From 3938af4f83432ee0df32a3bc97d682c1bc242543 Mon Sep 17 00:00:00 2001
From: kponder <kap146@pitt.edu>
Date: Wed, 9 Nov 2016 15:40:08 -0500
Subject: [PATCH] played with removing whole light curves

---
 Generate_Mock_Lightcurves.ipynb | 1399 +++++++------------------------
 1 file changed, 280 insertions(+), 1119 deletions(-)

diff --git a/Generate_Mock_Lightcurves.ipynb b/Generate_Mock_Lightcurves.ipynb
index d00a014..8d3a015 100644
--- a/Generate_Mock_Lightcurves.ipynb
+++ b/Generate_Mock_Lightcurves.ipynb
@@ -2,1021 +2,181 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload #Use this to reload modules if they are changed on disk while the notebook is running\n",
-    "from __future__ import division\n",
-    "from snmachine import sndata, snfeatures, snclassifier, tsne_plot\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import time, os, pywt,subprocess\n",
-    "from sklearn.decomposition import PCA\n",
-    "from astropy.table import Table,join,vstack\n",
-    "from astropy.io import fits\n",
-    "import sklearn.metrics \n",
-    "import sncosmo\n",
-    "%matplotlib nbagg\n",
-    "from astropy.table import Column"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SN numbers with classification code\n",
-    "SN001695   - 2\n",
-    "\n",
-    "2457 - 33\n",
-    "\n",
-    "2542 - 1\n",
-    "\n",
-    "5399 - 2\n",
-    "\n",
-    "13481 - 2\n",
-    "\n",
-    "13866 - 1 \n",
-    "\n",
-    "16742 - 21\n",
-    "\n",
-    "17270 - 32\n",
-    "\n",
-    "27266 -32\n",
-    "\n",
-    "64968 - 23\n",
-    "\n",
-    "149147 -21"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "dataset='spcc'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# WARNING...\n",
-    "#Multinest uses a hardcoded character limit for the output file names. I believe it's a limit of 100 characters\n",
-    "#so avoid making this file path to lengthy if using nested sampling or multinest output file names will be truncated\n",
-    "\n",
-    "#Change outdir to somewhere on your computer if you like\n",
-    "outdir=os.path.join('output_%s_no_z' %dataset,'')\n",
-    "out_features=os.path.join(outdir,'features') #Where we save the extracted features to\n",
-    "out_class=os.path.join(outdir,'classifications') #Where we save the classification probabilities and ROC curves\n",
-    "out_int=os.path.join(outdir,'int') #Any intermediate files (such as multinest chains or GP fits)\n",
-    "\n",
-    "subprocess.call(['mkdir',outdir])\n",
-    "subprocess.call(['mkdir',out_features])\n",
-    "subprocess.call(['mkdir',out_class])\n",
-    "subprocess.call(['mkdir',out_int])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "#Data root\n",
-    "rt=os.path.join('SPCC_SUBSET','')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading data...\n",
-      "11 objects read into memory.\n"
-     ]
-    }
-   ],
-   "source": [
-    "dat=sndata.Dataset(rt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "read_from_file=False #True #We can use this flag to quickly rerun from saved features\n",
-    "run_name=os.path.join(out_features,'%s_all' %dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "dat.object_names[0]\n",
-    "types=dat.get_types()\n",
-    "types['Type'][np.floor(types['Type']/10)==2]=2\n",
-    "types['Type'][np.floor(types['Type']/10)==3]=3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'];\n",
-       "    var y0 = fig.canvas.height - msg['y0'];\n",
-       "    var x1 = msg['x1'];\n",
-       "    var y1 = fig.canvas.height - msg['y1'];\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x;\n",
-       "    var y = canvas_pos.y;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<img 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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural networks not available in this version of scikit-learn. Neural networks are available from development version 0.18.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload #Use this to reload modules if they are changed on disk while the notebook is running\n",
+    "from __future__ import division\n",
+    "from snmachine import sndata, snfeatures, snclassifier, tsne_plot\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time, os, pywt,subprocess\n",
+    "from sklearn.decomposition import PCA\n",
+    "from astropy.table import Table,join,vstack\n",
+    "from astropy.io import fits\n",
+    "import sklearn.metrics \n",
+    "import sncosmo\n",
+    "%matplotlib nbagg\n",
+    "from astropy.table import Column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SN numbers with classification code\n",
+    "SN001695   - 2\n",
+    "\n",
+    "2457 - 33\n",
+    "\n",
+    "2542 - 1\n",
+    "\n",
+    "5399 - 2\n",
+    "\n",
+    "13481 - 2\n",
+    "\n",
+    "13866 - 1 \n",
+    "\n",
+    "16742 - 21\n",
+    "\n",
+    "17270 - 32\n",
+    "\n",
+    "27266 -32\n",
+    "\n",
+    "64968 - 23\n",
+    "\n",
+    "149147 -21"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dataset='spcc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
      "data": {
-      "text/html": [
-       "&lt;Table length=128&gt;\n",
-       "<table id=\"table4385618768\" class=\"table-striped table-bordered table-condensed\">\n",
-       "<thead><tr><th>mjd</th><th>filter</th><th>flux</th><th>flux_error</th><th>zp</th><th>zpsys</th></tr></thead>\n",
-       "<thead><tr><th>float64</th><th>str4</th><th>float64</th><th>float64</th><th>float64</th><th>str2</th></tr></thead>\n",
-       "<tr><td>0.0</td><td>desg</td><td>3.874</td><td>4.787</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>0.0160000000033</td><td>desr</td><td>-5.456</td><td>5.335</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>0.0310000000027</td><td>desi</td><td>-10.3</td><td>5.439</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>0.0619999999981</td><td>desz</td><td>1.439</td><td>4.691</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>2.0</td><td>desg</td><td>-0.7297</td><td>2.238</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>2.14</td><td>desr</td><td>-2.837</td><td>3.03</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>2.156</td><td>desi</td><td>-4.236</td><td>4.2</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>2.187</td><td>desz</td><td>-5.086</td><td>3.518</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>9.984</td><td>desg</td><td>0.03404</td><td>1.702</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>10.0</td><td>desr</td><td>0.8236</td><td>1.251</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
-       "<tr><td>135.918</td><td>desi</td><td>4.008</td><td>3.081</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>139.894</td><td>desz</td><td>8.095</td><td>4.252</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>143.863</td><td>desg</td><td>4.442</td><td>2.914</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>143.871</td><td>desr</td><td>0.7718</td><td>1.365</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>143.894</td><td>desi</td><td>3.976</td><td>3.218</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>148.867</td><td>desz</td><td>5.982</td><td>2.609</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>155.871</td><td>desg</td><td>-0.109</td><td>1.816</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>155.879</td><td>desr</td><td>0.3982</td><td>1.383</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>156.871</td><td>desi</td><td>1.149</td><td>2.177</td><td>27.5</td><td>ab</td></tr>\n",
-       "<tr><td>157.871</td><td>desz</td><td>16.46</td><td>3.394</td><td>27.5</td><td>ab</td></tr>\n",
-       "</table>"
-      ],
       "text/plain": [
-       "<Table length=128>\n",
-       "      mjd       filter   flux  flux_error    zp   zpsys\n",
-       "    float64      str4  float64  float64   float64  str2\n",
-       "--------------- ------ ------- ---------- ------- -----\n",
-       "            0.0   desg   3.874      4.787    27.5    ab\n",
-       "0.0160000000033   desr  -5.456      5.335    27.5    ab\n",
-       "0.0310000000027   desi   -10.3      5.439    27.5    ab\n",
-       "0.0619999999981   desz   1.439      4.691    27.5    ab\n",
-       "            2.0   desg -0.7297      2.238    27.5    ab\n",
-       "           2.14   desr  -2.837       3.03    27.5    ab\n",
-       "          2.156   desi  -4.236        4.2    27.5    ab\n",
-       "          2.187   desz  -5.086      3.518    27.5    ab\n",
-       "          9.984   desg 0.03404      1.702    27.5    ab\n",
-       "           10.0   desr  0.8236      1.251    27.5    ab\n",
-       "            ...    ...     ...        ...     ...   ...\n",
-       "        135.918   desi   4.008      3.081    27.5    ab\n",
-       "        139.894   desz   8.095      4.252    27.5    ab\n",
-       "        143.863   desg   4.442      2.914    27.5    ab\n",
-       "        143.871   desr  0.7718      1.365    27.5    ab\n",
-       "        143.894   desi   3.976      3.218    27.5    ab\n",
-       "        148.867   desz   5.982      2.609    27.5    ab\n",
-       "        155.871   desg  -0.109      1.816    27.5    ab\n",
-       "        155.879   desr  0.3982      1.383    27.5    ab\n",
-       "        156.871   desi   1.149      2.177    27.5    ab\n",
-       "        157.871   desz   16.46      3.394    27.5    ab"
+       "1"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "# WARNING...\n",
+    "#Multinest uses a hardcoded character limit for the output file names. I believe it's a limit of 100 characters\n",
+    "#so avoid making this file path to lengthy if using nested sampling or multinest output file names will be truncated\n",
+    "\n",
+    "#Change outdir to somewhere on your computer if you like\n",
+    "outdir=os.path.join('output_%s_no_z' %dataset,'')\n",
+    "out_features=os.path.join(outdir,'features') #Where we save the extracted features to\n",
+    "out_class=os.path.join(outdir,'classifications') #Where we save the classification probabilities and ROC curves\n",
+    "out_int=os.path.join(outdir,'int') #Any intermediate files (such as multinest chains or GP fits)\n",
+    "\n",
+    "subprocess.call(['mkdir',outdir])\n",
+    "subprocess.call(['mkdir',out_features])\n",
+    "subprocess.call(['mkdir',out_class])\n",
+    "subprocess.call(['mkdir',out_int])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Data root\n",
+    "rt=os.path.join('SPCC_SUBSET','')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading data...\n",
+      "11 objects read into memory.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dat=sndata.Dataset(rt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "read_from_file=False #True #We can use this flag to quickly rerun from saved features\n",
+    "run_name=os.path.join(out_features,'%s_all' %dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dat.object_names[0]\n",
+    "types=dat.get_types()\n",
+    "types['Type'][np.floor(types['Type']/10)==2]=2\n",
+    "types['Type'][np.floor(types['Type']/10)==3]=3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
     "#test_table = dat.data['DES_SN001695.DAT'] #get_lightcurve\n",
     "\n",
-    "dat.plot_all()\n",
-    "dat.data[dat.object_names[0]]"
+    "#dat.plot_all()\n",
+    "#dat.data[dat.object_names[0]]"
    ]
   },
   {
@@ -1030,18 +190,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "realspertype=100"
+    "realspertype=667"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -1049,12 +209,12 @@
    "source": [
     "type_II = []\n",
     "for i in range(len(dat.data['DES_SN002457.DAT']['flux'])):\n",
-    "    type_II.append(np.random.normal(dat.data['DES_SN002457.DAT']['flux'][i], dat.data['DES_SN002457.DAT']['flux_error'][i]+ dat.data['DES_SN002457.DAT']['flux_error'][i]*0.02, realspertype))"
+    "    type_II.append(np.random.normal(dat.data['DES_SN002457.DAT']['flux'][i], dat.data['DES_SN002457.DAT']['flux_error'][i], realspertype))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -1062,12 +222,12 @@
    "source": [
     "type_Ia = []\n",
     "for i in range(len(dat.data['DES_SN002542.DAT']['flux'])):\n",
-    "    type_Ia.append(np.random.normal(dat.data['DES_SN002542.DAT']['flux'][i], dat.data['DES_SN002542.DAT']['flux_error'][i]+ dat.data['DES_SN002542.DAT']['flux_error'][i]*0.02, realspertype))"
+    "    type_Ia.append(np.random.normal(dat.data['DES_SN002542.DAT']['flux'][i], dat.data['DES_SN002542.DAT']['flux_error'][i], realspertype))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "collapsed": true
    },
@@ -1075,12 +235,12 @@
    "source": [
     "type_Ibc = []\n",
     "for i in range(len(dat.data['DES_SN005399.DAT']['flux'])):\n",
-    "    type_Ibc.append(np.random.normal(dat.data['DES_SN005399.DAT']['flux'][i], dat.data['DES_SN005399.DAT']['flux_error'][i]+ dat.data['DES_SN005399.DAT']['flux_error'][i]*0.02, realspertype))"
+    "    type_Ibc.append(np.random.normal(dat.data['DES_SN005399.DAT']['flux'][i], dat.data['DES_SN005399.DAT']['flux_error'][i], realspertype))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -1093,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -1104,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -1115,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -1127,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -1138,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -1149,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -1160,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -1190,12 +350,12 @@
     "    #table.rename_column('filter ', 'FLT')\n",
     "    #table.rename_column('flux', 'FLUXCAL')\n",
     "    #table.rename_column('flux_error', 'FLUXCALERR')\n",
-    "    sncosmo.write_lc(table, 'new_mocks/nopercent_%s.dat'%output,pedantic=True, format = 'snana')"
+    "    sncosmo.write_lc(table, 'new_mocks/nodesz_%s.dat'%output,pedantic=True, format = 'snana')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -1205,65 +365,75 @@
     "filename_Ia = 'SPCC_SUBSET/DES_SN002542.DAT'\n",
     "filename_Ibc = 'SPCC_SUBSET/DES_SN005399.DAT'\n",
     "\n",
-    "test_table_II = dat.data['DES_SN002457.DAT']\n",
-    "test_table_Ia = dat.data['DES_SN002542.DAT']\n",
-    "test_table_Ibc = dat.data['DES_SN005399.DAT']\n",
-    "#print test_table_II\n",
+    "for i in range(realspertype):\n",
+    "    test_table_II = dat.data['DES_SN002457.DAT'].copy()\n",
+    "    test_table_Ia = dat.data['DES_SN002542.DAT'].copy()\n",
+    "    test_table_Ibc = dat.data['DES_SN005399.DAT'].copy()\n",
+    "    #print test_table_II\n",
     "\n",
-    "#test_table_Ia.replace_column('mjd', test_table_Ia['mjd']+56178)\n",
-    "#test_table_II.replace_column('mjd', test_table_II['mjd']+56178)\n",
-    "#test_table_Ibc.replace_column('mjd', test_table_Ibc['mjd']+56178)\n",
+    "    #test_table_Ia.replace_column('mjd', test_table_Ia['mjd']+56178)\n",
+    "    #test_table_II.replace_column('mjd', test_table_II['mjd']+56178)\n",
+    "    #test_table_Ibc.replace_column('mjd', test_table_Ibc['mjd']+56178)\n",
     "\n",
-    "test_table_II.rename_column('flux', 'FLUXCAL')\n",
-    "test_table_II.rename_column('flux_error', 'FLUXCALERR')\n",
+    "    test_table_II.rename_column('flux', 'FLUXCAL')\n",
+    "    test_table_II.rename_column('flux_error', 'FLUXCALERR')\n",
     "    \n",
-    "test_table_Ibc.rename_column('flux', 'FLUXCAL')\n",
-    "test_table_Ibc.rename_column('flux_error', 'FLUXCALERR')\n",
-    "    \n",
-    "test_table_Ia.rename_column('flux', 'FLUXCAL')\n",
-    "test_table_Ia.rename_column('flux_error', 'FLUXCALERR')\n",
+    "    test_table_Ibc.rename_column('flux', 'FLUXCAL')\n",
+    "    test_table_Ibc.rename_column('flux_error', 'FLUXCALERR')\n",
+    "        \n",
+    "    test_table_Ia.rename_column('flux', 'FLUXCAL')\n",
+    "    test_table_Ia.rename_column('flux_error', 'FLUXCALERR')\n",
     "\n",
-    "objs=[]\n",
-    "new_data={}\n",
+    "    objs=[]\n",
+    "    new_data={}\n",
+    "\n",
+    "    col_Ia = Table.Column(name='field',data=np.zeros(len(test_table_Ia)) )\n",
+    "    test_table_Ia.add_column(col_Ia, index = 2)\n",
+    "    col_Ibc = Table.Column(name='field',data=np.zeros(len(test_table_Ibc)) )\n",
+    "    test_table_Ibc.add_column(col_Ibc, index = 2)\n",
+    "    col_II = Table.Column(name='field',data=np.zeros(len(test_table_II)) )\n",
+    "    test_table_II.add_column(col_II, index = 2)\n",
     "\n",
-    "col_Ia = Table.Column(name='field',data=np.zeros(len(test_table_Ia)) )\n",
-    "test_table_Ia.add_column(col_Ia, index = 2)\n",
-    "col_Ibc = Table.Column(name='field',data=np.zeros(len(test_table_Ibc)) )\n",
-    "test_table_Ibc.add_column(col_Ibc, index = 2)\n",
-    "col_II = Table.Column(name='field',data=np.zeros(len(test_table_II)) )\n",
-    "test_table_II.add_column(col_II, index = 2)\n",
     "\n",
-    "for i in range(realspertype):\n",
     "    test_table_Ia.replace_column('FLUXCAL', type_Ia[:,i])\n",
     "    test_table_Ibc.replace_column('FLUXCAL', type_Ibc[:,i])\n",
     "    test_table_II.replace_column('FLUXCAL', type_II[:,i])\n",
     "    \n",
-    "    test_table_Ia.replace_column('FLUXCALERR', test_table_Ia['FLUXCALERR'] + test_table_Ia['FLUXCALERR']*0.1)\n",
-    "    test_table_Ibc.replace_column('FLUXCALERR', test_table_Ibc['FLUXCALERR'] + test_table_Ibc['FLUXCALERR']*0.1)\n",
-    "    test_table_II.replace_column('FLUXCALERR', test_table_II['FLUXCALERR'] + test_table_II['FLUXCALERR']*0.1)\n",
+    "    #test_table_Ia.replace_column('FLUXCALERR', test_table_Ia['FLUXCALERR'] + test_table_Ia['FLUXCALERR']*0.02)\n",
+    "    #test_table_Ibc.replace_column('FLUXCALERR', test_table_Ibc['FLUXCALERR'] + test_table_Ibc['FLUXCALERR']*0.02)\n",
+    "    #test_table_II.replace_column('FLUXCALERR', test_table_II['FLUXCALERR'] + test_table_II['FLUXCALERR']*0.02)\n",
+    "    \n",
+    "    wh_desr_II = np.where(test_table_II['filter']=='desz')\n",
+    "    wh_desr_Ia = np.where(test_table_Ia['filter']=='desz')\n",
+    "    wh_desr_Ibc = np.where(test_table_Ibc['filter']=='desz')\n",
+    "    \n",
+    "    test_table_II.remove_rows(wh_desr_II)\n",
+    "    test_table_Ia.remove_rows(wh_desr_Ia)\n",
+    "    test_table_Ibc.remove_rows(wh_desr_Ibc)\n",
     "    \n",
     "    make_new_data(filename_Ibc, test_table_Ibc, 'Ibc_%s'%i, 3)\n",
     "    make_new_data(filename_II, test_table_II, 'II_%s'%i, 2)\n",
     "    make_new_data(filename_Ia, test_table_Ia, 'Ia_%s'%i, 1)\n",
     "    \n",
+    "    \n",
     "    #objs.append((str)(i))\n",
-    "    new_data[(str)(i)]=test_table_II.copy()  #new_light_curve_Ia\n",
-    "    new_data[(str)(i+realspertype)]=test_table_Ia.copy()#new_light_curve_II\n",
-    "    new_data[(str)(i+realspertype)]=test_table_Ibc.copy()#new_light_curve_Ibc\n",
+    "    #new_data[(str)(i)]=test_table_II.copy()  #new_light_curve_Ia\n",
+    "    #new_data[(str)(i+realspertype)]=test_table_Ia.copy()#new_light_curve_II\n",
+    "    #new_data[(str)(i+realspertype)]=test_table_Ibc.copy()#new_light_curve_Ibc\n",
     "    \n",
     "#sncosmo.read_lc(new_data)\n",
     "#new_data = Counter(new_data_Ia) + Counter(new_data_II) + Counter(new_data_Ibc)\n",
     "\n",
-    "for i in range(3*realspertype):    \n",
-    "    objs.append((str)(i))\n",
+    "#for i in range(3*realspertype):    \n",
+    "#    objs.append((str)(i))\n",
     "    \n",
-    "dat.object_names=np.asarray(objs)\n",
-    "dat.data=new_data"
+    "#dat.object_names=np.asarray(objs)\n",
+    "#dat.data=new_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -1271,12 +441,16 @@
    "source": [
     "#print test_table_Ia\n",
     "#test_table_Ia.replace_column('FLUXCALERR', test_table_Ia['FLUXCALERR']*5.)\n",
-    "#print test_table_Ia"
+    "#print test_table_Ia\n",
+    "#test_table_II = dat.data['DES_SN002457.DAT']\n",
+    "#wh_desr = np.where(test_table_II['filter']=='desr')\n",
+    "#test_table_II.remove_rows(wh_desr)\n",
+    "#print test_table_II"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -1286,7 +460,7 @@
      "output_type": "stream",
      "text": [
       "Reading data...\n",
-      "300 objects read into memory.\n"
+      "2001 objects read into memory.\n"
      ]
     }
    ],
@@ -1297,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -1310,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -1321,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -1335,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -1356,18 +530,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "mod1Feats=snfeatures.ParametricFeatures('newling',sampler='leastsq')"
+    "mod1Feats=snfeatures.ParametricFeatures('karpenka',sampler='leastsq')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -1376,8 +550,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "300 objects fitted\n",
-      "Time taken is 18.58 seconds\n"
+      "2001 objects fitted\n",
+      "Time taken is 6.22 minutes\n"
      ]
     }
    ],
@@ -1394,54 +568,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       Object        desg-logA desg-phi ...  desz-logk  desz-logpsi\n",
-      "-------------------- --------- -------- ... ----------- -----------\n",
-      "  nopercent_II_0.dat   4.03912  26.8433 ...   0.0658591     1.67191\n",
-      "  nopercent_II_1.dat   4.34714  26.5489 ...    0.862824      2.6169\n",
-      " nopercent_II_10.dat   4.36297  26.5112 ...   0.0527303     1.87855\n",
-      " nopercent_II_11.dat   4.40101  26.9264 ...    0.611766     2.38311\n",
-      " nopercent_II_12.dat   3.98468  21.8126 ...    -3.13245     2.51939\n",
-      " nopercent_II_13.dat   4.00131  27.5361 ...    0.351197     1.79706\n",
-      " nopercent_II_14.dat   4.50532  26.4299 ...   -0.510369     2.31294\n",
-      " nopercent_II_15.dat   5.50773  24.8449 ...    0.650985     2.82435\n",
-      " nopercent_II_16.dat   4.37311  26.9319 ...      1.2302     2.59514\n",
-      " nopercent_II_17.dat   4.34726  26.5563 ...    -1.88135     1.44255\n",
-      "                 ...       ...      ... ...         ...         ...\n",
-      " nopercent_Ibc_9.dat   4.19459  25.9459 ...   -0.114828     3.06977\n",
-      "nopercent_Ibc_90.dat   4.54126  25.9682 ...  -0.0339263     2.40006\n",
-      "nopercent_Ibc_91.dat    4.4985  25.6471 ...   -0.285418     2.55361\n",
-      "nopercent_Ibc_92.dat   4.54184  25.9886 ...    -3.28157     2.92438\n",
-      "nopercent_Ibc_93.dat   4.50312  25.7288 ... -0.00932307     2.27001\n",
-      "nopercent_Ibc_94.dat   4.53296  25.9393 ...    0.137002     2.25307\n",
-      "nopercent_Ibc_95.dat   4.52959  25.9125 ...    0.524802     2.42223\n",
-      "nopercent_Ibc_96.dat   4.54366  25.9757 ...    0.515548      2.2981\n",
-      "nopercent_Ibc_97.dat   4.54291  25.9957 ...    0.464763     2.10457\n",
-      "nopercent_Ibc_98.dat   4.53932  25.9753 ...    0.222259     2.19148\n",
-      "nopercent_Ibc_99.dat   4.54295  25.9584 ...    -3.81337     3.38942\n",
-      "Length = 300 rows\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#Unfortunately, sometimes the fitting methods return NaN for some parameters for these models.\n",
     "for c in mod1_features.colnames[1:]:\n",
     "    mod1_features[c][np.isnan(mod1_features[c])]=0\n",
     "    \n",
-    "print mod1_features"
+    "#print mod1_features[0], len(mod1_features)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -1450,7 +592,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fitting supernova models...\n",
+      "Fitting supernova models...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/karaponder/Projects/Basic_Codes/snmachine/snmachine/parametric_models.py:153: RuntimeWarning: overflow encountered in exp\n",
+      "  return A*(1+B*(t-t1)*(t-t1))*np.exp(-(t-t0)/T_fall)/(1+np.exp(-(t-t0)/T_rise))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Models fitted.\n"
      ]
     }
@@ -1462,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -2234,7 +1390,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2259,7 +1415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -3031,7 +2187,7 @@
     {
      "data": {
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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3048,7 +2204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
@@ -3820,7 +2976,7 @@
     {
      "data": {
       "text/html": [
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\">"
+       "<img 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\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3837,6 +2993,7 @@
       "Created classifier of type:\n",
       "Created classifier of type:\n",
       "GaussianNB()\n",
+      "\n",
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
@@ -3845,27 +3002,31 @@
       "\n",
       "\n",
       "\n",
-      "\n",
       "Optimised parameters: {}\n",
-      "Optimised parameters: {'C': 100000.0, 'gamma': 0.0031622776601683794}\n",
-      "\n",
-      "WARNING: Upper boundary on parameter C may be too low. Optimum may not have been reached.\n",
-      "\n",
+      "Optimised parameters: {'C': 31.622776601683793, 'gamma': 0.0031622776601683794}\n",
       "Optimised parameters: {'n_estimators': 25, 'base_estimator': DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
-      "            max_features=None, max_leaf_nodes=None, min_samples_leaf=25,\n",
+      "            max_features=None, max_leaf_nodes=None, min_samples_leaf=15,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')}\n",
-      "Classifier nb: AUC = 0.738055555556 FoM = 0.24\n",
-      "Classifier svm: AUC = 0.976111111111 FoM = 0.76862745098\n",
+      "Classifier nb: AUC = 0.98743718593 FoM = 0.974874371859\n",
+      "Classifier svm: AUC = 1.0 FoM = 1.0\n",
       "Classifier boost_dt: AUC = 1.0 FoM = 1.0\n",
       "\n",
-      "Time taken  0.381607516607 minutes\n"
+      "Time taken  1.1021290501 minutes\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "//anaconda/lib/python2.7/site-packages/matplotlib/cbook.py:137: MatplotlibDeprecationWarning: The set_color_cycle attribute was deprecated in version 1.5. Use set_prop_cycle instead.\n",
+      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
      ]
     }
    ],
    "source": [
     "plt.figure()\n",
-    "clss=snclassifier.run_pipeline(mod1_features,types,output_name=os.path.join(out_class,'model1'),\n",
+    "clss=snclassifier.run_pipeline(mod1_features,types,output_name=os.path.join(out_class,'model2'),\n",
     "                          classifiers=['nb','svm','boost_dt'], nprocesses=4)"
    ]
   },