User_Interface_Justin_v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Title Goes Here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "SVC = svm.SVC()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import os.path\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data generation code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOTE: this is referencing a specific notebook !!! I might need to change this in the future to use the right code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fuvIBet999+nXx7kWm5qaxsmB5XKZ0dFRjEYjHo8HAIMhj90epakpjcMRxWoN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f85da20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'events2Model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/Users/justingosses/Code/Hackathon_Agile/hackathon2017/Project_hackathon_clean_yuriy3.ipynb\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtestUnseenEvents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevents2Model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mevents3_testEvents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'events2Model' is not defined"
     ]
    }
   ],
   "source": [
    "%run 'Project_hackathon_clean_yuriy3.ipynb'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "points_gen() missing 1 required positional argument: 'nMU'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-fb013b4b765b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Generate points\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mevents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpoints_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Convert generated points to data (t(x))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoints_to_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: points_gen() missing 1 required positional argument: 'nMU'"
     ]
    }
   ],
   "source": [
    "# Generate points\n",
    "events=points_gen(2,2,2)\n",
    "\n",
    "# Convert generated points to data (t(x))\n",
    "data, labels, x = points_to_data(events)\n",
    "\n",
    "# Convert data to seismic\n",
    "seism = data_to_seism(data, x)\n",
    "\n",
    "# Plot\n",
    "bob = data_plot(seism,data,labels,x,'yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getBothDataSets(input1,input2,input3,input4):\n",
    "    # generate points for training\n",
    "    eventsA = points_gen(200,200,200,200)\n",
    "    # Convert generated points A to data (t(x))\n",
    "    data, labels, x = points_to_data(eventsA)\n",
    "    # generate points for testing\n",
    "    eventsB = points_gen(200,200,200,200)\n",
    "    # Convert generated points A to data (t(x))\n",
    "    dataB, labelsB, xB = points_to_data(eventsB)\n",
    "    # make a model from training data\n",
    "    model = comboFunctionA(eventsA)\n",
    "    # generate predicted labels for B based on model from A\n",
    "    newLabels = predict_from_data(eventsB, model)\n",
    "    \n",
    "    # Convert data to seismic\n",
    "    seism = data_to_seism(data, x)\n",
    "    # Convert data to seismic\n",
    "    seismB = data_to_seism(dataB, xB)\n",
    "\n",
    "    dataA = data_plot(data,labels,x,'no')\n",
    "    dataBLabels = data_plot(data,labels,x,'yes')\n",
    "    dataB = data_plot(data,newLabels,x,'yes')\n",
    "    \n",
    "    fig = bob.get_figure()\n",
    "    fig.savefig(\"output.png\", bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ed68ac8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZbFaeCYYmTSbTmdGDLx2TlcvlMhYWFtSMJez+7PF4lCuN4clmswmHw6GK9vQ8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\ntdQcUEvNAbXUHFBLzQG11C7oj0vKOhM5IQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1f9d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fuvIBet999+nXx7kWm5qaxsmB5XKZ0dFRjEYjHo8HAIMhj90epakpjcMRxWoN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\nXuHEiccIhb5MpXIb6+sbiTM7xUQRVL+KotE1SqUP27+HDTR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1f9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dnD69gkBg/JwIfqXp6wOWp3NNqv4pu60SBRRgVd9U9L/BYKC6uhqv14sQ9Rw/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ea61b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate points for training\n",
    "events2 = points_gen(200,200,200,200)\n",
    "\n",
    "# make a model from training data\n",
    "model = comboFunctionA(events2)\n",
    "\n",
    "newLabels = predict_from_data(events, model)\n",
    "\n",
    "data_plot(data,labels,x,'no')\n",
    "data_plot(data,labels,x,'yes')\n",
    "data_plot(data,newLabels,x,'yes')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'bob' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-0de94e3486ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_figure\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      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"output.png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tight'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'bob' is not defined"
     ]
    }
   ],
   "source": [
    "fig = bob.get_figure()\n",
    "fig.savefig(\"output.png\", bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bigPythonFunction(input1,input2,input3,input4):\n",
    "    print(\"bigPythonFunction works, it returns = \",input1,input2,input3,input4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'input1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-e4d9de76cb45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtestPythonCode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbigPythonFunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minput2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minput3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minput4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'input1' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Load HTML/CSS/JavaScript requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require.config({\n",
       "  paths: {\n",
       "      d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min'\n",
       "  }\n",
       "});"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "require.config({\n",
    "  paths: {\n",
    "      d3: '//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min'\n",
    "  }\n",
    "});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require.config({\n",
       "  paths: {\n",
       "      bootstrapCSS: '//maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css'\n",
       "  }\n",
       "});"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "require.config({\n",
    "  paths: {\n",
    "      bootstrapCSS: '//maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css'\n",
    "  }\n",
    "});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require.config({\n",
       "  paths: {\n",
       "      bootstrap: '//maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js'\n",
       "  }\n",
       "});"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "require.config({\n",
    "  paths: {\n",
    "      bootstrap: '//maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js'\n",
    "  }\n",
    "});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User Interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eventually, this might be moved to a normal HTML/CSS/JS webpage outside of a jupyter notebook that is accessible like any other webpage and either talks to a server to run the models or if want to get really fancy (and more secure) does everything including applying the model in the browser by porting the python code to JavaScript via web assembly. For now though, we're just running it in the jupyter notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HTML, CSS, and JavaScript code for User Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "\n",
    "def on_button_clicked(b):\n",
    "    print(\"Button clicked.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       " body {\n",
       "    background-color: lightblue;\n",
       "}\n",
       "div#title{\n",
       "    text-align:center;\n",
       "    margin-top: 40px;\n",
       "    margin-bottom: 40px;\n",
       "    hieght: 150px;\n",
       "}\n",
       "div#parameters {\n",
       "    height: 100px;\n",
       "    text-align: center;\n",
       "}\n",
       "h2#parameters {\n",
       "    height: 50px; \n",
       "}\n",
       "img{\n",
       "    padding-left:50px;\n",
       "}\n",
       "</style>\n",
       "<div id=\"title\">\n",
       "    <h1>Seismic Shotgather Record Colorizer</h1>\n",
       "</div>\n",
       "<div id=\"parameters\" style=\"height:100px;\">\n",
       "    <h2>Parameters</h2>\n",
       "    <div>\n",
       "         direct: <input type=\"text\" id=\"direct\" value=\"10\">\n",
       "         reflections: <input type=\"text\" id=\"reflections\" value=\"8\">\n",
       "         multiples: <input type=\"text\" id=\"multiples\" value=\"7\">\n",
       "         Coherent Noise: <input type=\"text\" id=\"coherentNoise\" value=\"6\"><br>\n",
       "         <button onclick=\"set_value()\">Generate images & Train Model</button>\n",
       "         <button onclick=\"set_value()\">Use Model to label right image</button>\n",
       "    </div>\n",
       "</div>\n",
       "<div\">\n",
       "</div>\n",
       "<div class=\"col-md-4 marginL\">\n",
       "    <h4>Labeled Data A</h4>\n",
       "    <p>Came with labels, which are colored.</p>\n",
       "    <p>Labels used to generate SVM ML model.</p>\n",
       "    <img src=\"output.png\" alt=\"gathers\" align=\"middle\">\n",
       "    <div>\n",
       "        <button onclick=\"set_value()\">Export image used in training</button>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-4 marginL\">\n",
       "    <h4>Unlabled Data B</h4>\n",
       "    <p>Labels (colors) not original but found using model</p>\n",
       "    <p>Model came from example to left</p>\n",
       "    <img src=\"output.png\" alt=\"gathers\" align=\"middle\">\n",
       "    <div>\n",
       "        <button onclick=\"set_value()\">Export image now colorized by SVM model</button>\n",
       "    </div>\n",
       "</div>\n",
       "<div class=\"col-md-4 marginL\">\n",
       "    <h4>Labled Data B</h4>\n",
       "    <p>Labels (colors) are original </p>\n",
       "    <p>from this dataset</p>\n",
       "    <img src=\"output.png\" alt=\"gathers\" align=\"middle\">\n",
       "    <div>\n",
       "        <button onclick=\"set_value()\">Export image with original labels</button>\n",
       "    </div>\n",
       "</div>\n",
       "<p id=\"tester\">test</p>\n",
       "\n",
       "<script type=\"text/Javascript\">\n",
       "    function set_value(){\n",
       "        console.log(\"test the console workss\");\n",
       "        var direct = document.getElementById('direct').value;\n",
       "        var reflections = document.getElementById('reflections').value;\n",
       "        var multiples = document.getElementById('multiples').value;\n",
       "        var coherentNoise = document.getElementById('coherentNoise').value;\n",
       "        console.log(\"test the console workss\");\n",
       "        var_value = \"testheretest\"\n",
       "        var command = var_name + \" = '\" + var_value + \"'\";\n",
       "        var command1 = var_dir + \" = '\"+ direct +\"'\";\n",
       "        var command0 = var_dir + \" = \" + direct + \", \" + var_refl + \" = \" + reflections+ \", \" + var_mult + \" = \" + multiples + \", \"+ var_Noise + \" = \" + coherentNoise + \";\n",
       "        var command3 = var_dir + \" = '\" + var_value + \"'\";\n",
       "        var command4 = var_dir + \" = '\" + 4 + \"'\";\n",
       "        var command2 = 'bigPythonFunction(\"+direct+reflections+multiples+coherentNoise+\")'\n",
       "        var commandOrig = direct + \" = '\" + direct + \"'\";\n",
       "        console.log(\"test the console works: command is \",command);\n",
       "        var kernel = IPython.notebook.kernel;\n",
       "        kernel.execute(command);\n",
       "    }\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "input_form = \"\"\"\n",
    "<style>\n",
    " body {\n",
    "    background-color: lightblue;\n",
    "}\n",
    "div#title{\n",
    "    text-align:center;\n",
    "    margin-top: 40px;\n",
    "    margin-bottom: 40px;\n",
    "    hieght: 150px;\n",
    "}\n",
    "div#parameters {\n",
    "    height: 100px;\n",
    "    text-align: center;\n",
    "}\n",
    "h2#parameters {\n",
    "    height: 50px; \n",
    "}\n",
    "img{\n",
    "    padding-left:50px;\n",
    "}\n",
    "</style>\n",
    "<div id=\"title\">\n",
    "    <h1>Seismic Shotgather Record Colorizer</h1>\n",
    "</div>\n",
    "<div id=\"parameters\" style=\"height:100px;\">\n",
    "    <h2>Parameters</h2>\n",
    "    <div>\n",
    "         direct: <input type=\"text\" id=\"direct\" value=\"10\">\n",
    "         reflections: <input type=\"text\" id=\"reflections\" value=\"8\">\n",
    "         multiples: <input type=\"text\" id=\"multiples\" value=\"7\">\n",
    "         Coherent Noise: <input type=\"text\" id=\"coherentNoise\" value=\"6\"><br>\n",
    "         <button onclick=\"set_value()\">Generate images & Train Model</button>\n",
    "         <button onclick=\"set_value()\">Use Model to label right image</button>\n",
    "    </div>\n",
    "</div>\n",
    "<div\">\n",
    "</div>\n",
    "<div class=\"col-md-4 marginL\">\n",
    "    <h4>Labeled Data A</h4>\n",
    "    <p>Came with labels, which are colored.</p>\n",
    "    <p>Labels used to generate SVM ML model.</p>\n",
    "    <img src=\"output.png\" alt=\"gathers\" align=\"middle\">\n",
    "    <div>\n",
    "        <button onclick=\"set_value()\">Export image used in training</button>\n",
    "    </div>\n",
    "</div>\n",
    "<div class=\"col-md-4 marginL\">\n",
    "    <h4>Unlabled Data B</h4>\n",
    "    <p>Labels (colors) not original but found using model</p>\n",
    "    <p>Model came from example to left</p>\n",
    "    <img src=\"output.png\" alt=\"gathers\" align=\"middle\">\n",
    "    <div>\n",
    "        <button onclick=\"set_value()\">Export image now colorized by SVM model</button>\n",
    "    </div>\n",
    "</div>\n",
    "<div class=\"col-md-4 marginL\">\n",
    "    <h4>Labled Data B</h4>\n",
    "    <p>Labels (colors) are original </p>\n",
    "    <p>from this dataset</p>\n",
    "    <img src=\"output.png\" alt=\"gathers\" align=\"middle\">\n",
    "    <div>\n",
    "        <button onclick=\"set_value()\">Export image with original labels</button>\n",
    "    </div>\n",
    "</div>\n",
    "<p id=\"tester\">test</p>\n",
    "\"\"\"\n",
    "\n",
    "javascript = \"\"\"\n",
    "<script type=\"text/Javascript\">\n",
    "    function set_value(){\n",
    "        console.log(\"test the console workss\");\n",
    "        var direct = document.getElementById('direct').value;\n",
    "        var reflections = document.getElementById('reflections').value;\n",
    "        var multiples = document.getElementById('multiples').value;\n",
    "        var coherentNoise = document.getElementById('coherentNoise').value;\n",
    "        console.log(\"test the console workss\");\n",
    "        var_value = \"testheretest\"\n",
    "        var command = var_name + \" = '\" + var_value + \"'\";\n",
    "        var command1 = var_dir + \" = '\"+ direct +\"'\";\n",
    "        var command0 = var_dir + \" = \" + direct + \", \" + var_refl + \" = \" + reflections+ \", \" + var_mult + \" = \" + multiples + \", \"+ var_Noise + \" = \" + coherentNoise + \";\n",
    "        var command3 = var_dir + \" = '\" + var_value + \"'\";\n",
    "        var command4 = var_dir + \" = '\" + 4 + \"'\";\n",
    "        var command2 = 'bigPythonFunction(\"+direct+reflections+multiples+coherentNoise+\")'\n",
    "        var commandOrig = direct + \" = '\" + direct + \"'\";\n",
    "        console.log(\"test the console works: command is \",command);\n",
    "        var kernel = IPython.notebook.kernel;\n",
    "        kernel.execute(command);\n",
    "    }\n",
    "</script>\n",
    "\"\"\" \n",
    "# def testPythonCode():\n",
    "#     bigPythonFunction(direct,reflections,multiples,coherentNoise)\n",
    "# % testPythonCode\n",
    "\n",
    "# var command = 'bigPythonFunction(\"+direct+reflections+multiples+coherentNoise+\")'\n",
    "\n",
    "\n",
    "HTML(input_form + javascript)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_form = \"\"\"\n",
    "<style>\n",
    " body {\n",
    "    background-color: lightblue;\n",
    "}\n",
    "div#title{\n",
    "    text-align:center;\n",
    "    margin-top: 40px;\n",
    "    margin-bottom: 40px;\n",
    "    hieght: 150px;\n",
    "}\n",
    "div#parameters {\n",
    "    height: 100px;\n",
    "    text-align: center;\n",
    "}\n",
    "h2#parameters {\n",
    "    height: 50px; \n",
    "}\n",
    "img{\n",
    "    padding-left:50px;\n",
    "}\n",
    "</style>\n",
    "<div id=\"title\">\n",
    "    <h1>Seismic Shotgather Record Colorizer</h1>\n",
    "</div>\n",
    "<div id=\"parameters\" style=\"height:100px;\">\n",
    "    <h2>Parameters</h2>\n",
    "    <div>\n",
    "         direct: <input type=\"text\" id=\"direct\" value=\"10\">\n",
    "         reflections: <input type=\"text\" id=\"reflections\" value=\"8\">\n",
    "         multiples: <input type=\"text\" id=\"multiples\" value=\"7\">\n",
    "         Coherent Noise: <input type=\"text\" id=\"coherentNoise\" value=\"6\"><br>\n",
    "         <button onclick=\"set_value()\">Generate images & Train Model</button>\n",
    "         <button onclick=\"set_value()\">Use Model to label right image</button>\n",
    "    </div>\n",
    "</div>\n",
    "<div\">\n",
    "</div>\n",
    "<div class=\"col-md-4 marginL\">\n",
    "    <h4>Labeled Data A</h4>\n",
    "    <p>Came with labels, which are colored.</p>\n",
    "    <p>Labels used to generate SVM ML model.</p>\n",
    "    <img src=\"output.png\" alt=\"gathers\" align=\"middle\">\n",
    "    <div>\n",
    "        <button onclick=\"set_value()\">Export image used in training</button>\n",
    "    </div>\n",
    "</div>\n",
    "<div class=\"col-md-4 marginL\">\n",
    "    <h4>Unlabled Data B</h4>\n",
    "    <p>Labels (colors) not original but found using model</p>\n",
    "    <p>Model came from example to left</p>\n",
    "    <img src=\"output.png\" alt=\"gathers\" align=\"middle\">\n",
    "    <div>\n",
    "        <button onclick=\"set_value()\">Export image now colorized by SVM model</button>\n",
    "    </div>\n",
    "</div>\n",
    "<div class=\"col-md-4 marginL\">\n",
    "    <h4>Labled Data B</h4>\n",
    "    <p>Labels (colors) are original </p>\n",
    "    <p>from this dataset</p>\n",
    "    <img src=\"output.png\" alt=\"gathers\" align=\"middle\">\n",
    "    <div>\n",
    "        <button onclick=\"set_value()\">Export image with original labels</button>\n",
    "    </div>\n",
    "</div>\n",
    "<p id=\"tester\">test</p>\n",
    "\"\"\"\n",
    "\n",
    "javascript = \"\"\"\n",
    "<script type=\"text/Javascript\">\n",
    "    function set_value(){\n",
    "        console.log(\"test the console workss\");\n",
    "        var direct = document.getElementById('direct').value;\n",
    "        var reflections = document.getElementById('reflections').value;\n",
    "        var multiples = document.getElementById('multiples').value;\n",
    "        var coherentNoise = document.getElementById('coherentNoise').value;\n",
    "        console.log(\"test the console workss\");\n",
    "        var_value = \"testheretest\"\n",
    "        var command = var_name + \" = '\" + var_value + \"'\";\n",
    "        var command1 = var_dir + \" = '\"+ direct +\"'\";\n",
    "        var command0 = var_dir + \" = \" + direct + \", \" + var_refl + \" = \" + reflections+ \", \" + var_mult + \" = \" + multiples + \", \"+ var_Noise + \" = \" + coherentNoise + \";\n",
    "        var command3 = var_dir + \" = '\" + var_value + \"'\";\n",
    "        var command4 = var_dir + \" = '\" + 4 + \"'\";\n",
    "        var command2 = 'bigPythonFunction(\"+direct+reflections+multiples+coherentNoise+\")'\n",
    "        var commandOrig = direct + \" = '\" + direct + \"'\";\n",
    "        console.log(\"test the console works: command is \",command);\n",
    "        var kernel = IPython.notebook.kernel;\n",
    "        kernel.execute(command);\n",
    "    }\n",
    "</script>\n",
    "\"\"\" \n",
    "# def testPythonCode():\n",
    "#     bigPythonFunction(direct,reflections,multiples,coherentNoise)\n",
    "# % testPythonCode\n",
    "\n",
    "# var command = 'bigPythonFunction(\"+direct+reflections+multiples+coherentNoise+\")'\n",
    "\n",
    "\n",
    "HTML(input_form + javascript)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'var_name' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-83-2c93a2fe3505>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# button.on_click(on_button_clicked)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# button.on_click(bigPythonFunction(direct,reflections,multiples,coherentNoise))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mbutton\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_click\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'var_name' is not defined"
     ]
    }
   ],
   "source": [
    "button = widgets.Button(description=\"Click Me!\")\n",
    "display(button)\n",
    "# button.on_click(on_button_clicked)\n",
    "# button.on_click(bigPythonFunction(direct,reflections,multiples,coherentNoise))\n",
    "button.on_click(print(var_name))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"background-color:gainsboro; border:solid black; width:300px; padding:20px;\">\n",
       "Variable Name: <input type=\"text\" id=\"var_name\" value=\"foo\"><br>\n",
       "Variable Value: <input type=\"text\" id=\"var_value\" value=\"bar\"><br>\n",
       "<button onclick=\"set_value()\">Set Value</button>\n",
       "</div>\n",
       "\n",
       "<script type=\"text/Javascript\">\n",
       "    function set_value(){\n",
       "        var var_name = document.getElementById('var_name').value;\n",
       "        var var_value = document.getElementById('var_value').value;\n",
       "        var command = var_name + \" = '\" + var_value + \"'\";\n",
       "        console.log(\"Executing Command: \",command);\n",
       "        \n",
       "        var kernel = IPython.notebook.kernel;\n",
       "        kernel.execute(command);\n",
       "    }\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_form = \"\"\"\n",
    "<div style=\"background-color:gainsboro; border:solid black; width:300px; padding:20px;\">\n",
    "Variable Name: <input type=\"text\" id=\"var_name\" value=\"foo\"><br>\n",
    "Variable Value: <input type=\"text\" id=\"var_value\" value=\"bar\"><br>\n",
    "<button onclick=\"set_value()\">Set Value</button>\n",
    "</div>\n",
    "\"\"\"\n",
    "\n",
    "javascript = \"\"\"\n",
    "<script type=\"text/Javascript\">\n",
    "    function set_value(){\n",
    "        var var_name = document.getElementById('var_name').value;\n",
    "        var var_value = document.getElementById('var_value').value;\n",
    "        var command = var_name + \" = '\" + var_value + \"'\";\n",
    "        console.log(\"Executing Command: \",command);\n",
    "        \n",
    "        var kernel = IPython.notebook.kernel;\n",
    "        kernel.execute(command);\n",
    "    }\n",
    "</script>\n",
    "\"\"\"\n",
    "\n",
    "HTML(input_form + javascript)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bar234\n"
     ]
    }
   ],
   "source": [
    "print(foo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (<ipython-input-53-4a005d2d2384>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-53-4a005d2d2384>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    interact(f, x=1,widgets.Button(\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "def f(x):\n",
    "    print(x)\n",
    "\n",
    "interact(f, x=1,widgets.Button(\n",
    "    \n",
    "    description='Click me',\n",
    "    disabled=False,\n",
    "    button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    tooltip='Click me',\n",
    "    icon='check'\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Register a callback to execute when the button is clicked.\n",
      "\n",
      "        The callback will be called with one argument,\n",
      "        the clicked button widget instance.\n",
      "\n",
      "        Parameters\n",
      "        ----------\n",
      "        remove : bool (optional)\n",
      "            Set to true to remove the callback from the list of callbacks.\n"
     ]
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "print(widgets.Button.on_click.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "widgets.Button(\n",
    "    \n",
    "    description='Click me',\n",
    "    disabled=False,\n",
    "    button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    tooltip='Click me',\n",
    "    icon='check'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Button clicked.\n",
      "Button clicked.\n",
      "Button clicked.\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "button = widgets.Button(description=\"Click Me!\")\n",
    "display(button)\n",
    "\n",
    "def on_button_clicked(b):\n",
    "    print(\"Button clicked.\")\n",
    "\n",
    "button.on_click(on_button_clicked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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