notebooks/emgdecompy-worfklow.ipynb

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    "# EMGdecomPy Workflow\n",
    "\n",
    "This notebook allows for simple visualisation of the decomposition results obtained from the `EMGDecomPy` package."
   ]
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    "## Imports - dependencies\n",
    "\n",
    "Please run the cells below to add all the dependencies to run this notebook.   \n",
    "First, import the Python packages for loading in and saving data:"
   ]
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    "# loadmat is used to load MATLAB data\n",
    "from scipy.io import loadmat\n",
    "\n",
    "# Pickle allows saving and loading Python objects into a file\n",
    "import pickle"
   ]
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   "source": [
    "## Imports - EMGDecomPy\n",
    "\n",
    "Now, import the `EMGDecomPy` package scripts for decomposing and visualizing the electromyography data:"
   ]
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i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.jsdelivr.net/npm/vega@5\", \"https://cdn.jsdelivr.net/npm/vega-lite@5\", \"https://cdn.jsdelivr.net/npm/vega-embed@6\", \"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\", \"https://cdn.jsdelivr.net/npm/notyf@3/notyf.min.js\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var css_urls = [\"https://cdn.jsdelivr.net/npm/notyf@3/notyf.min.css\", \"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.1/css/all.min.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/debugger.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/markdown.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/loading.css\", \"https://unpkg.com/@holoviz/panel@0.13.1/dist/css/dataframe.css\"];\n  var inline_js = [    function(Bokeh) {\n      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n      background-size: auto calc(min(50%, 400px));\\n    }\\n    \");\n    },    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, js_modules, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     "data": {
      "text/html": [
       "<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
       "  font-family: var(--jp-ui-font-size1);\n",
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       "  color: var(--jp-ui-font-color1);\n",
       "}\n",
       "</style>"
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     "metadata": {},
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    }
   ],
   "source": [
    "from emgdecompy.decomposition import *\n",
    "from emgdecompy.contrast import *\n",
    "from emgdecompy.viz import *\n",
    "from emgdecompy.preprocessing import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7bd7505-a710-451e-a103-9b02e5539ddb",
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   "source": [
    "## Load the raw EMG data\n",
    "\n",
    "Please specify the filepath where the raw data is located, and use it to define the  `raw_data` variable.\n",
    "\n",
    "This raw data will be processed via an algorithm based on the the process proposed by `Negro et al. (2016)`.\n",
    "\n",
    "In order for the decomposition to accurately process the data, it needs to be in the correct format. The `decomposition` function and the `visualize_decomp` functions expect the `raw` argument to be a numpy array of arrays, where each inner element array is a time series of electromyography signal observations per channel.\n",
    "\n",
    "For instance, during this project, we used data in the MATLAB `.mat` format. A simplified example of the raw data dictionary can be seen below.\n",
    "\n",
    "Below, we load the data via the `loadmat` function, which we index into its signal series with the `SIG` key."
   ]
  },
  {
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   "source": [
    "raw = loadmat('../data/raw/gl_10.mat')['SIG']"
   ]
  },
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   "cell_type": "markdown",
   "id": "d98a4c6a-592a-4ca6-b9c6-1f23aa9f0157",
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   "source": [
    "Note the aforementioned `SIG` key-value pair, containing the array of arrays. For the purposes of decomposition, `SIG` is the only key needed within the dictionary. However, the rest of the information stored within the `.mat` file can be found below:"
   ]
  },
  {
   "cell_type": "raw",
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    "{'__header__': b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Thu Nov 19 19:29:51 2020',\n",
    " '__version__': '1.0',\n",
    " '__globals__': [],\n",
    " 'MUPulses': array([[array([[ 92900,  93164,  93402,  93617,  93774,  94013,  94203,  94402,\n",
    "                  94625,  94812,  94990,  95223,  95418,  95604,  95815,  96040,\n",
    "                  96268,  96476,  96761,  97012,  97253,  97463,  97689,  97941,\n",
    "                  98146,  98345,  98622,  98849,  99191,  99504,  99759,  99937,\n",
    "                 100097, 100292, 100464, 100629, 100825, 100982, 101162, 101320,\n",
    "                 101488, 101660, 101842, 102016, 102214, 102373, 102479, 102540,\n",
    "                 102845, 103045, 103249, 103498, 103715, 103909, 104139, 104382,\n",
    "                 104603, 104804, 105002, 105192, 105388, 105582, 105771, 105977,\n",
    "                 106200, 106367, 106559, 106781, 107007, 107183, 107369, 107583,\n",
    "                 107794, 107989, 108303, 108511, 108729, 108984, 109196, 109418,\n",
    "                 109653, 109857, 110041, 110202, 110373, 110651, 110875, 111075,\n",
    "                 111187, 111270, 111469, 111668, 111873, 112079, 112271, 112498,\n",
    "                 112702, 112890, 113082, 113296, 113458, 113645, 113852, 114036,\n",
    "                 114210, 114391, 114604, 114799, 114996, 115212, 115436, 115665,\n",
    "                 115895, 116080, 116290, 116512, 116721, 116919, 117015, 117119,\n",
    "                 117316, 117528, 117734, 117950, 118146, 118328, 118516, 118682,\n",
    "                 118870, 119061, 119251, 119446, 119640, 119827, 120013, 120206,\n",
    "                 120426, 120642, 120780, 120973, 121177, 121358, 121532, 121748,\n",
    "                 121952, 122169, 122359]], dtype=int32)                         ,\n",
    "         array([[ 92911,  93167,  93430,  93645,  93877,  94096,  94371,  94625,\n",
    "                  94802,  94999,  95234,  95467,  95725,  95945,  96264,  96524,\n",
    "                  97023,  97341,  97580,  97953,  98211,  98755,  99785,  99988,\n",
    "                 100179, 100440, 100597, 100835, 101014, 101241, 101406, 101616,\n",
    "                 101847, 102047, 102237, 102421, 102625, 102946, 103168, 103419,\n",
    "                 103666, 103898, 104161, 104436, 104661, 104883, 105078, 105292,\n",
    "                 105533, 105751, 106005, 106277, 106481, 106717, 107066, 107303,\n",
    "                 107546, 107803, 108064, 108426, 108715, 109033, 109323, 109687,\n",
    "                 109956, 110134, 110358, 110698, 110962, 111235, 111457, 111683,\n",
    "                 111934, 112188, 112490, 112740, 112980, 113195, 113389, 113591,\n",
    "                 113858, 114055, 114275, 114492, 114749, 114993, 115326, 115603,\n",
    "                 115864, 116100, 116321, 116562, 116825, 117039, 117285, 117583,\n",
    "                 117850, 118109, 118337, 118533, 118738, 118965, 119175, 119394,\n",
    "                 119625, 119851, 120074, 120299, 120561, 120736, 120901, 121121,\n",
    "                 121362, 121576, 121840, 122085, 122348]], dtype=int32) ,\n",
    "               dtype=int32)                                                       ]],\n",
    "       dtype=object),\n",
    " 'MUIDs': array([[array(['A1'], dtype='<U2'), array(['A2'], dtype='<U2'),]], dtype=object),\n",
    " 'Cost': array([[array([[0.17085427]]), array([[0.17596919]]),\n",
    "         array([[0.26433915]]), array([[0.24786325]]),\n",
    "         array([[0.30697674]])]], dtype=object),\n",
    " 'PNR': array([[33.8, 39.8, 32. , 38.5, 27.1]]),\n",
    " 'SIG': array([[array([], shape=(0, 0), dtype=uint8),\n",
    "         array([[46.79361979, 13.22428385, -7.12076823, ..., 26.44856771,\n",
    "                 50.86263021, 36.62109375]])                             ,\n",
    "         array([[76.29394531, 39.67285156, 29.50032552, ..., 34.58658854,\n",
    "                 47.8108724 , 57.98339844]])                             ,\n",
    "         array([[74.2594401 ,  4.06901042, -5.08626302, ..., 21.36230469,\n",
    "                 30.51757812, 36.62109375]])                             ,\n",
    "         array([[62.05240885, 28.48307292, 17.29329427, ...,  3.05175781,\n",
    "                 22.37955729, -5.08626302]])                             ],\n",
    "        [array([[88.50097656, 29.50032552, 12.20703125, ..., 17.29329427,\n",
    "                 48.828125  , 77.31119792]])                             ,\n",
    "         array([[108.84602865,  71.20768229,  25.4313151 , ...,  20.34505208,\n",
    "                  62.05240885,  67.13867188]])                               ,\n",
    "         array([[81.38020833, 35.60384115, 10.17252604, ..., 27.46582031,\n",
    "                 66.12141927, 89.51822917]])                             ,\n",
    "         array([[74.2594401 , 10.17252604,  7.12076823, ...,  2.03450521,\n",
    "                 62.05240885, 71.20768229]])                             ,\n",
    "         array([[70.19042969, 27.46582031, 12.20703125, ..., 11.18977865,\n",
    "                 47.8108724 , 99.69075521]])                             ],\n",
    "        [array([[84.43196615, 65.10416667, 22.37955729, ..., 32.55208333,\n",
    "                 67.13867188, 77.31119792]])                             ,\n",
    "         array([[85.44921875, 40.69010417, 14.24153646, ..., 39.67285156,\n",
    "                 76.29394531, 72.2249349 ]])                             ,\n",
    "         array([[75.27669271, 47.8108724 , 26.44856771, ..., 19.32779948,\n",
    "                 31.53483073, 75.27669271]])                             ,\n",
    "         array([[68.15592448, 40.69010417,  1.0172526 , ..., 15.25878906,\n",
    "                 54.93164062, 75.27669271]])                             ,\n",
    "         array([[78.32845052, 18.31054688, -5.08626302, ..., 15.25878906,\n",
    "                 63.06966146, 52.89713542]])                             ],\n",
    "        [array([[66.12141927, 16.27604167,  1.0172526 , ..., 29.50032552,\n",
    "                 52.89713542, 78.32845052]])                             ,\n",
    "         array([[ 82.39746094,   7.12076823, -18.31054688, ...,  13.22428385,\n",
    "                  70.19042969,  86.46647135]])                               ,\n",
    "         array([[68.15592448, 29.50032552,  6.10351562, ..., 11.18977865,\n",
    "                 51.87988281, 79.34570312]])                             ,\n",
    "         array([[80.36295573, 37.63834635, 16.27604167, ..., 52.89713542,\n",
    "                 74.2594401 , 77.31119792]])                             ,\n",
    "         array([[64.08691406, 27.46582031, -7.12076823, ..., 24.4140625 ,\n",
    "                 52.89713542, 77.31119792]])                             ]],\n",
    "       dtype=object),\n",
    " 'IPTs': array([[0., 0., 0., ..., 0., 0., 0.],\n",
    "        [0., 0., 0., ..., 0., 0., 0.],\n",
    "        [0., 0., 0., ..., 0., 0., 0.],\n",
    "        [0., 0., 0., ..., 0., 0., 0.],\n",
    "        [0., 0., 0., ..., 0., 0., 0.]]),\n",
    " 'fsamp': array([[2048]], dtype=uint16),\n",
    " 'startSIGInt': array([[45]], dtype=uint8),\n",
    " 'stopSIGInt': array([[60]], dtype=uint8),\n",
    " 'ref_signal': array([[66833496.09375, 67749023.4375 , 67138671.875  , ...,\n",
    "         54626464.84375, 50659179.6875 , 52490234.375  ]]),\n",
    " 'origRecMode': array(['MONO'], dtype='<U4'),\n",
    " 'SIGFilePath': array(['C:\\\\Users\\\\hug_f\\\\Documents\\\\Data\\\\InterDrive\\\\S10\\\\Raw\\\\'],\n",
    "       dtype='<U49'),\n",
    " 'SIGFileName': array(['vaMiKT_10.otb+'], dtype='<U14'),\n",
    " 'DecompRuns': array([[50]], dtype=uint8),\n",
    " 'IED': array([[8]], dtype=uint8),\n",
    " 'discardChannelsVec': array([[0, 0, 0, 0, 0],\n",
    "        [0, 1, 0, 0, 0],\n",
    "        [0, 0, 0, 0, 0],\n",
    "        [0, 0, 0, 0, 0],\n",
    "        [0, 0, 0, 0, 0],\n",
    "        [0, 0, 0, 0, 0],\n",
    "        [0, 0, 0, 0, 0]], dtype=uint8),\n",
    " 'description': array(['C:\\\\Users\\\\hug_f\\\\Documents\\\\Data\\\\InterDrive\\\\S10\\\\Raw\\\\'],\n",
    "       dtype='<U49'),\n",
    " 'SIGlength': array([[105.21142578]]),\n",
    " 'ProcTime': array([[  7.8472085,  14.0359775,  20.3020407,  26.6780681,  33.1434723,\n",
    "          39.5730774,  46.0549393,  52.4345215,  58.8978001,  65.4029372,\n",
    "          71.8402833,  78.3977499,  84.9143059,  91.414722 ,  97.8160689,\n",
    "         104.1980256, 110.6378523, 117.0923444, 123.4877615, 129.9538468,\n",
    "         299.2515221, 305.935486 , 312.5607617, 319.0872198, 325.7498793]]),\n",
    " 'DecompStat': array([[4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
    "         5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
    "         5, 5, 5, 5, 5, 5]], dtype=uint8)}"
   ]
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  {
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   "source": [
    "## Run decomposition\n",
    "\n",
    "Run the decomposition on raw data to retrieve a dictionary containing the results of the blind source separation algorithm. Further documentation for each function can be found [here](https://emgdecompy.readthedocs.io/en/stable/autoapi/emgdecompy/index.html).\n",
    "\n",
    "__There is a number of parameters you may choose to customize:__\n",
    "\n",
    "| Parameter    | Data Type                    | Explanation                                                                                  |\n",
    "|--------------|------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| x            | numpy.ndarray                | Raw EMG signal. This is expected inside the 'SIG' key within the `.mat` file loaded above.  \n",
    "| discard      | slice, int, or array of ints | Indices of channels to discard. Useful if you are looking to only analyze some channels, or expect some channels to contain significant noise.                               |\n",
    "| R            | int                          | How far to extend x during the extension step of the algorithm.                                                                                                            |\n",
    "| M            | int                          | Number of iterations to run the decomposition for.                                                                                                                             |\n",
    "| bandpass       | bool                         | Whether to band-pass filter the raw EMG signal or not.                                                                                                                     |\n",
    "| lowcut       | float                        | Lower range of band-pass filter.                                                                                                                                           |\n",
    "| highcut      | float                        | Upper range of band-pass filter.                                                                                                                                           |\n",
    "| fs           | float                        | Sampling frequency in Hz.                                                                                                                                                  |\n",
    "| order        | int                          | Order of band-pass filter.                                                                                                                                                 |                                                                                                                          |\n",
    "| Tolx         | float                        | Tolerance for element-wise comparison in separation.                                                                                                                       |\n",
    "| contrast_fun | function                     | Contrast function to use. `skew`, `og_cosh` or `exp_sq`                                                                                                                          |\n",
    "| ortho_fun    | function                     | Orthogonalization function to use. `gram_schmidt` or `deflate`                                                                                                                 |\n",
    "| max_iter_sep | int > 0                      | Maximum iterations for fixed point algorithm.                                                                                                                              |\n",
    "| l            | int                          | Required minimal horizontal distance between peaks in peak-finding algorithm. Default value of 31 samples is approximately equivalent to 15 ms at a 2048 Hz sampling rate. |\n",
    "| sil_pnr      | bool                         | Whether to use SIL or PNR as the acceptance criterion. Default value of True uses SIL.                                                                                         |\n",
    "| thresh       | float                        | SIL/PNR threshold for accepting a separation vector.                                                                                                                       |\n",
    "| max_iter_ref | int > 0                      | Maximum iterations for refinement.                                                                                                                                         |\n",
    "| random_seed  | int                          | Used to initialize the pseudo-random processes in the function.                                                                                                            |\n",
    "| verbose      | bool                         | If true, decomposition information is printed.                                                                                                                             |\n"
   ]
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     "text": [
      "Centred.\n",
      "Extended.\n",
      "Whitened.\n",
      "Extracted source at iteration 4.\n",
      "Extracted source at iteration 5.\n",
      "Extracted source at iteration 11.\n",
      "Extracted source at iteration 25.\n",
      "Extracted source at iteration 27.\n",
      "Extracted source at iteration 32.\n",
      "Extracted source at iteration 35.\n",
      "Extracted source at iteration 55.\n",
      "Extracted source at iteration 59.\n",
      "Extracted source at iteration 63.\n"
     ]
    }
   ],
   "source": [
    "output = decomposition(\n",
    "    raw,\n",
    "    discard=5,\n",
    "    R=16,\n",
    "    M=64,\n",
    "    bandpass=True,\n",
    "    lowcut=10,\n",
    "    highcut=900,\n",
    "    fs=2048,\n",
    "    order=6,\n",
    "    Tolx=10e-4,\n",
    "    contrast_fun=skew,\n",
    "    ortho_fun=gram_schmidt,\n",
    "    max_iter_sep=10,\n",
    "    l=31,\n",
    "    sil_pnr=True,\n",
    "    thresh=0.9,\n",
    "    max_iter_ref=10,\n",
    "    random_seed=None,\n",
    "    verbose=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6265802-ca36-4863-93f3-485d9976f5a1",
   "metadata": {},
   "source": [
    "The output of decomposition is a dictionary containing the following keys:\n",
    "\n",
    "__'B'__: Matrix whose columns contain the accepted separation vectors.\n",
    "\n",
    "__'MUPulses'__: Firing indices for each motor unit. This is the key used within this notebook, along with the raw data, for visualisation purposes.\n",
    "\n",
    "__'SIL'__: Corresponding silhouette scores for each accepted source.\n",
    "\n",
    "__PNR__: Corresponding pulse-to-noise ratio for each accepted source.\n",
    "\n",
    "A simplified example of the output of the `decomposition` function can be seen below:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a2ef2fe2-8cb7-444b-a239-3b6152996966",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "source": [
    "{'B': array([[ 0.00807129,  0.01498068, -0.05055092, ...,  0.03324424,\n",
    "          0.01098719, -0.04423392],\n",
    "        [ 0.00099551,  0.02325052, -0.04633348, ...,  0.01060206,\n",
    "          0.00206237, -0.03023024],\n",
    "        [-0.00079862,  0.04170231,  0.02396507, ..., -0.0069239 ,\n",
    "         -0.00353031, -0.05398121],\n",
    "        ...,\n",
    "        [ 0.03401527,  0.00262566, -0.0032439 , ...,  0.00472695,\n",
    "         -0.01023719,  0.02006415],\n",
    "        [ 0.06270893,  0.00852112,  0.00463019, ..., -0.00228336,\n",
    "         -0.03886996,  0.01297941],\n",
    "        [ 0.05614772, -0.00944031,  0.00570728, ..., -0.00063509,\n",
    "         -0.04714769, -0.00320849]]),\n",
    " 'MUPulses': array([array([ 88042,  88264,  88598,  88810,  89117,  89359,  89597,  89814,\n",
    "                90027,  90222,  90443,  90670,  90996,  91194,  91424,  91692,\n",
    "                91920,  92273,  92650,  92922,  93178,  93441,  93656,  93888,\n",
    "                94107,  94382,  94635,  94813,  95010,  95245,  95478,  95736,\n",
    "                95956,  96275,  96535,  97034,  97352,  97591,  97964,  98221,\n",
    "               122727, 122963, 123185, 123592, 123804, 124030, 124621, 124938,\n",
    "               125287, 125634, 126067, 126212, 126400, 126609, 126802, 127068,\n",
    "               127330, 127686, 128037, 128302, 128577, 128838, 129100, 129407,\n",
    "               129682, 130028, 130287, 130652, 131062, 131518, 131799, 132185,\n",
    "               132848])                                                       ,\n",
    "        array([  6745,   6964,   7368,   8119,   8329,   8497,   8662,   8880,\n",
    "                 9086,   9297,   9501,   9671,   9840,  10015,  10178,  10359,\n",
    "                10551,  10741,  10916,  11099,  11228,  11396,  11554,  11710,\n",
    "                11884,  12027,  12213,  12389,  12543,  12698,  12884,  13065,\n",
    "                13237,  13391,  13555,  13724,  13881,  14049,  14234,  14394,\n",
    "               202778, 203067, 203385, 203692, 203942, 204211, 204500, 204754,\n",
    "               205087, 205415, 205794, 206021, 206292, 206559, 206830, 207179,\n",
    "               207606, 207869, 208248, 209149, 209472, 209996])               ],\n",
    "       dtype=object),\n",
    " 'SIL': array([0.9935724 , 0.99662968]),\n",
    " 'PNR': array([22.64379066, 25.86925002])}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a1363f-1685-4df9-8b96-3745153cd0a8",
   "metadata": {},
   "source": [
    "## Optional: save the output object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0312c303-10c7-4d91-8985-1986e2142269",
   "metadata": {},
   "source": [
    "Cell below saves the output of the decomposition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2d5f3bb-d023-4bdd-90dd-44171e60526c",
   "metadata": {},
   "outputs": [],
   "source": [
    "decomp_sample = output \n",
    "decomp_sample_pkl = open('decomp_sample_pkl.obj', 'wb') \n",
    "pickle.dump(decomp_sample, decomp_sample_pkl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbd5d76-0117-4bf5-bf72-40a21a580c15",
   "metadata": {},
   "source": [
    "Cell below loads the output saved above. The sample pickle file is included in this directory, so you may uncomment the line below right away to become familiar with the UI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59450999-4ab4-41dc-b464-051807e9e56f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('decomp_sample_pkl.obj', 'rb') as f: output = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5134ac-7656-4d9c-b875-bf1f46d08ec8",
   "metadata": {},
   "source": [
    "## Visualize results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843abdd8-70a1-4cf8-bb31-e9b4e7fc90b3",
   "metadata": {
    "tags": []
   },
   "source": [
    "Finally, run the cells below to get an interactive dashboard of the decomposed data.\n",
    "\n",
    "There are two parameters for the `visualize_decomp` function. `decomp_results` and `raw` take in the decomposition results and raw signal, respectively. You are unlikely to want to change it within the function, and most likely will select those earlier. However, if you want to plug in the data directly, you may use those parameters.\n",
    "\n",
    "There are three widgets controlling the rest of the dashboard:   \n",
    "1. The `Motor Unit` widget for selecting the Motor Unit to examine.\n",
    "2. The `Preset` parameter allows you to select arrangements of the channel. Currently, you may select between 'standard' and 'vert63' arrangements. New arrangements may easily be added within the `channel_preset` function in the `viz.py` script.\n",
    "3. The `Method` widget allows you to select the metric for calculating the average mismatch score between peak contribution and average motor unit action potential (MUAP) shape. Currently, RMSE is the only available metric, but other metrics can be added easily in the future by adding functions to the `viz.py` script.\n",
    "\n",
    "Below the widgets, dashboard contains four charts:\n",
    "\n",
    "#### Chart 1: Signal Strength + Firing Rate. \n",
    "The top chart displays signal strength along with the instanteneous firing rate at that point.   \n",
    "This chart allows for interval control. Select the interval of interest by clicking and dragging the mouse. Move around the interval by clicking on the selection and moving it around. The plots below zoom in according to this selection.\n",
    "\n",
    "#### Chart 2: Firing Rate. \n",
    "The second chart displays the instanteneous firing rate of each spike. Outliers may be indicative of a false positive firing. Click on an individual data point to select corresponding the pulse. This will allow you to see the given firing's contribution to the MUAP shape.\n",
    "\n",
    "#### Chart 3: Signal Strength\n",
    "The third chart displays the signal strength of each firing. Click on an individual data point to select corresponding pulse. This will allow you to see the given firing's contribution to the MUAP shape.\n",
    "\n",
    "#### Chart 4: MUAP Shapes + Peak Contributions\n",
    "The last chart displays the average shape for each channel. The title displays the currently selected motor unit. If an individual peak has been selected using Chart 2 or 3, then an overlay will be displayed on each sub-chart that shows the contribution of the selected peak towards the shape for each channel. You may toggle the opacity of that overlay and the MUAP shape using the legend. Additionally, the peak firing time and RMSE will displayed in the title of the chart.\n",
    "\n",
    "Further documentation of the visualization functions can be found [here](https://emgdecompy.readthedocs.io/en/stable/autoapi/emgdecompy/viz/index.html)."
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "id": "80492304-cb14-46d4-bb63-a9a44fde8480",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[4032]}},\"selected\":{\"id\":\"1019\"},\"selection_policy\":{\"id\":\"1018\"}},\"id\":\"1014\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1019\",\"type\":\"Selection\"},{\"attributes\":{\"children\":[{\"id\":\"1013\"}],\"margin\":[0,0,0,0],\"name\":\"Column00120\"},\"id\":\"1012\",\"type\":\"Column\"},{\"attributes\":{\"children\":[{\"id\":\"1004\"},{\"id\":\"1005\"},{\"id\":\"1006\"}],\"margin\":[0,0,0,0],\"name\":\"Column00124\"},\"id\":\"1003\",\"type\":\"Column\"},{\"attributes\":{\"reload\":false},\"id\":\"1041\",\"type\":\"panel.models.location.Location\"}],\"root_ids\":[\"1002\",\"1028\",\"1041\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n",
       "    var render_items = [{\"docid\":\"a9cc501e-fac2-42b4-b4fe-1569e1b5fda2\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"2b44a6c3-ac07-439e-adcb-2ba379ba866c\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined && ( root['vega'] !== undefined)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined && (root['vega'] !== undefined)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       "Column\n",
       "    [0] Column\n",
       "        [0] Select(name='Motor Unit:', options=[0, 1, 2, 3, 4, ...], value=0)\n",
       "        [1] Select(name='Preset:', options=['standard', 'vert63'], value='standard')\n",
       "        [2] Select(name='Comparison Metric:', options=['RMSE'], value='RMSE')\n",
       "    [1] Row\n",
       "        [0] Column\n",
       "            [0] Vega(VConcatChart, debounce=10, selection=Selection)\n",
       "            [1] ParamFunction(function)"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_decomp(output, raw)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}