{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following line stops the cursor from blinking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var rate = 0;\n",
       "// apply setting to  all current CodeMirror instances\n",
       "IPython.notebook.get_cells().map(\n",
       "    function(c) {  return c.code_mirror.options.cursorBlinkRate=rate;  }\n",
       ");\n",
       "\n",
       "// make sure new CodeMirror instance also use this setting\n",
       "CodeMirror.defaults.cursorBlinkRate=rate;"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "var rate = 0;\n",
    "// apply setting to  all current CodeMirror instances\n",
    "IPython.notebook.get_cells().map(\n",
    "    function(c) {  return c.code_mirror.options.cursorBlinkRate=rate;  }\n",
    ");\n",
    "\n",
    "// make sure new CodeMirror instance also use this setting\n",
    "CodeMirror.defaults.cursorBlinkRate=rate;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import needed libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cmappertools v1.0.24.\n"
     ]
    }
   ],
   "source": [
    "import mapper\n",
    "#from mapper import cmappertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from itertools import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 1: Input data</b><br>\n",
    "    Load the circle data set, circle = $x^2 + y^2 = 1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Uncomment below to read in data via file in Desktop folder\n",
    "# filename = 'Desktop/datafile.txt'\n",
    "# data = np.loadtxt(str(filename), delimiter=',', dtype=np.float)\n",
    "\n",
    "### Load circle data set \n",
    "data = mapper.shapes.circle(samples=80)\n",
    "#data.shape\n",
    "#data[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following without modification, just preprocessing some variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Preprocessing\n",
    "point_labels = None\n",
    "mask = None\n",
    "Gauss_density = mapper.filters.Gauss_density\n",
    "kNN_distance  = mapper.filters.kNN_distance\n",
    "crop = mapper.crop\n",
    "data, point_labels = mapper.mask_data(data, mask, point_labels)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<b>Step 2: Choose metric </b><br>\n",
    "Below we are choosing to use the Euclidean metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "intrinsic_metric = False\n",
    "if intrinsic_metric:\n",
    "    is_vector_data = data.ndim != 1\n",
    "    if is_vector_data:\n",
    "        metric = Euclidean            # here we choose Euclidean metric\n",
    "        if metric != 'Euclidean':\n",
    "            raise ValueError('Not implemented')\n",
    "    data = mapper.metric.intrinsic_metric(data, k=1, eps=1.0)\n",
    "is_vector_data = data.ndim != 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 3: Choose Filter function</b><br>\n",
    "Below we are choosing to use projection to first principal component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalues:\n",
      "[-36.9533893]\n"
     ]
    }
   ],
   "source": [
    "if is_vector_data:\n",
    "    metricpar = {'metric': 'euclidean'}\n",
    "    f = mapper.filters.dm_eigenvector(data,\n",
    "        metricpar=metricpar,\n",
    "        k=0, mean_center=True)\n",
    "else:\n",
    "    f = mapper.filters.dm_eigenvector(data,\n",
    "        k=0, mean_center=True)\n",
    "    \n",
    "### Uncomment out the following to change filter function\n",
    "### to Gauss density\n",
    "#if is_vector_data:\n",
    "#    metricpar = {'metric': 'euclidean'}\n",
    "#    f = mapper.filters.Gauss_density(data,\n",
    "#        metricpar=metricpar,\n",
    "#        sigma=1.0)\n",
    "#else:\n",
    "#    f = mapper.filters.Gauss_density(data,\n",
    "#        sigma=1.0)\n",
    "\n",
    "# Filter transformation\n",
    "mask = None\n",
    "crop = mapper.crop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 4: Select more Mapper parameters</b>\n",
    "Below we choose to use 15 intervals with 50% overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cover = mapper.cover.cube_cover_primitive(intervals=5, overlap=40.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we choose to use single linkage clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cluster = mapper.single_linkage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following without modification to run mapper algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of CPU cores present: 4\n",
      "Start Mapper thread.\n",
      "Start Mapper thread.\n",
      "Start Mapper thread.\n",
      "Start Mapper thread.\n",
      "Mapper: Filter range in dimension 0: [-0.15, 0.15]\n",
      "Mapper: Cover: Hypercube cover. Intervals: (5,). Overlap: (40.0,)Filter level (0,) has 32 points.\n",
      "\n",
      "Mapper: Clustering: Single linkage clusteringFilter level (1,) has 14 points.Filter level (2,) has 11 points.Filter level (3,) has 17 points.Filter level (4,) has 30 points.\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Mapper: Cutoff: None\n",
      "Level: (0,)\n",
      "Level: (1,)\n",
      "Level: (2,)\n",
      "Level: (3,)\n",
      "Level: (4,)\n"
     ]
    }
   ],
   "source": [
    "if not is_vector_data:\n",
    "    metricpar = {}\n",
    "mapper_output = mapper.mapper(data, f,\n",
    "    cover=cover,\n",
    "    cluster=cluster,\n",
    "    point_labels=point_labels,\n",
    "    cutoff=None,\n",
    "    metricpar=metricpar)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose where to cut the dendrogram in order to create clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scale graph path: [1 3 2 2 1]\n",
      "There are 9 nodes.\n",
      "Generate the simplicial complex.\n",
      "Collect simplices of dimension 1:\n",
      "There are 7 simplices of dimension 1.\n",
      "Collect simplices of dimension 2:\n",
      "There are 0 simplices of dimension 2.\n",
      "Dimension: 1\n"
     ]
    }
   ],
   "source": [
    "cutoff = mapper.cutoff.first_gap(gap=0.1)\n",
    "mapper_output.cutoff(cutoff, f, cover=cover, simple=False)\n",
    "\n",
    "### Uncomment out the following to change how the dendrogram is cut\n",
    "### in order to determine clusters.\n",
    "#mapper.scale_graph(mapper_output, f, cover=cover,\n",
    "#    weighting='inverse', maxcluster=50, expand_intervals=False, exponent=1.0,\n",
    "#    simple=False)\n",
    "\n",
    "# mapper_output.draw_scale_graph()\n",
    "# plt.savefig('scale_graph.pdf')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 5: Choose display parameters</b><br>\n",
    "Choose how to color the vertices (nodes) in mapper output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nodes = mapper_output.nodes #list of node objects; variables\n",
    "                            #of node are level, points, attribute\n",
    "node_color = None\n",
    "#node_color = [1,2,3,4,5,6,7,8]\n",
    "point_color = data[:,0]       # coloring nodes in mapper output using average of first coordinate\n",
    "name = 'custom scheme'\n",
    "node_color = mapper_output.postprocess_node_color(node_color, point_color, point_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 6:  Output</b><br>\n",
    "Draw TDA mapper graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJiurVPfQcoEdtracbdiQXm+/Ta/+/SuqqUJUCgnIQogqpdPp2PHbb/zxxReY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\nw3kN4AGZw+FwOJzXgP8DtOtoaV8t0TUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7a2bbf0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minsizes = []\n",
    "mapper_output.draw_2D(minsizes=minsizes,\n",
    "    node_color=node_color,\n",
    "    node_color_scheme=name)\n",
    "plt.savefig('mapper_output.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<b>Step 7:  Graphing clusters in a given bin</b><br>\n",
    "Choose a value less than or equal to intervals for bin; i.e. bin = n where n <= intervals.\n",
    "\n",
    "\n",
    "\n",
    "Run the following code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bin 1\n",
      "[ 6 22]\n",
      "bin 1\n",
      "[34 35 50 60]\n",
      "bin 1\n",
      "[ 9 10 19 32 43 49 58 78]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZdW1E1HL/aOW0xQjtdcdV62ukr9MNLAQ2ZpqvAZ4n+QJcA3wRWNnEuHoiYlDJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7a41a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levelSets = mapper_output.levelsets\n",
    "bins = set()  #a set of numbers corsponding to bins\n",
    "for i in levelSets:\n",
    "    bins.add(i[0])\n",
    "bin = 1 ##### CHANGE THIS TO COLOR DIFFERENT BINS ##### \n",
    "fig, ax = plt.subplots()  #create a plt object to graph our data\n",
    "ax.scatter(data[:,0],data[:,1], marker='+') #graphing the entire data points with marker '+'\n",
    "\n",
    "for i in nodes:\n",
    "    if i.level[0] == bin:\n",
    "        print 'bin', i.level[0]  #printing bin index\n",
    "        print i.points   #printing the indecis of data points in the bin\n",
    "        #print data[i.points]\n",
    "        ax.scatter(data[i.points][:,0], data[i.points][:,1]) #graphing the points in the chosen bin \n",
    "                                                    #using different colors\n"
   ]
  },
  {
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}