nir-corr-po.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b89b701-6080-492c-9333-90c8483f8614",
   "metadata": {},
   "source": [
    "# Correlations of Near-Infrared Spectra to Bulk Properties in Polyolefins, using Principal Component Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b1e57cb-7c0a-4b1d-877a-f56d1edae6be",
   "metadata": {},
   "source": [
    "In connection with: add DOI INFO/LINK HERE\n",
    "\n",
    "This code was predominantly produced by Bradley P. Sutliff, with assistance from Tyler B. Martin, and Debra Audus\n",
    "\n",
    "This notebook is provided in an effort to further open research initiatives and to further the circular economy.\n",
    "\n",
    "Please direct any questions to Bradley.Sutliff@nist.gov"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e52a0c-a393-45f7-b83b-0430d7be3397",
   "metadata": {},
   "source": [
    "## Notebook setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e43ba75",
   "metadata": {},
   "source": [
    "If running in Google Colab, uncomment (delete the `# `) and run the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c7d4510",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install scikit-fda sklearn-xarray sliced"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c609c2-41af-4abe-b7ba-f20e995ea4ac",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Package Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bd61b5b-20b4-4912-8829-f4805f75e532",
   "metadata": {},
   "outputs": [],
   "source": [
    "# os based imports\n",
    "import datetime\n",
    "import itertools\n",
    "\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.markers as pltmarkers\n",
    "\n",
    "# data handling\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# machine learning\n",
    "import skfda\n",
    "import xarray as xr\n",
    "from matplotlib.lines import Line2D\n",
    "from scipy.signal import savgol_filter\n",
    "from scipy.stats import pearsonr\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import FunctionTransformer, StandardScaler\n",
    "from sklearn_xarray import wrap\n",
    "from sliced import SlicedInverseRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288e98af-9946-46c3-a6a1-8c5d33c0ce7b",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define color schemes and plotting dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ab4566c-db36-40cd-8791-a31a85881ffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up notebook for plotting nicely\n",
    "%matplotlib inline\n",
    "contxt = \"notebook\"\n",
    "sns.set(context=contxt, style=\"ticks\", palette=\"bright\")\n",
    "pd.options.display.html.use_mathjax = False  # don't render table values as LaTeX"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ec697a-2410-4279-bb7c-d2e4d2a409d4",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Some dictionaries to make plotting names and colors easy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccd0159b-b752-4acb-b44e-fbccfbec4fc0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# The colorblind friendly colors that were used in the manuscript\n",
    "dict_cBlind = {\n",
    "    \"Class1\": {\"PP\": \"#0173b2\", \"PE\": \"#d55e00\"},\n",
    "    \"Class2\": {\n",
    "        \"PP\": \"#0173b2\",\n",
    "        \"LDPE\": \"#ece133\",\n",
    "        \"LLDPE\": \"#ca9161\",\n",
    "        \"MDPE\": \"#de8f05\",\n",
    "        \"HDPE\": \"#d55e00\",\n",
    "        \"PP-co-PE\": \"#56b4e9\",\n",
    "    },\n",
    "    \"fPCA_loading\": {2: \"#029e73\", 3: \"#cc78bc\", 1: \"#949494\", 0: \"#fbafe4\"},\n",
    "}\n",
    "\n",
    "# An alternative color choice\n",
    "dict_colors = {\n",
    "    \"Class1\": {\"PP\": \"#cb1b4f\", \"PE\": \"#357ba3\"},\n",
    "    \"Class2\": {\n",
    "        \"PP\": \"#e13342\",\n",
    "        \"LDPE\": \"#40b7ad\",\n",
    "        \"LLDPE\": \"#348fa7\",\n",
    "        \"MDPE\": \"#37659e\",\n",
    "        \"HDPE\": \"#413d7b\",\n",
    "        \"PP-co-PE\": \"#ad1759\",\n",
    "    },\n",
    "}\n",
    "dict_zord = {\"PP\": 1, \"LDPE\": 2, \"LLDPE\": 3,\n",
    "             \"MDPE\": 6, \"HDPE\": 4, \"PP-co-PE\": 5}\n",
    "\n",
    "dict_shapes = {\n",
    "    \"Class1\": {\"PP\": \"o\", \"PE\": \"v\"},\n",
    "    \"Class2\": {\n",
    "        \"PP\": \"o\",\n",
    "        \"iPP\": \"h\",\n",
    "        \"rPP\": \"H\",\n",
    "        \"LDPE\": \"<\",\n",
    "        \"LLDPE\": \"^\",\n",
    "        \"MDPE\": \"v\",\n",
    "        \"HDPE\": \">\",\n",
    "        \"PP-co-PE\": \"H\",\n",
    "    },\n",
    "}\n",
    "\n",
    "dict_linesty = {'Class2' : {'PP':(0, ()),\n",
    "                            'LDPE':(0, (1, 1)),\n",
    "                            'LLDPE':(0, (5, 5)),\n",
    "                            'MDPE':(0, (3, 5, 1, 5)),\n",
    "                            'HDPE':(5, (10, 3)),\n",
    "                            'PP-co-PE':(0, (3, 5, 1, 5, 1, 5))},\n",
    "                'fPCA_loading' : {0:(0, ()),\n",
    "                                  1:(0, (1, 1)),\n",
    "                                  2:(0, (5, 5)),\n",
    "                                  3:(0, (3, 5, 1, 5))}\n",
    "               }\n",
    "\n",
    "axis_dict = {\n",
    "    \"Density\": \"Density [g/cm$^3$]\",\n",
    "    \"Crystallinity\": \"Crystallinity [%]\",\n",
    "    \"Mw(IR) (g_per_mol)\": \"Mw [g/mol]\",\n",
    "    \"PDI(IR)\": \"PDI(IR)\",\n",
    "    \"Mw(MALS) g_per_mol\": \"Mw [g/mol]\",\n",
    "    \"PDI(MALS)\": \"PDI\",\n",
    "    \"Mw(Viscometer) g_per_mol\": \"Mw(Viscometer) [g/mol]\",\n",
    "    \"PDI(Visco)\": \"PDI\",\n",
    "    \"MHa\": \"MHa\",\n",
    "    \"MHK dL_per_g\": \"MHK [dL/g]\",\n",
    "    \"SCB_per_1000C (IR)\": \"$\\mathregular{CH_3/1000 \\ C}$\",\n",
    "    \"LCBf_per_1000C (MALS)\": \"Long Chain Branching/1000 C\",\n",
    "    \"pc0\": \"First fPC\",\n",
    "    \"pc1\": \"Second fPC\",\n",
    "    \"pc2\": \"Third fPC\",\n",
    "    \"pc3\": \"Fourth fPC\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d6509f-889c-40c2-a67b-7c96772d15e8",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Set our color palette to match one of our dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6a62ef9-457e-4e0b-9154-99abbbb3a5f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cpalette = dict_cBlind"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177f4d64-ba70-41bf-ac72-69d2ec894610",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define functions used in this code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19cf9cb7-adae-47f3-bcf8-345c5778cf1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_markers(scatter_plot, marker_vector):\n",
    "    \"\"\"\n",
    "    A function to apply new markers to a scatter_plot\n",
    "    based on a vector of markers. marker_vector must have\n",
    "    the same dimension as the x and y for scatter_plot\n",
    "    \"\"\"\n",
    "    marker_locs=[]\n",
    "    for marker in marker_vector:\n",
    "        if marker in [i for i in Line2D.markers]:\n",
    "            obj_marker = pltmarkers.MarkerStyle(marker)\n",
    "        else:\n",
    "            print(f'{marker} is not a viable marker')\n",
    "        marker_loc = obj_marker.get_path(\n",
    "        ).transformed(obj_marker.get_transform())\n",
    "        marker_locs.append(marker_loc)\n",
    "    scatter_plot.set_paths(marker_locs)\n",
    "    return scatter_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bae8ae9d-5bb2-4605-97aa-33f420059e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define functions to then make function transformers:\n",
    "# each of these will read from an Xarray DataArray\n",
    "# and return another DataArray\n",
    "def MSC_correct_da(sub_da, wavenumber_dim=\"Wavenumber\", sample_dim=\"sample\"):\n",
    "    \"\"\"\n",
    "    Multiplicative  scatter correction first calculates a mean reference spectrum\n",
    "    from a set of data. Data is averaged (mean) by the sample_dim. The reference\n",
    "    spectrum is then used to fit a linear regression between the reference spectrum\n",
    "    and each of the sample spectra. The coefficients of this fit are then used to\n",
    "    normalize the data.\n",
    "\n",
    "    sub_da = an Xarray DataArray containing only the samples to be combined/normalized\n",
    "            using the same reference spectrum\n",
    "    wavenumber_dim = the name of the dimension holding relevant wavenumber info\n",
    "    sample_dim = the name of the dimension that should be reduced in the creation\n",
    "            of the reference spectrum\n",
    "    data_dim = the name of the DataArray in the Dataset that you want to normalize.\n",
    "    \"\"\"\n",
    "    sub_da_clean = sub_da.copy()\n",
    "    sub_da[\"MCC_mean\"] = sub_da.mean(wavenumber_dim)\n",
    "    sub_da[\"MCC\"] = sub_da - sub_da[\"MCC_mean\"]\n",
    "    sub_da[\"ref_spectra\"] = sub_da[\"MCC\"].mean(sample_dim)\n",
    "\n",
    "    polyfit = sub_da.swap_dims(Wavenumber=\"ref_spectra\")[\"MCC\"].polyfit(\n",
    "        \"ref_spectra\", 1\n",
    "    )[\"polyfit_coefficients\"]\n",
    "    sub_da[\"MSC\"] = (sub_da[\"MCC\"] - polyfit.sel(degree=0)) / \\\n",
    "        (polyfit.sel(degree=1))\n",
    "    sub_da[\"m\"] = polyfit.sel(degree=1).reset_coords(drop=True)\n",
    "    sub_da[\"b\"] = polyfit.sel(degree=0).reset_coords(drop=True)\n",
    "    sub_da_clean[\"MSC\"] = sub_da[\"MSC\"]\n",
    "    return sub_da_clean\n",
    "\n",
    "\n",
    "def MSC_correct_all(data):\n",
    "    \"\"\"\n",
    "    applies the MSC correction to an entire Xarray Dataset\n",
    "    \"\"\"\n",
    "    try:\n",
    "        da2 = data.swap_dims(sample=\"sample\").copy()\n",
    "        da3 = (da2.groupby(\"polymer\")\n",
    "               .map(MSC_correct_da)\n",
    "               .sortby(\"sample\"))\n",
    "    except:\n",
    "        da3 = ((data.groupby(\"polymer\")\n",
    "                .map(MSC_correct_da)\n",
    "                .sortby(\"sample\")))\n",
    "\n",
    "    data2 = data.copy()\n",
    "    data2 = data2.sortby(\"sample\")\n",
    "    data2.values = da3.MSC.values\n",
    "    data2 = data2.rename(\"MSC\")\n",
    "    return data2\n",
    "\n",
    "\n",
    "def xr_SG(data):\n",
    "    \"\"\"\n",
    "    applies the Savitzky-Golay filter for an Xarray Dataset\n",
    "    the delta is set to the average difference between\n",
    "    consecutive wavenumbers for this Dataset\n",
    "    \"\"\"\n",
    "    dlta = data.Wavenumber.diff('Wavenumber').mean().values\n",
    "    return data.groupby(\"sample\").map(\n",
    "        savgol_filter, window_length=249, delta=dlta,\n",
    "        polyorder=2, deriv=0\n",
    "    )\n",
    "\n",
    "\n",
    "def xr_SG2(data):\n",
    "    \"\"\"\n",
    "    applies the Savitzky-Golay filter and takes the second derivative\n",
    "    for an Xarray Dataset\n",
    "    \"\"\"\n",
    "    dlta = data.Wavenumber.diff('Wavenumber').mean().values\n",
    "    return data.groupby(\"sample\").map(\n",
    "        savgol_filter, window_length=249, delta=dlta,\n",
    "        polyorder=2, deriv=2\n",
    "    )\n",
    "\n",
    "\n",
    "def id_func(data):\n",
    "    \"\"\"\n",
    "    returns the same data with no changes\n",
    "    this is mostly for use as a pipeline placeholder\n",
    "    for when no function is applied (no-dt, no-filter)\n",
    "    \"\"\"\n",
    "    return data\n",
    "\n",
    "\n",
    "def my_fPCA(data, n_components=4):\n",
    "    \"\"\"\n",
    "    This takes an Xarray DataArray and converts it to an FDataGrid for fPCA,\n",
    "    runs fPCA, and then returns a DataArray with fPCA loadings\n",
    "    and the transformed data.\n",
    "    \n",
    "    Additionally, if n_components is between 0 and 1, my_fPCA will return\n",
    "    enough principal components to explain `n_components` worth of variance \n",
    "    \n",
    "    fPCA_loadings: the 'functions' that are used to transform the data. \n",
    "                  These will have dimensions of (Wavenumber, n_components, 1)\n",
    "    fPCs: the values for the transformed data\n",
    "          These will have the dimensions of (samples, n_components, 1)\n",
    "    \"\"\"\n",
    "\n",
    "    data2 = data.copy()\n",
    "    fdata = skfda.FDataGrid(data2, grid_points=data2.Wavenumber.values)\n",
    "    if n_components <=0:\n",
    "        print('Invalid number of components please provide a positive number')\n",
    "    if n_components < 1:\n",
    "        comps = 1\n",
    "        sum_ratio = 0\n",
    "        while sum_ratio < n_components:\n",
    "            fpca = skfda.preprocessing.dim_reduction.feature_extraction.FPCA(\n",
    "                n_components=comps\n",
    "            )\n",
    "            results = fpca.fit(fdata)\n",
    "            sum_ratio = sum(results.explained_variance_ratio_)\n",
    "            comps = comps + 1\n",
    "    else:\n",
    "        comps = n_components\n",
    "\n",
    "    fpca = skfda.preprocessing.dim_reduction.feature_extraction.FPCA(\n",
    "        n_components=comps)\n",
    "    results = fpca.fit_transform(fdata)\n",
    "    ds_fpca = xr.Dataset(\n",
    "        {\n",
    "            \"fPCA_loadings\": xr.DataArray(\n",
    "                data=fpca.components_.data_matrix.squeeze().T,\n",
    "                dims=[\"Wavenumber\", \"fPCA_loading\"],\n",
    "                coords={\n",
    "                    \"Wavenumber\": data2.Wavenumber.values,\n",
    "                    \"fPCA_loading\": np.arange(comps),\n",
    "                },\n",
    "            ),\n",
    "            \"fPCs\": xr.DataArray(\n",
    "                data=results,\n",
    "                dims=[\"sample\", \"fPCA_loading\"],\n",
    "                coords={\n",
    "                    \"sample\": data2.sample.values,\n",
    "                    \"fPCA_loading\": np.arange(comps),\n",
    "                },\n",
    "            ),\n",
    "            \"fPCA_explainedVariance\": xr.DataArray(\n",
    "                data=fpca.explained_variance_ratio_,\n",
    "                dims=[\"fPCA_loading\"],\n",
    "                coords={\n",
    "                    \"fPCA_loading\": np.arange(comps),\n",
    "                },\n",
    "            ),\n",
    "        }\n",
    "    )\n",
    "\n",
    "    return ds_fpca"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b7efff-4d31-4251-a346-52177e787880",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define transformers that will be used in the analysis pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfbc3e48-47a9-49d2-867d-5da627cf6f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# by making these functions into transformers they can be directly called\n",
    "# from within a pipeline\n",
    "msc_transformer = FunctionTransformer(MSC_correct_all, validate=False)\n",
    "sg_transformer = FunctionTransformer(xr_SG, validate=False)\n",
    "sg2_transformer = FunctionTransformer(xr_SG2, validate=False)\n",
    "id_transformer = FunctionTransformer(id_func, validate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a8d662c-3a5d-4dea-ba9d-737a9ca25b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a dictionary to match these based on strings that are easier to understand\n",
    "# we can string these together to make a longer pipeline down the road.\n",
    "# the basic idea is to apply raw OR MSC, then no-filter, SG, OR SG2\n",
    "transf_dict = {\n",
    "    \"raw\": id_transformer,  # for when we don't actually want to apply any transformations\n",
    "    \"MSC\": msc_transformer,  # for when we use the MSC step\n",
    "    \"no-filter\": id_transformer, # same as raw, but to be used when deciding to apply SG filter or not\n",
    "    \"SG\": sg_transformer,  # SG filter\n",
    "    \"SG2\": sg2_transformer, # uses SG filter then returns the second derivative of the smoothed data\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1284bd69-ad97-4dda-ae37-799c19ae0a9e",
   "metadata": {},
   "source": [
    "## Data import"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fada6097-633b-4197-b27f-52affafe30f5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Load data from our files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350bb148-545c-4f48-9c73-75514971ab3c",
   "metadata": {},
   "source": [
    "First we set our main directory (`main_dir`) based on where the notebook is being executed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54c5ba5-7b8f-403c-a281-0020e3a6f3ee",
   "metadata": {},
   "source": [
    "If you cloned the repo and have everything in a folder on you local computer, choose `local`.  \n",
    "If you are executing the code from Colab or a directory without the data structure outlined below \n",
    "try using `giturl` or `nisturl`. One may be faster than the other for your individual situation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "89d5e973-5f85-4558-a3c2-8b156bbd3e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is a list of locations to grab the data from, just set main_dir to\n",
    "# the appropriate variable.\n",
    "# if you have copied the data to your computer, local will be the fastest\n",
    "# however, it if running in a Google Colab environment, try the giturl\n",
    "giturl = 'https://raw.githubusercontent.com/usnistgov/nir_corr_po/main/'\n",
    "nisturl = 'https://data.nist.gov/od/ds/ark:/88434/mds2-3022/' #note: nisturl may not work yet\n",
    "local = ''\n",
    "\n",
    "# set main_dir to local, nisturl, or giturl\n",
    "main_dir = giturl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee56c19a-05f9-4156-83b1-840be342793f",
   "metadata": {},
   "source": [
    "It is assumed that the data and code are set up in the following directory structure:  \n",
    "```\n",
    "nir_corr_po  \n",
    "├── Data  \n",
    "│   ├── AdditionalMeasurementData.csv  \n",
    "│   ├── NIR  \n",
    "│   │   └── *.csv  \n",
    "│   ├── README_data.md  \n",
    "│   └── SampleInformation.csv  \n",
    "├── README.md  \n",
    "└── nir-corr-po.ipynb  \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17205d8d-156b-410e-bf7f-b2ae05cf64a8",
   "metadata": {},
   "source": [
    "First we load the file that has our general sample information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e88426a-cb70-4f70-9dd1-dac7ce33b827",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Resin Code/Original Identifier</th>\n",
       "      <th>Source</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Class1</th>\n",
       "      <th>Class2</th>\n",
       "      <th>Physical State</th>\n",
       "      <th>Alternate names</th>\n",
       "      <th>bigSMILES</th>\n",
       "      <th>CAS number</th>\n",
       "      <th>Material Keywords</th>\n",
       "      <th>reference URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LLDPE-2</td>\n",
       "      <td>Hawaii Pacific University Polymers Kit</td>\n",
       "      <td>H0005LLDPE</td>\n",
       "      <td>PE</td>\n",
       "      <td>LLDPE</td>\n",
       "      <td>Pellet</td>\n",
       "      <td>linear low density polyethylene,LLDPE,polyethy...</td>\n",
       "      <td>{[][$]CC[$],[$]CC({[$][$]C[$][$]}C)[$][]}</td>\n",
       "      <td>9002-88-4</td>\n",
       "      <td>polyolefins,semicrystalline,copolymer</td>\n",
       "      <td>https://www.hpu.edu/cncs/cmdr/img/polymerkit1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MDPE</td>\n",
       "      <td>Hawaii Pacific University Polymers Kit</td>\n",
       "      <td>H0006MDPE</td>\n",
       "      <td>PE</td>\n",
       "      <td>MDPE</td>\n",
       "      <td>Pellet</td>\n",
       "      <td>mid density polyethylene,MDPE,polyethylene</td>\n",
       "      <td>{[][$]CC[$],[$]CC([$])[$],[$]CC([$])([$])[$][]}</td>\n",
       "      <td>9002-88-4</td>\n",
       "      <td>polyolefins,semicrystalline</td>\n",
       "      <td>https://www.hpu.edu/cncs/cmdr/img/polymerkit1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HDPE-1</td>\n",
       "      <td>Hawaii Pacific University Polymers Kit</td>\n",
       "      <td>H0007HDPE</td>\n",
       "      <td>PE</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>Pellet</td>\n",
       "      <td>high density polyethylene,HDPE,polyethylene,HD...</td>\n",
       "      <td>{[][$]CC[$][]}</td>\n",
       "      <td>9002-88-4</td>\n",
       "      <td>polyolefins,semicrystalline,linear,homopolymer</td>\n",
       "      <td>https://www.hpu.edu/cncs/cmdr/img/polymerkit1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HDPE-2</td>\n",
       "      <td>Hawaii Pacific University Polymers Kit</td>\n",
       "      <td>H0008HDPE</td>\n",
       "      <td>PE</td>\n",
       "      <td>HDPE</td>\n",
       "      <td>Pellet</td>\n",
       "      <td>high density polyethylene,HDPE,polyethylene,HD...</td>\n",
       "      <td>{[][$]CC[$][]}</td>\n",
       "      <td>9002-88-4</td>\n",
       "      <td>polyolefins,semicrystalline,linear,homopolymer</td>\n",
       "      <td>https://www.hpu.edu/cncs/cmdr/img/polymerkit1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PP</td>\n",
       "      <td>Hawaii Pacific University Polymers Kit</td>\n",
       "      <td>H0009PP</td>\n",
       "      <td>PP</td>\n",
       "      <td>PP</td>\n",
       "      <td>Pellet</td>\n",
       "      <td>polypropylene,PP</td>\n",
       "      <td>{[][$]CC(C)[$][]}</td>\n",
       "      <td>9003-07-0</td>\n",
       "      <td>polyolefins,semicrystalline,linear,homopolymer</td>\n",
       "      <td>https://www.hpu.edu/cncs/cmdr/img/polymerkit1....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Resin Code/Original Identifier                                  Source  \\\n",
       "14                        LLDPE-2  Hawaii Pacific University Polymers Kit   \n",
       "15                           MDPE  Hawaii Pacific University Polymers Kit   \n",
       "16                         HDPE-1  Hawaii Pacific University Polymers Kit   \n",
       "17                         HDPE-2  Hawaii Pacific University Polymers Kit   \n",
       "18                             PP  Hawaii Pacific University Polymers Kit   \n",
       "\n",
       "        Sample Class1 Class2 Physical State  \\\n",
       "14  H0005LLDPE     PE  LLDPE         Pellet   \n",
       "15   H0006MDPE     PE   MDPE         Pellet   \n",
       "16   H0007HDPE     PE   HDPE         Pellet   \n",
       "17   H0008HDPE     PE   HDPE         Pellet   \n",
       "18     H0009PP     PP     PP         Pellet   \n",
       "\n",
       "                                      Alternate names  \\\n",
       "14  linear low density polyethylene,LLDPE,polyethy...   \n",
       "15         mid density polyethylene,MDPE,polyethylene   \n",
       "16  high density polyethylene,HDPE,polyethylene,HD...   \n",
       "17  high density polyethylene,HDPE,polyethylene,HD...   \n",
       "18                                   polypropylene,PP   \n",
       "\n",
       "                                          bigSMILES CAS number  \\\n",
       "14        {[][$]CC[$],[$]CC({[$][$]C[$][$]}C)[$][]}  9002-88-4   \n",
       "15  {[][$]CC[$],[$]CC([$])[$],[$]CC([$])([$])[$][]}  9002-88-4   \n",
       "16                                   {[][$]CC[$][]}  9002-88-4   \n",
       "17                                   {[][$]CC[$][]}  9002-88-4   \n",
       "18                                {[][$]CC(C)[$][]}  9003-07-0   \n",
       "\n",
       "                                 Material Keywords  \\\n",
       "14           polyolefins,semicrystalline,copolymer   \n",
       "15                     polyolefins,semicrystalline   \n",
       "16  polyolefins,semicrystalline,linear,homopolymer   \n",
       "17  polyolefins,semicrystalline,linear,homopolymer   \n",
       "18  polyolefins,semicrystalline,linear,homopolymer   \n",
       "\n",
       "                                        reference URL  \n",
       "14  https://www.hpu.edu/cncs/cmdr/img/polymerkit1....  \n",
       "15  https://www.hpu.edu/cncs/cmdr/img/polymerkit1....  \n",
       "16  https://www.hpu.edu/cncs/cmdr/img/polymerkit1....  \n",
       "17  https://www.hpu.edu/cncs/cmdr/img/polymerkit1....  \n",
       "18  https://www.hpu.edu/cncs/cmdr/img/polymerkit1....  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_info = pd.read_csv(f'{main_dir}Data/SampleInformation.csv')\n",
    "sample_info.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916956b1-0b43-49fc-b805-b9aed5e53d0f",
   "metadata": {},
   "source": [
    "Now we load the file that has our physical measurement information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3755afc4-db12-41c0-a6d4-349ee278bab5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Density [g_per_cm3]</th>\n",
       "      <th>Density SEM [g_per_cm3]</th>\n",
       "      <th>Crystallinity [%]</th>\n",
       "      <th>Mw(IR) [g_per_mol]</th>\n",
       "      <th>Dispersity (IR)</th>\n",
       "      <th>Mw(MALS) [g_per_mol]</th>\n",
       "      <th>Dispersity(MALS)</th>\n",
       "      <th>Mw(Viscometer) [g_per_mol]</th>\n",
       "      <th>Dispersity(Viscometer)</th>\n",
       "      <th>MHa</th>\n",
       "      <th>MHK [dL_per_g]</th>\n",
       "      <th>SCB_per_1000C (IR)</th>\n",
       "      <th>SCB_per_1000C StDev (IR)</th>\n",
       "      <th>LCBf_per_1000C  (MALS)</th>\n",
       "      <th>LCBf_per_1000C  StDev (MALS)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S0024LDPE</td>\n",
       "      <td>0.916497</td>\n",
       "      <td>0.001254</td>\n",
       "      <td>0.486853</td>\n",
       "      <td>159150</td>\n",
       "      <td>9.645</td>\n",
       "      <td>339950</td>\n",
       "      <td>17.995</td>\n",
       "      <td>514550</td>\n",
       "      <td>23.885</td>\n",
       "      <td>0.41355</td>\n",
       "      <td>0.007253</td>\n",
       "      <td>28.90</td>\n",
       "      <td>1.131371</td>\n",
       "      <td>0.73890</td>\n",
       "      <td>0.283408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S0011PP</td>\n",
       "      <td>0.911211</td>\n",
       "      <td>0.003405</td>\n",
       "      <td>0.438083</td>\n",
       "      <td>420500</td>\n",
       "      <td>7.480</td>\n",
       "      <td>246350</td>\n",
       "      <td>6.185</td>\n",
       "      <td>578300</td>\n",
       "      <td>8.170</td>\n",
       "      <td>0.73000</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>331.60</td>\n",
       "      <td>1.272792</td>\n",
       "      <td>0.00045</td>\n",
       "      <td>0.000495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S0040HDPE</td>\n",
       "      <td>0.956345</td>\n",
       "      <td>0.001766</td>\n",
       "      <td>0.624898</td>\n",
       "      <td>144400</td>\n",
       "      <td>7.980</td>\n",
       "      <td>134850</td>\n",
       "      <td>8.750</td>\n",
       "      <td>167250</td>\n",
       "      <td>8.210</td>\n",
       "      <td>0.71295</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>7.35</td>\n",
       "      <td>0.777817</td>\n",
       "      <td>0.20250</td>\n",
       "      <td>0.014566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H0007HDPE</td>\n",
       "      <td>0.951718</td>\n",
       "      <td>0.001085</td>\n",
       "      <td>0.557357</td>\n",
       "      <td>138000</td>\n",
       "      <td>12.640</td>\n",
       "      <td>134300</td>\n",
       "      <td>11.420</td>\n",
       "      <td>174200</td>\n",
       "      <td>13.435</td>\n",
       "      <td>0.69600</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>8.70</td>\n",
       "      <td>2.404163</td>\n",
       "      <td>0.13400</td>\n",
       "      <td>0.008000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H0008HDPE</td>\n",
       "      <td>0.953928</td>\n",
       "      <td>0.002385</td>\n",
       "      <td>0.655347</td>\n",
       "      <td>133700</td>\n",
       "      <td>7.510</td>\n",
       "      <td>111600</td>\n",
       "      <td>8.665</td>\n",
       "      <td>158850</td>\n",
       "      <td>7.985</td>\n",
       "      <td>0.69800</td>\n",
       "      <td>0.000576</td>\n",
       "      <td>6.75</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.07885</td>\n",
       "      <td>0.105005</td>\n",
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      "text/plain": [
       "      Sample  Density [g_per_cm3]  Density SEM [g_per_cm3]  Crystallinity [%]  \\\n",
       "0  S0024LDPE             0.916497                 0.001254           0.486853   \n",
       "1    S0011PP             0.911211                 0.003405           0.438083   \n",
       "2  S0040HDPE             0.956345                 0.001766           0.624898   \n",
       "3  H0007HDPE             0.951718                 0.001085           0.557357   \n",
       "4  H0008HDPE             0.953928                 0.002385           0.655347   \n",
       "\n",
       "   Mw(IR) [g_per_mol]  Dispersity (IR)  Mw(MALS) [g_per_mol]  \\\n",
       "0              159150            9.645                339950   \n",
       "1              420500            7.480                246350   \n",
       "2              144400            7.980                134850   \n",
       "3              138000           12.640                134300   \n",
       "4              133700            7.510                111600   \n",
       "\n",
       "   Dispersity(MALS)  Mw(Viscometer) [g_per_mol]  Dispersity(Viscometer)  \\\n",
       "0            17.995                      514550                  23.885   \n",
       "1             6.185                      578300                   8.170   \n",
       "2             8.750                      167250                   8.210   \n",
       "3            11.420                      174200                  13.435   \n",
       "4             8.665                      158850                   7.985   \n",
       "\n",
       "       MHa  MHK [dL_per_g]  SCB_per_1000C (IR)  SCB_per_1000C StDev (IR)  \\\n",
       "0  0.41355        0.007253               28.90                  1.131371   \n",
       "1  0.73000        0.000176              331.60                  1.272792   \n",
       "2  0.71295        0.000478                7.35                  0.777817   \n",
       "3  0.69600        0.000552                8.70                  2.404163   \n",
       "4  0.69800        0.000576                6.75                  0.353553   \n",
       "\n",
       "   LCBf_per_1000C  (MALS)  LCBf_per_1000C  StDev (MALS)  \n",
       "0                 0.73890                      0.283408  \n",
       "1                 0.00045                      0.000495  \n",
       "2                 0.20250                      0.014566  \n",
       "3                 0.13400                      0.008000  \n",
       "4                 0.07885                      0.105005  "
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     },
     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "add_data = pd.read_csv(f'{main_dir}Data/AdditionalMeasurementData.csv')\n",
    "add_data.head()"
   ]
  },
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   "cell_type": "markdown",
   "id": "7371b2b1-79ec-4e64-8d59-d3d7da795aad",
   "metadata": {},
   "source": [
    "Finally, we can use our file to generate a list of the csvs that hold the spectral data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0429231f-18be-4168-9bd6-f360670e8bb1",
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   "outputs": [],
   "source": [
    "samples = sample_info.Sample\n",
    "replicates = [1, 2, 3, 4, 5, 6]\n",
    "pre_filelist = itertools.product(samples, replicates)\n",
    "\n",
    "d_nir = 'Data/NIR/'\n",
    "filelist = [\n",
    "    f'{main_dir}{d_nir}{\"_\".join([fs[0], str(fs[1])])}.csv'\n",
    "    for fs in pre_filelist]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62399ba0-ce06-4021-b980-032425ecc71e",
   "metadata": {},
   "source": [
    "Now we can load in each `*.csv` file from our filelist and add it to an Xarray Dataset. When we load it in, we'll also add all of the information from our other 2 files, and we'll add units where necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b1e8b118-2e06-48b0-8b3e-2a3ea9de0f38",
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       "  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 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\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: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;\n",
       "Dimensions:                   (Wavenumber: 4149, sample: 114)\n",
       "Coordinates:\n",
       "  * Wavenumber                (Wavenumber) float64 4e+03 4.002e+03 ... 1.2e+04\n",
       "    polymer                   (sample) &lt;U10 &#x27;N1476LDPE&#x27; ... &#x27;H0009PP&#x27;\n",
       "  * sample                    (sample) &lt;U12 &#x27;N1476LDPE_1&#x27; ... &#x27;H0009PP_6&#x27;\n",
       "    state                     (sample) &lt;U6 &#x27;Pellet&#x27; &#x27;Pellet&#x27; ... &#x27;Pellet&#x27;\n",
       "    repeat                    (sample) int64 1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6\n",
       "    Class1                    (sample) &lt;U2 &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27; ... &#x27;PP&#x27; &#x27;PP&#x27; &#x27;PP&#x27;\n",
       "    Class2                    (sample) &lt;U8 &#x27;LDPE&#x27; &#x27;LDPE&#x27; &#x27;LDPE&#x27; ... &#x27;PP&#x27; &#x27;PP&#x27;\n",
       "Data variables:\n",
       "    Intensity                 (sample, Wavenumber) float64 4.443 4.447 ... 19.11\n",
       "    Density                   (sample) float64 0.9197 0.9197 ... 0.8873 0.8873\n",
       "    Density SEM               (sample) float64 0.001673 0.001673 ... 0.004277\n",
       "    Crystallinity             (sample) float64 0.3126 0.3126 ... 0.4379 0.4379\n",
       "    SCB_per_1000C (IR)        (sample) float64 21.2 21.2 21.2 ... 312.9 312.9\n",
       "    SCB_per_1000C StDev (IR)  (sample) float64 1.56 1.56 1.56 ... 0.495 0.495\n",
       "Attributes:\n",
       "    creation_date:      20230523\n",
       "    author:             Bradley Sutliff\n",
       "    email:              Bradley.Sutliff@nist.gov\n",
       "    data_collected_by:  Shailja Goyal</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-af91a1ec-df34-4f5c-9f14-b1f4b4b4262d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af91a1ec-df34-4f5c-9f14-b1f4b4b4262d' 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'>Wavenumber</span>: 4149</li><li><span class='xr-has-index'>sample</span>: 114</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2ce9ffa0-4f38-45d5-ae7c-0f2bf4b36cc7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ce9ffa0-4f38-45d5-ae7c-0f2bf4b36cc7' class='xr-section-summary' >Coordinates: <span>(7)</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'>Wavenumber</span></div><div class='xr-var-dims'>(Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4e+03 4.002e+03 ... 1.2e+04 1.2e+04</div><input id='attrs-3f89dcf3-6349-4222-b1b3-5c34358bffb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f89dcf3-6349-4222-b1b3-5c34358bffb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea694fca-9896-41c0-acfa-a1031d68cfc7' class='xr-var-data-in' type='checkbox'><label for='data-ea694fca-9896-41c0-acfa-a1031d68cfc7' 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>1/cm</dd></dl></div><div class='xr-var-data'><pre>array([ 3999.706,  4001.634,  4003.563, ..., 11995.26 , 11997.19 , 11999.12 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polymer</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;N1476LDPE&#x27; ... &#x27;H0009PP&#x27;</div><input id='attrs-1e4d2310-b3a8-432f-831b-65c825118ce2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e4d2310-b3a8-432f-831b-65c825118ce2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f3cd7aa-bdea-47bb-98c8-f4b06d3940fc' class='xr-var-data-in' type='checkbox'><label for='data-0f3cd7aa-bdea-47bb-98c8-f4b06d3940fc' 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([&#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;,\n",
       "       &#x27;N1476LDPE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;,\n",
       "       &#x27;N1474PE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;,\n",
       "       &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;,\n",
       "       &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;,\n",
       "       &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0024LDPE&#x27;,\n",
       "       &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;,\n",
       "       &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;,\n",
       "       &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;,\n",
       "       &#x27;S0048HDPE&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;,\n",
       "       &#x27;S0052PP&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;,\n",
       "       &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;,\n",
       "       &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;,\n",
       "       &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0003LDPE&#x27;,\n",
       "       &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;,\n",
       "       &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;,\n",
       "       &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;,\n",
       "       &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;,\n",
       "       &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;,\n",
       "       &#x27;H0006MDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;,\n",
       "       &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;,\n",
       "       &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;,\n",
       "       &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;N1476LDPE_1&#x27; ... &#x27;H0009PP_6&#x27;</div><input id='attrs-31a6ebe7-3fdc-4a93-8031-fa9e2037f8be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31a6ebe7-3fdc-4a93-8031-fa9e2037f8be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c545c259-fc11-4814-954a-debadd1bc35e' class='xr-var-data-in' type='checkbox'><label for='data-c545c259-fc11-4814-954a-debadd1bc35e' 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([&#x27;N1476LDPE_1&#x27;, &#x27;N1476LDPE_2&#x27;, &#x27;N1476LDPE_3&#x27;, &#x27;N1476LDPE_4&#x27;,\n",
       "       &#x27;N1476LDPE_5&#x27;, &#x27;N1476LDPE_6&#x27;, &#x27;N1474PE_1&#x27;, &#x27;N1474PE_2&#x27;, &#x27;N1474PE_3&#x27;,\n",
       "       &#x27;N1474PE_4&#x27;, &#x27;N1474PE_5&#x27;, &#x27;N1474PE_6&#x27;, &#x27;N1473LDPE_1&#x27;, &#x27;N1473LDPE_2&#x27;,\n",
       "       &#x27;N1473LDPE_3&#x27;, &#x27;N1473LDPE_4&#x27;, &#x27;N1473LDPE_5&#x27;, &#x27;N1473LDPE_6&#x27;,\n",
       "       &#x27;S0081LDPE_1&#x27;, &#x27;S0081LDPE_2&#x27;, &#x27;S0081LDPE_3&#x27;, &#x27;S0081LDPE_4&#x27;,\n",
       "       &#x27;S0081LDPE_5&#x27;, &#x27;S0081LDPE_6&#x27;, &#x27;S0036RPP_1&#x27;, &#x27;S0036RPP_2&#x27;, &#x27;S0036RPP_3&#x27;,\n",
       "       &#x27;S0036RPP_4&#x27;, &#x27;S0036RPP_5&#x27;, &#x27;S0036RPP_6&#x27;, &#x27;S0024LDPE_1&#x27;, &#x27;S0024LDPE_2&#x27;,\n",
       "       &#x27;S0024LDPE_3&#x27;, &#x27;S0024LDPE_4&#x27;, &#x27;S0024LDPE_5&#x27;, &#x27;S0024LDPE_6&#x27;, &#x27;S0011PP_1&#x27;,\n",
       "       &#x27;S0011PP_2&#x27;, &#x27;S0011PP_3&#x27;, &#x27;S0011PP_4&#x27;, &#x27;S0011PP_5&#x27;, &#x27;S0011PP_6&#x27;,\n",
       "       &#x27;S0048HDPE_1&#x27;, &#x27;S0048HDPE_2&#x27;, &#x27;S0048HDPE_3&#x27;, &#x27;S0048HDPE_4&#x27;,\n",
       "       &#x27;S0048HDPE_5&#x27;, &#x27;S0048HDPE_6&#x27;, &#x27;S0052PP_1&#x27;, &#x27;S0052PP_2&#x27;, &#x27;S0052PP_3&#x27;,\n",
       "       &#x27;S0052PP_4&#x27;, &#x27;S0052PP_5&#x27;, &#x27;S0052PP_6&#x27;, &#x27;S0040HDPE_1&#x27;, &#x27;S0040HDPE_2&#x27;,\n",
       "       &#x27;S0040HDPE_3&#x27;, &#x27;S0040HDPE_4&#x27;, &#x27;S0040HDPE_5&#x27;, &#x27;S0040HDPE_6&#x27;,\n",
       "       &#x27;H0001ULDPE_1&#x27;, &#x27;H0001ULDPE_2&#x27;, &#x27;H0001ULDPE_3&#x27;, &#x27;H0001ULDPE_4&#x27;,\n",
       "       &#x27;H0001ULDPE_5&#x27;, &#x27;H0001ULDPE_6&#x27;, &#x27;H0002LDPE_1&#x27;, &#x27;H0002LDPE_2&#x27;,\n",
       "       &#x27;H0002LDPE_3&#x27;, &#x27;H0002LDPE_4&#x27;, &#x27;H0002LDPE_5&#x27;, &#x27;H0002LDPE_6&#x27;,\n",
       "       &#x27;H0003LDPE_1&#x27;, &#x27;H0003LDPE_2&#x27;, &#x27;H0003LDPE_3&#x27;, &#x27;H0003LDPE_4&#x27;,\n",
       "       &#x27;H0003LDPE_5&#x27;, &#x27;H0003LDPE_6&#x27;, &#x27;H0004LLDPE_1&#x27;, &#x27;H0004LLDPE_2&#x27;,\n",
       "       &#x27;H0004LLDPE_3&#x27;, &#x27;H0004LLDPE_4&#x27;, &#x27;H0004LLDPE_5&#x27;, &#x27;H0004LLDPE_6&#x27;,\n",
       "       &#x27;H0005LLDPE_1&#x27;, &#x27;H0005LLDPE_2&#x27;, &#x27;H0005LLDPE_3&#x27;, &#x27;H0005LLDPE_4&#x27;,\n",
       "       &#x27;H0005LLDPE_5&#x27;, &#x27;H0005LLDPE_6&#x27;, &#x27;H0006MDPE_1&#x27;, &#x27;H0006MDPE_2&#x27;,\n",
       "       &#x27;H0006MDPE_3&#x27;, &#x27;H0006MDPE_4&#x27;, &#x27;H0006MDPE_5&#x27;, &#x27;H0006MDPE_6&#x27;,\n",
       "       &#x27;H0007HDPE_1&#x27;, &#x27;H0007HDPE_2&#x27;, &#x27;H0007HDPE_3&#x27;, &#x27;H0007HDPE_4&#x27;,\n",
       "       &#x27;H0007HDPE_5&#x27;, &#x27;H0007HDPE_6&#x27;, &#x27;H0008HDPE_1&#x27;, &#x27;H0008HDPE_2&#x27;,\n",
       "       &#x27;H0008HDPE_3&#x27;, &#x27;H0008HDPE_4&#x27;, &#x27;H0008HDPE_5&#x27;, &#x27;H0008HDPE_6&#x27;, &#x27;H0009PP_1&#x27;,\n",
       "       &#x27;H0009PP_2&#x27;, &#x27;H0009PP_3&#x27;, &#x27;H0009PP_4&#x27;, &#x27;H0009PP_5&#x27;, &#x27;H0009PP_6&#x27;],\n",
       "      dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;Pellet&#x27; &#x27;Pellet&#x27; ... &#x27;Pellet&#x27;</div><input id='attrs-79ef87be-6d39-4e08-baf4-0d24f16d6cd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79ef87be-6d39-4e08-baf4-0d24f16d6cd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5ff672c-ecfe-45af-b6e1-7ef8dc0dd046' class='xr-var-data-in' type='checkbox'><label for='data-f5ff672c-ecfe-45af-b6e1-7ef8dc0dd046' 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([&#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;],\n",
       "      dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>repeat</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6</div><input id='attrs-b9a8cb5e-1bc2-4aa1-ae8a-492dde244260' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9a8cb5e-1bc2-4aa1-ae8a-492dde244260' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc1dabda-3924-492a-82e4-5ad86524ec2f' class='xr-var-data-in' type='checkbox'><label for='data-cc1dabda-3924-492a-82e4-5ad86524ec2f' 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([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n",
       "       5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2,\n",
       "       3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6,\n",
       "       1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n",
       "       5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2,\n",
       "       3, 4, 5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Class1</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27; ... &#x27;PP&#x27; &#x27;PP&#x27; &#x27;PP&#x27;</div><input id='attrs-4541b59e-e139-483a-9f7e-d9b00cc50ddd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4541b59e-e139-483a-9f7e-d9b00cc50ddd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3aa0788-8bef-48f2-be4c-6125e5e425af' class='xr-var-data-in' type='checkbox'><label for='data-d3aa0788-8bef-48f2-be4c-6125e5e425af' 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([&#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;,\n",
       "       &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Class2</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;LDPE&#x27; &#x27;LDPE&#x27; &#x27;LDPE&#x27; ... &#x27;PP&#x27; &#x27;PP&#x27;</div><input id='attrs-a9f50b63-f0c9-4f8b-94e0-1d7252915aad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9f50b63-f0c9-4f8b-94e0-1d7252915aad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23709c60-3d6c-4c43-91d6-fa0565ea584b' class='xr-var-data-in' type='checkbox'><label for='data-23709c60-3d6c-4c43-91d6-fa0565ea584b' 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([&#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;,\n",
       "       &#x27;PP-co-PE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;PP&#x27;,\n",
       "       &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;,\n",
       "       &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;,\n",
       "       &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;,\n",
       "       &#x27;LLDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;],\n",
       "      dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-594ba19b-4e02-418e-b0f3-e4cb565f2a14' class='xr-section-summary-in' type='checkbox'  checked><label for='section-594ba19b-4e02-418e-b0f3-e4cb565f2a14' class='xr-section-summary' >Data variables: <span>(6)</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>Intensity</span></div><div class='xr-var-dims'>(sample, Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.443 4.447 4.446 ... 19.16 19.11</div><input id='attrs-5671cbff-35af-4019-beee-bdcbe14557f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5671cbff-35af-4019-beee-bdcbe14557f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73405bb0-a2fd-455c-a3ee-ed9a68d757c0' class='xr-var-data-in' type='checkbox'><label for='data-73405bb0-a2fd-455c-a3ee-ed9a68d757c0' 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>% Reflectance</dd></dl></div><div class='xr-var-data'><pre>array([[ 4.442991,  4.446667,  4.446404, ..., 21.11305 , 21.27505 ,\n",
       "        21.35377 ],\n",
       "       [ 2.902091,  2.902828,  2.900718, ..., 17.02094 , 17.10987 ,\n",
       "        17.13083 ],\n",
       "       [ 3.693243,  3.697879,  3.700639, ..., 19.06606 , 19.2397  ,\n",
       "        19.35522 ],\n",
       "       ...,\n",
       "       [ 2.593265,  2.591108,  2.589963, ..., 19.43818 , 19.41288 ,\n",
       "        19.48311 ],\n",
       "       [ 2.872113,  2.872563,  2.872946, ..., 19.10243 , 19.02049 ,\n",
       "        19.02297 ],\n",
       "       [ 3.172266,  3.168778,  3.167384, ..., 19.25504 , 19.15759 ,\n",
       "        19.10684 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Density</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9197 0.9197 ... 0.8873 0.8873</div><input id='attrs-13cdc80a-743f-4d31-a5dd-da26afff61ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13cdc80a-743f-4d31-a5dd-da26afff61ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01ba3ae0-36f4-4ed4-b85d-287d7407be27' class='xr-var-data-in' type='checkbox'><label for='data-01ba3ae0-36f4-4ed4-b85d-287d7407be27' 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>g/cm^3</dd></dl></div><div class='xr-var-data'><pre>array([0.91972693, 0.91972693, 0.91972693, 0.91972693, 0.91972693,\n",
       "       0.91972693, 0.92719814, 0.92719814, 0.92719814, 0.92719814,\n",
       "       0.92719814, 0.92719814, 0.91710051, 0.91710051, 0.91710051,\n",
       "       0.91710051, 0.91710051, 0.91710051, 0.92130818, 0.92130818,\n",
       "       0.92130818, 0.92130818, 0.92130818, 0.92130818, 0.90972612,\n",
       "       0.90972612, 0.90972612, 0.90972612, 0.90972612, 0.90972612,\n",
       "       0.91649674, 0.91649674, 0.91649674, 0.91649674, 0.91649674,\n",
       "       0.91649674, 0.91121074, 0.91121074, 0.91121074, 0.91121074,\n",
       "       0.91121074, 0.91121074, 0.95635806, 0.95635806, 0.95635806,\n",
       "       0.95635806, 0.95635806, 0.95635806, 0.89319665, 0.89319665,\n",
       "       0.89319665, 0.89319665, 0.89319665, 0.89319665, 0.95634513,\n",
       "       0.95634513, 0.95634513, 0.95634513, 0.95634513, 0.95634513,\n",
       "       0.91880138, 0.91880138, 0.91880138, 0.91880138, 0.91880138,\n",
       "       0.91880138, 0.92407408, 0.92407408, 0.92407408, 0.92407408,\n",
       "       0.92407408, 0.92407408, 0.90557274, 0.90557274, 0.90557274,\n",
       "       0.90557274, 0.90557274, 0.90557274, 0.91131919, 0.91131919,\n",
       "       0.91131919, 0.91131919, 0.91131919, 0.91131919, 0.91961018,\n",
       "       0.91961018, 0.91961018, 0.91961018, 0.91961018, 0.91961018,\n",
       "       0.93417556, 0.93417556, 0.93417556, 0.93417556, 0.93417556,\n",
       "       0.93417556, 0.95171828, 0.95171828, 0.95171828, 0.95171828,\n",
       "       0.95171828, 0.95171828, 0.95392818, 0.95392818, 0.95392818,\n",
       "       0.95392818, 0.95392818, 0.95392818, 0.88727493, 0.88727493,\n",
       "       0.88727493, 0.88727493, 0.88727493, 0.88727493])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Density SEM</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001673 0.001673 ... 0.004277</div><input id='attrs-bc804999-ecb7-44b3-8770-f01018ce9ee4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc804999-ecb7-44b3-8770-f01018ce9ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b7bc39d-df65-4879-acb0-14e62963e837' class='xr-var-data-in' type='checkbox'><label for='data-7b7bc39d-df65-4879-acb0-14e62963e837' 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([1.672701e-03, 1.672701e-03, 1.672701e-03, 1.672701e-03,\n",
       "       1.672701e-03, 1.672701e-03, 6.705817e-03, 6.705817e-03,\n",
       "       6.705817e-03, 6.705817e-03, 6.705817e-03, 6.705817e-03,\n",
       "       7.410060e-04, 7.410060e-04, 7.410060e-04, 7.410060e-04,\n",
       "       7.410060e-04, 7.410060e-04, 3.695250e-04, 3.695250e-04,\n",
       "       3.695250e-04, 3.695250e-04, 3.695250e-04, 3.695250e-04,\n",
       "       2.831310e-04, 2.831310e-04, 2.831310e-04, 2.831310e-04,\n",
       "       2.831310e-04, 2.831310e-04, 1.253648e-03, 1.253648e-03,\n",
       "       1.253648e-03, 1.253648e-03, 1.253648e-03, 1.253648e-03,\n",
       "       3.404925e-03, 3.404925e-03, 3.404925e-03, 3.404925e-03,\n",
       "       3.404925e-03, 3.404925e-03, 1.546178e-03, 1.546178e-03,\n",
       "       1.546178e-03, 1.546178e-03, 1.546178e-03, 1.546178e-03,\n",
       "       5.016647e-03, 5.016647e-03, 5.016647e-03, 5.016647e-03,\n",
       "       5.016647e-03, 5.016647e-03, 1.766161e-03, 1.766161e-03,\n",
       "       1.766161e-03, 1.766161e-03, 1.766161e-03, 1.766161e-03,\n",
       "       7.944570e-04, 7.944570e-04, 7.944570e-04, 7.944570e-04,\n",
       "       7.944570e-04, 7.944570e-04, 5.190740e-04, 5.190740e-04,\n",
       "       5.190740e-04, 5.190740e-04, 5.190740e-04, 5.190740e-04,\n",
       "       8.210101e-03, 8.210101e-03, 8.210101e-03, 8.210101e-03,\n",
       "       8.210101e-03, 8.210101e-03, 8.919850e-04, 8.919850e-04,\n",
       "       8.919850e-04, 8.919850e-04, 8.919850e-04, 8.919850e-04,\n",
       "       6.820000e-05, 6.820000e-05, 6.820000e-05, 6.820000e-05,\n",
       "       6.820000e-05, 6.820000e-05, 1.049327e-03, 1.049327e-03,\n",
       "       1.049327e-03, 1.049327e-03, 1.049327e-03, 1.049327e-03,\n",
       "       1.084829e-03, 1.084829e-03, 1.084829e-03, 1.084829e-03,\n",
       "       1.084829e-03, 1.084829e-03, 2.385313e-03, 2.385313e-03,\n",
       "       2.385313e-03, 2.385313e-03, 2.385313e-03, 2.385313e-03,\n",
       "       4.276687e-03, 4.276687e-03, 4.276687e-03, 4.276687e-03,\n",
       "       4.276687e-03, 4.276687e-03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Crystallinity</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3126 0.3126 ... 0.4379 0.4379</div><input id='attrs-fd43eab0-5ec0-46ec-b216-2a6648a13a57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd43eab0-5ec0-46ec-b216-2a6648a13a57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-098777e3-982a-4644-916a-e8cff7cd6834' class='xr-var-data-in' type='checkbox'><label for='data-098777e3-982a-4644-916a-e8cff7cd6834' 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>%</dd></dl></div><div class='xr-var-data'><pre>array([0.31264646, 0.31264646, 0.31264646, 0.31264646, 0.31264646,\n",
       "       0.31264646, 0.49529973, 0.49529973, 0.49529973, 0.49529973,\n",
       "       0.49529973, 0.49529973, 0.24711512, 0.24711512, 0.24711512,\n",
       "       0.24711512, 0.24711512, 0.24711512, 0.37084469, 0.37084469,\n",
       "       0.37084469, 0.37084469, 0.37084469, 0.37084469, 0.39028971,\n",
       "       0.39028971, 0.39028971, 0.39028971, 0.39028971, 0.39028971,\n",
       "       0.48685286, 0.48685286, 0.48685286, 0.48685286, 0.48685286,\n",
       "       0.48685286, 0.43808305, 0.43808305, 0.43808305, 0.43808305,\n",
       "       0.43808305, 0.43808305, 0.60381471, 0.60381471, 0.60381471,\n",
       "       0.60381471, 0.60381471, 0.60381471, 0.38284404, 0.38284404,\n",
       "       0.38284404, 0.38284404, 0.38284404, 0.38284404, 0.62489782,\n",
       "       0.62489782, 0.62489782, 0.62489782, 0.62489782, 0.62489782,\n",
       "       0.34625341, 0.34625341, 0.34625341, 0.34625341, 0.34625341,\n",
       "       0.34625341, 0.40023842, 0.40023842, 0.40023842, 0.40023842,\n",
       "       0.40023842, 0.40023842, 0.37581744, 0.37581744, 0.37581744,\n",
       "       0.37581744, 0.37581744, 0.37581744, 0.42735014, 0.42735014,\n",
       "       0.42735014, 0.42735014, 0.42735014, 0.42735014, 0.32923706,\n",
       "       0.32923706, 0.32923706, 0.32923706, 0.32923706, 0.32923706,\n",
       "       0.50395095, 0.50395095, 0.50395095, 0.50395095, 0.50395095,\n",
       "       0.50395095, 0.55735695, 0.55735695, 0.55735695, 0.55735695,\n",
       "       0.55735695, 0.55735695, 0.65534741, 0.65534741, 0.65534741,\n",
       "       0.65534741, 0.65534741, 0.65534741, 0.43788991, 0.43788991,\n",
       "       0.43788991, 0.43788991, 0.43788991, 0.43788991])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SCB_per_1000C (IR)</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>21.2 21.2 21.2 ... 312.9 312.9</div><input id='attrs-c2447710-cd70-42cd-8232-736330a24d1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2447710-cd70-42cd-8232-736330a24d1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db2e2b97-8304-4d40-aa4a-0279b67238c3' class='xr-var-data-in' type='checkbox'><label for='data-db2e2b97-8304-4d40-aa4a-0279b67238c3' 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([ 21.2 ,  21.2 ,  21.2 ,  21.2 ,  21.2 ,  21.2 ,  10.9 ,  10.9 ,\n",
       "        10.9 ,  10.9 ,  10.9 ,  10.9 ,  27.2 ,  27.2 ,  27.2 ,  27.2 ,\n",
       "        27.2 ,  27.2 ,  23.6 ,  23.6 ,  23.6 ,  23.6 ,  23.6 ,  23.6 ,\n",
       "       318.75, 318.75, 318.75, 318.75, 318.75, 318.75,  28.9 ,  28.9 ,\n",
       "        28.9 ,  28.9 ,  28.9 ,  28.9 , 331.6 , 331.6 , 331.6 , 331.6 ,\n",
       "       331.6 , 331.6 ,   9.7 ,   9.7 ,   9.7 ,   9.7 ,   9.7 ,   9.7 ,\n",
       "       293.65, 293.65, 293.65, 293.65, 293.65, 293.65,   7.35,   7.35,\n",
       "         7.35,   7.35,   7.35,   7.35,  15.4 ,  15.4 ,  15.4 ,  15.4 ,\n",
       "        15.4 ,  15.4 ,  27.4 ,  27.4 ,  27.4 ,  27.4 ,  27.4 ,  27.4 ,\n",
       "        28.8 ,  28.8 ,  28.8 ,  28.8 ,  28.8 ,  28.8 ,  18.6 ,  18.6 ,\n",
       "        18.6 ,  18.6 ,  18.6 ,  18.6 ,  20.2 ,  20.2 ,  20.2 ,  20.2 ,\n",
       "        20.2 ,  20.2 ,  12.65,  12.65,  12.65,  12.65,  12.65,  12.65,\n",
       "         8.7 ,   8.7 ,   8.7 ,   8.7 ,   8.7 ,   8.7 ,   6.75,   6.75,\n",
       "         6.75,   6.75,   6.75,   6.75, 312.95, 312.95, 312.95, 312.95,\n",
       "       312.95, 312.95])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SCB_per_1000C StDev (IR)</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.56 1.56 1.56 ... 0.495 0.495</div><input id='attrs-8ff5153f-4646-4d7d-b8de-86b283d3bdc1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ff5153f-4646-4d7d-b8de-86b283d3bdc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d1b74bb-af29-4229-92ff-6aed89c51bef' class='xr-var-data-in' type='checkbox'><label for='data-5d1b74bb-af29-4229-92ff-6aed89c51bef' 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([1.56      , 1.56      , 1.56      , 1.56      , 1.56      ,\n",
       "       1.56      , 2.26      , 2.26      , 2.26      , 2.26      ,\n",
       "       2.26      , 2.26      , 1.41      , 1.41      , 1.41      ,\n",
       "       1.41      , 1.41      , 1.41      , 0.28284271, 0.28284271,\n",
       "       0.28284271, 0.28284271, 0.28284271, 0.28284271, 0.91923882,\n",
       "       0.91923882, 0.91923882, 0.91923882, 0.91923882, 0.91923882,\n",
       "       1.13137085, 1.13137085, 1.13137085, 1.13137085, 1.13137085,\n",
       "       1.13137085, 1.27279221, 1.27279221, 1.27279221, 1.27279221,\n",
       "       1.27279221, 1.27279221, 0.42426407, 0.42426407, 0.42426407,\n",
       "       0.42426407, 0.42426407, 0.42426407, 1.06066017, 1.06066017,\n",
       "       1.06066017, 1.06066017, 1.06066017, 1.06066017, 0.77781746,\n",
       "       0.77781746, 0.77781746, 0.77781746, 0.77781746, 0.77781746,\n",
       "       1.41421356, 1.41421356, 1.41421356, 1.41421356, 1.41421356,\n",
       "       1.41421356, 0.56568542, 0.56568542, 0.56568542, 0.56568542,\n",
       "       0.56568542, 0.56568542, 3.67695526, 3.67695526, 3.67695526,\n",
       "       3.67695526, 3.67695526, 3.67695526, 0.14142136, 0.14142136,\n",
       "       0.14142136, 0.14142136, 0.14142136, 0.14142136, 0.42426407,\n",
       "       0.42426407, 0.42426407, 0.42426407, 0.42426407, 0.42426407,\n",
       "       0.49497475, 0.49497475, 0.49497475, 0.49497475, 0.49497475,\n",
       "       0.49497475, 2.40416306, 2.40416306, 2.40416306, 2.40416306,\n",
       "       2.40416306, 2.40416306, 0.353553  , 0.353553  , 0.353553  ,\n",
       "       0.353553  , 0.353553  , 0.353553  , 0.49497475, 0.49497475,\n",
       "       0.49497475, 0.49497475, 0.49497475, 0.49497475])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-678fb025-380d-4be0-bca9-4f498b74e1f5' class='xr-section-summary-in' type='checkbox'  ><label for='section-678fb025-380d-4be0-bca9-4f498b74e1f5' class='xr-section-summary' >Indexes: <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-index-name'><div>Wavenumber</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-62c4dcbb-c1c5-4f60-b28f-c2ecbc1e35b2' class='xr-index-data-in' type='checkbox'/><label for='index-62c4dcbb-c1c5-4f60-b28f-c2ecbc1e35b2' 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(Float64Index([3999.706, 4001.634, 4003.563, 4005.491,  4007.42, 4009.348,\n",
       "              4011.277, 4013.205, 4015.134, 4017.062,\n",
       "              ...\n",
       "              11981.76, 11983.69, 11985.62, 11987.55, 11989.47,  11991.4,\n",
       "              11993.33, 11995.26, 11997.19, 11999.12],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Wavenumber&#x27;, length=4149))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c7006ed-d3a6-4d7e-8534-e7e46192e380' class='xr-index-data-in' type='checkbox'/><label for='index-9c7006ed-d3a6-4d7e-8534-e7e46192e380' 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([&#x27;N1476LDPE_1&#x27;, &#x27;N1476LDPE_2&#x27;, &#x27;N1476LDPE_3&#x27;, &#x27;N1476LDPE_4&#x27;,\n",
       "       &#x27;N1476LDPE_5&#x27;, &#x27;N1476LDPE_6&#x27;, &#x27;N1474PE_1&#x27;, &#x27;N1474PE_2&#x27;, &#x27;N1474PE_3&#x27;,\n",
       "       &#x27;N1474PE_4&#x27;,\n",
       "       ...\n",
       "       &#x27;H0008HDPE_3&#x27;, &#x27;H0008HDPE_4&#x27;, &#x27;H0008HDPE_5&#x27;, &#x27;H0008HDPE_6&#x27;, &#x27;H0009PP_1&#x27;,\n",
       "       &#x27;H0009PP_2&#x27;, &#x27;H0009PP_3&#x27;, &#x27;H0009PP_4&#x27;, &#x27;H0009PP_5&#x27;, &#x27;H0009PP_6&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;sample&#x27;, length=114))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d271524-d35f-43a0-a89e-12ab0ff650b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d271524-d35f-43a0-a89e-12ab0ff650b4' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>creation_date :</span></dt><dd>20230523</dd><dt><span>author :</span></dt><dd>Bradley Sutliff</dd><dt><span>email :</span></dt><dd>Bradley.Sutliff@nist.gov</dd><dt><span>data_collected_by :</span></dt><dd>Shailja Goyal</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (Wavenumber: 4149, sample: 114)\n",
       "Coordinates:\n",
       "  * Wavenumber                (Wavenumber) float64 4e+03 4.002e+03 ... 1.2e+04\n",
       "    polymer                   (sample) <U10 'N1476LDPE' ... 'H0009PP'\n",
       "  * sample                    (sample) <U12 'N1476LDPE_1' ... 'H0009PP_6'\n",
       "    state                     (sample) <U6 'Pellet' 'Pellet' ... 'Pellet'\n",
       "    repeat                    (sample) int64 1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6\n",
       "    Class1                    (sample) <U2 'PE' 'PE' 'PE' ... 'PP' 'PP' 'PP'\n",
       "    Class2                    (sample) <U8 'LDPE' 'LDPE' 'LDPE' ... 'PP' 'PP'\n",
       "Data variables:\n",
       "    Intensity                 (sample, Wavenumber) float64 4.443 4.447 ... 19.11\n",
       "    Density                   (sample) float64 0.9197 0.9197 ... 0.8873 0.8873\n",
       "    Density SEM               (sample) float64 0.001673 0.001673 ... 0.004277\n",
       "    Crystallinity             (sample) float64 0.3126 0.3126 ... 0.4379 0.4379\n",
       "    SCB_per_1000C (IR)        (sample) float64 21.2 21.2 21.2 ... 312.9 312.9\n",
       "    SCB_per_1000C StDev (IR)  (sample) float64 1.56 1.56 1.56 ... 0.495 0.495\n",
       "Attributes:\n",
       "    creation_date:      20230523\n",
       "    author:             Bradley Sutliff\n",
       "    email:              Bradley.Sutliff@nist.gov\n",
       "    data_collected_by:  Shailja Goyal"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_add = ['Density [g_per_cm3]', 'Density SEM [g_per_cm3]',\n",
    "               'Crystallinity [%]',\n",
    "               'SCB_per_1000C (IR)', 'SCB_per_1000C StDev (IR)']\n",
    "ds_list = []\n",
    "for filepath in filelist:\n",
    "    sample = filepath.split('/')[-1].split('.')[0]\n",
    "    polymer = sample.split('_')[0]\n",
    "    repeat = sample.split('_')[1].split('.')[0]\n",
    "    s_info = sample_info.loc[sample_info.Sample == polymer, :]\n",
    "    add_info = add_data.loc[add_data.Sample == polymer, :]\n",
    "\n",
    "    # use pandas csv reader to read in file\n",
    "    dataframe = pd.read_csv(filepath,\n",
    "                            )\n",
    "    # convert to Xarray Dataset object\n",
    "    dataset = dataframe.set_index('Wavenumber').to_xarray()\n",
    "\n",
    "    # add in extra metadata from filename/filepath\n",
    "    dataset['polymer'] = str(polymer)\n",
    "    dataset['sample'] = str(sample)\n",
    "    dataset['state'] = str(s_info['Physical State'].values[0])\n",
    "    dataset['repeat'] = int(repeat)\n",
    "    dataset['Class1'] = str(s_info['Class1'].values[0])\n",
    "    dataset['Class2'] = str(s_info['Class2'].values[0])\n",
    "    for column in cols_to_add:\n",
    "        dataset[column.split('[')[0].strip()] = add_info[column].values[0]\n",
    "\n",
    "    # add units where applicable\n",
    "    dataset['Wavenumber'].attrs = {'units': '1/cm'}\n",
    "    dataset['Intensity'].attrs = {'units': '% Reflectance'}\n",
    "    dataset['Density'].attrs = {'units': 'g/cm^3'}\n",
    "    dataset['Crystallinity'].attrs = {'units': '%'}\n",
    "\n",
    "    # define global attributes\n",
    "    dataset.attrs = {'creation_date': datetime.datetime.now().strftime('%Y%m%d'),\n",
    "                     'author': 'Bradley Sutliff',\n",
    "                     'email': 'Bradley.Sutliff@nist.gov',\n",
    "                     'data_collected_by': 'Shailja Goyal'}\n",
    "    # aggregate data into a list\n",
    "    ds_list.append(dataset)\n",
    "\n",
    "ds = xr.concat(ds_list, dim='sample')\n",
    "ds = ds.set_coords(['sample', 'polymer', 'state',\n",
    "                   'Class2', 'Class1', 'repeat'])\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95290f0-0630-4652-894c-5f3ee0028ead",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Raw Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb51c5fe-56cd-4a47-b092-60e2723334eb",
   "metadata": {},
   "source": [
    "Take a look at the raw data. This will only plot one of each subclass to keep the plot neat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "864f46a6-3c05-4758-9231-311f7cc2af0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14c7c92d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set info for the plot\n",
    "color_scheme = \"Class2\"\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# loop through for each polymer subclass\n",
    "for ptype in pd.unique(ds[color_scheme]):\n",
    "    # grab data of only our chosen subclass\n",
    "    ds_sub = ds.where(ds[color_scheme] == ptype, drop=True)\n",
    "    # grab data for a single replicate, the first replicate\n",
    "    rep_samp = pd.unique(ds_sub.sample)[0]\n",
    "    ds_samp = ds_sub.where(ds_sub.sample == rep_samp, drop=True)\n",
    "\n",
    "    # grab our x and y values\n",
    "    x = ds_samp.Wavenumber.values.flatten()\n",
    "    y = ds_samp.Intensity.values.flatten()\n",
    "    # use one of our predefined dictionaries to choose a color\n",
    "    # based on the polymer subclass\n",
    "    color = cpalette[color_scheme][ptype]\n",
    "    linesty = dict_linesty[color_scheme][ptype]\n",
    "    # add the data\n",
    "    ax.plot(x,y, c=color, label=ptype, ls=linesty)\n",
    "\n",
    "# clean up the axis\n",
    "ax.set_ylabel(\"% Reflectance\")\n",
    "ax.set_xlabel(\"Wavenumber [cm$^{-1}$]\")\n",
    "ax.set_xlim(12000, 4000)\n",
    "ax.tick_params(\n",
    "    bottom=True,\n",
    "    top=True,\n",
    "    left=True,\n",
    "    right=True,\n",
    "    labelbottom=True,\n",
    "    labeltop=False,\n",
    "    labelleft=True,\n",
    "    labelright=False,\n",
    "    direction=\"inout\",\n",
    ")\n",
    "# fix the legend\n",
    "ax.legend(loc=\"best\", ncol=2, frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ce6fcb-ae27-4193-a6bf-bf8589b4e4bb",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Preprocessing Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "035e5a67-b99a-4bdd-a683-a80b112a8a77",
   "metadata": {},
   "source": [
    "### Multiplicative Scatter Correction (MSC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6217ef2d-053b-4d85-9042-0abfab979e7f",
   "metadata": {},
   "source": [
    "MSC is a common NIR scattering correction. It corrects for scattering artifacts, such as those that arise from differences in pellet or powder shape and size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a5f590df-0873-40e3-b72a-59fdf3e1929d",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ":root {\n",
       "  --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",
       "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 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\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: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;\n",
       "Dimensions:                   (Wavenumber: 4149, sample: 114)\n",
       "Coordinates:\n",
       "  * Wavenumber                (Wavenumber) float64 4e+03 4.002e+03 ... 1.2e+04\n",
       "  * sample                    (sample) &lt;U12 &#x27;H0001ULDPE_1&#x27; ... &#x27;S0081LDPE_6&#x27;\n",
       "    polymer                   (sample) &lt;U10 &#x27;H0001ULDPE&#x27; ... &#x27;S0081LDPE&#x27;\n",
       "    state                     (sample) &lt;U6 &#x27;Pellet&#x27; &#x27;Pellet&#x27; ... &#x27;Pellet&#x27;\n",
       "    repeat                    (sample) int64 1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6\n",
       "    Class1                    (sample) &lt;U2 &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27; ... &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27;\n",
       "    Class2                    (sample) &lt;U8 &#x27;LDPE&#x27; &#x27;LDPE&#x27; ... &#x27;LDPE&#x27; &#x27;LDPE&#x27;\n",
       "Data variables:\n",
       "    Intensity                 (sample, Wavenumber) float64 4.076 4.076 ... 12.66\n",
       "    Density                   (sample) float64 0.9188 0.9188 ... 0.9213 0.9213\n",
       "    Density SEM               (sample) float64 0.0007945 0.0007945 ... 0.0003695\n",
       "    Crystallinity             (sample) float64 0.3463 0.3463 ... 0.3708 0.3708\n",
       "    SCB_per_1000C (IR)        (sample) float64 15.4 15.4 15.4 ... 23.6 23.6 23.6\n",
       "    SCB_per_1000C StDev (IR)  (sample) float64 1.414 1.414 ... 0.2828 0.2828\n",
       "    MSC                       (sample, Wavenumber) float64 -4.927 ... 4.686\n",
       "Attributes:\n",
       "    creation_date:      20230523\n",
       "    author:             Bradley Sutliff\n",
       "    email:              Bradley.Sutliff@nist.gov\n",
       "    data_collected_by:  Shailja Goyal</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-a3438cf1-251f-4b57-a4ee-260d1fe27824' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3438cf1-251f-4b57-a4ee-260d1fe27824' 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'>Wavenumber</span>: 4149</li><li><span class='xr-has-index'>sample</span>: 114</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cf8a0312-a5ac-405f-a2ee-1b1d10b8c0b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf8a0312-a5ac-405f-a2ee-1b1d10b8c0b6' class='xr-section-summary' >Coordinates: <span>(7)</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'>Wavenumber</span></div><div class='xr-var-dims'>(Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4e+03 4.002e+03 ... 1.2e+04 1.2e+04</div><input id='attrs-1a12bd28-c849-425f-ae3f-267d0d7ed6d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a12bd28-c849-425f-ae3f-267d0d7ed6d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87015ab8-6c26-455f-8b4c-ca3a80891593' class='xr-var-data-in' type='checkbox'><label for='data-87015ab8-6c26-455f-8b4c-ca3a80891593' 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>1/cm</dd></dl></div><div class='xr-var-data'><pre>array([ 3999.706,  4001.634,  4003.563, ..., 11995.26 , 11997.19 , 11999.12 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;H0001ULDPE_1&#x27; ... &#x27;S0081LDPE_6&#x27;</div><input id='attrs-ab6ed595-448f-4032-89c4-ce1a59c7762d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab6ed595-448f-4032-89c4-ce1a59c7762d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0821b0fe-6fe3-4732-9173-291573a53b3c' class='xr-var-data-in' type='checkbox'><label for='data-0821b0fe-6fe3-4732-9173-291573a53b3c' 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([&#x27;H0001ULDPE_1&#x27;, &#x27;H0001ULDPE_2&#x27;, &#x27;H0001ULDPE_3&#x27;, &#x27;H0001ULDPE_4&#x27;,\n",
       "       &#x27;H0001ULDPE_5&#x27;, &#x27;H0001ULDPE_6&#x27;, &#x27;H0002LDPE_1&#x27;, &#x27;H0002LDPE_2&#x27;,\n",
       "       &#x27;H0002LDPE_3&#x27;, &#x27;H0002LDPE_4&#x27;, &#x27;H0002LDPE_5&#x27;, &#x27;H0002LDPE_6&#x27;,\n",
       "       &#x27;H0003LDPE_1&#x27;, &#x27;H0003LDPE_2&#x27;, &#x27;H0003LDPE_3&#x27;, &#x27;H0003LDPE_4&#x27;,\n",
       "       &#x27;H0003LDPE_5&#x27;, &#x27;H0003LDPE_6&#x27;, &#x27;H0004LLDPE_1&#x27;, &#x27;H0004LLDPE_2&#x27;,\n",
       "       &#x27;H0004LLDPE_3&#x27;, &#x27;H0004LLDPE_4&#x27;, &#x27;H0004LLDPE_5&#x27;, &#x27;H0004LLDPE_6&#x27;,\n",
       "       &#x27;H0005LLDPE_1&#x27;, &#x27;H0005LLDPE_2&#x27;, &#x27;H0005LLDPE_3&#x27;, &#x27;H0005LLDPE_4&#x27;,\n",
       "       &#x27;H0005LLDPE_5&#x27;, &#x27;H0005LLDPE_6&#x27;, &#x27;H0006MDPE_1&#x27;, &#x27;H0006MDPE_2&#x27;,\n",
       "       &#x27;H0006MDPE_3&#x27;, &#x27;H0006MDPE_4&#x27;, &#x27;H0006MDPE_5&#x27;, &#x27;H0006MDPE_6&#x27;,\n",
       "       &#x27;H0007HDPE_1&#x27;, &#x27;H0007HDPE_2&#x27;, &#x27;H0007HDPE_3&#x27;, &#x27;H0007HDPE_4&#x27;,\n",
       "       &#x27;H0007HDPE_5&#x27;, &#x27;H0007HDPE_6&#x27;, &#x27;H0008HDPE_1&#x27;, &#x27;H0008HDPE_2&#x27;,\n",
       "       &#x27;H0008HDPE_3&#x27;, &#x27;H0008HDPE_4&#x27;, &#x27;H0008HDPE_5&#x27;, &#x27;H0008HDPE_6&#x27;, &#x27;H0009PP_1&#x27;,\n",
       "       &#x27;H0009PP_2&#x27;, &#x27;H0009PP_3&#x27;, &#x27;H0009PP_4&#x27;, &#x27;H0009PP_5&#x27;, &#x27;H0009PP_6&#x27;,\n",
       "       &#x27;N1473LDPE_1&#x27;, &#x27;N1473LDPE_2&#x27;, &#x27;N1473LDPE_3&#x27;, &#x27;N1473LDPE_4&#x27;,\n",
       "       &#x27;N1473LDPE_5&#x27;, &#x27;N1473LDPE_6&#x27;, &#x27;N1474PE_1&#x27;, &#x27;N1474PE_2&#x27;, &#x27;N1474PE_3&#x27;,\n",
       "       &#x27;N1474PE_4&#x27;, &#x27;N1474PE_5&#x27;, &#x27;N1474PE_6&#x27;, &#x27;N1476LDPE_1&#x27;, &#x27;N1476LDPE_2&#x27;,\n",
       "       &#x27;N1476LDPE_3&#x27;, &#x27;N1476LDPE_4&#x27;, &#x27;N1476LDPE_5&#x27;, &#x27;N1476LDPE_6&#x27;, &#x27;S0011PP_1&#x27;,\n",
       "       &#x27;S0011PP_2&#x27;, &#x27;S0011PP_3&#x27;, &#x27;S0011PP_4&#x27;, &#x27;S0011PP_5&#x27;, &#x27;S0011PP_6&#x27;,\n",
       "       &#x27;S0024LDPE_1&#x27;, &#x27;S0024LDPE_2&#x27;, &#x27;S0024LDPE_3&#x27;, &#x27;S0024LDPE_4&#x27;,\n",
       "       &#x27;S0024LDPE_5&#x27;, &#x27;S0024LDPE_6&#x27;, &#x27;S0036RPP_1&#x27;, &#x27;S0036RPP_2&#x27;, &#x27;S0036RPP_3&#x27;,\n",
       "       &#x27;S0036RPP_4&#x27;, &#x27;S0036RPP_5&#x27;, &#x27;S0036RPP_6&#x27;, &#x27;S0040HDPE_1&#x27;, &#x27;S0040HDPE_2&#x27;,\n",
       "       &#x27;S0040HDPE_3&#x27;, &#x27;S0040HDPE_4&#x27;, &#x27;S0040HDPE_5&#x27;, &#x27;S0040HDPE_6&#x27;,\n",
       "       &#x27;S0048HDPE_1&#x27;, &#x27;S0048HDPE_2&#x27;, &#x27;S0048HDPE_3&#x27;, &#x27;S0048HDPE_4&#x27;,\n",
       "       &#x27;S0048HDPE_5&#x27;, &#x27;S0048HDPE_6&#x27;, &#x27;S0052PP_1&#x27;, &#x27;S0052PP_2&#x27;, &#x27;S0052PP_3&#x27;,\n",
       "       &#x27;S0052PP_4&#x27;, &#x27;S0052PP_5&#x27;, &#x27;S0052PP_6&#x27;, &#x27;S0081LDPE_1&#x27;, &#x27;S0081LDPE_2&#x27;,\n",
       "       &#x27;S0081LDPE_3&#x27;, &#x27;S0081LDPE_4&#x27;, &#x27;S0081LDPE_5&#x27;, &#x27;S0081LDPE_6&#x27;],\n",
       "      dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polymer</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;H0001ULDPE&#x27; ... &#x27;S0081LDPE&#x27;</div><input id='attrs-9b0e9c76-b2c3-40d1-a4a7-2553aef498b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b0e9c76-b2c3-40d1-a4a7-2553aef498b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d83d6474-2f64-4ed9-ad62-4276ef9e5316' class='xr-var-data-in' type='checkbox'><label for='data-d83d6474-2f64-4ed9-ad62-4276ef9e5316' 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([&#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;,\n",
       "       &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;,\n",
       "       &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;,\n",
       "       &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0004LLDPE&#x27;,\n",
       "       &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;,\n",
       "       &#x27;H0004LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;,\n",
       "       &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;,\n",
       "       &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0007HDPE&#x27;,\n",
       "       &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;,\n",
       "       &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;,\n",
       "       &#x27;H0008HDPE&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;,\n",
       "       &#x27;H0009PP&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;,\n",
       "       &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;,\n",
       "       &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;,\n",
       "       &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;S0011PP&#x27;,\n",
       "       &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0024LDPE&#x27;,\n",
       "       &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;,\n",
       "       &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;,\n",
       "       &#x27;S0036RPP&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;,\n",
       "       &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;,\n",
       "       &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;,\n",
       "       &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;, &#x27;S0081LDPE&#x27;,\n",
       "       &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;],\n",
       "      dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;Pellet&#x27; &#x27;Pellet&#x27; ... &#x27;Pellet&#x27;</div><input id='attrs-234ef693-ba4c-4a04-b6d8-a40b7641c1ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-234ef693-ba4c-4a04-b6d8-a40b7641c1ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb103a32-287b-49a6-9809-2ed96daf4ae5' class='xr-var-data-in' type='checkbox'><label for='data-fb103a32-287b-49a6-9809-2ed96daf4ae5' 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([&#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;],\n",
       "      dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>repeat</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6</div><input id='attrs-a9156d1e-cb57-4884-a59b-cc216a49a9ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9156d1e-cb57-4884-a59b-cc216a49a9ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-775675ef-f522-4d76-abaa-65a84cafc61c' class='xr-var-data-in' type='checkbox'><label for='data-775675ef-f522-4d76-abaa-65a84cafc61c' 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([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n",
       "       5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2,\n",
       "       3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6,\n",
       "       1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n",
       "       5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2,\n",
       "       3, 4, 5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Class1</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27; ... &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27;</div><input id='attrs-707c5c13-ecd7-47a7-8ac0-5ab64ba883a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-707c5c13-ecd7-47a7-8ac0-5ab64ba883a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41a459ff-d34c-4514-94e9-84c8b7e91d28' class='xr-var-data-in' type='checkbox'><label for='data-41a459ff-d34c-4514-94e9-84c8b7e91d28' 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([&#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;,\n",
       "       &#x27;PP&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;,\n",
       "       &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Class2</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;LDPE&#x27; &#x27;LDPE&#x27; ... &#x27;LDPE&#x27; &#x27;LDPE&#x27;</div><input id='attrs-04f2067d-089e-4c79-bba8-93f80105ff49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-04f2067d-089e-4c79-bba8-93f80105ff49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a931aff-9bfa-4453-b32a-71e58fa56722' class='xr-var-data-in' type='checkbox'><label for='data-4a931aff-9bfa-4453-b32a-71e58fa56722' 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([&#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;,\n",
       "       &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;,\n",
       "       &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;,\n",
       "       &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;PP-co-PE&#x27;,\n",
       "       &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c35d2c8d-e90d-44b7-97c1-0bf31f8d488e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c35d2c8d-e90d-44b7-97c1-0bf31f8d488e' class='xr-section-summary' >Data variables: <span>(7)</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>Intensity</span></div><div class='xr-var-dims'>(sample, Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.076 4.076 4.075 ... 12.67 12.66</div><input id='attrs-149b3434-7caf-408e-8f63-4d0450626cd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-149b3434-7caf-408e-8f63-4d0450626cd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0af8a77-3c96-47a2-ad9b-9f81875bf1ac' class='xr-var-data-in' type='checkbox'><label for='data-a0af8a77-3c96-47a2-ad9b-9f81875bf1ac' 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>% Reflectance</dd></dl></div><div class='xr-var-data'><pre>array([[ 4.076315,  4.075627,  4.074627, ..., 13.33561 , 13.43203 ,\n",
       "        13.39136 ],\n",
       "       [ 2.324348,  2.32657 ,  2.326511, ...,  7.550017,  7.602274,\n",
       "         7.747263],\n",
       "       [ 2.820138,  2.822901,  2.825287, ..., 11.00496 , 11.08309 ,\n",
       "        11.06562 ],\n",
       "       ...,\n",
       "       [ 2.942806,  2.941994,  2.941879, ..., 13.74364 , 13.68171 ,\n",
       "        13.80696 ],\n",
       "       [ 2.383012,  2.380709,  2.378454, ..., 14.51353 , 14.52364 ,\n",
       "        14.63612 ],\n",
       "       [ 2.793289,  2.792151,  2.792742, ..., 12.80865 , 12.67313 ,\n",
       "        12.65745 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Density</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9188 0.9188 ... 0.9213 0.9213</div><input id='attrs-61bb094f-7fcc-4606-bde7-0aa67658dcf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61bb094f-7fcc-4606-bde7-0aa67658dcf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52870876-9b0a-4fec-aeca-0f4333e283ea' class='xr-var-data-in' type='checkbox'><label for='data-52870876-9b0a-4fec-aeca-0f4333e283ea' 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>g/cm^3</dd></dl></div><div class='xr-var-data'><pre>array([0.91880138, 0.91880138, 0.91880138, 0.91880138, 0.91880138,\n",
       "       0.91880138, 0.92407408, 0.92407408, 0.92407408, 0.92407408,\n",
       "       0.92407408, 0.92407408, 0.90557274, 0.90557274, 0.90557274,\n",
       "       0.90557274, 0.90557274, 0.90557274, 0.91131919, 0.91131919,\n",
       "       0.91131919, 0.91131919, 0.91131919, 0.91131919, 0.91961018,\n",
       "       0.91961018, 0.91961018, 0.91961018, 0.91961018, 0.91961018,\n",
       "       0.93417556, 0.93417556, 0.93417556, 0.93417556, 0.93417556,\n",
       "       0.93417556, 0.95171828, 0.95171828, 0.95171828, 0.95171828,\n",
       "       0.95171828, 0.95171828, 0.95392818, 0.95392818, 0.95392818,\n",
       "       0.95392818, 0.95392818, 0.95392818, 0.88727493, 0.88727493,\n",
       "       0.88727493, 0.88727493, 0.88727493, 0.88727493, 0.91710051,\n",
       "       0.91710051, 0.91710051, 0.91710051, 0.91710051, 0.91710051,\n",
       "       0.92719814, 0.92719814, 0.92719814, 0.92719814, 0.92719814,\n",
       "       0.92719814, 0.91972693, 0.91972693, 0.91972693, 0.91972693,\n",
       "       0.91972693, 0.91972693, 0.91121074, 0.91121074, 0.91121074,\n",
       "       0.91121074, 0.91121074, 0.91121074, 0.91649674, 0.91649674,\n",
       "       0.91649674, 0.91649674, 0.91649674, 0.91649674, 0.90972612,\n",
       "       0.90972612, 0.90972612, 0.90972612, 0.90972612, 0.90972612,\n",
       "       0.95634513, 0.95634513, 0.95634513, 0.95634513, 0.95634513,\n",
       "       0.95634513, 0.95635806, 0.95635806, 0.95635806, 0.95635806,\n",
       "       0.95635806, 0.95635806, 0.89319665, 0.89319665, 0.89319665,\n",
       "       0.89319665, 0.89319665, 0.89319665, 0.92130818, 0.92130818,\n",
       "       0.92130818, 0.92130818, 0.92130818, 0.92130818])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Density SEM</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0007945 0.0007945 ... 0.0003695</div><input id='attrs-d33ad11d-2006-49e6-828f-86c8e716349c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d33ad11d-2006-49e6-828f-86c8e716349c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-911bd7e3-c218-40db-aac9-c5818034fdca' class='xr-var-data-in' type='checkbox'><label for='data-911bd7e3-c218-40db-aac9-c5818034fdca' 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([7.944570e-04, 7.944570e-04, 7.944570e-04, 7.944570e-04,\n",
       "       7.944570e-04, 7.944570e-04, 5.190740e-04, 5.190740e-04,\n",
       "       5.190740e-04, 5.190740e-04, 5.190740e-04, 5.190740e-04,\n",
       "       8.210101e-03, 8.210101e-03, 8.210101e-03, 8.210101e-03,\n",
       "       8.210101e-03, 8.210101e-03, 8.919850e-04, 8.919850e-04,\n",
       "       8.919850e-04, 8.919850e-04, 8.919850e-04, 8.919850e-04,\n",
       "       6.820000e-05, 6.820000e-05, 6.820000e-05, 6.820000e-05,\n",
       "       6.820000e-05, 6.820000e-05, 1.049327e-03, 1.049327e-03,\n",
       "       1.049327e-03, 1.049327e-03, 1.049327e-03, 1.049327e-03,\n",
       "       1.084829e-03, 1.084829e-03, 1.084829e-03, 1.084829e-03,\n",
       "       1.084829e-03, 1.084829e-03, 2.385313e-03, 2.385313e-03,\n",
       "       2.385313e-03, 2.385313e-03, 2.385313e-03, 2.385313e-03,\n",
       "       4.276687e-03, 4.276687e-03, 4.276687e-03, 4.276687e-03,\n",
       "       4.276687e-03, 4.276687e-03, 7.410060e-04, 7.410060e-04,\n",
       "       7.410060e-04, 7.410060e-04, 7.410060e-04, 7.410060e-04,\n",
       "       6.705817e-03, 6.705817e-03, 6.705817e-03, 6.705817e-03,\n",
       "       6.705817e-03, 6.705817e-03, 1.672701e-03, 1.672701e-03,\n",
       "       1.672701e-03, 1.672701e-03, 1.672701e-03, 1.672701e-03,\n",
       "       3.404925e-03, 3.404925e-03, 3.404925e-03, 3.404925e-03,\n",
       "       3.404925e-03, 3.404925e-03, 1.253648e-03, 1.253648e-03,\n",
       "       1.253648e-03, 1.253648e-03, 1.253648e-03, 1.253648e-03,\n",
       "       2.831310e-04, 2.831310e-04, 2.831310e-04, 2.831310e-04,\n",
       "       2.831310e-04, 2.831310e-04, 1.766161e-03, 1.766161e-03,\n",
       "       1.766161e-03, 1.766161e-03, 1.766161e-03, 1.766161e-03,\n",
       "       1.546178e-03, 1.546178e-03, 1.546178e-03, 1.546178e-03,\n",
       "       1.546178e-03, 1.546178e-03, 5.016647e-03, 5.016647e-03,\n",
       "       5.016647e-03, 5.016647e-03, 5.016647e-03, 5.016647e-03,\n",
       "       3.695250e-04, 3.695250e-04, 3.695250e-04, 3.695250e-04,\n",
       "       3.695250e-04, 3.695250e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Crystallinity</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3463 0.3463 ... 0.3708 0.3708</div><input id='attrs-9e0524fd-2bb0-4cc9-a6b8-1b305da1dcee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e0524fd-2bb0-4cc9-a6b8-1b305da1dcee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bd74af3-a729-4fbb-baa7-4425d6fdc9f4' class='xr-var-data-in' type='checkbox'><label for='data-9bd74af3-a729-4fbb-baa7-4425d6fdc9f4' 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>%</dd></dl></div><div class='xr-var-data'><pre>array([0.34625341, 0.34625341, 0.34625341, 0.34625341, 0.34625341,\n",
       "       0.34625341, 0.40023842, 0.40023842, 0.40023842, 0.40023842,\n",
       "       0.40023842, 0.40023842, 0.37581744, 0.37581744, 0.37581744,\n",
       "       0.37581744, 0.37581744, 0.37581744, 0.42735014, 0.42735014,\n",
       "       0.42735014, 0.42735014, 0.42735014, 0.42735014, 0.32923706,\n",
       "       0.32923706, 0.32923706, 0.32923706, 0.32923706, 0.32923706,\n",
       "       0.50395095, 0.50395095, 0.50395095, 0.50395095, 0.50395095,\n",
       "       0.50395095, 0.55735695, 0.55735695, 0.55735695, 0.55735695,\n",
       "       0.55735695, 0.55735695, 0.65534741, 0.65534741, 0.65534741,\n",
       "       0.65534741, 0.65534741, 0.65534741, 0.43788991, 0.43788991,\n",
       "       0.43788991, 0.43788991, 0.43788991, 0.43788991, 0.24711512,\n",
       "       0.24711512, 0.24711512, 0.24711512, 0.24711512, 0.24711512,\n",
       "       0.49529973, 0.49529973, 0.49529973, 0.49529973, 0.49529973,\n",
       "       0.49529973, 0.31264646, 0.31264646, 0.31264646, 0.31264646,\n",
       "       0.31264646, 0.31264646, 0.43808305, 0.43808305, 0.43808305,\n",
       "       0.43808305, 0.43808305, 0.43808305, 0.48685286, 0.48685286,\n",
       "       0.48685286, 0.48685286, 0.48685286, 0.48685286, 0.39028971,\n",
       "       0.39028971, 0.39028971, 0.39028971, 0.39028971, 0.39028971,\n",
       "       0.62489782, 0.62489782, 0.62489782, 0.62489782, 0.62489782,\n",
       "       0.62489782, 0.60381471, 0.60381471, 0.60381471, 0.60381471,\n",
       "       0.60381471, 0.60381471, 0.38284404, 0.38284404, 0.38284404,\n",
       "       0.38284404, 0.38284404, 0.38284404, 0.37084469, 0.37084469,\n",
       "       0.37084469, 0.37084469, 0.37084469, 0.37084469])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SCB_per_1000C (IR)</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.4 15.4 15.4 ... 23.6 23.6 23.6</div><input id='attrs-bafb65d7-f881-423a-b5e1-b0fdb914cd74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bafb65d7-f881-423a-b5e1-b0fdb914cd74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e502fa46-0dc1-41fb-9d38-29ad3c553fa1' class='xr-var-data-in' type='checkbox'><label for='data-e502fa46-0dc1-41fb-9d38-29ad3c553fa1' 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([ 15.4 ,  15.4 ,  15.4 ,  15.4 ,  15.4 ,  15.4 ,  27.4 ,  27.4 ,\n",
       "        27.4 ,  27.4 ,  27.4 ,  27.4 ,  28.8 ,  28.8 ,  28.8 ,  28.8 ,\n",
       "        28.8 ,  28.8 ,  18.6 ,  18.6 ,  18.6 ,  18.6 ,  18.6 ,  18.6 ,\n",
       "        20.2 ,  20.2 ,  20.2 ,  20.2 ,  20.2 ,  20.2 ,  12.65,  12.65,\n",
       "        12.65,  12.65,  12.65,  12.65,   8.7 ,   8.7 ,   8.7 ,   8.7 ,\n",
       "         8.7 ,   8.7 ,   6.75,   6.75,   6.75,   6.75,   6.75,   6.75,\n",
       "       312.95, 312.95, 312.95, 312.95, 312.95, 312.95,  27.2 ,  27.2 ,\n",
       "        27.2 ,  27.2 ,  27.2 ,  27.2 ,  10.9 ,  10.9 ,  10.9 ,  10.9 ,\n",
       "        10.9 ,  10.9 ,  21.2 ,  21.2 ,  21.2 ,  21.2 ,  21.2 ,  21.2 ,\n",
       "       331.6 , 331.6 , 331.6 , 331.6 , 331.6 , 331.6 ,  28.9 ,  28.9 ,\n",
       "        28.9 ,  28.9 ,  28.9 ,  28.9 , 318.75, 318.75, 318.75, 318.75,\n",
       "       318.75, 318.75,   7.35,   7.35,   7.35,   7.35,   7.35,   7.35,\n",
       "         9.7 ,   9.7 ,   9.7 ,   9.7 ,   9.7 ,   9.7 , 293.65, 293.65,\n",
       "       293.65, 293.65, 293.65, 293.65,  23.6 ,  23.6 ,  23.6 ,  23.6 ,\n",
       "        23.6 ,  23.6 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SCB_per_1000C StDev (IR)</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.414 1.414 1.414 ... 0.2828 0.2828</div><input id='attrs-1fd2e164-d887-44e1-b5bf-c9908d3bde37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fd2e164-d887-44e1-b5bf-c9908d3bde37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87efe637-62f9-46f2-a0d7-9ef517ea9042' class='xr-var-data-in' type='checkbox'><label for='data-87efe637-62f9-46f2-a0d7-9ef517ea9042' 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([1.41421356, 1.41421356, 1.41421356, 1.41421356, 1.41421356,\n",
       "       1.41421356, 0.56568542, 0.56568542, 0.56568542, 0.56568542,\n",
       "       0.56568542, 0.56568542, 3.67695526, 3.67695526, 3.67695526,\n",
       "       3.67695526, 3.67695526, 3.67695526, 0.14142136, 0.14142136,\n",
       "       0.14142136, 0.14142136, 0.14142136, 0.14142136, 0.42426407,\n",
       "       0.42426407, 0.42426407, 0.42426407, 0.42426407, 0.42426407,\n",
       "       0.49497475, 0.49497475, 0.49497475, 0.49497475, 0.49497475,\n",
       "       0.49497475, 2.40416306, 2.40416306, 2.40416306, 2.40416306,\n",
       "       2.40416306, 2.40416306, 0.353553  , 0.353553  , 0.353553  ,\n",
       "       0.353553  , 0.353553  , 0.353553  , 0.49497475, 0.49497475,\n",
       "       0.49497475, 0.49497475, 0.49497475, 0.49497475, 1.41      ,\n",
       "       1.41      , 1.41      , 1.41      , 1.41      , 1.41      ,\n",
       "       2.26      , 2.26      , 2.26      , 2.26      , 2.26      ,\n",
       "       2.26      , 1.56      , 1.56      , 1.56      , 1.56      ,\n",
       "       1.56      , 1.56      , 1.27279221, 1.27279221, 1.27279221,\n",
       "       1.27279221, 1.27279221, 1.27279221, 1.13137085, 1.13137085,\n",
       "       1.13137085, 1.13137085, 1.13137085, 1.13137085, 0.91923882,\n",
       "       0.91923882, 0.91923882, 0.91923882, 0.91923882, 0.91923882,\n",
       "       0.77781746, 0.77781746, 0.77781746, 0.77781746, 0.77781746,\n",
       "       0.77781746, 0.42426407, 0.42426407, 0.42426407, 0.42426407,\n",
       "       0.42426407, 0.42426407, 1.06066017, 1.06066017, 1.06066017,\n",
       "       1.06066017, 1.06066017, 1.06066017, 0.28284271, 0.28284271,\n",
       "       0.28284271, 0.28284271, 0.28284271, 0.28284271])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MSC</span></div><div class='xr-var-dims'>(sample, Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.927 -4.928 ... 4.704 4.686</div><input id='attrs-5b07b6c7-4a8a-46cd-a1f1-6ac8dc29c270' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b07b6c7-4a8a-46cd-a1f1-6ac8dc29c270' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da941356-5992-41c9-9881-15a08dba3d15' class='xr-var-data-in' type='checkbox'><label for='data-da941356-5992-41c9-9881-15a08dba3d15' 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>% Reflectance</dd></dl></div><div class='xr-var-data'><pre>array([[-4.92707958, -4.92770977, -4.92862576, ...,  3.55429289,\n",
       "         3.64261213,  3.60535903],\n",
       "       [-4.39280838, -4.38919462, -4.38929057, ...,  4.10598451,\n",
       "         4.19097295,  4.42677653],\n",
       "       [-4.45506582, -4.4520154 , -4.4493812 , ...,  4.58116217,\n",
       "         4.66741946,  4.64813218],\n",
       "       ...,\n",
       "       [-6.22286357, -6.22370801, -6.22382761, ...,  5.00945665,\n",
       "         4.94505259,  5.07530624],\n",
       "       [-6.43997904, -6.44209852, -6.44417383, ...,  4.72388651,\n",
       "         4.73319087,  4.8367076 ],\n",
       "       [-6.27893856, -6.28020357, -6.27954661, ...,  4.85421628,\n",
       "         4.70357118,  4.68614116]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e43f031-0ffc-44dd-ba16-fc8e1ec89ccb' class='xr-section-summary-in' type='checkbox'  ><label for='section-3e43f031-0ffc-44dd-ba16-fc8e1ec89ccb' class='xr-section-summary' >Indexes: <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-index-name'><div>Wavenumber</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-514bae58-0507-493b-979c-697b37ce27e6' class='xr-index-data-in' type='checkbox'/><label for='index-514bae58-0507-493b-979c-697b37ce27e6' 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(Float64Index([3999.706, 4001.634, 4003.563, 4005.491,  4007.42, 4009.348,\n",
       "              4011.277, 4013.205, 4015.134, 4017.062,\n",
       "              ...\n",
       "              11981.76, 11983.69, 11985.62, 11987.55, 11989.47,  11991.4,\n",
       "              11993.33, 11995.26, 11997.19, 11999.12],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Wavenumber&#x27;, length=4149))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-28eaa89f-261a-43e6-ba9d-7038377ca876' class='xr-index-data-in' type='checkbox'/><label for='index-28eaa89f-261a-43e6-ba9d-7038377ca876' 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([&#x27;H0001ULDPE_1&#x27;, &#x27;H0001ULDPE_2&#x27;, &#x27;H0001ULDPE_3&#x27;, &#x27;H0001ULDPE_4&#x27;,\n",
       "       &#x27;H0001ULDPE_5&#x27;, &#x27;H0001ULDPE_6&#x27;, &#x27;H0002LDPE_1&#x27;, &#x27;H0002LDPE_2&#x27;,\n",
       "       &#x27;H0002LDPE_3&#x27;, &#x27;H0002LDPE_4&#x27;,\n",
       "       ...\n",
       "       &#x27;S0052PP_3&#x27;, &#x27;S0052PP_4&#x27;, &#x27;S0052PP_5&#x27;, &#x27;S0052PP_6&#x27;, &#x27;S0081LDPE_1&#x27;,\n",
       "       &#x27;S0081LDPE_2&#x27;, &#x27;S0081LDPE_3&#x27;, &#x27;S0081LDPE_4&#x27;, &#x27;S0081LDPE_5&#x27;,\n",
       "       &#x27;S0081LDPE_6&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;sample&#x27;, length=114))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2875bcc5-1755-4765-b8db-23bb16895393' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2875bcc5-1755-4765-b8db-23bb16895393' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>creation_date :</span></dt><dd>20230523</dd><dt><span>author :</span></dt><dd>Bradley Sutliff</dd><dt><span>email :</span></dt><dd>Bradley.Sutliff@nist.gov</dd><dt><span>data_collected_by :</span></dt><dd>Shailja Goyal</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (Wavenumber: 4149, sample: 114)\n",
       "Coordinates:\n",
       "  * Wavenumber                (Wavenumber) float64 4e+03 4.002e+03 ... 1.2e+04\n",
       "  * sample                    (sample) <U12 'H0001ULDPE_1' ... 'S0081LDPE_6'\n",
       "    polymer                   (sample) <U10 'H0001ULDPE' ... 'S0081LDPE'\n",
       "    state                     (sample) <U6 'Pellet' 'Pellet' ... 'Pellet'\n",
       "    repeat                    (sample) int64 1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6\n",
       "    Class1                    (sample) <U2 'PE' 'PE' 'PE' ... 'PE' 'PE' 'PE'\n",
       "    Class2                    (sample) <U8 'LDPE' 'LDPE' ... 'LDPE' 'LDPE'\n",
       "Data variables:\n",
       "    Intensity                 (sample, Wavenumber) float64 4.076 4.076 ... 12.66\n",
       "    Density                   (sample) float64 0.9188 0.9188 ... 0.9213 0.9213\n",
       "    Density SEM               (sample) float64 0.0007945 0.0007945 ... 0.0003695\n",
       "    Crystallinity             (sample) float64 0.3463 0.3463 ... 0.3708 0.3708\n",
       "    SCB_per_1000C (IR)        (sample) float64 15.4 15.4 15.4 ... 23.6 23.6 23.6\n",
       "    SCB_per_1000C StDev (IR)  (sample) float64 1.414 1.414 ... 0.2828 0.2828\n",
       "    MSC                       (sample, Wavenumber) float64 -4.927 ... 4.686\n",
       "Attributes:\n",
       "    creation_date:      20230523\n",
       "    author:             Bradley Sutliff\n",
       "    email:              Bradley.Sutliff@nist.gov\n",
       "    data_collected_by:  Shailja Goyal"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make our transformer to apply the MSC correction\n",
    "trans1 = msc_transformer\n",
    "# actually apply the correction and transform our data\n",
    "ds_msc = trans1.fit_transform(ds.Intensity)\n",
    "# add this data back to the original Dataset\n",
    "ds = xr.merge([ds, ds_msc])\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82a5780d-baea-48b4-85ad-b85d15ec574f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15aedeb90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set info for the plot\n",
    "color_scheme = \"Class2\"\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# loop through for each poylmer subclass\n",
    "for ptype in pd.unique(ds[color_scheme]):\n",
    "    # grab data of only our chosen subclass\n",
    "    ds_sub = ds.where(ds[color_scheme] == ptype, drop=True)\n",
    "    # grab data for a single replicate, the first replicate\n",
    "    rep_samp = pd.unique(ds_sub.sample)[0]\n",
    "    ds_samp = ds_sub.where(ds_sub.sample == rep_samp, drop=True)\n",
    "\n",
    "    # grab our x and y values\n",
    "    x = ds_samp.Wavenumber.values.flatten()\n",
    "    y = ds_samp.MSC.values.flatten()\n",
    "    # use one of our predefined dictionaries to choose a color\n",
    "    # based on the polymer subclass\n",
    "    color = cpalette[color_scheme][ptype]\n",
    "    linesty = dict_linesty[color_scheme][ptype]\n",
    "    # add the data\n",
    "    ax.plot(x,y, c=color, label=ptype, ls=linesty)\n",
    "\n",
    "# clean up the axis\n",
    "ax.set_ylabel(\"MSC\\nRelative Intensity [AU]\")\n",
    "ax.set_xlabel(\"Wavenumber [cm$^{-1}$]\")\n",
    "ax.set_xlim(12000, 4000)\n",
    "ax.tick_params(\n",
    "    bottom=True,\n",
    "    top=True,\n",
    "    left=True,\n",
    "    right=True,\n",
    "    labelbottom=True,\n",
    "    labeltop=False,\n",
    "    labelleft=True,\n",
    "    labelright=False,\n",
    "    direction=\"inout\",\n",
    ")\n",
    "# fix the legend\n",
    "ax.legend(loc=\"best\", ncol=2, frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "941964c6-fb02-41d8-bd51-7a904b97137a",
   "metadata": {},
   "source": [
    "### Savitzky-Golay Filtering and Differentiation (SG and SG'')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d12ac8a-133b-4573-8162-25290d8c125c",
   "metadata": {},
   "source": [
    "SG filtering is a common signal smoothing filter. We use it here to clean up some noise, enabling better differentiation. This is also common in the NIR signal processing/chemometric community."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5ed3d64-b31a-48c3-a313-ddbc164e08f2",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                   (Wavenumber: 4149, sample: 114)\n",
       "Coordinates:\n",
       "  * Wavenumber                (Wavenumber) float64 4e+03 4.002e+03 ... 1.2e+04\n",
       "  * sample                    (sample) &lt;U12 &#x27;H0001ULDPE_1&#x27; ... &#x27;S0081LDPE_6&#x27;\n",
       "    polymer                   (sample) &lt;U10 &#x27;H0001ULDPE&#x27; ... &#x27;S0081LDPE&#x27;\n",
       "    state                     (sample) &lt;U6 &#x27;Pellet&#x27; &#x27;Pellet&#x27; ... &#x27;Pellet&#x27;\n",
       "    repeat                    (sample) int64 1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6\n",
       "    Class1                    (sample) &lt;U2 &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27; ... &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27;\n",
       "    Class2                    (sample) &lt;U8 &#x27;LDPE&#x27; &#x27;LDPE&#x27; ... &#x27;LDPE&#x27; &#x27;LDPE&#x27;\n",
       "Data variables:\n",
       "    Intensity                 (sample, Wavenumber) float64 4.076 4.076 ... 12.66\n",
       "    Density                   (sample) float64 0.9188 0.9188 ... 0.9213 0.9213\n",
       "    Density SEM               (sample) float64 0.0007945 0.0007945 ... 0.0003695\n",
       "    Crystallinity             (sample) float64 0.3463 0.3463 ... 0.3708 0.3708\n",
       "    SCB_per_1000C (IR)        (sample) float64 15.4 15.4 15.4 ... 23.6 23.6 23.6\n",
       "    SCB_per_1000C StDev (IR)  (sample) float64 1.414 1.414 ... 0.2828 0.2828\n",
       "    MSC                       (sample, Wavenumber) float64 -4.927 ... 4.686\n",
       "    SG2                       (sample, Wavenumber) float64 4.448e-06 ... -1.0...\n",
       "Attributes:\n",
       "    creation_date:      20230523\n",
       "    author:             Bradley Sutliff\n",
       "    email:              Bradley.Sutliff@nist.gov\n",
       "    data_collected_by:  Shailja Goyal</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-e8665c08-a755-4c2d-b1b7-b5e605c0ef61' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e8665c08-a755-4c2d-b1b7-b5e605c0ef61' 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'>Wavenumber</span>: 4149</li><li><span class='xr-has-index'>sample</span>: 114</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-31a29aea-dcfa-428f-9e7c-887abf59a3f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31a29aea-dcfa-428f-9e7c-887abf59a3f5' class='xr-section-summary' >Coordinates: <span>(7)</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'>Wavenumber</span></div><div class='xr-var-dims'>(Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4e+03 4.002e+03 ... 1.2e+04 1.2e+04</div><input id='attrs-43bf630e-663c-4936-8014-cafee9491c9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43bf630e-663c-4936-8014-cafee9491c9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2b0d12f-64b7-434c-8b59-e191bba9eba1' class='xr-var-data-in' type='checkbox'><label for='data-b2b0d12f-64b7-434c-8b59-e191bba9eba1' 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>1/cm</dd></dl></div><div class='xr-var-data'><pre>array([ 3999.706,  4001.634,  4003.563, ..., 11995.26 , 11997.19 , 11999.12 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;H0001ULDPE_1&#x27; ... &#x27;S0081LDPE_6&#x27;</div><input id='attrs-1ea944cf-81e9-42ed-b8dc-b444b8917ccc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ea944cf-81e9-42ed-b8dc-b444b8917ccc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf598cf9-7a1e-4622-931c-004633c3aabd' class='xr-var-data-in' type='checkbox'><label for='data-bf598cf9-7a1e-4622-931c-004633c3aabd' 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([&#x27;H0001ULDPE_1&#x27;, &#x27;H0001ULDPE_2&#x27;, &#x27;H0001ULDPE_3&#x27;, &#x27;H0001ULDPE_4&#x27;,\n",
       "       &#x27;H0001ULDPE_5&#x27;, &#x27;H0001ULDPE_6&#x27;, &#x27;H0002LDPE_1&#x27;, &#x27;H0002LDPE_2&#x27;,\n",
       "       &#x27;H0002LDPE_3&#x27;, &#x27;H0002LDPE_4&#x27;, &#x27;H0002LDPE_5&#x27;, &#x27;H0002LDPE_6&#x27;,\n",
       "       &#x27;H0003LDPE_1&#x27;, &#x27;H0003LDPE_2&#x27;, &#x27;H0003LDPE_3&#x27;, &#x27;H0003LDPE_4&#x27;,\n",
       "       &#x27;H0003LDPE_5&#x27;, &#x27;H0003LDPE_6&#x27;, &#x27;H0004LLDPE_1&#x27;, &#x27;H0004LLDPE_2&#x27;,\n",
       "       &#x27;H0004LLDPE_3&#x27;, &#x27;H0004LLDPE_4&#x27;, &#x27;H0004LLDPE_5&#x27;, &#x27;H0004LLDPE_6&#x27;,\n",
       "       &#x27;H0005LLDPE_1&#x27;, &#x27;H0005LLDPE_2&#x27;, &#x27;H0005LLDPE_3&#x27;, &#x27;H0005LLDPE_4&#x27;,\n",
       "       &#x27;H0005LLDPE_5&#x27;, &#x27;H0005LLDPE_6&#x27;, &#x27;H0006MDPE_1&#x27;, &#x27;H0006MDPE_2&#x27;,\n",
       "       &#x27;H0006MDPE_3&#x27;, &#x27;H0006MDPE_4&#x27;, &#x27;H0006MDPE_5&#x27;, &#x27;H0006MDPE_6&#x27;,\n",
       "       &#x27;H0007HDPE_1&#x27;, &#x27;H0007HDPE_2&#x27;, &#x27;H0007HDPE_3&#x27;, &#x27;H0007HDPE_4&#x27;,\n",
       "       &#x27;H0007HDPE_5&#x27;, &#x27;H0007HDPE_6&#x27;, &#x27;H0008HDPE_1&#x27;, &#x27;H0008HDPE_2&#x27;,\n",
       "       &#x27;H0008HDPE_3&#x27;, &#x27;H0008HDPE_4&#x27;, &#x27;H0008HDPE_5&#x27;, &#x27;H0008HDPE_6&#x27;, &#x27;H0009PP_1&#x27;,\n",
       "       &#x27;H0009PP_2&#x27;, &#x27;H0009PP_3&#x27;, &#x27;H0009PP_4&#x27;, &#x27;H0009PP_5&#x27;, &#x27;H0009PP_6&#x27;,\n",
       "       &#x27;N1473LDPE_1&#x27;, &#x27;N1473LDPE_2&#x27;, &#x27;N1473LDPE_3&#x27;, &#x27;N1473LDPE_4&#x27;,\n",
       "       &#x27;N1473LDPE_5&#x27;, &#x27;N1473LDPE_6&#x27;, &#x27;N1474PE_1&#x27;, &#x27;N1474PE_2&#x27;, &#x27;N1474PE_3&#x27;,\n",
       "       &#x27;N1474PE_4&#x27;, &#x27;N1474PE_5&#x27;, &#x27;N1474PE_6&#x27;, &#x27;N1476LDPE_1&#x27;, &#x27;N1476LDPE_2&#x27;,\n",
       "       &#x27;N1476LDPE_3&#x27;, &#x27;N1476LDPE_4&#x27;, &#x27;N1476LDPE_5&#x27;, &#x27;N1476LDPE_6&#x27;, &#x27;S0011PP_1&#x27;,\n",
       "       &#x27;S0011PP_2&#x27;, &#x27;S0011PP_3&#x27;, &#x27;S0011PP_4&#x27;, &#x27;S0011PP_5&#x27;, &#x27;S0011PP_6&#x27;,\n",
       "       &#x27;S0024LDPE_1&#x27;, &#x27;S0024LDPE_2&#x27;, &#x27;S0024LDPE_3&#x27;, &#x27;S0024LDPE_4&#x27;,\n",
       "       &#x27;S0024LDPE_5&#x27;, &#x27;S0024LDPE_6&#x27;, &#x27;S0036RPP_1&#x27;, &#x27;S0036RPP_2&#x27;, &#x27;S0036RPP_3&#x27;,\n",
       "       &#x27;S0036RPP_4&#x27;, &#x27;S0036RPP_5&#x27;, &#x27;S0036RPP_6&#x27;, &#x27;S0040HDPE_1&#x27;, &#x27;S0040HDPE_2&#x27;,\n",
       "       &#x27;S0040HDPE_3&#x27;, &#x27;S0040HDPE_4&#x27;, &#x27;S0040HDPE_5&#x27;, &#x27;S0040HDPE_6&#x27;,\n",
       "       &#x27;S0048HDPE_1&#x27;, &#x27;S0048HDPE_2&#x27;, &#x27;S0048HDPE_3&#x27;, &#x27;S0048HDPE_4&#x27;,\n",
       "       &#x27;S0048HDPE_5&#x27;, &#x27;S0048HDPE_6&#x27;, &#x27;S0052PP_1&#x27;, &#x27;S0052PP_2&#x27;, &#x27;S0052PP_3&#x27;,\n",
       "       &#x27;S0052PP_4&#x27;, &#x27;S0052PP_5&#x27;, &#x27;S0052PP_6&#x27;, &#x27;S0081LDPE_1&#x27;, &#x27;S0081LDPE_2&#x27;,\n",
       "       &#x27;S0081LDPE_3&#x27;, &#x27;S0081LDPE_4&#x27;, &#x27;S0081LDPE_5&#x27;, &#x27;S0081LDPE_6&#x27;],\n",
       "      dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polymer</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;H0001ULDPE&#x27; ... &#x27;S0081LDPE&#x27;</div><input id='attrs-8986b05f-abc0-4f97-9c11-632876a8f32d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8986b05f-abc0-4f97-9c11-632876a8f32d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60a261b7-cd39-4b9b-b1e9-31ca309c7646' class='xr-var-data-in' type='checkbox'><label for='data-60a261b7-cd39-4b9b-b1e9-31ca309c7646' 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([&#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;,\n",
       "       &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;,\n",
       "       &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;,\n",
       "       &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0004LLDPE&#x27;,\n",
       "       &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;,\n",
       "       &#x27;H0004LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;,\n",
       "       &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;,\n",
       "       &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0007HDPE&#x27;,\n",
       "       &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;,\n",
       "       &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;,\n",
       "       &#x27;H0008HDPE&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;, &#x27;H0009PP&#x27;,\n",
       "       &#x27;H0009PP&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;,\n",
       "       &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;,\n",
       "       &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;,\n",
       "       &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;S0011PP&#x27;,\n",
       "       &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0011PP&#x27;, &#x27;S0024LDPE&#x27;,\n",
       "       &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;,\n",
       "       &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;, &#x27;S0036RPP&#x27;,\n",
       "       &#x27;S0036RPP&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;,\n",
       "       &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;,\n",
       "       &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;,\n",
       "       &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;, &#x27;S0052PP&#x27;, &#x27;S0081LDPE&#x27;,\n",
       "       &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;],\n",
       "      dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;Pellet&#x27; &#x27;Pellet&#x27; ... &#x27;Pellet&#x27;</div><input id='attrs-dcfd067d-c143-42f5-96f8-10caf6baef12' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcfd067d-c143-42f5-96f8-10caf6baef12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8cba5f4-7b75-4a9a-a3f8-6a37455a52e0' class='xr-var-data-in' type='checkbox'><label for='data-a8cba5f4-7b75-4a9a-a3f8-6a37455a52e0' 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([&#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;],\n",
       "      dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>repeat</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6</div><input id='attrs-747a7f57-c779-4f39-9fd7-cb87f6ceb97e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-747a7f57-c779-4f39-9fd7-cb87f6ceb97e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccb21b37-472b-4bb7-83d0-8649d4b44c94' class='xr-var-data-in' type='checkbox'><label for='data-ccb21b37-472b-4bb7-83d0-8649d4b44c94' 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([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n",
       "       5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2,\n",
       "       3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6,\n",
       "       1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n",
       "       5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2,\n",
       "       3, 4, 5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Class1</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27; ... &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27;</div><input id='attrs-da8e372a-3392-4f20-8ed6-3e3304211696' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da8e372a-3392-4f20-8ed6-3e3304211696' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65fc4d92-b677-4d1e-b591-1aea841aa4ae' class='xr-var-data-in' type='checkbox'><label for='data-65fc4d92-b677-4d1e-b591-1aea841aa4ae' 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([&#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;,\n",
       "       &#x27;PP&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;,\n",
       "       &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Class2</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;LDPE&#x27; &#x27;LDPE&#x27; ... &#x27;LDPE&#x27; &#x27;LDPE&#x27;</div><input id='attrs-e2ddc3b1-9c1f-4a12-b57c-f5b5963cf342' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2ddc3b1-9c1f-4a12-b57c-f5b5963cf342' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35b667bc-9caa-461b-a915-6a9df8673d39' class='xr-var-data-in' type='checkbox'><label for='data-35b667bc-9caa-461b-a915-6a9df8673d39' 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([&#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;,\n",
       "       &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;,\n",
       "       &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;PP&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;,\n",
       "       &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;PP-co-PE&#x27;,\n",
       "       &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;PP-co-PE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1098a03f-369f-4434-b289-438226e3dbc4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1098a03f-369f-4434-b289-438226e3dbc4' class='xr-section-summary' >Data variables: <span>(8)</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>Intensity</span></div><div class='xr-var-dims'>(sample, Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.076 4.076 4.075 ... 12.67 12.66</div><input id='attrs-55e57151-0843-4c66-8cdf-f28c621a1522' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55e57151-0843-4c66-8cdf-f28c621a1522' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5afb731-f6ab-445a-a2a5-15dd6e77c9b4' class='xr-var-data-in' type='checkbox'><label for='data-c5afb731-f6ab-445a-a2a5-15dd6e77c9b4' 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>% Reflectance</dd></dl></div><div class='xr-var-data'><pre>array([[ 4.076315,  4.075627,  4.074627, ..., 13.33561 , 13.43203 ,\n",
       "        13.39136 ],\n",
       "       [ 2.324348,  2.32657 ,  2.326511, ...,  7.550017,  7.602274,\n",
       "         7.747263],\n",
       "       [ 2.820138,  2.822901,  2.825287, ..., 11.00496 , 11.08309 ,\n",
       "        11.06562 ],\n",
       "       ...,\n",
       "       [ 2.942806,  2.941994,  2.941879, ..., 13.74364 , 13.68171 ,\n",
       "        13.80696 ],\n",
       "       [ 2.383012,  2.380709,  2.378454, ..., 14.51353 , 14.52364 ,\n",
       "        14.63612 ],\n",
       "       [ 2.793289,  2.792151,  2.792742, ..., 12.80865 , 12.67313 ,\n",
       "        12.65745 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Density</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9188 0.9188 ... 0.9213 0.9213</div><input id='attrs-9adf1df7-a22f-4fbc-bf07-2d02fa0b3fc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9adf1df7-a22f-4fbc-bf07-2d02fa0b3fc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5481b9ce-fa85-4990-b981-a339531c6e88' class='xr-var-data-in' type='checkbox'><label for='data-5481b9ce-fa85-4990-b981-a339531c6e88' 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>g/cm^3</dd></dl></div><div class='xr-var-data'><pre>array([0.91880138, 0.91880138, 0.91880138, 0.91880138, 0.91880138,\n",
       "       0.91880138, 0.92407408, 0.92407408, 0.92407408, 0.92407408,\n",
       "       0.92407408, 0.92407408, 0.90557274, 0.90557274, 0.90557274,\n",
       "       0.90557274, 0.90557274, 0.90557274, 0.91131919, 0.91131919,\n",
       "       0.91131919, 0.91131919, 0.91131919, 0.91131919, 0.91961018,\n",
       "       0.91961018, 0.91961018, 0.91961018, 0.91961018, 0.91961018,\n",
       "       0.93417556, 0.93417556, 0.93417556, 0.93417556, 0.93417556,\n",
       "       0.93417556, 0.95171828, 0.95171828, 0.95171828, 0.95171828,\n",
       "       0.95171828, 0.95171828, 0.95392818, 0.95392818, 0.95392818,\n",
       "       0.95392818, 0.95392818, 0.95392818, 0.88727493, 0.88727493,\n",
       "       0.88727493, 0.88727493, 0.88727493, 0.88727493, 0.91710051,\n",
       "       0.91710051, 0.91710051, 0.91710051, 0.91710051, 0.91710051,\n",
       "       0.92719814, 0.92719814, 0.92719814, 0.92719814, 0.92719814,\n",
       "       0.92719814, 0.91972693, 0.91972693, 0.91972693, 0.91972693,\n",
       "       0.91972693, 0.91972693, 0.91121074, 0.91121074, 0.91121074,\n",
       "       0.91121074, 0.91121074, 0.91121074, 0.91649674, 0.91649674,\n",
       "       0.91649674, 0.91649674, 0.91649674, 0.91649674, 0.90972612,\n",
       "       0.90972612, 0.90972612, 0.90972612, 0.90972612, 0.90972612,\n",
       "       0.95634513, 0.95634513, 0.95634513, 0.95634513, 0.95634513,\n",
       "       0.95634513, 0.95635806, 0.95635806, 0.95635806, 0.95635806,\n",
       "       0.95635806, 0.95635806, 0.89319665, 0.89319665, 0.89319665,\n",
       "       0.89319665, 0.89319665, 0.89319665, 0.92130818, 0.92130818,\n",
       "       0.92130818, 0.92130818, 0.92130818, 0.92130818])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Density SEM</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0007945 0.0007945 ... 0.0003695</div><input id='attrs-b80bd180-7251-4759-982b-0e9aee8e4eeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b80bd180-7251-4759-982b-0e9aee8e4eeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b120c18b-4ef6-4791-9561-afdd0a81de89' class='xr-var-data-in' type='checkbox'><label for='data-b120c18b-4ef6-4791-9561-afdd0a81de89' 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([7.944570e-04, 7.944570e-04, 7.944570e-04, 7.944570e-04,\n",
       "       7.944570e-04, 7.944570e-04, 5.190740e-04, 5.190740e-04,\n",
       "       5.190740e-04, 5.190740e-04, 5.190740e-04, 5.190740e-04,\n",
       "       8.210101e-03, 8.210101e-03, 8.210101e-03, 8.210101e-03,\n",
       "       8.210101e-03, 8.210101e-03, 8.919850e-04, 8.919850e-04,\n",
       "       8.919850e-04, 8.919850e-04, 8.919850e-04, 8.919850e-04,\n",
       "       6.820000e-05, 6.820000e-05, 6.820000e-05, 6.820000e-05,\n",
       "       6.820000e-05, 6.820000e-05, 1.049327e-03, 1.049327e-03,\n",
       "       1.049327e-03, 1.049327e-03, 1.049327e-03, 1.049327e-03,\n",
       "       1.084829e-03, 1.084829e-03, 1.084829e-03, 1.084829e-03,\n",
       "       1.084829e-03, 1.084829e-03, 2.385313e-03, 2.385313e-03,\n",
       "       2.385313e-03, 2.385313e-03, 2.385313e-03, 2.385313e-03,\n",
       "       4.276687e-03, 4.276687e-03, 4.276687e-03, 4.276687e-03,\n",
       "       4.276687e-03, 4.276687e-03, 7.410060e-04, 7.410060e-04,\n",
       "       7.410060e-04, 7.410060e-04, 7.410060e-04, 7.410060e-04,\n",
       "       6.705817e-03, 6.705817e-03, 6.705817e-03, 6.705817e-03,\n",
       "       6.705817e-03, 6.705817e-03, 1.672701e-03, 1.672701e-03,\n",
       "       1.672701e-03, 1.672701e-03, 1.672701e-03, 1.672701e-03,\n",
       "       3.404925e-03, 3.404925e-03, 3.404925e-03, 3.404925e-03,\n",
       "       3.404925e-03, 3.404925e-03, 1.253648e-03, 1.253648e-03,\n",
       "       1.253648e-03, 1.253648e-03, 1.253648e-03, 1.253648e-03,\n",
       "       2.831310e-04, 2.831310e-04, 2.831310e-04, 2.831310e-04,\n",
       "       2.831310e-04, 2.831310e-04, 1.766161e-03, 1.766161e-03,\n",
       "       1.766161e-03, 1.766161e-03, 1.766161e-03, 1.766161e-03,\n",
       "       1.546178e-03, 1.546178e-03, 1.546178e-03, 1.546178e-03,\n",
       "       1.546178e-03, 1.546178e-03, 5.016647e-03, 5.016647e-03,\n",
       "       5.016647e-03, 5.016647e-03, 5.016647e-03, 5.016647e-03,\n",
       "       3.695250e-04, 3.695250e-04, 3.695250e-04, 3.695250e-04,\n",
       "       3.695250e-04, 3.695250e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Crystallinity</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3463 0.3463 ... 0.3708 0.3708</div><input id='attrs-eb432c40-80eb-4550-8ebb-09bc510b0c5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb432c40-80eb-4550-8ebb-09bc510b0c5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9b2a576-449c-455a-9a49-4ac363143ff2' class='xr-var-data-in' type='checkbox'><label for='data-c9b2a576-449c-455a-9a49-4ac363143ff2' 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>%</dd></dl></div><div class='xr-var-data'><pre>array([0.34625341, 0.34625341, 0.34625341, 0.34625341, 0.34625341,\n",
       "       0.34625341, 0.40023842, 0.40023842, 0.40023842, 0.40023842,\n",
       "       0.40023842, 0.40023842, 0.37581744, 0.37581744, 0.37581744,\n",
       "       0.37581744, 0.37581744, 0.37581744, 0.42735014, 0.42735014,\n",
       "       0.42735014, 0.42735014, 0.42735014, 0.42735014, 0.32923706,\n",
       "       0.32923706, 0.32923706, 0.32923706, 0.32923706, 0.32923706,\n",
       "       0.50395095, 0.50395095, 0.50395095, 0.50395095, 0.50395095,\n",
       "       0.50395095, 0.55735695, 0.55735695, 0.55735695, 0.55735695,\n",
       "       0.55735695, 0.55735695, 0.65534741, 0.65534741, 0.65534741,\n",
       "       0.65534741, 0.65534741, 0.65534741, 0.43788991, 0.43788991,\n",
       "       0.43788991, 0.43788991, 0.43788991, 0.43788991, 0.24711512,\n",
       "       0.24711512, 0.24711512, 0.24711512, 0.24711512, 0.24711512,\n",
       "       0.49529973, 0.49529973, 0.49529973, 0.49529973, 0.49529973,\n",
       "       0.49529973, 0.31264646, 0.31264646, 0.31264646, 0.31264646,\n",
       "       0.31264646, 0.31264646, 0.43808305, 0.43808305, 0.43808305,\n",
       "       0.43808305, 0.43808305, 0.43808305, 0.48685286, 0.48685286,\n",
       "       0.48685286, 0.48685286, 0.48685286, 0.48685286, 0.39028971,\n",
       "       0.39028971, 0.39028971, 0.39028971, 0.39028971, 0.39028971,\n",
       "       0.62489782, 0.62489782, 0.62489782, 0.62489782, 0.62489782,\n",
       "       0.62489782, 0.60381471, 0.60381471, 0.60381471, 0.60381471,\n",
       "       0.60381471, 0.60381471, 0.38284404, 0.38284404, 0.38284404,\n",
       "       0.38284404, 0.38284404, 0.38284404, 0.37084469, 0.37084469,\n",
       "       0.37084469, 0.37084469, 0.37084469, 0.37084469])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SCB_per_1000C (IR)</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.4 15.4 15.4 ... 23.6 23.6 23.6</div><input id='attrs-90d5a8d3-73be-4c43-a9d5-f36dae5e9020' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90d5a8d3-73be-4c43-a9d5-f36dae5e9020' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cec7f70b-5a04-4deb-84e4-6f4606cc3888' class='xr-var-data-in' type='checkbox'><label for='data-cec7f70b-5a04-4deb-84e4-6f4606cc3888' 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([ 15.4 ,  15.4 ,  15.4 ,  15.4 ,  15.4 ,  15.4 ,  27.4 ,  27.4 ,\n",
       "        27.4 ,  27.4 ,  27.4 ,  27.4 ,  28.8 ,  28.8 ,  28.8 ,  28.8 ,\n",
       "        28.8 ,  28.8 ,  18.6 ,  18.6 ,  18.6 ,  18.6 ,  18.6 ,  18.6 ,\n",
       "        20.2 ,  20.2 ,  20.2 ,  20.2 ,  20.2 ,  20.2 ,  12.65,  12.65,\n",
       "        12.65,  12.65,  12.65,  12.65,   8.7 ,   8.7 ,   8.7 ,   8.7 ,\n",
       "         8.7 ,   8.7 ,   6.75,   6.75,   6.75,   6.75,   6.75,   6.75,\n",
       "       312.95, 312.95, 312.95, 312.95, 312.95, 312.95,  27.2 ,  27.2 ,\n",
       "        27.2 ,  27.2 ,  27.2 ,  27.2 ,  10.9 ,  10.9 ,  10.9 ,  10.9 ,\n",
       "        10.9 ,  10.9 ,  21.2 ,  21.2 ,  21.2 ,  21.2 ,  21.2 ,  21.2 ,\n",
       "       331.6 , 331.6 , 331.6 , 331.6 , 331.6 , 331.6 ,  28.9 ,  28.9 ,\n",
       "        28.9 ,  28.9 ,  28.9 ,  28.9 , 318.75, 318.75, 318.75, 318.75,\n",
       "       318.75, 318.75,   7.35,   7.35,   7.35,   7.35,   7.35,   7.35,\n",
       "         9.7 ,   9.7 ,   9.7 ,   9.7 ,   9.7 ,   9.7 , 293.65, 293.65,\n",
       "       293.65, 293.65, 293.65, 293.65,  23.6 ,  23.6 ,  23.6 ,  23.6 ,\n",
       "        23.6 ,  23.6 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SCB_per_1000C StDev (IR)</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.414 1.414 1.414 ... 0.2828 0.2828</div><input id='attrs-2c7fc4e3-6a2a-4ccb-9dab-fe75e851187d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c7fc4e3-6a2a-4ccb-9dab-fe75e851187d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b89b8e22-5356-4df7-8267-0fb1659d47b8' class='xr-var-data-in' type='checkbox'><label for='data-b89b8e22-5356-4df7-8267-0fb1659d47b8' 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([1.41421356, 1.41421356, 1.41421356, 1.41421356, 1.41421356,\n",
       "       1.41421356, 0.56568542, 0.56568542, 0.56568542, 0.56568542,\n",
       "       0.56568542, 0.56568542, 3.67695526, 3.67695526, 3.67695526,\n",
       "       3.67695526, 3.67695526, 3.67695526, 0.14142136, 0.14142136,\n",
       "       0.14142136, 0.14142136, 0.14142136, 0.14142136, 0.42426407,\n",
       "       0.42426407, 0.42426407, 0.42426407, 0.42426407, 0.42426407,\n",
       "       0.49497475, 0.49497475, 0.49497475, 0.49497475, 0.49497475,\n",
       "       0.49497475, 2.40416306, 2.40416306, 2.40416306, 2.40416306,\n",
       "       2.40416306, 2.40416306, 0.353553  , 0.353553  , 0.353553  ,\n",
       "       0.353553  , 0.353553  , 0.353553  , 0.49497475, 0.49497475,\n",
       "       0.49497475, 0.49497475, 0.49497475, 0.49497475, 1.41      ,\n",
       "       1.41      , 1.41      , 1.41      , 1.41      , 1.41      ,\n",
       "       2.26      , 2.26      , 2.26      , 2.26      , 2.26      ,\n",
       "       2.26      , 1.56      , 1.56      , 1.56      , 1.56      ,\n",
       "       1.56      , 1.56      , 1.27279221, 1.27279221, 1.27279221,\n",
       "       1.27279221, 1.27279221, 1.27279221, 1.13137085, 1.13137085,\n",
       "       1.13137085, 1.13137085, 1.13137085, 1.13137085, 0.91923882,\n",
       "       0.91923882, 0.91923882, 0.91923882, 0.91923882, 0.91923882,\n",
       "       0.77781746, 0.77781746, 0.77781746, 0.77781746, 0.77781746,\n",
       "       0.77781746, 0.42426407, 0.42426407, 0.42426407, 0.42426407,\n",
       "       0.42426407, 0.42426407, 1.06066017, 1.06066017, 1.06066017,\n",
       "       1.06066017, 1.06066017, 1.06066017, 0.28284271, 0.28284271,\n",
       "       0.28284271, 0.28284271, 0.28284271, 0.28284271])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MSC</span></div><div class='xr-var-dims'>(sample, Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.927 -4.928 ... 4.704 4.686</div><input id='attrs-517241ab-28a0-4783-a142-d9907432536a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-517241ab-28a0-4783-a142-d9907432536a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e14e7557-fd3e-40e9-91ce-a7861c8dffcc' class='xr-var-data-in' type='checkbox'><label for='data-e14e7557-fd3e-40e9-91ce-a7861c8dffcc' 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>% Reflectance</dd></dl></div><div class='xr-var-data'><pre>array([[-4.92707958, -4.92770977, -4.92862576, ...,  3.55429289,\n",
       "         3.64261213,  3.60535903],\n",
       "       [-4.39280838, -4.38919462, -4.38929057, ...,  4.10598451,\n",
       "         4.19097295,  4.42677653],\n",
       "       [-4.45506582, -4.4520154 , -4.4493812 , ...,  4.58116217,\n",
       "         4.66741946,  4.64813218],\n",
       "       ...,\n",
       "       [-6.22286357, -6.22370801, -6.22382761, ...,  5.00945665,\n",
       "         4.94505259,  5.07530624],\n",
       "       [-6.43997904, -6.44209852, -6.44417383, ...,  4.72388651,\n",
       "         4.73319087,  4.8367076 ],\n",
       "       [-6.27893856, -6.28020357, -6.27954661, ...,  4.85421628,\n",
       "         4.70357118,  4.68614116]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SG2</span></div><div class='xr-var-dims'>(sample, Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.448e-06 4.448e-06 ... -1.057e-05</div><input id='attrs-ed1c084d-7ee7-4260-a6fb-a4326d365d17' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed1c084d-7ee7-4260-a6fb-a4326d365d17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e68327ff-6054-4f12-9ee6-caa63e3322a9' class='xr-var-data-in' type='checkbox'><label for='data-e68327ff-6054-4f12-9ee6-caa63e3322a9' 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([[ 4.44846034e-06,  4.44846034e-06,  4.44846034e-06, ...,\n",
       "        -4.97491163e-06, -4.97491163e-06, -4.97491163e-06],\n",
       "       [ 1.43017736e-07,  1.43017736e-07,  1.43017736e-07, ...,\n",
       "        -3.46260191e-06, -3.46260191e-06, -3.46260191e-06],\n",
       "       [ 2.28661490e-06,  2.28661490e-06,  2.28661490e-06, ...,\n",
       "        -2.16547024e-06, -2.16547024e-06, -2.16547024e-06],\n",
       "       ...,\n",
       "       [ 5.23292317e-06,  5.23292317e-06,  5.23292317e-06, ...,\n",
       "        -9.20212448e-06, -9.20212448e-06, -9.20212448e-06],\n",
       "       [ 7.32357480e-06,  7.32357480e-06,  7.32357480e-06, ...,\n",
       "        -1.01882409e-05, -1.01882409e-05, -1.01882409e-05],\n",
       "       [ 4.18751053e-06,  4.18751053e-06,  4.18751053e-06, ...,\n",
       "        -1.05689111e-05, -1.05689111e-05, -1.05689111e-05]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6270082a-1e41-4353-a58d-d263a948e739' class='xr-section-summary-in' type='checkbox'  ><label for='section-6270082a-1e41-4353-a58d-d263a948e739' class='xr-section-summary' >Indexes: <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-index-name'><div>Wavenumber</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5af3e941-2601-4c00-b91d-c2d95e8e1dfe' class='xr-index-data-in' type='checkbox'/><label for='index-5af3e941-2601-4c00-b91d-c2d95e8e1dfe' 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(Float64Index([3999.706, 4001.634, 4003.563, 4005.491,  4007.42, 4009.348,\n",
       "              4011.277, 4013.205, 4015.134, 4017.062,\n",
       "              ...\n",
       "              11981.76, 11983.69, 11985.62, 11987.55, 11989.47,  11991.4,\n",
       "              11993.33, 11995.26, 11997.19, 11999.12],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Wavenumber&#x27;, length=4149))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-edc66d42-4d42-45fc-b520-82dfee03c287' class='xr-index-data-in' type='checkbox'/><label for='index-edc66d42-4d42-45fc-b520-82dfee03c287' 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([&#x27;H0001ULDPE_1&#x27;, &#x27;H0001ULDPE_2&#x27;, &#x27;H0001ULDPE_3&#x27;, &#x27;H0001ULDPE_4&#x27;,\n",
       "       &#x27;H0001ULDPE_5&#x27;, &#x27;H0001ULDPE_6&#x27;, &#x27;H0002LDPE_1&#x27;, &#x27;H0002LDPE_2&#x27;,\n",
       "       &#x27;H0002LDPE_3&#x27;, &#x27;H0002LDPE_4&#x27;,\n",
       "       ...\n",
       "       &#x27;S0052PP_3&#x27;, &#x27;S0052PP_4&#x27;, &#x27;S0052PP_5&#x27;, &#x27;S0052PP_6&#x27;, &#x27;S0081LDPE_1&#x27;,\n",
       "       &#x27;S0081LDPE_2&#x27;, &#x27;S0081LDPE_3&#x27;, &#x27;S0081LDPE_4&#x27;, &#x27;S0081LDPE_5&#x27;,\n",
       "       &#x27;S0081LDPE_6&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;sample&#x27;, length=114))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5028b5c-e402-42f1-ab14-820045a9bc87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5028b5c-e402-42f1-ab14-820045a9bc87' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>creation_date :</span></dt><dd>20230523</dd><dt><span>author :</span></dt><dd>Bradley Sutliff</dd><dt><span>email :</span></dt><dd>Bradley.Sutliff@nist.gov</dd><dt><span>data_collected_by :</span></dt><dd>Shailja Goyal</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (Wavenumber: 4149, sample: 114)\n",
       "Coordinates:\n",
       "  * Wavenumber                (Wavenumber) float64 4e+03 4.002e+03 ... 1.2e+04\n",
       "  * sample                    (sample) <U12 'H0001ULDPE_1' ... 'S0081LDPE_6'\n",
       "    polymer                   (sample) <U10 'H0001ULDPE' ... 'S0081LDPE'\n",
       "    state                     (sample) <U6 'Pellet' 'Pellet' ... 'Pellet'\n",
       "    repeat                    (sample) int64 1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6\n",
       "    Class1                    (sample) <U2 'PE' 'PE' 'PE' ... 'PE' 'PE' 'PE'\n",
       "    Class2                    (sample) <U8 'LDPE' 'LDPE' ... 'LDPE' 'LDPE'\n",
       "Data variables:\n",
       "    Intensity                 (sample, Wavenumber) float64 4.076 4.076 ... 12.66\n",
       "    Density                   (sample) float64 0.9188 0.9188 ... 0.9213 0.9213\n",
       "    Density SEM               (sample) float64 0.0007945 0.0007945 ... 0.0003695\n",
       "    Crystallinity             (sample) float64 0.3463 0.3463 ... 0.3708 0.3708\n",
       "    SCB_per_1000C (IR)        (sample) float64 15.4 15.4 15.4 ... 23.6 23.6 23.6\n",
       "    SCB_per_1000C StDev (IR)  (sample) float64 1.414 1.414 ... 0.2828 0.2828\n",
       "    MSC                       (sample, Wavenumber) float64 -4.927 ... 4.686\n",
       "    SG2                       (sample, Wavenumber) float64 4.448e-06 ... -1.0...\n",
       "Attributes:\n",
       "    creation_date:      20230523\n",
       "    author:             Bradley Sutliff\n",
       "    email:              Bradley.Sutliff@nist.gov\n",
       "    data_collected_by:  Shailja Goyal"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make another transformer to apply the Savitzky-Golay smoothing w/ 2nd deriv.\n",
    "trans2 = sg2_transformer\n",
    "\n",
    "# actually apply the correction and transform our data\n",
    "ds_sg2 = trans2.fit_transform(ds.MSC)\n",
    "ds_sg2.name = \"SG2\"\n",
    "# add this data back to the original Dataset\n",
    "ds = xr.merge([ds, ds_sg2])\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a42a30c7-a32a-44cb-a8ac-28ad85d2896b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15af83310>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set info for the plot\n",
    "color_scheme = \"Class2\"\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# loop through for each poylmer subclass\n",
    "for ptype in pd.unique(ds[color_scheme]):\n",
    "    # grab data of only our chosen subclass\n",
    "    ds_sub = ds.where(ds[color_scheme] == ptype, drop=True)\n",
    "    # grab data for a single replicate, the first replicate\n",
    "    rep_samp = pd.unique(ds_sub.sample)[0]\n",
    "    ds_samp = ds_sub.where(ds_sub.sample == rep_samp, drop=True)\n",
    "\n",
    "    # grab our x and y values\n",
    "    x = ds_samp.Wavenumber.values.flatten()\n",
    "    y = ds_samp.SG2.values.flatten()\n",
    "    # use one of our predefined dictionaries to choose a color\n",
    "    # based on the polymer subclass\n",
    "    color = cpalette[color_scheme][ptype]\n",
    "    linesty = dict_linesty[color_scheme][ptype]\n",
    "    # add the data\n",
    "    ax.plot(x,y, c=color, label=ptype, ls=linesty)\n",
    "\n",
    "# clean up the axis\n",
    "ax.set_ylabel(\"f ''(MSC)\")\n",
    "ax.set_xlabel(\"Wavenumber [cm$^{-1}$]\")\n",
    "ax.set_xlim(12000, 4000)\n",
    "ax.tick_params(\n",
    "    bottom=True,\n",
    "    top=True,\n",
    "    left=True,\n",
    "    right=True,\n",
    "    labelbottom=True,\n",
    "    labeltop=False,\n",
    "    labelleft=True,\n",
    "    labelright=False,\n",
    "    direction=\"inout\",\n",
    ")\n",
    "# fix the legend\n",
    "ax.legend(loc=\"best\", ncol=2, frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105eab5e-6216-41d4-be42-26ef3684e037",
   "metadata": {},
   "source": [
    "## Functional Principal Component Analysis (fPCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c72264-48a0-448e-8fa1-63c7ce5e9af1",
   "metadata": {},
   "source": [
    "fPCA reduces the spectra to a set of basis functions (loadings) that describe the variance between all the samples given to the function. Each sample can then be modeled with a series of these basis functions and their corresponding coefficients or scores. The net effect is that large sets of data can be reduced to a few scores (fPCs), enabling easier visualization and faster computing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "27c91ffa-1591-4c3e-b253-0dd159a50eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the data so a given wavenumber/feature doesn't control the PCA\n",
    "sscaler = wrap(StandardScaler(), sample_dim=\"sample\")\n",
    "ds_scaled = sscaler.fit_transform(ds.MSC)\n",
    "\n",
    "# another transformer for fPCA\n",
    "fPCA_transformer = FunctionTransformer(\n",
    "    my_fPCA, validate=False, kw_args={\"n_components\": 4}\n",
    ")\n",
    "\n",
    "# actually apply the correction and transform our data\n",
    "ds_fPCA = fPCA_transformer.fit_transform(ds_scaled)\n",
    "# add this data back to the original Dataset\n",
    "ds = xr.merge([ds, ds_fPCA])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e4307e8c-f84e-481b-843b-c0c57df66598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set info for the plot\n",
    "color_scheme = \"Class2\"\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "x = ds.fPCs.where(ds.fPCA_loading == 0, drop=True).values.flatten()\n",
    "y = ds.fPCs.where(ds.fPCA_loading == 1, drop=True).values.flatten()\n",
    "\n",
    "col_vec = pd.Series(ds[color_scheme].values).map(cpalette[color_scheme])\n",
    "shape_vec = pd.Series(ds[color_scheme].values).map(dict_shapes[color_scheme])\n",
    "order = np.argsort(ds[color_scheme].values)\n",
    "\n",
    "# add the data\n",
    "# note: fix_markers is a custom function\n",
    "myscatter=ax.scatter(x[order], y[order],\n",
    "                    color=col_vec.values[order])\n",
    "fix_markers(myscatter, shape_vec.values[order])\n",
    "\n",
    "# adjust axis\n",
    "ax.set_xlabel(f'{axis_dict[\"pc0\"]} [AU]')\n",
    "ax.set_ylabel(f'{axis_dict[\"pc1\"]} [AU]', labelpad=-1)\n",
    "ax.tick_params(\n",
    "    bottom=True,\n",
    "    top=True,\n",
    "    left=True,\n",
    "    right=True,\n",
    "    labelbottom=True,\n",
    "    labeltop=False,\n",
    "    labelleft=True,\n",
    "    labelright=False,\n",
    "    direction=\"inout\",\n",
    ")\n",
    "\n",
    "# fix the legend\n",
    "leg_elems = [\n",
    "    Line2D(\n",
    "        [0],\n",
    "        [0],\n",
    "        marker=dict_shapes[color_scheme][key],\n",
    "        color=\"w\",\n",
    "        markerfacecolor=cpalette[color_scheme][key],\n",
    "        label=key,\n",
    "        markersize=10,\n",
    "    )\n",
    "    for key in cpalette[color_scheme].keys()\n",
    "]\n",
    "fig.legend(\n",
    "    handles=leg_elems,\n",
    "    loc=\"center\",\n",
    "    bbox_to_anchor=[0.5, 0],\n",
    "    ncol=len(leg_elems),\n",
    "    frameon=False,\n",
    ")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a5f7cd1-c3be-40b1-affc-588f5d0df603",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance ratios (in order): [0.75314103 0.15148145 0.07919624 0.01050397]\n"
     ]
    }
   ],
   "source": [
    "print(f'Explained variance ratios (in order): {pd.unique(ds.fPCA_explainedVariance)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7dfcd56-e2cf-4e9e-9f1c-fd8ba1307943",
   "metadata": {},
   "source": [
    "## Linear Regression and Pearson's Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe7498ef-0805-43bf-9003-a89f32622769",
   "metadata": {},
   "source": [
    "Now that we have reduced the data, we are going to evaluate the correlations between each of our fPCs and the physical correlations we have measured. We will save Pearson's correlation coefficient and the goodness of fit for each pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a0220b2b-4ea6-4450-b7e4-10b0600f7d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_LinReg = pd.DataFrame(columns=[\"Pair\", \"r_corr\", \"r^2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c9878d20-3f9b-42f8-9d1c-abf1fc562db4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up information for the data groupings to loop through\n",
    "# using the first 2 principal components\n",
    "pc_list = [0, 1]\n",
    "# checking correlation with the following\n",
    "corr_list = [\"Density\", \"Crystallinity\", \"SCB_per_1000C (IR)\"]\n",
    "# grab standard error/standard deviation where appropriate\n",
    "err_list = [\n",
    "    (None, \"Density SEM\"),\n",
    "    (None, \"Density SEM\"),\n",
    "    (None, None),\n",
    "    (None, None),\n",
    "    (None, \"SCB_per_1000C StDev (IR)\"),\n",
    "    (None, \"SCB_per_1000C StDev (IR)\"),\n",
    "]\n",
    "# making a list of all combinations of fPCs and physical measurements to evaluate\n",
    "plot_list = list(itertools.product(corr_list, pc_list))\n",
    "\n",
    "# note: this is set up to test correlations with either PE or PP\n",
    "ds_sub = ds.where(ds[\"Class1\"] == \"PE\", drop=True)\n",
    "\n",
    "fig, axes = plt.subplots(\n",
    "    len(corr_list),\n",
    "    len(pc_list),\n",
    "    sharey=\"row\",\n",
    "    sharex=\"col\",\n",
    "    gridspec_kw={\"wspace\": 0, \"hspace\": 0},\n",
    ")\n",
    "# loop through our property, fPC pairs\n",
    "for i, pair in enumerate(plot_list):\n",
    "    # split up the pairs so we can use them as indices\n",
    "    pc = pair[1]\n",
    "    corr = pair[0]\n",
    "    # grab the SEM/StDev data if available\n",
    "    corr_yerr = err_list[i][1]\n",
    "    # make sure plots go on the axis we expect\n",
    "    ax = axes.flatten()[i]\n",
    "    # set x and y and y_err\n",
    "    y = ds_sub[corr].values.flatten()\n",
    "    x = ds_sub.fPCs[:, pc].values.flatten()\n",
    "    if corr_yerr:\n",
    "        yerr = ds_sub[corr_yerr].values.flatten()\n",
    "    else:\n",
    "        yerr = None\n",
    "    # set the colors and shapes of the scatter points\n",
    "    col_vec = pd.Series(ds_sub[color_scheme].values).map(\n",
    "        cpalette[color_scheme])\n",
    "    shape_vec = pd.Series(ds_sub[color_scheme].values).map(\n",
    "        dict_shapes[color_scheme])\n",
    "\n",
    "    # make our linear regression model for this pair\n",
    "    X = x.reshape(-1, 1)\n",
    "    Y = y.reshape(-1, 1)\n",
    "    reg = LinearRegression().fit(X, Y)\n",
    "    Y_pred = reg.predict(X)\n",
    "    # Pearson's Correlation\n",
    "    r_corr, _ = pearsonr(x, y)\n",
    "    # r^2\n",
    "    r2 = reg.score(X, Y)\n",
    "    # save these results in a data frame\n",
    "    data = pd.DataFrame(columns=[\"Pair\", \"r_corr\", \"r^2\"])\n",
    "    data[\"Pair\"] = [pair]\n",
    "    data[\"r_corr\"] = r_corr\n",
    "    data[\"r^2\"] = r2\n",
    "    df_LinReg = pd.concat([df_LinReg, data])\n",
    "\n",
    "    # add the data\n",
    "    # we have to manipulate the plotting order so fewer points get covered up\n",
    "    order = np.argsort(ds_sub[color_scheme].values)\n",
    "\n",
    "    # plot the data\n",
    "    myscatter=ax.scatter(x[order], y[order],\n",
    "                    color=col_vec.values[order])\n",
    "    fix_markers(myscatter, shape_vec.values[order])\n",
    "    #add error bars\n",
    "    if yerr is not None:\n",
    "        ax.errorbar(\n",
    "            x[order],\n",
    "            y[order],\n",
    "            yerr=yerr[order],\n",
    "            fmt=\"none\",\n",
    "            ecolor=col_vec.values[order],\n",
    "        )\n",
    "    # add the linear regression line\n",
    "    ax.plot(x, Y_pred, color=\"k\")\n",
    "\n",
    "    # adjust the axes for all of the plots\n",
    "    ax.tick_params(\n",
    "        bottom=True,\n",
    "        top=True,\n",
    "        left=True,\n",
    "        right=True,\n",
    "        labelbottom=False,\n",
    "        labeltop=False,\n",
    "        labelleft=False,\n",
    "        labelright=False,\n",
    "        direction=\"inout\",\n",
    "    )\n",
    "\n",
    "# fixing axis for left and bottom plots\n",
    "axes.flatten()[0].tick_params(labelleft=True)\n",
    "axes.flatten()[2].tick_params(labelleft=True, labelbottom=True)\n",
    "axes.flatten()[4].tick_params(\n",
    "    labelbottom=True,\n",
    "    labelleft=True,\n",
    ")\n",
    "axes.flatten()[5].tick_params(labelbottom=True)\n",
    "axes.flatten()[0].set_ylabel(axis_dict[plot_list[0][0]])\n",
    "axes.flatten()[2].set_ylabel(axis_dict[plot_list[2][0]])\n",
    "axes.flatten()[4].set_ylabel(axis_dict[plot_list[4][0]])\n",
    "axes.flatten()[4].set_xlabel(axis_dict[f\"pc{plot_list[0][1]}\"])\n",
    "axes.flatten()[5].set_xlabel(axis_dict[f\"pc{plot_list[3][1]}\"])\n",
    "\n",
    "# custom legend\n",
    "leg_elems = [\n",
    "    Line2D(\n",
    "        [0],\n",
    "        [0],\n",
    "        marker=dict_shapes[color_scheme][key],\n",
    "        color=\"w\",\n",
    "        markerfacecolor=cpalette[color_scheme][key],\n",
    "        label=key,\n",
    "        markersize=10,\n",
    "    )\n",
    "    for key in cpalette[color_scheme].keys()\n",
    "]\n",
    "fig.legend(\n",
    "    handles=leg_elems,\n",
    "    loc=\"center\",\n",
    "    bbox_to_anchor=[0.5, 0],\n",
    "    ncol=len(leg_elems),\n",
    "    frameon=False,\n",
    ")\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "09cfb8d1-5d80-4da4-bafd-3202b844e084",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCB_per_1000C (IR)\n"
     ]
    },
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                   (sample: 90, Wavenumber: 4149, fPCA_loading: 4)\n",
       "Coordinates:\n",
       "  * Wavenumber                (Wavenumber) float64 4e+03 4.002e+03 ... 1.2e+04\n",
       "  * sample                    (sample) &lt;U12 &#x27;H0001ULDPE_1&#x27; ... &#x27;S0081LDPE_6&#x27;\n",
       "    polymer                   (sample) &lt;U10 &#x27;H0001ULDPE&#x27; ... &#x27;S0081LDPE&#x27;\n",
       "    state                     (sample) &lt;U6 &#x27;Pellet&#x27; &#x27;Pellet&#x27; ... &#x27;Pellet&#x27;\n",
       "    repeat                    (sample) int64 1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6\n",
       "    Class1                    (sample) &lt;U2 &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27; ... &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27;\n",
       "    Class2                    (sample) &lt;U8 &#x27;LDPE&#x27; &#x27;LDPE&#x27; ... &#x27;LDPE&#x27; &#x27;LDPE&#x27;\n",
       "  * fPCA_loading              (fPCA_loading) int64 0 1 2 3\n",
       "Data variables:\n",
       "    Intensity                 (sample, Wavenumber) float64 4.076 4.076 ... 12.66\n",
       "    Density                   (sample) float64 0.9188 0.9188 ... 0.9213 0.9213\n",
       "    Density SEM               (sample) float64 0.0007945 0.0007945 ... 0.0003695\n",
       "    Crystallinity             (sample) float64 0.3463 0.3463 ... 0.3708 0.3708\n",
       "    SCB_per_1000C (IR)        (sample) float64 15.4 15.4 15.4 ... 23.6 23.6 23.6\n",
       "    SCB_per_1000C StDev (IR)  (sample) float64 1.414 1.414 ... 0.2828 0.2828\n",
       "    MSC                       (sample, Wavenumber) float64 -4.927 ... 4.686\n",
       "    SG2                       (sample, Wavenumber) float64 4.448e-06 ... -1.0...\n",
       "    fPCA_loadings             (Wavenumber, fPCA_loading, sample) float64 -0.0...\n",
       "    fPCs                      (sample, fPCA_loading) float64 -96.24 ... -2.422\n",
       "    fPCA_explainedVariance    (fPCA_loading, sample) float64 0.7531 ... 0.0105\n",
       "Attributes:\n",
       "    creation_date:      20230523\n",
       "    author:             Bradley Sutliff\n",
       "    email:              Bradley.Sutliff@nist.gov\n",
       "    data_collected_by:  Shailja Goyal</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-859ece8a-42f9-4df8-9d65-8d16ff67de16' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-859ece8a-42f9-4df8-9d65-8d16ff67de16' 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'>sample</span>: 90</li><li><span class='xr-has-index'>Wavenumber</span>: 4149</li><li><span class='xr-has-index'>fPCA_loading</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f265e4c9-e10e-4c5b-ab73-bd65a0abcecb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f265e4c9-e10e-4c5b-ab73-bd65a0abcecb' class='xr-section-summary' >Coordinates: <span>(8)</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'>Wavenumber</span></div><div class='xr-var-dims'>(Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4e+03 4.002e+03 ... 1.2e+04 1.2e+04</div><input id='attrs-73d7b0ab-676d-4dbd-bfbe-9ca1f3036c23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73d7b0ab-676d-4dbd-bfbe-9ca1f3036c23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-284d83ff-1dfd-45ca-a194-fe14a31e4f50' class='xr-var-data-in' type='checkbox'><label for='data-284d83ff-1dfd-45ca-a194-fe14a31e4f50' 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>1/cm</dd></dl></div><div class='xr-var-data'><pre>array([ 3999.706,  4001.634,  4003.563, ..., 11995.26 , 11997.19 , 11999.12 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;H0001ULDPE_1&#x27; ... &#x27;S0081LDPE_6&#x27;</div><input id='attrs-09f9fd0a-c37a-44fb-bc93-4a964154c50f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09f9fd0a-c37a-44fb-bc93-4a964154c50f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5b6856d-55aa-4b5b-b496-4fe72a1fd772' class='xr-var-data-in' type='checkbox'><label for='data-e5b6856d-55aa-4b5b-b496-4fe72a1fd772' 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([&#x27;H0001ULDPE_1&#x27;, &#x27;H0001ULDPE_2&#x27;, &#x27;H0001ULDPE_3&#x27;, &#x27;H0001ULDPE_4&#x27;,\n",
       "       &#x27;H0001ULDPE_5&#x27;, &#x27;H0001ULDPE_6&#x27;, &#x27;H0002LDPE_1&#x27;, &#x27;H0002LDPE_2&#x27;,\n",
       "       &#x27;H0002LDPE_3&#x27;, &#x27;H0002LDPE_4&#x27;, &#x27;H0002LDPE_5&#x27;, &#x27;H0002LDPE_6&#x27;,\n",
       "       &#x27;H0003LDPE_1&#x27;, &#x27;H0003LDPE_2&#x27;, &#x27;H0003LDPE_3&#x27;, &#x27;H0003LDPE_4&#x27;,\n",
       "       &#x27;H0003LDPE_5&#x27;, &#x27;H0003LDPE_6&#x27;, &#x27;H0004LLDPE_1&#x27;, &#x27;H0004LLDPE_2&#x27;,\n",
       "       &#x27;H0004LLDPE_3&#x27;, &#x27;H0004LLDPE_4&#x27;, &#x27;H0004LLDPE_5&#x27;, &#x27;H0004LLDPE_6&#x27;,\n",
       "       &#x27;H0005LLDPE_1&#x27;, &#x27;H0005LLDPE_2&#x27;, &#x27;H0005LLDPE_3&#x27;, &#x27;H0005LLDPE_4&#x27;,\n",
       "       &#x27;H0005LLDPE_5&#x27;, &#x27;H0005LLDPE_6&#x27;, &#x27;H0006MDPE_1&#x27;, &#x27;H0006MDPE_2&#x27;,\n",
       "       &#x27;H0006MDPE_3&#x27;, &#x27;H0006MDPE_4&#x27;, &#x27;H0006MDPE_5&#x27;, &#x27;H0006MDPE_6&#x27;,\n",
       "       &#x27;H0007HDPE_1&#x27;, &#x27;H0007HDPE_2&#x27;, &#x27;H0007HDPE_3&#x27;, &#x27;H0007HDPE_4&#x27;,\n",
       "       &#x27;H0007HDPE_5&#x27;, &#x27;H0007HDPE_6&#x27;, &#x27;H0008HDPE_1&#x27;, &#x27;H0008HDPE_2&#x27;,\n",
       "       &#x27;H0008HDPE_3&#x27;, &#x27;H0008HDPE_4&#x27;, &#x27;H0008HDPE_5&#x27;, &#x27;H0008HDPE_6&#x27;,\n",
       "       &#x27;N1473LDPE_1&#x27;, &#x27;N1473LDPE_2&#x27;, &#x27;N1473LDPE_3&#x27;, &#x27;N1473LDPE_4&#x27;,\n",
       "       &#x27;N1473LDPE_5&#x27;, &#x27;N1473LDPE_6&#x27;, &#x27;N1474PE_1&#x27;, &#x27;N1474PE_2&#x27;, &#x27;N1474PE_3&#x27;,\n",
       "       &#x27;N1474PE_4&#x27;, &#x27;N1474PE_5&#x27;, &#x27;N1474PE_6&#x27;, &#x27;N1476LDPE_1&#x27;, &#x27;N1476LDPE_2&#x27;,\n",
       "       &#x27;N1476LDPE_3&#x27;, &#x27;N1476LDPE_4&#x27;, &#x27;N1476LDPE_5&#x27;, &#x27;N1476LDPE_6&#x27;,\n",
       "       &#x27;S0024LDPE_1&#x27;, &#x27;S0024LDPE_2&#x27;, &#x27;S0024LDPE_3&#x27;, &#x27;S0024LDPE_4&#x27;,\n",
       "       &#x27;S0024LDPE_5&#x27;, &#x27;S0024LDPE_6&#x27;, &#x27;S0040HDPE_1&#x27;, &#x27;S0040HDPE_2&#x27;,\n",
       "       &#x27;S0040HDPE_3&#x27;, &#x27;S0040HDPE_4&#x27;, &#x27;S0040HDPE_5&#x27;, &#x27;S0040HDPE_6&#x27;,\n",
       "       &#x27;S0048HDPE_1&#x27;, &#x27;S0048HDPE_2&#x27;, &#x27;S0048HDPE_3&#x27;, &#x27;S0048HDPE_4&#x27;,\n",
       "       &#x27;S0048HDPE_5&#x27;, &#x27;S0048HDPE_6&#x27;, &#x27;S0081LDPE_1&#x27;, &#x27;S0081LDPE_2&#x27;,\n",
       "       &#x27;S0081LDPE_3&#x27;, &#x27;S0081LDPE_4&#x27;, &#x27;S0081LDPE_5&#x27;, &#x27;S0081LDPE_6&#x27;],\n",
       "      dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polymer</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;H0001ULDPE&#x27; ... &#x27;S0081LDPE&#x27;</div><input id='attrs-ee20034d-2d3f-497e-ad95-c232201dd098' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee20034d-2d3f-497e-ad95-c232201dd098' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89a4cfce-6efa-4ede-a4f5-f5273119a63c' class='xr-var-data-in' type='checkbox'><label for='data-89a4cfce-6efa-4ede-a4f5-f5273119a63c' 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([&#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;,\n",
       "       &#x27;H0001ULDPE&#x27;, &#x27;H0001ULDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;,\n",
       "       &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0002LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;,\n",
       "       &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0003LDPE&#x27;, &#x27;H0004LLDPE&#x27;,\n",
       "       &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;, &#x27;H0004LLDPE&#x27;,\n",
       "       &#x27;H0004LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;,\n",
       "       &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0005LLDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;,\n",
       "       &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0006MDPE&#x27;, &#x27;H0007HDPE&#x27;,\n",
       "       &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;, &#x27;H0007HDPE&#x27;,\n",
       "       &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;, &#x27;H0008HDPE&#x27;,\n",
       "       &#x27;H0008HDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;,\n",
       "       &#x27;N1473LDPE&#x27;, &#x27;N1473LDPE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;,\n",
       "       &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1474PE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;,\n",
       "       &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;N1476LDPE&#x27;, &#x27;S0024LDPE&#x27;,\n",
       "       &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;, &#x27;S0024LDPE&#x27;,\n",
       "       &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;, &#x27;S0040HDPE&#x27;,\n",
       "       &#x27;S0040HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;,\n",
       "       &#x27;S0048HDPE&#x27;, &#x27;S0048HDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;,\n",
       "       &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;, &#x27;S0081LDPE&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;Pellet&#x27; &#x27;Pellet&#x27; ... &#x27;Pellet&#x27;</div><input id='attrs-2a9c38e7-73ec-48f1-8534-39e874e43408' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a9c38e7-73ec-48f1-8534-39e874e43408' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9e98027-efba-4bfb-8e2f-7dc7f6370a9d' class='xr-var-data-in' type='checkbox'><label for='data-c9e98027-efba-4bfb-8e2f-7dc7f6370a9d' 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([&#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;,\n",
       "       &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;, &#x27;Pellet&#x27;],\n",
       "      dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>repeat</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6</div><input id='attrs-ad5870d5-e8c2-4225-b1a6-353fed74f619' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad5870d5-e8c2-4225-b1a6-353fed74f619' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41e586a9-94f0-4678-b2f0-7633c8d7c630' class='xr-var-data-in' type='checkbox'><label for='data-41e586a9-94f0-4678-b2f0-7633c8d7c630' 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([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n",
       "       5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2,\n",
       "       3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6,\n",
       "       1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n",
       "       5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Class1</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27; ... &#x27;PE&#x27; &#x27;PE&#x27; &#x27;PE&#x27;</div><input id='attrs-c8a5eb95-f328-4827-b01d-092832bf4d2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8a5eb95-f328-4827-b01d-092832bf4d2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b843fc92-a141-47a7-b62b-ea2ea9608ebc' class='xr-var-data-in' type='checkbox'><label for='data-b843fc92-a141-47a7-b62b-ea2ea9608ebc' 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([&#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;, &#x27;PE&#x27;,\n",
       "       &#x27;PE&#x27;, &#x27;PE&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Class2</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;LDPE&#x27; &#x27;LDPE&#x27; ... &#x27;LDPE&#x27; &#x27;LDPE&#x27;</div><input id='attrs-5347d0f6-12ef-4294-9f8b-35f3b67042bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5347d0f6-12ef-4294-9f8b-35f3b67042bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cab04c15-fd22-4fa3-95bc-7c2683165062' class='xr-var-data-in' type='checkbox'><label for='data-cab04c15-fd22-4fa3-95bc-7c2683165062' 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([&#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;,\n",
       "       &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;, &#x27;LLDPE&#x27;,\n",
       "       &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;MDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;,\n",
       "       &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;HDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;,\n",
       "       &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;, &#x27;LDPE&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fPCA_loading</span></div><div class='xr-var-dims'>(fPCA_loading)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-0926ed33-3a26-4362-bcb5-0bf6cdbeef80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0926ed33-3a26-4362-bcb5-0bf6cdbeef80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c15d5fd6-f43e-47e4-af2b-4b862ea901d7' class='xr-var-data-in' type='checkbox'><label for='data-c15d5fd6-f43e-47e4-af2b-4b862ea901d7' 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([0, 1, 2, 3])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a25a034d-1356-461d-a04b-af7b520b85c7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a25a034d-1356-461d-a04b-af7b520b85c7' class='xr-section-summary' >Data variables: <span>(11)</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>Intensity</span></div><div class='xr-var-dims'>(sample, Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.076 4.076 4.075 ... 12.67 12.66</div><input id='attrs-cd2e893b-a06c-40b8-8412-4ed1e0143413' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd2e893b-a06c-40b8-8412-4ed1e0143413' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18b3c581-da84-49cd-a39b-725070cd041b' class='xr-var-data-in' type='checkbox'><label for='data-18b3c581-da84-49cd-a39b-725070cd041b' 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>% Reflectance</dd></dl></div><div class='xr-var-data'><pre>array([[ 4.076315,  4.075627,  4.074627, ..., 13.33561 , 13.43203 ,\n",
       "        13.39136 ],\n",
       "       [ 2.324348,  2.32657 ,  2.326511, ...,  7.550017,  7.602274,\n",
       "         7.747263],\n",
       "       [ 2.820138,  2.822901,  2.825287, ..., 11.00496 , 11.08309 ,\n",
       "        11.06562 ],\n",
       "       ...,\n",
       "       [ 2.942806,  2.941994,  2.941879, ..., 13.74364 , 13.68171 ,\n",
       "        13.80696 ],\n",
       "       [ 2.383012,  2.380709,  2.378454, ..., 14.51353 , 14.52364 ,\n",
       "        14.63612 ],\n",
       "       [ 2.793289,  2.792151,  2.792742, ..., 12.80865 , 12.67313 ,\n",
       "        12.65745 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Density</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9188 0.9188 ... 0.9213 0.9213</div><input id='attrs-b0ca0171-3e16-4cb0-852a-d8c3f17acbee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0ca0171-3e16-4cb0-852a-d8c3f17acbee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4764e49e-aa6f-49d7-b968-fdf598c2aa5e' class='xr-var-data-in' type='checkbox'><label for='data-4764e49e-aa6f-49d7-b968-fdf598c2aa5e' 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>g/cm^3</dd></dl></div><div class='xr-var-data'><pre>array([0.91880138, 0.91880138, 0.91880138, 0.91880138, 0.91880138,\n",
       "       0.91880138, 0.92407408, 0.92407408, 0.92407408, 0.92407408,\n",
       "       0.92407408, 0.92407408, 0.90557274, 0.90557274, 0.90557274,\n",
       "       0.90557274, 0.90557274, 0.90557274, 0.91131919, 0.91131919,\n",
       "       0.91131919, 0.91131919, 0.91131919, 0.91131919, 0.91961018,\n",
       "       0.91961018, 0.91961018, 0.91961018, 0.91961018, 0.91961018,\n",
       "       0.93417556, 0.93417556, 0.93417556, 0.93417556, 0.93417556,\n",
       "       0.93417556, 0.95171828, 0.95171828, 0.95171828, 0.95171828,\n",
       "       0.95171828, 0.95171828, 0.95392818, 0.95392818, 0.95392818,\n",
       "       0.95392818, 0.95392818, 0.95392818, 0.91710051, 0.91710051,\n",
       "       0.91710051, 0.91710051, 0.91710051, 0.91710051, 0.92719814,\n",
       "       0.92719814, 0.92719814, 0.92719814, 0.92719814, 0.92719814,\n",
       "       0.91972693, 0.91972693, 0.91972693, 0.91972693, 0.91972693,\n",
       "       0.91972693, 0.91649674, 0.91649674, 0.91649674, 0.91649674,\n",
       "       0.91649674, 0.91649674, 0.95634513, 0.95634513, 0.95634513,\n",
       "       0.95634513, 0.95634513, 0.95634513, 0.95635806, 0.95635806,\n",
       "       0.95635806, 0.95635806, 0.95635806, 0.95635806, 0.92130818,\n",
       "       0.92130818, 0.92130818, 0.92130818, 0.92130818, 0.92130818])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Density SEM</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0007945 0.0007945 ... 0.0003695</div><input id='attrs-dd14b535-b96a-4a6d-8bb3-0f85355aff74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd14b535-b96a-4a6d-8bb3-0f85355aff74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f52df49a-3b01-4a66-8118-a6fe2a9bc9a7' class='xr-var-data-in' type='checkbox'><label for='data-f52df49a-3b01-4a66-8118-a6fe2a9bc9a7' 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([7.944570e-04, 7.944570e-04, 7.944570e-04, 7.944570e-04,\n",
       "       7.944570e-04, 7.944570e-04, 5.190740e-04, 5.190740e-04,\n",
       "       5.190740e-04, 5.190740e-04, 5.190740e-04, 5.190740e-04,\n",
       "       8.210101e-03, 8.210101e-03, 8.210101e-03, 8.210101e-03,\n",
       "       8.210101e-03, 8.210101e-03, 8.919850e-04, 8.919850e-04,\n",
       "       8.919850e-04, 8.919850e-04, 8.919850e-04, 8.919850e-04,\n",
       "       6.820000e-05, 6.820000e-05, 6.820000e-05, 6.820000e-05,\n",
       "       6.820000e-05, 6.820000e-05, 1.049327e-03, 1.049327e-03,\n",
       "       1.049327e-03, 1.049327e-03, 1.049327e-03, 1.049327e-03,\n",
       "       1.084829e-03, 1.084829e-03, 1.084829e-03, 1.084829e-03,\n",
       "       1.084829e-03, 1.084829e-03, 2.385313e-03, 2.385313e-03,\n",
       "       2.385313e-03, 2.385313e-03, 2.385313e-03, 2.385313e-03,\n",
       "       7.410060e-04, 7.410060e-04, 7.410060e-04, 7.410060e-04,\n",
       "       7.410060e-04, 7.410060e-04, 6.705817e-03, 6.705817e-03,\n",
       "       6.705817e-03, 6.705817e-03, 6.705817e-03, 6.705817e-03,\n",
       "       1.672701e-03, 1.672701e-03, 1.672701e-03, 1.672701e-03,\n",
       "       1.672701e-03, 1.672701e-03, 1.253648e-03, 1.253648e-03,\n",
       "       1.253648e-03, 1.253648e-03, 1.253648e-03, 1.253648e-03,\n",
       "       1.766161e-03, 1.766161e-03, 1.766161e-03, 1.766161e-03,\n",
       "       1.766161e-03, 1.766161e-03, 1.546178e-03, 1.546178e-03,\n",
       "       1.546178e-03, 1.546178e-03, 1.546178e-03, 1.546178e-03,\n",
       "       3.695250e-04, 3.695250e-04, 3.695250e-04, 3.695250e-04,\n",
       "       3.695250e-04, 3.695250e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Crystallinity</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3463 0.3463 ... 0.3708 0.3708</div><input id='attrs-e8e97cf9-060f-4c84-878f-35c0a8b0b0f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8e97cf9-060f-4c84-878f-35c0a8b0b0f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65451ac1-30f8-4260-8c53-20bb50b827df' class='xr-var-data-in' type='checkbox'><label for='data-65451ac1-30f8-4260-8c53-20bb50b827df' 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>%</dd></dl></div><div class='xr-var-data'><pre>array([0.34625341, 0.34625341, 0.34625341, 0.34625341, 0.34625341,\n",
       "       0.34625341, 0.40023842, 0.40023842, 0.40023842, 0.40023842,\n",
       "       0.40023842, 0.40023842, 0.37581744, 0.37581744, 0.37581744,\n",
       "       0.37581744, 0.37581744, 0.37581744, 0.42735014, 0.42735014,\n",
       "       0.42735014, 0.42735014, 0.42735014, 0.42735014, 0.32923706,\n",
       "       0.32923706, 0.32923706, 0.32923706, 0.32923706, 0.32923706,\n",
       "       0.50395095, 0.50395095, 0.50395095, 0.50395095, 0.50395095,\n",
       "       0.50395095, 0.55735695, 0.55735695, 0.55735695, 0.55735695,\n",
       "       0.55735695, 0.55735695, 0.65534741, 0.65534741, 0.65534741,\n",
       "       0.65534741, 0.65534741, 0.65534741, 0.24711512, 0.24711512,\n",
       "       0.24711512, 0.24711512, 0.24711512, 0.24711512, 0.49529973,\n",
       "       0.49529973, 0.49529973, 0.49529973, 0.49529973, 0.49529973,\n",
       "       0.31264646, 0.31264646, 0.31264646, 0.31264646, 0.31264646,\n",
       "       0.31264646, 0.48685286, 0.48685286, 0.48685286, 0.48685286,\n",
       "       0.48685286, 0.48685286, 0.62489782, 0.62489782, 0.62489782,\n",
       "       0.62489782, 0.62489782, 0.62489782, 0.60381471, 0.60381471,\n",
       "       0.60381471, 0.60381471, 0.60381471, 0.60381471, 0.37084469,\n",
       "       0.37084469, 0.37084469, 0.37084469, 0.37084469, 0.37084469])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SCB_per_1000C (IR)</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.4 15.4 15.4 ... 23.6 23.6 23.6</div><input id='attrs-20e5c74c-b057-4004-b613-524335cd7718' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20e5c74c-b057-4004-b613-524335cd7718' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51dccb83-2429-4a93-87a9-df7483afbc9b' class='xr-var-data-in' type='checkbox'><label for='data-51dccb83-2429-4a93-87a9-df7483afbc9b' 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([15.4 , 15.4 , 15.4 , 15.4 , 15.4 , 15.4 , 27.4 , 27.4 , 27.4 ,\n",
       "       27.4 , 27.4 , 27.4 , 28.8 , 28.8 , 28.8 , 28.8 , 28.8 , 28.8 ,\n",
       "       18.6 , 18.6 , 18.6 , 18.6 , 18.6 , 18.6 , 20.2 , 20.2 , 20.2 ,\n",
       "       20.2 , 20.2 , 20.2 , 12.65, 12.65, 12.65, 12.65, 12.65, 12.65,\n",
       "        8.7 ,  8.7 ,  8.7 ,  8.7 ,  8.7 ,  8.7 ,  6.75,  6.75,  6.75,\n",
       "        6.75,  6.75,  6.75, 27.2 , 27.2 , 27.2 , 27.2 , 27.2 , 27.2 ,\n",
       "       10.9 , 10.9 , 10.9 , 10.9 , 10.9 , 10.9 , 21.2 , 21.2 , 21.2 ,\n",
       "       21.2 , 21.2 , 21.2 , 28.9 , 28.9 , 28.9 , 28.9 , 28.9 , 28.9 ,\n",
       "        7.35,  7.35,  7.35,  7.35,  7.35,  7.35,  9.7 ,  9.7 ,  9.7 ,\n",
       "        9.7 ,  9.7 ,  9.7 , 23.6 , 23.6 , 23.6 , 23.6 , 23.6 , 23.6 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SCB_per_1000C StDev (IR)</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.414 1.414 1.414 ... 0.2828 0.2828</div><input id='attrs-cd6c99b4-6c74-4adc-a1e6-8f530e1fe11a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd6c99b4-6c74-4adc-a1e6-8f530e1fe11a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0e805d8-cffe-47d3-ba0f-bd3a69328989' class='xr-var-data-in' type='checkbox'><label for='data-a0e805d8-cffe-47d3-ba0f-bd3a69328989' 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([1.41421356, 1.41421356, 1.41421356, 1.41421356, 1.41421356,\n",
       "       1.41421356, 0.56568542, 0.56568542, 0.56568542, 0.56568542,\n",
       "       0.56568542, 0.56568542, 3.67695526, 3.67695526, 3.67695526,\n",
       "       3.67695526, 3.67695526, 3.67695526, 0.14142136, 0.14142136,\n",
       "       0.14142136, 0.14142136, 0.14142136, 0.14142136, 0.42426407,\n",
       "       0.42426407, 0.42426407, 0.42426407, 0.42426407, 0.42426407,\n",
       "       0.49497475, 0.49497475, 0.49497475, 0.49497475, 0.49497475,\n",
       "       0.49497475, 2.40416306, 2.40416306, 2.40416306, 2.40416306,\n",
       "       2.40416306, 2.40416306, 0.353553  , 0.353553  , 0.353553  ,\n",
       "       0.353553  , 0.353553  , 0.353553  , 1.41      , 1.41      ,\n",
       "       1.41      , 1.41      , 1.41      , 1.41      , 2.26      ,\n",
       "       2.26      , 2.26      , 2.26      , 2.26      , 2.26      ,\n",
       "       1.56      , 1.56      , 1.56      , 1.56      , 1.56      ,\n",
       "       1.56      , 1.13137085, 1.13137085, 1.13137085, 1.13137085,\n",
       "       1.13137085, 1.13137085, 0.77781746, 0.77781746, 0.77781746,\n",
       "       0.77781746, 0.77781746, 0.77781746, 0.42426407, 0.42426407,\n",
       "       0.42426407, 0.42426407, 0.42426407, 0.42426407, 0.28284271,\n",
       "       0.28284271, 0.28284271, 0.28284271, 0.28284271, 0.28284271])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MSC</span></div><div class='xr-var-dims'>(sample, Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.927 -4.928 ... 4.704 4.686</div><input id='attrs-85014420-4b51-403e-88ab-4e4ca72e54b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85014420-4b51-403e-88ab-4e4ca72e54b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18ca7eea-6336-49da-b7ae-30032b5282c9' class='xr-var-data-in' type='checkbox'><label for='data-18ca7eea-6336-49da-b7ae-30032b5282c9' 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>% Reflectance</dd></dl></div><div class='xr-var-data'><pre>array([[-4.92707958, -4.92770977, -4.92862576, ...,  3.55429289,\n",
       "         3.64261213,  3.60535903],\n",
       "       [-4.39280838, -4.38919462, -4.38929057, ...,  4.10598451,\n",
       "         4.19097295,  4.42677653],\n",
       "       [-4.45506582, -4.4520154 , -4.4493812 , ...,  4.58116217,\n",
       "         4.66741946,  4.64813218],\n",
       "       ...,\n",
       "       [-6.22286357, -6.22370801, -6.22382761, ...,  5.00945665,\n",
       "         4.94505259,  5.07530624],\n",
       "       [-6.43997904, -6.44209852, -6.44417383, ...,  4.72388651,\n",
       "         4.73319087,  4.8367076 ],\n",
       "       [-6.27893856, -6.28020357, -6.27954661, ...,  4.85421628,\n",
       "         4.70357118,  4.68614116]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SG2</span></div><div class='xr-var-dims'>(sample, Wavenumber)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.448e-06 4.448e-06 ... -1.057e-05</div><input id='attrs-ce348108-9de3-495f-998a-7ddb87b74ef4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce348108-9de3-495f-998a-7ddb87b74ef4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e891864a-0c89-4b13-8ea7-6a2ef30ece92' class='xr-var-data-in' type='checkbox'><label for='data-e891864a-0c89-4b13-8ea7-6a2ef30ece92' 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([[ 4.44846034e-06,  4.44846034e-06,  4.44846034e-06, ...,\n",
       "        -4.97491163e-06, -4.97491163e-06, -4.97491163e-06],\n",
       "       [ 1.43017736e-07,  1.43017736e-07,  1.43017736e-07, ...,\n",
       "        -3.46260191e-06, -3.46260191e-06, -3.46260191e-06],\n",
       "       [ 2.28661490e-06,  2.28661490e-06,  2.28661490e-06, ...,\n",
       "        -2.16547024e-06, -2.16547024e-06, -2.16547024e-06],\n",
       "       ...,\n",
       "       [ 5.23292317e-06,  5.23292317e-06,  5.23292317e-06, ...,\n",
       "        -9.20212448e-06, -9.20212448e-06, -9.20212448e-06],\n",
       "       [ 7.32357480e-06,  7.32357480e-06,  7.32357480e-06, ...,\n",
       "        -1.01882409e-05, -1.01882409e-05, -1.01882409e-05],\n",
       "       [ 4.18751053e-06,  4.18751053e-06,  4.18751053e-06, ...,\n",
       "        -1.05689111e-05, -1.05689111e-05, -1.05689111e-05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fPCA_loadings</span></div><div class='xr-var-dims'>(Wavenumber, fPCA_loading, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0127 -0.0127 ... -0.003227</div><input id='attrs-ba529bf2-fe6d-423e-9612-e32d8e3a0664' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba529bf2-fe6d-423e-9612-e32d8e3a0664' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e481be7-df4f-4f35-b279-c7e0eb99922a' class='xr-var-data-in' type='checkbox'><label for='data-5e481be7-df4f-4f35-b279-c7e0eb99922a' 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([[[-0.01269797, -0.01269797, -0.01269797, ..., -0.01269797,\n",
       "         -0.01269797, -0.01269797],\n",
       "        [-0.00180511, -0.00180511, -0.00180511, ..., -0.00180511,\n",
       "         -0.00180511, -0.00180511],\n",
       "        [-0.00383661, -0.00383661, -0.00383661, ..., -0.00383661,\n",
       "         -0.00383661, -0.00383661],\n",
       "        [-0.0091852 , -0.0091852 , -0.0091852 , ..., -0.0091852 ,\n",
       "         -0.0091852 , -0.0091852 ]],\n",
       "\n",
       "       [[-0.01269849, -0.01269849, -0.01269849, ..., -0.01269849,\n",
       "         -0.01269849, -0.01269849],\n",
       "        [-0.00180347, -0.00180347, -0.00180347, ..., -0.00180347,\n",
       "         -0.00180347, -0.00180347],\n",
       "        [-0.00382369, -0.00382369, -0.00382369, ..., -0.00382369,\n",
       "         -0.00382369, -0.00382369],\n",
       "        [-0.0091698 , -0.0091698 , -0.0091698 , ..., -0.0091698 ,\n",
       "         -0.0091698 , -0.0091698 ]],\n",
       "\n",
       "       [[-0.01269868, -0.01269868, -0.01269868, ..., -0.01269868,\n",
       "         -0.01269868, -0.01269868],\n",
       "...\n",
       "        [-0.00377537, -0.00377537, -0.00377537, ..., -0.00377537,\n",
       "         -0.00377537, -0.00377537]],\n",
       "\n",
       "       [[ 0.01217469,  0.01217469,  0.01217469, ...,  0.01217469,\n",
       "          0.01217469,  0.01217469],\n",
       "        [-0.00542873, -0.00542873, -0.00542873, ..., -0.00542873,\n",
       "         -0.00542873, -0.00542873],\n",
       "        [-0.00864847, -0.00864847, -0.00864847, ..., -0.00864847,\n",
       "         -0.00864847, -0.00864847],\n",
       "        [-0.00302457, -0.00302457, -0.00302457, ..., -0.00302457,\n",
       "         -0.00302457, -0.00302457]],\n",
       "\n",
       "       [[ 0.012172  ,  0.012172  ,  0.012172  , ...,  0.012172  ,\n",
       "          0.012172  ,  0.012172  ],\n",
       "        [-0.00560756, -0.00560756, -0.00560756, ..., -0.00560756,\n",
       "         -0.00560756, -0.00560756],\n",
       "        [-0.0085421 , -0.0085421 , -0.0085421 , ..., -0.0085421 ,\n",
       "         -0.0085421 , -0.0085421 ],\n",
       "        [-0.00322705, -0.00322705, -0.00322705, ..., -0.00322705,\n",
       "         -0.00322705, -0.00322705]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fPCs</span></div><div class='xr-var-dims'>(sample, fPCA_loading)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-96.24 0.8871 ... 21.64 -2.422</div><input id='attrs-2c7db9e2-3d61-4a02-8a55-0adaeb9b058c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c7db9e2-3d61-4a02-8a55-0adaeb9b058c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21aacd35-1145-4c52-a189-57964bccbf40' class='xr-var-data-in' type='checkbox'><label for='data-21aacd35-1145-4c52-a189-57964bccbf40' 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([[-9.62369756e+01,  8.87099908e-01,  4.92178770e+00,\n",
       "         8.10851955e+00],\n",
       "       [-9.18124499e+01, -1.47032944e+01, -2.19879563e+01,\n",
       "        -1.23189344e+01],\n",
       "       [-9.55005596e+01, -1.69307783e+01, -3.10784085e+01,\n",
       "        -2.91429548e+00],\n",
       "       [-9.67623867e+01, -6.33011080e+00, -1.22947814e+01,\n",
       "         1.07719627e+01],\n",
       "       [-9.69924234e+01, -7.74409216e+00, -1.55106207e+01,\n",
       "         1.07801566e+01],\n",
       "       [-9.67349904e+01, -6.35446912e+00, -1.23465219e+01,\n",
       "         1.07136524e+01],\n",
       "       [-7.23568696e+01, -9.15780502e+00, -1.01167102e+01,\n",
       "        -5.29317501e+00],\n",
       "       [-7.44948362e+01,  2.51616970e+00,  1.18708225e+01,\n",
       "         5.70695699e+00],\n",
       "       [-7.29674312e+01, -1.06577086e+01, -1.48950996e+01,\n",
       "        -2.63557411e+00],\n",
       "       [-7.28627438e+01,  3.46937604e+00,  1.43310579e+01,\n",
       "         2.01587420e+00],\n",
       "...\n",
       "       [ 4.45082205e+01, -2.91653251e+01,  2.26195274e+01,\n",
       "        -1.72894372e+01],\n",
       "       [ 4.62544173e+01, -3.01678074e+01,  1.98338389e+01,\n",
       "        -1.68709614e+01],\n",
       "       [ 4.62541008e+01, -3.29680147e+01,  1.21574884e+01,\n",
       "        -1.58588481e+01],\n",
       "       [ 4.31316809e+01, -3.69648510e+01,  3.12321546e+00,\n",
       "        -1.52215001e+01],\n",
       "       [-2.36232805e+01, -1.08696025e+01, -2.36934559e+00,\n",
       "        -2.75273991e-01],\n",
       "       [-2.37220501e+01, -2.53959416e+00,  1.61066239e+01,\n",
       "        -9.78119170e-01],\n",
       "       [-2.58676728e+01, -3.57251459e+00,  1.23921964e+01,\n",
       "         3.09823647e+00],\n",
       "       [-2.29451602e+01, -5.89684778e+00,  1.01655543e+01,\n",
       "        -4.55268360e+00],\n",
       "       [-2.62168472e+01,  5.01939620e+00,  3.17185726e+01,\n",
       "         3.36431927e+00],\n",
       "       [-2.36915388e+01, -3.97902894e-01,  2.16429815e+01,\n",
       "        -2.42249200e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fPCA_explainedVariance</span></div><div class='xr-var-dims'>(fPCA_loading, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7531 0.7531 ... 0.0105 0.0105</div><input id='attrs-1fa889cb-ccc5-4284-be7e-55c810927116' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fa889cb-ccc5-4284-be7e-55c810927116' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5db04502-5dcc-4ea4-bd0b-b014ae8515c9' class='xr-var-data-in' type='checkbox'><label for='data-5db04502-5dcc-4ea4-bd0b-b014ae8515c9' 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([[0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103,\n",
       "        0.75314103, 0.75314103, 0.75314103, 0.75314103, 0.75314103],\n",
       "       [0.15148145, 0.15148145, 0.15148145, 0.15148145, 0.15148145,\n",
       "        0.15148145, 0.15148145, 0.15148145, 0.15148145, 0.15148145,\n",
       "...\n",
       "        0.07919624, 0.07919624, 0.07919624, 0.07919624, 0.07919624,\n",
       "        0.07919624, 0.07919624, 0.07919624, 0.07919624, 0.07919624],\n",
       "       [0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397,\n",
       "        0.01050397, 0.01050397, 0.01050397, 0.01050397, 0.01050397]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8743921-d787-464e-9ef2-2af3d84d4452' class='xr-section-summary-in' type='checkbox'  ><label for='section-c8743921-d787-464e-9ef2-2af3d84d4452' class='xr-section-summary' >Indexes: <span>(3)</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>Wavenumber</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0d00dcf5-503a-43f7-80e4-480ea5b330dd' class='xr-index-data-in' type='checkbox'/><label for='index-0d00dcf5-503a-43f7-80e4-480ea5b330dd' 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(Float64Index([3999.706, 4001.634, 4003.563, 4005.491,  4007.42, 4009.348,\n",
       "              4011.277, 4013.205, 4015.134, 4017.062,\n",
       "              ...\n",
       "              11981.76, 11983.69, 11985.62, 11987.55, 11989.47,  11991.4,\n",
       "              11993.33, 11995.26, 11997.19, 11999.12],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Wavenumber&#x27;, length=4149))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5e7e5944-1e2e-49a1-aff2-0fb690f376b8' class='xr-index-data-in' type='checkbox'/><label for='index-5e7e5944-1e2e-49a1-aff2-0fb690f376b8' 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([&#x27;H0001ULDPE_1&#x27;, &#x27;H0001ULDPE_2&#x27;, &#x27;H0001ULDPE_3&#x27;, &#x27;H0001ULDPE_4&#x27;,\n",
       "       &#x27;H0001ULDPE_5&#x27;, &#x27;H0001ULDPE_6&#x27;, &#x27;H0002LDPE_1&#x27;, &#x27;H0002LDPE_2&#x27;,\n",
       "       &#x27;H0002LDPE_3&#x27;, &#x27;H0002LDPE_4&#x27;, &#x27;H0002LDPE_5&#x27;, &#x27;H0002LDPE_6&#x27;,\n",
       "       &#x27;H0003LDPE_1&#x27;, &#x27;H0003LDPE_2&#x27;, &#x27;H0003LDPE_3&#x27;, &#x27;H0003LDPE_4&#x27;,\n",
       "       &#x27;H0003LDPE_5&#x27;, &#x27;H0003LDPE_6&#x27;, &#x27;H0004LLDPE_1&#x27;, &#x27;H0004LLDPE_2&#x27;,\n",
       "       &#x27;H0004LLDPE_3&#x27;, &#x27;H0004LLDPE_4&#x27;, &#x27;H0004LLDPE_5&#x27;, &#x27;H0004LLDPE_6&#x27;,\n",
       "       &#x27;H0005LLDPE_1&#x27;, &#x27;H0005LLDPE_2&#x27;, &#x27;H0005LLDPE_3&#x27;, &#x27;H0005LLDPE_4&#x27;,\n",
       "       &#x27;H0005LLDPE_5&#x27;, &#x27;H0005LLDPE_6&#x27;, &#x27;H0006MDPE_1&#x27;, &#x27;H0006MDPE_2&#x27;,\n",
       "       &#x27;H0006MDPE_3&#x27;, &#x27;H0006MDPE_4&#x27;, &#x27;H0006MDPE_5&#x27;, &#x27;H0006MDPE_6&#x27;,\n",
       "       &#x27;H0007HDPE_1&#x27;, &#x27;H0007HDPE_2&#x27;, &#x27;H0007HDPE_3&#x27;, &#x27;H0007HDPE_4&#x27;,\n",
       "       &#x27;H0007HDPE_5&#x27;, &#x27;H0007HDPE_6&#x27;, &#x27;H0008HDPE_1&#x27;, &#x27;H0008HDPE_2&#x27;,\n",
       "       &#x27;H0008HDPE_3&#x27;, &#x27;H0008HDPE_4&#x27;, &#x27;H0008HDPE_5&#x27;, &#x27;H0008HDPE_6&#x27;,\n",
       "       &#x27;N1473LDPE_1&#x27;, &#x27;N1473LDPE_2&#x27;, &#x27;N1473LDPE_3&#x27;, &#x27;N1473LDPE_4&#x27;,\n",
       "       &#x27;N1473LDPE_5&#x27;, &#x27;N1473LDPE_6&#x27;, &#x27;N1474PE_1&#x27;, &#x27;N1474PE_2&#x27;, &#x27;N1474PE_3&#x27;,\n",
       "       &#x27;N1474PE_4&#x27;, &#x27;N1474PE_5&#x27;, &#x27;N1474PE_6&#x27;, &#x27;N1476LDPE_1&#x27;, &#x27;N1476LDPE_2&#x27;,\n",
       "       &#x27;N1476LDPE_3&#x27;, &#x27;N1476LDPE_4&#x27;, &#x27;N1476LDPE_5&#x27;, &#x27;N1476LDPE_6&#x27;,\n",
       "       &#x27;S0024LDPE_1&#x27;, &#x27;S0024LDPE_2&#x27;, &#x27;S0024LDPE_3&#x27;, &#x27;S0024LDPE_4&#x27;,\n",
       "       &#x27;S0024LDPE_5&#x27;, &#x27;S0024LDPE_6&#x27;, &#x27;S0040HDPE_1&#x27;, &#x27;S0040HDPE_2&#x27;,\n",
       "       &#x27;S0040HDPE_3&#x27;, &#x27;S0040HDPE_4&#x27;, &#x27;S0040HDPE_5&#x27;, &#x27;S0040HDPE_6&#x27;,\n",
       "       &#x27;S0048HDPE_1&#x27;, &#x27;S0048HDPE_2&#x27;, &#x27;S0048HDPE_3&#x27;, &#x27;S0048HDPE_4&#x27;,\n",
       "       &#x27;S0048HDPE_5&#x27;, &#x27;S0048HDPE_6&#x27;, &#x27;S0081LDPE_1&#x27;, &#x27;S0081LDPE_2&#x27;,\n",
       "       &#x27;S0081LDPE_3&#x27;, &#x27;S0081LDPE_4&#x27;, &#x27;S0081LDPE_5&#x27;, &#x27;S0081LDPE_6&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;sample&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fPCA_loading</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c0afde3c-4e45-404a-82b6-6f6df938e60e' class='xr-index-data-in' type='checkbox'/><label for='index-c0afde3c-4e45-404a-82b6-6f6df938e60e' 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(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;fPCA_loading&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-803fbe1a-4920-4c14-b242-a18828cbd9b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-803fbe1a-4920-4c14-b242-a18828cbd9b5' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>creation_date :</span></dt><dd>20230523</dd><dt><span>author :</span></dt><dd>Bradley Sutliff</dd><dt><span>email :</span></dt><dd>Bradley.Sutliff@nist.gov</dd><dt><span>data_collected_by :</span></dt><dd>Shailja Goyal</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (sample: 90, Wavenumber: 4149, fPCA_loading: 4)\n",
       "Coordinates:\n",
       "  * Wavenumber                (Wavenumber) float64 4e+03 4.002e+03 ... 1.2e+04\n",
       "  * sample                    (sample) <U12 'H0001ULDPE_1' ... 'S0081LDPE_6'\n",
       "    polymer                   (sample) <U10 'H0001ULDPE' ... 'S0081LDPE'\n",
       "    state                     (sample) <U6 'Pellet' 'Pellet' ... 'Pellet'\n",
       "    repeat                    (sample) int64 1 2 3 4 5 6 1 2 ... 5 6 1 2 3 4 5 6\n",
       "    Class1                    (sample) <U2 'PE' 'PE' 'PE' ... 'PE' 'PE' 'PE'\n",
       "    Class2                    (sample) <U8 'LDPE' 'LDPE' ... 'LDPE' 'LDPE'\n",
       "  * fPCA_loading              (fPCA_loading) int64 0 1 2 3\n",
       "Data variables:\n",
       "    Intensity                 (sample, Wavenumber) float64 4.076 4.076 ... 12.66\n",
       "    Density                   (sample) float64 0.9188 0.9188 ... 0.9213 0.9213\n",
       "    Density SEM               (sample) float64 0.0007945 0.0007945 ... 0.0003695\n",
       "    Crystallinity             (sample) float64 0.3463 0.3463 ... 0.3708 0.3708\n",
       "    SCB_per_1000C (IR)        (sample) float64 15.4 15.4 15.4 ... 23.6 23.6 23.6\n",
       "    SCB_per_1000C StDev (IR)  (sample) float64 1.414 1.414 ... 0.2828 0.2828\n",
       "    MSC                       (sample, Wavenumber) float64 -4.927 ... 4.686\n",
       "    SG2                       (sample, Wavenumber) float64 4.448e-06 ... -1.0...\n",
       "    fPCA_loadings             (Wavenumber, fPCA_loading, sample) float64 -0.0...\n",
       "    fPCs                      (sample, fPCA_loading) float64 -96.24 ... -2.422\n",
       "    fPCA_explainedVariance    (fPCA_loading, sample) float64 0.7531 ... 0.0105\n",
       "Attributes:\n",
       "    creation_date:      20230523\n",
       "    author:             Bradley Sutliff\n",
       "    email:              Bradley.Sutliff@nist.gov\n",
       "    data_collected_by:  Shailja Goyal"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ds_sub[corr].values.flatten()\n",
    "print(corr)\n",
    "ds_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "45426168-9299-4724-b3c0-9618e13cfb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pair</th>\n",
       "      <th>r_corr</th>\n",
       "      <th>r^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Density, 0)</td>\n",
       "      <td>0.847096</td>\n",
       "      <td>0.717572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Density, 1)</td>\n",
       "      <td>-0.856166</td>\n",
       "      <td>0.733020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Crystallinity, 0)</td>\n",
       "      <td>0.783357</td>\n",
       "      <td>0.613648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Crystallinity, 1)</td>\n",
       "      <td>-0.800321</td>\n",
       "      <td>0.640514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(SCB_per_1000C (IR), 0)</td>\n",
       "      <td>-0.741796</td>\n",
       "      <td>0.550261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(SCB_per_1000C (IR), 1)</td>\n",
       "      <td>0.800924</td>\n",
       "      <td>0.641479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Pair    r_corr       r^2\n",
       "0             (Density, 0)  0.847096  0.717572\n",
       "0             (Density, 1) -0.856166  0.733020\n",
       "0       (Crystallinity, 0)  0.783357  0.613648\n",
       "0       (Crystallinity, 1) -0.800321  0.640514\n",
       "0  (SCB_per_1000C (IR), 0) -0.741796  0.550261\n",
       "0  (SCB_per_1000C (IR), 1)  0.800924  0.641479"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_LinReg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23fc043a-f7bb-4a70-9d5b-58e92d4cb935",
   "metadata": {},
   "source": [
    "We have a lot of correlation between variables, but not great goodness of fit, and a lot of cross-correlation. To take advantage of the correlations, and get around the cross correlation, we will turn to another technique to plot our physical measurements as a function of the first few fPCs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa25c60b-37b6-48d0-bffc-0741e7afea16",
   "metadata": {},
   "source": [
    "## Sliced Inverse Regression (SIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779ebe4a-1df4-4b9a-a155-293bcc071945",
   "metadata": {},
   "source": [
    "Similar to fPCA, SIR can take large sets of data and reduce them to fewer dimensions by using coefficients to weight the data. However, a key difference is that we get to tune SIR with the outputs we want the inputs to predict. This way we can provide the first few fPCs to the SIR, along with the corresponding physical measurement (e.g. density) so that SIR can apply weights to the fPCs($\\hat\\beta_{SIR}$). $\\hat\\beta_{SIR}$ will then provide different weighting to the fPCs for each physical measurement, enabling a linear combination of the weighted fPCs ($X\\hat\\beta_{SIR}$) to predict the physical measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5415c18d-8e12-449f-bc28-6887d9998b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xcRFvXZ9Op0anS7+wW3LiGSIfdiP2iTta9d1n5RLR6dRoNBHodLoM9aQk/4/Ih18S83hQuuQBQKeNe+s4BeFl4q+JIBRCYWFhjBw5Ep1OR9++fenSpYuxQxIEIQ89fvyYmTNnAjB27FiqVKli5IgKBzs7O8qWLUlISAhTJjRBq4nMUT06bQKJMd8Ref8j4iMXAJCceJqI++2ICxuGVh3y0hEaIkI+IOpBW5Litum3vi5xEITcJLowCUIho9VqGTlyJGFhYTg7OzNjxgxjhyQIAqldijRx4QCYWJcAQJv8bECuJEOnSUGTGI1MaYOJhc1bnWfSpElER0dTt25dBg8e/NaxC6l02nBmTtQyaAT8vDOert2D+NC1ZzaOTyApbiuJ0d+h08UDOlKSbxJxvx06bfibjgZAq36YaVclQchLIoEQhEJm5cqVHD9+HKVSSUBAAObm5sYOSRAEIPH6n9yZ2wIAZaVGOPZdSvi+hcRfDsKsbA0S7/4PtBrQqrFu0J3SX6/FxMo+2+f55Zdf+PXXXzE1NWXx4sWYmIh/9QajU9G4gSW9e1rw07YEvMf7EBTUFhub1yd8mSUOabQp/2YrBK06jJjHg3ISvSAYjOjCJAiFyNmzZ1m0KHXV0blz51KtWjUjRyQIQhrNC33mk26f5c7spsT9dwRtUiyJN0+DOhm0qf3hY8/v4Nrw4oQs6446G33tIyIimDJlCgAjR47k3XffNexFFHmpN/6jhlnjVFbOw4fPu4plWjqtq9KDjiRErUSni+PF5CEnJLk95jZfA2IxUMF4RAIhCIVEZGQkw4YNQ6PR0K1bN7FYlCAUALrk1w9wjT2/M1uJxLRp0wgPD8fZ2ZkRI0YYKkzhJRbmMmZNsUWSJDZv3kxQUFCGMjqdjqjQPiRErTBI4pBGkuRY2A7DruxBlNb9EbdygjGId50gFAI6nY6xY8fy8OFDKlasyPz588ViUYJQiGQlkTh06BA7d+5EJpOxePFiMW1zLmtQT8HAr1Mf1Hh7exMVFZVuvyRJmNsMQJLZAob/eyyT22Jp54Fd2UMikRDynHi3CUIh8O233+r7PAcEBOinGhQEoXCJPb+Tm5NqZtgeHR3NxIkTgdQV5+vVq5fHkRVN3uOGUqVKFUJDQ5k6dWqG/UqrrtiV2YuF7Zg3JBI5vx17cyIhbvUEwxPvKkEo4C5evMjs2bMBmDJlCnXq1DFyRIIg5BZJYYHDJzMybJ8zZw6hoaFUqlQJLy+vvA+sSHk+9kCpVOLn54dMJmPHjh38+uuvGUpLMiXmNr1TEwk7LySZHS8mEiaKWshNKz/77tltmWSOmVVvstNy8TyROIyF3XjMLHugsOiE3LRqDq5REF5PJBCCUIDFxcXh5uaGSqWibdu2DBw40NghCYKQG0zNceg+i+rfxGLvOjTdruPHj/Pjjz8CsHjxYjHzWi6SyUtiqmySbluDBg1wd3cHYPz48UREZN7FTJIpMbfuhV2ZX15IJECSl6CY409Yl1j8QiIhYWXvhb3TnzlIJIphbv0pVsUnYl1iJpIkbvUEwxNzuwlCAaXT6Zg4cSJ37tyhdOnSLF68WIx7EITCxtQchy4TKd5lcqYrScfHx+Pt7Q3AgAEDeP/99/M6wqJFMsWm5FLUyZdJSTqDzKQcAF5eXhw5coSrV68yefJkAgICXl3Fs0RCafkJyfH7kJtWRJIkFBYumJq3JCXxOFptTGpZSYGVvReWdh7ER65CnXwWpVXW150QhNwiEghBKKC2bt3Kjh07kMvlrFq1Cnv77M8XLwhCHnoxwZdkFGven+QHl0m6dRqZ0hptUuzz/W9IHNIsWLCAkJAQypYtqx8DIeQ+E7MamJjV0H9vZmaGv78/nTt3Zs+ePXTs2JEuXbq8tg5JpkRp3SP9tmeJRIaykgIre0+DxC4IhiASCEEogG7cuMHkyZOB1CdfjRs3NnJEgiC8iWUNV0p+vhCtKhG7ll9jal+O+Ksn0MQ8xqRERVQPg0l+dBWZqRnFu0x6beIAqeu+rF+/HoBFixaJyROMrE6dOnh4eODv78/EiRNp0qQJDg4Oxg5LEHKFSCAEoYBJTEzEzc2NxMREmjVrJuZ6F4QCQmZmSYmO49Jts6zeQv/aolLDLNeVlJSEl5cXOp2Ozz//HBeXjE+thbw3atQoDh06xOXLl5kwYQLr1q0TXUuFQkmMrBGEAmbWrFlcuXKF4sWLs3z5cuRysRqpIBQ1fn5+3Lx5k5IlSzJt2jRjhyM8o1Ao8Pf3x9TUlF9//ZWdO3caOyRByBUigRCEAmTfvn1s3LgRgGXLllGqVCkjRyQIQl77999/9YN0582bh62trXEDEtKpWbMmnp6eAEydOpXQ0FDjBiQIuUAkEIJQQISEhDB27FgAhg8fTqtWrYwbkCAIeU6lUjFmzBg0Gg0ff/wx7du3N3ZIQiaGDx9OnTp1iIqKwtvbG51OZ+yQBMGgRAIhCAVASkoKw4YNIyYmhvr16zNu3Lg3HyQIQqGzcuVKrly5gp2dnX4BSSH/MTU1xd/fH4VCQVBQEFu3bjV2SIJgUPk+gdBqtSxbtowWLVpQt25dvv76a+7evfvK8iEhIbi5udG4cWOaNWvGnDlzSExMTFfm33//pU+fPtSpUwcXFxeWLVuGVqvN7UsRhBxbuHAhf//9NzY2NqxatQpTU1NjhyQIQh4LDg5m6dKlAMyePZsSJUoYOSLhdapXr65vNZ4+fToPHjwwckSCYDj5PoFYtWoVmzdvZs6cOWzZsgVJkhg8eDAqlSpD2djYWHr37k10dDTr1q1j9erVXLp0ieHDh+vL3L59m379+lG+fHl2797NhAkT+PbbbwkMDMzLyxKELPvtt99YtWoVkLrKrJOTk5EjEgQhr2k0GsaOHUtKSgpt2rThk08+MXZIQha4ublRv359YmNjGTdunOjKJBQa+TqBUKlUrF+/Hg8PD1xcXHB2dsbPz4/Hjx9z+PDhDOV37txJXFwcK1eupE6dOtSuXRs/Pz9OnjzJuXPnAPjmm2+oWrUqPj4+VKpUiQ4dOvDVV1/x999/5/XlCcIbhYaGMmrUKAD69+9Px44djRyRIAjGsHbtWv755x+sra2ZN2+emBq0gJDL5fj5+aFUKjl27BibNm0ydkiCYBD5OoEIDg4mPj6eJk2a6LfZ2NhQo0YNzp49m6H87du3qVy5croVeUuXLo2dnR1nzpwB4MSJE3Tu3DndH9+RI0e+dtl5QTAGjUaDh4cH4eHh1KhRQ0zVKAhF1K1bt1i0aBGQ2hWmdOnSRo5IyI6qVasyfvx4IHUa7pCQECNHJAhvL18nEGlTn738x7JkyZI8evQoQ3kHBwfCwsLQaDT6bXFxcURHRxMeHk5cXBxPnz7F2tqaSZMm0bx5czp27MiaNWvSHSMI+cGyZcs4efIkFhYWBAQEoFQqjR2SIAh55O6Cj7gxqRY3ZzdjtNtXJCUlUb+4irpBg7i3rCcp4feMHaKQDQMHDqRx48bEx8czZswYMe5SKPDy9UrUaYOfFQpFuu1mZmZER0dnKN+pUydWr16Nj4+Pfpq7mTNnIkkSKpWKuLg4ABYsWEC/fv1Yu3YtV65cYe7cuSQmJuq7irysdevW+tdpicuL24KCgt7uQgXhJX/99RdLliwBwMfHh6pVqxq0fvGeFoT8LT74d9Bq+OW+BeeCbVHKtXg6R0KKhrjz27l+fjtWDXtS+ovFmBYvb+xw84WoqCiWLFnC77//TlxcHNWrV8fLy4uGDVNX+J44cSI7duxId0ypUqU4fvx4rseW1pXpo48+4uTJk2zYsIGvvvoq188rCLklX7dApD1xfXnAdHJyMubm5hnKV6hQgeXLl3PkyBEaNGhAy5YtKVOmDLVq1cLKyko/c03Tpk0ZMWIE7777Lt27d8fd3Z3vvvtODG4S8oWIiAiGDx+OVqulZ8+efPrpp8YOSRAEI3iSJGftdRsABlaJpbR5+pbyuHPbuD6mAveWfypaJIAxY8Zw4cIFlixZwrZt26hZsyYDBw7k5s2bAFy9ehU3Nzf++OMP/deuXbvyLL6KFSsyZcoUAObOncvt27fz7NyCYGj5ugUirevSkydPKF/++ROWJ0+e4OzsnOkxLi4uHDt2jLCwMKytrVEqlTRt2pTu3btja2uLmZkZ1apVS3fMO++8Q0JCAhERERQvXjxDnS8+jU17Siue0Aq5QafTMXr0aEJDQ6lcuTI+Pj65ch7xnhaE/E2nA78rxUjUyKhZLJmPneJfWTbu3Daun9tWpFsk7t69y59//slPP/1E/fr1AZg8eTLHjx9n7969jBgxghs3bjBs2DAcHByMFme/fv3Yt28fJ0+eZMyYMWzbtg25XG60eAQhp/J1C4SzszNWVlacPn1avy0mJobLly/rmyRfdP78efr27YtKpcLBwQGlUsmZM2eIjIykadOmyOVy6tevz4ULF9Idd/XqVWxsbLC1tc3tSxKE11q7di1HjhzBzMyMgIAALC0tjR2SIAhGcPihknPhSkxlOrxqRCHLwqRLqS0SFYk6/m3uB5jP2NnZsWbNGmrVqqXfJkkSOp2O6Oho7ty5Q3JyMlWqVDFilCCTyViyZAmWlpacOXNGTCEvFFj5OoFQKBT07dsXX19fgoKCCA4OZvTo0Tg6OtKmTRs0Gg1hYWEkJSUBUKVKFa5fv46Pjw8hISH6DL9Xr176ufPd3d05ceIEy5cv5969exw4cIA1a9bQv39/8RRAMKoLFy7oWxymTZuW7h+hIAhFx+PHjwm4ag1Av8qxOFlmY5IPUyXyYqVyKbL8y8bGBhcXl3RjJg8cOMC9e/do3rw5165dQ5IkNmzYgKurKx999BGzZ88mNjY2z2N1cnLSz6q3YMECbty4kecxCMLbytddmCB1ilW1Ws2UKVNISkqiUaNGBAYGolAouH//Pq1bt2bevHn6Lkpr1qxh3rx5dOnSBTs7O3r16oW7u7u+vvfff59vvvkGPz8/vvnmGxwcHBgyZAiDBg0y4lUKRV1sbCzu7u6kpKTQoUMH+vfvb+yQBEEwAp1Ox+TJk4lTy3jHWsWn5eOydqDclOJdJuPQcSwyM9Fyef78eSZNmkTr1q1xdXVl2bJlyGQyypYty+rVq7l79y4LFizg2rVrbNiwAZks8+epuTXhRJ8+fdi/fz/Hjh3D09OT3bt3i4eYQp4w1Hs63ycQcrmccePGMW7cuAz7ypUrx9WrV9Ntq1u3Lps3b35tnS1atKBFixYGjVMQckqn0zF+/Hju3r1LuXLl8PX1FYtECUIRtXfvXg4cOIBcSu26JH9TPwGROGRw5MgRxo4dS926dfWz2Xl4eDBgwABsbFIHpVerVg0HBwc+//xzLl68SN26dfM0RkmSWLRoEa1bt+aff/7hm2++YdiwYXkagyC8jXyfQAhCYffTTz/pnz6tXLlSjMURhCIqIiKCyZMnA9CrUjxVrNXP9kjAS7MEisQhUz/88ANz586lTZs2+Pr66rs0SZKkTx7SpE2oEhoa+soEIjcnnChbtiwzZ85kzJgx+mSievXqBqlbEF7FUO9pkUAIghFdvXqVqVOnAuDtNRqL5Y24DDivictwU6BNjid4iJX+e+c1qV0bXrcts3oEQcifpk+fTnh4ONWrV2dY78pI0Q8xtS+Lnas7j7eMJ+neP8jMrCjW4iuROGTixx9/ZPbs2Xz55ZdMmjQpXbckLy8voqKi0g1avnjxIoDB19nJjs8++4z9+/dz5MgRPD092bNnj37KeUHIz0QCIeR7OrUKTWIMcgtbUiJCuDG2crr9aTfJqse3uOGdOsNGteWPueZRKkMZrSoRXUoyckvbdDfkxrjRTkxMxG3oUJKSknBxccFtyCCuuY1JV0aTGIukMAe1KsPxWlUSMoVYnVoQCoPDhw+zY8cOZDIZixcvpuJ776XbX3H8YSNFVjDcvn0bHx8f2rRpw9ChQwkPD9fvUyqVdO7cGXd3dwICAujUqRO3b99m1qxZdO7c2agzM0mSxIIFC2jdujX//vsvK1euxNPT02jxCEJWiQRCyPfCDy3jydbxmFdtSuL1PzLsv7f0E4q3H0PIks76bZG/r820ric/TyIiaCX2rm7YtfbItZizYvJod65dv469hQkLxg3KMIhPkxjLNY+SyK2Ko45+jK1L+oH+1zxKYefqnm5b3KUgLGu65nrsgiAYTkxMDBMmTABg8ODBvPdS8iC82cGDB0lJSeHw4cMcPpw+2erWrRvz589n6dKlrF69mtWrV2NtbU2XLl3yxc26o6Mjs2fPxsPDA39/f9q0aUPNmjWNHZYgvJZIIIR8T5sQCehIvHEy0/0Jl4+S8N+RdNvCtk/JtKwmIRI0aiKCVhERFGDoULNs9+7dbPnlMBI6xjuHEevfmpDaHdKV0akS0KUkoY58AEDUb6vTV6LTEBm0It2m+8u6YlaxQa7GLgiCYc2ZM4fQ0FAqVqyY6YQhwpu5ubnh5ub22jLt2rWjXbt2eRRR9nTr1o39+/dz4MABRo0axf79+9NNSSsI+U2+XgdCEPRkJqDTZr7vVdtfEBG0Cq0q6VldMtBqQKvW73+8eSwpEQ8MEekb3blzB29vbwB6V4yjvn0iAPGXDunLJN67kOmxWZF85x/967hLQeh0uteUFgTBEMIPL+f+qt6kxDwh7BcfEm+dJSnkIuEHFqOJj9SX0yTGELZrFsmh1wH4448/2LRpEwC+vr6Ym5uj0+kI/3UJ8cHHjHItQt6TJIn58+djb2/PlStX8Pf3N3ZIgvBaIoEQioQnW7y5NtKR5AdXIJMb6sjf1nB9bCVCfxiZq4mESqVi2LBhxMXF0aDmO/Sr/MIiRrrni0XdndOMB2v65fAszxOq+8u6cntWE+IuHhKJhCDkoqe/zCPm9Gaue5QibNtkbs9sTIh/Vx5vHsu10U482TEdTXwkCVdPELZzOjcnVOfG8j6MHeMJQL9+/fjggw8A0CZE8fgnL+7Oa8UdHxeRSBQRJUqU0C8mumLFCi5cyPmDJEHIbSKBEIoMbWI0SbfPZN5iodOCJoWIw8u5NbUeOo06YxkDmDdvHhcuXMDW1hZf78GvneP9xRaJt5F06wz3fNsReWSlQerLT7RaLcuWLaNFixbUrVuXr7/+mrt3776yfEpKCosXL6ZFixbUq1ePvn37cuXKlTyMWChKUp7eBkCXHM/T3bO5NtqJ6JM/pO7U6Vj6435CHjzCsZiSsYN6ZVpHwvU/RSJRhHTp0oWPP/4YjUaDp6cnycnJxg5JEDIlEgihSJFbO4D06re9iV05HPv4I8kNPzzo4J7trFmzBgDfudMpZWvxhiMM8PGUUlc2tarXGav3urx9ffnMqlWr2Lx5M3PmzGHLli1IksTgwYNRqTLOWgUwY8YMtm3bxuzZs9m+fTu2trYMHjyY2NjYTMsLguHo0CXHE3M6daHTy1Gm7LxnDsDIKqE8nv0eD77pp+/apKdNbZkUiUTRMXfuXEqUKMG1a9dYvHjxa8vqdCqS4/ehScn44CQpdi8xjz1Qq4L12zTqJyTF/oxG/Zik2G3otAkGj18oGkQCIRQJZhXqU2Hi71jV7QiZrPIsty1D6a/X8Y7vLYo17WPw8z98+BCvidMA6OYURxvXVm88psLEo299Xstabag08zzlR/+CokSFt64vP1GpVKxfvx4PDw9cXFxwdnbGz8+Px48fZ5iFBSAkJIRt27Yxb948WrVqRZUqVfDx8UGhUHDp0iUjXIFQVKk04HvZFh0SbUon0Mg+AXQ6ok/9yM0J1Xn0nXvGg9ISiWt/6BOJ5IfBGcsJBZ69vT0LFy4EICAggPPnz2coo9OpSIr9mcgHnYkLn05S3Db9vqTYvYSHuBAfOYOU5FPERwWiUT8hPmIRUQ+7EB+5gKiHXYmPnE9K0pk8uy6hcBEJhFAwvGmg9GtaFQAqTT6OpbPLs7p0GZKIqj7/YecyEMnE8Av4qNVqPDw8iIyK4h1rFYPeicnScab2Ttk/2bMWhzROHtswr1g/+/UUAMHBwcTHx9OkSRP9NhsbG2rUqMHZs2czlP/jjz+wsbGhZcuW6cofPXpU3/dcEPLCD7etCUkwxV6hwa1a9PMdOg3odMSc2frqg3VaQCLh6nFi/9mT67EKxtGuXTt69OiBVqvF09OTxMTUyTZeTBziIxeg00YCctDp0iUO6OL1dWlUV4l62IWkuC1A2li71G66Ot48CYkgZEYkEEL+J6XOmiQpXtHlx1SJ3YdD021SONXNvCpJBjotJnblKNVv1fPtuZA4pPH39+evv/7C0sKCybUjUcjSYsnYEvJSsOm/N83k+l/6mVjWakOFyRnXyiiMQkNDAShdunS67SVLluTRo0cZyt+5cwcnJycOHTpE9+7dadasGYMHD+bmzZt5Eq8gAFyPMWXL3dQFLD2co7ExfTa5gcwEkLBu9CkVp5zI/GBZ6gMCZeVGlB97kOIdxZSvhdmsWbNwdHTk1q1bLFgwL5PEAUAH6EiK25YhcUij0z7keeIgCIYh1oEQ8j3bll+jKFkFi+otib2wn8c/pF8AruqCq5jaO6Eo/S6PN40EoKL3oXQrUaexbzcay5ptsGnUE51GxeONw3Itbp1Wy88D3sE/KHX6WJ+ZUyh7cAAAdxZ1AG3Ka4+XWzvg2HcZchtHNDGhWDfswXXPsvr9JXstxLbFQK4NL67f5uSxLbOqCqW0J3Ivz5VuZmZGdHR0hvJxcXHcu3ePVatW4e3tjY2NDQEBAXzxxRfs37+f4sWLZzgGoHXr1vrXaYnJi9uCgoLe+lqEokGtlbH4ii1anUTLkok0L5mUmjhoNVg36IbDJ9NQlquVbtpXIDVx0GpQVmxAye6zsazV5s0PIF5DvKcLBltbWxYtWsSXX37JunWBNK1fnAb1zJ7tfXFWPS2IlgQhj4kWCCHfUzhUwrbFABQlK2PX8qsM+02siiNJEnYuX7+xLqVTbYp90DtXWxzShD0MYfaJeHRA+zIJNLz/g36f6t7fqO5ffO3xkiRh38aDYu9/in0bD+QWxdLtt3cdhszU7BVHF35KpRIgw4Dp5ORkzM3NM5Q3NTUlNjYWPz8/mjdvTp06dfDz8wNg586duR+wUITJkEyV/GLWjpuxptiYahjhHAdIWDfoRuW5/+I0YivKcrVeOi41SVBWbED5sQepNO0vrGq3favkQShYPmz1Ad26WKPTwdQ5USQkipYEIX8QLRBCgSIzs6TGhszXM3h536vKZaWut6XVahnjPYEIlZzylikMqx6dYbXsN1E9uYnSqU6uxFcYpHVdevLkCeXLl9dvf/LkCc7OzhnKOzo6YmJiQpUqVfTblEolTk5O3L9//5XnefFpbNpTWvGEVniZ3MIWTfQjLN79kISrJ7Co3gJFaWeiT3yLfZuRhL/TjXU9egMwrHoM5Zt9om9xeJlkqkQys8KsbA2DtDi8TLynCw5JZs5snz38dbYL9x/E4b8yjkljbYwdliCIBEIQcsOaNWv47dhxFDIdU2pHYi7PfqJya0o9ijX7EoeuU1GUqvrKhCer2wobZ2dnrKysOH36tD6BiImJ4fLly/Tt2zdD+YYNG6JWq7l48SK1a9cGICkpiZCQEDp16pSnsQuFT+XZ/4BGg0xpgVaVhEyhRKfVUrqPP1pJzsCuXVGpVLRu3Zpha79BbpaxlSyNTGFO9RVhSKZmorVBwM6+Gkv81tK7d282b4/H1UVJk0bmPB/XYILMpAxadQjpuzYJQu4RXZgEwcD+/vtv5s2bB8CwatFUssrponSp0zreGF+d0E2eBouvsFAoFPTt2xdfX1+CgoIIDg5m9OjRODo60qZNGzQaDWFhYSQlpY5BadiwIU2bNmX8+PGcO3eOGzdu4O3tjVwup2vXrka+GqGgk5maIVOmTmogU6R2r5NkMiQTBYGBgfzzzz9YW1szf/781yYP+voUSpE8CHotW7bkyy+/BGDmAlNSdO1IvYWTAxoUyqbYlTuBwqItaV3f0shN62CieLk1W9z+CW9HvIMEwYCio6MZNmwYarWazh070LHs2y7SowOdluQH/6HTiSdLLxs5ciQ9e/ZkypQp9O7dG7lcTmBgIAqFgkePHtG8eXP279+vL798+XIaN27MiBEj6NmzJ3FxcWzcuBF7e3sjXoVQmN25c4cFCxYAMHXqVMqUKWPkiISCaurUqZQvX54HD0JZsjIJ2zK7MLPsAkhIMmtkMiXWJXwyJBJy0wrYlArEpmSAPpGQK94FQCYT3aGEnJF04q4kW0R/UeFVdDodQ4cOZd++fZQvX579u7fzaFzZNx/4GspKjSjZYzaWtXJv4KR4T2ed+FkJ2aHVavnss884deoUzZs3Z/PmzfmuVUG8p7MuP/ysTp06Rc+ePQHYtGkTrVq1QquJQpKZI0npJ9XQapNQq65jqqiKJEtt9dLpdOg0T5GZOKDVPEWSFc9370kh77zNe1q0QAiCgXz//ffs27cPU1NTAgICsLG2fqv6yo3cTaXpp7Gq3U78gReEAmjTpk2cOnUKc3NzFi1aJD7Hwlv74IMPGDhwIABeXl5ER0cjk9tmSB4AZDIlCmVtffIAqbP7yUwcUvfLS4j3pJBjIoEQDEKnUZMS+TDTfZr4KDSJqasva1VJqGOeZF6HVkvy4xuoox+nfq/TkRIekjsBG9jly5eZMWMGABMnTqRevXoZysgdKr+xHtOyz2dksarVWvxxF4QC6sGDB8yZMweACRMmpJspTBDexsSJE6lYsSKhoaH6/zuCkNdEAiEYRMzZbVz3LMu9JZ2Jv/oHl/tLXO4voU2O535AL66NLM2TbVN4sn0K1zxKcbm/RMLNv9KVi//vCDe93+HaSEfuLmxDxNHVXB9Tnsv9JaLP7USbHJ+ufH6RkJCAu7s7ycnJuLq6MnjwYCB1JhVJaY2pYzUqTDpOlWmn9MdINiXBImO/+/Keu/MsbkEQcodOp2PChAnExcXRoEEDvvoq4/o1gpBT5ubm+Pv7I0kSW7du5fDhw8YOSSiCxDSugkFoEqIAiLt4kLgL+9Lt08ZHoVMl8HTvPF6cHeLOrA8yrQMg/r8jxL+wbsKD5d1RVk1fPr+YMmUKN27cwNHRkaVLlyKTpeblktyEd7+J0Zd7seXlnbkXkZlZEjzEKl1dosVBEAq+7du3c/ToUczMzFiyZAlyudzYIQmFTKNGjRg6dCirV6/G29ubo0ePYmdnZ+ywhCJEtEAIBiSBNv2UpSHLejxvLdBpQZf5KpoP1w9GHR362tqTbvylfx0ffPztQjWQHTt2sGXLFmQyGcuXLxez+QhCEffkyROmT58OwOjRo6lataqRIxIKq3HjxlG1alWePHnCtGnTjB2OUMSIBELIVfH/HSH5waU3lov5awuPf/R8Q6nnE4aFLOnI7bktiL/y+1vF9zZu3brFhAkTgNQbhaZNmxotFkEQ8ofJkycTFRVFrVq1cHNzM3Y4QiGmVCrx9/dHJpOxY8cODhw4YOyQhCJEJBBC7npFi0NGWsjmjMKJ1/7g7vwPifsv76fUS05Oxt3dnfj4eD744ANGjRqV5zEIgpC/7Nu3j/3792NiYsLixYsxNTU1dkhCIffee+8xbNgwAMaPH094eLiRIxKKCjEGQiigZEimCuzbjsLinbx/8j9nzhwuXbqEvb09y5cvz1IfZxObktTYkD5Jevn7V20TBCF/i4iIYPLkyQAMGzaMWrVqveEIQTCMMWPGcOTIEYKDg5k0aRLffPONsUMSioB83wKh1WpZtmwZLVq0oG7dunz99dfcvXv3leVDQkJwc3OjcePGNGvWjDlz5pCYmJiujKurK9WrV0/3NXbs2Ny+lCJJUtpgYlfujeXkNqWwbTkoy/XatxvFO0vuUeqz+cgU5m8+wIAOHjzI+vXrAfD396d06dJ5en5BEPKfGTNmEBYWRrVq1fD09DR2OEIRYmZmhr+/PyYmJuzdu5fdu8VsfkLuy/cJxKpVq9i8eTNz5sxhy5YtSJLE4MGDUalUGcrGxsbSu3dvoqOjWbduHatXr+bSpUsMHz5cXyYuLo6HDx/yzTff8Mcff+i/0ga9CW8j7cn585mEqi4IxtTu2WrM0quf0leZdxnLWm1eX73J84VySvaYjYmNQ04DzbEHDx4wZswYAIYOHapfxVEQhKIrKCiI7du3I5PJWLx4MWZmGRf1EoTcVLt2bUaOHAnApEmTePIk8/WWBMFQ8nUCoVKpWL9+PR4eHri4uODs7Iyfnx+PHz/OdN7jnTt3EhcXx8qVK6lTpw61a9fGz8+PkydPcu7cOQCuXbuGTqejfv36ODg46L+s33LV4KJOklLfSjLzYpT45PlsEHJzG5ClJg5mTrWwqvexfp/DZwv0r2WmZkiy529HSWGBdePP9d/btR5B1fnBuRZ/VqSkpDBs2DCioqJ477339AOoBUEoumJjYxk/fjwAgwYNon79+kaOSCiqRo4cSc2aNYmKimLChAnosjmuUBCyI1+PgQgODiY+Pp4mTZrot9nY2FCjRg3Onj1Lp06d0pW/ffs2lStXTjeVZunSpbGzs+PMmTM0bNiQq1ev4uDggI2NTZ5dR1Fg3bA7yE2wadQTubkNJbvN0O8r1WsRmoRorOq0JyXsNolNPse6YQ9kpmY4dPLWl7Oq3R6HT+cht7DFtvkAQEd0DVesG3TTtzYYc3yAr68v586dw9rampUrV6JQKIwWiyAI+cOcOXN49OgRFStWxNvb+80HCEIuMTU1xd/fn44dO3Lw4EF27NhBjx49jB2WUEjl6wQiNDR1XYCX+5iXLFmSR48eZSjv4OBAWFgYGo1GP6g1Li6O6Oho/cwE165dw8LCAg8PD/755x/s7e3p3r07/fr10y8A9rIXu6mknffFbUFBeT8LUH5jYl0Cu5ZfZ7rvxUHOipKVUZSsnGk5mdIKh87pn+rbfTjEcEG+hePHj7Ny5UoAFi1aRIUKFYwc0dsR72lBeHt//vknP/zwA5D6d8HcPG/HYwnCy2rUqMHo0aNZuHAhU6dOpWnTpmKcnpAr8nUXprTBzy8/6TUzMyM5OTlD+U6dOhEdHY2Pjw/x8fHExMQwffp0JEnSj5m4fv06sbGxdOzYkcDAQD7//HOWLl3K8uXLc/+ChAIpLCwMDw8PdDodffv2pUuXLsYOSRAEI0tISGDcuHEAfPnll2IdGCHfGD58OHXr1iU6Ohpvb2/RlUnIFfm6BUKpVAKpYyHSXkPqHPyZPempUKECy5cvZ9q0aWzatAmlUsmXX35JrVq1sLKyAuDbb78lOTlZ/3316tWJj48nICAADw+PTFshXnwam/aUVjyhLRq0Wi0jR47k6dOnODs7M2PGDGOHZBDiPS0Ib2fhwoXcvXuXMmXK6KdvFYT8wMTEBH9/f9q3b8/Ro0fZsmULvXr1eu0xGvV9NCn3kJk4otNEYKpsqN+n0+lISTqNTF4WTcp1NJqHyOVlMFXWRya3zeWrEfKrfJ1ApDW7PXnyhPLly+u3P3nyBGdn50yPcXFx4dixY4SFhWFtbY1SqaRp06Z0794dSO0j+PLiPtWqVSMhIYHo6Gjs7Oxy6WqEgmjlypUcP34cpVJJQECA6KIgCPmcJj4KrSoeJDmSTIaJTcksHZd4+zxm5WohM339DEqahGj+/ucC69atA2DBggViEg4h36lWrRrjxo1jzpw5zJgxgxYtWlC2bNkM5TTq+yRGf0ty/B7SZlKUJCvsnX5PTRwSjxEftQqt+hagAJ7PgGlhOxpzmz55c0FCvpOvuzA5OztjZWXF6dOn9dtiYmK4fPkyDRs2zFD+/Pnz9O3bF5VKhYODA0qlkjNnzhAZGUnTpk3RarW4uroSEBCQ7riLFy9SokQJkTwI6Zw9e5ZFixYBMHfuXKpVq2bkiARBeJOQZd24PqYi10eV4ZpHKUJWfEpy6LXXHpMS8YDbMxoSPMSKhxuGoU3J2EU2ze2VXzDy68/Q6XR079oZV1dXQ1+CIBjEkCFDaNCgAbGxsYwdOzZdVyaN+j5x4bOJetiN5PjdPJ+GHXS6FFQJvxP16HNin459ljzAi8lD6nTtmry4DCGfytcJhEKhoG/fvvj6+hIUFERwcDCjR4/G0dGRNm3aoNFoCAsLIykpCYAqVapw/fp1fHx8CAkJ4eTJk4wZM4ZevXrh5OSETCajXbt2rFu3jgMHDnDv3j22bNnCunXrGDVqlJGvVshPIiMjGT58OBqNhm7duvH555+/+SBBEIxOEx8JWjVpN0SxZ7dxc3z11yYS2sToZy/URB0NeG0iEXj8Dvfi5NgpNPSN/4HHP09CHfs0ty5HEHJMLpfj5+eHUqnk+PHjfP/9969NHJ5LfilxyIz0mn1CUZCvEwhInde4Z8+eTJkyhd69eyOXywkMDEShUPDo0SOaN2/O/v37AbC1tWXNmjUEBwfTpUsXJk+eTK9evZgyZYq+Pi8vLwYNGsTixYvp2LEj3377LZMnT+azzz4z1iUK+YxOp2PcuHE8ePCAihUrMm/ePCRJ/LEUhIIsK4mE3isSiUuXLrHpnygAPKpHY00C4fsWcH1MeZFICPlSlSpVmDhxIgCzZ03l0tkur0kcBCHr8vUYCEjNoMeNG6ef7eJF5cqV4+rVq+m21a1bl82bN7+yPhMTE9zd3XF3dzd4rELh8N1333HgwAFMTU0JCAgQ/ZsFoRCJPbuN2LPbsG7Uk5I952Lm+Jquic8Siajf12LVYiBjfrqJRgctSibSolRqyzc6LTpVIuH7FhBxyB/7tp4Ubz8GE+sSeXNBgvAGX3/9NXt/WcvZc/eZNjeKdSvskcnEQzHh7eT7FghByEuXLl1i1qxZAEyZMoU6deoYOSJBEHJD7Nlt3JpcB0181JsLa9Ws+W4T/12+jI2ZjBHVozOWSUsk9s7j7sKPDB6vIOSUTCbD3/97LCwUnPtHxU/bEowdklAIiARCEJ6Ji4vDzc0NlUpFmzZtGDhwoLFDEgQhl8gsbHHsuxS5pe0by96NN+GH2zYAjGxWHHszbSYVpi5ealauFqV6+RoyVEF4axUrVWPy5OkALA1I5G6I8jWlxa2h8Gb5vguTIOQFnU7HxIkTuX37NqVLl2bJkiVi3IOBPHz4MMfHlilTxoCRCAJIShtK9pyLncsgZIrX3UQ9K29XnuX3y5OivYWrqyvtqt0m6cYLBWRy0GowK/MuJXvMweq9j8XfDiFf6tevHwcOHOCPP/5g5kI7Nn33Kclx34EuJn1BSYGt4w8kRK9FlXDwFbWJMRRFnUggBAHYunUrO3bsQCaTsXLlSuzt7Y0dUqHh6uqaoxsqSZK4fPlyLkQkFEXZTRxMilegdP9VbD7zgAs/z8DKyor58+ej+vbZglwicRAKGJlMxuLFi2ndujXnzv3ND1s6MXTIfpLitpEQvR50cSApkSQlctOKWJeYiyZlsD6RkGTF0WnDn9WmQ5IsjHo9gnHlOIFIG9WfXZIk4ePjk9PTCoLBXb9+Xb+SrJeXF++//76RIyp83Nzc0i0G+SZ3797lm2++ycWIhMJKetaVSP99VhIH2fN/hWmJg3Xdjty5c4f58z2A1DFRZcuW5Y4stXuHSByEgqhcuXJMnz6dcePGsXDhQlq3bs077/RFadUTreYJktwenfZ5i4Q+kVB7IMks0Gpi0WqeIpcXQyZ3NOKVCMaW4wRi586dlChRAoVCkeVjVCoV4eHhIoEQ8o3ExETc3d1JTEykWbNmeHh4GDukQunDDz/M1oD0CxcusHr16lyMSCisSvXxJ+nuP0gmSmQKJTaNP3tji4NZ6WrYthqCdf2uWNftCDyfzjkpKYkPPviAPn1SV9wt+dkCNHHhWNXtKBIHIU/ptBqubJtPCeemlKzzIf9unIQqNpzSjToTfvUvZDI5Jd5thmP9dq+tp3fv3uzfv5/ffvuN0aNHs2vXLkxMlMhlzx7yyKwyHCM3SU0WZDIbMM24orVQ9LxVF6ZVq1Zl66bgf//7H717936bUwqCQc2aNYsrV65QvHhxli9fjlwuf/NBQrYcOnSI0qVLZ+uYd999l0OHDuVSREJhZlm9BZbVW2T7uDJfpW/x2rRpEydPnkSpVOLr64vsWcuDRdUmBolTELIrPPgU8aE3SYp4iMxEQXJUKAAP/9pF2piE+3/toMS7zTAxz5gEpJEkSd/68M8//xAQECAengnZluOh9l26dMHOzi5bxxQvXpzOnTvn9JSCYFD79u1j48aNACxbtoxSpUoZOaLCqXz58piammbYHhERwa5du1i3bh07d+4kLCxMv0+hUGSry5MgGNLDhw+ZPXs2AOPHj6dixYrGDUjIkqioKKZNm0bLli2pX78+vXv35ty5c/r9V65coW/fvtSrV49WrVoRGBhoxGizR6fV8OD0bgA0qiTuHvvhxb3PX6nVhP7zqoHPz5UpU4aZM2cCsHjxYq5cuWLQeIXCL9sJRHR0NNeuXWPRokU4OTlx/PhxfvjhhwwLumXGycmJRYsW5ShQQTCkkJAQxo4dC8Dw4cNp1aqVcQMqYk6dOkXr1q1Zu3YtQUFBrFy5kjZt2nD48GFjhyYUcTqdjgkTJhAXF0f9+vXFdM4FyJgxY7hw4QJLlixh27Zt1KxZk4EDB3Lz5k0iIyP56quvqFixItu3b8fDw4OlS5eyfft2Y4edJeHBp1DFPh/AnLY6ekY6Qv93CHVi3Bvr/PTTT2nTpg0pKSl4enqSkpJisHiFwi9bXZh+//13xo4di1qtpkmTJjRr1ow///wTjUbDokWLWLp0qbgRE/K9lJQU3N3diYmJoX79+pmuci7kLn9/fxYsWEDbtm3129avX8+8efNo06aNESMTirodO3YQFBSEQqFg8eLFoltjAXH37l3+/PNPfvrpJ+rXrw/A5MmTOX78OHv37kWpVKJQKJgxYwYmJiZUqVKFu3fvsnbtWnr06GHk6F/vxdaHLJV/1gpRrunrryutK9OHH37IpUuXWLFiBaNHj37bcIUiIlstEH5+fqxfv55Nmzbx+++/U6VKFVavXs3atWuZN28eK1euzK04BcFgFi5cyD///EOxYsVYtWpVpt1rBMMZOHBghuZxtVqd4cZMkiQ0Gk1ehiYI6YSFhTFt2jQAPD09qVatmpEjErLKzs6ONWvWUKtWLf02SZLQ6XRER0dz7tw5GjVqhInJ8+emTZo04fbt24SHh2dWZb6RvvUhK7LeClGyZEnmzp0LpD7YuXTpUg6jFIqabLVAPHjwQD9oWqFQ0KTJ88Fk7dq1Y8aMGQYNThAM7bfffmPVqlUA+Pr64uTkZOSICj8XFxcGDRpEkyZN8PT0xMnJCQ8PD0aNGoWTkxPFihXj6dOnPHr0SMzQJhjVlClTiIqKombNmgwbNszY4QjZYGNjg4uLS7ptBw4c4N69ezRv3hw/P78MCWHJkiWB1DEvxYsXz7Te1q1b618/evQow7agoCCDxP8q2W190B+XxVYIgK5du7Jv3z7279+Pp6cn+/fvz9YMm0LBYqj3dLZaIMzMzFCr1QC0bdtWPysFpE7RqtOJlQmF/Cs0NJRRo0YB0L9/fzp27GjkiIqGfv36cfDgQcqVK0f37t2ZM2cOtWvX5rfffuOrr77CxcWFIUOGcPjwYTHJgmA0+/fvZ+/evcjlcpYsWSJaJgu48+fPM2nSJFq3bo2rqytJSUkZborNzMwASE5+1XgC48t+60OarLdCSJLEvHnzsLe358qVK/j5+eXgfEJRk60WiPfff5+7d+9SpUoVfH190+07ceKEaO4V8i2NRsPIkSMJDw+nRo0a+m4KQt6wsrJi9OjRfPHFFyxfvpwOHTrQr18/vvrqKywtLY0dnlDERUZGMmnSJACGDRuWrhuMUPAcOXKEsWPHUrduXZYsWQKAUqlEpVKlK5eWOFhYvHpF5RefxqY9pc3tVocXPTi9J8fH6tQpPL5whLJNPnlj2RIlSjB//nyGDBnCypUradeuHfXq1cvxuYX8y1Dv6Wy1QCxZsoQqVapkuq9+/fosXbo02wEIQl5Yvnw5f/75JxYWFgQEBKBUvn5hKSF3lCpVijlz5vDjjz/y33//0aZNG77//nt9y6YgGMOMGTMICwujatWqeHp6GjucQishISHDtgsXLhj0HD/88AMeHh60bNmStWvX6v/WOzo68uTJk3Rl077Pz1N425SvgXW5d7Eu9y5yZdYetkgmCuRmlliUrIjSPutr8HTq1ImuXbui0Wjw9PQkKSkpp2ELRcBbLST3ohIlShiqKkEwqNOnT7N48WIAfHx8qFq1qpEjKlqSkpIICAjg5MmTaDQaGjZsyIgRIwgICODcuXP4+vry3Xff4enpSZcuXYwdrlDEHD16lG3btiFJEr6+vuLhQi64cuUKEydOpG3btunGlkRHR9O7d28qVarEsmXLXvmAMqt+/PFHZs+ezZdffsmkSZPSdbNu1KgRmzdvRqPR6CdwOHXqFJUqVXrl+If8oFLrAXl6vjlz5nDy5EmuX7+Or68vU6ZMydPzCwVHjheSe1F0dDSzZ8+mW7dutG7dOsPXRx99ZIjTCEK2RUREMHz4cLRaLT179uTTTz81dkhFzvTp0/n111/p1KkTPXr04MqVK/qpAhs2bMjmzZsZN24cK1asoGvXrkaOVihKYmNjGT9+PJA6W1ijRo2MHFHhExISwoABA4iOjs7w8EahUDBp0iQSEhL44osvCA0NzfF5bt++jY+PD23atGHo0KGEh4cTFhZGWFgYsbGx9OjRg7i4OCZPnsyNGzfYsWMHGzZsYOjQoW97iYWKvb09CxcuBGD16tWcPXvWyBEJ+ZVBWiCmTp1KUFAQLVq0wNnZ2RBVCsJb0+l0jB49mkePHlGlShUxw4+RHD16lCVLltCiRQsAOnToQPPmzUlOTtYPYmzfvj1t2rRhy5YtxgxVKGJ8fHx4+PAhFSpU0CcSgmGtWbMGOzs7Nm/ejK2tbbp95ubm9O3blw4dOtCzZ09Wr16d49kcDx48SEpKCocPH86wIGW3bt2YP38+69atY+7cuXTr1g0HBwe8vb3p1q1bDq+s8Grbti09e/Zk27ZtjB49msOHD2Nubm7ssIR8xiAJxMmTJ/H29qZ///6GqE4QDGLdunUcOXIEMzMzAgICxGBdIylVqhSHDx+mbt26mJmZceDAAWxtbfXJQxq5XM4XX3xhpCiFoubUqVNs3LgRSF0b5nUDaYWcO3XqFG5ubhmShxcVL16cr776ik2bNuX4PG5ubri5ub22TJ06dcRDiiyaOXMmf/zxB7dv32bevHnMmjXL2CEJ+YxBujBZWlpSqVIlQ1QlCAZx4cIF/eI406ZNo2bNmkaOqOiaOXMmx44d4/3336devXqsXLmSefPmGTssoQhLTExk7NixAPTp04fmzZsbOaLCKywsjAoVKryxXLVq1d6qC5NgWLa2tixatAiAwMBATp06ZeSIhPzGIC0Qffr04dtvv6VBgwbiKa9gdLGxsQwbNoyUlBQ6dOggWsaMrEGDBhw5coSbN28iSRIVK1bM0PogCHlp0aJF3LlzB0dHRzFINJfZ29tnmP0oMxEREa9tpRDynqurK7179+ann35izJgxHDlyRNzjCXoGaYHo27cvoaGhuLi48Nlnn9GvX790X+IGTsgrOp2O8ePHc+fOHcqVK4evry+SJBk7rCJt6dKlRERE4OzsTPXq1bOUPDx+/FhMCy3kir///pu1a9cCsGDBAmxsbIwcUeHWqFEjduzY8cZyu3bt4t13382DiAqelIQYNCmpa1bodFqSY57q92nUKcQ+uE7yC4vNqWIj0GoMMzX29OnTKVu2LPfu3dO36gsCGCiBmDZtGrdv38bBwQEzMzN0Ol26L61Wa4jTCMIb/fTTT+zevRu5XM7KlSvFE618YPXq1Tx+/Dhbx4SGhrJ69epcikgoqpKTk/Hy8kKr1dK9e3cxQ2Ae+PLLLzl9+jTz58/PdMVnlUrFggULOHHiBH369DFChPmbVp3Cfz/N5PovywB4dG4//24YT2L4AwCCt84lePs8/v3Wm+ToMNSJcVz8YTJ3f9tokPNbW1vrFw7esGEDJ06cMEi9QsFnkC5MR48eZcyYMQwZMsQQ1QlCjly9epWpU6cCMH78eBo2bGjkiARIbRWaMWMGVlZWWT4mLi4uFyMSiqply5Zx7do1SpQowcyZM40dTpFQu3ZtJk6ciI+PD7t37+aDDz6gXLlyaDQaHj58yOnTp4mMjGTUqFH6mdqE555e+YOU+EhS4iOJvnORR+f3g07HgzN7cGzQgYSn956V1PHw7F5MLWzQpiTz9MqflG7YGaVtybeOoWXLlvTr14+NGzfi5eVFUFAQ1tbWb12vULAZJIFQKBTUrl3bEFUJQo4kJibi7u5OUlISLi4uuLu7Gzsk4Zm0ufV1Ol2Wj7G0tBQJoGBQ//33HytWrABSF8uyt7c3ckRFR58+fXB2diYwMJCgoCB9S4SlpSXNmzfn66+/pm7dukaOMv/RqlN4eHpP6jeSjDu/f49Wlfqzi7x+loSwkHTln14+gSQ3ffadxMOzv1C5zUCDxDJlyhSOHTvG3bt3mTVrln6AtVB0GSSB+OSTT/jpp594//330638KAh5Zfr06Vy9epWSJUuydOlS8T7MR77//ntjhyAUcWq1Gi8vL9RqNR06dKBz587GDqnIadCgAQ0aNAAgMjISmUxGsWLFjBxV/vb0yh+kJESnfqPTonph7ANAclTGWat0mhR9+fDgk5Rp1MUgrRCWlpYsWbKEHj168OOPP9KxY0c+/PDDt65XKLgMkkBYWVlx8uRJXF1dqVOnToZR+pIkiUW8hFyze/duNm3ahCRJLFu2DAcHB2OHJAhCPrJ69WouXryIra0tc+fOFRMrGJmdnZ2xQ8j30rU+5JhhWyGaNGnCwIEDCQwMZOzYsRw9elQkgUWYQR7T7tixAxsbG2QyGZcuXeL06dMZvnJKq9WybNkyWrRoQd26dfn666+5e/fuK8uHhITg5uZG48aNadasGXPmzCExMTHTsiqVii5dujBhwoQcxycY1507d/D29gbAw8ND9KEVBCGdGzdusGTJEiC1pbJUqVJGjkgQ3ixd60NOPWuFSIp68zS6WTVx4kQqVapEaGgo06ZNM1i9QsFjsEHUuWXVqlVs3ryZefPmUapUKRYtWsTgwYPZu3cvCoUiXdnY2Fh69+6Nk5MT69atQ5Ik5s6dy/Dhw1m/fn2GuhcuXMi1a9fEImMFlEqlYtiwYcTFxdG4cWO8vLyMHZIgCPmIRqPBy8uL5ORkWrVqxaeffmrskAThjQzT+pDGsK0Q5ubm+Pv7061bN7Zt20anTp1o27atQeoWChaDJBCZ0Wg0SJL0Vn3RVSoV69evZ9y4cbi4uADg5+dHixYtOHz4MJ06dUpXfufOncTFxbFy5Ur9ADk/Pz8+/PBDzp07l25Q5okTJzhw4ADvvPNOjuMTjGvevHlcuHABW1tbVqxYgYlJrr2dBUHIooQ7h0iJuPLaMsqyLTArVT/LdaoirhJ/dSuqpxfgpcH4muRotImpfcO1ydFICht9F6UtfyZw7lw8FmYS49pE8/TwYOxbLUWuEIthCfmXQVof0hh4LARAw4YNGTp0KAEBAXh7e9OwYUMxKUERZLA7roCAAM6dO0dgYCAA586dw9PTk6FDhzJgwIAc1RkcHEx8fDxNmjTRb7OxsaFGjRqcPXs2QwJx+/ZtKleunO6NXLp0aezs7Dhz5ow+gYiIiGDixInMnj2bb7/9NkexCcZ15MgR1qxZA6QmiWXLljVyREJWPHz4kDJlyhg7DCEXRRwfl5pASPLMC+g0WFbvRckOWZ+nPu6/b4k+vyRrheNT58d/GCVj1UF7QMK9RQzFog4RFwXWddyQO4oZvoT86/GFoNQXkuxZwpz1GezSk0ACdDrC/juGUzPDtcCNHTuWI0eOcP36daZOncrKlSsNVrdQMBgkgVi3bh0rVqygX79++m0VKlSga9euLF68GHNzcz7//PNs1xsamjrDQOnSpdNtL1myJI8ePcpQ3sHBgbCwMDQaDXJ56j+vuLg4oqOjCQ9/vkrj5MmT+fDDD3F1dc1SAtG6dWv967TzvrgtKCgoG1clvK2HDx/i6ekJwMCBA0XzaQ4Y6z3dunVrmjRpQvfu3Wnbtm2WVqUWChabesMJPzoCdJpXl6kzNFt1WtceRPR5P7J6I6XTwYKD1iSlSNQrp6JrvSQAZBalUIrkwShUKhXnzp3j9u3bxMfHI0kS1tbWVK1alTp16mToklyUlW/Ri8SIhwBoVIkkhN3T70tJjCUpMhR0OiR56i2cTqNGkstJzRZAZmKKpWMVLEtW1LfGFato2Kn2lUol/v7+fPzxx+zatYuOHTtmeKgrFG4GSSC2bt3K6NGjGTRokH6bo6MjEyZMwN7eno0bN+YogUgb/PzyHxYzMzOiozM273Xq1InVq1fj4+PDmDFj0Gg0zJw5E0mSUKlUAGzevJmbN2+yePHibMcjGJ9arcbDw4PIyEhq167N5MmTjR2SkA2+vr7s2rWLCRMmMHPmTDp27Ej37t2pV69ejurTarWsWLGCn3/+mZiYGBo0aMD06dOpUKFCpuV37tyZ6aQJhw4deuUxQvZY1+hP1Ok5aOIfk+GGXzJBWbY5yrLNslWnqW1VLN/tQ/yVHwHtG8vvvajk/D0FZiY6JrSPRfZs0qXiLcXc9cbwzTffsGbNGuLj4zPdX6xYMdzc3Pjqq6/yOLL8qViFWhSrUMvYYbxRvXr1GD58OMuWLWPChAm8//77lChRwthhCXnEIAnE48ePXzkQuXbt2jlu2lIqlUDqk4u01wDJycmYm5tnKF+hQgWWL1/OtGnT2LRpE0qlki+//JJatWphZWXFrVu3WLRoEYGBgVhYWGQ5jhefxqY9pRWtDsbh7+/PX3/9haWlJatWrRJPsHPIWO/pTp060alTJ8LCwti1axe7d+9m69atVKxYke7du9O1a9dszZKTnUkWIHW18saNG+tn5Ukj+u8ajmRihu37U1JbIV6mU2P3wfQc1Wv3/iTir2x61VlJS1aexMpY8VvqGIdBzeMpZ5eacMgsSmHl3CtH5xZybs2aNfj7+zNw4EDatWtHhQoV9FO9x8fHc/fuXX799VcWLVqEJEk57vIsGMfo0aM5fPgwV65cYeLEiaxZs0ZMk1xEGGQaVycnJ06ePJnpvtOnT+Po6JijetO6Lj15kn4KsidPnryyThcXF44dO8aJEyf466+/8PLyIiQkhIoVK7J//37i4+P56quveO+993jvvfc4d+4cv/zyC++99x4PHz7MUZxC3vjzzz/x9/cHYMGCBVSuXNm4AQk55uDgoL/R37lzJyVLlsTPzw9XV1fc3d05f/78G+tIm2TBw8MDFxcXnJ2d8fPz4/Hjxxw+fDjTY65du4azszMODg7pvtK6PAqGYV2jP3LLl/9Gy1GWa5Xt1oc0aa0Qad00XmZW+gN0Ojm+h62IV8l4t3QKnzV4PoW3aH0wjs2bN+Pm5sbYsWOpXbs2NjY2yOVy5HI5NjY21K5dm3HjxjFkyBA2bXpVgijkVwqFAn9/f0xMTNi/fz+7d+82dkhCHjFIAtG7d2/Wr1/PggULOH/+PHfu3OHvv/9m4cKFrFu3jt69e+eoXmdnZ6ysrNKtIxETE8Ply5fTzaiU5vz58/Tt2xeVSoWDgwNKpZIzZ84QGRlJ06ZN6du3LwcPHmTXrl36r1q1auHq6squXbsoWdIwMxQIhvf06VM8PDzQ6XT06tWLbt26GTsk4S2dO3eOqVOnMmDAAM6dO0ezZs2YNGkSarWavn37vnF80psmWcjM1atXqVq1qkGvQ8go6s9NJF17/NKESRpiTv5F2C/z0KYkpyv/ZNsUbs1oROw/v6B74aBbMxpzf2Uvkh9cBlJbITKSYfFOd4q7+HL4igknb5phItMxsX0s8mf/4UTrg/FEREToV6B+nfr16/P48eM8iEgwtFq1aunHJU6ePFn8HosIg3Rh6tOnD6GhoXz77bd89913+u1yuZz+/fvnuElSoVDQt29ffH19sbe3p2zZsixatAhHR0fatGmDRqMhIiICa2trlEolVapU4fr16/j4+DBw4EBCQkLw9vamV69eODk5AWBra5vuHEqlEktLS9H/OR/TarV4enry+PFj3nnnHWbPnm3skIQcunv3Lrt372bPnj08ePCAsmXL0q9fP3r06KFvVezTpw9jx44lICDgtX2iszvJQkREBE+fPuXs2bN8//33REVFUbduXcaOHUulSpVeeR4xiUL2JT+8jDpMhYmDCVqZjuSLaQOqUwjbNomwXbNAnTqwudryxyTd+4ek2+cI8f8YRdlaqB5c0teVdPssMWe2Yt2gO7HntwOgrKNAkqclGjrs3p9MDI4s/d0O0DDggwQql3g+iFu0PqSXl+/pKlWqsHfvXpo3b/7actu3b3/t51DI30aMGMHBgwe5ePEi48eP59tvvxVdmQq5HCcQa9asoWXLljg7OwPg5eXFkCFD+Oeff4iOjsbGxoY6deq89ZL1I0eORK1WM2XKFJKSkmjUqBGBgYEoFAru379P69atmTdvHt27d8fW1pY1a9Ywb948unTpgp2dHb169cLd3f2tYhCM65tvvuG3335DqVSyevXqbI1fEfKXdu3aYWZmxkcffcTs2bP54IMPMi1XuXJl7ty589q6sjvJwrVr14DUBxsLFiwgISGBVatW8cUXX/DLL7+IwX+GpoOUUC2mZTNp6H6WPACEH1yKTvv8Zl/1rLXh5cpi/96VvvJnLN7pjqJELaa4uREdr6GKg5q+7yfo94vWB+MaNWoUw4YN49atW7Rp04ZKlSphaWmJJEnExcVx7949Dh06xL///suyZcuMHa6QQ6ampvj7+9O+fXsOHz7Mtm3bxMKNhZyk0+lyNMHwsGHD9INZW7ZsiYuLC02bNsXKysrQMeYrYhB13vr777/p1q0barWaBQsW0LdvX2OHVOjk5Xt606ZNfPzxx1hbW791XQcPHmTkyJFcuHAh3SQLo0aNQqVSERAQkOGY6OhoihUrpv8+ISGBDz/8kIEDBzJkyJA3nlN8/rMm9KexRPy6GCQwc5aTfOXVU7oCz+a7f/PsSmlMyskwdUh9/lW273mOnrvPwIEDkcvlbPB6h8rmVzFzeI/EuwdxaL9RJBCvkRfv6b///puVK1dy5swZUlJS0u2Ty+U0bNgQd3f3dN0R8yPx+X+z5cuXM3/+fGxsbAgKChLr/uRzb/OeznELxKpVq0hJSeHMmTOcOHECPz8/xowZw3vvvUeLFi3StU4IQk5ER0czbNgw1Go1Xbp0oU+fPsYOSXhLBw8epEmTJpkmEMHBwYwbN45ffvklS3W9OMlC+fLl9dufPHnyyr89LyYPABYWFpQrV0702c0tOki+9obkAbKVPACo72tRP9EgoeXBsV+YOGcrAO7u7rgMG4VOFYtkXoLkR39hnsNB24Lh1K9fn8DAQFQqFSEhIcTGxqLVarGxsaF8+fJiDYhCxN3dnYMHD/LPP/8wbtw4fvjhB9GVqZB6qzEQpqamNGvWjGbNmjFhwgRCQkI4fvw4x48fJyAgAGtra1q2bEnLli3FYl9Ctuh0OsaNG0dISAjly5dn4cKF4o9QAXXu3Dn9wNgzZ85w9uxZIiIiMpT77bffCAkJyXK9L06ykJZApE2ykFlL1Y8//sjSpUs5duyYvsUiLi6OO3fu0LNnz5xcWp5Qq66TnHAQc5sByGSvbuFVq66QnBCEhc0gJJkyw36tJprkuJ2YKN/D1KwuAJqU+yTH78HMqgdyk1JZqk+nSyY57hckuT1mFq5vvoAs5A85otIisy7B4j1/8+TJE6pUqcLo0aORmSrBNLWbo0ge8heFQkGVKlWMHYaQi0xMTPDz86Ndu3b8/vvv/PTTT3zxxRfGDkvIBQYZRJ3GycmJPn360KdPH1QqFadPn+bYsWMsXrxYJBBCtnz//ffs27cPExMTVq1ahY2NjbFDEnJo27Zt7Nq1C0mSkCSJmTNnZiiTlmB07tw5y/Vmd5KFDz/8EH9/f7y9vfHw8CApKYklS5Zgb2+fL2f1UquukxC9hpTE3wAwNauPwrxpJuWukBD1DSlJfwCgMG+OqVk9/X6tJpqk2B9JjN0EuiTM1N2QyYqTEBOIKn4foEVmUga51SeZ16dsjqmyHjpdMklxu0iMDkSnjUBu+m7WEogskMxt0CXGZLm8zKoEDl2nckGqzs4BXyNJEosXL07XlU3IX8RK1EXHO++8g7e3N7Nnz2bmzJm0bNmScuXKGTsswcAMmkC8SKFQ0KJFC1q0aJFbpxAKqcuXLzNjxgwAJk6cyHvvvWfcgIS3MnnyZLp3745Op6N///5MmzYtw1SqMpkMGxsb3nnnnWzVnZ1JFkqXLs2GDRvw9fWld+/e6HQ6mjVrxsaNG/PVjWf6xOHV61Okv9HPWC594qAidQVniZSk8yTH7yZ1PQXtG+vT6VJIjN2iTxwMrZzXr0QdWUbchf2pG7IwHqLq/CskSUomuKYmMF9//TWNGjUyeGyCYYiVqIuewYMH8+uvv3L27FnGjBnD5s2bkckMsnKAkE8YJIF43QJsMpkMCwsL8QRZyJKEhATc3d1JTk7G1dU1SwNbhfzN2tqaxo0bA7Bx40Zq1qypX4n2bcnlcsaNG8e4ceMy7CtXrhxXr15Nt+3dd98lMDDQIOc2tMwTh4z9fzK/0X9eTqeJJyEq4KXEQb8XrSaEF2cxAkiK2YxGfSPT+uKeeqPTxeb8wuQK0KjSbXpxqlaLiu8RpS9rgm2Lr4n6fU36KqwdKN5xHE+2eAMgU5gzb5YPDx48wMnJifHjx+c8PiFXiZWoiya5XM6SJUto06YNf/75Jxs3bhS/20LGIAmEq6vrG/unFytWjH79+jFs2DBDnFIopCZPnsyNGzdwdHTE399fPLEoBHbt2oWLiwt2dnY8fPjwjSu+f/LJJ3kTWD6h1UQRFzH3WeKQ9n7PmDjotDHEPPF8lji8ulzs03GAmvSJQ7qaMmxJTR5ecd4cJA9WdToQd/Eg1u91waHrNNBpCVneE11KIiU/X4RF5fQLgdq83xuFY3WKtx+DqX05ynz1DQChP47BtERF7FoNRqYwp0TH1ETxr7/+0q85tGjRIoMlpILhpa1EPWrUqAz70lairl27NqampmzatEncZBYilStXZvLkyUydOpU5c+bQqlUrKlasaOywBAMxSAIxf/58pk2bRuPGjencuTMlSpQgPDycgwcP8vvvvzNs2DDi4+MJCAjA1tZWDKgRMrV9+3a2bt2KTCZjxYoVFC9e3NghCQYwYcIEtm7dip2dHRMmTHhtWUmSilwCoUm5RUri78++e3XXHY06hJSkP99YDlSv2Zc3rGq2pqrPxXTbKozd/8ryts36QrOMA98dv1iSYVtiYiJeXl4AfPHFF6KbbD6XnZWo169fnwcRCXlpwIAB7N+/n1OnTjFmzBi2bdsmHgwWEgZJIPbt20enTp2YN29euu1du3Zl+vTpXLp0idWrV2NjYyNG5AuZunnzpv7mcvTo0a9cYEwoeIKCgnBwcNC/FtIzVdbHtvR2EqLXoUo4QGrrQsaWABNFTYo5biYxei2qxCOkdjfKWM7MsgeqhIPodPFk1trwapnXl0rKZl25x9fXlzt37uDo6MjUqVONHY7wBmIl6qJNJpOxZMkSWrduzenTpwkMDGTw4MHGDkswAIMkEGfOnGHVqlWZ7mvbti3Dhw8HoEGDBnzzzTeGOKVQiCQnJ+Pu7k5CQgIffPBBpk3dQsFVtmzZTF8Lz8lNy2NdYhaalEGvTSRMFFWwdpiPWnXzlYmEmVUHLO1GkhT7M4kx372USKR1NU1ft3kxNzQpN1ElHM5Qn9J6AKqEA2g1jzF2IvG///2PNWtSx0ekLVYl5G9iJWqhfPnyTJ06lYkTJzJ//nxcXV3FdL6FgEESCFtbW4KDg2nWLOOc28HBwfrVqRMSEjA3NzfEKYVCZM6cOfz333/Y29uzfPly5PJXzzwjFHx//vknv/32G4mJiWi16bviSJKEj4+PkSIzvswTifSzJcGrEgmZvpwks8S82ACU1p9mSCQU5u2R5NYkx+14VpsGmbwEFsUGoU4ZnFpfwmF9fQrz5ljYupEcv4/E6DXPEom8p1Kp8PLyQqvV0q1bN9q0aWOUOITsadmyJRs3bmTlypUsW7bslStRr1+/Pt+vRC3k3Jdffsn+/fs5ceIEo0ePZufOneJ/fQFnkASiS5cuLFu2DBMTE9q3b4+9vT0REREcOnSIFStW0KtXL6Kjo9mwYQN169Y1xCkFA9CqYlE9vfjaMnLLMpgWq/jGunRaDYn3j6GOuv7SORLQJISijnuENikCE2snZGbPnxoePRvC+vXHAPCdO1G/urBQOK1btw5fX1/MzMywt7fPMPmCWCwwVcZE4ldk8hIZymVMJH5DJns+dihjIrEBuakTFrZDMbcZQGLMdyTH7dTXbWJaGesS815IJI4ik9sjSSYorbpiZtlJn0jITErm2c8DYPny5QQHB1O8eHFmzZqVp+cW3o5YiVpIW6vF1dWV8+fPs2bNGtzd3Y0dlvAWDJJAeHp6Eh4ezvz585k/f75+u0wmo0ePHowePZqDBw9y+fJlNmzYYIhTCgYQcWICsRfXvraMTGlPBbfQN9YVf20rYb/2z9b5Q2NkTN1gB8jo1TCBd8OmAWJ8TGG2adMmunTpwty5c8VNQxakJRI63Qwk6dUDD9MSCZ1Om2k5fSJh00+/X25SCiv78VjajctwTFoi8XJ9zxOJjw10hVlz+fJlffeWOXPmYG9vn6fnF97eqxaSi4qKEgvJFRFly5ZlxowZjB07lkWLFtG6dWuqVatm7LCEHMpxArFt2zZat26NnZ0dJiYmzJs3D3d3d06fPk1kZCSlSpWifv36ODk5AanNmLt37+aPP/7A2dnZYBcg5JzlOz3ekEBIWFb7NEt1mVdoAzIFaLM2A4xaCzP32hCbJOPd0ikMbRmPVXUxxW9hFx4eTs+ePcXNQja9LnnITrlMk4vXHPOqfXnZUqRWq/Hy8kKtVtO+fXu6dOmSZ+cWDEMsJCek6dWrF/v37+fo0aN4enqyZ88eTExybU1jIRfl+Lc2depUqlWrhp2dnX5b+fLlKV++fKblixUrxp07d5g6dSo9e/bM6WkFA1I6fYiZYxOSH58FXSazr0hybBt5Z6kuuXkJbOqPIubcoiyVD/zTgosPTLFUaJnROQZThRm2TUW3hMKuRo0aXL9+nffff9/YoQgFxJo1a/j3338pVqwYPj4+optbASMWkhNeJEkSCxcupHXr1ly4cIFVq1YxcuRIY4cl5ECOEwidTseqVavSJRBvEhkZmdPTCblAkiTsms4gdEf7THbKsa49EBNrpyzXZ9tgNDF/L31jK8S5u6b88JcFAN7t4ihrq6XYeyORycSAqsJu0qRJeHp6YmFhQd26dTOdVKFMmTJGiEzIj27cuIGvry8A06dPp1SpUkaOSMgusZCc8LLSpUsza9YsRo0axZIlS/joo4+oUaOGscMSsinHCUSZMmW4du1ato8Tg2TzF30rROgZ0s/0ImW59SFNVlohIuIlZu21QYfEx3USae2cDHLR+lBU9O7dG61Wy6RJk175JPnKlSt5HJWQH2m1WsaNG0dycjKtWrXis88+M3ZIQg6IheSEzPTo0YP9+/dz8OBBPD092bdvH6ampsYOS8iGHCcQR48eNWQcgpFk3gohy3brQxrbBqOJOe8PuvRT9SkrtCPhzkHm7LchIkFG5RJqRrnGAYjWhyJk9uzZoguKkCUbNmzgzJkzWFpasmDBAvG+KaDEQnJvT6dNIiluO6rEP7B2WAQoSYhaSXLcdmQmjljZj8NU2ShLdWm1cSTF/oQ6+T+sis8hOWEvqoTfsXZYhExmnbsX8gJJkpg/fz5nzpzhv//+Y9myZfoV5oWCQYxcEVA6fYiiZCNUT84CkHghmbJ9R2Qop02OJ3hI6poeVX1vcWNsZf0+5zWpycBVNweQgXndF58kyCjx0SoWjnLlzJ0EzEx0zOwSg5kpIJmK1ocipHv37sYOQSgAQkJC9OuBTJo0iXLlyhk5IiGnxEJyOZeWOCTGrEenjQYgPsL32fowqeMWtepbpCRffGMCkZY4JMZ8D7oEAKIeddXXq1U/QKbI2wluSpYsydy5cxk2bBjLli2jbdu21K5dO09jEHJOJBACkiRh22QyT/Z8ot9mYm24f9hWtb7mf8GhfHM4CYDRH8VRqUTqHz/r2oNF60Mht2vXLlxcXLCzs2PXrl1vLP/JJ5/kekxC/qXT6Rg3bhwJCQm8//779OvXz9ghCW9BLCSXfekThxheXP1dlbD3pdKvb5nLLHF4fp5oA0Wcc127dmX//v3s3bsXT09P9u/fj5mZmbHDErJAJBACAMpyLXOv8qqDGP6ZGxqtljbvJtGpVpJ+l03j8bl3XiFfmDBhAlu3bsXOzo4JEya8tqwkSSKBKOK2bNnCiRMnUCqV+Pr6IpNlbQpbIf8SC8llzesSh+x6XeKQ3/j4+HDq1CmCg4NZsmQJEydONHZIQhaIBEIAQK6wokyfC9wca4CZELTgNOg+CTe3I7d9h5GzVvLgwQMqVqyI34YAlLpoVGH/YlrcGYWVGFRf2AUFBeHg4KB/LQivEhoaysyZMwEYO3YslStXfsMRQkGiUCioUqVKnpxr1apVnDp1iu+//16/beLEiezYsSNduVKlSnH8+PE8iel1UpL/JTZs9FsnDgCqxD+IfToJdIlvXVdeKF68OAsWLGDQoEGsWrWKdu3aUb9+fWOHJbyBQRKIpKQklEqlIaoSjMjUJvM1PHJCZmqJTV13vv32W3799VdMTU0JCAjArmwdAMzLtTDYuYT8rWzZspm+FoQX6XQ6Jk6cSExMDHXr1mXw4MHGDkkooL777juWLVtGo0bpxwVcvXoVNzc3+vbtq98ml+eTLrQ6debrMeW0LjSkdm/K/wkEQIcOHejWrRs7d+5k9OjR/Prrr5lO8y3kHwZJIJo2bUrHjh3p3r27yBoFvUuXLjFrVuoA6SlTplCnTh0jRyQYQ3aaoyVJ0g+eFYqWPXv2cOjQIUxNTVm8eLFYnbaQOHv2bLbKv3zTnx2PHz9m8uTJnD9/PsOMThqNhhs3bjBs2DB9i2h+Yqqsj13ZfSTF/kxizHfodPHk9OZfYdEKO7N9JMVuIjH2R9CpclxXXpo9ezZ//vknN27cYNGiRUybNs3YIQmvYZC/0G5ubuzevZtt27ZRoUIFunfvTteuXXF0dDRE9UIBFBcXh5ubGyqVirZt2zJw4EBjhyQYyenTp7NcVkzVWTSFh4czZcoUAEaOHMm7775r5IgEQxk2bBhxcamz9Ol0uld+xtP2vc06MP/99x/FihVjz549rFyZ2nU2zZ07d0hOTs6zLlQ5IcksMS82AKX1p2+dSMjktljYDkdp3afAJBJ2dnYsXLiQAQMGsGbNGjp06PBWCaWQuwySQAwZMoQhQ4bw77//smPHDtavX8+yZcto0qQJPXr04KOPPhKDpIqYKdNncvv2bUqXLs3ixYvFjWERJtaMEd5k2rRpRERE8O677zJiRMYppIWCa8+ePXz11VdERUWxYMGCXO2W4urqiqura6b7rl27hiRJbNiwgePHjyOTyXBxccHT0xNr61evf9C6dWv960ePHmXYlhvjujJPJFKTMFPztqQkHuH5wq+vTwgyTySSU88jWenrzS/atGnDZ599xtatW/H09OTIkSOiK5OBGeo9bdA24jp16lCnTh0mT57Mn3/+SWBgIF5eXlhbW9O1a1f69euHk1P2FycTCpZDD83Zfnk3MpmMVatWYW9vb+yQBEHIpw4dOsSuXbuQyWQsXrxYPGwqZEqXLk1gYCDdu3fnr7/+Yvx448y8d/36dWQyGWXLlmX16tXcvXuXBQsWcO3aNTZs2JAvZ/tKl0jE/Ywq8QRWxScBM0iI9CM5fg8yeUlMFG+e/CR9IvEjKckXsS6xgOT4XagSfkcmzz8TmsyYMYPjx49z584d5s2bp+8KLeQvBu9k+vDhQ/bs2cOBAwe4evUqlStXxsXFhT/++IMtW7bg4+ND586ds1yfVqtlxYoV/Pzzz8TExNCgQQOmT59OhQoVMi0fEhLC3Llz+fvvvzE1NaVDhw54eXnpM1iNRsPKlSvZuXMn4eHhVK1alREjRrzyqYWQPffiTVgeXAwALy8vGjdubOSIBGNzdXXNcguUJEkcOXIklyMS8ovo6Gj91L5ubm7UrVvXyBEJuaFs2bJ4e3szc+ZMBgwYQKlSpfI8Bg8PDwYMGICNjQ0A1apVw8HBgc8//5yLFy++8r334tPYtKe0eT2bnCSzxNxmAOY2A/TbrIqPx6p49pOx1ERimP57c5t+mNvkr7VWihUrxuLFi+nTpw+BgYG0b9+epk2bGjusQsNQ72mDJBBxcXEcPHiQXbt2cf78eZRKJe3bt2f69On6QdXjx49n6NChzJ8/P1sJxKpVq9i8eTPz5s2jVKlSLFq0iMGDB7N3794MT6piY2Pp3bs3Tk5OrFu3DkmSmDt3LsOHD2f9+vUA+Pn5sWPHDubPn0+lSpXYu3cvI0aMYMuWLWIFxLeUmJTE3It2JGllNPugCR4eHsYOScgHGjduLLqwCZmaPXs2jx8/pnLlyowZM8bY4Qi5qHv37tSsWdNo3VEkSdInD2mqVasGpE4fLJLX/KVVq1b06dOHTZs2MWbMGIKCgrC0tDR2WMILDJJANGvWDJVKRd26dZk1axYdO3bEwsIiQ7natWtz+fLlLNerUqlYv34948aNw8XFBUhNAFq0aMHhw4fp1KlTuvI7d+4kLi6OlStX6rvN+Pn58eGHH3Lu3DkaNmyIWq1m8uTJtGyZunCau7s769ev5/Tp0yKBeEuz587nVpwptqYalvr55p/p8QSjmj9/vrFDEPKh48eP89NPPyFJEosXLxb9nAs5SZJwdnY22vm9vLyIiooiMDBQv+3ixYsAVK1a1VhhCa8xbdo0jh07RkhICLNnzxb/S/IZg3T669OnD/v27WPz5s307Nkz0+QB4KuvvuL333/Pcr3BwcHEx8enW97exsaGGjVqZDo13O3bt6lcuXK6PvelS5fGzs6OM2fOAKmr4qYlHomJiXz33XckJiby/vvvZzkuIaP9Bw7y/aYfAfCuFUWpkiWNHJGQnz19+pRHjx7x8OFDHj58yP3797l+/To//fSTsUMT8kB8fDzjxo0DYMCAAaKrYyG2Zs0agoODjR0GnTt35s8//yQgIIB79+5x7NgxJk2aROfOnfP1zExFmZWVFYsXLwbg+++/zxcL/gnPGaQF4sMPP3xln8aYmBhOnDhBp06dst38FBoaCqQmAS8qWbKkfuT4ixwcHAgLC0Oj0eiffsfFxREdHU14eHi6snv27MHb2xudToeHh4dofXgLoYlyxk2YBMDnFWJpVDzZyBEJ+VVwcDBjxozh9u3bme6XJInevXvncVRCXps/fz7379+nXLly2VonRCh4/ve//7F69WosLS1p2bIlLVu2pFmzZlhZWeVpHB9++CFLly5l9erVrF69Gmtra7p06YKnp2eexiFkT/PmzRkwYADfffcdXl5eBAUFZeiKJhiHQRKIfv36sWXLlkwXCrt8+TITJ07M0N0oKxITEwEyjHUwMzMjOjo6Q/lOnTqxevVqfHx8GDNmDBqNhpkzZyJJEiqVKl3ZRo0asWvXLk6dOoWvry/29vZ88cUXmcZhjGncCgq1Fnwu2RETG0uD+u8xwHafsUMSssBY7+mFCxcSExPD+PHj+e2331AoFHz44YccP36c48ePs3Hjxlw5r5B/nDlzRj8mbdGiRaJfcyG3atUqUlJSOHPmDCdOnMDf3x8vLy/ee+89WrRoQcuWLXOla1Nm3V3atWtHu3btDH4uIXdNnjyZ33//nTt37jBz5kx9q4RgXDlOIMaPH6+/8dDpdMyYMSPTJwp37tyhRIkSOTqHUqkEUsdCpL0GSE5OzrS/bIUKFVi+fDnTpk1j06ZNKJVKvvzyS2rVqpUhttKlS1O6dGmcnZ25c+cOgYGBr0wghFf79qY1V6IVFLOxYcXSJcTNFAmE8GoXLlxgwoQJfPrpp1hYWLB7926++OILvvjiC0aOHMn3339Pw4YNjR2mkEsSExPx8vICoFevXvqxaELhZmpqSrNmzWjWrBkTJkwgJCRE/9AgICAAa2trfetE27ZtjR2ukM9YWFiwZMkSevTowebNm+nYsWO6B16CceQ4gWjXrh3ffvttum06XfoFTeRyOfXq1aNPnz45Okda16UnT55Qvnx5/fYnT5688omFi4sLx44dIywsDGtra5RKJU2bNqV79+6kpKRw7Ngxatasma5bVLVq1di+ffsr48gP07jlR2efmrH1buoCPIvmz6Vc2bIYv6erkBXGek+rVCoqVaoEQOXKlbl69ap+X/fu3Zk+fXquxyAYj5+fH7du3aJUqVJMmzbN2OEIRuLk5ESfPn3o06cPKpWKv/76i+PHj7N48WKRQAiZev/99xk0aBBr167F29uboKAgbG1tjR1WkZbjBOLFFR+//PJLZsyYYfCBSM7OzlhZWXH69Gl9AhETE8Ply5fp27dvhvLnz5/Hz8+P9evX4+DgAKQ2l0dGRtK0aVPkcjmTJ0/miy++YNSoUfrjLly4IGZhyKYnYU9Z+J8tAB+Xi6dDe9EsLLxZmTJlCAkJoWHDhlSoUIG4uDh9X3iFQpFp10ShcLhw4QIBAQFAaveSYsWKGTkiIT9QKBT61gdBeJ3x48cTFBTErVu3mDZtGsuWLTN2SEWaQWZh+v7773NlFgOFQkHfvn3x9fUlKCiI4OBgRo8ejaOjI23atEGj0RAWFkZSUhIAVapU4fr16/j4+BASEsLJkycZM2YMvXr1wsnJCZlMxtdff813333Hvn37uHPnDmvWrOGXX34RaxZkg0YHnpNmEZUip7JVCkPfETd9Qta0bdsWX19ffv31VxwcHKhcuTJ+fn5cvXqV9evXi5XqCymVSoWXlxdarZauXbuKp8yCnkql4rvvvuOXX34hJiaGYcOG0bp1ayZOnEhcXJyxwxPyEXNzc/z9/ZHJZGzfvp2DBw8aO6QiLcctEBMnTmTYsGE4OTm9cRYNSZLw8fHJ0XlGjhyJWq1mypQpJCUl0ahRIwIDA1EoFNy/f5/WrVszb948unfvjq2tLWvWrGHevHl06dIFOzs7evXqhbu7u76+wYMHY2ZmxtKlS3n06BGVK1dm+fLloj9dNvx024pTt/5GKdcytXYECrHcg5BFI0aM4O7du2zfvp327dszceJERowYwf79+5HL5SxZssTYIQq5YOXKlVy5cgV7e3tmz55t7HCEfGT69OkkJiYSHh7O2rVrad68OaNHj+aXX35h1qxZLFy40NghCvlIgwYNcHd3Z+XKlXh7e9OoUaN0U/cLeSfHCcTp06fp37+//vXrvM0qtHK5nHHjxunnDH9RuXLl0vWhBqhbty6bN29+ZX0ymYwBAwYwYMCAHMdUmOi0GjQJUZhYFc90vzr2KXJLeySZDNXTu/wbqeD7W6njHkY6R1POUpOX4QoFnJmZGcuWLSMlJQWAFi1asHfvXi5dukTNmjXTjXUSCofg4GCWLl0KwJw5cyhePPO/NULRdPHiRfbu3UtcXByurq54e3sDMGbMGLp27Wrk6IT8yMvLiyNHjnD16lUmTZrE6tWrjR1SkZTjBOLo0aOZvhYKlpi/NvPgmy+xafwp9h3GptunTY7numc5TB0qYddqCNc3eDHvUkm0SHR+vzptrH/Tl0248RcWVZu8XL0gpPNiy2UaJycnnJycuHXrFm5ubuKfQSGiVqvx8vIiJSWFtm3b8vHHHxs7JCGf0el0qFQqrKys0vVmiIuLQ61WGzEyIb8yMzPD39+fzp0788svv9CpUye6dOli7LCKHIOMgYDUD/vjx4+B1D6N69atY86cOZmuGC3kH5r4CEBHzLkd3JmZfjVYbUoSOnUyqkdXCf1xDIsu2/E0WY6TRQpDzH9PV/bewo+4uzj7a30IhV/aatMPHz5k165dXLt2Ld22tK/jx49z8uRJY4crvEAVG8G/GycSc//5/GoP/trJtT3+6Wbdu3vsRy5vmcOlH6ejVaeuufPk4m/Mcu/B//73P6ytrZg3b95btUYLhVO3bt345JNP0Gg0dOvWDUhdfO6TTz7h888/N3J0Qn5Vp04d/djViRMnEhYWZuSIih6DLCT377//MmjQID777DPGjh3LnDlz2Lp1KzY2Nvz4449ijEF+J8lAm/5Jz4Nv+mHffsyz73TsDLHkr6dKTGU6JtWOxNxEl6GaxKsn9K8TbvyFVU3xOxdg1qxZHDt2TP/9iBEjMi2n0+lo1qxZXoUlZMGj8/tJjnpMyB9bqfH5VFRxkTw8uw90WmLu/UexCrVIjHjEkwtH9Mc8uXQMhxrNOb3nO74/9DcAwz9rg6Ojo7EuQ8jHBg0axPvvv49c/nwwnb29PfPmzaNRo0ZGjEzI70aNGsWhQ4f0CxavXbtWPKTIQwZJIPz8/KhcuTKff/45SUlJ/PLLL3zxxRdMmzaNadOmsXr1apFAFDCxf+8m9vwOAK7GmLL2eurS8W7vRFPV+lXNylr9q3sLP8LiXVdK9piNxTtNcztcIR+bOXMmJ0+eRKfTMWnSJNzd3TOMdZDJZNjY2PD+++8bKUrhZarYCMIu/Q5AwpM7xNy9ROTt/6XulCQe/LUTm/I1eXhmDyABqQ8VHp35hZSEeJbtOY9KraVuxeK8XzyBpOgnKIuVNMalCPlc7dq1031fvnx5MR5KeCOFQoG/vz+dOnXiwIED7Ny5k+7duxs7rCLDIAnEhQsX8PPzw8nJid9++42kpCT94KeOHTuyZ88eQ5xGyEu61MHR8WoJn4t2qHUSzR0S6VIuIctVJFw5yp05RynvfRirmh/lVqRCPleqVCl91wRJkmjVqhV2dnZGjkp4k0fn96PvpSTJCPnzZxLDH5CWKMQ/vs3T/04QcS39JBrqpDjWrw3g8v1IlKZyhrWviSRJPDq7j0offZW3FyEYXb9+/bJcVpIkNmzYkIvRCIVNzZo18fT0ZNGiRUydOpWmTZuK1s48YpAxEDKZDIVCAcCxY8ewsbGhTp06QOrYCKVSaYjTCHlMp5Pwv1KMh4kmlFKqGVMjiiy3DkoykJti38YDiypicLWQqlu3bsTFxXHjxg0gdWHImTNn4ubmxq5du4wbnKCnb33QPWtV1GlJDL9PWvKQSiLkz58zHPskOpGNv18B4EuXdyhVzBzQ8fTyHyRFP8nt0IV8RqfTZfg6c+YMcXFxGbZrtdo3VygILxk+fDh16tQhKioKb2/vdOOzhNxjkBaIWrVqsW3bNpRKJQcOHKBVq1ZIkqSf17lWrVqGOI2QlxSWHLdqy++PTyOTdEyqFYm1adY/lHYfDqFElymY2pfNxSCFgub48eMMGzaML7/8kvHjxzN9+nQOHTpEtWrVmDhxIikpKXz66afGDrPIS9f68Eo6NMnx6bfodKz69T+SVBreLWdLh/rpu6GIVoii5/vvv0/3vVqtplatWsyePZuaNWsaKSohq7SqOLRJEa8tI7csjSQ3RafTkhJ+Ba0qFp1OQ0r0bTRxD9OVleTmmFiVRZLJkRRWmJV8D7n5263jYGpqir+/P+3btycoKIitW7eKAfh5wCAJhLe3N4MGDWLfvn3Y29vrF27r3LkzWq2WwMBAQ5xGyDX6fgr617rB+/HtnfqP/qsqsdSw1744xCEjSZZuMHapXr7IzCxzL2ShQFq1ahXNmzdn+PDhxMbGcvjwYYYMGcKoUaPw8/Nj48aNIoEwsgytD9lw9OJD/ncnHFO5jBHtayFL12SZ2gpRulEnMRaiCBODXAuWB5saoo6+9doyVjW/wqHNN0T9NYuo09lbNFhS2FBx2NO3CRGA6tWrM3bsWHx8fJg+fTrNmzenbFnxADM3GaQLU40aNTh06BBbtmzhyJEjVKxYEYAZM2awd+9e0QKRr0mg0yGZWVG88wQAkjQSI7ynkpSURMPiSXxWIQ6Ld5qnO0ruUCXd93YfDqGKz+U8i1oomIKDg+nfvz9WVlacOHECjUZDu3btAGjWrBl37941coRC1lofMoqIS2b90dTpXnu3qErZ4pk/QHh0dt/bhCcIQh5SlmtF6sPFVzMv1zK1bNkW2a7fzKFe9oN6BTc3N+rXr09sbCzjxo0TXZlymcHWgbCysqJixYrExMTo53WvXbs2KSkpPHz48M0VCEZh06gnjv1WUc0vBIePJwMQcM2Ga9dvULJkSRbPmUbVOReo4H0Yu9Ye+uMqjT+crp5SvXwxtS+Tp7ELBY+ZmZl+cagTJ05QvHhxnJ2dAXj69Ck2NjbGDK/IS0mIyVHrg06n45tDl4lPVlPF0YaujSq8qiRPr/yBKvb1XSIEQcgfbBuPT+1dkCkZJsUqYVk9tbuQ0skV0xJ1slV/iTZr3zLC5+RyOX5+fiiVSo4dO8amTZsMVreQkUG6MN25c4cJEyZw4cKFV5a5cuWKIU4lGJiJrSP2rVO7nGmT4/k9VMn+B5ZIksSyZcuo2uL5E4VSn88jMmi5sUIVCoEGDRqwfv16oqOjOXDggH7KvUuXLrFixQrq169v5AiLNkkmw7bSe2hUifptqvgoUuKjXygEclMl6qQ4tOoUAE5efczp608wkUl4dKyFXCZLLSiTITMx03dbkZmYYlmqMpLcIP96BEHIZabFKmFVoz9xlzfoZ2d8Totdk2lIstTPsyRJFG+5gNAdHbJUt7JsS0xtKxk03qpVqzJ+/HhmzpzJrFmzcHFxwcnJyaDnEFIZ5K/47NmzuXPnDiNGjMDR0RGZzGANG0VaWvNbTvuMvtx8p9PpXvu7uXP3Hn5XbAEYMcyNFi2y3xwpCK8zceJEhg4dytixY6latap+vNTQoUMxNzdn7NixRo6waDNRWlG10/BsHRMREcHAVq0AGOk5mk+9vHIhMqGwEWMhCg7bxuNTE4h0ZJgUq6BvfUiT1gqR8vTfN9ZryNaHFw0cOJADBw5w5swZxowZw5YtW8R9aS4wSAJx7tw55s6dS+fOnQ1RXZGn06lJittFYtQKdLokLGyHYW6T9bm0tdoYEqN/ICn2B5Askcmt0Wmj0WmjsXZYjsL8gwzHqFQqho/0JEEjo2axZMaM8sikZkF4O05OTuzbt4/w8HBKlCih375y5Upq1Kihnw5aKDimTZtGeHg4zs7OeHiIvxtCeq6urpkmC25ubpiamqbbJkkSR44cyVBWMK7MWyHStz6kyWorRG60PqRJ68r00UcfcfLkSTZu3MiAAQNy5VxFmUESCCsrK4oVK2aIqoq09IlDnH67VhOWpeOfJw7fAynPKlWhVUe+UFfm87D7+Pjw78VLWJtomVQ7ChMT0cVAMLz+/fvTo0cP2rZtm257vXr1jBOQ8FYOHz7Mzp07kclkLF68WCSAQgaNGzcWrQ2FQPpWiMxbH9JkpRUit1of0lSsWJHJkyczZcoU5syZg4uLC5Uq5U7CUlQZ5C6xa9eubNq0iebNm4s/FDnwqsQhqzJNHLLh8OHDrF2b+mEeWzOSksqX+zkKgmGYmJgwceJEZs2aRYcOHejevTvvvfeescMSciA6OpoJE1Jnbhs6dKhIAoVMzZ8/39ghCAbwvBXiO9Bl3vqQ5k2tELnZ+vCi/v37s3//fk6ePMmYMWPYtm0bcrk8189bVBgkgTA3N+f8+fO0adOG2rVrZ1h5WpIkfHyyNzdwUWDsxAHg4cOHeHp6AvD1gH40vS/+2Au5JzAwkCdPnrB79252797Nzz//TIUKFejevTuffPIJpUqVMnaIQhbNmTOH0NBQKlWqhJcY9yBkQUJCAhYWFum2Xbhwgbp16xopIiE70lohXpx56VVebIUwsamETq5AE3kVyP3WhzQymYwlS5bQunVrzpw5Q2BgIEOGDMmTcxcFBhlVsnPnTqytrdFqtVy4cIHTp09n+BIyin7sTkLk/JwlD5pooh50JSl2PTlNHtRqNSNGjCAqKoratWszabx3juoRhOwoWbIkgwcPZu/evWzduhVXV1e2b9+Oq6srgwYN4siRI2L+7nzuxIkT/PjjjwD4+vpibm5u5IiE/OzKlSt88sknfPfdd+m2R0dH07t3bzp16sTNmzeNE5yQZabFKuHQfgMlO/74ytaHNJIkUbLjJqxqfkWpLj/j2HkzFlV7YvvBzDxpfUjj5OTEtGnTAFiwYAE3btzIs3MXdgZpgTh69KghqilylFafEB9xGUjO9rGSzBozqy4kxW7m9UtEv5q/vz+nT5/G0tKSVatWYWYm+i8LeUutVqNWq0lJSU2CIyMj8fT0pHz58vj5+VG9enUjRyi8LD4+Hm/v1IcNAwYMoEmTJkaOSMjPQkJCGDBgABYWFlStWjXdPoVCwaRJkwgMDOSLL75g9+7dODo6GilSISusqn+W5bIK++o4tPlG/32pzj/mRkhv1KdPH/bv38+xY8fw9PRk9+7doiuTARh0XqubN2+yceNGFi9ezOPHjzl37hxxcdl/ul5UKK06YV8uCHMbNyB7N++SJMPSbgx2ZfZhZvkpb1op8mV//vkn/v7+QGpWXrly5WwdLwg5FRISwooVK2jbti19+vTh2LFjfP755xw9epTt27cTFBSEiYlJlqZ01Wq1LFu2jBYtWlC3bl2+/vrrLK9m/csvv1C9enXu37//tpdUYGjUD4kLn0vs0ynEhs8kPtI/23UsWLCAe/fuUbZsWSZOnEhK0nlinowiOf5XtJpYYsImE36vGZEPP0ejDjX8RQgFypo1a7Czs2Pnzp0ZJk8wNzenb9++bNu2DQsLC1avXm2kKIXCTJIkFi1ahLW1Nf/88494nxmIQRIIjUbDlClT6Ny5Mz4+Pqxbt46nT5+ycuVKunbtSmio+CfyKpJMiYXtIOzLHX2WSJhl63iZiQNWxcdjV2b/s0Tizb/Sp0+f4uHhgU6no1evXnTr1i1nwQtCNvXq1Yu2bduybt066tWrx4YNGzh06BBubm768Q+lSpWibdu2PHr06I31rVq1is2bNzNnzhy2bNmCJEkMHjwYlUr12uMePHjAzJkzDXJNBUFq4jCHqIf/b+++w5uq3gCOfzOapnvRwQbZyBAQBQRREZWlgqIgIAoiirIpW7bMMgQZIkNcgMhy4EBQf4hscTAKiOzRlu6ZNMn9/VEaWtJC2qZNx/t5Hh7Sc88999wkN8l7z3oGQ/JWjCnfY0z+GmPq//JUzqFDh1izZg0AM2cMwJw8nITIgaSn7SU57gNirzxGeuoPgAGL6Szm9HOFcDaiJNm3bx+vvfYavr6+ueYJCAjg1VdfZd++fUVXMVGmVKxY0fqZHxYWRnh4uJNrVPI5JIBYvnw5X3/9NTNmzGDv3r3W/stjxoxBURQWLlzoiMOUarcCiV0FDCS+zSGQuNU6YbFYGDZsGBEREdSqVYvp06c7pP5C2MNkMjF58mR+++035s6dywMPPJBjvscff5zVq1ffsSyj0ciaNWsYPHgwbdu2pW7duixcuJCIiAh27tyZ634Wi4XQ0FDuvffeAp1LSZA9cNgG5H9sSVpaGiNHjkRRFLo+XYkmdT/AZPjTul0xXypQ+aJ0ioqKomrVqnfNV7t2bbnZKArVCy+8wOOPP47RaGT48OHWrrMifxwSQGzevJkhQ4bw3HPPZbvLULduXYYMGcLevXsdcZgyIVsg4fMmOvf26NyftHv/rIGE3qsfbt6v4eE3EXefIWj1LfjokzP8/PPP6PV6li9fbjMjhhCFKSAggCpVquDp6XnHfHXr1r3rzCzh4eEkJydn64Pv7e1N/fr1OXToUK77rVixgvT0dAYOHJi3ypcgjgwcMoXNG83Zs2cpF6Bm5Fumm6kSMIg78/f3JzIy5/WHsoqJibljK4UQBaVSqZgzZw6+vr78/fffvP/++86uUonmkEHUN27coF69ejluCw4OJiEhwRGHKVNUaj3uPv3ztI/FHIvFdBWt672otYF4+A3Ktv3EkXuYM/clAKZMHs89VaNIN5hxcW0AgNl0JdeyTcZ/UUyyFLwomMOHD9OvXz+HlJV5t7J8+fLZ0oOCgnLt/vT333+zZs0avvzySyIiIuw6Trt27ayPM8vNmrZr16481buwpcSvIzV+KfmdXOF2imJi///6svLDXwCYGOqDt7es91OSFeV7unnz5mzZsoVOnTrdMd+2bdty/R0hhKOEhIQwffp0Bg8ezKJFi2jfvj0NGjRwdrVKJIf8IqxatSq//vprjtsOHjxoV/OlyD+LOZbk2MXEXulEfEQ/FCX7QnAWSyLXLy3mjYG9MJnMdHiyDh3ariLpxggSIvphMv5H4o3JxF/raVO2yXCChMihxF/vQUrc0qI6JVFKPfTQQ2zatAmDIe8zj90uNTUVwGb1Y1dX1xzLT0lJYdSoUYwaNYpq1aoV+PjFl2NbBYxGIxMm/4HZDE897sZjbfV330mIm/r06cOBAweYPXt2jtel0Whkzpw57Nmzh169ejmhhqKs6dq1Kx06dMBkMjFs2LC7jpkTOXNIC0Tfvn2ZNGkS6enpPProo6hUKi5cuMCBAwdYs2aNdbVS4VgWcyypCZ/cnMrVxK07jhk/ICyWRNIS15MS/zFjx1/j6jUTFStomDgqDpUqM3a0EH/9RTJiyVt3LE2Gk6TFf0J62l4gY7ozRZH+gqJgXF1d+e6779i5cyeVKlUiICAg23aVSsW6devsKitzwUqj0Zht8UqDwZDjugQzZsygWrVq9OjRI091zno3NvMubXFrdcjK3ecVXD3akxq/BkPyVxQ0oFi27ANOnUnAz8+LiWObAyfJGFcl3ZdKqqJ8Tzds2JBx48Yxc+ZMtm/fTsuWLalUqRJms5mrV69y4MABYmNjGTp0KG3atCmUOgiRlUqlYtasWezfv5+TJ0/y3nvvERoa6uxqlTgOCSC6d+9OTEwMK1asYP369SiKwogRI3BxceG1116jZ0/bO9si/3IPHG5utyRhSPqC1IRPQEnjy61J7Pw5Da0G5k7zxcvz9oYnBcjeapEQOQCV9c6uGSEc4fr16zRp0sT69+0LxuVlAbnMrkuRkZFUqVLFmh4ZGUndunVt8m/evBmdTmc9vtmc8b7u3LkzTz/9NNOmTbP/RIo5jbYingHv4ObTr0CBxKlTp3jvvfcAmD59FtXrdSU97TApccsxGf9CAglhj169elG3bl1Wr17Nrl27rC0RHh4etG7dmn79+slq1GVc/IEvSDiwEf+nRnD9k7cxRvyLb5tXCX5hNmrXW2M1FUXhyrKe6MrXIeDJYWg8/Ei79A8RG0bh98gAvJp1Q6W+e+eawMBAZs2axRtvvMGSJUt44okn5D2YRw4JIAAGDhxIr169OHr0KHFxcXh7e9O4cWMZFOVAFnMcqQkf3wwc0sntizvuSmcgDYDT/6Yzd1HGGJShg7xoeK8d6024qFHpNEjgIBztk08+cVhZdevWxdPTkwMHDlgDiISEBE6cOEHv3r1t8v/444/Z/v7rr78IDQ1l5cqV1KhRw2H1Kk4KEkiYzWZGjhxJeno67du359lnnwXARX8/PiGrbwskQKWphGIuO2tqiLxp1qwZzZo1AzIWjFSr1fj4+Di5VqK4SDn5M4lHtpB4ZIs1LfanJcTuWopfu7duBRIWMwkHNwIQ/f18Ap4aidYnmORjP5J87Ed05esS9Nx0uwKJLl26sGPHDr766iuGDRvGd999l601W9yZwwIIi8WC0Wjk3nvvxc/PD5XKMYPsLBYL77//Pps2bSIhIYFmzZoxefLkXMdVXLp0iXfffZc//vgDFxcXOnTowMiRI61dGiwWC2vWrGHTpk1ERERQsWJFXnnlFbp37+6Q+ham1IQ1pCXas5JjRvCQkmohdGIsBiO0aeVKnx4eBa+Ei5rgSU1ITO0O7lqCx2VE7LERba3bs/7tX2kPKrVtdxJRNqWkpNjM/PXXX3/l686PTqejd+/ehIWF4e/vT8WKFZk3bx4hISG0b98es9lMTEwMXl5e6PV6m8+MzEHYFSpUsOlKVdrcHkhYlDRUaFFrfHPd58MPP+To0aN4eXkxa9Ysm8/0rIFEasI6XD06odW3IjlmFump/0OtrYDGpXohn5koifz8/JxdBVEcabRgNmVPUyxZAolBBD337q1NhmRufDUDlcbFmma8fprL73e3O5B499132bdvH6dPn2bBggWMHz/e4adVWhV4EPU333xD7969ady4MQ899BAPPfQQTZs2pX///vz0008FrmBeFopKTEykZ8+exMfHs2rVKlasWMGxY8d46623rHk++OADVq5cybBhw/jqq6/o27cvU6dOZevWrQWua2Fz8+6P3rsvqFy588rTGRH07PkJnLtgJqicmukTfVCrnTdzisUcg2JJcdrxhXOdPHmSZ599lo8++ihbenx8PD179qRTp06cPXs2z+UOGTKE559/nokTJ9KzZ080Gg2rV69Gp9Nx7do1WrduzY4dOxx0FiVfZiDhXe5dvMpNxcNveI75zp07x7x58wCYNGmSzUxXWbno78c7aAmuHk+h0XjjHTiLgCp78auwCY02pFDOQwhRGt3hN4piIfan9zn1pp9NumIyZPsbbgUSZ8ffS8KhL1EsOc9K5+/vz5w5c4CMNc2OHDlSoDMoS/IdQFgsFkaMGMGoUaO4ePEiHTt2pF+/fvTr14927dpx5swZBg8eXKAB1HldKGrr1q0kJSWxdOlSGjVqRMOGDVm4cCG///47hw8fBmDDhg3069ePDh06UKVKFV544QWeeeYZvvzyy3zXs6ioNT54+A7Gr8I36L1fvhlIZH8JFYuK+NN9+Pa7pmz7NhW1Gsa9XBPTf1UxJrkQ9WdFDLFuXP2tOtHHcvlyT7egGG27L5nSDuS5zmbTNZKiZxB75SlS4pfneX9R8l26dIlXXnmF+Ph4atasmW2bTqdj/PjxpKSk8NJLL+V5ISmNRkNoaCj79u3j6NGjrFy5kkqVKgFQqVIlTp06Rbdu3XLc98EHH+TUqVPW/CJD5kJ7aWlptG7dWsawCSGKETunp84hkDDF5fz98uSTT9KtWzcsFgvDhw+3zvAn7izfXZg+//xzvv/+e8aOHcvLL7+M+rYmIovFwvr165k5cyZt2rS56xzQObnbQlG3l3nu3Dnuuece/P39rWnly5fHz8+PgwcP0rRpU2bPnk316rbN6vHx8Xmun7OoNX54+A7Gzas3qYmfZoyJUNIBC4kX/fjnt91M+zjjx37PtnWo7lKNpEuQnqzDEONJSpQH5mQ9aTc88akZhVZvISMQuRU0eAUtJSluyG1HzvtUZ7dmeFJQLEn5PGNRkq1cuRI/Pz82bNhgMybKzc2N3r1706FDB55//nlWrFjBlClTnFJPkeHTTz9l3759uLm5MW/ePId1RxVCiCJ3c2IOiyEZxZz7TJLTp0/n999/5+zZs8yZM0e+h+yQ7xaILVu28OKLL/LKK6/YBA8AarWaXr168cILL/DFF1/k6xh5XSgqMDCQqKgo6+wqAElJScTHxxMdHY1araZly5aEhNy683758mW+/fZbWrduna86OlNmIJHRItEHRXEl8lggYV/9RaohnSb1a9Dt/lv9vg0xHoCCOVlP5kDKqKOV8QnZiM69AyqdCxVmd6feRxYUte3ifyqdhuBJt2bQyRz/kFXwpCbZ8tya4UkWoSur9u3bx2uvvXbHCRUCAgJ49dVX2bdvX9FVTNi4cuUKM2bMAGDcuHHZZrcSQoiSI+PGh9a/EuX7r6bWvLO4BFTONbevry9z584FYNWqVRw4kPceF2VNvlsgzp8/z5Aht9+httWmTZt8j4W400JRObUYdOrUiRUrVjBz5kxGjBiB2Wxm6tSpqFSqHMdMREVF8frrrxMQEMCbb76Zaz2KYtVOizkeQ9IWtK4NcdHfn2s+k/ECKbFz0breB5jQeTyF1qU6Hr6DSb5Uk9XfT+O/iES83VwY3K4ammzjHmwfp0Z4o5gC8Co3BVN6f4ype4m92hHFHFXgcxLFV1GuRBsVFWXXYpK1a9fOcxcm4TiKojBmzBiSk5Np3rw5r776qrOrJIQQN6nwevBFyvdewunBgbeS1RnrVGExW/OBgta/EoFdp+Dbqg8qrcvtheWoXbt29OjRgw0bNjBixAh27txpM+mHuCXft4VTU1PtmoLNz8+PmJiYfB0j60JRWeW2UFTVqlVZsmQJP/30E82aNePhhx+mQoUKNGjQAE9Pz2x5//vvP3r27ElycjJr16512nRyFnM8KXEriL3amZT4paQlfZVjPpPxAvHX+xF//TnSDQdITfiA1ITVpKf8crMcE5s//ohvj1wEYEinhvi72xcfnvtpbUYZpoukxoVJ8CAcyt/fn8jIyLvmi4mJKbPTPiuWVKIv3m/zL91wGmPqXgypv1vTLKaMz1OT8QIpcSsdVocvv/ySn3/+GVdXV8LCwnJsWb69roolFUUxY0zdh8Uc57C6CCHKoBy7GKnwerAHdZbFUHnQejQevre2aHT4Pz6Y4J7zrWlZWxz8Hu5nd/CQafLkyVSoUIHz588zc+bMfJ5I2ZDvFghFUdBoNHfNp1arseQy+v1u8rpQFEDbtm359ddfiYqKsk7f2KpVq2wDKY8cOcKbb75JYGAgn3zyyR1nGIHCWbXTnH6duGudb/6VuRiT7Re2MfUvEqP651LKrfzHfv2KRVv2A/ByTw9eGHINyLizfO7rBijm3GPF+PN/k54Sj4u+GTqPzhiTv0UWhyrdinIl2ubNm7Nly5a7joPatm0b9erVK5Q6lFQJEb24/Vo0pV8i9cYoTMa/AXDRP4iLvmALIEVGRlr7/I4YMcJmsHtuDCk7SU1Yi8V0CXeft3HzeaVA9RDCUZYtW8a+ffuyrT1z8uRJ3n33XY4dO4avry99+vShf//cvl9FUXKr2RLD9dMEdAzl2sdvY4q9jFezrlTouzxb0IBag0fDJ3EtX4+ATqNx8S1P6vk/cKvxIL6PvJ6nFoeceHt7M3/+fHr27MnatWt56qmnSmQX96JQrDumZ10oKlPmQlH332/bzefIkSP07t0bo9FIYGAger2egwcPEhsbS6tWrQD4+++/ee2116hVqxaff/75XYMHR8tscYi79nyWVNsf65ktDrkHD5DZDcmQlsqI8VNISjNRq4I3Q9/0ynO9zv20FpVaj1fAFPwq/oiL2+N5LkOInPTp04cDBw4we/Zs6wq0WRmNRubMmcOePXvo1auXE2rofIpiym2LTUpiVH9r8JCxr+1zmlcTJkwgLi6ORo0a8cYbb9wxr6LcGmOWHDMNi+kyoEYht3MQomh99NFHLF68OFtabGwsr776KtWqVWPz5s0MHjyY9957j82bNzupliIr39YvU23sLrwaPUXtsH+pvzrNpsUBQKVSUXXU94T0WoiLb8bvN7dqTak+aX++Whxy8vDDD9OnTx8ARo4cSWJiYoHLLI0KtJDclClTbLoG3S4pKf8z7+R1oagaNWpw5swZZs6cSf/+/bl06RKjR4+mR48eVK5cGZPJxKhRowgICGD27NkYjUaiojK662g0mmyzNxWG1IR1pMSvAsVAblORKYpCfMSbmAyH7CpTURSmjXmek5eicddpGfVMQ1xc/stz3aytEO4+qDV+eAVMJuZywdfxyJDZwiLKooYNGzJu3DhmzpzJ9u3badmyJZUqVcJsNnP16lUOHDhAbGwsQ4cOpU2bNs6ubpFSFBPGlB9Jjl3qkPLM6RdRa0NQqXJfcV5RLJhN59Foq6OYb/D1N9vZsWMHWq2WsLAwtNqMrwWz6ToqtSdqtefN/cwYU3bmUFcFuHtrtBCFLSIiggkTJnDkyBGb2Ra/+OILdDodU6ZMQavVUqNGDS5cuMCHH37Ic88956Qai+LqnXfe4ZdffuHSpUtMnz7dOsBa3JLvAKJ58+ZAxg/YO/Hw8MixtcBeQ4YMwWQyMXHiRNLS0mjevLl1oajLly/Trl07Zs2aRbdu3fD19WXlypXMmjWLLl264OfnR48ePawDpP/++28uXLgAwOOPZ7/DXrFiRXbv3p3vet6NophIS9wCyp3nF1YUEyaD/QuZ/LY3nHVf/gnAoKfuJcQv/wN+zv20ltpPD8txm4vb46Sn7iJrIOAV+AGJUQNzyO0OZCwa5+E/kdT4tVjMV7jz4neiNOvVqxd169Zl9erV7Nq1y9oS4eHhQevWrenXr1++VqMuyQzJ35MStxSL2XZGubxKTztKSvwKTIYjePhPQu/5tE0eRbFgTP2V1PjlmNP/Q+PajOjIQ0ycmHET5e233+bee+/FlP4fqfEfYkz5Cb1Xbzz8hmJI3klK3Ps3r2Mhiqfjx4/j4+PDV199xdKlS7ly5db79fDhwzRv3twaIAO0aNGCDz74gOjo6FK/Gr3IGw8PDxYsWED37t357LPP6NixI4888ohNPkPkf8T9to6Ap0aidfcmPfoSKq0OrU+wbd6rJ3EJqILa1aMIzqDw5TuAyNqvsDBlLhQVGhpqsy1zoaisGjduzIYNG3Isq2nTpjb5i4pKpcW3/EbSkraQmrAaxZLzuhNqtQt+Fb4nKWY66Wl77lhmdIyZ0HHbUIAnm1SmTYMgUOXUsqGQvQUg5x/yprTkXI/lFTAZRRlDSvwaDEnrAdC62K6n4REwDY2mOgmRGc1/ru5P4urRGWPq/9DkkF+UHc2aNaNZs2ZARncCtVrttMkLnM1siiApeqJDykqJW4g5/QyZrQCKkn3F99sDB2sdDEeYuyiBmFgLNWv4MujNLiTeGIcxZWeWspIxpV8kKXqcQ+oqRGF67LHHeOyxx3Lcdv36dWrXrp0tLSgoCICrV6/mGkAU5Yx1onhp1aoV/fr1Y82aNYwcOZLdu3fbfGddnPck6ZH/Er19GvpaD6GkJWG8ehK/doOsYzQALMY0zk5sjFrvSbnO4/Fv96bTAglHvacL1IVJ5I1KrcfN+yX0nt1ITdxIavyS23Jk/PhXa/3x8B9Fcqw76ak/5FiWxaIwfmosN6IN1KlTg5lLKuOiOUZOXQmqdzme5S8XAqrkb679jHUnBlkDiJy4uj1qk6ZSaXF1z/lDXZRNfn5+zq6CU2m0wXgHrSAlbikm4z8FKsucfjbzEVmHteUWOGTa83sa33yfsVr9tAnBpMX05tbnhxnQYDKGk3D95QLVT4jiIC0tLccp4YEcx2YJARnr4ezevZvz588zZcoUFi5cmG27kp5mfZx2Zq/1cczOJcTuWmYNJDRu3mBOx5IcS+QXo7nxzUynBxIFJQGEE2QEEr1Qq71IiXsfRbm1aJuipJAUPR1D8tdkthp4lpuHIemrbC0Saz5JZt9BA3pXFbMnx+OiybzraMG/0q+kJX5BasI6FCWZ/Iw/UKndCKhy+K7pOeW5U7oQIoOL/n68g9dgMhwhOXYx5vQT+SzptlZHRcGQ8nOugQNAYpKFaXMyWkF7v+hBg3qZgwTNWXKZMRvzWychihe9Xp/jlPDAHef6L8oZ60Tx4+7uzqJFi+jatStffPEFHTt2pH379nffUTGjmM3WQML3kQFZtilODSQc9Z4u1rMwlUaZYyFiLrUgOXZmtuABID31l5vBg4XMH/4qlc4aPGhdW3H0LyNLP8z4wh830psa1bPOOqAQc7ktKfFL8S3/JXrv14As29UB1jncE29MwWKKJDX+I9IS87dauBAi/1QqVUYgEbTCYWWa08+SdCM01+ABYOH7iUREWqhSScNbr3tRsEkONGQPPIQofkJCQmzWo8n8OzjYtr+6EJmaN2/O66+/DsDo0aOJjY21f2fFjGI2Ers7h8/4LIHE6eGVubEjDIsh967kxY0EEEVEUUwYkr4h7mpXkmPvtjhJ7utmxNw4x5jJsZjN0PEJPc92tl1QL1Nq0hekJX4G3FqcxcPvbetjY8o3xF57jpT490lNzL1bkhCicKlUuU0w4JpD2p3HjWhcquHmPQBUbuQ03ungEQNfbs9osZwyzgc3/Z0mN7jzxAdegYvRujYCFFQq/R3zCuFMzZs358iRI5jNt4Ldffv2Ub16dRlALe4qNDSUmjVrEhkZyTvvvJP3ApQ73GTJDCQ2hhL11bv5r2QRkwCiiCREvEFSzBQs5uv5LkNRFCbNOM71iIw7h++M9rnDDw9IS1gLtw2oTL594OZdZoUSQjiPb/kv8Qn5DO+gj7OkfYqH32hU6lzGkai0uPsOxK/it7h5v5YtkEhJtTBlVkbXpRe6uXN/05sBiiqz6Tzr54kGtaaC9XFOXFyb4BP8IT4hG9B7dssxjxDFwXPPPUdSUhITJkzg33//ZcuWLaxbt46BA3OaSVCI7Nzc3Fi4cCFqtZqtW7fy3Xff5a0A9R2mulZrQKPNGC/xxNCCVbQISQBRRFw9nkKlyl//tqTY9wDY8GUKu/9nwMUF5s7ww8Pjbi+frL0gREmQObbo9n8al/JodXVw0dfPlqb3egG/it/cDCSyrl9z65pXq71tAon3P0ji8hUzIcFqhg26teCkzu0RPANmodZWzqwRoODi9iA+IRvQuT1yM/3ml6BKT0CVw6jUGS2gWl1NVOr8TyEtRGELCAhg1apVnDt3jq5du/L+++8zevRounbt6uyqiRKiadOmDBo0CIAxY8YQHR19951uBg6eDZ7MeZtGi9+jA6k17z/Kv7w0x+lfiysZRF1E9F7Po/N4irTEjaTGryNznQR7KKZznDyVTtiSjPESI972pn6dgq+2KIQouVQqV/ReL+Dq+QyGpO2kxK9CscSguu1jPTOQOH6mLp998RIAk8b44OUViGKJvlmWFleP9ujc22FM2UVK/HIspouABq2uJl6BczAZ/81YHyJ1FypZOE4Uc7Nnz7ZJa9SoERs3bnRCbURpMWLECH766SfCw8MZP348I3PrRa7WgMWMZ6OOBHadgmv5OoS/7nlrm0qF3yOvU67TWFwCKudSSPEmAUQRUqs9cffpj6vH08Rd7WD3fsnJZka/E0t6Ojz6sCsvdZc7fUKIDFkDCWPqHlxcm9vkSUtLIzR0MooCzz//LE917oKLWztMhiOYTVfR6ZvfLEttDSTS037PtnbLrUDiLIolpsjOTwghigtXV1cWLVpE586d+eabb2jaSkObmz/JND7lUbt6kB75rzVwcKvWFACLMRVQgUZT4gOHTBJAOIFGG0hAlcOY0s+TFD0V8x3mgVcUhXfDErlwyUxwkJpp433vOO5BCFE2qVSuuLo/nuO2RYsWcebMGYKCgpgyZQauHhnjJ3RuLXIpS43OrXWO27S6GkANh9RZCCFKmoYNGzJkyBAWLFjA4mNu3N+9LfUGLMG9ckNSzx1Bpdagr3pftn3UOjeqjPoe1wr1SnzgkEnGQDiR1qUaviFr8Q7+NEtq9uDg253V+Ob7FDQamDPNDx+fu3QdUGWdTUICDSHKumPHjrFs2TIAZs6cWeYX8RNClH7m5DjOz3qM6B8XYzGmErl5IpeXv8S5aS2J/vE9LMZULMY0zoyuw9mJjUk5eyDXsq59MoSr694kPfqiNW3w4MHce++9xCUksfTyPbhVagCAW/VmNsFDJs+GT5Sa4AEkgCgWtC5VrY9dPZ4jo2FIxX/n05k++ygAb77mSdPGOlw9XwSyr6aJyi/L/rfuQKq1tWyO5eLexZFVF0IUY+np6YwYMQKz2Uznzp3p0MH+rpNCCFFSGW+cIyX8ZyI+G8qZEVWJ/XUNCfvXk3p2PxGfDeP0sIrc+HY26RGnMVz6m/PTWvDv+IY5BhKJhzcTt3sFZ0bV4OpHb5AefRGdTseiRYtwcXHhhx9+YPPmzU44S+eSAKKY8fAbil+Fr0H7HKHvJJKaaqB161b075Mx+MbD9y38Kn6Dq2cP6z5+Fb7Ew28Mak15NNpK1nSf4FV4Bb6H2qWuNc3Td3CWo/ng4T8Nja4BWpeahX5uQoiitXz5co4fP46fnx8zZsxwdnWEEKLImROjMMdfy5ZmSY7lxrap2dKMV47dMZDAYiLu11UZgcTagdQM8mDEiBEATJo0iWvXrtnuU4rJGIhiIHMKx6x/hy2J58y/BgICAli8+H2CsqyUqcINT/9RePqPsqbpvbqj9+oOgJt3T2u6zu0hdG4PZTte1mMB6D07OvR8hBDOd/r0aRYuXAjAtGnTCAwMdHKNhBCi+MsMJHQVG1Ch/6rsGy0ZC8LF/W81cf9bw7MPvcJ399bl7+PhjB49mo8//rjMjFOVFohi6Ntvv+XjjzMWjlq8eDHBwSVnXmAhhPOZzWZGjhyJ0WikXbt2+Z7r3mJJIiV+NWmJm+6Sz0BSzCzirvXAkHqEpOjpmE35XzQzK0PyT8Re7Y4headDyhNCCHtkBhKmpBzWe7CYwWIiae9ahvjsRafVsHv37jI1TbAEEMXMxYsXGTUqo2Vh0KBBPPLII86tkBCixFm9ejV//PEHXl5ezJ49O893xDICh1XEXulIavxyUhNz/lLMDBxiLz+MIWkz5vR/SYoaiCF5OybDnwU6B0PyT8Rcfoqk6LFYTOcwJH9ToPKEEMLxVFT1SGdQlwcAmDx5MleuXHFynYqGBBDFSHp6OoMGDSIhIYGmTZsyevRoZ1dJCFHCnD9/njlz5gDwzjvvUKFCBbv3zR44fABK5oKXym35sgcOYHZQ7bMHDorlhsPKFUKIvNCF1KHKuF/RegbYblRrQaXGp1Vvas45zYj3NtKsWTOSkpIYOXIkiqLY7lPKyBiIYmTOnDkcPXoUHx8fli1bhouLrDYthLCfxWJh1KhRpKWl8dBDD/HSSy/ZuV8SaYkbSE34GJRUbg8YbuUzkBK3AEPSNhwZNEBG4JAcGyZBgxDCqXQhdQh5dSWedR+23ajWgmLBp1VvAp+eiC741po4Cxcu5IknnmDPnj188sknvPzyy0VY66InLRDFxM8//8zy5csBCAsLo3Ll0jNXsBCiaHz22Wfs27cPNzc35s2bZ1fXpbSkbbe1OOQcPKQlflUoLQ4WcwIxlztIi4MQolCoPfzQeGcfS6rWe1Ou8/hsaZktDjXnhOccPGRpcag4YG224AGgRo0ajB07FoDp06dz4cIFx55IMSMtEEXMYooh9uoT2dIMmk8Z/HZGpPpiN3c6PPVogY+jWFKJudwGAP9Ke1Cp3QpcphCi+Lpy5Yp1qtYxY8ZQtWrVu+yRIT3tyM3AQU1uwQNAuuEojm51ADCbr0ngIIRwKF1AVdxqtsKrWVf8271J1LZpGCPPYoz8D5+WPfF/7E1Qa4g/sB6VVk/IKytyDhoAryZdsJiMBHaZYBM03K5///58//337N+/n5EjR/LFF1+gVpfOe/USQDiZ2awwdMQ4YuMs1KmlZdQQb2dXSQhRwiiKwtixY0lKSqJZs2b069fP7n09A6aR7tGJlPjlmI3HyQgkLDb5vMpNxpjSjqTY2Shmx8ywBOCiq4Nvxe9Ijp5GetrvDitXCFF2aTz9qf7OXuvfwS/OyTFfrbD/7lpW+VdW2H1ctVrNggULePzxx9m3bx9r1qzhtddes3v/kqR0hkUlyKp1Sfy+7yBubirmTvfD1bVszB8shHCczZs3s3v3bnQ6HfPnz0ej0di9r0qlQufWAp/gj/AKfB+Nrt7NLbZfDzr31vhX/AavcotQaUIcVHvQaMrhHbQY34o/4KJ/6O47CCFEMVW1alUmTJgAwKxZszh79qyTa1Q4JIBwosNHDSxfnQTAhFHeVK8qDUJCiLyJiopi8uTJAAwfPpxatWrlq5zcAwlbhRdIBOAd9J4EEkKIEu3ll1+mdevWpKWlMWLECMxmx3f/dDYJIJwkLt7C2MlxWCzw/HNP83RHd7v3tZhNXD34NcakWAAMiTFcPfQ1isWCMSmWy/u2cHn/Fmv+63/8QFpcpMPPQQjhfBMmTCAuLo4GDRrw5ptvFri82wMJra4hWl3tHPPeCiTeQ6UpD2hw9eyDSh2AWlsx33WwDSTUaFxq5rs8IYQoSmq1mvnz5+Pp6cnhw4f58MMPnV0lh5Nb3k6gKAoTp8cRGWWhWhUNM6ZPwBj/rN37R5/ax5X9W0mLi+CeJ17j8m8biTlzCFfvQOLO/03Mqf2oNBaqV8rIf+XANhKvnKP2M8ML54SEEE7x7bff8u2336LVapk/f75Dp37ODCR0bi3umlfn/hD+7l/fSvAf6pA6ZAYSQghR0lSqVInJkycTGhrK3Llzadeu3V1biBVFIeKzoejK10XrXZ7ITWPRBlbFvWYrDFdPYEmOw/uB7vi2fgWV1rlT/UsA4QSfbkzhf3sN6HQwb4YfHh7uGOPt29diNnFl/zYAosN/x7tKA2LOHALgwp6NmFMyClLMav7b1ohKjx9HMauJv/APCVdO4V2xTmGckhCiiMXExFj72Q4aNIgGDRrkuQxTahKnts2ncusX8K58q8vS5X1bMSTcwJgYjUqtRqVxQTEZqfXMCM7uWIpP1YYEN27nsHMRQojSqGfPnuzYsYOff/6ZYcOGsX37drTaO/z0NqcTs3MJACoXPUp6GsaI06Qc22nNknx8J5GbxhHUfZZTAwnpwlTE/vr7OAuXJgAwaog3dWrl7YWPPrWP9JtdlwAu7d1kfZwZPOTm2sFv8nQsIUTxNWXKFKKioqhVqxbDhg3LVxnX//yRlKgLXNyz3rpyqiHhBtcOf0vMqX0kXT1N4uVwEi78Q+KVU1z+7Qviz//N5b1fYkpLduDZCCFE6aNSqZg7dy7e3t78+eefLFu2zO59lfS0XLeZk6K5tvZ1Tg8tT+wvH6KY0h1R3TyRAKIIJSYm8tbboZhM0O4RPS92s3/cA9wc+3Bge7Y0U3JsLrltJVw6TlLEuTwdUwhR/OzatYvNmzejUqmYP38+rq6ueS7DlJpExNEfAUi9cZn4c38BcPXQN6DkvB5E5LFfQKXCYjYS8efOHPMIIYS4pUKFCkydOhWABQsWcPLkSYeV7cxAotgHEBaLhcWLF9OmTRsaN25Mv3797ri636VLl3jjjTd44IEHeOihh5gxYwapqak55j106BD16uU+04gjKYrCmDFjuHDhEhVCNEwd52PXKrFZRZ/ahzExpgC1UHH1ZvcnIUTJlJiYyJgxYwB47bXXaNasWb7Kuf7nj1jMN79sVCou799CWnwUN078Rq4LylnMGcGFonD9jx+kFUIIIezQvXt32rdvT3p6OsOGDSM93bE/9K2BxIgqpJzee/cdHKDYBxDLli1jw4YNzJgxg40bN6JSqRgwYABGo9Emb2JiIj179iQ+Pp5Vq1axYsUKjh07xltvvWWT98CBAwwaNAiLxXbBpMKwfv16tm/fjkajYc40X7y98/bU59T6kHcK8Rf+kVYIIUqwGTNmcO3aNapWrWoNJPLKlHaz9SGzpUFRSL1xmfM/f5xr68PtpBVCCCHso1KpmDNnDr6+vhw7dowlS5YUynHMCREYI84UStm3K9YBhNFoZM2aNQwePJi2bdtSt25dFi5cSEREBDt32n5xbd26laSkJJYuXUqjRo1o2LAhCxcu5Pfff+fw4cMAmEwmZsyYQb9+/ahcuXKRnMepU6d45513ABgdOpjGDXV5LqPgrQ83qdTSCiFECbV3714+/fRTAObNm4ebm1u+yrl+NEvrQyaVisSLx8m19eF20gohhBB2Cw4O5t133wXgvffe4+D21SSf3kvcb+tIvfAnkdunc33D6HyWrsL7gRepOecMvm1ecVid76RYBxDh4eEkJyfTosWtaQS9vb2pX78+hw4dssl/7tw57rnnHvz9/a1p5cuXx8/Pj4MHDwKQkpLCsWPHWLNmDb179y70c0hNTeWNN94gLS2NRx55hDcGvprnMhzT+nCTYpFWCCFKoJSUFEJDQwHo3bs3Dz2Uv4XWbFofMtnZ8pCVtEIIIYT9nnnmGTp27IjJZGLk2Hc4M701Vz98hXNTmnNjyyRid+Z12uqbgcPcM1R6awO64BqFUu+cFOtpXK9fvw5kBAFZBQUFce3aNZv8gYGBREVFYTab0Wg0ACQlJREfH090dDSQEYBs2LABgC1bttiUkZN27W5NV5h53Kxpu3btynXfSZMmcfr0aYKCgnjvvfdQq/Mes6WnxJOebOc8rzepNBaqdzlmfayYsx83+fp/eAZXz3NdROlQkPe0cI65c+dy4cIFypcvz8SJE/NdTo6tD/l1sxUi+L72aPUejilTCCFKKZVKxaxZs9i/fz//xcTw6X9e9KuZCBZTXkvC+4EXCHr+3SINGrIq1gFE5uBnnS57lx9XV1fi421/UHfq1IkVK1Ywc+ZM69LhU6dORaVS5ThmorBt376dzz//HJVKxeLFiylXrhwWU967Ibl6BdDk9SUo5ltvMJMhxXrH0GRIwZJuQFEsKIoFS3o6CgbgbQBqdhmGi6sfWr07Gp0bqECr93TIOQohCt+RI0dYtWoVAHPmzMHLyytf5ZgNqTm3PhSAxWQg4q9dVHzwaYeVKYQQpVW5cuWYPXs2r7/+OhvPe/JQYBp1fOy5qaNC5eqOZ+NOBD8/02mBQ6ZiHUDo9XogYyxE5mMAg8GQY9/fqlWrsmTJEiZNmsRnn32GXq+nT58+NGjQAE/P/P9gzno3NvMu7d3u0J4/f57RozP6sg0ZMoQ2bdrk+/gAGp0+299atzufj2JJJeZyxmOfSnVQqfPXV1qUTvl5TwvnMBgMjBw5EkVReO6557K1FOWVgoJXxTo24xbSk+MwpSVntEwodkwsodagcdGj9w0GwNU7IN91EkKIsqZTp048WtHEz1e0zD3hy/IHotBp7raXgmJIJu38H6Rd+ksCiDvJ7LoUGRlJlSpVrOmRkZHUrVs3x33atm3Lr7/+SlRUFF5eXuj1elq1akW3bt2KpM6QEfAMGjSIpKQkHnjgAUaMGFFkxxZClC6LFi3izJkzlCtXjilTphSoLI3OjartH8BivoqL/n60LnfuxqgoCmmJG0DliUZbDrUmCK2uRpbtBowp/8NFXzTTYQshRGkxuFE6f0WpuJjswrr/vBlQK8Gu/dIj/+Xq6v543dfFaatQQzEfRF23bl08PT05cOCANS0hIYETJ05w//332+Q/cuQIvXv3xmg0EhgYiF6v5+DBg8TGxtKqVasiq/fMmTP566+/8PX15f3337/zsuVCCJGLY8eOsXTpUgDefffdbBNE5IWiKBiSfybuaheSbgwlJXYOqfGr7pg/JeETYi49TErcfFJip5EYNZjUhA9vbjeQlvgFsVe6kBQ9DkPyjnzVSwghyiofHQytFwfApgseHI+zLxjwaf0K90w57NTgAYp5C4ROp6N3796EhYXh7+9PxYoVmTdvHiEhIbRv3x6z2UxMTIy1paFGjRqcOXOGmTNn0r9/fy5dusTo0aPp0aNHkU3ZunPnTj78MONLduHChVSsWLFIjiuEKF3S09MZOXIkZrOZjh070rlz5zyXoSgKxpRfSImbj8V8/battl2VFEUhNfFTUuNWAlkX4FRubk8nLfELUuJXoVhiANXNf0Wzno4QQpQmrQINtC+fws5r7sw74ceKB6PQa3Ieo+bT+hUCn57o9K5LmYp1AAEZ4wdMJhMTJ04kLS2N5s2bs3r1anQ6HZcvX6Zdu3bMmjWLbt264evry8qVK5k1axZdunTBz8+PHj168OabbxZJXa9du8bw4cMB6N+/P0888USRHFcIUfosX76cY8eO4evra5073F53Dhxyzp9z4JBdeurvpKf+mnVP4K4dd4UQQuRiUO14/ohx5UqKlrX/evFmnexdmYpb4JCp2AcQGo2G0NBQ6/znWVWqVIlTp05lS2vcuLF1mta76datm8PGRphMJt5++21iY2Np2LAhEyZMcEi5Qoiy58yZMyxcuBCAqVOnEhQUZPe+xtT9JMe8i8VsO9V1TlKTtpASuwiUFDtyO2j6VyGEKOM0bj4oJiOeJBN6v5qxe2HrJQ8eqqDQyCux2AYOmYp9AFFSLFq0iP379+Ph4cGyZctwdXV1dpWEECWQ2Wxm5MiRGI1GHnvsMZ577jm791UUhaToaSiWSLv3SYlZAKTlo6ZCCCHyq9o7v6N29cScEEEdnxD+GD6UL776nveu1uf7zZ/jU/EeZ1fxjor1IOqSYu/evSxatAjImKP9nnuK94suhCi+1q5dy5EjR/D09GT27NmoVCq791WpVPiErEHn9pTd+/iEfIRGJ7MoCSFEUXLxLY/GzQtdcE00ek+mzV1ExYoVuXjpEnOW5j7JRXEhAUQB3bhxg8GDB6MoCj169KBr167OrpIQooS6cOECs2fPBmDixIn5moRBow3BK3AGvhW+Ruf25F3za3U18Q35BJ+QDWhc7hxIqNSZs0DJuAchhHAkLy8vwsLCAFi3bh179uxxco3uTAKIArBYLAwfPpyIiAhq1arF9OnTnV0lIcoUi8XC4sWLadOmDY0bN6Zfv35cuHAh1/zHjh2jb9++NGnShBYtWjBp0iQSEuybe7uwKYpCaGgoqamptGzZkl69ehWoPI22PF6B7+YtkCh/50DCxbUx3sEf4aJ/IPMoyAxMQgjhGA8//DAvv/wyACNHjiQxMdHJNcqdBBAFsHLlSnbv3o1er2f58uW4u7s7u0pClCnLli1jw4YNzJgxg40bN6JSqRgwYABGo9Emb2RkJK+++ipVqlRh69atLFu2jD/++IMxY8Y4oea2Pv/8c/bu3YterycsLAy12jEfz7kFEipVzp9XOQcSWkCNSu2Bi2sDvIOWZAkkFFQqD4fUVQghyrqJEydSpUoVrly5UqxvTMsg6nz6448/mDVrFpAxS0q9evnvQ2w0Hs32t8WSjtnwD6hUaF1qotb4FaiukHF302Q8hkZbAbUmoMDlCeFsRqORNWvWEBoaStu2bYGMtVfatGnDzp076dSpU7b8V65coU2bNkyePBmtVku1atXo3r27dbYjZ7p69SrTpk0DYPTo0VSrVs3hx8gMJCzm4VgsSXf9HMgMJEzGC4CCWuMJqluTQ7i4NsAlaAlm0xXUmkCH11cIIcoiDw8PFixYwPPPP89nn31Ghw4dePTRR51dLRvSApEPFouFQYMGYTKZ6NKlS4G7GiTfyH4HNO7qMyRGDSIx8k3SEtcXqGwAY9pB4iNeJSHiVVITPilweUIUB+Hh4SQnJ9OiRQtrmre3N/Xr1+fQoUM2+Zs0acKCBQusK8P/+++/bN26lYceeqjI6pwTRVEYO3YsSUlJNGnShNdee61Qj6fWlEPrUg212suu/FpdVbS6aqg15XLcR6OtiEqlc3Q1hRCizGrZsiX9+/cHYNSoUcTHxzu5RrakBSIfYmJiuHHjBlWrVmXu3Ll5miVFUXJeYTC7zD5vKhQlf/OuZz1O0o2RZMSKalBsu3YIURJdv56xQFr58uWzpQcFBXHt2p3XQHjyySc5f/48FStWZNmyZXfM265dO+vjzHKzpu3atStP9b7d1q1b2bVrFzqdjgULFqDRyABlUbgK+z0thCi4cePGsXv3bs6dO8fkyZOts30WF9ICkQ8pKSlotVqWLVuGt7e3XfsoioIhZTdx1/PSWmF/YJL1OMbU/cRHvnHbFgvycovSJDU1Y8VknS773W9XV1cMBsMd9w0LC+PTTz8lMDCQl19+meTk5EKr551ERUXxzjvvADB06FBq167tlHoIUVZcuXKFOnXq2PzbtGmTs6smRDZubm4sXLgQtVrNpk2b+PHHH51dpWykBSKfxo0bx3333WdXXkPKryTHzkEx27+4U36kpx0iOW4pZuMx8hN8CFGS6PV6IGMsROZjAIPBgJub2x33bdiwIQBLliyhbdu27Ny5k2effTbHvFnvxmbepXXUHdqJEycSFxfHvffey1tvveWQMoW4m8J8Txd3p06dwtXVlZ9++ilb7wEvL/u69AlRlJo3b87AgQNZvnw5Y8aM4f7778ff3//uOxYBuSWdD4GBgQwcONCuvBZzLEk3RhV68KBY0kiIGnYzeACwp6uUECVXZtelyMjs11ZkZCQhISE2+c+ePcuvv/6aLS0oKAgfHx8iIiIKr6K5+O677/jmm2/QaDQsWLAAFxeXIq+DEGXN6dOnqV69OkFBQQQGBlr/Zb0JIURxMmrUKGrVqkVkZKS1xbo4kAAiH9zc3Owe96DW+OEVuBi1Ju8LQuWFSq3HO2gpWtdmmUcu1OMJ4Wx169bF09OTAwcOWNMSEhI4ceIE999/v03+PXv2MHToUJKSkqxpFy9eJDY2lho1ahRJnTPFxcUxfvx4AN58800aNGhQpMcXoqw6deoUNWvWdHY1hLCbXq9n0aJFaDQatm3bxrfffuvsKgHyK7NI6Nxa4lthG16B74EquNCO4+J6Hz7BH+AdtBKNrmGhHUeI4kCn09G7d2/CwsLYtWsX4eHhDB8+nJCQENq3b4/ZbCYqKoq0tDQAnnnmGby8vAgNDeXMmTMcPnyYIUOG0KhRoyKfIm/q1KlERkZSs2ZNhg8fXqTHFqIsO336NNHR0bz00ku0atWKnj17FvsVf4W47777rN1cx44dy40bN5xcIxkDUWRUKhU6t4fwDfmYuGt3XxU2Q/66Ibnom+IT9D4xl9tkSZUVY0XpM2TIEEwmExMnTiQtLY3mzZuzevVqdDodly9fpl27dsyaNYtu3brh5+fHxx9/zOzZs+nZsycajYZ27doxduzYIp356Oeff+aLL75ApVIRFhYmXSeEKCJGo5Hz58/j5ubG6NGjcXd356uvvmLAgAGsXbuWli1b2uwjM1aJ4mLYsGHs3LmTkydPMm7cOFauXJmnWUAzOeo9LQFEEbPrxVb5gRJLxgqvBZ9f3StwCakJazEZ/si2EJQQJZ1GoyE0NJTQ0FCbbZUqVeLUqVPZ0qpXr84HH3xQVNWzkZSUZF35un///jRv3txpdRGirNHpdBw6dAitVmudva1BgwacPXuW1atX5xhACFFcuLq6smjRIjp16sSOHTvYvn17rpN/FAUJIIoBz3KLSLoxzPq3b/mtmE3/olKp0GirFbh8F9f70AWvxGQ8hVpTeF2ohBB3NnPmTK5cuULVqlWtgYQQoui4u7vbpNWuXZvffvstx/xlecYqUfw0aNCAYcOGERYWxoQJE2jVqhVBQUF5KsNR72kZA1EMuOjqZ/tbrdag09+Hi2tj1Bofhx1Hq6uDWuPrsPKEEPbbt28f69atA2Du3Lk5/pARQhSe8PBwmjRpwuHDh7OlHzt2TAZWixLj7bffpmHDhsTFxTF69Gg7Fyh2PAkghBCikKWmpjJq1CgAevXqRevWrZ1cIyHKntq1a1OrVi2mTp3K4cOHOXv2LLNmzeLPP//kjTduX3xViOLJxcWFRYsW4eLiws6dO/nyyy+dUg8JIIQQopCFhYVx/vx5QkJCmDhxorOrI0SZpFarWbFiBQ0bNmTYsGF07dqVv/76i7Vr11KnTh1nV08Iu9WtW5eRI0cCMGnSJK5evVrkdZAxEEIIUYiOHj3KypUrAZgzZw7e3t5OrpEQZZe/vz8zZ850djWEKLA333yTH374gaNHjxIaGsqnn36ar1mZ8ktaIIQQopAYDAZGjhyJxWKhW7duPP74486ukhBCiFJAq9WyaNEi9Ho9v/zyC+vXry/S40sAIYQQhWTJkiWcOnWKcuXKMXXqVGdXRwghRClSs2ZN6zTmU6dO5fLly0V2bAkghBCiEJw4cYIlS5YAMGPGDPz9/Z1cIyGEEKXNgAEDaN68OUlJSYwYMQKLpWgWDZYAQgghHMxkMjFy5EhMJhMdO3akc+fOzq6SEEKIUkij0bBgwQL0ej179+7l448/LpLjSgAhhBAO9sEHH/D333/j6+vLu+++W6QD24QQQpQt99xzDxMmTAAyWrzPnz9f6MeUAEIIIRzo33//Zf78+QBMnjw5z6uECiGEEHn1yiuv0LJlS1JTU4ukK5MEEEII4SAWi4VRo0ZhMBh49NFH6d69u7OrJIQQogxQq9UsWLAAd3d3Dhw4wJo1awr3eIVaugNYLBYWL15MmzZtaNy4Mf369ePChQu55r906RJvvPEGDzzwAA899BAzZswgNTU1W57vvvuOjh070rBhQ7p06cL//ve/wj4NIUQZ8NFHH3Ho0CE8PDyYM2eOdF0SQghRZKpUqcI777wDwKxZszh79myhHavYBxDLli1jw4YNzJgxg40bN6JSqRgwYABGo9Emb2JiIj179iQ+Pp5Vq1axYsUKjh07xltvvWXNs3//fkJDQ3nppZfYtm0brVu35q233irUJ1kIUfpdvHiRWbNmATBhwgQqVqzo5BoJIYQoa/r06UObNm1IS0tj+PDhmM3mQjlOsQ4gjEYja9asYfDgwbRt25a6deuycOFCIiIi2Llzp03+rVu3kpSUxNKlS2nUqBENGzZk4cKF/P777xw+fBiADz/8kPbt29O7d29q1KjBmDFjuPfee1m3bl1Rn54QopRQFIXRo0eTkpJCixYt6NOnj7OrJIQQogxSqVTMnz8fLy8vjhw5wsqVKwvlOMU6gAgPDyc5OZkWLVpY07y9valfvz6HDh2yyX/u3DnuueeebPOtly9fHj8/Pw4ePIjFYuGPP/7IVh7Agw8+aA0whBAirzZs2MCePXvQ6/XMmzcPtbpYf7QKIYQoxSpWrMiUKVMAmDdvHqdPn3b4MbQOL9GBrl+/DmQEAVkFBQVx7do1m/yBgYFERUVhNpvRaDQAJCUlER8fT3R0NAkJCaSkpBASEmJXeZnatWtnfZyZL2varl278nhmQjiXvKcd59q1a9ZVpkNDQ7nnnnucXCMhhBBl3Ysvvsi3337L7t27GT58ONu3b0erddzP/mJ9myxz8LNOp8uW7urqisFgsMnfqVMn4uPjmTlzJsnJySQkJDB58mRUKhVGo5G0tLQ8lSeEEHeiKArjxo0jMTGRJk2aMGDAAGdXSQghhEClUjF37lx8fHz4888/WbZsmUPLL9YtEHq9HsgYC5H5GMBgMODm5maTv2rVqixZsoRJkybx2Wefodfr6dOnDw0aNMDT0xNXV1dreVnlVl6mrHdjM+/Syh1aUZLJe9oxtm/fzs6dO3FxcWH+/PnWlk8hhBDC2cqXL8+0adMYOnQoCxYs4PHHH6d+/foOKbtYt0Bkdl2KjIzMlh4ZGWnTDSlT27Zt+fXXX9mzZw/79+9n5MiRXLp0iWrVquHr64u7u3ueyhNCiJzcuHGDiRMnAjB06FDq1Knj5BoJIYQQ2T333HM8+eSTpKenM2zYsBxnMc2PYh1A1K1bF09PTw4cOGBNS0hI4MSJE9x///02+Y8cOULv3r0xGo0EBgai1+s5ePAgsbGxtGrVCpVKRdOmTTl48GC2/Q4cOECzZs0K/XyEEKXHO++8Q2xsLPXq1cs2VbQQQghRXKhUKmbPno2vry/Hjx9n8eLFDim3WAcQOp2O3r17ExYWxq5duwgPD2f48OGEhITQvn17zGYzUVFR1rENNWrU4MyZM8ycOZNLly7x+++/M2LECHr06EHlypUBePXVV/n2229Zu3YtZ8+eZe7cuZw8eZK+ffs681SFECXI999/z1dffYVGo2HBggU246qEEEKI4iIoKIiZM2cCsGTJEv75558Cl1msAwiAIUOG8PzzzzNx4kR69uyJRqNh9erV6HQ6rl27RuvWrdmxYwcAvr6+rFy5kvDwcLp06cKECRPo0aOHtZsBQOvWrZk5cybr16+na9eu7N+/nxUrVlCjRg1nnaIQogSxWCyMGzcWgFd6BVCr2t9OqUe6MZy4ay8RfbE5aYmb8rav4S9ir71C9MXWRF9sTmrC+kKqpRBCiOLg6aefplOnTphMJoYNG1bgyYOK9SBqAI1GQ2hoKKGhoTbbKlWqxKlTp7KlNW7cmA0bNtyxzGeffZZnn33WkdUUQpQRsbFRREXFUq2KhoGvqjGbLhXp8dON4SRHT8Ocfmteb1P6efv2NfxFUsxCLOnHsqWb0886sopCCCGKGZVKxaxZs9i/fz/h4eEsWLCgQOUV+xYIIYQoTpKTDahUMHW8L3q9S5EdN7PFIeF672zBg1373mxxSIjobxM8CCGEKBsCAgKYPXs2AMuWLSvQgGoJIIQQIo96Pu9Ok8ZFM+5BAgchhBCO0rFjR7p27YrFYiE6Ojrf5UgAIYQQeaBzUTHkDa8iOVZa4pf5ChwAUuJWSOAghBDCxvTp0wkKCiI9PT3fZRT7MRCljVrrT0CVwzbpOaUVhErt5vAyhRAQFKjG3b1o7r1odA1B5QVKYp731bo2BpU/KDGFUDMhhBAllZ+fH3PnzmXQoEH5LkNaIIQQophyca2Df6VduPuNAZVnnvbVubXEv9K3ePiPz/O+QgghSrf27dsXaBFlCSCEECJPVNz66FQK/2gqNW5e3fGvtDvPgYRK5YLesxv+lXZKICGEECKbgqxhJAGEEELkgVobjKvnc4AGMBfZcSWQEEIIUVzIGAghhMgTDZ7+Y3Dz7kta4hfo3FoW6dEzAwm953OkJW3GkLQNV/cn7Nw3I5Bw9eiCIflrjCn7MZsvoPPoXMi1FkIIUZpIACGEEPmg0Ybg4TfEacfPDCTcvLrnY9+MQELv2a0QaiaEEKK0ky5MQgghhBBCCLtJACGEEEIIIYSwmwQQQgghhBBCCLtJACGEEEIIIYSwmwQQAoB27drRrl07Z1dD5JO8fkVPnnPnkOddFBfyXnQMeR4LzhnPoQQQQgghhBBCCLtJACGEEEIIIYSwm0pRFMXZlShJGjZsiNlspnz58s6uikNdu3YNoNSdV1lRkNfv2rVraDQa/vnnH0dXq9TJev3LNeMc8rw7llz/9rv9+1/ei44hz2PB5fc5LMj1Ly0QeeTq6opWK+vvidJDq9Xi6urq7GqUCHL9i9JGrn/7yfUvSpuCXP/SAiEArINvdu3a5eSaiPyQ16/oyXPuHPK8i+JC3ouOIc9jwTnjOZQWCCGEEEIIIYTdJIAQQgghhBBC2E26MAkhhBBCCCHsJi0QQgghhBBCCLtJACGEEEIIIYSwmwQQQgghhBBCCLtJACGEEEIIIYSwmwQQAovFwuLFi2nTpg2NGzemX79+XLhwwdnVErdZtmwZffr0yZZ28uRJevfuzX333ccjjzzC6tWrs22X19bx5DktGnFxcUyaNImHH36Ypk2b0rNnTw4fPmzdfrf3vhCFST4H8kauZ8c7d+4cTZo0YcuWLda0onweJYAQLFu2jA0bNjBjxgw2btyISqViwIABGI1GZ1dN3PTRRx+xePHibGmxsbG8+uqrVKtWjc2bNzN48GDee+89Nm/ebM0jr63jyXNaNEaMGMFff/3FggUL+PLLL7n33nvp378/Z8+eteu9L0Rhks+BvJHr2bHS09MZNWoUKSkp1rQifx4VUaYZDAalSZMmyueff25Ni4+PVxo1aqR88803TqyZUBRFuX79utK/f3/lvvvuU5566imld+/e1m0rVqxQ2rRpo6Snp1vT5s+frzz55JOKoshrWxjkOS0a58+fV2rXrq0cOXLEmmaxWJT27dsrixYtuut7X4jCJJ8DeSPXs+PNnz9f6dOnj1K7dm1l8+bNiqLc/TeBo0kLRBkXHh5OcnIyLVq0sKZ5e3tTv359Dh065MSaCYDjx4/j4+PDV199RePGjbNtO3z4MM2bN0er1VrTWrRowblz54iOjpbXthDIc1o0/Pz8WLlyJQ0aNLCmqVQqFEUhPj7+ru99IQqTfA7kjVzPjnXo0CE2btzInDlzsqUX9fMoAUQZd/36dQDKly+fLT0oKIhr1645o0oii8cee4z58+dTuXJlm23Xr18nJCQkW1pQUBAAV69elde2EMhzWjS8vb1p27YtOp3Omvbdd99x8eJFWrdufdf3vhCFST4H8kauZ8dJSEhg9OjRTJw40eb9V9TPowQQZVxqaipAtgsbwNXVFYPB4IwqCTulpaXl+LoBGAwGeW0LgTynznHkyBHGjx9Pu3bteOyxx+763heiMMnnQMHI9Zx/U6ZM4b777qNLly4224r6eZQAoozT6/UANgO/DAYDbm5uzqiSsJNer8/xdQNwd3eX17YQyHNa9H766Sf69+9Po0aNWLBgAXD3974QhUk+B/JPruf827ZtG4cPH2bKlCk5bi/q51ECiDIuswksMjIyW3pkZKRNU5goXkJCQnJ83QCCg4PltS0E8pwWrU8//ZTBgwfz8MMP8+GHH1p/uN3tvS9EYZLPgfyR67lgNm/eTHR0NI888ghNmjShSZMmAEyePJlOnToV+fMoAUQZV7duXTw9PTlw4IA1LSEhgRMnTnD//fc7sWbibpo3b86RI0cwm83WtH379lG9enUCAgLktS0E8pwWnc8//5zp06fTq1cvFi1alK1p/m7vfSEKk3wO5J1czwUXFhbGjh072LZtm/UfwJAhQ1i5cmWRP48SQJRxOp2O3r17ExYWxq5duwgPD2f48OGEhITQvn17Z1dP3MFzzz1HUlISEyZM4N9//2XLli2sW7eOgQMHAvLaFgZ5TovGuXPnmDlzJu3bt2fgwIFER0cTFRVFVFQUiYmJd33vC1GY5HMgb+R6dozg4GCqVq2a7R9AQEAAFStWLPLnUXv3LKK0GzJkCCaTiYkTJ5KWlkbz5s1ZvXq1zWAcUbwEBASwatUq3n33Xbp27UpgYCCjR4+ma9eu1jzy2jqePKeF74cffiA9PZ2dO3eyc+fObNu6du3K7Nmz7/reF6IwyeeA/eR6Lhr2/CZwJJWiKEqhlCyEEEIIIYQodaQLkxBCCCGEEMJuEkAIIYQQQggh7CYBhBBCCCGEEMJuEkAIIYQQQggh7CYBhBBCCCGEEMJuEkAIIYQQQggh7CYBhBBCCCGEEMJuEkAIIYQQQggh7CYBhBBCCCGEEMJuEkCIYuPixYvcd9999OrVC1kgXYiyRa5/Icouuf5LHgkgRLGgKArjx4+nb9++pKam8umnnzq7SkKIIiLXvxBll1z/JZMEEKJY+Pjjj9FqtQwdOpRFixaxcuVKLl265OxqCSGKgFz/QpRdcv2XTCpF2oqEEEIIIYQQdpIWCCGEEEIIIYTdJIAQQgghhBBC2E0CCOFwK1asoE6dOpw4ccJm2+jRo6lfvz6//PJLtvTU1FTef/99nnrqKRo2bMiDDz7IiBEjiI+PL6JaCyEcQa5/Icouuf7LDgkghMP17t0bb29vVqxYkS39vffeY/v27UycOJFHHnnEmq4oCiNGjGDdunX06NGDDz/8kEGDBrFjxw7WrFlTxLUXQhSEXP9ClF1y/ZcdEkAIh/P09KR3797s3LmTs2fPArB582aWLVvGa6+9xksvvZQt/8GDB9m9ezfTp0/nlVdeoUWLFvTt25eAgABSU1OdcQpCiHyS61+Iskuu/7JDAghRKPr27Yter2flypX8/vvvTJ48mQ4dOjBq1CibvN9//z0+Pj60b9/emnbo0CFu3LhBixYtirLaQggHkOtfiLJLrv+yQevsCojSydfXl549e7Ju3Tp++uknGjVqxNy5c1GpVDZ5jx49SqNGjQCIiIjgt99+Y968ebRs2ZJHH320qKsuhCgguf6FKLvk+i8bpAVCFJrOnTtjMplwd3dn2bJl6HQ6mzwGg4EzZ87QuHFjVqxYwcMPP8z48eNxc3Nj9uzZ2T5wNm7cSJcuXWjatCktW7Zk0KBB1m2XL1+mXr16pKamcvnyZerUqUOTJk1o3LgxHTt2ZN++fUVyzkKIDHL9C1F2yfVf+kkAIQpFUlIS48aNAyA2Npa0tLQc8x0/fhyTyUSjRo14+umnWb16NYMHDyY5OZlevXqRnJwMwKeffsr69etZtGgRf/zxB9u2bePxxx+3lhMeHk7VqlVxc3MjPDycmjVrcvToUf7880+efPJJ3nnnncI/aSEEINe/EGWZXP9lgwQQwuHS09MZPHgwly5dYunSpSiKwqpVq3LM+/fffwPQsGFDKleuTOvWrXn77beZPHkyly9f5q+//gJgy5Yt9OrVixo1agAQHBxMt27drOWEh4dTt25dm8cqlYpmzZoRGxtbaOcrhLhFrn8hyi65/ssOCSCEw02cOJGDBw/y3nvv8fjjj/P000+zadMmoqOjbfL+888/VK5cGX9//xzLCgoKAsDV1ZX169fz008/We9KZHXq1Cnrh8apU6eoV68ekNGn8oMPPuCJJ55w1OkJIe5Arn8hyi65/ssOCSCEQy1cuJBt27YxZcoU2rRpA8DAgQNJT0/no48+ssn/999/ExgYmC1NURQ2bdpE7dq1qVmzJgBhYWHUr1+fSZMm0bJlSyZMmIDRaLTuc/sdiKVLl9KkSRMefvhhqlWrxtSpUwvpjIUQmeT6F6Lskuu/jFGEcJANGzYotWvXVhYuXGizbfjw4UrTpk2V+Ph4a1psbKxSu3ZtpXbt2sr48eOV33//Xdm1a5cyaNAgpUGDBsoff/xhU47FYlF+//13pXHjxsrWrVsVRVGUpKQkpU6dOsq1a9esj6OjoxWz2ax8+OGHytNPP11YpyyEuEmufyHKLrn+yx5pgRAO8euvvzJ16lSefvpphg0bZrP9jTfeIDk5mY8//tialtn/8dlnn2XPnj0MGDCA6dOno9Fo2LRpE02aNLEpR6VS0bJlSwIDA60Ds06dOoWPjw8hISGcOnWKcuXK4e/vj1qt5uWXX+bKlSscP368cE5cCCHXvxBlmFz/ZZOsAyEcom3btpw4cSLX7bVr1yY8PDxb2t9//42LiwvTpk3D1dU1130/+OADWrRoQf369TEajXz88cckJCRYZ2EIDw+nTp061se1a9e27qvT6XjwwQf5+eefuffeewtyikKIXMj1L0TZJdd/2SQtEMJp/vnnH2rXrn3HDw+A+Ph4Ro0axQMPPMCTTz5JeHg4GzdupFy5coBt/8datWpl279Vq1b88ssvhXIOQoj8ketfiLJLrv+ST6UoiuLsSoiyqVWrVrRv314GOAlRBsn1L0TZJdd/yScBhBBCCCGEEMJu0oVJCCGEEEIIYTcJIIQQQgghhBB2kwBCCCGEEEIIYTcJIIQQQgghhBB2kwBCCCGEEEIIYTcJIIQQQgghhBB2kwBCCCGEEEIIYTcJIIQQQgghhBB2kwBCCCGEEEIIYTcJIIQQQgghhBB2kwBCCCGEEEIIYTcJIIQQQgghhBB2kwBClEqKyYhiNjm7GsKJFMWMoqQ7uxrCDha5VksFxWws0v2EEM6jUhRFcXYlhCgMxsj/0AXdg2I2odJonVqXPn36ULFiRWbPnm2zbezYsVy5coVPPvmExx57jCtXrli3ubi4UK5cOR577DEGDx6Mn5+fddvtedVqNR4eHtSrV4+hQ4dy//33W8vfunVrrnXbuHEj9913nwPOsnhQFDMqlQZz+hU0LhUdXv5jjz1G165dGTx4sM22JUuW8P7771v/VqvVeHl50aBBAwYNGmR9TXLKC+Dq6krFihV59tlnef3111GpVBw4cICXX3451/q8+OKLTJs2zQFn5hyKxYxKrbH+XxTu9hpu3bqV3bt357jv5cuXadeuXbY0vV5P5cqVefbZZ3nllVfQarW55tVoNPj7+9O8eXNGjx5N+fLlrXXKej1npdPp+Oeff/J8nkVNsZgwJV4i8tseYM9PC5WKoE4b0HpVRqV2/Gd05nM6duxYXn31VZvtkyZNYuPGjbz99ts88MAD2a4zlUqFm5sb1atXp2fPnnTv3t26LadrUqvVEhgYSOvWrRk9ejTe3t4A1KlTJ9f6Va9ene+//76gpymEUzj3V5UQhejy0u5oPMsR9Nx03O55oFgEEvbo168f/fr1AyAtLY3Tp08zb948Dh06xPr16/H09Mwxr6IoxMXFsWDBAl577TW+//57QkJCAGjSpAlLlizJ8Xi+vr6Fe0JFxBo4GMNJiV+OxRyHb/lPi7weISEhfPnllwCYzWaioqL47LPP6Nu3LytWrKBNmzY55gUwGAz8+uuvzJgxA51Ol+1Hz6ZNm6w/NrNyc3MrxLMpfCq1hutHfyCkyZPOrkqeLFmyhCZNmqAoCklJSezfv5/58+dz+vRp5s6dm2NeAIvFwqVLl5gwYQIDBw5k+/btqFQqIPv1nFXm9uJOpdbi4lMdjVs5Ui/svGt+t6rtcfGpXqh1cnFx4fvvv7cJIEwmEz/++KPNc5t5nVksFhISEti9ezdTp07l6tWrDB06NMe8kHGtnzp1irFjx3Ljxg1WrFhhzTd+/Hg6duxoUzeNpmgCZiEKQ/H/NSVEfikKycd+5NyxH/Fo8ESJCSTc3d0JDAy0/l25cmXq1atHp06dWL16dbYvsdvzBgUFMXXqVB5++GF+/PFH610yFxeXbPlKk9sDh/S0/QBoXHK/81eYNBpNtuc6JCSE2bNnExcXx5QpU/jhhx+sd6hvzwvQq1cvdu3axVdffZXtR4+/v3+pew0Vi5nkyAtc2rMRz/K18AiqWmStEAXl4+NjfT2CgoK45557KFeuHIMHD6Zr1660bNkyx7wAwcHBvP3224waNYpTp05Rt25dwPZ6LokUiwm/llPsCiD8Wk5BsZgKpfUhU8uWLdmzZw/Xrl3LFoDv378fd3d3mwA863UWHBxMrVq10Ol0zJs3j2eeeYZq1arlmBcyrvW+ffuyaNEiEhMT8fLyAsDLy6vEv65C3E7GQIgyIfnYj5yb+iAX5j1J2oU/AErUGIkKFSrQvn17vvnmm7vmzfxxqtPpCrtaTqUoZgDMxnASIt8mPqKvNXgojvr27cvly5f5888/75pXo9GU+tcPMlofruzP6F53Zf/WEhM85KZ9+/ZUqFDBrus08+5zaXudVWotriHNcava/o753Kq2xzWkeaEGDwCNGjWiQoUKNl2FduzYQYcOHexq3XnhhRdwcXFhx44dd82r0WhQqVTWz2EhSisJIESZUpIDidq1a3Px4kWSk5NzzRMREcG0adNwd3fn4YcfLsLaFZ2SFjhkyuwLHR4enmuetLQ0tmzZwt69e3nqqaeKqmpOoVjMJF3/j4SLxwFIuHicpOv/oVjMTq5Z/qlUKmrVqsXJkydzzWOxWDh58iTLly+nXr162e5olxaZrRB3ktn6UBQ6dOiQLYAwGo389NNPdOrUya79PTw8qFSp0h2vXZPJxOHDh/n4449p27Ztie9aKMTdSIgsyiRndG36+uuv+eGHH2zSjUYjTZs2vev+mYPykpKS8PDwAOCDDz5gzZo1QMYXmNFopEaNGixatIgKFSpY9z18+LC1D3ZWderUYcOGDfk6n6KWW1elkiKzO0NiYqI17erVq9lel5SUFLy8vOjbty99+/bNtn/nzp1zvFu6YcOGOw7ULK6ytj5kurJ/K3WeHemkGjmGl5cX58+fz5Y2YMAAa4uD0WhEURTuv/9+pk+fjlp96z5e1us5q5deeonQ0NBCrbcjZW2FyKkrU2brQ1Hp0KEDq1evtnZj2rt3L35+ftSvX9/uMry8vLJdu5D9mkxLS0Oj0dC2bVubSQ0mT57M9OnTbcocNWoUvXr1yscZCeF8EkCIMi0zkAjuOZ+Ap0ZgMRlRawunS8Fjjz3GqFGjbNLDwsKIi4u76/6ZX15ZB1H36NGDPn36ABkz/vj6+lp/qGbVoEEDwsLCbNJLSvcJRUlHpXIhNeEzUuIWOrs6+ZL5+mV9fYKCgvjkk0+AjLvXer2ewMDAHAOFlStXEhwcbJOe08Dq4i5z7ENm60OmzFaIkjQW4nZJSUnZrlGAGTNm0LhxYyCji2FAQAB6vd5m36zXc1Y5XdPF3Z3GQhTF2IesGjRoQOXKla2DqXfs2EHnzp3zVEZSUhJBQUHZ0rJekzqdjoCAgBw/U4cMGcITTzxhk+7v75+nOghRnEgAIcq021sgCit4gIxm8KpVq+aYbk8Acfz4capVq2ZtfYCMwZk5lXk7vV5vV77iSqVyQVHMuHn3wsX1vhLZAnH8eMaP5ax3PbVard2vS4UKFahUqVKh1K2o5dT6kKkkt0IoisKJEyd45JFHsqUHBwfb9Trbez2XBLm1QhR160OmzG5ML730Ert27WLTpk1275ucnMz58+dtgg57r8mAgIBS87oKkUnGQIgyyaPBE1SffICqoT+gr5rRfag4z8x0/fp1du3aRZcuXZxdFadRqTLuSGt0dfEOeh+f4HW46Fs4uVb2++yzz6hcuXKpWnMjP24f+3C7kjwWYufOnURGRpbp6zSrnMZCFOXYh6w6dOjAX3/9xZdffknlypWpUaOG3ft+8cUXWCyWHKdiFaKsKr6/mIQoBLe3OEDxCxxSUlKIiooCMvrVnjp1ikWLFlGpUqUcF0OyR3p6urXM23l4eODu7p7v+ha12wMJk+F4kbdIXLhwgf/973/Z0lxdXYFbaz9AxoDZiIgINm7cyJ49e/jggw+y9XnPi5iYGOsxsspcmKykuFPrQ6aiaIW402uYlpZmsw2gYcOG1sfx8fFERUVZ14E4ePAg8+fP59lnn+WBBx7IV52yXvu38/X1xcXFJV/lOsvtrRDOan0AqFevHlWrVmXBggUMHDgw13yZ15miKMTHx/PLL7+wePFi3njjDapUqZKvYycmJub6ugYEBOT7M0EIZypev5yEKCQlIXDItGbNGutASnd3d0JCQnjiiSfo169ftu5LeXH06FFat26d47aRI0fy+uuv57u+zuLMQOLrr7/m66+/zpYWHBxM9+7duX79uvW51mq1lCtXjsaNG7N+/XoaNWqU72NmXQk3q5K0mm1uYx9uVxRjIe70GkZHRzNgwACbfdauXWv9EZl1FWtfX1+qVKnCiBEjePHFF/Ndp6zX/u1K6orxWcdCFPXYh9t16NCB5cuX37ElIfM6U6lU+Pn5UbNmTebMmVOg1oeZM2cyc+bMHLf99ttvskaEKJFUimLPevNClDz/TW5WIleiFvmTOUtTZiDhrJWoxZ2d2jb/rgEEgHeVe0vsWAhhK/6PRfg0HebsagghHEQCCFFqGSP/Qxd0jwQOZYx1utf0K2hcKjq7OuImxWLGkBjD2R3LAHu+dlTU6DgIVy//Ejsjk8iQ2ergzNYHIYRjSQAhSiXFZASVWgKHMixjwTkLKlXJ6jdeWlnMJtT5uB7zu58oXhSzEZWmZEwbLYS4OwkghBBCCCGEEHaTof9CCCGEEEIIu0kAIYQQQgghhLCbBBBCCCGEEEIIu0kAIYQQQgghhLCbBBBCCCGEEEIIu0kAIYQQQgghhLCbBBBCCCGEEEIIu0kAIYQQQgghhLCbBBBCCCGEEEIIu0kAIYQQQgghhLCbBBBCCCGEEEIIu0kAIYQQQgghhLCbBBBCCCGEEEIIu0kAIYQQQgghhLCbBBBCCCGEEEIIu/0fJa0fgvgNLQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using the first 3 principal components for SIR\n",
    "fpcs = 3\n",
    "# make a DataFrame to record our correlation and fit results\n",
    "df_SIR = pd.DataFrame()\n",
    "\n",
    "# setting up the loop information\n",
    "# checking correlation with the following\n",
    "plot_list = [\"Density\", \"Crystallinity\", \"SCB_per_1000C (IR)\"]\n",
    "# grab standard error/standard deviation where appropriate\n",
    "err_list = [\n",
    "    (None, \"Density SEM\"),\n",
    "    (None, \"Density SEM\"),\n",
    "    (None, None),\n",
    "    (None, None),\n",
    "    (None, \"SCB_per_1000C StDev (IR)\"),\n",
    "    (None, \"SCB_per_1000C StDev (IR)\"),\n",
    "]\n",
    "\n",
    "# note: this is set up to test correlations with either PE or PP\n",
    "ds_sub = ds.where(ds[\"Class1\"] == \"PE\", drop=True)\n",
    "sir_coeffs = np.zeros((len(plot_list), fpcs))\n",
    "\n",
    "fig, axes = plt.subplots(int(len(plot_list)/len(corr_list)), len(corr_list),\n",
    "                         figsize=(8, 4))\n",
    "# loop through our property, fPC pairs\n",
    "for i, corr in enumerate(plot_list):\n",
    "    # grab the SEM/StDev data if available\n",
    "    corr_yerr = err_list[i][1]\n",
    "    # make sure plots go on the axis we expect\n",
    "    ax = axes.flatten()[i]\n",
    "    # set x and y and y_err\n",
    "    y = ds_sub[corr].values.flatten()\n",
    "    x = ds_sub.fPCs[:, 0:fpcs].values.reshape(len(y), fpcs)\n",
    "    if corr_yerr:\n",
    "        yerr = ds_sub[corr_yerr].values.flatten()\n",
    "    else:\n",
    "        yerr = None\n",
    "    # set the colors and shapes of the scatter points\n",
    "    col_vec = pd.Series(ds_sub[color_scheme].values).map(\n",
    "        cpalette[color_scheme])\n",
    "    shape_vec = pd.Series(ds_sub[color_scheme].values).map(\n",
    "        dict_shapes[color_scheme])\n",
    "\n",
    "    # here is the actual SIR stuff\n",
    "    sir = SlicedInverseRegression(n_directions=1)\n",
    "    sir.fit(x, y)\n",
    "    coeffs = sir.directions_\n",
    "    x_new = sir.transform(x).T.reshape(-1, 1)\n",
    "\n",
    "    # add in a predicted fit\n",
    "    reg = LinearRegression().fit(x_new, y)\n",
    "    x_model = np.linspace(x_new.min() * 0.9, x_new.max()\n",
    "                          * 1.1, 50).reshape(-1, 1)\n",
    "    y_model = reg.predict(x_model)\n",
    "    score = reg.score(x_new, y)\n",
    "    # save it to dataframe for model comparisons\n",
    "    r_corr, _ = pearsonr(x_new[:, 0], y)\n",
    "    r2 = reg.score(x_new, y)\n",
    "    df_smallSIR = pd.DataFrame(\n",
    "        {\"polymer\": [\"PE\"], \"Property\": [corr],\n",
    "            \"r_corr\": r_corr, \"r^2\": [r2]}\n",
    "    )\n",
    "    df_SIR = pd.concat([df_SIR, df_smallSIR], ignore_index=True)\n",
    "    sir_coeffs[i] = sir.directions_\n",
    "\n",
    "    # add the data\n",
    "    # we have to manipulate the plotting order so fewer points get covered up\n",
    "    order = np.argsort(ds_sub[color_scheme].values)\n",
    "\n",
    "    # plot the data\n",
    "    myscatter=ax.scatter(x_new[order], y[order],\n",
    "                    color=col_vec.values[order])\n",
    "    fix_markers(myscatter, shape_vec.values[order])\n",
    "    # add error bars\n",
    "    if yerr is not None:\n",
    "        ax.errorbar(\n",
    "            x_new[order],\n",
    "            y[order],\n",
    "            yerr=yerr[order],\n",
    "            fmt=\"none\",\n",
    "            ecolor=col_vec.values[order],\n",
    "        )\n",
    "    # show the SIR model\n",
    "    ax.plot(x_model, y_model, \"k\")\n",
    "\n",
    "    # adjust the axes for all of the plots\n",
    "    ax.tick_params(\n",
    "        bottom=True,\n",
    "        top=True,\n",
    "        left=True,\n",
    "        right=True,\n",
    "        labelbottom=True,\n",
    "        labeltop=False,\n",
    "        labelleft=True,\n",
    "        labelright=False,\n",
    "        direction=\"inout\",\n",
    "    )\n",
    "    ax.set_ylabel(axis_dict[corr])\n",
    "    ax.set_xlabel(r\"$X\\hat\\beta_{SIR}$\")\n",
    "    ax.set_xlim(x_model.min(), x_model.max())\n",
    "\n",
    "\n",
    "# custom legend\n",
    "leg_elems = [\n",
    "    Line2D(\n",
    "        [0],\n",
    "        [0],\n",
    "        marker=dict_shapes[color_scheme][key],\n",
    "        color=\"w\",\n",
    "        markerfacecolor=cpalette[color_scheme][key],\n",
    "        label=key,\n",
    "        markersize=10,\n",
    "    )\n",
    "    for key in ['HDPE', 'LDPE', 'LLDPE', 'MDPE']\n",
    "]\n",
    "fig.legend(\n",
    "    handles=leg_elems,\n",
    "    loc=\"center\",\n",
    "    bbox_to_anchor=[0.5, 0],\n",
    "    ncol=len(leg_elems),\n",
    "    frameon=False,\n",
    ")\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bde33c66-fd31-4753-afc3-3daaa3921d20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>polymer</th>\n",
       "      <th>Property</th>\n",
       "      <th>r_corr</th>\n",
       "      <th>r^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PE</td>\n",
       "      <td>Density</td>\n",
       "      <td>0.887727</td>\n",
       "      <td>0.788059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PE</td>\n",
       "      <td>Crystallinity</td>\n",
       "      <td>0.866393</td>\n",
       "      <td>0.750637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PE</td>\n",
       "      <td>SCB_per_1000C (IR)</td>\n",
       "      <td>-0.879952</td>\n",
       "      <td>0.774316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  polymer            Property    r_corr       r^2\n",
       "0      PE             Density  0.887727  0.788059\n",
       "1      PE       Crystallinity  0.866393  0.750637\n",
       "2      PE  SCB_per_1000C (IR) -0.879952  0.774316"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SIR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab65ebe2-bca7-4922-a2fb-3321a624da0f",
   "metadata": {
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   },
   "source": [
    "## Feature Importance Ranking Measure\n",
    "\n",
    "To determine the relative contributions of the fPCs to the properties, we can make use of the sliced inverse regression coefficients. One challenge is that the fPCs are correlated and thus the common technique (denoted Isimp here) is incorrect. To fix this, we use the Feature Importance Ranking Measure as discussed in [Zien, Alexander, Nicole Krämer, Sören Sonnenburg, and Gunnar Rätsch. “The feature importance ranking measure.” *arXiv preprint arXiv:0906.4258* (2009)](https://arxiv.org/abs/0906.4258). Isimp is computed via equation 3 therein, while FIRM is calculated via $\\mathbf{Q} = \\mathbf{D}^{-1} \\mathbf{\\Sigma} \\mathbf{w}$ as described in the text after equation 12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "566f894c-95cc-43c8-b3e6-61b78d2b62ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Density\n",
      "Isimp [0.48310662 0.43716478 0.0797286 ]\n",
      "Ifirm [0.40600964 0.42319686 0.1707935 ]\n",
      "\n",
      "Crystallinity\n",
      "Isimp [0.22327124 0.56580185 0.21092691]\n",
      "Ifirm [0.36881638 0.43890243 0.19228119]\n",
      "\n",
      "SCB_per_1000C (IR)\n",
      "Isimp [0.10765276 0.61229239 0.28005485]\n",
      "Ifirm [0.45901945 0.44594743 0.09503312]\n"
     ]
    }
   ],
   "source": [
    "ds_pe = ds.where(ds.Class1 == 'PE', drop=True)\n",
    "\n",
    "pc0 = ds_pe.fPCs.where(ds_pe.fPCA_loading == 0,\n",
    "                       drop=True).values.flatten()\n",
    "pc1 = ds_pe.fPCs.where(ds_pe.fPCA_loading == 1,\n",
    "                       drop=True).values.flatten()\n",
    "pc2 = ds_pe.fPCs.where(ds_pe.fPCA_loading == 2,\n",
    "                       drop=True).values.flatten()\n",
    "\n",
    "pcvals = np.array([pc0, pc1, pc2])\n",
    "pcvar = np.var(pcvals, axis=1)\n",
    "\n",
    "\n",
    "Sigma = np.cov(pcvals, ddof=0)\n",
    "\n",
    "D = np.diagonal(Sigma)\n",
    "D = np.sqrt(D)\n",
    "Dinv = np.diag(1/D)\n",
    "\n",
    "\n",
    "# The following line assumes that pcs are uncorrelated. Use this *only* for Isimp\n",
    "SigmaSimp = np.diag(np.diagonal(Sigma))\n",
    "\n",
    "for i, pair in enumerate(plot_list):\n",
    "    print()\n",
    "    print(pair)\n",
    "\n",
    "    Isimp = np.dot(SigmaSimp, sir_coeffs[i])\n",
    "    Isimp = np.abs(np.dot(Dinv, Isimp))\n",
    "    Isimp = Isimp/np.sum(Isimp)\n",
    "    print(\"Isimp\", Isimp)\n",
    "\n",
    "    Ifirm = np.dot(Sigma, sir_coeffs[i])\n",
    "    Ifirm = np.abs(np.dot(Dinv, Ifirm))\n",
    "    Ifirm = Ifirm/np.sum(Ifirm)\n",
    "    print(\"Ifirm\", Ifirm)"
   ]
  }
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