From 477c258cd7c0c95e23c158b4a2933e0b3e500783 Mon Sep 17 00:00:00 2001
From: Brian Delhaisse <briandelhaisse@gmail.com>
Date: Fri, 21 Jun 2019 20:17:09 +0200
Subject: [PATCH] add dataset parsers

---
 pyrobolearn/datasets/README.md                |     0
 pyrobolearn/datasets/parsers/3d/README.md     |    54 +
 pyrobolearn/datasets/parsers/3d/cmu.ipynb     |  1769 +++
 .../datasets/parsers/3d/data/CMU/02.asf       |   338 +
 .../datasets/parsers/3d/data/CMU/02_01.amc    | 10293 ++++++++++++++++
 .../parsers/3d/data/CMU/download-link.txt     |     3 +
 .../datasets/parsers/3d/data/CMU/subject2.txt |    15 +
 .../3d/data/UTD/Skeleton_joint_order.txt      |    24 +
 .../parsers/3d/data/UTD/download-link.txt     |     3 +
 .../3d/figures/JHMDB-comparison-datasets.png  |   Bin 0 -> 92264 bytes
 .../MPIICooking-comparison-datasets.png       |   Bin 0 -> 110912 bytes
 .../datasets/parsers/3d/mocap_parser.py       |   230 +
 .../datasets/parsers/3d/utd-mhad.ipynb        |   967 ++
 .../handwritten/HWRT/download-link.txt        |     4 +
 .../handwritten/IAM-Online/download-link.txt  |     7 +
 .../handwritten/ICDAR2013/download-link.txt   |     5 +
 .../datasets/parsers/handwritten/README.md    |    38 +
 .../download-link.txt                         |     5 +
 .../UCI-Ujipen-Chars2/download-link.txt       |     4 +
 .../handwritten/handwritten-data.ipynb        |   388 +
 20 files changed, 14147 insertions(+)
 create mode 100644 pyrobolearn/datasets/README.md
 create mode 100644 pyrobolearn/datasets/parsers/3d/README.md
 create mode 100644 pyrobolearn/datasets/parsers/3d/cmu.ipynb
 create mode 100644 pyrobolearn/datasets/parsers/3d/data/CMU/02.asf
 create mode 100644 pyrobolearn/datasets/parsers/3d/data/CMU/02_01.amc
 create mode 100644 pyrobolearn/datasets/parsers/3d/data/CMU/download-link.txt
 create mode 100644 pyrobolearn/datasets/parsers/3d/data/CMU/subject2.txt
 create mode 100644 pyrobolearn/datasets/parsers/3d/data/UTD/Skeleton_joint_order.txt
 create mode 100644 pyrobolearn/datasets/parsers/3d/data/UTD/download-link.txt
 create mode 100644 pyrobolearn/datasets/parsers/3d/figures/JHMDB-comparison-datasets.png
 create mode 100644 pyrobolearn/datasets/parsers/3d/figures/MPIICooking-comparison-datasets.png
 create mode 100644 pyrobolearn/datasets/parsers/3d/mocap_parser.py
 create mode 100644 pyrobolearn/datasets/parsers/3d/utd-mhad.ipynb
 create mode 100644 pyrobolearn/datasets/parsers/handwritten/HWRT/download-link.txt
 create mode 100644 pyrobolearn/datasets/parsers/handwritten/IAM-Online/download-link.txt
 create mode 100644 pyrobolearn/datasets/parsers/handwritten/ICDAR2013/download-link.txt
 create mode 100644 pyrobolearn/datasets/parsers/handwritten/README.md
 create mode 100644 pyrobolearn/datasets/parsers/handwritten/UCI-Characters-Trajectories/download-link.txt
 create mode 100644 pyrobolearn/datasets/parsers/handwritten/UCI-Ujipen-Chars2/download-link.txt
 create mode 100644 pyrobolearn/datasets/parsers/handwritten/handwritten-data.ipynb

diff --git a/pyrobolearn/datasets/README.md b/pyrobolearn/datasets/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/pyrobolearn/datasets/parsers/3d/README.md b/pyrobolearn/datasets/parsers/3d/README.md
new file mode 100644
index 0000000..1f59f1b
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/README.md
@@ -0,0 +1,54 @@
+## 3D datasets
+
+This repo contains a brief description of 3D datasets that I could find online. It also contains the Python code to load, parse, and plot some of these datasets.
+
+
+### List of datasets
+
+Here is a non-exhaustive list of datasets that could be used:
+
+* [**TUM Kitchen**: "The TUM Kitchen Data Set of Everyday Manipulation Activities for Motion Tracking and Action Recognition", 2009](https://ias.in.tum.de/dokuwiki/software/kitchen-activity-data)
+    * scenarios: "everyday manipulation  activities  in  a  natural  kitchen  environment"
+    * labeled segmented data: 51 DOF articulated human model, 3D joint positions, videos and labels
+* [**CMU-MMAC**: "Guide to the Carnegie Mellon University Multimodal Activity database", 2009](http://kitchen.cs.cmu.edu/)
+    * scenarios: human activity of subjects performing the tasks involved in cooking and food preparation
+* [**HumanEva**: "HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion", 2010](http://humaneva.is.tue.mpg.de/)
+    * 6 scenarios: walking, jogging, gesturing, throwing and catching a ball, boxing, combo (=walking+jogging+balancing)
+    * nb of subjects: 4
+    * data: 2D and 3D pose estimation
+* [**MPII Cooking**: "A database for fine grained activity detection of cooking activities", 2012](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/human-activity-recognition/mpii-cooking-activities-dataset/)
+<img src="figures/MPIICooking-comparison-datasets.png" alt="Datasets" style="width: 400px;"/>
+    * nb of subjects: 12
+    * 65 fine-grained cooking activities: background activity, change temperature, cut apart, cut dice, cut in, cut off ends, cut out inside, cut slices, cut stripes, dry, fill water from tap, grate, lid: put on, lid: remove, mix, move from X to Y, open egg, open tin, open/close cupboard, open/close drawer, open/close fridge, open/close oven, package X, peel, plug in/out, pour, pull out, puree, put in bowl, put in pan/pot, put on bread/dough, put on cutting-board, put on plate, read, remove from package, rip open, scratch off, screw close, screw open, shake, smell, spice, spread, squeeze, stamp, stir, strew, take & put in cupboard, take & put in drawer, take & put in fridge, take & put in oven, take & put in spice holder, take ingredient apart, take out from cupboard, take out from drawer, take out from fridge, take out from oven, take out from spice holder, taste, throw in garbage, unroll dough, wash hands, wash objects, whisk, wipe clean
+    * labeled and segmented data: 2D body joint positions
+* [**J-HMDB**: "Joint-annotated Human Motion Data Base", 2013](http://jhmdb.is.tue.mpg.de/)
+<img src="figures/JHMDB-comparison-datasets.png" alt="Datasets" style="width: 400px;"/>
+    * 21 scenarios: brush hair, catch, clap, climb stairs, golf, jump, kick ball, pick, pour, pull-up, push, run, shoot
+ball, shoot bow, shoot gun, sit, stand, swing baseball, throw, walk, wave
+    * labeled and segmented data: 36-55 clips per action class with each clip containing 15-40 frames + 2D joint positions. 10 body parts connected by 13 joints (shoulder, elbow, wrist, hip, knee, ankle, neck)  and  two  landmarks  (face  and  belly).
+* [**Human 3.6M**: "Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments", 2014](http://vision.imar.ro/human3.6m/description.php)
+    * 3.6 million 3D human poses and corresponding images
+    * nb of subjects: 11
+    * 15 scenarios: direction, discussion, eating, activitie while seated, greeting, taking photo, posing, making purchases, smoking, waiting, walking, sitting on chair, talking on the phone, walking dog, walking together
+    * data: 3D joint positions and joint angles
+* [**UTD-MHAD**: "UTD-MHAD: A Multimodal Dataset for Human Action Recognition utilizing a Depth Camera and a Wearable Inertial Sensor", 2015](http://www.utdallas.edu/~kehtar/UTD-MHAD.html)
+    * nb of subjects: 8
+    * 27 different actions: (1) right arm swipe to the left, (2) right arm swipe to the right, (3) right hand wave, (4) two hand front clap, (5) right arm throw, (6) cross arms in the chest, (7) basketball shoot, (8) right hand draw x, (9) right hand draw circle (clockwise), (10) right hand draw circle (counter clockwise), (11) draw triangle, (12) bowling (right hand), (13) front boxing, (14) baseball swing from right, (15) tennis right hand forehand swing, (16) arm curl (two arms), (17) tennis serve, (18) two hand push, (19) right hand knock on door, (20) right hand catch an object, (21) right hand pick up and throw, (22) jogging in place, (23) walking in place, (24) sit to stand, (25) stand to sit, (26) forward lunge (left foot forward), (27) squat (two arms stretch out).
+    * labeled and segmented data: 861 data sequences, 3D skeleton joint positions
+* [**G3D**: "G3D: A Gaming Action Dataset and Real Time Action Recognition Evaluation Framework", 2012](http://dipersec.king.ac.uk/G3D/index.html)
+    * nb of subjects: 10
+    * 20 gaming actions: punch right, punch left, kick right, kick left, defend, golf swing, tennis swing forehand, tennis swing backhand, tennis serve, throw bowling ball, aim and fire gun, walk, run, jump, climb, crouch, steer a car, wave, flap and clap
+    * data: 3D joint positions
+* [**CMU-Datasets**](mocap.cs.cmu.edu/search.php)
+    * dataset: very diverse, contains data such as sport activities (soccer, dancing, etc), everyday movements/activities, locomotion (walk/run, jump, etc), interaction with environment and other agents, human subjects imitating animal behaviors (such as snake, dog, chicken, etc), stretching, finegrained movements, and so on.
+    * data: 3D joint positions and videos.
+* Others: MSRC-12 Kinect Gesture Datase, MPI HDM05 Motion Capture Database, MSR Action3D Database, VGG Human Pose Estimation datasets, etc. 
+ 
+
+
+### Python Code
+
+The python code to load, parse, and plot (using animations) has been implemented for the following datasets:
+
+* UTD-MHAD: see `utd-mhad.ipynb`
+* CMU-Datasets: see `cmu.ipynb`
diff --git a/pyrobolearn/datasets/parsers/3d/cmu.ipynb b/pyrobolearn/datasets/parsers/3d/cmu.ipynb
new file mode 100644
index 0000000..ae3fef6
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/cmu.ipynb
@@ -0,0 +1,1769 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### [CMU-Datasets](mocap.cs.cmu.edu/search.php)\n",
+    "\n",
+    "* dataset: very diverse, contains data such as sport activities (soccer, dancing, etc), everyday movements/activities, locomotion (walk/run, jump, etc), interaction with environment and other agents, human subjects imitating animal behaviors (such as snake, dog, chicken, etc), stretching, finegrained movements, and so on.\n",
+    "* data: 3D joint positions and videos.\n",
+    "\n",
+    "The CMU datasets is composed of '.asf/.amc' files. More info about the this file format can be found on this [link](http://research.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/ASF-AMC.html).\n",
+    "\n",
+    "To parse these files, we will need the `cgkit` module: http://cgkit.sourceforge.net/\n",
+    "\n",
+    "The relevant documentation can be found here: http://cgkit.sourceforge.net/doc2/asfamc.html\n",
+    "\n",
+    "Clone and install the following github repo: https://github.com/VanushVaswani/amcparser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import matplotlib.animation as animation\n",
+    "\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "#%matplotlib inline\n",
+    "%matplotlib notebook\n",
+    "\n",
+    "from amcparser.skeleton import Skeleton\n",
+    "from amcparser.motion import SkelMotion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "PATH = 'data/CMU'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 343/343 [00:00<00:00, 585.49it/s]    | 51/343 [00:00<00:00, 509.01it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define some variables\n",
+    "links = ['upperback', 'rfoot', 'lradius', 'lthumb', 'rradius', 'lwrist', 'rhand', 'rhumerus',\n",
+    "        'lowerneck', 'lhand', 'ltibia', 'head', 'rwrist', 'lfingers', 'rtoes', 'lowerback',\n",
+    "        'lhipjoint', 'rhipjoint', 'lclavicle', 'upperneck', 'rfemur', 'thorax', 'rclavicle',\n",
+    "        'rtibia', 'lfoot', 'lhumerus', 'rthumb', 'rfingers', 'root', 'ltoes', 'lfemur']\n",
+    "Nl = len(links)\n",
+    "\n",
+    "# File paths\n",
+    "skeleton_filename = PATH+'/02.asf'\n",
+    "motion_filename = PATH+'/02_01.amc'\n",
+    "#video_filename = PATH+'/02_01.avi'\n",
+    "framerate = 120.\n",
+    "\n",
+    "# Create skeleton and motion\n",
+    "skel = Skeleton(skeleton_filename)\n",
+    "skelMot = SkelMotion(skel, motion_filename, (1./framerate))\n",
+    "\n",
+    "# Compute all the trajectories\n",
+    "skelMot.traverse(None, 0, -1)\n",
+    "\n",
+    "# Data\n",
+    "X_TO = np.array([skel.bones[jnt].xyz_data for jnt in ['head', 'upperneck', 'lowerneck',\n",
+    "                                                      'upperback', 'thorax', 'lowerback', 'root']])\n",
+    "X_RA = np.array([skel.bones[jnt].xyz_data for jnt in ['rclavicle', 'rhumerus', 'rradius',\n",
+    "                                                      'rwrist', 'rhand', 'rthumb', 'rfingers']])\n",
+    "X_LA = np.array([skel.bones[jnt].xyz_data for jnt in ['lclavicle', 'lhumerus', 'lradius',\n",
+    "                                                      'lwrist', 'lhand', 'lthumb', 'lfingers']])\n",
+    "X_RL = np.array([skel.bones[jnt].xyz_data for jnt in ['rhipjoint', 'rfemur', 'rtibia', 'rfoot', 'rtoes']])\n",
+    "X_LL = np.array([skel.bones[jnt].xyz_data for jnt in ['lhipjoint', 'lfemur', 'ltibia', 'lfoot', 'ltoes']])\n",
+    "X = np.vstack((X_TO, X_RA, X_LA, X_RL, X_LL))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": 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"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Rescaling such that the skeleton it is in the right proportion and at the middle\n",
+    "xmin, xmax = X[...,2].min(), X[...,2].max()\n",
+    "ymin, ymax = X[...,0].min(), X[...,0].max()\n",
+    "zmin, zmax = X[...,1].min(), X[...,1].max()\n",
+    "x_len, y_len, z_len = (xmax - xmin), (ymax - ymin), (zmax - zmin)\n",
+    "max_len = max([x_len, y_len, z_len])\n",
+    "xmin, xmax = xmin + (x_len - max_len)/2., xmin + (x_len + max_len)/2.\n",
+    "ymin, ymax = ymin + (y_len - max_len)/2., ymin + (y_len + max_len)/2.\n",
+    "zmin, zmax = zmin + (z_len - max_len)/2., zmin + (z_len + max_len)/2.\n",
+    "\n",
+    "# Plot trajectories\n",
+    "x,y,z = skel.bones['rhand'].xyz_data.T\n",
+    "T = len(x)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca(projection='3d')\n",
+    "\n",
+    "def init():\n",
+    "    ax.set_title('movement')\n",
+    "    ax.set_xlabel('x')\n",
+    "    ax.set_xlim(xmin, xmax)\n",
+    "    ax.set_ylabel('y')\n",
+    "    ax.set_ylim(ymin, ymax)\n",
+    "    ax.set_zlabel('z')\n",
+    "    ax.set_zlim(zmin, zmax)\n",
+    "    #ax.scatter(x[0], y[0], z[0], marker='o')\n",
+    "    return fig,\n",
+    "\n",
+    "def animate(i):\n",
+    "    #ax.view_init(elev=10., azim=i)\n",
+    "    ax.scatter(x[i], y[i], z[i], marker='o')\n",
+    "    return fig,\n",
+    "    \n",
+    "def animate_skeleton(i):\n",
+    "    ax.clear()\n",
+    "    init()\n",
+    "    #ax.scatter(X[:,2,i], X[:,0,i], X[:,1,i], marker='o', c='b')\n",
+    "    for d in [X_TO, X_RA, X_LA, X_RL, X_LL]:\n",
+    "        ax.plot(d[:,i,2], d[:,i,0], d[:,i,1], marker='o', c='b')\n",
+    "    return fig,\n",
+    "\n",
+    "# Animate\n",
+    "#anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
+    "#                               frames=T, interval=20, blit=True)\n",
+    "anim = animation.FuncAnimation(fig, animate_skeleton, init_func=init,\n",
+    "                               frames=T, interval=20, blit=True)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": 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"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot trajectory profiles\n",
+    "plt.figure()\n",
+    "plt.subplot(2,2,1)\n",
+    "plt.title('X')\n",
+    "plt.plot(range(T), X[11,:,2] - X[6,:,2])\n",
+    "\n",
+    "plt.subplot(2,2,2)\n",
+    "plt.title('Y')\n",
+    "plt.plot(range(T), X[11,:,0] - X[6,:,0])\n",
+    "\n",
+    "plt.subplot(2,2,3)\n",
+    "plt.title('Z')\n",
+    "plt.plot(range(T), X[11,:,1] - X[6,:,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyrobolearn/datasets/parsers/3d/data/CMU/02.asf b/pyrobolearn/datasets/parsers/3d/data/CMU/02.asf
new file mode 100644
index 0000000..c2c869f
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/data/CMU/02.asf
@@ -0,0 +1,338 @@
+# AST/ASF file generated using VICON BodyLanguage
+# -----------------------------------------------
+:version 1.10
+:name VICON
+:units
+  mass 1.0
+  length 0.45
+  angle deg
+:documentation
+   .ast/.asf automatically generated from VICON data using
+   VICON BodyBuilder and BodyLanguage model FoxedUp or BRILLIANT.MOD
+:root
+   order TX TY TZ RX RY RZ
+   axis XYZ
+   position 0 0 0  
+   orientation 0 0 0 
+:bonedata
+  begin
+     id 1 
+     name lhipjoint
+     direction 0.655637 -0.713449 0.247245 
+     length 2.52691 
+     axis 0 0 0  XYZ
+  end
+  begin
+     id 2 
+     name lfemur
+     direction 0.34202 -0.939693 0  
+     length 7.59371  
+     axis 0 0 20  XYZ
+    dof rx ry rz
+    limits (-160.0 20.0)
+           (-70.0 70.0)
+           (-60.0 70.0)
+  end
+  begin
+     id 3 
+     name ltibia
+     direction 0.34202 -0.939693 0   
+     length 7.28717   
+     axis 0 0 20   XYZ
+    dof rx
+    limits (-10.0 170.0)
+  end
+  begin
+     id 4 
+     name lfoot
+     direction 0.0886837 -0.243657 0.965798   
+     length 2.2218   
+     axis -90 7.62852e-016 20   XYZ
+    dof rx rz
+    limits (-45.0 90.0)
+           (-70.0 20.0)
+  end
+  begin
+     id 5 
+     name ltoes
+     direction 1.53547e-011 -4.22004e-011 1   
+     length 1.11249   
+     axis -90 7.62852e-016 20   XYZ
+    dof rx
+    limits (-90.0 20.0)
+  end
+  begin
+     id 6 
+     name rhipjoint
+     direction -0.645062 -0.722004 0.250209   
+     length 2.49697  
+     axis 0 0 0  XYZ 
+  end
+  begin
+     id 7 
+     name rfemur
+     direction -0.34202 -0.939693 0   
+     length 7.58734   
+     axis 0 0 -20   XYZ
+    dof rx ry rz
+    limits (-160.0 20.0)
+           (-70.0 70.0)
+           (-70.0 60.0)
+  end
+  begin
+     id 8 
+     name rtibia
+     direction -0.34202 -0.939693 0   
+     length 7.21538   
+     axis 0 0 -20   XYZ
+    dof rx
+    limits (-10.0 170.0)
+ end
+ begin
+     id 9 
+     name rfoot
+     direction -0.102907 -0.282735 0.953662   
+     length 2.23735  
+     axis -90 -7.62852e-016 -20   XYZ
+    dof rx rz
+    limits (-45.0 90.0)
+           (-20.0 70.0)
+  end
+  begin
+     id 10 
+     name rtoes
+     direction -1.5354e-011 -4.21751e-011 1   
+     length 1.11569   
+     axis -90 -7.62852e-016 -20   XYZ
+    dof rx
+    limits (-90.0 20.0)
+  end
+  begin
+     id 11 
+     name lowerback
+     direction 0.00952301 0.997604 -0.0685217   
+     length 2.05943   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 12 
+     name upperback
+     direction 0.00494399 0.999577 -0.0286701   
+     length 2.06523   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 13 
+     name thorax
+     direction 0.000729686 0.999992 0.00388603   
+     length 2.06807   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 14 
+     name lowerneck
+     direction 0.00452882 0.995459 0.0950816   
+     length 1.57426   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 15 
+     name upperneck
+     direction 0.021922 0.997711 -0.0639762   
+     length 1.56399   
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 16
+     name head
+     direction 0.008024 0.999444 -0.0323691  
+     length 1.6265  
+     axis 0 0 0   XYZ
+    dof rx ry rz
+    limits (-20.0 45.0)
+           (-30.0 30.0)
+           (-30.0 30.0)
+  end
+  begin
+     id 17 
+     name lclavicle
+     direction 0.967826 0.247107 -0.0474446  
+     length 3.6598  
+     axis 0 0 0   XYZ
+    dof ry rz
+    limits (-20.0 10.0)
+           (0.0 20.0)
+  end
+  begin
+     id 18 
+     name lhumerus
+     direction 1 -4.48971e-011 -2.90544e-027   
+     length 4.86513   
+     axis 180 -30 -90   XYZ
+    dof rx ry rz
+    limits (-60.0 90.0)
+           (-90.0 90.0)
+           (-90.0 90.0)
+  end
+  begin
+     id 19
+     name lradius
+     direction 1 -4.48958e-011 1.79638e-026   
+     length 3.35554   
+     axis 180 -30 -90   XYZ
+    dof rx
+    limits (-10.0 170.0)
+  end
+  begin
+     id 20
+     name lwrist
+     direction 1 -4.48952e-011 3.34345e-026   
+     length 1.67777   
+     axis -2.22354e-014 90 90   XYZ
+    dof ry
+    limits (-180.0 0.0)
+ end
+  begin
+     id 21 
+     name lhand
+     direction 1 -4.49055e-011 6.6869e-026   
+     length 0.661175   
+     axis -4.26681e-014 90 90   XYZ
+    dof rx rz
+    limits (-90.0 90.0)
+           (-45.0 45.0)
+  end
+  begin
+     id 22 
+     name lfingers
+     direction 1 -4.48731e-011 1.33738e-025   
+     length 0.533057   
+     axis -8.53362e-014 90 90   XYZ
+    dof rx
+    limits (0.0 90.0)
+  end
+  begin
+     id 23
+     name lthumb
+     direction 0.707107 -6.35005e-011 0.707107   
+     length 0.765366   
+     axis -90 45 2.85299e-015   XYZ
+    dof rx rz
+    limits (-45.0 45.0)
+           (-45.0 45.0)
+  end
+  begin
+     id 24 
+     name rclavicle
+     direction -0.973174 0.211421 -0.0907389   
+     length 3.59444  
+     axis 0 0 0   XYZ 
+    dof ry rz
+    limits (-10.0 20.0)
+           (-20.0 0.0)
+  end
+  begin
+     id 25 
+     name rhumerus
+     direction -1 -4.48964e-011 1.56311e-027  
+     length 5.02649   
+     axis 180 30 90   XYZ
+    dof rx ry rz
+    limits (-90.0 60.0)
+           (-90.0 90.0)
+           (-90.0 90.0)
+  end
+  begin
+     id 26 
+     name rradius
+     direction -1 -4.48948e-011 2.01724e-026   
+     length 3.36431   
+     axis 180 30 90   XYZ
+    dof rx
+    limits (-10.0 170.0)
+  end
+  begin
+     id 27 
+     name rwrist
+     direction -1 -4.49008e-011 3.34345e-026   
+     length 1.68216   
+     axis -2.22354e-014 -90 -90   XYZ
+    dof ry
+    limits (-180.0 0.0)
+  end
+  begin
+     id 28
+     name rhand
+     direction -1 -4.49013e-011 6.6869e-026   
+     length 0.730406   
+     axis -4.26681e-014 -90 -90   XYZ
+    dof rx rz
+    limits (-90.0 90.0)
+           (-45.0 45.0)
+  end
+  begin
+     id 29
+     name rfingers
+     direction -1 -4.48867e-011 1.33738e-025   
+     length 0.588872   
+     axis -8.53362e-014 -90 -90   XYZ
+    dof rx
+    limits (0.0 90.0)
+  end
+  begin
+     id 30
+     name rthumb
+     direction -0.707107 -6.34907e-011 0.707107   
+     length 0.845506   
+     axis -90 -45 -2.85299e-015   XYZ
+    dof rx rz
+    limits (-45.0 45.0)
+           (-45.0 45.0)
+  end
+:hierarchy
+  begin
+    root lhipjoint rhipjoint lowerback
+    lhipjoint lfemur
+    lfemur ltibia
+    ltibia lfoot
+    lfoot ltoes
+    rhipjoint rfemur
+    rfemur rtibia
+    rtibia rfoot
+    rfoot rtoes
+    lowerback upperback
+    upperback thorax
+    thorax lowerneck lclavicle rclavicle
+    lowerneck upperneck
+    upperneck head
+    lclavicle lhumerus
+    lhumerus lradius
+    lradius lwrist
+    lwrist lhand lthumb
+    lhand lfingers
+    rclavicle rhumerus
+    rhumerus rradius
+    rradius rwrist
+    rwrist rhand rthumb
+    rhand rfingers
+  end
diff --git a/pyrobolearn/datasets/parsers/3d/data/CMU/02_01.amc b/pyrobolearn/datasets/parsers/3d/data/CMU/02_01.amc
new file mode 100644
index 0000000..c19815a
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/data/CMU/02_01.amc
@@ -0,0 +1,10293 @@
+#!OML:ASF H:\Terrain\Patient Classification 1\Takeo\Monday\TakeoMonday.ASF
+:FULLY-SPECIFIED
+:DEGREES
+1
+root 10.4194 16.7048 -30.1003 -2.48972 -9.82194 -3.00914
+lowerback 7.86573 1.57272 6.3571
+upperback 3.47255 2.55226 1.65684
+thorax -0.599593 1.31944 -2.05527
+lowerneck -12.4901 4.06706 1.03438
+upperneck 1.61784 5.61246 -6.40733
+head 2.97226 2.54741 -1.92752
+rclavicle -1.6201e-014 -1.43125e-014
+rhumerus -9.40273 14.8795 -82.1974
+rradius 46.2257
+rwrist -16.7798
+rhand -25.9429 18.5743
+rfingers 7.12502
+rthumb 0.600913 -11.4046
+lclavicle -1.6201e-014 -1.43125e-014
+lhumerus -51.9812 32.3861 64.939
+lradius 20.3824
+lwrist 12.631
+lhand -21.8909 22.8474
+lfingers 7.12502
+lthumb 4.5141 52.7793
+rfemur 16.1777 1.96704 29.9133
+rtibia 11.5302
+rfoot -15.9687 0.925564
+rtoes 4.33359
+lfemur -28.0191 -1.02078 -20.1783
+ltibia 22.3977
+lfoot -12.1104 13.478
+ltoes -10.2227
+2
+root 10.4117 16.684 -29.9168 -2.69729 -10.0082 -2.83243
+lowerback 7.44596 1.65374 6.09331
+upperback 3.91254 2.58553 1.67171
+thorax 0.0784557 1.35992 -1.86912
+lowerneck -13.0403 4.1508 1.03655
+upperneck 1.41954 5.71695 -6.43796
+head 3.01316 2.59206 -1.92353
+rclavicle -3.06128e-014 -7.95139e-016
+rhumerus -8.66985 14.6305 -81.9822
+rradius 46.032
+rwrist -16.5037
+rhand -25.3579 18.1254
+rfingers 7.12502
+rthumb 1.16593 -11.8512
+lclavicle -3.06128e-014 -7.95139e-016
+lhumerus -51.4595 32.9736 65.2094
+lradius 20.3072
+lwrist 12.3811
+lhand -21.901 23.0332
+lfingers 7.12502
+lthumb 4.50436 52.9653
+rfemur 17.2048 2.06921 29.6756
+rtibia 11.2385
+rfoot -15.6726 0.950482
+rtoes 2.11338
+lfemur -27.7269 -0.953135 -20.5822
+ltibia 22.4395
+lfoot -12.3785 13.6867
+ltoes -8.45391
+3
+root 10.4037 16.6749 -29.7319 -2.73684 -10.1104 -2.67905
+lowerback 7.31519 1.74924 5.93159
+upperback 3.96845 2.68906 1.61541
+thorax 0.211031 1.41449 -1.83734
+lowerneck -13.1188 4.14188 1.06949
+upperneck 1.08186 5.66983 -6.25219
+head 2.90215 2.582 -1.8696
+rclavicle 3.97569e-015 -1.03368e-014
+rhumerus -8.50177 14.3985 -81.9469
+rradius 45.6568
+rwrist -16.2262
+rhand -25.4816 18.2851
+rfingers 7.12502
+rthumb 1.04638 -11.6921
+lclavicle 3.97569e-015 -1.03368e-014
+lhumerus -51.3593 33.2859 65.0346
+lradius 20.0295
+lwrist 11.6134
+lhand -21.8378 22.427
+lfingers 7.12502
+lthumb 4.56542 52.3578
+rfemur 17.7116 2.03566 29.3786
+rtibia 11.6107
+rfoot -15.695 1.22609
+rtoes 0.922151
+lfemur -27.6281 -0.810148 -21.0041
+ltibia 22.6869
+lfoot -12.2625 13.6748
+ltoes -9.11447
+4
+root 10.3951 16.6696 -29.54 -2.6529 -10.1914 -2.43014
+lowerback 7.19363 1.82368 5.74365
+upperback 3.81888 2.77415 1.4497
+thorax 0.127573 1.4515 -1.90802
+lowerneck -12.9178 4.12991 1.31882
+upperneck 0.706102 5.58519 -6.09066
+head 2.73437 2.55894 -1.86943
+rclavicle 1.37658e-014 0
+rhumerus -8.68107 14.2787 -82.1702
+rradius 45.2994
+rwrist -16.0827
+rhand -25.8239 18.565
+rfingers 7.12502
+rthumb 0.715839 -11.4136
+lclavicle 1.37658e-014 0
+lhumerus -51.4118 33.3294 64.7565
+lradius 19.8149
+lwrist 11.1137
+lhand -21.9155 21.714
+lfingers 7.12502
+lthumb 4.49033 51.6464
+rfemur 18.2149 2.09324 28.9948
+rtibia 11.7512
+rfoot -15.3617 1.42214
+rtoes 0.396563
+lfemur -27.6637 -0.391122 -21.6105
+ltibia 23.2309
+lfoot -11.8145 13.2795
+ltoes -11.1406
+5
+root 10.3841 16.6488 -29.3447 -2.50765 -10.3526 -2.46894
+lowerback 6.93084 1.85711 5.71898
+upperback 3.70601 2.80336 1.40701
+thorax 0.158931 1.46518 -1.93228
+lowerneck -12.7095 4.17378 1.546
+upperneck 0.38896 5.58739 -5.9898
+head 2.59153 2.57436 -1.87971
+rclavicle -2.04251e-014 -7.15625e-015
+rhumerus -8.88558 14.3777 -82.4627
+rradius 45.0282
+rwrist -16.0025
+rhand -26.022 18.4229
+rfingers 7.12502
+rthumb 0.524504 -11.5562
+lclavicle -2.04251e-014 -7.15625e-015
+lhumerus -51.3723 33.2687 64.7988
+lradius 19.8026
+lwrist 11.3747
+lhand -21.8573 21.6385
+lfingers 7.12502
+lthumb 4.54656 51.5698
+rfemur 18.5238 2.05559 28.8937
+rtibia 12.2574
+rfoot -15.0286 1.70983
+rtoes -0.597689
+lfemur -27.6786 -0.161279 -21.8039
+ltibia 24.1117
+lfoot -11.5185 12.8466
+ltoes -11.8881
+6
+root 10.3736 16.6153 -29.1487 -2.27833 -10.4567 -2.74192
+lowerback 6.39393 1.89875 5.83274
+upperback 3.70818 2.82625 1.52352
+thorax 0.460678 1.48492 -1.85437
+lowerneck -12.6363 4.16435 1.46386
+upperneck 0.109727 5.56187 -5.76511
+head 2.46892 2.57522 -1.79365
+rclavicle -9.39258e-015 -3.33958e-014
+rhumerus -8.94255 14.3995 -82.5464
+rradius 44.7054
+rwrist -16.0208
+rhand -26.1876 18.2668
+rfingers 7.12502
+rthumb 0.36451 -11.7126
+lclavicle -9.39258e-015 -3.33958e-014
+lhumerus -51.2401 33.4019 64.9731
+lradius 19.7523
+lwrist 11.4084
+lhand -21.7171 21.9356
+lfingers 7.12502
+lthumb 4.68197 51.8639
+rfemur 18.7716 1.97435 29.0445
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+rfoot -15.001 1.96046
+rtoes -2.66505
+lfemur -27.737 -0.266955 -21.6658
+ltibia 25.1808
+lfoot -11.2938 12.5502
+ltoes -10.5823
+7
+root 10.3599 16.5715 -28.9534 -2.2599 -10.3619 -2.59234
+lowerback 6.0578 1.87772 5.45535
+upperback 3.7224 2.75505 1.55572
+thorax 0.634688 1.44776 -1.59433
+lowerneck -12.5777 4.15989 1.23641
+upperneck -0.237379 5.55495 -5.38302
+head 2.31528 2.59066 -1.62948
+rclavicle 1.9133e-014 -2.38542e-015
+rhumerus -8.99603 14.4498 -82.5553
+rradius 44.2949
+rwrist -16.2697
+rhand -26.0908 18.2072
+rfingers 7.12502
+rthumb 0.458083 -11.7721
+lclavicle 1.9133e-014 -2.38542e-015
+lhumerus -51.2191 33.6162 65.0468
+lradius 19.7851
+lwrist 11.1488
+lhand -21.4715 21.9958
+lfingers 7.12502
+lthumb 4.91905 51.9187
+rfemur 19.3145 2.03164 28.8841
+rtibia 13.2441
+rfoot -15.2026 1.8636
+rtoes -5.73873
+lfemur -27.7804 -0.346391 -22.0273
+ltibia 26.364
+lfoot -10.3924 12.4866
+ltoes -10.2688
+8
+root 10.3458 16.5643 -28.7542 -2.36002 -10.2713 -2.44292
+lowerback 5.94446 1.79342 4.97885
+upperback 3.73693 2.61242 1.65782
+thorax 0.673863 1.36867 -1.21366
+lowerneck -12.6639 4.23843 0.871191
+upperneck -0.503805 5.68322 -4.98816
+head 2.21473 2.66958 -1.42777
+rclavicle 1.9133e-014 1.31198e-014
+rhumerus -9.04388 14.801 -82.5439
+rradius 43.7541
+rwrist -16.5425
+rhand -25.8764 18.1624
+rfingers 7.12502
+rthumb 0.665108 -11.8163
+lclavicle 1.9133e-014 1.31198e-014
+lhumerus -51.1759 33.8174 65.2761
+lradius 19.8353
+lwrist 11.135
+lhand -21.4038 22.1568
+lfingers 7.12502
+lthumb 4.98449 52.0782
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+rtibia 13.9784
+rfoot -15.0329 1.75063
+rtoes -8.99435
+lfemur -27.7763 -0.479807 -22.3193
+ltibia 27.6883
+lfoot -9.30166 13.1214
+ltoes -11.1841
+9
+root 10.3343 16.5753 -28.5503 -2.28592 -10.2581 -2.54086
+lowerback 5.87195 1.73346 5.11106
+upperback 3.56428 2.54204 1.52084
+thorax 0.544173 1.33202 -1.42625
+lowerneck -12.8389 4.28669 0.757051
+upperneck -0.501307 5.7362 -4.30349
+head 2.21631 2.71548 -1.17916
+rclavicle -1.33186e-014 -1.03368e-014
+rhumerus -9.46188 14.6013 -83.1637
+rradius 43.0615
+rwrist -16.2744
+rhand -25.7951 18.6118
+rfingers 7.12502
+rthumb 0.743616 -11.3667
+lclavicle -1.33186e-014 -1.03368e-014
+lhumerus -50.921 33.424 65.3926
+lradius 19.7381
+lwrist 11.3958
+lhand -21.7051 22.6165
+lfingers 7.12502
+lthumb 4.69349 52.5445
+rfemur 19.9706 2.02266 28.6945
+rtibia 14.6633
+rfoot -14.231 1.88766
+rtoes -11.8154
+lfemur -27.9258 -0.751252 -22.2513
+ltibia 29.0729
+lfoot -8.74962 14.2092
+ltoes -10.7542
+10
+root 10.323 16.5366 -28.3523 -2.23404 -10.2098 -2.46793
+lowerback 5.65422 1.7393 4.99464
+upperback 3.45035 2.53558 1.41083
+thorax 0.546233 1.32679 -1.469
+lowerneck -12.567 4.24422 0.659075
+upperneck -0.942763 5.65924 -3.82541
+head 1.99407 2.70184 -1.02124
+rclavicle -1.70955e-014 -8.34896e-015
+rhumerus -9.9406 14.2338 -83.6317
+rradius 42.4407
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+lclavicle -1.70955e-014 -8.34896e-015
+lhumerus -50.6293 33.2714 65.479
+lradius 19.5341
+lwrist 11.2062
+lhand -21.9528 22.825
+lfingers 7.12502
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+lfemur -28.1653 -1.09442 -22.327
+ltibia 30.3575
+lfoot -8.43111 15.001
+ltoes -8.05829
+11
+root 10.3113 16.4997 -28.1612 -2.38897 -10.1522 -2.38425
+lowerback 5.69251 1.74104 4.85984
+upperback 3.4436 2.53459 1.32498
+thorax 0.515306 1.32455 -1.48382
+lowerneck -12.4188 4.20035 0.693845
+upperneck -1.41542 5.57606 -3.7145
+head 1.80474 2.67141 -1.00325
+rclavicle 3.23025e-015 3.97569e-016
+rhumerus -10.3454 13.8752 -84.0076
+rradius 41.8169
+rwrist -15.7038
+rhand -26.0009 19.0243
+rfingers 7.12502
+rthumb 0.544899 -10.9548
+lclavicle 3.23025e-015 3.97569e-016
+lhumerus -50.4439 33.6313 65.1218
+lradius 19.1743
+lwrist 10.1843
+lhand -21.7547 22.4258
+lfingers 7.12502
+lthumb 4.64563 52.3548
+rfemur 20.654 1.76938 28.3617
+rtibia 16.9931
+rfoot -13.6701 1.75689
+rtoes -19.9638
+lfemur -28.1914 -1.14051 -22.458
+ltibia 31.6336
+lfoot -8.26969 14.9866
+ltoes -3.29597
+12
+root 10.296 16.5225 -27.9723 -2.45678 -10.0637 -2.62042
+lowerback 5.77311 1.73311 4.96082
+upperback 3.41008 2.53532 1.36846
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+upperback 1.77665 -2.13258 1.8357
+thorax -1.63019 -1.06363 2.68346
+lowerneck -11.2823 0.504082 -1.52876
+upperneck -0.132911 0.997207 -3.69361
+head 1.90272 0.383805 -0.862521
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+rhumerus -59.358 -32.3857 -53.1865
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+337
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+lowerback 6.61127 -1.39872 -1.87141
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+thorax -1.58851 -0.97565 3.20913
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+thorax -0.950553 -1.09392 3.20896
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+lfoot -14.9779 0.774576
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+lowerback 6.08656 -1.74843 0.1647
+upperback 1.59999 -2.27025 2.15737
+thorax -1.68055 -1.133 2.29471
+lowerneck -11.2052 0.454015 -1.6437
+upperneck 0.552324 0.997883 -4.39699
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+upperback 1.78225 -2.40255 2.42913
+thorax -1.32314 -1.20217 2.10433
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+rhumerus -63.7887 -34.3173 -47.1111
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+lclavicle -7.23079e-015 -1.27222e-014
+lhumerus -32.0358 -3.35783 91.4085
+lradius 65.228
+lwrist 25.6386
+lhand -25.7351 12.7292
+lfingers 7.12502
+lthumb 0.801561 42.7075
+rfemur -23.2433 6.73071 25.0009
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+rfoot -4.35066 -15.1615
+rtoes -10.693
+lfemur 16.9276 -2.85303 -22.5749
+ltibia 27.7778
+lfoot -13.6748 -0.618019
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\ No newline at end of file
diff --git a/pyrobolearn/datasets/parsers/3d/data/CMU/download-link.txt b/pyrobolearn/datasets/parsers/3d/data/CMU/download-link.txt
new file mode 100644
index 0000000..3733e52
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/data/CMU/download-link.txt
@@ -0,0 +1,3 @@
+# CMU Dataset
+
+* webpage (link to download the dataset): mocap.cs.cmu.edu/search.php
diff --git a/pyrobolearn/datasets/parsers/3d/data/CMU/subject2.txt b/pyrobolearn/datasets/parsers/3d/data/CMU/subject2.txt
new file mode 100644
index 0000000..3cf8c36
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/data/CMU/subject2.txt
@@ -0,0 +1,15 @@
+
+			Subject #2 (various expressions and human behaviors)
+
+02_01	02_01.tvd	02_01.c3d	02_01.amc	02_01.avi	walk	
+02_02	02_02.tvd	02_02.c3d	02_02.amc	02_02.mpg	02_02.avi	walk	
+02_03	02_03.tvd	02_03.c3d	02_03.amc	02_03.mpg	02_03.avi	run/jog	
+02_04	02_04.tvd	02_04.c3d	02_04.amc	02_04.mpg	02_04.avi	jump, balance	
+02_05	02_05.tvd	02_05.c3d	02_05.amc	02_05.mpg	02_05.avi	punch/strike	
+02_06	02_06.tvd	02_06.c3d	02_06.amc	02_06.mpg	02_06.avi	bend over, scoop up, rise, lift arm	
+02_07	02_07.tvd	02_07.c3d	02_07.amc	02_07.mpg	02_07.avi	swordplay	
+02_08	02_08.tvd	02_08.c3d	02_08.amc	02_08.mpg	02_08.avi	swordplay	
+02_09	02_09.tvd	02_09.c3d	02_09.amc	02_09.mpg	02_09.avi	swordplay	
+02_10	02_10.tvd	02_10.c3d	02_10.amc	02_10.mpg	02_10.avi	wash self	
+
+
diff --git a/pyrobolearn/datasets/parsers/3d/data/UTD/Skeleton_joint_order.txt b/pyrobolearn/datasets/parsers/3d/data/UTD/Skeleton_joint_order.txt
new file mode 100644
index 0000000..074d121
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/data/UTD/Skeleton_joint_order.txt
@@ -0,0 +1,24 @@
+The skeleton joint order in UTD-MAD dataset:
+
+1. head; 
+2. shoulder_center;
+3. spine;
+4. hip_center;
+5. left_shoulder;
+6. left_elbow;
+7. left_wrist;
+8. left_hand;
+9. right_shoulder;
+10. right_elbow;
+11. right_wrist;
+12. right_hand;
+13. left_hip;
+14. left_knee;
+15. left_ankle;
+16. left_foot;
+17. right_hip;
+18. right_knee;
+19. right_ankle;
+20. right_foot;
+
+Each skeleton data is a 20 x 3 x num_frame matrix. Each row of a skeleton frame corresponds to three spatial coordinates of a joint.
\ No newline at end of file
diff --git a/pyrobolearn/datasets/parsers/3d/data/UTD/download-link.txt b/pyrobolearn/datasets/parsers/3d/data/UTD/download-link.txt
new file mode 100644
index 0000000..624a110
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/data/UTD/download-link.txt
@@ -0,0 +1,3 @@
+# UTD-MHAD Dataset
+
+* webpage (link to download the dataset): http://www.utdallas.edu/~kehtar/UTD-MHAD.html
diff --git a/pyrobolearn/datasets/parsers/3d/figures/JHMDB-comparison-datasets.png b/pyrobolearn/datasets/parsers/3d/figures/JHMDB-comparison-datasets.png
new file mode 100644
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diff --git a/pyrobolearn/datasets/parsers/3d/figures/MPIICooking-comparison-datasets.png b/pyrobolearn/datasets/parsers/3d/figures/MPIICooking-comparison-datasets.png
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diff --git a/pyrobolearn/datasets/parsers/3d/mocap_parser.py b/pyrobolearn/datasets/parsers/3d/mocap_parser.py
new file mode 100644
index 0000000..9ec4976
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/mocap_parser.py
@@ -0,0 +1,230 @@
+import numpy as np
+from scipy.interpolate import CubicSpline
+import matplotlib.pyplot as plt
+
+class MocapParser(object):
+
+    def __init__(self, filename):
+        """
+        Parser for motion capture. By default, if the data is described in Cartesian space, the x-axis should
+        be pointing in front of the human, the y-axis on his/her left, and z-axis upward.
+        :param filename:
+        """
+        self.filename = filename
+        self.num_samples = 0
+        self.joint_names = []
+        self.link_names = []
+        self.marker_names = []
+
+        self.data = self.loadFile(filename)
+
+
+    def loadFile(self, filename):
+        raise NotImplementedError("loadFile is not implemented.")
+
+    def interpolate(self, data, method='cubic', axis=-1)
+        """
+        Interpolate the Mocap data such that it is between 0 and 1, along the given axis.
+
+        :param data: mocap data
+        :param method: 'linear', 'cubic', 'hermite' interpolation
+        :param axis: The axis on which to interpolate. The length should be equal to the number of samples in the
+                     mocap data
+        :return: Interpolator - function that given the time [0,1] will give the corresponding data
+        """
+        self.num_samples = data.shape[axis]
+        x = np.linspace(0., 1., self.num_samples)
+        interpolator = CubicSpline(x, self.data, axis=axis)
+        return interpolator
+
+    def getMarkerName(self, marker_idx=None):
+        if marker_idx is None:
+            return self.getMarkerNames()
+        else:
+            return self.marker_names[marker_idx]
+
+    def getMarkerNames(self):
+        return self.marker_names
+
+    def getJointName(self, joint_idx=None):
+        if joint_idx is None:
+            return self.getJointNames()
+        else:
+            return self.joint_names[joint_idx]
+
+    def getJointNames(self):
+        return self.joint_names
+
+    def getLinkName(self, link_idx=None):
+        if link_idx is None:
+            return self.getLinkNames()
+        else:
+            return self.link_names[link_idx]
+
+    def getLinkNames(self):
+        return self.link_names
+
+    def getMarkerPosition(self, marker_idx=None):
+        if marker_idx is None:
+            return self.getMarkerPositions()
+        else:
+            pass
+
+    def getMarkerPositions(self):
+        pass
+
+    def getJointPosition(self, joint_idx=None):
+        if joint_idx is None:
+            return self.getJointPositions()
+        else:
+            pass
+
+    def getJointPositions(self):
+        pass
+
+    def getJointVelocity(self, joint_idx=None):
+        if joint_idx is None:
+            return self.getJointVelocities()
+        else:
+            pass
+
+    def getJointVelocities(self):
+        pass
+
+    def getLinkPosition(self, link_idx=None):
+        if link_idx is None:
+            return self.getLinkPositions()
+        else:
+            pass
+
+    def getLinkPositions(self):
+        pass
+
+    def getLinkVelocity(self, link_idx=None):
+        if link_idx is None:
+            return self.getLinkVelocities()
+        else:
+            pass
+
+    def getLinkVelocities(self):
+        pass
+
+    def getLinkOrientation(self, link_idx=None):
+        if link_idx is None:
+            return self.getLinkOrientations()
+        else:
+            pass
+
+    def getLinkOrientations(self):
+        pass
+
+    def getLinkAngularVelocity(self, link_idx=None):
+        if link_idx is None:
+            return self.getLinkAngularVelocities()
+        else:
+            pass
+
+    def getLinkAngularVelocities(self):
+        pass
+
+
+    ## Plotting ##
+    def plot3d(self, ax=None):
+        pass
+
+    def plotJointProfile(self, ax=None, joint_idx=None, pos=True, vel=True, acc=True):
+        pass
+
+    def plotLinkProfile(self, ax=None, link_idx=None, pos=True, vel=True, acc=True, wrt='world'):
+        pass
+
+    def plotMarkerProfile(self, ax=None, link_idx=None, pos=True, vel=True, acc=True, wrt='world'):
+        pass
+
+    def animate3d(self, ax=None, title=None):
+        pass
+
+
+
+
+from amcparser.skeleton import Skeleton
+from amcparser.motion import SkelMotion
+
+class CMUMocapParser(MocapParser):
+
+    def __init__(self, skeleton_filename, motion_filename, skeleton_scale=1.0):
+        super(CMUMocapParser, self).__init__(motion_filename)
+        self.joint_names = ['head', 'upperneck', 'lowerneck', 'upperback', 'thorax', 'lowerback', 'root', # Spine
+                            'rclavicle', 'rhumerus', 'rradius', 'rwrist', 'rhand', 'rthumb', 'rfingers', # Right arm
+                            'lclavicle', 'lhumerus', 'lradius', 'lwrist', 'lhand', 'lthumb', 'lfingers', # Left arm
+                            'rhipjoint', 'rfemur', 'rtibia', 'rfoot', 'rtoes', # Right leg
+                            'lhipjoint', 'lfemur', 'ltibia', 'lfoot', 'ltoes'] # Left leg
+        self.link_names = self.joint_names
+        self.marker_names = self.joint_names
+        self.base_name = 'root'
+
+        # Load skeleton
+        self.skeleton = Skeleton(skeleton_filename, scale=skeleton_scale)
+
+    def loadFile(self, filename, framerate=120.):
+        self.skeleton_motion = SkelMotion(self.skeleton, filename, (1./framerate))
+        # compute trajectories
+        #self.data = self.skeleton_motion.traverse(bone, start, end)
+        self.data = self.skeleton_motion.traverse(None, 0, -1)
+        # make sure that given axis
+
+    def animate3d(self, ax=None, title=None):
+        if ax is None:
+            fig = plt.figure()
+            ax = fig.gca(projection='3d')
+
+        # Rescaling such that the skeleton it is in the right proportion and at the middle
+        xmin, xmax = X[..., 2].min(), X[..., 2].max()
+        ymin, ymax = X[..., 0].min(), X[..., 0].max()
+        zmin, zmax = X[..., 1].min(), X[..., 1].max()
+        x_len, y_len, z_len = (xmax - xmin), (ymax - ymin), (zmax - zmin)
+        max_len = max([x_len, y_len, z_len])
+        xmin, xmax = xmin + (x_len - max_len) / 2., xmin + (x_len + max_len) / 2.
+        ymin, ymax = ymin + (y_len - max_len) / 2., ymin + (y_len + max_len) / 2.
+        zmin, zmax = zmin + (z_len - max_len) / 2., zmin + (z_len + max_len) / 2.
+
+        # Plot trajectories
+        x, y, z = skel.bones['rhand'].xyz_data.T
+        T = len(x)
+
+        def init():
+            ax.set_title('movement')
+            ax.set_xlabel('x')
+            ax.set_xlim(xmin, xmax)
+            ax.set_ylabel('y')
+            ax.set_ylim(ymin, ymax)
+            ax.set_zlabel('z')
+            ax.set_zlim(zmin, zmax)
+            # ax.scatter(x[0], y[0], z[0], marker='o')
+            return fig,
+
+        def animate(i):
+            # ax.view_init(elev=10., azim=i)
+            ax.scatter(x[i], y[i], z[i], marker='o')
+            return fig,
+
+        def animate_skeleton(i):
+            ax.clear()
+            init()
+            # ax.scatter(X[:,2,i], X[:,0,i], X[:,1,i], marker='o', c='b')
+            for d in [X_TO, X_RA, X_LA, X_RL, X_LL]:
+                ax.plot(d[:, i, 2], d[:, i, 0], d[:, i, 1], marker='o', c='b')
+            return [fig]  # fig,
+
+        # Animate
+        # anim = animation.FuncAnimation(fig, animate, init_func=init,
+        #                                frames=T, interval=20, blit=True)
+        anim = animation.FuncAnimation(fig, animate_skeleton, init_func=init,
+                                       frames=T, interval=20, blit=False)
+
+        plt.show()
+
+
+# Test
+if __name__ == "__main__":
+    pass
\ No newline at end of file
diff --git a/pyrobolearn/datasets/parsers/3d/utd-mhad.ipynb b/pyrobolearn/datasets/parsers/3d/utd-mhad.ipynb
new file mode 100644
index 0000000..b0ebacb
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/3d/utd-mhad.ipynb
@@ -0,0 +1,967 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### [UTD-MHAD: \"UTD-MHAD: A Multimodal Dataset for Human Action Recognition utilizing a Depth Camera and a Wearable Inertial Sensor\", 2015](http://www.utdallas.edu/~kehtar/UTD-MHAD.html)\n",
+    "\n",
+    "* nb of subjects: 8\n",
+    "* 27 different actions: (1) right arm swipe to the left, (2) right arm swipe to the right, (3) right hand wave, (4) two hand front clap, (5) right arm throw, (6) cross arms in the chest, (7) basketball shoot, (8) right hand draw x, (9) right hand draw circle (clockwise), (10) right hand draw circle (counter clockwise), (11) draw triangle, (12) bowling (right hand), (13) front boxing, (14) baseball swing from right, (15) tennis right hand forehand swing, (16) arm curl (two arms), (17) tennis serve, (18) two hand push, (19) right hand knock on door, (20) right hand catch an object, (21) right hand pick up and throw, (22) jogging in place, (23) walking in place, (24) sit to stand, (25) stand to sit, (26) forward lunge (left foot forward), (27) squat (two arms stretch out).\n",
+    "* labeled and segmented data: 861 data sequences, 3D skeleton joint positions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.io as sio\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import matplotlib.animation as animation\n",
+    "import glob\n",
+    "import re\n",
+    "\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "#%matplotlib inline\n",
+    "%matplotlib notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "PATH = 'data/UTD/*.mat'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb of demos: 861\n",
+      "Min T: 41\n",
+      "Max T: 125\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Definition of some variables\n",
+    "id_to_jnt = ['head', 'shoulder_center', 'spine', 'hip_center', 'left_shoulder', 'left_elbow', 'left_wrist',\n",
+    "             'left_hand', 'right_shoulder', 'right_elbow', 'right_wrist', 'right_hand', 'left_hip', 'left_knee',\n",
+    "             'left_ankle', 'left_foot', 'right_hip', 'right_knee', 'right_ankle', 'right_foot']\n",
+    "jnt_to_id = {name: idx for idx,name in enumerate(id_to_jnt)}\n",
+    "id_to_action = ['Swipe left', 'Swipe right', 'Wave', 'Clap', 'Throw',  'Arm cross', 'Basketball shoot', \n",
+    "                'Draw X', 'Draw circle (clockwise)', 'Draw circle (counter clockwise)',\n",
+    "                'Draw triangle', 'Bowling', 'Boxing', 'Baseball swing', 'Tennis swing',\n",
+    "                'Arm curl', 'Tennis serve', 'Push', 'Knock', 'Catch', 'Pickup and throw',\n",
+    "                'Jog', 'Walk', 'Sit to stand', 'Stand to sit', 'Lunge', 'Squat']\n",
+    "action_to_id = {name: idx for idx,name in enumerate(id_to_action)}\n",
+    "Nj = len(id_to_jnt) # 20\n",
+    "\n",
+    "# Load all the data\n",
+    "files = glob.glob(PATH)\n",
+    "data = [sio.loadmat(f)['d_skel'] for f in files] # list of shape Njx3xT\n",
+    "N = len(data)\n",
+    "print(\"Nb of demos: %d\" % len(data))\n",
+    "Ts = np.array([d.shape[2] for d in data])\n",
+    "print(\"Min T: %d\" % np.min(Ts))\n",
+    "print(\"Max T: %d\" % np.max(Ts))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": 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"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select a sequence\n",
+    "idx = np.random.randint(0, N)\n",
+    "X = np.copy(data[idx])\n",
+    "T = X.shape[2]\n",
+    "title_idx = int(re.findall(r'a[0-9]+', files[idx])[0][1:]) - 1\n",
+    "\n",
+    "# Get trajectory of one end-effector (x,y,z)\n",
+    "#x,y,z = X[jnt_to_id['right_wrist']] # left_arm\n",
+    "#x,y,z = z,x,y\n",
+    "#T = len(x)\n",
+    "\n",
+    "# Rescaling such that the skeleton it is in the right proportion and at the middle\n",
+    "xmin, xmax = X[:,2].min(), X[:,2].max()\n",
+    "ymin, ymax = X[:,0].min(), X[:,0].max()\n",
+    "zmin, zmax = X[:,1].min(), X[:,1].max()\n",
+    "x_len, y_len, z_len = (xmax - xmin), (ymax - ymin), (zmax - zmin)\n",
+    "max_len = max([x_len, y_len, z_len])\n",
+    "xmin, xmax = xmin + (x_len - max_len)/2., xmin + (x_len + max_len)/2.\n",
+    "ymin, ymax = ymin + (y_len - max_len)/2., ymin + (y_len + max_len)/2.\n",
+    "zmin, zmax = zmin + (z_len - max_len)/2., zmin + (z_len + max_len)/2.\n",
+    "\n",
+    "# Plot trajectory\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca(projection='3d')\n",
+    "\n",
+    "def init():\n",
+    "    ax.set_title(id_to_action[title_idx])\n",
+    "    ax.set_xlabel('x')\n",
+    "    #ax.set_xlim(xmin, xmax)\n",
+    "    ax.set_xlim(-xmax, -xmin)\n",
+    "    ax.set_ylabel('y')\n",
+    "    ax.set_ylim(ymin, ymax)\n",
+    "    ax.set_zlabel('z')\n",
+    "    ax.set_zlim(zmin, zmax)\n",
+    "    #ax.scatter(x[0], y[0], z[0], marker='o')\n",
+    "    return fig,\n",
+    "\n",
+    "def animate(i):\n",
+    "    #ax.view_init(elev=10., azim=i)\n",
+    "    ax.scatter(-x[i], y[i], z[i], marker='o')\n",
+    "    return fig,\n",
+    "    \n",
+    "def animate_skeleton(i):\n",
+    "    ax.clear()\n",
+    "    init()\n",
+    "    #ax.scatter(X[:,2,i], X[:,0,i], X[:,1,i], marker='o', c='b')\n",
+    "    for j in range(0,20,4):\n",
+    "        ax.plot(-X[j:j+4,2,i], X[j:j+4,0,i], X[j:j+4,1,i], marker='o', c='b')\n",
+    "    return fig,\n",
+    "    \n",
+    "\n",
+    "# Animate\n",
+    "#anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
+    "#                               frames=T, interval=20, blit=True)\n",
+    "anim = animation.FuncAnimation(fig, animate_skeleton, init_func=init,\n",
+    "                               frames=T, interval=20, blit=True)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create video with HTML\n",
+    "#HTML(anim.to_html5_video())"
+   ]
+  }
+ ],
+ "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": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyrobolearn/datasets/parsers/handwritten/HWRT/download-link.txt b/pyrobolearn/datasets/parsers/handwritten/HWRT/download-link.txt
new file mode 100644
index 0000000..0605923
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/handwritten/HWRT/download-link.txt
@@ -0,0 +1,4 @@
+# HWRT Database of Handwritten symbols
+
+* webpage: www.martin-thoma.de/write-math/data/
+* link to download the dataset: i13pc106.ira.uka.de/~mthoma/hwrt/2015-01-28-data.tar
diff --git a/pyrobolearn/datasets/parsers/handwritten/IAM-Online/download-link.txt b/pyrobolearn/datasets/parsers/handwritten/IAM-Online/download-link.txt
new file mode 100644
index 0000000..ec28cd2
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/handwritten/IAM-Online/download-link.txt
@@ -0,0 +1,7 @@
+# IAM Online Handwriting Database
+
+* webpage: www.fki.inf.unibe.ch/databases/iam-on-line-handwriting-database
+* link to download the dataset: http://www.fki.inf.unibe.ch/databases/iam-on-line-handwriting-database/download-the-iam-on-line-handwriting-database
+
+Note that you need to register in order to download the datasets.
+                    
diff --git a/pyrobolearn/datasets/parsers/handwritten/ICDAR2013/download-link.txt b/pyrobolearn/datasets/parsers/handwritten/ICDAR2013/download-link.txt
new file mode 100644
index 0000000..10c706d
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/handwritten/ICDAR2013/download-link.txt
@@ -0,0 +1,5 @@
+# ICDAR2013 - Handwriting Stroke Recovery from Offline Data (Kaggle)
+
+* webpage: https://www.kaggle.com/c/icdar2013-stroke-recovery-from-offline-data
+* link to download the dataset: https://www.kaggle.com/c/icdar2013-stroke-recovery-from-offline-data/data
+                    
diff --git a/pyrobolearn/datasets/parsers/handwritten/README.md b/pyrobolearn/datasets/parsers/handwritten/README.md
new file mode 100644
index 0000000..5db9d08
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/handwritten/README.md
@@ -0,0 +1,38 @@
+## Online Handwritten Datasets
+
+This repo contains the python code to load, parse and plot online handwritten characters.
+
+In this folder, I focus on the following datasets:
+
+1. Character Trajectories Data Set (2,858): https://archive.ics.uci.edu/ml/datasets/Character+Trajectories 
+2. UJI Pen Characters (V2) Data Set (11,640): archive.ics.uci.edu/ml/datasets/UJI+Pen+Characters+(Version+2)
+3. HWRT Database of Handwritten symbols: www.martin-thoma.de/write-math/data/
+4. ICDAR2013 - Handwriting Stroke Recovery from Offline Data (Kaggle): https://www.kaggle.com/c/icdar2013-stroke-recovery-from-offline-data
+5. IAM Online Handwriting Database: www.fki.inf.unibe.ch/databases/iam-on-line-handwriting-database
+
+
+There are of course other datasets (mostly offline) that could be used such as:
+
+* MNIST dataset (Offline)
+* NIST Handprinted Forms and Characters Database (Offline)
+* The IRESTE On/Off Dual Handwriting Database (75€): www.irccyn.ec-nantes.fr/~viardgau/IRONOFF/ICDAR99.htm
+* UCI online datasets:
+    * Online Handwritten Assamese Characters Dataset (8,235): archive.ics.uci.edu/ml/datasets/Online+Handwritten+Assamese+Characters+Dataset
+    * Pen-Based Recognition of Handwritten Digits Dataset (10,992): archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits
+* CASIA online and offline chinese handwriting Databases: www.nlpr.ia.ac.cn/databases/handwriting/Online_database.html
+* CEDAR Handwriting (Offline)
+
+
+## How to use it?
+
+The whole python code to load and parse the different datasets is inside the jupyter notebook `handwritten-data.ipynb`. To open the jupyter notebook, you will first need to install jupyter. For each dataset, I plotted 25 random samples to give you a general feeling about the dataset. You will need to download the dataset that you which to use (and put it in the corresponding folder if you do not wish to modify the python code).
+
+
+## FAQs
+
+* What is the meaning of 'online'? Online in this case means that you also have the trajectories (x,y), while offline would mean that you only have pictures of handwritten characters/words/sentences.
+
+* Do I need to cite the dataset that I am using? Yes, you should cite the corresponding paper which can usually be found on the website where you downloaded the dataset.
+
+* For what can I use these datasets? Mostly for demonstration / supervised learning. You could, for instance, use one of the following models to learn the trajectories: Gaussian Processes, Gaussian Mixture Models, Hidden Markov Models, (Deep) Neural Networks, Dynamic Movement Primitives, etc.
+                    
diff --git a/pyrobolearn/datasets/parsers/handwritten/UCI-Characters-Trajectories/download-link.txt b/pyrobolearn/datasets/parsers/handwritten/UCI-Characters-Trajectories/download-link.txt
new file mode 100644
index 0000000..1eb6e08
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/handwritten/UCI-Characters-Trajectories/download-link.txt
@@ -0,0 +1,5 @@
+# Character Trajectories Data Set
+
+* webpage: https://archive.ics.uci.edu/ml/datasets/Character+Trajectories
+* link to download the dataset: https://archive.ics.uci.edu/ml/machine-learning-databases/character-trajectories/
+                    
diff --git a/pyrobolearn/datasets/parsers/handwritten/UCI-Ujipen-Chars2/download-link.txt b/pyrobolearn/datasets/parsers/handwritten/UCI-Ujipen-Chars2/download-link.txt
new file mode 100644
index 0000000..ccf4c41
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/handwritten/UCI-Ujipen-Chars2/download-link.txt
@@ -0,0 +1,4 @@
+# UJI Pen Characters (V2) Data Set
+
+* webpage: archive.ics.uci.edu/ml/datasets/UJI+Pen+Characters+(Version+2)
+* link to download the dataset: http://archive.ics.uci.edu/ml/machine-learning-databases/uji-penchars/version2/
diff --git a/pyrobolearn/datasets/parsers/handwritten/handwritten-data.ipynb b/pyrobolearn/datasets/parsers/handwritten/handwritten-data.ipynb
new file mode 100644
index 0000000..d391ff9
--- /dev/null
+++ b/pyrobolearn/datasets/parsers/handwritten/handwritten-data.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Online Handwritten Datasets\n",
+    "\n",
+    "### Table of Contents\n",
+    "\n",
+    "1. [UCI - Character Trajectories Data Set](#uci-character)\n",
+    "2. [UCI - UJI Pen Characters (V2) Data Set](#uci-ujipen)\n",
+    "3. [HWRT Database of Handwritten symbols](#hwrt)\n",
+    "4. [ICDAR2013 - Handwriting Stroke Recovery from Offline Data (Kaggle)](#kaggle)\n",
+    "5. [IAM Online Handwriting Database](#IAMonline)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import scipy.io\n",
+    "from scipy import misc\n",
+    "from ast import literal_eval\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [UCI - Character Trajectories Data Set](https://archive.ics.uci.edu/ml/datasets/Character+Trajectories ) <a id='uci-character'></a>\n",
+    "\n",
+    "* Size: 2,858\n",
+    "* Short description: single stroke trajectories sampled at 200Hz."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3nHxFlaSIq4bmfeUCf877Y77Qt/jk5bsaSuz+acsbLbHnPr8bM3bDgvWO9+e/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\n/wODBHTrXPmSRwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3bdecd590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "uci_char_path = 'UCI-Character-Trajectories/mixoutALL_shifted.mat'\n",
+    "\n",
+    "# Load the data\n",
+    "uci_char = scipy.io.loadmat(uci_char_path)\n",
+    "#print(uci_char.keys()) # ['mixout', '__version__', '__header__', 'consts', '__globals__']\n",
+    "N = uci_char['mixout'].shape[1]\n",
+    "uci_char = uci_char['mixout'][0]\n",
+    "\n",
+    "# Get a list of (x,y) for each data\n",
+    "data = []\n",
+    "for ex in uci_char:\n",
+    "    x = [ex[0,i] for i,force_value in enumerate(ex[2]) if force_value != 0.0]\n",
+    "    y = [ex[1,i] for i,force_value in enumerate(ex[2]) if force_value != 0.0]\n",
+    "    data.append(np.array([x,y]))\n",
+    "\n",
+    "# Choose randomly 25 characters and plot them\n",
+    "idx = np.random.choice(N, 25, replace=False)\n",
+    "data = [data[i] for i in idx]\n",
+    "plt.figure(figsize=(8,8))\n",
+    "for i,d in enumerate(data):\n",
+    "    plt.subplot(5,5,i+1)\n",
+    "    plt.axis('off')\n",
+    "    plt.plot(d[0], d[1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [UCI - UJI Pen Characters (V2) Data Set](archive.ics.uci.edu/ml/datasets/UJI+Pen+Characters+(Version+2)) <a id='uci-ujipen'></a>\n",
+    "\n",
+    "* Size: 11,640 (Single stroke = 5,973)\n",
+    "* Short description: Single and multi-stroke characters and digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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E32f6zCF1iRLd0duJ2EByVEnHXx14vaQkWsIC6fEF4t/7zjQZqiaYMRfwKLAn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+KDkJ7LGVuoPokb8Y4e8xdts2Ks6I5BYivgLuBdYGEzLijl6iZdHR+LF/7/ADwicYzEeLWNOJRg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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3bab85b90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ujipen_path = 'UCI-Ujipen-Chars2/ujipenchars2.txt'\n",
+    "\n",
+    "# Load and parse the data\n",
+    "num_stroke = 0\n",
+    "data = []\n",
+    "with open(ujipen_path, 'rb') as f:\n",
+    "    for line in f.readlines():\n",
+    "        line = line.strip() # remove extra space before and after the sentence\n",
+    "        if not line.startswith('//'): # Not a comment\n",
+    "            if line.startswith('NUMSTROKES'): # check the nb of strokes\n",
+    "                num_stroke = int(line[-1])\n",
+    "            elif line.startswith('POINTS') and num_stroke == 1: # only keep single stroke characters\n",
+    "                hash_idx = line.find('#')\n",
+    "                coord = map(int, line[hash_idx+2:].split())\n",
+    "                x = coord[::2]\n",
+    "                y = coord[1::2]\n",
+    "                data.append(np.array([x,y]))\n",
+    "\n",
+    "#print(len(data))\n",
+    "\n",
+    "# Choose randomly 25 characters and plot them\n",
+    "idx = np.random.choice(len(data), 25, replace=False)\n",
+    "data = [data[i] for i in idx]\n",
+    "plt.figure(figsize=(8,8))\n",
+    "for i,d in enumerate(data):\n",
+    "    plt.subplot(5,5,i+1)\n",
+    "    plt.axis('off')\n",
+    "    plt.plot(d[0], d[1][::-1]) # note that the y coordinates goes from 'up' to 'bottom'\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [HWRT Database of Handwritten symbols](www.martin-thoma.de/write-math/data/) <a id='hwrt'></a>\n",
+    "\n",
+    "* Size: 151,160 (training data), 17,075 (test data)\n",
+    "* Short description: \"on-line data of handwritten symbols such as all alphanumeric characters, arrows, greek characters and mathematical symbols like the integral symbol\"\n",
+    "* Note: this dataset contains single and multiple strokes characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WkC6Zhfisq/UViaaV3p/mIN4LW9FQYD4zJssdiPxQKZPW5AFgOTOmzx2INIdUb7oN8I/U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\neAqtIiIiIlJ4/w8nQAMx11tQBQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3bbd75f10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hwrt_path = 'HWRT/train-data.csv'\n",
+    "#hwrt_path = 'HWRT/test-data.csv'\n",
+    "\n",
+    "# Read the csv file. WARNING: do not load the whole file!\n",
+    "# Header: symbol_id; user_id; data; user_agent\n",
+    "data = pd.read_csv(hwrt_path, sep=';', quotechar=\"'\", index_col=False, skiprows=0, nrows=2000)\n",
+    "# transform the string '[[{},{}]]' into a list of list of dict\n",
+    "data.data = data.data.apply(literal_eval)\n",
+    "\n",
+    "# Get a list of (x,y) for each data\n",
+    "xy = []\n",
+    "for i,ex in enumerate(data['data']):\n",
+    "    if len(ex) == 1: # single stroke\n",
+    "        x = [dic['x'] for dic in ex[0]]\n",
+    "        y = [dic['y'] for dic in ex[0]]\n",
+    "        xy.append([x,y])\n",
+    "\n",
+    "# Choose randomly 25 characters and plot them\n",
+    "idx = np.random.choice(len(xy), 25, replace=False)\n",
+    "xy = [xy[i] for i in idx]\n",
+    "plt.figure(figsize=(8,8))\n",
+    "for i,d in enumerate(xy):\n",
+    "    plt.subplot(5,5,i+1)\n",
+    "    plt.axis('off')\n",
+    "    plt.plot(d[0], d[1][::-1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [ICDAR2013 - Handwriting Stroke Recovery from Offline Data (Kaggle)](https://www.kaggle.com/c/icdar2013-stroke-recovery-from-offline-data) <a id='kaggle'></a>\n",
+    "\n",
+    "* Size: 1,081 (training: 605, test: 476)\n",
+    "* Short description: \"the data consists of 1,081 (arabic) signatures of 200 writers\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TLa20rFhZ41WxwVQqhGJaQURYBBql9NkNS5p/LPAx5s95Cpb/8CNsVDpMm0/N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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3bac78790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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6l+EjhiJUbihFrqS7iwghaNasEc2aPYmUsHrVd3S7vhOaCCnZUObo3PnKIm3f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\nijzounVL4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/OvvTebEycfhuOzMGGXhx78Py69/Dy0Ujz08HSuvPwPNPQ+GnYNn3/+BSlp\nWfiNYdDg3nTJSKBLl1SGjxjK0Scfi2U0Gpuly/OY+crLFGzdQjgUYvy4AznsqCl0y8qg+1GTOPKo\nSZGbi0VT9C/t7Iw2PHDfPyjcUsgB48dxzHFHAgZvJobqBCVtYR8bXNdl0OBBWLb9yyeuAUdZrF6V\ni1O2mUMOnSSnRhPRgOW6OEazMb+YB+6+i0v/dAm9+vVBNcPojNpjq8FYzP1qHu++PYs/3XQ1MQlx\nWChMWINlsX5tAZ9+8jG5a3K9pvPYONat30haaio3/OU6fD7ba82gpvIWMN7pvZ36boR3XRhj8+13\nX9Fv4ClNsMVt23dz51G0ZROxsWObfNmWA1U+xbPPv0Q3X5C0rt0iT//t7+3GKLTj8NWnn3LmSb/i\nkceewYcmKS6aY487nK5du3pdhxrYL7d61q45X33HTz98x2knn0hGZgbsZnat5tQpakAffeQ/XHDJ\n2TUBaNjArP/N4LiTjuWeux/kwkvOIiUlDVA88veHuejKC7Gs+sfmWmvKy4K8/PKrBAI+hg4dwshR\nw/HybHl9Bo3WNf02Nm7cxNdffkf+unyUFSQnpy8rVqwgrUsmhx12KFld00EbL0BtvzFki9eAua7L\nkmU/M2xQ/z0+jA2was0G+nZLQvnjKHU0W3LX0bt3D1CK0iqXhGh2W/NpMBijcEIuX341l9LSUo4+\n6nB8PgtvPLlC16l2+P67ZXzyycdEBRT7jNiHgycciGV5qTG8vJKeYChMOBxm9szZLFm2jF69ezF+\nwniysjLx+9vNYIC2UwOqXRxls/7nPB5/7HFG7j+aE04+kTaVWao6taRyqESxZslKnn/iMe68/+9s\nX61pANdrfjfR3H3zPWT2TOX8889vazXj7bcG1Hj39NyN+Tzx90eYev1UktKTvcyJzbCPvdHLFqEQ\nTH/iaaICAc468wyMbfji8zl89823bNy8kSFDhmA0JKUmM37cONLT0/EHFKGQg8/ntaQ17b1B4zXh\nKr78ci5ZWVn07ZfTkAW0yxrQ+//+L0771eFk9+uNaqIpN6uF0FSWa268Ziq3XH8BGT0G4yqwvZwq\ngBckVlY53Hv339EVpfTrlUPXvj0ZP348cXExmEgc0dBT0RhDOKR54omnyYiL5ZQzTwO/QhuNbTXp\ndta7ZJ0iAL3vzr9yzY1/rqnZyN20lZ5dElHsbHSpizH2Lg5u7VvKl1/M5dtv59GlSyaTJk0is0sK\nxpidTOlYk1ABY+DrOd+Sl5eHz7Lo078P+47YB6UUZeXlvPPO+2wtKIgkvIVx48fTt28v7/mj2l3t\nV4sHIGvXF5GZGk1UdPSe125gxfJl9B/ozSyxXR/QXX5Ho5WFNvDuOx/So2c3hg8b5M2wWP0RA7l5\nm/j40w9wqsL07NmdQw87FL+t9jCt4m5Wa7xAV2vDmtVreO2114iPS2DkqFEceODoSP/N6k83YB2R\n5v5mekq0mQA0bAwrFi1lxkuvcczJxzNsn6EEXU20r+10gjaRh73Wii8/m8O7r7/OmWefzpBRI3cy\n0loTxuLfj0xn85rl3Hzvnbu497Sq9h2AYrji0qlcdMFZDBoxHGiemnFjDGgo2FTIjBlvs35NHr6A\nn7LKCnzKIjUzg+Ej9uGQiQdi2Ra2bW03X8qO/9e0vJn3Fi1aCmj2GTGkoQF4+whADUAYoxSbSl3e\nfOZZ/njpH2i6/em9NBptUYTF/dffzZET92PslLH47Fi0sdDKBgMffTSHOZ9+ia2DRAcUx538awYO\nGdy4HRkZywIG11h8P38l33z+MeP3H82IMd60vc300ioBaDUHWPrtPIaOGVlzA3EAg4t/pz1hqi/o\n2pPPdV2+/vprli3JIzEpluOOO5pAwI9RYTAqErjs/GSd+fZ7lBSHQFdy1JRjScqIQWuNthT+mgeL\nU7NOHVl9WHsPlLLCCpYuXcryJUuxo20GDRrEiH2HNmV/jebSYgGIMRDUGreylNj4pHqt3EQaqnZ6\n1OqsyRiNUhYlFVUsXLyMYf0Hk5joI+SGsK1oFi1eTF7uOgoLtzJ2/7H0H9AbZTko/Nvli9u7EcqR\n5uE6HcldwNYalMWy5T8z6513SU5O5vgTjiElJSmyCSYycn4XazaGjRvL+fGnORx55BGNLt0utIkA\n1BhDbu4mpj/2Ly66/GKysrs0cbEaV6bqv2vOD2Ooqgzx+suvsuinH7n8isvI7Nl9hwdE5CwyinXr\nC3novnu5/sarSO3S+tu0E+0vADWAcgi7Pm79861MOeYQxh8yYa9rFb3KBxeUizF+UAqjYeumLcz7\n7kfmL/yJ4uJi0tLS6NM3h/33H0N2djdao6HDu9N4QYsyFt98vZCS4jIOO2JsY/ZD+whA8fJpamPz\nwn9nMXn8SLK6ZzVdFzYDKI2DxcIfcvng3ee45rqrCRODBpYsXMncL+dQsmUTWBYnnnIq77w9gwvO\nP4eo2FiMAqsRZXHxopJwCN58fRbrVy/m3D/+gYTEWJQV6VMuAWi9Naqgr778FiedcuweTyZjDK6j\nsH2GcMjhtdfeJi93A336ZnPyySd6H9pJLaQx2vujfThOmIcffpzk5ERGjx7N0KGD8fmri723d5O6\nHZS9ZQWDmpUrV7J06VKys7tTUlLCxEMOxO8PYNXp0wZw790Pc+31l3spOlqmpa7FAhDHhaL89aR3\n69YkazWA6xry1uWzavH3TJ5yDFiK/I1F/O/l1+jeLYW+ffoxYsTwSOto26mdNpHDPmfO1yycv4g+\n/Xtx4AEHEh8fi+M6O31xaab0Pa0egGpHU7hlGw89/DAnnnQCI0bus3cDSOqWoM5ivKPvoMMhlC8K\nrSxcDFWOhWUM0T5F8eZyKioqWTB/PlXFm0lKSqKishxjGyorK1m2ZDlbC7aSlpZGt27diIuLQ2tN\nQkIqFZWVFBVtJSEunj79+vLZ11+wZOES9t13X/r3789+++/HhvXrSUhMJCExBr/PR0pKCvjs2pyP\nlkK71bkefTXBbzMmUG93AWj1wu+8/e/ERykuv/pilBXVyId07f3ay+TkVS7MnvUhy5Yto7IySHxi\nNEOGDGH//fcnPj7aW4+iJu9ma/DKagiHXf774iv06pXDQePGYll03ADUGFylKCsL8snszznu2PFY\n/uima3WMtDQVbC1n+j8e4Oobp7JxcxFvvP4uFeVb0VozeGB/9h+7H127d2PN6nUUFRUyYt/hjX75\nMcbgKMXypbm88uJL9OnVjd+eeTrKV90S16xBgASgAA6G5//9DGef/7ud3kQMGrRFMBTizddnU1xc\nzLl/OBWfz7eLm47GGBelvNqted/NZ+GCZeTk5DDhkNFtqglMa69JLy9vLQo/tm2TnpGMz2dH+gs1\nuxYJQMKuQVcGiYoP0PibhY68+SvCIcOChSuJ8sHatbmsX7+eU087iYS4eJTdbkagb8cNa4qLi3n6\n6WcZPGQoWVlZjNh3GJb1y9r+JtQGAlDDLTfdwlHHHsNB48dGelntXT10NVXnf4yBkPZehGZ8+CEr\nl+RSkL+FBFOBQhOsrMQKGOLi4omJjaUy7JKcnMK2wq2EqyrJyckhFHYoq6yiS5cuFGzaRCgcJjY2\nlr59+7B162YyMzOxLZsVK5az4IfvSU5JITMzk5iYGGy/l35py5bNVAUrSE5OprS0lFh/FMFgEMuy\nSE5NJRQKkpqaxqRJE+jeowc+20b56xz7pj1i7SsANeAol5U/b+XZxx/mrrtvwVhWIzI8ew9/rRW2\nBevW5/PWGzPZtm0bgUCAbj26c/DB48jO7lInn7PZxVpapwp0/dqNfPLJFwwfMZRhwwfvTU1s+whA\nXZdKy+a5J56nX04yhx5xJGCzy4z+uyuR2v5/TCSjRbA8yJuvziI1yuWbH5biC/jp3jWZI486kqwe\n2SgLwsrCNt5kAEQSwdf//IusU7toy4d2Da+/MYNQZYjBg/oxYsQgsKO9MQc7z+bVlCQABVi2cj39\n+3bBUtvX+hgNq9fkEnY0/fv18qq3d7PLvBuK4cMPPmX16lyKi7dxxRWX4vP7W6WZpP6qA4yaHoqR\nv1uk0M0egGgNrzz3PKec/ZuanK2/VCfIijSxGQ1OZJBZ/votfPLBp1QGi+jerRtTjpgcSfgc6VcZ\n6STZTLWErcYY2LatmNmzZ+O3o0lJS2HChHH4fLVNwrW15Y0KVFs9AA1VhbjjtjuZdudttYe/sSs3\nXjXGls2FfPjhhxjHJS8vj/LycsKhMIFAgOTkZOzyIPuO6s1++w8lKiUbX1QMRtmR2agiZVDegBGv\nFtKblaQmxY4xWJbX39errfSOgxM2bN1SyFNPPcnkyYcyeqzX9xdT+5BSCiorg0RHR9UsT2uDZUF5\nSRWlpaWsWZPL6tWrsSyLoqIiKsvLyMjI4Pjjjyc+xUs7p7Vuipfp9hWAEsY1fm664V7O/d0x9B80\nBLWL3Inb1W4ax5seNzKMxHVgxpsfsK0gnyVLFnPkkUfSd3BPcnK8lHtt5QW2unnWK00YY2xvsNEX\n37Ns+RLOPfdMLHuvy9ouAlDjBinSfh669T7+Mu1iLLsxqZfMdj8ZvG4WRhtmvD2bRfN/IqAsHCfE\nwEH9+fXJJ3gVGtb2XbX2lnYNJeUVPP7Iv0lLiuGM355JbEICrm7RWnUJQI2B++++n6uuv7Lmxr56\nzTrS09NJiI8GdqzNcqhO1FQ9WuzJJ6ejlGLQoCFMmHAA2pjICGdRD80bgLiaiy65hkcf+9seBwcY\nA0HHpbKiivfe/YABA/uRlpJE/vpNjBo5nEBMgJp+lm2kKb1lVOf5i8zl5cUzrF+3idw16/AHFFld\ns+jZM7sxC2/VANQA2wqL+M9j07n8uiswlmrQ0fXSlUBVqIrZb73D9199Q0xMHGWhKuKSExk4eACj\nRo0iJ6cnxtQJ2AyA431ZBbz/r56uuQGqp0bU2sXCx0vPv8aynxcw9c9TiY1tuoTzrgNr1uSyZMkS\nygvLiI2LIqtrBvuM2peoGB9GOygrujGLbmcBqEvI2Ey9ZCr/+OcdYPt3CEBruzS5dc4iy4WfV+Xy\nyiuv4sdmW/FWhg0fzPCRwxk0cBDKUmhT23zdVgLQmsblyHk+8+13Wb16Db8++ddkZ2d6tW97X9R2\nEYCGdZgPP1nIwGybHgOH42tMf0sTQmF7LyOWoioET/zrCUq2FeG3DQMH9KVfv14M32+/Ot9quq5b\n1V2vZr37ET/N+5bjjjmcYSNHEqlGbekKFAlAv/n6R8bsPwRl1c4vYCIP3B0DFmMM5WVVPPXkf6gs\nLeHU00+hZ59uWFZkNHykE7GnMwUoe6V5AxAD7854jyN/NaUmwbLGuxCLikr47OPPSE1OY1vRZqYc\neQTRgQCWvDw0mOvqyAwprd7/q8EBaDAY5tY/38Ttf70df1SgQTdh19W8O+t9li5cii8QYOKkiQwa\n2IdATBTae9RQPTHN9kGFxsXyBolhqB5gaO8048Yuyl5nkNKCBct5Z+Y7DB82kKOPParZUnB5q9QY\nFMuWruKH738gJ6cnY8fuj69x+araVQBq0JQ5FvdefS13PHAHxrK3C0BNTfxp2LhhC5999hkLFywg\nJjaeIUMGcsyxR6EsFcnD6e5wTlh1VxTJauFl12qtgFS7UFFeyccffMjq3HXsN3pfDjpoLMpWRO6k\n1Ddd/W60iwC03IR5/v/e5bzfjiIY3Y2YRpTaxWuRW7ZsDe/Nep+N69aTHO9j4vjRjJ00Dp8dhVJ+\nr1nfNG23F2MMpSUVPPavp/DhcP4fziUuNQ6lvK6ERrV41UrnDkALNm4lPT0F5dtxlvZIEOlafPLJ\nF2zIW09efh5nnHEG3Xp0wbIstG5z6UzaqxYJQFauXElZWRl+v59+/foRFRXVxKsVjdT6TfDAc//3\nDH5czj73HG+ewfp26TLw0YefcuihE7xKTEVtzch2L6Sw463ddQ3PPPMSyUldGDNmFBldEoiOavjt\n3xgTmdnbYKnmf4TU3cGu63UFePPNmcTHBZg8eXJbSMHTjAGoi2Nspt1wB7ff8WdcO4CNQ1FxFflr\n8/lm7jesXrUa27bxxfiYMmUK/fv3IyGxPrXRGu1avPbqLL794jN8Ph9Dhw7ljD+c3lybs/NSGIPS\nsGLpMr6e8w0/LlrAqaedxtgD9mv10dANtNfngdcwoXGNxddzl1KWv4Kjj5+Ca+182m6IZB6JrNko\nvGm1sXFcw4cffMkP877BxmVAnxyO+dVxRMXGoE090vs1pvAKjNGgNN/OW8ysGTM49qgjGbn/qLaQ\nJ7rzBqBhbXhh+gv89tzTsCwLoy3CVWGefeZZ8jcVkJqWyu9/fxaxsbHoyO3du/baRtNIB9LqAYho\nVa1+/I0xhMNh7rv3UbpmJnLO738LPn/1L3e4Ue9dPsUdUyoVFRXx9ssvEayo5Nw/XYxttd8Xo7X5\nm8lMiCMQF9OQQKVdBaCgcTXc9Je/ct2fziMpIx2N4YMPPqF712wGDOiHz2+jNVh2dS2mqucYAI0x\niqVLVtNvQG8sC3Jz15LTs1uTV3aYSFWt13LnzWCktWbhgsV89PZ7lFSWEx0fxxlnnU63btmRiTCa\nTZsNQCPjjwkCM558kaOPn0J8WgKu8uHbVbGNC0qhsXANlJaHeG/m++SuXE1AlzH+kIMZM25/NAZb\n1ZnZvQn3gjGRBILKIlzl8I8HHibOZ3P+xRcQiI2KBKZN16e0kTpvAGpcuO2m26ioDFJeXs74gw/i\n9DNPRVktln6owdrACdMcWj0AEa2q1Y9/3YE9Tz7+FHk/55LVJY3zLroQf0zU9gWsO6y9kUWvnsyg\n+p6qIsnlQ0oTaMezHjsGvp3zDQceNKYhHQPbVwAa6bs7b0k+s198lutvuQ7HtrFVnVOj0VvkBaC/\nGNPcxHvIC4oNRYXFrFi6kk8//ZSKigqUUowdO5YDJh5IQkI8tlKEMfgaNca/QdpuAGrAVbAmt4Bv\n33mV0/54Plg+lNd3ZpfCwOaCUl567r9YjoNWVZxx5klkdu3mXRqRANU72jRp50tvMLQmZGxWLV3J\n3Dlfkhgbx8lnnBRJ4dVm0gF23gBUtBmtHoC0tnrNrtRxtaHj781uVri1iIcefIjy8nLiomM44IAD\nGD9+PPHx8XhdNGtX0RQvhNXPnvaeQcF1HdZvLCI7MwGfv941ue0rAKX2en3iiedYs2IJV19xGUlZ\nXbCV5a26JhLdm2u64TXtxhi0q7HsyMtNpO+x47isWraSvLw8li5dSkF+lprjXQAAIABJREFUITEx\nMfiifEw87BD23XcEUdHei08rVXC03QBUhwhaAZ596n9MmjCSPn361LlXR2YvNF7XDBcbB1i9PJ8f\nvvmG3BULGdSvJ8efeUYLdNerPl9ctPGxcMlaPnr9RdK6ZHD8yScRl5QQOT/bFAlARatrQwGIaAVt\n9vi7rsuqVauY991CFi9ejGVZDBkykLi4OKqqqkiLTcRxHLKysugzuC9R0VH4bB8uLuANyHKNxlIW\nBoOtq4e6K3AN+G1QGuNaXr9TDK4KAz4wFr6djKz2NORBsjddBhr23ZCBhd/MJysrnuycPvVdSbsL\nQKtrzF1t2Lh+Cw/87SGyM5L4zblnkdW1C7VdgBvXYrWzF1JjQLsa27IiY5yq02d5s5zl5a5l4cKF\n5OflU7StiMrKSiqqKklKSqKkpISsblnk5OQwfPhwcvr2wLIslIoMbac65VerabMBqNEu67eW88xj\nj3HdDVdH9lt1cWsD0HAY5n37A2/PeJP01Hj2Gz2aA8cfhO3zhhg2VwC64wY6Ghb8sJj33n2Pow8/\nmBFjImnYVLPXYjeGBKCi1bXZAKS9aEzXjDbUnaPNHv+aJnKlamad8R42DhUV5eTlricvL49gMMjC\n+YsoLy/Htm10OEx8fDw+n4+o6Ci6d+9Ojx498CfGEZ+SRkJKMjFx0YQd8PkUscrLvmThdQ2yquPR\nnWyS8Qq0063eebPt3nQZMDv/2ew8GM3fUsjcTz7nhJOO9TpA1k+7C0BrV6K92i+lyFuzkdmz3mPD\nujwy0lMYNWoUQ/cdRlJSUoOutermcWMUwWAVjuOyNm89BQUF5P68muKiIoLBIJs2bSItLc0LbIxN\nSkoKgwYNIrN7JhkZ6SQmJuIqsG2vkdeHieR6tVB1d0/HvAdU2+vzwDHwf9NfYlj3JMYfceQOv/UC\nUK0N9937T/bffxQxAR9jDxrt/dr2oWmCHAG7oY32jqFRbN1awv/9+2myslI54bijSUlPbetNKhKA\nilbXZgMQ0SLawfHfeRC33Yq0N3oYDE7YYe3aDaxdu5ZFCxaglEV5eTmmIoSrHYyrKXEMfmUDmsTE\nRCzbR2FhIYEoRVRUFGlpqZSWlZKZkUlVsIrYmGgsyyIlNYU+fXvTq1cvCjZtIjklhYDfjz8qyqsV\nA0ykCdYLelSdn3fIUGaqk9grdry/1/6biqSRqk75BCbk/VxZVcma1WtYsmQJmzdvpnvvHKZMmkAg\nPr4hcU27DUB/sVJjcF2X4qJK3n77LfLzNhAVFUVpaSn77DeSrKwsfD4f4XAVcXFxpKSkUFxcjN/v\np7KykqKiYtatW0dFRQWlhSWUlJSQlJREeXkx4w8+mO45PemSlUVUlB+f366TM7Q1trZJta0AtKYv\njKGkyjDt5ru5/ZqziMnsucMHNVqDMQob15taV4EV6dvZPOO2DDX5BJVBG4Nj4MUXXiYvdwO/OfFY\n+gzoi7FdrMgApzZ8ekgAKlpdOwhARDOS44/X3F/9dzAYJBgMsqWomHnz5rF50xZ8yo/rulRWVtak\nE3OcEKFQENu2ibUDGNclNjaWoqIi0tPTUUoRCAQIh8MkJydTVlbGwIEDyc/Px/gs0tLSKCgooKqq\nil69emGMITc3l+zsbEpLSykqKmLAgAGsWbOGiooKLMsiOjqakFtFOBzGtm2OPvYYunXrtjdpzTpM\nAFq76kiwXqfbgusYyssriAoEAEVVVRWO41BZWYVt29i2hc9nk5SUDIDPpiYxeN091IaDib3RZgJQ\nDViRbzlonvnvW7gb87jgTxdAm8hQocFYGAWOMXwz53s+eO9dDjpgDJOnHAqW3VQzlLUECUBFq5MA\npHOT478D13VRljcBYs30m5HfVd+Hq0fRm5oIReM4LlVVQUpLS/H7fZGuAhWkpCQTFRVNfn4+69at\nIzs7m8ryCuLi4sjNzSU+Pp7o6GjC4TADBw5g+fIV9OqVQ2HhNpKTk0hNTcXv99fZsZF+rKq2yXgv\nHngdLACtw9T2odVGb5d+q24WBI+C7RLTR4JXU+fn6o95S6zz3TY3uKSh2kwAGqnzR2tNpWtx6w03\nc8dNVxKVmNLqVc3V54ujFHl5G5k18z3iowIcfNAYeg/og7bUrlNDtU31Lmz7zQ0iRAfXhvpziibg\nBXPVA5aqn3sq8nNNKLrd7EoGC3/Awh/wk5AYv9Pl9k/oR/8B/ahdGgwY1P8Xnxsz1uvDltElY7t/\nr11znSkmO+F5V15eTmxs7J6vOVV3P9X+XP21X359J4HkLlfR7oPONkxjlGLlmnwSAjZ2tOU1e7fq\nqa5RykK7mulPvcjmTev5/Tm/pUvXDJSlUMpqX6FnA0kAKkQbJcFnR1STIbDen24pnf1si4uLa+0i\niGbiDRrSVGk/7814k3Fj9sUXSGiy+NObJclBa0MYH7YFm7cUsXD+cgKUM3HSJLBUZIpeB2MMjvKj\njMWKFbm8NeNt0lKimfrnPxEIBLZbdke+LiUAFUIIIUSH5Y098lIW2W6ISUdMxjThC74FoHw4Fsx4\n93N+/HwOg/v1IDs9k7yt62rGP9k4GGxCKCqqNP9+4F+kpURx2R/PJCousdPljZYAVAghhBAdlheA\n+pgz5yfCpYVYMVFUhIPE+GOaZPlegKnxK4sxIwbTMyWBHt0ySExMJD7+0JrPaeOjMuTy/HMvsTH3\nZ4444lDGjjsQo/R23Tk6i/YUgHbkmmghhBBCNANlwlQqPz9+9QmHT54Myk+s399ky7ci/1VAr67p\n9OqaXpvtIAzGhqCBuZ98yzdzvyAx3uLmW68E2+v2oZo1q2jb1Z4CUCHEXpKBTUKIzkehNWzbWsjg\nEcOaeVVeyi6DN7tV0B+mZGsV/374WZQu5PIrLiM+KQljmU5fqyYBqGgvOvu12iQk+BRCdD4KjKZk\n6zZi4po372d1CrWQazDK8OxTs9i6eRWTJ49k7AHjvdJYCk2bnEazRUkAKoQQQoiOSykKt4aIj4vF\nWLqZAz9vUtR3Zn7EgoVLOKBfGuedfSnYPsJKYWOw0VhGg+rcIVjn3nohhBBCdGjGGEpLi/H7LCyi\nm2ihdX5UGoOFBjZtLuapx54iLcrm4nNPJK1r98gsRoraSTTVdvlkOysJQIUQQgjRgSmyslJxHAet\nNVYTjfkxGAyKoLEIVoSY/sTTVJSWM6xPV078zclge+Pj28kUmi1OAlAhhBBCdFhKQUyMjbIsXMfB\n8gf2/KU9MmijqHQMn378Fd/M+YyslDguv+Yi7Gh/pIazs/fy3D0JQP+fvfuOk6JIGzj+q+qe2ZxY\ncpIcFVAJAgqCiKCYUETU8873jGfOYgIlmNMZ0UPPU88cDnPEBIpIlpwkSY6bZ7qr3j9mliDL7gK7\ns4Hn+/mswk7PTA/d0/3UU1VPCSGEEKL6suA6mh1ofv3me445ZcB+h4aW6AR3LFifkHJYtvQPvv3m\naxJNPheddzZNWrXAVpOYMxYVUyQAFUIIIUT1pRTWWDp2OJwZcxfQ/eQB0d/vz4t4+MrBt4o163KY\nP3sOC2ZPp1f3jnTucRwAxhpASbWRUlLW2pK3EkKIiiUXqqqlPO7Acg5ULeUVhR3AeWDxURSEfEbe\nejeDTzuJY47rCW7px2aGrGXl7+tYuWwZ06ZOpeex3enarSOOG0TJhKLdlfq4SwAqhKgK5EJVtUgA\nKipVAGpRWGtZvGgx4595kZNP6kvvAb3xdQBlDY7SYMEog8LFWvAVaB+WL/2DWVMnM2/ebE46dRBd\nunVBQbS7fdfHlLwnIAGoEEKIakZuVlVLJQpAo0+MjmucOnUOH777Dp07H8kJJ/YjMSUJpSMBqkVR\nUOCxdct2vvjsczauX4fxPLp2PpJefY9HB1zQUkS+GBKACiGEqFbkZlW1VLoAtJBvLVnbc3jj36+z\nfsNa3KCLrwCl0GjA4gZc6tesQ5u2rejSoysQXUlOIs+SSAAqhBCiWpGbVdVSaQNQawrwVBA8cLRi\n2ZIVLPxtPq7WtOnUllq1apCYHI+1BmPAOgEKiypJ/FkiCUCFEEJUK3KzqloqbQAqylWpj7tM3RJC\nCCGEEDEldUCFEEJUBdL7KUQ1IhlQIYQQQggRUxKACiGEEEKImJIAVAghhBBCxJQEoEIIIYQQIqYk\nABVCCCGEEDElAagQQgghhIgpCUCFEEIIIURMSQAqhBBCCCFiSgJQIYQQQggRUxKACiGEEEKImJIA\nVAghhBBCxJQEoEIIIYQQIqYkABVCCCGEEDElAagQQgghhIgpt6J3QAghhBCilFRF74AoG5IBFUII\nIYQQMSUBqBBCCCGEiCkJQIUQQgghRExJACqEEEIIIWJKAlAhhBBCCBFTEoAKIYQQQoiYkgBUCCGE\nEELElASgQgghhBAipiQAFUIIIYQQMSUBqBBCCCGEiCkJQIUQQgghRExJACqEEEIIIWJKAlAhhBBC\nCBFTEoAKIYQQQoiYkgBUCCGEEELElASgQgghhBAipiQAFUIIIYQQMSUBqBBCCCGEiCkJQIUQQggh\nRExJACqEEEIIIWLKregdEOIA2IreAbFPqqJ34E/kXDl4le2YlpX9PjestShVXf85qu1xPlhV/RpS\naY+rZECFEEKIP7F277ijGgefQsScBKBCCCHEn0iwKUT5kgBUCCGEEELElASgQgghRAmMMRW9C0JU\nKxKACiGEECXQWm6XQpQl+UYJIYQQQoiYkgBUCCGEqPQ8QIYBiOpDAlAhhBCikjIGPM9gjYsxpsjy\nUEJURVKIXgghqhQPsGBddtWYzgHlYAhgcXAqcO9E2TE+jB0xCtdx6XDUEXw18XuuvGwITdseiZbb\nd/VlAO2DdUAZqmuuUElrSlRBctJWXpWteGI1PFcKu2E14AMeBcThAo4BrAGnTG9Yle2YlpVKe24Y\nYxg7+jHat2nKyWedRlA70bqklhuvu49HHh4Obpkflup6nA9WTM8Tay0FRrFy8e9MmzKJc84ZjJOQ\ncDAvWWmPqwSgoiqSk7byqmwXu2p3rlgLSoENW7JztjNp0ncUbFrCkUd2o3H7o8lzgiSUbQ60sh3T\nslLpzg2Lhwm7jLl3NLePvB1Hq70K4nsW3n76Uc696oayPjDV9TgfrJieJzOmz+fNV8czctS9OAkJ\nPPf085xxch8aNmt5oIsjVNrjKgGoqIrkpK28KtvFrhqdK5GuOGvgyy8n8v0nn5OdnUN8XAJ5bgZx\n2RuolZjNDaOuw8loX5ZvXNmOaVmpVOeGsT6LFq7hnVdf5I5RI4oJNiy3XH0HDzw5VgLQ2IjZefLs\n0/9mQN/ONG1zOFYBGIzxuOGKa3j4uacJqANqWFba4yqDSIQQopIygLYeFk2u0iyYt4QPXnuLgm2b\naN6hDecMGUZ6egphYEPYMPKa+3j2/qe5aswYrJOBrCZZdXz1+XdQkM/w0SMrb8Qgyp4Fq+D1/77D\nqSd3p/5hLUEVRo0aRwfpd8Zfca0HBxaAVlpVKQCtVK1VUSK5hgpxkLQJY3WAAg/eeWsCM7+dSMum\ndbh45EiCgVTAYK1HEKgTpxn9+HDuvuNllvz4A017n4pCVdPpC9WHxfLrzzOplZzMkQP6lmJ7nwId\nQGGRy2w1oDzy8x3q1UiiYZPWRR7SXn07s2HRbOq0OTr2+1eO5NokhBCVlK8CbMv1GHHXKOZ/9wXX\nXP9/XHbbdbiBFAonIynl4CtDwNNkJliOP6sPr7z1NU54nVSNPACxHpa2aMlatm9aTYdjSx9cNGvW\nrBz3SMSStQ533HITvfr32+c2wWCAid/8WO1KcEkAKoQQlYaN9PUYsOSwOd9n7O33UysORjx2P4e1\nbo+j4tEoIpdvDSgcG8S6YNCc3LUOC7w6bJn5I37Ffpgq6QAneuw/GyY3ZJg96Vt6nTKo1MWzlHVJ\nSq0RmY0mqq7o4Vu/LY9bbr4WRwf2mdCOV4at2/1q1w0sAagQQlQWVmGVoUBb1m4I8vid99KwPtx4\n500kJCSVuB65AyQG4kivV4+vvp1C0EoOtLKxWLCGfAI89cBYhlx4HsHdAo+SslwWS272ZmRUWhWn\nPKyFh+65h7r1G5a4+Y4dO6huQy4kABVCiErCYimwmuXL1/LkfaPo2KYB19x0I7hx2OJuPtFJCy6K\nAJr+g05n/tL1WFsQs30XpaNQFBjN/beP5JY7h0djil3HtsQMrIKlSxZVt1jk0GMdLHBKv2Oxpajb\nGxcXV/77FGMSgAohRAWKJLwskfDTMnvOQp555Cn6dGvO0IuHYVQCPhpUKTJeNrJZ86ZpBFJqYZR0\nwlc21sKoux9kxNgRBzSrWeGR7GisrHdVtVnLK6+9Td9TBpRq8/z8/HLeodiTAFQIISqQUgYPS9gq\nPvr8J957+SUuHNyHfkPOA5WCoyLlSlRpUl4q8pOcCOsLFCokl/jKxOLx33cnMvKuyw54rGkYl+R4\nCT6rPKVZ+Os0UIGSv9lGE4yvft/lqlSGSQghqh3PD2GceF4a/w5zfv2Z6y8fRvOOh+OpwAFnCIIB\nh5zcHIxWkierRFZneTSxq9HxvQ/4NTTQvEnNstspUSHCyjJo0Iml2tZayEhJrXYZw+r2eYQQokrR\nOp5nnnmdZXN+4d6b/kqzTp0Iqzjcg5jlbIwlOTkZQqEy3FNxwCwUGMtr9z/MMWddgD6IW69nFY0P\na1CGOycqwsvjXqb7Cb1Kta1S0LhRo3Leo9iTDKgQQsSYxSfsF6BUAs8++xpbfp/DXXfdRGKNWigU\nAeBgljEyniFgPAKBhDLbZ3HgPBti1O0PMHrM7Xj64GYPbd1RwOHt2sscpCrMA1b9vhxUoFTb5xlo\n36lttZt4JhlQIYSIMWUdHJ3Iv55/jVXzpnDLbTeQXCMDe5DBye5cNyCVemKoyPJJFqz1mT1nJTde\ndwk4zkFnfbbm+SQ1KLlsj6i8tmzI4fyhZ5V6+3BeAUnJqeW4RxVDAlAhhIgxz8JHH33P6gVzuPqa\na0jOqIXVpcuGlIbvR8v5lFA3VJSdoiYVWQUeDjN/nEhanTpl8j5Tvp1O2CSVyWuJivH266/RsmPp\ns9gbtuaTXKd2ue5TRZCrkxBClDsvsnKmD5DPz1PmMmPSZ1xy5d9o1KolRkUuxbqMUpZbsiDJz8K6\nUoapInnWcs+d93LRPy5G7xagehZuvf5Wht94KxPeeI+CrZuwJhwtxrXrpyhL5kzBlZllVZLFYIHU\npHjsfpTgWrH8d1QZ9o5UFjIGtLKz7Br3Eb0iWWtBRR/avYixtTtLtVi1q2ygUdLSEKJCWRerDT6a\nxUs388m7r3LNlRdRu3ETlNqjDHmZvN0fqzeSmZaE51sCcpWvGNYyd/Fahl97+V7ZUQefsY89AEC+\ngSnfT2XHxvX8+P3XFIRCKK1wXZcrbriWJk2bsPt5YUJZeAqCsfskogxNnjSDYX8dit793l6CXyZ9\nT5/eHapdRQu5NFVydrcTNKws1hocrVHW4liLNZEoU2mFsTaaZLE4KlDtTlYhqirjG3wHtmbl8PxT\n4xhyci/qHta4TLvdYdc4xPzsLJo3b4By48v09UXpFaD4+NWnuf3eMXs9ptSuld8TNRx3fBeMgUFD\nBgG7sp9r12zkhiuu59EnHwI3cq6kqVwCngW3+mXEqj/Nt599SY8enYjcrSO/g93WmSjisHp5W1FW\nVbtJSBKAVlKFNxIfhYPFGHCNZebU6SxesJClCxexJWvzzu0UkKBcGjc/jL9e/H/o1ORIetRqtNGS\nAhWiAimnAM8k8OiYJ+h7dBOO6dsPq9zSFZffn/dRCt/32bh2Faf16IasBF8xrDU88sQL3H7PqBKP\ncOHju6/GWPi7+g1q8dAzj3H7Fdcy+rkn8IylUZN6KHc/0mei0pg8ZTadu7Rj5e9r8EIFfPrRx4TC\n+TRq2AirXDp06EDrti1Qu42xMMZQt27dCtzr8iMBaCVVGFgWZOcx4Z0PmP/bb+TsyEYHHFIy0glh\nadOpG4GAi+8blFJk79jBvEULGH7rHRzdqQ3nXXoZVuud60SLys6A1RhrQCmUAWzkAqSUwiqDox2s\ntahSrB0sKp4ljLIuvonjkSdfJSm8gYFDrkM7ZZv5BB9rHRQFbPAd1IalJDY+FR8JVCpCrqfo3awR\nqgxa/q4fJhSIQxsIGc2Qc4eC1XJYqxgfePeNNzl32Ol41iOYlMBl116F1gqtFeGwD1gefORJUlzD\nFTdeB4DRmhQ3jLYhUNVr4IUEoBXMFv7X+ijj4gHKt0z65gcmfvUNYS8HD0NyjQwGDjmVli1bkpmZ\nSW5uLklJkZmQ1tpIkKIdfpoynVmz5zP716kkvvk2pw0dgjFxuHKkKzcL1kYy1Vnbsvl9yVK+/OJr\ncnNz2bJlC67rkhQfT5fuXTnl9EG7Mt8HUStSxIKDrxQ/zVhJ7vJpjLj7JrxAXNlfeK3GKHCsy8q1\nOwiQg3LjyvpdRClY4KmHn+KW264qZZBoCPmKB0eOoU6tTC65+oo/PS+IE5+CwjJ3wVqOaZtJNeyN\nreYM1miaNqxN5y5d926WWAhGs54333old19xAyrya8Ihw8lnnFbtgk+QALTCKSwWi2c1BsOE1//H\ntKlTMF4BGemp/O2Kf9C4adNIfcBoJkwpRWJi4q7XiP4O5dO5c0e6dD2KV/N8Zs6cz+lDAe0hh7qy\nMoDGGJj4zWS++vwjAJxAgIaNG9OxS2fat29PQUEB3333A999/yML5s7mxjvviGRCJQCtvCwYpflj\nw1Y+fG08g/p2RWc2IGBCUNYjtKMRiWcUcxesxfF2gI6sIC9iK7vA55SeHfCwBEoRKb79zucEc3cw\n/J7bsWiuu/QfPDruKXRhCS0NrusCmm8+fJ9j2l5BJJ8mo/yrDp/Zs5dx1Q3/YPd5w0XR1sVPrR1J\nSChYuOh32rcomxJelY1EJRXMNwZQTPtlJhMm/A8nv4AGDWpzzvlDCcQFqVGz1s5ONLVbTb8/Bx46\nWqIh6EZaTedeej6j7ryb6VOmc9QxR8fs84jSM1jCVrNo/mLefuUVCrKzaNGhHYMHDyYzM3Ov7Rs1\nG8b6jf14eOSjTP7yC7r3OzFybkRiWFHJWFVAyMYx/rF/06JOkONOGxwtwlL2mclwtEfW0YqtW3fQ\nq0d3LAFc34IjQWhsWHwU/3zwCW6/6+oSu9+NtTz/4lsMO/lY0urVo/BL/MizzzDz16kc1bVLdEOf\ngk3rgTDb1q3Ciw6/kZt3FWLh/dde4MiO95fcJlQ+FORFezQ8pvw0hY7tzqqWbUk5hyuYQfHMky/x\nx9KlpCUaLrv5MtJr1cXXcZHagHa38664E9AqQO1cvU8nOCQlJ/HtVz9wVLeu1fLkrboiEWPujnye\nf/pZ/vjjDwaeMoA+ffqggvu+aWmgTs2anHTyAD755EN69OtXYmtaVByPOKZMmkqck8eFl1wK6DKf\ndFQogMXis8NzCW3dQscBR4BysLq83lHsxSqMhTMH9kHZwD6/lxZQNsyStTl0OKwmafX+tK67Y5jw\n/od06tIFrcBzDKkpAazK5ajDaslNuwrKswEuv+Qvpe6xys3NjfxBaTatXl5te7okbxIzlsgKsGGM\nNYSAn3+ewYibhhPevo7BgwcyfMx9ZNZriuMmENSa+EhMueunOLtto4A463H6ORewY/tm8j2ZC1sZ\nGGOw1mKN4oP3PuSOW4ZjvFyuvf4q+vY/ARtwdg2nKILC4gDH9z+OAg/mzpoB1sdUz2tTlbd9Rz6f\nv/ce5wwdSHxGJhCd9V4Ox8uggDAL1+RgV80nmJqCxcXsrO0iyptVIZ5+ZBxtju5U7HbKQogAbz/x\nCN1P6LP361jIysraVfeZAO3bNAeVTKcjW5fHrotyZK1h1OinqNui7R6/N5EH8XzLXbePZMzdI8Cz\neLjE6Xw0FovGy8+F/ShaX5VIYypmFIVjdvKz87l/zBi8/Dxq1anNP66+mriEeIw1qDIc15OWlkY4\nHCYglagrBa01BQUFjLj7IUw4h1MGnMDxp/dHKxfPQkmN3MLxwtp1QLl8MuFD2nfsgMGhGi6SUeW9\n8/pbNK/fkGbtW+FF6z6WV8Y6BATR/DJjNm3SA1gi4z8d8oCEsn9DsTcT4KjDW+6x4lFRrIJbrr2T\nxx4fVURj0+AoTeN6hxFdHIsteWFOHjKMjbkObY7rXT77LsqNNZq6ZONrZ4+7u8Iw4dOvWDZzKveM\nHkEYxU1X3cTYpx/EcSJbGh9q1kor9XsZY3aNHa4Cqs6eVmnRue5WMX/eEp54+FHSk5K49c7buO62\nmwkmxGMdvUftr4OllCI/P5+4uDisJEEqlLWWsLUsXbaSUcPHUivF4Z6Rd3DCqScTVHG4OASUKkVr\nUKOUQ1BBg1YtMWGD1VpakZWFBTAYC6vXb2H9iuWccc4pKJuAq0puYBwMlxDWxpGUl0i3AfVAu2g8\nJPiMnQeffI0eA3uWuF1Bfj5/HXRMsd2qvilcQtXy5OgHiU9P4fuJPyK37Kpne55hyIVD2L3wmsUS\n9jVzp07l2uG3o7UiThuGjx3NkllzqFWnDgZYuW4b3bt3K/V7VaXgE+RsLh+FC/kaDx9LGDCe4dXx\nr/Dmq6/RvmNbrrnpBlJr1gI0hemrsowTjVVs3bSBhNR0tHTDVRwLvq94afx/GP/ss/Tp240bbr+N\n+OSkA7pYFJZfCgQChEJhrEUaGJWFCuMTWYnskdGP0KxxQ2o2aICNwSQga13ygU3LZ1LrsNYoXKyV\ntHishC3E5/yBU8IEM0uYm2/9J51OOHkfW0SuCfUb14v+XZG/YQMKxaTPP8M60qCoap4ZPYZ6TZrv\n0fHho7jrhlsZPuKO3Roimsz0eF584SWatGmNYxWzpk/jqGNKbtRUVZI8KQ8KIDLez/fA+pb7Ro8h\nNSWJPicez3G9e0XG+u3cVu16WhmxaBbOn0P7jp1QZRraitLZNTX99VffZMWCBQw952yO6HJUpMj8\nAb5q4cUqMzOTDYu8P60jLiqUdfCVZvacRSQF4PShZxBCEYxB0UZ0J7ANAAAgAElEQVRlNZvyfVT+\nBoKpdQG3XDOuYk9fTfyZf9x8fclD9S00DK4kshB4UQy+p+hzYh+UjXTXt+zUDKN8UtzcMilsL2LH\nGENmrSKWw/Xh6KOPKHKZiP5nn0f7lnWxCjatW1+tJxJKAFouDNYafOOwcN5i3n/tNeo0rMtZw4ZQ\no0aNmKTJrYWs7B0MOm0gUi8u1gwYH09pXhr3ChvWrODK666iXsMGHFzFJINFY4C1G9aSlJoWuUPJ\nYjeVQoEqQJsEJrz2Dl26tCElvUbkgd2OTXnVbnXw+WmZT89G2zB+SqQEqIiZxVOnMLDvMaXatqQx\n+dOmzaBLl8hEpsWLf+eU0wbjGU3zRvUwSrotqw6PTVkhLrvm6r0e+fbHqZw5dGiRl+30WjWpUasm\nvrWsXrW8Wl/a5VwuFwqDy5RfZ/H+26/TsVMHLr78EjIzM9A2NjPSV6/awPbtW6lRIyMm7yd2pwkZ\nh4fvf4Q/Vi7i8sv/Tr0G9VGU3RculJtH3TrVszhxVeUQz88//kJuznZOGDBgZ9WDWMjTsOqnSXQ+\n+Uy0kugzdgw5nuG8c8/c6xGLj2e8PcdWGc3mrHT21WI0xjDhg/+hdCRr/sxjT9KgUSMKlMP5l1yM\nlvE2VYaPy3/GvQyBPVcwstby83c/4MQVvRzvzJ+nEh+XiO8rkuMNthoPoZMAtIxEhn0aQr5HyCje\nePUtvnjrLVoe1oDTh52FE3BRygGnfG8ONjr+dMG8OVxz3bWgDFqFy/U9xS7Gh4J8j/tGPUJB1nb+\ncsEwatSrs3MGysFeS4yNTHJZs3IVLVu0xEgffKXh+YovP/+cPsd3IyE1Hcdx9urtKK96fps8H3fx\nVNz6LTCS/oypV//zHpkNGuz1+zzj8P4bb7Nh80by8/MBMNrhqMFDsaGCIl9LKUVetAZkOBzm6qsv\nxSiYMnkmbpKM/6xKfAONMtP3GjahlCLseft83po/VmOAXANdunSlOl/g5UpVZjxA4+Dw4fufMn/2\nLI7v15t+A/vHdC+UAi9saNmyBUlJiUTGIlbfE7hyiFY5QJGfH2LM6AdICjgMH3EXuA6+1pFBEAfb\nkLWRDAlagzG0bRutKyeHt8JEiopbrFLMmLaAnB076N37WHCKzm4c0Hvso9veRt4cLMxbvpam6Q4F\nOgHHGHQ1rRtY2fhYwpv+iHwn/yRBw1lDz2X7tu38+tM0srKy+OHH77GhfD4bt54GTZrR76T+9Dz+\n2J3H18ehZYvm5Od5/PjjZPqecCyOhV8m/sAJ3Q/fueKdqPzee+MDhg47tcgRUjUzM6Pf372f5+dE\nGitr126iR88e5b2bFUoyoGXEYjE+vPP6O8z4ZTIDTu5P34EnYitgAtD8+Qtp0rRhNOmmkTGg5S2y\nCkC4wOe+MfeREGe4+uarMa5GOZqdK3KXZkGBYmk0irysAhLjIb1+HVz5ClcCCmPgvf++woAT+5Jc\nxDKq5cFiwfrko1g/dTv9z+uBi0tAJh3GTBiHYX89N9Jl/icK0I4iIzOdY/v0ZOBpAxj74FjGPv4I\nT736KhlJAY7tfdwejQvrweVXXEJCYoAT+/fGcRzCKoTdthotme0qw1rL/DnzID65yEv+P679O6OH\n3wVm7+9qnFOA8i1fvD+BQGL1znrL3auM+NZlwgefMPe32Vx9zSX07NMDoyi3pfeK06BBg2iSrHC8\nqbSay0c082kgVOBx/9iHSUtP5rwLzyclJWVnMeEyoyJ13hbOW0SrVq2wRla4qmiFM5VDnqFGWhwn\nnNQPlFOmTb59dtsrD6zDhnzIX/wRiQ0aR95XyWU9Vib9MIP02jX36zkKRcCBi2+7m//8a9zOccLW\nevz7+Vf2qgetCNKnd/XOhFUvBg9F/TrJeAA2MjwvbM3OEo0OcPldt/HEyPujz/EAgw+0bdMcCOFt\nX0Nk9bTqS65UB8oaIrOSwbeWNSvWMmfar5w1+DQy6tRBEcDFifnNwFpLjcxUlNp7/Jkoa7tar6Pu\nfZCAY7nyystp2rRFub2btZY1K1dx1FFHyfGtJHxg3m/LcLVBBxQ2WoatvGkTIF8rlvy2nIa1HGyg\nRiQarta3rEoiMvKB2d9/i3MAt1GtFImOofOxJ/K/19/Es+AbxeYNy9mzfKvH1tww3U8aWFZ7LmJg\n7pzfufiaKwhoQ8j6vPDi6zx05z2Mf24cn334ESuXLWf93HmcfdklPDByJNjIObRu3Q76DOyPr+No\nVK9WxX6IGJA72AGyqEihZwtLFi7j1XHjaN+hHe2O7ACudJUcEqzFWHj66XHo0A7+fvn/EZ+YWK71\nF30U6zespc3h7ZE64xXPKoO2hqkzZtH7xP5Y5UTDvzK6tNpd/yvwI+XdjF+AtT4FgCGX32es47jT\nBoB1MApZmSBGjDUkJQT3e8BDdnY2AGGtade2GR36nMRb455kw5Ysbrn11j9t7fLIyJEQDO71OqKS\nsoYX/zUu2vupeXn8f7nob+dx65iR/N/ll3HSoEHUrteIw7t0pk6Dmtxw9wjG3PMA1hhee/V1MjPr\nkl1g6d6rT0V/knInAegBsiqy2tCa1et4+aWX6dGzK4PPGYwbCOBUYAaivGbZir15yuH558ezfNEi\nTjl5EJnlPPbPWvjuu0nk5WcTlBtSpWCsITtfsXTurzRvUT6Z78L3WfbbIuYtXM/Ysc/w+auvkrV0\nPqFtBaQVzCS1TjuwwcgFXS4B5c4qi1Ka3v177Fdpi8VrNjPyrgewQBBAwWF10mnYtivPPfwoKiH4\npxVzIMEaOaRVSJ5yOaJF3Z09VBdfdAEBrXBQkYVDNMQnuOjo8suuMgwfcSvvvvMVTWsFMVhm/DyX\nhs0bVewHiQEJQPeLif542DBkb8vm0QcfolZmBj1O6IuxKtLqsQfxzxqGMCE8vMjVpxi+72OtxVpL\nXl4efn4OhHPB5Ef2keiYkxJeR+yfwn/zyd9PY+WSpVx86UV06XMsjuOUawPAWstPP02lf/8+5f5e\nopSUwzc/TCct6FMjs0a5HROtNIGMWvzrhWfJydrCF1N/44H332T0hffSqeZqbMBBaRud7CaX9fKm\nUBgLTZq2QO1HV8Sn70zgzLP/D7VbPWhHKbr17sz2HTvI3ZG1x+VaA52OPUYC0Cpk3cYChgw7g8Lw\nSrnFHz2lHLTS9OjXhw8//xGtfBYsmoF1i1/WtTqQvuIDonG04tGHH+bIIzty/l/OB0dFbz4HV5dx\n2YYtTP/hCwaffSbGLX6O88YNW5kzZw4//vgjGzduJNEqXNfF+IY2RzTngov+RjA+MbqUl1zCyoIx\nHka5rF3+B59+8gHnnHsObQ9vh7EGVc43/k0bd1AjJY227dqX6/uI0rMWZkyeTN3MjCJL8ewfg8Vg\nohMPFHaP722ThjW4/dbrqVkrDYNmZRjuWfEQK/9YTfNdexTpnpGve7kLhQwJcfsXJCQlJ7Fjx449\nfmeV5elHXuThZx7nb2cN4ZV33tx5Lm3fnsPAgTL+syrwAMdaJrz+Hlddc3ax2xpjUMrDV0FcwMPn\nucce5IVXXuDG60aQ7IZwlEOeb4n3DSpYPSvZSFN5Pxmj8D3FD19/T8B1GDxkMNpVaK3LJPuRFcpn\n5tRZLJu3AM8WXay4UEpaCst+/53rb7yRhx55mHuffIzrR93LKX/5GwsX/86YO0exYtFCLKGD3i8R\nZTXh/DCPP/44CYlxHN7hiMjs9Bjc8P/3wQd07XIUB19QVJQVCwSUzznnnMPBD/7VEC2spQqrB0Zf\nMhKQKhKT4lFK4SjDkqUbSQ2tpnXX46PFrg++ASxKxwDTZi7EagdU6SecNah/GH5KCiZavxUMW3eE\nuOriwTjW8Mo7b1OQv+u6/8hDj8twmyrCiR7SnK2/oym+DvBbr3/MiNvG4prIvdkah6BjiNOa+58Y\nxREdO+IDroYvPvyk/He+gkgAup+stUydOp2vv/ycCy88n4Sk+Ohc+LJxRON6XHnLTdRodBiuLb7V\nE5cQpHmrZiSnJhCXECDB9amVnkiPrh3567W34CSk8Z9//3evlRjEQVCap554kiZNGnPLLbcQCJR/\ny9RaS3Z2Nlnbt9GlW6e9yrSIirNth49XkBVd9OEAIz9rsXiEDUz87le2rd+Ist5e7Yz8gjAJ8UGM\n8clH88OEj2lfCxr0OOGgP4fYf6FQaL+TDj0HdGPb6mnRv3mE0Xzy5pu4qZmRMYPakJCwq/Zj7bTE\nMtxjUZ58BZ5R1K+dUeywYGst7dq15rb77ubrTz7Gtx7vvPE/ht82HKvAt9CpR1ccIomNmrViU1e4\nIkhksh+MsShHM/HLLzny2K40bNYErd0yXXVEO4p6dTKpkZFeYuFhVyn6Hd8bVylcpVAqgFaKgAut\nG9fimhuvITu/AD9XMqBlZcGixaxdtZrux3YnPj6IKaKQcHn4+uuvyUhLA+VJ/rMSWbXmD+LjFPHp\naQfxKpHigFbDrFmLeOu11/AK8vYKQBPjXDQKq1xmLN9OeMl0zj/3TDxHgpSKUK9+zSILiRcnGfh5\n4izAx0cz5vZ7uODvf9mZPNfoSDvGRtIa/U44fudzfQNYGdBfWbnWYDQM6Nez2PXblVKs27iOvE0b\nmbdkDRaXjauXoeMCKGDK5Ok0a9YMgK3bQrRu1ypGnyD2JADdD0ppvvj0a8LhAgYNGrSzgHBlpBXE\nx8WRn5/Ptu1bK3p3qiwbHZdniWSg3nz1LerUqcNRnY8EiC6NpymPr1K0ZjE5WbksnLeCc88bgjUu\n1ij86GM7NxIxYXf7U8jCjGlzadOkAZ6KO/DyR0qhcHB9OGvQSSSl1yIn20epwvWiI5MJPaVAFZBn\n4L/jx9OuSSrJ7TtjZSh/zGmgZdP6ZG3bgd2P774yEEytw4Oj/8nYW+5g5JgRoIsaoa+ZOX0hbTt2\n3Pmb/BDgS+9HZWXQ/Dx9JXUOa1zixLQT+/dizuxFbFm5mttvup2rb7x2Zzb911+m7pxB/83XE0lO\nTy33fa8oEoDuB2vhh4nf0rVbZxzHKfuVbsqQsors7TtITkmhRu3qX9C2/OwKLj///Eu8UB5nDxsS\nuxno1vLUU8/QoUM7snPyWb9uIwqLYwETmY1fEcu9HuoKG5+rli6iZZsWO1dZPaBGqY1WzlCGhk0y\nOPev55GcmYaNTmqzKDwLKt8nTDwrN2WRunktpw0dCk6chJ8VRCnFtq3717i3Tj6B8BpuvfMm7nro\nvn3W8rUKPnz/Hcxug8u//vgnjNyxKy2NYeKXP+A5CZEKNMVQQI9+XWl9eDseeHgUytl1YJPjdv15\nzk+T8Jzix5NWZXLt2g8v/+c14oNBThxwYkXvSomstYwbN47WbdqgKnGgXFVs2rSNL7/8ih6dO9Gs\nZbOYzPOw1idU4GMNNGpcnzfffI9lyxZSOzOdDkccwSlnnI7v+2hH7koxF12EoF6NNJq3aYl2nN1S\n0vtJeWDdaNbDi3TD7nwfsErx24LlvD5+PN26Hs2Hn3zFgJY1SGvYkALrEqc85FIeYxaMgtnTZtC4\nWbNSp3JCBGmYEpnE5LDvUcMFFlo1b4QCfAwazcwZX3PaWd3LZv9FmQsrS97qpQSc0k0aU8ShlEHt\nNoTP930Cu3Xfj3poVLWeVChXreJYQBmstYTDloVz5tK/bx8oYqXnwtqQFbc8YqQv1vcjBZLf+M8b\nWGs59/xzUXKYD5iyYXwV4LX/vEWDtDQGDz0HawyqjI5zyEBAGRQexjporbDWopTFGs37737EwFMG\n0e7wtrQ/oh1ZWTl89+2PTP75F6b+OpO+vXrS64S+GF2R596hxmCsIjffY8WSmcQnngsYrNIHWO7M\n3e0ms/d3VQFb120mMy2ZSZ9/g/XymbpoHafP/YEdR59GbbsDVI0D/zhi/ykIWssfazdirSn1RE/f\naq66/oLIJJViTpWcrGyO798vUvHAKgoUtGxSG4N0W1ZWbtgnJan4jLixFqtsZPlWC/UbHLbrQQth\n7TD43LN2/a4aB58g53Lxdh58hw8nfEpaSio9evcqsut98eLf8UORwvDltQp0dPl5rG8x/p8zLQpj\nwFEOL457kTlz53DR/12EG3DwjQxcP1AWly0bt7Nm1UqO73t8mZZEMcbw5ZeTeObpF1m+eDVqZ8NG\nAS5/rN5EIBCkY6e2EC2llZKSxCmD+jNm9F3UbdiAdz+YwLfffCPBZ4wUXhKsgiVL1tC2TZtozcZy\n+vePrPZLh+6dWLB6I0EKuOnmy2h9ylk8/9RH1F7zG8Ymlc97i+IpRVrjpvj7Uf/3lXcmojKalRhY\nvPfGu9StXxeIHP+N2/I46+wzo+W5RGVk3SDpifHFbvPHms3MmzYLay3LF6+g5/E9dz2oPH7+eSbp\n6enlvKeVh9y1ShTJacz9bRH9+vUjLqnoE+zV198klB/GeD5+OYSgxhiUD37IkL0jB20VnuftHHMW\nycAqnnj0n2xYs45hFwyjToPaKK3QTjVvRpUjD3j6qWfQeBzZtTNa6zIL9rTW9Op1DK1btWfTxu27\n3ZQU1sC33/5Im7bNUcrfrZvGoJTF2DAXXXYxTjCerVu2VuoJcdVK9J/ZaMWGteto3qQxfrRcmiqH\nY2AAbSEpweX0c86jTfu2tO/YntPOGcCvSYfx2+f/JTsWRWhFkU47tR/aZpdya8PiyT/hU3zVAmMs\nSxcv2DmnzTMFzPhhMsG0mge3s6JcKVWA0gnFbjNz+m9k1GqMF7a8/J+X0IFdCQ2Ly/rV6zGH0Epm\nh84nPRAGLCE2b8pGFeRSs0FNwBbZev37387n20nfYVA4+xwpHklhGhvCGg9jLR4envWjy2YWEbja\nSKWPnO25vPTUv/jyw8/56YcpPPrYU7z90n9Rfh7GD5FV4PPoqIdYt24dp593Fh06HYHrukTWQJLD\nfKDyCiAvN4+2rVsSn1T2maZAUFGzZgZ169XG8/OIjvtg3rwl1KyVSvvDWxJJgxUGoIVrq/p89OlX\n5Ic9Bg0aVOb7JfatsPDAgvnzSUiIR2l9wBPgS2IAjCXOQM0aaQz566Wg4qiD5YiBg1m9ZA2pprQB\nkChrwYDl2ceeL0UDMFLzs46zGaeEbbOz8hg1+t6dpZlcJ8ipg06IVkuQxkZlpSyECopfPCajRgYN\nGmUy8ZvJ1EtNxex2OA2watnCQ+oYS2RSIsWsmb+RmppM48aN9zn7uXHjRgwcOJBAwClmQZRIV51P\nAD8ENmRRvsW1PsqqnbNe99oDBdddcyMr1q9mc842jjuhFyefdjKLVq7mxXEvkbM9j7tvvoO8UB7X\n33g9rVtV37phMWEj5Zc84KuPvwYTot+A48vlreK0okOnVjRsWh/XicdYjQW+++Fb+vQ9lsh4Y72z\n0WOMwhiH1au3MuvnyfTsehSBxARZFz5GrAIV/b6Gs7dxeKcjIjcMxV4rIRWOCz8YDmAc2LQ9G7N5\nOYEa8SjfxVNhFJqAAzt0ykG9hzhwDi75TjqquMrjAMZle8hw/V03QTEr0xkb4olH/4kbdNm5lris\nblU1mLgSqxR8/fVnWOXTf8CxXHnzDXsGYMZSr27aIXWoJQAtjrJYNMuX/w7KK7brVSmL1pHcyN4X\no0hm0xqwxjJ/zkJGjRzNxK++59V/v8WkbyZh8/P2+dqeF7mR1W/amLPPG0pcYoAWrZvRt/9JTJ85\njztuvo0uHQ/nqhuuoVbdmrjuoXQKlxeFtfDzD19z3HE9adikabmMvlJK7RE8WmuZNXMeLVo0Iy4u\nbo/HIusHK7J25DH+hReomZnBeReejxQCjTVNyIDjF+AkJeyzDNann37JonkLS/WKxQWqCsXmDes5\ntl/fSNke4+MTZOX0yaQ1bUWCHP8KZLn8igsp2L69+K1UmLuvugk/rR4hve/148MEaN2iuTQoqyJr\nIVx0jGABY6AgOxsd/br++RBvzjacdMqp5buPlYwEoMWygGb9+g10796thIuC/dNP0Sb870s+fOtN\nLrvk73Q9tiuHH9mDTz+ZyMvjnoNwfpHP0VqRkJhAIOAScBTawpLfFjLh/bcJ5edhnDDn/f0c0moU\nDl6WG9LBU0yfPo9w7jaUBn+3LGSZv1M0CFVKsWrVWrKys+l7Qu+9zjelFDnZBTz22JPUzEjn6muu\nAmUw+7EWtSgDyqEgZNEmjAo42MIxmH/62iWnZLB0/hJMCVlQY0yxAWh+rh+5aVmNo8BqxTZA/zGf\nI/v1B2St8IqjSIp3GT32sWK32p4Ho4ZfR8CYYo/WxnVbGTJ0cNnuoogNBb4p5lqsIDlB7bP26+xZ\nc6mZeWhVs5AAtBi+MZiQJjFR06RF6xK21n/6+fNjEevXb+Too4+mzmF1SEpNJC0xnsTMNObNmsvW\n9av2elWrIutsDDrtNLI3b8aGPR4aPZZ333iDQYMGcu2dIwlG64k5ujCbJof14BhCWL775nvS6teh\nd98+xdbsOxiFzRVjwfNhytSZdOvWCY3GD/nk7sgBGwbyyQt5jL53LDUSA/zj2itBgVJOmS4FK4qn\nrKUAy8J5KwlaH4jfdW786QTpdVwXBp51StHnjYmM5A0beOrxcaxft4VZ0xdivaw93w/QjkerNs3x\ntcLxHPIcxf/e+4WmKWGcBg0PqS67yuqOsfeycc26vZr+PpHs9ogbbqZGk8bFT2A0Hv988EFsQI5o\nVRR2fPzaTYp8TNnIdf7YPn12q3aypxULf8NQ/BjS6kYKRBZDa012Vh5JiYmkpB3sOCsDSpGcHM/3\n3/8IWjF/4WKyd+SQ0aAef1iXyT9MZtCwln96nkJpOLF/H7779DNuveE24lKSaH14O9q0a0t2rkEp\njR8O4wSKn4EnSk+h2LpxI6ed0Z/ExPJfa9tayMnJ5fjex2A8xbvvf8iKxcvQGkIFuVxz7dW8PeFd\naqSncOUN1+I4jsx8ryAaxQ/ffUtqvINShXVbiw4aius1MRYee2wcR7dszi+Tf2LZguUccfhf9ggo\njTE7S38pPHAUvtHM+OIjLjyhA0ZnluVHEwcoTsNHk6Yx+IwBqOCuAMOxOTz93ASeePqxEluwRrn0\nH3gC6hCaBV2dBKxLj+N6YozZu6GhDF989gMD+vcs+snAypXLwAYOqfG+1T4A9TwvUsz3AFYDshby\n8nKpUaMGXsgr+Qn7fCGi2SrLucMG8wYeP/8yjRZNGnPqKf2Jb9iEJ5etZtPa9fvYD4vrKi6/7irW\nb9xIl26dsTqyg5//5x1cFMhqR2XGYNmyORsbDnH00UeV+3gsi90ZyNTKrMH4F14hlJfDZdf+gx1b\ntzL++ee5Y/hdZNZMZ/jdt0ZW3aH44EaUD0vk++gX5NGqXXPgII6DjYzpnTFzCi2OOJpVK1egzJ7X\nmd1vZMq4hFQBEycu5tiWaXQ/8xzyPIcEl0PqplUZKQVnD+7Pf15/m4a163D8ib0wRjHizn9y152X\nQilK4c2YsZQTTuwnh7LK8unRpR06HMYP6j3ynL5R/Pj5Zww4qddezyrsBWvbqiWHWtuj2gegrntw\nHzE1NY3Vq1djihvbUSrRCUraYfCwcyAMAeuhnDDbAomEcclIzyjymZEbnE+DFo1o2LwhxngoowiF\nfaZOmcKxRx0lhcjL2Ccff0ZaSqTs0t4BhmWPgTwl3TF2rnpSmLHc+wlr166jXr26GN8yc/pMTjn5\neOJSgqQHM1ABl1w/REJWNspRWG0PqVIdlY0BHGtod0S7A38RFcmknnXWmRRsWUXtlu35asJnbFyx\nhNqtaxXxBIvG4OMyZdIkbjuzGyEniXgbirSUVbW/lFd+ToC//GUovrXs2JJDclICY8YOxy/lpfmr\njz/g6KNuLN99FOWmQDm4vs+CxSto0675HrOMvpo0nXtH3b7Pxqox0LHj4Rxqw+cOrU+7n5QC6yg2\nbdpE1uatB15WRUGkoIqLUop4RxEfr3ASAmjXId5asjatJDUtteinAigHV4GjI9lc41sefehxEuMc\nzrhgCIrAgX9Q8Sea9WvWMeiUvgTj9l54wEbrtS6ev6ikOWcRe1xzzB4/1hg0igb166KwhC1Y3yMj\nJQMdMtx7292EQiFGP3Q/Ob7Hc48/WS4Fz0XpKBQhZdi6bgM16tY5mBfCUYomjTJpc8SRbCkIk6ws\ntZs13+cT8rXi2SffpXnCBlLadyGoQOkgaAk+KwUVOT9cpUnPTMGNd0EXtXDznxly8j2GnC31fKuy\nOMBxHA5r15wXxz0PFnxryM7zyF+zGJKLHsanLGzctJVWrVsdcmkFCUCLYa0lGHQIhcNMnjyZcDhc\n5t2eliBzf1tOSlBzZI/uxW6r0JHMm4EHxz5IKCeHv1/2dwKJiZhSXOZE6XhGkbN9K61at9hnbPnH\n2g28/fb7TPt1VrRbtuTXtVZhrYO17PzZfQZ0pBveJ6N2TX5ftpKH73+EuKRE7hoxnFoZyaTVqc2G\nDTvw8g+tgeqVje/71KxVk7j4fZfTKS2tNbiG+dMXkJERAHfPRqiJ/tezsHp9CG/jUi668u94Wma+\nVxcWn7de/5imbZpV9K6IMpCgFOdecBEjbr2Hh+95hEUzp3H6uefu+w6tLCtWrcNx3UOufo0EoMXQ\nWqMUHN6+PevXry/TdcALGRRvvvY6yXGa9Lp1i93WGsvqlWu4c/hdKKu47pbrada2FUoptDnU2k7l\nZ+68JYRyc4hL2Pe6vjUza3JY4xbUrl2XWbNm78yKFstaZs2cT16u5aMPv2L8v15j/vz5ewzvCLia\nI7t0YeaM2SiTy50jhuMGguBrzj/3PNZv3EpWMTVjRflzHZfNm7dQVgO2fAxTvp9Cp44tsWrPc84A\nWINnLW++9BwD+3XDS6yDK5W3qo0wAVIC27BWkgjVRWJykJEPjuCWkTdzZPduJWztM3nS5JjsV2Uj\nfTfFUmgFR3buzH9fewc/O4xODhx8mtwCeBgcvvt6OipnO+dceEGRM+CM8fDROEbx6bsf8cuvU6nX\nqAFXXHU5zu5rQEtTosysWLKCOjWTMXFxqJ3jN3dRyiEYB6JVx0wAACAASURBVKcOPpmtW7fSsWOH\n4gNQC1Z5LF22mdTEeKwJ075TZ+a+8TpPPv0yN158Dq06d0URJqADnH3mQPr16U5qauqusb0O1G5Y\nH+Vowlu3QYbMfq4Yhk0bwrg6FxN20QcYM1iInlsGzwTYumEhfa+6nlxg9wVfXQDlEq/glhuv3Lm8\nrnzfq48vvp3PWef99ZCbgFLdlTZOsBaSgwEMFh19VnGVNaoTOeVLZGjduhkJCUF+/HES1i+b1IOv\nXPI8y3tvv0zdOskc3uVIfF3UCadxleKDdz/g51+mMOjM07jyqivQRW4rysLcuXOpXbt2ZG5HMdul\npCbQ+LD60b+V/FVyHIdZM2eSmBBHAI9LLv07rVq14vPPP9tr27S0NLTWe4w51ha8UD4hGQNagRSh\nUIjkxDhU4MB7RCLnlcFHs2LVdgK2gEBGTRL3cWyttTLRsBoyxrDi508OubF/YrfVz5RL3Xo196x4\ncQgEnyABaKkoDe0Pb83kyZPQ+1rGYD8Z4J+PPElyvOLyqy+LrgO/981HoXnmn8+xatUqbht5B0d3\nOxqlQSF9cOXBWsu2bdtIT08vcVulfJQyKAWliQ20Bu04fPf19yyaO4u01HgyMzNJSdl7cHrhBWj3\nC9GsmXNJSYyjbsNGpf9AokxZYMXKlYTzczng9GfhKylLgQ+P33c/hzeri+8moqxf5NaFpeQOlRvT\nocFnw5Y8LrvhGrkTH4IKv8uehQ6tWlTw3lQMOe2LYwEMWltOGnACKM2qZb/jmaJvEqV+WQUPPvAo\n21Yu5qYbrieYlIHBxSki+/Huex9hwj7XXH81CckJmJ0zqEVZ862PbyEzLZWmzRoDep/rfEdoduZI\ni01KGkDRqGFNFi1dzsyZ0zju+O4YC8vn/0qnnj0j29g/jYixkQkKvrVk54b5/MN36NStM/ogS4uJ\ng6DA8SEtIwX2ESyWjkfYKhYvWE0wZzUXXHJZpBEj/bCHDqv5+IP30K7m4O4ooioLh30aNG9Y0btR\nIeROVhwF4KI1JCUFiE9P54MJH3HJFZfiJipAYQszVbs/b4+6jwYbnf9mfItWMP6lV9iybCm3Dr+J\nmk0igU7kRfbObgwefPKutcJhZxFyUU6Uwvc8EhMTQKkSSh6VshZotDyLVXDDjVeDZ3DiHbZuz8cP\n59OhU6ciXySSbNdo4IH7HqDDEe0ZOmzYPtcSFrHhex45uTnRQZwHxgIozcv/eo66dWuSkJpB5ESR\nAPRQsT3P44JhZ6EPKpMuqrrVq9bSsmlRtX+rP7na7YdLL7mI5q1a8dGEj8FGSiIVGQuo3f+gicx1\nh6zt2Tw45lHmz5zB4LPPJrNRwxJzmYUr5IjYKFz9Kj8vvwxfNXJCaG1xXI0T7+JZxcvjXyQpKRFn\nHzcgay3Gh5f+9QoBC2cOHhwJiqUbtkJt2LAB7TiY8IGvjmYJsGjxesjfwQUXX4x1XYxcjg8Zvgnx\n2vhXCCZGSnkd/EInoqr6buKPh+xCEofmpz5AiUlxdO3ZldlT5/PFR1/T76S+6GBklSLU7kFEpDp5\npO5jZBznY48+ye/LlhJ0A4x9YCyO66KdkovHKwk4YqawOeG4LsG48qizaAEf3yqWr9rKhjV/cOcd\n1+9zawOMe3Y861b8wR2330wgIIsNVAbp6ems8n108MCPh2/glVfeIC0hQKNWrQhZi6N26zwR1VrI\nBDjn7P6o6H1DJpgdmqy1JKekHLLfeTnr94slLT2dI4/qQH5+iPff+R+fTfiEUE4+BvCtxWAx1mIt\nhArCTPlpGnfcfDc7tmylR89jeOCRMcQlxuEEXJQ9qF48Uca00rjKklqjJtm5BWjj7xxicXAimXCL\nxkfjKYd/Pf44HY9oTWKNmqCcyCIDhcNJo6srLZizlN9/X8Ytd99IMC2Acp0KjU4kEw9YCCYnEacT\nS14Bazd+9LmRcb2GafNWYbav5JY7bgYVIKiDOEjweSiwWB576Clq1tt73J98xw4dlkjm21F+kcPv\nDgWSAd0PSmkCDqTVTOTkM07i448/ZenyFcx/6gVqZ9agdu3aZGVlkZefz/IVK9i+Ywf5+bnUyczg\nvAvPpWmrlqALZzdX8IcReylcajUhOZGvvpxIh6OPQpXF+KxoWstaA0rz4rMvo6zH0AuHFrl5fl6I\nab/O5auvP+bqq68kMTEeJWMDK42EhHjC4fB+PUcDKIO1irDVvP7C8/Ru35hAeo1y2UdRiVnF+Wf0\nK/Ih6e06tCilyM8vy+FeVYsEoAdAa40KGk478xTCYcNPP01l0rffsmz1apSC7OwcMlLTOP74Y+nT\npxeJadGSPnJtqdQKhzv0G3Aib4//F9pxKbtO0cgYr++/m86SeTMZNuQMrFF7zTkxBrZs2Ux6RgJ3\n3nUbWutK0z0nN8dIwzEtLXXXWqr79U9iCFuHmfOWk2K2c/qFt2BlSc1DisXyz+ff5dpLB1f0rohK\nQCtNzcwMDtXOaAlAD5DyI12mwYCmV68uHNerG8bYaMFohY4GLkopGddVxTRpVJvtOWG2rFlPRqO6\nZXDsLMYqfBQfvvs2zQ9rzNE9jwNn74uO1tCgYT0aNKx30O8qyp61EArn4eogVpf+e50LJHmasANv\nPPkaHTo0w8Yn4EiP6yElC0X/7plS7UCgiNTIqVVj7zrQhwr5Fhwoh13Vk6KlclytCDgaRymU0ruK\nif8/e/cdH1WVNnD8d869M+mNTui9K1IsCIgIiIi9rHXFsmJZseuqa1l1bYjlteuuaxdF7Ng7oIIo\nSEek955eZu495/1jJhQNIYSUSXi++4kbkpnJSe6Zc5/TnlNjhRQV4WhFcmoS06dNr5TXM8rHKHji\n4f8SsHlcds1VsgajllJAixaZFIXDKFv+5jOJQorQzJq1hBR3G6NGXxbZ9CbVYL/y4cTP6XzAkTVd\nDBEjrN2/NxlLACrEH2gshw8ayOYt29i6Zes+pUjxPA9jNK+/MpGVixdyy+23oLTavhZY1D7BoEOT\nZplgy78+2BpNWMOnr77OaeeMwCoHvRcBrKjlrEeBD4d3bL6Hwy0q+PKyealWstaS2XL/PdlOWkAh\n/kBh6HFgd7Zs3kpqauo+rcFUSjFzxlymfzeFESMPI6VeYzn1pJYLBCAlPQ2/sPwbkQpVHFddcQ1x\nOZvpeMjBoOVYzf2Jr1xe+t/rtOrSHh2u/Osudal2stZQv379Ur6+f3QoJAAVohTp6Ulktm3Dsvm/\nY62PxexV2h0s+MayaNEKXnvhZY4ZPoghx5wEWm1PtyO3jNpJAw0bt2RbTlbZD7QGi0dh2PLi8xPJ\nsEXc9MQ9aJ2yfQWG1IH9Q6Fvad8gFes4IOl8BQAG5ToEg3/eiLi/dCgkABXiTwyOgrjEFBYvXQlG\ng7V4Zs8RqLUWYwyFfohlizfx/BPP0rtXJ4Yff1zkCM39pGdbV1kU8dYnsX4jFs1ftIdHazzr8tbE\nD1kz91suu/RCtBOs9Ny/+8toSa1kIeyHeeN/b3DU8SMknZrYwei9G9Sog+TdIMSfBNBYho0YRL1G\nTXn68WfAKEw5OqXGGJRS/DxtLo8+fCvdD2jJWeefh1VyqlVdYBUoCmnRoQOL5s6LfG03AaAFfv11\nAbO+/pSTTzmeNt17YnQAZ48H8Io6QwE2wBH9DpJ133WYdAIrRgJQIf5IRY7ljHMduh7YhW49+/L+\nxI8IWg8wkYTypQQRxnigHd776Gs+HP8yh/cbwKiLLwPHxXEqIaG9qHEai6+SSU00rNuSR+HWTRgT\nOXIXYyMnHVmLbz2Wrl7Hm888w1GDj6BX/yEo5UQT0ldusyudmtjlAw/c+xRtO3eQ5RZ1UEngWaH3\noDLkZRdWcolqFwlAhdgNpRQpSS6HHd6L5atX8PZbk7BWR47NtBofs8vHls253HfnvUz74mMuunQ0\nZ5x7TmTHq3SO6wwLaMLEBV2sDvDGy6/h4GOVwdcWX4Gxis2bcnj6vgfJbNKEY046saaLLWpIGDj9\ntMNj5jAJse92Hu3c185fdk7Oft2BVLVo6LjWFFQAVbu/otrqQuQHGQoKQjz26FO0btGcrl270r5D\nWxKSEzDGsG1bFu+++x6L5i0iPTWVa6+7kvjkpP11h0ms/daVWlcih0p4WKtYvS6PN559lmbNG3Ha\nOWfhxLkY3zJx/ER+mPw9zZs14Mrrr8FNiKOW9/Vj7ZpWliptRyyWl194k7+eewo4teLMl7p6nfdV\n1dQT65OdW0xaamKVvPxOYva6SgAqqkqdCECxYJXF4mNxef+ND/ht8W+EQmHiAnG0atWSVq1a8c03\n33LAQZ0ZcvQxOHF6e7gRs+/8qhNrv3LlBqDR0zetLSak4/h5ylxmTP6K4uIi4pNSWLt6FYV52Zx2\nysn0Gz4QdGTphZIANBZVaTuSD+iCPBISk6vyx1Smunqd91UltyE2MuppPDZsyaNxw/TKfPnSxOx1\nlQBUVJW6EYD+gfEtxhpyc/PJzc4lKyuLzp074rguSlvZaBR7jV2V1pWQAeUbvvj4M1YuWUyXbl3p\nP3gQytF1qR7UmV/kD6q0bkz6cDIjju1fm+pBrSloNauSemIxrFixjtatmlXFy+8sZq+rBKCiqtTJ\nAHTXzUe1elSrqsRaY1fF06xm+y9so9ldS2pIHdp2FmvXtLJUUWABhdZSuHYt9Zo1q01/vFpU1GpV\nZW2IMaZK1geXFDjW0w3Xpjuoko9a9VFH6e0fFnb5EPufyHnwJR+Rau8AjlSI/ZYFnhz3DPWaNkZa\nBlETPN+r6SKUS20aARWiRLVX2u3rdsSexNofSRq4fRdr17SyVEndCPmWUHYWSRnpta3NqFWFrUYV\nqid7umdU5T0lkhUuMhfjqNhNQFubRkCFEEKI2GUNL73wKon1Mmpb8Cn2gTF/zgu9p+tf1fXDGIOO\n8ZO3ZARU1EZSaWNXrN11pa7su1i7ppWlcuuGhWIDBVnbSK+fURv/aLWwyNUiZtuQkpP3dhfMRkdZ\nY/a6xnZ4LIQQQtQCnoJPv55GWkaaRHKiyuw8aKh12dk2Yjj2BCQAFUIIIfaZY7PJDIbk1KP9XGnT\n8ZUp1oPKvSFT8KI2kkobu2KtdZS6su9i7ZpWlkqrGxbDrPkrObBTS7RTawPQunqd91Vtb0Ni9rrW\n2neKEEIIEQusheKc9bU5+BSi2skIqKiNpNLGrljrbUtd2Xexdk0rSyWOgNaJP1Id+BWqRG1vQ2L2\nukp3TQghhNgHMXuHFzFLBv8kABVCCCGEqFZ1aTNRRUkAKoQQQgghqpUEoEIIIYQQolrJJiQhhBBC\nCFGtZARUCCGEEEJUKwlAhRBCCCFEtZIAVAghhBBCVCsJQIUQQgghRLWSAFQIIYQQQlQrCUCFEEII\nIUS1kgBUCCGEEEJUKwlAhRBCCCFEtZIAVAghhBBCVCsJQIUQQgghRLWSAFQIIYQQQlQrCUCFEEII\nIUS1kgBUCCGEEEJUKwlAhRBCCCFEtZIAVAghhBBCVCsJQIUQQgghRLWSAFQIIYQQQlQrCUCFEEII\nIUS1kgBUCCGEEEJUKwlAhRBCCCFEtZIAVAghhBBCVCu3pgtQQ2xNFyBGqZougBBCCCHqPhkBFUII\nIYQQ1UoCUCGEEEIIUa0kABVCCCGEENVqf10DKoQQQojaQ/Zu1C573FMiI6BCCCGEEKJaSQAqhBBC\nCCGqlQSgQgghhBCiWkkAKoQQQgghqpUEoEIIIYQQolpJACqEEEIIIaqVBKBCCCGEEKJaSR5QIYQQ\nQohqUpLQ1PgGrRRYC0phFXjW4GqLLgnPLOXIqFk7SQAaA3xTjFZBAJSqozVt30kS4tqlsiuyXP/a\nRRoyIXbD2hAah61rN/Ld5O9p0KABixcvZunva2neogkjTj2V1i3rYa1BKUNdDdWUtftlux4Tv3Qx\nEGdhweIVjH/mKYYN7E+/kSNRTo0VKZZvGjFxzUS5SQC6f4vltkTUTnWiDTDGEM7O5bnnXqRFm47M\nmT+X9u3bM2jQEWRlb+OHLyYxZ/Fqhg4fwdHDB6KdWvtW2mPBJQCtQdb6LFm+geXzZjFk5DGMvugq\nnv7PozXZcsdyTY+JaybKTQLQ/VsstyWidqq1bYDFYKxGU8wXb3/Atz8tpn33Hpx55rEoP0wgEEAp\nFZltt7Bl7XrG//c5GvUaxqnH9kHV4KjUPpCjOGOVBULK4dN3JjBk5AhAcfCAI1DGq+miCSGEEKJS\nGJTxUV42915zO9/9upJLrxrDuWeNJM6xBIPB7UvvSiK2+k0acfwZJ7Ng2hSKbK0MPsulbi4sqBU8\nnrjnUa66acz2r3Tt2Rdrwigtl0UIIYSozSwGZQ0F2dk88O+nOfqc8zmoZ0cCChxCWAJ/HiZUYB1N\n8w5tqK98lq/PpktmWk0Uv8rJCGgNWfTbWi75+8VoFdj+td49mpO9eX0NlkoIIYQQlcJq8ELcd/v9\nHDfqfHr1ak+8A45WoIOo3cxSK8AjgVZt0/ngjbfwqcXrD8ogAWi18/CAedO+JjE1Zdfv+LBmdXbN\nFEsIIYQQlUb5hnvvHEffoSfTs3smLuVfYhdQ0G/wYDYsX1qFJaxZMtdb7TSvv/o+55559p+/o6F1\n69bVXyRRwyx/7t9K31CIP6iLg0B12X6/Ea0ov4CsnGwGHn0YjgVUXLmfa40hvUlT4m2YUDhEXCCw\n2xHT2kructXMYGjbKA7cP8f+2gej/BoolRBCCCEq08LZP9Gt7yCSgmavw3GjHXTAxSpDkQqgKK5z\nXTAJQKvZS899SL8jh5X6vfz8ECkZ6dVcIlHzFJG3osbaks9hP02RJoQQdUJeUSH9Du+HrkBbrjGA\nonHDDHJzLFD3dsNLAFqNwgZat0nFqNIrY3ZhPtbKCOj+TWFMJFmxEEKI2qtD507k5eVV8IRDg8Ul\nKejw/dSfqIvhWt37jWKUtR5PP/1fjjjqSJzdnGwwZ9Y8rFyS/drHH3/ClClTAAlChajLZIaj7mvY\nrA3dOjdFVeC+rgCFokHThqxfsxJrnDq3qlainWri45AeF4j2hEqvRdnbslFaLsn+xmIx+FgDJqzp\n0rEbSikcp+5NuQghxP5CKYVbyn6PvdGkSROW/b5kL/bP1x4S7VQDC8yet5yzR51Z5uOy1q5HKbkk\n+yOFwvMscXEBQuFCQEZIhKjLKjYtK2oTpdQ+X+cmjRsT0A5GQ127JUi0U9Us+Mbjh88/Qu/hhKO4\nBOrcLjdRXgqtFUOHHUmzFo0rpeESQghRMyLJ9TyMb4CK7O3Q+ErRoHEm4YJNkVOV6tiqLAlAq0G4\n2OXyKy6FPQQUScpiJQLdb0Vm3C3WyEY0IYSolWzJEZweuVuzMWgsFY8cHcfBGFsnZ8QkAK1iVlke\nGvc4xt3zaFYgENjjY0TssxbwwQOMb7GEMcYjsqvxz1T0f5FsTAqlg9VaXhH76uLNR+xgMNHjFuvq\noYv7ERu5ntYPc+v1NzN3/vLdtPzlk5ASOTGxLk6ISQBa5RSZGS56D9vXPKDv4b1R+9BTErFBRf/j\nW9iwZhPWM2il5LYiKiwUCtV0EUQVUsCiRcuZM+NXaSdigDUe69ZvImwsYKIf5XwuxfjG5bE77yap\nSVvm/bQg2rHYe46FULgYTSEGXef6JhKAViGLYd36HM7720VlP9BAcQiatG0LSnY+136RUy82bc3m\nsYcf54v3J2GMka6F2Gue9Xn5pff54v3PJC1XHbdx40Z+nj5rb2IdUUWM9nhh7Fj+efu/8dBYq8GW\nbx+60UFmff0xc9blcvno88nauhm7D0nk3bg4AkrjhZE0TGJvaD6YMAGCe641y5atJRBw6lwPZ39l\ngfjERArzQ8yf+Staabm0Yq+5SnP6X45n3ca1aEnRVmcpNIMGHMyGbdsIFRTWdHGEiWPIcUMoWrua\nm668idWrNhIykU3Ee1oO4/shPv12Bg8++SiNGiSzbMlv0RPuKkY5DmnJKbiurXMB6L4lqBJlMlhS\nU4PoPfV+FHw+aRLdrr6gzlWw/ZJSQJjUgENi40Zs27qKdUuW0rR9m8hBA6Ve48gqIWMVFoVrLBYV\n7SL6hH1wHE3Jk5WKrjW1oKIna/moyNejj3JsCGsVSunojnoJYGodqwjGwQWj9zCLIuqEMVdfTjBO\n1oDXNEcr+g4aRnz9piz4ZQb33fIPwm6Ag/oczulnnUxiajIJyuB7Ptp1UcrHU+AY+OGLKZx87ijS\nHIXBw1OBCrW8kXuFB1bjhQrIL9IkJYSAvawf0RuChcgu+t2nIq92EoBWod9/X8OpZ5yyPSDYPcOq\n5UsjEUWMVAyxrxRaKc4663T+M+5+XnjxFW76161YZctMr2QtzF+wlLdefpH6GWlkZjYhLhgkPjGZ\nTp06sW3bVoyxNGzQgPiEeJJTUiLtidYUhYrRWpO1LYvExERS05IjSZCdyB5MLXWr9onuTVOuLM3Z\nHyQkBEByQceM7t27061bZ04781zefesdZv7yM7dd8wNuIB4T7zB06BCGDBmMDgSJc+C7Tz/is+9m\nc+OAI0CHwdO0bNmSmsyf5CuzPeb0tUGjUDFyrrwEoFXo1f++yL/uuQkFTJ36E3169iCYFI+1UFBQ\nxGOPPcnSeYsIOhZrivjnNTezNTeH+qn1uHPc3TLlVqtpHEfRsVNzUjObk71tExuWr6JRu1aU1cuw\nxrJ+/Xq2bdlKODebZQvnk1ccJiE+iY9dl2AwSHFxMa52iI+Pp7AwMl3nOA7JqSkUFBTgeR7hcJhA\nQpCmDRtx9PChdOtzoHRu9jPW7ujs7Py5iF3qD3sArLWgLMoawJX3cDVTSqEI4AfhxDNP5YQzT0UB\n+bk5fPrhZ0z79lu++/wzgslpxPmWrVu30PPI4SQmuoDBWoemmc3Yl4W91ousPc3OLqRRwt4Hjsp6\naBTTv53CxG9+4F+33kx8bMSfEoBWDUOxVXRq2xAb3f9+4AEH8sTTz7F0zjw8v5COXTpyxRVXk5wS\nTyGWl8eO42/XXQvKsiG/mMmTJnHEccfV9C8iKsRSMt2tNBx/xjlMeuE/TBz/JpfedA2U0bHQjqZB\ngwYEVABNmIsuuRAnrSGO1UyZMoUWLVqQlZXFutVrWLtmDY1bNEdphaMdUtJSyc3Npbi4GGMMBaEw\nmzds4Z3xE2jZqgWpjRtV0+8valpJwFmyXk2Cz9pJqciSnGnT5+HnbaHfkUdgtZI4tDopIuOFasff\nPTk1jVPOOo1Tzjot8oWQz+svvUCw8VGcOOJwdHQ20yvOJzEjAW2Df+g87ByQljXQ5AIFFBYWkhAf\npEIJ7a3mhcefYMW6bRSbBJwYGteSALSKrFuzldPPOW977q7kFJdrrr0Ui8JaxYvP/Ze1a9fSsVNb\njK/o0bvH9sYmIzGeb7/7TgLQWqukpbE4KHq0bshH6c3YuHER2DA+kTVBqpTnaCzdu7bBTUsnoaiQ\nFp1ak5TaFEdBhy5tdyzwNJF1oCYaaCjAWg9fufjWUlzg4QIP3vFvCvK2si1rswSgtZ0Fo2DcuGfZ\num4VmQ3qU+gV0afPIaxevYa58+YScByKi4txHIecrCyCiYm4gQAHdenAWaP+Qjg+hTjflJx6IGoB\nZQ2L1mxi7icTmDlzJgcd0JtAehIHHdQTlMZg0VrjWB+FxqgwWrvbBz9E1Vu9dA4LZq/ksrvORWuN\ntQalID8vj/iE+FKeUf4oUAUCxMfH43keFRkC94G/XHABwcQkrCaa6jE2olAJQKuAxeedV9/kqutG\no7YvAI2OiBEJHM6/+MLtj1+zahMdu3Tb/u+gUoTCPuVYPCpiUklPOfLfQEKQwwb2473nZ/De629y\nwjmjSrmsanv+0IDrcmj/w/ll0icUFxaSlGJRJQs4S54YbT/0TiNb1jqRNYOe4v2J77Fg7mxCebkc\n1PNAWnXsVEW/q9hbxpgKHbXqKVAWNqxaQ9fOrcjNyoawz9Rvv2Pr1q0EggGSkpNo1aI5ycnJJCUl\nEggGmTN/AVN//pUVC+dxy8PjiJHlX6LcDPVTU2nasg1FaN7+5FP83Cxe9n204+CFw8TFx0PQpSC3\nmPNOP4ZDjxlR04XeryxfvR6tC3jhqWe58sqLiI+PI3okEulpabuccGiMoagoREJCfLmSyysgHA7j\nBgJEMobvnYBWBJJSIo0HBqL3iVggAWgVKDaKRhkJhJUt1361r7/+htHnnbr939ZacnJzqq6AolqF\nMBx+SFfeeN5lxk9zOPaUfALx8VilUCXJhXdqEJRS9O3bi+8+mMTyBUuo17Alf36rlpyYoqMpPiKj\n59s2ZPO/5/7HsiXLaFA/yPEnjWDA0KH4SuKOWGKMwdnLUUjXRm4/yRnp6HAhV954Fb5yULZkm6sl\nWhWwJjIyrpVl2MnHsWJzLuPGXEL2hjWkNG4mG9JqmYEDerPw2/e59uarMTigVCQLBjbSCVWRLBhj\n73+S3+fNlgC0mvU76mh69u3HC8/+h/tuv4vMlq048eRTWL16Jct/K+SgPu1wbBisi0KxdOZMWh1y\nGCluWaORBmU1eBAMJIMbxtq43QetO2WHstHNi5F7S0nAqYDYCT4hVsZh65itW4oZeNQAAmp3KXci\nDJH68fucWdu/pjBgwyQkpeHFUEURFaeUIt71OWTwUFau3cSrzz0D1se30QPaSkkr16hxBjoxgUWz\n5pW6ZDRy83GwVuN7ipzsIsa/NJ4Xn3mWhIDmpNOO49a77uDwIYOwOnIasah5PrB83Rb+ed2/ycra\nGlkJVt79CQocBSeedDyrV64DL4xPZBRcK9Ba4SiFg8LVkeUcGB9r4IF7nqZhZmPSGjb600YXUf3s\nTh97pCApqGh6wFC+//hjlI5cc0crHKVBWXwfnnrieYq3rODsq68AmX6vVkpBcmoql199FaP/fglJ\nqUm8Pv413n3lbT7/fDx3//N+1m/18XUkMly6diOuQ0lDXqaC3FzWbtpCWlIAVcbDfRUJPCPBZ+T0\nJluDu+/LQwLQSmaAia+8SPM2bfeQbsdHGY85i1ezBrQ9YAAAIABJREFUYdkCrrzkUjA+WIXFITkp\nHkeOxKgbfA9lQwweehR5xWHm/forednbypx+URqCSYmsX7Uueo78H2mwioL8MJ98/BUPjn2Uub/8\nwjHDhnDZ1ZdzxJCBuAmJGMcBDKoCUzei8mmgedMGFHr5/N/9jxIuKsbXe9c56N6jHUUqyOzp08oY\n1Y6sMt5SCGOuuIFMbxVXX3896MC+/QJinxkfJox/CxMu53vSOigFx488jM+mzNplH4rFYHzFRx9/\nRUI4j3/eeSfolKopuNgtFc2XplxN45aZnH3uWVx11RV07daJs0ZdwgmnHIvnF6NsmAW/zqWY+DKD\nyV1fXJHSsHFkY1MZz4l0OC3Ks2xdn4UfVoRNbHdDZAq+UnmAi19UvMf1XcY6/PO2uzmsa2eGHTuc\n7ocMZ+Xvv9G8Y2cUDp07d5IebG31h0Yi6Dj4BGiSYThy8FFsXDqPVcvX0TmjIWCi3cBd+4IulpEn\nns6nrzzBppVraNK23faXttZSkFPAF598TbPMVrgYBh7eh4P6HEC9BvVRWhPAQVMyDiL9zFgS0Ip7\nHriLO/9xB88/+RwXXfE3nEBcOZ8dySTduE1n5v0ymx6HH1HK9yPH9mVlFfDBu+/QplEq1958F44b\nJHKrlMXlNcsyZ9YcVi1bytX/uL4ca4Ej30+KAxOXSlFuDglpqVilCXuG11+cQMfWzTn+ijGEHSCG\nNpnsj5zoNLfFkr0th+7NWtK1UwMcC77SLJ89iw2qIUFddlL5aAI1tm1cTW4BGKV3c1kNFgPG5bXn\nH2Dm9E3oJIcER5MXUtx67x0kJ0ZSfDkxVi9iqzS1nXXBwogThpfRvhuKsVx+8TX866br6T38OE79\ny1/o3qMlX0/6EG0t+T707t0TuTy1k1Vmlw/QaCDoanr26UNObgHffv01yviR3eulXOcwlk4dW5Bb\nDFM+/3r7WHhxfoiXnnmNSe9/QbsOnWnfuRVHjxzCUUcfQb0G9dFax9JBF+IPSq5NYkKQ/kcOYMni\npYSLizDR0Y3iPT5f4ypFuz4dycuFYpO3/Xu+H8a3FmPgxeff5NGxDzFo4CHccPs/IRAXmSfcvhZM\n1Bhlue2u23DjEnhn/FvleHxJvbGcctapPPXYkxirmf3LIp57+nnOOPs0DhvcHxwIEKkjouYppUiv\nn8b8X+dG5sUJ4FjDtrytZDbNxJryzXCuXbOGuIR4nJLjjHYWHRRVFn6f8QNTp67j0ptv4u5x9/Gv\n++9k2OBeXH/NTRgbwLGxVy9ir0S1mseMn2bTsdPudxyH0dx25S089cz9uElxvPziK8QlJ+IayMrO\nxrOW776dTtvuXeRc+CpijMFai7UWY8wu/94XJa/he4aiwlBkOYUB40cSSWssbdq3YVtWLgvnzwev\nOJJap5TXcpSmXkocjVq1Z+H8BeRuyeabL79j3P0P0bRJY1q3bUXbDi1Iq5eM0hbtuKiSxaKW8k/v\niBpz7PHDCWF44rGn8KNv9rhyXreBfXqxYuMmcjduAeuBNaADzJm9mOf/+zJdurfhzvv+SZuOHXbK\nSitigdYa19Wcdf65LF+xmvXr15freZ4O0K19c/LCis8mTSIvL5vRl15IMN6RY85i1MH9+rJ4wQIs\nLkb5rF60gMnfz6BDx3ZoXb612MuWLSOzVUuc0u5P0ftHfnYOL7/xDn+98mqat0rH1QbfcTn6uBG0\nredw3z2PEIrBKiLtUiWyyvL9t1OwpTQGkXO+LTdeeTX3jfs3aBdtw3i5W7HWwddw1PATcXH47efp\nQEAGKqpIyZRXSTocz/N2SdpdUdZasrKyePOFCfzfff/HxxM/4eOJnxAq9sEYsIaElGQ835CbncWn\nH3yANQa/lNzCjm9xtGHg8BFsycll7F33snbpCg44oDMtWjfhkMMOJDHJRTvRJ9sdm9pE7WAVXH/z\nLfy2cAkP/utuDHaXdC1liTcQSE9n/i9zMLgYo3jgnnGsX72W0049gUMP74VvowMvIuYoBQ0z0jmo\n78E8dOeD2CJDaLeX3gcMrvGYN38161ctZ9u6dfQbeDDKdeSQgRiW2a49rpdL2Isk8X39+SfQyU1I\nqx8HNoApa5+H1YDP2hUb6HZIf+II/yli86Oz8kvmLCSt9UH06dmCOAUGjWPBOAlcdNXFhJcuYtwj\nj0VzicYOCUArk3UjC41LaRCUhbyw5S8nnYLv+pHAB5fsLZsiiWuBtPQ0LJDolnc9mNgTC/jW4Fkf\nY2HD+i08+9TLfPnRD7zz2sc8/cj/uPfW+3jwX/fz0lP/ZcbUHzFeMb4XomTNd1khgfUj+Vo9a1m1\negvvTvycvLw8evXqRVZWFrNnz+aFpx8Dq/C0Qzi/EDcxDs9zmPLlj4QKCnF1eKdtsRZrfULKMmfe\nctYvXIivksnN2cKSJQs5rN+hdD2wG2jQ2kFFJ91KTuvYPsMq96SYp5WiRYuGXHXDVSxZtIw7brgJ\ng8JidlPvonn88HCNz2EjBzN58k+sXL2RsfeM5YSRwxl2zCDSMlLROLiO2l4VpDrEmsgVOfLIw8ls\n24orLrucgKH0TrDRGKv5+OsZfPP+C4x98jGWrN6M8kJyA49xwWAKaQ3g3jseYPbXHzFvg09CQiJp\nSW7JeSK7ZRVgDdvyimnTummps2XagrI+H3/6FWf95QSMH5nviNwHLBqfhvUbUOAXMnfOiir6LStO\n6m8lKgaGDDm81O9ZBfNnL6TPob1xo3u/fKs4Jvp4ayGjXgpW+Yy++vzqKvJ+oORIQs2H73/Ga6++\ngdY+TVvUwzgeLVplEkgIsCV7K7PnzuH1V97k7lvvYt7MWSjrofYwnW20ImwteXkhHntwHOuW/MY5\no85g6MghnHHBmRw6dAirVm7gk4nv4wAvvvgC9VKTGTJkCPmFYd58dTza6O1RgjGK4iLDxxM/Zv7M\n2RTmbaM4lEvAjeOCCy6gYdPoaUYytFUn+L5Pt27dSMxIo379+rzx8pvsvlkuqYguxtUccuAB5Obk\n8dMPU7nobxfSpXs3GQ2rZZQ2XHrV5biOwzOPPFbqYzbl5HLzdf9k6U9fM/rGm0mMU7Ro14U5M2aU\nK42PqDlKKy65/Dryty3lv+M/5urrb0J5BcQBmLJPNjKA9UPkFvi0yEzAlhKAGs8w9+efKDYOjesn\nEIyes+kQyZBg/RAPPfAsBV4RY666AdeNrX3nsVWaWm7tqm307NW71O8pC+3atWb65B85bPCR4Bh+\nX72Nrn16AR6+5+Ikp6IlR18li6Qp2bwlj5SERM4fdS4JSfEEgpou3TuhUBx74nCUhVC4mCcff475\nc+bw+kvjWblkCSNPOwOwkfPbS2krlNJgLa+9PJ6AhutuHEMwLQGLxUdxSL9eTP7iG7779nsKlWHx\n3FnUT43n7IvPJ7lBY+bPmcWPX39LWpMmGKP48suv+X3xUtq3bIVvwjTJbMqlV17Ba//9H8Whosh6\nP12e4w1EbVCSjL55i9asWL2CQwYNZdWydbRo1RSlLLvm6rKAgzWwPjuPp+97iHwvTLIL6Q3Sojmm\nJQCtXTRBF/ofPZwFM6bz4rMvccSAfrTu0h7jWx544CEKcnK47Iq/0bJ1q+3PGXHSMTxx+810OeRQ\nXLnmMUspBW4cYx9/io1bNtEkw/LfwgBBLDgubhkJKbyQR/HWzRQUe6j8PExi0p8CNuVoJn/+HW06\ndkfryIt5ABZcwkz9YjIZLVsS/9ti2nWNvaOYJQCtRF989AkXX3JG6d9UltS0RGZM/Z7DhhwZefyk\nT7jiktMBl59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KjNLSglSyyJXRBDW0a9+K7eduR5XsmP7u28V0aN8apcBF0yAtkb9fOZqS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\nAAUAAEBSBCgAAACS+g/+clXiQOXW9QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3b2673d90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "icdar_traj_path = 'ICDAR2013/train.csv' # normalized data (0,1)\n",
+    "#icdar_traj_path = 'ICDAR2013/unnormalized_data.csv' # unnormalized data\n",
+    "icdar_img_path = 'ICDAR2013/images-handwritten/'\n",
+    "\n",
+    "# Read the csv file. WARNING: do not load the whole file!\n",
+    "# Header: train_id, signature_id, writer_id, occurence_id, time, x, y\n",
+    "data = pd.read_csv(icdar_traj_path, sep=',') #, skiprows=0, nrows=2000)\n",
+    "#print(data.dtypes)\n",
+    "\n",
+    "# Get a list of (x,y) for each data\n",
+    "N = data['signature_id'].max()\n",
+    "xy = []\n",
+    "for i in range(N):\n",
+    "    d = data.loc[data['signature_id'] == i+1]\n",
+    "    xy.append(np.array([d['x'].values, d['y'].values]))\n",
+    "\n",
+    "# Choose randomly 25 characters and plot them\n",
+    "idx = np.random.choice(len(xy), 25, replace=False)\n",
+    "idx = np.array(range(25))\n",
+    "xy = [xy[i] for i in idx]\n",
+    "plt.figure(figsize=(8,8))\n",
+    "for i,d in enumerate(xy):\n",
+    "    plt.subplot(5,5,i+1)\n",
+    "    plt.axis('off')\n",
+    "    plt.plot(d[0], 1.-d[1]) # note that we need to inverse the y\n",
+    "plt.show()\n",
+    "\n",
+    "# Plot the corresponding images\n",
+    "imgs = [misc.imread(icdar_img_path+'{:04d}.jpg'.format(i+1)) for i in idx]\n",
+    "plt.figure(figsize=(8,8))\n",
+    "for i,img in enumerate(imgs):\n",
+    "    plt.subplot(5,5,i+1)\n",
+    "    plt.axis('off')\n",
+    "    plt.imshow(img)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [IAM Online Handwriting Database](www.fki.inf.unibe.ch/databases/iam-on-line-handwriting-database) <a id='IAMonline'></a>\n",
+    "\n",
+    "* Size: 1,712 forms, 12,195 imgs/strokeset/sub-sentences/sequences.\n",
+    "* Short description: \" The IAM On-Line Handwriting Database (IAM-OnDB) contains forms of handwritten English text acquired on a whiteboard.\" It contains the images of the various forms and the corresponding trajectories/strokes. \n",
+    "* Note: You will need to register on the website in order to download the dataset. It is free!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rB2PGxH8erxuD4adPHxN+WLUq2j1JDsL+6K6q67HQFH9lmK3AYf4dESkNHEMh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d99zWzJ4SgERoyAE0+0gJCffJJR18V5R5cuZgr1xReWR1WlU6dOlJWZ48UYe0gqrVFy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PN5fv00+3SbF0wBlMjmKpV8+yUd1yi8XgtGtnBSMdO46Lc3KkKgsW2Oz+rbfCrruGJ8eKFebS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\n06qQo1xgNxGeg+6XMAj4whhzjb32of+3w5FORGQ4cII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XXqseS196STcMBi0zx43TNSRu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\nmmijuzxwfLn9zTvnD/+PxphtaPCqm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Q6To/zw++/mLFWpYvNwzZol+/727c5hKgrPHSYR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\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6qLsiOvFxP9Ih6YAssbIJ8WeRrR1ii9cDf1I5JiAdiqle+FA2n+sd73cPRP8rlHcOskzs\njjJcGzTs8wFvJnsPytUaCvsRQTqmjyMd6Q1IGT/NWuvrplJ7KkmXRW21hDrjNGvtVGPMQo1zgDHm\nWsQmb9a/e3gcmfx9ORmgxMfklaNWuvDLS4is7bbMvi4I4+ry06qUAaQeH4m0EXsC9xhjtrPW3l1B\nTr8tUBGbxQwTBTmesdbeaa1911p7C9LYbHRU1toh1trh1tp/WGsfQ9bUnm6MaZsxnr7IWtN1FUPm\nRyK3tfYOa+1oa+1ka+1gZP/K5TqqloZzEad0ETKqdBViAN2qlPFS/XtdGm9eHWuD5Cz9+STifI9F\nlpYkjUzngTtwYC/EKSRy55B5KtAJaQDfi+jYH8nGGLMDshdpMnBjgkx5Of4QvH8V0iE5HGkMbUAa\nIA7LkArrGKRBN5TSgY1KMkxEHJDjuNZ/2Vp7E1IxHYw4qTHAxcaYA4wxvZCRrkoNtJWeDIMozKwe\niUyrlx0NLyeDBlkKzAzy9GsJVOV0kcTRJYcczi+Ns9Y+ZK19x1r7Q6RBZMqlLwW+rH9Uft9GH6E4\n34Zq56UIWtE+A9xgrX05fB7EdSVShu7zdFsprpuRZRuPuSiDv7WMqwVi35dba0dYa8cD30Jmvo9L\niSe3/owxWyN7U8bo+12Q8jlMK+20NLn9q7d45aMbMnBTKyTqL29cFeyiDvEr51hr1zdRTpdXNyKD\nPqcDZyN5tWMeIi8/JiDLd76F5gdNK1vorGS1afT5vgr8D/Ana+29TeS4AhkMfkRlu7IKkZw/Goi0\nEb6JDNRaZO9GXtxD4eCCMxDbeRaZ9SmB1mtP6c9HmxAfFJfh55A0fVGf/QqZmeiGrKK5DSnD/sxY\nU9pT5WQYBDxkjDlAZ0n+CxmsXoHopQ5ZolhtW6bmclSri1qXlyZir1rIYK29yVo7VuvoW5GBsx/V\nSkg/ok1+UbrmuxUy/XhtEO5mgs1dwfNtKd5Ub4F/BmH20zB9kAZ4x+B5X2S63b/XEhkh76W/H1Ru\nX+auytca2XBbwp0i84Eatr0n8+ve861VF/V4J9sgjUG3b6JkT4HenwX0S9KF8rpp3U5JvDXQ8VHA\n5/XeG+hJVxnlvTFBx463E7J0rYQ7o8w3AhMTZLYUb17dHpnVehFvw2AGjsf196vIDFIjh9rdp2Vk\nnaiytvI43PrdVojDvAdxmlll8DdMtkJG8laUkeFk5RiMdOKs5td6vdypM+vL6GKMvvcw0gls8Djc\nvrcpabrwZPh9Sjpcni7PmR+pHJXk8PiGBWHGAWtz+o+ngjBdlfu+FDlc3p+V4D8+wdt36D0rSq/e\n66O2MxzvUI5ycSEV83rvcvm5Dri+xnH1UTvZMwj3CYW9aFXrDzgfmJsS5swK8dRTOFjGDXL4+nH2\nPbKCbTZQOOClkv4yxZXDLv5MYZO9y1Pr3etG8qlxsyj4aJdXfQIuS6G81yO+oJHL0+Fsj+t8ZEP6\nNypwPeXJcqx3368b+yKzElVzec/3pHDaqONz+neXf3BJP41v74Dn6UDf4ZUnnV013muCdDYEXBMT\nuMJ6/3h998wKOpuo4V3d+GoCVzX2vh7pLLTT8AcEYYYTnHibYPd+e6pcGS7JZy+OQcG9nSjY71z0\n5GOq8/epMuSVoxa6oLS8+HV9Vp9Qra+rWoYUXZysHCXtqlCGNI6ka7OYYbLSs/w7MmXt44uIk03D\noUgBn+uogH11OZRDT2QU5ThggrV2csAxFtjRGHOod+94ZJRrvBcGimfkegLTrLVLEeefxJ0mcwOF\nEXgLdPTWnrbS6yPldqj34p+AOODj3UMj+532obCMLdSF412JNFyTeNPkzarjN621i4wx7ZHR+mdy\nyOt0vEMC7xRr7fIU7iwyjwUOTpDZTfu70bOXkM2svWzpLGQ5jlVeOr8bcPQkYT+Hh9bIlLMbXRlL\nQU8tEDs8VO9nkcF/33Fsgey7ScOhiKPaAtkUOgQ58rWTXiAdO7c0NkmOnREHdw2y/O0+jwOkQTGd\ndF04GdzoWJgOVz7f8p7n1UXIUVYOa+0sZKlE5yBMewp7Y7L6j2OC9eU9Vc600XSX942zoSKORAAA\nB1xJREFUHzrSOhK431p7XcI7YXpdPGOVL2kmJSmu0ynkfSdkNsEi6+cH1jgutxy40fcbY3ZC7GmW\nF1e1+tuG0lFi12h1/i8tnmn67pYUTnH19QNi3309niTbXIosO3Nhyukva1xZ7aIdMov6ZeV5QeV5\nRH+/idh9ko92Zdbl1TyP6xikDnEny/4Sabj5XD2RDeptKPh7lx8jPK7OSJ6+73GF+WGAGUHdOBaZ\n4ZrvcV2icr2TkwtjzF5IB3k0shzP6czpaZzqzq83D9O/4dKhy/W9I5CDb87XdF+OHDGeR7YJyKDn\nEi+dX0E6i7OU62JKbc+ovH697/b4jqTYLj5AOoo+174U6sbHE7iqsfelSBndVu/ZIEyW9onfnkot\nw2E+eyjxV9baxdbaZbr6Zhfk4CXH31R/X06GvHIkIa8u/LLn23cWn+C326rxddXIUK5ddSjSGUpq\nV4UyZEee3lU1F7LetpMqpgGZmu4EtNHnpyIj4RcgRy1ehjTAjtLn7ZA9IZ2RbyX0Qk7eeSXgnQP8\nDVmfewLi3G9BRhIvTJFtGJIxhyHLfqYhI+VO5q8hBXk8st7yUuU7H5kqTeRGlif1o3BM+jkqz0OB\nzEuRgtANWQKyAWmQHYv01vsgFcn3PO57EOfWFXGar+lVThfrNP5E3ip0vAQZzWiLjBh8gDdzU0be\nMZ6OXSdnCrI8wOnqSZU3kbuMzCO9MC2U6w2V93sU9kB0QzppbyAVWFtk3ay7WuTgeIvCEdpdkM7D\nOqC7cjyBzJp2R9aKv6Bpvl/j/Qly+MkKxP5eRhrs6/SdSjK0RWx9per7bArHip+uMpyCLIU4D5kZ\n/QWFGU13EtB+Gu8tFE6kWe+l49eq41FI4/qPGsfDwSiT42hARhFdOsrKoHn6G03HIKTxM195umfM\njywcWXThZj6fVtmH6e9zK/kP73md6uJ5xGlfHaTX5X1npFHZBfEHCyicdtVR5X+QYvvcOUHnY5G9\nij/TfHsM8SmZ4krwY8cSjPLVMi7V7bvIDHVH5FCCdykc910L/XVAfN1AZE/gQUgZWwzs5sXzscZ9\nLnL07CrERjbgnVAV6CdcMdECaag/j/h+Vw/9Mo/+MsaVxy5ceb5U9fc2xTOwiT46iD8tr0YhS6ic\nDt3e1hspfM/IP464XH78LeB6HKl3f4+Uvf5InfG5DHV4KFdZLmRmaQbSQdjT16k+H4WcQvix6vxo\nZE+j24/zH6Fsgf721XCH5JVN378d2fDeAxlQHoIMDI6mcBT4O0jdcQaFFQMPBOn8PGI3Q1WW9sCt\nmo7xHtckpH6fiixpdp9pCevGLPa+FlnKdxzSSXSzWN2QwbrpSFvjMMR/X4WUhRMqtKf+N/C1Lm8O\nRNpTKyjMVv9a82xfxH7745U3pE10hMZ/LnKY0oAgD7P4+1QZaiFHLXSRYp+jyOETcuR9kv/pXiMZ\nTkHa4u54/Es0ndc1VYZU/eQJXM1FodKtDy4/g/sghWYlMqLtf4tjb6QwLUAc7TQ1shMTeC1S+bjO\n0vdUgTukyLYj0qN163nvRUY8QpndiOQKZEM0yPcQErkpzAws1jRNRpxrN5Jl3oA4wxuQEfoPKcwI\n9Qu4t0KWf7mPeQ1FOhTldHFXOd4qdDxc5V6DGPYNeEdZlpF31zI6Xq15d0U57jIybx/E/5im2VL4\nIOzxnm2Gdulk2qcJHNa7GhwH4lTWeveXAb/RZ3sgDbO5Gmat2sMs4LyMMjiORRSWjSxDbVXDnKB5\n4JZxrEeOu+0W6OsYZERztYbzHdgQZObX6WmtyhV+rM5xWMRxn5dFBi9Pl3jpWEzw/YgKuqjIkUMX\ng7x8Ww3cmMV/BGH+5OmyHj1CNSHv16huH0aXVmiY6ym10Xrg/QSduzQ1aPpfyhNXiu9OWlJbk7iQ\n5T73Ina7APENe9VSfxrueKRhuVjlHg4cFoTpqDx++WlMU4p+6kn+cO2zSN3g6qGk8pGqvyxx5bQL\nV55nIIMEIyn9QGSJj86SVz6X6nA0hY9vr0nhSsyPBK5ZFPtTl8ZvZ6jDc3Ehh7ok1gX6fCTSYeiI\nLPNZl8BVJFuQ5n31+SFNTGdLZH/GXLWXF5Gjrn2uNkj5qMTVGamTFijXa8iou891BqX1WVLdmMXe\n3ffMXFtnEsXHZO+vdjKXwje0envP09pTrRLK8KtIe+BfFNd/Q5AZTPfR5ZcCGfpT8CNTSVj2RTZ/\nnypDLeSohS5S7LMpPiGrryvyP7WSAanHJ2p+LNP/k741lVmGtMt91C8iIiIiIiIiIiIiIiIiwGax\nhykiIiIiIiIiIiIiImJzROwwRUREREREREREREREpCB2mCIiIiIiIiIiIiIiIlIQO0wRERERERER\nEREREREpiB2miIiIiIiIiIiIiIiIFMQOU0RERERERERERERERApihykiIiIiIiIiIiIiIiIFscMU\nERERERERERERERGRgthhioiIiIiIiIiIiIiISEHsMEVERERERERERERERKQgdpgiIiIiIiIiIiIi\nIiJS8H9HDwrLWTIrzgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff3b260bc90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import xml.etree.ElementTree as ET\n",
+    "from glob import glob\n",
+    "import os\n",
+    "\n",
+    "iam_imgs_dirpath = 'IAM-Online/lineImages/'\n",
+    "iam_strokes_dirpath = 'IAM-Online/lineStrokes/'\n",
+    "#iam_strokes_dirpath = 'IAM-Online/lineStrokes/a01/a01-000/a01-000u-01.xml'\n",
+    "\n",
+    "# Get all the paths\n",
+    "trajs = sorted(glob(iam_strokes_dirpath+'*/*/*.xml'))[:1000] # take only the first 1000\n",
+    "imgs = sorted(glob(iam_imgs_dirpath+'*/*/*.tif'))[:1000]\n",
+    "N = len(trajs)\n",
+    "\n",
+    "# Load all the xml files, and extract all the strokes\n",
+    "strokes = []\n",
+    "for i,traj in enumerate(trajs):\n",
+    "    tree = ET.parse(traj)\n",
+    "    # Get the data\n",
+    "    xy = []\n",
+    "    for stroke in tree.iter(tag='Stroke'):\n",
+    "        x = [int(point.attrib['x']) for point in stroke]\n",
+    "        y = [int(point.attrib['y']) for point in stroke]\n",
+    "        xy.append(np.array([x,y]))\n",
+    "    strokes.append(xy)\n",
+    "\n",
+    "\n",
+    "# Randomly choose a strokeset, and then randomly choose 25 strokes and plot them\n",
+    "idx = np.random.choice(N, 1)[0]\n",
+    "strokes = strokes[idx]\n",
+    "N_stroke = len(strokes)\n",
+    "size = N_stroke if N_stroke < 25 else 25\n",
+    "idx = np.random.choice(N_stroke, size, replace=False)\n",
+    "xy = [strokes[i] for i in idx]\n",
+    "plt.figure(figsize=(10,10))\n",
+    "for i,d in enumerate(xy):\n",
+    "    if i==25: break\n",
+    "    plt.subplot(5,5,i+1)\n",
+    "    plt.plot(d[0], d[1].max()-d[1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
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