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 "cells": [
  {
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   "metadata": {},
   "source": [
    "# More detailed walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the simple walkthrough for a quicker introduction to the PyDDM package. Here, we will go into a little more depth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> Before working through this, go through the simple walkthrough.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the necessary modules\n",
    "\n",
    "We will ned to import [numpy](https://numpy.org/), [matplotlib](https://matplotlib.org/), and [xarray](https://xarray.pydata.org/en/stable/). \n",
    "We will also import the [PyDDM](https://rmcgorty.github.io/PyDDM/) package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "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\n",
    "\n",
    "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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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",
    "\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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-10 14:00:30,212 - DDM Calculations - Running dt = 1...\n",
      "2022-02-10 14:00:37,571 - DDM Calculations - Running dt = 5...\n",
      "2022-02-10 14:00:43,229 - DDM Calculations - Running dt = 9...\n",
      "2022-02-10 14:00:47,878 - DDM Calculations - Running dt = 16...\n",
      "2022-02-10 14:00:51,469 - DDM Calculations - Running dt = 27...\n",
      "2022-02-10 14:00:54,913 - DDM Calculations - Running dt = 47...\n",
      "2022-02-10 14:00:58,136 - DDM Calculations - Running dt = 81...\n",
      "2022-02-10 14:01:01,219 - DDM Calculations - Running dt = 138...\n",
      "2022-02-10 14:01:04,153 - DDM Calculations - Running dt = 236...\n",
      "2022-02-10 14:01:07,126 - DDM Calculations - Running dt = 402...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DDM matrix took 39.838425159454346 seconds to compute.\n",
      " Background estimate ± std is 211.17 ± 1.49\n"
     ]
    },
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       "  * 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",
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       "    B                 float64 211.2\n",
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       "    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-55deaa26-59a5-41a2-8915-e53b1ba2663c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55deaa26-59a5-41a2-8915-e53b1ba2663c' 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-5f052fc3-ebaa-4a95-8195-5503f392c566' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f052fc3-ebaa-4a95-8195-5503f392c566' 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-543c4aa0-4d15-4668-98e6-8447b998089f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-543c4aa0-4d15-4668-98e6-8447b998089f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef9f26be-c027-425f-9e82-a48847cc9652' class='xr-var-data-in' type='checkbox'><label for='data-ef9f26be-c027-425f-9e82-a48847cc9652' 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-31772d5e-2e30-43ef-8762-ea5c5b90ce35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31772d5e-2e30-43ef-8762-ea5c5b90ce35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b05e1cdb-d82c-4cf3-821a-f83626fa0333' class='xr-var-data-in' type='checkbox'><label for='data-b05e1cdb-d82c-4cf3-821a-f83626fa0333' 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-78b1b5bb-e617-407d-a907-ee6cb87233b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78b1b5bb-e617-407d-a907-ee6cb87233b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cf347ae-8a9c-4549-8d57-bf147283b6c1' class='xr-var-data-in' type='checkbox'><label for='data-5cf347ae-8a9c-4549-8d57-bf147283b6c1' 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-407d74a5-7952-46a4-87fa-fe1c3b9efbe5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-407d74a5-7952-46a4-87fa-fe1c3b9efbe5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a81904e6-bee0-47d8-8100-9e9ea7853d63' class='xr-var-data-in' type='checkbox'><label for='data-a81904e6-bee0-47d8-8100-9e9ea7853d63' 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-40c35b1a-ab99-487e-a494-97e870e07f58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40c35b1a-ab99-487e-a494-97e870e07f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78bbb302-8854-4609-af4a-8af5e6e9b38f' class='xr-var-data-in' type='checkbox'><label for='data-78bbb302-8854-4609-af4a-8af5e6e9b38f' 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-bffc8ec9-0dc6-433d-b561-d2a1d4de212a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bffc8ec9-0dc6-433d-b561-d2a1d4de212a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eed5258d-0965-4b36-94ad-1c8ae00a5101' class='xr-var-data-in' type='checkbox'><label for='data-eed5258d-0965-4b36-94ad-1c8ae00a5101' 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-6f08c84e-b0c5-4c44-93f4-71e41a72c46f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f08c84e-b0c5-4c44-93f4-71e41a72c46f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04736fab-7d4d-443d-821d-49655cc3b205' class='xr-var-data-in' type='checkbox'><label for='data-04736fab-7d4d-443d-821d-49655cc3b205' 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-161876cb-8790-4a63-aeaf-1102f38fc8c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-161876cb-8790-4a63-aeaf-1102f38fc8c2' 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-459c7e7d-ea3b-4eb6-9ea3-d0d867127f6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-459c7e7d-ea3b-4eb6-9ea3-d0d867127f6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4315a467-e6a7-42c0-a391-5c0444356001' class='xr-var-data-in' type='checkbox'><label for='data-4315a467-e6a7-42c0-a391-5c0444356001' 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-2226cdb8-6ece-4c7a-85ef-88f9c38bc332' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2226cdb8-6ece-4c7a-85ef-88f9c38bc332' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8565024a-bb21-4173-bf99-fbba7b65a511' class='xr-var-data-in' type='checkbox'><label for='data-8565024a-bb21-4173-bf99-fbba7b65a511' 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-f52e1326-f47b-4fec-81ab-33b5f99d07a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f52e1326-f47b-4fec-81ab-33b5f99d07a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-650c729f-738c-4cf2-b462-9d78a96056e5' class='xr-var-data-in' type='checkbox'><label for='data-650c729f-738c-4cf2-b462-9d78a96056e5' 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-83f15301-7424-462c-8a3e-e5d6fa60da67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83f15301-7424-462c-8a3e-e5d6fa60da67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f96fcdb-f848-4a07-b1a6-1a6548666455' class='xr-var-data-in' type='checkbox'><label for='data-8f96fcdb-f848-4a07-b1a6-1a6548666455' 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-b67cbf8a-b5c7-417c-9c53-142cd0822c61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b67cbf8a-b5c7-417c-9c53-142cd0822c61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38ea42c5-df98-411f-b3fd-b6347b2fcd37' class='xr-var-data-in' type='checkbox'><label for='data-38ea42c5-df98-411f-b3fd-b6347b2fcd37' 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-7c05ad7b-c7c9-4085-ba8c-6ff9cf201b71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c05ad7b-c7c9-4085-ba8c-6ff9cf201b71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8558d876-03f6-45cd-8cb3-ae449005ee6e' class='xr-var-data-in' type='checkbox'><label for='data-8558d876-03f6-45cd-8cb3-ae449005ee6e' 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-4481d8d6-2324-4f00-985f-280e6d74ef09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4481d8d6-2324-4f00-985f-280e6d74ef09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-531eb25f-c2e2-45e7-a1bf-837052a1ee4c' class='xr-var-data-in' type='checkbox'><label for='data-531eb25f-c2e2-45e7-a1bf-837052a1ee4c' 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-f6253846-0332-4a7d-89de-811726ef1a01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6253846-0332-4a7d-89de-811726ef1a01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-510e330e-dba8-4f78-af4d-8e8379905749' class='xr-var-data-in' type='checkbox'><label for='data-510e330e-dba8-4f78-af4d-8e8379905749' 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-9f7a64d2-c7da-4590-95e8-2b9169c669a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f7a64d2-c7da-4590-95e8-2b9169c669a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdb67dcd-7a6e-48d8-bd69-674a6a8e4bff' class='xr-var-data-in' type='checkbox'><label for='data-bdb67dcd-7a6e-48d8-bd69-674a6a8e4bff' 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-1401b0bf-8a86-446f-856d-bf96c153f981' class='xr-section-summary-in' type='checkbox'  ><label for='section-1401b0bf-8a86-446f-856d-bf96c153f981' 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": 3,
     "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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      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddm_calc.calculate_DDM_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Digging into the DDM xarray.Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the dataset created after running `calculate_DDM_matrix`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "\n",
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       "\n",
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       ".xr-icon-file-text2 {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\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</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-975aa7ce-c30c-4ad5-884b-7f143af056c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-975aa7ce-c30c-4ad5-884b-7f143af056c0' 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-baf56383-6657-413c-a0a4-1b117fd1fd69' class='xr-section-summary-in' type='checkbox'  checked><label for='section-baf56383-6657-413c-a0a4-1b117fd1fd69' 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-e4345a7a-7325-4ad8-b049-d3ab817ca8b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4345a7a-7325-4ad8-b049-d3ab817ca8b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfcc5f8e-ac83-4c24-aaf4-8757d57a9150' class='xr-var-data-in' type='checkbox'><label for='data-dfcc5f8e-ac83-4c24-aaf4-8757d57a9150' 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-104e56ac-f63e-4be1-8947-cceaaf6cf1f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-104e56ac-f63e-4be1-8947-cceaaf6cf1f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61f9ef8d-5ff8-4c53-9d88-96324599ff41' class='xr-var-data-in' type='checkbox'><label for='data-61f9ef8d-5ff8-4c53-9d88-96324599ff41' 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-9b433553-fa24-4d8f-b951-1982f39b22e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b433553-fa24-4d8f-b951-1982f39b22e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-611b0467-9190-4b4c-9edf-04ab341a42c1' class='xr-var-data-in' type='checkbox'><label for='data-611b0467-9190-4b4c-9edf-04ab341a42c1' 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-982b31a6-f666-4bfb-9dc3-4471eb4d4c43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-982b31a6-f666-4bfb-9dc3-4471eb4d4c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-074e81db-2cf2-40d6-bcc7-849bf2ee83e8' class='xr-var-data-in' type='checkbox'><label for='data-074e81db-2cf2-40d6-bcc7-849bf2ee83e8' 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-1caa042b-1f37-4746-a9c7-b81d6cbd49b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1caa042b-1f37-4746-a9c7-b81d6cbd49b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b8ffc71-9403-4bf5-ae03-1d0b40629101' class='xr-var-data-in' type='checkbox'><label for='data-3b8ffc71-9403-4bf5-ae03-1d0b40629101' 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-43db0abd-41b2-400c-b08c-6e988c606e1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43db0abd-41b2-400c-b08c-6e988c606e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44cdc43f-4d5f-4028-bda1-d536dbfcb5d5' class='xr-var-data-in' type='checkbox'><label for='data-44cdc43f-4d5f-4028-bda1-d536dbfcb5d5' 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-ef2d8a81-5344-41c7-9a9d-edb1dc150a21' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef2d8a81-5344-41c7-9a9d-edb1dc150a21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-355e5233-1dde-4c8d-9922-d78c40df8eb9' class='xr-var-data-in' type='checkbox'><label for='data-355e5233-1dde-4c8d-9922-d78c40df8eb9' 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-9cef3772-aedf-4433-b7b6-71f5e7dc6f1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9cef3772-aedf-4433-b7b6-71f5e7dc6f1e' 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-3aef9af1-c060-4211-9534-c95e6af21608' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3aef9af1-c060-4211-9534-c95e6af21608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cacffa10-c279-4be2-8162-094c06a53445' class='xr-var-data-in' type='checkbox'><label for='data-cacffa10-c279-4be2-8162-094c06a53445' 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-9bd45ac8-c934-4c1e-bfdd-d44c0daeeb79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9bd45ac8-c934-4c1e-bfdd-d44c0daeeb79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1510d2a-676e-4a72-8b3b-8e8882dbacfa' class='xr-var-data-in' type='checkbox'><label for='data-a1510d2a-676e-4a72-8b3b-8e8882dbacfa' 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-a73eeba0-7c07-473a-837e-d310b01f5683' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a73eeba0-7c07-473a-837e-d310b01f5683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfefc635-0e30-4989-ab01-cc05e831477f' class='xr-var-data-in' type='checkbox'><label for='data-dfefc635-0e30-4989-ab01-cc05e831477f' 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-81bb25c9-fa25-4655-b58f-6fcd66c47de6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81bb25c9-fa25-4655-b58f-6fcd66c47de6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77a6799e-3e70-4e9d-bbd0-c8c28b866b56' class='xr-var-data-in' type='checkbox'><label for='data-77a6799e-3e70-4e9d-bbd0-c8c28b866b56' 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-c8c91368-5d69-4c50-a9b3-ea17dd7c4766' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8c91368-5d69-4c50-a9b3-ea17dd7c4766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d71a66-998b-4ff3-83fa-e9fcfc0a6e38' class='xr-var-data-in' type='checkbox'><label for='data-42d71a66-998b-4ff3-83fa-e9fcfc0a6e38' 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-e4faa2bc-0e72-40a1-a16f-b5ef968c97be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4faa2bc-0e72-40a1-a16f-b5ef968c97be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c81dc904-550d-47c1-8826-3c5d6b67302e' class='xr-var-data-in' type='checkbox'><label for='data-c81dc904-550d-47c1-8826-3c5d6b67302e' 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-0670f511-f18e-480e-947e-d3630ff13c4e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0670f511-f18e-480e-947e-d3630ff13c4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3ce4064-e886-4dc8-a906-6362efb8c45a' class='xr-var-data-in' type='checkbox'><label for='data-d3ce4064-e886-4dc8-a906-6362efb8c45a' 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-50a6602e-4bca-4f82-84b0-2b468e4ac00e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50a6602e-4bca-4f82-84b0-2b468e4ac00e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9f3d27b-9122-4140-8fda-697101165593' class='xr-var-data-in' type='checkbox'><label for='data-d9f3d27b-9122-4140-8fda-697101165593' 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-8752656e-9825-44c7-9bff-9e22e3c0f539' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8752656e-9825-44c7-9bff-9e22e3c0f539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbe3d4cc-c4dd-48fb-af95-689c2abd02a6' class='xr-var-data-in' type='checkbox'><label for='data-cbe3d4cc-c4dd-48fb-af95-689c2abd02a6' 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-7f8377db-84f8-449f-9979-ea7ac84ae3d2' class='xr-section-summary-in' type='checkbox'  ><label for='section-7f8377db-84f8-449f-9979-ea7ac84ae3d2' 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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ddm_calc.ddm_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notice the data variables: \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"
     ]
    }
   ],
   "source": [
    "print(\"Notice the data variables: \\n\", ddm_calc.ddm_dataset.data_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within this `ddm_datset`, we have the DDM matrix $D(q_x, q_y, \\Delta t)$ stored as `ddm_matrix_full`. We can take a look at a slice of this matrix with the `matshow` function in `matplotlib`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'DDM matrix for lag time of 0.288 s')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###############################################################\n",
    "# Displaying a slice of the full three-dimensional DDM matrix #\n",
    "###############################################################\n",
    "\n",
    "lagtime_for_ddmmatrix = 10\n",
    "fig,ax = plt.subplots()\n",
    "plt.matshow(ddm_calc.ddm_dataset.ddm_matrix_full[lagtime_for_ddmmatrix], fignum=0, cmap='gray')\n",
    "\n",
    "#Setting up the ticks and tick labels so that they match q_x and q_y\n",
    "ticks = np.linspace(0,ddm_calc.ddm_dataset.ddm_matrix_full.shape[1]-1,7,dtype=int)\n",
    "ticklabelsx = [\"{:4.2f}\".format(i) for i in ddm_calc.ddm_dataset.q_x[ticks].values]\n",
    "ticklabelsy = [\"{:4.2f}\".format(i) for i in ddm_calc.ddm_dataset.q_y[ticks].values]\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_xticklabels(ticklabelsx)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_yticklabels(ticklabelsy)\n",
    "\n",
    "plt.title(\"DDM matrix for lag time of %.3f s\" % ddm_calc.ddm_dataset.lagtime[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slice of the DDM matrix above looks pretty radially symmetric. This is expected as we are analyzing a video of silica particles diffusing randomly with no preferred direction. Because of this symmetry, we can radially average the DDM matrix to go from $D(q_x, q_y, \\Delta t)$ to $D(q, \\Delta t)$ where $q = \\sqrt{q_x^2 + q_y^2}$. \n",
    "\n",
    "Let's plot a slice of this radially averaged DDM matrix $D(q, \\Delta t)$ (which is stored as the variable `ddm_matrix` in the `ddm_dataset`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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"
    }
   ],
   "source": [
    "######################################################################\n",
    "# Plotting the radially averaged DDM matrix for a particular q-value #\n",
    "######################################################################\n",
    "\n",
    "q_index = 15 #index of the list of q values\n",
    "plt.figure(figsize=(10,10/1.618))\n",
    "plt.semilogx(ddm_calc.ddm_dataset.lagtime, ddm_calc.ddm_dataset.ddm_matrix[:,q_index], 'ro')\n",
    "plt.xlabel(\"Lag time (s)\")\n",
    "plt.ylabel(\"DDM matrix\")\n",
    "plt.title(\"DDM matrix for $q$ = %.3f $\\mu$m$^{-1}$\" % ddm_calc.ddm_dataset.q[q_index]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Different data selecting methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are slicing this [xarray dataset](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.html), let's use the opportunity to go over different ways of selecting data stored in a dataset. See the [xarray user guide](https://xarray.pydata.org/en/stable/user-guide/indexing.html) for more info. \n",
    "\n",
    "1. We can use the `isel` function (see [xarray.DataArray.isel](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.isel.html)). \n",
    "```python\n",
    "q_index = 15 #index of the list of q values\n",
    "plt.figure(figsize=(10,10/1.618))\n",
    "plt.semilogx(ddm_calc.ddm_dataset.lagtime, ddm_calc.ddm_dataset.ddm_matrix.isel(q=q_index), 'ro')\n",
    "plt.xlabel(\"Lag time (s)\")\n",
    "plt.ylabel(\"DDM matrix\")\n",
    "plt.title(\"DDM matrix for q = %.3f \" % ddm_calc.ddm_dataset.q[q_index])\n",
    "```\n",
    "\n",
    "\n",
    "2. We can select the desired *q* not based on its **index** but on its actual **value** with `sel` (see [xarray.DataArray.sel](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.sel.html). Note that here we have to use `method = 'nearest'` since we do not have a *q* value of exactly 3 $\\mu$m$^{-1}$.\n",
    "```python\n",
    "q_index = 15 #index of the list of q values\n",
    "plt.figure(figsize=(10,10/1.618))\n",
    "plt.semilogx(ddm_calc.ddm_dataset.lagtime, ddm_calc.ddm_dataset.ddm_matrix.sel(q=3, method='nearest'), 'ro')\n",
    "plt.xlabel(\"Lag time (s)\")\n",
    "plt.ylabel(\"DDM matrix\")\n",
    "plt.title(\"DDM matrix for q = %.3f \" % ddm_calc.ddm_dataset.q[q_index])\n",
    "```\n",
    "\n",
    "\n",
    "3. Lastly, we can create this plot (using whichever slicing method) with xarray's plotting function (which just wraps `matplotlib.pyplot`). For more, see [xarray.DataArray.plot](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.plot.html).\n",
    "```python\n",
    "q_index = 15\n",
    "ddm_calc.ddm_dataset.ddm_matrix.isel(q=q_index).plot(xscale='log', marker='o', ls='none')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Determing the A and B parameters to get the ISF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now look at the `avg_image_ft` data variable. This is a radially averaged average of all the Fourier transforms of the images (i.e., the radially average of $\\left< | \\tilde{I}(q, t) |^2 \\right>_t$). This function should equal $ (1/2) \\times \\left[ A(q) + B(q) \\right] $. \n",
    "\n",
    "If we make the assumption that at the highest wavevectors (large $q$) the amplitude, $A(q)$, goes to zero, then we can estimate the background, $B$. If we also assume that the background is a constant that's independent of $q$, then we can determine $A(q)$. \n",
    "\n",
    "Plotted below is this `avg_image_ft` along with a horizontal line which can be used to estimate **half** the background. This estimate is taken from the last 10% of the wavevectors. The background can be saved to the `B` data variable. And then we can determine the amplitude which is saved as the `Amplitude` variable.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "    <b>Note:</b> What is described above is the <b>default</b> method for getting the background. Other methods can be selected with the optional parameter 'background_method' passed to the method 'calculate_DDM_matrix'.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2299c86dbb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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"
    }
   ],
   "source": [
    "############################################################################################\n",
    "# Plotting the average squared Fourier-transformed image as a function of the wavevector q #\n",
    "############################################################################################\n",
    "\n",
    "plt.figure(figsize=(10,10/1.618))\n",
    "plt.semilogy(ddm_calc.ddm_dataset.q[1:], ddm_calc.ddm_dataset.avg_image_ft[1:], 'ro')\n",
    "plt.xlabel(\"Wavevector, q ($\\mu$m$^{-1}$)\")\n",
    "plt.ylabel(r\"$\\left< | \\tilde{I}(q, t) |^2 \\right>_t$\", fontsize=14)\n",
    "plt.title(\"Default method to estimate the background and find the amplitude\")\n",
    "\n",
    "number_of_hi_qs = int(0.1*len(ddm_calc.ddm_dataset.q)) #for getting highest 10% of q's\n",
    "plt.axhline(y = ddm_calc.ddm_dataset.avg_image_ft[-1*number_of_hi_qs:].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other methods for getting the background (and from the background then estimating the amplitude) are:\n",
    "* `background_method` = 1: $B$ will be the minimum of the DDM matrix\n",
    "* `background_method` = 2: $B$ will be the average (over all lag times) of the DDM matrix at the largest wavevector $q$\n",
    "* `background_method` = 3: $B$ = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we know $A(q)$ and $B$, then we can find the intermediate scattering function, $f(q, \\Delta t)$, by assuming that the DDM matrix is equal to $D (q, \\Delta t) = A(q) \\times \\left[ 1 - f(q, \\Delta t) \\right] + B$. \n",
    "\n",
    "The ISF is stored as the data variable `ISF` in the `ddm_dataset`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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"
    }
   ],
   "source": [
    "#############################################################################################################\n",
    "# Plotting the intermediate scattering function (ISF) for a particular value of q as a function of lag time #\n",
    "#############################################################################################################\n",
    "\n",
    "plt.figure(figsize=(10,10/1.618))\n",
    "plt.semilogx(ddm_calc.ddm_dataset.lagtime, ddm_calc.ddm_dataset.ISF.sel(q=1.5, method='nearest'), 'ro')\n",
    "plt.xlabel(\"Lag time (s)\")\n",
    "plt.ylabel(\"ISF\")\n",
    "plt.title(\"ISF for q = %.3f $\\mu$m$^{-1}$\" % ddm_calc.ddm_dataset.q.sel(q=1.5, method='nearest'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking the background estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We expect the ISF to go from 1 to 0. We may not see exactly this behavior in the ISF for all wavevectors. At larger wavevectors, the dynamics might be fast and we might not be taking images fast enough to see the early decay of the ISF. Conversely, at lower wavevectors, the dynamics might be slow and we may not be able to calculate the ISF for large enough lag times to see it decay all the way to zero. Additionally, for non-ergodic dynamics, the ISF may **not** decay to zero but to some value sometimes referred to as the non-ergodicity parameter. \n",
    "\n",
    "But another reason that the ISF may not go from 1 to 0 is that we did a poor job in estimating the background, $B$. Using the default method (`background_method = 0`), we try to estimate $B$ by looking at the average Fourier transforms of all the images, radially averaging that, and assuming that the amplitude goes to zero at large wavevectors. But another way to estimate $B$ is to look at the DDM matrix at large wavevectors, as is plotted below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x229a6721f70>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x444.994 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###################################################################################################\n",
    "# Plotting the radially averaged DDM matrix for the greatest value of q as a function of lag time #\n",
    "###################################################################################################\n",
    "\n",
    "q_index = -1 #index of the list of q values\n",
    "plt.figure(figsize=(10,10/1.618))\n",
    "plt.semilogx(ddm_calc.ddm_dataset.lagtime, ddm_calc.ddm_dataset.ddm_matrix[:,q_index], 'ro')\n",
    "plt.xlabel(\"Lag time (s)\")\n",
    "plt.ylabel(\"DDM matrix\")\n",
    "plt.title(\"DDM matrix for $q$ = %.3f $\\mu$m$^{-1}$\" % ddm_calc.ddm_dataset.q[q_index]);\n",
    "plt.axhline(y = ddm_calc.ddm_dataset.B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DDM matrix should go from the background, $B$, to the background plus amplitude, $B + A$, as a function of the lag time. From the above plot, it does look like we overestimated the background (perhaps the amplitude hadn't gone quite to zero by the last wavevectors we measure?). From the plot, clearly the background is less than 195. \n",
    "\n",
    "Let's estimate the background at  about 190. We can make this adjustment with the `recalculate_ISF_with_new_background` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  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:           (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                 int32 190\n",
       "    B_std             float64 1.491\n",
       "    Amplitude         (q) float64 -190.0 2.585e+05 1.026e+04 ... 19.47 20.65\n",
       "    ISF               (lagtime, q) float64 0.0 0.9996 0.9872 ... 0.432 0.4966\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-259e998e-19dc-44f4-833e-c228eb9ed16c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-259e998e-19dc-44f4-833e-c228eb9ed16c' 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-897c4196-3588-4bc9-9565-2fee4c57cb96' class='xr-section-summary-in' type='checkbox'  checked><label for='section-897c4196-3588-4bc9-9565-2fee4c57cb96' 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-1d0b059a-b035-4e10-a2a8-20df242ad6fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d0b059a-b035-4e10-a2a8-20df242ad6fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e75dd98-8ee2-439d-ac49-a9b35b804d00' class='xr-var-data-in' type='checkbox'><label for='data-3e75dd98-8ee2-439d-ac49-a9b35b804d00' 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-9a0055d2-c7ab-436f-9822-c6fb4913ce5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a0055d2-c7ab-436f-9822-c6fb4913ce5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e3ded91-66d4-4c89-b364-f88159d241ce' class='xr-var-data-in' type='checkbox'><label for='data-7e3ded91-66d4-4c89-b364-f88159d241ce' 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-100b249a-f791-4279-a32a-344bc2f8d8c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-100b249a-f791-4279-a32a-344bc2f8d8c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40bfc2fd-ad7b-42aa-981f-128db36f9dbe' class='xr-var-data-in' type='checkbox'><label for='data-40bfc2fd-ad7b-42aa-981f-128db36f9dbe' 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-366f6352-718a-47a2-97a8-5b3fc535dbec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-366f6352-718a-47a2-97a8-5b3fc535dbec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0a9f36b-2891-4f0d-90a8-6ab8633d5d82' class='xr-var-data-in' type='checkbox'><label for='data-b0a9f36b-2891-4f0d-90a8-6ab8633d5d82' 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-6547719a-cb8d-44e9-9582-002ecea41463' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6547719a-cb8d-44e9-9582-002ecea41463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32309a59-ae0f-4bf5-a26d-71e23b142830' class='xr-var-data-in' type='checkbox'><label for='data-32309a59-ae0f-4bf5-a26d-71e23b142830' 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-3609255a-7613-4cd4-b852-d8cfa151df29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3609255a-7613-4cd4-b852-d8cfa151df29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee0a4118-c739-4246-93e1-88e59e723e4b' class='xr-var-data-in' type='checkbox'><label for='data-ee0a4118-c739-4246-93e1-88e59e723e4b' 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-5d9508b9-dbba-4583-ba8e-988383b14ecb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d9508b9-dbba-4583-ba8e-988383b14ecb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5eff16a-b0b9-4538-8b8b-f95381b2a569' class='xr-var-data-in' type='checkbox'><label for='data-a5eff16a-b0b9-4538-8b8b-f95381b2a569' 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-3363628a-17e7-4bfc-9fd6-1feda2edb6aa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3363628a-17e7-4bfc-9fd6-1feda2edb6aa' 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-de8dd0b8-d626-4283-8807-179dff8cb501' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de8dd0b8-d626-4283-8807-179dff8cb501' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34922bcc-b47a-4311-8add-c4348aefc5d1' class='xr-var-data-in' type='checkbox'><label for='data-34922bcc-b47a-4311-8add-c4348aefc5d1' 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-c4856663-d3ad-4746-b3d8-3e15d754fa31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4856663-d3ad-4746-b3d8-3e15d754fa31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcdaa4a9-9ea4-4057-bce7-70df7b9293f4' class='xr-var-data-in' type='checkbox'><label for='data-bcdaa4a9-9ea4-4057-bce7-70df7b9293f4' 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-84b9b10a-241f-4113-bce3-53396f76ca73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84b9b10a-241f-4113-bce3-53396f76ca73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d59ad419-45f5-41bf-8b87-7def1d831b03' class='xr-var-data-in' type='checkbox'><label for='data-d59ad419-45f5-41bf-8b87-7def1d831b03' 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-ef5b6a28-7a46-4f13-8cf9-97a3a6d9536e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef5b6a28-7a46-4f13-8cf9-97a3a6d9536e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66a798c8-4c96-4d09-8e01-5d5e7a419af0' class='xr-var-data-in' type='checkbox'><label for='data-66a798c8-4c96-4d09-8e01-5d5e7a419af0' 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-bb2663dc-e6b1-4f95-99a0-64ea77eec3fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb2663dc-e6b1-4f95-99a0-64ea77eec3fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82b26cae-3855-40df-ae49-79bf7a03365c' class='xr-var-data-in' type='checkbox'><label for='data-82b26cae-3855-40df-ae49-79bf7a03365c' 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'>int32</div><div class='xr-var-preview xr-preview'>190</div><input id='attrs-a72abef0-b31d-47bb-8bde-cee79b2a72b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a72abef0-b31d-47bb-8bde-cee79b2a72b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7ed9c12-0d08-4d66-a714-34c900d4b5d0' class='xr-var-data-in' type='checkbox'><label for='data-e7ed9c12-0d08-4d66-a714-34c900d4b5d0' 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(190)</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-955bacb7-cd50-4e61-9791-3f3ce5e7682b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-955bacb7-cd50-4e61-9791-3f3ce5e7682b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5d2d1c4-45af-4c30-bb3e-63b0a2afedaf' class='xr-var-data-in' type='checkbox'><label for='data-b5d2d1c4-45af-4c30-bb3e-63b0a2afedaf' 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'>-190.0 2.585e+05 ... 19.47 20.65</div><input id='attrs-7156408f-b545-42fc-a35b-863af5042312' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7156408f-b545-42fc-a35b-863af5042312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b38d1492-2c1e-4631-9cb6-f47638fdfa85' class='xr-var-data-in' type='checkbox'><label for='data-b38d1492-2c1e-4631-9cb6-f47638fdfa85' 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([-1.90000000e+02,  2.58493018e+05,  1.02602346e+04,  1.15497914e+04,\n",
       "        1.68195629e+04,  1.91789308e+04,  1.97920750e+04,  2.04739243e+04,\n",
       "        1.98628174e+04,  2.14217457e+04,  2.30787136e+04,  2.75439244e+04,\n",
       "        3.36077443e+04,  3.95384367e+04,  4.21701356e+04,  4.26722045e+04,\n",
       "        4.10361202e+04,  3.58616144e+04,  2.83223278e+04,  2.06264656e+04,\n",
       "        1.43607517e+04,  9.18098831e+03,  5.57030056e+03,  3.36316630e+03,\n",
       "        1.93279536e+03,  1.17701023e+03,  8.05816138e+02,  6.46477276e+02,\n",
       "        4.86766556e+02,  3.54100454e+02,  2.81344652e+02,  2.59050811e+02,\n",
       "        2.16860258e+02,  1.66873759e+02,  1.40019053e+02,  1.08582782e+02,\n",
       "        9.24025916e+01,  7.62421017e+01,  6.95296420e+01,  6.63192247e+01,\n",
       "        5.82623495e+01,  5.81042089e+01,  5.24400299e+01,  4.81539029e+01,\n",
       "        4.50185732e+01,  4.06202012e+01,  4.05361388e+01,  3.51453041e+01,\n",
       "        3.36993461e+01,  3.35304868e+01,  3.04943557e+01,  2.94060037e+01,\n",
       "        2.76688057e+01,  2.93246657e+01,  2.71957975e+01,  2.54568387e+01,\n",
       "        2.55785786e+01,  2.38141133e+01,  2.40730764e+01,  2.19637827e+01,\n",
       "        2.02505220e+01,  2.06261336e+01,  1.94747592e+01,  2.06536633e+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 0.9996 0.9872 ... 0.432 0.4966</div><input id='attrs-5c39cc81-60b0-4f63-a61f-4159f2ebb8b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c39cc81-60b0-4f63-a61f-4159f2ebb8b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ecb59f9-dbb9-4baf-8bff-30529c627340' class='xr-var-data-in' type='checkbox'><label for='data-6ecb59f9-dbb9-4baf-8bff-30529c627340' 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.        ,  0.99959694,  0.9871915 , ...,  0.85778634,\n",
       "         0.82204821,  0.86604236],\n",
       "       [ 0.        ,  0.99906358,  0.97066658, ...,  0.76497186,\n",
       "         0.7397658 ,  0.78674034],\n",
       "       [ 0.        ,  0.99866393,  0.95809464, ...,  0.70299025,\n",
       "         0.6810774 ,  0.71946869],\n",
       "       ...,\n",
       "       [ 0.        ,  0.98539009,  0.4540191 , ...,  0.27275862,\n",
       "        -0.09683656, -0.09489651],\n",
       "       [ 0.        ,  0.98461401,  0.4867419 , ...,  0.32901286,\n",
       "         0.21002625,  0.13879852],\n",
       "       [ 0.        ,  0.98635641,  0.42712011, ...,  0.13873015,\n",
       "         0.43195264,  0.49663365]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b36d44d-3eb5-4b91-8ecb-355f27f9b378' class='xr-section-summary-in' type='checkbox'  ><label for='section-2b36d44d-3eb5-4b91-8ecb-355f27f9b378' 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>None</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                 int32 190\n",
       "    B_std             float64 1.491\n",
       "    Amplitude         (q) float64 -190.0 2.585e+05 1.026e+04 ... 19.47 20.65\n",
       "    ISF               (lagtime, q) float64 0.0 0.9996 0.9872 ... 0.432 0.4966\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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_ddm_matrix = ddm.recalculate_ISF_with_new_background(ddm_calc.ddm_dataset, background_val=190)\n",
    "display(new_ddm_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's save this `ddm_dataset`. When the `calculate_DDM_matrix` method is executed, the `ddm_dataset` is automatically saved to disk. But we can overwrite that with the `resave_ddm_dataset` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddm_calc.resave_ddm_dataset(new_ddm_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additional notes about the background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default way that **this** code estimates the background is to compute the power spectrum of the images, as described above. That is, we find $\\left< | \\tilde{I}(q, t) |^2 \\right>_t$ and recognize that $\\left< | \\tilde{I}(q, t) |^2 \\right>_t = 0.5 \\times \\left[ A(q) + B \\right]$ and assume that as $q \\rightarrow q_{max}$, $A(q) \\rightarrow 0$. Therefore, $\\left< | \\tilde{I}(q_{max}, t) |^2 \\right>_t \\approx 0.5\\times B$ (see [**Cerbino 2017**] or Eq. 5 in [**Giavazzi 2018**]). \n",
    "\n",
    "But, there are other ways. In [**Kurzthaler 2018**], they set the background, $B$, to zero. They note that for their fluorescence imaging \"the camera noise is usually negligible.\" However, they note that for bright-field of phase contrast modalities, the camera is not negligible and $B$ will be non-zero. \n",
    "\n",
    "In [**Bayles 2017**], they approximate $B$ by $\\min \\left[ D(q, \\Delta t_{min}) \\right]$. But they note that the frame rate needs to be \"sufficiently small\" and that this will give an overestimate of $B$. To try this, use `background_method = 1` as an optional keyword parameter in the function `calculate_DDM_matrix` (or set the value of `background_method` in the YAML file under `Analysis_parameters`).\n",
    "\n",
    "\n",
    ">[**Cerbino 2017**] Cerbino, R., Piotti, D., Buscaglia, M. & Giavazzi, F. [Dark field differential dynamic microscopy enables accurate characterization of the roto-translational dynamics of bacteria and colloidal clusters.](https://doi.org/10.1088/1361-648X/aa9bc5) J. Phys.: Condens. Matter 30, 025901 (2017).\n",
    "\n",
    "\n",
    ">[**Giavazzi 2018**] Giavazzi, F., Malinverno, C., Scita, G. & Cerbino, R. [Tracking-Free Determination of Single-Cell Displacements and Division Rates in Confluent Monolayers.](https://doi.org/10.3389/fphy.2018.00120) Front. Phys. 6, (2018).\n",
    "\n",
    "\n",
    ">[**Kurzthaler 2018**] Kurzthaler, C. et al. [Probing the Spatiotemporal Dynamics of Catalytic Janus Particles with Single-Particle Tracking and Differential Dynamic Microscopy.](https://doi.org/10.1103/PhysRevLett.121.078001) Phys. Rev. Lett. 121, 078001 (2018).\n",
    "\n",
    "\n",
    ">[**Bayles 2017**] Bayles, A. V., Squires, T. M. & Helgeson, M. E. [Probe microrheology without particle tracking by differential dynamic microscopy.](https://doi.org/10.1007/s00397-017-1047-7) Rheol Acta 56, 863–869 (2017).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiazing DDM_Fit class and fitting our data to a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We initlize the `DDM_Fit` class by passing it the yaml file containing our analysis parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "#Note that to initialize the DDM_Fit class we need to pass it the YAML file containing the parameters \n",
    "# *or* a dictionary with the same parameters. \n",
    "\n",
    "ddm_fit = ddm.DDM_Fit(ddm_calc.data_yaml)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on what was done above in terms of estimating the background, we found that $B$ should be about 190. We use that estimate of the background to determine $A(q)$ and the intermediate scattering function, $f(q,\\Delta t)$. We could proceed to then fit the ISF. But here we choose not to. We fit the DDM matrix and have the amplitude and background, $A$ and $B$, be fitting parameters. But we can constrain these parameters based on what we found above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 'Background' set to 190.\n",
      "Parameter 'Background' lower limit set to 10.\n",
      "Parameter 'Background' upper limit set to 220.\n"
     ]
    }
   ],
   "source": [
    "#We update the intial guess for the background and the lower and upper bounds\n",
    "ddm_fit.set_parameter_initial_guess('Background', 190)\n",
    "ddm_fit.set_parameter_bounds('Background', [10, 220])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our first attemp at fitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we execute the fit below, we can use the optional argument `use_A_from_images_as_guess` set to True. This will use the value of $A(q)$ we esimated from the average Fourier tranform of all images and the estimated background. We can further limit the bounds on $A(q)$ with the optional parameter `update_limits_on_A` set to True. This will restrict the range that $A(q)$ can take to be within 10% of the estimated $A(q)$. If we want the bounds to be stricter or looser than 10%, we can change the optional parameter `updated_lims_on_A_fraction` to be something other than 0.1 (the default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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"
     ]
    }
   ],
   "source": [
    "#Note that the method `fit` has many optional parameters\n",
    "\n",
    "fit01 = ddm_fit.fit(name_fit = 'fit01', display_table=False, use_A_from_images_as_guess=True, update_limits_on_A=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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       "</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 2.326e+05 ... 0.705 0.7446\n",
       "    theory           (lagtime, q) float64 10.0 315.0 338.5 ... 206.7 206.8 206.9\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9996 0.9872 ... 0.432 0.4966\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -190.0 2.585e+05 1.026e+04 ... 19.47 20.65\n",
       "    B                int32 190\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:      0.6168642315520588\n",
       "    tau_vs_q_slope:                 [-1.98005917]\n",
       "    msd_alpha:                      [1.01007082]\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:               None\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-47e2e60b-d87a-46b5-b014-61a5b7197ee9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-47e2e60b-d87a-46b5-b014-61a5b7197ee9' 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-86012f90-6c17-4b11-940d-09f1d7afff6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-86012f90-6c17-4b11-940d-09f1d7afff6b' 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-21f6cc8c-e988-4e2a-a766-624ee0414633' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21f6cc8c-e988-4e2a-a766-624ee0414633' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-620bd27d-0817-485b-a33e-1d818caa9328' class='xr-var-data-in' type='checkbox'><label for='data-620bd27d-0817-485b-a33e-1d818caa9328' 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-06dc6c06-f5c1-4c2b-9662-242af89355d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06dc6c06-f5c1-4c2b-9662-242af89355d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-127d1663-2a41-452c-aa6d-44e2ab864130' class='xr-var-data-in' type='checkbox'><label for='data-127d1663-2a41-452c-aa6d-44e2ab864130' 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-8903651e-79fc-4ebd-8e6b-9be11ae953fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8903651e-79fc-4ebd-8e6b-9be11ae953fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3619c601-c252-4343-bd72-671cf92421cc' class='xr-var-data-in' type='checkbox'><label for='data-3619c601-c252-4343-bd72-671cf92421cc' 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-01f9c352-5a70-431d-bc24-c35cce0dc937' class='xr-section-summary-in' type='checkbox'  checked><label for='section-01f9c352-5a70-431d-bc24-c35cce0dc937' 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 2.326e+05 ... 0.705 0.7446</div><input id='attrs-b81b69e4-0eed-48d1-9054-51ee23fa3706' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b81b69e4-0eed-48d1-9054-51ee23fa3706' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88aa3a69-48bd-415e-a881-4ae3d401bf2b' class='xr-var-data-in' type='checkbox'><label for='data-88aa3a69-48bd-415e-a881-4ae3d401bf2b' 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, 2.32643716e+05, 9.23421116e+03, 1.04425917e+04,\n",
       "        1.65958488e+04, 1.87741627e+04, 2.01262093e+04, 2.02699356e+04,\n",
       "        1.93938141e+04, 2.14281346e+04, 2.33743051e+04, 2.74778139e+04,\n",
       "        3.34522708e+04, 3.95131276e+04, 4.17820421e+04, 4.20664112e+04,\n",
       "        4.07569060e+04, 3.57098235e+04, 2.84201318e+04, 2.06082054e+04,\n",
       "        1.43591503e+04, 9.28277724e+03, 5.65119988e+03, 3.47725293e+03,\n",
       "        2.06716941e+03, 1.22311564e+03, 8.76927421e+02, 6.90124480e+02,\n",
       "        5.35443212e+02, 3.89510499e+02, 3.09479117e+02, 2.84955892e+02,\n",
       "        2.38546284e+02, 1.83561135e+02, 1.54020959e+02, 1.19441061e+02,\n",
       "        9.04713776e+01, 7.32803000e+01, 6.33150620e+01, 5.96873022e+01,\n",
       "        5.24361145e+01, 5.22937881e+01, 4.71960269e+01, 4.33385126e+01,\n",
       "        4.05167159e+01, 3.65581811e+01, 3.64825249e+01, 3.16307736e+01,\n",
       "        3.03294115e+01, 3.01774381e+01, 2.74449201e+01, 2.64654033e+01,\n",
       "        2.49019252e+01, 2.63921992e+01, 2.44762177e+01, 2.29111548e+01,\n",
       "        2.30207207e+01, 2.14327020e+01, 2.16657687e+01, 1.97674044e+01,\n",
       "        1.82254698e+01, 1.85635203e+01, 1.75272833e+01, 1.85882970e+01],\n",
       "       [1.00000000e+01, 1.00000000e+01, 1.00000000e+01, 3.86806763e+00,\n",
       "        2.55606871e+00, 1.42436450e+00, 1.03553610e+00, 7.42218975e-01,\n",
       "        5.52015441e-01, 4.65091456e-01, 3.96220641e-01, 3.21261104e-01,\n",
       "        2.90633158e-01, 2.48487749e-01, 2.13902731e-01, 1.92959729e-01,\n",
       "...\n",
       "        1.88872932e+02, 1.88581861e+02, 1.89252080e+02, 1.88386083e+02,\n",
       "        1.88981350e+02, 1.87924484e+02, 1.87679912e+02, 1.87688222e+02,\n",
       "        1.87969231e+02, 1.87747866e+02, 1.88648024e+02, 1.89106434e+02,\n",
       "        1.89427805e+02, 1.88135686e+02, 1.89288727e+02, 1.88358949e+02],\n",
       "       [1.10000000e+00, 1.10000000e+00, 5.61916398e-01, 7.10762250e-01,\n",
       "        8.11577778e-01, 9.09132560e-01, 9.11750595e-01, 9.52912598e-01,\n",
       "        9.57434030e-01, 9.79312824e-01, 9.69590565e-01, 1.00961966e+00,\n",
       "        1.01501443e+00, 1.04072569e+00, 1.03076437e+00, 1.03128893e+00,\n",
       "        1.04638759e+00, 1.04446796e+00, 1.04406777e+00, 1.05492850e+00,\n",
       "        1.03622512e+00, 1.02767912e+00, 1.01091421e+00, 9.75980332e-01,\n",
       "        9.19591679e-01, 9.11770097e-01, 8.60156028e-01, 8.59201102e-01,\n",
       "        7.98604397e-01, 7.94550993e-01, 7.81941075e-01, 7.57373189e-01,\n",
       "        7.63314218e-01, 7.33617919e-01, 6.92531764e-01, 6.47957554e-01,\n",
       "        6.96849842e-01, 7.07400255e-01, 7.43780117e-01, 6.79390232e-01,\n",
       "        7.06264342e-01, 6.76778617e-01, 6.50957583e-01, 6.69603282e-01,\n",
       "        6.90556765e-01, 6.62360386e-01, 7.05250808e-01, 6.76426769e-01,\n",
       "        7.21412072e-01, 7.22245349e-01, 6.46001050e-01, 6.55461922e-01,\n",
       "        6.71431019e-01, 6.24491328e-01, 6.21572597e-01, 6.59109603e-01,\n",
       "        6.24070701e-01, 6.10418310e-01, 6.90625300e-01, 6.72248157e-01,\n",
       "        7.68715009e-01, 7.20353863e-01, 7.04971605e-01, 7.44597078e-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'>10.0 315.0 338.5 ... 206.8 206.9</div><input id='attrs-531a720b-a557-476f-9087-01c0d93781db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-531a720b-a557-476f-9087-01c0d93781db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fefbc66-04b4-47c4-9eb5-493b83577ed0' class='xr-var-data-in' type='checkbox'><label for='data-3fefbc66-04b4-47c4-9eb5-493b83577ed0' 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([[1.00013109e+01, 3.14969814e+02, 3.38493932e+02, ...,\n",
       "        1.92529023e+02, 1.93101914e+02, 1.92287259e+02],\n",
       "       [1.00028078e+01, 6.63227156e+02, 4.80661006e+02, ...,\n",
       "        1.94802592e+02, 1.95066214e+02, 1.94459478e+02],\n",
       "       [1.00043826e+01, 1.02958077e+03, 5.91814578e+02, ...,\n",
       "        1.96469200e+02, 1.96522549e+02, 1.96093000e+02],\n",
       "       ...,\n",
       "       [1.06708801e+01, 1.56086044e+05, 6.05266368e+03, ...,\n",
       "        2.06699206e+02, 2.06816010e+02, 2.06947246e+02],\n",
       "       [1.07242679e+01, 1.68506368e+05, 6.30794233e+03, ...,\n",
       "        2.06699206e+02, 2.06816010e+02, 2.06947246e+02],\n",
       "       [1.07744986e+01, 1.80192244e+05, 6.55592511e+03, ...,\n",
       "        2.06699206e+02, 2.06816010e+02, 2.06947246e+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 0.9996 0.9872 ... 0.432 0.4966</div><input id='attrs-435f0f44-14d1-41f2-9108-053de5ee48ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-435f0f44-14d1-41f2-9108-053de5ee48ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdbe4291-5ad1-4e4a-a3b7-0420ada976d8' class='xr-var-data-in' type='checkbox'><label for='data-fdbe4291-5ad1-4e4a-a3b7-0420ada976d8' 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.        ,  0.99959694,  0.9871915 , ...,  0.85778634,\n",
       "         0.82204821,  0.86604236],\n",
       "       [ 0.        ,  0.99906358,  0.97066658, ...,  0.76497186,\n",
       "         0.7397658 ,  0.78674034],\n",
       "       [ 0.        ,  0.99866393,  0.95809464, ...,  0.70299025,\n",
       "         0.6810774 ,  0.71946869],\n",
       "       ...,\n",
       "       [ 0.        ,  0.98539009,  0.4540191 , ...,  0.27275862,\n",
       "        -0.09683656, -0.09489651],\n",
       "       [ 0.        ,  0.98461401,  0.4867419 , ...,  0.32901286,\n",
       "         0.21002625,  0.13879852],\n",
       "       [ 0.        ,  0.98635641,  0.42712011, ...,  0.13873015,\n",
       "         0.43195264,  0.49663365]])</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-719243b2-3be8-461e-b032-927a4a9c2912' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-719243b2-3be8-461e-b032-927a4a9c2912' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e6eed76-b6d8-4c4d-ac5f-68e9c81fb49b' class='xr-var-data-in' type='checkbox'><label for='data-9e6eed76-b6d8-4c4d-ac5f-68e9c81fb49b' 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'>-190.0 2.585e+05 ... 19.47 20.65</div><input id='attrs-059c66c9-3b6a-4da1-b924-2c585ef60638' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-059c66c9-3b6a-4da1-b924-2c585ef60638' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81feb072-ecb9-41be-9f10-d6aac9ad3340' class='xr-var-data-in' type='checkbox'><label for='data-81feb072-ecb9-41be-9f10-d6aac9ad3340' 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([-1.90000000e+02,  2.58493018e+05,  1.02602346e+04,  1.15497914e+04,\n",
       "        1.68195629e+04,  1.91789308e+04,  1.97920750e+04,  2.04739243e+04,\n",
       "        1.98628174e+04,  2.14217457e+04,  2.30787136e+04,  2.75439244e+04,\n",
       "        3.36077443e+04,  3.95384367e+04,  4.21701356e+04,  4.26722045e+04,\n",
       "        4.10361202e+04,  3.58616144e+04,  2.83223278e+04,  2.06264656e+04,\n",
       "        1.43607517e+04,  9.18098831e+03,  5.57030056e+03,  3.36316630e+03,\n",
       "        1.93279536e+03,  1.17701023e+03,  8.05816138e+02,  6.46477276e+02,\n",
       "        4.86766556e+02,  3.54100454e+02,  2.81344652e+02,  2.59050811e+02,\n",
       "        2.16860258e+02,  1.66873759e+02,  1.40019053e+02,  1.08582782e+02,\n",
       "        9.24025916e+01,  7.62421017e+01,  6.95296420e+01,  6.63192247e+01,\n",
       "        5.82623495e+01,  5.81042089e+01,  5.24400299e+01,  4.81539029e+01,\n",
       "        4.50185732e+01,  4.06202012e+01,  4.05361388e+01,  3.51453041e+01,\n",
       "        3.36993461e+01,  3.35304868e+01,  3.04943557e+01,  2.94060037e+01,\n",
       "        2.76688057e+01,  2.93246657e+01,  2.71957975e+01,  2.54568387e+01,\n",
       "        2.55785786e+01,  2.38141133e+01,  2.40730764e+01,  2.19637827e+01,\n",
       "        2.02505220e+01,  2.06261336e+01,  1.94747592e+01,  2.06536633e+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'>int32</div><div class='xr-var-preview xr-preview'>190</div><input id='attrs-45e52dd9-62a2-4553-ad08-45414c95e7e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45e52dd9-62a2-4553-ad08-45414c95e7e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b53c1114-e40a-4e6d-97cd-92f6bc05dc1d' class='xr-var-data-in' type='checkbox'><label for='data-b53c1114-e40a-4e6d-97cd-92f6bc05dc1d' 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(190)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75f65c34-e7c4-4d77-a532-a2873b367f68' class='xr-section-summary-in' type='checkbox'  ><label for='section-75f65c34-e7c4-4d77-a532-a2873b367f68' 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;: 190.0, &#x27;limits&#x27;: [10.0, 220.0], &#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>0.6168642315520588</dd><dt><span>tau_vs_q_slope :</span></dt><dd>[-1.98005917]</dd><dt><span>msd_alpha :</span></dt><dd>[1.01007082]</dd><dt><span>msd_effective_diffusion_coeff :</span></dt><dd>[0.6138703]</dd><dt><span>diffusion_coeff :</span></dt><dd>0.6104877159595499</dd><dt><span>diffusion_coeff_std :</span></dt><dd>0.04076205720351696</dd><dt><span>velocity :</span></dt><dd>1.0541545094734308</dd><dt><span>velocity_std :</span></dt><dd>0.41611807499452924</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>None</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 2.326e+05 ... 0.705 0.7446\n",
       "    theory           (lagtime, q) float64 10.0 315.0 338.5 ... 206.7 206.8 206.9\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9996 0.9872 ... 0.432 0.4966\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -190.0 2.585e+05 1.026e+04 ... 19.47 20.65\n",
       "    B                int32 190\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:      0.6168642315520588\n",
       "    tau_vs_q_slope:                 [-1.98005917]\n",
       "    msd_alpha:                      [1.01007082]\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:               None\n",
       "    OverlapMethod:                  2"
      ]
     },
     "execution_count": 16,
     "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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hGnWTu0hjZ2bzPMotHEWkfqTSZ1A1dJHkmpLsAESauJT5DKqGLiIikgJUQxcREUkBSugiIiIpQAldREQkBSihi4iIpAAldJEGwsx2M7NHzexvZjY62fGINEVmlmNmD5nZc8mOpaaU0EUSyMweNrO1ZrawQvlxZrbEzL40sxuDxacCz7n7JcBJ9R6sSIqqyefQ3Qvd/aLkRFo3SugiifUIcFxkgZmlA/cDxwN9gbPNrC/QDVgRXKy0HmMUSXWPEPvnsNFSQhdJIHd/F/i2QvEQ4MtgTWA7MB04GVhJIKmDPpsicVPDz2GjpS8Nkfq3Fz/WxCGQyPcCXgBOM7O/0ATvQy1Sz6J+Ds2so5n9FRhoZjclJ7TaaZbsAESaIItS5u7+PXBBfQcj0kRV9TlcD4yr72DiQTV0kfq3Etg7YrobsDpJsYg0VSn3OVRCF6l/HwC9zKynmTUHzgJeSXJMIk1Nyn0OldBFEsjMngLmArlmttLMLnL3ncCVwBvAIuAZd/80mXGKpLKm8jnUaGsiIiIpQDV0ERGRFKCELiIikgKU0EVERFKAErqIiEgKUEIXERFJAUroIiIiKUAJXUREJAUooYuIiKQAJXQRqTdmlmNmD5nZc8mORSTVKKGLCABm1tLMZptZeqL2ERx7+qIK+21uZu+amUZ/FKkDfYBEJORC4AV3L63rhswsD/hdxe27+9qKy7r7djObCZwJTKvrvkWaKiV0kSbAzCYAPwNWAMXAfHf/Y4XFRgPnBJfPBl519/2D078EWgOPAK8D7wEHAR8DU4FfA12A0e7+X3f/HzCiBiG+ROAHgBK6SC2pyV0kxZnZYAJDQw4ETgUOiLJMcyDH3ZfGsMl9gXuB/kAfAj8CDgN+CYzfRSwdzeyvwEAzuyli1sJocYlI7FRDF0l9hwMvuvtWADOLNuZzJ2BDjNv7OlgDx8w+BWa6u5vZ/4Ds6lZ09/XAuCjlpWa23czauPvmGOMQkQiqoYs0DbsaJ/kHoEWFMot4nhHxvCTieVnEdBl1qyRkAtvqsL5Ik6aELpL63gV+GuzF3gYYWXEBd/8OSDezyKTew8w6m1kacASQsN7vZtYRKHb3HYnah0iqU0IXSXHu/iHwNLAAeB6YU8WibxI4Fx6yHngMmE/gHPfPSFxSHwbMSNC2RZoEc99VS5yIpBIzuxXYUrGXu5kNBK519/Mq9nKvh5heAG5y9yX1sT+RVKQauogA4O4fAe8k8sYy0QR72L+kZC5SN6qhi4iIpADV0EVERFKAErqIiEgKUEIXERFJAUroIiIiKUAJXUREJAUooYuIiKQAJXQREZEUoIQuIiKSAv4/plT9I3gdONgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x355.995 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generate plots for inspecting the outcome of the fits\n",
    "\n",
    "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": [
    "### Our second attemp at fitting the data - updating initial guesses for tau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the results of the fit, we can estimate a diffusion coefficient of about 0.6 $\\mu m^2 / s$. We determine this by looking at the characteristic decay time vs the wavevector ($\\tau$ vs $q$). On a log-log plot, power laws relationships between the two variables will come across as a straight line where the slope of the line is equal to the power. And, for this data, we see a clear power law show a relationship of $\\tau \\propto q^{-2}$ which indicates diffusive motion. Note that our data deviates from this power law relationship for $q$ greater than about 4 $\\mu m^{-1}$. This could be due to poor optical resolution limiting the high wavevectors (which correspond to small length scales). Or it could be due to not having the temporal resolution necessary to detect fast decay times. Lastly, it could also be due to poor fit performance. We could try to use an initial guess for $\\tau$ for each $q$ that is based on an estimated diffusion coefficient. That is, when we fit $D(q, \\Delta t)$ (or $f(q,\\Delta t)$), for each $q$, we will calculate $\\tau_{guess} = 1 / (Dq^2)$ and use that as the initial guess in the fits. We do that below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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, use_A_from_images_as_guess=True, update_limits_on_A=True,\n",
    "                   update_tau_based_on_estimated_diffcoeff=True, estimated_diffcoeff=0.6, \n",
    "                   update_limits_on_tau=True, updated_lims_on_tau_fraction=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  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 2.326e+05 9.894e+03 ... 0.5 0.5\n",
       "    theory           (lagtime, q) float64 10.0 152.4 361.6 ... 202.0 201.7 201.6\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9996 0.9872 ... 0.432 0.4966\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -190.0 2.585e+05 1.026e+04 ... 19.47 20.65\n",
       "    B                int32 190\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:      0.6584064996820138\n",
       "    tau_vs_q_slope:                 [-1.89886818]\n",
       "    msd_alpha:                      [1.053259]\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:               None\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-8beacef4-eff2-4f87-bc39-f7ff97ffd10e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8beacef4-eff2-4f87-bc39-f7ff97ffd10e' 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-6e860ed1-c67a-41f1-b458-c9b180640a85' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e860ed1-c67a-41f1-b458-c9b180640a85' 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-581c13c8-9533-493e-a730-2c5de1bef035' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-581c13c8-9533-493e-a730-2c5de1bef035' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1c0b2e3-e3b7-4fc0-a609-b84a387dea3a' class='xr-var-data-in' type='checkbox'><label for='data-b1c0b2e3-e3b7-4fc0-a609-b84a387dea3a' 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-309a65aa-b2c8-486e-8896-e48921718c02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-309a65aa-b2c8-486e-8896-e48921718c02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd8a795e-d62f-4b90-ac45-7c3a589ef87e' class='xr-var-data-in' type='checkbox'><label for='data-dd8a795e-d62f-4b90-ac45-7c3a589ef87e' 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-eb6cc77a-74b0-45ca-bd4f-5f06a0a4ff67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb6cc77a-74b0-45ca-bd4f-5f06a0a4ff67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d8f0dd4-9060-4114-a3f4-8b1c9e26e8ce' class='xr-var-data-in' type='checkbox'><label for='data-8d8f0dd4-9060-4114-a3f4-8b1c9e26e8ce' 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-bae55faa-c316-4e87-8faa-249036a8ab63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bae55faa-c316-4e87-8faa-249036a8ab63' 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 2.326e+05 9.894e+03 ... 0.5 0.5</div><input id='attrs-0b4176e3-adc8-4ddf-bc30-9e30692b31ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b4176e3-adc8-4ddf-bc30-9e30692b31ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0604656f-77fb-4134-b9b7-36d46c5d8eaa' class='xr-var-data-in' type='checkbox'><label for='data-0604656f-77fb-4134-b9b7-36d46c5d8eaa' 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, 2.32643716e+05, 9.89373199e+03, 1.04425914e+04,\n",
       "        1.65958403e+04, 1.87741626e+04, 2.01262063e+04, 2.02699350e+04,\n",
       "        1.93938141e+04, 2.14281346e+04, 2.33743051e+04, 2.74778140e+04,\n",
       "        3.34522708e+04, 3.95131276e+04, 4.17820422e+04, 4.20664111e+04,\n",
       "        4.07569060e+04, 3.57098235e+04, 2.84201318e+04, 2.06082054e+04,\n",
       "        1.43591503e+04, 9.28277724e+03, 5.65119988e+03, 3.47725293e+03,\n",
       "        2.06716941e+03, 1.22311564e+03, 8.76927351e+02, 6.90124615e+02,\n",
       "        5.35443212e+02, 3.89510499e+02, 3.09479117e+02, 2.84955892e+02,\n",
       "        2.38546284e+02, 1.83561135e+02, 1.54020959e+02, 1.19441061e+02,\n",
       "        1.01642851e+02, 8.38663118e+01, 7.64826062e+01, 7.29511472e+01,\n",
       "        6.40885844e+01, 6.39146298e+01, 5.76840329e+01, 5.29692932e+01,\n",
       "        4.95204305e+01, 4.46822213e+01, 4.45897527e+01, 3.86598345e+01,\n",
       "        3.70692807e+01, 3.68835355e+01, 3.35437912e+01, 3.23466041e+01,\n",
       "        3.04356863e+01, 3.22571323e+01, 2.99153772e+01, 2.80025225e+01,\n",
       "        2.81364364e+01, 2.61955246e+01, 2.64803840e+01, 2.41601609e+01,\n",
       "        2.22755742e+01, 2.26887470e+01, 2.14222352e+01, 2.27190297e+01],\n",
       "       [1.00000000e+01, 8.10157916e+01, 1.53295774e+01, 3.86806746e+00,\n",
       "        2.55606574e+00, 1.42436448e+00, 1.03553582e+00, 7.42218942e-01,\n",
       "        5.52015441e-01, 4.65091456e-01, 3.96220640e-01, 3.21261107e-01,\n",
       "        2.90633158e-01, 2.48487749e-01, 2.13902731e-01, 1.92959728e-01,\n",
       "...\n",
       "        1.75920784e+02, 1.75462434e+02, 1.77385444e+02, 1.77265516e+02,\n",
       "        1.78234387e+02, 1.76044620e+02, 1.76896189e+02, 1.77554782e+02,\n",
       "        1.77379772e+02, 1.78268340e+02, 1.78255143e+02, 1.79515166e+02,\n",
       "        1.79915464e+02, 1.79294718e+02, 1.80274342e+02, 1.78884768e+02],\n",
       "       [1.10000000e+00, 1.10000000e+00, 5.13758578e-01, 7.10762358e-01,\n",
       "        8.11578193e-01, 9.09132572e-01, 9.11750832e-01, 9.52912637e-01,\n",
       "        9.57434030e-01, 9.79312823e-01, 9.69590566e-01, 1.00961966e+00,\n",
       "        1.01501443e+00, 1.04072569e+00, 1.03076437e+00, 1.03128894e+00,\n",
       "        1.04638759e+00, 1.04446796e+00, 1.04406777e+00, 1.05492850e+00,\n",
       "        1.03622512e+00, 1.02767912e+00, 1.01091421e+00, 9.75980332e-01,\n",
       "        9.19591679e-01, 9.11770096e-01, 8.60156128e-01, 8.59200858e-01,\n",
       "        7.98604406e-01, 7.94551000e-01, 7.81941080e-01, 7.57373234e-01,\n",
       "        7.63314258e-01, 7.43304844e-01, 7.05794318e-01, 6.59952326e-01,\n",
       "        6.13021408e-01, 5.87587545e-01, 5.74890649e-01, 5.32364098e-01,\n",
       "        5.32295111e-01, 5.18818644e-01, 5.05445433e-01, 5.02325262e-01,\n",
       "        5.10491451e-01, 5.00000000e-01, 5.05828686e-01, 5.00000000e-01,\n",
       "        5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01,\n",
       "        5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01,\n",
       "        5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01,\n",
       "        5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-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'>10.0 152.4 361.6 ... 201.7 201.6</div><input id='attrs-01bc22c2-ce7a-47af-bbd9-48360da0ca74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01bc22c2-ce7a-47af-bbd9-48360da0ca74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13e98e34-428d-4cbf-921a-e2defa6e9ba9' class='xr-var-data-in' type='checkbox'><label for='data-13e98e34-428d-4cbf-921a-e2defa6e9ba9' 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([[1.00013109e+01, 1.52384616e+02, 3.61633798e+02, ...,\n",
       "        1.94039740e+02, 1.94324216e+02, 1.93918518e+02],\n",
       "       [1.00028078e+01, 1.87321112e+02, 5.08207808e+02, ...,\n",
       "        1.96840289e+02, 1.96957227e+02, 1.96698370e+02],\n",
       "       [1.00043826e+01, 2.24123965e+02, 6.19965793e+02, ...,\n",
       "        1.98299076e+02, 1.98319989e+02, 1.98127945e+02],\n",
       "       ...,\n",
       "       [1.06708801e+01, 2.46219857e+04, 5.64750065e+03, ...,\n",
       "        2.01983465e+02, 2.01696577e+02, 2.01603797e+02],\n",
       "       [1.07242679e+01, 2.82783354e+04, 5.89647837e+03, ...,\n",
       "        2.01983465e+02, 2.01696577e+02, 2.01603797e+02],\n",
       "       [1.07744986e+01, 3.23548994e+04, 6.14246430e+03, ...,\n",
       "        2.01983465e+02, 2.01696577e+02, 2.01603797e+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 0.9996 0.9872 ... 0.432 0.4966</div><input id='attrs-683763e7-25d4-441a-812f-e77195de1b9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-683763e7-25d4-441a-812f-e77195de1b9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c88b6a1e-7ace-43c1-9f00-2d290298ddd1' class='xr-var-data-in' type='checkbox'><label for='data-c88b6a1e-7ace-43c1-9f00-2d290298ddd1' 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.        ,  0.99959694,  0.9871915 , ...,  0.85778634,\n",
       "         0.82204821,  0.86604236],\n",
       "       [ 0.        ,  0.99906358,  0.97066658, ...,  0.76497186,\n",
       "         0.7397658 ,  0.78674034],\n",
       "       [ 0.        ,  0.99866393,  0.95809464, ...,  0.70299025,\n",
       "         0.6810774 ,  0.71946869],\n",
       "       ...,\n",
       "       [ 0.        ,  0.98539009,  0.4540191 , ...,  0.27275862,\n",
       "        -0.09683656, -0.09489651],\n",
       "       [ 0.        ,  0.98461401,  0.4867419 , ...,  0.32901286,\n",
       "         0.21002625,  0.13879852],\n",
       "       [ 0.        ,  0.98635641,  0.42712011, ...,  0.13873015,\n",
       "         0.43195264,  0.49663365]])</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-6519680c-dc56-4980-9be8-06e9c2aec087' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6519680c-dc56-4980-9be8-06e9c2aec087' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-905ea8cd-1ae5-44e4-b406-4275108d149c' class='xr-var-data-in' type='checkbox'><label for='data-905ea8cd-1ae5-44e4-b406-4275108d149c' 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'>-190.0 2.585e+05 ... 19.47 20.65</div><input id='attrs-3ec89950-b338-49cd-baa9-c64e0625bf37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ec89950-b338-49cd-baa9-c64e0625bf37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a69106e9-2ead-4d1a-b183-4655ef50bfdd' class='xr-var-data-in' type='checkbox'><label for='data-a69106e9-2ead-4d1a-b183-4655ef50bfdd' 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([-1.90000000e+02,  2.58493018e+05,  1.02602346e+04,  1.15497914e+04,\n",
       "        1.68195629e+04,  1.91789308e+04,  1.97920750e+04,  2.04739243e+04,\n",
       "        1.98628174e+04,  2.14217457e+04,  2.30787136e+04,  2.75439244e+04,\n",
       "        3.36077443e+04,  3.95384367e+04,  4.21701356e+04,  4.26722045e+04,\n",
       "        4.10361202e+04,  3.58616144e+04,  2.83223278e+04,  2.06264656e+04,\n",
       "        1.43607517e+04,  9.18098831e+03,  5.57030056e+03,  3.36316630e+03,\n",
       "        1.93279536e+03,  1.17701023e+03,  8.05816138e+02,  6.46477276e+02,\n",
       "        4.86766556e+02,  3.54100454e+02,  2.81344652e+02,  2.59050811e+02,\n",
       "        2.16860258e+02,  1.66873759e+02,  1.40019053e+02,  1.08582782e+02,\n",
       "        9.24025916e+01,  7.62421017e+01,  6.95296420e+01,  6.63192247e+01,\n",
       "        5.82623495e+01,  5.81042089e+01,  5.24400299e+01,  4.81539029e+01,\n",
       "        4.50185732e+01,  4.06202012e+01,  4.05361388e+01,  3.51453041e+01,\n",
       "        3.36993461e+01,  3.35304868e+01,  3.04943557e+01,  2.94060037e+01,\n",
       "        2.76688057e+01,  2.93246657e+01,  2.71957975e+01,  2.54568387e+01,\n",
       "        2.55785786e+01,  2.38141133e+01,  2.40730764e+01,  2.19637827e+01,\n",
       "        2.02505220e+01,  2.06261336e+01,  1.94747592e+01,  2.06536633e+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'>int32</div><div class='xr-var-preview xr-preview'>190</div><input id='attrs-d79d4a1f-94c8-4bfe-b887-b9a1386c99ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d79d4a1f-94c8-4bfe-b887-b9a1386c99ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d7d311f-de1d-41ce-b5da-2e806100ab6c' class='xr-var-data-in' type='checkbox'><label for='data-3d7d311f-de1d-41ce-b5da-2e806100ab6c' 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(190)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0875d15-47f9-4ea3-8c8c-e9807a41232c' class='xr-section-summary-in' type='checkbox'  ><label for='section-b0875d15-47f9-4ea3-8c8c-e9807a41232c' 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;: 190.0, &#x27;limits&#x27;: [10.0, 220.0], &#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>0.6584064996820138</dd><dt><span>tau_vs_q_slope :</span></dt><dd>[-1.89886818]</dd><dt><span>msd_alpha :</span></dt><dd>[1.053259]</dd><dt><span>msd_effective_diffusion_coeff :</span></dt><dd>[0.64391314]</dd><dt><span>diffusion_coeff :</span></dt><dd>0.6338529924342144</dd><dt><span>diffusion_coeff_std :</span></dt><dd>0.04273736278332374</dd><dt><span>velocity :</span></dt><dd>1.0717563562746923</dd><dt><span>velocity_std :</span></dt><dd>0.38768117275140707</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>None</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 2.326e+05 9.894e+03 ... 0.5 0.5\n",
       "    theory           (lagtime, q) float64 10.0 152.4 361.6 ... 202.0 201.7 201.6\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9996 0.9872 ... 0.432 0.4966\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -190.0 2.585e+05 1.026e+04 ... 19.47 20.65\n",
       "    B                int32 190\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:      0.6584064996820138\n",
       "    tau_vs_q_slope:                 [-1.89886818]\n",
       "    msd_alpha:                      [1.053259]\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:               None\n",
       "    OverlapMethod:                  2"
      ]
     },
     "execution_count": 18,
     "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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o0SxZsqRFr7mlcnNzeeGFF7j88st55ZVXaG6M0OrVq+nfvz8DBw4E4IwzzuDll1+O6V7u3qLnx44dy//93/8xf/58Jk+e3OS1xx57LBkZGeTm5lJbWxv+YhL5/ixevJiRI0eSm5vLP/7xD1auXNloeY3d74c//CG5ublccMEF3HPPPU3G1FV0+YVlPqyuhpwcmDMHysshLw9yctr8n6BIR+uTmcnaKP9u+2RmtrrMDRs28I9//IO33noLM6O2thYz4+abbwYgM6LstLS08OO0tDS2b98efq7hFJyWTMnZaaedwn3aDWv20cpxd374wx/W++IRi5133rleGY2J9TW31MCBA1m2bBmLFi3iyiuv5Oijj+aaa65p9PzmknJTVqxYwZFHHhn1ua+//po1a9YwcOBAQjOJunXrxkEHHcScOXNYuXIlCxcubLTsyPcjIyMj/DsKvT9bt27l/PPPZ+nSpey///7MmjWryfnWkb+XSHV1daxatYoePXrwxRdf0Lt375heeyrr8jX08H92OTkwbVrgb9r2n6BIIszOzqZnWv2PdM+0NGZnZ7e6zMcee4zTTz+dtWvXsmbNGj766CP69+/Pq6++2qJyHn30Uerq6nj//feprKxk0KBBjZ6766678vXXX4cf9+vXL9yc/thjj4WPH3744ZSUlADw1ltv8a9//QuAQw89lNdee4333nsPgM2bN/Puu++2KN7DDz+cBQsWUFtby/r163n55Zc55JBDWlRGLDIyMsL9yp988gk9e/bktNNO49JLL63XtRES+d4MHjyYNWvWhF/nQw89xOjRo5u8n7vz+9//nv/85z87NOlDYLzE+eefzwknnMB3vvOdes9dcskl/OY3v2GPPfZo1WsNCSXvPffck02bNtX7nTb83Tfld7/7HUOGDGH+/PnMmDEj/D52ZV0+ocfjP0GRRJiWlcXcQYPom5mJAX0zM5k7aBDTsrJaXeb8+fM58cQT6x2bNGkSf/nLX1pUzqBBgxg9ejTHHnssd911F927d2/03ClTpnDLLbeEp5Ndeuml3HnnnXz/+9/n888/D5933nnnsWnTJoYNG8bNN98cTrh77bUX8+bNY+rUqQwbNoxDDz20xU38J554IsOGDWP48OEcccQR3Hzzzey9994tKiMWxcXFDBs2jGnTpvHmm29yyCGHkJeXx+zZs7n66qt3OD80wDAvLw935/777+eUU04hNzeXtLQ0zj333Kj3ueyyy8LT1pYsWcLixYvp1q1b+PkxY8YwdOhQDjnkEPr06cOf/vSnHcrIycnhjDPOaPNr3n333TnnnHPIzc3lhBNO4OCDD476+iIHOTb07rvvcs899zBnzhxGjRrF4Ycfzq9//es2x5bsrC3NNok2YsQIb8m808aUVFUxs7KSD6ur6ZOZyezs7Db9JyjSXlatWsWQIUMSHUabTJ8+nXHjxnHyyScnOhSRpBLt829my9x9RLTzk7IP3czGA+MHDBjQLuVNy8pSAhcRkaSWlAnd3RcCC0eMGKF5CiKd3Lx58xIdgkiX0OX70EVERFKBErqIiEgKUEIXERFJAUroIiIiKUAJXUSape1TW2/evHnstdde5OXlkZeXx+mnn94u5d511108+OCDrb6+vLycRYsWtUssTVm/fj0jR44kPz+fV155Je73i+ayyy4jJyeHyy67rN7xyH93qSApR7mLSMeK3D511qxZ7V7+hAkTmDBhAhBI6OPGjQtvMRovoe1Tr7vuunrH3R13Jy2t/eo7kydP5o477mi38rZv397oIjJNXbPTTt/+l19eXs7SpUs57rjjmj23LV588UUGDx7MAw88EPM1tbW1pKent8v9Af70pz+xfv36esv2Qv1/d6lANXSRFFNWVsaNN95IWVlZu5Sn7VNbv31qU377298ydOhQhg4dym233QYEdqUbOnRo+Jxbb701/AWqqKiIq666itGjR3P77bcza9Ysbr31ViCww9nYsWM56KCDGDVqVLglZfr06Vx88cWMGTOGyy+/PFzutm3buOaaa1iwYAF5eXksWLCAWbNmUVxczNFHH83pp5/OmjVrGDVqFAUFBRQUFPD666+H34OioiJOPvlkBg8ezLRp08Lryl9xxRUceOCBDBs2jEsvvZTy8nJ+8YtfsGjRovDqb/Pnzyc3N5ehQ4fWi2mXXXbhmmuuYeTIkZSVlbHLLrtw+eWXc9BBB3HUUUfxxhtvUFRURHZ2Ns8888wO76e7R/2dTZgwgW+++YaRI0fusCVt6N9d6L0677zzGDNmDNnZ2bz00kvMmDGDIUOGMH369PA15513HiNGjCAnJ4drr702fHzRokUMHjyYww47jJ/+9Kfh1qdvvvmGGTNmcPDBB5Ofn8/TTz8NwMqVK8MrAw4bNqze7natFvo2mox/DjroIBdJZW+//XaLzn/99de9R48enp6e7j169PDXX3+9zTE89NBDPmPGDHd3Lyws9GXLlrm7++LFi71Xr17+ySef+NatW33ffff1a665xt3db7vtNv/Zz37m7u5nnHGGH3PMMV5bW+vvvvuu77fffr5lyxZfvHixH3/88e7ufv/99/sFF1wQPv/RRx8N33/06NG+ZMkSd3dfv3699+3b193d58yZ42eeeaa7u1dUVHh6erovWbLE169f76NGjfJNmza5u/tNN93k11133Q6v69prr/VbbrnF3d0/+OADNzMvKytzd/fHHnvMjzrqKN++fbt/+umnvv/++/snn3wS82uOdP/99/uee+7pw4cP9+HDh/t9993nS5cu9aFDh/qmTZv866+/9gMPPNCXL1/uH3zwgefk5ISvveWWW/zaa68Nvw/nnXde1PiPOOIIf/fdd93d/Z///KePGTMm/F4ef/zxvn379qhxhd7zUHkFBQW+efNmd3f/5ptvfMuWLe7u/u6773ro/9vFixf7brvt5h999JHX1tb6oYce6q+88opv2LDBBw4c6HV1de7u/uWXX+5wn48//tj3339//+yzz7ympsbHjBnjTz75pLu7A75gwYJwPIAvWrTI3d1POOEE/+EPf+jbtm3z8vJyHz58+A6vp7Hfmbv7zjvvvMP5DWM744wzfPLkyV5XV+dPPfWU77rrrv6vf/3La2trvaCgwFesWOHu7hs2bHB39+3bt/vo0aO9oqLCt2zZ4r179/bKykp3d58yZUr43/aVV17pDz30UPg9OeCAA3zTpk1+4YUX+p///Gd3d6+urg6/75Giff6Bpd5ITlQNXSSFlJaWsm3bNmpra9m2bRulpaVtLlPbp7Z9+9TJkydTXl5OeXk5Z555Jq+++ionnngiO++8M7vssgsnnXRSTP3L0bYS3bRpE6+//jqnnHIKeXl5/OQnPwnv3Q5wyimnxNx8PWHChPA+5DU1NeE110855ZR64xoOOeQQevfuTVpaGnl5eaxZs4bddtuN7t27c/bZZ/PEE0/Qs2fPHcpfsmQJRUVF7LXXXuy0005MmzYtvOVreno6kyZNCp/brVu3eluvjh49Orwta7T3uanfWazGjx+PmZGbm0tWVlZ4jfycnJzwPR955BEKCgrIz89n5cqVvP3227zzzjtkZ2fTv39/AKZOnRou8/nnn+emm24iLy+PoqIitm7dyocffkhhYSE33HADv/nNb1i7dm34fW8L9aGLpJCioiK6devGtm3b6NatG0VFRW0qT9un7qg9tk9trPzI1wo7vt5oW4nW1dWx++67U15eHrXMxrYfbe7c3/3ud2RlZVFRUUFdXV29DXUi34P09PRwn/sbb7zBiy++yMMPP8wdd9zBP/7xj3rlN/W+du/evd4Xj4Zbrzb3PjdVdqwi79Hw97x9+3Y++OADbr31VpYsWcJ3vvMdpk+fztatW5u8t7vz+OOP77DD4JAhQxg5ciTPPfccxxxzDPfccw9HHHFEm+JXDV0khRQWFvLiiy/yq1/9ihdffJHCwsI2laftU+Ozferhhx/OU089xebNm/nmm2948sknGTVqFFlZWXz22Wds2LCB6urqqOMNGtptt93o378/jz76KBBIIBUVFc1e19xWpRs3bmSfffYhLS2Nhx56iNra2ibL27RpExs3buS4447jtttui/oFY+TIkbz00kt8/vnn1NbWMn/+/Ga3fI1VR2x5+9VXX7HzzjvTq1cvqqqq+Otf/woEtrKtrKwM1+Ij++qPOeYY/vCHP4ST/ooVKwCorKwkOzubn/70p0yYMCH877ctlNBFUkxhYSFXXnllm5M5aPvUeG2fWlBQwPTp0znkkEMYOXIkZ599Nvn5+WRkZIQHho0bN47BgwfHVF5JSQn33nsvw4cPJycnJzzwqiljxozh7bffDg+Ka+j888/ngQce4NBDD+Xdd99ttqb/9ddfM27cOIYNG8bo0aP53e9+t8M5++yzDzfeeCNjxoxh+PDhFBQUMHHixJheY3M6Ysvb4cOHk5+fT05ODjNmzOAHP/gBAD169OB///d/GTt2LIcddhhZWVn06tULgF/+8pfU1NQwbNgwhg4dyi9/+UsgkPSHDh1KXl4e77zzTrtMZ9T2qSKdmLZPFUkOmzZtYpdddsHdueCCCzjggAO46KKL2lRmS7dP7TQ1dDMrMrNXzOwuMytKdDwiIiKxuvvuu8nLyyMnJ4eNGzfyk5/8pMNjiOugODO7DxgHfObuQyOOjwVuB9KBe9z9JsCBTUB3YF084xKRjqPtU6UruOiii9pcI2+reNfQ5wFjIw+YWTrwR+BY4EBgqpkdCLzi7scClwPXISJA+4zeFZHk0prPfVwTuru/DHzR4PAhwHvuXunu24CHgYnuHpqr8SWQSSPMrNjMlprZ0vXr18clbpHOonv37mzYsEFJXaQLcXc2bNjQ5ODRaBIxD30/4KOIx+uAkWZ2EnAMsDvQ6KLH7j4XmAuBQXHxC1Mk8Xr37s26devQl1eRrqV79+707t27RdckIqFHW1HC3f0J4ImODkakM8vIyAivPiUi0pREjHJfB+wf8bg38ElLCjCz8WY2d+PGje0amIiISLJKREJfAhxgZv3NrBswBdhx65wmuPtCdy8OTdwXERHp6uKa0M1sPlAGDDKzdWZ2lrtvBy4E/g6sAh5x95XxjENERCTVxbUP3d2nNnJ8EbCoteWa2Xhg/IABA1pbhIiISErpNCvFtYSa3EVEROpLyoQuIiIi9SVlQtcodxERkfqSMqGryV1ERKS+pEzoIiIiUp8SuoiISApIyoSuPnQREZH6kjKhqw9dRESkvqRM6CIiIlKfErqIiEgKUEIXERFJAUmZ0DUoTkREpL6kTOgaFCciHa2kqop+ZWWklZbSr6yMkqqqRIckUk9cd1sTEUkmJVVVXPr003y6ZAl7H3wwt06cyLSsLEqqqihevZrNb74J5eWszcujuKYmfN3Myko+rK6mT2Yms7OzmZaVlcBXIV2VErqIdCklVVVRE3BJVRVnPf441RdfDDU1fPrQQ5y1fTtMmsTMyspAMr/kEqipgYwMNs+Zw8/M2OIeNdErqUtHU0IXkS6jqZr2zMpKqlesCCTsujqoqaF6xQpm5ufzYXU1lJfXe47ycjbk5MDKlTsk+pkZGUro0uGU0EWky2ispj0zIyOQtPPyICMj/Bx5eeGa/NoozwFRE/2HOTmJe5HSZSVlQjez8cD4AQMGJDoUEUkijdW0P8zJCSTtnByYMydwTl4eBI/Pzs6muKaGzRHP9czNpUdaGhuiJPo+mZmN9seLxEtSJnR3XwgsHDFixDmJjkVEOpfG+siBRmva4aS9ejWbc3IgWMPumZZW7/qZGRnh5D87OxuA4rq6HRL9cXvs0Wh/vJK6xEtSJnQRkWia6iOflpXVaE27XtJu5MvAtKysRpNxw0TfVH+8ErrEixK6iKSMpvrIIxNywwQcS9JuTLRrfrxqVaP98SLxooQuIimjqT7ykNYk7ZZqqj9eJF6ScqU4EZFo+mRmflszTkur10fekWZnZ9MzLS3QFz9tGuTk0DMtjeP22EOrzUncqIYuIkmjqQFvQJN95B0pWn/8cXvswQOffsrmujoA1lZXU7x6db3zRdoiKRO6pq2JdD3NDXiL/LuxPvKO1LBpv19ZWSCZr1wZ/rKxOSeHmZWVSujSLszdEx1Dq40YMcKXLl2a6DBEpAP0Kytj7fLl9Qa8MWcOfQsKWFNYmOjwmpVWWoo3WFWOOXOwnBzqiooSHZ4kCTNb5u4joj2nPnQRSQqNDnhLkpHjfTIzo8avgXLSXpTQRaRTaG570s4y4K21Zmdnk5mfXy/+zPz8Du/fl9SVlH3oIpJawv3jTQwY6ywD3lprWlYWTJrEpTvtpOVgJS7Uhy4iCdevrIy11dX1BoyRk0PfzMx6/ePNjXIXSXVN9aGrhi4icRVLEv4wlMwbDBhruGtZRywKI5Ks1IcukuSa63uO17Wxll+8ejVrly/HS0pYu3w5xatXR+8f78IDxuL9e5CuQTV0kSQWS99zs9c2Ma+7setibfZubm31kNnZ2ZyVn091xNrnXWXAWGt/DyINKaGLJEh79AfPrKxs9WIlsSbbhjG3JPnEsrZ6+NouOmCsNb8HkWiU0EUSoL1qZbH2PTd6bQzJNlJLk09T+483NC0ri2nFxVBcHPPrTwWt+T2IRJOUCV1Lv0qya69aWZ/MTNZG63suKIjt2hiTbUhLk0+yTzXrCK35PYhEk5QJ3d0XAgtHjBhxTqJjkdQXj6lS7VUra0vfc2uSbUuTT2daW72z0pceaS9JmdBFOkq8Biy1V62sLX3PrUm2rUk+mmrWNH3pkfaihWVEmhCvDUEaflEIJca5gwZ1+v/ItbiLSOJoYRmRVorXgKVkrpWpxi3SOSmhizQhngOWlBilKWoJkZZSQhdpggYsSSJosRlpDSV0kSYkc9O4JC8tNiOtoYQu0gw1jUtH02Iz0hranEVEpJPpk5kZ2EI2IwPS0rTYjMRENXQRkU5GYzekNZTQRUQ6GY3dkNZQQpekVFZWRmlpKUVFRRS2YYEXkc5KYzekpZTQJenMeu45rp80Ca+pwTIyuObxx5l1/PGJDktEJKE61aA4M9vZzJaZ2bhExyKdU0lVFTc8/TQeHP3rNTXc8PTTlFRVJTo0EZGEimtCN7P7zOwzM3urwfGxZrbazN4zsysinroceCSeMUnzSqqq2GfuXOycc9hn7txOlSxnVlZSM3x4vdG/NcOHM7OyMtGhiYgkVLyb3OcBdwAPhg6YWTrwR+CHwDpgiZk9A+wLvA10j3NM0oSSqirOevxxqi++GGpq+PShhzhr+3aYNKlT9Od9WF0NOTkQMfqXnJzAcZEUp+VgpSnNJnQzSwOGE0i4W4CV7h5Tlc3dXzazfg0OHwK85+6VwfIfBiYCuwA7AwcCW8xskbvXRYmnGCgG6NOnTyxhSAvMrKykesWKegtaVK9Ywcz8/E7xH0efzEzWhpJ6xCIbmp8rqS68HGxd4L/FtdXVFK9eDWg5WAlotMndzL5nZnOB94CbgKnA+cD/mdk/zezMYLJvqf2AjyIerwP2c/eZ7v5z4C/A3dGSOYC7z3X3Ee4+Yq+99mrF7aUpH1ZXR13QorPUgGdnZ9Mzrf4/u55paZqfKylvZmVlIJmvXAklJbByJZvr6tTdJGFN1dB/DdwJ/MQbbJpuZv8D/Aj4MfBAC+9pUY6Fy3f3eS0sT9pRn8xM1kZp0u4sNeDw/Fw1O0oX82F1dSCZR6zvzpw5Wg5WwhpN6O4+tYnnPgNua+U91wH7RzzuDXzSkgLMbDwwfsCAAa0MQRozOzs70KwX0aTd2WrAmp8rXVGfzEzWRlnfvU9BQaJDk06iVaPczWzvNtxzCXCAmfU3s27AFOCZlhTg7gvdvbhXr15tCEOimZaVxdxBg+ibmYkBfTMzmTtokBKoSILNzs4mMz+/XndYZn5+p/qyLYnV2lHu9wLNruRhZvOBImBPM1sHXOvu95rZhcDfgXTgPndf2co4JA5UAxbpfKZlZcGkSVy60058umQJex98MLdOnKjPqoRZg+7xpBDR5H7Ov//970SHIyIi0iHMbJm7j4j2XLNN7mbWJ9qf9g8zdmpyFxERqS+WJvfnCIxCNwKLvvQHVgMaWikiItJJNJvQ3T038rGZFQA/iVtEIiIi0mItHuXu7suBg+MQS8zMbLyZzd24cWMiwxAREek0Yln69eKIh2lAAbA+bhHFwN0XAgtHjBhxTiLjEBER6SxiqaHvGvEnk0Cf+sR4BiUiIs0rqaqiX1kZaaWl9Csr61Q7I0rHi6UP/bqOCKQltFKciHR14c1a3nwTystZm5dHcU0NoM1auqrWrhRX3N6BtISmrYlIVzezsjKQzC+5BO67Dy65hM1vvqnNWrqwViV0om+wIiIiHeTD6urABkoN1nbvLDsjSsdrVUJ39z+1dyAiIhK7PpmZUbc67iw7I0rHi2ktdzM7nsBCMt1Dx9z9+ngFJSIiTZudnU1xTQ2bI7Y67pmbq81aurBYpq3dBfQExgD3ACcDb8Q5ruZi0qA4EenSQgPfZmZk8GFODn0yM5mdna0BcV1Ys5uzmNm/3H1YxN+7AE+4+9EdE2LjRowY4UuXLk10GCIiIh2iTZuzAFuCf282s32BGgLruYuIiEgnEUsf+rNmtjtwC7CcwEYtd8czKBEREWmZWBaW+VXwx8fN7Fmgu7trEXUREZFOpNEmdzM7rOExd68OJXMz283MhsYzOBEREYlNUzX0SWZ2M/A3YBmBDVm6AwMIjHjvC1wS9wij0Ch3ERGR+poc5W5m3yEwTe0HwD4EBsitAp5z91c7JMImaJS7iMi3SqqqmFlZyYfV1ZrGlqKaGuXeZB+6u39JYACcBsGJiHRi2qxFYlopTkREOrd6m7XU1EBGBpvnzGFmRoYSehfR2s1ZRESkE9FmLaKELiKSArRZi8SylntPAqPZ+7j7OWZ2ADDI3Z+Ne3QiIhITbdYisfSh309g2lph8PE64FEgYQld09ZEROrTZi0Sy+YsS919hJmtcPf84LEKdx/eIRE2QdPWRESkK2nr5izbzKwHgTXcMbPvARplISIi0onE0uR+LYHV4vY3sxICi8xMj2dQIiIi0jKxbM7yf2a2HDgUMOBn7v553CMTERGRmDWa0M2soMGh/wT/7mNmfdx9efzCEhERkZZoqoY+J/h3d2AEUEGghj4M+H/ADruxiYiISGI0OijO3ce4+xhgLVDg7iPc/SAgH3ivowIUEZHWK6mqol9ZGWmlpfQrK6OkqirRIUmcxDIobrC7vxl64O5vmVle/EISEZH2EN6wpa4OgLXV1RSvXg1ow5ZUFMu0tVVmdo+ZFZnZaDO7m8AWqiIi0onNrKwMJ/OQzXV1zKysTFBEEk+x1NDPBM4DfhZ8/DJwZ9wiEhGRdhHemGXlyvBysOTkaMOWFBXLtLWtwO+CfzoFLf0qItK8PpmZrF2+vN6WqsyZQ5+ChpOYJBU02+RuZh+YWWXDPx0RXGPcfaG7F/fq1SuRYYiIdGqzs7PJqKiot6VqRkWFNmxJUbE0uUeuGdsdOAX4bnzCERGR9jItK4t/T5zI9Q8+iNfUYBkZXDVxogbEpahmN2eJepHZq+6e8Hno2pxFRKR5ZWVllJaWUlRURGFhYfMXSKfV1OYsseyHHtnZkkagxr5rO8UmIiJxVlhYqETeBcTS5D4n4uftwAfAqfEJR0RERFojloR+lrvXGwRnZv3jFI+IiIi0QiwLyzwW4zERERFJkKZ2WxsM5AC9zOykiKd2IzDaXURERDqJpprcBwHjgN2B8RHHvwbOiWNMIiISJyVVVcysrOTD6mr6ZGYyOztb09hSRKMJ3d2fBp42s0J3L+vAmEREJA60WUtqa6rJ/RfufjPwIzOb2vB5d/9pXCMTEZF2Fd6sJWJt9805OcysrFRCTwFNNbmHdlTTyi0iIingw+rqQDJvsLb7hzk5iQ5N2kFTTe4Lg38/0HHhiIhIvPTJzGRteXm9td0pL9dmLSmiqSb3hUCj68K6+4T2DMTMhhDYonVP4EV31xatIiLtaHZ2Nmfl51OdkRGuoWfm52uzlhTRVJP7rW0t3MzuIzBS/jN3HxpxfCxwO5AO3OPuN7n7KuBcM0sD7m7rvUVEpL5pWVkwaRKX7rQTny5Zwt4HH8yt2qwlZcS0OYuZdQMGE6ixr3b3bTEVbnY4sAl4MJTQzSwdeBf4IbAOWAJMdfe3zWwCcAVwh7v/pbnytTmLiIh0JU1tzhLLfujHA+8DvwfuAN4zs2NjubG7vwx80eDwIcB77l4Z/GLwMDAxeP4z7v59YFos5YuIiEhArJuzjHH39wDM7HvAc8BfW3nP/YCPIh6vA0aaWRFwEpAJLGrsYjMrBooB+vTp08oQREREi8ykllgS+mehZB5UCXzWhntalGPu7qVAaXMXu/tcYC4EmtzbEIeISJelRWZSTyybs6w0s0VmNt3MzgAWAkvM7KQGa7zHah2wf8Tj3sAnLSnAzMab2dyNGze24vYiIhJeZCbC5ro6ZlZWNnKFdHaxJPTuQBUwGigC1gPfJbC++7hW3HMJcICZ9Q8OtpsCPNOSAtx9obsX9+rVqxW3FxGRD6urAz+sXAklJYG/I49L0mm2yd3dz2xt4WY2n8CXgD3NbB1wrbvfa2YXAn8nMG3tPndf2dp7iIhIy/XJzGTt8uU7rBqnRWaSV7MJ3cz6A/8f0C/y/FgWlnH3HdaADx5fRBMD32KIaTwwfsCAAa0tQkSkS5udnc2Z999PTcSqcRkVFcw++eREhyatFEuT+1PAGuAPBEa8h/4kjJrcRUTaZlpWFldNnIhlZEBaGpaRwbgjj2RmZSVppaX0KyujpKoq0WFKC8Qyyn2ru/8+7pGIiEiHmnX88RyzeDGlpaVUDxvGLbvuyuZgH7pGvSefZleKM7MfAQcAzwPh0RLuvjy+oTVPK8WJiLSPfmVlrI0yIK5vZiZrCgsTEJFE09RKcbHU0HOBHwNHAKE5Dh58nBDqQxcRaV/1Rr0H90onJ0ej3pNILAn9RCA71vXbO0Jwa9eFI0aMOCfRsYiIpAKNek9+sQyKqwB2j3McIiKSQLOzs8moqKi3V3pGRYW2Vk0isST0LOAdM/u7mT0T+hPvwEREpONEG/V+lbZWTSqxNLlfG/coWkh96CIi7S9y1HtRURGV2dn0KyvT5i1JIqb90OtdYPYD4EfufkF8QoqdRrmLiMRHw81bAHqmpTF30CAl9QRq037owQLyzOxmM1sD/BpY1Y7xiYhIJ6PNW5JPo03uZjaQwMYpU4ENwAICNfoxHRSbiIgkiKaxJZ+m+tDfAV4Bxof2QzezizokqmaoD11EJL40jS35NNXkPgn4FFhsZneb2ZGAdUxYTdNa7iIi8aVpbMmn0YTu7k+6+2RgMFAKXARkmdmdZnZ0B8UnIiIJoGlsyadFo9zN7LvAKcBkd0/Y0q8hGuUuIhJfZWVl4WlshVrTPeGaGuXe4mlrnYkSuohIxyipqmJmZaXmpCdYWzdnERGRLqzhnHRtrdo5xTQPvbMxs/FmNnfjxo2JDkVEJOVpTnpySMqErlHuIiIdp96c9JKSwN+Rx6VTUJO7iIg0SXPSk0NS1tBFRKTjaE56clBCFxGRJjWck05GBj0KCvjxqlX0KyujpKoq0SEKanIXEZEYhLZWve3ZZ3l6v/34avBgQCPeOxPNQxcRkZj1KytjbZTBcOlAHWiOepy1eftUERERaHzEey3gfFtjVzN8x0vKhK556CIiidEnMzOQxC+5BO67L/B3MKmHaI56YiRlQtc8dBGRxIg24p3ycs1R7wSSMqGLiEhiRBvxzm677VBjd9AI+A6mUe4iItIioRHvpaWlVA8bxg1PP01Nwxp7To5GwHcwJXQREWmxwsLCetupXv/gg3hoFbm8vPDxUH+6Enr8KaGLiEibhGrs37/vvkAyz8mp97z60zuGErqIiLRZYWEhfQlMW2PlykCzezC598nMTHB0XYMGxYmISLuYnZ1N5ttv1xsgl/n221rzvYMooYuISLuYlpXFxI8/rjelbeLHH6v/vIMooYuISLv5+bhx9MjMJD09nW7duvFy376klZZqClsHSMo+dDMbD4wfMGBAokMREZEIhYWFvPjii+FNXD4dOBDQJi4dQZuziIhIu2tsE5e+mZmsiZjuJi2jzVlERKRDNTZVTVPY4kcJXURE2l14qlqDNd41hS1+lNBFRKTdRZvCxsqVrK2u1gC5OFFCFxGRdhdtChvl5YD2TI8XJXQREYmL0BS28K5sUdZ4l/ajhC4iInERmsLGjBkwZ07goPZMj5uknIcuIiLJIbzG+/LlgX700I5sc+bQp6Ag0eGlFNXQRUQkrmZnZ5NRUVGvPz2jokJrvLczJXQREYmraVlZXDVxIpaREe5P71FQwI9XrdKI93akJncREYm70J7poSVhvxo8GNCSsO1JCV1ERDpEYWEhU4HqBoPhQiPeldDbRk3uIiLSYbQkbPwooYuISIdpbEnYNFBfeht1qoRuZieY2d1m9rSZHZ3oeEREpH01tiRsLWj1uDaKe0I3s/vM7DMze6vB8bFmttrM3jOzKwDc/Sl3PweYDkyOd2wiItKxmloSVqvHtU1H1NDnAWMjD5hZOvBH4FjgQGCqmR0YccrVwedFRCTF/HzcuMDiMlGWhFVfeuvFfZS7u79sZv0aHD4EeM/dKwHM7GFgopmtAm4C/uruy6OVZ2bFQDFAnz594ha3iIjER2FhIXv//vd8umQJ7LZbuIZOTo62V22DRPWh7wd8FPF4XfDY/wccBZxsZudGu9Dd57r7CHcfsddee8U/UhERaXe3TpxIZn4+/PGP2l61nSQqoVuUY+7uv3f3g9z9XHe/q8OjEhGRDqHtVdtfohL6OmD/iMe9gU9ivdjMxpvZ3I0bN7Z7YCIi0jG0vWr7SlRCXwIcYGb9zawbMAV4JtaL3X2huxf36tUrbgGKiEh87bC9ak5OvefV/N4yHTFtbT5QBgwys3Vmdpa7bwcuBP4OrAIecfeV8Y5FREQ6l8LCQvrOmBFI5g0Wm4FAUj9t1Sr2fPVVJfZmdMQo96mNHF8ELGpNmWY2Hhg/YMCAtoQmIiKdwOzsbM56/HGqG+yXHllj37B9uzZxaUanWikuVmpyFxFJHVEHyP397zvU1tWv3jRz90TH0GojRozwpUuXJjoMERFpo7KyMo488ki2VFdDenrgYG1t4OexY+GYY8I19nSgFuibmcns7OwuVWM3s2XuPiLac0lZQ9codxGR1BIaIHfqFVeQfuyxgWQeqq0/+2x4njoEkjloeltDSZnQ1eQuIpJ6CgsLWTB7NlcXFwf60S24ZIl7vXnqkdQM/62kTOgiIpK6Zh1/PK8vXswRP/5xo2u+R1pbXa1aOupDFxGRTqysrIzjHnyQ/w4dGjhQXh5Y//2rrwIJPtiv3jMtjbmDBqV8f3pTfehJmdAjpq2d8+9//zvR4YiISByVVFUFprVdfDFs2xZogjeDbt12mN6W6gPlUm5QnPrQRUS6jnrT2kKVUPdAcp83b4eFaLrqQLmkTOgiItK1hNZ9T0sLpi2zQFJftqzeCHgIDJQ7Y9WqLpfU475SnIiISFuFprWVlpbyXkYG8x55hLqlS+uPgI9oeq+FLreynBK6iIgkhcLCQgoLCwHYf8gQrp80CQ8tFbvbboGV5SIGyoWmtHWVhK5BcSIikpTKysq47dlnebK6mpo77gjU1KOsLAepM1hOg+JERCTlhBaiOTEzs/468A1WloOuMVguKRO6iIhIyM/HjaNbt27NriyX6qvKKaGLiEhSKywspPQf/6i/slx6OlRVwcKF9XZtS+VV5ZKyDz1EK8WJiEikfebO5dNnn4W//Q22b4+6CI0BTnL2q6dcH7p2WxMRkWhunTiRjL33DuzWFrkITUQTfKgau7a6mtNWrWLPV19NiVp7UiZ0DYoTEZFopmVlcdXEiVjkbm3NbO6yYfv2lBgwp3noIiKSUmYdfzzHLF5MaWkpt/73v3zxxReBeeqhQXIR09lCQqvLQfIuRKM+dBERSVn1NnYJLUJzwQWB3dqi7NpmwBG77857W7bwYXU1fTpZP3tTfeiqoYuISMqalpXFMx9/zCOheerbtsHttwd+jjJgzoEX//vf8PWh+euhsjqzpOxDFxERiVVoY5f09HTS09O/TebQ6K5tkZJlsxfV0EVEJKVFbuyyxx578POf/5yt1dV4Xd23u7YtXQorVsDPfgbjxweSe3l5uDk+GTZ7UR+6iIh0KWVlZeFd2+5bsCCQzEPS0wNJ/Y9//LbPPdgcHylRc9ib6kNPyoSuzVlERKQ9nLRgAU9OmxaYtw6BGvu++8Inn3zbx37QQTB9etTR8dCxyT3lFpbRPHQREWkPT0yezIybbgrUzEPN7x9//G0yd4dly3bY7CVSZ9n4JSkTuoiISHu599JLef2VV8gdPfrbxWhCNfVQUg8NnGuwNnxIZxg4p4QuIiJdXmFhIX+64QZ6dO9Oeno6Pbp3Z8bPfx6Y0hY5cO63v4V7741aYw8NnEtUUk/KPvQQDYoTEZH2FBowV1RURGFhIbOee45fXXcddUuXfjvVDQJJ/vvfhyFD6i1ME7JHsAn/i+3b23VxmpQbFBeihC4iIvE267nnuH7SJHzbtvpJHXZYmKYxPdPSmDtoUJuTesoNihMREekos44/ntcWL+aG2bM56Nhjv+1nh5gWpoFAH/vMysq4xqkauoiISIzKyso48sgjqa6upi5yYZpoNfUGi9MYUFdU1Kb7ay13ERGRdtBw1bmf3H13YFpbqKY+f36gX3233XZYnKZPQUFcY1NCFxERaYHCwkIKCwsBuBb4tKIikLjd4bXX4PXXAzX2urrABTU1UF7O7JNPjmtcSdmHbmbjzWzuxo0bEx2KiIh0YbdOnEh6tH71UDIHSE9nt4KCuK8kl5QJXSvFiYhIZzAtK4uri4ux0Hx1qJ/czUg/9lj+96ST4h6LmtxFRETaYNbxx3PM4sXhfvUFr73GP+bPh9paLCODq4uLO2add41yFxERaV8NF6hpLxrlLiIi0oEiB851lKTsQxcREZH6lNBFRERSgBK6iIhIClBCFxERSQFK6CIiIilACV1ERCQFKKGLiIikACV0ERGRFJDUK8WZ2XpgbTsV1wvozLu9dHR88bpfe5XblnJac21LrmnJuXsCn7cwllSlz2DH3K8rfAZbcn6yfQb7uvteUZ9xd/0JfKmZm+gYOlN88bpfe5XblnJac21LrmnhuUs78vfamf/oM9gx9+sKn8GWnJ9Kn0E1uX9rYaIDaEZHxxev+7VXuW0ppzXXtuSazv5vqbPq7O+bPoPtV068P4OtvUdSS+omd5FkZ2ZLvZGNFkQk/lLpM6gaukhizU10ACJdXMp8BlVDFxERSQGqoYuIiKQAJXQREZEUoIQuIiKSApTQRUREUoASukgnYWY7m9kDZna3mU1LdDwiXZGZZZvZvWb2WKJjaSkldJE4MrP7zOwzM3urwfGxZrbazN4zsyuCh08CHnP3c4AJHR6sSIpqyefQ3Svd/azERNo2Sugi8TUPGBt5wMzSgT8CxwIHAlPN7ECgN/BR8LTaDoxRJNXNI/bPYdJSQheJI3d/GfiiweFDgPeCNYFtwMPARGAdgaQO+myKtJsWfg6Tlv7TEOl4+/FtTRwCiXw/4AlgkpndSRdch1qkg0X9HJrZHmZ2F5BvZlcmJrTW2SnRAYh0QRblmLv7N8CZHR2MSBfV2OdwA3BuRwfTHlRDF+l464D9Ix73Bj5JUCwiXVXKfQ6V0EU63hLgADPrb2bdgCnAMwmOSaSrSbnPoRK6SByZ2XygDBhkZuvM7Cx33w5cCPwdWAU84u4rExmnSCrrKp9D7bYmIiKSAlRDFxERSQFK6CIiIilACV1ERCQFKKGLiIikACV0ERGRFKCELiIikgKU0EVERFKAErqIiEgKUEIXkQ5jZtlmdq+ZPZboWERSjRK6iABgZj3M7CUzS4/XPYJ7T5/V4L7dzOxlM9PujyJtoA+QiITMAJ5w99q2FmRmucCNDct3988anuvu28zsRWAyUNLWe4t0VUroIl2Amc0ETgc+AtYDy9z91ganTQN+FDy/H/Csuw8NPr4U2AWYB/wNeBU4FKgA7geuA/4HmObub7j7m8C4FoT4FIEvAEroIq2kJneRFGdmBxHYGjIfOAk4OMo53YBsd18TQ5EDgNuBYcBgAl8CDgMuBa5qJpY9zOwuIN/Mrox46q1ocYlI7FRDF0l9o4An3X0zgJlF2/N5T+C/MZb3QbAGjpmtBF50dzezN4F+TV3o7huAc6McrzWzbWa2q7t/HWMcIhJBNXSRrqG5fZK3AN0bHLOInzMifq6O+Lku4nEdbaskZAJb23C9SJemhC6S+l4GTgyOYt8VGN/wBHf/Ekg3s8ik3tfM9jKzNOBwIG6j381sD2C9u9fE6x4iqU4JXSTFuftyYAFQDjwOvNLIqc8T6AsP2QA8CCwj0Md9OvFL6mOARXEqW6RLMPfmWuJEJJWY2SxgU8NR7maWD1zs7j9uOMq9A2J6ArjS3Vd3xP1EUpFq6CICgLuvABbHc2GZaIIj7J9SMhdpG9XQRUREUoBq6CIiIilACV1ERCQFKKGLiIikACV0ERGRFKCELiIikgKU0EVERFKAErqIiEgKUEIXERFJAf8/cz3myrRnkFwAAAAASUVORK5CYII=\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=False, show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It doesn't appear that that helped much. In fact, we see that the $\\tau$ values appear to be \"hitting the rails\" of the bounds we imposed on the parameter. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we plot $\\tau$ vs $q$ for the last two fittings. We see that updating the initial guess didn't improve things. So the reason that the $\\tau$ found for the high wavevectors is off does not seem to be related to the fitting algorithm using a poor initial guess. Perhaps at the high wavevectors the dynamics are too fast for us detect with the camera frame rate we used? Below we also plot a horizontal line corresponding the time interval between frames. We see that the fits are off even well above our temporal resolution. So it is also not the case that the fits at some of the high wavevectors is due to poor temporal resolution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x229a892e760>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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"
    }
   ],
   "source": [
    "################################################################################################\n",
    "# Plotting the characteristic decay time tau vs wavevector q determined from the last two fits #\n",
    "################################################################################################\n",
    "\n",
    "plt.figure(figsize=(10,10/1.618))\n",
    "plt.loglog(fit01.q, fit01.parameters.loc['Tau'], 'ro', label=\"Not updating tau's initial guess\")\n",
    "plt.loglog(fit02.q, fit02.parameters.loc['Tau'], 'bo', label=\"Updating tau's initial guess based on estimated diffusion coeff\")\n",
    "plt.axhline(y = 1.0/fit01.frame_rate)\n",
    "plt.legend(loc=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our third attemp at fitting the data - tightening the bounds on the stretching exponent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try one more thing with this fitting. Let's think about the stretching exponent. For normal diffusive motion, we expect the intermediate scattering function to be a simple exponential: $f(q,\\Delta t) = \\exp(-\\Delta t / \\tau)$. But sometimes when the dynamics are heterogeneous or subdiffusion is present, we see that a stretched exponential works better. That is, for the ISF we use: $f(q,\\Delta t) = \\exp(-\\Delta t / \\tau)^s$ where $s$ is the stretching exponent. \n",
    "\n",
    "For this example data, we just have movie of silica colloidal particles diffusing in water. The dynamics should be diffusive. So we could try constraining the stretching exponent to be around to 1 as we expect (notice how the stretching exponent is between 0.6 and 0.7 for the high $q$ values in the previous fits)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter 'StretchingExp' set to 1.\n",
      "Parameter 'StretchingExp' lower limit set to 0.85.\n",
      "Parameter 'StretchingExp' upper limit set to 1.\n"
     ]
    }
   ],
   "source": [
    "#We update the intial guess for the background and the lower and upper bounds\n",
    "ddm_fit.set_parameter_initial_guess('StretchingExp', 1)\n",
    "ddm_fit.set_parameter_bounds('StretchingExp', [0.85, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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 'fit03'.\n"
     ]
    }
   ],
   "source": [
    "fit03 = ddm_fit.fit(name_fit = 'fit03', display_table=False, use_A_from_images_as_guess=True, update_limits_on_A=True,\n",
    "                   update_tau_based_on_estimated_diffcoeff=True, estimated_diffcoeff=0.6, \n",
    "                   update_limits_on_tau=True, updated_lims_on_tau_fraction=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</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 2.326e+05 ... 0.85 0.85\n",
       "    theory           (lagtime, q) float64 10.0 78.85 304.9 ... 200.8 200.6 200.4\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9996 0.9872 ... 0.432 0.4966\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -190.0 2.585e+05 1.026e+04 ... 19.47 20.65\n",
       "    B                int32 190\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:      0.6779463827208101\n",
       "    tau_vs_q_slope:                 [-1.84532034]\n",
       "    msd_alpha:                      [1.08382266]\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:               None\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-59032ed1-58f8-464f-b9de-47af87c3adf2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59032ed1-58f8-464f-b9de-47af87c3adf2' 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-205c3d45-58dc-4bf5-b406-a86a98e00686' class='xr-section-summary-in' type='checkbox'  checked><label for='section-205c3d45-58dc-4bf5-b406-a86a98e00686' 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-5566f43b-d646-4e7b-8adf-119d7adc5aad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5566f43b-d646-4e7b-8adf-119d7adc5aad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aa53ab7-d939-4f9b-87a1-8f0279f4c5e4' class='xr-var-data-in' type='checkbox'><label for='data-3aa53ab7-d939-4f9b-87a1-8f0279f4c5e4' 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-78c6895a-fb0c-4300-b2ad-8c666bd75554' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78c6895a-fb0c-4300-b2ad-8c666bd75554' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-074c6144-cfd3-4aa8-b1b2-43e20b12b760' class='xr-var-data-in' type='checkbox'><label for='data-074c6144-cfd3-4aa8-b1b2-43e20b12b760' 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-af57747d-cc9f-477d-8fba-cf9710c60366' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af57747d-cc9f-477d-8fba-cf9710c60366' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29269d2a-c3b5-42fd-8814-f41187bf8279' class='xr-var-data-in' type='checkbox'><label for='data-29269d2a-c3b5-42fd-8814-f41187bf8279' 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-979b3b07-7688-4724-9bc1-397e09436ce9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-979b3b07-7688-4724-9bc1-397e09436ce9' 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 2.326e+05 ... 0.85 0.85</div><input id='attrs-bf2a0a96-ecef-44cc-83e1-9a3860981b48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf2a0a96-ecef-44cc-83e1-9a3860981b48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf7f2caa-a59c-4ee3-82c0-cf15e8393f3c' class='xr-var-data-in' type='checkbox'><label for='data-cf7f2caa-a59c-4ee3-82c0-cf15e8393f3c' 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, 2.32643716e+05, 9.23421116e+03, 1.03948122e+04,\n",
       "        1.60003202e+04, 1.87741628e+04, 2.01262090e+04, 2.02699354e+04,\n",
       "        1.93938140e+04, 2.14281346e+04, 2.33743043e+04, 2.75572551e+04,\n",
       "        3.35948001e+04, 3.99229074e+04, 4.20968490e+04, 4.23743650e+04,\n",
       "        4.11720321e+04, 3.60354402e+04, 2.86662660e+04, 2.08140281e+04,\n",
       "        1.44536527e+04, 9.32711827e+03, 5.66146702e+03, 3.47725293e+03,\n",
       "        2.06716941e+03, 1.22311562e+03, 8.76927279e+02, 6.90124549e+02,\n",
       "        5.11753536e+02, 3.71791825e+02, 2.92071418e+02, 2.62261447e+02,\n",
       "        2.25300404e+02, 1.74720965e+02, 1.45416169e+02, 1.10732817e+02,\n",
       "        9.10226873e+01, 7.62810056e+01, 6.92047238e+01, 6.19430712e+01,\n",
       "        5.66532390e+01, 5.56754998e+01, 4.99486147e+01, 4.76357944e+01,\n",
       "        4.62577150e+01, 4.13242391e+01, 4.33182867e+01, 3.73441604e+01,\n",
       "        3.70692807e+01, 3.68835355e+01, 3.34116359e+01, 3.23466041e+01,\n",
       "        3.04356863e+01, 3.22571323e+01, 2.99153772e+01, 2.80025225e+01,\n",
       "        2.81364364e+01, 2.61955246e+01, 2.64803840e+01, 2.41601609e+01,\n",
       "        2.22755742e+01, 2.26887470e+01, 2.14222352e+01, 2.27190297e+01],\n",
       "       [1.00000000e+01, 8.10157916e+01, 5.93151055e+00, 3.19823267e+00,\n",
       "        2.35940287e+00, 1.42436451e+00, 1.03553609e+00, 7.42218947e-01,\n",
       "        5.52015439e-01, 4.65091456e-01, 3.96220605e-01, 3.24285752e-01,\n",
       "        2.94651560e-01, 2.56813354e-01, 2.18980700e-01, 1.97344828e-01,\n",
       "...\n",
       "        1.74181629e+02, 1.73699760e+02, 1.75872232e+02, 1.75672665e+02,\n",
       "        1.76717670e+02, 1.74420790e+02, 1.75377440e+02, 1.76123228e+02,\n",
       "        1.75933358e+02, 1.76915945e+02, 1.76883833e+02, 1.78261549e+02,\n",
       "        1.78758553e+02, 1.78116378e+02, 1.79162804e+02, 1.77708018e+02],\n",
       "       [1.00000000e+00, 1.00000000e+00, 8.50000000e-01, 8.50000000e-01,\n",
       "        8.50000000e-01, 9.09132557e-01, 9.11750622e-01, 9.52912603e-01,\n",
       "        9.57434031e-01, 9.79312823e-01, 9.69590609e-01, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "        1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 9.75980335e-01,\n",
       "        9.19591679e-01, 9.11770112e-01, 8.60156233e-01, 8.59200975e-01,\n",
       "        8.50000000e-01, 8.50000000e-01, 8.50000000e-01, 8.50000000e-01,\n",
       "        8.50000000e-01, 8.50000000e-01, 8.50000000e-01, 8.50000000e-01,\n",
       "        8.50000000e-01, 8.50000000e-01, 8.50000000e-01, 8.50000000e-01,\n",
       "        8.50000000e-01, 8.50000000e-01, 8.50000000e-01, 8.50000000e-01,\n",
       "        8.50000000e-01, 8.50000000e-01, 8.50000000e-01, 8.50000000e-01,\n",
       "        8.50000000e-01, 8.50000000e-01, 8.50000000e-01, 8.50000000e-01,\n",
       "        8.50000000e-01, 8.50000000e-01, 8.50000000e-01, 8.50000000e-01,\n",
       "        8.50000000e-01, 8.50000000e-01, 8.50000000e-01, 8.50000000e-01,\n",
       "        8.50000000e-01, 8.50000000e-01, 8.50000000e-01, 8.50000000e-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'>10.0 78.85 304.9 ... 200.6 200.4</div><input id='attrs-2700783d-0e46-4d4b-9921-5ab6db2a62f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2700783d-0e46-4d4b-9921-5ab6db2a62f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-306c19cd-1c8b-4cc9-bb63-78d4e3d8c4be' class='xr-var-data-in' type='checkbox'><label for='data-306c19cd-1c8b-4cc9-bb63-78d4e3d8c4be' 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([[1.00023952e+01, 7.88527542e+01, 3.04935382e+02, ...,\n",
       "        1.93142984e+02, 1.93567415e+02, 1.93210146e+02],\n",
       "       [1.00047847e+01, 1.47685131e+02, 3.72530736e+02, ...,\n",
       "        1.97598835e+02, 1.97719290e+02, 1.97551808e+02],\n",
       "       [1.00071684e+01, 2.16497136e+02, 4.34561426e+02, ...,\n",
       "        1.99371895e+02, 1.99332618e+02, 1.99198835e+02],\n",
       "       ...,\n",
       "       [1.06673683e+01, 2.95649066e+04, 7.75262515e+03, ...,\n",
       "        2.00805125e+02, 2.00585039e+02, 2.00427047e+02],\n",
       "       [1.07160601e+01, 3.34939686e+04, 8.06747230e+03, ...,\n",
       "        2.00805125e+02, 2.00585039e+02, 2.00427047e+02],\n",
       "       [1.07622299e+01, 3.78089588e+04, 8.34673758e+03, ...,\n",
       "        2.00805125e+02, 2.00585039e+02, 2.00427047e+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 0.9996 0.9872 ... 0.432 0.4966</div><input id='attrs-8fdc5275-da7a-4a6e-bc64-fcc06dc4aacb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8fdc5275-da7a-4a6e-bc64-fcc06dc4aacb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c638b3e-9689-435e-a69d-e93756a77205' class='xr-var-data-in' type='checkbox'><label for='data-6c638b3e-9689-435e-a69d-e93756a77205' 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.        ,  0.99959694,  0.9871915 , ...,  0.85778634,\n",
       "         0.82204821,  0.86604236],\n",
       "       [ 0.        ,  0.99906358,  0.97066658, ...,  0.76497186,\n",
       "         0.7397658 ,  0.78674034],\n",
       "       [ 0.        ,  0.99866393,  0.95809464, ...,  0.70299025,\n",
       "         0.6810774 ,  0.71946869],\n",
       "       ...,\n",
       "       [ 0.        ,  0.98539009,  0.4540191 , ...,  0.27275862,\n",
       "        -0.09683656, -0.09489651],\n",
       "       [ 0.        ,  0.98461401,  0.4867419 , ...,  0.32901286,\n",
       "         0.21002625,  0.13879852],\n",
       "       [ 0.        ,  0.98635641,  0.42712011, ...,  0.13873015,\n",
       "         0.43195264,  0.49663365]])</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-db5d43d0-a76e-408c-96f9-5c44a25dd695' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db5d43d0-a76e-408c-96f9-5c44a25dd695' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-366b6d60-5a27-420e-8c3d-e8217e2bcaa8' class='xr-var-data-in' type='checkbox'><label for='data-366b6d60-5a27-420e-8c3d-e8217e2bcaa8' 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'>-190.0 2.585e+05 ... 19.47 20.65</div><input id='attrs-7bf5cfee-290b-4783-b0ca-eca8cc250487' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bf5cfee-290b-4783-b0ca-eca8cc250487' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12250277-1efc-4428-ae96-1519d2bda41d' class='xr-var-data-in' type='checkbox'><label for='data-12250277-1efc-4428-ae96-1519d2bda41d' 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([-1.90000000e+02,  2.58493018e+05,  1.02602346e+04,  1.15497914e+04,\n",
       "        1.68195629e+04,  1.91789308e+04,  1.97920750e+04,  2.04739243e+04,\n",
       "        1.98628174e+04,  2.14217457e+04,  2.30787136e+04,  2.75439244e+04,\n",
       "        3.36077443e+04,  3.95384367e+04,  4.21701356e+04,  4.26722045e+04,\n",
       "        4.10361202e+04,  3.58616144e+04,  2.83223278e+04,  2.06264656e+04,\n",
       "        1.43607517e+04,  9.18098831e+03,  5.57030056e+03,  3.36316630e+03,\n",
       "        1.93279536e+03,  1.17701023e+03,  8.05816138e+02,  6.46477276e+02,\n",
       "        4.86766556e+02,  3.54100454e+02,  2.81344652e+02,  2.59050811e+02,\n",
       "        2.16860258e+02,  1.66873759e+02,  1.40019053e+02,  1.08582782e+02,\n",
       "        9.24025916e+01,  7.62421017e+01,  6.95296420e+01,  6.63192247e+01,\n",
       "        5.82623495e+01,  5.81042089e+01,  5.24400299e+01,  4.81539029e+01,\n",
       "        4.50185732e+01,  4.06202012e+01,  4.05361388e+01,  3.51453041e+01,\n",
       "        3.36993461e+01,  3.35304868e+01,  3.04943557e+01,  2.94060037e+01,\n",
       "        2.76688057e+01,  2.93246657e+01,  2.71957975e+01,  2.54568387e+01,\n",
       "        2.55785786e+01,  2.38141133e+01,  2.40730764e+01,  2.19637827e+01,\n",
       "        2.02505220e+01,  2.06261336e+01,  1.94747592e+01,  2.06536633e+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'>int32</div><div class='xr-var-preview xr-preview'>190</div><input id='attrs-b10d32b7-3a03-4e5e-a133-2ccceede1c73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b10d32b7-3a03-4e5e-a133-2ccceede1c73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-054c5beb-23d5-4086-acf7-9658146c53f2' class='xr-var-data-in' type='checkbox'><label for='data-054c5beb-23d5-4086-acf7-9658146c53f2' 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(190)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50ae7a65-fa80-4d22-b5b2-eedfe428229d' class='xr-section-summary-in' type='checkbox'  ><label for='section-50ae7a65-fa80-4d22-b5b2-eedfe428229d' 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;: 190.0, &#x27;limits&#x27;: [10.0, 220.0], &#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;: [0.85, 1.0], &#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>0.6779463827208101</dd><dt><span>tau_vs_q_slope :</span></dt><dd>[-1.84532034]</dd><dt><span>msd_alpha :</span></dt><dd>[1.08382266]</dd><dt><span>msd_effective_diffusion_coeff :</span></dt><dd>[0.6562143]</dd><dt><span>diffusion_coeff :</span></dt><dd>0.6463363023540297</dd><dt><span>diffusion_coeff_std :</span></dt><dd>0.07295135160177549</dd><dt><span>velocity :</span></dt><dd>1.0725254218902054</dd><dt><span>velocity_std :</span></dt><dd>0.35877065376614453</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>None</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 2.326e+05 ... 0.85 0.85\n",
       "    theory           (lagtime, q) float64 10.0 78.85 304.9 ... 200.8 200.6 200.4\n",
       "    isf_data         (lagtime, q) float64 0.0 0.9996 0.9872 ... 0.432 0.4966\n",
       "    ddm_matrix_data  (lagtime, q) float64 0.0 294.2 321.4 ... 207.8 201.1 200.4\n",
       "    A                (q) float64 -190.0 2.585e+05 1.026e+04 ... 19.47 20.65\n",
       "    B                int32 190\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:      0.6779463827208101\n",
       "    tau_vs_q_slope:                 [-1.84532034]\n",
       "    msd_alpha:                      [1.08382266]\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:               None\n",
       "    OverlapMethod:                  2"
      ]
     },
     "execution_count": 22,
     "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(fit03, q_indices=[3,6,9,22], forced_qs=[4,16], use_new_tau=False, show=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x229a6934640>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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"
    }
   ],
   "source": [
    "##################################################################################################\n",
    "# Plotting the characteristic decay time tau vs wavevector q determined from the last three fits #\n",
    "##################################################################################################\n",
    "\n",
    "plt.figure(figsize=(10,10/1.618))\n",
    "plt.loglog(fit01.q, fit01.parameters.loc['Tau'], 'ro', label=\"Not updating tau's initial guess\")\n",
    "plt.loglog(fit02.q, fit02.parameters.loc['Tau'], 'bo', label=\"Updating tau's initial guess\")\n",
    "plt.loglog(fit03.q, fit03.parameters.loc['Tau'], 'go', label=\"Restricting the stretching exponent\")\n",
    "plt.xlabel(\"$q$ ($\\mu$m$^{-1})$\")\n",
    "plt.ylabel(\"Decay time, $\\\\tau$ (s)\")\n",
    "plt.axhline(y = 1.0/fit01.frame_rate)\n",
    "plt.legend(loc=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What to make of the results at high $q$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have tried a couple things to improve the results of our fits for $q \\gtrsim 6 \\mu m^{-1}$. We tried:\n",
    "1. Updating the intial guess for $\\tau$ for each $q$ based on an expected diffusion coefficient and constraining the bounds of $\\tau$ around that guess. \n",
    "2. Narrowing the bounds of the stretching exponent to be close to what we would expect for normal diffusive motion (i.e., near 1). \n",
    "\n",
    "But, these didn't seem to make the result for $\\tau (q)$ reasonable for the larger $q$ values. So what is going on? \n",
    "\n",
    "The horizontal line in the plot above corresponds to the minimum time lag we are measuring (24 ms given our frame rate of 41.7 Hz). So we are not lacking in temporal resolution. \n",
    "\n",
    "But what about **spatial resolution**? Even though our pixels are 0.242 $\\mu m$ in size doesn't mean that we can truly measure dynamics occurring around such small length scales. \n",
    "\n",
    "The data used here was acquired using a not-so-great microscope with a 40x 0.5NA objective and a 0.3NA condenser. Assuming light of a wavelength of 550 nm, that means our theoretical resolution is $\\frac{1.22 \\times 0.550}{0.5+0.3} = 0.763 \\mu m$. Note that this is more than 3 times the pixel size.\n",
    "\n",
    "As described in [**Safari 2015**], the maximum $q$ is determined either by (1) the pixel size, (2) the smallest distance a particle can be detected moving between two frames given the frame rate, and (3) the smallest resolvable distance given the optical resolution limit. In the case of (3): $q_{max} = \\frac{2 \\pi NA}{\\lambda}$. For our data, that would be $q_{max} = 5.7 \\mu m^{-1}$. \n",
    "\n",
    "**So it seems we have found the culprit of the poor fitting at large $q$: our limited optical resolution.**\n",
    "\n",
    ">[**Safari 2015**] Safari, M. S., Vorontsova, M. A., Poling-Skutvik, R., Vekilov, P. G. & Conrad, J. C. [Differential dynamic microscopy of weakly scattering and polydisperse protein-rich clusters.](https://doi.org/10.1103/PhysRevE.92.042712) Phys. Rev. E 92, 042712 (2015).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Lines correspond roughly to limits of temporal (horizontal line) and spatial (vertical line) resolution')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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"
    }
   ],
   "source": [
    "##################################################################################################\n",
    "# Plotting the characteristic decay time tau vs wavevector q determined from the last three fits #\n",
    "##################################################################################################\n",
    "\n",
    "plt.figure(figsize=(10,10/1.618))\n",
    "plt.loglog(fit01.q, fit01.parameters.loc['Tau'], 'ro', label=\"Not updating tau's initial guess\")\n",
    "plt.loglog(fit02.q, fit02.parameters.loc['Tau'], 'bo', label=\"Updating tau's initial guess\")\n",
    "plt.loglog(fit03.q, fit03.parameters.loc['Tau'], 'go', label=\"Restricting the stretching exponent\")\n",
    "plt.xlabel(\"$q$ ($\\mu$m$^{-1})$\")\n",
    "plt.ylabel(\"Decay time, $\\\\tau$ (s)\")\n",
    "plt.axhline(y = 1.0/fit01.frame_rate)\n",
    "plt.axvline(x = 5.7)\n",
    "plt.legend(loc=0)\n",
    "plt.title(\"Lines correspond roughly to limits of temporal (horizontal line) and spatial (vertical line) resolution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determining the mean squared displacement from the ISF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we assume that the particle displacements are Gaussian, then the ISF can be written as \n",
    "$f(q,\\Delta t) = \\exp \\left[ (-q^2 / 4) \\left< \\Delta r^2(\\Delta t) \\right> \\right]$. We can then find the MSD, $\\left< \\Delta r^2(\\Delta t) \\right>$ from the DDM matrix, $D(q, \\Delta t)$, using:\n",
    "$$ \\left< \\Delta r^2(\\Delta t) \\right> = \\frac{4}{q^2} \\ln \\left[ \\frac{A(q)}{A(q)-D(q,\\Delta t)+B(q)} \\right]$$\n",
    "\n",
    "See equation 6 in [**Bayles 2017**] or equation 7 in [**Edera 2017**]. \n",
    "\n",
    "We find $\\left< \\Delta r^2(\\Delta t) \\right>$ in this code with the method `extract_MSD` in the `DDM_Fit` class. The code will use the *'Amplitude'* and *'Background'* parameters of the fit. But, if those parameters are not present (as would be the case if we fit to the ISF instead of the DDM matrix) then the code will use the estimate for $A(q)$ and $B$ found using  $\\left< | \\tilde{I}(q, t) |^2 \\right>_t$ as described previously in this document.\n",
    "\n",
    "\n",
    ">[**Bayles 2017**] Bayles, A. V., Squires, T. M. & Helgeson, M. E. [Probe microrheology without particle tracking by differential dynamic microscopy.](https://doi.org/10.1007/s00397-017-1047-7) Rheol Acta 56, 863–869 (2017).\n",
    "\n",
    ">[**Edera 2017**] Edera, P., Bergamini, D., Trappe, V., Giavazzi, F. & Cerbino, R. [Differential dynamic microscopy microrheology of soft materials: A tracking-free determination of the frequency-dependent loss and storage moduli.](https://doi.org/10.1103/PhysRevMaterials.1.073804) Phys. Rev. Materials 1, 073804 (2017).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rmcgorty\\Anaconda3\\lib\\site-packages\\xarray\\core\\computation.py:742: RuntimeWarning: invalid value encountered in log\n",
      "  result_data = func(*input_data)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diffusion coefficient: 0.66499 um^2/s\n"
     ]
    },
    {
     "data": {
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3T0VdRZmZ+1c4qqlPn3bMn59JUdEA5s/PjPjDvjYn/5XXtVdREb+y4n51KYGISGy5w3PPwb77wuuvw+DBQfI48MByD6/uCKXi+kR5H/rRrFtUV2W1mWiqqNbRo8fuMR/6qy4sEYmdvLzgRk8TJ8IRRwQ1j332qfQptTVCKdaiVYOoyXWi0R2X1l1YWo1XJA6KimDYMGjfng3vT+a2Hc6j7ie9aN3twypHAdXGCKXaUNHriPbkv8paOtHqjquIWiAiEl3ffQeXXgoff8yiDkdw4o8n8t3aTf3uxa0GIG4jlGpLbbyOWLd0NAoLJRCRmNuwAYYMCW7w1LAhDB1K69vWkpe/eotDk607KpHFerRXWndhiUgt+PJLlrfdH265hZfW7c3B295MTr2DyV+wZfIAWLZsbUIuT56MKhooUBuJWC0QEYncmjUwaBBF9/2HxUUNuYLTGUMHoPK5GxWpjeXJpXoqa4HUK2+jiEiVPvooqHX88AMvbXskl/3ZlRU0LNldUFBIgwb1yp3ZXVFiieYcBYk9dWGJSPWsXg1XXhksPbJhA0yaRO+C0zZLHsUqmuT30EMnJOTy5FI9Kd8CKbUWVrxDEUl679/4GHsNvYVdN67gqe1PZLusezjvpM60ajW33JFAxZP8KuqPT4WRVulMNRARqdrSpcw77RJ2/3gcs9iFvpzNZ2RsNiQ3nus+SeyoBiIikXGH0aPh6qtpvmQ5t9OFuzmB9aGPjuKRU8XzDdSiSC9KICJSvp9/DlbNHTsWDj6YzksuYAa7bnFY8UzoyrqqJDVFVEQ3s23NrG60gxGRBFBUBNnZ0K4dTJoE998Pn37Kqoy9yz1cI6fSV1gJxMzqmNn5ZvammS0GvgN+MbNZZvYfM2sb2zBFpFbMmQMnngj9+8NBB8GMGXD99VC3bq2t7STJI9wWyPvAnsDNwF/cvaW77wwcDUwF7jGz/4tRjCISa4WFwTIkHTrAV18x9dJBtP7pXOq0HVNyf4l4zniWxBTWKCwzq+/uG2p6TDxpFJZIBb79Fvr2henToVcvXj3pWi7459caUSVAFNbCCicxJHLyEJEtjXzmax5ucjIbDujEki+/56OrH4AxY7h+yA9ap0rCUmUCMbMuZvaEmXUMPY7enVBqge4HIrKltwbm0PGSblyzcjwj6cg+RTfQbYSR88LsWruTniS/cFogVwA3Av9nZicAHWMaUZS5+zh3z2zcuHG8QxGJvz/+gGuuocugC2jo6+hGXy6iN8vZtqSVURv30pbUEE4CWeLuK9x9ANAVODjGMYlILLz1FrRvD48+ynCOoD038Bab3142P3+VRltJ2MJJIG8W/+DuNwHPxS4cEYm65cvhb3+Dbt1gm23go48YknEhf7DNFocWr12l0VYSjipnorv768U/m1ln4AQzuzj0XAsO8f1jF6KIRMSdj/7+IPsMG8gORX/yWKPuNP3nYHof2YnBg3cod+2q4laGZpVLOKq7lEkOQT1kBlAU/XBEJCoWLWLBqRdy9Bfv8gXN6UJfvlnVnIZXTaZoq61LkoPWrpKaqNZqvGY2xd2TsiNU80AkLbjDiBEwYABrVxVwq3dlKEezkU0rD2VkNCpZ/FCkKtFcjXegmT0JvAusK97o7q/WID4RiYaffoLMTHjvPTj2WPaffCg/stMWh2k4rkRLdRPIxcA+QH02dWE5oAQiEi8bN/LFhTfTbuSDrPe63LNjH9r3vYn18z+BCm7yJBIN1U0gB7h7h5hEIiLVN2MGS087n4PmzuR12nEFZ7BoeWMaXvYOF120H88+O6vCQrlITVV3OfepZqYqm0i8rVsHt90GBx4I8/M4h//jNP7GIoIJswUFhYwfP0/DcSWmqltEn02wKu88ghpI0gzjVRFdUsYnn8Cll8Ls2XDhhTR7bg+Wse0Wh5lBUdGAOAQoqSSaRfRuUYhHRCLw4ohpFFx3Exetfp9FdXfghxsf44T7+rPd5GyWqdYhcVCtLix3zyvvK1bBRYMWU5RU8N4/szms31+5aPX7DOMI9t14HT2HrSEnJ1dLj0jcVCuBmNkMM8sxs3+aWXcza2FmWbEKLhq0mKIktWXL4MILOeG+/hR4fY7iCq7hNP5gm5LFD7X0iMRLdWsgOwL7h74OAnoBue5+RGzCix7VQCSpuMPo0XD11fD779xZeCx3ciLrqL/ZYapzSKzV+IZSxdx9ubt/4O4Pu/tFBCvz/hiNIEUk8Ooj7zNp205w3nl8s7Ihbw4azZMZ52yRPEB1Domv6nZhtS392N1/JGiNiEhNFRXx2SW3c+I1PThyTS43cAoHrb+cc+7Mo0eP3VXnkIRT3Xkg2WaWb2afmtnjZvYsMNPMGsYiOJG08cMPcMIJHPr0HXxBczpwPQ9wLBupqzkdkrCqVQMpeZJZK4I7Ex4Q+r4/sNHd96nkaXGlGogkmpycXAbeMpmz88dxO5Owhg24oqAbIziYYIrVJqp1SLzUeB6ImZmXyjTung/kA2NLHaPOWJEw5eTkMuzSpxi9dhQH8jOv0J5/+FmsbNoUlq3d4njVOiQRhduF9b6ZXR1qeZQws63M7IRQV9YZ0Q9PJAWtWcOqK67nw7VD2ZVVnMkFnMVFzF0TzCZXrUOSRbgJpBuwERhpZovMLNfM5hGMwOoNDHX3Z2IUo0jqmDwZDjiAy1e9xbMcRDsG8GqpcSjLl69VrUOSRrVrIGZWH2gGrHH3FbEIKhZUA5F4ycnJ5Z6bJ3HVglH0Zyqrd25Jf85k5OLmWxyrmz1JoonaPBAAd9/g7r8kU/IQiZecnFxeu+QBJiwYyKV8xhCOYffVV9HkzB7qqpKkV+0EIiJh+u03GmdeyEvrR7CMhhzG1dxIT5atqaNhuZISIhrGm4zUhSW1xh2eew6uu451v69iEF24j+MoLHVfcg3LlWQRzeXcMbOdANx9SU0DE0k58+ZB//4waRIcdRQ95nbhvUVbzrPVsFxJBWF1YVngdjNbCnwH/GBmS8zsttiGJ5L4cnJy2SPjMa63UynYcx82fPQxDBsGkydzyX1nqdYhKSvcGsi1wJHAwe7e1N13AA4FjjSz62IVnEiiy8nJ5aFLn2Fk/l08wDje8z3ZjwHkND4O6tTRUuuS0sKqgZjZV0AXd19aZvtOwNvu3ilG8UWNaiASdevW8fAuZ3D5yomsoAHX0ItRdARMw3ElZURjGG/9sskDSuogW64xXQvMbFsze9bMnjCzPvGIQdJPTk4urVtnc5RdyZztWnPNyvGMpCP7ciOj6ETxGlb5+VveYlYk1YSbQNZHuK9azOwpM1tsZjPLbO9mZt+b2Rwzuym0+QzgZXfvB5warRhEKpKTk8sN/cZyY94zTGE49QrX0Z2+XERvlrHtZseqSC7pINxRWAeYWek/qUovFbpNFON5BngUeK7kQmZ1gWFAF2AhMM3MxgItgBmhwzZGMQaRcr173XCmrfkfzVnFgxzFv+jGn2yNWTByt5iK5JIuwmqBuHtdd29U6mv7Ul9R68Jy9w+B5WU2HwLMcfe57r4eGEVwK92FBEmkwtdhZplmNt3Mpi9ZolHHEqHFi6F3b55aMowVNOBwruI6evEnWwNB8lCRXNJRuMu5HwwscPdfQ48vBM4E5gN3uHvZD/1oag4sKPV4IcEIsIeBR83sZGBceU9092wgG4IiegxjlBSSk5NLVtYU8vNWcl3TXO5aN4at16/h/sancvPKw9lQ5tdGBXNJV+F2YT0OnARgZscA9wBXE9xMKhs4KxbBhVg529zd/wQujuF1JQ3l5OSSmfk2OxcsZiIv03XZj3xapzVL736OvzRvQ/3Mt9lQUFhyvLqrJJ2Fm0DqlmplnAtku/srwCtm9nVMIttkIdCy1OMWwKJwn2xmPYGebdq0iXZckoJuu2Uy/Qve5d+8RRF1uJLT+G/R4bQa/ivz5wdjNbKyppCfv4pWrRoxePBR6q6StBXuPJCZQEd3LzSz74DMUL0CM5vp7u2jFpBZa+CN4nOaWT3gB+BE4GdgGnC+u8+qznk1D0Sq9M03TOt4CgezkHHsyxWcwUKaAFq7StJXNNbCGglMDi1lsgb4KHTiNsDKqEQZnG8kcBzQzMwWAgPdfYSZXQW8BdQFnqpu8hApq6TOkb+Kti224ZWO39J+/FO0rtOQc4v6MJoDKN17qmG5IlsKK4G4+2Azexf4C8HM8+JmiwFXRSsYd+9dwfbxwPhoXUfSW3Gdo6CgkGP4iScWvMxeC5by09Gn8WWfG3nj+s9BdQ6RKoU7CmtsqYeXmJX8ZWaAk8AT+VQDkbKysqZQv2A1j/MmmXzGXHbkJPoxJ78z8/sfwfrtmqjOIRKGcGsgSwiG0o4EPqPMyCh3nxyT6KJINZD0VrrL6jSfwTDGsDN/MJSjuY2/soatVOcQKUc0aiB/IZgJ3hs4H3gTGKlahCSD4i6rJgXLeIUxnM4svmI3TuESviyZi6o6h0h1hVsD2QhMBCaa2dYEieQDMxvk7o/EMkCRmvrXLR9yQcFH3Mt4tqKQf9CDoRyz2R0CVecQqb6w70gYShwnEySP1gQzwV+NTVjRoxpImvvuO57Lv4+jmce7tKE/Z/ITzUp2m6E6h0iEwq2BPAu0ByYAo9x9ZhVPSTiqgaSZ9evhnntg8GBWFNbjuqJTeIbOlC7faQkSkapFowZyAfAnsBdwTdlRWO6uzmNJHJ9+Cv36waxZcO65vHvMFYy+8UsNzRWJsnBrIOHeN0SkVpUeXdWuRX1e2ecz9n5nFLRoAW+8ASefzJnA2sbNNDRXJMrC6sJKBerCSj2lJwSeQi7DeZXmrOKHrn3Y5+XhsP328Q5RJOlF45a2ScvMeppZ9sqVUVtxRRJEVtYUti9Yzij+xzieZgUNOIIr6fb90UoeIrUg5ROIu49z98zGjRvHOxSpoeL7kdepM4TWGY9zUt4kZjOE05jJv/grB/F3PiND9yMXqSVhD+MViafS3VVtWUJ2/n85jrlMZg8yOZMf2LnkWE0IFKkdSiCSFLKyprChYC23MJlbeYc11OdSzuJpDqaoVENao6tEao8SiCSFXfNmMI6X6cCvjGZ/rqEXvxG0NDIyGml0lUgcKIFIQik9LLdVq0bcm9WRc795jo8Zzs80oicX8wabEoQmA4rET8onEC1lkjxK1zkADsj7hKP6/wMPDc099qP9WLxm039ZdVeJxJdGYUnCyMqaQkFBIbuykpd4jtd5lmXegNN3+Sf7vPU8DzxxKhkZjTALWh7Z2V3VXSUSRynfApHksSBvBZcxlXuYwFYUchPduZ9j2fhbsGpunz7tlDBEEogSiCSGWbP4bOtsOq/7iXdow2WlVs3N0LBckYSkBCK1rnShvG3LBrzS6Vvaj3+a9ltvSz8/nyfXd6R41VzVOUQSV8rXQCSxFBfK8/JWcazPYVz+7bR/PZu5h3Znm3k/ctxTWWRkNFadQyQJqAUitSorawrbFKzkUd7gYqYzh6acRD/mLOjM/GbN6NOnmRKGSJJI+QSiYbwJxJ1j8t7nfsbRhDXczfEMogtrqY9p/SqRpJPyXVgaxpsg5syBLl14jlHMoSkHci230IO11Ae0fpVIMkr5BCK1a7MVc1tnM/LZr+Guu6BDB5g2jc8v+hddGvydmexa8hwVykWSkxKIRE3pArk77Jb3LQdc3B2ysuDkk2H2bA555t88/kQ3TQgUSQG6I6FETevW2eTlraIxa7ib8VzOVPJpwh07nc+IxcPiHZ6IRKCyOxKmfBFdak9+3krO4RseZCw78wcPcDS38VcKlm7NiHgHJyJRpwQi0TFvHu9t8yzHrZ3FdFpwCpfwJS0AzSQXSVWqgUjNbNgA994L++3HkcxjQP3TOZSrS5KHCuQiqSvlE4iZ9TSz7JUrV8Y7lNTzySdw4IFw003QrRv1f/yeTk/fScuMJiqQi6QBFdElLKXXr2rfvC6v7DWVtu+NhpYt4dFH4dRT4x2iiMSAiuhSI5tu9LSB8/iaoQvH0mxhAbO7X8S+ox+F7baLd4giEgdKIFKlrKwp/KXgV4Yzhr/yA5/Tku5cyu+5+zJfyUMkbSmBSOXWrePCvFe4hfdYT12uphfDOYIi6mj9KpE0l/JFdKmB996D/fdnEG/zGvuxDzfyKEdRFPpvo/WrRNKbEohs6bff4IIL4MQTYeNG3vvH4/Rt+Dd+YdOClBqeKyJKILJJURE89hjssw+8+CLceivMmMEJ92aSnd1V61eJyGY0jFcCX38Nl10Gn30Gxx8Pw4cHiURE0lplw3jVAkl3q1fD9dfDQQfBvHnwv//Bu+8qeYhIlTQKK82UTAjMW0lms594YONrNFyxGPr3D+7bscMO8Q5RRJKEEkgaKZ4QuHPBYsYxhpOXfse3thuLBubQbWDveIcnIklGXVhpZNAt73N9wQRy+Q/HMI9rOZUD/Roue3p1vEMTkSSU8i0QM+sJ9GzTpk28Q4mviRN5I/922rKUFzmAGziFn2kCQL4mBIpIBFK+BeLu49w9s3HjxlUfnIry8+GMM6B7d+rUq0sX+nEe/1eSPEATAkUkMimfQNLW+vVw993BaKqJE+Guu/j8iYl80nDzuRuaECgikUr5Lqy09M47cNVV8P33cPrpMHQoZGTQGyiqv1XJsuytWjVi8OCjNCFQRCKiBJJKFi6EG26A0aNhzz1h/Hjo3n2zQ/r0aaeEISJRoS6sVLBhAwwZEnRXjR0LgwbBzJlbJA8RkWhSCyTZffABXHkl5ObCKafAQw/BHnvEOyoRSQNqgSSrX36BPn2CdasKCoKWx7hxSh4iUmuUQJJNYSE8+CDsvTe8/HKwYu6sWdCzZ7wjE5E0oy6sJFC8flXLvG/Irv86+274Gbp1g0cegXSfICkicaMWSILLycnltn4vcXteNh8xnG03/Ml5W11MTp8hSh4iEldqgSSytWv5+apb+GbNBLZiI3dzPHdyEgXrt2Lqvz6mz//tF+8IRSSNKYEkInd45RW48Ub+sWI+Y9iPGzmFn2hWcojWrxKReFMXVqL56is47jg4+2zYfnt673wdZ/C3zZIHaP0qEYk/JZBE8euv0LdvcGfA3Nzg3uRffcUpD1xKw4abNxS1fpWIJAIlkHhbuxbuvRf22guefz64veyPPwZ3CKxblz592pGd3ZWMjEaYQUZGI7Kzu2o5EhGJO3P3eMdQKzp37uzTp0+PdxibuMOYMTBgQHAv8lNPDZYjads23pGJiJQwsy/cvXN5+9QCiYevv4YTToAzz4Rtt4VJk+D115U8RCSpJG0CMbM9zGyEmb0c71jC9ttv0K8fHHggzJgBw4cHRfOTTop3ZCIi1RaXBGJmT5nZYjObWWZ7NzP73szmmNlNlZ3D3ee6e9/YRhol69bBffcFLYxnnoFrr4U5c+Dyy6GeRlKLSHKK16fXM8CjwHPFG8ysLjAM6AIsBKaZ2VigLnB3medf4u6LayfUGnAPuqYGDICffgpWy73//qBgLiKS5OKSQNz9QzNrXWbzIcAcd58LYGajgF7ufjdwSiTXMbNMIBOgVatWkQcciW+/DVoa778P7drBW29B1661G4OISAwlUg2kObCg1OOFoW3lMrOmZvYY0MnMbi7vGHfPdvfO7t55p512im60FVm8OBiC26kTfPMNPPpo8F3JQ0RSTCJ1wFs52yocY+zuy4DLYhdONa1fH6yOO2hQcH+Oq6+G226DHXeMd2QiIjGRSAlkIdCy1OMWwKI4xRI+9+BGTjfcEBTGe/QI6hz77BPvyEREYiqRurCmAW3NbHcz2wo4Dxhb05OaWU8zy165cmWNA9zCjBlB11SvXsFoqgkT4M03lTxEJC3EaxjvSOBTYG8zW2hmfd29ELgKeAuYDYx291k1vZa7j3P3zMaNG9f0VJssWQJXXAEdO8IXX8DDDwdF827doncNEZEEF69RWL0r2D4eGF/L4YRv/XoYNgzuuAP++AOuvBIGDoSmTeMdmYhIrUukLqyYiEoXlju88QZ06BAsdnjYYUGL4+GHlTxEJG2lfAKpcRfWb78FXVM9e4JZUOOYMCGY2yEiksYSaRRWYmrSBH7/HR58MKh71K8f74hERBKCEkhVtt4aPvssaH2IiEiJlO/CqqmcnFxa7/4EdeoMoXXrbHJycuMdkohIQkj5FoiZ9QR6tmnTptrPzcnJJTPzbQoKCgHIy1tFZubbALojoIikvZRvgdSkiJ6VNaUkeRQrKCgkK2tKtMITEUlaKZ9AaiI/f1W1touIpBMlkEq0atWoWttFRNJJyieQmkwkHDz4KBo23LxM1LBhPQYPPipa4YmIJK2UTyA1qYH06dOO7OyuZGQ0wgwyMhqRnd1VBXQREcDcK7zlRkrp3LmzT58+Pd5hiIgkFTP7wt07l7cv5VsgIiISG0ogIiISESUQERGJSMonkJjekVBEJI2lfAKJyR0JRUQkfUZhmdkSIK+C3Y2BaDVRIj1XdZ5X1bGR7q/O9mbA0kquURuS6X0L57jKjqnuvnR4zyI9XzR/16o6JhV+1zLcfady97h72n8B2fE+V3WeV9Wxke6vznZgut638J8XznGVHVPdfenwnkV6vmj+rkX6viXb71pFXynfhRWmcQlwruo8r6pjI91f3e3xlkzvWzjHVXZMdfelw3sW6fmi+btW1TGp8rtWrrTpwpLoMrPpXsHkIklMes+SUyK/b2qBSKSy4x2AVJves+SUsO+bWiAiIhIRtUBERCQiSiAiIhIRJRAREYmIEohElZntYWYjzOzleMcilTOzbc3sWTN7wsz6xDseCU8i/Y4pgUgJM3vKzBab2cwy27uZ2fdmNsfMbqrsHO4+1937xjZSqUg138MzgJfdvR9waq0HKyWq874l0u+YEoiU9gzQrfQGM6sLDAO6A+2A3mbWzsw6mNkbZb52rv2QpYxnCPM9BFoAC0KHbazFGGVLzxD++5Yw6lV9iKQLd//QzFqX2XwIMMfd5wKY2Sigl7vfDZxSyyFKFarzHgILCZLI1+iPybiq5vuWW8vhVUj/aaQqzdn0VyoEHzrNKzrYzJqa2WNAJzO7OdbBSVgqeg9fBc40s/+SZEtopIly37dE+h1TC0SqYuVsq3D2qbsvAy6LXTgSgXLfQ3f/E7i4toORsFX0viXM75haIFKVhUDLUo9bAIviFItERu9hckr4900JRKoyDWhrZrub2VbAecDYOMck1aP3MDkl/PumBCIlzGwk8Cmwt5ktNLO+7l4IXAW8BcwGRrv7rHjGKRXTe5ickvV902KKIiISEbVAREQkIkogIiISESUQERGJiBKIiIhERAlEREQiogQiIiIRUQIRKcPM/ojBOW8p8/iTaF8jdN5OZvZkJft3MrOJsbi2pB8lEJHasVkCcfcjYnidRyra6e5LgF/M7MgYXV/SiBKISBjMrKeZfWZmX5nZO2a2S2j7TmY2ycy+NLPHzSzPzJqVee49QAMz+9rMckLb/gh9P87MJpvZaDP7wczuMbM+Zva5mc0wsz1LXecVM5sW+toiAZjZ9sD+7v5N6PGxoWt+HYp7+9ChrwG6A6HUmBKISHimAIe5eydgFPCP0PaBwHvufiAwBmhV9onufhOwxt07unt5H9wHAH8HOgAXAHu5+yHAk8DVoWMeAoa6+8HAmaF9ZXUGSt/RbgBwpbt3BI4G1oS2Tw89FqkRLecuEp4WwItmtiuwFTAvtP0o4HQAd59oZr9HcO5p7v4LgJn9BLwd2j4DOD7080lAO7OSFb4bmdn27r661Hl2BZaUevwx8ECo1fOquy8MbV8M7BZBnCKbUQtEJDyPAI+6ewegP7BNaHt592yornWlfi4q9biITX/k1QEOD7ViOrp78zLJA4IWRnFcuPs9wKVAA2Cqme0T2rUNm1ojIhFTAhEJT2Pg59DPF5XaPgU4B8DMugI7VPD8DWZWvwbXf5tgZVZC1+pYzjGzgTaljtnT3We4+70E3VbFCWQvNu/qEomIEojIlhqGltQu/roeuB14ycw+ApaWOvYOoKuZfQl0B34ByrYMALKBb4uL6BG4BuhsZt+aWS7l3JHO3b8DGpcqll9rZjPN7BuCFseE0PbjgTcjjEOkhJZzF6kBM9sa2OjuhWZ2OPDfUNE6XvFcB6x298rmgnwI9HL3SOo1IiVURBepmVbAaDOrA6wH+sU5nv8CZ1e008x2Ah5Q8pBoUAtEREQiohqIiIhERAlEREQiogQiIiIRUQIREZGIKIGIiEhElEBERCQi/w+YWne7aHXyZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###############################################################################################\n",
    "# Plotting the mean squared displacement as a function of lag time as determined from the ISF #\n",
    "###############################################################################################\n",
    "\n",
    "msd, msd_std = ddm_fit.extract_MSD(fit=fit01)\n",
    "poly_fit_results = np.polyfit(msd.lagtime[1:10], msd[1:10], 1)\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "plt.loglog(msd.lagtime, msd, 'o', color='darkblue')\n",
    "plt.plot(msd.lagtime, np.polyval(poly_fit_results,msd.lagtime), '-r')\n",
    "plt.xlabel(\"Lag time (s)\")\n",
    "plt.ylabel(\"MSD ($\\mu$m$^2$)\")\n",
    "plt.title(\"MSD vs lag time\")\n",
    "\n",
    "print(\"Diffusion coefficient: %.5f um^2/s\" % (poly_fit_results[0]/4.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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