{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2fad641c",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/usnistgov/AFL-agent/blob/main/docs/source/how-to/autosas_model_selection.ipynb)\n",
    "\n",
    "# Do Model Selection with AutoSAS\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced056ca",
   "metadata": {},
   "source": [
    "## Google Colab Setup\n",
    "\n",
    "Only uncomment and run the next cell if you are running this notebook in Google Colab or if don't already have the AFL-agent package installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5543936a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install git+https://github.com/usnistgov/AFL-agent.git"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607446da",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "First, let's import the necessary packages and load our example dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a3a2890",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from AFL.double_agent import *\n",
    "from AFL.double_agent.AutoSAS import AutoSAS\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7496ebc",
   "metadata": {},
   "source": [
    "Next, let's load the example dataset. We'll subselect only part of the dataset to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a0749bd",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 49kB\n",
       "Dimensions:  (sample: 30, q: 100)\n",
       "Coordinates:\n",
       "  * q        (q) float64 800B 0.001 0.001072 0.00115 ... 0.8697 0.9326 1.0\n",
       "Dimensions without coordinates: sample\n",
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       "    I        (sample, q) float64 24kB 5.465e+03 4.598e+03 ... 1.1 1.002\n",
       "    dI       (sample, q) float64 24kB 545.9 429.3 342.6 ... 0.09923 0.09923\n",
       "    model    (sample) object 240B &#x27;surface_fractal&#x27; ... &#x27;mass_fractal&#x27;</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-bc848e20-2251-4ef0-8bc1-72bbbcab51e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bc848e20-2251-4ef0-8bc1-72bbbcab51e4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>sample</span>: 30</li><li><span class='xr-has-index'>q</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7df28f4a-f4a2-4f61-a221-3441d5a754f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7df28f4a-f4a2-4f61-a221-3441d5a754f4' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001072 ... 0.9326 1.0</div><input id='attrs-a2753a6d-3148-4eff-a142-8859fbc5b64d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2753a6d-3148-4eff-a142-8859fbc5b64d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b152ef9f-5989-48da-a4c8-5e2ea758705c' class='xr-var-data-in' type='checkbox'><label for='data-b152ef9f-5989-48da-a4c8-5e2ea758705c' 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([0.001   , 0.001072, 0.00115 , 0.001233, 0.001322, 0.001417, 0.00152 ,\n",
       "       0.00163 , 0.001748, 0.001874, 0.002009, 0.002154, 0.00231 , 0.002477,\n",
       "       0.002656, 0.002848, 0.003054, 0.003275, 0.003511, 0.003765, 0.004037,\n",
       "       0.004329, 0.004642, 0.004977, 0.005337, 0.005722, 0.006136, 0.006579,\n",
       "       0.007055, 0.007565, 0.008111, 0.008697, 0.009326, 0.01    , 0.010723,\n",
       "       0.011498, 0.012328, 0.013219, 0.014175, 0.015199, 0.016298, 0.017475,\n",
       "       0.018738, 0.020092, 0.021544, 0.023101, 0.024771, 0.026561, 0.02848 ,\n",
       "       0.030539, 0.032745, 0.035112, 0.037649, 0.04037 , 0.043288, 0.046416,\n",
       "       0.04977 , 0.053367, 0.057224, 0.061359, 0.065793, 0.070548, 0.075646,\n",
       "       0.081113, 0.086975, 0.09326 , 0.1     , 0.107227, 0.114976, 0.123285,\n",
       "       0.132194, 0.141747, 0.151991, 0.162975, 0.174753, 0.187382, 0.200923,\n",
       "       0.215443, 0.231013, 0.247708, 0.265609, 0.284804, 0.305386, 0.327455,\n",
       "       0.351119, 0.376494, 0.403702, 0.432876, 0.464159, 0.497702, 0.53367 ,\n",
       "       0.572237, 0.613591, 0.657933, 0.70548 , 0.756463, 0.811131, 0.869749,\n",
       "       0.932603, 1.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3069ec66-94a7-4fdf-a347-46a92daade8a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3069ec66-94a7-4fdf-a347-46a92daade8a' class='xr-section-summary' >Data variables: <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-var-name'><span>I</span></div><div class='xr-var-dims'>(sample, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.465e+03 4.598e+03 ... 1.1 1.002</div><input id='attrs-10f4f7c1-472b-4fd2-b6fc-ba95e964d996' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10f4f7c1-472b-4fd2-b6fc-ba95e964d996' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-657d9367-758a-4d55-896a-1facc4fe6652' class='xr-var-data-in' type='checkbox'><label for='data-657d9367-758a-4d55-896a-1facc4fe6652' 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([[5.46461639e+03, 4.59795217e+03, 3.85947764e+03, ...,\n",
       "        8.23637450e-01, 1.08891674e+00, 8.86408253e-01],\n",
       "       [1.14146226e+04, 1.20480054e+04, 9.60389049e+03, ...,\n",
       "        1.09249128e+00, 1.03151090e+00, 1.02766190e+00],\n",
       "       [3.38673256e+03, 2.48217196e+03, 2.22022900e+03, ...,\n",
       "        1.16251134e+00, 1.01348467e+00, 1.15549165e+00],\n",
       "       ...,\n",
       "       [2.33984274e+04, 2.43947831e+04, 2.05533255e+04, ...,\n",
       "        1.04267181e+00, 9.85962747e-01, 9.07216458e-01],\n",
       "       [2.09634325e+00, 2.08464279e+00, 1.81224765e+00, ...,\n",
       "        1.10735048e+00, 9.92568176e-01, 9.57666868e-01],\n",
       "       [1.14052557e+04, 7.68845857e+03, 7.42318078e+03, ...,\n",
       "        1.03875573e+00, 1.10006506e+00, 1.00202285e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dI</span></div><div class='xr-var-dims'>(sample, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>545.9 429.3 ... 0.09923 0.09923</div><input id='attrs-1ca59c51-cfa2-41d6-9f3c-9722538efd54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ca59c51-cfa2-41d6-9f3c-9722538efd54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8589764c-084f-45d7-b76c-501174ce8ab3' class='xr-var-data-in' type='checkbox'><label for='data-8589764c-084f-45d7-b76c-501174ce8ab3' 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([[5.45853473e+02, 4.29328308e+02, 3.42588723e+02, ...,\n",
       "        9.70709140e-02, 9.70708823e-02, 9.70708568e-02],\n",
       "       [1.34833750e+03, 1.10406203e+03, 9.14697444e+02, ...,\n",
       "        1.00109160e-01, 1.00106587e-01, 1.00104428e-01],\n",
       "       [3.10385847e+02, 2.45619675e+02, 1.97113531e+02, ...,\n",
       "        9.93385928e-02, 9.93385623e-02, 9.93385376e-02],\n",
       "       ...,\n",
       "       [2.78764336e+03, 2.26312770e+03, 1.86001084e+03, ...,\n",
       "        9.80268288e-02, 9.80242931e-02, 9.80221832e-02],\n",
       "       [2.04875154e-01, 2.04855122e-01, 2.04833121e-01, ...,\n",
       "        1.02022577e-01, 1.02022027e-01, 1.02021575e-01],\n",
       "       [1.15537649e+03, 9.47070166e+02, 7.85415799e+02, ...,\n",
       "        9.92344457e-02, 9.92320281e-02, 9.92299984e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;surface_fractal&#x27; ... &#x27;mass_frac...</div><input id='attrs-27187011-13c3-4c01-86f0-82dda06eafe6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27187011-13c3-4c01-86f0-82dda06eafe6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8437c50-ffe0-4cd5-bc47-3dcccb6311f8' class='xr-var-data-in' type='checkbox'><label for='data-c8437c50-ffe0-4cd5-bc47-3dcccb6311f8' 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;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;surface_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;surface_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;polymer_excl_volume&#x27;, &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;,\n",
       "       &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-152bda70-dc5d-4d3a-abb4-3cd521cf257c' class='xr-section-summary-in' type='checkbox'  ><label for='section-152bda70-dc5d-4d3a-abb4-3cd521cf257c' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6036e6bb-7988-4e72-ac15-48cd6d38121d' class='xr-index-data-in' type='checkbox'/><label for='index-6036e6bb-7988-4e72-ac15-48cd6d38121d' 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([                0.001, 0.0010722672220103231, 0.0011497569953977356,\n",
       "       0.0012328467394420659, 0.0013219411484660286, 0.0014174741629268048,\n",
       "       0.0015199110829529332, 0.0016297508346206436,  0.001747528400007683,\n",
       "        0.001873817422860383,  0.002009233002565048, 0.0021544346900318843,\n",
       "       0.0023101297000831605, 0.0024770763559917113, 0.0026560877829466868,\n",
       "        0.002848035868435802, 0.0030538555088334154, 0.0032745491628777285,\n",
       "        0.003511191734215131, 0.0037649358067924675,  0.004037017258596553,\n",
       "        0.004328761281083057,  0.004641588833612782,  0.004977023564332114,\n",
       "        0.005336699231206312,   0.00572236765935022,  0.006135907273413176,\n",
       "        0.006579332246575682,  0.007054802310718645,  0.007564633275546291,\n",
       "        0.008111308307896872,  0.008697490026177835,    0.0093260334688322,\n",
       "                        0.01,  0.010722672220103232,  0.011497569953977356,\n",
       "        0.012328467394420659,  0.013219411484660293,  0.014174741629268055,\n",
       "         0.01519911082952934,  0.016297508346206444,   0.01747528400007684,\n",
       "         0.01873817422860384,   0.02009233002565047,  0.021544346900318846,\n",
       "        0.023101297000831605,  0.024770763559917114,  0.026560877829466867,\n",
       "         0.02848035868435802,  0.030538555088334154,   0.03274549162877728,\n",
       "         0.03511191734215131,  0.037649358067924674,   0.04037017258596556,\n",
       "         0.04328761281083059,  0.046415888336127795,  0.049770235643321115,\n",
       "          0.0533669923120631,   0.05722367659350217,   0.06135907273413173,\n",
       "         0.06579332246575682,   0.07054802310718646,   0.07564633275546291,\n",
       "         0.08111308307896872,   0.08697490026177834,     0.093260334688322,\n",
       "                         0.1,   0.10722672220103231,   0.11497569953977356,\n",
       "         0.12328467394420659,   0.13219411484660287,   0.14174741629268048,\n",
       "          0.1519911082952933,   0.16297508346206452,    0.1747528400007685,\n",
       "          0.1873817422860385,    0.2009233002565048,   0.21544346900318845,\n",
       "         0.23101297000831603,   0.24770763559917114,   0.26560877829466867,\n",
       "          0.2848035868435802,   0.30538555088334157,   0.32745491628777285,\n",
       "          0.3511191734215131,   0.37649358067924676,   0.40370172585965536,\n",
       "          0.4328761281083057,    0.4641588833612782,   0.49770235643321137,\n",
       "          0.5336699231206312,     0.572236765935022,    0.6135907273413176,\n",
       "          0.6579332246575682,    0.7054802310718645,    0.7564633275546291,\n",
       "          0.8111308307896873,    0.8697490026177834,    0.9326033468832199,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;q&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98f71dc3-4387-496a-944c-a61318d0f470' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-98f71dc3-4387-496a-944c-a61318d0f470' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 49kB\n",
       "Dimensions:  (sample: 30, q: 100)\n",
       "Coordinates:\n",
       "  * q        (q) float64 800B 0.001 0.001072 0.00115 ... 0.8697 0.9326 1.0\n",
       "Dimensions without coordinates: sample\n",
       "Data variables:\n",
       "    I        (sample, q) float64 24kB 5.465e+03 4.598e+03 ... 1.1 1.002\n",
       "    dI       (sample, q) float64 24kB 545.9 429.3 342.6 ... 0.09923 0.09923\n",
       "    model    (sample) object 240B 'surface_fractal' ... 'mass_fractal'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from AFL.double_agent.data import example_dataset2\n",
    "\n",
    "# Load example dataset and select the mass_fractal model\n",
    "ds = example_dataset2()\n",
    "ds = ds.where(ds.model.isin(['surface_fractal','mass_fractal','polymer_excl_volume']),drop=True)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e449bc1",
   "metadata": {},
   "source": [
    "Let's plot the data so we can see what we're trying to fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2190e8e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model,sds in ds.groupby('model'):\n",
    "    plt.figure()\n",
    "    sds.I.plot.line(x='q',xscale='log',yscale='log',marker='.',ls='None',add_legend=False);\n",
    "    plt.title(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e41bd2be",
   "metadata": {},
   "source": [
    "Great, we have three groups of data. Let's use AutoSAS to fit and classify this data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d84ef0",
   "metadata": {},
   "source": [
    "## Defining the Fit\n",
    "\n",
    "The first step to AutoSAS is to define which models we'd like to fit. Since we have three representations of data in our dataset, we'll define three models to fit it. In real, experimental scenarios, you often don't know how many structures you might encounter, so you can define as many as you'd like in the `model_input` list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dc5b21d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_inputs = [\n",
    "    {\n",
    "        \"name\": \"surface_fractal\", # your name for the model, can be anything\n",
    "        \"sasmodel\": \"power_law\", # the name of the sasmodel in the sasmodels library\n",
    "        'q_min':0.001,\n",
    "        'q_max':1.0,\n",
    "        \"fit_params\": {\n",
    "            \"power\": {\"value\": 4, \"bounds\": (3, 4)},\n",
    "            \"scale\": {\"value\": 1.0, \"bounds\": (1e-6,1e-3)},\n",
    "            \"background\": {\"value\": 1.0},\n",
    "        },\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"mass_fractal\", \n",
    "        \"sasmodel\": \"power_law\", \n",
    "        'q_min':0.001,\n",
    "        'q_max':1.0,\n",
    "        \"fit_params\": {\n",
    "            \"power\": {\"value\": 4, \"bounds\": (1.7, 3)},\n",
    "            \"scale\": {\"value\": 1.0, \"bounds\": (1e-4,1e-1)},\n",
    "            \"background\": {\"value\": 1.0},\n",
    "        },\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"polymer\", \n",
    "        \"sasmodel\": \"polymer_excl_volume\",\n",
    "        'q_min':0.001,\n",
    "        'q_max':1.0,\n",
    "        \"fit_params\": {\n",
    "            \"scale\": {\"value\": 1.0, \"bounds\": (1e-2,1e2)},\n",
    "            \"rg\": {\"value\": 60.0, \"bounds\": (10,150)},\n",
    "            \"background\": {\"value\": 1.0},\n",
    "        },\n",
    "    }   \n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "254188b5",
   "metadata": {},
   "source": [
    "## Building and Executing the Pipeline\n",
    "\n",
    "\n",
    "We then construct a pipeline with a single pipeline operation. We tell the operation what the data will be named in the dataset and give it the list of model inputs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9b922aa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PipelineOp                               input_variable ---> output_variable\n",
      "----------                               -----------------------------------\n",
      "0  ) <AutoSAS>                           ['q', 'I', 'dI'] ---> ['fit_all_chisq']\n",
      "\n",
      "Input Variables\n",
      "---------------\n",
      "0) q\n",
      "1) I\n",
      "2) dI\n",
      "\n",
      "Output Variables\n",
      "----------------\n",
      "0) fit_all_chisq\n"
     ]
    }
   ],
   "source": [
    "with Pipeline() as autosas_pipeline1:\n",
    "    AutoSAS(\n",
    "        sas_variable='I',\n",
    "        sas_err_variable='dI',\n",
    "        q_variable = 'q',\n",
    "        output_prefix='fit',\n",
    "        model_inputs=model_inputs,\n",
    "    )\n",
    "autosas_pipeline1.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a075d4fa",
   "metadata": {},
   "source": [
    "Now we're ready to fit! When we run the pipeline, it will fit each of the three models in model_input to *every* measurement in the dataset. For this dataset, that means that we'll carry out 90 fits. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5f26b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f828d41333041dab5fc339aac8cb5f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
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       "\n",
       ".xr-attrs,\n",
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       ".xr-var-data,\n",
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       "}\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",
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       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 202kB\n",
       "Dimensions:                    (sample: 30, q: 100, models: 3,\n",
       "                                surface_fractal_params: 3,\n",
       "                                mass_fractal_params: 3, polymer_params: 3,\n",
       "                                fit_q_surface_fractal: 100,\n",
       "                                fit_q_mass_fractal: 100, fit_q_polymer: 100)\n",
       "Coordinates:\n",
       "  * q                          (q) float64 800B 0.001 0.001072 ... 0.9326 1.0\n",
       "  * models                     (models) &lt;U15 180B &#x27;surface_fractal&#x27; ... &#x27;poly...\n",
       "  * surface_fractal_params     (surface_fractal_params) &lt;U26 312B &#x27;surface_fr...\n",
       "  * mass_fractal_params        (mass_fractal_params) &lt;U23 276B &#x27;mass_fractal_...\n",
       "  * polymer_params             (polymer_params) &lt;U18 216B &#x27;polymer_scale&#x27; ......\n",
       "  * fit_q_surface_fractal      (fit_q_surface_fractal) float64 800B 0.001 ......\n",
       "  * fit_q_mass_fractal         (fit_q_mass_fractal) float64 800B 0.001 ... 1.0\n",
       "  * fit_q_polymer              (fit_q_polymer) float64 800B 0.001 ... 1.0\n",
       "Dimensions without coordinates: sample\n",
       "Data variables: (12/18)\n",
       "    I                          (sample, q) float64 24kB 5.465e+03 ... 1.002\n",
       "    dI                         (sample, q) float64 24kB 545.9 429.3 ... 0.09923\n",
       "    model                      (sample) object 240B &#x27;surface_fractal&#x27; ... &#x27;ma...\n",
       "    sas_fit_sample             (sample) int64 240B 0 1 2 3 4 ... 25 26 27 28 29\n",
       "    fit_all_chisq              (sample, models) float64 720B 1.023 ... 27.2\n",
       "    probabilities              (sample, models) float64 720B 0.01182 ... 0.00...\n",
       "    ...                         ...\n",
       "    fit_I_surface_fractal      (sample, fit_q_surface_fractal) float64 24kB 5...\n",
       "    residuals_surface_fractal  (sample, fit_q_surface_fractal) float64 24kB -...\n",
       "    fit_I_mass_fractal         (sample, fit_q_mass_fractal) float64 24kB 956....\n",
       "    residuals_mass_fractal     (sample, fit_q_mass_fractal) float64 24kB -8.2...\n",
       "    fit_I_polymer              (sample, fit_q_polymer) float64 24kB 6.219 ......\n",
       "    residuals_polymer          (sample, fit_q_polymer) float64 24kB -10.0 ......</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-120c0b04-8e32-4353-9095-a241f516d5e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-120c0b04-8e32-4353-9095-a241f516d5e2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>sample</span>: 30</li><li><span class='xr-has-index'>q</span>: 100</li><li><span class='xr-has-index'>models</span>: 3</li><li><span class='xr-has-index'>surface_fractal_params</span>: 3</li><li><span class='xr-has-index'>mass_fractal_params</span>: 3</li><li><span class='xr-has-index'>polymer_params</span>: 3</li><li><span class='xr-has-index'>fit_q_surface_fractal</span>: 100</li><li><span class='xr-has-index'>fit_q_mass_fractal</span>: 100</li><li><span class='xr-has-index'>fit_q_polymer</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-275a1948-f6a7-4f67-9f95-92eebd197084' class='xr-section-summary-in' type='checkbox'  checked><label for='section-275a1948-f6a7-4f67-9f95-92eebd197084' 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'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001072 ... 0.9326 1.0</div><input id='attrs-92a89cc6-2af8-4612-98aa-3c8d0ce5ec4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92a89cc6-2af8-4612-98aa-3c8d0ce5ec4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-202842ef-5452-4175-a245-577a5cfa3007' class='xr-var-data-in' type='checkbox'><label for='data-202842ef-5452-4175-a245-577a5cfa3007' 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([0.001   , 0.001072, 0.00115 , 0.001233, 0.001322, 0.001417, 0.00152 ,\n",
       "       0.00163 , 0.001748, 0.001874, 0.002009, 0.002154, 0.00231 , 0.002477,\n",
       "       0.002656, 0.002848, 0.003054, 0.003275, 0.003511, 0.003765, 0.004037,\n",
       "       0.004329, 0.004642, 0.004977, 0.005337, 0.005722, 0.006136, 0.006579,\n",
       "       0.007055, 0.007565, 0.008111, 0.008697, 0.009326, 0.01    , 0.010723,\n",
       "       0.011498, 0.012328, 0.013219, 0.014175, 0.015199, 0.016298, 0.017475,\n",
       "       0.018738, 0.020092, 0.021544, 0.023101, 0.024771, 0.026561, 0.02848 ,\n",
       "       0.030539, 0.032745, 0.035112, 0.037649, 0.04037 , 0.043288, 0.046416,\n",
       "       0.04977 , 0.053367, 0.057224, 0.061359, 0.065793, 0.070548, 0.075646,\n",
       "       0.081113, 0.086975, 0.09326 , 0.1     , 0.107227, 0.114976, 0.123285,\n",
       "       0.132194, 0.141747, 0.151991, 0.162975, 0.174753, 0.187382, 0.200923,\n",
       "       0.215443, 0.231013, 0.247708, 0.265609, 0.284804, 0.305386, 0.327455,\n",
       "       0.351119, 0.376494, 0.403702, 0.432876, 0.464159, 0.497702, 0.53367 ,\n",
       "       0.572237, 0.613591, 0.657933, 0.70548 , 0.756463, 0.811131, 0.869749,\n",
       "       0.932603, 1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>models</span></div><div class='xr-var-dims'>(models)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;surface_fractal&#x27; ... &#x27;polymer&#x27;</div><input id='attrs-66481eda-f887-4be6-80de-73e6c1ae7fea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66481eda-f887-4be6-80de-73e6c1ae7fea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ca1fcb7-6d8d-45c3-9431-e62f1947eb1b' class='xr-var-data-in' type='checkbox'><label for='data-1ca1fcb7-6d8d-45c3-9431-e62f1947eb1b' 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;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>surface_fractal_params</span></div><div class='xr-var-dims'>(surface_fractal_params)</div><div class='xr-var-dtype'>&lt;U26</div><div class='xr-var-preview xr-preview'>&#x27;surface_fractal_power&#x27; ... &#x27;sur...</div><input id='attrs-87c58fe7-8182-4888-92c2-6d8c41878df1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87c58fe7-8182-4888-92c2-6d8c41878df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f1fb570-0064-4e15-bd0f-547d018f818c' class='xr-var-data-in' type='checkbox'><label for='data-5f1fb570-0064-4e15-bd0f-547d018f818c' 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;surface_fractal_power&#x27;, &#x27;surface_fractal_scale&#x27;,\n",
       "       &#x27;surface_fractal_background&#x27;], dtype=&#x27;&lt;U26&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mass_fractal_params</span></div><div class='xr-var-dims'>(mass_fractal_params)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>&#x27;mass_fractal_power&#x27; ... &#x27;mass_f...</div><input id='attrs-bc7fedf7-2c51-4a1f-8dce-68d1f226085f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc7fedf7-2c51-4a1f-8dce-68d1f226085f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6c95863-28ba-4570-aae1-7bb3e6d8b334' class='xr-var-data-in' type='checkbox'><label for='data-a6c95863-28ba-4570-aae1-7bb3e6d8b334' 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;mass_fractal_power&#x27;, &#x27;mass_fractal_scale&#x27;, &#x27;mass_fractal_background&#x27;],\n",
       "      dtype=&#x27;&lt;U23&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>polymer_params</span></div><div class='xr-var-dims'>(polymer_params)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;polymer_scale&#x27; ... &#x27;polymer_bac...</div><input id='attrs-3c536dcb-56c9-4e2a-b6e8-4cd8f715a5d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c536dcb-56c9-4e2a-b6e8-4cd8f715a5d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89f43007-c8d0-40ad-871e-e773509dd701' class='xr-var-data-in' type='checkbox'><label for='data-89f43007-c8d0-40ad-871e-e773509dd701' 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;polymer_scale&#x27;, &#x27;polymer_rg&#x27;, &#x27;polymer_background&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fit_q_surface_fractal</span></div><div class='xr-var-dims'>(fit_q_surface_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001072 ... 0.9326 1.0</div><input id='attrs-182b9a07-d8e1-4145-a7cc-e3d92be72cb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-182b9a07-d8e1-4145-a7cc-e3d92be72cb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d6f08e8-9413-4bcb-a555-7a8ca879afa5' class='xr-var-data-in' type='checkbox'><label for='data-1d6f08e8-9413-4bcb-a555-7a8ca879afa5' 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([0.001   , 0.001072, 0.00115 , 0.001233, 0.001322, 0.001417, 0.00152 ,\n",
       "       0.00163 , 0.001748, 0.001874, 0.002009, 0.002154, 0.00231 , 0.002477,\n",
       "       0.002656, 0.002848, 0.003054, 0.003275, 0.003511, 0.003765, 0.004037,\n",
       "       0.004329, 0.004642, 0.004977, 0.005337, 0.005722, 0.006136, 0.006579,\n",
       "       0.007055, 0.007565, 0.008111, 0.008697, 0.009326, 0.01    , 0.010723,\n",
       "       0.011498, 0.012328, 0.013219, 0.014175, 0.015199, 0.016298, 0.017475,\n",
       "       0.018738, 0.020092, 0.021544, 0.023101, 0.024771, 0.026561, 0.02848 ,\n",
       "       0.030539, 0.032745, 0.035112, 0.037649, 0.04037 , 0.043288, 0.046416,\n",
       "       0.04977 , 0.053367, 0.057224, 0.061359, 0.065793, 0.070548, 0.075646,\n",
       "       0.081113, 0.086975, 0.09326 , 0.1     , 0.107227, 0.114976, 0.123285,\n",
       "       0.132194, 0.141747, 0.151991, 0.162975, 0.174753, 0.187382, 0.200923,\n",
       "       0.215443, 0.231013, 0.247708, 0.265609, 0.284804, 0.305386, 0.327455,\n",
       "       0.351119, 0.376494, 0.403702, 0.432876, 0.464159, 0.497702, 0.53367 ,\n",
       "       0.572237, 0.613591, 0.657933, 0.70548 , 0.756463, 0.811131, 0.869749,\n",
       "       0.932603, 1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fit_q_mass_fractal</span></div><div class='xr-var-dims'>(fit_q_mass_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001072 ... 0.9326 1.0</div><input id='attrs-bba5a117-e1cd-477d-9155-8ebf88253dd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bba5a117-e1cd-477d-9155-8ebf88253dd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a204ad72-46e6-4126-b34b-12341090c407' class='xr-var-data-in' type='checkbox'><label for='data-a204ad72-46e6-4126-b34b-12341090c407' 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([0.001   , 0.001072, 0.00115 , 0.001233, 0.001322, 0.001417, 0.00152 ,\n",
       "       0.00163 , 0.001748, 0.001874, 0.002009, 0.002154, 0.00231 , 0.002477,\n",
       "       0.002656, 0.002848, 0.003054, 0.003275, 0.003511, 0.003765, 0.004037,\n",
       "       0.004329, 0.004642, 0.004977, 0.005337, 0.005722, 0.006136, 0.006579,\n",
       "       0.007055, 0.007565, 0.008111, 0.008697, 0.009326, 0.01    , 0.010723,\n",
       "       0.011498, 0.012328, 0.013219, 0.014175, 0.015199, 0.016298, 0.017475,\n",
       "       0.018738, 0.020092, 0.021544, 0.023101, 0.024771, 0.026561, 0.02848 ,\n",
       "       0.030539, 0.032745, 0.035112, 0.037649, 0.04037 , 0.043288, 0.046416,\n",
       "       0.04977 , 0.053367, 0.057224, 0.061359, 0.065793, 0.070548, 0.075646,\n",
       "       0.081113, 0.086975, 0.09326 , 0.1     , 0.107227, 0.114976, 0.123285,\n",
       "       0.132194, 0.141747, 0.151991, 0.162975, 0.174753, 0.187382, 0.200923,\n",
       "       0.215443, 0.231013, 0.247708, 0.265609, 0.284804, 0.305386, 0.327455,\n",
       "       0.351119, 0.376494, 0.403702, 0.432876, 0.464159, 0.497702, 0.53367 ,\n",
       "       0.572237, 0.613591, 0.657933, 0.70548 , 0.756463, 0.811131, 0.869749,\n",
       "       0.932603, 1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fit_q_polymer</span></div><div class='xr-var-dims'>(fit_q_polymer)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001072 ... 0.9326 1.0</div><input id='attrs-2d9bc213-a964-498b-aae4-19419ac86909' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d9bc213-a964-498b-aae4-19419ac86909' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ace73fd-787b-483f-a44c-18451fcc4c07' class='xr-var-data-in' type='checkbox'><label for='data-7ace73fd-787b-483f-a44c-18451fcc4c07' 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([0.001   , 0.001072, 0.00115 , 0.001233, 0.001322, 0.001417, 0.00152 ,\n",
       "       0.00163 , 0.001748, 0.001874, 0.002009, 0.002154, 0.00231 , 0.002477,\n",
       "       0.002656, 0.002848, 0.003054, 0.003275, 0.003511, 0.003765, 0.004037,\n",
       "       0.004329, 0.004642, 0.004977, 0.005337, 0.005722, 0.006136, 0.006579,\n",
       "       0.007055, 0.007565, 0.008111, 0.008697, 0.009326, 0.01    , 0.010723,\n",
       "       0.011498, 0.012328, 0.013219, 0.014175, 0.015199, 0.016298, 0.017475,\n",
       "       0.018738, 0.020092, 0.021544, 0.023101, 0.024771, 0.026561, 0.02848 ,\n",
       "       0.030539, 0.032745, 0.035112, 0.037649, 0.04037 , 0.043288, 0.046416,\n",
       "       0.04977 , 0.053367, 0.057224, 0.061359, 0.065793, 0.070548, 0.075646,\n",
       "       0.081113, 0.086975, 0.09326 , 0.1     , 0.107227, 0.114976, 0.123285,\n",
       "       0.132194, 0.141747, 0.151991, 0.162975, 0.174753, 0.187382, 0.200923,\n",
       "       0.215443, 0.231013, 0.247708, 0.265609, 0.284804, 0.305386, 0.327455,\n",
       "       0.351119, 0.376494, 0.403702, 0.432876, 0.464159, 0.497702, 0.53367 ,\n",
       "       0.572237, 0.613591, 0.657933, 0.70548 , 0.756463, 0.811131, 0.869749,\n",
       "       0.932603, 1.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dbd9296e-4c21-40b5-85fa-6ff60565408a' class='xr-section-summary-in' type='checkbox'  ><label for='section-dbd9296e-4c21-40b5-85fa-6ff60565408a' class='xr-section-summary' >Data variables: <span>(18)</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>I</span></div><div class='xr-var-dims'>(sample, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.465e+03 4.598e+03 ... 1.1 1.002</div><input id='attrs-92482956-6494-4346-919b-86d7c002db01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92482956-6494-4346-919b-86d7c002db01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de1956d4-7858-4da3-80a9-78604f693622' class='xr-var-data-in' type='checkbox'><label for='data-de1956d4-7858-4da3-80a9-78604f693622' 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([[5.46461639e+03, 4.59795217e+03, 3.85947764e+03, ...,\n",
       "        8.23637450e-01, 1.08891674e+00, 8.86408253e-01],\n",
       "       [1.14146226e+04, 1.20480054e+04, 9.60389049e+03, ...,\n",
       "        1.09249128e+00, 1.03151090e+00, 1.02766190e+00],\n",
       "       [3.38673256e+03, 2.48217196e+03, 2.22022900e+03, ...,\n",
       "        1.16251134e+00, 1.01348467e+00, 1.15549165e+00],\n",
       "       ...,\n",
       "       [2.33984274e+04, 2.43947831e+04, 2.05533255e+04, ...,\n",
       "        1.04267181e+00, 9.85962747e-01, 9.07216458e-01],\n",
       "       [2.09634325e+00, 2.08464279e+00, 1.81224765e+00, ...,\n",
       "        1.10735048e+00, 9.92568176e-01, 9.57666868e-01],\n",
       "       [1.14052557e+04, 7.68845857e+03, 7.42318078e+03, ...,\n",
       "        1.03875573e+00, 1.10006506e+00, 1.00202285e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dI</span></div><div class='xr-var-dims'>(sample, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>545.9 429.3 ... 0.09923 0.09923</div><input id='attrs-aad9ae86-9bbb-4acf-87e8-505fdd236366' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aad9ae86-9bbb-4acf-87e8-505fdd236366' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29284199-2045-4a31-be67-444b79ee1343' class='xr-var-data-in' type='checkbox'><label for='data-29284199-2045-4a31-be67-444b79ee1343' 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([[5.45853473e+02, 4.29328308e+02, 3.42588723e+02, ...,\n",
       "        9.70709140e-02, 9.70708823e-02, 9.70708568e-02],\n",
       "       [1.34833750e+03, 1.10406203e+03, 9.14697444e+02, ...,\n",
       "        1.00109160e-01, 1.00106587e-01, 1.00104428e-01],\n",
       "       [3.10385847e+02, 2.45619675e+02, 1.97113531e+02, ...,\n",
       "        9.93385928e-02, 9.93385623e-02, 9.93385376e-02],\n",
       "       ...,\n",
       "       [2.78764336e+03, 2.26312770e+03, 1.86001084e+03, ...,\n",
       "        9.80268288e-02, 9.80242931e-02, 9.80221832e-02],\n",
       "       [2.04875154e-01, 2.04855122e-01, 2.04833121e-01, ...,\n",
       "        1.02022577e-01, 1.02022027e-01, 1.02021575e-01],\n",
       "       [1.15537649e+03, 9.47070166e+02, 7.85415799e+02, ...,\n",
       "        9.92344457e-02, 9.92320281e-02, 9.92299984e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;surface_fractal&#x27; ... &#x27;mass_frac...</div><input id='attrs-ca2a3018-29ea-478f-9256-ba1e6f364e58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca2a3018-29ea-478f-9256-ba1e6f364e58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d60d4e8b-24a6-4b13-9575-058db47b4f3c' class='xr-var-data-in' type='checkbox'><label for='data-d60d4e8b-24a6-4b13-9575-058db47b4f3c' 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;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;surface_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;surface_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;polymer_excl_volume&#x27;, &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;,\n",
       "       &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sas_fit_sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 24 25 26 27 28 29</div><input id='attrs-9c21bdd2-12f6-4cdd-9390-5297bda4c38e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c21bdd2-12f6-4cdd-9390-5297bda4c38e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe38c7c2-6601-4591-b378-e0cde0e187d9' class='xr-var-data-in' type='checkbox'><label for='data-fe38c7c2-6601-4591-b378-e0cde0e187d9' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fit_all_chisq</span></div><div class='xr-var-dims'>(sample, models)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.023 14.07 27.32 ... 1.086 27.2</div><input id='attrs-03b7cb20-e2eb-4553-82ce-07d74620b072' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03b7cb20-e2eb-4553-82ce-07d74620b072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad0335c9-b078-42b8-9756-0bb88a5eaed1' class='xr-var-data-in' type='checkbox'><label for='data-ad0335c9-b078-42b8-9756-0bb88a5eaed1' 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>fit_calc_id :</span></dt><dd>ASAS-a69b1c5c-c1d3-4c5e-a758-0fc3f5c1525e</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[1.02275423e+00, 1.40722782e+01, 2.73224420e+01],\n",
       "       [4.09722407e+00, 9.50171680e-01, 2.63673388e+01],\n",
       "       [1.05413472e+00, 1.36841369e+01, 2.72866689e+01],\n",
       "       [1.14629265e+00, 1.04990624e+00, 3.17019675e+01],\n",
       "       [6.96376204e+05, 1.35878440e+02, 1.18324132e+00],\n",
       "       [1.08126923e+00, 1.19570931e+01, 2.58392185e+01],\n",
       "       [8.36611432e-01, 1.21540498e+01, 2.69767485e+01],\n",
       "       [8.92581487e-01, 1.12844843e+01, 2.53700558e+01],\n",
       "       [7.43408306e+05, 1.44644370e+02, 1.11691133e+00],\n",
       "       [4.08984295e+00, 9.12932757e-01, 2.64968640e+01],\n",
       "       [1.32050220e+00, 9.75581038e-01, 3.07720967e+01],\n",
       "       [7.06812364e+05, 1.37343734e+02, 9.18066715e-01],\n",
       "       [7.71372671e+05, 1.56599393e+02, 9.73842068e-01],\n",
       "       [1.57930523e+00, 1.01924014e+00, 3.04306793e+01],\n",
       "       [9.31217832e-01, 1.24766894e+01, 2.55126991e+01],\n",
       "       [9.27394330e-01, 1.03076313e+01, 2.44011743e+01],\n",
       "       [4.01568675e+00, 8.51332910e-01, 2.83843343e+01],\n",
       "       [1.07638344e+00, 1.05327569e+01, 2.38169213e+01],\n",
       "       [8.71404947e-01, 8.93694309e+00, 2.19797127e+01],\n",
       "       [1.08324978e+00, 9.81511077e+00, 2.41165279e+01],\n",
       "       [7.36691528e+05, 1.47836036e+02, 1.09358942e+00],\n",
       "       [7.15150775e+05, 1.38734930e+02, 1.10718714e+00],\n",
       "       [7.28863488e+05, 1.47481645e+02, 9.61265988e-01],\n",
       "       [2.05590837e+00, 1.13113537e+00, 2.80256684e+01],\n",
       "       [7.30479177e+05, 1.44870136e+02, 1.03919799e+00],\n",
       "       [1.23729309e+00, 8.66385752e-01, 2.81719466e+01],\n",
       "       [7.49889915e+05, 1.51181543e+02, 9.99128955e-01],\n",
       "       [2.42291207e+00, 1.16555890e+00, 3.01412948e+01],\n",
       "       [7.08550329e+05, 1.41412767e+02, 1.08373657e+00],\n",
       "       [4.75962943e+00, 1.08625395e+00, 2.72039930e+01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>probabilities</span></div><div class='xr-var-dims'>(sample, models)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01182 0.9238 ... 0.9984 0.001121</div><input id='attrs-24cf285e-f593-4688-8801-9bc710f450cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24cf285e-f593-4688-8801-9bc710f450cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57344d05-4c65-4f84-979c-716faabcff24' class='xr-var-data-in' type='checkbox'><label for='data-57344d05-4c65-4f84-979c-716faabcff24' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[1.18241649e-02, 9.23820654e-01, 6.43551809e-02],\n",
       "       [1.23607135e-03, 9.96341125e-01, 2.42280337e-03],\n",
       "       [8.23266617e-03, 9.40641961e-01, 5.11253726e-02],\n",
       "       [4.27533439e-01, 5.72377505e-01, 8.90553923e-05],\n",
       "       [0.00000000e+00, 1.73073808e-64, 1.00000000e+00],\n",
       "       [1.49740488e-03, 9.57828584e-01, 4.06740111e-02],\n",
       "       [3.55950627e-03, 9.80632154e-01, 1.58083397e-02],\n",
       "       [9.17293426e-04, 9.47240896e-01, 5.18418105e-02],\n",
       "       [0.00000000e+00, 1.78346827e-68, 1.00000000e+00],\n",
       "       [1.27297309e-02, 9.85285111e-01, 1.98515819e-03],\n",
       "       [3.07295385e-01, 6.92659281e-01, 4.53336987e-05],\n",
       "       [0.00000000e+00, 2.87852843e-65, 1.00000000e+00],\n",
       "       [0.00000000e+00, 9.22602333e-74, 1.00000000e+00],\n",
       "       [2.61966631e-01, 7.37981212e-01, 5.21571954e-05],\n",
       "       [3.16094856e-03, 9.03018069e-01, 9.38209827e-02],\n",
       "       [3.49580420e-04, 9.71200269e-01, 2.84501501e-02],\n",
       "       [1.29078990e-02, 9.86763088e-01, 3.29012466e-04],\n",
       "       [3.03155123e-04, 7.98369591e-01, 2.01327254e-01],\n",
       "       [8.19662930e-05, 9.22293393e-01, 7.76246408e-02],\n",
       "       [1.01633723e-04, 5.47733346e-01, 4.52165021e-01],\n",
       "       [0.00000000e+00, 7.95457604e-70, 1.00000000e+00],\n",
       "       [0.00000000e+00, 6.45880860e-66, 1.00000000e+00],\n",
       "       [0.00000000e+00, 8.57179161e-70, 1.00000000e+00],\n",
       "       [1.74573275e-01, 8.24542384e-01, 8.84340955e-04],\n",
       "       [0.00000000e+00, 1.81958266e-68, 1.00000000e+00],\n",
       "       [3.19004059e-01, 6.80291474e-01, 7.04467341e-04],\n",
       "       [0.00000000e+00, 2.68265729e-71, 1.00000000e+00],\n",
       "       [1.02787938e-01, 8.97121386e-01, 9.06757543e-05],\n",
       "       [0.00000000e+00, 5.10032208e-67, 1.00000000e+00],\n",
       "       [5.20020767e-04, 9.98358893e-01, 1.12108653e-03]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_fractal_fit_val</span></div><div class='xr-var-dims'>(surface_fractal_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.243 3.0 3.163 3.0 ... 1.0 1.0 1.0</div><input id='attrs-9a342cb5-e354-4ae3-b032-cc5b2b65ddc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a342cb5-e354-4ae3-b032-cc5b2b65ddc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2ecbe75-a11c-4a5f-a6c0-7daf4278c2aa' class='xr-var-data-in' type='checkbox'><label for='data-f2ecbe75-a11c-4a5f-a6c0-7daf4278c2aa' 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>fit_calc_id :</span></dt><dd>ASAS-a69b1c5c-c1d3-4c5e-a758-0fc3f5c1525e</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[3.24265739e+00, 3.00000000e+00, 3.16252070e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.08615609e+00, 3.15552503e+00, 3.05113167e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.25242366e+00, 3.03037286e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.00000000e+00],\n",
       "       [1.00000000e-06, 1.88805902e-05, 1.00000000e-06, 8.07491165e-05,\n",
       "        1.00000000e-06, 1.00000000e-06, 1.24393004e-06, 1.00000000e-06,\n",
       "        1.00000000e-06, 2.18202275e-05, 6.10328588e-05, 1.00000000e-06,\n",
       "        1.00000000e-06, 5.96185299e-05, 1.11492212e-06, 1.00000000e-06,\n",
       "        2.25881511e-05, 1.00000000e-06, 1.00000000e-06, 1.00000000e-06,\n",
       "        1.00000000e-06, 1.00000000e-06, 1.00000000e-06, 5.01149203e-05,\n",
       "        1.00000000e-06, 6.92824955e-05, 1.00000000e-06, 3.59572491e-05,\n",
       "        1.00000000e-06, 1.64517485e-05],\n",
       "       [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_fractal_fit_err</span></div><div class='xr-var-dims'>(surface_fractal_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0227 0.01792 0.02407 ... 0.0 0.0</div><input id='attrs-1e7a0cbf-06a6-406c-8cf3-81b17f31d059' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e7a0cbf-06a6-406c-8cf3-81b17f31d059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d287e82a-cec1-4db9-8c37-5c2d8e8eb04f' class='xr-var-data-in' type='checkbox'><label for='data-d287e82a-cec1-4db9-8c37-5c2d8e8eb04f' 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>fit_calc_id :</span></dt><dd>ASAS-a69b1c5c-c1d3-4c5e-a758-0fc3f5c1525e</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[2.27048704e-02, 1.79223126e-02, 2.40659471e-02, 1.30755205e-02,\n",
       "        7.27887423e-04, 2.55743991e-02, 2.29994739e-02, 2.63328387e-02,\n",
       "        7.04419584e-04, 1.73100251e-02, 1.37324240e-02, 7.22326241e-04,\n",
       "        6.91839641e-04, 1.39774626e-02, 2.28313482e-02, 2.70634988e-02,\n",
       "        1.68284235e-02, 2.78456852e-02, 2.78247365e-02, 2.77249316e-02,\n",
       "        7.07711080e-04, 7.18157333e-04, 7.11603537e-04, 1.46252174e-02,\n",
       "        7.10825868e-04, 1.38115165e-02, 7.01610295e-04, 1.51664322e-02,\n",
       "        7.21627230e-04, 1.82691043e-02],\n",
       "       [1.30712675e-07, 1.99407460e-06, 1.39863344e-07, 5.68296258e-06,\n",
       "        4.93677731e-09, 1.49719861e-07, 1.64556199e-07, 1.55220020e-07,\n",
       "        4.77760508e-09, 2.21210410e-06, 4.59230273e-06, 4.89904463e-09,\n",
       "        4.69228908e-09, 4.58996792e-06, 1.45467891e-07, 1.59749280e-07,\n",
       "        2.22355435e-06, 1.64812550e-07, 1.64903795e-07, 1.64396226e-07,\n",
       "        4.79992379e-09, 4.87077236e-09, 4.82632551e-09, 4.07911051e-06,\n",
       "        4.82106811e-09, 5.20163539e-06, 4.75856119e-09, 3.10007783e-06,\n",
       "        4.89431125e-09, 1.77906351e-06],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mass_fractal_fit_val</span></div><div class='xr-var-dims'>(mass_fractal_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.327 2.708 2.263 ... 1.0 1.0 1.0</div><input id='attrs-d2e99f6f-390a-439b-9f72-363a8c7bf76b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2e99f6f-390a-439b-9f72-363a8c7bf76b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dcf92c8-f87a-4f40-9b29-906e42d3653d' class='xr-var-data-in' type='checkbox'><label for='data-0dcf92c8-f87a-4f40-9b29-906e42d3653d' 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>fit_calc_id :</span></dt><dd>ASAS-a69b1c5c-c1d3-4c5e-a758-0fc3f5c1525e</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[2.32667232e+00, 2.70771805e+00, 2.26272645e+00, 2.95928814e+00,\n",
       "        1.70000000e+00, 2.20397864e+00, 2.29631053e+00, 2.17898167e+00,\n",
       "        1.70000000e+00, 2.72327751e+00, 2.90766327e+00, 1.70000000e+00,\n",
       "        1.70000000e+00, 2.89774061e+00, 2.35502215e+00, 2.16469122e+00,\n",
       "        2.72861467e+00, 2.13300103e+00, 2.13477745e+00, 2.14056161e+00,\n",
       "        1.70000000e+00, 1.70000000e+00, 1.70000000e+00, 2.86340973e+00,\n",
       "        1.70000000e+00, 2.91737002e+00, 1.70000000e+00, 2.81452282e+00,\n",
       "        1.70000000e+00, 2.68510041e+00],\n",
       "       [1.00000000e-04, 1.00000000e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.00000000e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.04785464e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.03066892e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.05075202e-04, 1.00000000e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.00000000e-04, 1.00000000e-04, 1.04777291e-04,\n",
       "        1.00000000e-04, 1.07215235e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.00000000e-04],\n",
       "       [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mass_fractal_fit_err</span></div><div class='xr-var-dims'>(mass_fractal_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03115 0.01507 0.03165 ... 0.0 0.0</div><input id='attrs-86dcd9f2-32d8-421d-84b9-79e3e157d06d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86dcd9f2-32d8-421d-84b9-79e3e157d06d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7307fadc-c443-47b3-a392-d8879506cfaf' class='xr-var-data-in' type='checkbox'><label for='data-7307fadc-c443-47b3-a392-d8879506cfaf' 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>fit_calc_id :</span></dt><dd>ASAS-a69b1c5c-c1d3-4c5e-a758-0fc3f5c1525e</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[3.11538645e-02, 1.50731929e-02, 3.16536073e-02, 1.29313013e-02,\n",
       "        2.54503819e-02, 3.16613936e-02, 2.96995833e-02, 3.15895469e-02,\n",
       "        2.46166729e-02, 1.47327752e-02, 1.33044826e-02, 2.52264042e-02,\n",
       "        2.41889926e-02, 1.34897817e-02, 3.12569447e-02, 3.15930608e-02,\n",
       "        1.44385458e-02, 3.31186418e-02, 3.30138173e-02, 3.19064838e-02,\n",
       "        2.47254105e-02, 2.50868816e-02, 2.48625273e-02, 1.39016032e-02,\n",
       "        2.48696932e-02, 1.34768498e-02, 2.45398366e-02, 1.39221047e-02,\n",
       "        2.52168618e-02, 1.51065202e-02],\n",
       "       [1.48852173e-05, 8.14818887e-06, 1.54933083e-05, 6.84638978e-06,\n",
       "        1.69408717e-05, 1.58106544e-05, 1.44407776e-05, 1.59900730e-05,\n",
       "        1.63857670e-05, 8.30277128e-06, 7.03419636e-06, 1.67913949e-05,\n",
       "        1.61012315e-05, 7.36494592e-06, 1.48565822e-05, 1.60709381e-05,\n",
       "        8.16838404e-06, 1.69550162e-05, 1.69592147e-05, 1.64106194e-05,\n",
       "        1.64580270e-05, 1.66985964e-05, 1.65493330e-05, 7.70810369e-06,\n",
       "        1.65545487e-05, 7.59938920e-06, 1.63348826e-05, 7.42614119e-06,\n",
       "        1.67852396e-05, 8.16621099e-06],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polymer_fit_val</span></div><div class='xr-var-dims'>(polymer_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.258 40.23 4.006 ... 1.0 1.0 1.0</div><input id='attrs-fbb2ad89-60db-489e-b0e3-b77408a226e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbb2ad89-60db-489e-b0e3-b77408a226e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c46b60ae-6e25-4356-b18d-edfb67c23a03' class='xr-var-data-in' type='checkbox'><label for='data-c46b60ae-6e25-4356-b18d-edfb67c23a03' 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>fit_calc_id :</span></dt><dd>ASAS-a69b1c5c-c1d3-4c5e-a758-0fc3f5c1525e</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[  5.25833492,  40.22505817,   4.00558774, 100.        ,\n",
       "          1.04442146,   3.29761   ,   4.8720121 ,   3.06146257,\n",
       "          1.00713336,  44.27147743,  78.17551856,   1.02892542,\n",
       "          0.93875659, 100.        ,   6.08637701,   2.94546786,\n",
       "         45.0702793 ,   2.50345304,   2.73405192,   2.70524025,\n",
       "          0.96785773,   1.03149347,   1.02593748,  71.97009094,\n",
       "          1.01287684, 100.        ,   0.94249659,  56.17376216,\n",
       "          1.04479582,  36.40920265],\n",
       "       [150.        , 150.        , 150.        , 150.        ,\n",
       "         51.06176077, 150.        , 150.        , 150.        ,\n",
       "         66.89120632, 150.        , 150.        ,  56.50715476,\n",
       "         60.19825947, 150.        , 150.        , 150.        ,\n",
       "        150.        , 150.        , 150.        , 150.        ,\n",
       "         57.51120818,  65.91860794,  66.08437723, 150.        ,\n",
       "         51.03465948, 150.        ,  54.99540066, 150.        ,\n",
       "         57.39639901, 150.        ],\n",
       "       [  1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polymer_fit_err</span></div><div class='xr-var-dims'>(polymer_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.557 3.071 0.4278 ... 0.0 0.0 0.0</div><input id='attrs-20d5442b-de8f-48dc-832d-b31e42374a6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20d5442b-de8f-48dc-832d-b31e42374a6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e18dc13-9730-4a23-bd58-e8f4ddffb395' class='xr-var-data-in' type='checkbox'><label for='data-0e18dc13-9730-4a23-bd58-e8f4ddffb395' 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>fit_calc_id :</span></dt><dd>ASAS-a69b1c5c-c1d3-4c5e-a758-0fc3f5c1525e</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.55701407,  3.07117713,  0.42778412,  8.69419771,  0.03408775,\n",
       "         0.33168065,  0.46750051,  0.30125613,  0.03525603,  3.39906167,\n",
       "         6.95871167,  0.03454602,  0.03374983,  6.96250167,  0.63187833,\n",
       "         0.28329694,  3.39953852,  0.25694993,  0.25926761,  0.25897734,\n",
       "         0.03405919,  0.03578345,  0.03549022,  6.14599674,  0.03336914,\n",
       "         7.93845586,  0.03350642,  4.68010318,  0.03472009,  2.74813752],\n",
       "       [ 8.43420851,  5.0489181 ,  9.08329964,  5.2036293 ,  3.21497204,\n",
       "         9.0828923 ,  7.97555919,  9.20181048,  4.55893797,  5.00749523,\n",
       "         5.41144915,  3.59425279,  4.22852841,  4.24658521,  8.14522953,\n",
       "         9.14026989,  4.9375889 , 10.01209928,  9.33676861,  9.42076471,\n",
       "         3.90038145,  4.44092837,  4.44246663,  5.27284562,  3.29848751,\n",
       "         4.78430279,  3.83062797,  5.25408604,  3.66863617,  5.04400228],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fit_I_surface_fractal</span></div><div class='xr-var-dims'>(sample, fit_q_surface_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.346e+03 4.264e+03 ... 1.0 1.0</div><input id='attrs-e0f9663d-d086-4f2d-b887-59b91acbcaa3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0f9663d-d086-4f2d-b887-59b91acbcaa3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3815a0af-c94b-40dd-af80-1282d5c4fdef' class='xr-var-data-in' type='checkbox'><label for='data-3815a0af-c94b-40dd-af80-1282d5c4fdef' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[5.34630102e+03, 4.26394591e+03, 3.40075387e+03, ...,\n",
       "        1.00000157e+00, 1.00000125e+00, 1.00000100e+00],\n",
       "       [1.88815902e+04, 1.53156289e+04, 1.24231676e+04, ...,\n",
       "        1.00002870e+00, 1.00002328e+00, 1.00001888e+00],\n",
       "       [3.07399683e+03, 2.46549619e+03, 1.97748804e+03, ...,\n",
       "        1.00000155e+00, 1.00000125e+00, 1.00000100e+00],\n",
       "       ...,\n",
       "       [3.59582491e+04, 2.91670333e+04, 2.36584688e+04, ...,\n",
       "        1.00005465e+00, 1.00004433e+00, 1.00003596e+00],\n",
       "       [1.00100000e+03, 8.12130831e+02, 6.58933225e+02, ...,\n",
       "        1.00000152e+00, 1.00000123e+00, 1.00000100e+00],\n",
       "       [1.64527485e+04, 1.33455205e+04, 1.08251520e+04, ...,\n",
       "        1.00002501e+00, 1.00002028e+00, 1.00001645e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>residuals_surface_fractal</span></div><div class='xr-var-dims'>(sample, fit_q_surface_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2168 -0.778 ... -1.008 -0.02022</div><input id='attrs-7f4f32dd-5b58-41e8-af6d-7318c7f81f8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f4f32dd-5b58-41e8-af6d-7318c7f81f8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7484b4bb-d8a2-4274-8d42-efed59195c53' class='xr-var-data-in' type='checkbox'><label for='data-7484b4bb-d8a2-4274-8d42-efed59195c53' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[-2.16752990e-01, -7.77973996e-01, -1.33899259e+00, ...,\n",
       "         1.81685857e+00, -9.15985177e-01,  1.17020443e+00],\n",
       "       [ 5.53790700e+00,  2.95963752e+00,  3.08219636e+00, ...,\n",
       "        -9.23617634e-01, -3.14540965e-01, -2.76141815e-01],\n",
       "       [-1.00757084e+00, -6.78926389e-02, -1.23147789e+00, ...,\n",
       "        -1.63591791e+00, -1.35732062e-01, -1.56526014e+00],\n",
       "       ...,\n",
       "       [ 4.50553393e+00,  2.10869684e+00,  1.66942220e+00, ...,\n",
       "        -4.34749911e-01,  1.43654008e-01,  9.46923398e-01],\n",
       "       [ 4.87567006e+03,  3.95423936e+03,  3.20807969e+03, ...,\n",
       "        -1.05220784e+00,  7.28573742e-02,  4.14952742e-01],\n",
       "       [ 4.36869962e+00,  5.97322363e+00,  4.33142700e+00, ...,\n",
       "        -3.90295162e-01, -1.00819040e+00, -2.02196915e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fit_I_mass_fractal</span></div><div class='xr-var-dims'>(sample, fit_q_mass_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>956.0 812.9 691.3 ... 1.0 1.0 1.0</div><input id='attrs-0006c7ba-8ff4-4875-b28e-05d4de0a24c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0006c7ba-8ff4-4875-b28e-05d4de0a24c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-350b2adc-d85f-4621-a875-c37016ec1618' class='xr-var-data-in' type='checkbox'><label for='data-350b2adc-d85f-4621-a875-c37016ec1618' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[9.56029898e+02, 8.12917208e+02, 6.91250173e+02, ...,\n",
       "        1.00013836e+00, 1.00011763e+00, 1.00010000e+00],\n",
       "       [1.32796570e+04, 1.09936422e+04, 9.10118097e+03, ...,\n",
       "        1.00014592e+00, 1.00012080e+00, 1.00010000e+00],\n",
       "       [6.15015531e+02, 5.25338670e+02, 4.48759099e+02, ...,\n",
       "        1.00013713e+00, 1.00011710e+00, 1.00010000e+00],\n",
       "       ...,\n",
       "       [2.77705263e+04, 2.28191222e+04, 1.87505708e+04, ...,\n",
       "        1.00014811e+00, 1.00012170e+00, 1.00010000e+00],\n",
       "       [1.35892541e+01, 1.21811082e+01, 1.09304676e+01, ...,\n",
       "        1.00012677e+00, 1.00011259e+00, 1.00010000e+00],\n",
       "       [1.13589838e+04, 9.41847690e+03, 7.80950485e+03, ...,\n",
       "        1.00014546e+00, 1.00012061e+00, 1.00010000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>residuals_mass_fractal</span></div><div class='xr-var-dims'>(sample, fit_q_mass_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-8.26 -8.816 ... -1.007 -0.01938</div><input id='attrs-727f45f3-709c-4ac6-b704-6a252a0d7752' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-727f45f3-709c-4ac6-b704-6a252a0d7752' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc63a1ba-5d93-489c-bad5-49fc15092444' class='xr-var-data-in' type='checkbox'><label for='data-dc63a1ba-5d93-489c-bad5-49fc15092444' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[-8.25970103e+00, -8.81617842e+00, -9.24790354e+00, ...,\n",
       "         1.81826773e+00, -9.14786336e-01,  1.17122431e+00],\n",
       "       [ 1.38321038e+00, -9.54985533e-01, -5.49591048e-01, ...,\n",
       "        -9.22446716e-01, -3.13566813e-01, -2.75331467e-01],\n",
       "       [-8.92990791e+00, -7.96692403e+00, -8.98705375e+00, ...,\n",
       "        -1.63455312e+00, -1.34565789e-01, -1.56426355e+00],\n",
       "       ...,\n",
       "       [ 1.56838534e+00, -6.96231550e-01, -9.69217278e-01, ...,\n",
       "        -4.33796542e-01,  1.44443299e-01,  9.47576748e-01],\n",
       "       [ 5.60971432e+01,  4.92858819e+01,  4.45153589e+01, ...,\n",
       "        -1.05098013e+00,  7.39489142e-02,  4.15923125e-01],\n",
       "       [-4.00492428e-02,  1.82670555e+00,  4.91872047e-01, ...,\n",
       "        -3.89081356e-01, -1.00717941e+00, -1.93777258e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fit_I_polymer</span></div><div class='xr-var-dims'>(sample, fit_q_polymer)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.219 6.213 6.207 ... 1.0 1.0 1.0</div><input id='attrs-24f0e243-f8ad-444d-8cf6-95d85d0757c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24f0e243-f8ad-444d-8cf6-95d85d0757c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82caee86-88a4-4858-8677-74e424b17559' class='xr-var-data-in' type='checkbox'><label for='data-82caee86-88a4-4858-8677-74e424b17559' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[ 6.21908453,  6.2132386 ,  6.20652748, ...,  1.00000988,\n",
       "         1.00000801,  1.0000065 ],\n",
       "       [40.92480168, 40.88008163, 40.82874308, ...,  1.00007555,\n",
       "         1.00006128,  1.0000497 ],\n",
       "       [ 4.97568837,  4.97123518,  4.96612292, ...,  1.00000752,\n",
       "         1.0000061 ,  1.00000495],\n",
       "       ...,\n",
       "       [56.75445795, 56.69200699, 56.62031338, ...,  1.0001055 ,\n",
       "         1.00008557,  1.00006941],\n",
       "       [ 2.04364931,  2.04347775,  2.04328055, ...,  1.00003502,\n",
       "         1.00002841,  1.00002304],\n",
       "       [37.13742929, 37.0969515 , 37.05048306, ...,  1.00006838,\n",
       "         1.00005546,  1.00004499]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>residuals_polymer</span></div><div class='xr-var-dims'>(sample, fit_q_polymer)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -10.7 ... -1.008 -0.01993</div><input id='attrs-16249d55-c357-4bd6-9cff-5149a0841c4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16249d55-c357-4bd6-9cff-5149a0841c4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dfbbc81-3bee-4e5d-a121-9bef1b2e7bc2' class='xr-var-data-in' type='checkbox'><label for='data-2dfbbc81-3bee-4e5d-a121-9bef1b2e7bc2' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[ -9.99974824, -10.69516929, -11.24751298, ...,   1.81694411,\n",
       "         -0.91591557,   1.17026107],\n",
       "       [ -8.43534933, -10.87540827, -10.45489065, ...,  -0.92314966,\n",
       "         -0.31416137,  -0.27583391],\n",
       "       [-10.89533207, -10.08551423, -11.23851245, ...,  -1.63585783,\n",
       "         -0.13568319,  -1.56522039],\n",
       "       ...,\n",
       "       [ -8.3732637 , -10.75418374, -11.01966974, ...,  -0.43423121,\n",
       "          0.14407475,   0.94726468],\n",
       "       [ -0.25720025,  -0.20094706,   1.12790789, ...,  -1.05187945,\n",
       "          0.07312374,   0.4151688 ],\n",
       "       [ -9.83931935,  -8.07898073,  -9.40410201, ...,  -0.38985808,\n",
       "         -1.00783586,  -0.01993211]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9fe40a0c-7507-48c3-beae-49c7b9f92b19' class='xr-section-summary-in' type='checkbox'  ><label for='section-9fe40a0c-7507-48c3-beae-49c7b9f92b19' class='xr-section-summary' >Indexes: <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-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a16e6f0f-d050-495a-862c-e396fbdd8d85' class='xr-index-data-in' type='checkbox'/><label for='index-a16e6f0f-d050-495a-862c-e396fbdd8d85' 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([                0.001, 0.0010722672220103231, 0.0011497569953977356,\n",
       "       0.0012328467394420659, 0.0013219411484660286, 0.0014174741629268048,\n",
       "       0.0015199110829529332, 0.0016297508346206436,  0.001747528400007683,\n",
       "        0.001873817422860383,  0.002009233002565048, 0.0021544346900318843,\n",
       "       0.0023101297000831605, 0.0024770763559917113, 0.0026560877829466868,\n",
       "        0.002848035868435802, 0.0030538555088334154, 0.0032745491628777285,\n",
       "        0.003511191734215131, 0.0037649358067924675,  0.004037017258596553,\n",
       "        0.004328761281083057,  0.004641588833612782,  0.004977023564332114,\n",
       "        0.005336699231206312,   0.00572236765935022,  0.006135907273413176,\n",
       "        0.006579332246575682,  0.007054802310718645,  0.007564633275546291,\n",
       "        0.008111308307896872,  0.008697490026177835,    0.0093260334688322,\n",
       "                        0.01,  0.010722672220103232,  0.011497569953977356,\n",
       "        0.012328467394420659,  0.013219411484660293,  0.014174741629268055,\n",
       "         0.01519911082952934,  0.016297508346206444,   0.01747528400007684,\n",
       "         0.01873817422860384,   0.02009233002565047,  0.021544346900318846,\n",
       "        0.023101297000831605,  0.024770763559917114,  0.026560877829466867,\n",
       "         0.02848035868435802,  0.030538555088334154,   0.03274549162877728,\n",
       "         0.03511191734215131,  0.037649358067924674,   0.04037017258596556,\n",
       "         0.04328761281083059,  0.046415888336127795,  0.049770235643321115,\n",
       "          0.0533669923120631,   0.05722367659350217,   0.06135907273413173,\n",
       "         0.06579332246575682,   0.07054802310718646,   0.07564633275546291,\n",
       "         0.08111308307896872,   0.08697490026177834,     0.093260334688322,\n",
       "                         0.1,   0.10722672220103231,   0.11497569953977356,\n",
       "         0.12328467394420659,   0.13219411484660287,   0.14174741629268048,\n",
       "          0.1519911082952933,   0.16297508346206452,    0.1747528400007685,\n",
       "          0.1873817422860385,    0.2009233002565048,   0.21544346900318845,\n",
       "         0.23101297000831603,   0.24770763559917114,   0.26560877829466867,\n",
       "          0.2848035868435802,   0.30538555088334157,   0.32745491628777285,\n",
       "          0.3511191734215131,   0.37649358067924676,   0.40370172585965536,\n",
       "          0.4328761281083057,    0.4641588833612782,   0.49770235643321137,\n",
       "          0.5336699231206312,     0.572236765935022,    0.6135907273413176,\n",
       "          0.6579332246575682,    0.7054802310718645,    0.7564633275546291,\n",
       "          0.8111308307896873,    0.8697490026177834,    0.9326033468832199,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>models</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1e0fb86d-ecdd-4c7f-96bd-8d3df124dcb1' class='xr-index-data-in' type='checkbox'/><label for='index-1e0fb86d-ecdd-4c7f-96bd-8d3df124dcb1' 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;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer&#x27;], dtype=&#x27;object&#x27;, name=&#x27;models&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>surface_fractal_params</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b31dda86-5551-46d1-b099-9b03b70ec5f0' class='xr-index-data-in' type='checkbox'/><label for='index-b31dda86-5551-46d1-b099-9b03b70ec5f0' 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;surface_fractal_power&#x27;, &#x27;surface_fractal_scale&#x27;,\n",
       "       &#x27;surface_fractal_background&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;surface_fractal_params&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mass_fractal_params</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-819f5182-2d95-4663-a3d4-41e3bcfd4d72' class='xr-index-data-in' type='checkbox'/><label for='index-819f5182-2d95-4663-a3d4-41e3bcfd4d72' 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;mass_fractal_power&#x27;, &#x27;mass_fractal_scale&#x27;, &#x27;mass_fractal_background&#x27;], dtype=&#x27;object&#x27;, name=&#x27;mass_fractal_params&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>polymer_params</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f1b48f39-e377-4e1b-b1fa-db8d9500ae2e' class='xr-index-data-in' type='checkbox'/><label for='index-f1b48f39-e377-4e1b-b1fa-db8d9500ae2e' 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;polymer_scale&#x27;, &#x27;polymer_rg&#x27;, &#x27;polymer_background&#x27;], dtype=&#x27;object&#x27;, name=&#x27;polymer_params&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fit_q_surface_fractal</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-76db3b03-dcfa-43b6-ba9b-e3fb1ea2ce1a' class='xr-index-data-in' type='checkbox'/><label for='index-76db3b03-dcfa-43b6-ba9b-e3fb1ea2ce1a' 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([                0.001, 0.0010722672220103231, 0.0011497569953977356,\n",
       "       0.0012328467394420659, 0.0013219411484660286, 0.0014174741629268048,\n",
       "       0.0015199110829529332, 0.0016297508346206436,  0.001747528400007683,\n",
       "        0.001873817422860383,  0.002009233002565048, 0.0021544346900318843,\n",
       "       0.0023101297000831605, 0.0024770763559917113, 0.0026560877829466868,\n",
       "        0.002848035868435802, 0.0030538555088334154, 0.0032745491628777285,\n",
       "        0.003511191734215131, 0.0037649358067924675,  0.004037017258596553,\n",
       "        0.004328761281083057,  0.004641588833612782,  0.004977023564332114,\n",
       "        0.005336699231206312,   0.00572236765935022,  0.006135907273413176,\n",
       "        0.006579332246575682,  0.007054802310718645,  0.007564633275546291,\n",
       "        0.008111308307896872,  0.008697490026177835,    0.0093260334688322,\n",
       "                        0.01,  0.010722672220103232,  0.011497569953977356,\n",
       "        0.012328467394420659,  0.013219411484660293,  0.014174741629268055,\n",
       "         0.01519911082952934,  0.016297508346206444,   0.01747528400007684,\n",
       "         0.01873817422860384,   0.02009233002565047,  0.021544346900318846,\n",
       "        0.023101297000831605,  0.024770763559917114,  0.026560877829466867,\n",
       "         0.02848035868435802,  0.030538555088334154,   0.03274549162877728,\n",
       "         0.03511191734215131,  0.037649358067924674,   0.04037017258596556,\n",
       "         0.04328761281083059,  0.046415888336127795,  0.049770235643321115,\n",
       "          0.0533669923120631,   0.05722367659350217,   0.06135907273413173,\n",
       "         0.06579332246575682,   0.07054802310718646,   0.07564633275546291,\n",
       "         0.08111308307896872,   0.08697490026177834,     0.093260334688322,\n",
       "                         0.1,   0.10722672220103231,   0.11497569953977356,\n",
       "         0.12328467394420659,   0.13219411484660287,   0.14174741629268048,\n",
       "          0.1519911082952933,   0.16297508346206452,    0.1747528400007685,\n",
       "          0.1873817422860385,    0.2009233002565048,   0.21544346900318845,\n",
       "         0.23101297000831603,   0.24770763559917114,   0.26560877829466867,\n",
       "          0.2848035868435802,   0.30538555088334157,   0.32745491628777285,\n",
       "          0.3511191734215131,   0.37649358067924676,   0.40370172585965536,\n",
       "          0.4328761281083057,    0.4641588833612782,   0.49770235643321137,\n",
       "          0.5336699231206312,     0.572236765935022,    0.6135907273413176,\n",
       "          0.6579332246575682,    0.7054802310718645,    0.7564633275546291,\n",
       "          0.8111308307896873,    0.8697490026177834,    0.9326033468832199,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;fit_q_surface_fractal&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fit_q_mass_fractal</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ddf7e832-60ad-4576-a95e-d69947efbc6c' class='xr-index-data-in' type='checkbox'/><label for='index-ddf7e832-60ad-4576-a95e-d69947efbc6c' 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([                0.001, 0.0010722672220103231, 0.0011497569953977356,\n",
       "       0.0012328467394420659, 0.0013219411484660286, 0.0014174741629268048,\n",
       "       0.0015199110829529332, 0.0016297508346206436,  0.001747528400007683,\n",
       "        0.001873817422860383,  0.002009233002565048, 0.0021544346900318843,\n",
       "       0.0023101297000831605, 0.0024770763559917113, 0.0026560877829466868,\n",
       "        0.002848035868435802, 0.0030538555088334154, 0.0032745491628777285,\n",
       "        0.003511191734215131, 0.0037649358067924675,  0.004037017258596553,\n",
       "        0.004328761281083057,  0.004641588833612782,  0.004977023564332114,\n",
       "        0.005336699231206312,   0.00572236765935022,  0.006135907273413176,\n",
       "        0.006579332246575682,  0.007054802310718645,  0.007564633275546291,\n",
       "        0.008111308307896872,  0.008697490026177835,    0.0093260334688322,\n",
       "                        0.01,  0.010722672220103232,  0.011497569953977356,\n",
       "        0.012328467394420659,  0.013219411484660293,  0.014174741629268055,\n",
       "         0.01519911082952934,  0.016297508346206444,   0.01747528400007684,\n",
       "         0.01873817422860384,   0.02009233002565047,  0.021544346900318846,\n",
       "        0.023101297000831605,  0.024770763559917114,  0.026560877829466867,\n",
       "         0.02848035868435802,  0.030538555088334154,   0.03274549162877728,\n",
       "         0.03511191734215131,  0.037649358067924674,   0.04037017258596556,\n",
       "         0.04328761281083059,  0.046415888336127795,  0.049770235643321115,\n",
       "          0.0533669923120631,   0.05722367659350217,   0.06135907273413173,\n",
       "         0.06579332246575682,   0.07054802310718646,   0.07564633275546291,\n",
       "         0.08111308307896872,   0.08697490026177834,     0.093260334688322,\n",
       "                         0.1,   0.10722672220103231,   0.11497569953977356,\n",
       "         0.12328467394420659,   0.13219411484660287,   0.14174741629268048,\n",
       "          0.1519911082952933,   0.16297508346206452,    0.1747528400007685,\n",
       "          0.1873817422860385,    0.2009233002565048,   0.21544346900318845,\n",
       "         0.23101297000831603,   0.24770763559917114,   0.26560877829466867,\n",
       "          0.2848035868435802,   0.30538555088334157,   0.32745491628777285,\n",
       "          0.3511191734215131,   0.37649358067924676,   0.40370172585965536,\n",
       "          0.4328761281083057,    0.4641588833612782,   0.49770235643321137,\n",
       "          0.5336699231206312,     0.572236765935022,    0.6135907273413176,\n",
       "          0.6579332246575682,    0.7054802310718645,    0.7564633275546291,\n",
       "          0.8111308307896873,    0.8697490026177834,    0.9326033468832199,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;fit_q_mass_fractal&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fit_q_polymer</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0d956fc4-fa76-4294-8845-7a66d1c3406c' class='xr-index-data-in' type='checkbox'/><label for='index-0d956fc4-fa76-4294-8845-7a66d1c3406c' 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([                0.001, 0.0010722672220103231, 0.0011497569953977356,\n",
       "       0.0012328467394420659, 0.0013219411484660286, 0.0014174741629268048,\n",
       "       0.0015199110829529332, 0.0016297508346206436,  0.001747528400007683,\n",
       "        0.001873817422860383,  0.002009233002565048, 0.0021544346900318843,\n",
       "       0.0023101297000831605, 0.0024770763559917113, 0.0026560877829466868,\n",
       "        0.002848035868435802, 0.0030538555088334154, 0.0032745491628777285,\n",
       "        0.003511191734215131, 0.0037649358067924675,  0.004037017258596553,\n",
       "        0.004328761281083057,  0.004641588833612782,  0.004977023564332114,\n",
       "        0.005336699231206312,   0.00572236765935022,  0.006135907273413176,\n",
       "        0.006579332246575682,  0.007054802310718645,  0.007564633275546291,\n",
       "        0.008111308307896872,  0.008697490026177835,    0.0093260334688322,\n",
       "                        0.01,  0.010722672220103232,  0.011497569953977356,\n",
       "        0.012328467394420659,  0.013219411484660293,  0.014174741629268055,\n",
       "         0.01519911082952934,  0.016297508346206444,   0.01747528400007684,\n",
       "         0.01873817422860384,   0.02009233002565047,  0.021544346900318846,\n",
       "        0.023101297000831605,  0.024770763559917114,  0.026560877829466867,\n",
       "         0.02848035868435802,  0.030538555088334154,   0.03274549162877728,\n",
       "         0.03511191734215131,  0.037649358067924674,   0.04037017258596556,\n",
       "         0.04328761281083059,  0.046415888336127795,  0.049770235643321115,\n",
       "          0.0533669923120631,   0.05722367659350217,   0.06135907273413173,\n",
       "         0.06579332246575682,   0.07054802310718646,   0.07564633275546291,\n",
       "         0.08111308307896872,   0.08697490026177834,     0.093260334688322,\n",
       "                         0.1,   0.10722672220103231,   0.11497569953977356,\n",
       "         0.12328467394420659,   0.13219411484660287,   0.14174741629268048,\n",
       "          0.1519911082952933,   0.16297508346206452,    0.1747528400007685,\n",
       "          0.1873817422860385,    0.2009233002565048,   0.21544346900318845,\n",
       "         0.23101297000831603,   0.24770763559917114,   0.26560877829466867,\n",
       "          0.2848035868435802,   0.30538555088334157,   0.32745491628777285,\n",
       "          0.3511191734215131,   0.37649358067924676,   0.40370172585965536,\n",
       "          0.4328761281083057,    0.4641588833612782,   0.49770235643321137,\n",
       "          0.5336699231206312,     0.572236765935022,    0.6135907273413176,\n",
       "          0.6579332246575682,    0.7054802310718645,    0.7564633275546291,\n",
       "          0.8111308307896873,    0.8697490026177834,    0.9326033468832199,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;fit_q_polymer&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7842eb2-4e32-43c2-8a25-7b9cacace41e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d7842eb2-4e32-43c2-8a25-7b9cacace41e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 202kB\n",
       "Dimensions:                    (sample: 30, q: 100, models: 3,\n",
       "                                surface_fractal_params: 3,\n",
       "                                mass_fractal_params: 3, polymer_params: 3,\n",
       "                                fit_q_surface_fractal: 100,\n",
       "                                fit_q_mass_fractal: 100, fit_q_polymer: 100)\n",
       "Coordinates:\n",
       "  * q                          (q) float64 800B 0.001 0.001072 ... 0.9326 1.0\n",
       "  * models                     (models) <U15 180B 'surface_fractal' ... 'poly...\n",
       "  * surface_fractal_params     (surface_fractal_params) <U26 312B 'surface_fr...\n",
       "  * mass_fractal_params        (mass_fractal_params) <U23 276B 'mass_fractal_...\n",
       "  * polymer_params             (polymer_params) <U18 216B 'polymer_scale' ......\n",
       "  * fit_q_surface_fractal      (fit_q_surface_fractal) float64 800B 0.001 ......\n",
       "  * fit_q_mass_fractal         (fit_q_mass_fractal) float64 800B 0.001 ... 1.0\n",
       "  * fit_q_polymer              (fit_q_polymer) float64 800B 0.001 ... 1.0\n",
       "Dimensions without coordinates: sample\n",
       "Data variables: (12/18)\n",
       "    I                          (sample, q) float64 24kB 5.465e+03 ... 1.002\n",
       "    dI                         (sample, q) float64 24kB 545.9 429.3 ... 0.09923\n",
       "    model                      (sample) object 240B 'surface_fractal' ... 'ma...\n",
       "    sas_fit_sample             (sample) int64 240B 0 1 2 3 4 ... 25 26 27 28 29\n",
       "    fit_all_chisq              (sample, models) float64 720B 1.023 ... 27.2\n",
       "    probabilities              (sample, models) float64 720B 0.01182 ... 0.00...\n",
       "    ...                         ...\n",
       "    fit_I_surface_fractal      (sample, fit_q_surface_fractal) float64 24kB 5...\n",
       "    residuals_surface_fractal  (sample, fit_q_surface_fractal) float64 24kB -...\n",
       "    fit_I_mass_fractal         (sample, fit_q_mass_fractal) float64 24kB 956....\n",
       "    residuals_mass_fractal     (sample, fit_q_mass_fractal) float64 24kB -8.2...\n",
       "    fit_I_polymer              (sample, fit_q_polymer) float64 24kB 6.219 ......\n",
       "    residuals_polymer          (sample, fit_q_polymer) float64 24kB -10.0 ......"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_result = autosas_pipeline1.calculate(ds)\n",
    "ds_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccc903fa",
   "metadata": {},
   "source": [
    "Now, this is a large dataset. You should see 18 data variables. This includes:\n",
    "\n",
    "1. Input data variables (`I`, `dI`, `q`, `model`, `sas_fit_sample`)\n",
    "2. Fit quality metrics (`fit_all_chisq`, `probabilities`) \n",
    "3. Model parameters for each model type (`surface_fractal_params`, `mass_fractal_params`, `polymer_params`)\n",
    "4. Fitted intensity curves for each model (`fit_I_surface_fractal`, `fit_I_mass_fractal`, `fit_I_polymer`)\n",
    "5. Residuals for each model (`residuals_surface_fractal`, `residuals_mass_fractal`, `residuals_polymer`)\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51db5d04",
   "metadata": {},
   "source": [
    "## Evaluating the Fit Results\n",
    "\n",
    "Okay, now we must evaluate the quality of the fits. If the parameters of the model_inputs dialog are too constraining, then the fit might not be able to be fully optimized. \n",
    "\n",
    "\n",
    "We'll plot all three fits to a given measurement (defined by `data_index`) below and along with the residual for the fit. Vary the `data_index` in order to assess the quality of the models for different data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2785c78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(5, 6), height_ratios=[4, 1], sharex=True)\n",
    "\n",
    "data_index = 0\n",
    "\n",
    "# Top plot - data and fits\n",
    "ds_result.isel(sample=data_index).I.plot.line(x='q', xscale='log', yscale='log', marker='.', ls='None', label='data', ax=ax1)\n",
    "ds_result.isel(sample=data_index).fit_I_surface_fractal.plot.line(x='fit_q_surface_fractal', xscale='log', yscale='log', label='surface_fractal', ax=ax1)\n",
    "ds_result.isel(sample=data_index).fit_I_mass_fractal.plot.line(x='fit_q_mass_fractal', xscale='log', yscale='log', label='mass_fractal', ax=ax1)\n",
    "ds_result.isel(sample=data_index).fit_I_polymer.plot.line(x='fit_q_polymer', xscale='log', yscale='log', label='polymer', ax=ax1)\n",
    "ax1.legend()\n",
    "ax1.set(xlabel=None,ylabel='Intensity [A.U.]')\n",
    "\n",
    "ax1.get_lines()[0].set_color('C0')  # Keep data points as first color\n",
    "\n",
    "\n",
    "# Bottom plot - residuals\n",
    "ds_result.isel(sample=data_index).residuals_surface_fractal.plot(x='fit_q_surface_fractal', xscale='log',  ax=ax2)\n",
    "ds_result.isel(sample=data_index).residuals_mass_fractal.plot(x='fit_q_mass_fractal', xscale='log',  ax=ax2)\n",
    "ds_result.isel(sample=data_index).residuals_polymer.plot(x='fit_q_polymer', xscale='log',  ax=ax2)\n",
    "ax2.axhline(y=0, color='k', linestyle='--', alpha=0.5)\n",
    "ax2.get_lines()[1].set_color('C2')  # Skip C1, use C2 for surface_fractal\n",
    "ax2.get_lines()[2].set_color('C3')  # Use C3 for mass_fractal  \n",
    "ax2.get_lines()[3].set_color('C4')  # Use C4 for polymer\n",
    "ax2.set(xlabel='q [$\\AA^{-1}$]',ylabel='Residual')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1ff133",
   "metadata": {},
   "source": [
    "The residuals (differences between the model and data) provide a key way to assess the quality of the fits. A good fit should show residuals that:\n",
    "\n",
    "1. Are randomly scattered around zero\n",
    "2. Have no clear systematic trends or patterns\n",
    "3. Are roughly within ±2-3 standard deviations of zero\n",
    "\n",
    "Let's plot the residuals for the first sample to assess the quality of our surface fractal fits:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a701dd42",
   "metadata": {},
   "source": [
    "## Adding Model Selection to the Pipeline\n",
    "\n",
    "\n",
    "Okay, now to the hard part. We've fit the data, but we need to assess which model is best for each measurement. AutoSAS has a number of built in model selection tools for making this decision:\n",
    "\n",
    "1. Bayesian Information Criterion (BIC) - Balances model complexity with goodness of fit\n",
    "2. Akaike Information Criterion (AIC) - Similar to BIC but penalizes complexity less\n",
    "3. Probability-based selection - Uses Bayesian probability theory to calculate model likelihoods\n",
    "4. Chi-squared test - Compares the goodness of fit between models\n",
    "\n",
    "We'll use the Chi-square option for this demonstration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0f3d73d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PipelineOp                               input_variable ---> output_variable\n",
      "----------                               -----------------------------------\n",
      "0  ) <AutoSAS>                           ['q', 'I', 'dI'] ---> ['fit_all_chisq']\n",
      "1  ) <ModelSelection_BestChiSq>          ['fit_all_chisq', 'models'] ---> ['BestChiSq_labels', 'BestChiSq_label_names']\n",
      "\n",
      "Input Variables\n",
      "---------------\n",
      "0) q\n",
      "1) I\n",
      "2) dI\n",
      "3) models\n",
      "\n",
      "Output Variables\n",
      "----------------\n",
      "0) BestChiSq_labels\n",
      "1) BestChiSq_label_names\n"
     ]
    }
   ],
   "source": [
    "from AFL.double_agent.AutoSAS import ModelSelectBestChiSq\n",
    "\n",
    "with Pipeline() as autosas_pipeline2:\n",
    "    AutoSAS(\n",
    "        sas_variable='I',\n",
    "        sas_err_variable='dI',\n",
    "        q_variable = 'q',\n",
    "        output_prefix='fit',\n",
    "        model_inputs=model_inputs,\n",
    "    )\n",
    "\n",
    "    ModelSelectBestChiSq(\n",
    "        all_chisq_var='fit_all_chisq',\n",
    "        model_names_var='models',\n",
    "        sample_dim='sample',\n",
    "        output_prefix='BestChiSq',\n",
    "    )\n",
    "autosas_pipeline2.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b99dbfa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc1dd5cf35d2417dbe619cc8b94a31c2",
       "version_major": 2,
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 205kB\n",
       "Dimensions:                    (sample: 30, q: 100, models: 3,\n",
       "                                surface_fractal_params: 3,\n",
       "                                mass_fractal_params: 3, polymer_params: 3,\n",
       "                                fit_q_surface_fractal: 100,\n",
       "                                fit_q_mass_fractal: 100, fit_q_polymer: 100)\n",
       "Coordinates:\n",
       "  * q                          (q) float64 800B 0.001 0.001072 ... 0.9326 1.0\n",
       "  * models                     (models) &lt;U15 180B &#x27;surface_fractal&#x27; ... &#x27;poly...\n",
       "  * surface_fractal_params     (surface_fractal_params) &lt;U26 312B &#x27;surface_fr...\n",
       "  * mass_fractal_params        (mass_fractal_params) &lt;U23 276B &#x27;mass_fractal_...\n",
       "  * polymer_params             (polymer_params) &lt;U18 216B &#x27;polymer_scale&#x27; ......\n",
       "  * fit_q_surface_fractal      (fit_q_surface_fractal) float64 800B 0.001 ......\n",
       "  * fit_q_mass_fractal         (fit_q_mass_fractal) float64 800B 0.001 ... 1.0\n",
       "  * fit_q_polymer              (fit_q_polymer) float64 800B 0.001 ... 1.0\n",
       "Dimensions without coordinates: sample\n",
       "Data variables: (12/21)\n",
       "    I                          (sample, q) float64 24kB 5.465e+03 ... 1.002\n",
       "    dI                         (sample, q) float64 24kB 545.9 429.3 ... 0.09923\n",
       "    model                      (sample) object 240B &#x27;surface_fractal&#x27; ... &#x27;ma...\n",
       "    sas_fit_sample             (sample) int64 240B 0 1 2 3 4 ... 25 26 27 28 29\n",
       "    fit_all_chisq              (sample, models) float64 720B 1.023 ... 27.2\n",
       "    probabilities              (sample, models) float64 720B 0.01182 ... 0.00...\n",
       "    ...                         ...\n",
       "    residuals_mass_fractal     (sample, fit_q_mass_fractal) float64 24kB -8.2...\n",
       "    fit_I_polymer              (sample, fit_q_polymer) float64 24kB 6.219 ......\n",
       "    residuals_polymer          (sample, fit_q_polymer) float64 24kB -10.0 ......\n",
       "    BestChiSq_labels           (sample) int64 240B 0 1 0 1 2 0 0 ... 2 1 2 1 2 1\n",
       "    BestChiSq_ChiSq            (sample) float64 240B 1.023 0.9502 ... 1.086\n",
       "    BestChiSq_label_names      (sample) &lt;U15 2kB &#x27;surface_fractal&#x27; ... &#x27;mass_...</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-a4b176dd-06b6-4aa3-bc9f-0269d03c0074' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4b176dd-06b6-4aa3-bc9f-0269d03c0074' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>sample</span>: 30</li><li><span class='xr-has-index'>q</span>: 100</li><li><span class='xr-has-index'>models</span>: 3</li><li><span class='xr-has-index'>surface_fractal_params</span>: 3</li><li><span class='xr-has-index'>mass_fractal_params</span>: 3</li><li><span class='xr-has-index'>polymer_params</span>: 3</li><li><span class='xr-has-index'>fit_q_surface_fractal</span>: 100</li><li><span class='xr-has-index'>fit_q_mass_fractal</span>: 100</li><li><span class='xr-has-index'>fit_q_polymer</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ea2c4371-e72b-492d-8ea6-75262c10c1e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea2c4371-e72b-492d-8ea6-75262c10c1e9' 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'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001072 ... 0.9326 1.0</div><input id='attrs-932ef913-98b0-49d0-9b1e-b916507c2500' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-932ef913-98b0-49d0-9b1e-b916507c2500' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc1b4d4-2b3e-4b81-86cf-b5c4af9f262e' class='xr-var-data-in' type='checkbox'><label for='data-3dc1b4d4-2b3e-4b81-86cf-b5c4af9f262e' 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([0.001   , 0.001072, 0.00115 , 0.001233, 0.001322, 0.001417, 0.00152 ,\n",
       "       0.00163 , 0.001748, 0.001874, 0.002009, 0.002154, 0.00231 , 0.002477,\n",
       "       0.002656, 0.002848, 0.003054, 0.003275, 0.003511, 0.003765, 0.004037,\n",
       "       0.004329, 0.004642, 0.004977, 0.005337, 0.005722, 0.006136, 0.006579,\n",
       "       0.007055, 0.007565, 0.008111, 0.008697, 0.009326, 0.01    , 0.010723,\n",
       "       0.011498, 0.012328, 0.013219, 0.014175, 0.015199, 0.016298, 0.017475,\n",
       "       0.018738, 0.020092, 0.021544, 0.023101, 0.024771, 0.026561, 0.02848 ,\n",
       "       0.030539, 0.032745, 0.035112, 0.037649, 0.04037 , 0.043288, 0.046416,\n",
       "       0.04977 , 0.053367, 0.057224, 0.061359, 0.065793, 0.070548, 0.075646,\n",
       "       0.081113, 0.086975, 0.09326 , 0.1     , 0.107227, 0.114976, 0.123285,\n",
       "       0.132194, 0.141747, 0.151991, 0.162975, 0.174753, 0.187382, 0.200923,\n",
       "       0.215443, 0.231013, 0.247708, 0.265609, 0.284804, 0.305386, 0.327455,\n",
       "       0.351119, 0.376494, 0.403702, 0.432876, 0.464159, 0.497702, 0.53367 ,\n",
       "       0.572237, 0.613591, 0.657933, 0.70548 , 0.756463, 0.811131, 0.869749,\n",
       "       0.932603, 1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>models</span></div><div class='xr-var-dims'>(models)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;surface_fractal&#x27; ... &#x27;polymer&#x27;</div><input id='attrs-80f7cb38-4333-4c35-99d8-39692b7cc5b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80f7cb38-4333-4c35-99d8-39692b7cc5b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99a2638f-1dab-4da7-9e2e-354c9fb5e2bd' class='xr-var-data-in' type='checkbox'><label for='data-99a2638f-1dab-4da7-9e2e-354c9fb5e2bd' 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;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>surface_fractal_params</span></div><div class='xr-var-dims'>(surface_fractal_params)</div><div class='xr-var-dtype'>&lt;U26</div><div class='xr-var-preview xr-preview'>&#x27;surface_fractal_power&#x27; ... &#x27;sur...</div><input id='attrs-8450d14a-8407-4e24-9f3a-5b0dcf13ae93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8450d14a-8407-4e24-9f3a-5b0dcf13ae93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-431cb697-d570-4ba3-936f-0ae293815f82' class='xr-var-data-in' type='checkbox'><label for='data-431cb697-d570-4ba3-936f-0ae293815f82' 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;surface_fractal_power&#x27;, &#x27;surface_fractal_scale&#x27;,\n",
       "       &#x27;surface_fractal_background&#x27;], dtype=&#x27;&lt;U26&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mass_fractal_params</span></div><div class='xr-var-dims'>(mass_fractal_params)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>&#x27;mass_fractal_power&#x27; ... &#x27;mass_f...</div><input id='attrs-e6ac7700-d955-4ad9-8f15-0da4e7f4f12f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6ac7700-d955-4ad9-8f15-0da4e7f4f12f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2888b70b-409c-4cb7-a97f-68b257d10c57' class='xr-var-data-in' type='checkbox'><label for='data-2888b70b-409c-4cb7-a97f-68b257d10c57' 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;mass_fractal_power&#x27;, &#x27;mass_fractal_scale&#x27;, &#x27;mass_fractal_background&#x27;],\n",
       "      dtype=&#x27;&lt;U23&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>polymer_params</span></div><div class='xr-var-dims'>(polymer_params)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;polymer_scale&#x27; ... &#x27;polymer_bac...</div><input id='attrs-3e76eacf-8d89-48d5-aea5-75d09762c461' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e76eacf-8d89-48d5-aea5-75d09762c461' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b2031cd-8720-42a7-ba1b-1ec706639c9f' class='xr-var-data-in' type='checkbox'><label for='data-5b2031cd-8720-42a7-ba1b-1ec706639c9f' 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;polymer_scale&#x27;, &#x27;polymer_rg&#x27;, &#x27;polymer_background&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fit_q_surface_fractal</span></div><div class='xr-var-dims'>(fit_q_surface_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001072 ... 0.9326 1.0</div><input id='attrs-2c872d94-12af-48a3-8b7e-675db82b3f94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c872d94-12af-48a3-8b7e-675db82b3f94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1aa56b16-43d6-401e-aac6-f0857eb06d85' class='xr-var-data-in' type='checkbox'><label for='data-1aa56b16-43d6-401e-aac6-f0857eb06d85' 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([0.001   , 0.001072, 0.00115 , 0.001233, 0.001322, 0.001417, 0.00152 ,\n",
       "       0.00163 , 0.001748, 0.001874, 0.002009, 0.002154, 0.00231 , 0.002477,\n",
       "       0.002656, 0.002848, 0.003054, 0.003275, 0.003511, 0.003765, 0.004037,\n",
       "       0.004329, 0.004642, 0.004977, 0.005337, 0.005722, 0.006136, 0.006579,\n",
       "       0.007055, 0.007565, 0.008111, 0.008697, 0.009326, 0.01    , 0.010723,\n",
       "       0.011498, 0.012328, 0.013219, 0.014175, 0.015199, 0.016298, 0.017475,\n",
       "       0.018738, 0.020092, 0.021544, 0.023101, 0.024771, 0.026561, 0.02848 ,\n",
       "       0.030539, 0.032745, 0.035112, 0.037649, 0.04037 , 0.043288, 0.046416,\n",
       "       0.04977 , 0.053367, 0.057224, 0.061359, 0.065793, 0.070548, 0.075646,\n",
       "       0.081113, 0.086975, 0.09326 , 0.1     , 0.107227, 0.114976, 0.123285,\n",
       "       0.132194, 0.141747, 0.151991, 0.162975, 0.174753, 0.187382, 0.200923,\n",
       "       0.215443, 0.231013, 0.247708, 0.265609, 0.284804, 0.305386, 0.327455,\n",
       "       0.351119, 0.376494, 0.403702, 0.432876, 0.464159, 0.497702, 0.53367 ,\n",
       "       0.572237, 0.613591, 0.657933, 0.70548 , 0.756463, 0.811131, 0.869749,\n",
       "       0.932603, 1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fit_q_mass_fractal</span></div><div class='xr-var-dims'>(fit_q_mass_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001072 ... 0.9326 1.0</div><input id='attrs-f3fbaae9-9517-4917-894f-aaeac23f70a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3fbaae9-9517-4917-894f-aaeac23f70a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8eb6bebe-c6ae-4274-8a0c-b76d1ca83dae' class='xr-var-data-in' type='checkbox'><label for='data-8eb6bebe-c6ae-4274-8a0c-b76d1ca83dae' 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([0.001   , 0.001072, 0.00115 , 0.001233, 0.001322, 0.001417, 0.00152 ,\n",
       "       0.00163 , 0.001748, 0.001874, 0.002009, 0.002154, 0.00231 , 0.002477,\n",
       "       0.002656, 0.002848, 0.003054, 0.003275, 0.003511, 0.003765, 0.004037,\n",
       "       0.004329, 0.004642, 0.004977, 0.005337, 0.005722, 0.006136, 0.006579,\n",
       "       0.007055, 0.007565, 0.008111, 0.008697, 0.009326, 0.01    , 0.010723,\n",
       "       0.011498, 0.012328, 0.013219, 0.014175, 0.015199, 0.016298, 0.017475,\n",
       "       0.018738, 0.020092, 0.021544, 0.023101, 0.024771, 0.026561, 0.02848 ,\n",
       "       0.030539, 0.032745, 0.035112, 0.037649, 0.04037 , 0.043288, 0.046416,\n",
       "       0.04977 , 0.053367, 0.057224, 0.061359, 0.065793, 0.070548, 0.075646,\n",
       "       0.081113, 0.086975, 0.09326 , 0.1     , 0.107227, 0.114976, 0.123285,\n",
       "       0.132194, 0.141747, 0.151991, 0.162975, 0.174753, 0.187382, 0.200923,\n",
       "       0.215443, 0.231013, 0.247708, 0.265609, 0.284804, 0.305386, 0.327455,\n",
       "       0.351119, 0.376494, 0.403702, 0.432876, 0.464159, 0.497702, 0.53367 ,\n",
       "       0.572237, 0.613591, 0.657933, 0.70548 , 0.756463, 0.811131, 0.869749,\n",
       "       0.932603, 1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>fit_q_polymer</span></div><div class='xr-var-dims'>(fit_q_polymer)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001072 ... 0.9326 1.0</div><input id='attrs-ffd806d5-2ce8-4e8e-b2de-8bc311c36283' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ffd806d5-2ce8-4e8e-b2de-8bc311c36283' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a993a848-f0c7-4a1c-835e-74618587028e' class='xr-var-data-in' type='checkbox'><label for='data-a993a848-f0c7-4a1c-835e-74618587028e' 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([0.001   , 0.001072, 0.00115 , 0.001233, 0.001322, 0.001417, 0.00152 ,\n",
       "       0.00163 , 0.001748, 0.001874, 0.002009, 0.002154, 0.00231 , 0.002477,\n",
       "       0.002656, 0.002848, 0.003054, 0.003275, 0.003511, 0.003765, 0.004037,\n",
       "       0.004329, 0.004642, 0.004977, 0.005337, 0.005722, 0.006136, 0.006579,\n",
       "       0.007055, 0.007565, 0.008111, 0.008697, 0.009326, 0.01    , 0.010723,\n",
       "       0.011498, 0.012328, 0.013219, 0.014175, 0.015199, 0.016298, 0.017475,\n",
       "       0.018738, 0.020092, 0.021544, 0.023101, 0.024771, 0.026561, 0.02848 ,\n",
       "       0.030539, 0.032745, 0.035112, 0.037649, 0.04037 , 0.043288, 0.046416,\n",
       "       0.04977 , 0.053367, 0.057224, 0.061359, 0.065793, 0.070548, 0.075646,\n",
       "       0.081113, 0.086975, 0.09326 , 0.1     , 0.107227, 0.114976, 0.123285,\n",
       "       0.132194, 0.141747, 0.151991, 0.162975, 0.174753, 0.187382, 0.200923,\n",
       "       0.215443, 0.231013, 0.247708, 0.265609, 0.284804, 0.305386, 0.327455,\n",
       "       0.351119, 0.376494, 0.403702, 0.432876, 0.464159, 0.497702, 0.53367 ,\n",
       "       0.572237, 0.613591, 0.657933, 0.70548 , 0.756463, 0.811131, 0.869749,\n",
       "       0.932603, 1.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4330d15e-0c11-4608-8311-5f68739ac944' class='xr-section-summary-in' type='checkbox'  ><label for='section-4330d15e-0c11-4608-8311-5f68739ac944' class='xr-section-summary' >Data variables: <span>(21)</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>I</span></div><div class='xr-var-dims'>(sample, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.465e+03 4.598e+03 ... 1.1 1.002</div><input id='attrs-6b2206c1-4042-4309-93d0-feaaef3dbc75' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b2206c1-4042-4309-93d0-feaaef3dbc75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83f16d23-8706-41eb-b218-218f99c52e10' class='xr-var-data-in' type='checkbox'><label for='data-83f16d23-8706-41eb-b218-218f99c52e10' 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([[5.46461639e+03, 4.59795217e+03, 3.85947764e+03, ...,\n",
       "        8.23637450e-01, 1.08891674e+00, 8.86408253e-01],\n",
       "       [1.14146226e+04, 1.20480054e+04, 9.60389049e+03, ...,\n",
       "        1.09249128e+00, 1.03151090e+00, 1.02766190e+00],\n",
       "       [3.38673256e+03, 2.48217196e+03, 2.22022900e+03, ...,\n",
       "        1.16251134e+00, 1.01348467e+00, 1.15549165e+00],\n",
       "       ...,\n",
       "       [2.33984274e+04, 2.43947831e+04, 2.05533255e+04, ...,\n",
       "        1.04267181e+00, 9.85962747e-01, 9.07216458e-01],\n",
       "       [2.09634325e+00, 2.08464279e+00, 1.81224765e+00, ...,\n",
       "        1.10735048e+00, 9.92568176e-01, 9.57666868e-01],\n",
       "       [1.14052557e+04, 7.68845857e+03, 7.42318078e+03, ...,\n",
       "        1.03875573e+00, 1.10006506e+00, 1.00202285e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dI</span></div><div class='xr-var-dims'>(sample, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>545.9 429.3 ... 0.09923 0.09923</div><input id='attrs-2a2bdc56-53e7-4e05-85db-8c3f4657cbf9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a2bdc56-53e7-4e05-85db-8c3f4657cbf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62f72259-ed88-457f-ab07-dd596eeb7ebd' class='xr-var-data-in' type='checkbox'><label for='data-62f72259-ed88-457f-ab07-dd596eeb7ebd' 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([[5.45853473e+02, 4.29328308e+02, 3.42588723e+02, ...,\n",
       "        9.70709140e-02, 9.70708823e-02, 9.70708568e-02],\n",
       "       [1.34833750e+03, 1.10406203e+03, 9.14697444e+02, ...,\n",
       "        1.00109160e-01, 1.00106587e-01, 1.00104428e-01],\n",
       "       [3.10385847e+02, 2.45619675e+02, 1.97113531e+02, ...,\n",
       "        9.93385928e-02, 9.93385623e-02, 9.93385376e-02],\n",
       "       ...,\n",
       "       [2.78764336e+03, 2.26312770e+03, 1.86001084e+03, ...,\n",
       "        9.80268288e-02, 9.80242931e-02, 9.80221832e-02],\n",
       "       [2.04875154e-01, 2.04855122e-01, 2.04833121e-01, ...,\n",
       "        1.02022577e-01, 1.02022027e-01, 1.02021575e-01],\n",
       "       [1.15537649e+03, 9.47070166e+02, 7.85415799e+02, ...,\n",
       "        9.92344457e-02, 9.92320281e-02, 9.92299984e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;surface_fractal&#x27; ... &#x27;mass_frac...</div><input id='attrs-f844c5c5-9ef6-4195-ad95-dcbcbb191f51' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f844c5c5-9ef6-4195-ad95-dcbcbb191f51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fa34cbf-6a11-4302-a526-cd600d637d5b' class='xr-var-data-in' type='checkbox'><label for='data-5fa34cbf-6a11-4302-a526-cd600d637d5b' 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;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;surface_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;surface_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;polymer_excl_volume&#x27;, &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;,\n",
       "       &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;polymer_excl_volume&#x27;, &#x27;mass_fractal&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sas_fit_sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 24 25 26 27 28 29</div><input id='attrs-ee7c7ad5-3b96-4a47-9fec-d6147bb86d0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee7c7ad5-3b96-4a47-9fec-d6147bb86d0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df691671-d765-45e7-936b-e444d984d9b2' class='xr-var-data-in' type='checkbox'><label for='data-df691671-d765-45e7-936b-e444d984d9b2' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fit_all_chisq</span></div><div class='xr-var-dims'>(sample, models)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.023 14.07 27.32 ... 1.086 27.2</div><input id='attrs-b75ed9f1-03c9-42c6-8f98-0700bd3801b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b75ed9f1-03c9-42c6-8f98-0700bd3801b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd9e1e64-2e67-4d19-837d-f8200e5fc6e3' class='xr-var-data-in' type='checkbox'><label for='data-dd9e1e64-2e67-4d19-837d-f8200e5fc6e3' 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>fit_calc_id :</span></dt><dd>ASAS-44f48a96-589b-473b-8411-8df15b6c95a4</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[1.02275423e+00, 1.40722782e+01, 2.73224420e+01],\n",
       "       [4.09722407e+00, 9.50171680e-01, 2.63673388e+01],\n",
       "       [1.05413472e+00, 1.36841369e+01, 2.72866689e+01],\n",
       "       [1.14629265e+00, 1.04990624e+00, 3.17019675e+01],\n",
       "       [6.96376204e+05, 1.35878440e+02, 1.18324132e+00],\n",
       "       [1.08126923e+00, 1.19570931e+01, 2.58392185e+01],\n",
       "       [8.36611432e-01, 1.21540498e+01, 2.69767485e+01],\n",
       "       [8.92581487e-01, 1.12844843e+01, 2.53700558e+01],\n",
       "       [7.43408306e+05, 1.44644370e+02, 1.11691133e+00],\n",
       "       [4.08984295e+00, 9.12932757e-01, 2.64968640e+01],\n",
       "       [1.32050220e+00, 9.75581038e-01, 3.07720967e+01],\n",
       "       [7.06812364e+05, 1.37343734e+02, 9.18066715e-01],\n",
       "       [7.71372671e+05, 1.56599393e+02, 9.73842068e-01],\n",
       "       [1.57930523e+00, 1.01924014e+00, 3.04306793e+01],\n",
       "       [9.31217832e-01, 1.24766894e+01, 2.55126991e+01],\n",
       "       [9.27394330e-01, 1.03076313e+01, 2.44011743e+01],\n",
       "       [4.01568675e+00, 8.51332910e-01, 2.83843343e+01],\n",
       "       [1.07638344e+00, 1.05327569e+01, 2.38169213e+01],\n",
       "       [8.71404947e-01, 8.93694309e+00, 2.19797127e+01],\n",
       "       [1.08324978e+00, 9.81511077e+00, 2.41165279e+01],\n",
       "       [7.36691528e+05, 1.47836036e+02, 1.09358942e+00],\n",
       "       [7.15150775e+05, 1.38734930e+02, 1.10718714e+00],\n",
       "       [7.28863488e+05, 1.47481645e+02, 9.61265988e-01],\n",
       "       [2.05590837e+00, 1.13113537e+00, 2.80256684e+01],\n",
       "       [7.30479177e+05, 1.44870136e+02, 1.03919799e+00],\n",
       "       [1.23729309e+00, 8.66385752e-01, 2.81719466e+01],\n",
       "       [7.49889915e+05, 1.51181543e+02, 9.99128955e-01],\n",
       "       [2.42291207e+00, 1.16555890e+00, 3.01412948e+01],\n",
       "       [7.08550329e+05, 1.41412767e+02, 1.08373657e+00],\n",
       "       [4.75962943e+00, 1.08625395e+00, 2.72039930e+01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>probabilities</span></div><div class='xr-var-dims'>(sample, models)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01182 0.9238 ... 0.9984 0.001121</div><input id='attrs-093ee18a-950e-4497-810d-56525c833a48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-093ee18a-950e-4497-810d-56525c833a48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-474fe053-7447-4eaa-bc82-b7e082ebc39e' class='xr-var-data-in' type='checkbox'><label for='data-474fe053-7447-4eaa-bc82-b7e082ebc39e' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[1.18241649e-02, 9.23820654e-01, 6.43551809e-02],\n",
       "       [1.23607135e-03, 9.96341125e-01, 2.42280337e-03],\n",
       "       [8.23266617e-03, 9.40641961e-01, 5.11253726e-02],\n",
       "       [4.27533439e-01, 5.72377505e-01, 8.90553923e-05],\n",
       "       [0.00000000e+00, 1.73073808e-64, 1.00000000e+00],\n",
       "       [1.49740488e-03, 9.57828584e-01, 4.06740111e-02],\n",
       "       [3.55950627e-03, 9.80632154e-01, 1.58083397e-02],\n",
       "       [9.17293426e-04, 9.47240896e-01, 5.18418105e-02],\n",
       "       [0.00000000e+00, 1.78346827e-68, 1.00000000e+00],\n",
       "       [1.27297309e-02, 9.85285111e-01, 1.98515819e-03],\n",
       "       [3.07295385e-01, 6.92659281e-01, 4.53336987e-05],\n",
       "       [0.00000000e+00, 2.87852843e-65, 1.00000000e+00],\n",
       "       [0.00000000e+00, 9.22602333e-74, 1.00000000e+00],\n",
       "       [2.61966631e-01, 7.37981212e-01, 5.21571954e-05],\n",
       "       [3.16094856e-03, 9.03018069e-01, 9.38209827e-02],\n",
       "       [3.49580420e-04, 9.71200269e-01, 2.84501501e-02],\n",
       "       [1.29078990e-02, 9.86763088e-01, 3.29012466e-04],\n",
       "       [3.03155123e-04, 7.98369591e-01, 2.01327254e-01],\n",
       "       [8.19662930e-05, 9.22293393e-01, 7.76246408e-02],\n",
       "       [1.01633723e-04, 5.47733346e-01, 4.52165021e-01],\n",
       "       [0.00000000e+00, 7.95457604e-70, 1.00000000e+00],\n",
       "       [0.00000000e+00, 6.45880860e-66, 1.00000000e+00],\n",
       "       [0.00000000e+00, 8.57179161e-70, 1.00000000e+00],\n",
       "       [1.74573275e-01, 8.24542384e-01, 8.84340955e-04],\n",
       "       [0.00000000e+00, 1.81958266e-68, 1.00000000e+00],\n",
       "       [3.19004059e-01, 6.80291474e-01, 7.04467341e-04],\n",
       "       [0.00000000e+00, 2.68265729e-71, 1.00000000e+00],\n",
       "       [1.02787938e-01, 8.97121386e-01, 9.06757543e-05],\n",
       "       [0.00000000e+00, 5.10032208e-67, 1.00000000e+00],\n",
       "       [5.20020767e-04, 9.98358893e-01, 1.12108653e-03]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_fractal_fit_val</span></div><div class='xr-var-dims'>(surface_fractal_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.243 3.0 3.163 3.0 ... 1.0 1.0 1.0</div><input id='attrs-72adf50c-d3b3-474f-8f0c-f48960feb2be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72adf50c-d3b3-474f-8f0c-f48960feb2be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b0af610-283b-4214-8d3a-b574f4ead1d1' class='xr-var-data-in' type='checkbox'><label for='data-6b0af610-283b-4214-8d3a-b574f4ead1d1' 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>fit_calc_id :</span></dt><dd>ASAS-44f48a96-589b-473b-8411-8df15b6c95a4</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[3.24265739e+00, 3.00000000e+00, 3.16252070e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.08615609e+00, 3.15552503e+00, 3.05113167e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.25242366e+00, 3.03037286e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
       "        3.00000000e+00, 3.00000000e+00],\n",
       "       [1.00000000e-06, 1.88805902e-05, 1.00000000e-06, 8.07491165e-05,\n",
       "        1.00000000e-06, 1.00000000e-06, 1.24393004e-06, 1.00000000e-06,\n",
       "        1.00000000e-06, 2.18202275e-05, 6.10328588e-05, 1.00000000e-06,\n",
       "        1.00000000e-06, 5.96185299e-05, 1.11492212e-06, 1.00000000e-06,\n",
       "        2.25881511e-05, 1.00000000e-06, 1.00000000e-06, 1.00000000e-06,\n",
       "        1.00000000e-06, 1.00000000e-06, 1.00000000e-06, 5.01149203e-05,\n",
       "        1.00000000e-06, 6.92824955e-05, 1.00000000e-06, 3.59572491e-05,\n",
       "        1.00000000e-06, 1.64517485e-05],\n",
       "       [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_fractal_fit_err</span></div><div class='xr-var-dims'>(surface_fractal_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0227 0.01792 0.02407 ... 0.0 0.0</div><input id='attrs-9aeabb98-9010-4cf2-b8dc-5db8d5a4cf13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9aeabb98-9010-4cf2-b8dc-5db8d5a4cf13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-becd360c-960d-4c99-a001-5da3442fad14' class='xr-var-data-in' type='checkbox'><label for='data-becd360c-960d-4c99-a001-5da3442fad14' 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>fit_calc_id :</span></dt><dd>ASAS-44f48a96-589b-473b-8411-8df15b6c95a4</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[2.27048704e-02, 1.79223126e-02, 2.40659471e-02, 1.30755205e-02,\n",
       "        7.27887423e-04, 2.55743991e-02, 2.29994739e-02, 2.63328387e-02,\n",
       "        7.04419584e-04, 1.73100251e-02, 1.37324240e-02, 7.22326241e-04,\n",
       "        6.91839641e-04, 1.39774626e-02, 2.28313482e-02, 2.70634988e-02,\n",
       "        1.68284235e-02, 2.78456852e-02, 2.78247365e-02, 2.77249316e-02,\n",
       "        7.07711080e-04, 7.18157333e-04, 7.11603537e-04, 1.46252174e-02,\n",
       "        7.10825868e-04, 1.38115165e-02, 7.01610295e-04, 1.51664322e-02,\n",
       "        7.21627230e-04, 1.82691043e-02],\n",
       "       [1.30712675e-07, 1.99407460e-06, 1.39863344e-07, 5.68296258e-06,\n",
       "        4.93677731e-09, 1.49719861e-07, 1.64556199e-07, 1.55220020e-07,\n",
       "        4.77760508e-09, 2.21210410e-06, 4.59230273e-06, 4.89904463e-09,\n",
       "        4.69228908e-09, 4.58996792e-06, 1.45467891e-07, 1.59749280e-07,\n",
       "        2.22355435e-06, 1.64812550e-07, 1.64903795e-07, 1.64396226e-07,\n",
       "        4.79992379e-09, 4.87077236e-09, 4.82632551e-09, 4.07911051e-06,\n",
       "        4.82106811e-09, 5.20163539e-06, 4.75856119e-09, 3.10007783e-06,\n",
       "        4.89431125e-09, 1.77906351e-06],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mass_fractal_fit_val</span></div><div class='xr-var-dims'>(mass_fractal_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.327 2.708 2.263 ... 1.0 1.0 1.0</div><input id='attrs-8f1023ad-390d-41ee-8d60-d93c36caa106' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f1023ad-390d-41ee-8d60-d93c36caa106' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58ea7a55-3522-478b-a687-5f5d66499120' class='xr-var-data-in' type='checkbox'><label for='data-58ea7a55-3522-478b-a687-5f5d66499120' 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>fit_calc_id :</span></dt><dd>ASAS-44f48a96-589b-473b-8411-8df15b6c95a4</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[2.32667232e+00, 2.70771805e+00, 2.26272645e+00, 2.95928814e+00,\n",
       "        1.70000000e+00, 2.20397864e+00, 2.29631053e+00, 2.17898167e+00,\n",
       "        1.70000000e+00, 2.72327751e+00, 2.90766327e+00, 1.70000000e+00,\n",
       "        1.70000000e+00, 2.89774061e+00, 2.35502215e+00, 2.16469122e+00,\n",
       "        2.72861467e+00, 2.13300103e+00, 2.13477745e+00, 2.14056161e+00,\n",
       "        1.70000000e+00, 1.70000000e+00, 1.70000000e+00, 2.86340973e+00,\n",
       "        1.70000000e+00, 2.91737002e+00, 1.70000000e+00, 2.81452282e+00,\n",
       "        1.70000000e+00, 2.68510041e+00],\n",
       "       [1.00000000e-04, 1.00000000e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.00000000e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.04785464e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.03066892e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.05075202e-04, 1.00000000e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.00000000e-04, 1.00000000e-04, 1.04777291e-04,\n",
       "        1.00000000e-04, 1.07215235e-04, 1.00000000e-04, 1.00000000e-04,\n",
       "        1.00000000e-04, 1.00000000e-04],\n",
       "       [1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mass_fractal_fit_err</span></div><div class='xr-var-dims'>(mass_fractal_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03115 0.01507 0.03165 ... 0.0 0.0</div><input id='attrs-ceac26a2-37d3-4de2-9603-18d0af4900d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceac26a2-37d3-4de2-9603-18d0af4900d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f7c5015-cb58-49f1-8d5b-3cb1681201d0' class='xr-var-data-in' type='checkbox'><label for='data-9f7c5015-cb58-49f1-8d5b-3cb1681201d0' 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>fit_calc_id :</span></dt><dd>ASAS-44f48a96-589b-473b-8411-8df15b6c95a4</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[3.11538645e-02, 1.50731929e-02, 3.16536073e-02, 1.29313013e-02,\n",
       "        2.54503819e-02, 3.16613936e-02, 2.96995833e-02, 3.15895469e-02,\n",
       "        2.46166729e-02, 1.47327752e-02, 1.33044826e-02, 2.52264042e-02,\n",
       "        2.41889926e-02, 1.34897817e-02, 3.12569447e-02, 3.15930608e-02,\n",
       "        1.44385458e-02, 3.31186418e-02, 3.30138173e-02, 3.19064838e-02,\n",
       "        2.47254105e-02, 2.50868816e-02, 2.48625273e-02, 1.39016032e-02,\n",
       "        2.48696932e-02, 1.34768498e-02, 2.45398366e-02, 1.39221047e-02,\n",
       "        2.52168618e-02, 1.51065202e-02],\n",
       "       [1.48852173e-05, 8.14818887e-06, 1.54933083e-05, 6.84638978e-06,\n",
       "        1.69408717e-05, 1.58106544e-05, 1.44407776e-05, 1.59900730e-05,\n",
       "        1.63857670e-05, 8.30277128e-06, 7.03419636e-06, 1.67913949e-05,\n",
       "        1.61012315e-05, 7.36494592e-06, 1.48565822e-05, 1.60709381e-05,\n",
       "        8.16838404e-06, 1.69550162e-05, 1.69592147e-05, 1.64106194e-05,\n",
       "        1.64580270e-05, 1.66985964e-05, 1.65493330e-05, 7.70810369e-06,\n",
       "        1.65545487e-05, 7.59938920e-06, 1.63348826e-05, 7.42614119e-06,\n",
       "        1.67852396e-05, 8.16621099e-06],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polymer_fit_val</span></div><div class='xr-var-dims'>(polymer_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.258 40.23 4.006 ... 1.0 1.0 1.0</div><input id='attrs-165f66c5-be8b-4a7b-9745-8ba2c4d60a0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-165f66c5-be8b-4a7b-9745-8ba2c4d60a0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c778908-0b89-4c15-8390-08c7a4aabc06' class='xr-var-data-in' type='checkbox'><label for='data-7c778908-0b89-4c15-8390-08c7a4aabc06' 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>fit_calc_id :</span></dt><dd>ASAS-44f48a96-589b-473b-8411-8df15b6c95a4</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[  5.25833492,  40.22505817,   4.00558774, 100.        ,\n",
       "          1.04442146,   3.29761   ,   4.8720121 ,   3.06146257,\n",
       "          1.00713336,  44.27147743,  78.17551856,   1.02892542,\n",
       "          0.93875659, 100.        ,   6.08637701,   2.94546786,\n",
       "         45.0702793 ,   2.50345304,   2.73405192,   2.70524025,\n",
       "          0.96785773,   1.03149347,   1.02593748,  71.97009094,\n",
       "          1.01287684, 100.        ,   0.94249659,  56.17376216,\n",
       "          1.04479582,  36.40920265],\n",
       "       [150.        , 150.        , 150.        , 150.        ,\n",
       "         51.06176077, 150.        , 150.        , 150.        ,\n",
       "         66.89120632, 150.        , 150.        ,  56.50715476,\n",
       "         60.19825947, 150.        , 150.        , 150.        ,\n",
       "        150.        , 150.        , 150.        , 150.        ,\n",
       "         57.51120818,  65.91860794,  66.08437723, 150.        ,\n",
       "         51.03465948, 150.        ,  54.99540066, 150.        ,\n",
       "         57.39639901, 150.        ],\n",
       "       [  1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ,   1.        ,   1.        ,\n",
       "          1.        ,   1.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polymer_fit_err</span></div><div class='xr-var-dims'>(polymer_params, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.557 3.071 0.4278 ... 0.0 0.0 0.0</div><input id='attrs-680d88fe-3aba-4cb4-b51a-8105e4692a40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-680d88fe-3aba-4cb4-b51a-8105e4692a40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-660ae46d-f018-475f-8a2d-3e0c3fc6eca9' class='xr-var-data-in' type='checkbox'><label for='data-660ae46d-f018-475f-8a2d-3e0c3fc6eca9' 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>fit_calc_id :</span></dt><dd>ASAS-44f48a96-589b-473b-8411-8df15b6c95a4</dd><dt><span>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.55701407,  3.07117713,  0.42778412,  8.69419771,  0.03408775,\n",
       "         0.33168065,  0.46750051,  0.30125613,  0.03525603,  3.39906167,\n",
       "         6.95871167,  0.03454602,  0.03374983,  6.96250167,  0.63187833,\n",
       "         0.28329694,  3.39953852,  0.25694993,  0.25926761,  0.25897734,\n",
       "         0.03405919,  0.03578345,  0.03549022,  6.14599674,  0.03336914,\n",
       "         7.93845586,  0.03350642,  4.68010318,  0.03472009,  2.74813752],\n",
       "       [ 8.43420851,  5.0489181 ,  9.08329964,  5.2036293 ,  3.21497204,\n",
       "         9.0828923 ,  7.97555919,  9.20181048,  4.55893797,  5.00749523,\n",
       "         5.41144915,  3.59425279,  4.22852841,  4.24658521,  8.14522953,\n",
       "         9.14026989,  4.9375889 , 10.01209928,  9.33676861,  9.42076471,\n",
       "         3.90038145,  4.44092837,  4.44246663,  5.27284562,  3.29848751,\n",
       "         4.78430279,  3.83062797,  5.25408604,  3.66863617,  5.04400228],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fit_I_surface_fractal</span></div><div class='xr-var-dims'>(sample, fit_q_surface_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.346e+03 4.264e+03 ... 1.0 1.0</div><input id='attrs-23fe51a4-c37d-4a53-a06b-089535b3eda4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23fe51a4-c37d-4a53-a06b-089535b3eda4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b82e468-65e6-4be4-9961-164d267e86ce' class='xr-var-data-in' type='checkbox'><label for='data-6b82e468-65e6-4be4-9961-164d267e86ce' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[5.34630102e+03, 4.26394591e+03, 3.40075387e+03, ...,\n",
       "        1.00000157e+00, 1.00000125e+00, 1.00000100e+00],\n",
       "       [1.88815902e+04, 1.53156289e+04, 1.24231676e+04, ...,\n",
       "        1.00002870e+00, 1.00002328e+00, 1.00001888e+00],\n",
       "       [3.07399683e+03, 2.46549619e+03, 1.97748804e+03, ...,\n",
       "        1.00000155e+00, 1.00000125e+00, 1.00000100e+00],\n",
       "       ...,\n",
       "       [3.59582491e+04, 2.91670333e+04, 2.36584688e+04, ...,\n",
       "        1.00005465e+00, 1.00004433e+00, 1.00003596e+00],\n",
       "       [1.00100000e+03, 8.12130831e+02, 6.58933225e+02, ...,\n",
       "        1.00000152e+00, 1.00000123e+00, 1.00000100e+00],\n",
       "       [1.64527485e+04, 1.33455205e+04, 1.08251520e+04, ...,\n",
       "        1.00002501e+00, 1.00002028e+00, 1.00001645e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>residuals_surface_fractal</span></div><div class='xr-var-dims'>(sample, fit_q_surface_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2168 -0.778 ... -1.008 -0.02022</div><input id='attrs-d2589b91-471c-4b89-b38e-6cd0870a848d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2589b91-471c-4b89-b38e-6cd0870a848d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5716b0a-3856-4d9c-a08e-2460f10705d2' class='xr-var-data-in' type='checkbox'><label for='data-b5716b0a-3856-4d9c-a08e-2460f10705d2' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[-2.16752990e-01, -7.77973996e-01, -1.33899259e+00, ...,\n",
       "         1.81685857e+00, -9.15985177e-01,  1.17020443e+00],\n",
       "       [ 5.53790700e+00,  2.95963752e+00,  3.08219636e+00, ...,\n",
       "        -9.23617634e-01, -3.14540965e-01, -2.76141815e-01],\n",
       "       [-1.00757084e+00, -6.78926389e-02, -1.23147789e+00, ...,\n",
       "        -1.63591791e+00, -1.35732062e-01, -1.56526014e+00],\n",
       "       ...,\n",
       "       [ 4.50553393e+00,  2.10869684e+00,  1.66942220e+00, ...,\n",
       "        -4.34749911e-01,  1.43654008e-01,  9.46923398e-01],\n",
       "       [ 4.87567006e+03,  3.95423936e+03,  3.20807969e+03, ...,\n",
       "        -1.05220784e+00,  7.28573742e-02,  4.14952742e-01],\n",
       "       [ 4.36869962e+00,  5.97322363e+00,  4.33142700e+00, ...,\n",
       "        -3.90295162e-01, -1.00819040e+00, -2.02196915e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fit_I_mass_fractal</span></div><div class='xr-var-dims'>(sample, fit_q_mass_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>956.0 812.9 691.3 ... 1.0 1.0 1.0</div><input id='attrs-77af5b1d-3d10-48c2-8e1e-751e297fc0d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77af5b1d-3d10-48c2-8e1e-751e297fc0d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c360125-f700-4001-8ccb-c6a9676b3fe2' class='xr-var-data-in' type='checkbox'><label for='data-4c360125-f700-4001-8ccb-c6a9676b3fe2' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[9.56029898e+02, 8.12917208e+02, 6.91250173e+02, ...,\n",
       "        1.00013836e+00, 1.00011763e+00, 1.00010000e+00],\n",
       "       [1.32796570e+04, 1.09936422e+04, 9.10118097e+03, ...,\n",
       "        1.00014592e+00, 1.00012080e+00, 1.00010000e+00],\n",
       "       [6.15015531e+02, 5.25338670e+02, 4.48759099e+02, ...,\n",
       "        1.00013713e+00, 1.00011710e+00, 1.00010000e+00],\n",
       "       ...,\n",
       "       [2.77705263e+04, 2.28191222e+04, 1.87505708e+04, ...,\n",
       "        1.00014811e+00, 1.00012170e+00, 1.00010000e+00],\n",
       "       [1.35892541e+01, 1.21811082e+01, 1.09304676e+01, ...,\n",
       "        1.00012677e+00, 1.00011259e+00, 1.00010000e+00],\n",
       "       [1.13589838e+04, 9.41847690e+03, 7.80950485e+03, ...,\n",
       "        1.00014546e+00, 1.00012061e+00, 1.00010000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>residuals_mass_fractal</span></div><div class='xr-var-dims'>(sample, fit_q_mass_fractal)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-8.26 -8.816 ... -1.007 -0.01938</div><input id='attrs-15cd6880-565e-420a-b644-ad3dd0b3942b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15cd6880-565e-420a-b644-ad3dd0b3942b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfc0adc5-e452-4a5b-ad59-7c08e2a15a87' class='xr-var-data-in' type='checkbox'><label for='data-dfc0adc5-e452-4a5b-ad59-7c08e2a15a87' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[-8.25970103e+00, -8.81617842e+00, -9.24790354e+00, ...,\n",
       "         1.81826773e+00, -9.14786336e-01,  1.17122431e+00],\n",
       "       [ 1.38321038e+00, -9.54985533e-01, -5.49591048e-01, ...,\n",
       "        -9.22446716e-01, -3.13566813e-01, -2.75331467e-01],\n",
       "       [-8.92990791e+00, -7.96692403e+00, -8.98705375e+00, ...,\n",
       "        -1.63455312e+00, -1.34565789e-01, -1.56426355e+00],\n",
       "       ...,\n",
       "       [ 1.56838534e+00, -6.96231550e-01, -9.69217278e-01, ...,\n",
       "        -4.33796542e-01,  1.44443299e-01,  9.47576748e-01],\n",
       "       [ 5.60971432e+01,  4.92858819e+01,  4.45153589e+01, ...,\n",
       "        -1.05098013e+00,  7.39489142e-02,  4.15923125e-01],\n",
       "       [-4.00492428e-02,  1.82670555e+00,  4.91872047e-01, ...,\n",
       "        -3.89081356e-01, -1.00717941e+00, -1.93777258e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fit_I_polymer</span></div><div class='xr-var-dims'>(sample, fit_q_polymer)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.219 6.213 6.207 ... 1.0 1.0 1.0</div><input id='attrs-5d017282-d0c6-4a2b-8dbc-4c3e8fa27d53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d017282-d0c6-4a2b-8dbc-4c3e8fa27d53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f78f291f-7abb-4072-b205-7807d05d5067' class='xr-var-data-in' type='checkbox'><label for='data-f78f291f-7abb-4072-b205-7807d05d5067' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[ 6.21908453,  6.2132386 ,  6.20652748, ...,  1.00000988,\n",
       "         1.00000801,  1.0000065 ],\n",
       "       [40.92480168, 40.88008163, 40.82874308, ...,  1.00007555,\n",
       "         1.00006128,  1.0000497 ],\n",
       "       [ 4.97568837,  4.97123518,  4.96612292, ...,  1.00000752,\n",
       "         1.0000061 ,  1.00000495],\n",
       "       ...,\n",
       "       [56.75445795, 56.69200699, 56.62031338, ...,  1.0001055 ,\n",
       "         1.00008557,  1.00006941],\n",
       "       [ 2.04364931,  2.04347775,  2.04328055, ...,  1.00003502,\n",
       "         1.00002841,  1.00002304],\n",
       "       [37.13742929, 37.0969515 , 37.05048306, ...,  1.00006838,\n",
       "         1.00005546,  1.00004499]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>residuals_polymer</span></div><div class='xr-var-dims'>(sample, fit_q_polymer)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -10.7 ... -1.008 -0.01993</div><input id='attrs-132ccf62-cf7c-4cc7-a740-fa91645d9771' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-132ccf62-cf7c-4cc7-a740-fa91645d9771' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6caeb6c1-97ba-4f4f-bf17-9f034133cb55' class='xr-var-data-in' type='checkbox'><label for='data-6caeb6c1-97ba-4f4f-bf17-9f034133cb55' 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>name :</span></dt><dd>AutoSAS</dd><dt><span>input_variable :</span></dt><dd>[&#x27;q&#x27;, &#x27;I&#x27;, &#x27;dI&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>fit</dd><dt><span>server_id :</span></dt><dd>None</dd><dt><span>fit_method :</span></dt><dd>None</dd><dt><span>model_inputs :</span></dt><dd>[{&#x27;name&#x27;: &#x27;surface_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (3, 4)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (1e-06, 0.001)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;mass_fractal&#x27;, &#x27;sasmodel&#x27;: &#x27;power_law&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;power&#x27;: {&#x27;value&#x27;: 4, &#x27;bounds&#x27;: (1.7, 3)}, &#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.0001, 0.1)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}, {&#x27;name&#x27;: &#x27;polymer&#x27;, &#x27;sasmodel&#x27;: &#x27;polymer_excl_volume&#x27;, &#x27;q_min&#x27;: 0.001, &#x27;q_max&#x27;: 1.0, &#x27;fit_params&#x27;: {&#x27;scale&#x27;: {&#x27;value&#x27;: 1.0, &#x27;bounds&#x27;: (0.01, 100.0)}, &#x27;rg&#x27;: {&#x27;value&#x27;: 60.0, &#x27;bounds&#x27;: (10, 150)}, &#x27;background&#x27;: {&#x27;value&#x27;: 1.0}}}]</dd><dt><span>results :</span></dt><dd>{}</dd><dt><span>q_variable :</span></dt><dd>q</dd><dt><span>sas_variable :</span></dt><dd>I</dd><dt><span>sas_err_variable :</span></dt><dd>dI</dd><dt><span>resolution :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_dim :</span></dt><dd>models</dd><dt><span>q_min :</span></dt><dd>None</dd><dt><span>q_max :</span></dt><dd>None</dd></dl></div><div class='xr-var-data'><pre>array([[ -9.99974824, -10.69516929, -11.24751298, ...,   1.81694411,\n",
       "         -0.91591557,   1.17026107],\n",
       "       [ -8.43534933, -10.87540827, -10.45489065, ...,  -0.92314966,\n",
       "         -0.31416137,  -0.27583391],\n",
       "       [-10.89533207, -10.08551423, -11.23851245, ...,  -1.63585783,\n",
       "         -0.13568319,  -1.56522039],\n",
       "       ...,\n",
       "       [ -8.3732637 , -10.75418374, -11.01966974, ...,  -0.43423121,\n",
       "          0.14407475,   0.94726468],\n",
       "       [ -0.25720025,  -0.20094706,   1.12790789, ...,  -1.05187945,\n",
       "          0.07312374,   0.4151688 ],\n",
       "       [ -9.83931935,  -8.07898073,  -9.40410201, ...,  -0.38985808,\n",
       "         -1.00783586,  -0.01993211]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BestChiSq_labels</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 0 1 2 0 0 0 ... 2 1 2 1 2 1 2 1</div><input id='attrs-79c6ca23-a6dc-479c-b888-aedd741b65d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79c6ca23-a6dc-479c-b888-aedd741b65d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06524d5e-d275-45f1-a40c-acb1319cf392' class='xr-var-data-in' type='checkbox'><label for='data-06524d5e-d275-45f1-a40c-acb1319cf392' 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>name :</span></dt><dd>ModelSelection_BestChiSq</dd><dt><span>input_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;, &#x27;models&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;BestChiSq_labels&#x27;, &#x27;BestChiSq_label_names&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>BestChiSq</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_names_var :</span></dt><dd>models</dd><dt><span>all_chisq_var :</span></dt><dd>fit_all_chisq</dd><dt><span>dataset :</span></dt><dd>&lt;xarray.Dataset&gt; Size: 202kB\n",
       "Dimensions:                    (sample: 30, q: 100, models: 3,\n",
       "                                surface_fractal_params: 3,\n",
       "                                mass_fractal_params: 3, polymer_params: 3,\n",
       "                                fit_q_surface_fractal: 100,\n",
       "                                fit_q_mass_fractal: 100, fit_q_polymer: 100)\n",
       "Coordinates:\n",
       "  * q                          (q) float64 800B 0.001 0.001072 ... 0.9326 1.0\n",
       "  * models                     (models) &lt;U15 180B &#x27;surface_fractal&#x27; ... &#x27;poly...\n",
       "  * surface_fractal_params     (surface_fractal_params) &lt;U26 312B &#x27;surface_fr...\n",
       "  * mass_fractal_params        (mass_fractal_params) &lt;U23 276B &#x27;mass_fractal_...\n",
       "  * polymer_params             (polymer_params) &lt;U18 216B &#x27;polymer_scale&#x27; ......\n",
       "  * fit_q_surface_fractal      (fit_q_surface_fractal) float64 800B 0.001 ......\n",
       "  * fit_q_mass_fractal         (fit_q_mass_fractal) float64 800B 0.001 ... 1.0\n",
       "  * fit_q_polymer              (fit_q_polymer) float64 800B 0.001 ... 1.0\n",
       "Dimensions without coordinates: sample\n",
       "Data variables: (12/18)\n",
       "    I                          (sample, q) float64 24kB 5.465e+03 ... 1.002\n",
       "    dI                         (sample, q) float64 24kB 545.9 429.3 ... 0.09923\n",
       "    model                      (sample) object 240B &#x27;surface_fractal&#x27; ... &#x27;ma...\n",
       "    sas_fit_sample             (sample) int64 240B 0 1 2 3 4 ... 25 26 27 28 29\n",
       "    fit_all_chisq              (sample, models) float64 720B 1.023 ... 27.2\n",
       "    probabilities              (sample, models) float64 720B 0.01182 ... 0.00...\n",
       "    ...                         ...\n",
       "    fit_I_surface_fractal      (sample, fit_q_surface_fractal) float64 24kB 5...\n",
       "    residuals_surface_fractal  (sample, fit_q_surface_fractal) float64 24kB -...\n",
       "    fit_I_mass_fractal         (sample, fit_q_mass_fractal) float64 24kB 956....\n",
       "    residuals_mass_fractal     (sample, fit_q_mass_fractal) float64 24kB -8.2...\n",
       "    fit_I_polymer              (sample, fit_q_polymer) float64 24kB 6.219 ......\n",
       "    residuals_polymer          (sample, fit_q_polymer) float64 24kB -10.0 ......</dd></dl></div><div class='xr-var-data'><pre>array([0, 1, 0, 1, 2, 0, 0, 0, 2, 1, 1, 2, 2, 1, 0, 0, 1, 0, 0, 0, 2, 2,\n",
       "       2, 1, 2, 1, 2, 1, 2, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BestChiSq_ChiSq</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.023 0.9502 1.054 ... 1.084 1.086</div><input id='attrs-85ea7c6b-ecf3-40a4-880b-fd98985826ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85ea7c6b-ecf3-40a4-880b-fd98985826ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-663e13c7-58e1-4125-b557-3bd78617ae9a' class='xr-var-data-in' type='checkbox'><label for='data-663e13c7-58e1-4125-b557-3bd78617ae9a' 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>name :</span></dt><dd>ModelSelection_BestChiSq</dd><dt><span>input_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;, &#x27;models&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;BestChiSq_labels&#x27;, &#x27;BestChiSq_label_names&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>BestChiSq</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_names_var :</span></dt><dd>models</dd><dt><span>all_chisq_var :</span></dt><dd>fit_all_chisq</dd><dt><span>dataset :</span></dt><dd>&lt;xarray.Dataset&gt; Size: 202kB\n",
       "Dimensions:                    (sample: 30, q: 100, models: 3,\n",
       "                                surface_fractal_params: 3,\n",
       "                                mass_fractal_params: 3, polymer_params: 3,\n",
       "                                fit_q_surface_fractal: 100,\n",
       "                                fit_q_mass_fractal: 100, fit_q_polymer: 100)\n",
       "Coordinates:\n",
       "  * q                          (q) float64 800B 0.001 0.001072 ... 0.9326 1.0\n",
       "  * models                     (models) &lt;U15 180B &#x27;surface_fractal&#x27; ... &#x27;poly...\n",
       "  * surface_fractal_params     (surface_fractal_params) &lt;U26 312B &#x27;surface_fr...\n",
       "  * mass_fractal_params        (mass_fractal_params) &lt;U23 276B &#x27;mass_fractal_...\n",
       "  * polymer_params             (polymer_params) &lt;U18 216B &#x27;polymer_scale&#x27; ......\n",
       "  * fit_q_surface_fractal      (fit_q_surface_fractal) float64 800B 0.001 ......\n",
       "  * fit_q_mass_fractal         (fit_q_mass_fractal) float64 800B 0.001 ... 1.0\n",
       "  * fit_q_polymer              (fit_q_polymer) float64 800B 0.001 ... 1.0\n",
       "Dimensions without coordinates: sample\n",
       "Data variables: (12/18)\n",
       "    I                          (sample, q) float64 24kB 5.465e+03 ... 1.002\n",
       "    dI                         (sample, q) float64 24kB 545.9 429.3 ... 0.09923\n",
       "    model                      (sample) object 240B &#x27;surface_fractal&#x27; ... &#x27;ma...\n",
       "    sas_fit_sample             (sample) int64 240B 0 1 2 3 4 ... 25 26 27 28 29\n",
       "    fit_all_chisq              (sample, models) float64 720B 1.023 ... 27.2\n",
       "    probabilities              (sample, models) float64 720B 0.01182 ... 0.00...\n",
       "    ...                         ...\n",
       "    fit_I_surface_fractal      (sample, fit_q_surface_fractal) float64 24kB 5...\n",
       "    residuals_surface_fractal  (sample, fit_q_surface_fractal) float64 24kB -...\n",
       "    fit_I_mass_fractal         (sample, fit_q_mass_fractal) float64 24kB 956....\n",
       "    residuals_mass_fractal     (sample, fit_q_mass_fractal) float64 24kB -8.2...\n",
       "    fit_I_polymer              (sample, fit_q_polymer) float64 24kB 6.219 ......\n",
       "    residuals_polymer          (sample, fit_q_polymer) float64 24kB -10.0 ......</dd></dl></div><div class='xr-var-data'><pre>array([1.02275423, 0.95017168, 1.05413472, 1.04990624, 1.18324132,\n",
       "       1.08126923, 0.83661143, 0.89258149, 1.11691133, 0.91293276,\n",
       "       0.97558104, 0.91806671, 0.97384207, 1.01924014, 0.93121783,\n",
       "       0.92739433, 0.85133291, 1.07638344, 0.87140495, 1.08324978,\n",
       "       1.09358942, 1.10718714, 0.96126599, 1.13113537, 1.03919799,\n",
       "       0.86638575, 0.99912896, 1.1655589 , 1.08373657, 1.08625395])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BestChiSq_label_names</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;surface_fractal&#x27; ... &#x27;mass_frac...</div><input id='attrs-b3b67f2a-f398-47e2-94f9-7e0f1b0c3baa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3b67f2a-f398-47e2-94f9-7e0f1b0c3baa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-534198bf-9c39-426a-ad84-a28e3b74e887' class='xr-var-data-in' type='checkbox'><label for='data-534198bf-9c39-426a-ad84-a28e3b74e887' 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>name :</span></dt><dd>ModelSelection_BestChiSq</dd><dt><span>input_variable :</span></dt><dd>[&#x27;fit_all_chisq&#x27;, &#x27;models&#x27;]</dd><dt><span>output_variable :</span></dt><dd>[&#x27;BestChiSq_labels&#x27;, &#x27;BestChiSq_label_names&#x27;]</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>BestChiSq</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>model_names_var :</span></dt><dd>models</dd><dt><span>all_chisq_var :</span></dt><dd>fit_all_chisq</dd><dt><span>dataset :</span></dt><dd>&lt;xarray.Dataset&gt; Size: 202kB\n",
       "Dimensions:                    (sample: 30, q: 100, models: 3,\n",
       "                                surface_fractal_params: 3,\n",
       "                                mass_fractal_params: 3, polymer_params: 3,\n",
       "                                fit_q_surface_fractal: 100,\n",
       "                                fit_q_mass_fractal: 100, fit_q_polymer: 100)\n",
       "Coordinates:\n",
       "  * q                          (q) float64 800B 0.001 0.001072 ... 0.9326 1.0\n",
       "  * models                     (models) &lt;U15 180B &#x27;surface_fractal&#x27; ... &#x27;poly...\n",
       "  * surface_fractal_params     (surface_fractal_params) &lt;U26 312B &#x27;surface_fr...\n",
       "  * mass_fractal_params        (mass_fractal_params) &lt;U23 276B &#x27;mass_fractal_...\n",
       "  * polymer_params             (polymer_params) &lt;U18 216B &#x27;polymer_scale&#x27; ......\n",
       "  * fit_q_surface_fractal      (fit_q_surface_fractal) float64 800B 0.001 ......\n",
       "  * fit_q_mass_fractal         (fit_q_mass_fractal) float64 800B 0.001 ... 1.0\n",
       "  * fit_q_polymer              (fit_q_polymer) float64 800B 0.001 ... 1.0\n",
       "Dimensions without coordinates: sample\n",
       "Data variables: (12/18)\n",
       "    I                          (sample, q) float64 24kB 5.465e+03 ... 1.002\n",
       "    dI                         (sample, q) float64 24kB 545.9 429.3 ... 0.09923\n",
       "    model                      (sample) object 240B &#x27;surface_fractal&#x27; ... &#x27;ma...\n",
       "    sas_fit_sample             (sample) int64 240B 0 1 2 3 4 ... 25 26 27 28 29\n",
       "    fit_all_chisq              (sample, models) float64 720B 1.023 ... 27.2\n",
       "    probabilities              (sample, models) float64 720B 0.01182 ... 0.00...\n",
       "    ...                         ...\n",
       "    fit_I_surface_fractal      (sample, fit_q_surface_fractal) float64 24kB 5...\n",
       "    residuals_surface_fractal  (sample, fit_q_surface_fractal) float64 24kB -...\n",
       "    fit_I_mass_fractal         (sample, fit_q_mass_fractal) float64 24kB 956....\n",
       "    residuals_mass_fractal     (sample, fit_q_mass_fractal) float64 24kB -8.2...\n",
       "    fit_I_polymer              (sample, fit_q_polymer) float64 24kB 6.219 ......\n",
       "    residuals_polymer          (sample, fit_q_polymer) float64 24kB -10.0 ......</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;polymer&#x27;, &#x27;surface_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;polymer&#x27;, &#x27;mass_fractal&#x27;, &#x27;mass_fractal&#x27;,\n",
       "       &#x27;polymer&#x27;, &#x27;polymer&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;surface_fractal&#x27;,\n",
       "       &#x27;surface_fractal&#x27;, &#x27;surface_fractal&#x27;, &#x27;polymer&#x27;, &#x27;polymer&#x27;,\n",
       "       &#x27;polymer&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer&#x27;,\n",
       "       &#x27;mass_fractal&#x27;, &#x27;polymer&#x27;, &#x27;mass_fractal&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6bb9db0f-298c-4091-baed-abca0b5ae909' class='xr-section-summary-in' type='checkbox'  ><label for='section-6bb9db0f-298c-4091-baed-abca0b5ae909' class='xr-section-summary' >Indexes: <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-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9322faa7-2b6c-44e0-8cb3-2f70d1aeb610' class='xr-index-data-in' type='checkbox'/><label for='index-9322faa7-2b6c-44e0-8cb3-2f70d1aeb610' 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([                0.001, 0.0010722672220103231, 0.0011497569953977356,\n",
       "       0.0012328467394420659, 0.0013219411484660286, 0.0014174741629268048,\n",
       "       0.0015199110829529332, 0.0016297508346206436,  0.001747528400007683,\n",
       "        0.001873817422860383,  0.002009233002565048, 0.0021544346900318843,\n",
       "       0.0023101297000831605, 0.0024770763559917113, 0.0026560877829466868,\n",
       "        0.002848035868435802, 0.0030538555088334154, 0.0032745491628777285,\n",
       "        0.003511191734215131, 0.0037649358067924675,  0.004037017258596553,\n",
       "        0.004328761281083057,  0.004641588833612782,  0.004977023564332114,\n",
       "        0.005336699231206312,   0.00572236765935022,  0.006135907273413176,\n",
       "        0.006579332246575682,  0.007054802310718645,  0.007564633275546291,\n",
       "        0.008111308307896872,  0.008697490026177835,    0.0093260334688322,\n",
       "                        0.01,  0.010722672220103232,  0.011497569953977356,\n",
       "        0.012328467394420659,  0.013219411484660293,  0.014174741629268055,\n",
       "         0.01519911082952934,  0.016297508346206444,   0.01747528400007684,\n",
       "         0.01873817422860384,   0.02009233002565047,  0.021544346900318846,\n",
       "        0.023101297000831605,  0.024770763559917114,  0.026560877829466867,\n",
       "         0.02848035868435802,  0.030538555088334154,   0.03274549162877728,\n",
       "         0.03511191734215131,  0.037649358067924674,   0.04037017258596556,\n",
       "         0.04328761281083059,  0.046415888336127795,  0.049770235643321115,\n",
       "          0.0533669923120631,   0.05722367659350217,   0.06135907273413173,\n",
       "         0.06579332246575682,   0.07054802310718646,   0.07564633275546291,\n",
       "         0.08111308307896872,   0.08697490026177834,     0.093260334688322,\n",
       "                         0.1,   0.10722672220103231,   0.11497569953977356,\n",
       "         0.12328467394420659,   0.13219411484660287,   0.14174741629268048,\n",
       "          0.1519911082952933,   0.16297508346206452,    0.1747528400007685,\n",
       "          0.1873817422860385,    0.2009233002565048,   0.21544346900318845,\n",
       "         0.23101297000831603,   0.24770763559917114,   0.26560877829466867,\n",
       "          0.2848035868435802,   0.30538555088334157,   0.32745491628777285,\n",
       "          0.3511191734215131,   0.37649358067924676,   0.40370172585965536,\n",
       "          0.4328761281083057,    0.4641588833612782,   0.49770235643321137,\n",
       "          0.5336699231206312,     0.572236765935022,    0.6135907273413176,\n",
       "          0.6579332246575682,    0.7054802310718645,    0.7564633275546291,\n",
       "          0.8111308307896873,    0.8697490026177834,    0.9326033468832199,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>models</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-64f45d94-1a45-4ff6-80f4-5b7e415def3e' class='xr-index-data-in' type='checkbox'/><label for='index-64f45d94-1a45-4ff6-80f4-5b7e415def3e' 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;surface_fractal&#x27;, &#x27;mass_fractal&#x27;, &#x27;polymer&#x27;], dtype=&#x27;object&#x27;, name=&#x27;models&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>surface_fractal_params</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-155da6b8-8979-41bf-9af7-57fb2db7fa1f' class='xr-index-data-in' type='checkbox'/><label for='index-155da6b8-8979-41bf-9af7-57fb2db7fa1f' 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;surface_fractal_power&#x27;, &#x27;surface_fractal_scale&#x27;,\n",
       "       &#x27;surface_fractal_background&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;surface_fractal_params&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mass_fractal_params</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-061fe1df-c9bc-4117-90af-f53d93a37f30' class='xr-index-data-in' type='checkbox'/><label for='index-061fe1df-c9bc-4117-90af-f53d93a37f30' 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;mass_fractal_power&#x27;, &#x27;mass_fractal_scale&#x27;, &#x27;mass_fractal_background&#x27;], dtype=&#x27;object&#x27;, name=&#x27;mass_fractal_params&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>polymer_params</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b52bb8a9-9460-4853-8f74-0108c673a2b3' class='xr-index-data-in' type='checkbox'/><label for='index-b52bb8a9-9460-4853-8f74-0108c673a2b3' 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;polymer_scale&#x27;, &#x27;polymer_rg&#x27;, &#x27;polymer_background&#x27;], dtype=&#x27;object&#x27;, name=&#x27;polymer_params&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fit_q_surface_fractal</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cd5b7467-9e5e-4351-b438-f7d17a78035e' class='xr-index-data-in' type='checkbox'/><label for='index-cd5b7467-9e5e-4351-b438-f7d17a78035e' 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([                0.001, 0.0010722672220103231, 0.0011497569953977356,\n",
       "       0.0012328467394420659, 0.0013219411484660286, 0.0014174741629268048,\n",
       "       0.0015199110829529332, 0.0016297508346206436,  0.001747528400007683,\n",
       "        0.001873817422860383,  0.002009233002565048, 0.0021544346900318843,\n",
       "       0.0023101297000831605, 0.0024770763559917113, 0.0026560877829466868,\n",
       "        0.002848035868435802, 0.0030538555088334154, 0.0032745491628777285,\n",
       "        0.003511191734215131, 0.0037649358067924675,  0.004037017258596553,\n",
       "        0.004328761281083057,  0.004641588833612782,  0.004977023564332114,\n",
       "        0.005336699231206312,   0.00572236765935022,  0.006135907273413176,\n",
       "        0.006579332246575682,  0.007054802310718645,  0.007564633275546291,\n",
       "        0.008111308307896872,  0.008697490026177835,    0.0093260334688322,\n",
       "                        0.01,  0.010722672220103232,  0.011497569953977356,\n",
       "        0.012328467394420659,  0.013219411484660293,  0.014174741629268055,\n",
       "         0.01519911082952934,  0.016297508346206444,   0.01747528400007684,\n",
       "         0.01873817422860384,   0.02009233002565047,  0.021544346900318846,\n",
       "        0.023101297000831605,  0.024770763559917114,  0.026560877829466867,\n",
       "         0.02848035868435802,  0.030538555088334154,   0.03274549162877728,\n",
       "         0.03511191734215131,  0.037649358067924674,   0.04037017258596556,\n",
       "         0.04328761281083059,  0.046415888336127795,  0.049770235643321115,\n",
       "          0.0533669923120631,   0.05722367659350217,   0.06135907273413173,\n",
       "         0.06579332246575682,   0.07054802310718646,   0.07564633275546291,\n",
       "         0.08111308307896872,   0.08697490026177834,     0.093260334688322,\n",
       "                         0.1,   0.10722672220103231,   0.11497569953977356,\n",
       "         0.12328467394420659,   0.13219411484660287,   0.14174741629268048,\n",
       "          0.1519911082952933,   0.16297508346206452,    0.1747528400007685,\n",
       "          0.1873817422860385,    0.2009233002565048,   0.21544346900318845,\n",
       "         0.23101297000831603,   0.24770763559917114,   0.26560877829466867,\n",
       "          0.2848035868435802,   0.30538555088334157,   0.32745491628777285,\n",
       "          0.3511191734215131,   0.37649358067924676,   0.40370172585965536,\n",
       "          0.4328761281083057,    0.4641588833612782,   0.49770235643321137,\n",
       "          0.5336699231206312,     0.572236765935022,    0.6135907273413176,\n",
       "          0.6579332246575682,    0.7054802310718645,    0.7564633275546291,\n",
       "          0.8111308307896873,    0.8697490026177834,    0.9326033468832199,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;fit_q_surface_fractal&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fit_q_mass_fractal</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-4307ccc3-740b-4d9c-869b-9f818724f783' class='xr-index-data-in' type='checkbox'/><label for='index-4307ccc3-740b-4d9c-869b-9f818724f783' 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([                0.001, 0.0010722672220103231, 0.0011497569953977356,\n",
       "       0.0012328467394420659, 0.0013219411484660286, 0.0014174741629268048,\n",
       "       0.0015199110829529332, 0.0016297508346206436,  0.001747528400007683,\n",
       "        0.001873817422860383,  0.002009233002565048, 0.0021544346900318843,\n",
       "       0.0023101297000831605, 0.0024770763559917113, 0.0026560877829466868,\n",
       "        0.002848035868435802, 0.0030538555088334154, 0.0032745491628777285,\n",
       "        0.003511191734215131, 0.0037649358067924675,  0.004037017258596553,\n",
       "        0.004328761281083057,  0.004641588833612782,  0.004977023564332114,\n",
       "        0.005336699231206312,   0.00572236765935022,  0.006135907273413176,\n",
       "        0.006579332246575682,  0.007054802310718645,  0.007564633275546291,\n",
       "        0.008111308307896872,  0.008697490026177835,    0.0093260334688322,\n",
       "                        0.01,  0.010722672220103232,  0.011497569953977356,\n",
       "        0.012328467394420659,  0.013219411484660293,  0.014174741629268055,\n",
       "         0.01519911082952934,  0.016297508346206444,   0.01747528400007684,\n",
       "         0.01873817422860384,   0.02009233002565047,  0.021544346900318846,\n",
       "        0.023101297000831605,  0.024770763559917114,  0.026560877829466867,\n",
       "         0.02848035868435802,  0.030538555088334154,   0.03274549162877728,\n",
       "         0.03511191734215131,  0.037649358067924674,   0.04037017258596556,\n",
       "         0.04328761281083059,  0.046415888336127795,  0.049770235643321115,\n",
       "          0.0533669923120631,   0.05722367659350217,   0.06135907273413173,\n",
       "         0.06579332246575682,   0.07054802310718646,   0.07564633275546291,\n",
       "         0.08111308307896872,   0.08697490026177834,     0.093260334688322,\n",
       "                         0.1,   0.10722672220103231,   0.11497569953977356,\n",
       "         0.12328467394420659,   0.13219411484660287,   0.14174741629268048,\n",
       "          0.1519911082952933,   0.16297508346206452,    0.1747528400007685,\n",
       "          0.1873817422860385,    0.2009233002565048,   0.21544346900318845,\n",
       "         0.23101297000831603,   0.24770763559917114,   0.26560877829466867,\n",
       "          0.2848035868435802,   0.30538555088334157,   0.32745491628777285,\n",
       "          0.3511191734215131,   0.37649358067924676,   0.40370172585965536,\n",
       "          0.4328761281083057,    0.4641588833612782,   0.49770235643321137,\n",
       "          0.5336699231206312,     0.572236765935022,    0.6135907273413176,\n",
       "          0.6579332246575682,    0.7054802310718645,    0.7564633275546291,\n",
       "          0.8111308307896873,    0.8697490026177834,    0.9326033468832199,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;fit_q_mass_fractal&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>fit_q_polymer</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f9d541de-3ed6-4db5-8371-d9572bb50cb3' class='xr-index-data-in' type='checkbox'/><label for='index-f9d541de-3ed6-4db5-8371-d9572bb50cb3' 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([                0.001, 0.0010722672220103231, 0.0011497569953977356,\n",
       "       0.0012328467394420659, 0.0013219411484660286, 0.0014174741629268048,\n",
       "       0.0015199110829529332, 0.0016297508346206436,  0.001747528400007683,\n",
       "        0.001873817422860383,  0.002009233002565048, 0.0021544346900318843,\n",
       "       0.0023101297000831605, 0.0024770763559917113, 0.0026560877829466868,\n",
       "        0.002848035868435802, 0.0030538555088334154, 0.0032745491628777285,\n",
       "        0.003511191734215131, 0.0037649358067924675,  0.004037017258596553,\n",
       "        0.004328761281083057,  0.004641588833612782,  0.004977023564332114,\n",
       "        0.005336699231206312,   0.00572236765935022,  0.006135907273413176,\n",
       "        0.006579332246575682,  0.007054802310718645,  0.007564633275546291,\n",
       "        0.008111308307896872,  0.008697490026177835,    0.0093260334688322,\n",
       "                        0.01,  0.010722672220103232,  0.011497569953977356,\n",
       "        0.012328467394420659,  0.013219411484660293,  0.014174741629268055,\n",
       "         0.01519911082952934,  0.016297508346206444,   0.01747528400007684,\n",
       "         0.01873817422860384,   0.02009233002565047,  0.021544346900318846,\n",
       "        0.023101297000831605,  0.024770763559917114,  0.026560877829466867,\n",
       "         0.02848035868435802,  0.030538555088334154,   0.03274549162877728,\n",
       "         0.03511191734215131,  0.037649358067924674,   0.04037017258596556,\n",
       "         0.04328761281083059,  0.046415888336127795,  0.049770235643321115,\n",
       "          0.0533669923120631,   0.05722367659350217,   0.06135907273413173,\n",
       "         0.06579332246575682,   0.07054802310718646,   0.07564633275546291,\n",
       "         0.08111308307896872,   0.08697490026177834,     0.093260334688322,\n",
       "                         0.1,   0.10722672220103231,   0.11497569953977356,\n",
       "         0.12328467394420659,   0.13219411484660287,   0.14174741629268048,\n",
       "          0.1519911082952933,   0.16297508346206452,    0.1747528400007685,\n",
       "          0.1873817422860385,    0.2009233002565048,   0.21544346900318845,\n",
       "         0.23101297000831603,   0.24770763559917114,   0.26560877829466867,\n",
       "          0.2848035868435802,   0.30538555088334157,   0.32745491628777285,\n",
       "          0.3511191734215131,   0.37649358067924676,   0.40370172585965536,\n",
       "          0.4328761281083057,    0.4641588833612782,   0.49770235643321137,\n",
       "          0.5336699231206312,     0.572236765935022,    0.6135907273413176,\n",
       "          0.6579332246575682,    0.7054802310718645,    0.7564633275546291,\n",
       "          0.8111308307896873,    0.8697490026177834,    0.9326033468832199,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;fit_q_polymer&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54946c55-f81b-4f4b-be62-cc9185af61e3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-54946c55-f81b-4f4b-be62-cc9185af61e3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 205kB\n",
       "Dimensions:                    (sample: 30, q: 100, models: 3,\n",
       "                                surface_fractal_params: 3,\n",
       "                                mass_fractal_params: 3, polymer_params: 3,\n",
       "                                fit_q_surface_fractal: 100,\n",
       "                                fit_q_mass_fractal: 100, fit_q_polymer: 100)\n",
       "Coordinates:\n",
       "  * q                          (q) float64 800B 0.001 0.001072 ... 0.9326 1.0\n",
       "  * models                     (models) <U15 180B 'surface_fractal' ... 'poly...\n",
       "  * surface_fractal_params     (surface_fractal_params) <U26 312B 'surface_fr...\n",
       "  * mass_fractal_params        (mass_fractal_params) <U23 276B 'mass_fractal_...\n",
       "  * polymer_params             (polymer_params) <U18 216B 'polymer_scale' ......\n",
       "  * fit_q_surface_fractal      (fit_q_surface_fractal) float64 800B 0.001 ......\n",
       "  * fit_q_mass_fractal         (fit_q_mass_fractal) float64 800B 0.001 ... 1.0\n",
       "  * fit_q_polymer              (fit_q_polymer) float64 800B 0.001 ... 1.0\n",
       "Dimensions without coordinates: sample\n",
       "Data variables: (12/21)\n",
       "    I                          (sample, q) float64 24kB 5.465e+03 ... 1.002\n",
       "    dI                         (sample, q) float64 24kB 545.9 429.3 ... 0.09923\n",
       "    model                      (sample) object 240B 'surface_fractal' ... 'ma...\n",
       "    sas_fit_sample             (sample) int64 240B 0 1 2 3 4 ... 25 26 27 28 29\n",
       "    fit_all_chisq              (sample, models) float64 720B 1.023 ... 27.2\n",
       "    probabilities              (sample, models) float64 720B 0.01182 ... 0.00...\n",
       "    ...                         ...\n",
       "    residuals_mass_fractal     (sample, fit_q_mass_fractal) float64 24kB -8.2...\n",
       "    fit_I_polymer              (sample, fit_q_polymer) float64 24kB 6.219 ......\n",
       "    residuals_polymer          (sample, fit_q_polymer) float64 24kB -10.0 ......\n",
       "    BestChiSq_labels           (sample) int64 240B 0 1 0 1 2 0 0 ... 2 1 2 1 2 1\n",
       "    BestChiSq_ChiSq            (sample) float64 240B 1.023 0.9502 ... 1.086\n",
       "    BestChiSq_label_names      (sample) <U15 2kB 'surface_fractal' ... 'mass_..."
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_result2 = autosas_pipeline2.calculate(ds_result)\n",
    "ds_result2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373a7ea1",
   "metadata": {},
   "source": [
    "This dataset is similar to the one above but it has three new data variables:\n",
    "\n",
    "1. `BestChiSq_labels` - An array of integers indicating which model had the best chi-squared value for each sample (0 = surface_fractal, 1 = mass_fractal, 2 = polymer)\n",
    "2. `BestChiSq_ChiSq` - The chi-squared value for the best model for each sample\n",
    "3. `BestChiSq_label_names` - The name of the best model for each sample as a string (e.g. 'surface_fractal', 'mass_fractal', 'polymer')\n",
    "\n",
    "Let's plot the in groups based on the auto labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "85e060d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model,sds in ds_result2.groupby('BestChiSq_label_names'):\n",
    "    plt.figure()\n",
    "    sds.I.plot.line(x='q',xscale='log',yscale='log',marker='.',ls='None',add_legend=False);\n",
    "    plt.title(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2cf7b94",
   "metadata": {},
   "source": [
    "Excellent! It looks like we got a perfect separation, even between the two fractal models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a08b975d",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    " In this example, we demonstrated a complete workflow for fitting multiple SAS models to data and automatically selecting the best model based on chi-squared values. While this example used simulated data that was specifically generated to be well-separated between different model types (making the fitting and model selection relatively straightforward), it illustrates the key components and capabilities of the AutoSAS pipeline:\n",
    " \n",
    "1. Fitting multiple models to the same dataset\n",
    "2. Comparing fit quality across models\n",
    "3. Automatically selecting the best model for each sample\n",
    " \n",
    "With real experimental data, the fitting and model selection process may be more challenging due to:\n",
    "- Noise and experimental uncertainties\n",
    "- Samples that could be described by multiple models\n",
    "- More complex scattering patterns requiring more sophisticated models\n",
    " \n",
    "However, the workflow demonstrated here provides a foundation for approaching these more complex cases in a systematic way."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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