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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heart of the DDM code (found in the `ddm_calc.py` file) is the computation of the image structure function. This is found by taking the average of the Fourier transforms of all image *differences*. By \"image differences,\" I mean the result of subtracting two images separated by a given lag time $\\Delta t$.\n",
    "\n",
    "To describe the process mathematically, we find the difference between images separated by some lag time $\\Delta t$:\n",
    "$$\\Delta I = I(x,y;t) - I(x,y;t + \\Delta t)$$\n",
    "\n",
    "For a given $\\Delta t$ all such image differences are calculated. We then Fourier transform each $\\Delta I$ and average all of the same $\\Delta t$.\n",
    "\n",
    "This results in the image structure function $D(q_x,q_y,\\Delta t)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the necessary modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modules for plotting\n",
    "We use [matplotlib](https://matplotlib.org/) for creating figures and plots. Note that we use:\n",
    "```python\n",
    "%matplotlib inline\n",
    "```\n",
    "This sets the backend of matplotlib to `inline` which means the plots are included in the notebook. If you want the plots to also be interactive (e.g., having the ability to zoom, scroll, and save) then use:\n",
    "```python\n",
    "%matplotlib notebook\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modules for numerical work\n",
    "Here, we import [numpy](https://numpy.org/) and [xarray](https://xarray.pydata.org/en/stable/). \n",
    "\n",
    "Note that `xarray` might not have come with your Anaconda Python distribution (or whichever other distribution you installed). If that is the case, you'll need to [install](https://xarray.pydata.org/en/stable/getting-started-guide/installing.html) this package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #numerical python used for working with arrays, mathematical operations\n",
    "import xarray as xr #package for labeling and adding metadata to multi-dimensional arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the PyDDM package\n",
    "Make sure you append to `sys.path` the directory containing the [PyDDM](https://rmcgorty.github.io/PyDDM/) code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../../PyDDM\") #must point to the PyDDM folder\n",
    "import ddm_analysis_and_fitting as ddm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initializing DDM_Analysis class and computing the DDM matrix\n",
    "The instance of the `DDM_Analysis` class we create will need, when initialized, metadata about the images to analyze and the analysis and fitting parameters. This can be done by using a [yaml](https://yaml.org/) file as shown in the following cell of code (there, the metadata is saved in the file \"*example_data_silica_beads.yml*\". But one can also initialize `DDM_Analysis` with a dictionary containing the necessary metadata. One way to create such a dictionary and then using it to initialize `DDM_Analysis` is shown below. \n",
    "```python\n",
    "import yaml\n",
    "ddm_analysis_parameters_str = \"\"\"\n",
    "DataDirectory: 'C:/Users/rmcgorty/Documents/GitHub/DDM-at-USD/ExampleData/'\n",
    "FileName: 'images_nobin_40x_128x128_8bit.tif'\n",
    "Metadata:\n",
    "  pixel_size: 0.242 # size of pixel in um\n",
    "  frame_rate: 41.7 #frames per second\n",
    "Analysis_parameters:\n",
    "  number_lag_times: 40\n",
    "  last_lag_time: 600\n",
    "  binning: no \n",
    "Fitting_parameters:\n",
    "  model: 'DDM Matrix - Single Exponential' \n",
    "  Tau: [1.0, 0.001, 10]\n",
    "  StretchingExp: [1.0, 0.5, 1.1]\n",
    "  Amplitude: [1e2, 1, 1e6]\n",
    "  Background: [2.5e4, 0, 1e7]\n",
    "  Good_q_range: [5, 20]\n",
    "  Auto_update_good_q_range: True\n",
    "\"\"\"\n",
    "parameters_as_dictionary = yaml.safe_load(ddm_analysis_parameters_str)\n",
    "ddm_calc = ddm.DDM_Analysis(parameters_as_dictionary)\n",
    "```"
   ]
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   "cell_type": "code",
   "execution_count": 4,
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    {
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     "text": [
      "Provided metadata: {'pixel_size': 0.242, 'frame_rate': 41.7}\n",
      "Image shape: 3000-by-128-by-128\n",
      "Number of frames to use for analysis: 3000\n",
      "Maximum lag time (in frames): 600\n",
      "Number of lag times to compute DDM matrix: 40\n"
     ]
    }
   ],
   "source": [
    "#The yaml file `example_data_silica_beads.yml` contains the metadata and parameters above\n",
    "ddm_calc = ddm.DDM_Analysis(\"../../Examples/example_data_silica_beads.yml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, with the method `calculate_DDM_matrix`, we compute the DDM matrix and some associated data. The data will be stored as an [xarray Dataset](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.html) as an attribute to `ddm_calc` called `ddm_dataset`.\n",
    "\n",
    "**Note**: There are a few optional arguments we can pass to `calculate_DDM_matrix`. There is an optional argument `quiet` (*True* or *False*, *False* by default). Then we have some optional keyword arguments (all of which could also be specified in the YAML file). These are: `overlap_method` which sets the degree of overlap between image pairs when finding all image differences for a each lag time and is either *0*, *1*, *2*, or *3*, `background_method` which sets how to estimate the background parameter *B* and is either *0*, *1*, *2*, or *3*, and `number_lag_times`. If any of these three keyword arguments are set here, the value specified in the YAML file will be overwritten. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "2022-02-10 13:11:37,747 - DDM Calculations - Running dt = 1...\n",
      "2022-02-10 13:11:44,311 - DDM Calculations - Running dt = 5...\n",
      "2022-02-10 13:11:48,710 - DDM Calculations - Running dt = 9...\n",
      "2022-02-10 13:11:52,546 - DDM Calculations - Running dt = 16...\n",
      "2022-02-10 13:11:55,853 - DDM Calculations - Running dt = 27...\n",
      "2022-02-10 13:11:59,025 - DDM Calculations - Running dt = 47...\n",
      "2022-02-10 13:12:02,002 - DDM Calculations - Running dt = 81...\n",
      "2022-02-10 13:12:04,828 - DDM Calculations - Running dt = 138...\n",
      "2022-02-10 13:12:07,484 - DDM Calculations - Running dt = 236...\n",
      "2022-02-10 13:12:09,998 - DDM Calculations - Running dt = 402...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DDM matrix took 34.66797375679016 seconds to compute.\n",
      " Background estimate ± std is 211.17 ± 1.49\n"
     ]
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       "  * q_x               (q_x) float64 -12.98 -12.78 -12.58 ... 12.37 12.58 12.78\n",
       "  * q                 (q) float64 0.0 0.2028 0.4057 0.6085 ... 12.37 12.58 12.78\n",
       "  * y                 (y) int32 0 1 2 3 4 5 6 7 ... 121 122 123 124 125 126 127\n",
       "  * x                 (x) int32 0 1 2 3 4 5 6 7 ... 121 122 123 124 125 126 127\n",
       "Dimensions without coordinates: frames\n",
       "Data variables:\n",
       "    ddm_matrix_full   (lagtime, q_y, q_x) float64 194.5 183.5 ... 192.0 196.8\n",
       "    ddm_matrix        (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    first_image       (y, x) float64 128.0 149.0 173.0 ... 175.0 229.0 215.0\n",
       "    avg_image_ft      (q) float64 0.0 1.293e+05 5.225e+03 ... 105.3 104.7 105.3\n",
       "    num_pairs_per_dt  (lagtime) int32 2999 2998 2997 1498 1498 ... 20 17 15 13\n",
       "    B                 float64 211.2\n",
       "    B_std             float64 1.491\n",
       "    Amplitude         (q) float64 -211.2 2.585e+05 1.024e+04 ... -1.699 -0.52\n",
       "    ISF               (lagtime, q) float64 0.0 0.9997 0.9892 ... -4.952 -19.73\n",
       "Attributes: (12/24)\n",
       "    units:                   Intensity\n",
       "    lagtime:                 sec\n",
       "    q:                       μm$^{-1}$\n",
       "    x:                       pixels\n",
       "    y:                       pixels\n",
       "    info:                    ddm_matrix is the averages of FFT difference ima...\n",
       "    ...                      ...\n",
       "    split_into_4_rois:       no\n",
       "    use_windowing_function:  no\n",
       "    binning:                 no\n",
       "    bin_size:                2\n",
       "    central_angle:           no\n",
       "    angle_range:             no</pre><div class='xr-wrap' hidden><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-dbcb41e2-3f71-4e1a-93bf-1bc3c98dcea2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dbcb41e2-3f71-4e1a-93bf-1bc3c98dcea2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lagtime</span>: 40</li><li><span class='xr-has-index'>q_y</span>: 128</li><li><span class='xr-has-index'>q_x</span>: 128</li><li><span class='xr-has-index'>q</span>: 64</li><li><span class='xr-has-index'>y</span>: 128</li><li><span class='xr-has-index'>x</span>: 128</li><li><span>frames</span>: 40</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fb6256db-2f10-404e-aae5-0c9f02707d6e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb6256db-2f10-404e-aae5-0c9f02707d6e' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lagtime</span></div><div class='xr-var-dims'>(lagtime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02398 0.04796 ... 12.59 14.36</div><input id='attrs-95b4cf46-2ea7-4bbc-9809-3ffd1129f2b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95b4cf46-2ea7-4bbc-9809-3ffd1129f2b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce35785-d530-4e77-a314-9d9185d855e9' class='xr-var-data-in' type='checkbox'><label for='data-7ce35785-d530-4e77-a314-9d9185d855e9' 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([ 0.023981,  0.047962,  0.071942,  0.095923,  0.119904,  0.143885,\n",
       "        0.167866,  0.191847,  0.215827,  0.263789,  0.28777 ,  0.335731,\n",
       "        0.383693,  0.431655,  0.503597,  0.57554 ,  0.647482,  0.743405,\n",
       "        0.863309,  0.983213,  1.127098,  1.294964,  1.486811,  1.702638,\n",
       "        1.942446,  2.206235,  2.541966,  2.901679,  3.309353,  3.788969,\n",
       "        4.316547,  4.940048,  5.659472,  6.450839,  7.386091,  8.441247,\n",
       "        9.640288, 11.007194, 12.589928, 14.364508])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>framelag</span></div><div class='xr-var-dims'>(frames)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 352 402 459 525 599</div><input id='attrs-84efcbbf-0097-41e0-991f-08772b1bea77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84efcbbf-0097-41e0-991f-08772b1bea77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a799836e-9a2c-4ecb-8ff0-e29136f0603d' class='xr-var-data-in' type='checkbox'><label for='data-a799836e-9a2c-4ecb-8ff0-e29136f0603d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  11,  12,  14,  16,\n",
       "        18,  21,  24,  27,  31,  36,  41,  47,  54,  62,  71,  81,  92,\n",
       "       106, 121, 138, 158, 180, 206, 236, 269, 308, 352, 402, 459, 525,\n",
       "       599])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q_y</span></div><div class='xr-var-dims'>(q_y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-12.98 -12.78 ... 12.58 12.78</div><input id='attrs-e7c4361b-9a47-4431-8918-a5b4007be383' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7c4361b-9a47-4431-8918-a5b4007be383' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b53c6d72-bc16-406b-8636-0c0cc02a418f' class='xr-var-data-in' type='checkbox'><label for='data-b53c6d72-bc16-406b-8636-0c0cc02a418f' 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([-12.981788, -12.778947, -12.576107, -12.373267, -12.170426, -11.967586,\n",
       "       -11.764745, -11.561905, -11.359064, -11.156224, -10.953383, -10.750543,\n",
       "       -10.547703, -10.344862, -10.142022,  -9.939181,  -9.736341,  -9.5335  ,\n",
       "        -9.33066 ,  -9.12782 ,  -8.924979,  -8.722139,  -8.519298,  -8.316458,\n",
       "        -8.113617,  -7.910777,  -7.707937,  -7.505096,  -7.302256,  -7.099415,\n",
       "        -6.896575,  -6.693734,  -6.490894,  -6.288053,  -6.085213,  -5.882373,\n",
       "        -5.679532,  -5.476692,  -5.273851,  -5.071011,  -4.86817 ,  -4.66533 ,\n",
       "        -4.46249 ,  -4.259649,  -4.056809,  -3.853968,  -3.651128,  -3.448287,\n",
       "        -3.245447,  -3.042607,  -2.839766,  -2.636926,  -2.434085,  -2.231245,\n",
       "        -2.028404,  -1.825564,  -1.622723,  -1.419883,  -1.217043,  -1.014202,\n",
       "        -0.811362,  -0.608521,  -0.405681,  -0.20284 ,   0.      ,   0.20284 ,\n",
       "         0.405681,   0.608521,   0.811362,   1.014202,   1.217043,   1.419883,\n",
       "         1.622723,   1.825564,   2.028404,   2.231245,   2.434085,   2.636926,\n",
       "         2.839766,   3.042607,   3.245447,   3.448287,   3.651128,   3.853968,\n",
       "         4.056809,   4.259649,   4.46249 ,   4.66533 ,   4.86817 ,   5.071011,\n",
       "         5.273851,   5.476692,   5.679532,   5.882373,   6.085213,   6.288053,\n",
       "         6.490894,   6.693734,   6.896575,   7.099415,   7.302256,   7.505096,\n",
       "         7.707937,   7.910777,   8.113617,   8.316458,   8.519298,   8.722139,\n",
       "         8.924979,   9.12782 ,   9.33066 ,   9.5335  ,   9.736341,   9.939181,\n",
       "        10.142022,  10.344862,  10.547703,  10.750543,  10.953383,  11.156224,\n",
       "        11.359064,  11.561905,  11.764745,  11.967586,  12.170426,  12.373267,\n",
       "        12.576107,  12.778947])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q_x</span></div><div class='xr-var-dims'>(q_x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-12.98 -12.78 ... 12.58 12.78</div><input id='attrs-2ac1fdf9-a4b5-48a3-9260-5226d1817390' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ac1fdf9-a4b5-48a3-9260-5226d1817390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b43d431f-7dd0-450c-bea0-0148b560298b' class='xr-var-data-in' type='checkbox'><label for='data-b43d431f-7dd0-450c-bea0-0148b560298b' 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([-12.981788, -12.778947, -12.576107, -12.373267, -12.170426, -11.967586,\n",
       "       -11.764745, -11.561905, -11.359064, -11.156224, -10.953383, -10.750543,\n",
       "       -10.547703, -10.344862, -10.142022,  -9.939181,  -9.736341,  -9.5335  ,\n",
       "        -9.33066 ,  -9.12782 ,  -8.924979,  -8.722139,  -8.519298,  -8.316458,\n",
       "        -8.113617,  -7.910777,  -7.707937,  -7.505096,  -7.302256,  -7.099415,\n",
       "        -6.896575,  -6.693734,  -6.490894,  -6.288053,  -6.085213,  -5.882373,\n",
       "        -5.679532,  -5.476692,  -5.273851,  -5.071011,  -4.86817 ,  -4.66533 ,\n",
       "        -4.46249 ,  -4.259649,  -4.056809,  -3.853968,  -3.651128,  -3.448287,\n",
       "        -3.245447,  -3.042607,  -2.839766,  -2.636926,  -2.434085,  -2.231245,\n",
       "        -2.028404,  -1.825564,  -1.622723,  -1.419883,  -1.217043,  -1.014202,\n",
       "        -0.811362,  -0.608521,  -0.405681,  -0.20284 ,   0.      ,   0.20284 ,\n",
       "         0.405681,   0.608521,   0.811362,   1.014202,   1.217043,   1.419883,\n",
       "         1.622723,   1.825564,   2.028404,   2.231245,   2.434085,   2.636926,\n",
       "         2.839766,   3.042607,   3.245447,   3.448287,   3.651128,   3.853968,\n",
       "         4.056809,   4.259649,   4.46249 ,   4.66533 ,   4.86817 ,   5.071011,\n",
       "         5.273851,   5.476692,   5.679532,   5.882373,   6.085213,   6.288053,\n",
       "         6.490894,   6.693734,   6.896575,   7.099415,   7.302256,   7.505096,\n",
       "         7.707937,   7.910777,   8.113617,   8.316458,   8.519298,   8.722139,\n",
       "         8.924979,   9.12782 ,   9.33066 ,   9.5335  ,   9.736341,   9.939181,\n",
       "        10.142022,  10.344862,  10.547703,  10.750543,  10.953383,  11.156224,\n",
       "        11.359064,  11.561905,  11.764745,  11.967586,  12.170426,  12.373267,\n",
       "        12.576107,  12.778947])</pre></div></li><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'>0.0 0.2028 0.4057 ... 12.58 12.78</div><input id='attrs-fa1dde6f-824f-4d4d-8f0c-b5b98685da77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa1dde6f-824f-4d4d-8f0c-b5b98685da77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f48d4406-0a53-4ffb-9a4d-e92c3ba89250' class='xr-var-data-in' type='checkbox'><label for='data-f48d4406-0a53-4ffb-9a4d-e92c3ba89250' 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.      ,  0.20284 ,  0.405681,  0.608521,  0.811362,  1.014202,\n",
       "        1.217043,  1.419883,  1.622723,  1.825564,  2.028404,  2.231245,\n",
       "        2.434085,  2.636926,  2.839766,  3.042607,  3.245447,  3.448287,\n",
       "        3.651128,  3.853968,  4.056809,  4.259649,  4.46249 ,  4.66533 ,\n",
       "        4.86817 ,  5.071011,  5.273851,  5.476692,  5.679532,  5.882373,\n",
       "        6.085213,  6.288053,  6.490894,  6.693734,  6.896575,  7.099415,\n",
       "        7.302256,  7.505096,  7.707937,  7.910777,  8.113617,  8.316458,\n",
       "        8.519298,  8.722139,  8.924979,  9.12782 ,  9.33066 ,  9.5335  ,\n",
       "        9.736341,  9.939181, 10.142022, 10.344862, 10.547703, 10.750543,\n",
       "       10.953383, 11.156224, 11.359064, 11.561905, 11.764745, 11.967586,\n",
       "       12.170426, 12.373267, 12.576107, 12.778947])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 123 124 125 126 127</div><input id='attrs-02406226-8b5f-4be3-8772-c87c819337f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02406226-8b5f-4be3-8772-c87c819337f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8f19e38-a786-4fdf-b423-d33259a28ab5' class='xr-var-data-in' type='checkbox'><label for='data-d8f19e38-a786-4fdf-b423-d33259a28ab5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 123 124 125 126 127</div><input id='attrs-fc67e2de-b987-47e0-8373-6ebe1c3ab750' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc67e2de-b987-47e0-8373-6ebe1c3ab750' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e9794c7-8cea-4916-90ca-ecd6a0fe6236' class='xr-var-data-in' type='checkbox'><label for='data-7e9794c7-8cea-4916-90ca-ecd6a0fe6236' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d344052-bd6f-4511-9b9b-e29f3718cd32' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d344052-bd6f-4511-9b9b-e29f3718cd32' class='xr-section-summary' >Data variables: <span>(9)</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>ddm_matrix_full</span></div><div class='xr-var-dims'>(lagtime, q_y, q_x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>194.5 183.5 189.9 ... 192.0 196.8</div><input id='attrs-83d67dd8-6503-4a8b-835f-1f9ae4b7b6a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83d67dd8-6503-4a8b-835f-1f9ae4b7b6a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69714a6b-b15d-4bc7-b71c-c56339931a2a' class='xr-var-data-in' type='checkbox'><label for='data-69714a6b-b15d-4bc7-b71c-c56339931a2a' 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([[[194.53933642, 183.46865602, 189.88360447, ..., 185.13501835,\n",
       "         189.88360447, 183.46865602],\n",
       "        [180.54977527, 184.19554752, 188.62431121, ..., 190.98754022,\n",
       "         197.43847912, 186.62178023],\n",
       "        [192.70890797, 195.93098872, 189.09845247, ..., 190.12737145,\n",
       "         189.83523821, 192.21421257],\n",
       "        ...,\n",
       "        [178.74506797, 186.95925279, 189.9972449 , ..., 190.31796269,\n",
       "         193.86216905, 184.71498966],\n",
       "        [192.70890797, 192.21421257, 189.83523821, ..., 194.82373533,\n",
       "         189.09845247, 195.93098872],\n",
       "        [180.54977527, 186.62178023, 197.43847912, ..., 189.320714  ,\n",
       "         188.62431121, 184.19554752]],\n",
       "\n",
       "       [[192.53678443, 184.98536047, 183.00178339, ..., 189.93758386,\n",
       "         183.00178339, 184.98536047],\n",
       "        [182.22340665, 190.38208686, 185.90468391, ..., 188.79454507,\n",
       "         188.52514631, 188.48204681],\n",
       "        [192.71415943, 196.71513926, 189.84504279, ..., 187.27424593,\n",
       "         187.74393152, 186.99244495],\n",
       "...\n",
       "        [224.60947387, 108.81451286, 189.02957545, ..., 137.44506309,\n",
       "         348.30437843, 228.73579002],\n",
       "        [216.96920343, 166.46373793, 174.79469182, ..., 166.69258334,\n",
       "         126.31953212, 140.52349943],\n",
       "        [164.20961535, 169.57957548, 133.76022938, ..., 125.77861768,\n",
       "         159.69240297, 247.86589588]],\n",
       "\n",
       "       [[ 75.33297495, 174.05006796, 155.35027909, ..., 233.98667431,\n",
       "         155.35027909, 174.05006796],\n",
       "        [167.22701098, 196.84765156, 192.02546007, ..., 131.6447673 ,\n",
       "         119.41026107, 235.53961919],\n",
       "        [132.75085561, 149.1865022 , 144.63052109, ..., 237.29431965,\n",
       "         217.6141243 , 128.83335999],\n",
       "        ...,\n",
       "        [244.06105601, 195.72999615, 198.70767797, ..., 346.86825864,\n",
       "         208.46454057, 182.9136883 ],\n",
       "        [132.75085561, 128.83335999, 217.6141243 , ..., 137.87697986,\n",
       "         144.63052109, 149.1865022 ],\n",
       "        [167.22701098, 235.53961919, 119.41026107, ..., 165.83630492,\n",
       "         192.02546007, 196.84765156]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ddm_matrix</span></div><div class='xr-var-dims'>(lagtime, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 294.2 321.4 ... 201.1 200.4</div><input id='attrs-5acb7084-7480-44e6-83b4-7bb300018377' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5acb7084-7480-44e6-83b4-7bb300018377' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8f5c45d-7aed-4234-a99a-36f42260aea6' class='xr-var-data-in' type='checkbox'><label for='data-a8f5c45d-7aed-4234-a99a-36f42260aea6' 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.        ,  294.18943955,  321.41822078, ...,  192.93331805,\n",
       "         193.46556826,  192.76671589],\n",
       "       [   0.        ,  432.05714374,  490.9677582 , ...,  194.84772175,\n",
       "         195.06799833,  194.40459328],\n",
       "       [   0.        ,  535.36374381,  619.95883018, ...,  196.12616285,\n",
       "         196.21094092,  195.79399928],\n",
       "       ...,\n",
       "       [   0.        , 3966.56056507, 5791.89215548, ...,  205.00017796,\n",
       "         211.36062787,  212.61362392],\n",
       "       [   0.        , 4167.1712352 , 5456.14848464, ...,  203.8398704 ,\n",
       "         205.38454854,  207.78696548],\n",
       "       [   0.        , 3716.77402137, 6067.88205869, ...,  207.76466695,\n",
       "         201.06258553,  200.39635905]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>first_image</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>128.0 149.0 173.0 ... 229.0 215.0</div><input id='attrs-aa0563b1-3b21-420e-92ee-7cb6b2277a20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa0563b1-3b21-420e-92ee-7cb6b2277a20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09857491-94cc-4c79-8663-171264fe4572' class='xr-var-data-in' type='checkbox'><label for='data-09857491-94cc-4c79-8663-171264fe4572' 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([[128., 149., 173., ..., 178., 224., 255.],\n",
       "       [164., 163., 166., ..., 182., 197., 255.],\n",
       "       [208., 182., 175., ..., 178., 210., 255.],\n",
       "       ...,\n",
       "       [147., 162., 162., ..., 182., 196., 165.],\n",
       "       [201., 214., 234., ..., 189., 174., 178.],\n",
       "       [255., 255., 255., ..., 175., 229., 215.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>avg_image_ft</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.293e+05 ... 104.7 105.3</div><input id='attrs-18d069e0-a00b-4a00-b6cc-9a1aad9736e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18d069e0-a00b-4a00-b6cc-9a1aad9736e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f8c018a-6a1c-4071-ab5c-4398fee7cf2b' class='xr-var-data-in' type='checkbox'><label for='data-8f8c018a-6a1c-4071-ab5c-4398fee7cf2b' 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([0.00000000e+00, 1.29341509e+05, 5.22511731e+03, 5.86989569e+03,\n",
       "       8.50478146e+03, 9.68446541e+03, 9.99103750e+03, 1.03319622e+04,\n",
       "       1.00264087e+04, 1.08058729e+04, 1.16343568e+04, 1.38669622e+04,\n",
       "       1.68988722e+04, 1.98642184e+04, 2.11800678e+04, 2.14311022e+04,\n",
       "       2.06130601e+04, 1.80258072e+04, 1.42561639e+04, 1.04082328e+04,\n",
       "       7.27537584e+03, 4.68549415e+03, 2.88015028e+03, 1.77658315e+03,\n",
       "       1.06139768e+03, 6.83505113e+02, 4.97908069e+02, 4.18238638e+02,\n",
       "       3.38383278e+02, 2.72050227e+02, 2.35672326e+02, 2.24525405e+02,\n",
       "       2.03430129e+02, 1.78436880e+02, 1.65009527e+02, 1.49291391e+02,\n",
       "       1.41201296e+02, 1.33121051e+02, 1.29764821e+02, 1.28159612e+02,\n",
       "       1.24131175e+02, 1.24052104e+02, 1.21220015e+02, 1.19076951e+02,\n",
       "       1.17509287e+02, 1.15310101e+02, 1.15268069e+02, 1.12572652e+02,\n",
       "       1.11849673e+02, 1.11765243e+02, 1.10247178e+02, 1.09703002e+02,\n",
       "       1.08834403e+02, 1.09662333e+02, 1.08597899e+02, 1.07728419e+02,\n",
       "       1.07789289e+02, 1.06907057e+02, 1.07036538e+02, 1.05981891e+02,\n",
       "       1.05125261e+02, 1.05313067e+02, 1.04737380e+02, 1.05326832e+02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_pairs_per_dt</span></div><div class='xr-var-dims'>(lagtime)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>2999 2998 2997 1498 ... 20 17 15 13</div><input id='attrs-35ed41d2-c5a7-46da-8200-94f8b7e07c31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35ed41d2-c5a7-46da-8200-94f8b7e07c31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-774971b2-bc30-4bf1-90c1-099c874c44a8' class='xr-var-data-in' type='checkbox'><label for='data-774971b2-bc30-4bf1-90c1-099c874c44a8' 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([2999, 2998, 2997, 1498, 1498, 1497,  998,  998,  997,  748,  747,\n",
       "        598,  498,  497,  426,  372,  331,  270,  247,  212,  185,  164,\n",
       "        140,  123,  109,   94,   81,   71,   63,   54,   47,   41,   35,\n",
       "         31,   27,   23,   20,   17,   15,   13])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>211.2</div><input id='attrs-bd900d80-ad54-4809-9c70-3e71aed229aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd900d80-ad54-4809-9c70-3e71aed229aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-395aee10-65d5-4c1e-a08b-cab541610183' class='xr-var-data-in' type='checkbox'><label for='data-395aee10-65d5-4c1e-a08b-cab541610183' 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(211.17365621)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B_std</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.491</div><input id='attrs-257722de-1996-4ebb-a5c1-15c5412f9f35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-257722de-1996-4ebb-a5c1-15c5412f9f35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5344d01-acfb-40e4-a1ce-0db7a3736873' class='xr-var-data-in' type='checkbox'><label for='data-a5344d01-acfb-40e4-a1ce-0db7a3736873' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.49105881)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Amplitude</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-211.2 2.585e+05 ... -1.699 -0.52</div><input id='attrs-b97b1e61-15c3-4870-b69f-90016561ea95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b97b1e61-15c3-4870-b69f-90016561ea95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b878ee4-9427-411f-a318-9ae523ba3b8d' class='xr-var-data-in' type='checkbox'><label for='data-5b878ee4-9427-411f-a318-9ae523ba3b8d' 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([-2.11173656e+02,  2.58471844e+05,  1.02390610e+04,  1.15286177e+04,\n",
       "        1.67983893e+04,  1.91577572e+04,  1.97709014e+04,  2.04527506e+04,\n",
       "        1.98416438e+04,  2.14005721e+04,  2.30575400e+04,  2.75227507e+04,\n",
       "        3.35865707e+04,  3.95172631e+04,  4.21489620e+04,  4.26510308e+04,\n",
       "        4.10149466e+04,  3.58404407e+04,  2.83011541e+04,  2.06052919e+04,\n",
       "        1.43395780e+04,  9.15981465e+03,  5.54912690e+03,  3.34199265e+03,\n",
       "        1.91162170e+03,  1.15583657e+03,  7.84642482e+02,  6.25303620e+02,\n",
       "        4.65592900e+02,  3.32926798e+02,  2.60170996e+02,  2.37877154e+02,\n",
       "        1.95686602e+02,  1.45700103e+02,  1.18845397e+02,  8.74091262e+01,\n",
       "        7.12289354e+01,  5.50684455e+01,  4.83559858e+01,  4.51455685e+01,\n",
       "        3.70886933e+01,  3.69305527e+01,  3.12663737e+01,  2.69802467e+01,\n",
       "        2.38449170e+01,  1.94465450e+01,  1.93624826e+01,  1.39716478e+01,\n",
       "        1.25256899e+01,  1.23568306e+01,  9.32069947e+00,  8.23234748e+00,\n",
       "        6.49514954e+00,  8.15100953e+00,  6.02214129e+00,  4.28318246e+00,\n",
       "        4.40492236e+00,  2.64045708e+00,  2.89942014e+00,  7.90126460e-01,\n",
       "       -9.23134186e-01, -5.47522559e-01, -1.69889697e+00, -5.19992889e-01])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ISF</span></div><div class='xr-var-dims'>(lagtime, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9997 0.9892 ... -4.952 -19.73</div><input id='attrs-f8a15982-e6a8-4142-84c1-b6584af7d9a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8a15982-e6a8-4142-84c1-b6584af7d9a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c231a910-d1e3-4b19-bf16-a652e16675a1' class='xr-var-data-in' type='checkbox'><label for='data-c231a910-d1e3-4b19-bf16-a652e16675a1' 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.        ,   0.99967882,   0.98923294, ..., -32.31431348,\n",
       "         -9.42328539, -34.39844623],\n",
       "       [  0.        ,   0.99914543,   0.97267385, ..., -28.81782979,\n",
       "         -8.48006746, -31.24863895],\n",
       "       [  0.        ,   0.99874574,   0.96007591, ..., -26.48287376,\n",
       "         -7.80731178, -28.57666779],\n",
       "       ...,\n",
       "       [  0.        ,   0.98547081,   0.45495798, ..., -10.27529479,\n",
       "          1.11005474,   3.76920654],\n",
       "       [  0.        ,   0.98469467,   0.48774845, ..., -12.39449068,\n",
       "         -2.40756842,  -5.51295585],\n",
       "       [  0.        ,   0.98643721,   0.42800337, ...,  -5.22620786,\n",
       "         -4.95155025, -19.7258549 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90595fff-b35e-4d94-85ad-92f7e9a02db8' class='xr-section-summary-in' type='checkbox'  ><label for='section-90595fff-b35e-4d94-85ad-92f7e9a02db8' class='xr-section-summary' >Attributes: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Intensity</dd><dt><span>lagtime :</span></dt><dd>sec</dd><dt><span>q :</span></dt><dd>μm$^{-1}$</dd><dt><span>x :</span></dt><dd>pixels</dd><dt><span>y :</span></dt><dd>pixels</dd><dt><span>info :</span></dt><dd>ddm_matrix is the averages of FFT difference images, ravs are the radial averages</dd><dt><span>BackgroundMethod :</span></dt><dd>0</dd><dt><span>OverlapMethod :</span></dt><dd>2</dd><dt><span>DataDirectory :</span></dt><dd>C:/Users/rmcgorty/Documents/GitHub/PyDDM/Examples/</dd><dt><span>FileName :</span></dt><dd>images_nobin_40x_128x128_8bit.tif</dd><dt><span>pixel_size :</span></dt><dd>0.242</dd><dt><span>frame_rate :</span></dt><dd>41.7</dd><dt><span>starting_frame_number :</span></dt><dd>no</dd><dt><span>ending_frame_number :</span></dt><dd>no</dd><dt><span>number_lagtimes :</span></dt><dd>40</dd><dt><span>first_lag_time :</span></dt><dd>yes</dd><dt><span>last_lag_time :</span></dt><dd>600</dd><dt><span>crop_to_roi :</span></dt><dd>no</dd><dt><span>split_into_4_rois :</span></dt><dd>no</dd><dt><span>use_windowing_function :</span></dt><dd>no</dd><dt><span>binning :</span></dt><dd>no</dd><dt><span>bin_size :</span></dt><dd>2</dd><dt><span>central_angle :</span></dt><dd>no</dd><dt><span>angle_range :</span></dt><dd>no</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (lagtime: 40, q_y: 128, q_x: 128, q: 64, y: 128, x: 128, frames: 40)\n",
       "Coordinates:\n",
       "  * lagtime           (lagtime) float64 0.02398 0.04796 0.07194 ... 12.59 14.36\n",
       "    framelag          (frames) int32 1 2 3 4 5 6 7 ... 308 352 402 459 525 599\n",
       "  * q_y               (q_y) float64 -12.98 -12.78 -12.58 ... 12.37 12.58 12.78\n",
       "  * q_x               (q_x) float64 -12.98 -12.78 -12.58 ... 12.37 12.58 12.78\n",
       "  * q                 (q) float64 0.0 0.2028 0.4057 0.6085 ... 12.37 12.58 12.78\n",
       "  * y                 (y) int32 0 1 2 3 4 5 6 7 ... 121 122 123 124 125 126 127\n",
       "  * x                 (x) int32 0 1 2 3 4 5 6 7 ... 121 122 123 124 125 126 127\n",
       "Dimensions without coordinates: frames\n",
       "Data variables:\n",
       "    ddm_matrix_full   (lagtime, q_y, q_x) float64 194.5 183.5 ... 192.0 196.8\n",
       "    ddm_matrix        (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    first_image       (y, x) float64 128.0 149.0 173.0 ... 175.0 229.0 215.0\n",
       "    avg_image_ft      (q) float64 0.0 1.293e+05 5.225e+03 ... 105.3 104.7 105.3\n",
       "    num_pairs_per_dt  (lagtime) int32 2999 2998 2997 1498 1498 ... 20 17 15 13\n",
       "    B                 float64 211.2\n",
       "    B_std             float64 1.491\n",
       "    Amplitude         (q) float64 -211.2 2.585e+05 1.024e+04 ... -1.699 -0.52\n",
       "    ISF               (lagtime, q) float64 0.0 0.9997 0.9892 ... -4.952 -19.73\n",
       "Attributes: (12/24)\n",
       "    units:                   Intensity\n",
       "    lagtime:                 sec\n",
       "    q:                       μm$^{-1}$\n",
       "    x:                       pixels\n",
       "    y:                       pixels\n",
       "    info:                    ddm_matrix is the averages of FFT difference ima...\n",
       "    ...                      ...\n",
       "    split_into_4_rois:       no\n",
       "    use_windowing_function:  no\n",
       "    binning:                 no\n",
       "    bin_size:                2\n",
       "    central_angle:           no\n",
       "    angle_range:             no"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#If a file is found that matches what, by default, the program will save the computed DDM matrix as, \n",
    "#then you will be prompted as to whether or not you want to proceed with this calculation. If you \n",
    "#do not, then enter 'n' and the program will read from the existing file to load the DDM dataset \n",
    "#into memory. If you enter 'y', then the DDM matrix will be calculated.\n",
    "\n",
    "ddm_calc.calculate_DDM_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiazing DDM_Fit class and fitting our data to a model\n",
    "Next, we initialize the `DDM_Fit` class which will help us fit the calculated DDM matrix to a model of our choosing. \n",
    "\n",
    "When the `DDM_Fit` class is initialized, it will display a table of the parameters, with the initial guesses and bounds, that were in the parameters of the yaml file. It will also indicate if, for the chosen model, initial guesses for parameters are missing. \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> The intial guesses and bounds for the parameters are given in the yaml file. But you may realize that you need to adjust the bounds. You could change the yaml file and reload it. But you can also change the initial guesses and bounds for the parameters as shown in this code snippet:\n",
    "</div>\n",
    "\n",
    "```python\n",
    "ddm_fit.set_parameter_initial_guess('Tau', 0.5)\n",
    "ddm_fit.set_parameter_bounds('Tau', [0.001, 1000])\n",
    "```\n",
    "\n",
    "Also, note that to initialize the `DDM_Fit` class, we need to pass it the YAML file which contains the metadata and parameters. Below, we use the fact that the path to the YAML file is stored in the variable `ddm_calc.data_yaml`. But we could also use the path to the YAML file just like we did when initializing the DDM_Analysis class. That is, this would work as well:\n",
    "\n",
    "```python\n",
    "ddm_fit = ddm.DDM_Fit(\"../../Examples/example_data_silica_beads.yml\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initial guess</th>\n",
       "      <th>Minimum</th>\n",
       "      <th>Maximum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amplitude</th>\n",
       "      <td>100.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tau</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Background</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StretchingExp</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Initial guess  Minimum     Maximum\n",
       "Amplitude              100.0    1.000   1000000.0\n",
       "Tau                      1.0    0.001        10.0\n",
       "Background           25000.0    0.000  10000000.0\n",
       "StretchingExp            1.0    0.500         1.1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading file C:/Users/rmcgorty/Documents/GitHub/PyDDM/Examples/images_nobin_40x_128x128_8bit_ddmmatrix.nc ...\n"
     ]
    }
   ],
   "source": [
    "ddm_fit = ddm.DDM_Fit(ddm_calc.data_yaml)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, the fit is performed and saved into an xarray Dataset. We also have a table of the parameters for each q. Since this table may be long (especially if you have a large image size), you might want to not display it. If that's the case, then run this command with the `display_table` parmeter set to False, i.e.:\n",
    "```python\n",
    "fit01 = ddm_fit.fit(name_fit = 'fit01', display_table=False)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In function 'get_tau_vs_q_fit', using new tau...\n",
      "Fit is saved in fittings dictionary with key 'fit01'.\n"
     ]
    },
    {
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       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
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       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow1_col1\" class=\"data row1 col1\" >5009.160959</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow1_col2\" class=\"data row1 col2\" >10.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow1_col3\" class=\"data row1 col3\" >125.265537</td>\n",
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       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow3_col4\" class=\"data row3 col4\" >0.710762</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
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       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow4_col1\" class=\"data row4 col1\" >16595.852548</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow4_col2\" class=\"data row4 col2\" >2.556070</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow4_col3\" class=\"data row4 col3\" >83.269636</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow4_col4\" class=\"data row4 col4\" >0.811578</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
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       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow5_col1\" class=\"data row5 col1\" >18774.162108</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow5_col2\" class=\"data row5 col2\" >1.424364</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow5_col3\" class=\"data row5 col3\" >101.316228</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow5_col4\" class=\"data row5 col4\" >0.909133</td>\n",
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       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow6_col2\" class=\"data row6 col2\" >1.035536</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow6_col3\" class=\"data row6 col3\" >37.943122</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow6_col4\" class=\"data row6 col4\" >0.911751</td>\n",
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       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow7_col0\" class=\"data row7 col0\" >1.419883</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow7_col1\" class=\"data row7 col1\" >20269.935277</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow7_col2\" class=\"data row7 col2\" >0.742219</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow7_col3\" class=\"data row7 col3\" >30.165661</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow7_col4\" class=\"data row7 col4\" >0.952913</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow8_col0\" class=\"data row8 col0\" >1.622723</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow8_col1\" class=\"data row8 col1\" >19408.933976</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow8_col2\" class=\"data row8 col2\" >0.552100</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow8_col3\" class=\"data row8 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow8_col4\" class=\"data row8 col4\" >0.955882</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow9_col0\" class=\"data row9 col0\" >1.825564</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow9_col1\" class=\"data row9 col1\" >21442.103307</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow9_col2\" class=\"data row9 col2\" >0.465083</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow9_col3\" class=\"data row9 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow9_col4\" class=\"data row9 col4\" >0.977973</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow10_col0\" class=\"data row10 col0\" >2.028404</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow10_col1\" class=\"data row10 col1\" >23388.120235</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow10_col2\" class=\"data row10 col2\" >0.396199</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow10_col3\" class=\"data row10 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow10_col4\" class=\"data row10 col4\" >0.968451</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow11_col0\" class=\"data row11 col0\" >2.231245</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow11_col1\" class=\"data row11 col1\" >27490.759703</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow11_col2\" class=\"data row11 col2\" >0.321218</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow11_col3\" class=\"data row11 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow11_col4\" class=\"data row11 col4\" >1.008676</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow12_col0\" class=\"data row12 col0\" >2.434085</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow12_col1\" class=\"data row12 col1\" >33464.835149</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow12_col2\" class=\"data row12 col2\" >0.290591</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow12_col3\" class=\"data row12 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow12_col4\" class=\"data row12 col4\" >1.014272</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow13_col0\" class=\"data row13 col0\" >2.636926</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow13_col1\" class=\"data row13 col1\" >39525.290291</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow13_col2\" class=\"data row13 col2\" >0.248451</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow13_col3\" class=\"data row13 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow13_col4\" class=\"data row13 col4\" >1.040113</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow14_col0\" class=\"data row14 col0\" >2.839766</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow14_col1\" class=\"data row14 col1\" >41793.934306</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow14_col2\" class=\"data row14 col2\" >0.213868</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow14_col3\" class=\"data row14 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow14_col4\" class=\"data row14 col4\" >1.030231</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow15_col0\" class=\"data row15 col0\" >3.042607</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow15_col1\" class=\"data row15 col1\" >42078.124470</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow15_col2\" class=\"data row15 col2\" >0.192926</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow15_col3\" class=\"data row15 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow15_col4\" class=\"data row15 col4\" >1.030781</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow16_col0\" class=\"data row16 col0\" >3.245447</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow16_col1\" class=\"data row16 col1\" >40768.461703</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow16_col2\" class=\"data row16 col2\" >0.172733</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow16_col3\" class=\"data row16 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow16_col4\" class=\"data row16 col4\" >1.045874</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow17_col0\" class=\"data row17 col0\" >3.448287</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow17_col1\" class=\"data row17 col1\" >35721.127412</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow17_col2\" class=\"data row17 col2\" >0.155423</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow17_col3\" class=\"data row17 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow17_col4\" class=\"data row17 col4\" >1.043916</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow18_col0\" class=\"data row18 col0\" >3.651128</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow18_col1\" class=\"data row18 col1\" >28431.332255</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow18_col2\" class=\"data row18 col2\" >0.142240</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow18_col3\" class=\"data row18 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow18_col4\" class=\"data row18 col4\" >1.043399</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow19_col0\" class=\"data row19 col0\" >3.853968</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow19_col1\" class=\"data row19 col1\" >20619.257623</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow19_col2\" class=\"data row19 col2\" >0.128071</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow19_col3\" class=\"data row19 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow19_col4\" class=\"data row19 col4\" >1.054034</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow20_col0\" class=\"data row20 col0\" >4.056809</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow20_col1\" class=\"data row20 col1\" >14370.154840</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow20_col2\" class=\"data row20 col2\" >0.121052</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow20_col3\" class=\"data row20 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow20_col4\" class=\"data row20 col4\" >1.035008</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow21_col0\" class=\"data row21 col0\" >4.259649</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow21_col1\" class=\"data row21 col1\" >9293.652992</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow21_col2\" class=\"data row21 col2\" >0.111708</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow21_col3\" class=\"data row21 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow21_col4\" class=\"data row21 col4\" >1.025896</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow22_col0\" class=\"data row22 col0\" >4.462490</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow22_col1\" class=\"data row22 col1\" >5662.007475</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow22_col2\" class=\"data row22 col2\" >0.102243</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow22_col3\" class=\"data row22 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow22_col4\" class=\"data row22 col4\" >1.008149</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow23_col0\" class=\"data row23 col0\" >4.665330</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow23_col1\" class=\"data row23 col1\" >3487.994425</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow23_col2\" class=\"data row23 col2\" >0.097775</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow23_col3\" class=\"data row23 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow23_col4\" class=\"data row23 col4\" >0.971809</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow24_col0\" class=\"data row24 col0\" >4.868170</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow24_col1\" class=\"data row24 col1\" >2077.865135</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow24_col2\" class=\"data row24 col2\" >0.090379</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow24_col3\" class=\"data row24 col3\" >0.000000</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow24_col4\" class=\"data row24 col4\" >0.913350</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow25_col0\" class=\"data row25 col0\" >5.071011</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow25_col1\" class=\"data row25 col1\" >1223.115625</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow25_col2\" class=\"data row25 col2\" >0.089518</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow25_col3\" class=\"data row25 col3\" >83.952238</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow25_col4\" class=\"data row25 col4\" >0.911770</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow26_col0\" class=\"data row26 col0\" >5.273851</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow26_col1\" class=\"data row26 col1\" >876.927434</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow26_col2\" class=\"data row26 col2\" >0.083446</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow26_col3\" class=\"data row26 col3\" >84.578516</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow26_col4\" class=\"data row26 col4\" >0.860156</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow27_col0\" class=\"data row27 col0\" >5.476692</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow27_col1\" class=\"data row27 col1\" >690.124583</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow27_col2\" class=\"data row27 col2\" >0.081228</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow27_col3\" class=\"data row27 col3\" >107.731737</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow27_col4\" class=\"data row27 col4\" >0.859201</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow28_col0\" class=\"data row28 col0\" >5.679532</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow28_col1\" class=\"data row28 col1\" >559.962532</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow28_col2\" class=\"data row28 col2\" >0.074982</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow28_col3\" class=\"data row28 col3\" >89.841197</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow28_col4\" class=\"data row28 col4\" >0.757985</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow29_col0\" class=\"data row29 col0\" >5.882373</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow29_col1\" class=\"data row29 col1\" >417.404173</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow29_col2\" class=\"data row29 col2\" >0.077395</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow29_col3\" class=\"data row29 col3\" >113.007189</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow29_col4\" class=\"data row29 col4\" >0.732307</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow30_col0\" class=\"data row30 col0\" >6.085213</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow30_col1\" class=\"data row30 col1\" >339.585926</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow30_col2\" class=\"data row30 col2\" >0.074093</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow30_col3\" class=\"data row30 col3\" >121.429878</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow30_col4\" class=\"data row30 col4\" >0.702539</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow31_col0\" class=\"data row31 col0\" >6.288053</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow31_col1\" class=\"data row31 col1\" >328.928162</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow31_col2\" class=\"data row31 col2\" >0.063312</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow31_col3\" class=\"data row31 col3\" >104.741958</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow31_col4\" class=\"data row31 col4\" >0.648010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow32_col0\" class=\"data row32 col0\" >6.490894</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow32_col1\" class=\"data row32 col1\" >273.021363</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow32_col2\" class=\"data row32 col2\" >0.065949</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow32_col3\" class=\"data row32 col3\" >123.344201</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow32_col4\" class=\"data row32 col4\" >0.657741</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow33_col0\" class=\"data row33 col0\" >6.693734</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow33_col1\" class=\"data row33 col1\" >210.366431</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow33_col2\" class=\"data row33 col2\" >0.069732</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow33_col3\" class=\"data row33 col3\" >138.341989</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow33_col4\" class=\"data row33 col4\" >0.630490</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow34_col0\" class=\"data row34 col0\" >6.896575</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow34_col1\" class=\"data row34 col1\" >168.823091</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow34_col2\" class=\"data row34 col2\" >0.078894</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow34_col3\" class=\"data row34 col3\" >151.432022</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow34_col4\" class=\"data row34 col4\" >0.624047</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow35_col0\" class=\"data row35 col0\" >7.099415</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow35_col1\" class=\"data row35 col1\" >120.146768</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow35_col2\" class=\"data row35 col2\" >0.096006</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow35_col3\" class=\"data row35 col3\" >168.399637</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow35_col4\" class=\"data row35 col4\" >0.643459</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow36_col0\" class=\"data row36 col0\" >7.302256</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow36_col1\" class=\"data row36 col1\" >90.471468</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow36_col2\" class=\"data row36 col2\" >0.114569</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow36_col3\" class=\"data row36 col3\" >179.212366</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow36_col4\" class=\"data row36 col4\" >0.696849</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow37_col0\" class=\"data row37 col0\" >7.505096</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow37_col1\" class=\"data row37 col1\" >73.280303</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow37_col2\" class=\"data row37 col2\" >0.143625</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow37_col3\" class=\"data row37 col3\" >183.974447</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow37_col4\" class=\"data row37 col4\" >0.707400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow38_col0\" class=\"data row38 col0\" >7.707937</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow38_col1\" class=\"data row38 col1\" >63.315036</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow38_col2\" class=\"data row38 col2\" >0.145241</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow38_col3\" class=\"data row38 col3\" >186.298290</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow38_col4\" class=\"data row38 col4\" >0.743781</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow39_col0\" class=\"data row39 col0\" >7.910777</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow39_col1\" class=\"data row39 col1\" >54.490768</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow39_col2\" class=\"data row39 col2\" >0.146684</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow39_col3\" class=\"data row39 col3\" >188.785276</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow39_col4\" class=\"data row39 col4\" >0.771679</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow40_col0\" class=\"data row40 col0\" >8.113617</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow40_col1\" class=\"data row40 col1\" >48.703834</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow40_col2\" class=\"data row40 col2\" >0.161850</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow40_col3\" class=\"data row40 col3\" >189.738060</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow40_col4\" class=\"data row40 col4\" >0.784964</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow41_col0\" class=\"data row41 col0\" >8.316458</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow41_col1\" class=\"data row41 col1\" >46.274244</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow41_col2\" class=\"data row41 col2\" >0.157325</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow41_col3\" class=\"data row41 col3\" >189.710146</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow41_col4\" class=\"data row41 col4\" >0.805431</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow42_col0\" class=\"data row42 col0\" >8.519298</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow42_col1\" class=\"data row42 col1\" >42.999737</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow42_col2\" class=\"data row42 col2\" >0.150650</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow42_col3\" class=\"data row42 col3\" >188.305785</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow42_col4\" class=\"data row42 col4\" >0.739396</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow43_col0\" class=\"data row43 col0\" >8.722139</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow43_col1\" class=\"data row43 col1\" >41.307921</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow43_col2\" class=\"data row43 col2\" >0.160369</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow43_col3\" class=\"data row43 col3\" >188.695165</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow43_col4\" class=\"data row43 col4\" >0.715729</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow44_col0\" class=\"data row44 col0\" >8.924979</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow44_col1\" class=\"data row44 col1\" >37.804395</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow44_col2\" class=\"data row44 col2\" >0.155632</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow44_col3\" class=\"data row44 col3\" >188.636134</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow44_col4\" class=\"data row44 col4\" >0.761223</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow45_col0\" class=\"data row45 col0\" >9.127820</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow45_col1\" class=\"data row45 col1\" >35.459070</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow45_col2\" class=\"data row45 col2\" >0.163358</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow45_col3\" class=\"data row45 col3\" >188.442035</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow45_col4\" class=\"data row45 col4\" >0.690826</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow46_col0\" class=\"data row46 col0\" >9.330660</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow46_col1\" class=\"data row46 col1\" >31.309845</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow46_col2\" class=\"data row46 col2\" >0.158481</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow46_col3\" class=\"data row46 col3\" >191.127615</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow46_col4\" class=\"data row46 col4\" >0.879080</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow47_col0\" class=\"data row47 col0\" >9.533500</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow47_col1\" class=\"data row47 col1\" >27.495879</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow47_col2\" class=\"data row47 col2\" >0.159557</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow47_col3\" class=\"data row47 col3\" >190.705808</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow47_col4\" class=\"data row47 col4\" >0.826357</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow48_col0\" class=\"data row48 col0\" >9.736341</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow48_col1\" class=\"data row48 col1\" >29.578351</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow48_col2\" class=\"data row48 col2\" >0.164955</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow48_col3\" class=\"data row48 col3\" >189.489710</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow48_col4\" class=\"data row48 col4\" >0.747781</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow49_col0\" class=\"data row49 col0\" >9.939181</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow49_col1\" class=\"data row49 col1\" >27.750185</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow49_col2\" class=\"data row49 col2\" >0.173154</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow49_col3\" class=\"data row49 col3\" >190.573335</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow49_col4\" class=\"data row49 col4\" >0.817289</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow50_col0\" class=\"data row50 col0\" >10.142022</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow50_col1\" class=\"data row50 col1\" >26.022042</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow50_col2\" class=\"data row50 col2\" >0.169798</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow50_col3\" class=\"data row50 col3\" >190.445764</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow50_col4\" class=\"data row50 col4\" >0.694703</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow51_col0\" class=\"data row51 col0\" >10.344862</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow51_col1\" class=\"data row51 col1\" >25.817798</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow51_col2\" class=\"data row51 col2\" >0.149565</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow51_col3\" class=\"data row51 col3\" >188.926426</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow51_col4\" class=\"data row51 col4\" >0.677988</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow52_col0\" class=\"data row52 col0\" >10.547703</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow52_col1\" class=\"data row52 col1\" >20.964509</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow52_col2\" class=\"data row52 col2\" >0.171498</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow52_col3\" class=\"data row52 col3\" >192.258742</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow52_col4\" class=\"data row52 col4\" >0.865291</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow53_col0\" class=\"data row53 col0\" >10.750543</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow53_col1\" class=\"data row53 col1\" >22.212659</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow53_col2\" class=\"data row53 col2\" >0.186064</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow53_col3\" class=\"data row53 col3\" >191.315356</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow53_col4\" class=\"data row53 col4\" >0.803059</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddblevel0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow54_col0\" class=\"data row54 col0\" >10.953383</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow54_col1\" class=\"data row54 col1\" >20.296560</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow54_col2\" class=\"data row54 col2\" >0.175117</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow54_col3\" class=\"data row54 col3\" >191.111831</td>\n",
       "                        <td id=\"T_26388b9e_8ab6_11ec_90a3_d89ef334cddbrow54_col4\" class=\"data row54 col4\" >0.815027</td>\n",
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      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x28915ad0b50>"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "#Note that the method `fit` has many optional parameters, but here we \n",
    "# allow those optional parameter to take their default values\n",
    "\n",
    "fit01 = ddm_fit.fit(name_fit = 'fit01')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Inspecting the outcome of the fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now take a look at the best fit parameters determined by the fitting code. We can generate a set of plots and have the output saved as PDF with the function `fit_report`. This takes a few arguments, including:\n",
    "* the result of the fit in an xarray Dataset (here `fit01`)\n",
    "* `q_indices`: the DDM matrix or the ISF (whichever was used in the fitting) will be plotted at these $q$-values specified by their index in the array of $q$-values\n",
    "* `forced_qs`: range of $q$-values (specified, again, by the index) for which to extract power law relationship between the decay time $\\tau$ and $q$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In function 'get_tau_vs_q_fit', using new tau...\n",
      "In hf.plot_one_tau_vs_q function, using new tau... \n"
     ]
    },
    {
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       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (parameter: 4, q: 64, lagtime: 40)\n",
       "Coordinates:\n",
       "  * parameter        (parameter) &lt;U13 &#x27;Amplitude&#x27; &#x27;Tau&#x27; ... &#x27;StretchingExp&#x27;\n",
       "  * q                (q) float64 0.0 0.2028 0.4057 0.6085 ... 12.37 12.58 12.78\n",
       "  * lagtime          (lagtime) float64 0.02398 0.04796 0.07194 ... 12.59 14.36\n",
       "Data variables:\n",
       "    parameters       (parameter, q) float64 1.0 5.009e+03 ... 0.9077 0.9903\n",
       "    theory           (lagtime, q) float64 0.001311 364.7 349.3 ... 206.3 206.3\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9997 0.9892 ... -4.952 -19.73\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -211.2 2.585e+05 1.024e+04 ... -1.699 -0.52\n",
       "    B                float64 211.2\n",
       "Attributes: (12/18)\n",
       "    model:                          DDM Matrix - Single Exponential\n",
       "    data_to_use:                    DDM Matrix\n",
       "    initial_params_dict:            [&quot;{&#x27;n&#x27;: 0, &#x27;value&#x27;: 100.0, &#x27;limits&#x27;: [1.0...\n",
       "    effective_diffusion_coeff:      0.616760567419975\n",
       "    tau_vs_q_slope:                 [-1.9799845]\n",
       "    msd_alpha:                      [1.01010892]\n",
       "    ...                             ...\n",
       "    DataDirectory:                  C:/Users/rmcgorty/Documents/GitHub/PyDDM/...\n",
       "    FileName:                       images_nobin_40x_128x128_8bit.tif\n",
       "    pixel_size:                     0.242\n",
       "    frame_rate:                     41.7\n",
       "    BackgroundMethod:               0\n",
       "    OverlapMethod:                  2</pre><div class='xr-wrap' hidden><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-edbf0a41-9e69-4eca-a32f-e797fa6340c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-edbf0a41-9e69-4eca-a32f-e797fa6340c0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>parameter</span>: 4</li><li><span class='xr-has-index'>q</span>: 64</li><li><span class='xr-has-index'>lagtime</span>: 40</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-beba6370-6a54-4797-8fab-4d916b8cdccc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-beba6370-6a54-4797-8fab-4d916b8cdccc' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>parameter</span></div><div class='xr-var-dims'>(parameter)</div><div class='xr-var-dtype'>&lt;U13</div><div class='xr-var-preview xr-preview'>&#x27;Amplitude&#x27; ... &#x27;StretchingExp&#x27;</div><input id='attrs-d6c8f2bd-c60c-4267-93a5-fc03c2ea16fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6c8f2bd-c60c-4267-93a5-fc03c2ea16fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6016213b-b7f8-4186-83d9-f06cef477cba' class='xr-var-data-in' type='checkbox'><label for='data-6016213b-b7f8-4186-83d9-f06cef477cba' 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;Amplitude&#x27;, &#x27;Tau&#x27;, &#x27;Background&#x27;, &#x27;StretchingExp&#x27;], dtype=&#x27;&lt;U13&#x27;)</pre></div></li><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'>0.0 0.2028 0.4057 ... 12.58 12.78</div><input id='attrs-baa786dc-de2a-493b-815e-d55a74e1bc05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-baa786dc-de2a-493b-815e-d55a74e1bc05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a265b38-3036-4959-b7ba-64ca50e532df' class='xr-var-data-in' type='checkbox'><label for='data-2a265b38-3036-4959-b7ba-64ca50e532df' 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.      ,  0.20284 ,  0.405681,  0.608521,  0.811362,  1.014202,\n",
       "        1.217043,  1.419883,  1.622723,  1.825564,  2.028404,  2.231245,\n",
       "        2.434085,  2.636926,  2.839766,  3.042607,  3.245447,  3.448287,\n",
       "        3.651128,  3.853968,  4.056809,  4.259649,  4.46249 ,  4.66533 ,\n",
       "        4.86817 ,  5.071011,  5.273851,  5.476692,  5.679532,  5.882373,\n",
       "        6.085213,  6.288053,  6.490894,  6.693734,  6.896575,  7.099415,\n",
       "        7.302256,  7.505096,  7.707937,  7.910777,  8.113617,  8.316458,\n",
       "        8.519298,  8.722139,  8.924979,  9.12782 ,  9.33066 ,  9.5335  ,\n",
       "        9.736341,  9.939181, 10.142022, 10.344862, 10.547703, 10.750543,\n",
       "       10.953383, 11.156224, 11.359064, 11.561905, 11.764745, 11.967586,\n",
       "       12.170426, 12.373267, 12.576107, 12.778947])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lagtime</span></div><div class='xr-var-dims'>(lagtime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02398 0.04796 ... 12.59 14.36</div><input id='attrs-dd21d643-7b10-4151-8f61-6f2fd74766a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd21d643-7b10-4151-8f61-6f2fd74766a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-214eb781-c27b-48e8-a7f4-921df798b414' class='xr-var-data-in' type='checkbox'><label for='data-214eb781-c27b-48e8-a7f4-921df798b414' 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([ 0.023981,  0.047962,  0.071942,  0.095923,  0.119904,  0.143885,\n",
       "        0.167866,  0.191847,  0.215827,  0.263789,  0.28777 ,  0.335731,\n",
       "        0.383693,  0.431655,  0.503597,  0.57554 ,  0.647482,  0.743405,\n",
       "        0.863309,  0.983213,  1.127098,  1.294964,  1.486811,  1.702638,\n",
       "        1.942446,  2.206235,  2.541966,  2.901679,  3.309353,  3.788969,\n",
       "        4.316547,  4.940048,  5.659472,  6.450839,  7.386091,  8.441247,\n",
       "        9.640288, 11.007194, 12.589928, 14.364508])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-387a74be-fb8b-4596-a0b3-c0c2bb82ebd0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-387a74be-fb8b-4596-a0b3-c0c2bb82ebd0' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>parameters</span></div><div class='xr-var-dims'>(parameter, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 5.009e+03 ... 0.9077 0.9903</div><input id='attrs-509cd66a-00c8-4efa-8a03-761086288b3f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-509cd66a-00c8-4efa-8a03-761086288b3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e643f816-f645-4fa3-88bb-108f72e0c00c' class='xr-var-data-in' type='checkbox'><label for='data-e643f816-f645-4fa3-88bb-108f72e0c00c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.00000000e+00, 5.00916096e+03, 8.48280850e+03, 1.04425910e+04,\n",
       "        1.65958525e+04, 1.87741621e+04, 2.01262065e+04, 2.02699353e+04,\n",
       "        1.94089340e+04, 2.14421033e+04, 2.33881202e+04, 2.74907597e+04,\n",
       "        3.34648351e+04, 3.95252903e+04, 4.17939343e+04, 4.20781245e+04,\n",
       "        4.07684617e+04, 3.57211274e+04, 2.84313323e+04, 2.06192576e+04,\n",
       "        1.43701548e+04, 9.29365299e+03, 5.66200747e+03, 3.48799443e+03,\n",
       "        2.07786513e+03, 1.22311562e+03, 8.76927434e+02, 6.90124583e+02,\n",
       "        5.59962532e+02, 4.17404173e+02, 3.39585926e+02, 3.28928162e+02,\n",
       "        2.73021363e+02, 2.10366431e+02, 1.68823091e+02, 1.20146768e+02,\n",
       "        9.04714679e+01, 7.32803029e+01, 6.33150357e+01, 5.44907683e+01,\n",
       "        4.87038336e+01, 4.62742438e+01, 4.29997373e+01, 4.13079210e+01,\n",
       "        3.78043951e+01, 3.54590704e+01, 3.13098451e+01, 2.74958795e+01,\n",
       "        2.95783508e+01, 2.77501849e+01, 2.60220424e+01, 2.58177979e+01,\n",
       "        2.09645095e+01, 2.22126591e+01, 2.02965596e+01, 1.77334874e+01,\n",
       "        2.00125344e+01, 1.77408714e+01, 1.91646171e+01, 1.79749177e+01,\n",
       "        1.58011389e+01, 1.55611976e+01, 1.48803921e+01, 1.55394006e+01],\n",
       "       [1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 3.86806703e+00,\n",
       "        2.55607003e+00, 1.42436441e+00, 1.03553585e+00, 7.42218949e-01,\n",
       "        5.52099808e-01, 4.65083145e-01, 3.96199462e-01, 3.21218394e-01,\n",
       "        2.90591357e-01, 2.48450750e-01, 2.13867737e-01, 1.92925869e-01,\n",
       "...\n",
       "        1.89489710e+02, 1.90573335e+02, 1.90445764e+02, 1.88926426e+02,\n",
       "        1.92258742e+02, 1.91315356e+02, 1.91111831e+02, 1.91985542e+02,\n",
       "        1.90372072e+02, 1.90855691e+02, 1.90699793e+02, 1.90556119e+02,\n",
       "        1.91322769e+02, 1.90627761e+02, 1.91407404e+02, 1.90803455e+02],\n",
       "       [1.10000000e+00, 5.00000000e-01, 5.25256250e-01, 7.10762423e-01,\n",
       "        8.11577597e-01, 9.09132603e-01, 9.11750806e-01, 9.52912612e-01,\n",
       "        9.55881909e-01, 9.77973210e-01, 9.68450925e-01, 1.00867592e+00,\n",
       "        1.01427194e+00, 1.04011253e+00, 1.03023058e+00, 1.03078082e+00,\n",
       "        1.04587435e+00, 1.04391625e+00, 1.04339937e+00, 1.05403372e+00,\n",
       "        1.03500754e+00, 1.02589585e+00, 1.00814937e+00, 9.71809223e-01,\n",
       "        9.13350170e-01, 9.11770109e-01, 8.60156008e-01, 8.59200914e-01,\n",
       "        7.57984830e-01, 7.32306884e-01, 7.02538757e-01, 6.48010499e-01,\n",
       "        6.57740648e-01, 6.30489631e-01, 6.24047453e-01, 6.43459316e-01,\n",
       "        6.96848874e-01, 7.07400213e-01, 7.43780605e-01, 7.71678627e-01,\n",
       "        7.84963506e-01, 8.05431476e-01, 7.39396254e-01, 7.15729270e-01,\n",
       "        7.61222935e-01, 6.90825910e-01, 8.79080214e-01, 8.26357425e-01,\n",
       "        7.47781032e-01, 8.17288832e-01, 6.94703179e-01, 6.77987575e-01,\n",
       "        8.65290831e-01, 8.03058811e-01, 8.15027059e-01, 9.75116037e-01,\n",
       "        7.64394075e-01, 7.96393191e-01, 8.27180284e-01, 7.71979700e-01,\n",
       "        9.65798637e-01, 9.41685954e-01, 9.07700392e-01, 9.90261864e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theory</span></div><div class='xr-var-dims'>(lagtime, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001311 364.7 ... 206.3 206.3</div><input id='attrs-ccbebf8c-5c42-4cd3-a742-78717182b510' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccbebf8c-5c42-4cd3-a742-78717182b510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-391abf5b-ae47-4d85-9b04-24a44ac8ba42' class='xr-var-data-in' type='checkbox'><label for='data-391abf5b-ae47-4d85-9b04-24a44ac8ba42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.31088782e-03, 3.64655869e+02, 3.49299499e+02, ...,\n",
       "        1.92929945e+02, 1.93473002e+02, 1.92743417e+02],\n",
       "       [2.80784354e-03, 4.60431845e+02, 4.98126848e+02, ...,\n",
       "        1.94747318e+02, 1.95045541e+02, 1.94419819e+02],\n",
       "       [4.38258463e-03, 5.32617276e+02, 6.11996774e+02, ...,\n",
       "        1.96271689e+02, 1.96363908e+02, 1.95882577e+02],\n",
       "       ...,\n",
       "       [6.70880118e-01, 3.38004449e+03, 5.51938876e+03, ...,\n",
       "        2.06188959e+02, 2.06287796e+02, 2.06342855e+02],\n",
       "       [7.24267866e-01, 3.50338308e+03, 5.73872448e+03, ...,\n",
       "        2.06188959e+02, 2.06287796e+02, 2.06342855e+02],\n",
       "       [7.74498650e-01, 3.62346204e+03, 5.95207817e+03, ...,\n",
       "        2.06188959e+02, 2.06287796e+02, 2.06342855e+02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isf_data</span></div><div class='xr-var-dims'>(lagtime, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9997 0.9892 ... -4.952 -19.73</div><input id='attrs-9e38e798-7714-4994-9921-bf92ffc28e50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e38e798-7714-4994-9921-bf92ffc28e50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4aac9daa-1213-4d04-af01-b5c3037108ae' class='xr-var-data-in' type='checkbox'><label for='data-4aac9daa-1213-4d04-af01-b5c3037108ae' 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.        ,   0.99967882,   0.98923294, ..., -32.31431348,\n",
       "         -9.42328539, -34.39844623],\n",
       "       [  0.        ,   0.99914543,   0.97267385, ..., -28.81782979,\n",
       "         -8.48006746, -31.24863895],\n",
       "       [  0.        ,   0.99874574,   0.96007591, ..., -26.48287376,\n",
       "         -7.80731178, -28.57666779],\n",
       "       ...,\n",
       "       [  0.        ,   0.98547081,   0.45495798, ..., -10.27529479,\n",
       "          1.11005474,   3.76920654],\n",
       "       [  0.        ,   0.98469467,   0.48774845, ..., -12.39449068,\n",
       "         -2.40756842,  -5.51295585],\n",
       "       [  0.        ,   0.98643721,   0.42800337, ...,  -5.22620786,\n",
       "         -4.95155025, -19.7258549 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ddm_matrix_data</span></div><div class='xr-var-dims'>(lagtime, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 294.2 321.4 ... 201.1 200.4</div><input id='attrs-a53cbcf7-0d2d-4399-90b6-f92d30f12f7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a53cbcf7-0d2d-4399-90b6-f92d30f12f7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8b286fc-269c-4df2-b95e-c70027a1f560' class='xr-var-data-in' type='checkbox'><label for='data-d8b286fc-269c-4df2-b95e-c70027a1f560' 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.        ,  294.18943955,  321.41822078, ...,  192.93331805,\n",
       "         193.46556826,  192.76671589],\n",
       "       [   0.        ,  432.05714374,  490.9677582 , ...,  194.84772175,\n",
       "         195.06799833,  194.40459328],\n",
       "       [   0.        ,  535.36374381,  619.95883018, ...,  196.12616285,\n",
       "         196.21094092,  195.79399928],\n",
       "       ...,\n",
       "       [   0.        , 3966.56056507, 5791.89215548, ...,  205.00017796,\n",
       "         211.36062787,  212.61362392],\n",
       "       [   0.        , 4167.1712352 , 5456.14848464, ...,  203.8398704 ,\n",
       "         205.38454854,  207.78696548],\n",
       "       [   0.        , 3716.77402137, 6067.88205869, ...,  207.76466695,\n",
       "         201.06258553,  200.39635905]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>A</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-211.2 2.585e+05 ... -1.699 -0.52</div><input id='attrs-973414fa-bd8a-4f86-b749-7b96d072f6e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-973414fa-bd8a-4f86-b749-7b96d072f6e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c556f8f6-0869-439a-9203-460e79b8cb9e' class='xr-var-data-in' type='checkbox'><label for='data-c556f8f6-0869-439a-9203-460e79b8cb9e' 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([-2.11173656e+02,  2.58471844e+05,  1.02390610e+04,  1.15286177e+04,\n",
       "        1.67983893e+04,  1.91577572e+04,  1.97709014e+04,  2.04527506e+04,\n",
       "        1.98416438e+04,  2.14005721e+04,  2.30575400e+04,  2.75227507e+04,\n",
       "        3.35865707e+04,  3.95172631e+04,  4.21489620e+04,  4.26510308e+04,\n",
       "        4.10149466e+04,  3.58404407e+04,  2.83011541e+04,  2.06052919e+04,\n",
       "        1.43395780e+04,  9.15981465e+03,  5.54912690e+03,  3.34199265e+03,\n",
       "        1.91162170e+03,  1.15583657e+03,  7.84642482e+02,  6.25303620e+02,\n",
       "        4.65592900e+02,  3.32926798e+02,  2.60170996e+02,  2.37877154e+02,\n",
       "        1.95686602e+02,  1.45700103e+02,  1.18845397e+02,  8.74091262e+01,\n",
       "        7.12289354e+01,  5.50684455e+01,  4.83559858e+01,  4.51455685e+01,\n",
       "        3.70886933e+01,  3.69305527e+01,  3.12663737e+01,  2.69802467e+01,\n",
       "        2.38449170e+01,  1.94465450e+01,  1.93624826e+01,  1.39716478e+01,\n",
       "        1.25256899e+01,  1.23568306e+01,  9.32069947e+00,  8.23234748e+00,\n",
       "        6.49514954e+00,  8.15100953e+00,  6.02214129e+00,  4.28318246e+00,\n",
       "        4.40492236e+00,  2.64045708e+00,  2.89942014e+00,  7.90126460e-01,\n",
       "       -9.23134186e-01, -5.47522559e-01, -1.69889697e+00, -5.19992889e-01])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>211.2</div><input id='attrs-b2af1fed-7c8b-4f72-b600-316517f4d9c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2af1fed-7c8b-4f72-b600-316517f4d9c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f2bcf8b-6490-474e-9364-12acd81d4bfa' class='xr-var-data-in' type='checkbox'><label for='data-0f2bcf8b-6490-474e-9364-12acd81d4bfa' 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(211.17365621)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d22ab98-7afe-463e-af7f-60355c9ca5c5' class='xr-section-summary-in' type='checkbox'  ><label for='section-4d22ab98-7afe-463e-af7f-60355c9ca5c5' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>model :</span></dt><dd>DDM Matrix - Single Exponential</dd><dt><span>data_to_use :</span></dt><dd>DDM Matrix</dd><dt><span>initial_params_dict :</span></dt><dd>[&quot;{&#x27;n&#x27;: 0, &#x27;value&#x27;: 100.0, &#x27;limits&#x27;: [1.0, 1000000.0], &#x27;limited&#x27;: [True, True], &#x27;fixed&#x27;: False, &#x27;parname&#x27;: &#x27;Amplitude&#x27;, &#x27;error&#x27;: 0, &#x27;step&#x27;: 0}&quot;, &quot;{&#x27;n&#x27;: 1, &#x27;value&#x27;: 1.0, &#x27;limits&#x27;: [0.001, 10.0], &#x27;limited&#x27;: [True, True], &#x27;fixed&#x27;: False, &#x27;parname&#x27;: &#x27;Tau&#x27;, &#x27;error&#x27;: 0, &#x27;step&#x27;: 0}&quot;, &quot;{&#x27;n&#x27;: 2, &#x27;value&#x27;: 25000.0, &#x27;limits&#x27;: [0.0, 10000000.0], &#x27;limited&#x27;: [True, True], &#x27;fixed&#x27;: False, &#x27;parname&#x27;: &#x27;Background&#x27;, &#x27;error&#x27;: 0, &#x27;step&#x27;: 0}&quot;, &quot;{&#x27;n&#x27;: 3, &#x27;value&#x27;: 1.0, &#x27;limits&#x27;: [0.5, 1.1], &#x27;limited&#x27;: [True, True], &#x27;fixed&#x27;: False, &#x27;parname&#x27;: &#x27;StretchingExp&#x27;, &#x27;error&#x27;: 0, &#x27;step&#x27;: 0}&quot;]</dd><dt><span>effective_diffusion_coeff :</span></dt><dd>0.616760567419975</dd><dt><span>tau_vs_q_slope :</span></dt><dd>[-1.9799845]</dd><dt><span>msd_alpha :</span></dt><dd>[1.01010892]</dd><dt><span>msd_effective_diffusion_coeff :</span></dt><dd>[0.61375481]</dd><dt><span>diffusion_coeff :</span></dt><dd>0.6103536649149923</dd><dt><span>diffusion_coeff_std :</span></dt><dd>0.04066429200992886</dd><dt><span>velocity :</span></dt><dd>1.0539003757076701</dd><dt><span>velocity_std :</span></dt><dd>0.4160131054430251</dd><dt><span>good_q_range :</span></dt><dd>[4, 16]</dd><dt><span>DataDirectory :</span></dt><dd>C:/Users/rmcgorty/Documents/GitHub/PyDDM/Examples/</dd><dt><span>FileName :</span></dt><dd>images_nobin_40x_128x128_8bit.tif</dd><dt><span>pixel_size :</span></dt><dd>0.242</dd><dt><span>frame_rate :</span></dt><dd>41.7</dd><dt><span>BackgroundMethod :</span></dt><dd>0</dd><dt><span>OverlapMethod :</span></dt><dd>2</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:          (parameter: 4, q: 64, lagtime: 40)\n",
       "Coordinates:\n",
       "  * parameter        (parameter) <U13 'Amplitude' 'Tau' ... 'StretchingExp'\n",
       "  * q                (q) float64 0.0 0.2028 0.4057 0.6085 ... 12.37 12.58 12.78\n",
       "  * lagtime          (lagtime) float64 0.02398 0.04796 0.07194 ... 12.59 14.36\n",
       "Data variables:\n",
       "    parameters       (parameter, q) float64 1.0 5.009e+03 ... 0.9077 0.9903\n",
       "    theory           (lagtime, q) float64 0.001311 364.7 349.3 ... 206.3 206.3\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9997 0.9892 ... -4.952 -19.73\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -211.2 2.585e+05 1.024e+04 ... -1.699 -0.52\n",
       "    B                float64 211.2\n",
       "Attributes: (12/18)\n",
       "    model:                          DDM Matrix - Single Exponential\n",
       "    data_to_use:                    DDM Matrix\n",
       "    initial_params_dict:            [\"{'n': 0, 'value': 100.0, 'limits': [1.0...\n",
       "    effective_diffusion_coeff:      0.616760567419975\n",
       "    tau_vs_q_slope:                 [-1.9799845]\n",
       "    msd_alpha:                      [1.01010892]\n",
       "    ...                             ...\n",
       "    DataDirectory:                  C:/Users/rmcgorty/Documents/GitHub/PyDDM/...\n",
       "    FileName:                       images_nobin_40x_128x128_8bit.tif\n",
       "    pixel_size:                     0.242\n",
       "    frame_rate:                     41.7\n",
       "    BackgroundMethod:               0\n",
       "    OverlapMethod:                  2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x444.994 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x444.994 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x355.995 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x355.995 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddm.fit_report(fit01, q_indices=[3,6,9,22], forced_qs=[4,16], use_new_tau=True, show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying a different mathematical model\n",
    "Above, we fit the DDM matrix to a model where the intermediate scattering function (ISF or $f(q,\\Delta t)$) is an exponential. That is, we fit our data to:\n",
    "$$ D(q,\\Delta t) = A(q) \\left[ 1 - \\exp \\left(-\\Delta t / \\tau(q) \\right) ^{s(q)} \\right] + B(q)$$\n",
    "where $A$ is the amplitude, $B$ is the background, $\\tau$ is the decay time, and $s$ is the stretching exponent. \n",
    "\n",
    "An alternative is to use other information to determine $A(q)$ and $B$ and then fit only the ISF. We will try this below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initial guess</th>\n",
       "      <th>Minimum</th>\n",
       "      <th>Maximum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tau</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StretchingExp</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Initial guess  Minimum  Maximum\n",
       "Tau                      1.0    0.001     10.0\n",
       "StretchingExp            1.0    0.500      1.1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddm_fit.reload_fit_model_by_name(\"ISF - Single Exponential\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In function 'get_tau_vs_q_fit', using new tau...\n",
      "Fit is saved in fittings dictionary with key 'fit02'.\n"
     ]
    }
   ],
   "source": [
    "fit02 = ddm_fit.fit(name_fit = 'fit02', display_table=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In function 'get_tau_vs_q_fit', using new tau...\n",
      "In hf.plot_one_tau_vs_q function, using new tau... \n"
     ]
    },
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       "Dimensions:          (parameter: 2, q: 64, lagtime: 40)\n",
       "Coordinates:\n",
       "  * parameter        (parameter) &lt;U13 &#x27;Tau&#x27; &#x27;StretchingExp&#x27;\n",
       "  * q                (q) float64 0.0 0.2028 0.4057 0.6085 ... 12.37 12.58 12.78\n",
       "  * lagtime          (lagtime) float64 0.02398 0.04796 0.07194 ... 12.59 14.36\n",
       "Data variables:\n",
       "    parameters       (parameter, q) float64 0.001886 10.0 10.0 ... 1.1 1.093\n",
       "    theory           (lagtime, q) float64 4.761e-07 0.9987 0.9812 ... 0.0 0.0\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9997 0.9892 ... -4.952 -19.73\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -211.2 2.585e+05 1.024e+04 ... -1.699 -0.52\n",
       "    B                float64 211.2\n",
       "Attributes: (12/18)\n",
       "    model:                          ISF - Single Exponential\n",
       "    data_to_use:                    ISF\n",
       "    initial_params_dict:            [&quot;{&#x27;n&#x27;: 0, &#x27;value&#x27;: 1.0, &#x27;limits&#x27;: [0.001...\n",
       "    effective_diffusion_coeff:      0.6044361168424172\n",
       "    tau_vs_q_slope:                 [-1.97401294]\n",
       "    msd_alpha:                      [1.01316459]\n",
       "    ...                             ...\n",
       "    DataDirectory:                  C:/Users/rmcgorty/Documents/GitHub/PyDDM/...\n",
       "    FileName:                       images_nobin_40x_128x128_8bit.tif\n",
       "    pixel_size:                     0.242\n",
       "    frame_rate:                     41.7\n",
       "    BackgroundMethod:               0\n",
       "    OverlapMethod:                  2</pre><div class='xr-wrap' hidden><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-1b4efb7d-d5fc-4a59-bb7a-7669ff6cd634' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1b4efb7d-d5fc-4a59-bb7a-7669ff6cd634' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>parameter</span>: 2</li><li><span class='xr-has-index'>q</span>: 64</li><li><span class='xr-has-index'>lagtime</span>: 40</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-521271da-4ac4-4a2b-8e33-3b95b9cd7549' class='xr-section-summary-in' type='checkbox'  checked><label for='section-521271da-4ac4-4a2b-8e33-3b95b9cd7549' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>parameter</span></div><div class='xr-var-dims'>(parameter)</div><div class='xr-var-dtype'>&lt;U13</div><div class='xr-var-preview xr-preview'>&#x27;Tau&#x27; &#x27;StretchingExp&#x27;</div><input id='attrs-618b0cf6-d74c-4e9f-97c6-d7838f8e9598' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-618b0cf6-d74c-4e9f-97c6-d7838f8e9598' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e7519ef-8145-444b-9e94-e1590a55daba' class='xr-var-data-in' type='checkbox'><label for='data-7e7519ef-8145-444b-9e94-e1590a55daba' 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;Tau&#x27;, &#x27;StretchingExp&#x27;], dtype=&#x27;&lt;U13&#x27;)</pre></div></li><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'>0.0 0.2028 0.4057 ... 12.58 12.78</div><input id='attrs-a0982188-7e71-4979-9c10-af20d6721049' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0982188-7e71-4979-9c10-af20d6721049' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-050aad15-9240-48b6-8ee5-b4f32049d5d0' class='xr-var-data-in' type='checkbox'><label for='data-050aad15-9240-48b6-8ee5-b4f32049d5d0' 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.      ,  0.20284 ,  0.405681,  0.608521,  0.811362,  1.014202,\n",
       "        1.217043,  1.419883,  1.622723,  1.825564,  2.028404,  2.231245,\n",
       "        2.434085,  2.636926,  2.839766,  3.042607,  3.245447,  3.448287,\n",
       "        3.651128,  3.853968,  4.056809,  4.259649,  4.46249 ,  4.66533 ,\n",
       "        4.86817 ,  5.071011,  5.273851,  5.476692,  5.679532,  5.882373,\n",
       "        6.085213,  6.288053,  6.490894,  6.693734,  6.896575,  7.099415,\n",
       "        7.302256,  7.505096,  7.707937,  7.910777,  8.113617,  8.316458,\n",
       "        8.519298,  8.722139,  8.924979,  9.12782 ,  9.33066 ,  9.5335  ,\n",
       "        9.736341,  9.939181, 10.142022, 10.344862, 10.547703, 10.750543,\n",
       "       10.953383, 11.156224, 11.359064, 11.561905, 11.764745, 11.967586,\n",
       "       12.170426, 12.373267, 12.576107, 12.778947])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lagtime</span></div><div class='xr-var-dims'>(lagtime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02398 0.04796 ... 12.59 14.36</div><input id='attrs-5430ac71-54a7-4417-b506-5b19b84540a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5430ac71-54a7-4417-b506-5b19b84540a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c84a854-dea1-4d99-be3a-37ad78c2df79' class='xr-var-data-in' type='checkbox'><label for='data-3c84a854-dea1-4d99-be3a-37ad78c2df79' 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([ 0.023981,  0.047962,  0.071942,  0.095923,  0.119904,  0.143885,\n",
       "        0.167866,  0.191847,  0.215827,  0.263789,  0.28777 ,  0.335731,\n",
       "        0.383693,  0.431655,  0.503597,  0.57554 ,  0.647482,  0.743405,\n",
       "        0.863309,  0.983213,  1.127098,  1.294964,  1.486811,  1.702638,\n",
       "        1.942446,  2.206235,  2.541966,  2.901679,  3.309353,  3.788969,\n",
       "        4.316547,  4.940048,  5.659472,  6.450839,  7.386091,  8.441247,\n",
       "        9.640288, 11.007194, 12.589928, 14.364508])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f207f10e-b13e-41b1-8931-be0d52d88ea5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f207f10e-b13e-41b1-8931-be0d52d88ea5' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>parameters</span></div><div class='xr-var-dims'>(parameter, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001886 10.0 10.0 ... 1.1 1.093</div><input id='attrs-e90962d8-ba2e-4385-964d-923f41d0cfcd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e90962d8-ba2e-4385-964d-923f41d0cfcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c91cf0c-f836-4b3d-9f0b-6d463f9fff97' class='xr-var-data-in' type='checkbox'><label for='data-6c91cf0c-f836-4b3d-9f0b-6d463f9fff97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.88589103e-03, 1.00000000e+01, 1.00000000e+01, 4.91794800e+00,\n",
       "        2.65628838e+00, 1.50327300e+00, 1.01994490e+00, 7.73490610e-01,\n",
       "        5.96273909e-01, 4.76451564e-01, 3.95261921e-01, 3.29426444e-01,\n",
       "        2.98344562e-01, 2.52353941e-01, 2.21023606e-01, 2.01537369e-01,\n",
       "        1.77734983e-01, 1.59432944e-01, 1.44271986e-01, 1.32113236e-01,\n",
       "        1.26393210e-01, 1.16714583e-01, 1.10908961e-01, 1.09684454e-01,\n",
       "        1.08269318e-01, 1.15043361e-01, 1.11857630e-01, 1.14807481e-01,\n",
       "        1.19359419e-01, 1.22539709e-01, 1.24224230e-01, 1.30311108e-01,\n",
       "        1.29868326e-01, 1.46928516e-01, 1.79917017e-01, 2.29897133e-01,\n",
       "        3.00841481e-01, 3.70318524e-01, 4.25745120e-01, 5.32776300e-01,\n",
       "        6.05123405e-01, 7.15176380e-01, 9.19222068e-01, 9.37440036e-01,\n",
       "        1.23116692e+00, 1.66636757e+00, 1.61883949e+00, 4.16763356e+00,\n",
       "        3.13511306e+00, 4.03316708e+00, 8.47590873e+00, 1.00000000e+01,\n",
       "        1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01,\n",
       "        1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 1.00000000e+01,\n",
       "        1.00000000e-03, 1.19234103e-03, 1.00005677e-03, 1.16635749e-03],\n",
       "       [1.05319182e+00, 1.10000000e+00, 6.57192482e-01, 7.19198817e-01,\n",
       "        8.34029429e-01, 9.18452835e-01, 9.44371329e-01, 9.69649418e-01,\n",
       "        9.62634582e-01, 9.98357963e-01, 9.94382337e-01, 1.01920184e+00,\n",
       "        1.01976533e+00, 1.04708563e+00, 1.02648698e+00, 1.02019083e+00,\n",
       "        1.04248931e+00, 1.04339305e+00, 1.05283303e+00, 1.05848968e+00,\n",
       "        1.04109533e+00, 1.04954794e+00, 1.03737037e+00, 1.03106364e+00,\n",
       "        1.01864683e+00, 9.71396006e-01, 9.80075298e-01, 9.51053861e-01,\n",
       "        9.24786563e-01, 9.55424623e-01, 9.58598542e-01, 9.07784710e-01,\n",
       "        9.48445041e-01, 9.63368267e-01, 9.58958089e-01, 9.99925144e-01,\n",
       "        1.03313591e+00, 1.10000000e+00, 1.10000000e+00, 1.10000000e+00,\n",
       "        1.10000000e+00, 1.10000000e+00, 1.10000000e+00, 1.10000000e+00,\n",
       "        1.10000000e+00, 1.10000000e+00, 1.10000000e+00, 1.10000000e+00,\n",
       "        1.10000000e+00, 1.10000000e+00, 1.10000000e+00, 1.10000000e+00,\n",
       "        1.10000000e+00, 1.10000000e+00, 1.10000000e+00, 1.10000000e+00,\n",
       "        1.10000000e+00, 1.10000000e+00, 1.10000000e+00, 1.10000000e+00,\n",
       "        1.10000000e+00, 1.09262247e+00, 1.09999795e+00, 1.09332614e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theory</span></div><div class='xr-var-dims'>(lagtime, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.761e-07 0.9987 0.9812 ... 0.0 0.0</div><input id='attrs-bb361791-0e7a-4b3d-8a5e-8e6e097764a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb361791-0e7a-4b3d-8a5e-8e6e097764a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1241483c-9954-4dd5-b092-f1e4a80d0d28' class='xr-var-data-in' type='checkbox'><label for='data-1241483c-9954-4dd5-b092-f1e4a80d0d28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4.76121049e-07, 9.98689112e-01, 9.81208843e-01, ...,\n",
       "        2.92454151e-12, 4.91078571e-15, 1.44497734e-12],\n",
       "       [7.59506876e-14, 9.97192156e-01, 9.70527228e-01, ...,\n",
       "        2.52673726e-25, 2.12987589e-31, 5.46062080e-26],\n",
       "       [7.79395343e-21, 9.95617415e-01, 9.61702126e-01, ...,\n",
       "        4.91870075e-39, 1.22685721e-48, 4.41016073e-40],\n",
       "       ...,\n",
       "       [0.00000000e+00, 3.29119882e-01, 3.44694074e-01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 2.75732134e-01, 3.12417562e-01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 2.25501350e-01, 2.81187600e-01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isf_data</span></div><div class='xr-var-dims'>(lagtime, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9997 0.9892 ... -4.952 -19.73</div><input id='attrs-37e8bc63-8b7e-497f-93d8-9c230406849f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37e8bc63-8b7e-497f-93d8-9c230406849f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3db89806-da69-4fa0-96fe-7a3636dd8482' class='xr-var-data-in' type='checkbox'><label for='data-3db89806-da69-4fa0-96fe-7a3636dd8482' 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.        ,   0.99967882,   0.98923294, ..., -32.31431348,\n",
       "         -9.42328539, -34.39844623],\n",
       "       [  0.        ,   0.99914543,   0.97267385, ..., -28.81782979,\n",
       "         -8.48006746, -31.24863895],\n",
       "       [  0.        ,   0.99874574,   0.96007591, ..., -26.48287376,\n",
       "         -7.80731178, -28.57666779],\n",
       "       ...,\n",
       "       [  0.        ,   0.98547081,   0.45495798, ..., -10.27529479,\n",
       "          1.11005474,   3.76920654],\n",
       "       [  0.        ,   0.98469467,   0.48774845, ..., -12.39449068,\n",
       "         -2.40756842,  -5.51295585],\n",
       "       [  0.        ,   0.98643721,   0.42800337, ...,  -5.22620786,\n",
       "         -4.95155025, -19.7258549 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ddm_matrix_data</span></div><div class='xr-var-dims'>(lagtime, q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 294.2 321.4 ... 201.1 200.4</div><input id='attrs-c30c2f9f-6cba-4674-aecd-f6447e3dff23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c30c2f9f-6cba-4674-aecd-f6447e3dff23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b23eaf56-9435-4082-8c54-81b8576b9631' class='xr-var-data-in' type='checkbox'><label for='data-b23eaf56-9435-4082-8c54-81b8576b9631' 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.        ,  294.18943955,  321.41822078, ...,  192.93331805,\n",
       "         193.46556826,  192.76671589],\n",
       "       [   0.        ,  432.05714374,  490.9677582 , ...,  194.84772175,\n",
       "         195.06799833,  194.40459328],\n",
       "       [   0.        ,  535.36374381,  619.95883018, ...,  196.12616285,\n",
       "         196.21094092,  195.79399928],\n",
       "       ...,\n",
       "       [   0.        , 3966.56056507, 5791.89215548, ...,  205.00017796,\n",
       "         211.36062787,  212.61362392],\n",
       "       [   0.        , 4167.1712352 , 5456.14848464, ...,  203.8398704 ,\n",
       "         205.38454854,  207.78696548],\n",
       "       [   0.        , 3716.77402137, 6067.88205869, ...,  207.76466695,\n",
       "         201.06258553,  200.39635905]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>A</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-211.2 2.585e+05 ... -1.699 -0.52</div><input id='attrs-2aaa0fe6-4088-4193-9648-4fb341caf478' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2aaa0fe6-4088-4193-9648-4fb341caf478' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba302ea8-4bc3-4f08-82f0-feff2c43a01d' class='xr-var-data-in' type='checkbox'><label for='data-ba302ea8-4bc3-4f08-82f0-feff2c43a01d' 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([-2.11173656e+02,  2.58471844e+05,  1.02390610e+04,  1.15286177e+04,\n",
       "        1.67983893e+04,  1.91577572e+04,  1.97709014e+04,  2.04527506e+04,\n",
       "        1.98416438e+04,  2.14005721e+04,  2.30575400e+04,  2.75227507e+04,\n",
       "        3.35865707e+04,  3.95172631e+04,  4.21489620e+04,  4.26510308e+04,\n",
       "        4.10149466e+04,  3.58404407e+04,  2.83011541e+04,  2.06052919e+04,\n",
       "        1.43395780e+04,  9.15981465e+03,  5.54912690e+03,  3.34199265e+03,\n",
       "        1.91162170e+03,  1.15583657e+03,  7.84642482e+02,  6.25303620e+02,\n",
       "        4.65592900e+02,  3.32926798e+02,  2.60170996e+02,  2.37877154e+02,\n",
       "        1.95686602e+02,  1.45700103e+02,  1.18845397e+02,  8.74091262e+01,\n",
       "        7.12289354e+01,  5.50684455e+01,  4.83559858e+01,  4.51455685e+01,\n",
       "        3.70886933e+01,  3.69305527e+01,  3.12663737e+01,  2.69802467e+01,\n",
       "        2.38449170e+01,  1.94465450e+01,  1.93624826e+01,  1.39716478e+01,\n",
       "        1.25256899e+01,  1.23568306e+01,  9.32069947e+00,  8.23234748e+00,\n",
       "        6.49514954e+00,  8.15100953e+00,  6.02214129e+00,  4.28318246e+00,\n",
       "        4.40492236e+00,  2.64045708e+00,  2.89942014e+00,  7.90126460e-01,\n",
       "       -9.23134186e-01, -5.47522559e-01, -1.69889697e+00, -5.19992889e-01])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>B</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>211.2</div><input id='attrs-6737c194-12a9-4f5b-8658-77a0e26f5901' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6737c194-12a9-4f5b-8658-77a0e26f5901' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a192a62-13d3-484f-991a-4802c118764f' class='xr-var-data-in' type='checkbox'><label for='data-8a192a62-13d3-484f-991a-4802c118764f' 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(211.17365621)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7554893-039f-426d-b159-f8b8c4800073' class='xr-section-summary-in' type='checkbox'  ><label for='section-f7554893-039f-426d-b159-f8b8c4800073' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>model :</span></dt><dd>ISF - Single Exponential</dd><dt><span>data_to_use :</span></dt><dd>ISF</dd><dt><span>initial_params_dict :</span></dt><dd>[&quot;{&#x27;n&#x27;: 0, &#x27;value&#x27;: 1.0, &#x27;limits&#x27;: [0.001, 10.0], &#x27;limited&#x27;: [True, True], &#x27;fixed&#x27;: False, &#x27;parname&#x27;: &#x27;Tau&#x27;, &#x27;error&#x27;: 0, &#x27;step&#x27;: 0}&quot;, &quot;{&#x27;n&#x27;: 1, &#x27;value&#x27;: 1.0, &#x27;limits&#x27;: [0.5, 1.1], &#x27;limited&#x27;: [True, True], &#x27;fixed&#x27;: False, &#x27;parname&#x27;: &#x27;StretchingExp&#x27;, &#x27;error&#x27;: 0, &#x27;step&#x27;: 0}&quot;]</dd><dt><span>effective_diffusion_coeff :</span></dt><dd>0.6044361168424172</dd><dt><span>tau_vs_q_slope :</span></dt><dd>[-1.97401294]</dd><dt><span>msd_alpha :</span></dt><dd>[1.01316459]</dd><dt><span>msd_effective_diffusion_coeff :</span></dt><dd>[0.60044326]</dd><dt><span>diffusion_coeff :</span></dt><dd>0.5887020998284563</dd><dt><span>diffusion_coeff_std :</span></dt><dd>0.055442747427343994</dd><dt><span>velocity :</span></dt><dd>1.0262600559215465</dd><dt><span>velocity_std :</span></dt><dd>0.4159865239946986</dd><dt><span>good_q_range :</span></dt><dd>[4, 16]</dd><dt><span>DataDirectory :</span></dt><dd>C:/Users/rmcgorty/Documents/GitHub/PyDDM/Examples/</dd><dt><span>FileName :</span></dt><dd>images_nobin_40x_128x128_8bit.tif</dd><dt><span>pixel_size :</span></dt><dd>0.242</dd><dt><span>frame_rate :</span></dt><dd>41.7</dd><dt><span>BackgroundMethod :</span></dt><dd>0</dd><dt><span>OverlapMethod :</span></dt><dd>2</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:          (parameter: 2, q: 64, lagtime: 40)\n",
       "Coordinates:\n",
       "  * parameter        (parameter) <U13 'Tau' 'StretchingExp'\n",
       "  * q                (q) float64 0.0 0.2028 0.4057 0.6085 ... 12.37 12.58 12.78\n",
       "  * lagtime          (lagtime) float64 0.02398 0.04796 0.07194 ... 12.59 14.36\n",
       "Data variables:\n",
       "    parameters       (parameter, q) float64 0.001886 10.0 10.0 ... 1.1 1.093\n",
       "    theory           (lagtime, q) float64 4.761e-07 0.9987 0.9812 ... 0.0 0.0\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9997 0.9892 ... -4.952 -19.73\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -211.2 2.585e+05 1.024e+04 ... -1.699 -0.52\n",
       "    B                float64 211.2\n",
       "Attributes: (12/18)\n",
       "    model:                          ISF - Single Exponential\n",
       "    data_to_use:                    ISF\n",
       "    initial_params_dict:            [\"{'n': 0, 'value': 1.0, 'limits': [0.001...\n",
       "    effective_diffusion_coeff:      0.6044361168424172\n",
       "    tau_vs_q_slope:                 [-1.97401294]\n",
       "    msd_alpha:                      [1.01316459]\n",
       "    ...                             ...\n",
       "    DataDirectory:                  C:/Users/rmcgorty/Documents/GitHub/PyDDM/...\n",
       "    FileName:                       images_nobin_40x_128x128_8bit.tif\n",
       "    pixel_size:                     0.242\n",
       "    frame_rate:                     41.7\n",
       "    BackgroundMethod:               0\n",
       "    OverlapMethod:                  2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x444.994 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x444.994 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x355.995 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x355.995 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddm.fit_report(fit02, q_indices=[3,6,9,22], forced_qs=[4,16], use_new_tau=True, show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive with matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `Browse_DDM_Fits` class as shown below, one can interactively inspect the fits (to either the DDM matrix or the ISF) by selecting the appropriate point on the $\\tau$ vs $q$ plot or by pressing 'N' or 'P' for next or previous. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Click on a point in the tau vs q plot to see a fit.\n",
      "Or press 'N' or 'P' to display next or previous fit.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'tau (s)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "fig, (ax, ax2) = plt.subplots(2, 1, figsize=(10,10/1.618))\n",
    "browser = ddm.Browse_DDM_Fits(fig, ax, ax2, fit02)\n",
    "\n",
    "fig.canvas.mpl_connect('pick_event', browser.on_pick)\n",
    "fig.canvas.mpl_connect('key_press_event', browser.on_press)\n",
    "\n",
    "ax.set_title('Decay time vs wavevector')\n",
    "ax.set_xlabel(\"q\")\n",
    "ax.set_ylabel(\"tau (s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Saving the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a few options for saving the results of the fit. We can save the information contained in the xarray Dataset `fit01` as an Excel file. Or we can save it as a netCDF file using the xarray function [`to_netcdf`](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.to_netcdf.html). If we have the fit results as a netCDF file, then we can reload it easily with teh xarray function [`open_dataset`](https://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddm.save_fit_results_to_excel(fit01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit01.to_netcdf(\"fit_of_ddmmatrix.nc\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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