ECG data display bokeh.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wfdb\n",
    "import os\n",
    "from bokeh.plotting import figure, output_file, show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.99244289 0.00755711]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(X)\n",
    "\n",
    "print(pca.explained_variance_ratio_)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "all the input arrays must have same number of dimensions",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-5979ce86897a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mX_Axis_Array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m240\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mY_Axis_Array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_Axis_Array\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_Axis_Array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mY_Axis_Array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m#Y_Axis_Array = np.vstack((Y_Axis_Array, X_Axis_Array))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: all the input arrays must have same number of dimensions"
     ]
    }
   ],
   "source": [
    "X_List = list(range(0,240,1))\n",
    "print(X_List)\n",
    "\n",
    "import numpy as np\n",
    "#np.set_printoptions(suppress=True)\n",
    "#Y_Axis_Array = np.array([])\n",
    "Y_Axis_Array = np.empty((0,0))\n",
    "np.set_printoptions(suppress=True)\n",
    "np.set_printoptions(precision=3)\n",
    "#print(Y_Axis_Array)\n",
    "Y_Axis_Array#[0:5,:]\n",
    "X_Axis_Array = np.arange(240)\n",
    "\n",
    "Y_Axis_Array = np.concatenate((Y_Axis_Array, X_Axis_Array))\n",
    "Y_Axis_Array\n",
    "#Y_Axis_Array = np.vstack((Y_Axis_Array, X_Axis_Array))\n",
    "#print(Y_Axis_Array)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Record to plot: 101\n",
      "Init sample: 0\n",
      "Final sample: 1000\n",
      "Annotations enabled? (type True or False): True\n",
      "Symbols enabled? (type True or False): True\n",
      "Read channel 1? (type True or False): True\n",
      "Read channel 2? (type True or False): False\n",
      "[1]\n",
      "Time units (samples, seconds, minutes and hours): seconds\n",
      "Width of the plot: 15\n",
      "Height of the plot: 4\n"
     ]
    }
   ],
   "source": [
    "iRecord = -1\n",
    "iSampleIni = -1\n",
    "iSampleFin = -1\n",
    "iAnnotation = True\n",
    "iSymbols = True\n",
    "iTime_Units = ''\n",
    "iWidth = -1\n",
    "iHeight = -1\n",
    "\n",
    "records = [100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
    "           111, 112, 113, 114, 115, 116, 117, 118, 119, 121,\n",
    "           122, 123, 124, 200, 201, 202, 203, 205, 207, 208,\n",
    "           209, 210, 212, 213, 214, 215, 217, 219, 220, 221,\n",
    "           222, 223, 228, 230, 231, 232, 233, 234]\n",
    "\n",
    "bools = ['True', 'False']\n",
    "\n",
    "time_units = ['samples', 'seconds', 'minutes', 'hours']\n",
    "    \n",
    "# Record to plot    \n",
    "while not iRecord in records:\n",
    "    iRecord_str = input('Record to plot: ') #100-124 - {110, 120}, 200-234 - {204, 206, 211, 216, 218, 224-227, 229}\n",
    "    iRecord = int(iRecord_str)\n",
    "    if not iRecord in records:\n",
    "        print('This record is not in the database. Available records: 100-124 - {110, 120}, 200-234 - {204, 206, 211, 216, 218, 224-227, 229}')\n",
    "        \n",
    "# Init sample (only can put 0 or 1. Why?)        \n",
    "iSampleIni_str = input('Init sample: ')\n",
    "iSampleIni = int(iSampleIni_str)\n",
    "\n",
    "# Final sample\n",
    "iSampleFin_str = input('Final sample: ')\n",
    "iSampleFin = int(iSampleFin_str)\n",
    "\n",
    "# Annotations\n",
    "iAnnotation_str = input('Annotations enabled? (type True or False): ')\n",
    "while not iAnnotation_str in bools:\n",
    "    print('Incorrect input.')\n",
    "    iAnnotation_str = input('Annotations enabled? (type True or False): ')\n",
    "iAnnotation = bool(iAnnotation_str)\n",
    "\n",
    "# Symbols\n",
    "iSymbols_str = input('Symbols enabled? (type True or False): ')\n",
    "while not iSymbols_str in bools:\n",
    "    print('Incorrect input.')\n",
    "    iSymbols_str = input('Symbols enabled? (type True or False): ')\n",
    "iSymbols = bool(iSymbols_str)\n",
    "\n",
    "# Channels\n",
    "channelList = []\n",
    "\n",
    "# Channel 1\n",
    "iChannel1 = input('Read channel 1? (type True or False): ')\n",
    "while not iChannel1 in bools:\n",
    "    print('Incorrect input.')\n",
    "    iChannel1 = input('Read channel 1? (type True or False): ')\n",
    "if iChannel1 == 'True':\n",
    "    channelList = channelList + [1]\n",
    "\n",
    "# Channel 2\n",
    "iChannel2 = input('Read channel 2? (type True or False): ')\n",
    "while not iChannel2 in bools:\n",
    "    print('Incorrect input.')\n",
    "    iChannel2 = input('Read channel 2? (type True or False): ')\n",
    "if iChannel2 == 'True':\n",
    "    channelList = channelList + [2]\n",
    "\n",
    "print(channelList)\n",
    "\n",
    "# Time units\n",
    "iTime_Units = input('Time units (samples, seconds, minutes and hours): ')\n",
    "while not iTime_Units in time_units:\n",
    "    print('Incorrect input.')\n",
    "    iTime_Units = input('Time units (samples, seconds, minutes and hours): ')\n",
    "\n",
    "# Width\n",
    "iWidth_str = input('Width of the plot: ')\n",
    "iWidth = int(iWidth_str)\n",
    "while not iWidth > 0:\n",
    "    print('Invalid input.')\n",
    "    iWidth_str = input('Width of the plot: ')\n",
    "    iWidth = int(iWidth_str)    \n",
    "\n",
    "# Height\n",
    "iHeight_str = input('Height of the plot: ')\n",
    "iHeight = int(iHeight_str)\n",
    "while not iHeight > 0:\n",
    "    print('Invalid input.')\n",
    "    iHeight_str = input('Height of the plot: ')\n",
    "    iHeight = int(iHeight_str)\n",
    "\n",
    "iRecord_Str = 'mitdb/' + str(iRecord)\n",
    "iAnnotation_Str = 'mitdb/' + str(iRecord)\n",
    "iTitle = 'MIT-BIH Record ' + str(iRecord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import wfdb\n",
    "wfdb.plot_wfdb(record = wfdb.rdrecord('mitdb/102', sampto = 3000), annotation = wfdb.rdann('mitdb/102', 'atr', sampto = 3000), plot_sym = True, time_units='samples', title = 'Finally dude', figsize = (10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'QRS of type: MLII_N')"
      ]
     },
     "execution_count": 42,
     "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": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import wfdb\n",
    "import os\n",
    "from collections import Counter\n",
    "from numpy import nan\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "twoch_df = pd.DataFrame()\n",
    "firstch_df = pd.DataFrame()\n",
    "\n",
    "# We exctract 120 samples to the left and right from the beat label \n",
    "half_qrs=120\n",
    "\n",
    "# Search in each file name of the folder 'mitdb'\n",
    "for filename in os.listdir('mitdb'):\n",
    "    # If file name is a .dat and is not one of (102, 104, 107 or 217) recordings\n",
    "    if filename.endswith(\".dat\") and not filename.startswith(('102','104','107','217')):\n",
    "        # Read annotation (strip the .dat)\n",
    "        ann = wfdb.rdann('mitdb/' + filename.strip('.dat'), 'atr')\n",
    "        # Read signal file\n",
    "        record = wfdb.rdsamp('mitdb/' + filename.strip('.dat'))\n",
    "        \n",
    "        # The signal is in the position 0 of 'record'\n",
    "        data = record[0]\n",
    "        \n",
    "        # Prepare containers\n",
    "        signals, classes = [], []\n",
    "        firstch_signals = []\n",
    "        firstch_class = []\n",
    "        \n",
    "        # Beat extraction\n",
    "        for it, beat in enumerate(ann.symbol):\n",
    "            #if beat in good_beats:\n",
    "                \n",
    "                sta = ann.sample[it] - half_qrs\n",
    "                end = ann.sample[it] + half_qrs\n",
    "                qrs = data[sta : end, :]\n",
    "            \n",
    "                # This may happen at the edges\n",
    "                if len(qrs) != 2 * half_qrs: continue\n",
    "                \n",
    "                # Exctracting only first channel signals and classes \n",
    "                firstch_beat_class = '{}_{}'.format(record[1]['sig_name'][0], beat)\n",
    "                firstch_signals.append(qrs[:,0].reshape(240,))\n",
    "                firstch_class.append(firstch_beat_class)\n",
    "            \n",
    "                # Keep the channel type in the class name (both channels)\n",
    "                for ch in range(2):\n",
    "                    beat_class = '{}_{}'.format(record[1]['sig_name'][ch], beat)\n",
    "                    signals.append(qrs[:, ch].reshape(240,))\n",
    "                    classes.append(beat_class)\n",
    "                \n",
    "        new_frame = pd.DataFrame({'qrs_data' : signals,\n",
    "                              'qrs_type' : classes})\n",
    "        \n",
    "        new_frame_firstch = pd.DataFrame({'qrs_data' : firstch_signals,\n",
    "                              'qrs_type' : firstch_class})\n",
    "        # Cumulate\n",
    "        twoch_df = twoch_df.append(new_frame)\n",
    "        firstch_df = firstch_df.append(new_frame_firstch)\n",
    "        \n",
    "firstch_df['qrs_type'].value_counts()\n",
    "\n",
    "plt.figure()\n",
    "#plt.plot(data[0:3000])\n",
    "plt.plot(firstch_df.iloc[1111]['qrs_data'])\n",
    "plt.title('QRS of type: {}'.format(firstch_df.iloc[1111]['qrs_type']))\n",
    "\n",
    "#list30 = []\n",
    "#for ele in range (0, 3000):\n",
    "#    list30 = list30 + [ele]\n",
    "\n",
    "#new_vec_Y = []\n",
    "#for el in range (0, 3000):\n",
    "#    new_vec_Y = new_vec_Y + [data[el][0]]\n",
    "    \n",
    "#from bokeh.plotting import figure, output_file, show\n",
    "#plot = figure(title = \"Plot test\", plot_width=1000, plot_height = 500)\n",
    "#plot.line(list30, new_vec_Y)\n",
    "#show(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.756 0.082 0.041 0.023 0.015 0.011 0.007 0.006 0.006]\n",
      "[[ 0.062  0.063  0.063 ...  0.062  0.063  0.063]\n",
      " [-0.075 -0.076 -0.076 ...  0.064  0.062  0.059]\n",
      " [ 0.078  0.078  0.078 ... -0.039 -0.042 -0.046]\n",
      " ...\n",
      " [-0.079 -0.087 -0.095 ...  0.067  0.071  0.081]\n",
      " [-0.007 -0.017 -0.047 ... -0.061 -0.049 -0.023]\n",
      " [ 0.045  0.108  0.08  ...  0.009 -0.047 -0.025]]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import wfdb\n",
    "import os\n",
    "from collections import Counter\n",
    "from numpy import nan\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "twoch_df = pd.DataFrame()\n",
    "firstch_df = pd.DataFrame()\n",
    "#MR20190501\n",
    "Y_List = []\n",
    "X_List = list(range(0,240,1))\n",
    "\n",
    "# We exctract 120 samples to the left and right from the beat label \n",
    "half_qrs=120\n",
    "\n",
    "# Search in each file name of the folder 'mitdb'\n",
    "#for filename in os.listdir('mitdb'): # DEBUG: To avoid reading every file\n",
    "for i in range (1): # DEBUG: To avoid reading every file\n",
    "    filename = '100.dat' # DEBUG: To avoid reading every file\n",
    "    # If file name is a .dat and is not one of (102, 104, 107 or 217) recordings\n",
    "    if filename.endswith(\".dat\") and not filename.startswith(('102','104','107','217')):\n",
    "        # Read annotation (strip the .dat)\n",
    "        ann = wfdb.rdann('mitdb/' + filename.strip('.dat'), 'atr')\n",
    "        # Read signal file\n",
    "        record = wfdb.rdsamp('mitdb/' + filename.strip('.dat'))\n",
    "        \n",
    "        # The signal is in the position 0 of 'record'\n",
    "        data = record[0]\n",
    "        \n",
    "        # Prepare containers\n",
    "        signals, classes = [], []\n",
    "        firstch_signals = []\n",
    "        firstch_class = []\n",
    "                \n",
    "        # Beat extraction\n",
    "        for it, beat in enumerate(ann.symbol):\n",
    "            #if beat in good_beats:\n",
    "                \n",
    "                start = ann.sample[it] - half_qrs # getting first index of beat window\n",
    "                end = ann.sample[it] + half_qrs # gettint last index of beat window\n",
    "                qrs = data[start : end, :] # takes 240 samples, and both channels (:)\n",
    "                #MR20190501\n",
    "                qrs_firstch = data[start : end, 0] # takes 240 samples, only first channel (0)\n",
    "                    \n",
    "                # This may happen at the edges. len(qrs) must be 240\n",
    "                if len(qrs) != 2 * half_qrs: continue\n",
    "                    \n",
    "                # Here conditional and different Y_List for every kind of beat    \n",
    "                Y_List.append(qrs_firstch)\n",
    "                \n",
    "                # Extracting only first channel signals and classes \n",
    "                firstch_beat_class = '{}_{}'.format(record[1]['sig_name'][0], beat)\n",
    "                firstch_signals.append(qrs[:,0].reshape(240,))\n",
    "                firstch_class.append(firstch_beat_class)\n",
    "            \n",
    "                # Keep the channel type in the class name (both channels)\n",
    "                for ch in range(1): #2 DEBUG: Only 1 channel.\n",
    "                    beat_class = '{}_{}'.format(record[1]['sig_name'][ch], beat)\n",
    "                    signals.append(qrs[:, ch].reshape(240,))\n",
    "                    classes.append(beat_class)\n",
    "                \n",
    "        # Build new frame of full signals (to append afterwards)       \n",
    "        new_frame = pd.DataFrame({'qrs_data' : signals,\n",
    "                              'qrs_type' : classes})\n",
    "        \n",
    "        # Build new frame of first channel (to append afterwards)\n",
    "        new_frame_firstch = pd.DataFrame({'qrs_data' : firstch_signals,\n",
    "                              'qrs_type' : firstch_class})\n",
    "        \n",
    "        # Append full channel data frame\n",
    "        twoch_df = twoch_df.append(new_frame)\n",
    "        # Append first channel data frame\n",
    "        firstch_df = firstch_df.append(new_frame_firstch)\n",
    "        \n",
    "#MR20190505\n",
    "pca_df = pd.DataFrame(Y_List, columns = X_List) # 2271 rows, 240 columns (240 samples)\n",
    "#pca_df.head() # just to print\n",
    "\n",
    "# Scale before PCA #MR20190509\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Scaler\n",
    "scaler.fit(pca_df)\n",
    "mean_vector = scaler.mean_\n",
    "\n",
    "# Apply transform to both the training set and the test set.\n",
    "pca_df_scale = scaler.transform(pca_df)\n",
    "#pca_df_scale # just to print\n",
    "\n",
    "# --------------------------------------------------------------------------- # \n",
    "# -------------------------------- NO SCALE --------------------------------- # \n",
    "# --------------------------------------------------------------------------- # \n",
    "# PCA #MR20190505\n",
    "# Number of components\n",
    "#pca = PCA().fit(pca_df)\n",
    "#plt.figure(figsize=(10,5));\n",
    "#plt.plot(np.linspace(1,240,240),np.cumsum(pca.explained_variance_ratio_))\n",
    "#plt.xlim([1,10])\n",
    "#plt.xlabel('Number of components')\n",
    "#plt.ylabel('Cumulative explained variance');\n",
    "#plt.grid(True)\n",
    "#plt.yticks(np.arange(0.5, 1.01, 0.05))\n",
    "#plt.show()\n",
    "\n",
    "# Chosen 7 components (95% of explained variance)\n",
    "#pca = PCA(n_components=7)\n",
    "#pca.fit(pca_df)\n",
    "#print(pca.explained_variance_ratio_)\n",
    "#print(pca.components_) #7x240\n",
    "\n",
    "# --------------------------------------------------------------------------- # \n",
    "# --------------------------------- SCALE ----------------------------------- # \n",
    "# --------------------------------------------------------------------------- # \n",
    "# Number of components\n",
    "pca = PCA().fit(pca_df_scale)\n",
    "plt.figure(figsize=(10,5));\n",
    "plt.plot(np.linspace(1,240,240),np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlim([1,10])\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Cumulative explained variance');\n",
    "plt.grid(True)\n",
    "plt.yticks(np.arange(0.5, 1.01, 0.05))\n",
    "plt.show()\n",
    "\n",
    "# Chosen 9 components (95% of explained variance)\n",
    "pca = PCA(n_components=9)\n",
    "pca.fit(pca_df_scale)\n",
    "print(pca.explained_variance_ratio_)\n",
    "print(pca.components_) #9x240\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slide bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.281 -0.282 -0.283 -0.283 -0.284 -0.284 -0.285 -0.285 -0.285 -0.286\n",
      " -0.286 -0.286 -0.286 -0.286 -0.286 -0.287 -0.286 -0.286 -0.286 -0.286\n",
      " -0.286 -0.286 -0.286 -0.285 -0.285 -0.285 -0.285 -0.284 -0.284 -0.284\n",
      " -0.283 -0.282 -0.282 -0.281 -0.28  -0.279 -0.278 -0.276 -0.273 -0.27\n",
      " -0.266 -0.26  -0.254 -0.248 -0.241 -0.234 -0.226 -0.219 -0.213 -0.208\n",
      " -0.202 -0.196 -0.19  -0.185 -0.18  -0.176 -0.174 -0.173 -0.174 -0.176\n",
      " -0.178 -0.181 -0.183 -0.185 -0.186 -0.186 -0.185 -0.184 -0.182 -0.181\n",
      " -0.184 -0.19  -0.201 -0.214 -0.228 -0.242 -0.255 -0.265 -0.275 -0.282\n",
      " -0.288 -0.291 -0.294 -0.296 -0.297 -0.298 -0.3   -0.301 -0.303 -0.304\n",
      " -0.306 -0.307 -0.308 -0.309 -0.309 -0.31  -0.31  -0.311 -0.311 -0.312\n",
      " -0.313 -0.316 -0.323 -0.334 -0.35  -0.373 -0.402 -0.432 -0.458 -0.481\n",
      " -0.502 -0.505 -0.458 -0.354 -0.215 -0.058  0.135  0.381  0.645  0.857\n",
      "  0.977  0.975  0.8    0.456  0.055 -0.259 -0.417 -0.446 -0.421 -0.388\n",
      " -0.366 -0.356 -0.352 -0.349 -0.347 -0.346 -0.347 -0.348 -0.35  -0.351\n",
      " -0.352 -0.352 -0.353 -0.353 -0.353 -0.353 -0.353 -0.353 -0.353 -0.354\n",
      " -0.354 -0.354 -0.354 -0.354 -0.354 -0.354 -0.354 -0.355 -0.355 -0.354\n",
      " -0.355 -0.355 -0.355 -0.355 -0.355 -0.355 -0.355 -0.355 -0.355 -0.355\n",
      " -0.355 -0.355 -0.355 -0.355 -0.355 -0.355 -0.355 -0.355 -0.355 -0.355\n",
      " -0.355 -0.355 -0.355 -0.355 -0.355 -0.354 -0.354 -0.354 -0.354 -0.354\n",
      " -0.354 -0.354 -0.353 -0.354 -0.354 -0.355 -0.355 -0.356 -0.357 -0.359\n",
      " -0.36  -0.361 -0.363 -0.365 -0.367 -0.369 -0.371 -0.373 -0.375 -0.376\n",
      " -0.378 -0.379 -0.38  -0.38  -0.38  -0.379 -0.378 -0.377 -0.375 -0.372\n",
      " -0.368 -0.364 -0.359 -0.353 -0.347 -0.34  -0.333 -0.325 -0.317 -0.31\n",
      " -0.301 -0.294 -0.287 -0.28  -0.274 -0.269 -0.264 -0.259 -0.256 -0.253]\n",
      "[-0.325 -0.326 -0.327 -0.328 -0.328 -0.327 -0.329 -0.33  -0.331 -0.33\n",
      " -0.328 -0.327 -0.329 -0.33  -0.329 -0.329 -0.326 -0.327 -0.326 -0.327\n",
      " -0.328 -0.327 -0.326 -0.324 -0.324 -0.326 -0.326 -0.324 -0.323 -0.323\n",
      " -0.323 -0.321 -0.32  -0.32  -0.317 -0.316 -0.315 -0.314 -0.311 -0.308\n",
      " -0.305 -0.299 -0.293 -0.287 -0.28  -0.271 -0.261 -0.251 -0.244 -0.239\n",
      " -0.234 -0.227 -0.221 -0.213 -0.207 -0.203 -0.198 -0.194 -0.193 -0.194\n",
      " -0.197 -0.2   -0.204 -0.205 -0.206 -0.205 -0.205 -0.204 -0.2   -0.195\n",
      " -0.19  -0.193 -0.201 -0.216 -0.228 -0.241 -0.251 -0.261 -0.271 -0.28\n",
      " -0.288 -0.291 -0.293 -0.294 -0.297 -0.299 -0.299 -0.299 -0.299 -0.3\n",
      " -0.3   -0.303 -0.305 -0.305 -0.302 -0.303 -0.305 -0.307 -0.308 -0.306\n",
      " -0.307 -0.309 -0.314 -0.323 -0.337 -0.356 -0.382 -0.411 -0.439 -0.468\n",
      " -0.513 -0.548 -0.516 -0.422 -0.289 -0.135  0.056  0.302  0.567  0.781\n",
      "  0.902  0.9    0.73   0.39  -0.014 -0.334 -0.494 -0.503 -0.434 -0.381\n",
      " -0.356 -0.345 -0.337 -0.327 -0.318 -0.313 -0.31  -0.309 -0.31  -0.309\n",
      " -0.307 -0.305 -0.3   -0.297 -0.295 -0.296 -0.294 -0.292 -0.29  -0.289\n",
      " -0.287 -0.286 -0.285 -0.284 -0.282 -0.279 -0.28  -0.28  -0.279 -0.277\n",
      " -0.275 -0.273 -0.272 -0.272 -0.272 -0.27  -0.268 -0.265 -0.264 -0.265\n",
      " -0.266 -0.264 -0.263 -0.262 -0.262 -0.263 -0.263 -0.261 -0.26  -0.259\n",
      " -0.258 -0.257 -0.256 -0.254 -0.253 -0.25  -0.25  -0.25  -0.249 -0.247\n",
      " -0.244 -0.243 -0.242 -0.242 -0.24  -0.24  -0.239 -0.239 -0.239 -0.241\n",
      " -0.242 -0.241 -0.242 -0.242 -0.244 -0.246 -0.248 -0.247 -0.248 -0.249\n",
      " -0.25  -0.251 -0.252 -0.251 -0.251 -0.249 -0.248 -0.247 -0.244 -0.241\n",
      " -0.237 -0.233 -0.229 -0.226 -0.221 -0.215 -0.209 -0.203 -0.198 -0.193\n",
      " -0.187 -0.182 -0.176 -0.173 -0.169 -0.168 -0.166 -0.164 -0.163 -0.162]\n"
     ]
    }
   ],
   "source": [
    "from tkinter import *\n",
    "\n",
    "control = Tk()\n",
    "control.geometry(\"750x650+100+50\")\n",
    "control.title('Weights of the principal directions')\n",
    "\n",
    "iFont = 'Arial Bold'\n",
    "iSize = 14\n",
    "\n",
    "scale1 = Scale(control,orient=HORIZONTAL,length=600,width=20,sliderlength=10,from_=0,to=100,tickinterval=10)\n",
    "scale1.pack()\n",
    "\n",
    "scale2 = Scale(control,orient=HORIZONTAL,length=600,width=20,sliderlength=10,from_=0,to=100,tickinterval=10)\n",
    "scale2.pack()\n",
    "\n",
    "scale3 = Scale(control,orient=HORIZONTAL,length=600,width=20,sliderlength=10,from_=0,to=100,tickinterval=10)\n",
    "scale3.pack()\n",
    "\n",
    "scale4 = Scale(control,orient=HORIZONTAL,length=600,width=20,sliderlength=10,from_=0,to=100,tickinterval=10)\n",
    "scale4.pack()\n",
    "\n",
    "scale5 = Scale(control,orient=HORIZONTAL,length=600,width=20,sliderlength=10,from_=0,to=100,tickinterval=10)\n",
    "scale5.pack()\n",
    "\n",
    "scale6 = Scale(control,orient=HORIZONTAL,length=600,width=20,sliderlength=10,from_=0,to=100,tickinterval=10)\n",
    "scale6.pack()\n",
    "\n",
    "scale7 = Scale(control,orient=HORIZONTAL,length=600,width=20,sliderlength=10,from_=0,to=100,tickinterval=10)\n",
    "scale7.pack()\n",
    "\n",
    "scale8 = Scale(control,orient=HORIZONTAL,length=600,width=20,sliderlength=10,from_=0,to=100,tickinterval=10)\n",
    "scale8.pack()\n",
    "\n",
    "scale9 = Scale(control,orient=HORIZONTAL,length=600,width=20,sliderlength=10,from_=0,to=100,tickinterval=10)\n",
    "scale9.pack()\n",
    "\n",
    "def getValues():\n",
    "    p1 = scale1.get()\n",
    "    p2 = scale2.get()\n",
    "    p3 = scale3.get()\n",
    "    p4 = scale4.get()\n",
    "    p5 = scale5.get()\n",
    "    p6 = scale6.get()\n",
    "    p7 = scale7.get()\n",
    "    p8 = scale8.get()\n",
    "    p9 = scale9.get()\n",
    "        \n",
    "    #print(p1)\n",
    "    #print(p2)\n",
    "    #print(p3)\n",
    "    #print(p4)\n",
    "    #print(p5)\n",
    "    #print(p6)\n",
    "    #print(p7)\n",
    "    #print(p8)\n",
    "    #print(p9)\n",
    "    \n",
    "    pc = pca.components_\n",
    "    mv = mean_vector\n",
    "    Result_Vector_100 = p1*pc[0] + p2*pc[1] + p3*pc[2] + p4*pc[3] + p5*pc[4] + p6*pc[5] + p7*pc[6] + p8*pc[7] + p9*pc[8]\n",
    "    Result_Vector = mv + Result_Vector_100 / 100\n",
    "    print(Result_Vector) # len = 240\n",
    "    \n",
    "    # ----------------------------------------------------------------------------------------------------\n",
    "    # BOKEH PLOT\n",
    "    # ----------------------------------------------------------------------------------------------------\n",
    "    from bokeh.plotting import figure, output_file, show\n",
    "    list240 = list(range(0, 240))\n",
    "    plot = figure(title = 'Signal', plot_width = 60*iWidth, plot_height = 60*iHeight)\n",
    "    #plot.add_tools(LassoSelectTool())\n",
    "    plot.xaxis.axis_label = 'Time (samples)'\n",
    "    plot.yaxis.axis_label = 'mV'\n",
    "    # Mean vector\n",
    "    plot.line(list240, mv, line_color=\"blue\")\n",
    "    # Positive vector\n",
    "    plot.line(list240, Result_Vector, line_color=\"green\")\n",
    "    # Negative vector\n",
    "    plot.line(list240, -Result_Vector+2*mv, line_color=\"red\")\n",
    "    \n",
    "    show(plot)\n",
    "    \n",
    "btn = Button(control, text='OK', command=getValues, font = (iFont, 14))\n",
    "btn.pack()\n",
    "    \n",
    "    \n",
    "control.mainloop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotations of the PCA cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#firstch_df['qrs_type'].value_counts()\n",
    "#print(len(Y_List)) #Result 2271, is this 2271 beats in 101.dat?\n",
    "\n",
    "#plt.figure()\n",
    "#plt.plot(data[0:3000])\n",
    "#plt.plot(firstch_df.iloc[1111]['qrs_data'])\n",
    "#plt.title('QRS of type: {}'.format(firstch_df.iloc[1111]['qrs_type']))\n",
    "\n",
    "\n",
    "# Result: 240\n",
    "#print(len(signals[0]))\n",
    "#print(signals[0])\n",
    "\n",
    "# Class of beats of every beat\n",
    "#print(classes) # classes[0] corresponds to beat of 240 samples called signals[0]\n",
    "\n",
    "# Result: 240 and 240 (segmentation of the beat)\n",
    "#print(len(firstch_df.iloc[0]['qrs_data']), len(twoch_df.iloc[0]['qrs_data']))\n",
    "#print(len(firstch_df.iloc[0]))\n",
    "\n",
    "#print(data)\n",
    "#data2 = data[:, 0]\n",
    "\n",
    "#print(qrs)\n",
    "#print(len(qrs))\n",
    "\n",
    "\n",
    "#with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also\n",
    "#    display(firstch_df.iloc[1])\n",
    "    \n",
    "#X = firstch_df.iloc[0]['qrs_data']    \n",
    "#pca = PCA(n_components=2)\n",
    "#pca.fit(X)\n",
    "\n",
    "#list30 = []\n",
    "#for ele in range (0, 3000):\n",
    "#    list30 = list30 + [ele]\n",
    "\n",
    "#new_vec_Y = []\n",
    "#for el in range (0, 3000):\n",
    "#    new_vec_Y = new_vec_Y + [data[el][0]]\n",
    "    \n",
    "#from bokeh.plotting import figure, output_file, show\n",
    "#plot = figure(title = \"Plot test\", plot_width=1000, plot_height = 500)\n",
    "#plot.line(list30, new_vec_Y)\n",
    "#show(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tkinter import *\n",
    "#from tkinter.ttk import *\n",
    "import wfdb\n",
    "%matplotlib inline\n",
    "import wfdb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "iRecord = -1\n",
    "iSampleIni = 0\n",
    "iSampleFin = 3000\n",
    "iAnnotation = True\n",
    "iSymbols = True\n",
    "iTime_Units = ''\n",
    "iWidth = 20\n",
    "iHeight = 10\n",
    "\n",
    "window = Tk()\n",
    "\n",
    "# Window configurations\n",
    "window.geometry('600x320')\n",
    "#window.configure(background = 'black')\n",
    "window.title('Plot ECG MIT-BIH database with bokeh')\n",
    "\n",
    "iFont = 'Arial Bold'\n",
    "iSize = 14\n",
    "\n",
    "# --------------------------------\n",
    "# Record\n",
    "# --------------------------------\n",
    "# Label\n",
    "lbl01 = Label(window, text='Record', font = (iFont, iSize))\n",
    "lbl01.grid(column = 0, row = 0)\n",
    "# Combobox\n",
    "combo01 = Combobox(window, width = 9, font = (iFont, iSize))\n",
    "combo01['values'] = (100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 124, 200, 201, 202, 203, 205, 207, 208, 209, 210, 212, 213, 214, 215, 217, 219, 220, 221, 222, 223, 228, 230, 231, 232, 233, 234)\n",
    "combo01.current(0)\n",
    "combo01.grid(column = 1, row = 0)\n",
    "\n",
    "\n",
    "# --------------------------------\n",
    "# Init sample\n",
    "# --------------------------------\n",
    "# Label\n",
    "lbl02 = Label(window, text='Init sample', font = (iFont, iSize))\n",
    "lbl02.grid(column = 0, row = 1)\n",
    "# Input\n",
    "txt02 = Entry(window, width = 10, font = (iFont, iSize))\n",
    "txt02.insert(END, iSampleIni)\n",
    "txt02.grid(column = 1, row = 1)\n",
    "\n",
    "\n",
    "# --------------------------------\n",
    "# Final sample\n",
    "# --------------------------------\n",
    "# Label\n",
    "lbl03 = Label(window, text='Final sample', font = (iFont, iSize))\n",
    "lbl03.grid(column = 0, row = 2)\n",
    "# Input\n",
    "txt03 = Entry(window, width = 10, font = (iFont, iSize))\n",
    "txt03.insert(END, iSampleFin)\n",
    "txt03.grid(column = 1, row = 2)\n",
    "\n",
    "\n",
    "# --------------------------------\n",
    "# Annotations\n",
    "# --------------------------------\n",
    "# Label\n",
    "lbl04 = Label(window, text='Annotations', font = (iFont, iSize))\n",
    "lbl04.grid(column = 0, row = 3)\n",
    "# Check button\n",
    "chk04_state = BooleanVar()\n",
    "chk04_state.set(True) # Initially marked\n",
    "chk04 = Checkbutton(window, text='Enabled', var=chk04_state, font = (iFont, iSize))\n",
    "chk04.grid(column = 1, row = 3)\n",
    "\n",
    "\n",
    "# --------------------------------\n",
    "# Symbols\n",
    "# --------------------------------\n",
    "# Label\n",
    "lbl05 = Label(window, text='Symbols', font = (iFont, iSize))\n",
    "lbl05.grid(column = 0, row = 4)\n",
    "# Check button\n",
    "chk05_state = BooleanVar()\n",
    "chk05_state.set(True) # Initially marked\n",
    "chk05 = Checkbutton(window, text='Enabled', var=chk05_state, font = (iFont, iSize))\n",
    "chk05.grid(column = 1, row = 4)\n",
    "\n",
    "\n",
    "# --------------------------------\n",
    "# Channels\n",
    "# --------------------------------\n",
    "# Label\n",
    "lbl06 = Label(window, text='Channels', font = (iFont, iSize))\n",
    "lbl06.grid(column = 0, row = 5)\n",
    "# Check button 1\n",
    "chk06_1_state = BooleanVar()\n",
    "chk06_1_state.set(True) # Initially marked\n",
    "chk06_1 = Checkbutton(window, text='Channel 1', var=chk06_1_state, font = (iFont, iSize))\n",
    "chk06_1.grid(column = 1, row = 5)\n",
    "# Check button 2\n",
    "chk06_2_state = BooleanVar()\n",
    "chk06_2_state.set(True) # Initially marked\n",
    "chk06_2 = Checkbutton(window, text='Channel 2', var=chk06_2_state, font = (iFont, iSize))\n",
    "chk06_2.grid(column = 2, row = 5)\n",
    "\n",
    "\n",
    "# --------------------------------\n",
    "# Time units\n",
    "# --------------------------------\n",
    "# Label\n",
    "lbl07 = Label(window, text='Time units', font = (iFont, iSize))\n",
    "lbl07.grid(column = 0, row = 6)\n",
    "# Radio button\n",
    "selected07 = IntVar()\n",
    "rad07_1 = Radiobutton(window, text = 'Samples', value = 1, variable = selected07, font = (iFont, iSize))\n",
    "rad07_2 = Radiobutton(window, text = 'Seconds', value = 2, variable = selected07, font = (iFont, iSize))\n",
    "rad07_3 = Radiobutton(window, text = 'Minutes', value = 3, variable = selected07, font = (iFont, iSize))\n",
    "rad07_4 = Radiobutton(window, text = 'Hours', value = 4, variable = selected07, font = (iFont, iSize))\n",
    "selected07.set(1) # initial value\n",
    "rad07_1.grid(column = 1, row = 6)\n",
    "rad07_2.grid(column = 2, row = 6)\n",
    "rad07_3.grid(column = 3, row = 6)\n",
    "rad07_4.grid(column = 4, row = 6)\n",
    "\n",
    "# --------------------------------\n",
    "# Width of the plot\n",
    "# --------------------------------\n",
    "# Label\n",
    "lbl08 = Label(window, text='Plot width', font = (iFont, iSize))\n",
    "lbl08.grid(column = 0, row = 7)\n",
    "# Input\n",
    "txt08 = Entry(window, width = 10, font = (iFont, iSize))\n",
    "txt08.insert(END, iWidth)\n",
    "txt08.grid(column = 1, row = 7)\n",
    "#txt08.focus()\n",
    "\n",
    "# --------------------------------\n",
    "# Height of the plot\n",
    "# --------------------------------\n",
    "# Label\n",
    "lbl09 = Label(window, text='Plot height', font = (iFont, iSize))\n",
    "lbl09.grid(column = 0, row = 8)\n",
    "# Input\n",
    "txt09 = Entry(window, width = 10, font = (iFont, iSize))\n",
    "txt09.insert(END, iHeight)\n",
    "txt09.grid(column = 1, row = 8)\n",
    "#txt09.focus()\n",
    "\n",
    "\n",
    "# Button Plot\n",
    "def plotClicked():\n",
    "    # Obtain configured values\n",
    "    iRecord = int(combo01.get())\n",
    "    iSampleIni = int(txt02.get())\n",
    "    iSampleFin = int(txt03.get())\n",
    "    iAnnotations = chk04_state.get()\n",
    "    iSymbols = chk05_state.get()\n",
    "    # Channels\n",
    "    iChannel1 = chk06_1_state.get()\n",
    "    iChannel2 = chk06_2_state.get()\n",
    "    iTime_Units_Temp = selected07.get()\n",
    "    if iTime_Units_Temp == 1:\n",
    "        iTime_Units = 'samples'\n",
    "        iTime_Units_Label = 'Time (samples)'\n",
    "    elif iTime_Units_Temp == 2:\n",
    "        iTime_Units = 'seconds'\n",
    "        iTime_Units_Label = 'Time (seconds)'\n",
    "    elif iTime_Units_Temp == 3:\n",
    "        iTime_Units = 'minutes'\n",
    "        iTime_Units_Label = 'Time (minutes)'\n",
    "    elif iTime_Units_Temp == 4:\n",
    "        iTime_Units = 'hours'\n",
    "        iTime_Units_Label = 'Time (hours)'\n",
    "    iWidth = int(txt08.get())\n",
    "    iHeight = int(txt09.get())\n",
    "    \n",
    "    # Prepare strings\n",
    "    iRecord_Str = 'mitdb/' + str(iRecord)\n",
    "    iAnnotation_Str = 'mitdb/' + str(iRecord)\n",
    "    iTitle = 'MIT-BIH Record ' + str(iRecord)\n",
    "        \n",
    "    # Channels\n",
    "    channelList = []\n",
    "    # Channel 1\n",
    "    if iChannel1 == True:\n",
    "        channelList = channelList + [0]\n",
    "    # Channel 2\n",
    "    if iChannel2 == True:\n",
    "        channelList = channelList + [1]\n",
    "            \n",
    "    # Debug prints\n",
    "    #print('Record:', iRecord)\n",
    "    #print('Init sample:', iSampleIni)\n",
    "    #print(channelList)\n",
    "    #print(type(iRecord_Str))\n",
    "    #print('Final sample:', iSampleFin)\n",
    "    #print('Annotations:', iAnnotations)\n",
    "    #print('Symbols:', iSymbols)\n",
    "    #print('Time units:', iTime_Units)\n",
    "    #print('Plot width:', iWidth)\n",
    "    #print('Plot height:', iHeight)\n",
    "    \n",
    "    # ----------------------------------------------------------------- #\n",
    "    # -------------------------- WFDB PLOT ---------------------------- #\n",
    "    # ----------------------------------------------------------------- #\n",
    "    \n",
    "    wfdb.plot_wfdb(record = wfdb.rdrecord(iRecord_Str, sampfrom = iSampleIni, sampto = iSampleFin, channels = channelList), annotation = wfdb.rdann(iRecord_Str, 'atr', sampfrom = iSampleIni, sampto = iSampleFin), plot_sym = iSymbols, time_units = iTime_Units, title = iTitle, figsize = (iWidth, iHeight))\n",
    "    \n",
    "    # ----------------------------------------------------------------- #\n",
    "    # -------------------------- BOKEH PLOT --------------------------- #\n",
    "    # ----------------------------------------------------------------- #\n",
    "    # Read signal file\n",
    "    record = wfdb.rdsamp('mitdb/' + str(iRecord))\n",
    "    # Read annotation (strip the .dat)\n",
    "    ann = wfdb.rdann('mitdb/' + str(iRecord), 'atr')\n",
    "\n",
    "    # The signal is in the position 0 of 'record'\n",
    "    data = record[0]\n",
    "\n",
    "    # X vector\n",
    "    list30 = []\n",
    "    for ele in range (0, iSampleFin): #iSampleIni?\n",
    "        if iTime_Units == 'seconds':\n",
    "            ele = ele/360 # Fs = 360 Hz\n",
    "        elif iTime_Units == 'minutes':\n",
    "            ele = ele/21600\n",
    "        elif iTime_Units == 'hours':\n",
    "            ele = ele/1296000\n",
    "        list30 = list30 + [ele]\n",
    "\n",
    "        \n",
    "    new_vec_Y = []\n",
    "    new_vec_Y2 = []\n",
    "    for el in range (0, iSampleFin): #iSampleIni?\n",
    "        new_vec_Y = new_vec_Y + [data[el][0]]\n",
    "        new_vec_Y2 = new_vec_Y2 + [data[el][1]]\n",
    "\n",
    "    from bokeh.plotting import figure, output_file, show\n",
    "    from bokeh.models import ColumnDataSource, Range1d, LabelSet, Label\n",
    "    from bokeh.layouts import column\n",
    "    \n",
    "    # ----------------------------------------------------------------------------------------------------\n",
    "    # Channel 1\n",
    "    # ----------------------------------------------------------------------------------------------------\n",
    "    plot = figure(title = iTitle + ' Channel 1', plot_width = 60*iWidth, plot_height = 60*iHeight)\n",
    "    #plot.add_tools(LassoSelectTool())\n",
    "    plot.xaxis.axis_label = iTime_Units_Label\n",
    "    plot.yaxis.axis_label = 'mV'\n",
    "    plot.line(list30, new_vec_Y)\n",
    "    \n",
    "    \n",
    "    # ----------------------------------------------------------------------------------------------------\n",
    "    # Annotations (only channel 1)\n",
    "    # ----------------------------------------------------------------------------------------------------\n",
    "    if iAnnotations:\n",
    "        annotation_Samples = []\n",
    "        for it, beat in enumerate(ann.symbol):\n",
    "            annotation_Sample = ann.sample[it]\n",
    "            #if iTime_Units == 'seconds': # Try this with different units\n",
    "            #    annotation_Sample = annotation_Sample/360 # Fs = 360 Hz\n",
    "            #elif iTime_Units == 'minutes':\n",
    "            #    annotation_Sample = annotation_Sample/21600\n",
    "            #elif iTime_Units == 'hours':\n",
    "            #    annotation_Sample = annotation_Sample/1296000\n",
    "            annotation_Samples = annotation_Samples + [annotation_Sample]\n",
    "        #for it in annotation_Samples:\n",
    "        for i in range (1, len(annotation_Samples)): # 1 to avoid first '+'\n",
    "            if annotation_Samples[i] < iSampleFin:\n",
    "                labels = Label(x=annotation_Samples[i] + 20, y=new_vec_Y[annotation_Samples[i]], x_units='data', text=ann.symbol[i], render_mode='css',\n",
    "              border_line_color='black', border_line_alpha=1.0,\n",
    "              background_fill_color='white', background_fill_alpha=1.0)\n",
    "                plot.add_layout(labels)\n",
    "\n",
    "    #LabelSet(x='x', y='y', text='names', level='glyph', x_offset=5, y_offset=5)#, source=source)\n",
    "        \n",
    "    # ----------------------------------------------------------------------------------------------------\n",
    "    # Channel 2\n",
    "    # ----------------------------------------------------------------------------------------------------\n",
    "    if (iChannel1 and iChannel2) or (iChannel1 == False and iChannel2 == False):\n",
    "        plot2 = figure(title = iTitle + ' Channel 2', plot_width = 60*iWidth, plot_height = 60*iHeight, x_range = plot.x_range)\n",
    "    else:\n",
    "        plot2 = figure(title = iTitle + ' Channel 2', plot_width = 60*iWidth, plot_height = 60*iHeight)    \n",
    "    plot2.xaxis.axis_label = iTime_Units_Label\n",
    "    plot2.yaxis.axis_label = 'mV'\n",
    "    plot2.line(list30, new_vec_Y2)\n",
    "    \n",
    "    # Show figures\n",
    "    if iChannel1 and not iChannel2:\n",
    "        show(plot)\n",
    "    elif iChannel2 and not iChannel1:\n",
    "        show(plot2)\n",
    "    else:\n",
    "        show(column(plot, plot2))\n",
    "        \n",
    "    # Close window\n",
    "    window.destroy()    \n",
    "\n",
    "    \n",
    "#btn = Button(window, text='Plot', bg='blue', fg='white', command=plotClicked)\n",
    "btn = Button(window, text='Plot', command=plotClicked, font = (iFont, 20))\n",
    "btn.grid(column = 3, row = 9)\n",
    "\n",
    "\n",
    "window.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import wfdb\n",
    "import os\n",
    "from collections import Counter\n",
    "from numpy import nan\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "iRecord = 100\n",
    "\n",
    "# Read signal file\n",
    "record = wfdb.rdsamp('mitdb/' + str(iRecord))\n",
    "# Read annotation (strip the .dat)\n",
    "ann = wfdb.rdann('mitdb/' + str(iRecord), 'atr')\n",
    "\n",
    "# The signal is in the position 0 of 'record'\n",
    "data = record[0]\n",
    "\n",
    "list30 = []\n",
    "for ele in range (0, 3000):\n",
    "    list30 = list30 + [ele]\n",
    "\n",
    "new_vec_Y = []\n",
    "for el in range (0, 3000):\n",
    "    new_vec_Y = new_vec_Y + [data[el][0]]\n",
    "\n",
    "from bokeh.plotting import figure, output_file, show\n",
    "plot = figure(title = \"Plot test\", plot_width=1000, plot_height = 500)\n",
    "plot.line(list30, new_vec_Y)\n",
    "show(plot)    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, output_file, show\n",
    "\n",
    "x = [1, 2, 3, 4, 5]\n",
    "y = [6, 7, 2, 4, 5]\n",
    "x2 = [1, 2, 3, 4, 5]\n",
    "y2 = [3, 1, 6, 4, 3]\n",
    "\n",
    "plot = figure(title = \"Plot test\", plot_width=300, plot_height = 300)\n",
    "\n",
    "# Circle\n",
    "#plot.circle(x=[1, 2, 3], y=[1, 2, 3], color=(100, 100, 255), size=20)\n",
    "\n",
    "# Scatter plot\n",
    "#plot.scatter(x, y, marker=\"square\", fill_color = \"red\")\n",
    "\n",
    "# Line plot\n",
    "plot.line(x, y)\n",
    "\n",
    "# Multi line (superposition of lines)\n",
    "#plot.multi_line(xs = [x, x2], ys = [y, y2], color = ['red', 'green'])\n",
    "\n",
    "\n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on http://127.0.0.1:8050/\n",
      "Running on http://127.0.0.1:8050/\n",
      "Running on http://127.0.0.1:8050/\n",
      "Debugger PIN: 482-601-531\n",
      "Debugger PIN: 482-601-531\n",
      "Debugger PIN: 482-601-531\n",
      " * Serving Flask app \"__main__\" (lazy loading)\n",
      " * Environment: production\n",
      "   WARNING: Do not use the development server in a production environment.\n",
      "   Use a production WSGI server instead.\n",
      " * Debug mode: on\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/miguel/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3299: UserWarning:\n",
      "\n",
      "To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import dash\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "\n",
    "external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n",
    "\n",
    "app = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n",
    "\n",
    "app.layout = html.Div(children=[\n",
    "    html.H1(children='Hello Dash'),\n",
    "\n",
    "    html.Div(children='''\n",
    "        Dash: A web application framework for Python.\n",
    "    '''),\n",
    "\n",
    "    dcc.Graph(\n",
    "        id='example-graph',\n",
    "        figure={\n",
    "            'data': [\n",
    "                {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},\n",
    "                {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},\n",
    "            ],\n",
    "            'layout': {\n",
    "                'title': 'Dash Data Visualization'\n",
    "            }\n",
    "        }\n",
    "    )\n",
    "])\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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