzKqMynIaoPw1b0eV373ls7r9OY/8cZRWYERXkOuGEiOhao0gArFevHgDAZuNG7US+xOowC7iiACiT7F9RRpv7AGizOXT9ioXwbzAVALDkZGeXsv6JnyP/+HOwGWPKfbuMnKJywx8AZBWUf46I6Fqi6CxgADh8+DBWr16NjIwMl0D4xhtvKFQrIvIExy7gSgVAm31R9vK6gIsspbOFBXWe/HzDGftEj9k9Z+OHgz/gn1P/AAD8Ej9F3sG3YbbaoFG5vn+vTyqefHaJ28oRUS2haAD88ssv8dRTTyE8PBzR0dFOY4IEQWAAJKplbDYJEIq7gCuxCIFU0gJYThdwgckhADosHg0AQdogXB91Pbad2yYHQEGQYKj7FR6bG4VnejTCTQ3q4EBaDk5k5iO70IyL+a5Bs1uTCEiShPUpx/HKom3oeV0fLNxxGjclhqFenaqtV0pEVFMoGgDffvttTJo0CS+99JKS1SAiL7FJkNcBFMUqdAFb3AfAlQcySl+IzuGtQXADqEU1eiX2wozdM+Tjav8UbDi2H+tmZeLElD5OYwjdVkGSkJpzFv4N30eROQT3zgzCjpO5iAsxYMPLrpPYiIiuBYquA3jp0iUMGjRIySoQkRdZbQ5dwNXQAvjiL6VLSgllAmDdoLoA7EFw1X2rnM75N/yo0nU2S/m4EPYGBNEMle48dp07AMC+TRwR0bVK0QA4aNAgrFixQskqEJEXSQ6TQCq1q0bxGMCKZgGXEATnAFgvqJ78PEwf5uYCM+ZtOen+XqpcaOushCZsLf7TPOd0TtSWrk/Y8s3ll92HmIioJlK0C7hRo0Z4/fXXsXnzZrRq1Qoajcbp/LPPPqtQzYjIE2wOy8BUtBB0iZIWwIrWAZSpnFvk6gbWlZ+Lgoj3u72PsWvHyscEsQiv/ua8XEyJ8PiNKPJb5facoM6Vn+cZLXh07lZseeX2y9ePiKgGUTQAzpo1CwEBAVizZg3WrFnjdE4QBAZAolrGWsmFoGWS/ZfCAnNRhcU0oRugj3be8q1RSCOn170Se+GpORkIaPS+/f1VhZCsgW7vlxifiQOlm4AgRBeKLOOl4uvynMqey7l86yQRUU2jaABMSUlR8u2JyMtsVV0H0Gb/isoqLKiwWNnwN6DRADQIaeBS7oHr2+H3zDCI2ouA6D5UCqo8HLi4V349vuN4DGwyEE3eHwtd5DKIDi2AAKAWnYPskr1pmPTXAUwb0g7t6oZWWG8iIqUoOgaQiHyLzWESSFW6gPPctAA6LQLt4N4m9+Ktzm+5DZhBeg0km30nD0HlfhJHeMPv5eef3/Y5BjYZaH8/SwAA2LenE4wQ1FkQ1Fmw2CSsPXxevmbkvB04k1WIJ7/bftnPR0SkFEVbAB977LEKz8+ZM8dLNSEib7BntuIu4Ersy1uyDEy+yTms5RstaPHm8pJCTuf81H7l3u7Jbg2wcH4AjAA0oVtgM9WBZA6Xz+uiFqFIdUJ+HaQNkp8/3bkb5qQsBACog/ZBH/0rYNMh79iLGDpnK05M6eP0XkYLdzoioppL8WVgHB8ZGRlYuXIlfv31V2RlZSlZNSLyAKtNgiBcfi/gElLxLODCMi2AK/any89F7Xmnc03DmpZ7v1B/Lbo0sO8HrAnc77QcjKDOgjZss1P5IF1pABzdrQfCdPawqItYAUG0QlAXQGVIlT8bEdG1QtEWwN9++83lmM1mw8iRI9Gggev4HSK6tklV3QquuAXw+IUs+ZDFasPoBbvl15o6a50u6Rzrug+wI8cWQnsYtUEVcBh+CXNdyjq2AALAdeHNsP7MeoiabPmYqM2ENb8xTl8qcNoZRKxEAycRkVIU3wu4LFEUMXr0aHTv3h0vvvii0tUhompkk1C1SSDFs4APpF+QD+UUOW/5Joil3cMPXfcQ6hjqVHhLrUrr9Dqw+StOrxuFNIJVskKSJITqnCdxBGpcZw2LWvtuJC/9sgeF5tJu30rNciYiUkiNC4AAcOzYMVgslssXJKJrin0ZmKp3AUNV2gVstFidysQG++G8BLx848t4sPmDl73nsFbDsDdzLw5fOuz2fGxALD7p8Qlskg0q0Xmiisnmuh6hNmwTjOf6YfPxi07Hy8Y/SZIYComoxlA0AI4ZM8bptSRJSEtLw19//YWHH35YoVoRkadUuQu4uKwmcD/eS94B0eaPiECdfHbzuNvw2ubfcD4dCNYFV6oOCYEJ+KXfL2j1TSu356P8oqAW3X815ppy3R4XNBecJpMAwIV8E5q+thQfDGqDE5n5mLflFH4Z2QlxIYZK1ZOIyJMUDYA7d+50ei2KIiIiIvDhhx9edoYwEV17rLaqrQMo6kq3XZu5cQOshaVjgyMDdYgO1iPHZN+KLVhbuQBY4rqgrtifs87pWN3Auhjeani510T6Rbo9HtDoA5iz2qMozXlvc6PFhv/9WPo99/mqo3jnbvfBk4jImxQNgKtWud9qiYhqp6ouA2PO6gBtqH1mrqDOdzqXkWvE8azjOHDxAADnGbuVMTDhBexcrYUu4h8A9pbBP+/+s8Ju2ueufw4mqwndE7ojQBOAubsXY8fFZACAJmQ7is71AWzlL0NjtXKmMBHVDDViDOD58+dx6NAhCIKAJk2aICIiQukqEZEH2KrYAmgrioe1MB4qw2kI6ryyZzF8RWlrXVVbADUqLSw5reUAGKAJuOwYvWj/aHzY/UP59f6L++UACAAqXbpTK2VZKhXHABJRzaDoOoD5+fl47LHHEBMTg27duqFr166IjY3FsGHDUFBQ8dZPRHTtsTlMAilvJ5D7b0hwem0tigPgugevqD+D84WlawDWDapb9fqYw+TnRmvV9/SNMDj/sipqL5RT0k7DtWGIqIZQNACOGTMGa9asweLFi5GVlYWsrCz8/vvvWLNmDZ5//nklq0ZEHmCVAKGkC7ic1rbJ97TC+pd6oF4de1eqVLwFm7bMen+Rde2v20e1x96H91ZyUkkp+3wUjfy6wFL1XzrLBkB1yHb4N3wPqoD9bsvvOJWFs1nut6AjIvImRQPgL7/8gtmzZyMpKQlBQUEICgpC79698eWXX2LhwoVKVo2IPMAmXb4LWBAExIf62QMaAJupeHat5NxiKKnPAajcnsLulNy/IHUoArVBeLL1k1W+R6S/86QQtd8JiNqL8Ev41m35vWey0fndlVV+HyKi6qboGMCCggJERUW5HI+MjGQXMFEtVJVlYEL9tTh1sQCW3BYAAEFVhDF3xiFUH4L/zuZgs8WGgiLggWYPXFWdrHnX4e97RsNPV/Wvwzj/uArO2j+rLvo3CKIJRWcHAVBDkoBBX2xE96aR+L+b6iHYT1PBPYiIPEPRFsCOHTvizTffRFFR6SKvhYWFmDBhAjp27KhgzYjIE+z75VZuIeiP72uDdnVDMHtoJ3kP3ltaiHioY31MGdga5uJFmesH1b+iujjOxxWvcGxesC4YAxsPxPWR1yNMH+Z0TlDlQRuxAtrQf6EJ3g1VwBH53L8nLuH95Yfw4i+7y96SiMgrFG0B/OSTT9CrVy/Ex8ejTZs2EAQBu3btgl6vx/Lly5WsGhF5gNNWcJf5/bNBRAB+G2nf1/e7k/Vw8Vwmdp3fia3pW3B347vl9f/KdsNWliSVRkDVFQZAQRAwvtN4AMDzq5/HipMrSu9pOAVd+GqH16mw5jWD4x4hK/bbu7ELTBb4aWvEogxE5CMU/cZp2bIljhw5gu+//x4HDx6EJEm4//778eCDD8Jg4Gr5RLWNzVbVnUDs6gbWxfZz2/HBtg8AAL8f+x0AYFAb3O7PW1WqatiiTafSOb02JHwHNfxhgX39Ql34SmhDN6HwzBBY8xsDsI9DnL/1FF7+dS8AIGVyb24XR0ReofivnAaDAY8//rjS1SAiL7BPAinuAhYrHwATAp2XhjmZcxKAfdu26ghM1ZG5RrQZgX0X9uFkzknYJPtnLAl/8vuoCmGI+w55hyfKx0rCHwAUmKzwv4KxiEREVaXoGMDJkydjzpw5LsfnzJmDd999t8r3mz59OhITE6HX69G+fXusW7euwvLz5s1DmzZt4Ofnh5iYGDz66KO4cKHidbyI6MrZN8KoeB1Ad5qENnF7vLL7/7rjOAawOkJk3aC6+GPAH9g8ZHOF5QSVqdxzRovtqutBRFQZigbAmTNnolmzZi7HW7RogS+++KJK91qwYAFGjRqFV199FTt37kTXrl2RlJSEU6dOuS2/fv16DB06FMOGDcN///2Hn3/+Gf/++y+GDy9/H1AiujqSJEGowk4gJbrGd3V7PEATUC31qk4GtQHdE7pfppQEiK4LT+cbLR6pExFRWYoGwPT0dMTExLgcj4iIQFpaWpXu9dFHH2HYsGEYPnw4mjdvjqlTpyIhIQEzZsxwW37z5s2oX78+nn32WSQmJqJLly548sknsW3btiv6LER0eZXZCcQdURBxT+N7XI4PazXsyivjwW153+1a2oPRt0Ffl/P+Dd9HYNM3oQ7a5XR8+upjnqsUEZEDRQNgQkICNmzY4HJ8w4YNiI2NrfR9TCYTtm/fjp49ezod79mzJzZu3Oj2mk6dOuH06dNYsmQJJEnCuXPnsHDhQvTp06dqH4KIKs1axb2AHb3Q4QUMazkMywYuw96H92Lvw3txQ/QNV1wXyYMJ0E/jh+R7kzG6/Wi83vF1l/Oi9iIAwBA33+n4j1tPIS2bO4UQkecpOtp4+PDhGDVqFMxmM2699VYAwD///IMXX3yxSlvBZWZmwmq1uiwqHRUVhfT0dLfXdOrUCfPmzcPgwYNRVFQEi8WCfv364bPPPiv3fYxGI4zG0m6bnJycSteRiIqXgankOoBlBWoDMar9qGqvk6dE+0fjsZaPVaKkBMelYTpOXokTU/iLKBF5lqIB8MUXX8TFixcxcuRImEz2gdF6vR4vvfQSxo0bV+X7lR3IbR9v5H5w9/79+/Hss8/ijTfewJ133om0tDSMHTsWI0aMwOzZs91eM3nyZEyYMKHK9SIiuytdBsYTJA92AVckUBOIXHOu/FrQZkIyhcMxBFpt0hWvTUhEVBmKfgMLgoB3330X58+fx+bNm7F7925cvHgRb7zxRpXuEx4eDpVK5dLal5GR4XarOcAe5jp37oyxY8eidevWuPPOOzF9+nTMmTOn3PGH48aNQ3Z2tvxITU2tUj2JfJ3jMjBXuodvdfFm/gs32Hcy8df4u7RiBjT8ENqIFU7HLhWUP1OYiKg6KPsreLGAgADccMMNaNmyJXQ63eUvKEOr1aJ9+/ZITk52Op6cnIxOnTq5vaagoMBlHTKVyv4PklRO04BOp0NQUJDTg4gqzyoBQg1pAfSmT3p8gvZR7fFVz68wqMkgfH3n107ndeGrnF53ePtvJBfvEkJE5Ale/wYeMWJEpVvOFixYgHnz5lWq7JgxY/DVV19hzpw5OHDgAEaPHo1Tp05hxIgRAOytd0OHDpXL9+3bF7/++itmzJiB48ePY8OGDXj22Wdx4403VmkCChFVnlSTWgC92ATYOqI15vaai5bhLSEIAjpEd0DD4IZOZfzqTYc2onQLzMe/3VbuL6NERFfL62MAIyIi0LJlS3Tq1An9+vVDhw4dEBsbC71ej0uXLmH//v1Yv3495s+fj7i4OMyaNatS9x08eDAuXLiAiRMnIi0tDS1btsSSJUtQr149AEBaWprTmoCPPPIIcnNzMW3aNDz//PMICQnBrbfeekULUBNR5TguA6N0C6AnZwFXxjdJ36DL/C7ya5XfKaj8TsGcdQMkcxgA7gxCRJ4jSAr8ipmRkYHZs2dj/vz52Ldvn9O5wMBA3H777XjiiSdclnWpaXJychAcHIzs7Gx2BxNVQtIn63DM/Ct0EX/jvib3uV0ixVuW7k3DU/N2AIBis27rv/wHApqOhyCanY6bs9ui6Ox92DTudsQEc190ourGf78VmgUcGRmJcePGYdy4ccjKysLJkydRWFiI8PBwNGzYkJuhE9VS9t83a8YYwJ4tojHkprq4vm6ogrVQwVYUA5Wf845FmuBdMGX2QE6hBTFXvtsdEVG5FO9bCAkJQUhIiNLVICIvsC8EXTO6gFWigHfubqVoHQDAWhQvB0BJEiGU/Hx0GRg8axM61AvFjP9rD43KdybNEJHn8RuFiLzGVoNaAGsKa1HppLOvevyGtqHdAQCG+HnINmXi7wMZWH3ovDKVI6Jai9/AROQ1NglyC5fSs4BrCktec9hMoWgW1gw31q2P9nGls4MDGk8GYLG3nBIRVSMGQCLyGqdZwCK/fgAAVn+YTozF/D7zIQoiYvxjnE6L+nS8+ce+ci4mIroy/AYmIq+xjwG0t2axBdCRCirR/vNoG9nW6YyouYhzOUY31xARXTlFA+D48eNx8uRJJatARF5kX3SqZkwCqamahjXFo/Ffw5LbHAAgqPMVrhER1UaKfgMvXrwYDRs2xG233YYffvgBRUVFSlaHiDzMvhcwWwDLEuC89FWgJgw2S6D9nIoBkIiqn6IBcPv27dixYwdat26N0aNHIyYmBk899RT+/fdfJatFRB5in8zAFkAXZZY+DdSrIVn9AQC6iL8h6k/DYrUpUDEiqq0U/wZu3bo1Pv74Y5w5cwZz5szBmTNn0LlzZ7Rq1QqffPIJsrOzla4iEVUT+2RWLgNzOQPaxaFeaOkC1f6J05BdaK7gCiKiqqkx38A2mw0mkwlGoxGSJCEsLAwzZsxAQkICFixYoHT1iKga2CSpdKFjBkBEBekAADc3qON0XK9R4dGOTZ2OrT+9yWv1IqLaT/Fv4O3bt+OZZ55BTEwMRo8ejXbt2uHAgQNYs2YNDh48iDfffBPPPvus0tUkomrguBA0xwACC0d0wv9ubYSP72vjcu7uxnejX8N+8uvXtvzPm1UjolpO0QDYunVr3HzzzUhJScHs2bORmpqKKVOmoFGjRnKZoUOH4vx5roJPVBvUpK3gaoKEMD8837Mp6gToXM4Z1AZM6jIJgq30XJGFE+WIqHoo+g08aNAgnDhxAn/99RcGDBgAlcq1RSAiIgI2Gwc/E9UGksMYQLYAVk50/kvy8wtFFxSsCRHVJooGQEmSEOow0LlEYWEhJk6cqECNiMiT7MvAsAWwKoJUcbCZgwEAP+3Yr3BtiKi2UPQbeMKECcjLy3M5XlBQgAkTJihQIyLyJMdlYNgCWDnbTl6CVLwm4LS1uyBJ3BeYiK6e4i2AgiC4HN+9ezfCwsIUqBEReZIkQV4ImnsBV55kCQAAiOpc5BRZFK4NEdUGaiXeNDQ0FIIgQBAENGnSxCkEWq1W5OXlYcSIEUpUjYg8yCpJUHEMYJXJu4Ko8/Dj1lMYcUtDhWtERNc6RQLg1KlTIUkSHnvsMUyYMAHBwcHyOa1Wi/r166Njx45KVI2IPMhxDGDZ7c/IvamD2+LFlcsBAIIqD1OWHkSXRuFoGRd8mSuJiMqnSAB8+OGHAQCJiYno1KkTNBqNEtUgomJp2YWICNBBrfJct6wkSc6zgEW2AFbGgHZxGLvC3gUsqO1jpg+l5zIAEtFV8fognJycHPl5u3btUFhYiJycHLcPIvK87ScvoePklXh0rmf34LbJcxc4C7iqJLkLOAeABc//vFvZChHRNc/rLYChoaFIS0tDZGQkQkJC3E4CKZkcYrVavV09Ip/z/eaTAIB1RzJRZLZCr/FMy5ytZPaqwFnAVWWz2Fv71H4n4d/4XeQfe17hGhHRtc7rAXDlypXyDN9Vq1Z5++2JqAzR4ZewKUsPYny/Fh55H6vcBFg8C5gtgJU2beBdeGn7DAD2mcBqv2MK14iIrnVeD4C33HKL2+dEpAzHYX/fbz7psQAoL1/HFsAq692yLl7a7nBAVahYXYiodlD0V/Bly5Zh/fr18uvPP/8cbdu2xZAhQ3Dp0iUFa0bkOzQOCdBi89wiw9biBCiwBfCqGWIXIrMgU+lqENE1TNFv4LFjx8qTPfbu3YsxY8agd+/eOH78OMaMGaNk1Yh8RoCutCMgLsTgsffhGMCrM+r6UU6vv9v/gzIVIaJaQdEAmJKSguuuuw4A8Msvv6Bv37545513MH36dCxdulTJqhH5jIv5Jvm5Qeu5UGYrMwbQ3QQwKt/QFkNxS3zpsJk5/32pYG2I6FqnaADUarUoKCgAAPz999/o2bMnACAsLIzLwBB5iWMAzCk0e+x9bBwDeFU0ogZvdnzT6Rj3BSaiK6VoAOzSpQvGjBmDt956C1u3bkWfPn0AAIcPH0Z8fLySVSPyGTlFZrfPqxtnAV+9CL8I5B4qDYGZhRwHSERXRtFv4GnTpkGtVmPhwoWYMWMG4uLiAABLly5Fr169lKwakc9wnPdRZLbBaPHM+pslrVUCWwCvjs0Am8W+M8i7yZ5dvJuIai9FtoIrUbduXfz5558uxz/++GMFakPkm2wO3Yii9hwy8/MQF1z924xZ5UkgbAG8Gu/c3Qpv7/IH1Hn4bc9hTLmrl0e38COi2knRAAgANpsNR48eRUZGBmw2m9O5bt26KVQrIt9RkstU/ofhV3cOJm7ZgZk9p1X7+5R0AQvFW8FxL+Arc1vzSLy13R8AIKjycbHAhMhAvcK1IqJrjaK/Nm7evBmNGjVC8+bN0a1bN3Tv3l1+9OjRo8r3mz59OhITE6HX69G+fXusW7euwvJGoxGvvvoq6tWrB51Oh4YNG2LOnDlX+nGIrkklLYDa8H8AABvT1njkfeQxgGwBvCpRQXpE+Nl3UxJU+cjIMSpcIyK6FinaAjhixAh06NABf/31F2JiYq5qWYgFCxZg1KhRmD59Ojp37oyZM2ciKSkJ+/fvR926dd1ec9999+HcuXOYPXs2GjVqhIyMDFgsliuuA9G1Jt9owZ7T2QAkqAypHn0vs7VkDKD9T44BvHJdGtTH4pRtEFT5uFRguvwFRERlKBoAjxw5goULF6JRo0ZXfa+PPvoIw4YNw/DhwwEAU6dOxfLlyzFjxgxMnjzZpfyyZcuwZs0aHD9+XN6buH79+lddD6JryW87zwAABHW2PDkDsE/YqO51+srOAuY6gFcuMSQWAKAypOKh2Vsx66H26NkiWuFaEdG1RNE+mJtuuglHjx696vuYTCZs375dXkewRM+ePbFx40a31/zxxx/o0KED3nvvPcTFxaFJkyZ44YUXUFhY/h6bRqMROTk5Tg+ia5nJYg99ouai03Gjtfq7FS3FY3w5C/jq9Uq0r5KgDjgCffy3eP7n3QrXiIiuNYq2AP7vf//D888/j/T0dLRq1QoajcbpfOvWrSt1n8zMTFitVkRFRTkdj4qKQnp6uttrjh8/jvXr10Ov1+O3335DZmYmRo4ciYsXL5Y7DnDy5MmYMGFCpepEdC2QW+VURU7HiyxF0Kurd2JBaQtgcejkGMArlhCYID/XBO5HiDFPwdoQ0bVI0QA4cOBAAMBjjz0mHxMEQe5+slqrth5Z2S6lirqxbDYbBEHAvHnzEFy85MVHH32Ee++9F59//jkMBtc9UceNG+e0R3FOTg4SEhJcyhFdK8wlrXKic4tfoaUQIQip1veylJkEwhbAqxNl7Y9zqt/tz+vkKlwbIrrWKBoAU1JSquU+4eHhUKlULq19GRkZLq2CJWJiYhAXFyeHPwBo3rw5JEnC6dOn0bhxY5drdDoddDpdtdSZqCawlEzMKNMCWGgtfyjE1b4XWwCrx5Otn8QbWw5CHXAIFvHi5S8gInKgaACsV69etdxHq9Wiffv2SE5Oxt133y0fT05ORv/+/d1e07lzZ/z888/Iy8tDQIB9Vf3Dhw9DFEVuQ0c+w2ItnvghOM8kLbIUuSl9le8lr/PJFsDqIAgCpOIdQfafO4tTFwpQt46fwrUiomuF4r+Cf/fdd+jcuTNiY2Nx8uRJAPYZvL///nuV7jNmzBh89dVXmDNnDg4cOIDRo0fj1KlTGDFiBAB79+3QoUPl8kOGDEGdOnXw6KOPYv/+/Vi7di3Gjh2Lxx57zG33L1FtZC5ZnFlwXv6o0FL9LYCl6wCyBbA6RATqYLMEAgDUQbtx1+fJCteIiK4lin4Dz5gxA2PGjEHv3r2RlZUlj/kLCQnB1KlTq3SvwYMHY+rUqZg4cSLatm2LtWvXYsmSJXIrY1paGk6dOiWXDwgIQHJyMrKystChQwc8+OCD6Nu3Lz799NNq+3xENZ0cykTn8baeaQG0v5cEzgKuDt0ahyNIEwoAUOnTYQx03VaTiKg8inYBf/bZZ/jyyy8xYMAATJkyRT7eoUMHvPDCC1W+38iRIzFy5Ei35+bOnetyrFmzZkhO5m/N5LvM1pKlWZxbAD0RAK1W53UA2QJ4dQRBwOB21+Hb478BALRhm3E0Iw+NIgMUrhkRXQsU/QZOSUlBu3btXI7rdDrk5+crUCMi3yJPzCgTAAssBdX/XvIYQHYBV5fO8TfLz63GSOQUmRWsDRFdSxT9Bk5MTMSuXbtcji9duhTXXXed9ytE5GPkUCaU6QK2eqoLWOJewNWoeWQs8o//DwAgiIXILeJWlkRUOYp2AY8dOxZPP/00ioqKIEkStm7dih9//BGTJ0/GV199pWTViHxC6f68ZSaBmD01CUSSX3MM4NUL0KshSVoAgCCakMV9gYmokhQNgI8++igsFgtefPFFFBQUYMiQIYiLi8Mnn3yC+++/X8mqEfmE0mVgyowB9EQLoFVyamlUi4p+/dQKGpUI2OwBEKIZ+Ua2ABJR5SjeB/P444/j5MmTyMjIQHp6OlJTUzFs2DClq0XkE0p357AHB8lmb5XzyCQQm4SS8X8AA2B1kWz2xekFwYbJS/cpXBsiulYoGgBvvfVWZGVlAbDv5hEZGQnAvsXarbfeqmDNiHyDvBOIWBIA7WtgemIdQLPNJq8BCAAqkV3A1cJWuod6rqn6J+8QUe2kaABcvXo1TCbXMStFRUVYt26dAjUi8i1lJ4FIVs8FQKtNguAYADkGsFp8MOh6SFZ7K6CoyVK2MkR0zVCkD2bPnj3y8/379zvt4Wu1WrFs2TLExcUpUTUin2IuuwyM1b6VmKfHAIqCyFnA1eTe9vGYf6olDuVsh3+DT3HoYjc0DWuqdLWIqIZTJAC2bdsWgiBAEAS3Xb0GgwGfffaZAjUj8i0lLYAls4DlFkCPzQK2v59a4Pi/6tQ5rjMO5WwHAIxe/AOebf8UerWMVrhWRFSTKfItnJKSAkmS0KBBA2zduhURERHyOa1Wi8jISKhU7B4i8rSyLYAlAdBj6wAWdwFz/F/1ur/pA/hq9/cQtRdx7NIZjPh+O05M6aN0tYioBlMkAJbsz2uz2S5Tkog8yb4MjBUqvX0YhmQr7gL2xF7AVhvYAugZ0UH+MF3oDn3MrxDV2UpXh4iuAYp/Cx8+fBirV69GRkaGSyB84403FKoVkW+w2iSo/I+WHpDsLXOemARisUkQiscAsgWwegmCgDYx9XAIgKCxB8DvNp/EQzfXU7ZiRFRjKRoAv/zySzz11FMIDw9HdHQ0BEGQzwmCwABI5GFmqwRBVRr2LPkNoK2z1mOzgEu6gLkGYPW7Mb4BDp2G3AL4+qJ9aBDuj86NwhWuGRHVRIp+C7/99tuYNGkSXnrpJSWrQeSzLDabPDO3YUB77LH6AwCMVqMH3sthDCCXgKl2sYH2SR+CugAQTICkxZrD5xkAicgtRddhuHTpEgYNGqRkFYh8msUqyTOANaIGkOyLCnsiAFptDmMA2QJY7e5t1wgiincFKe4G1msYtInIPUUD4KBBg7BixQolq0Dk00xWmzwDWCNqIdnswcwTAdBsdRgDyBbAaqfXqFEvOBYAENDwQ2jDk/HpP0cUrhUR1VSK/hreqFEjvP7669i8eTNatWoFjUbjdP7ZZ59VqGZEvsFksQEaeyjTqUpbAIssnmgB5BhAT2sb0RYp2SkAAF3EPzBfutk+0UcULnMlEfkaRb+FZ82ahYCAAKxZswZr1qxxOicIAgMgkYeZrDYIWnsAjA0ORIBGDwCwShZYbJZqDWoWh4WgOQvYM8Z3Go//zqfgcPYuAICgzsW2Excx7re96NcmFqNub6JsBYmoxlA0AKakpCj59kQ+z2Qp7QI2aHQI0vshp+Sc1VStAdDqMOGE6wB6hiiI+GXAd2g9pwskVTZEfRoGz9oMAJj69xEGQCKScTNOIh9mD4D2UKYRNdCptfK56t4NhLOAvWdgk34AALXjGo9ERA68/mv4mDFj8NZbb8Hf3x9jxoypsOxHH33kpVoR+R6bTYLFJkFXMglEpYFerYFkU0EQrTBW8zhAi1WCwFnAXtE54XosPPYdNME7UZTeD7AZlK4SEdUwXv8W3rlzJ8xms/y8PI6LQhNR9TNZi3feKW4B1Ipa6DRi8UQQa7XPBLZyL2CvCdOHyc8Dm05A7sG3AUnNCSEA/txzFttOXMLrd12HbzaewLHzeejVMhpdG0dc/mKiWsTrAXDVqlVunxORdxktJQGwdB1AnVqEJGkgoKjaA6CFYwC9JsLgHGZEXTpsRfHIN1kQpNeUc1XtdOx8Hg6k5aBPqxgcz8zHMz/YGx4y84z4c08aAGDellN4/97WMGhVeOaHnVjybFdcFxukZLWJPI7fwkQ+ylQcAEsWgtaqtNCpVYDV/rVQ3WMArZwF7DUJQQmID4jH6bzTAAC1/2GYiuKx+dgF9GwRrXDtqo8kSZftLbrtQ/sKE89gJ6KCdPLxkvBXYuzCPfLz3p+uw4kpfaqxpkQ1DyeBEPmoki5gUbT/aQ+A9hZAANU/BpDrAHrV/Lvmy891kSsg6tLx1LwdCtaoeqVnF+Hmyf/go+TDAOxh8N8TF5FdYJZfT1y83+maczmV/zu9NeVi9VWWqAZiACTyUSUtgKLKYRawRgXYiheDru5ZwFYJAmcBe02wLhiCOVJ+rQndVNwKWzt8vuoozuUY5d1Ovtl4AoO+2IT7v9yMrAITtqZcxJwNV77U2H0zNyG70Fxd1SWqcRgAiXyUHADFMmMAbfZusnxzfrW+n9MYQLYAeoXe0kJ+rg3dAlF/Cot2nlGwRtWjwGTBiQulfz+zCkwYX9zadyAtB20nJsvrH16NBf+egslik/9fIapN+C1M5KNKxwDaIMG+DIw9ANp3A8k15Vbr+zmNAWQLoFc83eZZvH9oNQTB3vKnCdqDUQvqYkC7OIVrdmXMVhse+XorNhy94HS87cTkK7yjFfrYnyBZgmEpaABt2FpYcltBE7QLpgvd8M4S4J0lBwEAieH+eKFnU/RpHXOVn4KoZmALIJGPMlntrXElLYBasWQSiD0A5pnzqvX9OAbQ+x66qSEG1Bsuv9aEbHVbzmy14YWfd+O3nae9VbUrcuRcnkv4qyxBnQNN6HpAKOnWtUIbkQxN8G5o66yFPmox1P7HoY/+HSq/kzAkfOd0fUpmPp7+YQckqfZ0o5NvYwAk8lHyMjBi8TqAKvs6gCVdwHmm6g2AVlvpQtBsAfQOURTQIqZ0SRhBZYImdAN+3paK53/aDXPxRKBftp/Gwu2nMXrBbqWqWikFJssVX6uP/Qn66D+hj/kFAKAO3gld+Gr5vKh1DZa6yMUux/4+kHHFdSCqSRgAiXxUaRew41ZwIiSbfTu46l4H0GyVOAZQAQKcl0nRRy/G2IV78MuO01iy174UyoV8kxJVq7ICk/WyZdRBO6EO3o6S4Qby8eJt8TTBuyBoz8MQu/Cy99LW2QBBneN0bOm+tHJK1zxp2YXYcDRT6WpQDVWrAuD06dORmJgIvV6P9u3bY926dZW6bsOGDVCr1Wjbtq1nK0hUg5jKLAQtrwNYvAxMkaW61wG0cS9gBWQbs10PFneD5hmvvEVNCZdrAVQZjsMQtwCG2J8R0Ow16KIXAQDaNHYOQfWb/1rp99SEboCgPQ+Vv30sYGUmhEiSVCNmXHecvBIPfrUFGxkCyY1aEwAXLFiAUaNG4dVXX8XOnTvRtWtXJCUl4dSpUxVel52djaFDh+K2227zUk2JagZ5KziUzgIO1Ks9twwMxwAqomNsRwCAzRIgHxPU9lCoV187QVySJLmlUhO6CYb4uQ7j+ezUgQfk54JggzZ0M1R+R3FWPdepXKap8svDhIdmIaDhh/CrOxfaOqvw575jeOTrrTh1oQBL9qa5HRM4dM5W3PHxmhoze3jz8SsbN0m1W60JgB999BGGDRuG4cOHo3nz5pg6dSoSEhIwY8aMCq978sknMWTIEHTs2NFLNSWqGUpbAIu7gFUahPhp5IWgq78FkDuBKKF1RGvM7zMfk274BlajfTygqMkCAMxYc0zBmlXe5CUHkDhuCV79bR8AQB/9O9SBB6EJ2VJcQoI+7gdo67j2+vjV+wpFcN8CNrfXXAxsPLDC985VlY6L1EUuh3/Dj7H60Hl0e38VRs7bgbunb8T2k6WLRpssNqw7konj5/OxPy3H3S2JaoRaEQBNJhO2b9+Onj17Oh3v2bMnNm7cWO51X3/9NY4dO4Y333zT01UkqnFKAqAEeyuKVtQi1E8rtwBW+17AVkkeb8i9gL2rRXgLtIyOhWQOBgAIGnsL4NGMPOQWmXEovXqX/KkuNpuEJ77dhplrj7s9r4/+EwFNX0dg83HQBO1xW6YibSLaIDYgVn7dIaoDPur+UYXXiOpcqAP3yq93pWZh4IxNuJRvgs0m4VxO6S9OFW9SR6SsWvEtnJmZCavViqioKKfjUVFRSE9Pd3vNkSNH8PLLL2PdunVQqyv3YzAajTAaS/9RzMnhb3d07SrpApaE0lnABq3KYy2AFocxgOwC9r5GkQG4uV5DbLtwFKI6Sz7eavwK5Sp1GeuPZmLF/nNljjp3uQpi5XfreK/DYqy/9CX+OPYHRrQZAbWoxuCmg7Hy1ErcGH0jnrv+OahEFRb2XYgjWUew+NhibDzr2oigjUiGJbeV07G3/tyPX8sssp1/jY2xJN9Sq76Fy24KXt5G4VarFUOGDMGECRPQpEmTSt9/8uTJmDBhwlXXk6gmKG0BLB0DqFWJcgtgobWwWt+PXcDK6xCfiG0XAF1kMiBYYcrsefmLFLT3TPkTWKqqYXBDJLWojyRMwqQuk+Tjwbpgp32TAaBpWFM0DWuKO+rdgRdWv4A9GUdx0VS6RqJKlwFBlQvJGigfKxv+AGD2+hRcXy8Ues219/fdaLHiodlbcUP9UIy9s5nS1SEPqBVdwOHh4VCpVC6tfRkZGS6tggCQm5uLbdu24ZlnnoFarYZarcbEiROxe/duqNVqrFy50u37jBs3DtnZ2fIjNTXVI5+HyBvs6wBKsJV0Aau00KpFuQXQaKnmLmCHSSCcBayMGP/SXSx0Ee6/52oCm03CxmOZeH/5IZdzgsq5ZbpdSBKS7012O6zg0x6fys/DDeFVrodOpcNnt32GWXdOhVbUOp0zJMy97PX/HMxAs9eXwVLc2n4hz3jNLCS9bF86tqZcxOerro1xolR1taIFUKvVon379khOTsbdd98tH09OTkb//v1dygcFBWHv3r1Ox6ZPn46VK1di4cKFSExMdPs+Op0OOp2ueitPpBD7IsClsxQ1ogYahxbA6p4FbOU6gIprENzA6bWguQDJXMdr72+22jBh8X/o3DAcSa3cb6mWXWBGm4nldEsLFugilsovBzUZhNdufg2iIOLf//sXb258E1F+UegY2xGnc0+jR90emN1zNmbumYlxN4274no3DWuKfwb9gzOXTLh/hX3FCJXhDNRBu2DJbQ5D/DzYjJEwZtzl9vp3lx1Ey7hgPDd/F57p0Qgv3Nn0iutyJa4kctaUGczkObXmW3jMmDF46KGH0KFDB3Ts2BGzZs3CqVOnMGLECAD21rszZ87g22+/hSiKaNmypdP1kZGR0Ov1LseJaiuTxSavAQgUdwGrBQ+OASzdCYSTQJSRGOz8y21Ao/dReOZ+WHLaysdeW7QXY3s2Q7Cfptrf/7cdZ/D95lP4fvMpnJjSx+lcocmK+2dtcjtzVuV3HJrQTdAElf7iHhcQhzc6viG/Votqp67dG6JvAADcGHMjboy58arrHqIPQUgMMLvnbAxbMQwAYIibD8uF26EOOAwEHIbpYleIunSo/E5Cshqg0p9FUdrd+HJdCnRqe4fbtFVHvR4Ar4S74VNUu9Sab+HBgwfjwoULmDhxItLS0tCyZUssWbIE9erVAwCkpaVddk1AIl9itNgAsTQAalVaaFSW0hZAD0wCUQkcA6ikYF0wusZ1xbozpculGOLmIzenDSAWAjY/fL/5FCxWCVMGtq7293ecIVui0GSFIACLd5/F7tNuxvzBCr96s1yOBmoD3ZT1vA7RHZxeq+v8LT83xM2Dys/53xlrYRzMl7qUbr2ogGuk15m8rFaMASwxcuRInDhxAkajEdu3b0e3bt3kc3PnzsXq1avLvXb8+PHYtWuX5ytJVEOYLDZ5WRZREKEW1dCpSmcBV+cyMJIkFY8BLH0/Usb026dj4+BtTsd0kUsR0ORtqAPt6+zN/zcVe09n47tNJ6p1txBRLG1VysgpwtA5W9H8jWW4cdLfmPr3YbfXCKoCt8eVCoCiIOK5Vu4nA5YNf4D7+q/4Lx1GixW2GrBbSHnY/lf78VuYyEc5dgFrRHvo06gFpzGA1TVg3WyV7K0QDkvOkHL8tFrkHn5dfq2tsxaCYIMh/nsIGvuiyX2nrcfrv/+H8X/8d1XvlV1gxvaTF2G0WDHLYT2/KcsOYu3h8wCAnCILzma7b3EWdefdHg/VhV5Vva7GY+0GoFFIo0qV1YRuhV/iVHn3FQB44rvtaPraMjz41ZYKrlQWe4BrPwZAIh9lstpKA1nxDEetSoQklYaz6moFNFrs71PS4lgSOEkZKlFATEAdSFbXSW0BjT5wer38v3Qk7z+HBf+WP4Smol8Ukj5Zi4EzNqHbe6uQXVi6hMuvO1yXTSlL1Ke67f5tG9EWT7V56rLXe4ooiGgf1V5+naDpKj8f1nIYxrYrbSEU1XlQ6dMR0Hiyy302Hb8gh2BPkq5oGgjVdgyARD7KsQtYo7IHMrVKhCiVDg2urnGApeOfOAu4pljwREfU8SuvG7U0MOQWWfD4t9vw0i97cfx8nkvJMQt24baP1qDIbHU6XhIaS1r2zuVU5ZcJKwx1Z8E/8XOXM01Cm+C73t+hUWjlWuA8pV1kO/n5yJt7y897JfbC0Nb34NUbXXeYErUZLseGztmKzccvwGy1YduJizVm9i1bAGs/fgsT+Sijmy5gwB4GJUkFQbBW21IwJf+oiSJbAGuKunX8EKwPxEWj6z65hrqzUJR+DyRThNPxczlGNIgIwOlLBdh3Jht3toiWF0BefSgDHRuEy7OHH/92m8t9K0vUXoDa33n7t5tibkKr8FYY1GTQFd+3OvWs1xMZBRnoFt8NJ3NOysdLtpYr+eXKkSHhG+QfewFlR9jdP2szHu1cH19vOIE+rWPwaKf6CPXXomFEgEc/Q0UEhzrabJLT+E2qHRgAiXyUUxeww5g8rVqEZNMAKmu1twCKov1PBsCaQa/Suz2u9k+BLmIFis486HS8ZPvAPp+ud+rOBYDXFu1DZp4JANAg3P+K6mPQGWHx+xeSJcjlXHxAPJ67/rkruq8naFQaPNryUQBAqD4U9YPqo2NsRwRp7XV3t/SMqL0AVcAhSKYwqIN2wXypMySr/Wf19YYTAIC/9qThrz1pAIAtr9yGqCD3/42q4kqG8jq2AFolCSKnhdQ67AIm8lEmixVCcQug4y4H9nGA1TsTWB4DyBbAGuVMXuk4vMIz9zudE9WuS7I8PGcrcovMLuEPgBz+AOB4Zn6l66CNWAFt+N8I1KsxvG8K9NF/whD/g0u5KD/XXZ1qijB9GBbfvRiv3PSKfCwxOBFTe0wFAPgJsfJxdcAB+Df8CLqIldCErSt7Kyc3vfOPR+pbVdYaPFuZrhwDIJGPcpoFrCoNZFq1w37AlurZD9hotrcclR1zSMpyDPiOi0EDAEQT3Gk1vpxdOqpAG/43dNG/QtSfhi58JXQRf+PeG4OwOnV1udcMaT7kqt/X226rext2PbQLt/iXLlCtDS2d+asJ3gXAAlF3FiXjY8vadyZbke3jHBeCZgCsnRgAiXyUu1nAAKBRFXcBozpbAMsEQLYA1ghvd34bicGJyD/+rMs5lT4d2ohl9gWiq4HK/xB00Ysg6s9AF/E3tKFb4Z84TT7fIO4ick25bq9dN3gdgnXB1VIPb1OJKoy6rQVC8v/P5ZygyoM+diH8G3wKbdh6t9ff9dl6/O/HncjIKcJjc//FqoOuE0k8zcqVpGsljgEk8lFGs03uAnaclatVi0A1bwdX0gUMBsAapVdiL/RK7IX6m/8CAATk90ee/+/yeV34avt2Zun9AZteHq9WVYIqF351vwYAxEZkI9PN2tLv7XrZ5diHt3yIhMAEhOhDruh9a4rYEAPeG9ANTyR/73RcEC3FrYCALmopbOYQWPKaQ1AVQh24D5qQbShMHYo/9wBnsgqx81QWVh7McNlGzxMc2v9gtlgB8P/Z2oYtgEQ+qtxJIA4tgNU1C7ikC5gBsGYLKLoDW4Y4L06sDjiMgEbvwy9xKuC0npwNov6U037SMsEsHxf1ZxDQpLQLNNNyoMI6JCUmoW+DvhjcdDDuqHcHmtdpfoWfpmaJ9o++bBlD/I/wq/8FAhpPhj56MVT6NPg3eh8AsPNUllzO3VIxNpvkshTP1bPCr8FUPL16GFIy8zHky81Yf8R11nhFNh27gMlLDpT+Ekg1BlsAiXyU4xhAxwBo0KqqfT9geR1ABsAaTQLgp/Fze07U5EJbZzVMF24BIEITtg76qKUwZ7dF0Vn7BJIXezXFoYx0rCy0L9Lcv/5DuGQ6i7VnK1+HiZ0mQq+++pmvNY1jAFx932oczTqK4SuGu5RT6Z1/WIJgswdqqfT/mSavLcXBt3ph4fbTuLVZJIINGtw5dS2yC81YO7YHzmQVwubQbVuVDtwisxUX802wSRJE3XmodOfx34XzeGreZhxMK8LGYxcw66H26NkiGvvO2CcKtYwrv3v+gS83AwDCA3R4vFuDKtSEPI0BkMhHmSw2CBrXQBakV0PK80wXsATXSSdU8wxuOhgLDi1wOa6LXA5d5HIUnBwOXfhKAPaJDEVp90DQZOPxrj1x/fd3y+V/P/Fdue9ReHYQDLE/Ox1LSkyqleEPAAxqA96/5X1YbVbUMdRBHUMdvNHxDUzcNBE3Rd+ELenlbwunDjgIS24rp2Nv/v4fFmxLRUywHtHBepy+ZB+r+cGKQ5i3pfxdWy7nnukbsT8tBwAgOmwUc+h8OoAQAPat7GY/3AHDvrGv9fjfhDvhr1NDkiSk5xQhJtjgct+UC5WfGU7ewQBI5KNMFhugdV0GJsigAXKrtws49WLJRAK2ANZkJeO+Xr3pVYxpPwapuam4d/G9LuXatNyNw9mlXXqG+HlQBxxC/99/qvR7WbLboHnzU/DXW3FHvTuw6OgijGk/5mo/Qo3Wq34vp9eDmgxyWti61Tetyl4CAFD5pbgEwAXbUgEAadlFSHPYR9ld+Cs7h2PfmWxMW3kUgXo1Jt/TCmqVfTTY3tPZcvgDAIilk8AEVQEkS4j8uiT8AcDpS4VoGh2Il3/ZiwXbUvH5kOvRp3VMuXU4mJ6DIL0GKw9moGFEADo2rOP2c5NnMQAS+SiT1XUrOAAI1KtLxwBWUwvgkr32hW1tcN15hGoeQRDgp/FD07CmiMx6E6etq6Cts1Y+fyT3XwgOI8jVAYcAAKm5qeXe8/WbX8dbm98CAFjyGwBQ47FGb6FXS3tQuBaXealuX9z+BUavGoNCa4HTccHNmoxV4bgXcGaeEXd9Vjrj+Oftp/HJ/W1xZ4to9J3mPBNZEEv//xdUBQAkqAP3wlqQCMlauo3g3wfOoX64nxxKn/5hB9ok9ECsQ0vgj1tPYWT3hli85yzeW3bI6X28MamFXHESCJGPstokQHQNZHq1Sh5vVF3LwMSG6AHYAIE7gVxrHrj+ehgzeiM+/41KX3NjtPMuGM3DmuOuBnehbmBdtI1oi8LUx6q7mrVC57jO2PLgZvRv2N/puCboPwQ2fxmh170BdeA+aOushKA9X+n75hZZ8NvO0/jnwDmMmr/L5fxz83dh6t9HXI4LZVoAVf6HYYj/Af6N33Eq9/7yQ1h32HlySJd3V6HBK0ucjnV9b5VL+AOAk+weVgRbAIl8kCRJsNgkaN3MAtZpREg2++tqWwjaUjrjGOAYwJpKcLPb16OdE9E0OhBtEkKw5ower6x/xbVQGV/1/AopOSmYtnMa4gPicV/T++Cn8cOfd/8JCRIarF3qgdrXDoIgIEQX4vacRTLBEG9fSkYb8TfyDr4FQFV81gp10B7YzGGQzKFO2+n9sOUUfrjMuMDvN590PiCYoIsoXfRbUBVALJ6gIggS/OpNh+liF+hjfwYkNR7/8RkAV9aV+78fd+KPZ7pc0bV05RgAiXxQycr+7raC06lVgGT/aqiuFsACk9U5ALIFsEZyt96vShTQtXEEAKBvw76I9o9Gen46Pt35KdLz053K3ppwK167+TUIgoAGwQ3wUfePnM4LggCBe8pe1uBmg/HN/m/QMaYjNqVtcltGEGwQ9emwFcUBALQRydCFrwYA2Ex1kH9s7GXfR+V/GNrQjSg61xd5Rufwpov6C6KutFVPUOVD1GSVXut3Cga/ki37zNBHLUbh6UcgqLOgj/kV5pw2sGS3L/+9DSn2VQgE4JxZA4AB0NsYAIl8kKVkayc3W8Hp1KUtgNU1BrDQZJXHGwLOC0/TteWG6BsA2Lc5u+mHm+TjI9qMwNNtn1aqWrVKQmAC1g1eB3+tP+75/R6cyDmBEF0IsoxZTuX8Ez9D7sG3AEmDoMh/YSxebUnUXgAEIyDpAMFsn0Wc18xpKRlBnQO/unMAAAGBB1GU3hfmS53l8+oA5/UadZHJFdZZ1Nm7pA0Jc6HSp0MdcBhG7QWYLnSDJmgPrEUxsBUlQFDnQBf1JzRBe+RrCwDsPd8NrSLcT4Ihz+C3MJEPkvf2dLMVnCgIpTuBVNMs4AKzRQ6bKkEFUeDw45rIXRdweQxqA26IvgE7zu3Ar/1/RYPgyq/xFh6gRWaeCR3qh11BLX1Dye4nU7pOwdozazGk2RAIgoAOn0yEOnAfVIYzAAD/hh/AknsdjDbncXSiJhsQi2CI/wGiJguWvKYoTH0UACBoMuHf4BOn8vroxTBfugmAGhCLIKiqNvxD1F6ALvJPiLpz8jFd+Ep5uSDJ4oe8I69BH7MQ6oDDLtfvyNjBAOhlDIBEPshSpgvYsQVw0/EL1T4LuNBklWcUBmgDquWepCxBEDDrjlnIMeUgTF+1ILf+pVtRYLIizF97+cI+rkV4C7QIbyG/nnLbKEz8awfE+hNgtpkgarKhDXPtJvZv6Nz9XjJTW9CeR0DDD92+l6hPh80YBV3UnxBEM3SiP3IvNoc2ZJvb8mVp67jfzxgABHUBRO15t+EPAHaf312p96DqwwBI5IMs1vJ35ogK0gFn7a8PpF+olvcrMFkhqOwtFKG60Gq5JylPLaqrHP4AQK9RQa9RXb4gubjn+njc3S4OPx8ukpfVqSxRfxrx9f7FpXLO+ydOc3rdt2ESwhq3xqxDpQGwdURrtI9sj6//+7qqVYd/w4/LPZdZWLUt5ujqsR+GyAeVTgJxnQX8zK2N5RbAc3m5V/1eNptkD4Bq+9pmwbryt40iZXGCxrVBEAR0T+jucrzoXG9YcpuVe51/4jRkq0rDXIs6FXe51g+uj06JdZ2OfdrjU4zpMAZvdX4Lj7Z4FMn3JmPlva4tf8+0HY2pPaZW/EEcXCisnl82qfIYAIl8kNwFLLrOAg4P0JYOFhfMV/1eRSWbwBd3AQdqAysoTUSVEekXiU0PbJL3GP6q51f4JGkUkHUb4v0bl3udTbL//2hQG/BVz1nYM3QPHr7uYbdlu8Z3RavwVmgaag+VEfoYucV3QKMBGNNhDKL9oxHuF+R0XR19HTzZ5jF0j++OLnFd0Dq8tcu9NaIG/es+hoKTjwMA8sx5VfsB0FVjFzCRDyppARRF151AtCoRKG4BFMSrD4DHz+cX38u+pEyAhmMAiapDgDYAc3rOwbmCc+gQ3QGIAZJaPgmLbRgeT34c4YZw6FQ6/HHsD5drtz64VX7eo24PfLP/G6fz/9f8/+SJPQv7/Yz0/HRoRA0ENzOFBEGAX1FnFOg3AAASgxMBACpRhRm3zwDgvM3dzod2Qi2qsf3kRcxb+SeA6ltzlCqPAZDIB1nKdgE7tAAKggCpGlsAf9hqX4BWUNlbAP01/ld9TyKySwhKQEJQgvxaEARoVBrM7TVXPtY+qj3Wn1mP5JP2pVwi/SKd7tEusp3Lfcvu5lLS0lie5Ic/wpx9c7DkxB94vsPzLufH3TgOH2z7AFN7TJWXgdKqVIDDovOSJLkNmOQZDIBEPqh0EojrLGAA1doCaLbY3ys6BLgEdgETeds9je/BPY3vQYG5ALP2zEK/hv2czouCiDc7vonfj/6OXed3oY6+TpWXZAnS+2FUh2cwqsMzbs8PaT4Eg5sOhkosnfyjUQuQpNJfPousRTCoDe4uJw9gACTyQaVjAK2Q4LozR8kkkMq0AGYXmBFkULv9zX3J3jT8vP00ACAmVMClPLYA1mRsfKnd/DR+GNV+lNtz9za5F/c2uRdmqxmCIHhksXbH8AcAGofhJoC9FZAB0HsYAIl8UJHZ3vUrCPYA6DgLGIA8CUQQLbBJtnIXbt556hLunr4RSS2j0So+GP3axCI+1A8AkF1oxsh5O+SyFwtzAHAMIFFN5s19urUqEYAIyaaGIFqqbd1RqhwGQCIfVGAqDoCi6zqAAOSt4ACg0FwIf637VrtP/zkCAFi6Lx1L96XjvWWHEOavxZPdGqB/2zinsjGhAtIz2QJIRHZadfEvljYtIFo4EcTLuAwMkQ/KN9rH/pXsBOI4CQQAIGnkENj787/kFsM/95zF0DlbkZZt/6KW9xR2cDHfhMlLD+Lmyf84n+BOIETkIMTP/otnyaQzBkDvYgAk8kH5Jnvwg5uFoItPwGa2L9h8OjcN64/YV+l/5oedWHv4PDpOXokv1hyDqXiCx+XUr+OHS0X2/QdCdCFXXX8iuvbp1CpEBOrkXzYZAL2LAZDIB5V0AUsls4BFN+N+rPbB2IJoRK7RLHf3lpiy9CBM1soFwBsTw+SV/rkVHBGViA3WOy0FQ95TqwLg9OnTkZiYCL1ej/bt22PdunXllv31119xxx13ICIiAkFBQejYsSOWL1/uxdoSKaek5U5COcvAwGEcoGjC6AW78VGy6ybuO09lVer9HuyiR645F6IgIso/6soqTUS1jlYtyt81+eZ8hWvjW2pNAFywYAFGjRqFV199FTt37kTXrl2RlJSEU6dOuS2/du1a3HHHHViyZAm2b9+OHj16oG/fvti5c6eXa07kfWarcwAsOwZw2pB28vpcgmi66ve7YDoDAGga2pTrABKRTKMSIVnt3wmZhZkK18a31JoA+NFHH2HYsGEYPnw4mjdvjqlTpyIhIQEzZsxwW37q1Kl48cUXccMNN6Bx48Z455130LhxYyxevNjLNSfyPrNVAmCDBNet4ADgrtaxcrcMqiEApuWnAQBi/GOu+l5EVHto1SIkCwOgEmpFADSZTNi+fTt69uzpdLxnz57YuHFjpe5hs9mQm5uLsLCwcssYjUbk5OQ4PYiuRWarTZ4AAriZBQxAsukAAIJYtXE5TaKcZ/mKurOYsnUKAKB5neZVrSoR1WL2FkA9AKDAXKBwbXxLrQiAmZmZsFqtiIpyHlsUFRWF9PT0St3jww8/RH5+Pu67775yy0yePBnBwcHyIyEhodyyRDWZSwB0mQUMSJYgAICgqcovOjZMGNAQ6sB9UAXsBwD4N/hUPtu/Yf8rqzAR1UpatSgvPF9k5ULQ3lQrAmCJsltRVXZj6R9//BHjx4/HggULEBkZWW65cePGITs7W36kpqZedZ2JlGC2SvIagADcbvtkEOyt4XF1Kt8FfPstK/DE2iQY4r+HX8K3eOVu522dYgLYBUxEpXRqUd56kjuBeFetCIDh4eFQqVQurX0ZGRkurYJlLViwAMOGDcNPP/2E22+/vcKyOp0OQUFBTg+ia5HJYgMcFoF2t9XbpL5dAQBBAc4z84Z2rFe8Z6wFgH0yiSAAC0d0xJaM1U5lPzv4XHVXnTyoMr8wE1WnIL2GLYAKqRUBUKvVon379khOTnY6npycjE6dOpV73Y8//ohHHnkEP/zwA/r06ePpahLVGPYuYHsA1Kl0bstcF2Uf4nCu4JzT8Se6NcD8pxsiuPl4DEnagYNv34ak25biYMGSCt9zwV0LqqHmRFSbBBs0bAFUSK3ZC3jMmDF46KGH0KFDB3Ts2BGzZs3CqVOnMGLECAD27tszZ87g22+/BWAPf0OHDsUnn3yCm2++WW49NBgMCA4OVuxzEHmD2WqDIBa3ALoZ/wcA0X7RAIA8c559GzebfaD28tRfMHXnewCAxScW4vqYFlh3Zg3WnVlT4XtyBjARlRVkKG0BNFqNCtfGt9SaADh48GBcuHABEydORFpaGlq2bIklS5agXr16AIC0tDSnNQFnzpwJi8WCp59+Gk8//bR8/OGHH8bcuXO9XX0iryoy2wDBDKD8FkA/jR8CtYHINeXCEDsfkqRBUfoAOfyVmLBpQqXek3sAE1FZ/loVWwAVUmsCIACMHDkSI0eOdHuubKhbvXq15ytEVEMVmCwQigNgeS2AABDlF4VcUy7UgQftByRV5d8j9WHUb7IMGYX2LmS3280RkU8zaFVAcQDkVnDeVSvGABJR1RSYrIBY8RhAd+c0wbsqdX9LXhNY85ojVF/+uppERHqNSt51iF3A3sUASORBNpukdBXcyjdZLzsJBACOZR2r9D2/vvNrAEDLOm1RmPowAOCZNmOgElQY2dZ9yzzVLJwDTN5m0JS2ALIL2LtqVRcwUU3y39ls3D9zM567vTGGd22gdHWcFJos8jqAFXUB92vYDz8d/qnc89/0+gZ/Hf8LkX6R6BDdAavvW438Qi26rV8LALg+qj02D9lcYcgk5bVNCMGu1Czc1yFe6aqQj/HTqiBxGRhFMAASecgrv+1DrtGCt/86UOMCYG6RBRArngQCAGM6jEGzOs3w4jc2aOushbbOWvncrQm3ok1EG1wfdb18rI6hDkRb6cLRalGAXq33wCeg6jRv+E3Yn5aD9nVDla4K+Rh9mRbAym7gQFePAZDIU6Sa2f0LANmFZgh+l28B9Nf4Y1CTQRhr/QvmrA5yAJzYaSLubny322tC/bWY2L8FVKIAPy2/Yq4F/jo1bqjP8ZrkfQaHFkAJEkw2E3sMvITfzkQ+xmSxocBkhcb/8mMAHdnMpa1DBo2hgpLA0I71r7h+ROQ7HMcAAvZWQAZA72AAJPKQmtr+l11o7/oVKjEL2ImkQQN9FwQG5qJ7fHcP1Y6IfIlBowKggiSJEAQbiixFCNZxMwZvYAAk8pCa2gOcXWgfo6fX2CChcgFw6XNd8ff+c3i82zT7mB0iompg0BZ/n9g0gMrIpWC8iAGQyMeUtADqtFYUoeIxgCWaxwSheUyQh2tGRL5Gp7avRidJWggwcjFoL+I6gEQeItXQTuCcQnvXr0ZjD4J+Gj8lq0NEPkwQBMQG67kbiAIYAIncKDRZcfx83lXdo6Z2ARstVgCAINq7gv01/kpWh4h8XN06fpBs9qEoBeYChWvjOxgAidzo89k63PrhGvx74qLSVal2RovN/kS0j7XxVzMAEpFyNCpRDoB55qv7xZsqjwGwlrmUb4JUU5ueriHHz+cDABbvPqtwTaqfHAAF+6r77AImIiVpVCJgtS8Yn2/OV7g2voMBsBZZfSgD7d5Kxuu/71O6KrXG1WTpmprDTcUBUBLsLYAMgESkJI1KkFsAGQC9hwHwGrTl+AWsPpThcvz95YcAAN9vPuXtKtE1pKQF0FYSANUMgESkHDW7gBXBAHiNMVqsGDxrMx75+l95OY8SjtsnZheYMW/LSWQVmHAlcovM+GrdcZzNqvyMLJtNwp7TWTBZbLBYbdh3Jhs2Ww1tBqukq5nJ66lPfinfhJ2nLl3x9SUtgDYUjwHkJBAiUpBWJQJsAfQ6BsAaTpIkpzF9py6UzpAqMFnk53PWp2DfmRz59S0frMKrv+3D/37cCQD4fNVRvL/8YIXvtfbwebz8yx7kGy14Z8lBvP3XAQyetQlnswrRb9p6fFDcwmix2vDMDzvw1brjcpgAgFnrjqPftA146Zc9eOOP/3DXZ+sxffXRSn0ud4wWK+6buQkfrjhUYTlP+n7zKbz8y56rvo/jf6urdcv7q3D39I3YeDTziq4vmQVsgT3cMwASkZLYBawMLgRdg2XkFKHTlJWw2CT8+b8uaBkXjCMZpc3jExfvR982sXhnyQGcvuTcUpdVYG8dXHckEw3G/YWShrh+beLwvx93IC2rCC8lNcOPW0/hgRvrYtuJi1i0yz7hITJQJ3cxp14sRKcpKwEAe05n48lbGmDjsQv4c08a/tyThmmrjmLl890R5q/FlKX2gPnbzjNyPT5YcRjP3NrYqW4miw19P1uPmBA95j56I9YcPo+3/9yPtwa0RJOoQIT52xcmXrYvHVtTLmJrykX0ahkNrUpE46hAl59TvtECf93l/ypn5hmxdF86+reNRZBec9nyJeb/m4oX7myK8ICq7U/pGHDv+mw9Vj7fvUrXlyenyB4m/z6QgU6Nwqt8fW7x9VapeBIIu4CJSEHsAlYGA2ANZNuzELlbv8epHBXeEAXkiH74Y/piZLZoiD/35aCL6I8cyQ8H/kvDln2HkQs/VPSf0rEX9s6pa+Xnry3a5/RniU9Xum+1A4BW41c4vc4qsHcV/9/N9Sr8TJ/+cwQrD2Zg3vCbsCs1C4fO5eLQuVxIkoSH52wFANw/azNEAehQPwzxoQbcnFhHvr7Pp+sBAL881Qnt64XKx7efvIiBMzbh8a6J6NYkApl5RtzdLh4X803YdOwC7rguCtrileaf+HYbdpzKwpbjFzBtyPVO9cs3WmC22hDi535XDNtlWislSYLg2AdfRsmsYm+SJAnn84yIDNQ7HZu9PgWABTbYgyAngRCRknacvATY7N+9RZYihWvjOxgAa6A1G9aix7lV6ACgg+N/ocNA93J27SqQdMiBH3IkP+TAv/hPP+RI/m6OO5/PhR/MV/FXYfrqY5i++li55z9OPoxP/jkCwB4EZ649Lp+bsHi/U1mbhOJWP+DXHWdQ1sAZG3HwrV7Qa1TIKjBh4IxNAIAv16Xgy3UpAIDW8SF4et4OHEzPxajbG+O52xpjytKD2HEqCwDsLZdD7PfLM1pw/HweBs7YCLNVwr4JdyLATWuixVp+AEzefw4v/7IHHw9ui25NIsotd7mQWFWXG5/45h//4dtNJ/HV0A64/booAJB/BhBLx4YyABKRkg6m50IdZP/HjTuBeA8DYA10sW4Sxp7WIQgFCBIKEIT84j8LECTkIwgFCJSP2/9n8ROM8IMR0cKVTQ4wShrkwoA8yYA82B+5kgH50Bcf80NuybniPx3Ll5wrhA6Ac8gpCX8AnMIfAMzdeKLKdV15MAMtYoOwcPtpt+fTs4twMD0XgH0MX7PoIJf3lSQJB9Jy0fvTdU7H56xPwcjuDV3u6TjWUZIk5JusclB8/NttAIBH5/6LY+/0LrfeHd7+Gw/eVBdjejZ1OVc2HBaarNiVmoUb6odCrXI/VPdyy8x8u+kkAOC95QfRvWkEsgvNyC2yDw0o2QVEK2qhESvfHU5EVN2ev6MJPtls74liC6D3MADWQLd2vw3Pr7NdviAAETYEFAfFYIeAWPpn5QKkTjBDBzPChZzLvGPFrJJQHA79kOcQIMuGyzzJgALokS/p7H9CjwKp5E8d8qFHPgxuWyZfW7QPF/PLn92853S2/Dwzz4gR3293KZM4bonbaz9KPux2C7iZa4/huduaIECvxnvLDuLbTScxbUg7BDqMJbSWmfFcEkJLXMg34dOVR10C4JbjFzBy3g6M79cCfdvEAgCemrcdqw+dx/N3NMH/bnMeQ1liw9FM1H/5L/RpHYPP7m8HUXTfuihJwBPfbcfKgxl4pXczAIAgcgYwEdUM/dvGYerG4i5gKwOgtzAA1kCh/uX087phg4gcBCBHCsBpoMprj9gDZKH9Idj/DBQK4V/8OtDNOcfX9jIFCEAhVIIElSAhGPYwimro7TRJqjIBUYcCkx75Gn1xgCw+5xAmD69YhztF1zBZct4IDSqqXMlkGEc/bk3Fj1tT0TwmCAfS7CH5mR92upSTJAkvLtwDk7X8AP/nnrO4q3UsFu08g1ELdsnH//fjThgtNlxfNwSrD50HAHyYfBgjujdETqEZWrXoFDhLJgT9tScNNzeog0YRAZAg4ZVf92Lg9fFO5UrKvrOkeCa4yv4lywBIREoLNmjkMYB5Js4C9hZB4r5hVywnJwfBwcHIzs5GUFBQtd576d40PDVvR7nnb28eib8POC8G3TgywGmWcIkujcKx/gqWDHl3YCvUDfPHA19uLreMXiOiyGwDIMEAoxwSI7UmiOa8cgJkAfwEI/xRBD8UwV8ogh/sr/2FQvjDCJ1gLvc9r5ZVElAIHQqhRaGkc3iuRyG0KIAORdChQLIfd3xeCF3xNVoUQI8iyV7eflyLVvVjsOZEPqxQVVt9gw0aec3HCf1a4M0//rvqe4aFH4c5YhaahTXDz31/vur7ERFdKZtNQqM358O/0TsQIGDTkE0e/+XUk/9+XyvYAlhDJbWKQdfG4Vh3JBPhATq8PaCl3JUZoFNj2pDrce8XG7HvTA4+uq8NeraIhkoQkJlnxJRlB/HXnjQAQLPoQHw//CZc98YyFJisSAgzQKdW4WhxUHyiWwM837MJikw2bDp+wam7tHlMEFrHhyBlcm8IgoD6L//lUs8FT3RE/883ABBQCD0Kocd5KRS/vdQTbSaucClfWWpY4Aejc0AUigNj8bHSAOlYrvi4YIQ/Ch2eF8Eg2LuNVYKEABQhAEXV0krpJB2AHjBKahQVB8YCqThQFofEkudFkhZF0MAI+59Fkrb4ubb4XPF5oxZFggZF0OK7xacRL2hhLD5vhAYmqFHVDzKsayy+OMgWQCJSnigKCFTXgdWqBVQmZBZm8rvJCxgAa7BP7m+Hn7el4u52cU5r0P06shP0GhX+/F9Xl2sSwvww7YF2+HBQGxSYrAgx2LsMf3qyIz795whe7NUUWpUK7y0/iBG3NETLuGAAgE6tQq+W0YgPNchrCjYpXnOvZHLCH890xq7ULPRpFYPJSw/igRvrymXKCvbTYMaD1+PZ+TvRMCLAZTxcCZUouIydAwAL1MiBGjnwL+3Wvsq2ahE2+KMIehhhEEzwgxEGGKEXTPBDEQwwwSDYjxlggp9ghB72yTUGwVT6HCYYhOLyMBZfY7+fKNgrqRMs0MFSbV3hFbFKQmmILA6PJUHSWBwsixxCZhG0CEyzLw0TmJsBbJoOaPSA2gCodaUPlQ5Q6wG11v6nqvhPx/MqfoUQ0dUL8dMg0+YHQWVCtjH78hfQVeO3dw0W5q/Fk7eUzkj9362NcCHfhMaRARVeJwgC9BoV9JrSbsiWccGYNbSD/LrsOnglPnugHYZ8uQUv9mrqdD1gX16ldXwIAOCDQW1crh3asR6+3XQS/lr7dUmtYnCkVQwAoNPkf3A2u3Rwb/+2sdh7Jhu/P90ZeUYLOk5e6XSvtgkh2JWaVeHn3Pba7UjLKkLfaesrLFfilT4t8PZfB+zrJhaHybgQAwQBLgtpXxkJOpihLw6DpWGyNHDqYYRfcWDUwwSdYP/T/jBDL5jke+iLz+lKzgvm4tf2P0vCpkqQiltL7RM7KhM4vzkeCNQJhX/GQeC/DVf+kQWxTDgsCYs6N2GygiDpUvYy51Sa4gCqsd9PpbU/r8ZldojIe4INGpy3GgBNFnJMVzcZkSqHAfAa8ryb5UOqW7u6odg/8c4qrVf3Vv8W2HbyEt646zo8cGNdxATrXcp89fANGPfrHuw+nY1ggwZTB7cFYA+rjhMb1o7tgfhQAwCgwSvuZ+qWCA/QITxAh2Pv9MbXG1Lw9l8HKiz/fzfXg06jwuvFC193bRyO1++6DqF+Wnz6zxF8t/kkxt7ZFO8vd956btHTnTHg88qEJHtLnBFaZCPAucWy2kfaStDCUhoQBXuALBsWnYOkGYPahKNhqAr52buB/MMICGsERHUHzEWApRCwmABLEWA1Apbih7X4WMk5yepQDRtgLrA/agLHMKjSlgmJxX+q3QTHsmFSrXVzLzfHXO5VQRlRU/xn9Y0PJaotgg0aSEb7mqRsAfQOBkByUdXFih/qWB8PdawPwD5u0J3rYoPw+zNdkF1ohkYluLzH5nG34WK+CXXruC5KHBOsR1q289IAv43sJD9XiQKGd20AURAw8c/9eO62xgjQqTFpiXMg1GtUeOjmeoAkISWzAK/2aQ5V8dIpbw1oibcGtAQABBk0eH/ZQVhtEvq3i0PbhBAcmZSESX8dcLtu4aD28TickYfm0YHIM1qw81QWZj7UHh+sOCTP5v11ZCfcM31jBT/FUvd1iMdP2+xrHPZtE4vFu11nJQMCTNDABA3g2E1eopzA+fp9fQAAuVvfBQ4cRkCT3kD7UZWql8xqKRMQHZ7Lrx0CY9kAaTVWcK7kdTn3thQBVrO9nNXopm4m+6NGExwCodoeEkueyyGx7Gt1OceL/7zsNRXcw6lMVe7BFleqPsEGDaQCBkBvYgCsgbakbcE/p/7BzTE349a6typdnStWZCnCX8f/wh3170CQ1h4Mgw3uFx2ODtYjukzL4ZR7WuHtvw7g0wfaYdAXm5zO6dSurSiPdUlEUqtoRAfpIQgC6tXxQ3SwHuP/+A89mkbK5UrCankeurke/u+muk4hVaMS5bAIACmTe+NoRh7q1fGXt5or64WeTeUAmBDqfreNOY90QGyIAYnh/hg8czNaxQXjrQEt5QDYOi7YbQC8u10cftuVAkilSwbtfrMn2kxYIT8PNmhQaLLiaEYe3llyAM/3bCKXzTXZx2QGaCseTuCWSm1/aBUepC1JgM1qD4JWk0MwLH5uMboec1fWYipTxlTmcQVlHcs5tpjaK156znOT3b1DEO2hsNyHquLXqiqWF1X28Fnp8sXHVFW9pux5FSCU+VNUMwBXoxA/DaQMe+8Pu4C9gwGwBtqcthk/HvwRhZbCCgNgdW8tdjl5pjxkFGSgQUiDSpX/ZMcn+P7A91hxcgVm3jHT5bzZakZGYQbiAuLkY/nmfKTmpqJJaBMMbB+D+zoklC5wLJghqAohWYKgVrn/3DHBBvl5zxbRAIBfR3au7EdEkaUIerXe7c/VcbKKIAhoXM4EmBIt44Lx6QPtkBBqQHiA69qOZfc1XvR0aT0fuDEByfszcG/7eOSbLJj69xGna/2ikhHUbB7GtPwUny0xQqMSEahTY+frd6DIYpWDtkGrQqv4YPz4xM2QJAnHs4+jXmA9pOTYt81z/NlfcwShNIyiBs8YtFlLg6HN4vDcbG9NtZntx0rOuX3tWK74T7fnKnsPS2lgrfAeDsddgizsQwCuiVZXTxEcQmFxWBTE8o+JaocgWZVj6nJCaNlAKl7mWEV1VAGiWOa9yzumsv//51hHp+diFY+r7C2A1uIAaGQA9IZaFQCnT5+O999/H2lpaWjRogWmTp2Krl1dZ8qWWLNmDcaMGYP//vsPsbGxePHFFzFixAgv1ti9NhH2CRa7z+8ut8zrG17H7vO7Mb/PfHkv1wJzAfLMeYj0iyz3OncyCzPhp/ZzuydsgbkA5wvP41jWMTy36jkAwPy75qNuYF3o1XqXbcRSc1JxOOswbqt7G74/8D0AYONZe9fn4UuHsensJgxpNgRqUY1hK4ZhZ8ZOzOs9D60jWgMApmydgkVHFwEAdCod5veZj0ahjQAA/g0+hKjNQt6Rl6EuZ9eLP4//iV0Zu/DSDS9Bo6raFmeTt0zGT4d+wqyes3BD9A0u592FuBI2yYaLRRcRbgh3Ot6veGcPwN7a9+LCvfaleu5p5RT+XOpyT2s8cetJTN09CY/e8ChWHQzG7tPZqFfHDyEGDf44+S0AYNahl/Hrswtx5NIh2GB1WURckiRsOLsBofpQ7Dm/B+9seQeiIMIm2ReqbhzifpcRqkYl/1BrXMfGXlNsNns4lIOitfS1zeLw2vFR9pjVIWBW5Rrz5e9Z6feo6LXZ9bxU0a5Mkr0MLO6HJFCljQUQGByEzxGC/H9nAckfQxJUEAQR6Doa6DJa6SrWOrUmAC5YsACjRo3C9OnT0blzZ8ycORNJSUnYv38/6tat61I+JSUFvXv3xuOPP47vv/8eGzZswMiRIxEREYGBAwcq8AlKlQTAlOwUZBuzEawLdilTEpKSTyajf6P+kCQJ/Rb1w/nC85jfZz6a12kul5UkCT8c/AH1guqhS1wX2CQbnljxBLakb0GwLhjZxmw0DG6In/v97BToJEnCI8sewYGLzmPpvtrzFTae3YiW4S0x+87ZTuceT34cZ/LOuNTXbDNj4B/2n2uQNghJiUnYmWHfSePBJQ9i99DdEAVR/lwAYLQasSltkxwARW0WAEDtf6Tcls9x68YBABKDE/Fg8wfdljFZTRAFEfnmfKw4uQIHLhzACx1ewA8HfwAAPLb8MWwZsgV+Gj/8m/4v1p1ZhyOXjiDaLw5JLZOQVDyz2dH7/76P7w98j3e6vIO+Dfu6nF95aiWe2/IcYprFYOVdfyGoeOJLnikPU3dMRZ8GfdAush0A4ELhBezK2IWZe2biwMUD+PXIr9j6xDaYbaJ8Xatv7PfNNefilfVjsSdzD+5qcBc6xnZE3wZ95Z/P7H2z8cmOT6AW1bDYLAAghz8AqBdcz+3PiMiFKAKiFkDldyqqFWw2ewi0WeytoDZr6Z+Oz90dk6+xVfKYtfS9nO7t5pjb9y4OrPIxi8P1VTjm8lnLfn6b8/VOzyX3xyshsPi7KQ/2yWUl3/JmUxG4Y3n1qzU7gdx00024/vrrMWPGDPlY8+bNMWDAAEyePNml/EsvvYQ//vgDBw6UhpsRI0Zg9+7d2LRpk0t5dzy5kvhdv92FkzknMf226Vh3Zh0uFV3Cu93ehSiIMFlNaP99ewCASlAhPjAeJ3NOOl3/74P/Qq/WI8eUgxfXvIgNZ+2zWIe3Go4ovyhM2jLJ5T3n95mPFuEt8M/JfzBq9SioBBWsl/kf9/n2z+PAxQMosBRg6HVD8djyxy772fo37I9NaZuQUVC6k8mXPb/E9ZHXy5+rRPOw5hjcdDDG/bEOuoh/AABF6X0x4957YRTSEGGIwOLji3FD9A3wV/tj1OpR8rUlIe67/d/BYrPg0ZaP4kzeGdz9+92oG1gXhy45z/Z1pBbU2P7QdrT51nm5m+7x3dEivAWebP0kVqeuxvhN4zGpyyQ89fdTcpmven6FM3lnsPnsZrx444sIN4Tjtp9vkz9voCYQCUEJaBTSCOvPrMfFoosAgPdveR8NghvIQbmsLUO24OfDPyNUH4pX179abt0fafEInu/wPACg96+9kZqb6rbcJz0+uabHmBLRNcQxGMohsuS5hCKTCZ2+mApz7BKo8uoh8PQ9ECFBBRveGXILbm7V5PLvUQXcCaSWBECTyQQ/Pz/8/PPPuPvuu+Xjzz33HHbt2oU1a9a4XNOtWze0a9cOn3zyiXzst99+w3333YeCggJoNJf/fcOTf4FeXf8q/jj2B26MvhFb07cCAD645QN0i++GXFMubvv5tgqvH9thLLrGd8WXe77E4uOLK/WeYzuMxc6Mnfj71N9XVOe4gDi3rX9KaRPRxqkb/Y56d+DAhQM4nXe6Utf3bdC33J/dTTE3YUvaFgBA09CmFYbJ7gndsTp1daXrXZ5HWjyCuf/NrXT5x1s9jtn7Zju1+Dna/n/boVX5WGsOEdVYDd/6CH51vwYA5B6YIh//5P626N+2escrMwDWki7gzMxMWK1WREVFOR2PiopCenq622vS09PdlrdYLMjMzERMjGs3n9FohNFYOs4jJ8dzA1XbRLTBH8f+kMMfALyw5gUAwE3RN132+ve3vY/3t71fpfesavmy3IW/+kH1cSLnxFXd90qVHUOZfDK5StdXFJxLwh8AnCs4V+F9qiP8AahS+AOAL/d+CQDQiBqYbc7TTW+KuYnhj4hqFJspQn6uCVsH80X7GP6cwmt9unzN5H79imtU2XFhl5sl6668u+MlJk+ejODgYPmRkJBwlTUuX/uo9uWe25K+xe3xr3p+BT+1++VGrlRcQBy+Tfq2yteNbDsSawevRZCu9DerEW1cJ9gY1AaXYyXC9GFVft+5veaiX8N+Vb5ueKvhGNZyGLYM2eJSz34N+6FX/V4I1Lqf9ZtlzKry+3lTQmAC3uv2nvz6xz4/4tMenypYIyIiNyyl3/n6qL8gGuxDm7IZAD2iVgTA8PBwqFQql9a+jIwMl1a+EtHR0W7Lq9Vq1KlTx+0148aNQ3Z2tvxITXU/tqo6NAxpiFdvehW3xN9S6Wtahbdymvzh6Kbom9AopBECNAFYNnAZ2ka0lc+tvm+129C046Ed+GPAH2gX2Q57H96LPUP3oEWdFi7lZt4+E3pV6QzH75K+w1NtnkKoPhTDWw4HAPRO7I1Qneus18FNB2N+n/lu6zyv9zxsemCTU3gpsXvobnSKtS8G3SaiDbSiFkn1k9A+qj1evOFFt/cDgKfaPIU76t0Bg9qAV28qHUf33PXPYVT7UfDT+GFkm5FIDE6Uz714w4t4/5b3sWbwGnSP717uvbvFd8OOh3a4PdcqvBXe7Pim07EHmz+I8R3Hl3s/wP7zWX3farfnbqt7m9N/x/X3r0fbiLYY0mwIfuv3m3z8mXbPoFNsJ+hUOjQKaYSW4S3dzvgmIlLS0ue6wloYL7/WxywEBLPL6gZUPWpFF7BWq0X79u2RnJzsNAYwOTkZ/fv3d3tNx44dsXixcxffihUr0KFDh3LH/+l0Ouh0uuqr+GXc3+x+3N/sfvx48Ee8s+Udl/PNwprhs1s/Q/9F/XF7vdvhp/HDW53fwpd7vsTezL04mnVULjur5yxYJSuMFiMCtAG4JeEW7Dq/C8G6YNQx1MGkLpMwovUIbErbhI1nN6JDVAeXJV4EQcCo9qOw+NhijGw7Eu//+z7CDeHoFNcJRdbSnTpahJeGxO4J3fF7/98RHxiPv47/5XS/BsEN5MkKW4ZswU0/lHZtD2g0AHEBcRAEAY1CGrl8dlEQMfOOmbBJNoiCiEtFl+RFjYN1wXij4xuYuGkirqtzHV6+8WUMXToUANApthNGtBmBfHM+ArWBCNGFID4w3unegiDgsZaP4fUNr0MjauRZ2BpRg8HNBmP16dVu/3s91eYpaEQNXrnpFby79V083vpxnMw+iQGNBqBTXCeYbWZM2DQBgH1yy+j2o6FT6dAlrgs+2fEJeiX2wpKUJRAhYlirYdhwZgMGNxsMncr93zmdSod3uryDu/+4G70TeyNYF4zven8nn9/78F6n8sn3JkOvvsaXIiGiWqtZdBDqRwhIzbO/VunO47VBVjx4PVcr8IRaMQkEsC8D89BDD+GLL75Ax44dMWvWLHz55Zf477//UK9ePYwbNw5nzpzBt9/auzNTUlLQsmVLPPnkk3j88cexadMmjBgxAj/++GOll4Hx1iBSSZLw4bYPYbKZkG/Oxx/H/gBg78prGd4SFpsFAgSoHPYYtUk2rDi5Ai+tfQnd4rrhs9s+c7qnyWrCoqOL0Dmuc7UsBnzjvBtRaCkE4Bo8SixLWYaxa8fKr0tmHZf4dMenWH5iOb5J+sZlPb1Gk96FIf57+XV57+Ho0MVDCDeEQyWo0POXnjBajdj64NZyA5Ujm2TD70d/x00xNyE2oHQtP5PVhOdWPYdjWcfw0o0voWNMR2QUZKCOoY5TF7HRanT7Pq2+aQXAPsHkna6uob48kzZPwvxD89EjoQdWpa4CAAxsPBDjO41HgbkABrXBq4uCExF5wqc7PpXHL5fY+dBOqMXqba/iJJBaFAAB+0LQ7733HtLS0tCyZUt8/PHH6NatGwDgkUcewYkTJ7B69Wq5/Jo1azB69Gh5IeiXXnqpSgtBXwt/gbKN2QjUBkIUPNvbv/b0Wry58U1M7joZN8fc7LaM0WrE5C2T0TmuM5qFNkNCUOXHUNZ/+S/4N3oHosY+8aYyAdDRyZyT0Kv0iPJ3PyTAW4YvH44t6Vswt9fcCsd5lmW2mfHnsT/RLb4buv/UHQAwpesU9GnQx0M1JSLyviJLEVacXIEovygMX2EfQvRpj0/Ro26Pan2fa+Hfb0+rVQHQ2/gXyHvqv/wXNGHroI/6C3fUuwMfdf9I6SpdkSJLEdLy05zGGFbVkUtHsDdzL+5udDdb/Yio1vrg3w/wzf5v5N6O6sR/vxkArwr/AnlP/Zf/AmCDqD+NA68/VqluXCIiunblm/Ox/sx69KzXs9p/2eW/37VkEgj5ChG2oroMf0REPsBf448769+pdDVqrVqxDAzVfrc3jwQAJLWMVrgmRERE1z62ANI1Yer97bDqYAZubRapdFWIiIiueQyAdE0I0KnRt03s5QsSERHRZbELmIiIiMjHMAASERER+RgGQCIiIiIfwwBIRERE5GMYAImIiIh8DAMgERERkY9hACQiIiLyMQyARERERD6GAZCIiIjIxzAAEhEREfkYBkAiIiIiH8MASERERORjGACJiIiIfIxa6QpcyyRJAgDk5OQoXBMiIiKqrJJ/t0v+HfdFDIBXITc3FwCQkJCgcE2IiIioqnJzcxEcHKx0NRQhSL4cf6+SzWbD2bNnERgYCEEQlK6OonJycpCQkIDU1FQEBQUpXZ1ahz9fz+LP17P48/Us/nyrTpIk5ObmIjY2FqLom6Ph2AJ4FURRRHx8vNLVqFGCgoL4BeRB/Pl6Fn++nsWfr2fx51s1vtryV8I3Yy8RERGRD2MAJCIiIvIxDIBULXQ6Hd58803odDqlq1Ir8efrWfz5ehZ/vp7Fny9dCU4CISIiIvIxbAEkIiIi8jEMgEREREQ+hgGQiIiIyMcwABIRERH5GAZAcmvy5Mm44YYbEBgYiMjISAwYMACHDh1yKXfgwAH069cPwcHBCAwMxM0334xTp07J541GI/73v/8hPDwc/v7+6NevH06fPu3Nj1IjVebnm5eXh2eeeQbx8fEwGAxo3rw5ZsyY4VSGP9/yzZgxA61bt5YXx+3YsSOWLl0qn5ckCePHj0dsbCwMBgO6d++O//77z+ke/PmWr6Kfr9lsxksvvYRWrVrB398fsbGxGDp0KM6ePet0D/58y3e5v7+OnnzySQiCgKlTpzod58+XKsIASG6tWbMGTz/9NDZv3ozk5GRYLBb07NkT+fn5cpljx46hS5cuaNasGVavXo3du3fj9ddfh16vl8uMGjUKv/32G+bPn4/169cjLy8Pd911F6xWqxIfq8aozM939OjRWLZsGb7//nscOHAAo0ePxv/+9z/8/vvvchn+fMsXHx+PKVOmYNu2bdi2bRtuvfVW9O/fXw557733Hj766CNMmzYN//77/+3de1CU1RsH8C+w3AQiFLmJoogiooKAmkjg5GXVYWV0Jsy8LbdJA2+pjXhNw5DGS9YEqOlaaqIVEQpeIBBIvAQMAwgDWIjFgIyGOAoisM/vD3+8+bIsLHZR2+czszPsOec973u+rvpwdl/4GTY2Npg2bZrwO74Bzrc73eXb1NSEgoICbN68GQUFBUhMTERFRQVmz54tmoPzVa+n12+HpKQkXL16FXZ2dipzcL6sW8SYBurr6wkAZWVlCW3z5s2jhQsXqj3m3r17pK+vTwkJCUJbTU0N6erq0rlz5/7R633ZdJWvq6srbd++XTTOw8ODNm3aRESc77OwsLCgL774gpRKJdnY2NDOnTuFvkePHpG5uTnFx8cTEef7LDry7cq1a9cIAFVXVxMR5/ssOuf7+++/04ABA6ikpIQcHBxo7969Qh/ny3rCO4BMI42NjQCAvn37AgCUSiVSUlIwfPhwSKVSWFlZYcKECUhKShKOyc/PR2trK6ZPny602dnZYdSoUcjNzf1Xr/9F1zlfAPDx8UFycjJqampARMjMzERFRQWkUikAzrc32tvbkZCQgIcPH2LixImoqqpCXV2dKDtDQ0P4+fkJ2XG+muucb1caGxuho6ODV199FQDn2xtd5atUKrFo0SKsW7cOrq6uKsdwvqwnXACyHhER3nvvPfj4+GDUqFEAgPr6ejx48AA7d+7EjBkzcOHCBcyZMwdz585FVlYWAKCurg4GBgawsLAQzWdtbY26urp/fR0vqq7yBYBPP/0UI0eOhL29PQwMDDBjxgzExsbCx8cHAOerieLiYpiamsLQ0BBLly7F999/j5EjRwr5WFtbi8Y/nR3n2zN1+Xb26NEjrF+/Hm+//TZeeeUVAJyvJrrLNyYmBhKJBCtWrOjyWM6X9UTyvC+AvfgiIiJQVFSEn376SWhTKpUAgICAAKxevRoA4O7ujtzcXMTHx8PPz0/tfEQEHR2df/aiXyJd5Qs8KQCvXLmC5ORkODg4IDs7G++++y5sbW0xdepUtfNxvn9ydnZGYWEh7t27h++++w5LliwRvkEBoJKTJtlxvn9Sl+/TRWBrayveeustKJVKxMbG9jgn5/sndfk2Nzdj3759KCgo6HVWnC/rwDuArFvLly9HcnIyMjMzYW9vL7RbWlpCIpGofLfv4uIi3AVsY2ODx48fo6GhQTSmvr5eZedFW6nLt7m5GRs2bMCePXsgk8kwZswYREREYN68edi1axcAzlcTBgYGcHJygpeXF6Kjo+Hm5oZ9+/bBxsYGAFR2Qp7OjvPtmbp8O7S2tiIwMBBVVVVIS0sTdv8AzlcT6vLNyclBfX09Bg0aBIlEAolEgurqaqxZswaDBw8GwPmynnEByLpERIiIiEBiYiIyMjIwZMgQUb+BgQHGjRun8qNLKioq4ODgAADw9PSEvr4+0tLShP7a2lqUlJTA29v7n1/EC6ynfFtbW9Ha2gpdXfFfUT09PWH3lfPtPSJCS0sLhgwZAhsbG1F2jx8/RlZWlpAd59t7HfkCfxZ/lZWVSE9PR79+/URjOd/e68h30aJFKCoqQmFhofCws7PDunXrcP78eQCcL9PA87jzhL34li1bRubm5nTx4kWqra0VHk1NTcKYxMRE0tfXpwMHDlBlZSV99tlnpKenRzk5OcKYpUuXkr29PaWnp1NBQQG98cYb5ObmRm1tbc9jWS8MTfL18/MjV1dXyszMpF9//ZUUCgUZGRlRbGysMIbzVS8yMpKys7OpqqqKioqKaMOGDaSrq0sXLlwgIqKdO3eSubk5JSYmUnFxMc2fP59sbW3p/v37whycr3rd5dva2kqzZ88me3t7KiwsFL3GW1pahDk4X/V6ev121vkuYCLOl3WPC0DWJQBdPhQKhWjcoUOHyMnJiYyMjMjNzY2SkpJE/c3NzRQREUF9+/YlY2Nj8vf3p1u3bv2LK3kxaZJvbW0tyeVysrOzIyMjI3J2dqbdu3eTUqkUxnC+6gUHB5ODgwMZGBhQ//79acqUKaL/PJVKJW3dupVsbGzI0NCQfH19qbi4WDQH56ted/lWVVWpfY1nZmYKc3C+6vX0+u2sqwKQ82Xd0SEi+rd3HRljjDHG2PPDnwFkjDHGGNMyXAAyxhhjjGkZLgAZY4wxxrQMF4CMMcYYY1qGC0DGGGOMMS3DBSBjjDHGmJbhApAxxhhjTMtwAcgYY51MnjwZq1atem7nv3v3LqysrHDz5s2/dd7i4mLY29vj4cOHf+u8jLGXDxeAjDH2gomOjoZMJsPgwYM1Gi+TyTB16tQu+y5fvgwdHR0UFBRg9OjRGD9+PPbu3fs3Xi1j7GXEBSBj7KXU2tr6vC/hH9Hc3IxDhw4hNDRU42NCQkKQkZGB6upqlb7Dhw/D3d0dHh4eAICgoCDExcWhvb39b7tmxtjLhwtAxthfQkT4+OOP4ejoCGNjY7i5ueHbb78V+i9evAgdHR38+OOP8PLyQp8+feDt7Y3y8nLRPKdPn4anpyeMjIzg6OiIbdu2oa2tTejX0dFBfHw8AgICYGJigqioKABAVFQUrKysYGZmhtDQUKxfvx7u7u4AgOzsbOjr66Ourk50rjVr1sDX11fjNTY0NGDx4sWwsLBAnz59MHPmTFRWVgr91dXVkMlksLCwgImJCVxdXZGamiocu2DBAvTv3x/GxsYYNmwYFAqF2nOdPXsWEokEEydOFLWXlpZi1qxZMDU1hbW1NRYtWoQ7d+4AAPz9/WFlZYUjR46IjmlqasLJkycREhIitEmlUty9exdZWVkar58x9t/DBSBj7C/ZtGkTFAoF4uLicP36daxevRoLFy5UKTA2btyI3bt3Iy8vDxKJBMHBwULf+fPnsXDhQqxYsQKlpaXYv38/jhw5gh07dojm2Lp1KwICAlBcXIzg4GAcP34cO3bsQExMDPLz8zFo0CDExcUJ4319feHo6IijR48KbW1tbTh27BiCgoI0XqNcLkdeXh6Sk5Nx+fJlEBFmzZol7EKGh4ejpaUF2dnZKC4uRkxMDExNTQEAmzdvRmlpKc6ePYuysjLExcXB0tJS7bmys7Ph5eUlaqutrYWfnx/c3d2Rl5eHc+fO4fbt2wgMDAQASCQSLF68GEeOHMHTv979m2++wePHj7FgwQKhzcDAAG5ubsjJydF4/Yyx/yBijLFn9ODBAzIyMqLc3FxRe0hICM2fP5+IiDIzMwkApaenC/0pKSkEgJqbm4mI6PXXX6ePPvpINMfRo0fJ1tZWeA6AVq1aJRozYcIECg8PF7VNmjSJ3NzchOcxMTHk4uIiPE9KSiJTU1N68OCB2nX5+fnRypUriYiooqKCANClS5eE/jt37pCxsTGdOnWKiIhGjx5NH3zwQZdzyWQyCgoKUnuuzgICAig4OFjUtnnzZpo+fbqo7bfffiMAVF5eTkREZWVlBIAyMjKEMb6+vsKfw9PmzJlDcrlc42tijP338A4gY+yZlZaW4tGjR5g2bRpMTU2Fx1dffYVffvlFNHbMmDHC17a2tgCA+vp6AEB+fj62b98umiMsLAy1tbVoamoSjuu8M1ZeXo7x48eL2jo/l8vluHHjBq5cuQLgyWfiAgMDYWJigpycHNE5jx8/rrLGsrIySCQSTJgwQWjr168fnJ2dUVZWBgBYsWIFoqKiMGnSJGzduhVFRUXC2GXLliEhIQHu7u54//33kZub222mzc3NMDIyErXl5+cjMzNTdK0jRowAACHnESNGwNvbG4cPHxbac3JyRDutHYyNjUW5Msa0j+R5XwBj7OWlVCoBACkpKRgwYICoz9DQUPRcX19f+FpHR0d0vFKpxLZt2zB37lyVczxdDJmYmKj0d8zVgZ56CxQArKysIJPJoFAo4OjoiNTUVFy8eBHAk4KysLBQGGttba0yf+f5nm7vOHdoaCikUilSUlJw4cIFREdHY/fu3Vi+fDlmzpyJ6upqpKSkID09HVOmTEF4eDh27drV5byWlpZoaGgQtSmVSshkMsTExKiM7yimgSc3g0RERODzzz+HQqGAg4MDpkyZonLMH3/8gaFDh3Z5fsaYduAdQMbYMxs5ciQMDQ1x69YtODk5iR4DBw7UeB4PDw+Ul5erzOHk5ARdXfX/TDk7O+PatWuitry8PJVxoaGhSEhIwP79+zF06FBMmjQJwJOdsKfPZWZm1uUa29racPXqVaHt7t27qKiogIuLi9A2cOBALF26FImJiVizZg0OHjwo9PXv3x9yuRzHjh3DJ598ggMHDqhd09ixY1FaWqqSz/Xr1zF48GCVfJ4uigMDA6Gnp4evv/4aX375JYKCglQKZAAoKSnB2LFj1V4DY+y/j3cAGWPPzMzMDGvXrsXq1auhVCrh4+OD+/fvIzc3F6ampliyZIlG82zZsgX+/v4YOHAg3nzzTejq6qKoqAjFxcXC3b5dWb58OcLCwuDl5QVvb2+cPHkSRUVFcHR0FI2TSqUwNzdHVFQUtm/f3qs1Dhs2DAEBAQgLC8P+/fthZmaG9evXY8CAAQgICAAArFq1CjNnzsTw4cPR0NCAjIwMoTjcsmULPD094erqipaWFpw5c0ZUOHYmlUoRGRmJhoYGWFhYAHhyk8nBgwcxf/58rFu3DpaWlrhx4wYSEhJw8OBB6OnpAQBMTU0xb948bNiwAY2NjZDL5Srz37x5EzU1NWp/biBjTDvwDiBj7C/58MMPsWXLFkRHR8PFxQVSqRSnT5/GkCFDNJ5DKpXizJkzSEtLw7hx4/Daa69hz549cHBw6Pa4BQsWIDIyEmvXroWHhweqqqogl8tVPkOnq6sLuVyO9vZ2LF68uNdrVCgU8PT0hL+/PyZOnAgiQmpqqvC2dnt7O8LDw+Hi4oIZM2bA2dkZsbGxAJ7cdRsZGYkxY8bA19cXenp6SEhIUHuu0aNHw8vLC6dOnRLa7OzscOnSJbS3t0MqlWLUqFFYuXIlzM3NVXZIQ0JC0NDQgKlTp2LQoEEq8584cQLTp0/vMVvG2H+bDqn7gAtjjL2Epk2bBhsbG9GPfgGAsLAw3L59G8nJyc/pyjSXmpqKtWvXoqSkpNu3wHurpaUFw4YNw4kTJ4S3wRlj2onfAmaMvbSampoQHx8PqVQKPT09nDhxAunp6UhLSxPGNDY24ueff8bx48fxww8/PMer1dysWbNQWVmJmpqaXn2WsifV1dXYuHEjF3+MMd4BZIy9vJqbmyGTyVBQUICWlhY4Oztj06ZNoruJJ0+ejGvXruGdd97h34HLGGP/xwUgY4wxxpiW4ZtAGGOMMca0DBeAjDHGGGNahgtAxhhjjDEtwwUgY4wxxpiW4QKQMcYYY0zLcAHIGGOMMaZluABkjDHGGNMyXAAyxhhjjGkZLgAZY4wxxrTM/wDroTdCcfhsrQAAAABJRU5ErkJggg==",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "background, p = eels_tools.power_law_background(core_loss, core_loss.energy_loss, [250, 278])\n",
+    "spectrum = core_loss-background\n",
+    "spectrum.metadata = dataset.metadata.copy()\n",
+    "dataset.plot()\n",
+    "plt.gca().plot(spectrum.energy_loss, background)\n",
+    "plt.gca().plot(spectrum.energy_loss, spectrum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": false,
+    "hidePrompt": false,
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "Everything we did in the dashboard above we do now one by one:\n",
+    "### Fix energy scale and determine resolution function\n",
+    "> please see [Fitting the Zero-Loss Peak](./CH4_02-Fit_Zero_Loss.ipynb) for details\n",
+    "\n",
+    "### Determine Relative Thickness \n",
+    "The probabaility of an low-loss function in a solid angle is then:\n",
+    "$$\\frac{\\partial^2 P}{(\\partial E \\partial \\Omega) }= t*  \\frac{e}{\\pi^2 a_0 m_0 v^2} {\\rm Im} \\left[ \\frac{-1}{\\varepsilon(q,E)} \\right]\n",
+    "         \\left( \\frac{1}{\\theta^2+\\theta_E^2}\\right)$$\n",
+    "         \n",
+    ">See [Notebook: Analysing Low-Loss Spectra with Drude Theory](https://raw.githubusercontent.com/gduscher/MSE672-Introduction-to-TEM/main/Spectroscopy/CH4_03-Drude.ipynb) of the MSE672-Introduction-to-TEM Lecture in my Github account.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.23899302] sidpy.Dataset of type SPECTRUM with:\n",
+      " dask.array<array, shape=(2048,), dtype=float32, chunksize=(2048,), chunktype=numpy.ndarray>\n",
+      " data contains: intensity (counts)\n",
+      " and Dimensions: \n",
+      "energy_loss:  energy-loss (eV) of size (2048,)\n",
+      " with metadata: ['experiment']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1f6eac55290>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d8734dfe6e644a59a242674b8af641e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "low_loss = eels_tools.align_zero_loss(infoWidget.selected_dataset)\n",
+    "zero_loss = eels_tools.get_resolution_functions(low_loss, -.5, .5)\n",
+    "plt.close('all')\n",
+    "plt.figure()\n",
+    "plt.plot(low_loss.energy_loss, zero_loss, label='resolution funcion')\n",
+    "plt.plot(low_loss.energy_loss, low_loss, label = 'spectrum')\n",
+    "plt.plot(low_loss.energy_loss, low_loss-zero_loss, label = 'difference')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'acceleration_voltage': 200000.0,\n",
+       "  'beam_current': 0,\n",
+       "  'collection_angle': 50.0,\n",
+       "  'convergence_angle': 30.0,\n",
+       "  'count_conversion': 1,\n",
+       "  'exposure_time': 10.0,\n",
+       "  'flux_ppm': 0.0,\n",
+       "  'number_of_frames': 10000,\n",
+       "  'single_exposure_time': 0.001},\n",
+       " 'peak_fit': {'edge_model': array([0., 0., 0., ..., 0., 0., 0.]),\n",
+       "  'fit_end': 30.0,\n",
+       "  'fit_start': 1.0,\n",
+       "  'peak_model': array([0., 0., 0., ..., 0., 0., 0.]),\n",
+       "  'peak_out_list': array([[1.14401631e+01, 2.92439760e+06, 2.45501896e+01],\n",
+       "         [7.97104419e-01, 2.89095444e+07, 1.71055819e+00]]),\n",
+       "  'peaks': {'0': {'amplitude': 20986284.461096264,\n",
+       "    'associated_edge': '',\n",
+       "    'position': 0.8433632583641564,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 1.6347046209768021,\n",
+       "    'asymmetry': 0.0},\n",
+       "   '1': {'amplitude': 60297550.94024267,\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0,\n",
+       "    'position': 0.3943234078051374,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 0.8462042318305517},\n",
+       "   '2': {'amplitude': 5149807.779796465,\n",
+       "    'associated_edge': '',\n",
+       "    'position': 4.681561552802686,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 3.805046085043105,\n",
+       "    'asymmetry': 0.0},\n",
+       "   '3': {'amplitude': 1768787.351821872,\n",
+       "    'associated_edge': '',\n",
+       "    'position': 15.717359457313327,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 5.034726824660322,\n",
+       "    'asymmetry': 0.0},\n",
+       "   '4': {'amplitude': 1860806.817558061,\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0,\n",
+       "    'position': 18.651735459709663,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 9.474450049577818},\n",
+       "   '5': {'amplitude': 871061.5868498409,\n",
+       "    'associated_edge': '',\n",
+       "    'position': 27.65565717924107,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 11.173122920182822,\n",
+       "    'asymmetry': 0.0},\n",
+       "   '6': {'amplitude': 747444.1958128264,\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0,\n",
+       "    'position': 15.508181435473912,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 1.0964231231036516},\n",
+       "   '7': {'amplitude': 582735.7941706154,\n",
+       "    'associated_edge': '',\n",
+       "    'position': 8.793443515617403,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 3.2503281905987924,\n",
+       "    'asymmetry': 0.0},\n",
+       "   '8': {'amplitude': 1490223.5949046034,\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0,\n",
+       "    'position': 5.072798469661356,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 0.8350422095351262},\n",
+       "   '9': {'amplitude': 4264163.850603235,\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0,\n",
+       "    'position': 2.27260716852653,\n",
+       "    'type': 'Gauss',\n",
+       "    'width': 1.7783602300920873}}},\n",
+       " 'zero_loss': {'endFitEnergy': 0.5,\n",
+       "  'fit_parameter': array([ 1.55362639e-01,  1.46387345e+05,  4.26253898e-01, -7.36465028e-02,\n",
+       "          2.54673199e+05,  2.91093028e-01]),\n",
+       "  'original_low_loss': 'EELS_0290_new',\n",
+       "  'shifted': array([-0.2791726]),\n",
+       "  'startFitEnergy': -0.5}}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.dataset.metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## peakfit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyTEMlib import peak_dialog\n",
+    "    \n",
+    "peakFitWidget = peak_dialog.PeakFitWidget({'low_loss':infoWidget.dataset})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###### from pyTEMlib import peak_dialog"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ef52d9fcf024756b0449abc600442fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "AppLayout(children=(GridspecLayout(children=(Button(description='Fit Area', layout=Layout(grid_area='widget001…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "    \n",
+    "peakFitWidget = peak_dialog.PeakFitWidget({'core_loss': spectrum})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'single_exposure_time': 5.0,\n",
+       "  'exposure_time': 100.0,\n",
+       "  'number_of_frames': 20,\n",
+       "  'collection_angle': 50.0,\n",
+       "  'convergence_angle': 5.4,\n",
+       "  'acceleration_voltage': 60000.0,\n",
+       "  'flux_ppm': 1470027.5390625,\n",
+       "  'count_conversion': 1,\n",
+       "  'beam_current': 0}}"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fi.metadata\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e145189778264685b2b323832802cce2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "AppLayout(children=(GridspecLayout(children=(Button(description='Fit Area', layout=Layout(grid_area='widget001…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "datasets = {'core_loss': spectrum, 'dataset': core_loss}\n",
+    "low_loss.metadata = infoWidget.selected_dataset.metadata\n",
+    "peakFitWidget = peak_dialog.PeakFitWidget(datasets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\py11\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n",
+      "  warn('validate_h5_dimension may be removed in a future version',\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\py11\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n",
+      "  warn('validate_h5_dimension may be removed in a future version',\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<HDF5 group \"/Measurement_000\" (2 members)>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datasets = {'core_loss': spectrum, 'dataset': core_loss}\n",
+    "pyTEMlib.file_tools.save_dataset(datasets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function gauss in module pyTEMlib.eels_tools:\n",
+      "\n",
+      "gauss(x, p)\n",
+      "    Gaussian Function\n",
+      "    \n",
+      "    p[0]==mean, p[1]= amplitude p[2]==fwhm\n",
+      "    area = np.sqrt(2* np.pi)* p[1] * np.abs(p[2] / 2.3548)\n",
+      "    FWHM = 2 * np.sqrt(2 np.log(2)) * sigma = 2.3548 * sigma\n",
+      "    sigma = FWHM/3548\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1f6ee04c410>]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a9a368e4f6894cfca5c2a8583574166a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "model = spectrum.metadata['peak_fit']['peak_model'].copy()\n",
+    "spectrum.energy_loss -= 250\n",
+    "spectrum.energy_loss *= 0.05\n",
+    "spectrum.energy_loss += 250 \n",
+    "\n",
+    "import scipy\n",
+    "help(eels_tools.gauss)\n",
+    "gauss = eels_tools.gauss(spectrum.energy_loss, [spectrum.energy_loss[1024], 1, .2])\n",
+    "gauss2 = eels_tools.gauss(spectrum.energy_loss, [spectrum.energy_loss[1024], 1, .1])\n",
+    "plt.figure()\n",
+    "plt.plot(gauss)\n",
+    "plt.plot(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12.5 , 12.55, 12.6 , 12.65])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spectrum.energy_loss.values[:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1f6e72f33d0>"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64136d3e706f4d3ba20fce25a69a5f5d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "spectrum = infoWidget.dataset.copy()\n",
+    "model = spectrum.metadata['peak_fit']['peak_model']\n",
+    "zero_loss = np.array(np.roll(zero_loss, -np.argmax(np.array(zero_loss))))\n",
+    "j = np.fft.fft(model/model.sum())\n",
+    "z = np.fft.fft(zero_loss/zero_loss.sum()+1e-12)\n",
+    "#z2 = np.fft.fft(gauss2/gauss2.sum()*2)\n",
+    "\n",
+    "red =(np.fft.ifft(j/z).real)#\n",
+    "plt.figure()\n",
+    "plt.plot(spectrum.energy_loss, red/red.sum(), label='deconvoluted', linewidth=3)\n",
+    "#plt.plot(zero_loss/zero_loss.sum())\n",
+    "plt.plot(spectrum.energy_loss, model/model.sum(), label='Gaussian mixing')\n",
+    "# plt.plot(red-model)\n",
+    "plt.ylim(0,.005)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "285.8, 1.15, 58843\n",
+      "304.8, 13.34, 77560\n",
+      "293.0, 2.01, 80868\n",
+      "292.1, 0.64, 74606\n",
+      "296.5, 6.59, 64373\n",
+      "301.8, 2.73, 12192\n",
+      "308.9, 2.47, 3753\n",
+      "287.2, 2.06, 13455\n",
+      "290.2, 0.99, 6659\n",
+      "329.1, 26.32, 40827\n",
+      "289.0, 1.82, 11961\n"
+     ]
+    }
+   ],
+   "source": [
+    "for peak in spectrum.metadata['peak_fit']['peaks'].values():\n",
+    "    print (f\"{peak['position']:.1f}, {peak['width']:.2f}, {peak['amplitude']:.0f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'peakFitWidget' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[29], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpeakFitWidget\u001b[49m\u001b[38;5;241m.\u001b[39mfit_peaks()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'peakFitWidget' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "peakFitWidget.fit_peaks()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1f109ea6310>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67101ddd2de9426b8e6a99bb6314cb2d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.close('all')\n",
+    "plt.figure()\n",
+    "plt.plot(peakFitWidget.energy_scale,peakFitWidget.peak_model, label='model')\n",
+    "plt.plot(peakFitWidget.energy_scale,peakFitWidget.dataset, label='spectrum')\n",
+    "plt.plot(peakFitWidget.energy_scale, peakFitWidget.dataset-peakFitWidget.peak_model-resolution_functions, label='dif')\n",
+    "plt.ylim(-4e7,1e8)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "too many values to unpack (expected 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[215], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m dataset \u001b[38;5;241m=\u001b[39m peakFitWidget\u001b[38;5;241m.\u001b[39mdataset\n\u001b[1;32m----> 2\u001b[0m peak_model, peak_out_list \u001b[38;5;241m=\u001b[39m find_peaks(dataset\u001b[38;5;241m-\u001b[39mresolution_functions, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,  \u001b[38;5;241m30\u001b[39m)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(peak_out_list\u001b[38;5;241m.\u001b[39mreshape([\u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(peak_out_list)\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m3\u001b[39m),\u001b[38;5;241m3\u001b[39m]))\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n",
+      "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 2)"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = peakFitWidget.dataset\n",
+    "peak_model, peak_out_list = find_peaks(dataset-resolution_functions, -1,  30)\n",
+    "print(peak_out_list.reshape([int(len(peak_out_list)/3),3]))\n",
+    "plt.figure()\n",
+    "plt.plot(peak_model)\n",
+    "plt.plot(dataset-resolution_functions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'single_exposure_time': 5.0,\n",
+       "  'exposure_time': 100.0,\n",
+       "  'number_of_frames': 20,\n",
+       "  'collection_angle': 50.0,\n",
+       "  'convergence_angle': 5.42,\n",
+       "  'acceleration_voltage': 60000.0,\n",
+       "  'flux_ppm': 1477088.59375,\n",
+       "  'count_conversion': 1,\n",
+       "  'beam_current': 0},\n",
+       " 'zero_loss': {'shifted': array([-0.2791726])},\n",
+       " 'peak_fit': {'fit_start': 270.05000372603536,\n",
+       "  'fit_end': 350.3000052496791,\n",
+       "  'peaks': {'0': {'position': 285.5328133398505,\n",
+       "    'amplitude': 56813.851980084386,\n",
+       "    'width': 1.1447315680844505,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '1': {'position': 291.8502789181616,\n",
+       "    'amplitude': 80137.36537116366,\n",
+       "    'width': 0.631699853337263,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '2': {'position': 292.68460898549887,\n",
+       "    'amplitude': 67856.46722391486,\n",
+       "    'width': 1.6722959462172458,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '3': {'position': 288.33077096412796,\n",
+       "    'amplitude': 15200.2171172901,\n",
+       "    'width': 4.03592603054892,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '4': {'position': 300.97498872546737,\n",
+       "    'amplitude': 29204.484305388254,\n",
+       "    'width': 2.9452493043214063,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '5': {'position': 431.4963301648659,\n",
+       "    'amplitude': 62303.24268512067,\n",
+       "    'width': 160.13636902692892,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '6': {'position': 297.57744196974863,\n",
+       "    'amplitude': 82438.00185689674,\n",
+       "    'width': 4.570549606775482,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '7': {'position': 306.55294236999856,\n",
+       "    'amplitude': 64324.25014690288,\n",
+       "    'width': 8.408828675311156,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '8': {'position': 327.23866847395425,\n",
+       "    'amplitude': 24332.78308524786,\n",
+       "    'width': 16.520135069307326,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '9': {'position': 302.73254089477655,\n",
+       "    'amplitude': 30379.325543758885,\n",
+       "    'width': 4.215055093093726,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '10': {'position': 313.39578279938394,\n",
+       "    'amplitude': 13507.040368003669,\n",
+       "    'width': 9.086317779116715,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0},\n",
+       "   '11': {'position': 293.7938526566573,\n",
+       "    'amplitude': 56893.252049088034,\n",
+       "    'width': 3.7967168653233117,\n",
+       "    'type': 'Gauss',\n",
+       "    'associated_edge': '',\n",
+       "    'asymmetry': 0.0}},\n",
+       "  'edge_model': array([0., 0., 0., ..., 0., 0., 0.]),\n",
+       "  'peak_model': array([0., 0., 0., ..., 0., 0., 0.])}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['Channel_000'].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'resolution_functions' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[28], line 73\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m p_out\n\u001b[0;32m     72\u001b[0m fit_end \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m40\u001b[39m\n\u001b[1;32m---> 73\u001b[0m peak_out_list \u001b[38;5;241m=\u001b[39m find_peaks(infoWidget\u001b[38;5;241m.\u001b[39mselected_dataset\u001b[38;5;241m-\u001b[39m\u001b[43mresolution_functions\u001b[49m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, fit_end)\n\u001b[0;32m     74\u001b[0m p \u001b[38;5;241m=\u001b[39m fit_peaks(dataset\u001b[38;5;241m-\u001b[39mresolution_functions,peak_out_list,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, fit_end)\n\u001b[0;32m     75\u001b[0m model \u001b[38;5;241m=\u001b[39m model_ll(energy_scale,np\u001b[38;5;241m.\u001b[39marray(p), \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'resolution_functions' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy\n",
+    "def residuals_ll(p, x, y, only_positive_intensity):\n",
+    "    \"\"\"part of fit\"\"\"\n",
+    "\n",
+    "    err = (y - model_ll(x, p, only_positive_intensity)) / np.sqrt(np.abs(y))\n",
+    "    return err\n",
+    "\n",
+    "def model_ll(x, p, only_positive_intensity):\n",
+    "    \"\"\"part of fit\"\"\"\n",
+    "\n",
+    "    y = np.zeros(len(x))\n",
+    "\n",
+    "    number_of_peaks = int(len(p) / 3)\n",
+    "    for i in range(number_of_peaks):\n",
+    "        if only_positive_intensity:\n",
+    "            p[i * 3 + 1] = abs(p[i * 3 + 1])\n",
+    "        p[i * 3 + 2] = abs(p[i * 3 + 2])\n",
+    "        if p[i * 3 + 2] > abs(p[i * 3]) * 4.29193 / 2.0:\n",
+    "            p[i * 3 + 2] = abs(p[i * 3]) * 4.29193 / 2.  # ## width cannot extend beyond zero, maximum is FWTM/2\n",
+    "\n",
+    "        y = y + gauss(x, p[i * 3:])\n",
+    "\n",
+    "    return y\n",
+    "\n",
+    "def gauss(x, p):  # p[0]==mean, p[1]= amplitude p[2]==fwhm,\n",
+    "    \"\"\"Gaussian Function\n",
+    "\n",
+    "        p[0]==mean, p[1]= amplitude p[2]==fwhm\n",
+    "        area = np.sqrt(2* np.pi)* p[1] * np.abs(p[2] / 2.3548)\n",
+    "        FWHM = 2 * np.sqrt(2 np.log(2)) * sigma = 2.3548 * sigma\n",
+    "        sigma = FWHM/3548\n",
+    "    \"\"\"\n",
+    "    if p[2] == 0:\n",
+    "        return x * 0.\n",
+    "    else:\n",
+    "        return p[1] * np.exp(-(x - p[0]) ** 2 / (2.0 * (p[2] / 2.3548) ** 2))\n",
+    "\n",
+    "def find_peaks(dataset, fit_start, fit_end):\n",
+    "    energy_scale = dataset.get_spectral_dims(return_axis=True)[0].values\n",
+    "\n",
+    "    start_channel = np.searchsorted(energy_scale, fit_start)\n",
+    "    end_channel = np.searchsorted(energy_scale, fit_end)\n",
+    "    spectrum = np.abs(np.array(dataset)[start_channel:end_channel])\n",
+    "    i_pk = scipy.signal.find_peaks_cwt(spectrum, widths=range(3, len(energy_scale) // 30)) # \n",
+    "    \n",
+    "    p_in = np.ravel([[energy_scale[i]-fit_start, spectrum[i], .7] for i in i_pk])\n",
+    "    \n",
+    "    return p_in\n",
+    "    \n",
+    "def fit_peaks(spectrum, pin, start_fit, end_fit, only_positive_intensity=False):\n",
+    "\n",
+    "    energy_scale =  spectrum.get_spectral_dims(return_axis=True)[0]\n",
+    "    start_fit = np.searchsorted(energy_scale, start_fit)\n",
+    "    end_fit = np.searchsorted(energy_scale, end_fit)\n",
+    "    \n",
+    "    fit_energy = energy_scale[start_fit:end_fit]\n",
+    "    spectrum = np.array(spectrum)\n",
+    "    fit_spectrum = spectrum[start_fit:end_fit]\n",
+    "\n",
+    "    #pin_flat = [item for sublist in pin for item in sublist]\n",
+    "    [p_out, _] = leastsq(residuals_ll, np.array(pin), args=(fit_energy, fit_spectrum,\n",
+    "                                                                        only_positive_intensity))\n",
+    "    #p_out, pcov = curve_fit(residuals_ll, fit_energy, fit_spectrum, p0=np.array(pin))\n",
+    "    p = []\n",
+    "    for i in range(int(len(pin)/3)):\n",
+    "        if only_positive_intensity:\n",
+    "            p_out[i * 3 + 1] = abs(p_out[i * 3 + 1])\n",
+    "        p.append([p_out[i * 3], p_out[i * 3 + 1], abs(p_out[i * 3 + 2])])\n",
+    "    return p_out\n",
+    "\n",
+    "\n",
+    "fit_end = 40\n",
+    "peak_out_list = find_peaks(infoWidget.selected_dataset-resolution_functions, -1, fit_end)\n",
+    "p = fit_peaks(dataset-resolution_functions,peak_out_list,-1, fit_end)\n",
+    "model = model_ll(energy_scale,np.array(p), False)\n",
+    "\n",
+    "print(len(peak_out_list)/3)\n",
+    "\n",
+    "print(fit_end)\n",
+    "print(p.reshape([int(len(p)/3),3]))\n",
+    "print(len(p)/3)\n",
+    "plt.figure()\n",
+    "plt.plot(energy_scale, model)\n",
+    "plt.plot(energy_scale, dataset-resolution_functions)\n",
+    "plt.plot(energy_scale, dataset-resolution_functions-model)\n",
+    "plt.ylim(0,1e8)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.0\n",
+      "1877529801.672969\n",
+      "23.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\py11\\Lib\\site-packages\\scipy\\optimize\\_minpack_py.py:494: RuntimeWarning: Number of calls to function has reached maxfev = 14000.\n",
+      "  warnings.warn(errors[info][0], RuntimeWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1f146ce6ed0>"
+      ]
+     },
+     "execution_count": 254,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa9bc54f5e194e18a5e19cefcf40c501",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "peak_out_list2 = find_peaks(infoWidget.selected_dataset-resolution_functions-model, -1, fit_end)\n",
+    "peak_out_list = np.append(peak_out_list,peak_out_list2)\n",
+    "print(len(peak_out_list)/3)\n",
+    "p = fit_peaks(dataset-resolution_functions,peak_out_list,-1, fit_end )\n",
+    "model = model_ll(energy_scale,np.array(p), False)\n",
+    "model2 = model_ll(energy_scale,np.array(peak_out_list2), False)\n",
+    "print(np.std(np.array(dataset-resolution_functions)-model))\n",
+    "print(len(p)/3)\n",
+    "plt.figure()\n",
+    "plt.plot(energy_scale, model+resolution_functions, label='model')\n",
+    "plt.plot(energy_scale, model2, label='model1')\n",
+    "plt.plot(energy_scale, dataset, label='spectrum')\n",
+    "plt.plot(energy_scale, dataset-resolution_functions-model, label='difference')\n",
+    "plt.ylim(0,1e8)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1198221.9850117   2628210.59194557  4581554.52393588 ...\n",
+      " -2260492.70274396 -2410574.75171163 -2582093.5075449 ]\n",
+      "(2048,)\n",
+      "11.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1f14181eed0>"
+      ]
+     },
+     "execution_count": 237,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ff30cdd8d574d8597a71059766a4316",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model[:130] = 0.\n",
+    "model2_f = scipy.fft.fft(np.array(dataset)) #model+np.array(resolution_functions))\n",
+    "\n",
+    "res_f =scipy.fft.fft(np.array(resolution_functions))\n",
+    "smear = gauss(energy_scale, [0,1, .2])\n",
+    "smear *= resolution_functions.sum()/smear.sum()\n",
+    "gaus_f = scipy.fft.fft(smear)  # p[0]==mean, p[1]= amplitude p[2]==fwhm,\n",
+    "model2 =  scipy.fft.ifft(model2_f/res_f*gaus_f).real\n",
+    "print(model2)\n",
+    "#model2 = model+np.array(resolution_functions)\n",
+    "print(model2.shape)\n",
+    "print(len(p)/3)\n",
+    "plt.figure()\n",
+    "plt.plot(energy_scale, model+resolution_functions, label='model')\n",
+    "plt.plot(energy_scale, model2, label='model1')\n",
+    "plt.plot(energy_scale, dataset, label='spectrum')\n",
+    "plt.plot(energy_scale, dataset-resolution_functions-model, label='difference')\n",
+    "plt.ylim(0,1e8)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1f132c62b10>]"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a189faa4ebf64464ab15201f031af421",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "#plt.plot(energy_scale, model+resolution_functions)\n",
+    "plt.plot(energy_scale, model2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5, 6])"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.append([1,2,3], [4,5,6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.close('all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0., 0., 0., ..., 0., 0., 0.]),\n",
+       " array([2.03888490e+01, 6.60088441e+05, 9.51555853e+00]),\n",
+       " 7)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "peak_model, peak_out_list = eels_tools.find_peaks(infoWidget.selected_dataset, 1,40)\n",
+    "\n",
+    "new_list = np.reshape(peak_out_list, [len(peak_out_list) // 3, 3])\n",
+    "area = np.sqrt(2 * np.pi) * np.abs(new_list[:, 1]) * np.abs(new_list[:, 2] / np.sqrt(2 * np.log(2)))\n",
+    "arg_list = np.argsort(area)[::-1]\n",
+    "area = area[arg_list]\n",
+    "peak_out_list = new_list[arg_list]\n",
+    "\n",
+    "number_of_peaks = np.searchsorted(area * -1, -np.average(area))\n",
+    "\n",
+    "peak_model, peak_out_list[0], number_of_peaks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(19, 3)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "peak_model, peak_out_list[0], number_of_peaks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peakmodel, peak_out_list, number_of_peaks = smooth(infoWidget.selected_dataset, 1, False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def smooth(dataset, iterations, advanced_present):\n",
+    "    \"\"\"Gaussian mixture model (non-Bayesian)\n",
+    "\n",
+    "    Fit lots of Gaussian to spectrum and let the program sort it out\n",
+    "    We sort the peaks by area under the Gaussians, assuming that small areas mean noise.\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # TODO: add sensitivity to dialog and the two functions below\n",
+    "    \n",
+    "    # peaks = dataset.metadata['peak_fit']\n",
+    "\n",
+    "    peaks ={'fit_start':1,\n",
+    "            'fit_end': 40}\n",
+    "\n",
+    "    peak_model, peak_out_list = eels_tools.find_peaks(dataset, peaks['fit_start'], peaks['fit_end'])\n",
+    "    peak_out_list = [peak_out_list]\n",
+    "\n",
+    "    flat_list = [item for sublist in peak_out_list for item in sublist]\n",
+    "    new_list = np.reshape(flat_list, [len(flat_list) // 3, 3])\n",
+    "    area = np.sqrt(2 * np.pi) * np.abs(new_list[:, 1]) * np.abs(new_list[:, 2] / np.sqrt(2 * np.log(2)))\n",
+    "    arg_list = np.argsort(area)[::-1]\n",
+    "    area = area[arg_list]\n",
+    "    peak_out_list = new_list[arg_list]\n",
+    "\n",
+    "    number_of_peaks = np.searchsorted(area * -1, -np.average(area))\n",
+    "\n",
+    "    return peak_model, peak_out_list, number_of_peaks\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(peakFitWidget.peak_out_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peakFitWidget.sidebar[7,0].value = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7,\n",
+       " [('Peak 1', 0),\n",
+       "  ('ll', -2),\n",
+       "  ('Peak 3', 2),\n",
+       "  ('Peak 4', 3),\n",
+       "  ('Peak 5', 4),\n",
+       "  ('Peak 6', 5),\n",
+       "  ('add peak', -1)])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "options = list(peakFitWidget.sidebar[7,0].options)\n",
+    "options.insert(-1, (f'Peak {len(options)}', len(options)-1))\n",
+    "len(options), options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1deb9892dd0>]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67a009afa3b84bae87ca9c3395503b13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(resolution_functions)\n",
+    "plt.plot(eels_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "                                                        \n",
+    "view = resolution_functions.plot()\n",
+    "view.gca().plot(eels_dataset.energy_loss, eels_dataset)\n",
+    "eels_dataset.metadata\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'tuple' object has no attribute 'metadata'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\EELS\\Analyse_Low_Loss.ipynb Cell 11\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/gduscher/Documents/Github/pyTEMlib/notebooks/EELS/Analyse_Low_Loss.ipynb#X13sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m energy_shift \u001b[39m=\u001b[39m resolution_functions\u001b[39m.\u001b[39;49mmetadata[\u001b[39m'\u001b[39m\u001b[39mlow_loss\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mshifts\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/gduscher/Documents/Github/pyTEMlib/notebooks/EELS/Analyse_Low_Loss.ipynb#X13sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m fwhm \u001b[39m=\u001b[39m resolution_functions\u001b[39m.\u001b[39mmetadata[\u001b[39m'\u001b[39m\u001b[39mlow_loss\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mwidths\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/gduscher/Documents/Github/pyTEMlib/notebooks/EELS/Analyse_Low_Loss.ipynb#X13sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m t_mfp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlog(eels_dataset\u001b[39m.\u001b[39msum(axis\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\u001b[39m/\u001b[39mresolution_functions\u001b[39m.\u001b[39msum(axis\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m))\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'metadata'"
+     ]
+    }
+   ],
+   "source": [
+    "energy_shift = resolution_functions.metadata['low_loss']['shifts']\n",
+    "fwhm = resolution_functions.metadata['low_loss']['widths']\n",
+    "\n",
+    "t_mfp = np.log(eels_dataset.sum(axis=2)/resolution_functions.sum(axis=2))\n",
+    "\n",
+    "plt.figure()\n",
+    "ax1 = plt.subplot(131)\n",
+    "plt.imshow(energy_shift)\n",
+    "plt.colorbar()\n",
+    "plt.title(f' energy shift - mean: {np.mean(energy_shift):.2f}, std {np.std(energy_shift):.3f}')\n",
+    "ax2 = plt.subplot(132)\n",
+    "plt.imshow(fwhm)\n",
+    "plt.colorbar()\n",
+    "plt.title(f' peak widths - mean: {np.mean(fwhm):.2f}, std {np.std(fwhm):.3f}')\n",
+    "ax3 = plt.subplot(133)\n",
+    "plt.imshow(t_mfp)\n",
+    "plt.colorbar()\n",
+    "plt.title(f' thickness - mean: {np.mean(np.array(t_mfp)):.2f}, std {np.std(np.array(t_mfp)):.3f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "## Shift energy scale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c34d26fa707f4790989755df5871d0d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "                    4-eels_new_new_new_new\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eels_dataset.metadata['low_loss'].update(resolution_functions.metadata['low_loss'])\n",
+    "shifted_dataset = eels_tools.shift_on_same_scale(eels_dataset)\n",
+    "view = shifted_dataset.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\scipy\\optimize\\_minpack_py.py:492: RuntimeWarning: Number of calls to function has reached maxfev = 2000.\n",
+      "  warnings.warn(errors[info][0], RuntimeWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b50f9e31b574e01af3568f77bb2d214",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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gOztb7qFu7AEk0g5MAImI1EgqlWLy5Mno2rUrWrVqVWa9pk2bYvXq1di5cyd+//13SKVSdOnSBffv3y+zTXh4OKytrWUPV1dXVbyEcjH9I9IOIoH/rhERqc348ePxzz//4Pjx46hXr16F2xUVFaF58+YYMmQI5s2bV2qdgoICFBQUyJ5nZ2fD1dUVWVlZsLKyqnLs5WkwczcA4Ku3WmBUN3eVXotI32RnZ8Pa2rpaf5c5B5CISE2Cg4Oxa9cuHD16tFLJHwAYGxujXbt2uH37dpl1xGIxxGJxVcOsEvYpEGkHDgETEamYIAgIDg7G9u3bcejQIbi7V76HTCKR4PLly3B2dlZBhFXDpI9I+7AHkIhIxSZMmIANGzZg586dsLS0REpKCgDA2toaZmZmAICgoCDUrVsX4eHhAIC5c+eic+fO8PDwQGZmJhYtWoS7d+9i9OjRGnsdZZG8dP835oJE2oEJIBGRiq1YsQIA4OvrK1e+Zs0aDB8+HACQlJQEA4MXgzJPnjzBmDFjkJKSAhsbG3h5eeHkyZNo0aKFusKusJfv/ytlBkikFZgAEhGpWEWGSKOjo+WeL126FEuXLlVRRNXr5aSP6R+RduAcQCIiqpKXh4DZA0ikHZgAEhFRlcj1ADL/I9IKTACJiKhKpFJNR0BElcUEkIiIqkS+B5BdgETagAkgERFViUR4eQ6gBgMhogpjAkhERFXycg+ghBkgkVZgAkhERFXy8hxArgIm0g5MAImIqEpeTvqK2QNIpBWYABIRUZW8nPJxCJhIOzABJCKiKnl55W+xhAkgkTZgAkhERFUi8F7ARFqHCSAREVWbYu4KTaQVmAASEVGVvNzpxzmARNqBCSAREVWJlHMAibQOE0AiIqoSuVXAnANIpBWYABIRUZUIvBMIkdZhAkhERFXycsrHjaCJtAMTQCIiqhK5RSCcA0ikFZgAEhFRFb00BMw5gERagQkgERFVCbeBIdI+TACJiKhKXs75OAeQSDswASQioioRXh4C5p1AiLQCE0AiIqqSl4eAuRE0kXZgAkhERFXycgIo5SIQIq3ABJCIiKrk5SFgzgEk0g5MAImIqEq4CphI+zABJCKiKuEcQCLtwwSQiIiq5OUhYM4BJNIOTACJiKhKXs75iiTcBoZIGzABJCKiKnm5z48dgETagQkgERFViSDwXsBE2oYJIBERVcnLKR9XARNpByaAREQqFh4ejo4dO8LS0hIODg4IDAxEfHz8a9tt2bIFzZo1g6mpKVq3bo09e/aoIdrKe7nTjx2ARNqBCSARkYodOXIEEyZMwKlTp3DgwAEUFRWhT58+yM3NLbPNyZMnMWTIEIwaNQoXLlxAYGAgAgMDceXKFTVGXjFyQ8DsASTSCiJB4P9rRETq9OjRIzg4OODIkSPo0aNHqXXef/995ObmYteuXbKyzp07o23btoiIiKjQdbKzs2FtbY2srCxYWVlVS+ylOZv4GIMjYgAA9pZinP3CT2XXItJHqvhdZg8gEZGaZWVlAQBsbW3LrBMTEwM/P/lEyt/fHzExMWW2KSgoQHZ2ttxDHeSHgNmnQKQNmAASEamRVCrF5MmT0bVrV7Rq1arMeikpKXB0dJQrc3R0REpKSpltwsPDYW1tLXu4urpWW9zl4RAwkfZhAkhEpEYTJkzAlStXsHHjxmo/d2hoKLKysmSPe/fuVfs1SvNyysf8j0g7GGk6ACIifREcHIxdu3bh6NGjqFevXrl1nZyckJqaKleWmpoKJyenMtuIxWKIxeJqibUyXh71lTIDJNIK7AEkIlIxQRAQHByM7du349ChQ3B3d39tGx8fH0RFRcmVHThwAD4+PqoKU2ncCJpI+7AHkIhIxSZMmIANGzZg586dsLS0lM3js7a2hpmZGQAgKCgIdevWRXh4OABg0qRJ6NmzJxYvXoz+/ftj48aNOHfuHFauXKmx11EW+SFgJoBE2oA9gEREKrZixQpkZWXB19cXzs7OssemTZtkdZKSkpCcnCx73qVLF2zYsAErV66Ep6cntm7dih07dpS7cERT5IeANRcHEVUcewCJiFSsIlujREdHK5QNHjwYgwcPVkFE1UsAh4CJtA17AImIqErkegCZABJpBSaARERUJS+nfILAzaCJtAETQCIiqpJXEz7uBENU8zEBJCKiKnm1w493AyGq+ZgAEhFRlQh4tQeQCSBRTafWBHD58uVo0KABTE1N4e3tjTNnzpRbf8uWLWjWrBlMTU3RunVr7NmzR02REhFRRb2a7zEBJKr51JYAbtq0CSEhIZg9ezbOnz8PT09P+Pv7Iy0trdT6J0+exJAhQzBq1ChcuHABgYGBCAwMxJUrV9QVMhERVQCHgIm0j9oSwCVLlmDMmDEYMWIEWrRogYiICNSqVQurV68utf53332Hvn37Ytq0aWjevDnmzZuH9u3b48cff1RXyEREVAGvpnvcDJqo5lPLRtCFhYWIjY1FaGiorMzAwAB+fn6IiYkptU1MTAxCQkLkyvz9/bFjx45S6xcUFKCgoED2XCqV4vHjx6hTpw5EIlHVXwQRaYwgCHj69ClcXFxgYMCpyzWN4ipg9gAS1XRqSQDT09MhkUjg6OgoV+7o6IgbN26U2iYlJaXU+s/vofmq8PBwhIWFVU/ARFQj3bt3D/Xq1dN0GPSKV9M93g2EqObTmVvBhYaGyvUYZmVloX79+rgb4QArq0r2GBQBKBSAjmZArcq1jc5Trnci9No7SrUrvG6tVDsAMMhTrmfUQKLkBavwmSBSckhJpGysVek0VvJ1ivTlM1OJ1ykpzMfNVXNhaWlZ/fFQlbEHkEj7qCUBtLOzg6GhIVJTU+XKU1NT4eTkVGobJyenStUXi8UQi8UK5VZWBrCyreTLzJcCuQJgaVDpBNDcyLBy1/p/hrUUY68IA1NTpdoBgKGUCWDZDZVsBzABfJ2q/BxwOkeNpLAKmHMAiWo8tUymMTExgZeXF6KiomRlUqkUUVFR8PHxKbWNj4+PXH0AOHDgQJn1iYhIMzgETKR91DYEHBISgmHDhqFDhw7o1KkTli1bhtzcXIwYMQIAEBQUhLp16yI8PBwAMGnSJPTs2ROLFy9G//79sXHjRpw7dw4rV65UV8hERFQBij2ATACJajq1JYDvv/8+Hj16hFmzZiElJQVt27bF3r17ZQs9kpKS5Fb3denSBRs2bMCXX36Jzz//HI0bN8aOHTvQqlUrdYVMREQVwDuBEGkftS4CCQ4ORnBwcKnHoqOjFcoGDx6MwYMHqzgqIiKqCm4ETaR9uKEWERFVyas9fsz/iGo+JoBERFStOARMVPMxASQioipRWATCBJCoxtOZjaCJiMpz/fp1bNy4EceOHcPdu3fx7Nkz2Nvbo127dvD398e7775b6l6i9HqvLgLhHECimo89gESk086fPw8/Pz+0a9cOx48fh7e3NyZPnox58+bhww8/hCAI+OKLL+Di4oKFCxfK3VOcKoYbQRNpH/YAEpFOe/fddzF16lRs3boVtWvXLrNeTEwMvvvuOyxevBiff/65+gLUARwCJtI+up8AFqHk1m6VbUNEOuHmzZswNjZ+bT0fHx/4+PigqIh/ACqLdwIh0j46nwAm5+UhR4kbu0rNRSgQpEBR5dreKGhe6WsBgNhIuZvW5il362EAgKGyEwCKlWtWpXvdKttWi24dK1QhVq26j7AysVbh9RkbG+PHH3/Ehx9+WG4P4Mv1qXIUtoHhHECiGk/nE8D8jiYwtqh8piMYAjDRouyBiMr0xRdfYPr06QgMDMTo0aPxxhtvaDok3aIwBKyZMIio4nR/EYipCIJZ5R9M/oh0R0pKCiIiIpCcnIw333wT7u7umDdvHu7du6fp0HQCVwETaR/dTwCJSO+ZmZkhKCgIhw8fxq1bt/DRRx/h119/hbu7O/r27YstW7Zw7l8VvDrlT+AcQKIajwkgEemVhg0bYu7cuUhISMA///yDOnXqYPjw4ahbt66mQ9NaXARCpH2YABKRXhKJRDAyMoJIJIIgCOwBrIJX8z0OARPVfEwAiUiv3Lt3D3PnzkXDhg3x5ptv4uHDh/jll1+QnJys6dC01qurgNkBSFTz6fwqYCKiwsJCbNu2DatXr8ahQ4fg7OyMYcOGYeTIkWjYsKGmw9N6CkPA7AEkqvGYABKRznNycsKzZ8/w1ltv4e+//4a/vz8MDDgAUm1e6fLjHECimo9/AYlI53355Ze4d+8etm7dioCAAI0kf0ePHsWAAQPg4uICkUiEHTt2lFs/OjoaIpFI4ZGSkqKegCvh1XSPq4CJaj4mgESk80JCQmBvbw8AyMzMxKpVqxAaGorHjx8DAM6fP48HDx6oNIbc3Fx4enpi+fLllWoXHx+P5ORk2cPBwUFFESpPcRGIZuIgoorjEDAR6Y1Lly7Bz88P1tbWSExMxJgxY2Bra4tt27YhKSkJ69atU9m1AwICEBAQUOl2Dg4OFbqFnSa92uP36qIQIqp52ANIRHojJCQEw4cPx61bt2Bqaior79evH44eParByMrWtm1bODs7480338SJEyc0HU6pXk33mAAS1XzsASQivXH27Fn8/PPPCuV169atcXPrnJ2dERERgQ4dOqCgoACrVq2Cr68vTp8+jfbt25fapqCgAAUFBbLn2dnZaon11UW/XAVMVPMxASQivSEWi0tNim7evCmbI1hTNG3aFE2bNpU979KlC+7cuYOlS5fit99+K7VNeHg4wsLC1BWijOIQsNpDIKJK0vkE8LFUjAJp5Ua6TaD8DOYRVneUajfY84ZS7X5v0FqpdgCw4lIPpdoZ3DFTqp3RM5FS7QBApOxkhWKlL6k0pV9lFT40BeXfWqWIqvIBr0ys1fT6Bg4ciLlz52Lz5s0lpxWJkJSUhBkzZuDdd9+tnouoUKdOnXD8+PEyj4eGhiIkJET2PDs7G66uruoITY5EylUgRDUd5wASkd5YvHgxcnJy4ODggLy8PPTs2RMeHh6wtLTEggULNB3ea8XFxcHZ2bnM42KxGFZWVnIPdXh1yl+RhF2ARDWdWnoAw8PDsW3bNty4cQNmZmbo0qULFi5cKDe88arIyEiMGDFCrkwsFiM/P1/V4RKRjrK2tsaBAwdw/PhxXLp0CTk5OWjfvj38/PxUfu2cnBzcvn1b9jwhIQFxcXGwtbVF/fr1ERoaigcPHshWIi9btgzu7u5o2bIl8vPzsWrVKhw6dAj79+9XeayVJbzSfV3EfWCIajy1JIBHjhzBhAkT0LFjRxQXF+Pzzz9Hnz59cO3aNZibm5fZzsrKCvHx8bLnIpGax7mISCcEBQVh0KBB8Pf3h4WFBbp164Zu3bqpNYZz586hV69esufPh2qHDRuGyMhIJCcnIykpSXa8sLAQU6ZMwYMHD1CrVi20adMGBw8elDtHTaHYA8gEkKimU0sCuHfvXrnnkZGRcHBwQGxsLHr0KHsemkgkgpOTk6rDIyId5+Hhga+//hoffvghfH19MXDgQAwcOBB169ZVWwy+vr7l3iEjMjJS7vn06dMxffp0FUdVPV5d9MEhYKKaTyNzALOysgAAtra25dbLycmBm5sbXF1dMWjQIFy9elUd4RGRjpk1axZiY2Nx69YtDBgwADt27ECjRo3g5eWFuXPnIi4uTtMharVXh4ALi9kDSFTTqT0BlEqlmDx5Mrp27YpWrVqVWa9p06ZYvXo1du7cid9//x1SqRRdunTB/fv3S61fUFCA7OxsuQcR0cvq1auHTz75BPv27cOjR48wY8YMxMfH44033oCbmxuCg4P5j6YSXu3YLOYqYKIaT+0J4IQJE3DlyhVs3Lix3Ho+Pj4ICgpC27Zt0bNnT2zbtg329valbuIKlCw0sba2lj00sfUBEWkPS0tLvPfee1i/fj0ePXqE1atXw9DQEDExMZoOTetxCJio5lPrPoDBwcHYtWsXjh49inr16lWqrbGxMdq1aye3iu5lNWX/KyLSDunp6Th9+jQkEgk6deqE3r17o3fv3poOSyu9OreRQ8BENZ9aEkBBEPDpp59i+/btiI6Ohru7e6XPIZFIcPnyZfTr16/U42KxGGKxuKqhEpEe+PPPPzFq1Cg0adIERUVFiI+Px/LlyxW2nqKK4SpgIu2jlgRwwoQJ2LBhA3bu3AlLS0vZPTetra1hZlZyV4mgoCDUrVsX4eHhAIC5c+eic+fO8PDwQGZmJhYtWoS7d+9i9OjR6giZiHRITk4OLCwsZM/DwsJw5swZNGnSBACwe/dujBkzhgmgkl4d8GUCSFTzqWUO4IoVK5CVlQVfX184OzvLHps2bZLVSUpKQnJysuz5kydPMGbMGDRv3hz9+vVDdnY2Tp48iRYtWqgjZCLSIV5eXti5c6fsuZGREdLS0mTPU1NTYWJioonQdIJUeHUjaM4BJKrp1DYE/DrR0dFyz5cuXYqlS5eqKCIi0if79u3DhAkTEBkZieXLl+O7777D+++/D4lEguLiYhgYGCjsw0cV9+qf+EL2ABLVeGpdBEJEpAkNGjTA7t278ccff6Bnz56YOHEibt++jdu3b0MikaBZs2YwNTXVdJha63n+Z2QgQrFUQBEXgRDVeDqfAB542gpiwbhSbYxFEqWvd8igSKl2LUwfKNWulkGBUu0AoJHTI6Xa3cyo3Aru54zyDJVqB0BxklFFKXv3wCqMYAma2F5dyXhFyr7Oqny+K/M9qaa7QA4ZMgQBAQGYOnUqfH19sXLlSrRt27Z6Tq7P/r8L0MTIAMWFEs4BJNICOp8AEhEBwJ49e3D9+nV4enpi1apVOHLkCIYOHYqAgADMnTtXtiCNKu/5/xEmRgZ4VijhHEAlnE18jKJiKbp42Gk6FNITGrkVHBGROk2ZMgUjRozA2bNn8fHHH2PevHno2bMnzp8/D1NTU7Rr1w7//POPpsPUWs/nAJoYlnykcA5g5XyyPhaDI2Lw31WnkZadr+lwSE8wASQinRcZGYk9e/Zg48aNOHv2LH777TcAgImJCebNm4dt27bh66+/1nCU2uv5KmCxcclHCoeAK+Z80hP8fOQO9lxOkZUt3n8Tv526i8e5hRqMjPQBh4CJSOeZm5sjISEBXl5euHfvnsKCjxYtWuDYsWMaik77yYaADZkAVlSxRIp3fjqpUL7p3D1sOncP+6+m4LdR3hqIjPQFewCJSOeFh4cjKCgILi4u6NmzJ+bNm6fpkHSKbAjYqGShV1Ex5wCWJ79Igt5LjpRb59itdPYCkkoxASQinTd06FDcu3cPO3fuRGJiIgYNGqTpkHSKgBergAH2AL5OfMpT3M149tp6A344roZoSF9xCJiI9EKdOnVQp04dTYehm/6/w0/MRSAVMmj5iQrVe5CZp+JISJ+xB5CIdNq4ceNw//79CtXdtGkT1q9fr+KIdEvotkv4+ei/ANgDWBE3UrI1HQIRAPYAEpGOs7e3R8uWLdG1a1cMGDAAHTp0gIuLC0xNTfHkyRNcu3YNx48fx8aNG+Hi4oKVK1dqOmSt8seZe7KvjQ1LduzmPoCle1ZYjL7LFBcbJX7TH6nZ+fjp8G2sjbkrd+z0vxnwbsiea6p+7AEkIp02b9483Lx5E127dsVPP/2Ezp07o379+nBwcEDTpk0RFBSEf//9FytXrsSpU6fQpk0bTYestWQ9gLwVnAJBEJCQnqtQ7lanFgDA0coUYYNa4dM3POSOv7/ylFriI/3DHkAi0nmOjo744osv8MUXX+DJkydISkpCXl4e7Ozs0KhRI4hE1XSvOT1nxDmAZdp09h5mbrusUP7DkHZyzwNaOeOHQ7dlz4d3aaDq0EhPMQEkIr1iY2MDGxsbTYehk7gPYNlKS/4Sv+mvUNbCxQpjurvjl2MJAIBaJlW4hzpROTgETERE1eJ5AigVAImU8wCfKy0h3jOxe5n1v+jfAsG9SoaCnxVKVBYX6Ted7wG8mFUPxsUmarueFMoNJSWaKzfJ11ik/B+HWkbKbTIqqq1cO2m6mVLtAMBQ2dtjKjmyJ1ThXyOJWLl2oip8Xir9/miio0aZa7JDSSs8nwMIAIXFUpix9woAkPa0QKGshYtVuW1qiUveu8iTiZjQywP2lkr+YSEqA3sAiYioWryc8OUXsefqudRs+f/Qjk7r9do25iYv+mc6LjiItKfK/pdHVDomgEREVC0MRCLZVjD5xUwAn3v5nr8XZ/dB/f9f+Vsec7H8AN3vMXeRU1Bc7bGR/mICSER6Y/bs2bh79+7rK5LSTP//fsB5nLuGIokUkzZekCuzNjOuUNtXZ698f+g2Ws3eh5g7GdUUHek7JoBEpDd27tyJRo0aoXfv3tiwYQMKChTnZpHyRCLA9P+HgfOLOHFz7clE7Ix7qFTbZ4Wl9/Yt2HOtKiERyTABJCK9ERcXh7Nnz6Jly5aYNGkSnJycMH78eJw9e1bToekMU+OSj5U8zgHE/mupSrcd6FkXjlZc+EGqwwSQiPRKu3bt8P333+Phw4f49ddfcf/+fXTt2hVt2rTBd999h6ysLE2HqLVEeDEEXMAEEGIj+Y/YdvVrV7itdS1jnP7cD9P8m8qVi5Td2oDoFUwAiUgvCYKAoqIiFBYWQhAE2NjY4Mcff4Srqys2bdqk6fC01vOVwPq+COT0vxk4ditdruyXoA5VPu/lB/wHhaoHE0Ai0iuxsbEIDg6Gs7MzPvvsM7Rr1w7Xr1/HkSNHcOvWLSxYsAATJ07UdJha68UiEP2eA/jqPXw/fcMDdhaVH9Id1c1doUwQuMk2VR0TQCLSG61bt0bnzp2RkJCAX3/9Fffu3cM333wDDw8PWZ0hQ4bg0aNHGoxSe4lEgPj/5wDq8z6A1x5mK5RN7N1YqXOZGitups35lVQd1JIAzpkzByKRSO7RrFmzctts2bIFzZo1g6mpKVq3bo09e/aoI1Qi0mHvvfceEhMTsXv3bgQGBsLQUPHD1c7ODlJp9fdeHT16FAMGDICLiwtEIhF27Njx2jbR0dFo3749xGIxPDw8EBkZWe1xVTczYw4B9/v+mNzz9vVrw9hQ+Y/blR95yT2/WkqCSVRZausBbNmyJZKTk2WP48ePl1n35MmTGDJkCEaNGoULFy4gMDAQgYGBuHLlirrCJSId9Hyu36vy8vIwd+5clV47NzcXnp6eWL58eYXqJyQkoH///ujVqxfi4uIwefJkjB49Gvv27VNpnFUhgkjWY8V9AF8oruJ9kfu0dELiN/1lz4evPlPVkIjUlwAaGRnByclJ9rCzsyuz7nfffYe+ffti2rRpaN68OebNm4f27dvjxx9/VFe4RKSDwsLCkJOTo1D+7NkzhIWFqfTaAQEBmD9/Pt5+++0K1Y+IiIC7uzsWL16M5s2bIzg4GP/5z3+wdOlSlcZZVc+3gSko1s85gPEpTxXKBndwrdZr5DK5pmqgtgTw1q1bcHFxQcOGDTF06FAkJSWVWTcmJgZ+fn5yZf7+/oiJiVF1mESkwwRBgEikuI3GxYsXYWtrq4GIyqbM38GCggJkZ2fLPdRNNgSsp/PU/JcdlX294O1WWD/aGx9619dgRESlM3p9larz9vZGZGQkmjZtiuTkZISFhaF79+64cuUKLC0tFeqnpKTA0dFRrszR0REpKSllXqOgoEBuV39N/OEjoprJxsZGNv+4SZMmckmgRCJBTk4Oxo0bp8EIFZX1dzA7Oxt5eXkwMzNTaBMeHq7ynszyiETgEPBLnKxM0dWj7NGuqsgtKFa4XzBRZajlpycgIED2dZs2beDt7Q03Nzds3rwZo0aNqpZrlPWHz7XWE5iYV+zei9VBbKDczbpb1nqgVDtbQ8XhrIqyMVbuv9I7j5X7g5ZvZKpUOwAwMFBu81NlZ97k1VX+w8vQVrnbixUXKC5IqKhaN5W7Y4CRkveWF6l5dE9UxV0vli1bBkEQMHLkSISFhcHa2lp2zMTEBA0aNICPj08Vo9S80NBQhISEyJ5nZ2fD1bV6hx9fR8xFIDImRtU7yDa9b1N8uzceAJCRU8gEkKpEIz89tWvXRpMmTXD79u1Sjzs5OSE1Vf4WOqmpqXBycirznDXhDx8R1UzDhg0DALi7u6NLly4wNlbfP4XKKuvvoJWVVam9fwAgFoshFmv29mGmsm1g9G8O4JPcQrnnRZLqfQ/G9WgkSwAf5RSgfp1a1Xp+0i8a2QcwJycHd+7cgbOzc6nHfXx8EBUVJVd24MCBcv9DF4vFsLKyknsQEb08HaRdu3bIy8tTmCenqfly5VHm76CmifBiDqC+7VVXLJGi3bwDcmWt6lqXUVs5L4+EvLviJO5m5Fbr+Um/qCUBnDp1Ko4cOYLExEScPHkSb7/9NgwNDTFkyBAAQFBQEEJDQ2X1J02ahL1792Lx4sW4ceMG5syZg3PnziE4OFgd4RKRDrGxsUFaWhqAktEHGxsbhcfzclXKyclBXFwc4uLiAJRs8xIXFydbEBcaGoqgoCBZ/XHjxuHff//F9OnTcePGDfz000/YvHkzPvvsM5XGWVUW/z8s+TRfyfkFWmrpwZtyz6/N9YeDpfLTXiqi56JolZ6fdJtahoDv37+PIUOGICMjA/b29ujWrRtOnToFe3t7AEBSUhIMDF7kol26dMGGDRvw5Zdf4vPPP0fjxo2xY8cOtGrVSh3hEpEOOXTokGyF7+HDhzUWx7lz59CrVy/Z8+dTVoYNG4bIyEgkJyfL7Y7g7u6O3bt347PPPsN3332HevXqYdWqVfD391d77BUmEsHW3ASA4nCorlt++I7c81omnJ9HNZtafkI3btxY7vHo6GiFssGDB2Pw4MEqioiI9EXPnj1L/VrdfH19y72Ha2l3+fD19cWFCxdUGFX1MzPR721gVG1i78b4PuqW7Hl2fhGsTGv+nFaqefgvChHptEuXLlW4bps2bVQYiX4w1cM5gGcTH6vtWiFvNkF6TgE2nC7pLR7/eyzWj+6stuuT7mACSEQ6rW3bthCJROX2vgGASCSCRKI/SYsqvLwIRJ9WAQ+OkN+ce3iXBiq9XsibTWQJ4InbGfjncjL6tnIqdZNzorIwASQinZaQkKDpEPTK8x7A9JwCSKWC0nt4aouEdMWVuLMHtFDpNa3N5Id8x68/j+6N7fDbKG+VXpd0CxNAItJpbm5umg5Bb4hEgNFLCV9s0hN0bFCzbrFXnZIynmHypjiFclX3xBkbKm7gcexWOhrM3I1zX/rBzkKze0GSdmACSEQ67a+//kJAQACMjY3x119/lVt34MCBaopKd9lbvkg+svOKNBiJaj3OLUSPRYqryr/7oK1art+5oS1O/as497DD/INI/Ka/WmIg7cYEkIh0WmBgIFJSUuDg4IDAwMAy63EOYPUwNTaEZz1rXLyfhcc6vBXM9WTFjcPDBrbEoLZ11XL9yBGdEL7nOtbG3FXL9Uj3aOROIERE6iKVSuHg4CD7uqwHk7+qE6Fk6NPdzhwA8OSZ7iaAzwrlf15aOFthmIoXf7zM1NgQE97wUCjv6lFHbTGQdmMCSERE1crWvGQYOENHewBXHr2DMevOyZU9vweyOjlYmmLPxO5yZbfTctQeB2knnR8C9rRIgplF5V5mrlT5CbRSQbk/Ak5GWUq1szVU/pf9kXGmUu2crZS7Z+ptM+Xvi2lQpNyk6nxn5W5HVdc9Xal2AJD2xFKpdsZmys+XEgzVO+lbyR9zAIBIw7uDREVFYenSpbh+/ToAoHnz5pg8eTL8/Pw0G5gOeL72wda8ZJWqLt4NpFgixdd7biiUG5WyMEMdWrhY4eb8AHT6+iAynxUhNbsAgiBwSxh6LfYAEpHe+Omnn9C3b19YWlpi0qRJmDRpEqysrNCvXz8sX75c0+HpjOc9gLo4B7C05A8oSQw1xcTIAKuCOsiePy3Qr/swk3J0vgeQiOi5r7/+GkuXLkVwcLCsbOLEiejatSu+/vprTJgwQYPR6Y7nPYC6mACuPlH6vpISafkbjatah5e223maX4yiYiksTY1hYsR+HiodfzKISG9kZmaib9++CuV9+vRBVpZy0zDoheeDjrrcA1gaQwMRpvRpqukwZLp+cwhe8w/ig5Uxr69MeosJIBHpjYEDB2L79u0K5Tt37sRbb72lgYh0ky73AL5q3+QeOPN5b/RoYq/pUBScT8rE2z+dwL3HzzQdCtVAHAImIp32/fffy75u0aIFFixYgOjoaPj4+AAATp06hRMnTmDKlCmaClFnvFgEUtIDmJ1fjCKJtNQ7V2ijV+8nvWZ4RzR1Um7Rlyr0aGKPozcfyZVdSMpE928Pc3NoUsAEkIh02tKlS+We29jY4Nq1a7h27ZqsrHbt2li9ejW+/PJLdYenk16+V+3Ba6kIaO2swWiqx5mEx9h4Jkn2/Npcf9QyqVkfoZHDO2LwzzGIvftE4djZxMcoLJaiq4edBiKjmqhm/fQSEVWzhITSJ+2T6hi+dD/gdTF3dSIBfO/nF/PpGtmb17jkDwAMDERYN7ITZu28ij/P35c7NjiiJP64WW+idi0TTYRHNYxu9MsTEZHGlbb3XJdG2n9nilfn0Dlbm2koktczFxth8XueeK9DvVKPp+fo/rxMqpia9y8MEZEK3b9/H3/99ReSkpJQWCj/YbhkyRINRaV7RnRtgDUnEpFXpN232PvncjIijtyRK7uZ+lRD0VTcO+3rYfO5+wrl30fdwpL3PDW2cTXVHEwAiUhvREVFYeDAgWjYsCFu3LiBVq1aITExEYIgoH379poOT6dYmZbMA3yar52bEguCgNxCCcavP69wrI6Feu+8o4yODWzh29Qe0fHyi0L+uvgQf118iHfa1UXnRnXg39JJbs4m6Q/+C0BEeiM0NBRTp07F5cuXYWpqij///BP37t1Dz549MXjwYE2Hp1McrEqSpLtauAVJ1PVUdJh/EENWnir1+I//bafmiCrP0ECEyBGd4N/SsdTj2y48wPStlzB0VemvkXQfE0Ai0hvXr19HUFAQAMDIyAh5eXmwsLDA3LlzsXDhQg1Hp1sa1DEHADx4on0J4Ki155CRW4jLD0rfHLyRvYWaI1JebbPyF3xceaDcvd1J+zEBJCK9YW5uLpv35+zsjDt3XsztSk9P11RYOuPlNSCOVqYAgNTsAg1FoxoN6tTSdAiVMvnNxjA2VFyc87IGM3ej/bwDOPLKHoKk23R+DqBEMIBEqFyeayoqUv6C5f+elSmxULm9mVxrZSp3QQCdzZTbHsPQRbmbnkfkK79y7tEN5d4fQ2vlVryZ9/1XqXYAYLC5tVLtigqU/3U0VnaevZL3rxep+7731XSb1c6dO+P48eNo3rw5+vXrhylTpuDy5cvYtm0bOnfuXD0XIQCAk3VJAphTUIyn+UWwNK3588wkUgFHb5WdBNlZmGDHhK5qjKjqnK3NcGtBPwDA7kvJmLBBcU4jUHLXlmGrz3DDaD2i8wkgEdFzS5YsQU5ODgAgLCwMOTk52LRpExo3bswVwJU0KvKsQrIkeuk/YAuxESzFRnhaUIzU7PwanwDmFhSj5ex95db5aaiXVu+h17+NMxIzmmLRvvgy6zzJLYSNufa+Rqo4JoBEpDcaNmwo+9rc3BwREREajEZ7FUukiLqR9tp61rWM8bSgGH5LjuLSnD6ylcE1za/HEzBv17VSj33coyGm9GmK1Ox8uNpq1/BvaSb08sDIru5oPmtvqcfbzTuAlR95wdTYsEbe35iqj1rmADZo0AAikUjhMWHChFLrR0ZGKtQ1NTVVR6hEpAfOnTuH3377Db/99htiY2M1HY7WKZRUbA7A/Sd5sq+3lrInXU3w6GlBmcnfp294ILRfc5gYGehE8vecmYkhdn3aDWO6u+Pb/7RROD72t1gErT6D+JSnWLj3hsJG2KQb1NIDePbsWUgkLyYpXblyBW+++Wa52y5YWVkhPv5FN3VpO8wTEVXG/fv3MWTIEJw4cQK1a9cGAGRmZqJLly7YuHEj6tUr/e4JJK+gqPQE8NU/010a1cHJOxkAUGM3hP5fGcOhdWubYUqfpmqORn1a1bVGq7rWAIBBbV3Q9EvFHkH/ZUcBACui7+D0571lC3tIN6ilB9De3h5OTk6yx65du9CoUSP07NmzzDYikUiujaNj6XsZERFV1OjRo1FUVITr16/j8ePHePz4Ma5fvw6pVIrRo0drOjytUVBcsR7AX4d1VHEkVXPwWip2XXpY6rHwd5RbzKWNxEaGr63j/XWUGiIhdVL7NjCFhYX4/fffMXLkyHJ79XJycuDm5gZXV1cMGjQIV69eVWOURKSLjhw5ghUrVqBp0xc9O02bNsUPP/yAo0ePajAy7VJQXHpv3qt/0c1MDDG8SwMAwLPCmnVHkJO30zF63TnkFpb+WsRG3CXtVe/8dAKL9t2ARFpNy/JJo9T+E75jxw5kZmZi+PDhZdZp2rQpVq9ejZ07d+L333+HVCpFly5dcP9+2XNICgoKkJ2dLfcgInqZq6sriooUt3mSSCRwcXHRQETaqbCCPYBASRIIAHmF6t47qHzrzySVWv5eh3p4o5kDOjSwVXNENd/5pEwsP3wHjT7fg5j/H9on7aX2BPDXX39FQEBAuX9sfXx8EBQUhLZt26Jnz57Ytm0b7O3t8fPPP5fZJjw8HNbW1rKHq6urKsInIi22aNEifPrppzh37pys7Ny5c5g0aRL+97//aTAy7VLRIWAAqGVckgAejn/9qmF1uZ32FLsvJZd67Jt32mD18I4wNNDPeeeju7njjWYOr6035JdTkLInUKupdRuYu3fv4uDBg9i2bVul2hkbG6Ndu3a4fft2mXVCQ0MREhIie56dnc0kkIhgY2MjN90kNzcX3t7eMDIq+fNXXFwMIyMjjBw5EoGBgRqKUruUOQRcSs5051HJvosJ6bkokkhhbKj5odXJm+IUygxEQJBPAxjoaeL3Rb/m2HbhAT7p5YFnhcXo0qgO5u++Xm6bhp/vQX3bWviyf3P0aemkpkipuqg1AVyzZg0cHBzQv3/ldhqXSCS4fPky+vXrV2YdsVgMsVhc1RCJSMcsW7ZM0yHILF++HIsWLUJKSgo8PT3xww8/oFOnTqXWjYyMxIgRI+TKxGIx8vPz1RFqucpaBVyalzcVTsmqGXvp2Vsoflac/cIPdUop1xdjejTEmB4l+2TamptgdPeG+K93fQxZeQoX75d+T2QASHr8DGN/i0WPJvZYPNgTdhYm3LVDS6gtAZRKpVizZg2GDRsm+8/7uaCgINStWxfh4eEAgLlz56Jz587w8PBAZmYmFi1ahLt373KVHhFV2rBhwzQdAgBg06ZNCAkJQUREBLy9vbFs2TL4+/sjPj4eDg6lD7nV1O2wyhoCFpVyL8wpfZpizYlEADVjK5iCYgkOxyve7k2b7/ChKrVMjLAzuBuuPcyGmYkhev0vusy6R28+QscFB9GnhSOWD21fI3p6qXxqSwAPHjyIpKQkjBw5UuFYUlISDAxe/LA8efIEY8aMQUpKCmxsbODl5YWTJ0+iRYsW6gqXiHSURCLBjh07cP16yfBWy5YtMXDgQBgavn4rjKpYsmQJxowZI+vVi4iIwO7du7F69WrMnDmz1DbPt8OqacoaAi6NhdgIdWub4UFmHp6VseJWncL+frHpc+9mDgjt1wwOVqZ6O+evIlq4WAEAEr/pj6y8Inzzzw38UcYimv3XUtH4i39gbmKINvVqY/bAFsgrlGDsb7GY2bcZ3vXiXps1hdoSwD59+kAQSp8wGh0dLfd86dKlWLp0qRqiIiJ9cvv2bfTr1w8PHjyQbQUTHh4OV1dX7N69G40aNVLJdQsLCxEbG4vQ0FBZmYGBAfz8/BATE1Nmu+fbYUmlUrRv3x5ff/01WrZsqZIYKyM9p7BS9Z+vBK4JW8FsOP0icRndvSE8HCw1GI32sTYzRvg7rfFBR1d8u+8GTtwufTVwbqEEMf9moO+yY7KyKVsuMgGsQdhHS0R6Y+LEiWjUqBHu3buH8+fP4/z580hKSoK7uzsmTpyosuump6dDIpEobGjv6OiIlJSUUtvU5O2w0p4WlFpe1gh1rf9PAPM1NAQsCAKKJVKE/S2/n6ypMT8CleXpWhvrR3eu0jkq05NM1U+ti0A0wd34EcxNKje0Y2mg/CRrQyi3LD5fUO5bUU8D38GzSrZ7mqf8BGvBrnI9Ds+Z1yr9g+p10nY2U6odABQ8Vm4ukdEj5ecgGSr3MpVXhdEykRLbwVXX4NyRI0dw6tQp2Nq+2OOtTp06+Oabb9C1a9dqukr18PHxgY+Pj+x5ly5d0Lx5c/z888+YN29eqW3Cw8MRFham8tie5ivupVgeU+PnPYCa+cAP2XwRB6+l4mnBix5IdztzeNarrZF4dEn0VF+sOv4vhnq7IeC7Y6+tP+evq6hnY4bF+28ir0iCBnVqIa9IgpUfdYCna23VB0wy/PeHiPSGWCzG06dPFcpzcnJgYqK6RQB2dnYwNDREamqqXHlqamqF5/hVdDusrKws2ePevXtVirsseZVM5LKelSSMh65XfC/AZ4XF+OjX01gRfadS1yrN9gsP5JI/ADg81Vdvt3ypTg3szDE/sDWaO1vh7+Bur60feTIR83dfly0ISsx4htTsAgxafgI3UngDB3ViAkhEeuOtt97C2LFjcfr0aQiCAEEQcOrUKYwbNw4DBw5U2XVNTEzg5eWFqKgX91OVSqWIioqS6+Urz/PtsJydncusIxaLYWVlJfdQhcqu5o1PLUm6t114UOE2B6+n4ditdCzce6PM+eMVseey4obP/duU/R6S8lrXs8bVMH/cnB+AuYMqP1e177JjGPdbLDKfFcp+P0l1dH4ImIjoue+//x7Dhg2Dj48PjI2NAZRsBD1w4EB89913Kr12SEgIhg0bhg4dOqBTp05YtmwZcnNzZauCtWk7rMr2APZt6YS9V0uf61iWr3ZckX0ddy8TjR0tYSGu3EfWlQdZ+GT9ebmyAZ4u+P6DtpU6D1Wc+f9/j4J8GsDK1BiX7mfh2K1HuJWWU6H2e6+m4GlBEfKLpLibkYs9E7vDwcpUlSHrLSaARKQXBEFAdnY2Nm7ciAcPHsi2gWnevDk8PDxUfv33338fjx49wqxZs5CSkoK2bdti7969soUh2rQdVlk9gGXtUzixd2PsvZoCO4uKD7Nn5b2YZ/j2TycBAP9M6o7mzhXr1SyWSPHxb7FyZXYWJvhhSLsKx0BVE9iuLgLb1UVBsQTZecXotvBQhW4j+PLK4k//uIBNH5f0kucWFCP27hP4NKrDfQarARNAItILgiDAw8MDV69eRePGjdWS9L0qODgYwcHBpR7Tpu2wKtsD6Gxd0oOTnlOIjJyCUu+4cfJ2Og7dSMO0vk2RklX6QrxlB2/i5486VOiavRZH40Fmnux5I3tz7P+sZ6XipuohNjKEvaUh4ucHwG/JEdz+/97AivQMn054jEX7bsBAJMK1h9mIupGGyX6NMdmviTpC12lMoYlILxgYGKBx48bIyCh93zKquDJ7AMuoX7uWsezrOS9txPyy/646jVXHExDw3TH0XBRdap0iSflzwvIKJdhx4QGuPczGvcd5csfC32nDzZ5rgJYuL3pwIz7ywrzAVrLnv4/yLrXN8sN38MOh24i6UbKIaNnBWxAEAcsP30bg8hNIycrH1tj7+ONMEv59lFMj9pvUBuwBJCK98c0332DatGlYsWIFWrVq9foGVKrKLgIp7xZ2giDIbQ/z76PcMuseupGGk7fT0cXDDh+uOo3jt9Nx4LMe8HCwwD9XUhTm+72sYwObSsVMqhHyZhOkZOVjdPeS+w4P7VQfgiCgc8M6aOJoiZ8/8sLXe67jbsazcs/TcUEU0nNK9r/qHB4ld6xNPWv8VYEVyfqOCSAR6Y2goCA8e/YMnp6eMDExgZmZmdzxx48faygy7VLZIWAAmNS7Mb6LuoW/Lz7E4sGeMDEygFQqoOHne8ps49OwDgZ3qIeQzRdlZf9ddRrX5vrj+O10AMCbS4+We10vNxusHt6xxtxHWd+51TGXzekDAAMDEYJ8Gsie+7d0gm9Te4yKPCf7HpfmefJXmkv3s3A3IxdudcyrJWZdxQSQiPTGsmXLNB2CTih7EUjZbXwa1cF3UbcAAMsP38bdjFzsiHtY7nXWjuwEAFi0Lx7JL80LTMuu+M7nf47vUuG6VDOIjQzx+2hvZD0rgufc/Uqdo+eiaCR+0x9z/roKC7ERpvo3lR27npwNZ2tTWIiNYKTHi0mYABKR3hg2bJimQ9AJyvQAeru/uPvK80TwdUyMSj6cd33aDV7zD8rK/7lSsS1lvnmndSUipJrGupYxNn/sg6M3H2FL7D241DZDHXMTHKzghuINZu6WfX06IQO303IwpkdDfLs3HkDJwqB9k3vAyNAA+UUSiI0M9KqnmAkgEekViUSC7du3y7aBadGiBQYNGgQjI/45rAiJVKjQVh6vEolEeL+DKzadq9jdSS7O7iP72tbcBCaGBiiUlFx34d4bFTrHB53qVzpOqlk6uduik7stJvs1hqGBCMlZ+Th4/VClz3M28QkAyJI/ALjzKBcz/rwMJ2sxlh++AzsLE/g1d8T8wFZ60TOo+6+QiOj/Xb16FU2aNMGwYcOwfft2bN++HcOGDUPjxo1x5cqV15+AkF/OApCq9J0se78tNo7tDADYMMYb1mYvVg6LRCKc+8pPoc2r+wpGjugIU+OSj7U/xnSuQjRU0xgZlvTOudQ2w84JL+7b3bOJPcLfaY3Eb/qXuYq4PH+ev4/lh0tuN5ieU4iNZ++VO/dQl/BfXiLSG6NHj0bLli1x7tw52NiUrAp98uQJhg8fjrFjx+LkyZMajrDme5r/YouNl3vlKtS2oKjUct+m9ghsVxcAkPhN/1LrWJkaI7iXB348/OJeyCO7ucNCbIT0nEJ86F0fDlamOP/Vm7j/JA9NHC0rHBdplzb1rPFRZzfUt62FMT0aysq7NbZD4jf95YZ+lTF8zVkAJfeLXnrgJsb7NsI/V1JQz8YM73VwrdK5axKdTwCzpLVQJDWsVJs0ifJ/OExFpf+Be518wfj1lUpxp0j5b2Ej44rfmP1lrsbK7aM2vOkppdoBQFRaM6XapT5V7nuZmWGhVDsAMMxQ7nspfqw/c080JS4uTi75AwAbGxssWLAAHTt21GBk2mPDmSTZ14YGIuClDsHXzZ/6zK8J9lxOQacGtjA2EsFAJMJkv8Zo4WxdoWtP9W+Ke0+eYef/Lx7p29IJDe3lf1drmRgx+dNxIpFIbv/AV535ojc+WnUG8alPYWQgwjT/pgj/p2LTBl7W63/RAIC/Lr5YrGRmbIg3mjnIbnmnzbT/FRARVVCTJk2QmpqKli3lb1SflpamkTuDaKOUrBcbLFd2Y+XGjpZICO9XpYn2cwe1grO1Gd5o5qCQ/BEBgIOlKfZ91gOCIKBIIsDEyABBPg2QXyTBv+m5aOdaGwYGIoRuu4Q/zlRsTupzn/5xAQDQv7UzjAxF6N7YHu+2r4uCYimirqehW2M72fSFp/lFsDRVrkNAHTgHkIj0Rnh4OCZOnIitW7fi/v37uH//PrZu3YrJkydj4cKFyM7Olj2odM9X5k58w6PcbV/KUtVVltZmxpgZ0AydXlpVTFQakUgk+3k1MzGEjbkJvNxsYPD//7h82NlNVnfeoJa4tSCgwufefTkZO+MeYuqWi3AP3YNuCw9jwobzCN5Qshn5uphEtJ6zH3uvJFfjK6pe7AEkIr3x1ltvAQDee+89WSIiCCW3FxswYIDsuUgkgkRS+a1O9MGjpyV78NlbihV6APVoBw3SAS1drHFi5huwtxDLEsVaJoZyd6apqOcbUx+7lY5Vx/7F/N0luwxM2HABd752rr6gqxETQCLSG4cPH9Z0CFpPLgFkxkdarm5t+bsBHZnWC0mPn+He42eYtvUiWte1xp1HucjKq/j8/ufJH1CybdLLi1JGd3NHoUSK2QNaavze1EwAiUhv9OzZU9MhaL1HOS8SwFeHc5kOkraztxTD3lIMLzcb9GnpCDNjQ4hEIhQUS5CVV4ROC0ruO9y5oS1O/Vv5W0euOp4AoOQ2hw5WYtQxF6OBnWZuWccEkIiIKkQQhBc9gBam0IO9ckmP1TJ5kSKJjQzhYGmI7z5oC0MDEQJaOWPb+ftYdvAWHmSWLIzq3tgOx25VbA/B8evPy74ua+sjVWMCSEREFZJTUIz8opJ9/+wsTTgETHpnUNu6sq8Hd3DF4A6uKCiWQBCApMfP0Gfp0UqfM/NZIWrXMnl9xWrGBJCIiCrkYWY+AMDIQIRaJkaKK3qZEJIeEhuV7DXs8tJ8wpjQN/AwMx/3nzzD/N3X8aG3G/7rXR8dFxxUaJ/0+BkTQCIiqrm2xpbsmVYsLVk5relJ7EQ1iYXYCNFTfWFkKIKztRmcrc3g5WaDgZ4usn+WZgY0wzcvbUq97P22aFOvtkbirZYZHEePHsWAAQPg4lLyInfs2CF3XBAEzJo1C87OzjAzM4Ofnx9u3br12vMuX74cDRo0gKmpKby9vXHmzJnqCJeIiJSQ9PgZAMDcpKTH49X8j+kg6bsGduaoZ1NLruzlnvJR3dzRzcMOAPDtf9rIboGoCdXSA5ibmwtPT0+MHDkS77zzjsLxb7/9Ft9//z3Wrl0Ld3d3fPXVV/D398e1a9dgampa6jk3bdqEkJAQREREwNvbG8uWLYO/vz/i4+Ph4OBQHWETkZ5p165dqRsRi0QimJqawsPDA8OHD0evXr00EF3Nt+9qKoCSW7IBkG2oS0QVY2xogN9GdcKjnAI4WJae/6hLtfQABgQEYP78+Xj77bcVjgmCgGXLluHLL7/EoEGD0KZNG6xbtw4PHz5U6Cl82ZIlSzBmzBiMGDECLVq0QEREBGrVqoXVq1dXR8hEpIf69u2Lf//9F+bm5ujVqxd69eoFCwsL3LlzBx07dkRycjL8/Pywc+dOTYda4zzfMBt4sRcgF4EQVZ5IJNJ48geoYQ5gQkICUlJS4OfnJyuztraGt7c3YmJi8MEHHyi0KSwsRGxsLEJDQ2VlBgYG8PPzQ0xMTKnXKSgoQEFBgew5b+VERK9KT0/HlClT8NVXX8mVz58/H3fv3sX+/fsxe/ZszJs3D4MGDdJQlDXT8+FfAAjyaQAAMHh1H0Dmg0RaQ+UJYEpKCgDA0dFRrtzR0VF27FXp6emQSCSltrlx40apbcLDwxEWFqZQfqfQAaYFlXuZUkH5jlEDkVTptspIKbBWuu2houZKtSuQKvdjUyg1VKodADwrUm6FVGaylVLtzO4r/6sxdsgepdqt/rWf0tcUCa+vU53tqkSZa1ZTnJs3b0ZsbKxC+QcffAAvLy/88ssvGDJkCJYsWVI9F9QhEUfuAACaOFrAybqk94JDwETaS2e28QwNDUVWVpbsce/ePU2HREQ1jKmpKU6ePKlQfvLkSdl8ZKlUWubcZH11O+0p/jhT8jfV0erFe/PqRtAiLgMh0hoq7wF0cnICAKSmpsLZ+cUNkVNTU9G2bdtS29jZ2cHQ0BCpqaly5ampqbLzvUosFkMsFldP0ESkkz799FOMGzcOsbGx6NixIwDg7NmzWLVqFT7//HMAwL59+8r826SvriU/lX1976WhYCLSXirvAXR3d4eTkxOioqJkZdnZ2Th9+jR8fHxKbWNiYgIvLy+5NlKpFFFRUWW2ISJ6nS+//BK//PILzpw5g4kTJ2LixIk4c+YMfvnlF3zxxRcAgHHjxuHvv//WcKQ1y8FrL/4Z79nEXvb1lQeca02kraqlBzAnJwe3b9+WPU9ISEBcXBxsbW1Rv359TJ48GfPnz0fjxo1l28C4uLggMDBQ1qZ37954++23ERwcDAAICQnBsGHD0KFDB3Tq1AnLli1Dbm4uRowYUR0hE5GeGjp0KIYOHVrmcTMzszKP6au/Lj6UfR3Sp6kGIyGi6lItCeC5c+fk9s0KCQkBAAwbNgyRkZGYPn06cnNzMXbsWGRmZqJbt27Yu3ev3DybO3fuID39xU2U33//fTx69AizZs1CSkoK2rZti7179yosDCEiqqzCwkKkpaVBKpVftFW/fn0NRVRzNZi5W/b1nondYW1mXGZdobpW6xCRylVLAujr6yu3R9SrRCIR5s6di7lz55ZZJzExUaEsODhY1iNIRFRVt27dwsiRIxUWggiCAJFIBIlEoqHIaqZiiXyC3NjRotz6XARCpD14L2Ai0hvDhw+HkZERdu3aBWdn51LvCkIvpOcUyr7e9kkXGL+67PcV7AEk0h5MAIlIb8TFxSE2NhbNmjXTdChaYfuFB7Kv29e30WAkRFTddGYfQCKi12nRooXcXGMqX0ZOyd2VzE2U38SdiGomJoBEpDcWLlyI6dOnIzo6GhkZGcjOzpZ7qNry5cvRoEEDmJqawtvbG2fOnCm3/pYtW9CsWTOYmpqidevW2LNHubvMKCs5Kx8AV/4S6SImgESkN/z8/HDq1Cn07t0bDg4OsLGxgY2NDWrXrg0bG9UOcW7atAkhISGYPXs2zp8/D09PT/j7+yMtLa3U+idPnsSQIUMwatQoXLhwAYGBgQgMDMSVK1dUGufLHmblAQBcrHlnFCJdwzmARKQ3Dh8+rLFrL1myBGPGjJHtZRoREYHdu3dj9erVmDlzpkL97777Dn379sW0adMAAPPmzcOBAwfw448/IiIiQmVxDvrxOC7ez5Irc2ICSKRzmAASkd7o2bOnRq5bWFiI2NhYhIaGysoMDAzg5+eHmJiYUtvExMTI9lR9zt/fHzt27FBZnMlZeQrJHwA0cbRU2TWJSDOYABKRTrt06RJatWoFAwMDXLp0qdy6bdq0UUkM6enpkEgkChvZOzo64saNG6W2SUlJKbV+SkpKmdcpKChAQUGB7Hll5zUeu6W4QKZ1XWuYi/lRQaRrdP63OrXACmLjsneur24WRgWvr1RaO8N8pdo5iRX/W68oM8MipdpJBeX2TmtiVvYH1+tcrVVXqXaPssvfuLYswkPlezx+jeynVDuDqmyhJn19lVIpeU1RVWJV5senCtv1tW3bFikpKXBwcEDbtm0hEolK3bheFzaCDg8PR1hYmNLtT9wuSQCDe3ngszeb4PjtdLRysaqu8IioBtH5BJCI9FtCQgLs7e1lX2uCnZ0dDA0NkZqaKleempoKJyenUts4OTlVqj4AhIaGyg0bZ2dnw9XVtUIxSqWCLAHs1tgOhgYi9GxiX6G2RKR9uAqYiHSam5sbRCIRioqKEBYWBqlUCjc3t1IfqmJiYgIvLy9ERUXJyqRSKaKiouDj41NqGx8fH7n6AHDgwIEy6wOAWCyGlZWV3KOi4lOfIj2nEGbGhkpv+lzOHUGJqIZhAkhEesHY2Bh//vmnxq4fEhKCX375BWvXrsX169cxfvx45ObmylYFBwUFyS0SmTRpEvbu3YvFixfjxo0bmDNnDs6dO6ey+6Mf///5f94NbWFixI8GIl3H33Ii0huBgYEqXUVbnvfffx//+9//MGvWLLRt2xZxcXHYu3evbKFHUlISkpOTZfW7dOmCDRs2YOXKlfD09MTWrVuxY8cOtGrVSiXxHX8+/Othp5LzE1HNwjmARKQ3GjdujLlz5+LEiRPw8vKCubm53PGJEyeq9PrBwcFl9uBFR0crlA0ePBiDBw9WaUwAUFAswemEDAAl8/+ISPcxASQivfHrr7+idu3aiI2NRWxsrNwxkUik8gSwpjp/NxP5RVLYWYjRlHv+EekFJoBEpDc0tQq4pqtnY4aQN5vA0EAEkajie+40sjfHnUe5KoyMiFSFcwCJSO8UFhYiPj4excXFmg6lRnC1rYWJvRtjQi+PSrX7xLdy9Ymo5mACSER649mzZxg1ahRq1aqFli1bIikpCQDw6aef4ptvvtFwdNqnEp2FRFTDMAEkIr0RGhqKixcvIjo6GqamprJyPz8/bNq0SYORERGpF+cAEpHe2LFjBzZt2oTOnTvLzXVr2bIl7ty5o8HIiIjUiz2ARKQ3Hj16BAcHB4Xy3NzcSi1+oBKvvmW8EQiR9mACSER6o0OHDti9e7fs+fOkb9WqVeXeYo2ISNfo/BDw9SwnGBWLK9VGJFL+/1gL4wKl2hkZSJVrJ1KuXVXUNs5Tqt3NvLJvYv86D/JqK91WKfzXqFxCFTrLqvDrVWVff/01AgICcO3aNRQXF+O7777DtWvXcPLkSRw5ckRzgRERqRk/5ohIb3Tr1g1xcXEoLi5G69atsX//fjg4OCAmJgZeXl6aDo+ISG10vgeQiOhljRo1wi+//KLpMHSCCJw3SaStqqUH8OjRoxgwYABcXFwgEonkbrZeVFSEGTNmoHXr1jA3N4eLiwuCgoLw8OHDcs85Z84ciEQiuUezZs2qI1wi0lOGhoZIS0tTKM/IyIChoaEGIiIi0oxqSQBzc3Ph6emJ5cuXKxx79uwZzp8/j6+++grnz5/Htm3bEB8fj4EDB772vC1btkRycrLscfz48eoIl4j0lCCUPgGxoKAAJiYmao6GiEhzqmUIOCAgAAEBAaUes7a2xoEDB+TKfvzxR3Tq1AlJSUmoX79+2cEZGcHJSfmFA0REAPD9998DKFn1u2rVKlhYWMiOSSQSHD16lCMMSuDOOUTaSyNzALOysiASiVC7du1y6926dQsuLi4wNTWFj48PwsPDy0wYCwoKUFDwYgVudnZ2dYZMRFps6dKlAEp6ACMiIuSGe01MTNCgQQNERERoKjzdUUYPKxHVPGpPAPPz8zFjxgwMGTIEVlZWZdbz9vZGZGQkmjZtiuTkZISFhaF79+64cuUKLC0tFeqHh4cjLCxMlaETkZZKSEgAAPTq1Qvbtm2DjY2NhiMiItIstW4DU1RUhPfeew+CIGDFihXl1g0ICMDgwYPRpk0b+Pv7Y8+ePcjMzMTmzZtLrR8aGoqsrCzZ4969e6p4CUSkxXr16gWxWHFf0Ly8PMydO1cDERERaYbaEsDnyd/du3dx4MCBcnv/SlO7dm00adIEt2/fLvW4WCyGlZWV3IOI6GVhYWHIyclRKH/27BlHEIhIr6glAXye/N26dQsHDx5EnTp1Kn2OnJwc3LlzB87OziqIkIj0gSAIpd7z9+LFi7C1tdVAREREmlEtcwBzcnLkeuYSEhIQFxcHW1tbODs74z//+Q/Onz+PXbt2QSKRICUlBQBga2sr23qhd+/eePvttxEcHAwAmDp1KgYMGAA3Nzc8fPgQs2fPhqGhIYYMGVIdIRORHrGxsZHtJ9qkSRO5JFAikSAnJwfjxo3TYITaqbRkmoi0Q7UkgOfOnUOvXr1kz0NCQgAAw4YNw5w5c/DXX38BANq2bSvX7vDhw/D19QUA3LlzB+np6bJj9+/fx5AhQ5CRkQF7e3t069YNp06dgr29fXWETER6ZNmyZRAEASNHjkRYWBisra1lx56vAvbx8dFghERE6lUtCaCvr2+ZG6wCZW+++rLExES55xs3bqxqWEREAEr+GQUAd3d3dOnSBcbGxhqOiIhIs3T+XsCFUkNIpZW7xVORRPlbQj0rUu5uAsaGEqXamRkVKdUOAKSCcsM3OUWKqygrwtTIXKl2AFCo5PfE2jxPqXaPaisfq2G+klNri5W+pPKUHMETSas3DHXp2bOn7Ov8/HwUFhbKHefiMSLSF2rdBoaISJOePXuG4OBgODg4wNzcHDY2NnIPIiJ9wQSQiPTGtGnTcOjQIaxYsQJisRirVq1CWFgYXFxcsG7dOk2Hp3Ve7UDmfUCItIfODwETET33999/Y926dfD19cWIESPQvXt3eHh4wM3NDevXr8fQoUM1HSIRkVqwB5CI9Mbjx4/RsGFDACXz/R4/fgwA6NatG44eParJ0LQSd4Eh0l5MAIlIbzRs2FB2X+BmzZrJbi35999/o3bt2hqMjIhIvZgAEpHeGDFiBC5evAgAmDlzJpYvXw5TU1N89tlnmDZtmoajIyJSH84BJCK98dlnn8m+9vPzw40bNxAbGwsPDw+0adNGg5EREakXE0Ai0ltubm5wc3PTdBhERGrHIWAi0hsTJ07E999/r1D+448/YvLkyeoPSMuJlN1JnIg0jgkgEemNP//8E127dlUo79KlC7Zu3aqBiIiINIMJIBHpjYyMDFhbWyuUW1lZIT09XWXXffz4MYYOHQorKyvUrl0bo0aNQk5OTrltfH19IRKJ5B7jxo1TWYzK4DYwRNqLCSAR6Q0PDw/s3btXofyff/6R7Q+oCkOHDsXVq1dx4MAB7Nq1C0ePHsXYsWNf227MmDFITk6WPb799luVxVgdBN4KhEhrcBEIEemNkJAQBAcH49GjR3jjjTcAAFFRUVi8eDGWLVumkmtev34de/fuxdmzZ9GhQwcAwA8//IB+/frhf//7H1xcXMpsW6tWLTg5OakkLiLSb+wBJCK9MXLkSCxevBi//vorevXqhV69euH333/HihUrMGbMGJVcMyYmBrVr15Ylf0DJFjQGBgY4ffp0uW3Xr18POzs7tGrVCqGhoXj27Fm59QsKCpCdnS33ICIqjc73AIoNi2FkaFipNiYGEhVFUzYzoyKl2tmIy/9AUAUjkVSpdh610pS+poGS17xmUnbvSnkeO9VSqh0ASDMslGonKlb6kjBQduhNyXZCFeZ+KfWtrMahxfHjx2P8+PF49OgRzMzMYGGh3PerolJSUuDg4CBXZmRkBFtbW6SkpJTZ7r///S/c3Nzg4uKCS5cuYcaMGYiPj8e2bdvKbBMeHo6wsLBqi52IdBd7AIlIb+Tl5cl60ezt7ZGRkYFly5Zh//79lT7XzJkzFRZpvPq4ceOG0rGOHTsW/v7+aN26NYYOHYp169Zh+/btuHPnTpltQkNDkZWVJXvcu3dP6etXBNeAEGkvne8BJCJ6btCgQXjnnXcwbtw4ZGZmolOnTjAxMUF6ejqWLFmC8ePHV/hcU6ZMwfDhw8ut07BhQzg5OSEtTb73u7i4GI8fP67U/D5vb28AwO3bt9GoUaNS64jFYojF4gqfk4j0FxNAItIb58+fx9KlSwEAW7duhZOTEy5cuIA///wTs2bNqlQCaG9vD3t7+9fW8/HxQWZmJmJjY+Hl5QUAOHToEKRSqSypq4i4uDgAgLOzc4XbqBq3gSHSXhwCJiK98ezZM1haWgIA9u/fj3feeQcGBgbo3Lkz7t69q5JrNm/eHH379sWYMWNw5swZnDhxAsHBwfjggw9kK4AfPHiAZs2a4cyZMwCAO3fuYN68eYiNjUViYiL++usvBAUFoUePHrxnMRFVCyaARKQ3PDw8sGPHDty7dw/79u1Dnz59AABpaWmwsrJS2XXXr1+PZs2aoXfv3ujXrx+6deuGlStXyo4XFRUhPj5eNj/RxMQEBw8eRJ8+fdCsWTNMmTIF7777Lv7++2+VxUhE+oVDwESkN2bNmoX//ve/+Oyzz9C7d2/4+PgAKOkNbNeuncqua2triw0bNpR5vEGDBhBe2kXZ1dUVR44cUVk8RERMAIlIb/znP/9Bt27dkJycDE9PT1l579698fbbb2swMiIi9WICSER6xcnJSWH1badOnTQUjbbjKhAibVUtcwCPHj2KAQMGwMXFBSKRCDt27JA7Pnz4cIX9sfr27fva8y5fvhwNGjSAqakpvL29ZROkiYiIiEh51ZIA5ubmwtPTE8uXLy+zTt++feVuav7HH3+Ue85NmzYhJCQEs2fPxvnz5+Hp6Ql/f3+F/bSIiEgzuA0MkfaqliHggIAABAQElFtHLBZXatPTJUuWYMyYMRgxYgQAICIiArt378bq1asxc+bMKsVLREREpM/Utg1MdHQ0HBwc0LRpU4wfPx4ZGRll1i0sLERsbCz8/PxkZQYGBvDz80NMTIw6wiUiotd4tQPw5ZXMRFSzqWURSN++ffHOO+/A3d0dd+7cweeff46AgADExMTA0NBQoX56ejokEgkcHR3lyh0dHcu8t2ZBQQEKCgpkz7OysgAAxc8KKx2vUJU73SupyKhYqXaFRZV/fVUlFUmVapcvLVL6mgYi5T5YCnOUe3+kz/KVagcAkgIlf60KXl+lLIJyPz5Q8lsJVOFzXplvpaSw5PvBBKNm4XeDSHupJQH84IMPZF+3bt0abdq0QaNGjRAdHY3evXtXyzXCw8MRFhamUH7svdXVcn4i0rynT5/C2tpa02EQEWk9jWwD07BhQ9jZ2eH27dulJoB2dnYwNDREamqqXHlqamqZ8whDQ0MREhIie56ZmQk3NzckJSXxA6MU2dnZcHV1xb1791R6BwRtxfenbJp4bwRBwNOnT2W3TqOagWtAiLSXRhLA+/fvIyMjo8ybmpuYmMDLywtRUVEIDAwEAEilUkRFRSE4OLjUNmKxGGKxWKHc2tqaH+DlsLKy4vtTDr4/ZVP3e8N/5IiIqk+1LALJyclBXFwc4uLiAAAJCQmIi4tDUlIScnJyMG3aNJw6dQqJiYmIiorCoEGD4OHhAX9/f9k5evfujR9//FH2PCQkBL/88gvWrl2L69evY/z48cjNzZWtCiYiIs0ScR8YIq1VLT2A586dQ69evWTPnw/FDhs2DCtWrMClS5ewdu1aZGZmwsXFBX369MG8efPkeuzu3LmD9PR02fP3338fjx49wqxZs5CSkoK2bdti7969CgtDiIiIiKhyqiUB9PX1LXd13r59+157jsTERIWy4ODgMod8X0csFmP27NmlDgsT35/X4ftTNr439Bz7/4i0l0jgvgpERDopOzsb1tbWyMrKUsl8zQPXUjFm3TnZ8zkDWmB4V/dqvw6RvlPF77LaNoImIiIiopqBCSARESlF4U4gGomCiJTBBJCIiIhIzzABJCIipXAXGCLtpbMJ4PLly9GgQQOYmprC29sbZ86c0XRINcKcOXMgEonkHs2aNdN0WBpx9OhRDBgwAC4uLhCJRNixY4fccUEQMGvWLDg7O8PMzAx+fn64deuWZoLVgNe9P8OHD1f4Werbt69mgiUiokrRyQRw06ZNCAkJwezZs3H+/Hl4enrC398faWlpmg6tRmjZsiWSk5Nlj+PHj2s6JI3Izc2Fp6cnli9fXurxb7/9Ft9//z0iIiJw+vRpmJubw9/fH/n5+WqOVDNe9/4AQN++feV+lv744w81Rkiaxh5AIu2lkVvBqdqSJUswZswY2V1DIiIisHv3bqxevRozZ87UcHSaZ2RkVOY9lfVJQEAAAgICSj0mCAKWLVuGL7/8EoMGDQIArFu3Do6OjtixYwc++OADdYaqEeW9P8+JxWL+LOkxbiJGpL10rgewsLAQsbGx8PPzk5UZGBjAz88PMTExGoys5rh16xZcXFzQsGFDDB06FElJSZoOqcZJSEhASkqK3M+RtbU1vL29+XP0kujoaDg4OKBp06YYP348MjIyNB0SERFVgM4lgOnp6ZBIJAq3jHN0dERKSoqGoqo5vL29ERkZib1792LFihVISEhA9+7d8fTpU02HVqM8/1nhz1HZ+vbti3Xr1iEqKgoLFy7EkSNHEBAQAIlEounQSE04BEykvXRyCJjK9vKQXps2beDt7Q03Nzds3rwZo0aN0mBkpG1eHgZv3bo12rRpg0aNGiE6Ohq9e/fWYGSkKRwSJtIeOtcDaGdnB0NDQ6SmpsqVp6amcq5SKWrXro0mTZrg9u3bmg6lRnn+s8Kfo4pr2LAh7Ozs+LOkR0S8GzCR1tK5BNDExAReXl6IioqSlUmlUkRFRcHHx0eDkdVMOTk5uHPnDpydnTUdSo3i7u4OJycnuZ+j7OxsnD59mj9HZbh//z4yMjL4s0REpAV0cgg4JCQEw4YNQ4cOHdCpUycsW7YMubm5slXB+mzq1KkYMGAA3Nzc8PDhQ8yePRuGhoYYMmSIpkNTu5ycHLneqoSEBMTFxcHW1hb169fH5MmTMX/+fDRu3Bju7u746quv4OLigsDAQM0FrUblvT+2trYICwvDu+++CycnJ9y5cwfTp0+Hh4cH/P39NRg1qRU7AIm0lk4mgO+//z4ePXqEWbNmISUlBW3btsXevXsVJvTro/v372PIkCHIyMiAvb09unXrhlOnTsHe3l7ToanduXPn0KtXL9nzkJAQAMCwYcMQGRmJ6dOnIzc3F2PHjkVmZia6deuGvXv3wtTUVFMhq1V578+KFStw6dIlrF27FpmZmXBxcUGfPn0wb948iMViTYVM6sY5f0RaSyQInLZLRKSLsrOzYW1tjaysLFhZWVX7+Q/fSMOIyLOy57PeaoGR3dyr/TpE+k4Vv8s6NweQiIjUhEPARFqLCSARERGRnmECSERESmEHIJH2YgJIRKRiCxYsQJcuXVCrVi3Url27Qm0EQcCsWbPg7OwMMzMz+Pn54datW6oNlIj0BhNAIiIVKywsxODBgzF+/PgKt/n222/x/fffIyIiAqdPn4a5uTn8/f2Rn5+vwkgrR/TKveC4opBIe+jkNjBERDVJWFgYACAyMrJC9QVBwLJly/Dll19i0KBBAIB169bB0dERO3bskLsNnyZxEwki7cUeQCKiGiYhIQEpKSnw8/OTlVlbW8Pb2xsxMTEajIyIdAUTQD3l6+uLyZMny543aNAAy5Yt01g8qjJnzhyIRCKIRKJKvb7ExESIRCLExcVVWyyVeY99fX1lcVdnDKQdUlJSAEBh83pHR0fZsdIUFBQgOztb7qFKrw4BE5H2YAJIAICzZ89i7NixFaqrbcliy5YtkZycXOHXBwCurq5ITk5Gq1atVBhZ2bZt24YzZ85o5NpUMTNnzpQl6WU9bty4odaYwsPDYW1tLXu4urqq9fpEpD04B5AAQKdvBWdkZAQnJ6dKtTE0NKx0m+pka2ur8t4bqpopU6Zg+PDh5dZp2LChUud+/rOXmpoKZ2dnWXlqairatm1bZrvQ0FDZLfuAkrsHqDIJZP8fkfZiD6AeyM3NRVBQECwsLODs7IzFixcr1Hm5V08QBMyZMwf169eHWCyGi4sLJk6cCKBkaPLu3bv47LPPZL0cAJCRkYEhQ4agbt26qFWrFlq3bo0//vhD7hq+vr6YOHEipk+fDltbWzg5OWHOnDlydTIzM/Hxxx/D0dERpqamaNWqFXbt2iU7fvz4cXTv3h1mZmZwdXXFxIkTkZubW+n3RCQSYcWKFQgICICZmRkaNmyIrVu3yo6/OgQ8d+5cuLi4ICMjQ1anf//+6NWrF6RSaaVjK+89Ju1gb2+PZs2alfswMTFR6tzu7u5wcnJCVFSUrCw7OxunT5+Gj49Pme3EYjGsrKzkHkREpWECqAemTZuGI0eOYOfOndi/fz+io6Nx/vz5Muv/+eefWLp0KX7++WfcunULO3bsQOvWrQGUDE3Wq1cPc+fORXJyMpKTkwEA+fn58PLywu7du3HlyhWMHTsWH330kcIw5tq1a2Fubo7Tp0/j22+/xdy5c3HgwAEAgFQqRUBAAE6cOIHff/8d165dwzfffANDQ0MAwJ07d9C3b1+8++67uHTpEjZt2oTjx48jODhYqfflq6++wrvvvouLFy9i6NCh+OCDD3D9+vVS637xxRdo0KABRo8eDQBYvnw5Tp48ibVr18LAwKDSsZX3HpPuSUpKQlxcHJKSkiCRSBAXF4e4uDjk5OTI6jRr1gzbt28HUPIPyuTJkzF//nz89ddfuHz5MoKCguDi4oLAwEANvQpFnAJIpMUE0mlPnz4VTExMhM2bN8vKMjIyBDMzM2HSpEmyMjc3N2Hp0qWCIAjC4sWLhSZNmgiFhYWlnvPluuXp37+/MGXKFNnznj17Ct26dZOr07FjR2HGjBmCIAjCvn37BAMDAyE+Pr7U840aNUoYO3asXNmxY8cEAwMDIS8vr9Q2s2fPFjw9PRXKAQjjxo2TK/P29hbGjx8vCIIgJCQkCACECxcuyI7fuXNHsLS0FGbMmCGYmZkJ69evr1RslXmPy4qBtNOwYcMElGyTJ/c4fPiwrA4AYc2aNbLnUqlU+OqrrwRHR0dBLBYLvXv3LvN3oyxZWVkCACErK6uaXom8ozfTBLcZu2SPVcf+Vcl1iPSdKn6X2QOo4+7cuYPCwkJ4e3vLymxtbdG0adMy2wwePBh5eXlo2LAhxowZg+3bt6O4uLjc60gkEsybNw+tW7eGra0tLCwssG/fPiQlJcnVa9OmjdxzZ2dnpKWlAQDi4uJQr149NGnSpNRrXLx4EZGRkbCwsJA9/P39IZVKkZCQUG58pXl1KM3Hx6fMHkCgZD7X//73PyxcuBADBw7Ef//7X6VjU+Y9Ju0VGRkJQRAUHr6+vrI6giDIzSkUiUSYO3cuUlJSkJ+fj4MHD5b5u6Ep3AaQSHsxASQFrq6uiI+Px08//QQzMzN88skn6NGjB4qKispss2jRInz33XeYMWMGDh8+jLi4OPj7+6OwsFCunrGxsdxzkUgkm0NnZmZWblw5OTn4+OOPZcNncXFxuHjxIm7duoVGjRop+Wor5+jRozA0NERiYqJcwlbZ2JR5j4lqmlfzP4EZIZHWYAKo4xo1agRjY2OcPn1aVvbkyRPcvHmz3HZmZmYYMGAAvv/+e0RHRyMmJgaXL18GAJiYmEAikcjVP3HiBAYNGoQPP/wQnp6eaNiw4Wuv8ao2bdrg/v37ZbZr3749rl27Bg8PD4WHMpPtT506pfC8efPmZdbftGkTtm3bhujoaCQlJWHevHlViq2895iIiEiVuA2MjrOwsMCoUaMwbdo01KlTBw4ODvjiiy9gYFB27h8ZGQmJRAJvb2/UqlULv//+O8zMzODm5gagZMXw0aNH8cEHH0AsFsPOzg6NGzfG1q1bcfLkSdjY2GDJkiVITU1FixYtKhxrz5490aNHD7z77rtYsmQJPDw8cOPGDYhEIvTt2xczZsxA586dERwcjNGjR8Pc3BzXrl3DgQMH8OOPP1b6vdmyZQs6dOiAbt26Yf369Thz5gx+/fXXUuvev38f48ePx8KFC9GtWzesWbMGb731FgICAtC5c+dKx/a695iIiEiV2AOoBxYtWoTu3btjwIAB8PPzQ7du3eDl5VVm/dq1a+OXX35B165d0aZNGxw8eBB///036tSpA6BkS5TExEQ0atRItn/gl19+ifbt28Pf3x++vr5wcnJSarXin3/+iY4dO2LIkCFo0aIFpk+fLuttbNOmDY4cOYKbN2+ie/fuaNeuHWbNmgUXF5fKvykouT/rxo0b0aZNG6xbtw5//PFHqQnr87lZnTp1kq3q9ff3x/jx4/Hhhx8iJyen0rG97j0mIiJSJZHASRukw+bMmYMdO3Yo3E5NJBJh+/btNWpLjVclJibC3d0dFy5cKHfzX6KyZGdnw9raGllZWSrZEzA6Pg3D15yVPf+yf3OM7q7c5tdEVDZV/C6zB5B03uXLl2FhYYGffvpJ06FUWEBAAFq2bKnpMIiISEdxDiDptIkTJ+LDDz8EoF23u1u1ahXy8vIAAPXr19dwNESl4/ARkfZiAkg6zdbWFra2tgrlNX3mQ926dTUdAtHr1exfIyIqB4eAiYioWliI2adApC2YABIRUZW92cIR77Svp+kwiKiC+O8aERFV2S9BHTQdAhFVAnsAiYhIKQInARJpLSaARERERHqGCSARESmlhi+mJ6JyMAEkIiKlMAEk0l5MAImIiIj0DBNAIiIiIj3DBJCIiIhIzzABJCIipXAKIJH2YgJIREREpGeYABIRkVIELgMm0lpMAImISClM/4i0FxNAIiIiIj3DBJCIiIhIzzABJCIiItIzTACJiEgpXANCpL2YABIRERHpGSaARESkJHYBEmkrJoBEREREeoYJIBEREZGeYQJIRERK4SIQIu3FBJCIiJTi6Vpb0yEQkZKMNB0AERFpJ5faZjgyzRdWpsaaDoWIKok9gEREKrZgwQJ06dIFtWrVQu3atSvUZvjw4RCJRHKPvn37qjZQJbjVMYeNuYmmwyCiSmIPIBGRihUWFmLw4MHw8fHBr7/+WuF2ffv2xZo1a2TPxWKxKsIjIj3EBJCISMXCwsIAAJGRkZVqJxaL4eTkpIKIiEjfcQiYiKiGio6OhoODA5o2bYrx48cjIyOj3PoFBQXIzs6WexARlYYJIBFRDdS3b1+sW7cOUVFRWLhwIY4cOYKAgABIJJIy24SHh8Pa2lr2cHV1VWPERKRNmAASESlh5syZCos0Xn3cuHFD6fN/8MEHGDhwIFq3bo3AwEDs2rULZ8+eRXR0dJltQkNDkZWVJXvcu3dP6esTkW7jHEAiIiVMmTIFw4cPL7dOw4YNq+16DRs2hJ2dHW7fvo3evXuXWkcsFnOhCBFVCBNAIiIl2Nvbw97eXm3Xu3//PjIyMuDs7Ky2axKR7uIQMBGRiiUlJSEuLg5JSUmQSCSIi4tDXFwccnJyZHWaNWuG7du3AwBycnIwbdo0nDp1ComJiYiKisKgQYPg4eEBf39/Tb0MItIh7AEkIlKxWbNmYe3atbLn7dq1AwAcPnwYvr6+AID4+HhkZWUBAAwNDXHp0iWsXbsWmZmZcHFxQZ8+fTBv3jwO8RJRtRAJAm/nTUSki7Kzs2FtbY2srCxYWVlpOhwiUpIqfpc5BExERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGe4SpgIiId9XyNH+8JTKTdnv8OV+e6XSaAREQ66unTpwDAewIT6YinT5/C2tq6Ws7FbWCIiHSUVCrFw4cPYWlpCZFIpLLrZGdnw9XVFffu3eN2M6/ge1M2vjdle/W9EQQBT58+hYuLCwwMqmf2HnsAiYh0lIGBAerVq6e261lZWfGDvAx8b8rG96ZsL7831dXz9xwXgRARERHpGSaARERERHqGCSAREVWJWCzG7NmzeZ/iUvC9KRvfm7Kp473hIhAiIiIiPcMeQCIiIiI9wwSQiIiISM8wASQiIiLSM0wAiYhIacuXL0eDBg1gamoKb29vnDlzRtMhqV14eDg6duwIS0tLODg4IDAwEPHx8XJ18vPzMWHCBNSpUwcWFhZ49913kZqaqqGINeebb76BSCTC5MmTZWX6/N48ePAAH374IerUqQMzMzO0bt0a586dkx0XBAGzZs2Cs7MzzMzM4Ofnh1u3blXLtZkAEhGRUjZt2oSQkBDMnj0b58+fh6enJ/z9/ZGWlqbp0NTqyJEjmDBhAk6dOoUDBw6gqKgIffr0QW5urqzOZ599hr///htbtmzBkSNH8PDhQ7zzzjsajFr9zp49i59//hlt2rSRK9fX9+bJkyfo2rUrjI2N8c8//+DatWtYvHgxbGxsZHW+/fZbfP/994iIiMDp06dhbm4Of39/5OfnVz0AgYiISAmdOnUSJkyYIHsukUgEFxcXITw8XINRaV5aWpoAQDhy5IggCIKQmZkpGBsbC1u2bJHVuX79ugBAiImJ0VSYavX06VOhcePGwoEDB4SePXsKkyZNEgRBv9+bGTNmCN26dSvzuFQqFZycnIRFixbJyjIzMwWxWCz88ccfVb4+ewCJiKjSCgsLERsbCz8/P1mZgYEB/Pz8EBMTo8HINC8rKwsAYGtrCwCIjY1FUVGR3HvVrFkz1K9fX2/eqwkTJqB///5y7wGg3+/NX3/9hQ4dOmDw4MFwcHBAu3bt8Msvv8iOJyQkICUlRe69sba2hre3d7W8N0wAiYio0tLT0yGRSODo6ChX7ujoiJSUFA1FpXlSqRSTJ09G165d0apVKwBASkoKTExMULt2bbm6+vJebdy4EefPn0d4eLjCMX1+b/7991+sWLECjRs3xr59+zB+/HhMnDgRa9euBQDZ61fV75hRlc9AREREAEp6uq5cuYLjx49rOpQa4d69e5g0aRIOHDgAU1NTTYdTo0ilUnTo0AFff/01AKBdu3a4cuUKIiIiMGzYMJVfnz2ARERUaXZ2djA0NFRYrZmamgonJycNRaVZwcHB2LVrFw4fPox69erJyp2cnFBYWIjMzEy5+vrwXsXGxiItLQ3t27eHkZERjIyMcOTIEXz//fcwMjKCo6Oj3r43zs7OaNGihVxZ8+bNkZSUBACy16+q3zEmgEREVGkmJibw8vJCVFSUrEwqlSIqKgo+Pj4ajEz9BEFAcHAwtm/fjkOHDsHd3V3uuJeXF4yNjeXeq/j4eCQlJen8e9W7d29cvnwZcXFxskeHDh0wdOhQ2df6+t507dpVYbugmzdvws3NDQDg7u4OJycnufcmOzsbp0+frpb3hkPARESklJCQEAwbNgwdOnRAp06dsGzZMuTm5mLEiBGaDk2tJkyYgA0bNmDnzp2wtLSUzc+ytraGmZkZrK2tMWrUKISEhMDW1hZWVlb49NNP4ePjg86dO2s4etWytLSUzYV8ztzcHHXq1JGV6+t789lnn6FLly74+uuv8d577+HMmTNYuXIlVq5cCQCy/RLnz5+Pxo0bw93dHV999RVcXFwQGBhY9QCqvI6YiIj01g8//CDUr19fMDExETp16iScOnVK0yGpHYBSH2vWrJHVycvLEz755BPBxsZGqFWrlvD2228LycnJmgtag17eBkYQ9Pu9+fvvv4VWrVoJYrFYaNasmbBy5Uq541KpVPjqq68ER0dHQSwWC7179xbi4+Or5doiQRCEqqeRRERERKQtOAeQiIiISM8wASQiIiLSM0wAiYiIiPQME0AiIiIiPcMEkIiIiEjPMAEkIiIi0jNMAImIiIj0DBNAIiIiIj3DBJCIiEgL+fr6YvLkyRq7fkZGBhwcHJCYmFit57127Rrq1auH3Nzcaj0vyWMCSERERJW2YMECDBo0CA0aNKhQ/QEDBqBv376lHjt27BhEIhEuXbqEFi1aoHPnzliyZEk1RkuvYgJIRESkIkVFRZoOQSWePXuGX3/9FaNGjapwm1GjRuHAgQO4f/++wrE1a9agQ4cOaNOmDQBgxIgRWLFiBYqLi6stZpLHBJCIiHSeVCpFeHg43N3dYWZmBk9PT2zdulV2PDo6GiKRCFFRUejQoQNq1aqFLl26ID4+Xu48O3fuRPv27WFqaoqGDRsiLCxMLkkRiURYsWIFBg4cCHNzcyxYsAAAMH/+fDg4OMDS0hKjR4/GzJkz0bZtWwDA0aNHYWxsjJSUFLlrTZ48Gd27d6/wa3zy5AmCgoJgY2ODWrVqISAgALdu3ZIdv3v3LgYMGAAbGxuYm5ujZcuW2LNnj6zt0KFDYW9vDzMzMzRu3Bhr1qwp81p79uyBWCxG586d5cqvXLmCgIAAWFhYwNHRER999BHS09MBAG+99Rbs7e0RGRkp1yYnJwdbtmyRSybffPNNPH78GEeOHKnw66fKYQJIREQ6Lzw8HOvWrUNERASuXr2Kzz77DB9++KFCgvHFF19g8eLFOHfuHIyMjDBy5EjZsWPHjiEoKAiTJk3CtWvX8PPPPyMyMlKW5D03Z84cvP3227h8+TJGjhyJ9evXY8GCBVi4cCFiY2NRv359rFixQla/R48eaNiwIX777TdZWVFREdavXy93/dcZPnw4zp07h7/++gsxMTEQBAH9+vWT9UJOmDABBQUFOHr0KC5fvoyFCxfCwsICAPDVV1/h2rVr+Oeff3D9+nWsWLECdnZ2ZV7r2LFj8PLykivLzMzEG2+8gXbt2uHcuXPYu3cvUlNT8d577wEAjIyMEBQUhMjISAiCIGu3ZcsWSCQSDBkyRFZmYmKCtm3b4tixYxV+/VRJAhERkQ7Lz88XatWqJZw8eVKufNSoUcKQIUMEQRCEw4cPCwCEgwcPyo7v3r1bACDk5eUJgiAIvXv3Fr7++mu5c/z222+Cs7Oz7DkAYfLkyXJ1vL29hQkTJsiVde3aVfD09JQ9X7hwodC8eXPZ8z///FOwsLAQcnJyynxdPXv2FCZNmiQIgiDcvHlTACCcOHFCdjw9PV0wMzMTNm/eLAiCILRu3VqYM2dOqecaMGCAMGLEiDKv9apBgwYJI0eOlCubN2+e0KdPH7mye/fuCQCE+Ph4QRAE4fr16wIA4fDhw7I63bt3Fz788EOFa7z99tvC8OHDKxwTVQ57AImISKfdvn0bz549w5tvvgkLCwvZY926dbhz545c3edz0ADA2dkZAJCWlgYAuHjxIubOnSt3jjFjxiA5ORnPnj2TtevQoYPcOePj49GpUye5slefDx8+HLdv38apU6cAAJGRkXjvvfdgbm6OY8eOyV1z/fr1Cq/x+vXrMDIygre3t6ysTp06aNq0Ka5fvw4AmDhxIubPn4+uXbti9uzZuHTpkqzu+PHjsXHjRrRt2xbTp0/HyZMny31P8/LyYGpqKld28eJFHD58WC7WZs2aAYDsfW7WrBm6dOmC1atXAyj53hw7dqzUuYRmZmZy7ytVLyNNB0BERKRKOTk5AIDdu3ejbt26csfEYrHcc2NjY9nXIpEIQMn8wefnCQsLwzvvvKNwjZeTIXNz80rH6ODggAEDBmDNmjVwd3fHP//8g+joaAAlCWVcXJysrqOjY6XPDwCjR4+Gv78/du/ejf379yM8PByLFy/Gp59+ioCAANy9exd79uzBgQMH0Lt3b0yYMAH/+9//Sj2XnZ0dnjx5IleWk5ODAQMGYOHChQr1nyfTQMlikE8//RTLly/HmjVr0KhRI/Ts2VOhzePHj9GoUSOlXiu9HnsAiYhIp7Vo0QJisRhJSUnw8PCQe7i6ulb4PO3bt0d8fLzCOTw8PGBgUPbHadOmTXH27Fm5slefAyUJ2qZNm7By5Uo0atQIXbt2BVDSE/bytSwtLRXaNm/eHMXFxTh9+rSsLCMjA/Hx8WjRooWszNXVFePGjcO2bdswZcoU/PLLL7Jj9vb2GDZsGH7//XcsW7YMK1euLPM1tWvXDteuXVN4f65evYoGDRoovD8vJ8XvvfceDAwMsGHDBqxbtw4jR46UJdsvu3LlCtq1a1dmDFQ17AEkIiKdZmlpialTp+Kzzz6DVCpFt27dkJWVhRMnTsDKygrDhg2r0HlmzZqFt956C/Xr18d//vMfGBgY4OLFi7hy5Qrmz59fZrtPP/0UY8aMQYcOHdClSxds2rQJly5dQsOGDeXq+fv7w8rKCvPnz8fcuXMr9RobN26MQYMGYcyYMfj5559haWmJmTNnom7duhg0aBCAklXFAQEBaNKkCZ48eYLDhw+jefPmstfm5eWFli1boqCgALt27ZIdK42/vz9CQ0Px5MkT2NjYAChZZPLLL79gyJAhmD59OmxtbXH79m1s3LgRq1atgqGhIQDAwsIC77//PkJDQ5GdnY3hw4crnD8xMREPHjyAn59fpd4Hqjj2ABIRkc6bN28evvrqK4SHh6N58+bo27cvdu/eDXd39wqfw9/fH7t27cL+/fvRsWNHdO7cGUuXLoWbm1u57YYOHYrQ0FBMnToV7du3R0JCAoYPH64wh87AwADDhw+HRCJBUFBQpV/jmjVr4OXlhbfeegs+Pj4QBAF79uyRDWtLJBJMmDBB9vqbNGmCn376CUDJqtvQ0FC0adMGPXr0gKGhITZu3FjmtVq3bo327dtj8+bNsjIXFxecOHECEokEffr0QevWrTF58mTUrl1boYd01KhRePLkCfz9/eHi4qJw/j/++AN9+vR57XtLyhMJwktrsYmIiEjl3nzzTTg5Oclt/QKUJEaPHj3CX3/9paHIKm737t2YNm0arly5Uu4QeGUVFhaicePG2LBhg2wYnKofh4CJiIhU6NmzZ4iIiIC/vz8MDQ3xxx9/4ODBgzhw4ICsTlZWFi5fvowNGzZoRfIHAP3798etW7fw4MGDSs2lfJ2kpCR8/vnnTP5UjD2AREREKpSXl4cBAwbgwoULyM/PR9OmTfHll1/KrSb29fXFmTNn8PHHH2Pp0qUajJb0BRNAIiIiIj3DRSBEREREeoYJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRnmAASERER6RkmgERERER6hgkgERERkZ5hAkhERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGe+T/P8F9qaqXWwQAAAABJRU5ErkJggg==",
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+       "                </div>\n",
+       "                <img 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25ubnB1dcWgQYNw9erVcusXFBQgOztb7qFu7AEk0g5MAImI1EgqlWLy5Mno2rUrWrVqVWa9pk2bYvXq1di5cyd+//13SKVSdOnSBffv3y+zTXh4OKytrWUPV1dXVbyEcjH9I9IOIoH/rhERqc348ePxzz//4Pjx46hXr16F2xUVFaF58+YYMmQI5s2bV2qdgoICFBQUyJ5nZ2fD1dUVWVlZsLKyqnLs5WkwczcA4Ku3WmBUN3eVXotI32RnZ8Pa2rpaf5c5B5CISE2Cg4Oxa9cuHD16tFLJHwAYGxujXbt2uH37dpl1xGIxxGJxVcOsEvYpEGkHDgETEamYIAgIDg7G9u3bcejQIbi7V76HTCKR4PLly3B2dlZBhFXDpI9I+7AHkIhIxSZMmIANGzZg586dsLS0REpKCgDA2toaZmZmAICgoCDUrVsX4eHhAIC5c+eic+fO8PDwQGZmJhYtWoS7d+9i9OjRGnsdZZG8dP835oJE2oEJIBGRiq1YsQIA4OvrK1e+Zs0aDB8+HACQlJQEA4MXgzJPnjzBmDFjkJKSAhsbG3h5eeHkyZNo0aKFusKusJfv/ytlBkikFZgAEhGpWEWGSKOjo+WeL126FEuXLlVRRNXr5aSP6R+RduAcQCIiqpKXh4DZA0ikHZgAEhFRlcj1ADL/I9IKTACJiKhKpFJNR0BElcUEkIiIqkS+B5BdgETagAkgERFViUR4eQ6gBgMhogpjAkhERFXycg+ghBkgkVZgAkhERFXy8hxArgIm0g5MAImIqEpeTvqK2QNIpBWYABIRUZW8nPJxCJhIOzABJCKiKnl55W+xhAkgkTZgAkhERFUi8F7ARFqHCSAREVWbYu4KTaQVmAASEVGVvNzpxzmARNqBCSAREVWJlHMAibQOE0AiIqoSuVXAnANIpBWYABIRUZUIvBMIkdZhAkhERFXycsrHjaCJtAMTQCIiqhK5RSCcA0ikFZgAEhFRFb00BMw5gERagQkgERFVCbeBIdI+TACJiKhKXs75OAeQSDswASQioioRXh4C5p1AiLQCE0AiIqqSl4eAuRE0kXZgAkhERFXycgIo5SIQIq3ABJCIiKrk5SFgzgEk0g5MAImIqEq4CphI+zABJCKiKuEcQCLtwwSQiIiq5OUhYM4BJNIOTACJiKhKXs75iiTcBoZIGzABJCKiKnm5z48dgETagQkgERFViSDwXsBE2oYJIBERVcnLKR9XARNpByaAREQqFh4ejo4dO8LS0hIODg4IDAxEfHz8a9tt2bIFzZo1g6mpKVq3bo09e/aoIdrKe7nTjx2ARNqBCSARkYodOXIEEyZMwKlTp3DgwAEUFRWhT58+yM3NLbPNyZMnMWTIEIwaNQoXLlxAYGAgAgMDceXKFTVGXjFyQ8DsASTSCiJB4P9rRETq9OjRIzg4OODIkSPo0aNHqXXef/995ObmYteuXbKyzp07o23btoiIiKjQdbKzs2FtbY2srCxYWVlVS+ylOZv4GIMjYgAA9pZinP3CT2XXItJHqvhdZg8gEZGaZWVlAQBsbW3LrBMTEwM/P/lEyt/fHzExMWW2KSgoQHZ2ttxDHeSHgNmnQKQNmAASEamRVCrF5MmT0bVrV7Rq1arMeikpKXB0dJQrc3R0REpKSpltwsPDYW1tLXu4urpWW9zl4RAwkfZhAkhEpEYTJkzAlStXsHHjxmo/d2hoKLKysmSPe/fuVfs1SvNyysf8j0g7GGk6ACIifREcHIxdu3bh6NGjqFevXrl1nZyckJqaKleWmpoKJyenMtuIxWKIxeJqibUyXh71lTIDJNIK7AEkIlIxQRAQHByM7du349ChQ3B3d39tGx8fH0RFRcmVHThwAD4+PqoKU2ncCJpI+7AHkIhIxSZMmIANGzZg586dsLS0lM3js7a2hpmZGQAgKCgIdevWRXh4OABg0qRJ6NmzJxYvXoz+/ftj48aNOHfuHFauXKmx11EW+SFgJoBE2oA9gEREKrZixQpkZWXB19cXzs7OssemTZtkdZKSkpCcnCx73qVLF2zYsAErV66Ep6cntm7dih07dpS7cERT5IeANRcHEVUcewCJiFSsIlujREdHK5QNHjwYgwcPVkFE1UsAh4CJtA17AImIqErkegCZABJpBSaARERUJS+nfILAzaCJtAETQCIiqpJXEz7uBENU8zEBJCKiKnm1w493AyGq+ZgAEhFRlQh4tQeQCSBRTafWBHD58uVo0KABTE1N4e3tjTNnzpRbf8uWLWjWrBlMTU3RunVr7NmzR02REhFRRb2a7zEBJKr51JYAbtq0CSEhIZg9ezbOnz8PT09P+Pv7Iy0trdT6J0+exJAhQzBq1ChcuHABgYGBCAwMxJUrV9QVMhERVQCHgIm0j9oSwCVLlmDMmDEYMWIEWrRogYiICNSqVQurV68utf53332Hvn37Ytq0aWjevDnmzZuH9u3b48cff1RXyEREVAGvpnvcDJqo5lPLRtCFhYWIjY1FaGiorMzAwAB+fn6IiYkptU1MTAxCQkLkyvz9/bFjx45S6xcUFKCgoED2XCqV4vHjx6hTpw5EIlHVXwQRaYwgCHj69ClcXFxgYMCpyzWN4ipg9gAS1XRqSQDT09MhkUjg6OgoV+7o6IgbN26U2iYlJaXU+s/vofmq8PBwhIWFVU/ARFQj3bt3D/Xq1dN0GPSKV9M93g2EqObTmVvBhYaGyvUYZmVloX79+rgb4QArq0r2GBQBKBSAjmZArcq1jc5Trnci9No7SrUrvG6tVDsAMMhTrmfUQKLkBavwmSBSckhJpGysVek0VvJ1ivTlM1OJ1ykpzMfNVXNhaWlZ/fFQlbEHkEj7qCUBtLOzg6GhIVJTU+XKU1NT4eTkVGobJyenStUXi8UQi8UK5VZWBrCyreTLzJcCuQJgaVDpBNDcyLBy1/p/hrUUY68IA1NTpdoBgKGUCWDZDZVsBzABfJ2q/BxwOkeNpLAKmHMAiWo8tUymMTExgZeXF6KiomRlUqkUUVFR8PHxKbWNj4+PXH0AOHDgQJn1iYhIMzgETKR91DYEHBISgmHDhqFDhw7o1KkTli1bhtzcXIwYMQIAEBQUhLp16yI8PBwAMGnSJPTs2ROLFy9G//79sXHjRpw7dw4rV65UV8hERFQBij2ATACJajq1JYDvv/8+Hj16hFmzZiElJQVt27bF3r17ZQs9kpKS5Fb3denSBRs2bMCXX36Jzz//HI0bN8aOHTvQqlUrdYVMREQVwDuBEGkftS4CCQ4ORnBwcKnHoqOjFcoGDx6MwYMHqzgqIiKqCm4ETaR9uKEWERFVyas9fsz/iGo+JoBERFStOARMVPMxASQioipRWATCBJCoxtOZjaCJiMpz/fp1bNy4EceOHcPdu3fx7Nkz2Nvbo127dvD398e7775b6l6i9HqvLgLhHECimo89gESk086fPw8/Pz+0a9cOx48fh7e3NyZPnox58+bhww8/hCAI+OKLL+Di4oKFCxfK3VOcKoYbQRNpH/YAEpFOe/fddzF16lRs3boVtWvXLrNeTEwMvvvuOyxevBiff/65+gLUARwCJtI+up8AFqHk1m6VbUNEOuHmzZswNjZ+bT0fHx/4+PigqIh/ACqLdwIh0j46nwAm5+UhR4kbu0rNRSgQpEBR5dreKGhe6WsBgNhIuZvW5il362EAgKGyEwCKlWtWpXvdKttWi24dK1QhVq26j7AysVbh9RkbG+PHH3/Ehx9+WG4P4Mv1qXIUtoHhHECiGk/nE8D8jiYwtqh8piMYAjDRouyBiMr0xRdfYPr06QgMDMTo0aPxxhtvaDok3aIwBKyZMIio4nR/EYipCIJZ5R9M/oh0R0pKCiIiIpCcnIw333wT7u7umDdvHu7du6fp0HQCVwETaR/dTwCJSO+ZmZkhKCgIhw8fxq1bt/DRRx/h119/hbu7O/r27YstW7Zw7l8VvDrlT+AcQKIajwkgEemVhg0bYu7cuUhISMA///yDOnXqYPjw4ahbt66mQ9NaXARCpH2YABKRXhKJRDAyMoJIJIIgCOwBrIJX8z0OARPVfEwAiUiv3Lt3D3PnzkXDhg3x5ptv4uHDh/jll1+QnJys6dC01qurgNkBSFTz6fwqYCKiwsJCbNu2DatXr8ahQ4fg7OyMYcOGYeTIkWjYsKGmw9N6CkPA7AEkqvGYABKRznNycsKzZ8/w1ltv4e+//4a/vz8MDDgAUm1e6fLjHECimo9/AYlI53355Ze4d+8etm7dioCAAI0kf0ePHsWAAQPg4uICkUiEHTt2lFs/OjoaIpFI4ZGSkqKegCvh1XSPq4CJaj4mgESk80JCQmBvbw8AyMzMxKpVqxAaGorHjx8DAM6fP48HDx6oNIbc3Fx4enpi+fLllWoXHx+P5ORk2cPBwUFFESpPcRGIZuIgoorjEDAR6Y1Lly7Bz88P1tbWSExMxJgxY2Bra4tt27YhKSkJ69atU9m1AwICEBAQUOl2Dg4OFbqFnSa92uP36qIQIqp52ANIRHojJCQEw4cPx61bt2Bqaior79evH44eParByMrWtm1bODs7480338SJEyc0HU6pXk33mAAS1XzsASQivXH27Fn8/PPPCuV169atcXPrnJ2dERERgQ4dOqCgoACrVq2Cr68vTp8+jfbt25fapqCgAAUFBbLn2dnZaon11UW/XAVMVPMxASQivSEWi0tNim7evCmbI1hTNG3aFE2bNpU979KlC+7cuYOlS5fit99+K7VNeHg4wsLC1BWijOIQsNpDIKJK0vkE8LFUjAJp5Ua6TaD8DOYRVneUajfY84ZS7X5v0FqpdgCw4lIPpdoZ3DFTqp3RM5FS7QBApOxkhWKlL6k0pV9lFT40BeXfWqWIqvIBr0ys1fT6Bg4ciLlz52Lz5s0lpxWJkJSUhBkzZuDdd9+tnouoUKdOnXD8+PEyj4eGhiIkJET2PDs7G66uruoITY5EylUgRDUd5wASkd5YvHgxcnJy4ODggLy8PPTs2RMeHh6wtLTEggULNB3ea8XFxcHZ2bnM42KxGFZWVnIPdXh1yl+RhF2ARDWdWnoAw8PDsW3bNty4cQNmZmbo0qULFi5cKDe88arIyEiMGDFCrkwsFiM/P1/V4RKRjrK2tsaBAwdw/PhxXLp0CTk5OWjfvj38/PxUfu2cnBzcvn1b9jwhIQFxcXGwtbVF/fr1ERoaigcPHshWIi9btgzu7u5o2bIl8vPzsWrVKhw6dAj79+9XeayVJbzSfV3EfWCIajy1JIBHjhzBhAkT0LFjRxQXF+Pzzz9Hnz59cO3aNZibm5fZzsrKCvHx8bLnIpGax7mISCcEBQVh0KBB8Pf3h4WFBbp164Zu3bqpNYZz586hV69esufPh2qHDRuGyMhIJCcnIykpSXa8sLAQU6ZMwYMHD1CrVi20adMGBw8elDtHTaHYA8gEkKimU0sCuHfvXrnnkZGRcHBwQGxsLHr0KHsemkgkgpOTk6rDIyId5+Hhga+//hoffvghfH19MXDgQAwcOBB169ZVWwy+vr7l3iEjMjJS7vn06dMxffp0FUdVPV5d9MEhYKKaTyNzALOysgAAtra25dbLycmBm5sbXF1dMWjQIFy9elUd4RGRjpk1axZiY2Nx69YtDBgwADt27ECjRo3g5eWFuXPnIi4uTtMharVXh4ALi9kDSFTTqT0BlEqlmDx5Mrp27YpWrVqVWa9p06ZYvXo1du7cid9//x1SqRRdunTB/fv3S61fUFCA7OxsuQcR0cvq1auHTz75BPv27cOjR48wY8YMxMfH44033oCbmxuCg4P5j6YSXu3YLOYqYKIaT+0J4IQJE3DlyhVs3Lix3Ho+Pj4ICgpC27Zt0bNnT2zbtg329valbuIKlCw0sba2lj00sfUBEWkPS0tLvPfee1i/fj0ePXqE1atXw9DQEDExMZoOTetxCJio5lPrPoDBwcHYtWsXjh49inr16lWqrbGxMdq1aye3iu5lNWX/KyLSDunp6Th9+jQkEgk6deqE3r17o3fv3poOSyu9OreRQ8BENZ9aEkBBEPDpp59i+/btiI6Ohru7e6XPIZFIcPnyZfTr16/U42KxGGKxuKqhEpEe+PPPPzFq1Cg0adIERUVFiI+Px/LlyxW2nqKK4SpgIu2jlgRwwoQJ2LBhA3bu3AlLS0vZPTetra1hZlZyV4mgoCDUrVsX4eHhAIC5c+eic+fO8PDwQGZmJhYtWoS7d+9i9OjR6giZiHRITk4OLCwsZM/DwsJw5swZNGnSBACwe/dujBkzhgmgkl4d8GUCSFTzqWUO4IoVK5CVlQVfX184OzvLHps2bZLVSUpKQnJysuz5kydPMGbMGDRv3hz9+vVDdnY2Tp48iRYtWqgjZCLSIV5eXti5c6fsuZGREdLS0mTPU1NTYWJioonQdIJUeHUjaM4BJKrp1DYE/DrR0dFyz5cuXYqlS5eqKCIi0if79u3DhAkTEBkZieXLl+O7777D+++/D4lEguLiYhgYGCjsw0cV9+qf+EL2ABLVeGpdBEJEpAkNGjTA7t278ccff6Bnz56YOHEibt++jdu3b0MikaBZs2YwNTXVdJha63n+Z2QgQrFUQBEXgRDVeDqfAB542gpiwbhSbYxFEqWvd8igSKl2LUwfKNWulkGBUu0AoJHTI6Xa3cyo3Aru54zyDJVqB0BxklFFKXv3wCqMYAma2F5dyXhFyr7Oqny+K/M9qaa7QA4ZMgQBAQGYOnUqfH19sXLlSrRt27Z6Tq7P/r8L0MTIAMWFEs4BJNICOp8AEhEBwJ49e3D9+nV4enpi1apVOHLkCIYOHYqAgADMnTtXtiCNKu/5/xEmRgZ4VijhHEAlnE18jKJiKbp42Gk6FNITGrkVHBGROk2ZMgUjRozA2bNn8fHHH2PevHno2bMnzp8/D1NTU7Rr1w7//POPpsPUWs/nAJoYlnykcA5g5XyyPhaDI2Lw31WnkZadr+lwSE8wASQinRcZGYk9e/Zg48aNOHv2LH777TcAgImJCebNm4dt27bh66+/1nCU2uv5KmCxcclHCoeAK+Z80hP8fOQO9lxOkZUt3n8Tv526i8e5hRqMjPQBh4CJSOeZm5sjISEBXl5euHfvnsKCjxYtWuDYsWMaik77yYaADZkAVlSxRIp3fjqpUL7p3D1sOncP+6+m4LdR3hqIjPQFewCJSOeFh4cjKCgILi4u6NmzJ+bNm6fpkHSKbAjYqGShV1Ex5wCWJ79Igt5LjpRb59itdPYCkkoxASQinTd06FDcu3cPO3fuRGJiIgYNGqTpkHSKgBergAH2AL5OfMpT3M149tp6A344roZoSF9xCJiI9EKdOnVQp04dTYehm/6/w0/MRSAVMmj5iQrVe5CZp+JISJ+xB5CIdNq4ceNw//79CtXdtGkT1q9fr+KIdEvotkv4+ei/ANgDWBE3UrI1HQIRAPYAEpGOs7e3R8uWLdG1a1cMGDAAHTp0gIuLC0xNTfHkyRNcu3YNx48fx8aNG+Hi4oKVK1dqOmSt8seZe7KvjQ1LduzmPoCle1ZYjL7LFBcbJX7TH6nZ+fjp8G2sjbkrd+z0vxnwbsiea6p+7AEkIp02b9483Lx5E127dsVPP/2Ezp07o379+nBwcEDTpk0RFBSEf//9FytXrsSpU6fQpk0bTYestWQ9gLwVnAJBEJCQnqtQ7lanFgDA0coUYYNa4dM3POSOv7/ylFriI/3DHkAi0nmOjo744osv8MUXX+DJkydISkpCXl4e7Ozs0KhRI4hE1XSvOT1nxDmAZdp09h5mbrusUP7DkHZyzwNaOeOHQ7dlz4d3aaDq0EhPMQEkIr1iY2MDGxsbTYehk7gPYNlKS/4Sv+mvUNbCxQpjurvjl2MJAIBaJlW4hzpROTgETERE1eJ5AigVAImU8wCfKy0h3jOxe5n1v+jfAsG9SoaCnxVKVBYX6Ted7wG8mFUPxsUmarueFMoNJSWaKzfJ11ik/B+HWkbKbTIqqq1cO2m6mVLtAMBQ2dtjKjmyJ1ThXyOJWLl2oip8Xir9/miio0aZa7JDSSs8nwMIAIXFUpix9woAkPa0QKGshYtVuW1qiUveu8iTiZjQywP2lkr+YSEqA3sAiYioWryc8OUXsefqudRs+f/Qjk7r9do25iYv+mc6LjiItKfK/pdHVDomgEREVC0MRCLZVjD5xUwAn3v5nr8XZ/dB/f9f+Vsec7H8AN3vMXeRU1Bc7bGR/mICSER6Y/bs2bh79+7rK5LSTP//fsB5nLuGIokUkzZekCuzNjOuUNtXZ698f+g2Ws3eh5g7GdUUHek7JoBEpDd27tyJRo0aoXfv3tiwYQMKChTnZpHyRCLA9P+HgfOLOHFz7clE7Ix7qFTbZ4Wl9/Yt2HOtKiERyTABJCK9ERcXh7Nnz6Jly5aYNGkSnJycMH78eJw9e1bToekMU+OSj5U8zgHE/mupSrcd6FkXjlZc+EGqwwSQiPRKu3bt8P333+Phw4f49ddfcf/+fXTt2hVt2rTBd999h6ysLE2HqLVEeDEEXMAEEGIj+Y/YdvVrV7itdS1jnP7cD9P8m8qVi5Td2oDoFUwAiUgvCYKAoqIiFBYWQhAE2NjY4Mcff4Srqys2bdqk6fC01vOVwPq+COT0vxk4ditdruyXoA5VPu/lB/wHhaoHE0Ai0iuxsbEIDg6Gs7MzPvvsM7Rr1w7Xr1/HkSNHcOvWLSxYsAATJ07UdJha68UiEP2eA/jqPXw/fcMDdhaVH9Id1c1doUwQuMk2VR0TQCLSG61bt0bnzp2RkJCAX3/9Fffu3cM333wDDw8PWZ0hQ4bg0aNHGoxSe4lEgPj/5wDq8z6A1x5mK5RN7N1YqXOZGitups35lVQd1JIAzpkzByKRSO7RrFmzctts2bIFzZo1g6mpKVq3bo09e/aoI1Qi0mHvvfceEhMTsXv3bgQGBsLQUPHD1c7ODlJp9fdeHT16FAMGDICLiwtEIhF27Njx2jbR0dFo3749xGIxPDw8EBkZWe1xVTczYw4B9/v+mNzz9vVrw9hQ+Y/blR95yT2/WkqCSVRZausBbNmyJZKTk2WP48ePl1n35MmTGDJkCEaNGoULFy4gMDAQgYGBuHLlirrCJSId9Hyu36vy8vIwd+5clV47NzcXnp6eWL58eYXqJyQkoH///ujVqxfi4uIwefJkjB49Gvv27VNpnFUhgkjWY8V9AF8oruJ9kfu0dELiN/1lz4evPlPVkIjUlwAaGRnByclJ9rCzsyuz7nfffYe+ffti2rRpaN68OebNm4f27dvjxx9/VFe4RKSDwsLCkJOTo1D+7NkzhIWFqfTaAQEBmD9/Pt5+++0K1Y+IiIC7uzsWL16M5s2bIzg4GP/5z3+wdOlSlcZZVc+3gSko1s85gPEpTxXKBndwrdZr5DK5pmqgtgTw1q1bcHFxQcOGDTF06FAkJSWVWTcmJgZ+fn5yZf7+/oiJiVF1mESkwwRBgEikuI3GxYsXYWtrq4GIyqbM38GCggJkZ2fLPdRNNgSsp/PU/JcdlX294O1WWD/aGx9619dgRESlM3p9larz9vZGZGQkmjZtiuTkZISFhaF79+64cuUKLC0tFeqnpKTA0dFRrszR0REpKSllXqOgoEBuV39N/OEjoprJxsZGNv+4SZMmckmgRCJBTk4Oxo0bp8EIFZX1dzA7Oxt5eXkwMzNTaBMeHq7ynszyiETgEPBLnKxM0dWj7NGuqsgtKFa4XzBRZajlpycgIED2dZs2beDt7Q03Nzds3rwZo0aNqpZrlPWHz7XWE5iYV+zei9VBbKDczbpb1nqgVDtbQ8XhrIqyMVbuv9I7j5X7g5ZvZKpUOwAwMFBu81NlZ97k1VX+w8vQVrnbixUXKC5IqKhaN5W7Y4CRkveWF6l5dE9UxV0vli1bBkEQMHLkSISFhcHa2lp2zMTEBA0aNICPj08Vo9S80NBQhISEyJ5nZ2fD1bV6hx9fR8xFIDImRtU7yDa9b1N8uzceAJCRU8gEkKpEIz89tWvXRpMmTXD79u1Sjzs5OSE1Vf4WOqmpqXBycirznDXhDx8R1UzDhg0DALi7u6NLly4wNlbfP4XKKuvvoJWVVam9fwAgFoshFmv29mGmsm1g9G8O4JPcQrnnRZLqfQ/G9WgkSwAf5RSgfp1a1Xp+0i8a2QcwJycHd+7cgbOzc6nHfXx8EBUVJVd24MCBcv9DF4vFsLKyknsQEb08HaRdu3bIy8tTmCenqfly5VHm76CmifBiDqC+7VVXLJGi3bwDcmWt6lqXUVs5L4+EvLviJO5m5Fbr+Um/qCUBnDp1Ko4cOYLExEScPHkSb7/9NgwNDTFkyBAAQFBQEEJDQ2X1J02ahL1792Lx4sW4ceMG5syZg3PnziE4OFgd4RKRDrGxsUFaWhqAktEHGxsbhcfzclXKyclBXFwc4uLiAJRs8xIXFydbEBcaGoqgoCBZ/XHjxuHff//F9OnTcePGDfz000/YvHkzPvvsM5XGWVUW/z8s+TRfyfkFWmrpwZtyz6/N9YeDpfLTXiqi56JolZ6fdJtahoDv37+PIUOGICMjA/b29ujWrRtOnToFe3t7AEBSUhIMDF7kol26dMGGDRvw5Zdf4vPPP0fjxo2xY8cOtGrVSh3hEpEOOXTokGyF7+HDhzUWx7lz59CrVy/Z8+dTVoYNG4bIyEgkJyfL7Y7g7u6O3bt347PPPsN3332HevXqYdWqVfD391d77BUmEsHW3ASA4nCorlt++I7c81omnJ9HNZtafkI3btxY7vHo6GiFssGDB2Pw4MEqioiI9EXPnj1L/VrdfH19y72Ha2l3+fD19cWFCxdUGFX1MzPR721gVG1i78b4PuqW7Hl2fhGsTGv+nFaqefgvChHptEuXLlW4bps2bVQYiX4w1cM5gGcTH6vtWiFvNkF6TgE2nC7pLR7/eyzWj+6stuuT7mACSEQ6rW3bthCJROX2vgGASCSCRKI/SYsqvLwIRJ9WAQ+OkN+ce3iXBiq9XsibTWQJ4InbGfjncjL6tnIqdZNzorIwASQinZaQkKDpEPTK8x7A9JwCSKWC0nt4aouEdMWVuLMHtFDpNa3N5Id8x68/j+6N7fDbKG+VXpd0CxNAItJpbm5umg5Bb4hEgNFLCV9s0hN0bFCzbrFXnZIynmHypjiFclX3xBkbKm7gcexWOhrM3I1zX/rBzkKze0GSdmACSEQ67a+//kJAQACMjY3x119/lVt34MCBaopKd9lbvkg+svOKNBiJaj3OLUSPRYqryr/7oK1art+5oS1O/as497DD/INI/Ka/WmIg7cYEkIh0WmBgIFJSUuDg4IDAwMAy63EOYPUwNTaEZz1rXLyfhcc6vBXM9WTFjcPDBrbEoLZ11XL9yBGdEL7nOtbG3FXL9Uj3aOROIERE6iKVSuHg4CD7uqwHk7+qE6Fk6NPdzhwA8OSZ7iaAzwrlf15aOFthmIoXf7zM1NgQE97wUCjv6lFHbTGQdmMCSERE1crWvGQYOENHewBXHr2DMevOyZU9vweyOjlYmmLPxO5yZbfTctQeB2knnR8C9rRIgplF5V5mrlT5CbRSQbk/Ak5GWUq1szVU/pf9kXGmUu2crZS7Z+ptM+Xvi2lQpNyk6nxn5W5HVdc9Xal2AJD2xFKpdsZmys+XEgzVO+lbyR9zAIBIw7uDREVFYenSpbh+/ToAoHnz5pg8eTL8/Pw0G5gOeL72wda8ZJWqLt4NpFgixdd7biiUG5WyMEMdWrhY4eb8AHT6+iAynxUhNbsAgiBwSxh6LfYAEpHe+Omnn9C3b19YWlpi0qRJmDRpEqysrNCvXz8sX75c0+HpjOc9gLo4B7C05A8oSQw1xcTIAKuCOsiePy3Qr/swk3J0vgeQiOi5r7/+GkuXLkVwcLCsbOLEiejatSu+/vprTJgwQYPR6Y7nPYC6mACuPlH6vpISafkbjatah5e223maX4yiYiksTY1hYsR+HiodfzKISG9kZmaib9++CuV9+vRBVpZy0zDoheeDjrrcA1gaQwMRpvRpqukwZLp+cwhe8w/ig5Uxr69MeosJIBHpjYEDB2L79u0K5Tt37sRbb72lgYh0ky73AL5q3+QeOPN5b/RoYq/pUBScT8rE2z+dwL3HzzQdCtVAHAImIp32/fffy75u0aIFFixYgOjoaPj4+AAATp06hRMnTmDKlCmaClFnvFgEUtIDmJ1fjCKJtNQ7V2ijV+8nvWZ4RzR1Um7Rlyr0aGKPozcfyZVdSMpE928Pc3NoUsAEkIh02tKlS+We29jY4Nq1a7h27ZqsrHbt2li9ejW+/PJLdYenk16+V+3Ba6kIaO2swWiqx5mEx9h4Jkn2/Npcf9QyqVkfoZHDO2LwzzGIvftE4djZxMcoLJaiq4edBiKjmqhm/fQSEVWzhITSJ+2T6hi+dD/gdTF3dSIBfO/nF/PpGtmb17jkDwAMDERYN7ITZu28ij/P35c7NjiiJP64WW+idi0TTYRHNYxu9MsTEZHGlbb3XJdG2n9nilfn0Dlbm2koktczFxth8XueeK9DvVKPp+fo/rxMqpia9y8MEZEK3b9/H3/99ReSkpJQWCj/YbhkyRINRaV7RnRtgDUnEpFXpN232PvncjIijtyRK7uZ+lRD0VTcO+3rYfO5+wrl30fdwpL3PDW2cTXVHEwAiUhvREVFYeDAgWjYsCFu3LiBVq1aITExEYIgoH379poOT6dYmZbMA3yar52bEguCgNxCCcavP69wrI6Feu+8o4yODWzh29Qe0fHyi0L+uvgQf118iHfa1UXnRnXg39JJbs4m6Q/+C0BEeiM0NBRTp07F5cuXYWpqij///BP37t1Dz549MXjwYE2Hp1McrEqSpLtauAVJ1PVUdJh/EENWnir1+I//bafmiCrP0ECEyBGd4N/SsdTj2y48wPStlzB0VemvkXQfE0Ai0hvXr19HUFAQAMDIyAh5eXmwsLDA3LlzsXDhQg1Hp1sa1DEHADx4on0J4Ki155CRW4jLD0rfHLyRvYWaI1JebbPyF3xceaDcvd1J+zEBJCK9YW5uLpv35+zsjDt3XsztSk9P11RYOuPlNSCOVqYAgNTsAg1FoxoN6tTSdAiVMvnNxjA2VFyc87IGM3ej/bwDOPLKHoKk23R+DqBEMIBEqFyeayoqUv6C5f+elSmxULm9mVxrZSp3QQCdzZTbHsPQRbmbnkfkK79y7tEN5d4fQ2vlVryZ9/1XqXYAYLC5tVLtigqU/3U0VnaevZL3rxep+7731XSb1c6dO+P48eNo3rw5+vXrhylTpuDy5cvYtm0bOnfuXD0XIQCAk3VJAphTUIyn+UWwNK3588wkUgFHb5WdBNlZmGDHhK5qjKjqnK3NcGtBPwDA7kvJmLBBcU4jUHLXlmGrz3DDaD2i8wkgEdFzS5YsQU5ODgAgLCwMOTk52LRpExo3bswVwJU0KvKsQrIkeuk/YAuxESzFRnhaUIzU7PwanwDmFhSj5ex95db5aaiXVu+h17+NMxIzmmLRvvgy6zzJLYSNufa+Rqo4JoBEpDcaNmwo+9rc3BwREREajEZ7FUukiLqR9tp61rWM8bSgGH5LjuLSnD6ylcE1za/HEzBv17VSj33coyGm9GmK1Ox8uNpq1/BvaSb08sDIru5oPmtvqcfbzTuAlR95wdTYsEbe35iqj1rmADZo0AAikUjhMWHChFLrR0ZGKtQ1NTVVR6hEpAfOnTuH3377Db/99htiY2M1HY7WKZRUbA7A/Sd5sq+3lrInXU3w6GlBmcnfp294ILRfc5gYGehE8vecmYkhdn3aDWO6u+Pb/7RROD72t1gErT6D+JSnWLj3hsJG2KQb1NIDePbsWUgkLyYpXblyBW+++Wa52y5YWVkhPv5FN3VpO8wTEVXG/fv3MWTIEJw4cQK1a9cGAGRmZqJLly7YuHEj6tUr/e4JJK+gqPQE8NU/010a1cHJOxkAUGM3hP5fGcOhdWubYUqfpmqORn1a1bVGq7rWAIBBbV3Q9EvFHkH/ZUcBACui7+D0571lC3tIN6ilB9De3h5OTk6yx65du9CoUSP07NmzzDYikUiujaNj6XsZERFV1OjRo1FUVITr16/j8ePHePz4Ma5fvw6pVIrRo0drOjytUVBcsR7AX4d1VHEkVXPwWip2XXpY6rHwd5RbzKWNxEaGr63j/XWUGiIhdVL7NjCFhYX4/fffMXLkyHJ79XJycuDm5gZXV1cMGjQIV69eVWOURKSLjhw5ghUrVqBp0xc9O02bNsUPP/yAo0ePajAy7VJQXHpv3qt/0c1MDDG8SwMAwLPCmnVHkJO30zF63TnkFpb+WsRG3CXtVe/8dAKL9t2ARFpNy/JJo9T+E75jxw5kZmZi+PDhZdZp2rQpVq9ejZ07d+L333+HVCpFly5dcP9+2XNICgoKkJ2dLfcgInqZq6sriooUt3mSSCRwcXHRQETaqbCCPYBASRIIAHmF6t47qHzrzySVWv5eh3p4o5kDOjSwVXNENd/5pEwsP3wHjT7fg5j/H9on7aX2BPDXX39FQEBAuX9sfXx8EBQUhLZt26Jnz57Ytm0b7O3t8fPPP5fZJjw8HNbW1rKHq6urKsInIi22aNEifPrppzh37pys7Ny5c5g0aRL+97//aTAy7VLRIWAAqGVckgAejn/9qmF1uZ32FLsvJZd67Jt32mD18I4wNNDPeeeju7njjWYOr6035JdTkLInUKupdRuYu3fv4uDBg9i2bVul2hkbG6Ndu3a4fft2mXVCQ0MREhIie56dnc0kkIhgY2MjN90kNzcX3t7eMDIq+fNXXFwMIyMjjBw5EoGBgRqKUruUOQRcSs5051HJvosJ6bkokkhhbKj5odXJm+IUygxEQJBPAxjoaeL3Rb/m2HbhAT7p5YFnhcXo0qgO5u++Xm6bhp/vQX3bWviyf3P0aemkpkipuqg1AVyzZg0cHBzQv3/ldhqXSCS4fPky+vXrV2YdsVgMsVhc1RCJSMcsW7ZM0yHILF++HIsWLUJKSgo8PT3xww8/oFOnTqXWjYyMxIgRI+TKxGIx8vPz1RFqucpaBVyalzcVTsmqGXvp2Vsoflac/cIPdUop1xdjejTEmB4l+2TamptgdPeG+K93fQxZeQoX75d+T2QASHr8DGN/i0WPJvZYPNgTdhYm3LVDS6gtAZRKpVizZg2GDRsm+8/7uaCgINStWxfh4eEAgLlz56Jz587w8PBAZmYmFi1ahLt373KVHhFV2rBhwzQdAgBg06ZNCAkJQUREBLy9vbFs2TL4+/sjPj4eDg6lD7nV1O2wyhoCFpVyL8wpfZpizYlEADVjK5iCYgkOxyve7k2b7/ChKrVMjLAzuBuuPcyGmYkhev0vusy6R28+QscFB9GnhSOWD21fI3p6qXxqSwAPHjyIpKQkjBw5UuFYUlISDAxe/LA8efIEY8aMQUpKCmxsbODl5YWTJ0+iRYsW6gqXiHSURCLBjh07cP16yfBWy5YtMXDgQBgavn4rjKpYsmQJxowZI+vVi4iIwO7du7F69WrMnDmz1DbPt8OqacoaAi6NhdgIdWub4UFmHp6VseJWncL+frHpc+9mDgjt1wwOVqZ6O+evIlq4WAEAEr/pj6y8Inzzzw38UcYimv3XUtH4i39gbmKINvVqY/bAFsgrlGDsb7GY2bcZ3vXiXps1hdoSwD59+kAQSp8wGh0dLfd86dKlWLp0qRqiIiJ9cvv2bfTr1w8PHjyQbQUTHh4OV1dX7N69G40aNVLJdQsLCxEbG4vQ0FBZmYGBAfz8/BATE1Nmu+fbYUmlUrRv3x5ff/01WrZsqZIYKyM9p7BS9Z+vBK4JW8FsOP0icRndvSE8HCw1GI32sTYzRvg7rfFBR1d8u+8GTtwufTVwbqEEMf9moO+yY7KyKVsuMgGsQdhHS0R6Y+LEiWjUqBHu3buH8+fP4/z580hKSoK7uzsmTpyosuump6dDIpEobGjv6OiIlJSUUtvU5O2w0p4WlFpe1gh1rf9PAPM1NAQsCAKKJVKE/S2/n6ypMT8CleXpWhvrR3eu0jkq05NM1U+ti0A0wd34EcxNKje0Y2mg/CRrQyi3LD5fUO5bUU8D38GzSrZ7mqf8BGvBrnI9Ds+Z1yr9g+p10nY2U6odABQ8Vm4ukdEj5ecgGSr3MpVXhdEykRLbwVXX4NyRI0dw6tQp2Nq+2OOtTp06+Oabb9C1a9dqukr18PHxgY+Pj+x5ly5d0Lx5c/z888+YN29eqW3Cw8MRFham8tie5ivupVgeU+PnPYCa+cAP2XwRB6+l4mnBix5IdztzeNarrZF4dEn0VF+sOv4vhnq7IeC7Y6+tP+evq6hnY4bF+28ir0iCBnVqIa9IgpUfdYCna23VB0wy/PeHiPSGWCzG06dPFcpzcnJgYqK6RQB2dnYwNDREamqqXHlqamqF5/hVdDusrKws2ePevXtVirsseZVM5LKelSSMh65XfC/AZ4XF+OjX01gRfadS1yrN9gsP5JI/ADg81Vdvt3ypTg3szDE/sDWaO1vh7+Bur60feTIR83dfly0ISsx4htTsAgxafgI3UngDB3ViAkhEeuOtt97C2LFjcfr0aQiCAEEQcOrUKYwbNw4DBw5U2XVNTEzg5eWFqKgX91OVSqWIioqS6+Urz/PtsJydncusIxaLYWVlJfdQhcqu5o1PLUm6t114UOE2B6+n4ditdCzce6PM+eMVseey4obP/duU/R6S8lrXs8bVMH/cnB+AuYMqP1e177JjGPdbLDKfFcp+P0l1dH4ImIjoue+//x7Dhg2Dj48PjI2NAZRsBD1w4EB89913Kr12SEgIhg0bhg4dOqBTp05YtmwZcnNzZauCtWk7rMr2APZt6YS9V0uf61iWr3ZckX0ddy8TjR0tYSGu3EfWlQdZ+GT9ebmyAZ4u+P6DtpU6D1Wc+f9/j4J8GsDK1BiX7mfh2K1HuJWWU6H2e6+m4GlBEfKLpLibkYs9E7vDwcpUlSHrLSaARKQXBEFAdnY2Nm7ciAcPHsi2gWnevDk8PDxUfv33338fjx49wqxZs5CSkoK2bdti7969soUh2rQdVlk9gGXtUzixd2PsvZoCO4uKD7Nn5b2YZ/j2TycBAP9M6o7mzhXr1SyWSPHxb7FyZXYWJvhhSLsKx0BVE9iuLgLb1UVBsQTZecXotvBQhW4j+PLK4k//uIBNH5f0kucWFCP27hP4NKrDfQarARNAItILgiDAw8MDV69eRePGjdWS9L0qODgYwcHBpR7Tpu2wKtsD6Gxd0oOTnlOIjJyCUu+4cfJ2Og7dSMO0vk2RklX6QrxlB2/i5486VOiavRZH40Fmnux5I3tz7P+sZ6XipuohNjKEvaUh4ucHwG/JEdz+/97AivQMn054jEX7bsBAJMK1h9mIupGGyX6NMdmviTpC12lMoYlILxgYGKBx48bIyCh93zKquDJ7AMuoX7uWsezrOS9txPyy/646jVXHExDw3TH0XBRdap0iSflzwvIKJdhx4QGuPczGvcd5csfC32nDzZ5rgJYuL3pwIz7ywrzAVrLnv4/yLrXN8sN38MOh24i6UbKIaNnBWxAEAcsP30bg8hNIycrH1tj7+ONMEv59lFMj9pvUBuwBJCK98c0332DatGlYsWIFWrVq9foGVKrKLgIp7xZ2giDIbQ/z76PcMuseupGGk7fT0cXDDh+uOo3jt9Nx4LMe8HCwwD9XUhTm+72sYwObSsVMqhHyZhOkZOVjdPeS+w4P7VQfgiCgc8M6aOJoiZ8/8sLXe67jbsazcs/TcUEU0nNK9r/qHB4ld6xNPWv8VYEVyfqOCSAR6Y2goCA8e/YMnp6eMDExgZmZmdzxx48faygy7VLZIWAAmNS7Mb6LuoW/Lz7E4sGeMDEygFQqoOHne8ps49OwDgZ3qIeQzRdlZf9ddRrX5vrj+O10AMCbS4+We10vNxusHt6xxtxHWd+51TGXzekDAAMDEYJ8Gsie+7d0gm9Te4yKPCf7HpfmefJXmkv3s3A3IxdudcyrJWZdxQSQiPTGsmXLNB2CTih7EUjZbXwa1cF3UbcAAMsP38bdjFzsiHtY7nXWjuwEAFi0Lx7JL80LTMuu+M7nf47vUuG6VDOIjQzx+2hvZD0rgufc/Uqdo+eiaCR+0x9z/roKC7ERpvo3lR27npwNZ2tTWIiNYKTHi0mYABKR3hg2bJimQ9AJyvQAeru/uPvK80TwdUyMSj6cd33aDV7zD8rK/7lSsS1lvnmndSUipJrGupYxNn/sg6M3H2FL7D241DZDHXMTHKzghuINZu6WfX06IQO303IwpkdDfLs3HkDJwqB9k3vAyNAA+UUSiI0M9KqnmAkgEekViUSC7du3y7aBadGiBQYNGgQjI/45rAiJVKjQVh6vEolEeL+DKzadq9jdSS7O7iP72tbcBCaGBiiUlFx34d4bFTrHB53qVzpOqlk6uduik7stJvs1hqGBCMlZ+Th4/VClz3M28QkAyJI/ALjzKBcz/rwMJ2sxlh++AzsLE/g1d8T8wFZ60TOo+6+QiOj/Xb16FU2aNMGwYcOwfft2bN++HcOGDUPjxo1x5cqV15+AkF/OApCq9J0se78tNo7tDADYMMYb1mYvVg6LRCKc+8pPoc2r+wpGjugIU+OSj7U/xnSuQjRU0xgZlvTOudQ2w84JL+7b3bOJPcLfaY3Eb/qXuYq4PH+ev4/lh0tuN5ieU4iNZ++VO/dQl/BfXiLSG6NHj0bLli1x7tw52NiUrAp98uQJhg8fjrFjx+LkyZMajrDme5r/YouNl3vlKtS2oKjUct+m9ghsVxcAkPhN/1LrWJkaI7iXB348/OJeyCO7ucNCbIT0nEJ86F0fDlamOP/Vm7j/JA9NHC0rHBdplzb1rPFRZzfUt62FMT0aysq7NbZD4jf95YZ+lTF8zVkAJfeLXnrgJsb7NsI/V1JQz8YM73VwrdK5axKdTwCzpLVQJDWsVJs0ifJ/OExFpf+Be518wfj1lUpxp0j5b2Ej44rfmP1lrsbK7aM2vOkppdoBQFRaM6XapT5V7nuZmWGhVDsAMMxQ7nspfqw/c080JS4uTi75AwAbGxssWLAAHTt21GBk2mPDmSTZ14YGIuClDsHXzZ/6zK8J9lxOQacGtjA2EsFAJMJkv8Zo4WxdoWtP9W+Ke0+eYef/Lx7p29IJDe3lf1drmRgx+dNxIpFIbv/AV535ojc+WnUG8alPYWQgwjT/pgj/p2LTBl7W63/RAIC/Lr5YrGRmbIg3mjnIbnmnzbT/FRARVVCTJk2QmpqKli3lb1SflpamkTuDaKOUrBcbLFd2Y+XGjpZICO9XpYn2cwe1grO1Gd5o5qCQ/BEBgIOlKfZ91gOCIKBIIsDEyABBPg2QXyTBv+m5aOdaGwYGIoRuu4Q/zlRsTupzn/5xAQDQv7UzjAxF6N7YHu+2r4uCYimirqehW2M72fSFp/lFsDRVrkNAHTgHkIj0Rnh4OCZOnIitW7fi/v37uH//PrZu3YrJkydj4cKFyM7Olj2odM9X5k58w6PcbV/KUtVVltZmxpgZ0AydXlpVTFQakUgk+3k1MzGEjbkJvNxsYPD//7h82NlNVnfeoJa4tSCgwufefTkZO+MeYuqWi3AP3YNuCw9jwobzCN5Qshn5uphEtJ6zH3uvJFfjK6pe7AEkIr3x1ltvAQDee+89WSIiCCW3FxswYIDsuUgkgkRS+a1O9MGjpyV78NlbihV6APVoBw3SAS1drHFi5huwtxDLEsVaJoZyd6apqOcbUx+7lY5Vx/7F/N0luwxM2HABd752rr6gqxETQCLSG4cPH9Z0CFpPLgFkxkdarm5t+bsBHZnWC0mPn+He42eYtvUiWte1xp1HucjKq/j8/ufJH1CybdLLi1JGd3NHoUSK2QNaavze1EwAiUhv9OzZU9MhaL1HOS8SwFeHc5kOkraztxTD3lIMLzcb9GnpCDNjQ4hEIhQUS5CVV4ROC0ruO9y5oS1O/Vv5W0euOp4AoOQ2hw5WYtQxF6OBnWZuWccEkIiIKkQQhBc9gBam0IO9ckmP1TJ5kSKJjQzhYGmI7z5oC0MDEQJaOWPb+ftYdvAWHmSWLIzq3tgOx25VbA/B8evPy74ua+sjVWMCSEREFZJTUIz8opJ9/+wsTTgETHpnUNu6sq8Hd3DF4A6uKCiWQBCApMfP0Gfp0UqfM/NZIWrXMnl9xWrGBJCIiCrkYWY+AMDIQIRaJkaKK3qZEJIeEhuV7DXs8tJ8wpjQN/AwMx/3nzzD/N3X8aG3G/7rXR8dFxxUaJ/0+BkTQCIiqrm2xpbsmVYsLVk5relJ7EQ1iYXYCNFTfWFkKIKztRmcrc3g5WaDgZ4usn+WZgY0wzcvbUq97P22aFOvtkbirZYZHEePHsWAAQPg4lLyInfs2CF3XBAEzJo1C87OzjAzM4Ofnx9u3br12vMuX74cDRo0gKmpKby9vXHmzJnqCJeIiJSQ9PgZAMDcpKTH49X8j+kg6bsGduaoZ1NLruzlnvJR3dzRzcMOAPDtf9rIboGoCdXSA5ibmwtPT0+MHDkS77zzjsLxb7/9Ft9//z3Wrl0Ld3d3fPXVV/D398e1a9dgampa6jk3bdqEkJAQREREwNvbG8uWLYO/vz/i4+Ph4OBQHWETkZ5p165dqRsRi0QimJqawsPDA8OHD0evXr00EF3Nt+9qKoCSW7IBkG2oS0QVY2xogN9GdcKjnAI4WJae/6hLtfQABgQEYP78+Xj77bcVjgmCgGXLluHLL7/EoEGD0KZNG6xbtw4PHz5U6Cl82ZIlSzBmzBiMGDECLVq0QEREBGrVqoXVq1dXR8hEpIf69u2Lf//9F+bm5ujVqxd69eoFCwsL3LlzBx07dkRycjL8/Pywc+dOTYda4zzfMBt4sRcgF4EQVZ5IJNJ48geoYQ5gQkICUlJS4OfnJyuztraGt7c3YmJi8MEHHyi0KSwsRGxsLEJDQ2VlBgYG8PPzQ0xMTKnXKSgoQEFBgew5b+VERK9KT0/HlClT8NVXX8mVz58/H3fv3sX+/fsxe/ZszJs3D4MGDdJQlDXT8+FfAAjyaQAAMHh1H0Dmg0RaQ+UJYEpKCgDA0dFRrtzR0VF27FXp6emQSCSltrlx40apbcLDwxEWFqZQfqfQAaYFlXuZUkH5jlEDkVTptspIKbBWuu2houZKtSuQKvdjUyg1VKodADwrUm6FVGaylVLtzO4r/6sxdsgepdqt/rWf0tcUCa+vU53tqkSZa1ZTnJs3b0ZsbKxC+QcffAAvLy/88ssvGDJkCJYsWVI9F9QhEUfuAACaOFrAybqk94JDwETaS2e28QwNDUVWVpbsce/ePU2HREQ1jKmpKU6ePKlQfvLkSdl8ZKlUWubcZH11O+0p/jhT8jfV0erFe/PqRtAiLgMh0hoq7wF0cnICAKSmpsLZ+cUNkVNTU9G2bdtS29jZ2cHQ0BCpqaly5ampqbLzvUosFkMsFldP0ESkkz799FOMGzcOsbGx6NixIwDg7NmzWLVqFT7//HMAwL59+8r826SvriU/lX1976WhYCLSXirvAXR3d4eTkxOioqJkZdnZ2Th9+jR8fHxKbWNiYgIvLy+5NlKpFFFRUWW2ISJ6nS+//BK//PILzpw5g4kTJ2LixIk4c+YMfvnlF3zxxRcAgHHjxuHvv//WcKQ1y8FrL/4Z79nEXvb1lQeca02kraqlBzAnJwe3b9+WPU9ISEBcXBxsbW1Rv359TJ48GfPnz0fjxo1l28C4uLggMDBQ1qZ37954++23ERwcDAAICQnBsGHD0KFDB3Tq1AnLli1Dbm4uRowYUR0hE5GeGjp0KIYOHVrmcTMzszKP6au/Lj6UfR3Sp6kGIyGi6lItCeC5c+fk9s0KCQkBAAwbNgyRkZGYPn06cnNzMXbsWGRmZqJbt27Yu3ev3DybO3fuID39xU2U33//fTx69AizZs1CSkoK2rZti7179yosDCEiqqzCwkKkpaVBKpVftFW/fn0NRVRzNZi5W/b1nondYW1mXGZdobpW6xCRylVLAujr6yu3R9SrRCIR5s6di7lz55ZZJzExUaEsODhY1iNIRFRVt27dwsiRIxUWggiCAJFIBIlEoqHIaqZiiXyC3NjRotz6XARCpD14L2Ai0hvDhw+HkZERdu3aBWdn51LvCkIvpOcUyr7e9kkXGL+67PcV7AEk0h5MAIlIb8TFxSE2NhbNmjXTdChaYfuFB7Kv29e30WAkRFTddGYfQCKi12nRooXcXGMqX0ZOyd2VzE2U38SdiGomJoBEpDcWLlyI6dOnIzo6GhkZGcjOzpZ7qNry5cvRoEEDmJqawtvbG2fOnCm3/pYtW9CsWTOYmpqidevW2LNHubvMKCs5Kx8AV/4S6SImgESkN/z8/HDq1Cn07t0bDg4OsLGxgY2NDWrXrg0bG9UOcW7atAkhISGYPXs2zp8/D09PT/j7+yMtLa3U+idPnsSQIUMwatQoXLhwAYGBgQgMDMSVK1dUGufLHmblAQBcrHlnFCJdwzmARKQ3Dh8+rLFrL1myBGPGjJHtZRoREYHdu3dj9erVmDlzpkL97777Dn379sW0adMAAPPmzcOBAwfw448/IiIiQmVxDvrxOC7ez5Irc2ICSKRzmAASkd7o2bOnRq5bWFiI2NhYhIaGysoMDAzg5+eHmJiYUtvExMTI9lR9zt/fHzt27FBZnMlZeQrJHwA0cbRU2TWJSDOYABKRTrt06RJatWoFAwMDXLp0qdy6bdq0UUkM6enpkEgkChvZOzo64saNG6W2SUlJKbV+SkpKmdcpKChAQUGB7Hll5zUeu6W4QKZ1XWuYi/lRQaRrdP63OrXACmLjsneur24WRgWvr1RaO8N8pdo5iRX/W68oM8MipdpJBeX2TmtiVvYH1+tcrVVXqXaPssvfuLYswkPlezx+jeynVDuDqmyhJn19lVIpeU1RVWJV5senCtv1tW3bFikpKXBwcEDbtm0hEolK3bheFzaCDg8PR1hYmNLtT9wuSQCDe3ngszeb4PjtdLRysaqu8IioBtH5BJCI9FtCQgLs7e1lX2uCnZ0dDA0NkZqaKleempoKJyenUts4OTlVqj4AhIaGyg0bZ2dnw9XVtUIxSqWCLAHs1tgOhgYi9GxiX6G2RKR9uAqYiHSam5sbRCIRioqKEBYWBqlUCjc3t1IfqmJiYgIvLy9ERUXJyqRSKaKiouDj41NqGx8fH7n6AHDgwIEy6wOAWCyGlZWV3KOi4lOfIj2nEGbGhkpv+lzOHUGJqIZhAkhEesHY2Bh//vmnxq4fEhKCX375BWvXrsX169cxfvx45ObmylYFBwUFyS0SmTRpEvbu3YvFixfjxo0bmDNnDs6dO6ey+6Mf///5f94NbWFixI8GIl3H33Ii0huBgYEqXUVbnvfffx//+9//MGvWLLRt2xZxcXHYu3evbKFHUlISkpOTZfW7dOmCDRs2YOXKlfD09MTWrVuxY8cOtGrVSiXxHX8+/Othp5LzE1HNwjmARKQ3GjdujLlz5+LEiRPw8vKCubm53PGJEyeq9PrBwcFl9uBFR0crlA0ePBiDBw9WaUwAUFAswemEDAAl8/+ISPcxASQivfHrr7+idu3aiI2NRWxsrNwxkUik8gSwpjp/NxP5RVLYWYjRlHv+EekFJoBEpDc0tQq4pqtnY4aQN5vA0EAEkajie+40sjfHnUe5KoyMiFSFcwCJSO8UFhYiPj4excXFmg6lRnC1rYWJvRtjQi+PSrX7xLdy9Ymo5mACSER649mzZxg1ahRq1aqFli1bIikpCQDw6aef4ptvvtFwdNqnEp2FRFTDMAEkIr0RGhqKixcvIjo6GqamprJyPz8/bNq0SYORERGpF+cAEpHe2LFjBzZt2oTOnTvLzXVr2bIl7ty5o8HIiIjUiz2ARKQ3Hj16BAcHB4Xy3NzcSi1+oBKvvmW8EQiR9mACSER6o0OHDti9e7fs+fOkb9WqVeXeYo2ISNfo/BDw9SwnGBWLK9VGJFL+/1gL4wKl2hkZSJVrJ1KuXVXUNs5Tqt3NvLJvYv86D/JqK91WKfzXqFxCFTrLqvDrVWVff/01AgICcO3aNRQXF+O7777DtWvXcPLkSRw5ckRzgRERqRk/5ohIb3Tr1g1xcXEoLi5G69atsX//fjg4OCAmJgZeXl6aDo+ISG10vgeQiOhljRo1wi+//KLpMHSCCJw3SaStqqUH8OjRoxgwYABcXFwgEonkbrZeVFSEGTNmoHXr1jA3N4eLiwuCgoLw8OHDcs85Z84ciEQiuUezZs2qI1wi0lOGhoZIS0tTKM/IyIChoaEGIiIi0oxqSQBzc3Ph6emJ5cuXKxx79uwZzp8/j6+++grnz5/Htm3bEB8fj4EDB772vC1btkRycrLscfz48eoIl4j0lCCUPgGxoKAAJiYmao6GiEhzqmUIOCAgAAEBAaUes7a2xoEDB+TKfvzxR3Tq1AlJSUmoX79+2cEZGcHJSfmFA0REAPD9998DKFn1u2rVKlhYWMiOSSQSHD16lCMMSuDOOUTaSyNzALOysiASiVC7du1y6926dQsuLi4wNTWFj48PwsPDy0wYCwoKUFDwYgVudnZ2dYZMRFps6dKlAEp6ACMiIuSGe01MTNCgQQNERERoKjzdUUYPKxHVPGpPAPPz8zFjxgwMGTIEVlZWZdbz9vZGZGQkmjZtiuTkZISFhaF79+64cuUKLC0tFeqHh4cjLCxMlaETkZZKSEgAAPTq1Qvbtm2DjY2NhiMiItIstW4DU1RUhPfeew+CIGDFihXl1g0ICMDgwYPRpk0b+Pv7Y8+ePcjMzMTmzZtLrR8aGoqsrCzZ4969e6p4CUSkxXr16gWxWHFf0Ly8PMydO1cDERERaYbaEsDnyd/du3dx4MCBcnv/SlO7dm00adIEt2/fLvW4WCyGlZWV3IOI6GVhYWHIyclRKH/27BlHEIhIr6glAXye/N26dQsHDx5EnTp1Kn2OnJwc3LlzB87OziqIkIj0gSAIpd7z9+LFi7C1tdVAREREmlEtcwBzcnLkeuYSEhIQFxcHW1tbODs74z//+Q/Onz+PXbt2QSKRICUlBQBga2sr23qhd+/eePvttxEcHAwAmDp1KgYMGAA3Nzc8fPgQs2fPhqGhIYYMGVIdIRORHrGxsZHtJ9qkSRO5JFAikSAnJwfjxo3TYITaqbRkmoi0Q7UkgOfOnUOvXr1kz0NCQgAAw4YNw5w5c/DXX38BANq2bSvX7vDhw/D19QUA3LlzB+np6bJj9+/fx5AhQ5CRkQF7e3t069YNp06dgr29fXWETER6ZNmyZRAEASNHjkRYWBisra1lx56vAvbx8dFghERE6lUtCaCvr2+ZG6wCZW+++rLExES55xs3bqxqWEREAEr+GQUAd3d3dOnSBcbGxhqOiIhIs3T+XsCFUkNIpZW7xVORRPlbQj0rUu5uAsaGEqXamRkVKdUOAKSCcsM3OUWKqygrwtTIXKl2AFCo5PfE2jxPqXaPaisfq2G+klNri5W+pPKUHMETSas3DHXp2bOn7Ov8/HwUFhbKHefiMSLSF2rdBoaISJOePXuG4OBgODg4wNzcHDY2NnIPIiJ9wQSQiPTGtGnTcOjQIaxYsQJisRirVq1CWFgYXFxcsG7dOk2Hp3Ve7UDmfUCItIfODwETET33999/Y926dfD19cWIESPQvXt3eHh4wM3NDevXr8fQoUM1HSIRkVqwB5CI9Mbjx4/RsGFDACXz/R4/fgwA6NatG44eParJ0LQSd4Eh0l5MAIlIbzRs2FB2X+BmzZrJbi35999/o3bt2hqMjIhIvZgAEpHeGDFiBC5evAgAmDlzJpYvXw5TU1N89tlnmDZtmoajIyJSH84BJCK98dlnn8m+9vPzw40bNxAbGwsPDw+0adNGg5EREakXE0Ai0ltubm5wc3PTdBhERGrHIWAi0hsTJ07E999/r1D+448/YvLkyeoPSMuJlN1JnIg0jgkgEemNP//8E127dlUo79KlC7Zu3aqBiIiINIMJIBHpjYyMDFhbWyuUW1lZIT09XWXXffz4MYYOHQorKyvUrl0bo0aNQk5OTrltfH19IRKJ5B7jxo1TWYzK4DYwRNqLCSAR6Q0PDw/s3btXofyff/6R7Q+oCkOHDsXVq1dx4MAB7Nq1C0ePHsXYsWNf227MmDFITk6WPb799luVxVgdBN4KhEhrcBEIEemNkJAQBAcH49GjR3jjjTcAAFFRUVi8eDGWLVumkmtev34de/fuxdmzZ9GhQwcAwA8//IB+/frhf//7H1xcXMpsW6tWLTg5OakkLiLSb+wBJCK9MXLkSCxevBi//vorevXqhV69euH333/HihUrMGbMGJVcMyYmBrVr15Ylf0DJFjQGBgY4ffp0uW3Xr18POzs7tGrVCqGhoXj27Fm59QsKCpCdnS33ICIqjc73AIoNi2FkaFipNiYGEhVFUzYzoyKl2tmIy/9AUAUjkVSpdh610pS+poGS17xmUnbvSnkeO9VSqh0ASDMslGonKlb6kjBQduhNyXZCFeZ+KfWtrMahxfHjx2P8+PF49OgRzMzMYGGh3PerolJSUuDg4CBXZmRkBFtbW6SkpJTZ7r///S/c3Nzg4uKCS5cuYcaMGYiPj8e2bdvKbBMeHo6wsLBqi52IdBd7AIlIb+Tl5cl60ezt7ZGRkYFly5Zh//79lT7XzJkzFRZpvPq4ceOG0rGOHTsW/v7+aN26NYYOHYp169Zh+/btuHPnTpltQkNDkZWVJXvcu3dP6etXBNeAEGkvne8BJCJ6btCgQXjnnXcwbtw4ZGZmolOnTjAxMUF6ejqWLFmC8ePHV/hcU6ZMwfDhw8ut07BhQzg5OSEtTb73u7i4GI8fP67U/D5vb28AwO3bt9GoUaNS64jFYojF4gqfk4j0FxNAItIb58+fx9KlSwEAW7duhZOTEy5cuIA///wTs2bNqlQCaG9vD3t7+9fW8/HxQWZmJmJjY+Hl5QUAOHToEKRSqSypq4i4uDgAgLOzc4XbqBq3gSHSXhwCJiK98ezZM1haWgIA9u/fj3feeQcGBgbo3Lkz7t69q5JrNm/eHH379sWYMWNw5swZnDhxAsHBwfjggw9kK4AfPHiAZs2a4cyZMwCAO3fuYN68eYiNjUViYiL++usvBAUFoUePHrxnMRFVCyaARKQ3PDw8sGPHDty7dw/79u1Dnz59AABpaWmwsrJS2XXXr1+PZs2aoXfv3ujXrx+6deuGlStXyo4XFRUhPj5eNj/RxMQEBw8eRJ8+fdCsWTNMmTIF7777Lv7++2+VxUhE+oVDwESkN2bNmoX//ve/+Oyzz9C7d2/4+PgAKOkNbNeuncqua2triw0bNpR5vEGDBhBe2kXZ1dUVR44cUVk8RERMAIlIb/znP/9Bt27dkJycDE9PT1l579698fbbb2swMiIi9WICSER6xcnJSWH1badOnTQUjbbjKhAibVUtcwCPHj2KAQMGwMXFBSKRCDt27JA7Pnz4cIX9sfr27fva8y5fvhwNGjSAqakpvL29ZROkiYiIiEh51ZIA5ubmwtPTE8uXLy+zTt++feVuav7HH3+Ue85NmzYhJCQEs2fPxvnz5+Hp6Ql/f3+F/bSIiEgzuA0MkfaqliHggIAABAQElFtHLBZXatPTJUuWYMyYMRgxYgQAICIiArt378bq1asxc+bMKsVLREREpM/Utg1MdHQ0HBwc0LRpU4wfPx4ZGRll1i0sLERsbCz8/PxkZQYGBvDz80NMTIw6wiUiotd4tQPw5ZXMRFSzqWURSN++ffHOO+/A3d0dd+7cweeff46AgADExMTA0NBQoX56ejokEgkcHR3lyh0dHcu8t2ZBQQEKCgpkz7OysgAAxc8KKx2vUJU73SupyKhYqXaFRZV/fVUlFUmVapcvLVL6mgYi5T5YCnOUe3+kz/KVagcAkgIlf60KXl+lLIJyPz5Q8lsJVOFzXplvpaSw5PvBBKNm4XeDSHupJQH84IMPZF+3bt0abdq0QaNGjRAdHY3evXtXyzXCw8MRFhamUH7svdXVcn4i0rynT5/C2tpa02EQEWk9jWwD07BhQ9jZ2eH27dulJoB2dnYwNDREamqqXHlqamqZ8whDQ0MREhIie56ZmQk3NzckJSXxA6MU2dnZcHV1xb1791R6BwRtxfenbJp4bwRBwNOnT2W3TqOagWtAiLSXRhLA+/fvIyMjo8ybmpuYmMDLywtRUVEIDAwEAEilUkRFRSE4OLjUNmKxGGKxWKHc2tqaH+DlsLKy4vtTDr4/ZVP3e8N/5IiIqk+1LALJyclBXFwc4uLiAAAJCQmIi4tDUlIScnJyMG3aNJw6dQqJiYmIiorCoEGD4OHhAX9/f9k5evfujR9//FH2PCQkBL/88gvWrl2L69evY/z48cjNzZWtCiYiIs0ScR8YIq1VLT2A586dQ69evWTPnw/FDhs2DCtWrMClS5ewdu1aZGZmwsXFBX369MG8efPkeuzu3LmD9PR02fP3338fjx49wqxZs5CSkoK2bdti7969CgtDiIiIiKhyqiUB9PX1LXd13r59+157jsTERIWy4ODgMod8X0csFmP27NmlDgsT35/X4ftTNr439Bz7/4i0l0jgvgpERDopOzsb1tbWyMrKUsl8zQPXUjFm3TnZ8zkDWmB4V/dqvw6RvlPF77LaNoImIiIiopqBCSARESlF4U4gGomCiJTBBJCIiIhIzzABJCIipXAXGCLtpbMJ4PLly9GgQQOYmprC29sbZ86c0XRINcKcOXMgEonkHs2aNdN0WBpx9OhRDBgwAC4uLhCJRNixY4fccUEQMGvWLDg7O8PMzAx+fn64deuWZoLVgNe9P8OHD1f4Werbt69mgiUiokrRyQRw06ZNCAkJwezZs3H+/Hl4enrC398faWlpmg6tRmjZsiWSk5Nlj+PHj2s6JI3Izc2Fp6cnli9fXurxb7/9Ft9//z0iIiJw+vRpmJubw9/fH/n5+WqOVDNe9/4AQN++feV+lv744w81Rkiaxh5AIu2lkVvBqdqSJUswZswY2V1DIiIisHv3bqxevRozZ87UcHSaZ2RkVOY9lfVJQEAAAgICSj0mCAKWLVuGL7/8EoMGDQIArFu3Do6OjtixYwc++OADdYaqEeW9P8+JxWL+LOkxbiJGpL10rgewsLAQsbGx8PPzk5UZGBjAz88PMTExGoys5rh16xZcXFzQsGFDDB06FElJSZoOqcZJSEhASkqK3M+RtbU1vL29+XP0kujoaDg4OKBp06YYP348MjIyNB0SERFVgM4lgOnp6ZBIJAq3jHN0dERKSoqGoqo5vL29ERkZib1792LFihVISEhA9+7d8fTpU02HVqM8/1nhz1HZ+vbti3Xr1iEqKgoLFy7EkSNHEBAQAIlEounQSE04BEykvXRyCJjK9vKQXps2beDt7Q03Nzds3rwZo0aN0mBkpG1eHgZv3bo12rRpg0aNGiE6Ohq9e/fWYGSkKRwSJtIeOtcDaGdnB0NDQ6SmpsqVp6amcq5SKWrXro0mTZrg9u3bmg6lRnn+s8Kfo4pr2LAh7Ozs+LOkR0S8GzCR1tK5BNDExAReXl6IioqSlUmlUkRFRcHHx0eDkdVMOTk5uHPnDpydnTUdSo3i7u4OJycnuZ+j7OxsnD59mj9HZbh//z4yMjL4s0REpAV0cgg4JCQEw4YNQ4cOHdCpUycsW7YMubm5slXB+mzq1KkYMGAA3Nzc8PDhQ8yePRuGhoYYMmSIpkNTu5ycHLneqoSEBMTFxcHW1hb169fH5MmTMX/+fDRu3Bju7u746quv4OLigsDAQM0FrUblvT+2trYICwvDu+++CycnJ9y5cwfTp0+Hh4cH/P39NRg1qRU7AIm0lk4mgO+//z4ePXqEWbNmISUlBW3btsXevXsVJvTro/v372PIkCHIyMiAvb09unXrhlOnTsHe3l7ToanduXPn0KtXL9nzkJAQAMCwYcMQGRmJ6dOnIzc3F2PHjkVmZia6deuGvXv3wtTUVFMhq1V578+KFStw6dIlrF27FpmZmXBxcUGfPn0wb948iMViTYVM6sY5f0RaSyQInLZLRKSLsrOzYW1tjaysLFhZWVX7+Q/fSMOIyLOy57PeaoGR3dyr/TpE+k4Vv8s6NweQiIjUhEPARFqLCSARERGRnmECSERESmEHIJH2YgJIRKRiCxYsQJcuXVCrVi3Url27Qm0EQcCsWbPg7OwMMzMz+Pn54datW6oNlIj0BhNAIiIVKywsxODBgzF+/PgKt/n222/x/fffIyIiAqdPn4a5uTn8/f2Rn5+vwkgrR/TKveC4opBIe+jkNjBERDVJWFgYACAyMrJC9QVBwLJly/Dll19i0KBBAIB169bB0dERO3bskLsNnyZxEwki7cUeQCKiGiYhIQEpKSnw8/OTlVlbW8Pb2xsxMTEajIyIdAUTQD3l6+uLyZMny543aNAAy5Yt01g8qjJnzhyIRCKIRKJKvb7ExESIRCLExcVVWyyVeY99fX1lcVdnDKQdUlJSAEBh83pHR0fZsdIUFBQgOztb7qFKrw4BE5H2YAJIAICzZ89i7NixFaqrbcliy5YtkZycXOHXBwCurq5ITk5Gq1atVBhZ2bZt24YzZ85o5NpUMTNnzpQl6WU9bty4odaYwsPDYW1tLXu4urqq9fpEpD04B5AAQKdvBWdkZAQnJ6dKtTE0NKx0m+pka2ur8t4bqpopU6Zg+PDh5dZp2LChUud+/rOXmpoKZ2dnWXlqairatm1bZrvQ0FDZLfuAkrsHqDIJZP8fkfZiD6AeyM3NRVBQECwsLODs7IzFixcr1Hm5V08QBMyZMwf169eHWCyGi4sLJk6cCKBkaPLu3bv47LPPZL0cAJCRkYEhQ4agbt26qFWrFlq3bo0//vhD7hq+vr6YOHEipk+fDltbWzg5OWHOnDlydTIzM/Hxxx/D0dERpqamaNWqFXbt2iU7fvz4cXTv3h1mZmZwdXXFxIkTkZubW+n3RCQSYcWKFQgICICZmRkaNmyIrVu3yo6/OgQ8d+5cuLi4ICMjQ1anf//+6NWrF6RSaaVjK+89Ju1gb2+PZs2alfswMTFR6tzu7u5wcnJCVFSUrCw7OxunT5+Gj49Pme3EYjGsrKzkHkREpWECqAemTZuGI0eOYOfOndi/fz+io6Nx/vz5Muv/+eefWLp0KX7++WfcunULO3bsQOvWrQGUDE3Wq1cPc+fORXJyMpKTkwEA+fn58PLywu7du3HlyhWMHTsWH330kcIw5tq1a2Fubo7Tp0/j22+/xdy5c3HgwAEAgFQqRUBAAE6cOIHff/8d165dwzfffANDQ0MAwJ07d9C3b1+8++67uHTpEjZt2oTjx48jODhYqfflq6++wrvvvouLFy9i6NCh+OCDD3D9+vVS637xxRdo0KABRo8eDQBYvnw5Tp48ibVr18LAwKDSsZX3HpPuSUpKQlxcHJKSkiCRSBAXF4e4uDjk5OTI6jRr1gzbt28HUPIPyuTJkzF//nz89ddfuHz5MoKCguDi4oLAwEANvQpFnAJIpMUE0mlPnz4VTExMhM2bN8vKMjIyBDMzM2HSpEmyMjc3N2Hp0qWCIAjC4sWLhSZNmgiFhYWlnvPluuXp37+/MGXKFNnznj17Ct26dZOr07FjR2HGjBmCIAjCvn37BAMDAyE+Pr7U840aNUoYO3asXNmxY8cEAwMDIS8vr9Q2s2fPFjw9PRXKAQjjxo2TK/P29hbGjx8vCIIgJCQkCACECxcuyI7fuXNHsLS0FGbMmCGYmZkJ69evr1RslXmPy4qBtNOwYcMElGyTJ/c4fPiwrA4AYc2aNbLnUqlU+OqrrwRHR0dBLBYLvXv3LvN3oyxZWVkCACErK6uaXom8ozfTBLcZu2SPVcf+Vcl1iPSdKn6X2QOo4+7cuYPCwkJ4e3vLymxtbdG0adMy2wwePBh5eXlo2LAhxowZg+3bt6O4uLjc60gkEsybNw+tW7eGra0tLCwssG/fPiQlJcnVa9OmjdxzZ2dnpKWlAQDi4uJQr149NGnSpNRrXLx4EZGRkbCwsJA9/P39IZVKkZCQUG58pXl1KM3Hx6fMHkCgZD7X//73PyxcuBADBw7Ef//7X6VjU+Y9Ju0VGRkJQRAUHr6+vrI6giDIzSkUiUSYO3cuUlJSkJ+fj4MHD5b5u6Ep3AaQSHsxASQFrq6uiI+Px08//QQzMzN88skn6NGjB4qKispss2jRInz33XeYMWMGDh8+jLi4OPj7+6OwsFCunrGxsdxzkUgkm0NnZmZWblw5OTn4+OOPZcNncXFxuHjxIm7duoVGjRop+Wor5+jRozA0NERiYqJcwlbZ2JR5j4lqmlfzP4EZIZHWYAKo4xo1agRjY2OcPn1aVvbkyRPcvHmz3HZmZmYYMGAAvv/+e0RHRyMmJgaXL18GAJiYmEAikcjVP3HiBAYNGoQPP/wQnp6eaNiw4Wuv8ao2bdrg/v37ZbZr3749rl27Bg8PD4WHMpPtT506pfC8efPmZdbftGkTtm3bhujoaCQlJWHevHlViq2895iIiEiVuA2MjrOwsMCoUaMwbdo01KlTBw4ODvjiiy9gYFB27h8ZGQmJRAJvb2/UqlULv//+O8zMzODm5gagZMXw0aNH8cEHH0AsFsPOzg6NGzfG1q1bcfLkSdjY2GDJkiVITU1FixYtKhxrz5490aNHD7z77rtYsmQJPDw8cOPGDYhEIvTt2xczZsxA586dERwcjNGjR8Pc3BzXrl3DgQMH8OOPP1b6vdmyZQs6dOiAbt26Yf369Thz5gx+/fXXUuvev38f48ePx8KFC9GtWzesWbMGb731FgICAtC5c+dKx/a695iIiEiV2AOoBxYtWoTu3btjwIAB8PPzQ7du3eDl5VVm/dq1a+OXX35B165d0aZNGxw8eBB///036tSpA6BkS5TExEQ0atRItn/gl19+ifbt28Pf3x++vr5wcnJSarXin3/+iY4dO2LIkCFo0aIFpk+fLuttbNOmDY4cOYKbN2+ie/fuaNeuHWbNmgUXF5fKvykouT/rxo0b0aZNG6xbtw5//PFHqQnr87lZnTp1kq3q9ff3x/jx4/Hhhx8iJyen0rG97j0mIiJSJZHASRukw+bMmYMdO3Yo3E5NJBJh+/btNWpLjVclJibC3d0dFy5cKHfzX6KyZGdnw9raGllZWSrZEzA6Pg3D15yVPf+yf3OM7q7c5tdEVDZV/C6zB5B03uXLl2FhYYGffvpJ06FUWEBAAFq2bKnpMIiISEdxDiDptIkTJ+LDDz8EoF23u1u1ahXy8vIAAPXr19dwNESl4/ARkfZiAkg6zdbWFra2tgrlNX3mQ926dTUdAtHr1exfIyIqB4eAiYioWliI2adApC2YABIRUZW92cIR77Svp+kwiKiC+O8aERFV2S9BHTQdAhFVAnsAiYhIKQInARJpLSaARERERHqGCSARESmlhi+mJ6JyMAEkIiKlMAEk0l5MAImIiIj0DBNAIiIiIj3DBJCIiIhIzzABJCIipXAKIJH2YgJIREREpGeYABIRkVIELgMm0lpMAImISClM/4i0FxNAIiIiIj3DBJCIiIhIzzABJCIiItIzTACJiEgpXANCpL2YABIRERHpGSaARESkJHYBEmkrJoBEREREeoYJIBEREZGeYQJIRERK4SIQIu3FBJCIiJTi6Vpb0yEQkZKMNB0AERFpJ5faZjgyzRdWpsaaDoWIKok9gEREKrZgwQJ06dIFtWrVQu3atSvUZvjw4RCJRHKPvn37qjZQJbjVMYeNuYmmwyCiSmIPIBGRihUWFmLw4MHw8fHBr7/+WuF2ffv2xZo1a2TPxWKxKsIjIj3EBJCISMXCwsIAAJGRkZVqJxaL4eTkpIKIiEjfcQiYiKiGio6OhoODA5o2bYrx48cjIyOj3PoFBQXIzs6WexARlYYJIBFRDdS3b1+sW7cOUVFRWLhwIY4cOYKAgABIJJIy24SHh8Pa2lr2cHV1VWPERKRNmAASESlh5syZCos0Xn3cuHFD6fN/8MEHGDhwIFq3bo3AwEDs2rULZ8+eRXR0dJltQkNDkZWVJXvcu3dP6esTkW7jHEAiIiVMmTIFw4cPL7dOw4YNq+16DRs2hJ2dHW7fvo3evXuXWkcsFnOhCBFVCBNAIiIl2Nvbw97eXm3Xu3//PjIyMuDs7Ky2axKR7uIQMBGRiiUlJSEuLg5JSUmQSCSIi4tDXFwccnJyZHWaNWuG7du3AwBycnIwbdo0nDp1ComJiYiKisKgQYPg4eEBf39/Tb0MItIh7AEkIlKxWbNmYe3atbLn7dq1AwAcPnwYvr6+AID4+HhkZWUBAAwNDXHp0iWsXbsWmZmZcHFxQZ8+fTBv3jwO8RJRtRAJAm/nTUSki7Kzs2FtbY2srCxYWVlpOhwiUpIqfpc5BExERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGe4SpgIiId9XyNH+8JTKTdnv8OV+e6XSaAREQ66unTpwDAewIT6YinT5/C2tq6Ws7FbWCIiHSUVCrFw4cPYWlpCZFIpLLrZGdnw9XVFffu3eN2M6/ge1M2vjdle/W9EQQBT58+hYuLCwwMqmf2HnsAiYh0lIGBAerVq6e261lZWfGDvAx8b8rG96ZsL7831dXz9xwXgRARERHpGSaARERERHqGCSAREVWJWCzG7NmzeZ/iUvC9KRvfm7Kp473hIhAiIiIiPcMeQCIiIiI9wwSQiIiISM8wASQiIiLSM0wAiYhIacuXL0eDBg1gamoKb29vnDlzRtMhqV14eDg6duwIS0tLODg4IDAwEPHx8XJ18vPzMWHCBNSpUwcWFhZ49913kZqaqqGINeebb76BSCTC5MmTZWX6/N48ePAAH374IerUqQMzMzO0bt0a586dkx0XBAGzZs2Cs7MzzMzM4Ofnh1u3blXLtZkAEhGRUjZt2oSQkBDMnj0b58+fh6enJ/z9/ZGWlqbp0NTqyJEjmDBhAk6dOoUDBw6gqKgIffr0QW5urqzOZ599hr///htbtmzBkSNH8PDhQ7zzzjsajFr9zp49i59//hlt2rSRK9fX9+bJkyfo2rUrjI2N8c8//+DatWtYvHgxbGxsZHW+/fZbfP/994iIiMDp06dhbm4Of39/5OfnVz0AgYiISAmdOnUSJkyYIHsukUgEFxcXITw8XINRaV5aWpoAQDhy5IggCIKQmZkpGBsbC1u2bJHVuX79ugBAiImJ0VSYavX06VOhcePGwoEDB4SePXsKkyZNEgRBv9+bGTNmCN26dSvzuFQqFZycnIRFixbJyjIzMwWxWCz88ccfVb4+ewCJiKjSCgsLERsbCz8/P1mZgYEB/Pz8EBMTo8HINC8rKwsAYGtrCwCIjY1FUVGR3HvVrFkz1K9fX2/eqwkTJqB///5y7wGg3+/NX3/9hQ4dOmDw4MFwcHBAu3bt8Msvv8iOJyQkICUlRe69sba2hre3d7W8N0wAiYio0tLT0yGRSODo6ChX7ujoiJSUFA1FpXlSqRSTJ09G165d0apVKwBASkoKTExMULt2bbm6+vJebdy4EefPn0d4eLjCMX1+b/7991+sWLECjRs3xr59+zB+/HhMnDgRa9euBQDZ61fV75hRlc9AREREAEp6uq5cuYLjx49rOpQa4d69e5g0aRIOHDgAU1NTTYdTo0ilUnTo0AFff/01AKBdu3a4cuUKIiIiMGzYMJVfnz2ARERUaXZ2djA0NFRYrZmamgonJycNRaVZwcHB2LVrFw4fPox69erJyp2cnFBYWIjMzEy5+vrwXsXGxiItLQ3t27eHkZERjIyMcOTIEXz//fcwMjKCo6Oj3r43zs7OaNGihVxZ8+bNkZSUBACy16+q3zEmgEREVGkmJibw8vJCVFSUrEwqlSIqKgo+Pj4ajEz9BEFAcHAwtm/fjkOHDsHd3V3uuJeXF4yNjeXeq/j4eCQlJen8e9W7d29cvnwZcXFxskeHDh0wdOhQ2df6+t507dpVYbugmzdvws3NDQDg7u4OJycnufcmOzsbp0+frpb3hkPARESklJCQEAwbNgwdOnRAp06dsGzZMuTm5mLEiBGaDk2tJkyYgA0bNmDnzp2wtLSUzc+ytraGmZkZrK2tMWrUKISEhMDW1hZWVlb49NNP4ePjg86dO2s4etWytLSUzYV8ztzcHHXq1JGV6+t789lnn6FLly74+uuv8d577+HMmTNYuXIlVq5cCQCy/RLnz5+Pxo0bw93dHV999RVcXFwQGBhY9QCqvI6YiIj01g8//CDUr19fMDExETp16iScOnVK0yGpHYBSH2vWrJHVycvLEz755BPBxsZGqFWrlvD2228LycnJmgtag17eBkYQ9Pu9+fvvv4VWrVoJYrFYaNasmbBy5Uq541KpVPjqq68ER0dHQSwWC7179xbi4+Or5doiQRCEqqeRRERERKQtOAeQiIiISM8wASQiIiLSM0wAiYiIiPQME0AiIiIiPcMEkIiIiEjPMAEkIiIi0jNMAImIiIj0DBNAIiIiIj3DBJCIiEgL+fr6YvLkyRq7fkZGBhwcHJCYmFit57127Rrq1auH3Nzcaj0vyWMCSERERJW2YMECDBo0CA0aNKhQ/QEDBqBv376lHjt27BhEIhEuXbqEFi1aoHPnzliyZEk1RkuvYgJIRESkIkVFRZoOQSWePXuGX3/9FaNGjapwm1GjRuHAgQO4f/++wrE1a9agQ4cOaNOmDQBgxIgRWLFiBYqLi6stZpLHBJCIiHSeVCpFeHg43N3dYWZmBk9PT2zdulV2PDo6GiKRCFFRUejQoQNq1aqFLl26ID4+Xu48O3fuRPv27WFqaoqGDRsiLCxMLkkRiURYsWIFBg4cCHNzcyxYsAAAMH/+fDg4OMDS0hKjR4/GzJkz0bZtWwDA0aNHYWxsjJSUFLlrTZ48Gd27d6/wa3zy5AmCgoJgY2ODWrVqISAgALdu3ZIdv3v3LgYMGAAbGxuYm5ujZcuW2LNnj6zt0KFDYW9vDzMzMzRu3Bhr1qwp81p79uyBWCxG586d5cqvXLmCgIAAWFhYwNHRER999BHS09MBAG+99Rbs7e0RGRkp1yYnJwdbtmyRSybffPNNPH78GEeOHKnw66fKYQJIREQ6Lzw8HOvWrUNERASuXr2Kzz77DB9++KFCgvHFF19g8eLFOHfuHIyMjDBy5EjZsWPHjiEoKAiTJk3CtWvX8PPPPyMyMlKW5D03Z84cvP3227h8+TJGjhyJ9evXY8GCBVi4cCFiY2NRv359rFixQla/R48eaNiwIX777TdZWVFREdavXy93/dcZPnw4zp07h7/++gsxMTEQBAH9+vWT9UJOmDABBQUFOHr0KC5fvoyFCxfCwsICAPDVV1/h2rVr+Oeff3D9+nWsWLECdnZ2ZV7r2LFj8PLykivLzMzEG2+8gXbt2uHcuXPYu3cvUlNT8d577wEAjIyMEBQUhMjISAiCIGu3ZcsWSCQSDBkyRFZmYmKCtm3b4tixYxV+/VRJAhERkQ7Lz88XatWqJZw8eVKufNSoUcKQIUMEQRCEw4cPCwCEgwcPyo7v3r1bACDk5eUJgiAIvXv3Fr7++mu5c/z222+Cs7Oz7DkAYfLkyXJ1vL29hQkTJsiVde3aVfD09JQ9X7hwodC8eXPZ8z///FOwsLAQcnJyynxdPXv2FCZNmiQIgiDcvHlTACCcOHFCdjw9PV0wMzMTNm/eLAiCILRu3VqYM2dOqecaMGCAMGLEiDKv9apBgwYJI0eOlCubN2+e0KdPH7mye/fuCQCE+Ph4QRAE4fr16wIA4fDhw7I63bt3Fz788EOFa7z99tvC8OHDKxwTVQ57AImISKfdvn0bz549w5tvvgkLCwvZY926dbhz545c3edz0ADA2dkZAJCWlgYAuHjxIubOnSt3jjFjxiA5ORnPnj2TtevQoYPcOePj49GpUye5slefDx8+HLdv38apU6cAAJGRkXjvvfdgbm6OY8eOyV1z/fr1Cq/x+vXrMDIygre3t6ysTp06aNq0Ka5fvw4AmDhxIubPn4+uXbti9uzZuHTpkqzu+PHjsXHjRrRt2xbTp0/HyZMny31P8/LyYGpqKld28eJFHD58WC7WZs2aAYDsfW7WrBm6dOmC1atXAyj53hw7dqzUuYRmZmZy7ytVLyNNB0BERKRKOTk5AIDdu3ejbt26csfEYrHcc2NjY9nXIpEIQMn8wefnCQsLwzvvvKNwjZeTIXNz80rH6ODggAEDBmDNmjVwd3fHP//8g+joaAAlCWVcXJysrqOjY6XPDwCjR4+Gv78/du/ejf379yM8PByLFy/Gp59+ioCAANy9exd79uzBgQMH0Lt3b0yYMAH/+9//Sj2XnZ0dnjx5IleWk5ODAQMGYOHChQr1nyfTQMlikE8//RTLly/HmjVr0KhRI/Ts2VOhzePHj9GoUSOlXiu9HnsAiYhIp7Vo0QJisRhJSUnw8PCQe7i6ulb4PO3bt0d8fLzCOTw8PGBgUPbHadOmTXH27Fm5slefAyUJ2qZNm7By5Uo0atQIXbt2BVDSE/bytSwtLRXaNm/eHMXFxTh9+rSsLCMjA/Hx8WjRooWszNXVFePGjcO2bdswZcoU/PLLL7Jj9vb2GDZsGH7//XcsW7YMK1euLPM1tWvXDteuXVN4f65evYoGDRoovD8vJ8XvvfceDAwMsGHDBqxbtw4jR46UJdsvu3LlCtq1a1dmDFQ17AEkIiKdZmlpialTp+Kzzz6DVCpFt27dkJWVhRMnTsDKygrDhg2r0HlmzZqFt956C/Xr18d//vMfGBgY4OLFi7hy5Qrmz59fZrtPP/0UY8aMQYcOHdClSxds2rQJly5dQsOGDeXq+fv7w8rKCvPnz8fcuXMr9RobN26MQYMGYcyYMfj5559haWmJmTNnom7duhg0aBCAklXFAQEBaNKkCZ48eYLDhw+jefPmstfm5eWFli1boqCgALt27ZIdK42/vz9CQ0Px5MkT2NjYAChZZPLLL79gyJAhmD59OmxtbXH79m1s3LgRq1atgqGhIQDAwsIC77//PkJDQ5GdnY3hw4crnD8xMREPHjyAn59fpd4Hqjj2ABIRkc6bN28evvrqK4SHh6N58+bo27cvdu/eDXd39wqfw9/fH7t27cL+/fvRsWNHdO7cGUuXLoWbm1u57YYOHYrQ0FBMnToV7du3R0JCAoYPH64wh87AwADDhw+HRCJBUFBQpV/jmjVr4OXlhbfeegs+Pj4QBAF79uyRDWtLJBJMmDBB9vqbNGmCn376CUDJqtvQ0FC0adMGPXr0gKGhITZu3FjmtVq3bo327dtj8+bNsjIXFxecOHECEokEffr0QevWrTF58mTUrl1boYd01KhRePLkCfz9/eHi4qJw/j/++AN9+vR57XtLyhMJwktrsYmIiEjl3nzzTTg5Oclt/QKUJEaPHj3CX3/9paHIKm737t2YNm0arly5Uu4QeGUVFhaicePG2LBhg2wYnKofh4CJiIhU6NmzZ4iIiIC/vz8MDQ3xxx9/4ODBgzhw4ICsTlZWFi5fvowNGzZoRfIHAP3798etW7fw4MGDSs2lfJ2kpCR8/vnnTP5UjD2AREREKpSXl4cBAwbgwoULyM/PR9OmTfHll1/KrSb29fXFmTNn8PHHH2Pp0qUajJb0BRNAIiIiIj3DRSBEREREeoYJIBEREZGeYQJIREREpGeYABIRERHpGSaARERERHqGCSARERGRnmECSERERKRnmAASERER6RkmgERERER6hgkgERERkZ5hAkhERESkZ5gAEhEREekZJoBEREREeoYJIBEREZGe+T/P8F9qaqXWwQAAAABJRU5ErkJggg==' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shifted_resolution_functions = eels_tools.get_resolution_functions(shifted_dataset, zero_loss_fit_width=.9)\n",
+    "dif = shifted_dataset - shifted_resolution_functions\n",
+    "view = dif.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\jupyter_client\\session.py:719: UserWarning: Message serialization failed with:\n",
+      "Out of range float values are not JSON compliant\n",
+      "Supporting this message is deprecated in jupyter-client 7, please make sure your message is JSON-compliant\n",
+      "  content = self.pack(content)\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "array of sample points is empty",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[8], line 5\u001b[0m\n\u001b[0;32m      1\u001b[0m datasets \u001b[39m=\u001b[39m fileWidget\u001b[39m.\u001b[39mdatasets\n\u001b[0;32m      2\u001b[0m \u001b[39m#datasets['energy_corrected'] = shifted_dataset\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[39m#datasets['energy_corrected_resolution_function'] = shifted_resolution_functions\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m infoWidget\u001b[39m=\u001b[39m interactive_eels\u001b[39m.\u001b[39;49mInfoWidget(datasets)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\EELS\\../..\\pyTEMlib\\info_dialog.py:428\u001b[0m, in \u001b[0;36mInfoWidget.__init__\u001b[1;34m(self, datasets)\u001b[0m\n\u001b[0;32m    423\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mtoolbar_visible \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m    426\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m--> 428\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_dataset()\n\u001b[0;32m    429\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_action()\n\u001b[0;32m    431\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstart_cursor \u001b[39m=\u001b[39m ipywidgets\u001b[39m.\u001b[39mFloatText(value\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, description\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mStart:\u001b[39m\u001b[39m'\u001b[39m, disabled\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblack\u001b[39m\u001b[39m'\u001b[39m, layout\u001b[39m=\u001b[39mipywidgets\u001b[39m.\u001b[39mLayout(width\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m200px\u001b[39m\u001b[39m'\u001b[39m))\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\EELS\\../..\\pyTEMlib\\info_dialog.py:527\u001b[0m, in \u001b[0;36mInfoWidget.set_dataset\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msidebar[\u001b[39m13\u001b[39m,\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mvalue \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdatasets[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey]\u001b[39m.\u001b[39mmetadata[\u001b[39m'\u001b[39m\u001b[39mexperiment\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mbeam_current\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m    526\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mclear()\n\u001b[1;32m--> 527\u001b[0m view \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset\u001b[39m.\u001b[39;49mplot(figure\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfigure)\n\u001b[0;32m    528\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39mview, \u001b[39m'\u001b[39m\u001b[39maxes\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[0;32m    529\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39mview\u001b[39m.\u001b[39maxes[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\sidpy\\sid\\dataset.py:556\u001b[0m, in \u001b[0;36mDataset.plot\u001b[1;34m(self, verbose, figure, **kwargs)\u001b[0m\n\u001b[0;32m    554\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mview \u001b[39m=\u001b[39m ComplexSpectralImageVisualizer(\u001b[39mself\u001b[39m, figure\u001b[39m=\u001b[39mfigure, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    555\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 556\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mview \u001b[39m=\u001b[39m SpectralImageVisualizer(\u001b[39mself\u001b[39;49m, figure\u001b[39m=\u001b[39;49mfigure, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m    557\u001b[0m     \u001b[39m# plt.show()\u001b[39;00m\n\u001b[0;32m    558\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    559\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mDatasets with data_type \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m cannot be plotted, yet.\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata_type))\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\sidpy\\viz\\dataset_viz.py:593\u001b[0m, in \u001b[0;36mSpectralImageVisualizer.__init__\u001b[1;34m(self, dset, figure, horizontal, **kwargs)\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39menergy_scale)\u001b[39m!=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mspectrum\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]:\n\u001b[0;32m    592\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mspectrum \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mspectrum\u001b[39m.\u001b[39mT\n\u001b[1;32m--> 593\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49maxes[\u001b[39m1\u001b[39;49m]\u001b[39m.\u001b[39;49mplot(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49menergy_scale, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mspectrum\u001b[39m.\u001b[39;49mcompute())\n\u001b[0;32m    594\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes[\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mset_title(\u001b[39m'\u001b[39m\u001b[39mspectrum \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mx, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39my))\n\u001b[0;32m    595\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mxlabel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdset\u001b[39m.\u001b[39mlabels[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mspec_dim]\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1690\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1688\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[0;32m   1689\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m-> 1690\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madd_line(line)\n\u001b[0;32m   1691\u001b[0m \u001b[39mif\u001b[39;00m scalex:\n\u001b[0;32m   1692\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_request_autoscale_view(\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2304\u001b[0m, in \u001b[0;36m_AxesBase.add_line\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m   2301\u001b[0m \u001b[39mif\u001b[39;00m line\u001b[39m.\u001b[39mget_clip_path() \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   2302\u001b[0m     line\u001b[39m.\u001b[39mset_clip_path(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpatch)\n\u001b[1;32m-> 2304\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_update_line_limits(line)\n\u001b[0;32m   2305\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m line\u001b[39m.\u001b[39mget_label():\n\u001b[0;32m   2306\u001b[0m     line\u001b[39m.\u001b[39mset_label(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m_child\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_children)\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2327\u001b[0m, in \u001b[0;36m_AxesBase._update_line_limits\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m   2323\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_update_line_limits\u001b[39m(\u001b[39mself\u001b[39m, line):\n\u001b[0;32m   2324\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   2325\u001b[0m \u001b[39m    Figures out the data limit of the given line, updating self.dataLim.\u001b[39;00m\n\u001b[0;32m   2326\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2327\u001b[0m     path \u001b[39m=\u001b[39m line\u001b[39m.\u001b[39;49mget_path()\n\u001b[0;32m   2328\u001b[0m     \u001b[39mif\u001b[39;00m path\u001b[39m.\u001b[39mvertices\u001b[39m.\u001b[39msize \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m   2329\u001b[0m         \u001b[39mreturn\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\lines.py:1029\u001b[0m, in \u001b[0;36mLine2D.get_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1027\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Return the `~matplotlib.path.Path` associated with this line.\"\"\"\u001b[39;00m\n\u001b[0;32m   1028\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_invalidy \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_invalidx:\n\u001b[1;32m-> 1029\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrecache()\n\u001b[0;32m   1030\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_path\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\lines.py:681\u001b[0m, in \u001b[0;36mLine2D.recache\u001b[1;34m(self, always)\u001b[0m\n\u001b[0;32m    679\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x_filled \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x\u001b[39m.\u001b[39mcopy()\n\u001b[0;32m    680\u001b[0m     indices \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39mlen\u001b[39m(x))\n\u001b[1;32m--> 681\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x_filled[nanmask] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49minterp(\n\u001b[0;32m    682\u001b[0m         indices[nanmask], indices[\u001b[39m~\u001b[39;49mnanmask], \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_x[\u001b[39m~\u001b[39;49mnanmask])\n\u001b[0;32m    683\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    684\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x_filled \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x\n",
+      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36minterp\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\numpy\\lib\\function_base.py:1595\u001b[0m, in \u001b[0;36minterp\u001b[1;34m(x, xp, fp, left, right, period)\u001b[0m\n\u001b[0;32m   1592\u001b[0m     xp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mconcatenate((xp[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m:]\u001b[39m-\u001b[39mperiod, xp, xp[\u001b[39m0\u001b[39m:\u001b[39m1\u001b[39m]\u001b[39m+\u001b[39mperiod))\n\u001b[0;32m   1593\u001b[0m     fp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mconcatenate((fp[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m:], fp, fp[\u001b[39m0\u001b[39m:\u001b[39m1\u001b[39m]))\n\u001b[1;32m-> 1595\u001b[0m \u001b[39mreturn\u001b[39;00m interp_func(x, xp, fp, left, right)\n",
+      "\u001b[1;31mValueError\u001b[0m: array of sample points is empty"
+     ]
+    }
+   ],
+   "source": [
+    "datasets = fileWidget.datasets\n",
+    "#datasets['energy_corrected'] = shifted_dataset\n",
+    "#datasets['energy_corrected_resolution_function'] = shifted_resolution_functions\n",
+    "\n",
+    "infoWidget= interactive_eels.InfoWidget(datasets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0970d602c87c472aa7363a33a620a2c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "                    4-eels_new_new_new_new\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "v = shifted_dataset.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "object of too small depth for desired array",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[40], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m FWHM, energy_shift \u001b[39m=\u001b[39m eels_tools\u001b[39m.\u001b[39;49mfix_energy_scale(eels_dataset[\u001b[39m0\u001b[39;49m,\u001b[39m0\u001b[39;49m])\n\u001b[0;32m      3\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mZero Loss with energy resolution of \u001b[39m\u001b[39m{\u001b[39;00mFWHM\u001b[39m:\u001b[39;00m\u001b[39m.2f\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m eV at position \u001b[39m\u001b[39m{\u001b[39;00menergy_shift\u001b[39m:\u001b[39;00m\u001b[39m.3f\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m eV\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      4\u001b[0m eels_dataset\u001b[39m.\u001b[39menergy_loss \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m energy_shift\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\EELS\\../..\\pyTEMlib\\eels_tools.py:1116\u001b[0m, in \u001b[0;36mfix_energy_scale\u001b[1;34m(spec, energy)\u001b[0m\n\u001b[0;32m   1113\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(spec, np\u001b[39m.\u001b[39mndarray):\n\u001b[0;32m   1114\u001b[0m         \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m-> 1116\u001b[0m start \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49msearchsorted(energy, \u001b[39m-\u001b[39;49m\u001b[39m10\u001b[39;49m)\n\u001b[0;32m   1117\u001b[0m end \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msearchsorted(energy, \u001b[39m10\u001b[39m)\n\u001b[0;32m   1118\u001b[0m startx \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margmax(spec[start:end]) \u001b[39m+\u001b[39m start\n",
+      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36msearchsorted\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:1413\u001b[0m, in \u001b[0;36msearchsorted\u001b[1;34m(a, v, side, sorter)\u001b[0m\n\u001b[0;32m   1345\u001b[0m \u001b[39m@array_function_dispatch\u001b[39m(_searchsorted_dispatcher)\n\u001b[0;32m   1346\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msearchsorted\u001b[39m(a, v, side\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mleft\u001b[39m\u001b[39m'\u001b[39m, sorter\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m   1347\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   1348\u001b[0m \u001b[39m    Find indices where elements should be inserted to maintain order.\u001b[39;00m\n\u001b[0;32m   1349\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1411\u001b[0m \n\u001b[0;32m   1412\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1413\u001b[0m     \u001b[39mreturn\u001b[39;00m _wrapfunc(a, \u001b[39m'\u001b[39;49m\u001b[39msearchsorted\u001b[39;49m\u001b[39m'\u001b[39;49m, v, side\u001b[39m=\u001b[39;49mside, sorter\u001b[39m=\u001b[39;49msorter)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:54\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     52\u001b[0m bound \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(obj, method, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m     53\u001b[0m \u001b[39mif\u001b[39;00m bound \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m---> 54\u001b[0m     \u001b[39mreturn\u001b[39;00m _wrapit(obj, method, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[0;32m     56\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     57\u001b[0m     \u001b[39mreturn\u001b[39;00m bound(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n",
+      "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:43\u001b[0m, in \u001b[0;36m_wrapit\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m:\n\u001b[0;32m     42\u001b[0m     wrap \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m---> 43\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39;49m(asarray(obj), method)(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[0;32m     44\u001b[0m \u001b[39mif\u001b[39;00m wrap:\n\u001b[0;32m     45\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(result, mu\u001b[39m.\u001b[39mndarray):\n",
+      "\u001b[1;31mValueError\u001b[0m: object of too small depth for desired array"
+     ]
+    }
+   ],
+   "source": [
+    "FWHM, energy_shift = eels_tools.fix_energy_scale(eels_dataset)\n",
+    "\n",
+    "print(f'Zero Loss with energy resolution of {FWHM:.2f} eV at position {energy_shift:.3f} eV')\n",
+    "eels_dataset.energy_loss -= energy_shift\n",
+    "\n",
+    "zero_loss, _ = eels_tools.resolution_function(eels_dataset.energy_loss, eels_dataset, .4)\n",
+    "print(zero_loss)\n",
+    "plt.figure()\n",
+    "plt.plot(eels_dataset.energy_loss, eels_dataset, label='spectrum')\n",
+    "plt.plot(eels_dataset.energy_loss, zero_loss, label = 'zero-loss')\n",
+    "plt.plot(eels_dataset.energy_loss, np.array(eels_dataset)-zero_loss , label = 'difference')\n",
+    "\n",
+    "plt.title ('Lorentzian Product Fit of Zero-Loss Peak')\n",
+    "#plt.xlim(-5,30)\n",
+    "plt.legend();\n",
+    "Izl = zero_loss.sum()\n",
+    "Itotal = np.array(eels_dataset).sum()\n",
+    "tmfp = np.log(Itotal/Izl)\n",
+    "print(f'Sum of Zero-Loss: {Izl:.3f} %')\n",
+    "print(f'Sum of Spectrum: {Itotal:.3f} %')\n",
+    "print (f'thickness [IMFP]: {tmfp:.5f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fitting a Drude Function to Plasmon\n",
+    "\n",
+    "The position and the width are important materials parameters and we can derive them by fitting the Drude function to the volume plasmon region.\n",
+    "### Drude Function\n",
+    "\n",
+    "Most of the inelastically scattered electron arise from interaction with outer shell electrons. These interactions, therefore, have a high intensity and are easy to obtain. \n",
+    "\n",
+    "The energy-loss function $F_{el}$ on the other hand is determined by the dielectric function $\\varepsilon$ through:\n",
+    "\n",
+    "$$\n",
+    "F_{el} = \\Im \\left[\\frac{-1}{\\varepsilon(\\omega)} \\right]\n",
+    "$$\n",
+    "\n",
+    "The dielectric function in the Drude theory is given by two input parameters the position of the plasmon energy $E_p$\n",
+    "and the width of the plasmon $\\Gamma$\n",
+    "\n",
+    "$$ ε(ω) = ε1 + iε2 = 1 + χ = 1 − \\frac{\\omega_p^2}{\\omega^2+\\Gamma^2} + \\frac{i\\Gamma \\omega_p^2}{\\omega(\\omega^2+\\Gamma^2)}$$\n",
+    "Here $\\omega$ is the angular frequency (rad/s) of forced oscillation and $\\omega_p$ is the natural or resonance frequency for plasma oscillation, given by\n",
+    "$$ ω_p = \\sqrt{\\frac{ne^2}{(ε_0m_0)}} $$\n",
+    "A transmitted electron represents a sudden impulse of applied electric field, containing\n",
+    "all angular frequencies (Fourier components). Setting up a plasma oscillation of the loosely bound outer-shell electrons in a solid is equivalent to creating a pseudoparticle of energy $E_p = \\hbar \\omega_p$, known as a plasmon (Pines, 1963)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'spec' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moptimize\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m leastsq, curve_fit\n\u001b[1;32m----> 2\u001b[0m eels_dataset \u001b[38;5;241m=\u001b[39m spec\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mDrude\u001b[39m(E,Ep,Ew, gamma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m      4\u001b[0m     eps \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m Ep\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m/\u001b[39m(E\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m+\u001b[39mEw\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39mj\u001b[38;5;241m*\u001b[39m Ew\u001b[38;5;241m*\u001b[39m Ep\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m/\u001b[39mE\u001b[38;5;241m/\u001b[39m(E\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m+\u001b[39mEw\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'spec' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.optimize import leastsq, curve_fit\n",
+    "eels_dataset = spec\n",
+    "def Drude(E,Ep,Ew, gamma=1):\n",
+    "    eps = 1 - Ep**2/(E**2+Ew**2) +1j* Ew* Ep**2/E/(E**2+Ew**2)\n",
+    "    eps = 1 - (Ep**2 - Ew * E * 1j) / (E**2 + 2 * E * gamma * 1j)  # Mod drude ter\n",
+    "    elf = (-1/eps).imag\n",
+    "    return eps,elf\n",
+    "\n",
+    "def errfDrude(p, y, x):\n",
+    "    eps,elf = Drude(x,p[0],p[1])\n",
+    "    err = y - p[2]*elf\n",
+    "    #print (p,sum(np.abs(err)))\n",
+    "    return np.abs(err)#/np.sqrt(y)\n",
+    "\n",
+    "\n",
+    "pin2 = np.array([9,1,.7, 1.11])\n",
+    "E = energy_scale = eels_dataset.energy_loss\n",
+    "startFit =np.argmin(abs(energy_scale-6))\n",
+    "endFit = np.argmin(abs(energy_scale-15))\n",
+    "    \n",
+    "p2, lsq = leastsq(errfDrude, pin2, args=(eels_dataset[startFit:endFit], energy_scale[startFit:endFit]), maxfev=2000)\n",
+    "\n",
+    "eps, elf =Drude(energy_scale,p2[0],p2[1],p2[3])\n",
+    "drudePSD = p2[2]* elf\n",
+    "plt.figure()\n",
+    "\n",
+    "plt.plot(energy_scale,eels_dataset)\n",
+    "plt.plot(energy_scale,drudePSD)\n",
+    "plt.plot(energy_scale,eels_dataset-drudePSD)\n",
+    "plt.axhline(0, color='black')\n",
+    "\n",
+    "#plt.gca().set_xlim(0,40)\n",
+    "#plt.gca().set_ylim(-0.01,0.2)\n",
+    "print(f\"Drude Theory with Plamson Energy: {p2[0]:2f} eV and plasmon Width {p2[1]:.2f} eV\") \n",
+    "print(f\"Max of Plasmon at {energy_scale[drudePSD.argmax(0)]:.2f} eV\")\n",
+    "print(f\"Amplitude of  {p2[2]:.2f} was deteremined by fit \")\n",
+    "p2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": false,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:3: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:4: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:3: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "<>:4: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_17524\\467939617.py:3: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "  plt.plot(energy_scale,eps.real,label = 'Re($\\epsilon)$')\n",
+      "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_17524\\467939617.py:4: SyntaxWarning: invalid escape sequence '\\e'\n",
+      "  plt.plot(energy_scale,eps.imag,label = 'Im($\\epsilon)$')\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d236541265e430fa5408488c5cfbf4b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.title ('Drude Fit: Dielectric Function - Permittivity')\n",
+    "plt.plot(energy_scale,eps.real,label = 'Re($\\epsilon)$')\n",
+    "plt.plot(energy_scale,eps.imag,label = 'Im($\\epsilon)$')\n",
+    "plt.plot(energy_scale,drudePSD,label = 'loss-function$_{Drude}$')\n",
+    "plt.plot(energy_scale,eels_dataset,label = 'loss-function$_{exp}$')\n",
+    "plt.axhline(0, color='black')\n",
+    "\n",
+    "plt.gca().set_xlim(0,40)\n",
+    "plt.gca().set_ylim(-2.5,5.3)\n",
+    "\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "173\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1601c417250>]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cc20b277e93457c962dd8f5cd789562",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tmfp = .3\n",
+    "zl2 = zero_loss\n",
+    "        \n",
+    "LLene = np.arange(1,2048)/5\n",
+    "\n",
+    "zero_loss, _ = eels_tools.resolution_function(eels_dataset.energy_loss, eels_dataset, .4)\n",
+    "\n",
+    "ZP = np.array(zl2).argmax(0) # zl peak position \n",
+    "print(ZP)\n",
+    "\n",
+    "_, SSD = Drude(LLene,15, 2 )\n",
+    "PSD = np.zeros(len(LLene))\n",
+    "FAC = 1.\n",
+    "\n",
+    "ssd  = np.fft.fft(SSD)\n",
+    "ssd2 = ssd.copy()\n",
+    "SSD2 = SSD.copy()\n",
+    "for order in range(20):\n",
+    "    # This order convoluted spectum \n",
+    "    PPSD = np.zeros(len(LLene))\n",
+    "    # convoluted SSD is SSD2\n",
+    "    SSD2 = np.fft.ifft(ssd).real\n",
+    "\n",
+    "    # scale right (could be done better? GERD)\n",
+    "    mult = sum(SSD)/sum(SSD2)\n",
+    "\n",
+    "    SSD2 *= mult.real/np.exp(-tmfp)\n",
+    "    EP = np.array(SSD2).argmax(0)\n",
+    "    PPSD = SSD2/FAC*np.power(tmfp,(order))*np.exp(-tmfp)*1e12\n",
+    "    # Add this order t0 final spectrum\n",
+    "    PSD += PPSD\n",
+    "    # Get next order factor\n",
+    "    FAC=FAC*(order+2.)\n",
+    "\n",
+    "    # convolute next order\n",
+    "    ssd = ssd * ssd2\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "            \n",
+    "plt.figure()\n",
+    "plt.plot(SSD/SSD.max())\n",
+    "plt.plot(PSD/PSD.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "229957.93374337783 45464262.67521657\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1ffaea9a120>"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f35ed77d2a7a4b7ebb0bc8705a557d02",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.interpolate import splev,splrep,splint\n",
+    "def MakeDrudeVL(dataset, tmfp, zero_loss, Ep, Ew, Eh):\n",
+    "        energy_scale = np.arange(1,2049)/5\n",
+    "        \n",
+    "        ZP = np.array(zero_loss).argmax(0) # zl peak position \n",
+    "        _, PSD = Drude(energy_scale,Ep, Ew )\n",
+    "        PSD /= PSD.sum()\n",
+    "        \n",
+    "        FAC = 1.\n",
+    "\n",
+    "        ### sum contribution from each order of scattering:\n",
+    "        ssd  = np.fft.fft(PSD)\n",
+    "        \n",
+    "        SSD_sum = PSD.sum()\n",
+    "        ssd2 = ssd.copy()\n",
+    "        PSD /= FAC*np.power(tmfp,(0))*np.exp(-tmfp)\n",
+    "        for order in range(1,15):\n",
+    "            FAC=FAC*(order+2.)\n",
+    "            ssd *= ssd2   \n",
+    "            # convoluted SSD is SSD2\n",
+    "            SSD2 = np.fft.ifft(ssd).real\n",
+    "\n",
+    "            # scale right (could be done better? GERD)\n",
+    "            mult = SSD_sum/sum(SSD2)\n",
+    "\n",
+    "            SSD2 *= mult.real/np.exp(-tmfp)\n",
+    "            EP = np.array(SSD2).argmax(0)\n",
+    "            PPSD = SSD2/FAC*np.power(tmfp,(order))*np.exp(-tmfp)\n",
+    "            # Add this order t0 final spectrum\n",
+    "            PSD += PPSD\n",
+    "            \n",
+    "            # convolute next order\n",
+    "        dispersion = dataset.energy_loss[1] - dataset.energy_loss[0]\n",
+    "        \n",
+    "        factorZL =  Eh/dispersion*2 #, #zero_loss.sum()*(np.exp(tmfp)-1)/PSD.sum()*Eh/dispersion/4\n",
+    "        #print(factorZL, Eh)\n",
+    "        \n",
+    "        BGDcoef = splrep(energy_scale,PSD,s=0)\n",
+    "        dispersion = dataset.energy_loss[1] - dataset.energy_loss[0]\n",
+    "        cts =splev( dataset.energy_loss, BGDcoef)*factorZL #*p[1]\n",
+    "        \n",
+    "        #cts += zero_loss\n",
+    "        \n",
+    "        return cts\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "#zero_loss, _ = eels_tools.resolution_function(eels_dataset.energy_loss, eels_dataset, .4)\n",
+    "#Izl = zero_loss.sum()\n",
+    "#Itotal = np.array(eels_dataset).sum()\n",
+    "#tmfp = np.log(Itotal/Izl)\n",
+    "tmfp = 0.3\n",
+    "zero_loss = infoWidget.datasets['resolution_function']\n",
+    "\n",
+    "LL = MakeDrudeVL(spec, tmfp, zero_loss, p0[0]-5,.5, p0[2])\n",
+    "print(LL.max(), LL.sum())\n",
+    "plt.figure()\n",
+    "plt.plot(LL, label='Multi')\n",
+    "plt.plot(eels_dataset, label='spec')\n",
+    "plt.plot(eels_dataset-LL, label='dif')\n",
+    "plt.legend()\n",
+    "#plt.ylim(-.1,.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_17524\\2622750204.py:19: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  plt.legend()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1ffb3faff80>"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b294f37f8f54034a83eb1d33455d968",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "def errfDrude(p, y):\n",
+    "    LL = MakeDrudeVL(y, p[3], zero_loss, p[0],p[1],p[2])\n",
+    "    err = y - LL\n",
+    "    #print (p,sum(np.abs(err)))\n",
+    "    return np.abs(err)#/np.sqrt(y)\n",
+    "\n",
+    "\n",
+    "pin2 = np.array([15,1,.7, 0.3])\n",
+    "E = energy_scale = eels_dataset.energy_loss\n",
+    "startFit =np.argmin(abs(energy_scale-13))\n",
+    "endFit = np.argmin(abs(energy_scale-18))\n",
+    "    \n",
+    "p2, lsq = leastsq(errfDrude, pin2, args=(eels_dataset), maxfev=2000)\n",
+    "\n",
+    "LL = MakeDrudeVL(eels_dataset, p2[3], zero_loss, p2[0],p2[1],p2[2])\n",
+    "plt.figure()\n",
+    "plt.plot(LL)\n",
+    "plt.plot(eels_dataset)\n",
+    "plt.legend()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8326588466281821"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p2[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7408182206817179"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.power(0.3,(0))*np.exp(-.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(1.68808473)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero_loss.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def newDrudeBgd(x, p):\n",
+    "    tmfp = 500  #p[3]\n",
+    "    startB = x[0]\n",
+    "    endB = x[-1]\n",
+    "    p = np.abs(p)\n",
+    "\n",
+    "\n",
+    "    LLene = np.linspace(1, 2047,2048)\n",
+    "    eps = pyTEMlib.eels_tools.drude(LLene,p[0], p[1], p[2])\n",
+    "    SSD = (-1/eps).imag\n",
+    "    ssd  = np.fft.fft(SSD)\n",
+    "\n",
+    "    ssd2 = ssd.copy()\n",
+    "    SSD2 = SSD.copy()\n",
+    "\n",
+    "    ### sum contribution from each order of scattering:\n",
+    "    PSD = np.zeros(len(LLene))\n",
+    "    for order in range(1):\n",
+    "        # This order convoluted spectum \n",
+    "        PPSD = np.zeros(len(LLene))\n",
+    "        # convoluted SSD is SSD2\n",
+    "        SSD2 = np.fft.ifft(ssd).real\n",
+    "\n",
+    "        # scale right (could be done better? GERD) \n",
+    "        print( sum(SSD)/sum(SSD2))\n",
+    "        mult = sum(SSD)/sum(SSD2)\n",
+    "        SSD2 *= abs(mult)\n",
+    "\n",
+    "        PPSD = SSD2/scipy.special.factorial(order+1)*np.power(tmfp,(order+1))*np.exp(-tmfp) #using equation 4.1 of egerton ed2\n",
+    "       # Add this order to final spectrum\n",
+    "        PSD += PPSD\n",
+    "\n",
+    "        # next order convolution\n",
+    "        ssd = ssd * ssd2\n",
+    "\n",
+    "\n",
+    "    cts = np.zeros(len(x))\n",
+    "\n",
+    "    if startB < 0:\n",
+    "        startB = 0\n",
+    "    BGDcoef = scipy.interpolate.splrep(LLene[int(startB):int(endB)],PSD[int(startB):int(endB)],s=0)\n",
+    "\n",
+    "\n",
+    "    lin = np.zeros(len(x))\n",
+    "\n",
+    "    cts = scipy.interpolate.splev( x, BGDcoef)*p[1]\n",
+    "    return cts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'p0' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[7], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m LLene \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2047\u001b[39m,\u001b[38;5;241m2048\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m p \u001b[38;5;241m=\u001b[39m p0\n\u001b[0;32m      3\u001b[0m eps \u001b[38;5;241m=\u001b[39m pyTEMlib\u001b[38;5;241m.\u001b[39meels_tools\u001b[38;5;241m.\u001b[39mdrude(LLene,p[\u001b[38;5;241m0\u001b[39m], p[\u001b[38;5;241m1\u001b[39m], p[\u001b[38;5;241m2\u001b[39m])\n\u001b[0;32m      4\u001b[0m SSD \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39meps)\u001b[38;5;241m.\u001b[39mimag\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'p0' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "LLene = np.linspace(1, 2047,2048)\n",
+    "p = p0\n",
+    "eps = pyTEMlib.eels_tools.drude(LLene,p[0], p[1], p[2])\n",
+    "SSD = (-1/eps).imag\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(SSD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'spec' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m newDrudeBgd( np\u001b[38;5;241m.\u001b[39marray(spec\u001b[38;5;241m.\u001b[39menergy_loss[\u001b[38;5;241m200\u001b[39m:]), p0)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'spec' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "newDrudeBgd( np.array(spec.energy_loss[200:]), p0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def DrudeBgd(y, x, imfp, p):\n",
+    "\n",
+    "\n",
+    "\n",
+    "        # Fit function is the spectrum - new LL bgd devided by poinson noise\n",
+    "        def newLL(p, y, x):\n",
+    "            \n",
+    "            err = (y - newDrudeBgd( x,p))\n",
+    "            #print(p, sum( err))\n",
+    "            return err\n",
+    "\n",
+    "        # Least square fit\n",
+    "        pDLLBgd, lsq = scipy.optimize.leastsq(newLL, p0, args=(y, x), maxfev=2000)\n",
+    "        #print(sum(newLL(pZL, y, x)))\n",
+    "        # cts is the result of the fit\n",
+    "        cts=newDrudeBgd(x, abs(pDLLBgd))\n",
+    "        #print(\"new LLL background \", pZL)\n",
+    "        #tags['DrudeLLBgd'] = pDLLBgd\n",
+    "        print(pDLLBgd)\n",
+    "        \n",
+    "        \n",
+    "        return cts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'experiment': {'single_exposure_time': 0.1,\n",
+       "  'exposure_time': 10.0,\n",
+       "  'number_of_frames': 100,\n",
+       "  'collection_angle': 100.0,\n",
+       "  'convergence_angle': 0.0,\n",
+       "  'microscope': 'Libra 200 MC',\n",
+       "  'acceleration_voltage': 199990.28125,\n",
+       "  'flux_ppm': 4875.3037109375,\n",
+       "  'count_conversion': 1,\n",
+       "  'beam_current': 0},\n",
+       " 'zero_loss': {'shifted': array([-0.14012023]),\n",
+       "  'startFitEnergy': -0.5,\n",
+       "  'endFitEnergy': 0.5,\n",
+       "  'fit_parameter': array([-1.63207010e-02,  2.06835590e+04,  1.92327897e-01,  3.38795374e-02,\n",
+       "          2.25742582e+04,  2.93662067e-01]),\n",
+       "  'original_low_loss': 'EELS90muOAonaxis3_new_new'},\n",
+       " 'plasmon': {'parameter': array([1.50312722e+01, 7.45905381e-01, 3.48811157e+05]),\n",
+       "  'epsilon': array([     0.           +0.j        ,      0.           +0.j        ,\n",
+       "              0.           +0.j        , ...,\n",
+       "         293283.07757369+9900.8221703j , 293342.39439935+9892.35528781j,\n",
+       "         293401.61631948+9883.90252999j])}}"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "infoWidget.datasets['plasmon'].metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0000000000000007\n",
+      "1.0000000000000007\n",
+      "1.0000000000000007\n",
+      "0.9999999999999997\n",
+      "1.0\n",
+      "0.9999999999999997\n",
+      "1.0000000000000007\n",
+      "1.0000000000000007\n",
+      "[1.50312722e+01 7.45905381e-01 3.48811157e+05 1.70000000e+01]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x21050ae7fb0>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99ba61bedc474e729cc9147018d5c40a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipy\n",
+    "p0 = np.zeros(4)\n",
+    "#if 'Drude P Pos' in tags:\n",
+    "p0[:3] = [1.50312722e+01, 7.45905381e-01, 3.48811157e+05]\n",
+    "#p0[0] = tags['Drude P Pos']\n",
+    "#p0[1] = tags['Drude P Width']\n",
+    "#p0[2] = tags['Drude P thick']\n",
+    "p0[3] = 17. #tags['Drude P Assym']\n",
+    "spec = infoWidget.dataset\n",
+    "spec.energy_loss[200]\n",
+    "\n",
+    "cts = DrudeBgd(np.array(spec[200:]), np.array(spec.energy_loss[200:]), .15, p0)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(cts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def PMFP(ssdLL, energy_scale):\n",
+    "        # Calculate plasmon mean free paths using a free-electron formula Eq.(3.58)\n",
+    "        # with m = m0 and assuming small width of the plasmon peak.\n",
+    "        # Equally good for calculating total-inelastic MFP using a value of\n",
+    "        # Em in Eq.(5.38) or a more approximate value using Eq.(5.2).\n",
+    "        # Probe convergence alpha incorporated using Scheinfein & Isaacson formula.\n",
+    "        # Above values assume dipole conditions (beta* < Bethe-ridge angle).\n",
+    "        # The program also estimates a total-inelastic MFP by using dipole formula\n",
+    "        # with effective collection angle bstar = Bethe-ridge angle.\n",
+    "        # To obtain this value, enter alarge value (~ 100 mrad) for alpha or beta.\n",
+    "        E0 = 200000            #Incident-electron energy E0 (keV): ');\n",
+    "        Ep = energy_scale[0]+ssdLL.argmax(0)*(energy_scale[1]-energy_scale[0]) #'Plasmon energy of mean energy loss (eV): ');\n",
+    "        print(Ep, ssdLL.argmax(0))\n",
+    "        alpha = 10 #'Convergence semiangle (mrad) [can be 0]: ');\n",
+    "        beta  = 30 #'Collection semiangle (mrad): ');\n",
+    "        \n",
+    "        F = (1.0+E0/1022.0)/(1.0+E0/511.0)**2;\n",
+    "        Fg = (1.0+E0/1022.0)/(1.0+E0/511.0);\n",
+    "        T = E0*F; #keV\n",
+    "        tgt = 2.0*Fg*E0;\n",
+    "        a0 = 0.0529;  #nm\n",
+    "        #print('2.gamma.T = ',tgt);\n",
+    "\n",
+    "        # calculation of convergence correction\n",
+    "        #tgt=2.*E0.*(1.+E0./1022.)./(1.+E0./511.); % keV\n",
+    "        thetae=(Ep+1e-6)/tgt;          # in mrad, avoid NaN for e=0\n",
+    "        a2=alpha*alpha*1e-6 + 1e-10;  #radians^2, avoiding inf for alpha=0\n",
+    "        b2=beta*beta*1e-6;            #radians^2\n",
+    "        t2=thetae*thetae*1e-6;        #radians^2\n",
+    "        eta1=np.sqrt((a2+b2+t2)**2-4*a2*b2)-a2-b2-t2;\n",
+    "        eta2=2*b2*np.log(0.5/t2*(np.sqrt((a2+t2-b2)**2+4*b2*t2)+a2+t2-b2));\n",
+    "        eta3=2*a2*np.log(0.5/t2*(np.sqrt((b2+t2-a2)**2+4*a2*t2)+b2+t2-a2));\n",
+    "        eta=(eta1+eta2+eta3)/a2/np.log(4/t2);\n",
+    "        f1=(eta1+eta2+eta3)/2/a2/np.log(1+b2/t2);\n",
+    "        f2=f1;\n",
+    "        if(alpha/beta>1):\n",
+    "            f2=f1*a2/b2;\n",
+    "\n",
+    "        bstar=thetae*np.sqrt(np.exp(f2*np.log(1+b2/t2))-1); #% mrad\n",
+    "        #print('effective semiangle beta* = %g mrad\\n',bstar);\n",
+    "        bstar = 40\n",
+    "        \n",
+    "        thetabr = 1000 * (Ep/E0/1000.0)**0.5;\n",
+    "        print('Bethe Ridge Angle', thetabr)\n",
+    "        #print('Bethe-ridge angle(mrad) = ',tags['Bethe Ridge Angle'],'nm\\n')\n",
+    "\n",
+    "        pmfp = 0.0\n",
+    "        imfp = 0.0\n",
+    "        if (bstar < thetabr):\n",
+    "            pmfp = 4000*a0*T/Ep/np.log(1+bstar**2/thetae**2);\n",
+    "            imfp = 106*F*E0/Ep/np.log(2.0*bstar*E0/Ep);\n",
+    "            #print('Free-electron   MFP(nm) = %g nm\\n',pmfp);\n",
+    "            #print('Using Eq.(5.2), MFP(nm) = %g nm\\n',imfp);\n",
+    "            \n",
+    "        else:\n",
+    "            #print('Dipole range is exceeded\\n');\n",
+    "            imfp = 4000*a0*T/Ep/np.log(1+thetabr**2/thetae**2);\n",
+    "            #print('total-inelastic MFP(nm) = %g nm\\n',imfp);\n",
+    "        \n",
+    "\n",
+    "        return pmfp, imfp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15.040794864773837, 920)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ep = np.array(eels_dataset.energy_loss)[0]+np.array(ssdLL).argmax(0)*(np.array(eels_dataset.energy_loss)[1]-np.array(eels_dataset.energy_loss)[0])\n",
+    "Ep,  np.array(ssdLL).argmax(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15.040794864773837 920\n",
+      "Bethe Ridge Angle 0.27423343035426806\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 219.06514501302627)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PMFP(np.array(ssdLL), np.array(eels_dataset.energy_loss))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.17584541453708571\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1f219a1ace0>]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa46026986f74f8fb3f7f5c1e7abfa79",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Use resolution Function as ZL if existing\n",
+    "# print len(LLSpec)\n",
+    "LLSpec = eels_dataset\n",
+    "zero_loss, _ = eels_tools.resolution_function(eels_dataset.energy_loss, eels_dataset, .4)\n",
+    "\n",
+    "j = np.fft.fft(eels_dataset)\n",
+    "z = np.fft.fft(zero_loss)\n",
+    "j1 = z*np.log(j/z)\n",
+    "ssdLL =np.fft.ifft(j1).real#,'fourier-log deconvolution')\n",
+    "\n",
+    "#parent.text2.append('\\n Single Scattering Deconvolution, Done')\n",
+    "if np.array(eels_dataset).sum() > 0.0:          \n",
+    "    tmfp = np.log(np.array(eels_dataset).sum()/zero_loss.sum())\n",
+    "else:\n",
+    "    tmfp = 0.0\n",
+    "Ep = np.array(eels_dataset.energy_loss)[0]+np.array(ssdLL).argmax(0)*(np.array(eels_dataset.energy_loss)[1]-np.array(eels_dataset.energy_loss)[0])\n",
+    "\n",
+    "print(tmfp)\n",
+    "plt.figure()\n",
+    "plt.plot(ssdLL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drude(dataset,  ep, ew, tnm, eb, verbose=False):\n",
+    "       \n",
+    "    \n",
+    "    e0 = 200\n",
+    "    beta = 30\n",
+    "    e = dataset.energy_loss\n",
+    "    epc = e[1]-e[0]\n",
+    "\n",
+    "    b = beta/1000.0 # %rad\n",
+    "    T = 1000.0*e0*(1.+e0/1022.12)/(1.0+e0/511.06)**2;# %eV # equ.5.2a or Appendix E p 427 \n",
+    "    tgt = 1000*e0*(1022.12 + e0)/(511.06 + e0);# %eV  Appendix E p 427 \n",
+    "    rk0 = 2590*(1.0+e0/511.06)*np.sqrt(2.0*T/511060);\n",
+    "    os = e[0]\n",
+    "    ewMod = eb\n",
+    "    eps = 1 - (ep**2-ewMod*e*1j)/(e**2+2*e*ew*1j) #Mod Drude term\n",
+    "    eps[np.nonzero(eps==0.0)]= 1e-19\n",
+    "    elf = np.imag(-1/eps)\n",
+    "\n",
+    "    the = e/tgt; #% varies with energy loss! # Appendix E p 427 \n",
+    "    srfelf=np.imag(-4./(1.0+eps))-elf; #% for 2 surfaces\n",
+    "    angdep = np.arctan(b/the)/the - b/(b*b+the*the);\n",
+    "    srfint = angdep*srfelf/(3.1416*0.05292*rk0*T); #% probability per eV\n",
+    "    anglog = np.log(1.0+ b*b/the/the);\n",
+    "    I0 = eels_dataset.sum() *1     \n",
+    "    volint = abs(tnm/(np.pi*0.05292*T*2)*elf*anglog); #S equ 4.26% probability per eV\n",
+    "    volint = (volint+srfint) *I0  *epc #S probability per channel\n",
+    "    ssd = volint #+ srfint;\n",
+    "    if os <-1.0:\n",
+    "        xs = int(abs(-os/epc))\n",
+    "\n",
+    "        ssd[0:xs]=0.0\n",
+    "        volint[0:xs]=0.0\n",
+    "        srfint[0:xs]=0.0\n",
+    "        \n",
+    "        Ps = np.trapz(e,srfint); #% 2 surfaces but includes negative begrenzungs contribn.\n",
+    "        Pv = abs(np.trapz(e,abs(volint/np.array(eels_dataset)))); #% integrated volume probability\n",
+    "        Pv = (volint/I0).sum() ## our data have he same epc and the trapz formula does not include \n",
+    "        lam = tnm/Pv; #% does NOT depend on free-electron approximation (no damping). \n",
+    "        lamfe = 4.0*0.05292*T/ep/np.log(1+(b* tgt / ep) **2); #% Eq.(3.44) approximation\n",
+    "    if verbose:\n",
+    "        print('Ps(2surfaces+begrenzung terms) =', Ps, 'Pv=t/lambda(beta)= ',Pv,'\\n');\n",
+    "        print('Volume-plasmon MFP(nm) = ', lam,' Free-electron MFP(nm) = ',lamfe,'\\n');\n",
+    "        print('--------------------------------\\n');\n",
+    "\n",
+    "            \n",
+    "    return ssd#/np.pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1f21d52a560>]"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "027f84e640224d78831fb5680c8f6b54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
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+       "\n",
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+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ssd = drude(eels_dataset,  15, .5, 3, 1)\n",
+    "plt.figure()\n",
+    "plt.plot(eels_dataset.energy_loss, ssd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def doSSD(LLSpec):\n",
+    "        \n",
+    "        # Use resolution Function as ZL if existing\n",
+    "        # print len(LLSpec)\n",
+    "        extract_zero_loss(LLSpec)\n",
+    "        \n",
+    "        j = np.fft.fft(LLSpec)\n",
+    "        z = np.fft.fft(tags['zero_loss'])\n",
+    "        z2 = z ## Could be a zl extracted from Spectrum\n",
+    "        j1 = z2*np.log(j/z)\n",
+    "        ssdLL =np.fft.ifft(j1).real#,'fourier-log deconvolution')\n",
+    "        tags['ssdLL']=ssdLL.copy()\n",
+    "        \n",
+    "        \n",
+    "        #parent.text2.append('\\n Single Scattering Deconvolution, Done')\n",
+    "        if np.array(LLSpec).sum() > 0.0:          \n",
+    "            tmfp = np.log(np.array(LLSpec).sum()/tags['zero_loss'].sum())\n",
+    "        else:\n",
+    "            tmfp = 0.0\n",
+    "\n",
+    "        # Use resolution function if available, use ZL otherwise\n",
+    "        zl2 = tags['zero_loss']\n",
+    "        \n",
+    "        \n",
+    "        #####################\n",
+    "        ####### for SSD convoluted Spectra\n",
+    "        #####################\n",
+    "        startE = (6.0-tags['offset'])/tags['dispersion']\n",
+    "        SSD = ssdLL.copy()\n",
+    "        SSD2 = SSD.copy()\n",
+    "        SSD2[0:startE]=0.0\n",
+    "        EP = np.array(SSD2).argmax(0) # plasmon peak position\n",
+    "        ZP = np.array(zl2).argmax(0) # zl peak position \n",
+    "        #print ('\\n EP: ',EP,startE, tags['offset']+EP*tags['dispersion'])\n",
+    "\n",
+    "\n",
+    "        guess = [tags['offset']+EP*tags['dispersion'], 10000.0, 6.0, 0.98]\n",
+    "        pin = np.array(guess)\n",
+    "\n",
+    "        def errfct(p, y, x):\n",
+    "            err = (y - Lorentzian(x,p))\n",
+    "            return err\n",
+    "\n",
+    "        def Lorentzian(x,p):\n",
+    "            y = ((0.5 *  p[1]* p[2]/3.14)/((x- p[0])**2+(( p[2]/2)**2)))\n",
+    "            return y\n",
+    "\n",
+    "        p, lsq = leastsq(errfct, pin, args=(SSD, tags['ene']), maxfev=2000)\n",
+    "        tags['PLpos'] = p[0]\n",
+    "        tags['PLwidth'] = p[2]\n",
+    "        tags['PLarea'] = p[1]\n",
+    "        #parent.text2.insertPlainText('\\n Position 1  Amplitude 1,  Width 1,  \\n')\n",
+    "        #parent.text2.insertPlainText(str(p[0:3]))\n",
+    "        PL1 =  Lorentzian(tags['ene'],p)\n",
+    "\n",
+    "        pmfp, imfp = PMFP()\n",
+    "        startxE = tags['Drude Fit Start']\n",
+    "        endxE = tags['Drude Fit End']\n",
+    "        startx = (startxE-tags['offset'])/tags['dispersion']\n",
+    "        endx = (endxE-tags['offset'])/tags['dispersion']\n",
+    "\n",
+    "        if p[0] < startxE:\n",
+    "            p[0] = startxE\n",
+    "        if p[0] > endxE:\n",
+    "            p[0] = endxE\n",
+    "        if p[2] > (endxE-startxE)/2.0:\n",
+    "            p[2] = (endxE-startxE)/2.0\n",
+    "        \n",
+    "        \n",
+    "        guess = [p[0],p[2],tmfp*imfp,0.1,1.0]\n",
+    "        guess = [22,10,50,0.1,1.0]\n",
+    "        pin2 = np.array(guess)\n",
+    "\n",
+    "        \n",
+    "        def errfDrude(p, y, x):\n",
+    "            p = abs(p)\n",
+    "            if p[0] < startxE:\n",
+    "                p[0] = startxE\n",
+    "            if p[0] > endxE:\n",
+    "                p[0] = endxE\n",
+    "            if p[1] > endxE-startxE/3.0:\n",
+    "                p[1] = endxE-startxE/3.0\n",
+    "            if p[2] > 200:\n",
+    "                p[2] = 200\n",
+    "            if p[2]<0:\n",
+    "                p[2] =0\n",
+    "            if p[3] > 10:\n",
+    "                p[3] = 10\n",
+    "            if p[3]<0:\n",
+    "                p[3] =0\n",
+    "            if not tags['Drude Fit Asymm'] :\n",
+    "                p[3] = 0\n",
+    "            \n",
+    "            err = (y - drude(x,p[0],p[1],p[2],abs(p[3])))\n",
+    "\n",
+    "            y[np.nonzero(y<=0)] = 1e-12\n",
+    "            return np.abs(err)/np.sqrt(y)\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "        \n",
+    "        p2, lsq = leastsq(errfDrude, pin2, args=(tags['spec'][startx:endx], tags['ene'][startx:endx]), maxfev=2000)\n",
+    "        p2[3] = abs(p2[3])\n",
+    "        drudePSD = drude(tags['ene'],p2[0],p2[1],p2[2],abs(p2[3]))\n",
+    "        tags['Drude SSD'] = drudePSD\n",
+    "        \n",
+    "        tags['Drude P Pos'] = p2[0]\n",
+    "        tags['Drude P Width'] = p2[1]\n",
+    "        tags['Drude P thick'] = p2[2]\n",
+    "        tags['Drude P Assym'] = abs(p2[3])\n",
+    "        Pv = drudePSD.sum()/tags['spec'].sum()\n",
+    "        tags['Drude P Probab'] = Pv\n",
+    "        tags['Drude P IMFP'] =  p2[2]/Pv #(Wave vs. intensity)\n",
+    "        #tags['Drude P/LL IMFP',p2[2]/tmfp,'nm')\n",
+    "        tags['LLthick'] = tmfp\n",
+    "\n",
+    "        e = 1.60217646E-19; #% electron charge in Coulomb\n",
+    "        eps0 =  8.854187817*1e-12 # vacuum permittivity\n",
+    "        mel = 9.109e-31; #% REST electron mass in kg\n",
+    "        h = 4.135667516*1e-15; #% Planck's constant\n",
+    "        hbar = h/2.0/np.pi;\n",
+    "\n",
+    "        tags['Drude e- density']= np.sqrt( (p2[0]/hbar)**2/e**2*eps0*mel)*1e-7 #gerd true? /nm^2\n",
+    "\n",
+    "        tags['Drude VL'] = MakeDrudeVL()\n",
+    "        \n",
+    "        \n",
+    "        return tmfp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Surface Plasmon\n",
+    "\n",
+    "Spectra from thin specimen show the excitations of the surface plasmons on each side of the specimen. For any normal specimen these surface plasmons do interact, but this is not true for extremely thick specimen ($>> 10$nm).\n",
+    "The surface plasmon frequency $\\omega_S$ for thin specimen  is related to the bulk plasmon frequency $\\omega_P$ by Ritchie [Ritchie-PR1957]:  \n",
+    "$$\n",
+    "\\omega_S=\\omega_P\\left[  \\frac{1\\pm \\exp(-q_st) }{1+\\varepsilon} \\right]^{1/2}\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "The symmetric mode, where like charges face one another, corresponds to the higher angular frequency $q_s$. Please note, that this relationship does only apply for large $q_s$\n",
+    "\n",
+    "The differential probability for surface excitation at both surfaces of a sample with thickness $t$ can be expressed (normal incident, no retardation effects) by:\n",
+    "$$\n",
+    "\\frac{d^2 P_s}{d\\Omega d E}=\\frac{2\\hbar}{\\pi^2 \\gamma a_0 m_0^2 \\mu^3}\\frac{\\theta}{(\\theta^2+\\theta^2_E)^2} \\Im\\left[  \\frac{(\\varepsilon_a - \\varepsilon_b)^2 } {\\varepsilon_a^2 \\varepsilon_b}\\right]\n",
+    "$$\n",
+    "with \n",
+    "$$\n",
+    "R_c = \\frac{\\varepsilon_a \\sin^2(tE/2\\hbar\\mu)}{\\varepsilon_b + \\varepsilon_z }\\tanh (q_s t/2) \n",
+    "+ \\frac{\\varepsilon_a \\cos^2(tE/2\\hbar\\mu)}{\\varepsilon_b + \\varepsilon_a} \\coth (q_s t/2) \n",
+    "$$\n",
+    "and $\\varepsilon_a$ and $\\varepsilon_b$ are the permitivities of the two surfaces.\n",
+    "\n",
+    "\n",
+    "A secondary effect of the surface excitation is the reduced intensity of the bulk plasmon peak. The effect is usually smaller than 1\\%, but can be larger for spectra with small collection angle, because the preferred scattering of surfuce losses into small angles.\n",
+    "The correction for surface plasmon will be discussed in the Kramers--Kronig Analysis.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "The beauty of ``Low--Loss spectroscopy`` is its derivation of the dielectric function to high energies without prior knowledge of the composition. The signal is strong and the acquisition time is mostly restricted by the dynamic range of the spectrum.\n",
+    "\n",
+    "\n",
+    "**Think of low-loss spectroscopy as Electrodynamics**\n",
+    "\n",
+    "The advantages of EELS is the derivation of these values spatially resolved.\n",
+    "And from a linescan across an Si/SiO$_2$ interface the dielectric function per pixel can be obtained. From that we can calculate the dielectric polarizability $\\alpha_e (E)$, which may be  a measure of the dielectric strength.\n",
+    "\n",
+    "\n",
+    "We obtain more or less easily:\n",
+    "- relative thickness\n",
+    "- absolute thickness \n",
+    "- inelastic mean free path\n",
+    "- plasmon frequency\n",
+    "- plasmon width\n",
+    "- band gap\n",
+    "- dielectric function\n",
+    "- reflectivity \n",
+    "- absorption\n",
+    "- effective number of electrons per atoms \n",
+    " \n",
+    "\n",
+    "\n",
+    "The analysis of the optical data requires the exact knowledge of the zero-loss peak. Because of the weighting in the Fourier Analysis, the low energy part contributes heavily to the dielectric function. Therefore, energy resolution is critical for an exact determination of all the optical values from EELS. The new monochromated TEMs are now able to achieve an energy resolution of 10 meV (one is at the oak Ridge National Laboratory), which allows for a sharper zero-loss peak. Such a sharp zero-loss peak will enable us to extract this low energy data more accurately. The dielectric function and the parameters derived from it, can be more precisely determined from such EELS spectra.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "## Navigation\n",
+    "- <font size = \"3\">  **Up Chapter 4: [Imaging](CH4_00-Spectroscopy.ipynb)** </font>\n",
+    "- <font size = \"3\">  **Back: [Zero-Loss](CH4_02-Fit_Zero_Loss.ipynb)** </font>\n",
+    "- <font size = \"3\">  **Next: [Introduction to Core-Loss](./CH4_07-Introduction_Core_Loss.ipynb)** </font>\n",
+    "- <font size = \"3\">  **List of Content: [Front](../_MSE672_Intro_TEM.ipynb)** </font>"
+   ]
+  },
+  {
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+   "base_numbering": "3",
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+   "number_sections": true,
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