Merge remote-tracking branch 'goog/stage-aosp-master' into HEAD

112 files changed, 20775 insertions, 0 deletions

diff --git a/.dir-locals.el b/.dir-locals.el
new file mode 100644
index 0000000..4aacf85
--- /dev/null
+++ b/.dir-locals.el
@@ -0,0 +1,5 @@
+((nil . ((eval . (progn
+                   (require 'projectile)
+                   (puthash (projectile-project-root)
+                            "nosetests"
+                            projectile-test-cmd-map))))))
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..4219856
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,5 @@
+*.pyc
+doc/.ipynb_checkpoints
+/dist/
+/build/
+/TRAPpy.egg-info/
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..2886473
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,29 @@
+language: python
+python:
+  - "2.7"
+before_install:
+  - sudo apt-get update -qq
+  - sudo apt-get install -qq libfreetype6-dev
+  - sudo apt-get install -qq libpng12-dev
+  - wget http://ftp.us.debian.org/debian/pool/main/t/trace-cmd/trace-cmd_2.4.0-1_amd64.deb
+  - sudo dpkg -i trace-cmd_2.4.0-1_amd64.deb
+install:
+  - pip install matplotlib
+  - pip install Cython --install-option="--no-cython-compile"
+  - pip install pandas
+  # IPython 6.0.0 requires Python 3.3. Use an older version so we can keep using
+  # Python 2.7
+  - pip install "ipython[all]<6.0.0"
+env:
+  - MPLBACKEND=agg
+script: nosetests
+virtualenv:
+  system_site_packages: true
+notifications:
+  email:
+    recipients:
+      - javi.merino@arm.com
+    on_success: never
+    on_failure: always
+cache:
+  - pip
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..d645695
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..a62d731
--- /dev/null
+++ b/README.md
@@ -0,0 +1,69 @@
+TRAPpy [![Build Status](https://travis-ci.org/ARM-software/trappy.svg?branch=master)](https://travis-ci.org/ARM-software/trappy) [![Version](https://img.shields.io/pypi/v/trappy.svg)](https://pypi.python.org/pypi/trappy)
+======
+
+TRAPpy (Trace Analysis and Plotting in Python) is a visualization tool to help
+analyze data generated on a device. It parses ftrace-like logs and creates
+in-memory data structures to be used for plotting and data analysis.
+
+# Installation
+
+The following instructions are for Ubuntu 14.04 LTS but they should
+also work with Debian jessie.  Older versions of Ubuntu or Debian
+(e.g. Ubuntu 12.04 or Debian wheezy) will likely require to install
+more packages from pip as the ones present in Ubuntu 12.04 or Debian
+wheezy will probably be too old.
+
+## Required dependencies
+
+##### Install additional tools required for some tests and functionalities
+
+	$ sudo apt install trace-cmd kernelshark
+
+##### Install the Python package manager
+
+	$ sudo apt install python-pip python-dev
+
+##### Install required python packages
+
+	$ sudo apt install libfreetype6-dev libpng12-dev python-nose
+	$ sudo pip install numpy matplotlib pandas ipython[all]
+
+##### Install TRAPpy
+
+    $ sudo pip install --upgrade trappy
+
+# Quickstart
+
+Now launch the ipython notebook server:
+
+    $ ipython notebook
+
+This should pop up a browser. If it doesn't, open a web browser and go
+to http://localhost:8888/tree/
+
+In the `doc/` folder there's a `00 - Quick start` which describes how to
+run TRAPpy. Other notebooks in that directory describe other functions
+of TRAPpy.
+
+# Documentation
+
+API reference can be found in https://pythonhosted.org/TRAPpy/
+
+# For developers
+
+## Clone the repository
+
+The code of the TRAPpy toolkit with all the supported tests and
+Notebooks can be cloned from the official GitHub repository with this
+command:
+
+    $ git clone https://github.com/ARM-software/trappy.git
+
+## Testing your installation
+
+An easy way to test your installation is to use the `nosetests` command from
+TRAPpy's home directory:
+
+	$ nosetests
+
+If the installation is correct all tests will succeed.
diff --git a/doc/00 - Quick start.ipynb b/doc/00 - Quick start.ipynb
new file mode 100644
index 0000000..ff804ae
--- /dev/null
+++ b/doc/00 - Quick start.ipynb
@@ -0,0 +1,168 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:720461edd626c0943c76f621fa2a8f74512d5a5d5ab9e4f730e70cb2abca6609"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Quick Start"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To analyze a `trace.txt` that you have as the result of a run, you need to know the cpu mask of the clusters and the order of the actors as seen by the power allocator governor. This is platform specific information that can't be automatically guessed by `trappy`, so we need to tell it about them. For the cpumasks, look at the trace to know the masks, for example:\n",
+      "    \n",
+      "    $ grep -o '\\Wcpus=[0-9a-f,]\\+' trace.txt | sort -u\n",
+      "     cpus=00000000,00000006\n",
+      "     cpus=00000000,00000039\n",
+      "     \n",
+      "For this system we know that cpus 1-2 are the A57 cluster and cpus 0,3,4,5 are the A53 cluster. We call this the `map_label` and for this system that is:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "map_label = {\"00000000,00000006\": \"A57\", \"00000000,00000039\": \"A53\"}"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We will need this later. We also need to know how many actors are in the system and their order. We can get the number of actors from the trace:\n",
+      "\n",
+      "    $ grep -m 1 -o 'req_power={[0-9 ]\\+}' trace.txt\n",
+      "    req_power={0 1581 4112}\n",
+      "\n",
+      "This tells us that there are three actors. We need to guess which one they are and their order, you can usually do that by looking at the data. For this example, we'll assume the first one is the GPU and the others the A57 and A53 cluster:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "actor_order = [\"GPU\", \"A57\", \"A53\"]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To analyze the example `trace.txt` that is in the current folder add the following to a cell in an ipython notebook. Substitute `map_label` and `actor_order` with the ones you calculated before:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "import sys\n",
+      "sys.path.append(\"..\")\n",
+      "import trappy\n",
+      "\n",
+      "map_label = {\"00000000,00000006\": \"A57\", \"00000000,00000039\": \"A53\"}\n",
+      "actor_order = [\"GPU\", \"A57\", \"A53\"]\n",
+      "\n",
+      "trappy.summary_plots(actor_order, map_label, path=\"./trace.txt\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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G8Hn79rLKeLiQuHd/ItzIsLu7fA/ZurXMvt66tW//xz/uf2zPnmTp0uFD6N5j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\nrAf20nhfj1o92erPfOYz/v2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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f24f2a28c10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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1S/rHP6yIPHu2fRu9/34rBB06jEtpql7dCsgDBtgwBtOn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\nEBAMOQQEQw4BwRSWQ9OWT1OTmk10RpMzvN/nGz++oXlr51F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PaUKeHw2WdHJtulSy8Nh997z23myy+Hw7fe6jbTRkGvh668Mhx+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\nbs632z/2n1uUxuzkjrx5hnL9QMOjB6Ner+fmlTdn+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GX8P7UIvVpWmHDeAAAAAElFTkSu\nQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f24cd7c2590>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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CAAAg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\n2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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f24cd39b350>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f24ccc5af90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+9Nf51/Tp0uuvWxFf+/aWy8oOf42hgwetF/JHH9n5c+/eVoVdpIjz505JsedP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\n262Q85JLrHvBBRfkskTzgQO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KGO4SEhISEhKZAUlolpDIIkixlCTE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hSgj/EKiMefPvFEvEQJIgeHzLVQ7+fHYUBmzSIqVUr0BbIsz/v3LDAVKcJexSLvAFxzfQ212NdC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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f24cc445790>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+33Gs59ioQvfIAvfg3OkzpzN/3vxxC9HjFqfntWRe/bzMq5+XxobGoeuSo72p\nPc9e9Ow0NjRO90sMAACXJG00AAAoNlAZSHdf96hi9Mhd2GPnus9058zAmUk9jp4+ml1du3L1kqtz\nw/Ibcv3y64fO6y5bN/iNLwAAMEUUm+ESobcSlJEhKPNMZehU36k8cuCR/HT/T/PQvofy0wPV88m+\nk7l++fW5ftn1uWHFcCF6SeuSKV8TTAZfh6CMDEE5OWIi9GwGAGDOaG1szc2rb87Nq28eNX/o1KFq\nAXr/Q/np/p/m7p/enZ/u/2naGttG7YC+YcUNuXbZtWltbJ2mzwAAAOYmO5sBAJizKpVKfnX8V0O7\noAcL0Y8deixrOtbkhuU35IblN+S2tbfltjW32QUNAAAXoI0GAACM0Nffl8cPP56H9j+UbXu35b5d\n9+X+3fdnRduK3L729qHjhuU3eDNCAAAYQbEZLhF6K0EZGYIysz1D/QP9efTgo7l3571Dx46jO/KC\nVS8YVYBe3b56upfKHDXbMwTTTYagnBzNXoN/0ffw/ofzyIFH8vCB6vnMwJksblmcxS2Ls6RlydD1\n4pbFWdK6ZNRti1oWZV79hbsu69kMAAAX0FDfUH2DweXX5zdf8JtJkmOnj+X+3ffn3p335vMPfD7v\n+vq70trYOqr4/IJVL8j8efOnefUAAAxUBnLo1KHsPbE3e0/szaHuQ+nt7x119JzpOWuut783Pf1n\nz4831zKvJWs71mZtx9pc3nH50PXajrVZ3b46zfOap/RzHK+o/PCBh/PogUezoGlBrlt+Xa5dem1u\nX3t73vH8d2T+vPk5dOpQDncfzuHuwznUfShPHnsyD+x9IIe6h+cPdx/Oke4jaWtqG78wPWJ8PnY2\nAwDABFUqlTxx5IlRu58fPfhorlt23agC9LMWPmtwxwcAAAUqlUpO9J4YKiCfdZwcvj5w8kAum39Z\nVi5YmZULVmZxy+I0NzSnqaEpTQ1No65HHs3zzjE/zv1P9p3MzuM7xz32nNiThfMXnlWEHntMZKPC\nRIvK1y2/Ltctuy7XLrs2i1oWFb3WA5WBHO85Xi1KjylQj7z+i3/6F4k2GgAAMPlO9Z3KT/b8ZKj4\n/MOdP8yJ3hO5avFVuWrJVbly0ZW5aslVQ+MlLUumtRDd3dedXV27svP4zvT29+bm1TdfcIcKAMBk\n6uvvy/Ge4znWc2zUTuTxCsh7T+xNkqxasGqoiHyuY0Xbiml9v43+gf7sP7n/7EJ018786tivsvP4\nzuzq2pUAiCrSAAAeVUlEQVSO5o7h4nN79bymY00OnTo01P7ikQOPTFlRuZSezXCJ0FsJysgQlJGh\nYYe7D+fnh36enx/++fC5dl1XVzdUeL5qcfW4cnG1IF1a9D3ZO3qnza+O/+qsH3ZO9J7Imo41Wdux\nNvV19fnx7h9n5YKVQ7uyb1tzW5634nneGHEayBCUkSEod6EcVSqVnD5zOsd6juXY6WNDBeNjp4+d\nc268+/T296ajuSOXzb8si1sWX7CQvKBpwTP3IkyxgcpADp46OPz9Wq0IvbNrZxbPXzyjisrnomcz\nAAA8gxa3LM5ta2/LbWtvGzVfqVRy8NTBPH748aHi89cf+/rQdWND41mF6MHrurq60YXkET+YDM71\nnOk56880b1xxY/7JVf9kaLy0demondVj3xjxcz/6XH555JfZsHLDqAL02o61WoMAcEk6feZ0Ht7/\ncPac2JNlrcuyvG15VixYkdbG1ule2rj6+vtyqu/UuMfJvpPnvq33ZJ74yRP5L4f+y1nzXb1dQ4Xk\n+rr6oULxZc2XjTp3NFXn17SvybXLrs1lzZeNe9/WxtZL9vuK+rr6LG9bnuVty/OCVS+Y7uVMupn2\nX9XOZgAALkmVSiX7T+4ftRt6ZFE6SS6/7PKz/uRybcfaoflF8xdNyg9ux3uO50e7fzSqN3VjQ+NQ\n4fn2tbfnplU3pa2prfi5AGAm2XdiX7bt25ate7dm275t2bZ3W5448kSuWnxV1naszcFTB7P/5P7s\nO7kvDXUNWbFgRbX43DbmPGZ+Ucui1NfVP6219Pb3juqde6j70NnXp6u3D/bUPXTqUPoG+tLW2JbW\nxtaho61p9Li1sTWt884xP/gxtcdoaWxJe1P7UKF4qt8Aj5lPGw0AAJjFBr9Hnq4dQJVKJTuO7si9\nO+/Nfbvuy707781D+x/K1Uuuzu1rbs9ta6sF6KuXXH3eH6QH//T2RO+JdPV2pauna9zrE70n0tXT\nNXTd1tiWW9bcktvW3JZrll7ztH9YB4CxzgycyfaD24cKylv3bc22vdvSN9CXG1fcmBtX3JgNKzfk\nxpU35rlLn3tWgXXwTev2ndxXLT6f2DdUhB51rs2f6D2Rpa1LzypEt8xrGS4ejygYH+o+lNNnTmdx\ny+IsblmcJS1LsqR1ydD1eHOD1y3zWi7ZXcM8MxSb4RKhRxmUkSEoI0OXlp4zPdm6d2t15/Oue3Pf\nzvty5PSR3LL6lrQ2tp6zmNxQ35D2pva0N7dnQdOCs67Hmzt6+mju331/7tt1Xw6eOpibV9+cW1ff\nmlvXVI81HWum++WYFDIEZWSIczl6+mi27d02VFjetm9bHjnwSNZ2rK0WlFfcmBtXVovLa9rXTEmh\ntre/NwdOHjirCN19pnt0Abl1ydB1R3PHM140liMmQs9mAABgUjXPax7qS/3+vD9J9U+P7999f/r6\n+8YtJi9oWpCmhqai5z146mDu33V/tuzakj994E/zrr99V5obmocKz7euuTU3r745Hc0dk/FpAlxS\nKpVKus9050TviaG/Mhm8Pn3mdBobGtPU0JTG+uq5qaHprLnB8eBcY0PjRf9FykBlIKfPnB736O7r\nPvdtZ7pz9PTRPLT/oWzbuy2Hug/lhuU35MYVN+bWNbfmnTe9M9cvv/4ZfdO5poamrOlYM2d+QQrn\nYmczAAAwaw22+Lhv133ZsmtLtuzakq17t2bdZeuGis+3rbktN6y4objQDTBTVSqVHOs5NrRj9uCp\ng6PaEg0WjEe2KzrXuKmhadRfmyxoWpD25vY0NzSnb6Avff196e3vTd9A7Vwbn2+uoa7hnMXppoam\n1NfVp6e/56yicV9/X+bPm3/W0dLYMu78/Ibh29qb2nPd8uty44ob85zFz9GCCSaRNhoAAMAlo6+/\nLw8feHio+HzfrvvyiyO/yPNWPG+o/cZNq2+6YI9pgOl0ZuDM8JvRjegHvO/Evuw/dXaP4OaG5qF+\nwEtbl6ajuSMLGoeLxUOF4zFF5JHzbU1tmVc/uX8EX6lUcmbgzFAhemQxenCuf6B/3OJxU0OT3sMw\nAyk2wyVCbyUoI0NQRoaYybp6uvKTPT+pFqB3b8mPd/84B08dzIaVG3LTqpvyglUvyE2rb8o1S65J\nQ33DtKxRhqDMM5Gh/Sf350j3kZwZOHPOo2+g77y3D92vv3q/nv6eHDx1cLiQXCsgH+k+ksUti4cK\nyCvaht9UbuybzC1vW56WxpYp/dy5NPhaxETo2QwAAFzS2pvb89L1L81L1790aO5w9+H8ZM9P8pM9\nP8nf/vxv89HvfjR7T+zNjStvzE2rbqoeq2/KP1j6DyZ9px8w81Uqlfzy6C/zvSe/l3uevCffe+p7\nOXjqYJa1Lsu8+nlpbGjMvPp5Fzwa689/v6aGplxx2RW5ZfUtowrIS1qX+LcHmHXsbAYAAKg5evpo\nHtjzQH6858f58Z4f5yd7fpJdx3flhhU3DO+AXnVTrl12bRobGqd7ucAkGqgM5JEDjwwVlr/35PdS\nqVTykiteMnRcu+xa7XeAS542GgAAABfpeM/xoQL0T/b8JD/e8+M8efTJXL/8+qHdzzetuinXLL0m\nrY2t071cYILODJzJA3seqO5cfuqe3PPUPVk0f9FQYfmOdXfk2YuerWcwwBiKzXCJ0FsJysgQlJEh\nLiVdPV3Ztm9bfrx7eAf044cfz5LWJXnOoufkysVXDp8XPyfPWfScLGpZdN7HlCEoc6EMdfd1Z8uu\nLbnnqXvyvSe/l3t33pv1C9cPFZbvuOKOrG5f/cwtGGYgX4uYCD2bAQAAJlF7c3tevO7FefG6Fw/N\n9Q/0Z1fXrjx++PE8cfiJPHHkifzVI3+VJ448kccPP56mhqZxC9FXLr4yK9pWTONnA3NLpVLJ0dNH\ns/P4zuw4uiM/3PnD3PPUPXlgzwO5fvn1uWPdHfmdW38nX3rDl7K4ZfF0LxdgTrGzGQAAYIpVKpUc\nOHUgTxyuFp4HC9CD5+6+7jx70bNHFaKvWnJVrl12bVa0rfBn/FAzUBnIgZMHsvP4zuzq2pWdx3eO\nOgbnGusbs6ZjTS7vuDy3rbktL7niJbl97e1pa2qb7k8BYNbTRgMAAGAGO95z/KxC9GOHHssjBx5J\nJZVct+y66rF8+Ly8bfl0Lxsm1ZmBM9nTtee8heTdXbvT0dyRtR1rq0f72qzpWDM87libNe1r0t7c\nPt2fDsCcpdgMlwi9laCMDEEZGYIy42WoUqlk/8n9efjAw3l4/8PVc+26ob5h3CL00tal0/MJwNOw\np2tPHtj7QLbu3Tp0fvLok1nWtmyoYDyygDx4rG5fnfnz5o/7mL4OQTk5YiL0bAYAAJiF6urqsmLB\niqxYsCIve9bLhuYrlUr2ntg7VHjeundrvvjQF/Pw/ofTPK953CK03rRMh4HKQB4//Hge2DO6sNw3\n0Jfnr3x+nr/y+XntNa/NRzs/mqsWX5XGhsbpXjIABexsBgAAmCMqlUp2d+0+ayf0IwceSWtja9Zd\nti71dfWT/ryN9Y1pamgaOprnNVev68eMR96nYZy52v3aGtty5eIrs6ZjzZSsl6nRc6YnP93/06Gi\n8gN7H8iD+x7M0tal2bByQ56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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f24cc07a450>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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rfyi5fOj+y1MK7ST5RpJbDj12i2bdLrZu3ZqlpaUkyaZN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mq8b++uJ8klSV7d/fnGlPNam5obNX2Eg8OTdW6HtW6DdW6DdZ60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\n3Xbbbbfddrud7XAi9XNhjDKWfqxM7wmcAewLPA6cClxLnMXjA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zbdftoYl8+nvmq/Anhr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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f24cbda5dd0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f24cb820990>"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/doc/BarPlotter.ipynb b/doc/BarPlotter.ipynb
new file mode 100644
index 0000000..0159075
--- /dev/null
+++ b/doc/BarPlotter.ipynb
@@ -0,0 +1,177 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:f9a4dac23e4eb9ae2d6a54b24b420bc7ab1c50118b6f10b8e1bf6d90ae86657b"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "import pandas as pd\n",
+      "import trappy"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dfr = pd.DataFrame({\"foo\": [1, 2, 3], \"bar\": [2, 3, 1], \"baz\":[3, 2, 1]})"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Simple bar plot of one column"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.BarPlot(dfr, column=\"foo\").view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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nESiFfQdQTfAGAACAhARvgER0HoFS2HcA1QRvAAAASEjwBkhE5xEohX0HUE3w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\ngg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4ccb7c04d0>"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Multiple columns"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.BarPlot(dfr, column=[\"foo\", \"bar\"], title=\"Multiple columns\").view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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qumB4JXSAqIJ9B9SFjjdVcJ6llTQN3iNHjsz48eOTJEOGDMnYsWOzcuXKfscs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\nxN/RqV2dqQAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4cb691dc90>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Stacked plot"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.BarPlot(dfr, column=[\"foo\", \"bar\", \"baz\"], stacked=True).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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k0DEEcmHeARQTqgEAACCRUA2QQMcQyIV5B1BMqAYAAIBEQjVAAh1DIBfmHUAxoRoAAAAS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\nAwcOLHspQMn+H7aWafSb1eZBAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4cb6c77290>"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Signals"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Signals only work with trappy traces.  Let's first get a trace."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace = trappy.BareTrace()\n",
+      "trace.add_parsed_event(\"my_event\", dfr)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If you have a trappy trace, you can use the signal syntax instead of columns and templates"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.BarPlot(trace, signals=[\"my_event:baz\", \"my_event:foo\"]).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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egaaw3wFUE7wBAACgIMEboBCdR6Ap7HcA1QRvAAAAKEjwBihE5xFoCvsdQDXB\nGwAAAAoSvAEK0XkEmsJ+B1BN8AYAAICCBG+AQnQegaaw3wFUE7wBAACgIMEboBCdR6Ap7HcA1QRv\nAAAAKEjwBihE5xFoCvsdQDXBGwAAAAoSvAEK0XkEmsJ+B1BN8AYAAICCBG+AQnQegaaw3wFUE7wB\nAACgIMEboBCdR6Ap7HcA1QRvAAAAKEjwBihE5xFoCvsdQDXBGwAAAAoSvAEK0XkEmsJ+B1BN8AYA\nAICCBG+AQnQegaaw3wFUE7wBAACgIMEboBCdR6Ap7HcA1QRvAAAAKEjwBihE5xFoCvsdQDXBGwAA\nAAoSvAEK0XkEmsJ+B1BN8AYAAICCBG+AQnQegaaw3wFUE7wBAACgIMEboBCdR6Ap7HcA1QRvAAAA\nKEjwBihE5xFoCvsdQDXBGwAAAAoSvAEK0XkEmsJ+B1BN8AYAAICCBG+AQnQegaaw3wFUE7wBAACg\nIMEboBCdR6Ap7HcA1QRvAAAAKEjwBihE5xFoCvsdQDXBGwAAAAoSvAEK0XkEmsJ+B1BN8AYAAICC\nBG+AQnQegaaw3wFUE7wBAACgIMEboBCdR6Ap7HcA1QYPdMG8efNy9dVXZ/Xq1XnHO96R9773vWtc\nc9VVV2XevHlpb2/PCSeckLFjxxZZLAAAAGxoKn/jvXr16lx55ZWZMWNGLrjggtx2221ZvHhxv2vu\nvPPOLF26NBdddFGOO+64XHHFFUUXDLCh0HkEmsJ+B1CtMngvXLgwo0ePzpZbbpnBgwdn0qRJmTNn\nTr9r5syZk3322SdJMm7cuPT09LjdCAAAAP6kMnh3dXVl1KhRfY9HjhyZrq6uymtGjRq1xjUATeSH\nkEBT2O8Aqg3Y8X4xWq3Wi752xIgRmfWTn6yLb8sGpOPeBfnJvQvqXgY1mVX3Auqy5Zb5tf2ukex5\nzWW/o2nsd83V1P3ur63WVAbvkSNH5vHHH+97/Pjjj2fkyJFrfc2f23333f+qhQIAAMCGqPJW8x12\n2CFLlizJsmXL0tvbm5/97GeZOHFiv2smTpyYW2+9NUmyYMGCDBs2zAtsAAAAwJ+0tQa4T3zu3Ln9\n3k7s4IMPzsyZM5MkU6ZMSZJceeWVmTdvXjo6OnL88cdn++23L79yAAAA2AAMGLwBAACAv17lreYA\nAADASyN4AwAAQEHr5O3Ens+8efP6dcPf+973rnHNVVddlXnz5qW9vT0nnHBCxo4dW2o5NMRAc3fP\nPffk3HMNyEVxAAASKklEQVTPzVZbbZUk2XPPPXPIIYfUsVQ2Epdeemnmzp2bV73qVTn//POf9xp7\nHevaQHNnr6OExx57LJdcckmeeOKJtLW1ZfLkyTnwwAPXuM6ex7r0YubOnse69sc//jFnnnlmVq1a\nld7e3uyxxx458sgj17hurfa7VgHPPPNM6xOf+ERr6dKlrVWrVrX+4R/+obVo0aJ+19xxxx2tL3zh\nC61Wq9VasGBBa8aMGSWWQoO8mLn71a9+1friF79Y0wrZGP36179u/eY3v2l96lOfet7n7XWUMNDc\n2esoYfny5a0HH3yw1Wq1WitWrGh98pOf9O87insxc2fPo4SVK1e2Wq1Wq7e3tzVjxozW/Pnz+z2/\ntvtdkVvNFy5cmNGjR2fLLbfM4MGDM2nSpMyZM6ffNXPmzMk+++yTJBk3blx6enrS3d1dYjk0xIuZ\nuyRpeT1B1qGddtopw4YNe8Hn7XWUMNDcJfY61r0RI0Zku+22S5J0dHRkzJgxWb58eb9r7Hmsay9m\n7hJ7Hutee3t7kqS3tzerV6/O8OHD+z2/tvtdkeDd1dWVUaNG9T0eOXJkurq6Kq8ZNWrUGtfA2ngx\nc9fW1pYFCxZk+vTpOfvss7N48eL1vUwaxl5HHex1lLZs2bI89NBDGTduXL+P2/Mo6YXmzp5HCatX\nr8706dNz7LHHZvz48ens7Oz3/Nrud8U63i+Gn0yxvo0dOzaXXXZZ2tvbM3fu3Jx33nm58MIL614W\nGzl7HeubvY6SVq5cmQsuuCBHH310Ojo61njenkcJVXNnz6OEQYMG5bzzzsvTTz+ds846K/fcc0/G\njx/f75q12e+K/MZ75MiRefzxx/seP/744xk5cuRaXwNr48XM1NChQ/tuG5kwYUJ6e3vz1FNPrdd1\n0iz2Oupgr6OU3t7enH/++Xnb296Wv/3bv13jeXseJQw0d/Y8SnrFK16RCRMm5IEHHuj38bXd74oE\n7x122CFLlizJsmXL0tvbm5/97GeZOHFiv2smTpyYW2+9NUmyYMGCDBs2LCNGjCixHBrixcxdd3d3\n30+mFi5cmCRr9DVgXbLXUQd7HSW0Wq1cfvnlGTNmTA466KDnvcaex7r2YubOnse69uSTT6anpyfJ\ns69wfvfdd6/xiuVru9+1tQrdDzR37tx+b+t08MEHZ+bMmUmSKVOmJEmuvPLKzJs3Lx0dHTn++OOz\n/fbbl1gKDTLQ3N18882ZOXNmBg0alPb29hx11FF5/etfX/Oq2ZB9+ctfzvz58/Pkk09mxIgROeyw\nw/LMM88ksddRzkBzZ6+jhHvvvTef//zns+2226atrS1JcsQRR+Sxxx5LYs+jjBczd/Y81rXf/e53\nueSSS7J69eq0Wq3svffemTZt2kvKs8WCNwAAAFDoVnMAAADgWYI3AAAAFCR4AwAAQEGCNwAAABQk\neAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABA\nQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAA\nABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgD\nAABAQYI3AAAAFCR4AwAAQEGCNwAAABQkeAMAAEBBgjcAAAAUJHgDAABAQYI3AAAAFCR4AwAAQEGC\nNwAAABQ0uO4FAKwvv//979PT05O2tra6lwJAklarlWHDhmXMmDF1LwWgKMEbaITly5dn1apV/nEH\n8DLz6KOPZvny5dl8883rXgpAMW41Bxrh0UcfzRZbbFH3MgD4C1tssUUeffTRupcBUJTgDTRCW1ub\nW8wBXobsz0ATCN4AAABQkOANAAAABQneAPBXuPXWW/M3f/M3dS8D1quvfe1rGTt2bLbeeussX768\n7uUAbDC8qjnQWKsXLkzbokXFvn5rm20y6HWvq7xmq6226us29vT0pKOjI5tsskmS5KKLLsr73//+\nYut7Ib/97W/z8Y9/PHfccUc6Oztz/vnnZ999913v66jbK1/5ytx1110ZO3Zs3UtZZxaufiaLClZp\nt2klrxu0SeU1Zv7la6CZX7VqVWbMmJHZs2dn/Pjx63l1ABs2wRtorLZFizL83e8u9vWf+sEPkgGC\n99KlS/v+e/z48bnkkkvy9re/fY3rent7M3jw+tmyP/KRj+TNb35zvve97+Xmm2/Ohz70ocybN6+R\nrwrfarXqXsI6tagteffwIcW+/g+eWpXqiTfzL3dVM7906dKsXLkyO+6443pcEcDGwa3mAC9Dt956\na17/+tfnS1/6UnbYYYeccMIJ6e7uzqGHHprtttsu22yzTQ477LA8/PDDfZ/T1dWVj3/84xk3bly2\n2WabfOADH+h77qabbspb3vKWdHZ2Zr/99suvfvWr5/2+999/f375y1/ms5/9bNrb2/Oe97wn48eP\nzw033PC813/jG9/IfvvtlzPOOCOdnZ3Zddddc/vtt+frX/96dtxxx4wdOzb/8R//kSS54447sv32\n2/f7h/0NN9yQt7zlLZV/F61WK+eff3523XXXbLvttjnqqKP6bnE9+OCD85WvfKXf9W9+85vzgx/8\nIEly33335d3vfne23XbbTJgwId/5znf6rvvYxz6WU089NYceemi23nrr7LvvvnnwwQeTJPvvv3+S\n5C1veUtGjx7d7/P+0r/8y7/kta99bcaPH59vfetbfR+/+eab89a3vjWvec1rsuOOO+YLX/hC33Of\n+tSnMnr06L7/jRgxot/zTWTm/8/Lcebvv//+7L777kmSMWPG5F3veleS5Pbbb8/ee++dMWPGZJ99\n9snPf/7zvs955JFH8v73vz/bbrtt3vjGN+bqq6+u/HMDbMwEb4CXqWXLlqW7uzv33ntvLrrooqxe\nvTpHHXVU7r333syfPz8dHR351Kc+1Xf9sccem5UrV2bOnDl58MEHc9JJJyVJfvnLX+aEE07IxRdf\nnEWLFuWYY47J4Ycfnj/+8Y9rfM/58+dnu+22y7Bhw/o+tssuu2T+/PlJkkWLFqWzszO///3v+56f\nM2dOdtlllyxatCiHHnpojjrqqMybNy933313rrzyypx22ml5+umns/vuu2fkyJH5yU9+0ve511xz\nTT74wQ9W/j1ceumlufHGG3PLLbfkgQceyIgRI/r+3O9///tz/fXX91v/4sWLM3Xq1PT09GTatGn5\nwAc+kIceeij/9m//llNPPTX33ntv3/Xf/va3M2PGjCxevDjbb799/umf/ilJ8uMf/zjJs6FiyZIl\ned/73pck6ezszO233973+UuXLk1XV1cWLlyYr3zlKznppJNy//33J0mGDRuWK664Ig8//HCuv/76\nXHHFFfnhD3+YJLnggguyZMmSLFmyJD/+8Y8zYsSIvLvg3RcbCjP/rJfjzI8bNy6/+MUvkiQPP/xw\nfvjDH6arqyuHHnpoTjzxxCxatCif+MQncuihh/b9kODDH/5wOjs7s3DhwnzjG9/ImWeemf/6r/+q\n/LMDbKwEb4CXqUGDBuWzn/1shgwZko6OjowcOTLTpk1LR0dHhg8fnunTp+e2225LkixZsiQzZ87M\nhRdemM022yyDBw/OpEmTkiRXXXVVjjnmmOy+++5pa2vLkUcemU033TT/+7//u8b37OnpyWabbdbv\nY6985Svz1FNPJUm22WabLF68OGPGjOl7frvttssHP/jBtLW15X3ve18eeeSRnHHGGRkyZEje8Y53\nZMiQIfnNb36TJDnyyCPzzW9+M8mzv638z//8zxx22GGVfw9XXXVVPve5z2XrrbfOkCFDMmPGjHzv\ne9/L6tWr8653vSt33XVXFi9enCS59tpr8573vCdDhgzJTTfdlNe+9rX54Ac/mEGDBmXXXXfNtGnT\n8t3vfrfva0+bNi277bZbNtlkkxx++OG56667KteyePHivPnNb+73sc997nMZMmRI9tprr7zzne/s\n+03h2972tuy8885Jkje84Q059NBD89///d/9PvfRRx/NBz7wgZx//vnZZZddKr93E5j59K3/5Tjz\nf3kb+i233JJx48bl8MMPz6BBg3LYYYfl9a9/fW688cYsXrw4P//5z/PP//zP2XTTTbPLLrvk6KOP\nzjXXXFP5/QA2VjreAC9TW2yxRTbddNO+x08//XROP/30zJo1K93d3UmSp556Kq1WK4sXL87mm2++\nRoBInv2N3TXXXNPv9tRVq1ZlyZIla1w7bNiwPPnkk/0+9sQTT+SVr3zlC67z1a9+dd9/Dx069Hk/\n9lyIOfzww7PHHnvk6aefzne+851MmjQpW221VeXfw+9+97sceeSRGTTo/35WPHjw4CxbtiyjR4/O\nO9/5zlx33XU59dRT8+1vfzsXX3xx3597zpw56ezs7Pu83t7eHHHEEUmStra2bLnlln3PdXR0pKen\np3Itf2nEiBF9f+Yk2XbbbfPII48kSX7xi1/kH//xHzN//vysWrUqf/jDH/p+i5g8+//B3/3d3+Xw\nww/PIYccslbfd2Nl5p/1cp75P/fII4/0+17Jsz+oeOSRR7JkyZJsvvnm/e4k6OzszJ133vlXfz+A\nDZngDfAy9dwrPz/noosuysKFCzN79uxsueWWueuuuzJp0qS0Wq10dnZm+fLleeKJJ9YIIp2dnZk+\nfXqmT58+4Pfcaaed8tBDD+Wpp57K8OHDkyR333133z/cX6oxY8Zkjz32yPe///1ce+21OfbYYwf8\nnM7Ozlx++eXZc889n/f5ww47LGeffXbe+ta3ZuXKldlnn336Pm+vvfbK97///XWy9ufT3d2dp59+\nOq94xSuSPBuY3vCGNyRJjjnmmHz84x/PDTfckE033TSnn356Hn/88b7PPe200zJixIh8/vOfL7a+\nDY2Zf9bLeeb/3Gte85o1vteiRYuy//77973d2J//vf7lnQMATeJWc4ANRE9PT4YOHZrNNtssXV1d\n/V6Ma/To0dl///1z6qmnpru7O6tWreq7rfkjH/lIrrzyysyZMyetVis9PT25+eab+34j9+fGjRuX\nXXfdNWeffXZWrlyZG264IfPnz8973vOedfbnOPLII3PBBRfk17/+daZNmzbg9X//93+fM888M4v+\n9NZvjz76aH70ox/1Pf/Od74zixYtyllnnZVDDz207+MHHHBAFi5cmG9+85tZtWpVVq1alTvuuCP3\n3XdfkoFfsXzLLbfse+GpKmeddVZWrVqV2267LbfccksOPvjgJM/+ZnbzzTfPpptumjlz5uRb3/pW\nX7C88sorc9ttt+WKK64Y8Os3mZl/ec78c/bff/8sXLgw3/rWt9Lb25vrr78+CxYsyNSpUzNmzJjs\nueee+fznP58//OEP+dWvfpV///d/z+GHH/6ivz7AxsRvvIHGam2zzbNv+VXw67+Ut0z+y9/+nXji\nifnIRz6S1772tdl6661z0kkn5cYbb+x7/mtf+1rOOOOM7Lbbblm1alX23nvv7LXXXpkwYUIuvvji\nnHbaaXnggQfS0dGRt771rdlrr72e9/teffXV+djHPpZtttkm22yzTb7xjW9k1KhRSZ79bdYee+yR\nO+64I2PGjElbW9sa6/zLx39p2rRpOfXUU/u6uwM54YQT0mq1Mm3atCxZsiSvfvWrc8ghh+Sggw5K\nkmy66aaZNm1avv71r/e9UFSSDB8+PDfccEM+85nP5Iwzzsjq1auz66675otf/GLfOqvWPmPGjBx3\n3HFZuXJl/vVf/zUHH3xwRo8ene9+97t9r0r93CuSv+51r8uwYcNy0UUXZdy4cUmSL33pS5kxY0ZO\nO+207LXXXnnf+97Xd0vz9ddfn9/+9rd91ybJ9OnTc9pppw349/FSbNN69i2/Sn79lzL0Zv5ZL+eZ\n//PrR44cmeuuuy6f/vSnc+qpp2aHHXbIddddl5EjR/b9vZ588skZN25cRowYkf/3//7f8751HEAT\ntLU2tjcpBXge999/f17zmtfUvQz+5I1vfGMuvPBC/winMcx8tYcffrjfD6IANjZuNQdgvbrhhhvS\n1tYmgNAYZh4At5oDsN5MnTo1CxYsWKPbfPDBB+d//ud/1rh+fdx+DSWZeQASt5oDDeFWc4CXL7ea\nAxs7t5oDAABAQYI3AAAAFCR4A40wZMiQ9PT01L0MAP5CT09PhgwZUvcyAIr6/8eVv6GBOSbBAAAA\nAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f4cb6da0350>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/doc/BareTrace class.ipynb b/doc/BareTrace class.ipynb
new file mode 100644
index 0000000..990b25b
--- /dev/null
+++ b/doc/BareTrace class.ipynb
@@ -0,0 +1,96 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:07509ec03cc71263d49f2d8bb69f21bd039d5ddeeb3f3c81573002794049a1aa"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The `BareTrace` class lets you create a trace class that has been parsed outside of trappy.  This lets you leverage trappy's and bart's facilities with any type of trace."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let's assume that we have events available in a csv file.  We can import them into a trace object by creating a `BareTrace` object and adding them using `add_parsed_event`."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import trappy"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First we import the parsed events into a pandas DataFrame"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "event = pd.DataFrame.from_csv(\"sample_trace.csv\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now we create an trappy trace object and add our event to it.  "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace = trappy.BareTrace()\n",
+      "trace.add_parsed_event(\"http_access\", event)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can add more events if we had collected them from different sources.  Just call `.add_parsed_event()` for each of them"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.ILinePlot(trace, trace.http_access, column=[\"response_time\"]).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/doc/Displaying results.ipynb b/doc/Displaying results.ipynb
new file mode 100644
index 0000000..b7e15a8
--- /dev/null
+++ b/doc/Displaying results.ipynb
@@ -0,0 +1,425 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:d03f0300a24dadc9bd282e0fc96d108fd5c057ee71fb302185a4a5ffc75f7a64"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "import sys\n",
+      "sys.path.append('..')\n",
+      "import trappy.wa"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Displaying Results"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "At the end of running an agenda, Workload Automation produces a summary of the results for the workloads. trappy can parse the results and tabulate it in a notebook. Additionally, an optional id argument an be passed which if supplied overrides the id in the results file."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "results = trappy.wa.get_results(\"../tests\")\n",
+      "results"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr>\n",
+        "      <th></th>\n",
+        "      <th colspan=\"2\" halign=\"left\">geekbench</th>\n",
+        "      <th colspan=\"2\" halign=\"left\">antutu</th>\n",
+        "      <th colspan=\"2\" halign=\"left\">egypt_offscreen</th>\n",
+        "      <th colspan=\"2\" halign=\"left\">thechase</th>\n",
+        "      <th colspan=\"2\" halign=\"left\">t-rex_offscreen</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th></th>\n",
+        "      <th>power_allocator</th>\n",
+        "      <th>step_wise</th>\n",
+        "      <th>power_allocator</th>\n",
+        "      <th>step_wise</th>\n",
+        "      <th>power_allocator</th>\n",
+        "      <th>step_wise</th>\n",
+        "      <th>power_allocator</th>\n",
+        "      <th>step_wise</th>\n",
+        "      <th>power_allocator</th>\n",
+        "      <th>step_wise</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>3</td>\n",
+        "      <td>8</td>\n",
+        "      <td>5</td>\n",
+        "      <td>4</td>\n",
+        "      <td>652</td>\n",
+        "      <td>504</td>\n",
+        "      <td>491.615669</td>\n",
+        "      <td>242.052226</td>\n",
+        "      <td>1777</td>\n",
+        "      <td>2365</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>3</td>\n",
+        "      <td>9</td>\n",
+        "      <td>555</td>\n",
+        "      <td>2507</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>397</td>\n",
+        "      <td>429</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>5</td>\n",
+        "      <td>2</td>\n",
+        "      <td>2</td>\n",
+        "      <td>7</td>\n",
+        "      <td>790</td>\n",
+        "      <td>325</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>512</td>\n",
+        "      <td>424</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "        geekbench                    antutu           egypt_offscreen  \\\n",
+        "  power_allocator step_wise power_allocator step_wise power_allocator   \n",
+        "0               3         8               5         4             652   \n",
+        "1               1         4               3         9             555   \n",
+        "2               5         2               2         7             790   \n",
+        "\n",
+        "                   thechase             t-rex_offscreen            \n",
+        "  step_wise power_allocator   step_wise power_allocator step_wise  \n",
+        "0       504      491.615669  242.052226            1777      2365  \n",
+        "1      2507             NaN         NaN             397       429  \n",
+        "2       325             NaN         NaN             512       424  "
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In addition to displaying tabulated results, trappy can graphically plot individual benchmarks as well as all the benchmarks in the result."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "results.plot_results_benchmark(\"antutu\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f968a4d5950>"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "results.plot_results()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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HIkmSJEmSJEmSJEmSJEmSJEmSJOmk/H8Y6byOfAp6jQAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f968a392150>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f968a176a10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ZCdDw/1LKorZevKUUeBT4m2j5daAC2EZIhTwNnEJjbcat0e1SYA6wKdpmVNQ+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\nTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f968a118e90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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SRqyZiKdHayYkZZ01E8pVrkxIkqSMGExIacy75y/nTkqGwYQkScqINRPx9GjN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\n6kD/Hy5oKUUzDcd9AAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f968a0e96d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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G88BrwP8M23oDu8Pt3eE+QAmwI+G5O4B+p9i/pCxUWNiDvLy8tN0KC3tk+pCl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\nTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f9689f75c10>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Combining Results"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "trappy allows combination of results across different agenda runs as well. This is useful if you want to compare / display results from different runs of Workload Automation"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "unconstrained = trappy.wa.get_results(\"../tests/unconstrained.csv\", name=\"Unconstrained\")\n",
+      "constrained = trappy.wa.get_results(\"../tests/constrained.csv\", name=\"Constrained\")\n",
+      "\n",
+      "results = trappy.wa.combine_results([unconstrained, constrained])\n",
+      "results"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr>\n",
+        "      <th></th>\n",
+        "      <th colspan=\"2\" halign=\"left\">antutu</th>\n",
+        "      <th colspan=\"2\" halign=\"left\">egypt_offscreen</th>\n",
+        "      <th colspan=\"2\" halign=\"left\">t-rex_offscreen</th>\n",
+        "      <th colspan=\"2\" halign=\"left\">geekbench</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th></th>\n",
+        "      <th>Unconstrained</th>\n",
+        "      <th>Constrained</th>\n",
+        "      <th>Unconstrained</th>\n",
+        "      <th>Constrained</th>\n",
+        "      <th>Unconstrained</th>\n",
+        "      <th>Constrained</th>\n",
+        "      <th>Unconstrained</th>\n",
+        "      <th>Constrained</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>2</td>\n",
+        "      <td>2</td>\n",
+        "      <td>864</td>\n",
+        "      <td>334</td>\n",
+        "      <td>185</td>\n",
+        "      <td>560</td>\n",
+        "      <td>6</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>802</td>\n",
+        "      <td>242</td>\n",
+        "      <td>878</td>\n",
+        "      <td>872</td>\n",
+        "      <td>6</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>6</td>\n",
+        "      <td>7</td>\n",
+        "      <td>47</td>\n",
+        "      <td>190</td>\n",
+        "      <td>262</td>\n",
+        "      <td>918</td>\n",
+        "      <td>1</td>\n",
+        "      <td>3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>4</td>\n",
+        "      <td>4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>588</td>\n",
+        "      <td>5</td>\n",
+        "      <td>7</td>\n",
+        "      <td>2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>6</td>\n",
+        "      <td>2</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>559</td>\n",
+        "      <td>494</td>\n",
+        "      <td>9</td>\n",
+        "      <td>8</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>9</td>\n",
+        "      <td>8</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "         antutu             egypt_offscreen             t-rex_offscreen  \\\n",
+        "  Unconstrained Constrained   Unconstrained Constrained   Unconstrained   \n",
+        "0             2           2             864         334             185   \n",
+        "1           NaN         NaN             802         242             878   \n",
+        "2             6           7              47         190             262   \n",
+        "3             4           4             NaN         NaN             588   \n",
+        "4             6           2             NaN         NaN             559   \n",
+        "5             9           8             NaN         NaN             NaN   \n",
+        "\n",
+        "                  geekbench              \n",
+        "  Constrained Unconstrained Constrained  \n",
+        "0         560             6           1  \n",
+        "1         872             6           1  \n",
+        "2         918             1           3  \n",
+        "3           5             7           2  \n",
+        "4         494             9           8  \n",
+        "5         NaN           NaN         NaN  "
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "results.plot_results()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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0QJT0xUoaGhqora0FoKqqirq6uvQea0ePuuv4kI5xfZZjYAMwrtNzshh3rK2H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\nZaTzicOSKXUAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f9689fc3090>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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du7cZMGCAmTx5stm4caM5fvy4ufvuu01eXp7Jy8sz99xzjzlx4oQxxpgNGzaY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\ngg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f968a1119d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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RZ/AgSZISYvAgSSlmzoOizuBBkiQlxOBBvaq4eBA5OTkJD1ImM+dBUZff2wVQ\n31Zfvx9oTmJLAwhJ6i22PEhSipnzoKgzeJAkSQkxeJCkFDPnQVFn8CBJkhJi8CBJKWbOg6LO4EGS\nJCXE4EGSUsycB0WdwYMkSUpIssHDKOAZ4EXg/4CvhPMHAb8FtgBPAqUx28wHXgE2A+cleVxJynjm\nPCjqkg0eGoCvAicCk4A5wAnAPILg4XjgqXAaYDxwefj3fOCebhxbkiT1omQv4G8CL4TjB4CXgBHA\ndGBlOH8lcHE4/gXgpwRBx3agGjglyWNLUkYz50FRl4pf/xXAScCzwFBgTzh/TzgNUA68EbPNGwTB\nhiRJyjLdfTHWQOBnwD8B9Ucta6bzNx7FXTZ79mwqKioAKC0tZcKECa1RfEs/YqqmAdgGHBszThem\nQ6kuT0fTMUcM/07u4jTRrl9X65Ol9UtX+fz8Uj8d+2/TU/tfsWIFQOv3pZRO3Xk1YT9gDfBr4N/C\neZsJvhneBIYTJFWOoy334bvh37XAQoLWiljNzc3JvGExOTk5OVCZxIaVkPZyJvvmycoEN6mMcN0g\nq+qXznImK+qfX7KqqqrS2nURvqbeV80qbZLttsgB7gM20RY4AKwGZoXjs4DHY+ZfAfQn+B3xMWBD\nkseWpIxmzoOiLtlui9OBa4C/AM+H8+YTtCw8ClxHkBg5I1y2KZy/CWgEbiS5nytxFRcPor5+f6p2\nJ0mSOpFs8PD/6LjV4twO5n87HFIuCBySbDqVpBRLd7eFlG4+a0GSJCXE4EGSUsxWB0WdwYMkSUqI\nwYMkpZjvtlDUGTxIkqSEGDxIUoqZ86CoM3iQJEkJMXiQpBQz50FRZ/AgSZISYvAgSSlmzoOizuBB\nkiQlxOBBklLMnAdFncGDJElKiMGDJKWYOQ+KOoMHSZKUEIMHSUoxcx4UdQYPkiQpIQYPkpRi5jwo\n6gweJElSQgweJCnFzHlQ1Bk8SJKkhBg8SFKKmfOgqDN4kCRJCTF4kKQUM+dBUWfwIEmSEmLwIEkp\nZs6Dos7gQZIkJcTgQZJSzJwHRZ3BgyRJSojBgySlmDkPijqDB0mSlBCDB0lKMXMeFHUGD5IkKSEG\nD5KUYuY8KOoMHiRJUkLSHTycD2wGXgFuTfOxJSktzHlQ1KUzeMgDvk8QQIwHrgROSOPxJSktXnjh\nhd4ugtSj0hk8nAJUA9uBBuBh4AtpPL4kpcU777zT20WQelQ6g4cRwOsx02+E8yTFkZOTk/BQXFrc\n28WW1Ad9FguRAAACXElEQVTkp/FYzWk8lpT9KhPfpL6yPuXFUOK2b9/e20WQelROGo81ieDr8Pxw\nej7QBCyOWecF4FNpLJMkRcFGYEJvF0LqCfnAVqAC6E8QKJgwKUmSOjUVeJkgcXJ+L5dFkiRJkiT1\ntLzeLkAanQD8I3A5cAHwSeBtoKY3C6UuOYEgF2Yv8EHM/PMJWrGy3eeAgQT1mwxcChQA23qxTErO\nGcBlQBFBN62kLHYrQY7FPOCacJgfzot698mXersA3fQVgq6ux4HXgItjlj3fKyVKre8AfwKeA74X\njn8T+D3w9V4sV0/6z94uQAptiBn/MsF3ykLgD0T/u0WKvFeAfnHm9ycav1w78/qHr5LR/o/gVzkE\nybZ/Bm4Op6MQPGwiSCYuBOqBknB+AfCX3ipUCv0SWB3+bRnejZmf7WLPwT8DHw3HP0Jw7kqRlM7n\nPPSmwwQPpNp+1PzycFm2+2sny/4mbaXoGTnAgXB8O0Gz/s+AMaT3VuOe8gHQGA5bgdpw/iGCW5mz\n3UiCAOknBPXJASYCS3qzUCmUBwwiqFceQdcTBAFSY28VSlJqtPSNrwV+HA5rCb6sp/ZiuVJlD3AS\nwS/zo4ddvVOklHmG9vev9yNo+o7CxfVZglYHOPKJr6XA/6a/OCmXB8wF1hGcoxCtXI7tBPXZBrwK\nDA/nFxF0YUjKcnnAqQTJaP9A8NCqqLS8LCdI1Irnp+ksSA8YBQyLMz+HINEw2w3oYP4Q4BPpLEgP\nGwk8BvyA7O9K64pC4NjeLoQkSVEwDfh2bxdCkiRJkiRJkiRJkiRJkiRJkjr1/wG2jAnFMmcsbAAA\nAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f968a390590>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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4lUBJSQm9VVFVQeu21v7fPiiK49cfyy0tLTQ3NwN0fl5KhZbrp9MA4EngJ8A9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\nSZIkSZIkSZIkSZJUtP4/Y3wp7Q/ryrMAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f9689fe2150>"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/doc/Dynamic traces.ipynb b/doc/Dynamic traces.ipynb
new file mode 100644
index 0000000..887505f
--- /dev/null
+++ b/doc/Dynamic traces.ipynb
@@ -0,0 +1,275 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:4e50c9159c1a001f1a581f813b7e533f0ef5d8ec4220406180bad11eb9cdba5c"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Registering arbitrary trace events"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`trappy` knows about some trace events. You can add your own in the notebook without having to change any code in `trappy`.  After the trace is registered, the next time you parse a trace file that information will be part of the trace object as a pandas `DataFrame` and can be analyzed like the other `DataFrame`s in the `FTrace` class.\n",
+      "\n",
+      "The trace event must follow the following format:\n",
+      "\n",
+      "    title: key0=value0 key1=value1 key2=value2 ...\n",
+      "\n",
+      "The title should be something that's unique in the trace.  For example, you can generate trace with the following `trace_printk()`:\n",
+      "\n",
+      "    trace_printk(\"thermal_gpu_power_get: frequency=%u load=%d\\n\", freq, load);\n",
+      "\n",
+      "which will appear in the `trace.txt` as:\n",
+      "\n",
+      "    kworker/6:1-457   [006]   144.439566: bprint:               0xc042f8a0f: thermal_gpu_power_get: frequency=177 load=0\n",
+      "\n",
+      "You can add this event to the trace instance using `register_dynamic_ftrace()`"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First import trappy"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "sys.path.append(\"..\")\n",
+      "%matplotlib inline\n",
+      "import trappy"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Register it.  The first argument is the name under which you will find it in the trace instance.  It will be changed to lower_case_with_underscores_to_separate_words.  The second argument is some unique text in the trace, usually the title:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.register_dynamic_ftrace(\"gpu_power_in\", \"thermal_gpu_power_get\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "trappy.dynamic.gpu_power_in"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now we can parse the trace"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace = trappy.FTrace(\"/path/to/trace\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`trace` now has a `gpu_power_in` member that contains the information.  We can see the first few lines of the generated dataframe:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace.gpu_power_in.data_frame.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>frequency</th>\n",
+        "      <th>load</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Time</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>109.999957</th>\n",
+        "      <td> 480</td>\n",
+        "      <td>  96</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>110.099925</th>\n",
+        "      <td> 480</td>\n",
+        "      <td> 100</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>110.199930</th>\n",
+        "      <td> 480</td>\n",
+        "      <td> 100</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>110.299938</th>\n",
+        "      <td> 480</td>\n",
+        "      <td> 100</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>110.399924</th>\n",
+        "      <td> 480</td>\n",
+        "      <td> 100</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "            frequency  load\n",
+        "Time                       \n",
+        "109.999957        480    96\n",
+        "110.099925        480   100\n",
+        "110.199930        480   100\n",
+        "110.299938        480   100\n",
+        "110.399924        480   100"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can now plot it or manipulate it as any other `DataFrame` in pandas"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "figsize(18, 7)\n",
+      "trace.gpu_power_in.data_frame[\"frequency\"].plot()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "<matplotlib.axes.AxesSubplot at 0x7f7766c67f50>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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bH5OBNJPoRnpsTH6RIfkKy/SNeHKRQeYmXfbNEkWkFxmy6q1qApg1ebVRt6wl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\nCCGEEEIIIYQQUgb+P2cpksZaSSpwAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f7766c76610>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Advanced usage"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`register_dynamic_ftrace()` is useful for simple traces in which you don't need to do any post-processing.  If you need to register a full-featured trace class you can use `trappy.register_ftrace_parser()` for this.  For example, a class that parses trace for `capacity_per_group:` and wants to limit the cpumasks to 8-digit could declare it like this:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from trappy.base import Base\n",
+      "class GroupCapacity(Base):\n",
+      "\n",
+      "    unique_word = \"capacity_per_group:\"\n",
+      "    name = \"group_capacity\"\n",
+      "    _cpus_column = \"cpus\"\n",
+      "\n",
+      "    def __init__(self):\n",
+      "        super(GroupCapacity, self).__init__(\n",
+      "            unique_word=self.unique_word,\n",
+      "        )\n",
+      "\n",
+      "    def finalize_object(self):\n",
+      "        if self._cpus_column in self.data_frame.columns:\n",
+      "            self.data_frame[self._cpus_column] = self.data_frame[self._cpus_column].apply('{:0>8}'.format)\n",
+      "\n",
+      "trappy.register_ftrace_parser(GroupCapacity)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now after parsing your trace using `trappy.FTrace()` you can access it's group_capacity member.  For example:\n",
+      "\n",
+      "    trappy.LinePlot(trace, GroupCapacity, column=\"group_capacity\", pivot=\"cpus\", marker='.', linestyle='none', per_line=2).view()"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/doc/FTrace class.ipynb b/doc/FTrace class.ipynb
new file mode 100644
index 0000000..4e1ef28
--- /dev/null
+++ b/doc/FTrace class.ipynb
@@ -0,0 +1,2178 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:26eddef33113120cd011fe5e26150939f2cb0ccd2dc2a20dc72e6988808ee6c8"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "The FTrace class"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The `FTrace` class allows you to inspect the data for the run.  Its main purpose is to parse the trace information into pandas DataFrames.  Each tracepoint gets its own DataFrame."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First some hacks to allow importing trappy from here"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "sys.path.append(\"..\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "import trappy"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The `FTrace` class has one mandatory parameter: the path to `trace.dat` or `trace.txt`."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace = trappy.FTrace(\"./trace.txt\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "You can access the DataFrame for each tracepoint using the `.data_frame` property.  For example, the `trace_thermal` tracepoint is in the `.thermal` property of `FTrace`"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace.thermal.data_frame"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>__comm</th>\n",
+        "      <th>__cpu</th>\n",
+        "      <th>__pid</th>\n",
+        "      <th>id</th>\n",
+        "      <th>temp</th>\n",
+        "      <th>temp_prev</th>\n",
+        "      <th>thermal_zone</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Time</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0.000000</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68786</td>\n",
+        "      <td>68794</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.099539</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68781</td>\n",
+        "      <td>68786</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.199957</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68782</td>\n",
+        "      <td>68781</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.299112</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68835</td>\n",
+        "      <td>68782</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.399646</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68882</td>\n",
+        "      <td>68835</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.499651</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68907</td>\n",
+        "      <td>68882</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.609645</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68943</td>\n",
+        "      <td>68907</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.708361</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68959</td>\n",
+        "      <td>68943</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.810043</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68947</td>\n",
+        "      <td>68959</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.909460</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68950</td>\n",
+        "      <td>68947</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.008723</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68952</td>\n",
+        "      <td>68950</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.108719</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68965</td>\n",
+        "      <td>68952</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.209270</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68978</td>\n",
+        "      <td>68965</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.309819</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68989</td>\n",
+        "      <td>68978</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.408818</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69007</td>\n",
+        "      <td>68989</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.509182</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69008</td>\n",
+        "      <td>69007</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.619889</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68996</td>\n",
+        "      <td>69008</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.718734</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68990</td>\n",
+        "      <td>68996</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.819079</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69007</td>\n",
+        "      <td>68990</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.918692</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69026</td>\n",
+        "      <td>69007</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.018747</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69074</td>\n",
+        "      <td>69026</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.118722</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69113</td>\n",
+        "      <td>69074</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.219281</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69131</td>\n",
+        "      <td>69113</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.319764</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69161</td>\n",
+        "      <td>69131</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.419244</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69155</td>\n",
+        "      <td>69161</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.519402</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69203</td>\n",
+        "      <td>69155</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.618870</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69229</td>\n",
+        "      <td>69203</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.718664</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69269</td>\n",
+        "      <td>69229</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.818393</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69294</td>\n",
+        "      <td>69269</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.919531</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69313</td>\n",
+        "      <td>69294</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>...</th>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3.719973</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69498</td>\n",
+        "      <td>69480</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3.818846</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69513</td>\n",
+        "      <td>69498</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3.919578</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69556</td>\n",
+        "      <td>69513</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.018333</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69607</td>\n",
+        "      <td>69556</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.118291</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69647</td>\n",
+        "      <td>69607</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.219773</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69657</td>\n",
+        "      <td>69647</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.318951</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69663</td>\n",
+        "      <td>69657</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.419577</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69685</td>\n",
+        "      <td>69663</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.519842</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69710</td>\n",
+        "      <td>69685</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.618309</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69733</td>\n",
+        "      <td>69710</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.719834</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69714</td>\n",
+        "      <td>69733</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.818769</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69700</td>\n",
+        "      <td>69714</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.920188</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69667</td>\n",
+        "      <td>69700</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.022114</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69621</td>\n",
+        "      <td>69667</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.119599</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69577</td>\n",
+        "      <td>69621</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.218946</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69546</td>\n",
+        "      <td>69577</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.319060</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69530</td>\n",
+        "      <td>69546</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.418773</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69509</td>\n",
+        "      <td>69530</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.528891</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69523</td>\n",
+        "      <td>69509</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.628737</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69515</td>\n",
+        "      <td>69523</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.728319</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69489</td>\n",
+        "      <td>69515</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.829462</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69469</td>\n",
+        "      <td>69489</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.928346</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69478</td>\n",
+        "      <td>69469</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.029079</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69453</td>\n",
+        "      <td>69478</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.139673</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69442</td>\n",
+        "      <td>69453</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.248760</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69427</td>\n",
+        "      <td>69442</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.349737</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69406</td>\n",
+        "      <td>69427</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.458919</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69424</td>\n",
+        "      <td>69406</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.559456</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69418</td>\n",
+        "      <td>69424</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.658977</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>69417</td>\n",
+        "      <td>69418</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>67 rows \u00d7 7 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "               __comm  __cpu  __pid  id   temp  temp_prev thermal_zone\n",
+        "Time                                                                  \n",
+        "0.000000  kworker/4:1      4     51   0  68786      68794  soc_thermal\n",
+        "0.099539  kworker/4:1      4     51   0  68781      68786  soc_thermal\n",
+        "0.199957  kworker/4:1      4     51   0  68782      68781  soc_thermal\n",
+        "0.299112  kworker/4:1      4     51   0  68835      68782  soc_thermal\n",
+        "0.399646  kworker/4:1      4     51   0  68882      68835  soc_thermal\n",
+        "0.499651  kworker/4:1      4     51   0  68907      68882  soc_thermal\n",
+        "0.609645  kworker/4:1      4     51   0  68943      68907  soc_thermal\n",
+        "0.708361  kworker/4:1      4     51   0  68959      68943  soc_thermal\n",
+        "0.810043  kworker/4:1      4     51   0  68947      68959  soc_thermal\n",
+        "0.909460  kworker/4:1      4     51   0  68950      68947  soc_thermal\n",
+        "1.008723  kworker/4:1      4     51   0  68952      68950  soc_thermal\n",
+        "1.108719  kworker/4:1      4     51   0  68965      68952  soc_thermal\n",
+        "1.209270  kworker/4:1      4     51   0  68978      68965  soc_thermal\n",
+        "1.309819  kworker/4:1      4     51   0  68989      68978  soc_thermal\n",
+        "1.408818  kworker/4:1      4     51   0  69007      68989  soc_thermal\n",
+        "1.509182  kworker/4:1      4     51   0  69008      69007  soc_thermal\n",
+        "1.619889  kworker/4:1      4     51   0  68996      69008  soc_thermal\n",
+        "1.718734  kworker/4:1      4     51   0  68990      68996  soc_thermal\n",
+        "1.819079  kworker/4:1      4     51   0  69007      68990  soc_thermal\n",
+        "1.918692  kworker/4:1      4     51   0  69026      69007  soc_thermal\n",
+        "2.018747  kworker/4:1      4     51   0  69074      69026  soc_thermal\n",
+        "2.118722  kworker/4:1      4     51   0  69113      69074  soc_thermal\n",
+        "2.219281  kworker/4:1      4     51   0  69131      69113  soc_thermal\n",
+        "2.319764  kworker/4:1      4     51   0  69161      69131  soc_thermal\n",
+        "2.419244  kworker/4:1      4     51   0  69155      69161  soc_thermal\n",
+        "2.519402  kworker/4:1      4     51   0  69203      69155  soc_thermal\n",
+        "2.618870  kworker/4:1      4     51   0  69229      69203  soc_thermal\n",
+        "2.718664  kworker/4:1      4     51   0  69269      69229  soc_thermal\n",
+        "2.818393  kworker/4:1      4     51   0  69294      69269  soc_thermal\n",
+        "2.919531  kworker/4:1      4     51   0  69313      69294  soc_thermal\n",
+        "...               ...    ...    ...  ..    ...        ...          ...\n",
+        "3.719973  kworker/4:1      4     51   0  69498      69480  soc_thermal\n",
+        "3.818846  kworker/4:1      4     51   0  69513      69498  soc_thermal\n",
+        "3.919578  kworker/4:1      4     51   0  69556      69513  soc_thermal\n",
+        "4.018333  kworker/4:1      4     51   0  69607      69556  soc_thermal\n",
+        "4.118291  kworker/4:1      4     51   0  69647      69607  soc_thermal\n",
+        "4.219773  kworker/4:1      4     51   0  69657      69647  soc_thermal\n",
+        "4.318951  kworker/4:1      4     51   0  69663      69657  soc_thermal\n",
+        "4.419577  kworker/4:1      4     51   0  69685      69663  soc_thermal\n",
+        "4.519842  kworker/4:1      4     51   0  69710      69685  soc_thermal\n",
+        "4.618309  kworker/4:1      4     51   0  69733      69710  soc_thermal\n",
+        "4.719834  kworker/4:1      4     51   0  69714      69733  soc_thermal\n",
+        "4.818769  kworker/4:1      4     51   0  69700      69714  soc_thermal\n",
+        "4.920188  kworker/4:1      4     51   0  69667      69700  soc_thermal\n",
+        "5.022114  kworker/4:1      4     51   0  69621      69667  soc_thermal\n",
+        "5.119599  kworker/4:1      4     51   0  69577      69621  soc_thermal\n",
+        "5.218946  kworker/4:1      4     51   0  69546      69577  soc_thermal\n",
+        "5.319060  kworker/4:1      4     51   0  69530      69546  soc_thermal\n",
+        "5.418773  kworker/4:1      4     51   0  69509      69530  soc_thermal\n",
+        "5.528891  kworker/4:1      4     51   0  69523      69509  soc_thermal\n",
+        "5.628737  kworker/4:1      4     51   0  69515      69523  soc_thermal\n",
+        "5.728319  kworker/4:1      4     51   0  69489      69515  soc_thermal\n",
+        "5.829462  kworker/4:1      4     51   0  69469      69489  soc_thermal\n",
+        "5.928346  kworker/4:1      4     51   0  69478      69469  soc_thermal\n",
+        "6.029079  kworker/4:1      4     51   0  69453      69478  soc_thermal\n",
+        "6.139673  kworker/4:1      4     51   0  69442      69453  soc_thermal\n",
+        "6.248760  kworker/4:1      4     51   0  69427      69442  soc_thermal\n",
+        "6.349737  kworker/4:1      4     51   0  69406      69427  soc_thermal\n",
+        "6.458919  kworker/4:1      4     51   0  69424      69406  soc_thermal\n",
+        "6.559456  kworker/4:1      4     51   0  69418      69424  soc_thermal\n",
+        "6.658977  kworker/4:1      4     51   0  69417      69418  soc_thermal\n",
+        "\n",
+        "[67 rows x 7 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "You can use pandas' infrastructure to filter data.  For example, the `cpu_in_power` trace has information for both clusters"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace.cpu_in_power.data_frame.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>__comm</th>\n",
+        "      <th>__cpu</th>\n",
+        "      <th>__pid</th>\n",
+        "      <th>cpus</th>\n",
+        "      <th>dynamic_power</th>\n",
+        "      <th>freq</th>\n",
+        "      <th>load0</th>\n",
+        "      <th>load1</th>\n",
+        "      <th>load2</th>\n",
+        "      <th>load3</th>\n",
+        "      <th>static_power</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Time</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0.002264</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000006</td>\n",
+        "      <td>250</td>\n",
+        "      <td>1100000</td>\n",
+        "      <td>24</td>\n",
+        "      <td>19</td>\n",
+        "      <td>0</td>\n",
+        "      <td>0</td>\n",
+        "      <td>178</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.002279</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>166</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>36</td>\n",
+        "      <td>49</td>\n",
+        "      <td>48</td>\n",
+        "      <td>7</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.100949</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000006</td>\n",
+        "      <td>227</td>\n",
+        "      <td>1100000</td>\n",
+        "      <td>28</td>\n",
+        "      <td>11</td>\n",
+        "      <td>0</td>\n",
+        "      <td>0</td>\n",
+        "      <td>178</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.100970</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>178</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>32</td>\n",
+        "      <td>28</td>\n",
+        "      <td>46</td>\n",
+        "      <td>44</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.200914</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000006</td>\n",
+        "      <td>256</td>\n",
+        "      <td>1100000</td>\n",
+        "      <td>20</td>\n",
+        "      <td>24</td>\n",
+        "      <td>0</td>\n",
+        "      <td>0</td>\n",
+        "      <td>178</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "               __comm  __cpu  __pid               cpus  dynamic_power  \\\n",
+        "Time                                                                    \n",
+        "0.002264  kworker/4:1      4     51  00000000,00000006            250   \n",
+        "0.002279  kworker/4:1      4     51  00000000,00000039            166   \n",
+        "0.100949  kworker/4:1      4     51  00000000,00000006            227   \n",
+        "0.100970  kworker/4:1      4     51  00000000,00000039            178   \n",
+        "0.200914  kworker/4:1      4     51  00000000,00000006            256   \n",
+        "\n",
+        "             freq  load0  load1  load2  load3  static_power  \n",
+        "Time                                                         \n",
+        "0.002264  1100000     24     19      0      0           178  \n",
+        "0.002279   850000     36     49     48      7           131  \n",
+        "0.100949  1100000     28     11      0      0           178  \n",
+        "0.100970   850000     32     28     46     44           131  \n",
+        "0.200914  1100000     20     24      0      0           178  "
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can filter it to return only the data for cpus \"00000000,00000039\":"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace.cpu_in_power.data_frame[trace.cpu_in_power.data_frame.cpus == \"00000000,00000039\"]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>__comm</th>\n",
+        "      <th>__cpu</th>\n",
+        "      <th>__pid</th>\n",
+        "      <th>cpus</th>\n",
+        "      <th>dynamic_power</th>\n",
+        "      <th>freq</th>\n",
+        "      <th>load0</th>\n",
+        "      <th>load1</th>\n",
+        "      <th>load2</th>\n",
+        "      <th>load3</th>\n",
+        "      <th>static_power</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Time</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0.002279</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>166</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>36</td>\n",
+        "      <td>49</td>\n",
+        "      <td>48</td>\n",
+        "      <td>7</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.100970</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>178</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>32</td>\n",
+        "      <td>28</td>\n",
+        "      <td>46</td>\n",
+        "      <td>44</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.200929</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>174</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>28</td>\n",
+        "      <td>44</td>\n",
+        "      <td>42</td>\n",
+        "      <td>33</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.300253</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>166</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>52</td>\n",
+        "      <td>31</td>\n",
+        "      <td>49</td>\n",
+        "      <td>8</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.401033</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>197</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>38</td>\n",
+        "      <td>39</td>\n",
+        "      <td>34</td>\n",
+        "      <td>55</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.500595</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>149</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>46</td>\n",
+        "      <td>48</td>\n",
+        "      <td>4</td>\n",
+        "      <td>28</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.610771</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>168</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>42</td>\n",
+        "      <td>64</td>\n",
+        "      <td>20</td>\n",
+        "      <td>16</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.709190</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>190</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>80</td>\n",
+        "      <td>41</td>\n",
+        "      <td>23</td>\n",
+        "      <td>16</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.811388</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>185</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>56</td>\n",
+        "      <td>60</td>\n",
+        "      <td>10</td>\n",
+        "      <td>30</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.910507</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>171</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>29</td>\n",
+        "      <td>25</td>\n",
+        "      <td>43</td>\n",
+        "      <td>47</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.009501</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>159</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>33</td>\n",
+        "      <td>29</td>\n",
+        "      <td>52</td>\n",
+        "      <td>20</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.109501</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>208</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>38</td>\n",
+        "      <td>36</td>\n",
+        "      <td>93</td>\n",
+        "      <td>8</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.210615</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>207</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>38</td>\n",
+        "      <td>35</td>\n",
+        "      <td>27</td>\n",
+        "      <td>74</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.311017</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>164</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>22</td>\n",
+        "      <td>24</td>\n",
+        "      <td>68</td>\n",
+        "      <td>24</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.409793</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>180</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>48</td>\n",
+        "      <td>30</td>\n",
+        "      <td>7</td>\n",
+        "      <td>67</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.510247</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>195</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>92</td>\n",
+        "      <td>44</td>\n",
+        "      <td>11</td>\n",
+        "      <td>17</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.621442</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>172</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>25</td>\n",
+        "      <td>25</td>\n",
+        "      <td>63</td>\n",
+        "      <td>32</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.719513</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>165</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>69</td>\n",
+        "      <td>45</td>\n",
+        "      <td>18</td>\n",
+        "      <td>7</td>\n",
+        "      <td>131</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.820367</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>148</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>41</td>\n",
+        "      <td>37</td>\n",
+        "      <td>40</td>\n",
+        "      <td>7</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1.919996</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>111</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>42</td>\n",
+        "      <td>39</td>\n",
+        "      <td>6</td>\n",
+        "      <td>7</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.020034</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>161</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>44</td>\n",
+        "      <td>39</td>\n",
+        "      <td>8</td>\n",
+        "      <td>45</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.119842</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>130</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>31</td>\n",
+        "      <td>26</td>\n",
+        "      <td>6</td>\n",
+        "      <td>47</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.220430</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>167</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>61</td>\n",
+        "      <td>57</td>\n",
+        "      <td>17</td>\n",
+        "      <td>6</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.321255</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>166</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>40</td>\n",
+        "      <td>24</td>\n",
+        "      <td>57</td>\n",
+        "      <td>19</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.420036</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>170</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>57</td>\n",
+        "      <td>38</td>\n",
+        "      <td>38</td>\n",
+        "      <td>10</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.520509</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>117</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>40</td>\n",
+        "      <td>36</td>\n",
+        "      <td>17</td>\n",
+        "      <td>6</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.620426</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>129</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>56</td>\n",
+        "      <td>25</td>\n",
+        "      <td>22</td>\n",
+        "      <td>6</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.719428</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>173</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>44</td>\n",
+        "      <td>29</td>\n",
+        "      <td>66</td>\n",
+        "      <td>7</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.819184</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>185</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>42</td>\n",
+        "      <td>40</td>\n",
+        "      <td>62</td>\n",
+        "      <td>12</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2.921334</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>172</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>26</td>\n",
+        "      <td>23</td>\n",
+        "      <td>30</td>\n",
+        "      <td>66</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>...</th>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3.721862</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>91</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>33</td>\n",
+        "      <td>29</td>\n",
+        "      <td>9</td>\n",
+        "      <td>6</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3.820099</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>142</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>54</td>\n",
+        "      <td>51</td>\n",
+        "      <td>9</td>\n",
+        "      <td>6</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3.920987</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>122</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>34</td>\n",
+        "      <td>27</td>\n",
+        "      <td>8</td>\n",
+        "      <td>34</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.019102</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>154</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>38</td>\n",
+        "      <td>34</td>\n",
+        "      <td>19</td>\n",
+        "      <td>39</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.119037</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>185</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>36</td>\n",
+        "      <td>29</td>\n",
+        "      <td>79</td>\n",
+        "      <td>12</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.221545</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>119</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>25</td>\n",
+        "      <td>23</td>\n",
+        "      <td>32</td>\n",
+        "      <td>20</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.320729</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>186</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>30</td>\n",
+        "      <td>28</td>\n",
+        "      <td>64</td>\n",
+        "      <td>35</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.421109</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>168</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>52</td>\n",
+        "      <td>48</td>\n",
+        "      <td>11</td>\n",
+        "      <td>31</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.521129</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>171</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>28</td>\n",
+        "      <td>23</td>\n",
+        "      <td>10</td>\n",
+        "      <td>83</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.619289</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>179</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>29</td>\n",
+        "      <td>25</td>\n",
+        "      <td>54</td>\n",
+        "      <td>43</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.721126</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>224</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>45</td>\n",
+        "      <td>44</td>\n",
+        "      <td>84</td>\n",
+        "      <td>16</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.820703</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>166</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>26</td>\n",
+        "      <td>24</td>\n",
+        "      <td>7</td>\n",
+        "      <td>83</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4.921670</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>163</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>44</td>\n",
+        "      <td>22</td>\n",
+        "      <td>7</td>\n",
+        "      <td>64</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.022998</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>216</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>46</td>\n",
+        "      <td>41</td>\n",
+        "      <td>84</td>\n",
+        "      <td>11</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.120968</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>174</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>49</td>\n",
+        "      <td>26</td>\n",
+        "      <td>37</td>\n",
+        "      <td>35</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.220168</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>152</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>25</td>\n",
+        "      <td>46</td>\n",
+        "      <td>8</td>\n",
+        "      <td>49</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.320038</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>167</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>78</td>\n",
+        "      <td>31</td>\n",
+        "      <td>28</td>\n",
+        "      <td>4</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.419996</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>190</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>47</td>\n",
+        "      <td>52</td>\n",
+        "      <td>17</td>\n",
+        "      <td>44</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.530364</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>141</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>34</td>\n",
+        "      <td>74</td>\n",
+        "      <td>7</td>\n",
+        "      <td>4</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.629523</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>177</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>34</td>\n",
+        "      <td>51</td>\n",
+        "      <td>5</td>\n",
+        "      <td>59</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.729082</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>199</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>40</td>\n",
+        "      <td>52</td>\n",
+        "      <td>26</td>\n",
+        "      <td>50</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.830771</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>155</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>26</td>\n",
+        "      <td>41</td>\n",
+        "      <td>31</td>\n",
+        "      <td>33</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5.929531</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>184</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>34</td>\n",
+        "      <td>27</td>\n",
+        "      <td>31</td>\n",
+        "      <td>63</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.030359</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>199</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>61</td>\n",
+        "      <td>40</td>\n",
+        "      <td>40</td>\n",
+        "      <td>27</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.141069</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>151</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>43</td>\n",
+        "      <td>22</td>\n",
+        "      <td>38</td>\n",
+        "      <td>24</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.251660</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>201</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>40</td>\n",
+        "      <td>36</td>\n",
+        "      <td>55</td>\n",
+        "      <td>38</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.351220</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>160</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>34</td>\n",
+        "      <td>89</td>\n",
+        "      <td>6</td>\n",
+        "      <td>6</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.460227</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>170</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>48</td>\n",
+        "      <td>49</td>\n",
+        "      <td>20</td>\n",
+        "      <td>26</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.560918</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>180</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>64</td>\n",
+        "      <td>37</td>\n",
+        "      <td>42</td>\n",
+        "      <td>9</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6.659959</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>00000000,00000039</td>\n",
+        "      <td>164</td>\n",
+        "      <td>850000</td>\n",
+        "      <td>26</td>\n",
+        "      <td>57</td>\n",
+        "      <td>20</td>\n",
+        "      <td>35</td>\n",
+        "      <td>137</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>67 rows \u00d7 11 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "               __comm  __cpu  __pid               cpus  dynamic_power    freq  \\\n",
+        "Time                                                                            \n",
+        "0.002279  kworker/4:1      4     51  00000000,00000039            166  850000   \n",
+        "0.100970  kworker/4:1      4     51  00000000,00000039            178  850000   \n",
+        "0.200929  kworker/4:1      4     51  00000000,00000039            174  850000   \n",
+        "0.300253  kworker/4:1      4     51  00000000,00000039            166  850000   \n",
+        "0.401033  kworker/4:1      4     51  00000000,00000039            197  850000   \n",
+        "0.500595  kworker/4:1      4     51  00000000,00000039            149  850000   \n",
+        "0.610771  kworker/4:1      4     51  00000000,00000039            168  850000   \n",
+        "0.709190  kworker/4:1      4     51  00000000,00000039            190  850000   \n",
+        "0.811388  kworker/4:1      4     51  00000000,00000039            185  850000   \n",
+        "0.910507  kworker/4:1      4     51  00000000,00000039            171  850000   \n",
+        "1.009501  kworker/4:1      4     51  00000000,00000039            159  850000   \n",
+        "1.109501  kworker/4:1      4     51  00000000,00000039            208  850000   \n",
+        "1.210615  kworker/4:1      4     51  00000000,00000039            207  850000   \n",
+        "1.311017  kworker/4:1      4     51  00000000,00000039            164  850000   \n",
+        "1.409793  kworker/4:1      4     51  00000000,00000039            180  850000   \n",
+        "1.510247  kworker/4:1      4     51  00000000,00000039            195  850000   \n",
+        "1.621442  kworker/4:1      4     51  00000000,00000039            172  850000   \n",
+        "1.719513  kworker/4:1      4     51  00000000,00000039            165  850000   \n",
+        "1.820367  kworker/4:1      4     51  00000000,00000039            148  850000   \n",
+        "1.919996  kworker/4:1      4     51  00000000,00000039            111  850000   \n",
+        "2.020034  kworker/4:1      4     51  00000000,00000039            161  850000   \n",
+        "2.119842  kworker/4:1      4     51  00000000,00000039            130  850000   \n",
+        "2.220430  kworker/4:1      4     51  00000000,00000039            167  850000   \n",
+        "2.321255  kworker/4:1      4     51  00000000,00000039            166  850000   \n",
+        "2.420036  kworker/4:1      4     51  00000000,00000039            170  850000   \n",
+        "2.520509  kworker/4:1      4     51  00000000,00000039            117  850000   \n",
+        "2.620426  kworker/4:1      4     51  00000000,00000039            129  850000   \n",
+        "2.719428  kworker/4:1      4     51  00000000,00000039            173  850000   \n",
+        "2.819184  kworker/4:1      4     51  00000000,00000039            185  850000   \n",
+        "2.921334  kworker/4:1      4     51  00000000,00000039            172  850000   \n",
+        "...               ...    ...    ...                ...            ...     ...   \n",
+        "3.721862  kworker/4:1      4     51  00000000,00000039             91  850000   \n",
+        "3.820099  kworker/4:1      4     51  00000000,00000039            142  850000   \n",
+        "3.920987  kworker/4:1      4     51  00000000,00000039            122  850000   \n",
+        "4.019102  kworker/4:1      4     51  00000000,00000039            154  850000   \n",
+        "4.119037  kworker/4:1      4     51  00000000,00000039            185  850000   \n",
+        "4.221545  kworker/4:1      4     51  00000000,00000039            119  850000   \n",
+        "4.320729  kworker/4:1      4     51  00000000,00000039            186  850000   \n",
+        "4.421109  kworker/4:1      4     51  00000000,00000039            168  850000   \n",
+        "4.521129  kworker/4:1      4     51  00000000,00000039            171  850000   \n",
+        "4.619289  kworker/4:1      4     51  00000000,00000039            179  850000   \n",
+        "4.721126  kworker/4:1      4     51  00000000,00000039            224  850000   \n",
+        "4.820703  kworker/4:1      4     51  00000000,00000039            166  850000   \n",
+        "4.921670  kworker/4:1      4     51  00000000,00000039            163  850000   \n",
+        "5.022998  kworker/4:1      4     51  00000000,00000039            216  850000   \n",
+        "5.120968  kworker/4:1      4     51  00000000,00000039            174  850000   \n",
+        "5.220168  kworker/4:1      4     51  00000000,00000039            152  850000   \n",
+        "5.320038  kworker/4:1      4     51  00000000,00000039            167  850000   \n",
+        "5.419996  kworker/4:1      4     51  00000000,00000039            190  850000   \n",
+        "5.530364  kworker/4:1      4     51  00000000,00000039            141  850000   \n",
+        "5.629523  kworker/4:1      4     51  00000000,00000039            177  850000   \n",
+        "5.729082  kworker/4:1      4     51  00000000,00000039            199  850000   \n",
+        "5.830771  kworker/4:1      4     51  00000000,00000039            155  850000   \n",
+        "5.929531  kworker/4:1      4     51  00000000,00000039            184  850000   \n",
+        "6.030359  kworker/4:1      4     51  00000000,00000039            199  850000   \n",
+        "6.141069  kworker/4:1      4     51  00000000,00000039            151  850000   \n",
+        "6.251660  kworker/4:1      4     51  00000000,00000039            201  850000   \n",
+        "6.351220  kworker/4:1      4     51  00000000,00000039            160  850000   \n",
+        "6.460227  kworker/4:1      4     51  00000000,00000039            170  850000   \n",
+        "6.560918  kworker/4:1      4     51  00000000,00000039            180  850000   \n",
+        "6.659959  kworker/4:1      4     51  00000000,00000039            164  850000   \n",
+        "\n",
+        "          load0  load1  load2  load3  static_power  \n",
+        "Time                                                \n",
+        "0.002279     36     49     48      7           131  \n",
+        "0.100970     32     28     46     44           131  \n",
+        "0.200929     28     44     42     33           131  \n",
+        "0.300253     52     31     49      8           131  \n",
+        "0.401033     38     39     34     55           131  \n",
+        "0.500595     46     48      4     28           131  \n",
+        "0.610771     42     64     20     16           131  \n",
+        "0.709190     80     41     23     16           131  \n",
+        "0.811388     56     60     10     30           131  \n",
+        "0.910507     29     25     43     47           131  \n",
+        "1.009501     33     29     52     20           131  \n",
+        "1.109501     38     36     93      8           131  \n",
+        "1.210615     38     35     27     74           131  \n",
+        "1.311017     22     24     68     24           131  \n",
+        "1.409793     48     30      7     67           137  \n",
+        "1.510247     92     44     11     17           137  \n",
+        "1.621442     25     25     63     32           131  \n",
+        "1.719513     69     45     18      7           131  \n",
+        "1.820367     41     37     40      7           137  \n",
+        "1.919996     42     39      6      7           137  \n",
+        "2.020034     44     39      8     45           137  \n",
+        "2.119842     31     26      6     47           137  \n",
+        "2.220430     61     57     17      6           137  \n",
+        "2.321255     40     24     57     19           137  \n",
+        "2.420036     57     38     38     10           137  \n",
+        "2.520509     40     36     17      6           137  \n",
+        "2.620426     56     25     22      6           137  \n",
+        "2.719428     44     29     66      7           137  \n",
+        "2.819184     42     40     62     12           137  \n",
+        "2.921334     26     23     30     66           137  \n",
+        "...         ...    ...    ...    ...           ...  \n",
+        "3.721862     33     29      9      6           137  \n",
+        "3.820099     54     51      9      6           137  \n",
+        "3.920987     34     27      8     34           137  \n",
+        "4.019102     38     34     19     39           137  \n",
+        "4.119037     36     29     79     12           137  \n",
+        "4.221545     25     23     32     20           137  \n",
+        "4.320729     30     28     64     35           137  \n",
+        "4.421109     52     48     11     31           137  \n",
+        "4.521129     28     23     10     83           137  \n",
+        "4.619289     29     25     54     43           137  \n",
+        "4.721126     45     44     84     16           137  \n",
+        "4.820703     26     24      7     83           137  \n",
+        "4.921670     44     22      7     64           137  \n",
+        "5.022998     46     41     84     11           137  \n",
+        "5.120968     49     26     37     35           137  \n",
+        "5.220168     25     46      8     49           137  \n",
+        "5.320038     78     31     28      4           137  \n",
+        "5.419996     47     52     17     44           137  \n",
+        "5.530364     34     74      7      4           137  \n",
+        "5.629523     34     51      5     59           137  \n",
+        "5.729082     40     52     26     50           137  \n",
+        "5.830771     26     41     31     33           137  \n",
+        "5.929531     34     27     31     63           137  \n",
+        "6.030359     61     40     40     27           137  \n",
+        "6.141069     43     22     38     24           137  \n",
+        "6.251660     40     36     55     38           137  \n",
+        "6.351220     34     89      6      6           137  \n",
+        "6.460227     48     49     20     26           137  \n",
+        "6.560918     64     37     42      9           137  \n",
+        "6.659959     26     57     20     35           137  \n",
+        "\n",
+        "[67 rows x 11 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Custom trace events"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The `FTrace()` object by default parses a number of thermal and scheduler classes that are known to trappy.  If you know which events you are interested in, you can create a `FTrace()` object that only parses those.  For example, if you have collected your trace with\n",
+      "\n",
+      "    trace-cmd -e thermal_temperature cdev_update\n",
+      "   \n",
+      "you can get a custom FTrace object that only parses those:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace_custom = trappy.FTrace(\"./trace.txt\", scope=\"custom\", events=[\"thermal_temperature\", \"cdev_update\"])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace_custom.thermal_temperature.data_frame.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>__comm</th>\n",
+        "      <th>__cpu</th>\n",
+        "      <th>__pid</th>\n",
+        "      <th>id</th>\n",
+        "      <th>temp</th>\n",
+        "      <th>temp_prev</th>\n",
+        "      <th>thermal_zone</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Time</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0.000000</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68786</td>\n",
+        "      <td>68794</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.099539</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68781</td>\n",
+        "      <td>68786</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.199957</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68782</td>\n",
+        "      <td>68781</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.299112</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68835</td>\n",
+        "      <td>68782</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.399646</th>\n",
+        "      <td>kworker/4:1</td>\n",
+        "      <td>4</td>\n",
+        "      <td>51</td>\n",
+        "      <td>0</td>\n",
+        "      <td>68882</td>\n",
+        "      <td>68835</td>\n",
+        "      <td>soc_thermal</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "               __comm  __cpu  __pid  id   temp  temp_prev thermal_zone\n",
+        "Time                                                                  \n",
+        "0.000000  kworker/4:1      4     51   0  68786      68794  soc_thermal\n",
+        "0.099539  kworker/4:1      4     51   0  68781      68786  soc_thermal\n",
+        "0.199957  kworker/4:1      4     51   0  68782      68781  soc_thermal\n",
+        "0.299112  kworker/4:1      4     51   0  68835      68782  soc_thermal\n",
+        "0.399646  kworker/4:1      4     51   0  68882      68835  soc_thermal"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Writing to CSV"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "You can use the `.write_csv()` method of each tracepoint class to output the trace to CSV for analyzing with other programs.  For example, let's write the `trace_thermal_power_allocator` tracepoint information (available in the `.thermal_governor` property of the `FTrace` instance) to file `thermal_power_allocator.csv`"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace.thermal_governor.write_csv(\"thermal_power_allocator.csv\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/doc/InteractivePlotter.ipynb b/doc/InteractivePlotter.ipynb
new file mode 100644
index 0000000..5ae8cc6
--- /dev/null
+++ b/doc/InteractivePlotter.ipynb
@@ -0,0 +1,609 @@
+{
+ "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": "2.7.6"
+  },
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "TRAPpy: Interactive Plotting"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<span style=\"font-size: 150%\">Re Run the cells to generate the graphs</span>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys,os\n",
+      "sys.path.append(\"..\")\n",
+      "import numpy.random\n",
+      "import pandas as pd\n",
+      "import shutil\n",
+      "import tempfile\n",
+      "import trappy\n",
+      "trace_thermal = \"./trace.txt\"\n",
+      "trace_sched = \"../tests/raw_trace.dat\"\n",
+      "TEMP_BASE = \"/tmp\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": true,
+     "input": [
+      "def setup_thermal():\n",
+      "    tDir = tempfile.mkdtemp(dir=\"/tmp\", prefix=\"trappy_doc\", suffix = \".tempDir\")\n",
+      "    shutil.copyfile(trace_thermal, os.path.join(tDir, \"trace.txt\"))\n",
+      "    return tDir\n",
+      "\n",
+      "def setup_sched():\n",
+      "    tDir = tempfile.mkdtemp(dir=\"/tmp\", prefix=\"trappy_doc\", suffix = \".tempDir\")\n",
+      "    shutil.copyfile(trace_sched, os.path.join(tDir, \"trace.dat\"))\n",
+      "    return tDir"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": true,
+     "input": [
+      "temp_thermal_location = setup_thermal()\n",
+      "trace1 = trappy.FTrace(temp_thermal_location)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Interactive Line Plotting of Data Frames"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Interactive Line Plots Supports the same API as the LinePlot but provide an interactive plot that can be zoomed by clicking and dragging on the desired area. Double clicking resets the zoom."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can create an interactive plot easily from a dataframe by passing the data frame and the columns we want to plot as parameters:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "columns = [\"tick\", \"tock\"]\n",
+      "df = pd.DataFrame(numpy.random.randn(1000, 2), columns=columns).cumsum()\n",
+      "\n",
+      "trappy.ILinePlot(df, column=columns).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Plotting independent series"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "It is also possible to plot traces with different index values (i.e. x-axis values)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "columns = [\"tick\", \"tock\", \"bang\"]\n",
+      "df_len = 1000\n",
+      "df1 = pd.DataFrame(numpy.random.randn(df_len, 3), columns=columns, index=range(df_len)).cumsum()\n",
+      "df2 = pd.DataFrame(numpy.random.randn(df_len, 3), columns=columns, index=(numpy.arange(0.5, df_len, 1))).cumsum()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.ILinePlot([df1, df2], column=\"tick\").view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This does not affect filtering or pivoting in any way"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df1[\"bang\"] = df1[\"bang\"].apply(lambda x: numpy.random.randint(0, 4))\n",
+      "df2[\"bang\"] = df2[\"bang\"].apply(lambda x: numpy.random.randint(0, 4))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.ILinePlot([df1, df2], column=\"tick\", filters = {'bang' : [2]}, title=\"tick column values for which bang is 2\").view()"
+     ],
+     "language": "python",
+     "metadata": {
+      "scrolled": true
+     },
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": true,
+     "input": [
+      "trappy.ILinePlot([df1, df2], column=\"tick\", pivot=\"bang\", title=\"tick column pivoted on bang column\").view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Interactive Line Plotting of Traces"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can also create them from trace objects"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "map_label = {\n",
+      "        \"00000000,00000006\" : \"A57\",\n",
+      "        \"00000000,00000039\" : \"A53\",\n",
+      "}\n",
+      "\n",
+      "l = trappy.ILinePlot(\n",
+      "        trace1,                                          # TRAPpy FTrace Object\n",
+      "        trappy.cpu_power.CpuInPower,                     # TRAPpy Event (maps to a unique word in the Trace)\n",
+      "        column=[                                         # Column(s)\n",
+      "            \"dynamic_power\",\n",
+      "            \"load1\"],\n",
+      "\n",
+      "        filters={                                        # Filter the data  \n",
+      "            \"cdev_state\": [\n",
+      "                1,\n",
+      "                0]},\n",
+      "        pivot=\"cpus\",                                    # One plot for each pivot will be created\n",
+      "        map_label=map_label,                             # Optionally, provide an alternative label for pivots\n",
+      "        per_line=1)                                      # Number of graphs per line\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "You can also change the drawstyle to \"steps-post\" for step plots. These are suited if the data is discrete \n",
+      "and linear interploation is not required between two data points"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.ILinePlot(\n",
+      "    trace1,                                          # TRAPpy FTrace Object\n",
+      "    trappy.cpu_power.CpuInPower,                     # TRAPpy Event (maps to a unique word in the Trace)\n",
+      "    column=[                                         # Column(s)\n",
+      "        \"dynamic_power\",\n",
+      "        \"load1\"],\n",
+      "\n",
+      "    filters={                                        # Filter the data  \n",
+      "        \"cdev_state\": [\n",
+      "            1,\n",
+      "            0]},\n",
+      "    pivot=\"cpus\",                                    # One plot for each pivot will be created\n",
+      "\n",
+      "    per_line=1,                                      # Number of graphs per line\n",
+      "    drawstyle=\"steps-post\")                                     \n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Plots for a given time range"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Performance can suffer if ILinePlot tries to make a huge plot.  One way of fixing it is by limiting the period of time plotted using the `xlim` parameter:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.ILinePlot(\n",
+      "    trace1,\n",
+      "    signals=[\"thermal:temp\"],\n",
+      "    xlim=(1, 4),           # Only between seconds 1 and 4\n",
+      ").view()"
+     ],
+     "language": "python",
+     "metadata": {
+      "run_control": {
+       "marked": true
+      }
+     },
+     "outputs": []
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Synchronized zoom in multiple plots"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "ILinePlots can zoom all at the same time.  You can do so using the `group` and `sync_zoom` parameter.  All ILinePlots using the same group name zoom at the same time.  Note the use of signals with colors."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.ILinePlot(\n",
+      "    trace1,\n",
+      "    signals=[\"cpu_in_power:dynamic_power:18,140,171\", \"cpu_in_power:load1:0xcf,0x36,0x4a\"],\n",
+      "    pivot=\"cpus\",\n",
+      "    group=\"synchronized\",\n",
+      "    sync_zoom=True\n",
+      ").view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Styling ILinePlots"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Use `fill=True` to colour-fill the space under the line. `fill_alpha` optionally sets the opacity."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.ILinePlot(\n",
+      "    trace1,\n",
+      "    signals=[\"thermal:temp\"],\n",
+      "    fill=True,\n",
+      "    fill_alpha=0.5\n",
+      ").view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "EventPlot"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "TRAPpy's  Interactive Plotter features an Interactive Event TimeLine Plot. It accepts an input data of the type\n",
+      "<pre>\n",
+      "<code>\n",
+      "                   { \"A\" : [\n",
+      "                                    [event_start, event_end, lane],\n",
+      "                                    .\n",
+      "                                    .\n",
+      "                                    [event_start, event_end, lane],\n",
+      "                                 ],\n",
+      "                     .\n",
+      "                     .\n",
+      "                     .\n",
+      "\n",
+      "                     \"B\" : [\n",
+      "                                    [event_start, event_end, lane],\n",
+      "                                    .\n",
+      "                                    .\n",
+      "                                    [event_start, event_end, lane],\n",
+      "                    .\n",
+      "                    .\n",
+      "                    .\n",
+      "                   }\n",
+      "                                \n",
+      "</code>\n",
+      "</pre>\n",
+      "\n",
+      "Hovering on the rectangles gives the name of the process element and scrolling on the Plot Area and the window in the summary controls the zoom. One can also click and drag for panning a zoomed graph.\n",
+      "\n",
+      "For Example:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "A = [\n",
+      "    \n",
+      "        [0, 3, 0],\n",
+      "        [4, 5, 2],\n",
+      "]\n",
+      "\n",
+      "B = [\n",
+      "        [0, 2, 1],\n",
+      "        [2, 3, 3],\n",
+      "        [3, 4, 0],\n",
+      "]\n",
+      "\n",
+      "C =  [\n",
+      "        [0, 2, 3],\n",
+      "        [2, 3, 2],\n",
+      "        [3, 4, 1],\n",
+      "]\n",
+      "\n",
+      "EVENTS = {}\n",
+      "EVENTS[\"A\"] = A\n",
+      "EVENTS[\"B\"] = B\n",
+      "EVENTS[\"C\"] = C\n",
+      "\n",
+      "trappy.EventPlot(EVENTS,\n",
+      "                 keys=EVENTS.keys,                     # Name of the Process Element\n",
+      "                 lane_prefix=\"LANE: \",                 # Name of Each TimeLine\n",
+      "                 num_lanes=4,                          # Number of Timelines\n",
+      "                 domain=[0,5]                          # Time Domain\n",
+      "                ).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Lane names can also be specified as strings (or hashable objects that have an str representation) as follows"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "A = [\n",
+      "    \n",
+      "        [0, 3, \"zero\"],\n",
+      "        [4, 5, \"two\"],\n",
+      "]\n",
+      "\n",
+      "B = [\n",
+      "        [0, 2, 1],\n",
+      "        [2, 3, \"three\"],\n",
+      "        [3, 4, \"zero\"],\n",
+      "]\n",
+      "\n",
+      "C =  [\n",
+      "        [0, 2, \"three\"],\n",
+      "        [2, 3, \"two\"],\n",
+      "        [3, 4, 1],\n",
+      "]\n",
+      "\n",
+      "EVENTS = {}\n",
+      "EVENTS[\"A\"] = A\n",
+      "EVENTS[\"B\"] = B\n",
+      "EVENTS[\"C\"] = C\n",
+      "\n",
+      "trappy.EventPlot(EVENTS,\n",
+      "                 keys=EVENTS.keys,                     # Name of the Process Element\n",
+      "                 lanes=[\"zero\", 1, \"two\", \"three\"],\n",
+      "                 domain=[0,5]                          # Time Domain\n",
+      "                ).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "It is also possible to define a colour map to associate a specific colour to each event. A colour string can be:\n",
+      "\n",
+      " - a colour name, `green`, `red`, `blue`, etc.\n",
+      " \n",
+      " - the HEX representation of the colour, `#0000FF` for blue, `#FF0000` for red\n",
+      " "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Using colour names\n",
+      "trappy.EventPlot(EVENTS,\n",
+      "                 keys=EVENTS.keys,                     # Name of the Process Element\n",
+      "                 lanes=[\"zero\", 1, \"two\", \"three\"],\n",
+      "                 domain=[0,5],                         # Time Domain\n",
+      "                 color_map={\"A\" : \"blue\", \"B\" : \"red\", \"C\" : \"green\"}\n",
+      "                ).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Using HEX representation of colours\n",
+      "trappy.EventPlot(EVENTS,\n",
+      "                 keys=EVENTS.keys,                     # Name of the Process Element\n",
+      "                 lanes=[\"zero\", 1, \"two\", \"three\"],\n",
+      "                 domain=[0,5],                         # Time Domain\n",
+      "                 color_map={\"A\" : \"\t#ffa07a\", \"B\" : \"#f08080\", \"C\" : \"#add8e6\"}\n",
+      "                ).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "TracePlot"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A specification of the EventPlot creates a kernelshark like plot if the sched_switch event is enabled in the traces"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "f = setup_sched()\n",
+      "trappy.plotter.plot_trace(f)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": null
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Exporting notebooks with interactive plots to HTML"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Notebooks with ILinePlot or EventPlot can't be exported to HTML using File->Download as->HTML.  They need to be converted from the command line:\n",
+      "```\n",
+      "jupyter nbconvert --to=trappy.nbexport.HTML notebook.ipynb\n",
+      "```\n",
+      "You need nbconvert >= 4.2 and trappy has to be in your `PYTHONPATH`."
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
diff --git a/doc/Plotter.ipynb b/doc/Plotter.ipynb
new file mode 100644
index 0000000..c06f631
--- /dev/null
+++ b/doc/Plotter.ipynb
@@ -0,0 +1,604 @@
+{
+ "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": "2.7.6"
+  },
+  "name": "",
+  "toc": {
+   "toc_cell": false,
+   "toc_number_sections": true,
+   "toc_threshold": 6,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys,os\n",
+      "import tempfile\n",
+      "import shutil\n",
+      "sys.path.append(\"..\")\n",
+      "%matplotlib inline\n",
+      "import trappy\n",
+      "trace_thermal = \"./trace.txt\"\n",
+      "trace_sched = \"../tests/trace_sched.txt\"\n",
+      "TEMP_BASE = \"/tmp\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def setup_thermal():\n",
+      "    tDir = tempfile.mkdtemp(dir=\"/tmp\", prefix=\"trappy_doc\", suffix = \".tempDir\")\n",
+      "    shutil.copyfile(trace_thermal, os.path.join(tDir, \"trace.txt\"))\n",
+      "    return tDir\n",
+      "\n",
+      "def setup_sched():\n",
+      "    tDir = tempfile.mkdtemp(dir=\"/tmp\", prefix=\"trappy_doc\", suffix = \".tempDir\")\n",
+      "    shutil.copyfile(trace_sched, os.path.join(tDir, \"trace.txt\"))\n",
+      "    return tDir"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "temp_thermal_location = setup_thermal()\n",
+      "trace1 = trappy.FTrace(temp_thermal_location)\n",
+      "trace2 = trappy.FTrace(temp_thermal_location)\n",
+      "trace2.thermal.data_frame[\"temp\"] = trace1.thermal.data_frame[\"temp\"] * 2\n",
+      "trace2.cpu_out_power.data_frame[\"power\"] = trace1.cpu_out_power.data_frame[\"power\"] * 2"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Usage of Signals"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A signal is  a string which represents a combination of a TRAPpy event and a column. The format of the signal is, \"event_name:column\". e.g, thermal:temp. \n",
+      "\n",
+      "The Plotter API can accept either a list of signals as an input or a combination of templates and columns. Here is an example on how to use signals:\n",
+      "\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot(trace1, signals=[\"thermal:temp\"])\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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tWgV9+sCKFTBmDLRrF3VFklQ8TjkF/vlPmDkTBgwI+220avXtO+iZRbyn2bQp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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb40a3c7bd0>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Use an additional parameter to specify the color of the line in rgb: \"event_name:column:color\".  The color is specified as a comma separated of rgb values, from 0 to 255 or from 0x0 to 0xff.  E.g. 0xff,0x0,0x0 is red and 100,40,32 is brown.  Similar to the example above, to plot the temperature in purple and the previous temperature in green you can do:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.LinePlot(trace1, signals=[\"thermal:temp:118,95,151\", \"thermal:temp_prev:0,0xa9,0x60\"]).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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YuXIlo0aNKr+rlWrtepeRSM2i+pDSqDbkv+VdLmTh3PUMH9sTFzcni+tj6uFo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\ngg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f1c2c12d9d0>"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Limiting axes to focus on a given region"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "It is possible to set the limits of the X and Y axes by means of the `xlim` and `ylim` parameters:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot(trace1, signals=[\"thermal:temp\"], xlim=(2, 3), ylim=(69000, 70000))\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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P4YB7+OGHc8EFF/T3NgAAADgc9fQk69fvuZTvWL/8cnLMMc1SvmtB33V93HHJ\n8OF9fvy+dlpn4AEAACjH5s27F/HXf7xmTdLaunspnzo1+dSnetejRiUDDt5kugIPNdHR0eEurRRH\n7imNzFMiuWe/2b49ef75N7+c/Q9/aF72vmsxP+645Pjjk7PO6nvWfMiQ/j6i3SjwAAAAHLoajeal\n6nu7nH3duqStbfez5h/6UN/1O9+ZtLT091HtEzPwAAAA9I9t25qXq7/Z5ex/+EPza99ovnzX9bHH\nJkcc0b/HU5EZeAAAAA4NjUbS2bn3y9lffDEZM2b3Un7aaX3Xra21PWu+PynwUBPmwyiR3FMamadE\ncl8z27c3787+/PPNS9aff7537nzXkr56dXOG/PWz5u97X/Kxj/Wujz46GTiwv4+qNhR4AACAkm3d\n2lvGdy3lb/Taiy8mI0c2i/eYMc33Rx/dLOTvf3/fm8ANG9bfR3bYMQMPAABwOGk0kldeefMivuvH\nW7b0LeM7Pn6j10aNSgY5D/x2mYEHAAA4XG3f3pwpr1LK161rluw3KuCnnZacf37f10aONF9eEwo8\n1IT5MEok95RG5ilR0bnv6kpeeKFaKd+wITnqqDcu5WedtftZ86FD+/voOAAUeAAAgP2h0Ug2bdr7\nJes7Pn711WT06N0vWz/22GTy5L5lfdSo2jwijQPHDDwAAMCe9PQ0L12vWspbWqrNku+4dH3AgP4+\nQvqBGXgAAIAqtm1rFu4qpXz9+mT48Dcu4Geeuftr7rzOAaTAQ00UPR9GseSe0sg8JdpvuX/11Wp3\nXF+3Lnn55eYl6W9UyidN6vv66NHJ4MFvf3+wHyjwAADAoe2115Lnnkuefbb3beXK3o9XrWpe6v5G\nl62ffHLywQ/2LeVtbS5dp5bMwAMAAP2n0UhefPGNi/mOt/Xrmzd2O+GE5Pjj+76dcEIyblzS2upR\naNSGGXgAAODQs23b7mfPX1/Wjzhi92L+/vf3fnzsscnAgf19JNDvFHioCXORlEjuKY3MUzuNRrJx\n454vbX/22ebM+bHH9i3nU6Ykn/pUcvzx+ceVK/Phiy7q7yOBWlDgAQCAN7ZtW7J69Z4vbV+5snnZ\n+o5L23e8nzy5t6wfd1wyaM+1Y/v69QfxgKDezMADAECpXn/2/PVF/fnnmzd+29Ps+fHHJ0cd1d9H\nAbVjBh4AAOjV3d179nxPZ9B7enYv5xdd1Pvaccc159OBQ4ICDzVhLpISyT2lkXnekpdffvNL29eu\nbT4ybdd+nPlEAAAgAElEQVRyfvrpycc/3rseObLf79wu91CdAg8AAIea7u5kzZo3vznctm27z57/\ns3/WW87Hjk0GD+7vIwH2IzPwAABwsL3yyptf2r56dTJqVN9y/vrZ83e+s9/PngP7xgw8AAAcCrZv\nb16+/vpivuu6q2v3Yn7hhb2vjR2bHHlkfx8JcIhR4KEmzIdRIrmnNDJfM93dyZIlyaJFyZNPJr/8\nZfL73zfPnre19S3n73538sd/3Ltub3f2/P+Re6hOgQcAgL3ZtKlZ0Bct6n379a+TceOSKVOazz3/\nxjeaRX3cuGTIkP7eMXAYMgMPAAC7WrOmt6Q/+WTz/XPPJaed1izqOwr7pElJa2t/7xaoITPwAADw\nVmzfnixd2lvSd7xt29Zb0j/5yeTf/tvklFOSQf7qDPQvfwpBTZgPo0RyT2lk/gDavDl56qm+Z9YX\nL24+J33HWfWvfa358bhx5tMPIrmH6hR4AAAOL+vX9738fdGiZNmy5D3v6T2zfsklyRlnJCNH9vdu\nASozAw8AQD319CTLl/e9/P3JJ5vPWJ88ue+8+qmneiwbcMgwAw8AwOGrqyt5+um+Z9Z/+ctkxIje\nkn7ppckddyTvepdL4IHDkgIPNWE+jBLJPaWR+f9n48bdz6o/80wyYULvWfVPfap5CfyoUf29W94m\nuYfqFHgAAPpHo5GsWrX7I9teeCF53/uaRf2DH2zeXO7005N3vKO/dwzQr8zAAwBw4G3blvz2t7s/\nsm3w4N3n1SdMSAYO7O8dAxwwZuABADg0vPJK8qtf9T2r/vTTyfjxvSX9L/6i+f6YY/p7twC1ocBD\nTZgPo0RyT2lql/lGI1m7dvdHtv3hD8lppzUL+h/9UXLZZcmkScnw4f29Yw5Btcs99CMFHgCAvdu+\nPfnd73afV9++ve+N5W64ofm89UH+mgmwv5mBBwCgr82bk6ee6lvUFy9Ojj5693n1sWM9sg3gLTID\nDwDAW7d+/e43llu+vHkWfUdJ/7M/az6y7aij+nu3AEVT4KEmzIdRIrmnNAc08z09zWL++kvgN23q\nPat+4YXJX/5lcuqpzbvDw0Hgz3qoToEHADjcdHU17/q+65n1X/6yeQZ9x+Xvl13W/PjEE10CD1AT\nZuABAOpm+/Zk9epkxYpk5crd3z/3XPNZ6rvOq59xRtLe3s8bByAxAw8AcPjYtq1ZwvdU0P/wh2TU\nqOSEE5pn0E84IZk6Nfnn/7y5PvHEZMiQ/jwCAA4ABR5qwnwYJZJ7DltbtybPPrtbOd/4q19l5Isv\nJuvWJccc07egf+hDyec/31yPH58ceWQ/HwTsH/6sh+oUeACA/e3VV5ul/I3Onq9YkXR2JuPG9Zbz\nE09MLrwwy6dOzZRPf7r5aLYjjujXQwDg0KPAQ034zTQlknsOWS+99OYFfdOm5Pjj+xb0P/3T3vWx\nxyYDB+72Y6cc1IOAQ4M/66E6BR4AYFeNRvMM+a6l/PUFfdu2vpe3n3hicuaZvesxY5IBA/rzKAA4\nDCnwUBPmwyiR3HNANBrNGfM9nT1fubJZvnfcDG5HQf/wh3vX7e0H5NFrMk+J5B6qU+ABgMNLT0+y\nZs2eC/qzzyZDh/Y9g/6e9yR/8ie965Ej+/UQAOCNeA48AFAv3d3Nx6jtqaA/91yzgL/+Evdd3w8f\n3p9HAEDhDuhz4Ddv3py77747q1atSktLS2bOnJnBgwfn3nvvTVdXV0aPHp0rr7wyQ4YMya9+9av8\n3d/9XbZv355Bgwbl85//fE4//fQkybJlyzJnzpxs27YtU6ZMyYwZM5Ik3d3dmT17dpYtW5bW1tbM\nmjUro0aNessHAwAcBl57LVm1as+Xt69e3Zwx37WUf+ADySWXNNfHH5+84x39eggAcCBUKvD3339/\npkyZkm984xvZvn17urq68ld/9Vf54he/mFNOOSUdHR2ZO3duLrnkkowYMSLXXHNNRo4cmVWrVuWm\nm27K3XffnSS577778tWvfjUTJ07MzTffnEWLFmXy5Ml55JFHMnz48Hz3u9/N/Pnz88ADD+Tqq68+\noAcOdWM+jBLJ/WFqy5bmZex7Kujr1iXHHde3oJ97bvLFLzbX48cngwf36yEcKDJPieQeqttrgd+8\neXOWLFmSr33ta0mSgQMHZujQoVm7dm1OOeWUJMmkSZPy4x//OJdccklOPPHEnd87fvz4bNu2Ld3d\n3dm0aVO2bNmSiRMnJknOOeecLFiwIJMnT86CBQty8cUXJ0mmT5+e73//+/v7OAGAg+mll5Jf/CL5\n3e92L+gvvtgs4bsW9I9+tHc9dmwyyG16AOD19vr/juvWrUtra2vmzJmTlStX5qSTTsqMGTMybty4\nLFy4MFOnTs0TTzyRDRs27Pa9P/vZz/Kud70rgwYNSmdnZ9rb23d+rr29PZ2dnUnS53MDBgzIsGHD\nsmnTpgw3nwY7+c00JZL7mmg0kqVLk/nze9+WL0/+6I+SU09tFvPJk/s+A90j1t6QzFMiuYfq9lrg\ne3p6snz58lx22WWZMGFCfvCDH2Tu3LmZOXNm7r///jz44IN5//vfn0Gv+035qlWr8nd/93f51re+\n9ZY3VcP76gFAOTZvThYu7C3rTzzRnDk/66zk7LOTyy9PzjjjsL3MHQD6y14LfFtbW9rb2zNhwoQk\nzUvcH3rooVx88cW5/vrrkyRr1qzJk08+ufN7NmzYkL/5m7/J17/+9YwZM2bnz9n1LP2GDRvS1tbW\n53NtbW3p6enJli1b9nr2fddZmY6OjiSxtj6s1zteO1T2Y219MNa33357Jk+efMjsp8h1o5HzJkxI\nnngiz/3932fEr3+dEc8+m0yalOfGj89LU6bktLvuSsaP7/3+M888dPZfs/WiRYt23gfoUNiPtfXB\nWO/4+FDZj7X1wVrvi0qPkbvhhhvyla98Jccdd1x++MMfpqurK5/85CczYsSI9PT0ZM6cOTn99NNz\n3nnn5dVXX82/+3f/Lp/97Gczbdq0Pj/n+uuvz6WXXpoJEybk29/+dj72sY9l8uTJmTdvXlatWpXL\nL788jz/+eBYsWPCmN7HzGDlK1NHRsfM/eiiF3PeD115Lnnyy98z6/PnN184+u/l21lnJ1Knu8n6A\nyDwlkntKtK+dtlKBX7FiRe655550d3fn6KOPzhVXXJGOjo7MmzcvLS0tmTZtWj73uc8lSf77f//v\neeihh3Lsscem0WikpaUl119/fUaMGJFly5blrrvu2vkYuUsvvTRJsm3bttx5551ZsWJFWltbc9VV\nV+08c78/DxYAeJ3nn+8t6k88kfzTPyUTJ/Yt7BMmJC0t/b1TADhsHNACf6hR4AFgH2zfnjz1VN/Z\n9fXrk+nTewv7tGnJiBH9vVMAOKzta6cdcAD2AhwAb2dWBupK7t+mjRuTf/iH5N/+2+SP/zh55zuT\niy9uPt7tnHOSuXOTzs6+X6O89yuZp0RyD9V5yCoAHA4ajeSZZ/o+yu3ZZ5vz6medlVx9dfNM+6hR\n/b1TAGAfuYQeAOro1VeTBQv6Xg7f2to7t3722cn73pcccUR/7xQAeJ197bTOwAPAoa7RSFau7L3Z\n3Pz5yZIlzYJ+9tnJjBnJPfckY8f2904BgAPIDDzUhPkwSlRs7ru6mmX9O99J/sW/aBbz6dOTH/4w\nOeGE5M47kw0bdv8aaq/YzFM0uYfqnIEHgP62Zk3fR7ktWpS8+93Ns+uf+Uxy663JiSd6lBsAFM4M\nPAAcTN3dyeLFfWfXN27s+yi3M89szrMDAIclM/AAcCjq7Ex+9rPewr5gQTJ+fPNGcx/5SPKtbyXv\neU8ywFQbAPDm/G0BasJ8GCWqXe57epLf/Cb5/veTyy5LTj21ObP+ne80L3////6/5s3onn6679co\n7/w/tcs87AdyD9U5Aw8A++qVV5Jf/KJ3fv1nP0tGjuy9FP7rX08mTUoG+b9bAODtMwMPAFU0Gsny\n5b1z6/PnJ888k0ye3FvYzzorOeaY/t4pAHCIMwMPAPvT1q3J//2/fW82N2BAb1n/8z9PpkxJjjyy\nv3cKABRCgYea6OjoyHnnndff2+Ct2Lw5WbYsWbq0+faHPzQvpT7yyGTIkOb7HW/7sh48+LCfnT6o\nuV+9uresz5/fvFP8qac2z6pffHFy++3J8cd7lBsHlD/rKZHcQ3UKPMDb8corye9/31vSd31bvz55\n17uSiRObb+PGJdu3J11dzbO7L73UfN/V1fvajo/3tt66NXntteSII/bPLwP2x/cOHNjf/2tUt21b\n8qtf9S3smzb1Xgb/7W83H+U2bFh/7xQAYCcz8AB7s3HjGxf0pUuTl19OJkzoLem7vo0bd2BLbaPR\nLPFVyv5b+cXAvn7tgAH775cBb/eXEoMG9T1Tvn5930e5LVyYnHhib2E/++zk3e92dh0AOCjMwAPs\nq0Yj2bBhzyW9qys5+eTeov7hDyeXXtr8+Nhj++8y9paW3sI6YkT/7GGHRqN5Vnt//GJg48a3/wuH\npPffzRFHNF//wAeaRf2aa5ofv/Od/fvvDADgLVLgoSbMh71NjUaydu0bF/Tf/75Zhk8+uffs+Z/8\nSXLFFc2PR492ZnZvWlqaM/mDByetrfvtx+5z7ru7+44ajBlTr0v8KZY/6ymR3EN1Cjxw+Ojpad4o\nbk8lfejQvpe4f+pTvR+3tfX37tmfBg1qvplhBwAOI2bggXrp7k5WrXrjkr5sWfOy6DeaR58wITnq\nqP7ePQAAmIEHDiPbtiUrVrxxSV+5snk59K7l/IMfbL4/6aRk+PD+3j0AABwQCjzUxGE3H7Z1a+8z\n0l//GLbnnkvGju1b0i+4oPn+Xe9K3vGO/t49B8lhl3vYC5mnRHIP1SnwwIHz6qt7fkb6unXJCSf0\nFvRTTkk+8Ynmxyec0LwZGgAAsJMZeODtefnlPT9+7cUXm5e1v9FM+vjxzZuMAQBAYczAAwdOZ+ee\nS/qrr/Yt5medlfz5nzc/Hju2/56RDgAAhxkFHmrigM6HNRrJCy/suaR3d/d9Rvr55ydf+lLz42OO\n8Yx0DhhzkZRG5imR3EN1CjyUoNFINm1qlvQdj2B7/Wz6EUf0PZP+sY/1fjxqlJIOAAD9zAw81NXm\nzc0bwb3wwhu/f/1rLS3Nx6+9/u7uO56R3tbW30cEAABFMAMPdbd1a7No762I73jf3d0s5KNHN9/v\n+vF739t3PXp0MmxYfx8hAADwNijwcKC89lqyfn31s+Rbt/Yt3Lu+P/nkLH7++Uy64ILe14YPd1k7\nhz1zkZRG5imR3EN1CjxU1d2dbNhQ7XL1deuaM+ejRr1xKT/zzN1fP+qoNy3kGzo6kunTD97xAgAA\nhxQz8JSrp6f5eLQql6uvW5ds3NicE3+jS9bf6P073+kRagAAwG7MwEOjkbz4YvWbunV2JiNGvHEB\n3zFDvutrbW3JwIH9fZQAAEChFHgOXY1G8vLL1W/qtn59MnToG58dP/nk5Oyz+77e3t58dFpNmA+j\nRHJPaWSeEsk9VKfAc3A1GslzzyWrV1e7uduRR+5+dnzMmOSEE3afIx89Ohk8uL+PEAAA4IAwA8+B\ns2VL8utfJ7/8Ze/br36VDBmSjB//xpeuv/61IUP6+ygAAAD2KzPw9J9GI1mzpm9R/+Uvk+XLk3e/\nOznjjObbJz/ZfD96dH/vGAAAoHYUeN6a115LfvOb3ct60lvUP/ax5JprklNPdUn7fmQ+jBLJPaWR\neUok91CdAs+evfDC7kX9mWeSd72rt6z/63/dfH/ssW/6DHMAAADeHjPwJN3dzWL++rK+eXNvUd/x\ndtppyTve0d87BgAAqC0z8FSzcePuRf03v0mOO663pM+c2Xx//PHOqgMAABwiFPjDVU9P8vvf717W\nOzuTSZOaBf3MM5PLL2+uhw/v7x2zF+bDKJHcUxqZp0RyD9Up8IeDV15JFi/uW9Sfeippb+89q/7F\nLzbfn3RSMmBAf+8YAACAt8gMfJ00GsnKlbufVV+zJnnve/vOqr/vfcnIkf29YwAAAF7HDPzhZsuW\n5ln0XYv6r36VDBvWW9I/+9nkP/yH5OSTk0H+pwQAADicaX39rdFIVq/e/az6ihXJe97TW9Y//enm\nWfXRo/t7x/QT82GUSO4pjcxTIrmH6hT4g+m115Knn969rA8Y0FvUL7ooue665JRTksGD+3vHAAAA\nHCLMwB8o69btXtR/97vmTeRe/2z1Y47xuDYAAIBCmIHvL93dyW9/u3tZ37q1t6Cff35y9dXNG829\n4x39vWMAAABqSIF/K158cfei/pvfJOPG9Zb1r32t+X78eGfV2a/Mh1Eiuac0Mk+J5B6qU+DfSE9P\nsnTp7mX9xRebN5I744zkAx9Ivvzl5PTTk+HD+3vHAAAAHObMwL/ySvPxbLsW9aeeat7t/fWz6u96\nV/OGcwAAALCPzMBX8dpryZNPJvPnN9/+6Z+StWuT007rLelf+ELzLPtRR/X3bgEAAGCnw/t08vPP\nJw89lPzlXyYf+lDS1pZ85SvNu8F/8pPJ//yfycsvJ7/4RXLvvcnXv558+MPKO4ekjo6O/t4CHHRy\nT2lknhLJPVR3+JyB3769een7jrPr8+cnnZ3J9OnJ2WcnN96YTJuWtLb2904BAADgLavvDPz735/8\n7Ge9Zf0Xv0iOO65Z1ne8nXKKmXUAAAAOKeXNwI8fn0yd2izqs2Y1z7S3t/f3rgAAAOCAqO/p6c7O\n5Cc/SW66KbnoIuWdw575MEok95RG5imR3EN19S3wRxzR3zsAAACAg6a+BR4Kc9555/X3FuCgk3tK\nI/OUSO6hOgUeAAAAakCBh5owH0aJ5J7SyDwlknuoToEHAACAGlDgoSbMh1Eiuac0Mk+J5B6qU+AB\nAACgBhR4qAnzYZRI7imNzFMiuYfqFHgAAACoAQUeasJ8GCWSe0oj85RI7qE6BR4AAABqQIGHmjAf\nRonkntLIPCWSe6hOgQcAAIAaUOChJsyHUSK5pzQyT4nkHqpT4AEAAKAGFHioCfNhlEjuKY3MUyK5\nh+oUeAAAAKgBBR5qwnwYJZJ7SiPzlEjuoToFHgAAAGpAgYeaMB9GieSe0sg8JZJ7qE6BBwAAgBpQ\n4KEmzIdRIrmnNDJPieQeqlPgAQAAoAYUeKgJ82GUSO4pjcxTIrmH6gZV+aLNmzfn7rvvzqpVq9LS\n0pKZM2dm8ODBuffee9PV1ZXRo0fnyiuvzJAhQ5IkP/rRj/KTn/wkAwcOzIwZM3LGGWckSZYtW5Y5\nc+Zk27ZtmTJlSmbMmJEk6e7uzuzZs7Ns2bK0trZm1qxZGTVq1IE5YgAAAKihSmfg77///kyZMiW3\n3XZbbr311owdOzZ33313vvCFL+TWW2/NtGnTMnfu3CTJc889lyeeeCK33XZbrr322tx3331pNBpJ\nkvvuuy9f/epXc8cdd2TNmjVZtGhRkuSRRx7J8OHD893vfjcXXXRRHnjggQN0uFBf5sMokdxTGpmn\nRHIP1e21wG/evDlLlizJ+eefnyQZOHBghg4dmrVr1+aUU05JkkyaNCk///nPkyQLFy7M2WefnYED\nB2bMmDE59thjs3Tp0mzcuDFbtmzJxIkTkyTnnHNOFixYkCRZsGBBzj333CTJ9OnTs3jx4v1/pAAA\nAFBjey3w69atS2tra+bMmZNvfvObueeee9LV1ZVx48Zl4cKFSZInnngiGzZsSJJ0dnb2ufy9ra0t\nnZ2d6ezsTHt7+87X29vb09nZufN7dnxuwIABGTZsWDZt2rT/jhIOA+bDKJHcUxqZp0RyD9XtdQa+\np6cny5cvz2WXXZYJEybkBz/4QebOnZuZM2fm/vvvz4MPPpj3v//9GTSo0jh9JTsuuX8zDz/88H77\n50FdyD0lkntKI/OUSO6hmr227ra2trS3t2fChAlJmpe4P/TQQ7n44otz/fXXJ0nWrFmTJ598cufX\nr1+/fuf3b9iwIW1tbWlra9t5ln7X13d8z451T09PtmzZkuHDh+9xTxdccME+HCoAAADU114voR85\ncmTa29uzevXqJMnixYszbty4vPzyy0maZ+gffPDBXHjhhUmSqVOnZv78+enu7s66deuydu3aTJw4\nMSNHjszQoUOzdOnSNBqNPPbYYznzzDN3fs+jjz6apHk5/umnn35ADhYAAADqqqVR4Xr1FStW5J57\n7kl3d3eOPvroXHHFFeno6Mi8efPS0tKSadOm5XOf+9zOr//Rj36URx55JIMGDdrtMXJ33XXXzsfI\nXXrppUmSbdu25c4778yKFSvS2tqaq666KmPGjDlAhwwAAAD1U6nAAwAAAP2r0nPgAQAAgP6lwAMA\nAEAN7L9nv+1nGzZsyOzZs/PSSy+lpaUlF1xwQT7+8Y/v9nX/+T//5yxatChHHnlkvva1r+XEE088\n+JuF/aRK7n/6059m7ty5SZIhQ4bkS1/6Uo4//vj+2C68bVX/rE+SpUuX5t/8m3+Tq6++Oh/4wAcO\n8k5h/6ma+1//+tf5L//lv2T79u0ZMWJEbrjhhn7YLewfVXL/yiuv5M4778yLL76Ynp6e/Omf/mnO\nO++8/tkwvE3btm3LDTfckO7u7nR3d2fq1Kl97hu3w1vus41D1IsvvthYvnx5o9FoNLZs2dK48sor\nG88991yfr/mnf/qnxn/8j/+x0Wg0Gs8880zjuuuuO9jbhP2qSu5/+9vfNl599dVGo9FoPPnkk3JP\nrVXJfKPRaGzfvr1x4403Nm6++ebGz372s4O8S9i/quT+1VdfbcyaNauxYcOGRqPRaLz00ksHe5uw\nX1XJ/d///d83/tt/+2+NRqOZ+UsvvbTR3d19sLcK+83WrVsbjUbz7zHXXXdd4ze/+U2fz+9Lnz1k\nL6EfOXLkzt8+DBkyJGPHjk1nZ2efr1mwYEHOPffcJMnJJ5+czZs3Z+PGjQd7q7DfVMn9u9/97gwd\nOjRJM/ev/zzUSZXMJ8k//MM/ZPr06RkxYsRB3iHsf1Vy/9Of/jQf+MAH0tbWliSyT+1Vyf3IkSOz\nZcuWJMnWrVvT2tqagQMHHuytwn5z5JFHJmmeje/p6cnw4cP7fH5f+uwhW+B3tW7duqxcuTInn3xy\nn9c7OzvT3t6+c93W1qbMcNjYU+539fDDD2fy5MkHcVdw4LzZn/ULFizIn/zJn/TTzuDA2VPuV69e\nnU2bNuXGG2/Mtddem8cee6yfdgj7355yf8EFF+S5557LV77ylfzFX/xFZsyY0T8bhP2kp6cnf/mX\nf5kvf/nLOe200zJu3Lg+n9+XPnvIF/itW7fmP/2n/5QZM2ZkyJAh/b0dOCiq5P6pp55KR0dHPv/5\nzx/k3cH+92aZ/8EPftAn5w1PP+Uw8Wa57+npyfLly3Pttdfmuuuuy4MPPpi1a9f2005h/3mz3D/0\n0EM54YQTcs899+SWW27J97///WzdurWfdgpv34ABA/LXf/3X+d73vpff/OY3efrpp9/2zzxkb2KX\nJNu3b893vvOdnHPOOTnzzDN3+3xbW1s2bNiwc71hw4adl5pBXe0t90mycuXK/O3f/m2uu+663S7F\ngbrZW+aXLVuW22+/PY1GI6+88kqefPLJDBo0KFOnTu2H3cL+UeXvOK2trRk8eHAGDx6cU089NStW\nrMgxxxzTD7uF/WNvuf/tb3+bz3zmM0mSY445JmPGjMkf/vCHTJgw4WBvFfaroUOHZsqUKfn973+f\n9773vTtf35c+e0ifgf/e976XcePG7fGOxFOnTs2jjz6aJHnmmWcybNiwjBw58mBuEfa7veV+/fr1\n+c53vpOvf/3r/iLHYWFvmZ89e3Zmz56du+66K9OnT8/ll1+uvFN7e8v9mWeemSVLlqSnpyddXV35\n3e9+t9ull1A3e8v92LFjs3jx4iTJxo0bs2bNmhx99NEHc4uw37z88svZvHlzkuS1117L4sWLd7vD\n/L702ZbGIXot4pIlS3LDDTfk+OOPT0tLS1paWvJnf/ZneeGFF/7/9u4/pqr6j+P462LXi0G3LH7c\ne7n3UhYjYboQ606W//QDi1ByLMZIMlysX9PZps6ylasN22wta1qjlNnCRHMGqWxiq/XjD7QE9S7K\nphH3AgqSidy5Irz9wdfzFURBvsn1fH0+Njbu55z7Oe/Phzu21zmfc64sFoseeOABSdL69evV2Nio\n6OhoPfvss5o0aVKEKwdGbySf+/fff1979+5VfHy8wuGwxo0bp1WrVkW6dGBURvq//px169YpMzOT\nr5GDqY30c19TU6OvvvpKUVFRuv/++/Xwww9HuHJg9Ebyue/u7ta6devU1dWlcDisRx99VPfee2+k\nSwdGpaWlRWvXrlU4HFY4HNbMmTM1Z84c1dXV/U959qoN8AAAAAAA4L+u6iX0AAAAAACgHwEeAAAA\nAAATIMADAAAAAGACBHgAAAAAAEyAAA8AAAAAgAkQ4AEAAAAAMAECPAAAAAAAJkCABwAAAADABAjw\nAAAAAACYAAEeAAAAAAATIMADAAAAAGACBHgAAAAAAEyAAA8AAAAAgAkQ4AEAAAAAMAECPAAAAAAA\nJkCABwAAAADABAjwAAAAAACYAAEeAAAAAAATIMADAAAAAGACBHgAAAAAAEyAAA8AAAAAgAkQ4AEA\nAAAAMAECPAAAAAAAJkCABwAAAADABAjwAAAAAACYAAEeAAAAAAATIMADAAAAAGACBHgAAAAAAEyA\nAA8AAAAAgAkQ4AEAAAAAMAECPAAAAAAAJkCABwAAAADABAjwAAAAAACYAAEeAAAAAAATIMADAAAA\nAGACBHgAAAAAAEyAAA8AAAAAgAkQ4AEAAAAAMAECPAAAAAAAJkCABwAAAADABK6LdAEAgGtba2ur\nQqGQLBZLpEsBhhUOhxUTE6OkpKRIlwIAuAYR4AEAEXPy5En19vYShmAqnZ2dOnnypCZOnBjpUgAA\n1xiW0AMAIqazs1NxcXGRLgO4LHFxcers7Ix0GQCAaxABHgAQMRaLhaXzMB0+twCASCHAAwAAAABg\nAhPEpsIAAAfoSURBVAR4AAAAAABMgAAPAMA1JDU1Vd9++22kyxjSU089pVWrVkW6DAAArloEeAAA\nBnE4HHI6nXI6nbLb7YqPjzfatmzZEtHavvjiC2VkZCgxMVG5ubkKBoMX3ff/KRB/+eWXSk9Pj3QZ\nAABEFAEeAIBBjh07pvb2drW3t8vr9erTTz812goKCi7Yv6+vb0zq6ujoUHFxsV5//XW1tLRoypQp\nKikpGZNjDzZWYz4fD44DAFzrCPAAAFxCOBxWOBwe0Pbaa69p/vz5KikpkcvlUlVVlfbu3av77rtP\nbrdbKSkpWrp06YCQ6/f7NXv2bHm9Xt1xxx1as2aN0f/q1as1depU3XrrrSopKdGpU6eGrKW6ulpT\np05Vbm6ubDabVqxYoYaGBv36668X7PvBBx9o27ZtevPNN+V0OlVUVGRsa2xslM/nk9vt1oIFC9Tb\n22ts27lzp2bMmCG3263s7Gz9+OOPxrbU1FStWbNGPp9PiYmJRts777yje+65R06nUwsXLtTx48eV\nl5cnl8uluXPnqru72xjrvHnzdPvtt8vj8SgnJ0c///zzsH+D7u5uFRQUKBAIGCshTpw4ccm5O3r0\nqG644QZVVlYqNTVVXq9XFRUV2rdvn3w+nzwej5YtW2YcY+PGjZo1a5ZeeOEFJSUlafr06fr666+H\nrQ0AgLFEgAcAYBR27NihwsJCtbW1KT8/X1arVatXr1YgENCePXu0Z88erV+/XlJ/AJ0zZ44eeeQR\nHTlyRI2NjZo5c6Yk6d1339Xu3btVV1enw4cPKyYmRkuWLBnymE1NTZoyZYrxOjY2VsnJyWpqapIk\nbd682ei3tLRU+fn5WrJkidrb27Vp0ybjfdu3b9eOHTt06NAhNTQ06JNPPpEk/fDDD1q0aJHee+89\nBQIBPfHEEyosLNTff/9tvHfbtm2qrq5Wa2ur0fb555+rtrZW+/fvV3V1tR577DGVlZWpublZZ86c\nUXl5ubFvTk6ODh06pCNHjigtLU2lpaVDjvXs2bNyu936/vvvZbfbtWXLFnk8HmMlRFxc3IjmrqGh\nQX6/Xx9++KGWLl2qt956S7W1taqvr9fmzZtVX19v7FtfX68777xTLS0tWrZsmR5//HHj5AMAAFcD\nAjwA4KoVExv7r/xcCTNmzNCsWbMkSTabTRkZGcrMzJTFYlFycrJKSkqMh8Xt2rVLXq9XzzzzjKxW\nq2JjYzVt2jRJ0oYNG7Ry5UolJiZq/PjxWr58ubZv3z7kMUOhkOx2+4A2u92unp4eSVJhYaG++eab\nYWt//vnnFR8fr4kTJ+qhhx7SwYMHJUkVFRUqLS3VXXfdJYvFonnz5knqD/bnv9fhcMhmsxltzz33\nnG655RY5nU5lZWXJ5/MpPT1d48eP1+zZs3XgwAFJ/Uvgi4qKdP311xtjbWho0JkzZy6oMSoqSsFg\nUNOnT7/oOIabO4vFouXLl8tqtSo7O1s2m01FRUW6+eab5XK5lJWVZdQmSU6nU08//bTGjRungoIC\nJScna/fu3cPOJwAAY+W6SBcAAMDFhP4TTK9Gbrd7wOvDhw/rpZdeMgJpX1+fET6DwaBuu+22IfsJ\nBAIqKChQVFT/OfVwOKyoqCh1dHQoISFhwL4xMTE6ffr0gLZTp04p9jJPUpzf74QJE/THH38YtWzd\nulVr1641aunt7VVbW5uxv8vluqC/+Ph44/fo6OgB/UdHRysUCknqv6r+yiuvqLq6Wr///rssFoss\nFou6uroumM+RuNTcnRMXFzeglsG1nqtNkpKSkgb0f+6KPwAAVwuuwAMAMAqDH6i2aNEipaeny+/3\nq62tTStWrDDunXe73Tp69OiQ/bjdbtXU1CgQCCgQCCgYDA4Z3iVp8uTJxtVyqX9p/m+//abJkyeP\nqMbhuN1uvfjiiwNqOX78uObOnTvqPs9XWVmpuro61dbWqrW1VQcOHBjyGQNDGeq4lzN3I3H+bQFS\n/4kXh8Mxqr4AALgSCPAAAPwLenp6ZLfbNWHCBP3000/asGGDsS0nJ0fBYFDl5eX666+/dPr0aWNZ\n+oIFC/Tqq68aXwfX0dGhXbt2DXmMvLw8+f1+7dy5U3/++afKysqUmZl50av7CQkJam5uHvEYnnzy\nSZWXl2v//v3GmGpra4dc4j4aPT09stlsuummmxQKhbRy5coRnxBISEhQV1eXcbuANPzcjeTEwPmO\nHTum8vJy9fX1aevWrWpubtaDDz54WX0AAHAlEeABALiEkQbMsrIyffzxx3I6nVq8eLHy8/ONbXa7\nXTU1Nfrss880adIkZWRk6LvvvpMkLVy4UNnZ2crNzZXL5VJ2drYRoAdLSEjQRx99pJdfflkej0d+\nv18VFRXG9k2bNikrK8t4PX/+fB08eFBer1fFxcXDjufuu+/W22+/rcWLF8vj8WjatGmqqqq65FwM\nbrtU/8XFxXI4HEpJSZHP5xtQ6+D3nj17Vg6HQ/v27ZMkpaWlKS8vT2lpafJ4PDpx4sSwczdcbYNf\n+3w+NTU1yePx6I033lBlZaVuvPHGi44HAICxZglf7ulpAAD+Jb/88suQ91QDY23jxo2qqqq66OqH\nwdra2pSSknKFqwIAYCCuwAMAAAAAYAIEeAAAAAAATIAl9ACAiGEJPcyKJfQAgEjgCjwAAAAAACZA\ngAcAAAAAwAQI8ACAiLFarQqFQpEuA7gsoVBIVqs10mUAAK5B/wDVsmlJBUX9QwAAAABJRU5ErkJg\ngg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb409ebbd90>"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "No Pivot Simple"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot(trace1, trappy.thermal.Thermal, column=\"temp\")\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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tWgV9+sCKFTBmDLRrF3VFklQ8TjkF/vlPmDkTBgwI+220avXtO+iZRbyn2bQp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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb409dfcf90>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "No Pivot Multi Trace"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot([trace1, trace2], trappy.thermal.Thermal, column=\"temp\")\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+KCIblJEPGqWQBwAAgBaiRx4AAACagB55AAAAmIEU8rQNvUgUkQ3KyAdl5IMi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\nKABgqv8D0Ro/jcAlSNQAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb409d24250>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "No Pivot Mutiple Columns and Traces"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot([trace1, trace2], [trappy.thermal.Thermal,trappy.thermal.ThermalGovernor], column=[\"temp\", \"power_range\"])\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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sWZJ/9s/+Wfbt25df//rX+f3f//28+uqr2blzZ5588sm8/vrr+drXvpadO3em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\nrkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb409d1d2d0>"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "No Pivot With Filters"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot([trace1, trace2], [trappy.cpu_power.CpuOutPower], column=\"power\", filters={\"cdev_state\": [0]})\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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MzpR7s3jSlItORGvK2zPOSMRaeePQDKsJu0OE3UiGM7jcz8bBwcHs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Pln+oxyiEg57xd65fnecO0ZFdRb7ynX6Ovs7yX4rbY/VusZ1tBrlQbNC71Zj0PzMTWkXF0TDSnX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\nY3rDO3kNAV1JWi0ebBIrZcPn2Ktj7CwV7JXvOZ8HS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80cqhsarLVQmpdi5047j9LnZo+w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Gkz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\nmDAh08NQFCUL+P8DUz2Ws5NIpQAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb409be3450>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Pivoted Data"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot(trace1, trappy.thermal.Thermal, column=\"temp\", pivot=\"thermal_zone\")\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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KFdi+fXsML1PFif1CisbcUF7MD0WzoXt3OPPMcIqz9D+ifnaULAn/+EfYRjF5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\nTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb409b30f50>"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Pivoted Multi Trace Data"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot([trace1, trace2], trappy.cpu_power.CpuOutPower, column=\"power\", pivot=\"cpus\")\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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z4eBdcDEeD1dRRQfgvOPodmfLfrElB9iVJVKN3jvdvHkza9eupXfv3syYMQOX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\ndLoMkUrz/wFIVdi/Eu5POgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb4099c0650>"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Labelling Pivots"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A dictionary at that maps pivot values into possibly useful labels can be provided"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "map_label = {\n",
+      "            \"00000000,00000006\" : \"A57\",\n",
+      "            \"00000000,00000039\" : \"A53\",\n",
+      "}\n",
+      "    \n",
+      "l = trappy.LinePlot([trace1, trace2],\n",
+      "                    trappy.cpu_power.CpuOutPower,\n",
+      "                    column=\"power\",\n",
+      "                    pivot=\"cpus\",\n",
+      "                    map_label=map_label)\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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b8E8Q5HaCvEwKZzedczsJM5oGtpNKVjefh6qLW6Z9IyISexrjI9JN8vHiAOs1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\nnS5DpML8fw1FeQJlXVCYAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb40988ab10>"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`xlim` can be used to limit the X axes to focus on a given range for both plots:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot([trace1, trace2],\n",
+      "                    trappy.cpu_power.CpuOutPower,\n",
+      "                    column=\"power\",\n",
+      "                    pivot=\"cpus\",\n",
+      "                    map_label=map_label,\n",
+      "                    xlim=(3.5,5.5))\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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E16lCCA62yH0JiyH4GNMDXiaKi4uZNWsWd911V2TfG2+8wdy5c/n2t7/Njh07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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb408533850>"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`color` can be used to specify different colors for the lines.  For instance, to make the lines dark grey and dark blue you can do:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.LinePlot([trace1, trace2],\n",
+      "                trappy.cpu_power.CpuOutPower,\n",
+      "                column=\"power\",\n",
+      "                pivot=\"cpus\",\n",
+      "                map_label=map_label,\n",
+      "                colors=[[69, 69, 69], [0, 0, 0xc8]],\n",
+      "                ).view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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z5lqlyyFSBM/F1nHbEBEpj2N2iGrp3ns9MXhwcyxZclHpUoiIiIioltjYcTCZ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\noEaNGnKXQVRl/heyc7yaBWY++QAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb6b614cb50>"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Pivoted Data Multiple Traces and Columns"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot([trace1, trace2],\n",
+      "                    [trappy.cpu_power.CpuInPower, trappy.cpu_power.CpuOutPower],\n",
+      "                    column=[\"dynamic_power\", \"power\"],\n",
+      "                    pivot=\"cpus\")\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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qVSQnJwMwbty48KdjzflwOJFYuAsfn2EYTTWvEE+iih8RERGRVu20a3y8Xi8T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\nIYQQkgcSiQSCwSBEIlGmi0Ly1E81ArcVh/dXjAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb409a5d4d0>"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Pivoted Data with filters"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot(trace1, trappy.cpu_power.CpuInPower, column=[\"dynamic_power\", \"load1\"],\n",
+      "                    filters={\"cdev_state\": [1, 0]}, pivot=\"cpus\")\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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SlHPjCJxkC+F8FM/X6mqgTx9+v5owgf87c4aLHRvjpGvPKbbYxo7KSu6FBMzl\n3NTUIDEzk3tuHFBQwDbHJUSQ54YwT4SIGy+ksLSYGB5y0dZmfB9GPDeA+o1VHpbWowf38HR0GO8P\nQRCEWdxuft9JSPB+Py3NEYM6opsiD0uLj+fPeiNjlZoaICWFPDcRCokbC3FSTKOQLW43XwzT5uLG\nL+dG8qiY9d4YFTdq8b5yceNy8dA0jd+y251fEYKTbCGcj9/5KvfayImAQZ2Trj2n2GIbO+QFBcws\n5FlTg4rWVj7G6egAGhuD088QYZvjEiJI3BDmaWriN4xIqvIlhaUB5sRNRwcXIDU12sm2kogC1G+q\ncnFjtj8EQRCBIIkbXxySSE10U+SeG8B4aJqUc+Ny0bUQgZC4sRAnxTQK59wkJ9vec6O4zg1gTky0\ntPAQMpdLe1uRsLSGhq6cG0C3qEC3O78iBCfZQjgfv/NVTdxEQFiak649p9hiGzvkBQUAU56brHPP\n5X9HgBdTD9sclxBB4oYwj9sdEeLGCynnBjAnbpqbuQhJSdEOTfP13OiFpZntD0EQRCCQ54ZwGowp\nixsj5aBrarrGCg4pKtCdIHFjIU6KaRSyJUI8N162yMPSEhKCJ258c24sCEvrdudXhOAkWyKVyspK\nPPnkk/j1r3+NBx98EGvWrAEA1NfXY8mSJViwYAGefvppNMri5vPz8/HAAw8gNzcXu3btClfXQ45q\nzo0vEeC5cdK15xRbbGFHXR1/Tvfo0fWeCc9NyenT/G8HeG5scVxCCJWCJszT1NQ1OG9vB6Kjw9sf\nEQINSxMRN4wZr5Ym9SeS8pcIwiZER0fj1ltvRXZ2NtxuNxYtWoTx48ejoKAA48aNw/XXX4/Vq1cj\nPz8fN998M8rKyrB582bk5eWhsrISTz31FF544QW4fJPquwNa4ibCB3REN0VeTEDCaM5NbS3aBg/m\nf0eA0Ce8Ic+NhTgpplHIFrebez969LC198Yv5ybYYWlNTUBsLP8HqLvD5Yt4CvSn251fEYKTbIlU\nUlJSkJ2dDQCIj49H//79UVlZiaKiIlx22WUA+HHatm0bAKCoqAjTpk1DdHQ0MjIykJmZidLS0nB1\nP6QI59z07Gn7KlFOuvacYost7PAtJgCY8tycO3Uq/9sBIZqGj0turq3HdXqQuCHM09TEB+Q2Fzde\nhMJzI/faANphaQYKChAEoU9FRQWOHj2KkSNHora2FikpKQC4AKrtnGSoqqpCenq6Z5vU1FRUVVWF\npb9hR02go60uAAAgAElEQVTcuFzkvSEiE998G8Bczk3nvaPbXQcdHcALLwAnT4a7J6YhcWMhTopp\nFM65iQDPjceW9nY+CykJCrPiJj6eDwaqq5XbyAUUQDk3CpAtRDBwu914/vnncdtttyE+Pt7v80DC\nzuTHubCwMGJfS397Pq+uxv5Tp5Tbd85Y26n/SjbYpT+BvF6+fLmt+mP2te+xCUd/9m7ciIr2dq/P\nD1VWep7DIvtrOH4c20pKAAD7Tp/GqT17wmaPFa+NnF9fr1nDBU5nzpEd+i9/LQQLM+vWrQt3Fyyj\noKAg3F2wDCFbnnuOsd/8hrGsLMaOHQt6n8zisaW6mrHk5K4PfvITxj74wNjOvvmGsSlTGPuf/2Hs\n6aeV23z3HWMXXND1Oj+fseuv928XH89YQ0PX6//4D8ZWr1b96m53fkUITrEl0u/FbW1tbMmSJezT\nTz/1vLdw4UJWXV3NGGOsurqaLVy4kDHGWH5+PsvPz/e0W7JkCSsuLlbdd6T/NnL8ztfrrlO/78yc\nydjatUHvk1mccu0x5hxbbGHHiy8ydt99/u/df7/4Pvr3Z5vef5//vWYNY7NmWdc/s2zbxpjJe9FX\nn3zC2MsvizUuLWUMYOzf/zb1XcFG5H5MnhsLsUWsqUUIr3MTAWFpHlt8PSrBCktT8tz4usPb2/lv\nlpDg3R+NggLd7vyKEJxkSyTz6quvYsCAAbj22ms9702aNMkz41dYWIicnBwAQE5ODjZt2oS2tjZU\nVFSgvLwcw4cPD0e3Q45wzg1g+1wDJ117TrHFFnYoFRQwEZY2dfZs/rddwtLee4//M8Elra3AM8+I\nNZZCdCO4iAJVSyPM09TEc0vi4mwtbjzIy0ADgYubgweV24jk3DQ0AImJPK49kP4QBIH9+/dj48aN\nGDRoEB5++GG4XC7Mnz8fc+fORV5eHgoKCtC3b1/k5uYCAAYMGICpU6ciNzcXMTExuOuuu7pnpTRA\nW9xQlSgiEqmsBIYO9X7PSEGB1lb+LJZC2O2yzk1JifmqtFu2iNsgCTk72GwSEjcWUlhYaI9ZCwsQ\nssXtBvr2tb3nxmOLvFIawMVEU5OxnZkpKKBUgtI33wbQLSjQ7c6vCMFJtkQqo0ePxv/+7/8qfvb4\n448rvj9v3jzMmzcvmN1SZ906YOJE/9nlEOB3vuqJGzvMWKvgpGvPKbbYwo7KSmDyZO/3jIib2log\nORmFGzZwW+xyHRQXAxkZpjat+ewzpDQ1deVKa+EAzw2FpRHmiZCCAh7sFJamJG7Ic0MQ3YPHHgO+\n+ircveBEcFgaQSiiVC3NyDo38kppAH+Gu93hHee0tfFoETPXY1sbkoqL+aSryPZVVXwpCxI3BGCT\nWFOLcOQ6N1aHpQVSCtqEuOl251eE4CRbiBBx/Lh6tcUg43W+NjfzQVNionJjm4elOenac4ottrBD\nbZ0b0Zyb2logJaXLFpeLe1nDKfR//JGHpJnpw549iBk8GBg8WGz7ykpg+HBPtbRIhMQNYZ4IKSjg\nQSksLRSem/h4XlZRXiygocF7jRupnUZBAYIgHEB7O18/IkzixouaGu61Ucs3Muu5OXkSOHIkoK4R\nhGnUCgqY9dwA4Q9NKy4GcnJ4Hxgztu2WLcCUKeLXc1UVMGqUrSc29CBxYyGG63DbGCFb5GFpNh6U\ne2wJVViar+fG5eLfW1fX9Z4Jz023O78iBCfZQoSAigoucMK0aKjX+aoVkgaY99z88Y/AkiXGtzOI\nk649p9hiCzuUPDcmwtK8bAl3UYGSEmDcOD7eko8lRNiyBQf69BEXaJWVwMiRJG6IbkqEhKV5sCos\nLT6e3yhrapRnUOrqvL8H8HeJmygoQBCEAzh+nP9vB8+NiLgxM1u9ZQuwf7/5fhGEWdra+PPV1/MS\nF+cfQaGGXT03I0aY68eWLTg7Zoz4ZEVVFYkbogtbxJpahGPXuQk0LM3t5jfJ+Hge/6pUbe3sWW/P\nDeDvEqecm3B3wTKcZAsRAsIsbvwqpfkO4uSYma1uaQF27AiJuHHStecUW8Juh3ROR/kMb6UIChHv\nTae48bIl3J6b4mIuOIyGilZXA2VlmHzbbeLCSBI3VVVcEEYgJG4I80hhaZGyzo1vWFpCgvmwNEA9\nNM03LA3wd4lTtTSC6J4cPw4MGhQZnhszVaK+/x4YNozPoEfwzC8RoSiFpEkYETfyiVAg/J6bkhIu\nOIz249tvgUmTgJgYY2Fp55zD84KNLHxqI0jcWIgtYk0tQsiWCAlL89hiZbU0QF3c+IoowD8szURB\ngW53fkUITrKFCAHHjwPnnRcZOTcul/HBlJS8fO65QffeOOnac4otYbdDqZiAhGjejVLOTTjFTXMz\nv29kZxvPg+u8HgsLC415btLSuJcoQiumkbghzBNoWNqpU0BBgfX9UsPKammAMc+NBWFpBEE4AEnc\nRILnBjA+mNq6lYub0aMp74YIPXqeGxFPhFLOTTjD0g4d4mWcY2ONiyzpegTEQtra2z2lsNG3b8R6\nX0ncWEjYY00tRDjnJhDPzdq1wLJlxrcziFfOTbg8NyJhaToFBbrd+RUhOMkWIgSEWdz45dzoiRuj\nMf5btgAXXcTFzb59pvooipOuPafYEnY7rAhL813nBgiv50YqJmC0H4xxcXPRRdwWkW1ra/nkbHR0\n+POMAoDEDWGeQMPSKiuNlzQMhFCFpal5bvSqpZHnhiCcjyRu1Kot6tHaCvz1r+a29cVqz01lJS91\nfe655LkhwoMUUqWEwYICXlgx0G9uBlatMr6dVExA6oeouCkp4WORzEz+WuRalv9+Thc3jY2NeP75\n55Gbm4tf//rXKCkpQX19PZYsWYIFCxbg6aefRmNjo6d9fn4+HnjgAeTm5mLXrl1B67zdCHusqVn+\n/ne/+G/hdW6ksDQz69ycORMSceO1zk2ww9IYEwtLU8u5oXVuIg4n2UKEACl2Pi7O3P1v3z7gzjvN\nDZJgMOcGMDZTvHUrMHkyn/UNgbhx0rXnFFvCboeW5ybcOTe7dgG/+Y3x7aRiAkb7IeW/AeI5N/Kc\nJaeLm5UrV2LChAnIy8vDsmXL0L9/f6xevRrjxo3DihUrMHbsWOTn5wMAysrKsHnzZuTl5WHx4sV4\n8803wayYYSKCxx/+AKxZY2wbxrrWfIkUz41VYWnx8fxvJXHjdvMHe48e3u+LVkuz8WKoBEEESH09\n97ykpHBRYSY0raQEGDMGePjhrrLSZrE6LE0KSQOAoUN5/8gbTYQSrYICgeTcWCFuSkvNlVf2DUsT\nFRzy6xHgNtXV8UqGalRVdQ9x09jYiP3792PmzJkAgOjoaCQmJqKoqAiXXXYZAB5juW3bNgBAUVER\npk2bhujoaGRkZCAzMxOlpaVBNME+hD3W1Czl5fwikKFrS3MzH8BHRZkvBR0icTNjxgx+MTc3A4mJ\nXR8Ess4NoCxulLw2AK1z4wPZQnRLjh8H+vfnVchSU82Jm+Ji4NprgfvvB+65x3B4muGcGyODKXny\ncmwsFzglJYb6ZwQnXXtOsSXsdlhVCto35yYlhW+rJQz0KC3lwkakD3Is8NzMmDGDT7ympGjfd+S/\nX9++zq2WVlFRgV69euGVV17BokWL8Nprr6G5uRm1tbVI6VS2KSkpqO1Uw1VVVUhPT/dsn5qaiqow\nlbwkBGhu5if65s3GtpNC0oDI8NxIXhuXq+u9+HjlRTi10AtLUyomAIjl3OgUFCAIIsKRxA0QmOdm\n5Ehg8WKgrAz429/M98dKz01Hhyd52QPl3RChJtCwtLY2oLHR//ksIgz0kCb6jXiA6uv5dw4YwF+L\nipvGRn7tTZjg/b7e9t3Fc9PR0YHDhw9j1qxZePbZZxEXF4fVq1f7tXPJB40Gkcc1FhYWRuxr6W+7\n9Efk9ebVq9GSksIvgoYGPxtUt++slFZYWIjSH3/0iBtD319ZCbjd2LB+fVDtXb58uUd0eH0eH4/W\n+npD+6s5dQo7pQpAKSmoKC72+ryooAD1smvBs33njJHndX090LOnX3/ctbWq3+97bIL1e4Xita9N\n4e5PIK+XL19uq/4E8poIMlaJmxEj+KTSypU8hv/ECeHNvY63lZ6bAwf4oCgjo+u9IFdMc9K56xRb\nwm6HXkEBvbC0s2d59EVUlL8tgQ72S0p4tIsRcVNSwhfFjYrq6oPI9t99B4wb55mE9tgiIm4cUFAA\nTIfq6mp2//33e17v27ePLV26lC1cuJBVV1d72ixcuJAxxlh+fj7Lz8/3tF+yZAkrLi5W3f+6dev0\nuhAxFBQUhLsLxtm6lbGcHMYuuoixDRs8b+vacvAgY0OG8L9ffpmxe+81/t0DBzIGMFZVZXxbAxQU\nFDC2axdj48Z5f1Bfz1hCgrGdXXQRY5s28b8/+4yxq67y/nzDBsYuucR/u+++Y2zChK7XAwcyduSI\nd5uKCsbS01W/OiLPLxXIFvvhpHux1Vj22yxdythDD/G/b7+dsTfeML6Pc85hrKys6/XjjzM2Zw5j\nHR1Cm3vO15YWxmJi9LfbtInf9/RYuZKx+fO931u1yv89C3HKtceYc2wJ2I69e4XPZUX692fsxx+V\nP1u9mrHrrtPe/uBBxrKzGWMKtkybxtjGjeb7lp7O2PnnM7Zmjfg2//u/jN1wQ9frjg7GYmMZa2rS\n3u655xh74AHPS48tc+bw30GNX/2KsRUr+N8HDjA2fLh4X0OEyP1Y13OTkpKCtLQ0nOicGdq9ezcG\nDBiASZMmec2+5uTkAABycnKwadMmtLW1oaKiAuXl5Rg+fHjw1JmNCHusqRnKy4F+/Xhcpiw0TdcW\nq8LSpAS3IDJjxgz/MtBAVxiYkZh1kbA0tZwbkVLQGgUFIvL8UoFsIbolcs+NmZybs2f5/TIrq+u9\nxx4Djh4F3nlHaBee87W6mt/D9KIuRMNgZPH9Hs49N6hhaU669pxiS8B2XHEF8MMP5rfXKyigF5Ym\nKybgZ0sgRQVqavi4aexYv+q0msjLQAP8ehXph8/16LGFwtK6uP322/Hiiy/ioYcewtGjR3HDDTdg\n7ty52L17NxYsWIAffvgBc+fOBQAMGDAAU6dORW5uLpYuXYq77roroJA1IsiUlwPnnANMnWos70Za\nwBMwVwra7eZVgzIzQ5N341sGGgBiYngcbWur+H70xE0QCwoQBBHhBBqWVlICDB/uLUik8LRf/9pY\norJISBogHpbmW5kJAEaN4uFqRqtDOYWmJuDee61ZkyiSqavj1f1EOHsW+P57c9/T2Mh/a3nhIDki\nOTedC3gqEshg/+BBfu0aFUhSGKocvX0wxsdzvtejyLbysLSUlK4KjxGGkLjJzs7G0qVLsWzZMvzm\nN79BYmIikpKS8Pjjj2PFihV47LHH0FO2Zse8efPw4osvIi8vD+PHjw9a5+1G2GNNzSD33GzZ4rkJ\n69oiLeAJmPPcSEl/vXoFXdwUFhaqJ/obFRRmCwrIb6otLfx37tHDu01MDE9mbG9Xt8MhkC1Et8QK\nceM70AGASZO4l0RggspzvoqKG5HysQ0NvG8XXOD9fu/efPuyMv3vMYHtr72vvgL+/GehinG2t0UQ\nRTsOHOALz+rR0cHPJbPrI0rjCrUJdZGcG5nnxs+WQDw3paVc3KSmGtuHr+dGpB9lZfx6HTLE85bH\nFr2cHbnnKyrKeH9tgpC4IRzMqVNc3AwaxE/kI0fEtgs0LC2E4gaAclgaEJi4SU72X2VczXMjbdPc\n3LWAp+8N2OWitW4Iwsn4ihujlUTlJWF9mT4d+OYb8X2JihupSpRWX4uKgPPP77rPyenOFdPWruW/\n35dfhrsn4eX4cbHnfEMD/9+s50arUhpgOCzNDyNrPvkiiZu0NONhab4TGnr92LmTT3goiTwjYWnS\nd0VgaBqJGwuJyJhZyXPjcnmFpgnl3EieGzPr3IRQ3MyYMUM5LA0wJ24kURcfzx9c8nLSap4boGvW\nSCkkTaA/EXl+qUC2EN2O9nagooKH4gLWem4AYXHjlXMjIm4A/cGUbwloOUGsmGb7a++LL4A77wQK\nCnSb2t4WQRTtOH6cjxH0xgn19fyZalbcaFVKA6zJuTE70JeLG1GBVFnJPTDyCoRSP7T2cewYMHiw\n11vCOTe+ApHEDWE7tm/XbyOJG6ArNE2EQMPSzpwJredGTXQkJBgTN/JFPAH/0DQ1zw3QFZqmJ27I\nc0MQzuPUKT4jGhvLX5spKKAlbqZOBb79VnyRQSPiRm9Qp1RMQKK7em7Ky/kgc9EiLm66c97N8eP8\nf71nfX09H5Q3NZlbPFLPcyNNTGo987U8N1aEpRnZh+Sp9fXA6F2PZWVd6+L4orVtezs/RvKJYBI3\nhO1iZi+5hN9gtZCLG5nnRtcWK8LS0tNDl3MTjLA0QFncaHluRMSNSn9sd34FANlCdDvkIWmAOc+N\nUoiKRGoqDy/WyVcwnHMDaA/IGNMWN0GsmGbra2/dOmDmTGDoUH6/37NHs3lAttx9N/Dxx+a3txBF\nO6R1mPSe9dLz8/zzzXlvtCqlSeh5b2pqPIN7S9e5MZNzozaZIZJz4yNuhNa5kWyPju56r29fEjeE\nzXC7eYlQNRjzFjeTJvEbsDzMSg3famk2DksDYE1YWkcHrxoiLwTgK27USkED3mFpsgIcXkjlqQmC\ncBZK4sZI7H1VFb//+IaoyJk2Ddi0SWx/VoWllZXxGV+fMBgP3dVzs3YtcNVV/O/LLxcKTTPNyZPA\n4cPB23+gSJ6b+nrtdtLEXyDiRstzA4iJG6s9N3V1/NmflWUs50apmIBIP44dU/fcaF3LSuIwPd2c\nFy3MkLixEFvFzLa18cH4jz+qt6mv5+5OyYuQkMBrsH/3nb4tbre358ZoKFU4cm4C9dy0tHBb5S5i\nI2Fp0k21oYFybsgWorvhK25SUviAR7RMsjSLq7W0gkDejamcG61QFslro9av/v35s8a3sqQF2Pba\nY8xb3MycqStuArKlvt42s+uqOTeJiWJhaZK4MVMxTUTc6JWD1sq5MVtQ4OBBYNgwXrTJiEBSEzd6\n/SgrAwYO9HrLK+emqko5TNK3mID0XTY5t4xA4sapSGJDy3MjrXEjR3S9m0jz3FgRluYbkgYoe27U\nwtJEc27Ic0MQzsNX3MTE8PuA6No0WpXSJIxUTLPKc6NVTADgomfUqO7lvdm7lz8rhg3jr2fOBDZs\nCN56P/X19p5dP36cnwMi4qZXL2D8eHOeG72CAoB+OWgtz42UJ2f0OEohadL3NzSIrR1jJiyNMS5u\n5PcaOT168HNT6VgoiUMz4mbrVuDnPze2jcWQuLEQW8X/SuJGy3MjD0mT6CwqIJRzEyHixrPOTaBh\naSLiRsRzY7KggK3OrwAhW4huh6+4AYzl3WgVE5AYPpzfPzTu+6ZzbtQGOEVFwOTJ2tsHKTTNttee\n5LWRvFlZWTx3QcMbEZAtNhI3fnY0NPBzcvBgsZybpCQeQbJ/v3hxDAkrwtJki3j62RITw0PK9dbK\n8UUubqKixK57xrTFjdr1WFnJxxE+YwwvW9TEkVWem7ffBj76KKyL95K4cSqinhtfcSN5bvQqu8jD\n0mxeChqANWFpouJGpBS0Ws4NeW4IwpmoiRsj8fd64sblEvfeWOG56egAduwAJk7U3j6IRQVsiTwk\nTUIgNM00NhI3fpw4wcWdyLNemvjr2ZPnjBw4IP49LS3Atm38XNMikJwbwNxgXy5uALHQtPJyPoGs\n1Bet7RVC0hS3V7JBSdwYLSjQ0QHk5/O/S0vFt7MYEjcWYqv4XxHPjbSAp5zsbKC9HTOGDtXefwR5\nbizLuRENS9MrBa2Vc6NRUMBW51eAkC1ERMMY8LOfiS96DITGcwNwcaNRVMB0zo3SYKqkhA+G9GbL\ng+S5seW119ICbNzIiwjI0RE3js25kc57UXEjPT+NhqZ9/DE/z/SukUBybgBzRQXMiBu1fBuAX7dn\nz/JCHr6olIH2skXt+60IS9u0iW9z9dXcqxsmSNw4leZmfoEa9dxIi3nqrXcTQeIGAPeYqIWliVSH\nA7y9VRJyccOYNWFp5LkhCHuzezfwwQfA3Lldq6rroSRuRNe60QpR8SUYnhu1md7vvgNycvS3704V\n0zZv5vklvoPEGTO46DEaaqUHY/b23Bw/Lu65kcLSAONFBV5/HfjFL/TbaeXcdHTw31It+gIwV1TA\nV9yIlIPWEjfR0Xw8o3Tv0FrjRkLNBrWwNCPn1ocfAj/9Ka+++9134ttZDIkbC7FV/G9zM3+QNjer\n31CUxA0ATJ2Ksg8+0N6/b7U0I+KmvZ3fXPr0CYm42bB2LX+g+AoTwFrPTXMzF4e+bSTkYWl2Wedm\n5UrrH7YC2OpaCRAn2UIIsm4dcMcdwAUXALffrh/GK82y+k6wiHpuTp/mAxo9DwnAQ8QOHFC9rxYW\nFvJk5qYm9YkYX9QGQ999xwcxegwfzr1cIknUBrDltacUkgbwEt4DB6ourm3aFum5U12tPJMfYvzs\nMOq5kYsbUc/NoUM8PPKGG/TbaoWlnT3LQ+Ki+NBY8ZjoLaDpS2Mjv3bkgkOkHPSRI8CQIeqfq3lf\njh1TDEsTyrlR8twkJvL7W2Ojdn8BLg7/8Q8ubnJyyHNDBIGWFj7IHjRIPTRNTdxMmYLeOguOBeS5\nqanhN7qYGP6/aLUgk0Q3NvIbmlKpUivFjZbXBhCvlma0rHYgPPggXyOBIAhxpAHsn//MveNLl2q3\nlwZ4vvcgUXEjUilNIi4OmDCBVyxSQwq90SorLUetSpSouImL4wOuMMbghww1cQMEZ72b+nr+bElO\nNrZuUqg4ccKcuDESlvbmm7w6l9IEpi9a4kYv3wYwHpZ26BAXKfKFMUVzbpTGZ/J9KIksEc+NkYIC\nLpd4aNq2bfw4jxnDJ1l27AhbUQESNxZiq/hfaSBuRtzk5KD30aPag363u0vcxMby79ObvZQ4c6Zr\ndiAEnpuLx41TDkkDrBU3WmWgAe+wNBMFBYJyfjU2hiUMzlbXSoA4yRZCgOZmHvY1cya/XvPzgVde\nAT75RH0bpZA0QLyggGhImoRGaNqMGTO4UNEbxMmRJqLk+YUdHdwLoVdMQCIIRQVsd+1VVwP79vHF\nVJXQyLsxbUtDA3+e2GQl+YBybuQThFJ1NT0R0NrKIxDuvlusg8nJ6mFpPuJG8ZgYzUEpKfEOSQPE\nwtJOnfJfqkOOmkAJJOdGSdwA4ufWhx8CP/lJ13ekp3P7wwCJG6ciDcQHD1bPu1Fa5wbgN8rRo7nq\nVqOpqWuWJDqa/xMNb6qs5Cc90HXDExVGZtASHaH03EhhaSYLClhOezu3STTniCAIno84enRXvkpW\nFn+o33EHH9gqoSZuRHNuRCqlydHLuzGSbyPhO1NcUsLv4yKhckD3yLv58kvg4ovVQ5Mvu4wnXBvN\nUdVC8nbYdSV5ubipr9duK/fcuFxioWn/+he/NvSqpEmE2nPjm28j7UNvUkNP3KiFiqqEpfl9v5JY\nUSulLSLoGOsKSZOYNClsoWkkbizEVvG/ep6bjg6gokL14jndqxefAVBDHpYGGCsHLb+AevTgwiiI\nA/odGzaETtyIem7skHMjiZoweG5sda0EiJNsIQRQCjuaMgV49lng+uuVCwxoeW5Ew9KMiJtp03hY\nmkIORmFhoXlxIx9MiYakSQRB3Nju2tMKSQP4bz5iBA/f8cG0LdLzpG9fW4gbxZwbo6WgJUTEjWgh\nAQkD4kbxmBj13KiJm2B4bjQW8PSyxUhBAam93rm1Ywcfy51/ftd7OTlhKypA4sapyD03SuKmqorf\nRFRmmNr0FqqSh6UBxvJufGcHghyaFtPQELywtORkfkNkTLsMtNTWTtXSpARBqs5GEOKsWwdceaX/\n+7ffzqtk/b//5/9ZqMVNejqQmQn88IPy5zU1xsWN74CoqCjs4sZ2fPGFtrgBrF/vxmbixouODh4h\nYkTcyJ+hehXTDh/m56EUCiWCVilo2QKeqgwYoL3Ehi9mxA1jmpPPqvuoruZjMb1CIUrbtrV15W/5\nIiLopJA0eR4feW6cga3if+WeG6WwNKU1bmRkjh6tLW7kYWmArcXNuMGD1T0qCQmBiZv4eD5b0dSk\nH5YmFU/Qy7lRKSgwY8AA4LXXxPoqgiRuwhCWZqtrJUCcZAuhQ00NsGePek7FvfcqX6OB5NwwxgdI\nRsQNoBqa5sm5CTQsTbQMtIQkbiwMQbbVtXfiBH8GjB2r3W7mTB6+5oNpW+Tixm45N2fO8OdefDzv\no5FS0IB+UYG//IWvNyWfaNVDqxS0SM7NyJF8skH0PFYSN3o5N2fP8jGVll1KoWXHjqkWE9DNuZHy\n8KIUZIGeuGGsqwS0HKmoQBiq+JG4cSrNzfziUPPc6FXi0Eq6A/zD0mwsbizNuVGqxiKFpukVFIiL\n4zeOM2fMeW6+/RZ47z2xvopAnhuCMEZBARcNajkVs2bx2XPfcr+BeG5OnuSTIWreZzW08m7MiBu5\n56ajgw9aRIsJAHxAFx/v3OqM5eXKFfF8ueQSHpZm1X3Xzjk38vPeTFjaeecBe/cq5/O2tgJ//at4\nIQGJQHNuUlP5hKbIb+1284nkQYO839fLudELSZP24StQysr0823UtlULSQP0hfMPP/Dxn68nNzWV\nl0AvLtbvk8WQuLEQW8X/Sl6G/v35Tdf35qAjbkpPn/aujONLBIWlHdqxw5qwNLdbeVAjiRs9zw3Q\ntfCWiYICJVu3ah8To4TRc2OrayVAnGQLoYNaSJpEdDQfbPl6bwIpKGC0mIDEtGmK4iagnBtpgFNc\nzAfTaoMhNS67jM/wWoStrj21ZGxfevfmAz6fnFZH5tyYETfyZ2hSEg9pU6q49emnwNCh+p4yXyRx\no+R5Ecm5Abq8N3ocPswnmGNivN/XC0s7dYqfI1oo5c1olIH2siUpiY/X5GMNrfNXz3MjeW2UhH2Y\nFvMkceNUpHVuYmP5RXLihPfnOuJGN+dGKSxNdH2WEIub6IaG4BUUAMQ9N0DX5yZKQcecPRsccUOe\nGx8TQqUAACAASURBVIIQY906/ZyKO+4A3n+/a3a4rY0POpXut8nJfECnFbZhNN9GYtQoXtxAqTBM\noJ4bo8UEJB58EMjLC8vCwUGnqkq8cpyRcGg9bCZuvJCKCQD6lVHb2vgz1jcUSyk0raYGeOop4J57\njPdJiqBQ+v1rasQ8pCNGiHkjlELSAL4wJqC+MKZZz41GWJoXLpf/9lqeGxFxo5b3FKaiAiRuLMRW\n8b/ygbhS3o2OuBk9ZYpjwtIG9+kTGnEj4rnp3btLdBrszxC9UEGjUM6NJTjJFkKDH3/komDcOO12\nmZl8scZ33+Wvy8v54EDpmo+K8l8/xhez4sbl4vkd69d7vR1Qzo1c3BjJt5G46CIeNvOPfxjfVgHL\nr73GRj5ANENlpbgnKz7e777ryJwbaQFPgD/3XC71SVBpvR7f2X/fimnl5cCMGTy87+abzXVSLTRN\nJOcGEPfcqIkbgJ8raqFpQQhL87PF1/Ojdf5qhTzu28fHJRddpPx5mIoKkLhxKr7ixjfvRm2NGwmt\ngXRbG4+5lj+sbSxuUFsbvGppAL8ZVlfrl4IGeD/UvDZSf9Ru/lVV3BarVvwlzw1BiLNuHXDFFcoJ\nt77ccw8PTWNMPSRNQi/vxqy4AXgI3bp1/u8HGpZm1nMDAL/5DbBsWXDXNjNDRwcwfz7wy1+a2140\nLA2w1nMjiQK759wA2s9635A0ifHjuyqmHT7MRc1PfsI9gCLXohKC4kYVUc+N0gKeElqhaUbEjfw6\n0ghL0/1+Lc+jlufm/fd5SJrasZg4Edi5M+RFBUjcWIit4n/lA3GlhTx1PDdFJSXq4sbt5oNw+QyL\n2XVuAH6jCaK4OXPoUHA9N336dIWliXhu1PJtdPpTWVLCH8B6C6GJQjk3luAkWwgN9NYwkXPllfx+\nsG2bvrjRy7spKeEzxWa46ioubmQDINM5N9JMr5liAnLmzOH3sA0bzG0vw9Jr7/e/50Vb9KrXqWFE\n3Ch4bizLuQmzaFTNuQG0xY1vpTQJyXPzww9c2OTmAo8/rl+4QQtBcaN6TEaMCNxzE6i4iYvjk8ry\n31M050bp+/XC0nyFFMBfv/MOcNNN6v3s04fbcuCAepsgQOLGqYh4bjTETXtSknqohG9IGiDuuWGM\nzwCEMuemsdFeYWla4kajoECM9BtZlXdDnhuCEGf9eu1iAnKiovjCgq+9FpjnpqMDOHhQfYCkx5Ah\nPL7fd72bQDw3xcV8IG20mIBEVBTPvVm2zNz2weCjj3hZ4XfeMR/6qzU49CUYOTcJCTyaIphREEYx\n6rlRejZmZ/Nn3uWX83PmvvsC75daZIoRz01pqX4UhV5YWiDiBvAOLWOMh1SKVEsD/MWNljiPjeXe\nQd/frKiIf+/kydrfFYaiAiRuLMRWsfd6nhuddW4uuvpqbc+NWXHT0MArCkkJdUDQxU0fl0tb3Ih6\nLqwoKJCcbNpzk9zWxquuWJV3E0ZxY6trJUCcZAuhQUqKf0lXLW6/neeW7N2rL27UvAVHjvABh1Yo\nqx6S96aTgHJuqqqML96pxM9/zgc7e/YEtBtLrr39+3mFuw8/5GvxmJ08CtBzE3DODWCLvBu/nBup\noADAn/VqkQdq4iYqih+fVat42KAVqHlufBbxVD0mvXrxffgWapLT0sLF3eDByp9rlYMWFTdygVJT\nw8cHKhOsfrYY8dwAyqFp777LvTZ6XrScnJDn3ZC4cSrSOjeAv+emtZU/3NLT1beXVvFVcnH7VkoD\nxMWN0gMgFOvcBDvnxojnRi/nRq0/VVVds1hW0NjI+xKGsDSCiDhEQ9IkMjL4ujerVpn33OzcCVxw\ngbHv9eXKK3lInUR7O59k0puI8SUujv8rKAhc3MTHA7/6FfCnPwW2H1G2bwd++1seJiifba+tBa6/\nHnjmGZ4QnZwcGnETDM8NYK+8G7ebPxPl4wwzOTcAP09mz7aub0ripqNDbIJSQq+owOHDPERMGof5\nohWWVlEhLm4kwWEk3wbwFyt6BTF827e383X3RIo6kOcmsrFV7L2S50YSKqdP8xM1Olp188JNm7gr\nUqlUYSBhaWEQNy2VldaEpUm5Rr7IxY1IKWg9z41SQQHG0FFVxcNMrBQ3qalh8dzY6loJECfZQmgg\nGpIm55e/5PdLszk3O3cCEyYY/145l18OfP215/789Sef8PuQmWTstDTgiy/MVUrz5d57gfz8gBb1\nFL72Pv0U+Ne/gFtv5QPAe+7h7/3851y03nEHb5eYyCf/RJc1kGOkFHQwcm4AW5SD9thx4gSvHCg/\nz8zk3AQDafJWTn09P/6yNWk0j4leUYEdO3gxBDUCzbnx3YdOSJpQzo3W+esrnL/8kl9LIvmAYSgq\nQOLGqcjFTe/e/IKVHqA6+TYe1OJS1cLSRB4IVomblhbgs8+EmsaEcp2bYBUUqKtDR48e/OFlZVha\nWhp5bghChJkzjW8zYwb/N2qUeptge25SU3m41ebNAIDY+nrjIWkS6el8hthsMQE5aWl81veFFwLf\nlx4VFcBtt/EQwQ0b+PFYtoyH0+TldbVzufj93Mw91kgp6GB5bmwgbjwo5ZolJRnPuQkG2dm8ype8\nL6Jr3EjoeW62blUvjwyol4JuaOAiQOS3kAsUo56bQMPSpJA0EVJSuNDdv1+8fwFC4sZCbBV7Ly3i\nKSHPuxEQNzNmzFAXN3YIS9uzB3jgAf12bW2I6uhQ9rgA/DdqbharMGNFWNrs2XzGUg21ggJVVYjp\n27fru6wgjJ4bW10rAeIkWwgNRBKNfXG5eBiXVq5OsMUN4BWadtHIkebFTVoaXxXe7Pa+5OYCb7xh\nugKk8LUnnwkfMQL49a+BwkJeSMB3/SEz4qa9nW8j+rt0h5wbJXFjNizNanJzgXPP5V5NSQwqFBPQ\nPCZ6npstW7TFjZrnRjpXRarByQsK6Igb3ZwbPXEuP7eamoDVq4H/+3/1+ygR4sU8Sdw4Fd+BuDzv\nRm+NGwm1+GOlsDTRUtBWiZu6OjGPg1Jf5URFiXudtMTNqVNcICl9Lic7m5ezVEPNcyPNqgRD3JDn\nhiDCh1pBgcpKfq0PGRL4d8iLCpgpJiCRnh54vo2cYcP4IHPLFuv2qcSpUzwHSgQzeTe1tfw5Jgtp\n0qQ75Nz4FhMA7BOWFh0N/PnPPCfu4ov5xK9opTQJLc9NSwsvX60VvqknbkQwEJamuW1rKx8PaHmu\n5J6bTz7h94HMTPHvC/FiniRuLMRWsfe+A3GDnpvCwkLjYWk2FTcteg8c0dA0LXFz4gS3I5Da+1p9\nqapCtcsVWMKrL1JYmtmH7Oefm46Xt9W1EiBOsoUIA2o5Nzt38ph9swsVypk2jXu7q6ux5+uvzYub\nQYP4YNBKJk7k+QkmEL72RBO0AXMTSEZC0gBrc26kRTwBW4Sleeww47kJlbgB+LN6yRJeWvrii4Fv\nvvETN5rHZNgwXjSgrc3/s127eAloLXvUSkEbFTeCBQU0c26qqvg9QWv8Ihc3774rVkhAzqRJvLBH\niCBx41T0PDciOTdq7nk7hKUZEDcdet6UQMVNcjIPSzBafUgJSYj53jCrqtDau7f5eHAlAvXcrFjB\nE5UJgjCPWliaFcUEJOLigOnTgcLCwHJu/vAH4L//25o+SUycGPxBjxHPjVlxI1pMALDOc9Pe7j3Z\naANx48HOYWlyFizg1fKeeMKY5yY+no+jfNcQBPRD0gD1UtBmPTdGc2769OFjifZ2sWIYkriprubF\nBG64Qfy7AC4gP//c2DYBQOLGQmwVe6/ludFZ4wYQyLmxg+emsVE/V6apCfF6NyzRB42auImL4/uw\n6sasVDGtqgoZo0ZZG5bW1BSY56ahQbmangC2ulYCxEm2EGFAS9xYkW8j0Zl3M7JvX/PixuUK3Dvt\ny4QJpj03QtdeSwsfOIt6VsxMIBmplAZYl3PT2MgrfEnePaW1SEKM6ZybUIal+XLzzbxAkY83QveY\nqOXdbN0KTJmiva3ksfUdw5gRN9ICnkZybqKj+RivulpsAVop5PHDD3mYq5HiC0DXQqAhgsSNU7HC\nc6MVlmal5yYpid+k9Vb7lSOVcdTLldHLuQEC99wA/IFohecGUC4qIN18rA5LC6SgQEMD5esQRKCo\n5dxYLW6kvJtAcm6Cwbnn8meTyaICukhLH4iG95m5xxoNS7PKc+MbymUnz82JE/YOS/PliiuAa64x\nto1a3o1epTSAD/YTE/3HWEbEjVRQoLaWn99GBYckjkTOX6mgwDvvGA9JCwMkbizEVrH38kU8AWtz\nbtQ8N2ZLQUdF8f01NOhvLyHdIPUG101NqG1t1W5jlbix0nOjIG4OVldbX1AgkFLQAYgbW10rAeIk\nW4gw0KsXv47k9ym3GygtBcaMse57xo0DampQVVBgL3ETGwuMHcsTsA3y9Ucf6XuPjYSkAaEJS7Mq\n58aG4qawsJB7EowWFAi3uFFA95goeW7OnOE5XqNH63+BUjloM54bgZA0RVuk7UXD0n78kV+nVi6o\nGiRI3DgV34F4v358xs7tNua5Ea2WJuq5OXPGe8ViCaOhaVJbvQdbUxNfH0YLUXGjtognEBJx09qr\nV3BybszOIDY2mg5LIwiik6go/wH1nj184KR2vzH7PVdeiT7bt9tL3AA8NM1E3s3QN94A/vpX7UZG\nigkAkZVz4ysIkpP5fs0sQmolVVX83E1M9H6/Vy91D53IUgp2Q8lz8+23wOTJmouke1CqmGbkfE1K\n4uOu0lJj+Ta+3y/iuUlJ4RMwN9xg7X0pSJC4sRBbxd77rnMTFcVP/uJifuPTcV9q5twoDfIDKQUN\nmBc3Ap6bPr6zR77YLSxNJedm9LRp9ioFHYDnxlbXSoA4yRYiTPjm3ezYYV0xATlXXQVXR4c9xY2J\nvJusHj2UE7rlmPHcBDvnJiHBmpwbX3HjcnlX0AoDM2bMUM63ASLOc2Mq50akmICEkrgxcr5Kx3vX\nLt0y0Iq2SGFtIjk3UVG8vejCnWGGxI1TURqIDxrEZxVEF4jSqpZmxnPT2soHxErCKoiem5Dl3ATZ\nc+OVcyOy6KgeVuTckOeGIALHN+/G6nwbiSuv7Po+O2G2HHR1NQ9/0sKo5yYUOTeizxw9lASBDULT\nnCJudMnO5ueffDJSJN9GQk3cGDlf09P5/cKs5+bMGXFxvn49MHOm8e8JAyRuLMRWsfdKA/HBg7m4\nEQhJM5VzoydupFrqSomdQfTclOvNwtnNc6NSUGDbwYO8r1FR1jwYGxv58WhpMVbMAegqQUo5N46y\nhQgTvp6bYImbgQNx+pJL+ESXnRg3Dti/X8z7L6OurIwPpLUwOlgMVViaVTk3vhWowixuCgsLlYsJ\nAPYsBa2B7jGJjeXX0qFD/HVHBx9jiYob35wbt7vruSyK5LkJJOdGVJyfd5711RKDhNByuvfffz8S\nExPhcrkQHR2NpUuXor6+HsuXL8fp06eRkZGB3NxcJHbGV+bn56OgoADR0dG47bbbMH78+KAaQSig\n5rn5+GMuckSwulqa1gPAjLhxuYTEjSXr3HR0cM+TWv7OFVeEJucG6Hr46nmktGht5d6fHj34edLc\nbGx/kseGPDcEETjyhTw7OnjSbpCem3t+/3vM6Ns3KPs2TWIiMGQIzzUyEI4XW1cnJm7GjRPvixlx\nY6YUtBUTVA0N9vXcKIWDJyXxZzdj/oPkcJaCDgQp7+bcc3mIWkqKuXVqAO5lzMgwJiDS0oANG3TD\n0lS3PXLE+PkbAQh5blwuF37729/iueeew9KlSwEAq1evxrhx47BixQqMHTsW+fn5AICysjJs3rwZ\neXl5WLx4Md58800wK0JoIgBbxd6reW527xby3HhybqwsKGC1uElPFwpLyxo2TLuNQuUaP1pauI1q\nN52f/AS4+mrtfYji++BjDKiqwrQ5c/hrK/JupPURXC4x+32RKttRzo2jbCHChNxzc+gQf20kzMkA\ntj1fTYSmxTc18YG01hjDTEEBozk3ZkpBByPnBgj7WjeaOTexsfyf73OjpYVHA+hNRIYYoWMiz7sx\nEpIG+Isbo15GaR+ArudG0RajnpsIQkjcMMb8BEpRUREuu+wyAPxH27Ztm+f9adOmITo6GhkZGcjM\nzERpaanF3SY0aW/ns38xPo65QYP4+yKV0gDrw9KsFjfnnCPkubEk50YrJM1qfPvT1MRFiGSHFWvd\nSOIGMFe5hzw3BGEd8pybYBUTsDtGiwq0t/PBvcvVte6ZEkYLCpjNuQmH5ybScm4A5Wd9QwN/P0JC\nnryQV0wTWbxTjm9YWhDFjSJGCgpEGMKemyVLlmDx4sVYv349AKC2thYpnSu/p6SkoLZzEFxVVYV0\nWanf1NRUVCktUOZAbBN7Lw3EfW8UUjiakZwbyYUsRy0sTa/8pNXiJiNDSNwcLi/XbmNHcSP/LTtv\nPJ7zy4py0HJxY+ZBG6DnxjbXigU4yRYiTMg9N8HKt+nEtuer0XLQtbVoTUzkgzqt0DSjA8Zevfj9\nra1NrH1LC79/Gsm5tDLnxmbiRjPnBlB+1ts0JE3omIwY0SVujFRKA6zx3KSn899OpwJuwOvcRBhC\nOTdPPfUU+vTpg7Nnz2LJkiXIUoildAWguAsLCz0uM+kA0Gvzr2Pq6nBxZ26I1+edMZk/nDmD8wDN\n/QEAYmLQHhuLTf/+Ny659lrP5xeUlyOl04vg2b7Tc6PZv8pKHGtsxEGl4915wxO2t1PcHNixAycH\nDFBt/2NxMcoaGzFEw96hFRUY1HlDUf2+oUOBuLiQHL9R1dXI7BQbhYWF6HnwICZ3zqoUFhZiTEsL\nMjpnFk1/X1oakJiIwsJCTO7oQM/OB63w9p3itra8HDtMXL8SdrheAn29c+dOW/UnkNdEmEhN5fkm\nABc3d98d3v6EgwkTeK5Re7vYGiE1NWjr1QuxWVlc3CgteNrRwUO0jOQYRUVxoXL2rNhstjTrbWQM\nZKXnxnfduEj03ERipTSJkSN5WFpjI3DggDGvq1VhaQMGmPN6paVxIep2266YQ8Awg7z//vvs448/\nZgsXLmTV1dWMMcaqq6vZwoULGWOM5efns/z8fE/7JUuWsOLiYtX9rVu3zmgXCD1OnmSsb1/lzzIy\nGNu0SXxfWVmMHTvm/d7kyYxt3er93ldfMXbxxdr7evhhxv7wB+XP/vQnxjrPIV3a2hiLimLsV79i\nbPly7bb338/Yiy9qt/ntbxl74gntNsXFjA0bJta/QPnlLxl75ZWu1wUFjF16adfrX/yCsVdfDew7\ntmzhx5ExxiZOZKyoyNj269Yx1qcPY+PHB9YPwjbQvVidoP82//wnY9dfz//OymLs8OHgfp9dGTKE\nsf37xdoWFTE2YQJjP/sZYytXKrc5fZrfp4ySnc3YoUNibX/4gbHRo43tv6ODMZeLsfZ2432Tc+ed\njL3xhvd7X37p/bwINc3NjMXG8ue0EtOnM7Zhg/d7W7cylpMT/L4Fg/Z2xhISGPvss65nqiilpfyc\nl1iwgI+FjPDFF4z9n/9jbBsJt5sxQH28aFNE7se6YWnNzc1wd84wuN1ufP/99xg0aBAmTZrkmfUr\nLCxETk4OACAnJwebNm1CW1sbKioqUF5ejuHDhwdPnRH++C7gKed3v+Pl/ERRyrtpagpvtTSp/GVi\nYvfIufGNh1XLhTKCb86NmYICfftSzg1BWIEUllZRwa8t0YqWTsNIaFp1Nf/d+vdXX+vGaDEBCSN5\nN2ZCelyuriqVgWDDsDScPMl/czXvW69evN9y6uoi13MQFQUMGwa8846xkDTAmpybK68EPvjA2DYS\ncXF8LOWwkDRAIOemtrYWjz/+OB5++GE8+uijmDRpEsaPH4+5c+di9+7dWLBgAX744QfMnTsXADBg\nwABMnToVubm5WLp0Ke66666AQtYiCd+Qm7ChNRC/916hm4jHFqWbvNmCAmfO+LvQJYyIG+lGmJgo\nVC1t7+HD2m0iRNx4jomV1dKUvk+EhgZ+LCnnxlG2EGFCKiiwaxfPtwniM9PW56uRimk1NTjd1sbF\njVrOjdFiAhJG7rFGiwlI+EwqmTouNhQ32z/5RLkMtEQEhaUJH5MRI4B//tO4uElO9s7vMiNu5MWG\nNFC1JS3NccUEAIGcm4yMDCxbtszv/aSkJDz++OOK28ybNw/z5s0LvHeEOawciCt5Cdzu8FZLk8RN\nQoL+TdyqdW5sUFDAQ0oKcOxYYN/hK26MipTGRi5u9u8PrB8EQXR5brprpTSJCROAvDyxttXVaEtK\n4gPpzkJHfpgZLALGxY2ZwaEVeTdKi3impfFzSTR3qbwceP114IknAutLJz0qKx0jboQZORLIzzdW\nKQ3gXp+UFP6Mz8gwf74GQnq6Iz03QgUFCDFsk5RrwUDcY4tSZa5wh6XJxY1AWNp5kydrtxEphRxK\ncRMX5/1g7RQ3nmNih1LQAXpubHOtWICTbIlUXn31VWzfvh3Jycn44x//CAD44IMPsH79eiR3VhGa\nP38+LuisQma7haalRTx37gRmzQrqV9n6fJXKQSst8uhLdTUyx4zR9tyYDUszUpHSIs+NqeOiJApi\nYnhBhOpq9UgJOcXFwHvvWSZuzktNNSdubBiWJnxMRozg54DemnpKSKFpQRY3qrZ0V88NEYEE23Oj\nFpYWqlLQBsPSHJFzM2RI1+tglII2k3OTns77KTIQIYggMnPmTMyePRsvvfSS1/tz5szBHGnx207k\nC01XVlbiqaeewgsvvBDe8OmePYHWVr5OxqJF4etHuMnM5Is8HjvG12XToqZGP+fGbFhasHNuAGs8\nNw0Nyh4PKTRNRNw0NBhbhkGPkyf5cVQjgkpBCzN9Oq9waLZiWWUlv/5ra0PvRXGouBFa54YQwzax\nzBYMxL1ybuQDacbU17nR8twwpv0QCKLn5ru9e7XbRIK4CWbOjVnPTVIS/01MPKBtc61YgJNsiVRG\njx6Nnr7hOYDf4tOATReadrm6wk3PPTeoX2X781V0Mc/qahRXVPB12yoqlNelCcRzE+ywtGDl3ADG\n8m4sFjcnt2/X9twkJUVMWJrwMRk9Gli61NyXSOLm9Gn+t0gooQlUbXFoWBqJGyditedGfpNvaeFu\nb98LMC5OW9ycPcsH7T16KH8eRHETkTk3WtXS7FJQoGdPc5XWCCJEfPbZZ3jooYfw5z//GY2dXl7b\nLjTdpw8wdqz6PbK7ICpuOte5QWwsH5ydOuXfJlQ5N+Hy3FghburrlRfrNklcZaVxz41Nw9JCgiRu\nwpFvAwAPPgjceWfovzfIkLixkJDEMnd08H9aNDcH/ID0yu+Qe27Uwrz0PDd6D4AghqVNvvRS7TYi\nDxklb1WwUCkoENScGzNhaVI5bhPloG0d928QJ9niJGbNmoWXXnoJy5YtQ0pKCt5+++2A9ief+Sws\nLLT8dW1MjKeYQDD2L1+wNZj7D/j1xIk4s3atbvuqgwcxZto0AEBd79747uOP/dt3hqUZ7c/+8nKc\nPHBArH1VFXYeO2bY3iq323PflX8mun1hQYGnoIDv5ydaW3Hgm2/E9tfQAHR04KvPPzf2/SqvU91u\nbDt+XL19r144VVrq3d8DB1AsCy20y/koX+w4aN+XmorSbdvw/dq1HnETjO+T4/V5djYK9++3xe8t\n+lqI4C+3ow0tHGeQRYsYe/117Tbvv8/YT35izfe9/TZjN93U9frkScbOOce/nbRwlxrffssXi1Sj\no4Ox6GjGWlr0+/TMM4w99BBfCExv4dCsLMbKyrTbbNnC2IUXard5/XW+YFoo+Oc/GZs7t+v1wIGM\nHTnS9bqujrHExMC+44EHGMvL438/8QRjv/udse1vvpmxv/2NseHD+QKnRMQT6ffiiooK9uCDD+p+\nZnShacZC9NvMns3YihXB/x67c/AgYwMG6Le78ELGNm/mf193Hb9v+pKdzfdnlH/8w/serMV55zG2\na5fx75gzh7GPPza+nYTbzViPHsqfLV7M2FNPie3nmWf4Qo7l5eb7Iic9/f+z9+bRcZRnvv+3uiVZ\nkiVLli3J8oaNjQEb4wHMYgNjEwMhLDP4ALmTEBKYyWTCgTu2s3G5Ey6ZxLlkIBPjJITJXDK/QLiT\nSzIZOWFLAoxFcGwINpv3BS94l2VJLcmWtfX7++PtV11dXctb+6Lncw4Hd6uXerqrq95vfZ+FsePH\njf/e3My/LzWf/jQ/n4xEVq7k39dPf8rPq4QlngzxJOSxrSydcPQozyM2w8uaG23xul6nNICnBgwM\nGFvbVs6Noujn4uphMy1t3aZN5o+JSVra8HcyejTfHqvudGZ44dxUVjp2bgL5rQREkmKJM4yxghqb\nTpW7+dZbb2HKlCkAIjxo+oEHgDvu8P1tIr+/Tp/Oj/FtbeaP6+zEW7t28X/rNRVgLLg5N2HU3JjV\nqdituQG8qbvp70e2s9O8kUGM0tIC+a2ItDSn9WGSRP537zHULS1uZDL5g5ERfnZLM0pLU5S8wCkr\nK/67TF6yOOhZnSi6u/nBUzItTarmxmpxH5a46evj/6lPYqL4OJPhJzEnUM0NkSBWr16Nbdu2obu7\nG/feey8++clPYuvWrdi/fz8URUF9fT2+8IUvACgcNF1SUhKdQdOLFoW9BdFAUYB584APPgA+9jHj\nx4k5NwAvYNe2gz51Kn/RzC6y4saqUY4ZbmturMTN22/LvY6X4ubYMfSPHYvylMl18yR2S3ODaAUd\nVs1NQiFx4yGB5N5nMtYLei/n3GjFjd4AT0FZGXcT9MSNtiheD9m6G1nnhjGgrw9/fv315q8XZeem\no4N/bopSuH+J78UrceO05qaigmpuEhRLXFm2bFnRfddcc43h40fyoOlY7K+TJxu3dwb4sb2zE1eK\nNt+TJgGvv174GDeLRVlxc/o0H8QoMSG+CLdzbvQGeArGj7d2vtSvA3gjbo4cQbl6bIEe1dX591Rv\nQwTFTSC/FeHclJcDc+f69jax+N17CImbuBGGc6M+yBulpQH5WTd6BykZ29lrcXPmDN8mq6uyX4Fb\nnAAAIABJREFUURQ3oqGAkSh02zGtt9d9K2jRUICcG4IgvKShwTz9+vRpnikgLqTpDfJ0mpIG6M93\n08NpShrgv3MTRlqa1YwbwDgtLYLiJhCEuCkpIefGQ6jmxkMCyWns6gpE3AzHIpuWBph3TJM5eNkV\nN1ZpablttfxeoiZu1LNjVOKmIA634iZk5yZJ+b9JioVIPrHYX63ETUcHUFubj0Wv5sZNDYM471l1\nJnWakga4r7kxGuAJ2G8FXVrK1xZuOXIEh61aSlPNTSEBtYKOxe/eQ0jcxI2gnZsxY/iBRxzkzVoi\nm8268UPcWDk3ZkJMTdTEjXp7/HJuvBjiSc4NQRB+0NioP7dG0NnJ5wIJ9Gpu3Dg3JSX82KZNn9Li\ndMYN4K9zM348Fzcys2tOneJui0fOTb/V51FVxd9TvW1Uc0M1Nx5D4sZDfM9pZCwwcTMcSzrNF7Hi\nwOfGubG6MjNmjD1xU1rKRdfAgP7jcttq+b2UlfHXGBoyfsyZM6GLG92aG6e4dW7E86nmJlGxEMkn\nFvurpHMzHMvYsfzcoxYjbrtPyVxAciNuvKi5MRIElZVcoFmJM8BbcXPkCKZfeaX5Y9Jpfs4R6xjG\nIpuWFshvpbKSr2Pa2pzX0EoQi9+9h5C4iRNnzvBFuExDAb2ifqeoF9JOxY3MlRm7zo2imDsHss6N\nohQPztTS1xfsEE9ybgiCGKlYOTcdHYXOjaIU1924vRIucwFJplGOEX46N4B8alpPDzBhQnA1N0Dh\n2If+ft6Uwcs1S5xQFC6Qa2v5BVvCE0jceIjvOY0iJ9bKuenv967mBig8yJulpQVdcwOYp6bJ1twA\n1ieaCDQU8LzmRgg/uydZ8R2XlTluBZ2k/N8kxUIkn1jsr1bOTS4trSAWbd2Nm7Q0IHDnxtM5N4C8\nuDl1yjtxc+QINpp1uROoz/URTkkL7Lcybpy7fVWCWPzuPYTETZzIZPgVjiBrboDCjmlRaigASIkb\nKazcizAaCogZCnpXBrVd7OziZoincG0Ax0M8CYIgDBHixqhmJJeWVoDWuYl6WlqUnBsPa276ZJws\n9bk+oilpgVJXR/U2HkPixkN8z2nMZPgPIMiaG8BeWppRapdX4qa/n+enivjMFteyNTdAtJybdJqL\n2MFB45obMcTTCYy5c27U4sahc+Pqt9LTA2zb5vz5HjPScpmJeBOL/bWigp9PjDp45dLSCmLRNhUI\nyrlxmpbm55wbIN9UwApZ5+bYMWDNGuO/9/fz2UO33mr9nlpxE8FOaUCAv5Vx43wXN7H43XsIiZs4\nkcnwA3jQzo16IR12Wpq63gbwzrmJkrhRb48fNTd9fTy3t6Qk/15xcm5++1vg/vuDfU+CIILFLDVN\n2y0N8N65ka25iapzM3as9Tkim+XHbxlxs3498I1vGP9diMmUxLIyJmlpgRGAuBlpkLjxkEBqbpqa\n+MHIrMWjl3NuAHnnxqoVtBdDPNUpaUAya27U2+NHzY06JQ2w31DATUpbDle/lc5O4KOPnD/fY0Za\nLjMRb2KzvzY0GDcV0M65AQprbkTnNK0AskPca25kzqdiKHdNjfVjOzuBHTuMu4oeOQI0NcnFEZO0\ntMB+K7NmAeef7+tbxOZ37xEkbuJEJsOt5lTKWEQA/tTcRKVbmlbcSKSlSRFFcdPXZ15z4zQtTStu\n3Dg3DltBl7a3A++/b/t5AHjcBw9aD9gjCCK+NDY6d25aW/PnSqf4nZbm1rkxG+IJyI1WEMdymXNv\nZyc/J+3bp//3o0d5ZokM1dX5NtURFjeB8ZWvAF/8YthbkShI3HhIIDU3NTX8YGSWmuZHzY04yDtJ\nS8tmCxfERvjk3MSu5gbINxUwq7nxyrkRscsMfAOK09IcODdXtrUB3/mO7ecB4L+D/n7zVrEBMtJy\nmYl4E5v91Swtzarmxm1KGiB3jHWTlubnnBtA7nwq6nZkxQ1gXO+Yc26k4qCam8BJUiwykLiJE5kM\nvxpTWWktbqI050ZY3+m0+fv4JG6ksBI3QQ7xFNvT08P/q6kp/ruX4iad5vU3Zm6gGg+cG3R1GRcL\nWyHiPnDA2fMJgog+ZrNu9LqlTZzIi96zWffNBABrdzyb5ceiqM65qa62PsYK90dW3FRUGIsbu84N\n1dwQPkLixkN8z2mUdW78nHPjRNzI2s4+paV5VnMT1BBPsT3HjvHPPpdaURDHmDH8czXKfzZDK24A\ne3U3Hjg3+zdvdi5uxL4YEXEz0nKZiXgTm/1VoqFAQSyjRvHj5YkTwTg3mQw/DorGLHaJQs2N3bS0\nyy6zdG6o5iaaJCkWGUjcxImurry4MbtaHrVuaX6KGy+dG7NFehg1N0eOGF8VTKUKpzzbQU/c2LmK\n6IFzkz592nnNUCYDTJsWqaYCBEF4jFVamta5AfJ1N8eP+y9ujOohZQnCuZFJS6uq4sfzM2fML5Z1\ndgILF3rv3EQ4LY2ILyRuPCTRNTdu5tzIHrzEAc+s9kNP3CRtzo3YnqNHC06eRXE4TU0zEjeyDoz6\n+Q6dmyk1Ne6cmwsvjIxzM9JymYl4E5v91SgtbWCAH4+rqopjEXU3XqSlWR1f3XRKA/yvubHTUEBR\n+P9Fkb8enZ3AggW8Y5peMxc7NTfqC3MRTkuLzW9FgiTFIgOJmzgham4CEDcFuG0FLXvwErNXzESG\nXlpaEufcjBpl7twA3oobp2lpbmpu3Dg38+aRc0MQScbIuens5Mc+MetMjXBuvEhLs6q5cStuZJyb\n9nbji31WQzxlam7Ur2Hl9HR2AlOn8i51esdeN85NRMUNEV9I3HhIYDU3Mg0FvK65cdMtzc7By+oA\nO5Lm3GjETVEc6u/FDm6dGw9qbtr37+cnXtkObWoyGWDu3Mg4NyMtl5mIN7HZX43m3KhS0opiEbNu\nvEhLE8dXo2OUF2lpfX3Dr6/7vdx8Mx+eqWVoiB93tcdxNbI1N+LcLCNuamv5PBZtatrAAP886uud\n1dxENC0tNr8VCZIUiwwkbuKEuuYmis5NWOLG7zk32Sw/eHvZgU5me2ScGyfuh9fOjQNxkz59Ot8i\n3C6dnTwtjZwbgkguY8fyc4f2nJJrA62LuubGbVqa6PBpdHxz69woinEqt/o9tm8vvl8cw83m+Nhp\nBS3zeCFuZs8uFjfi87bqiKq3bRFOSyPiC4kbDwm05sbnhgIFsVRX8/cbGuIL4CiJG4m0NNc1N/39\nPDa9NAi/0BE3kam50RM3Nh2YGvFZ2q27YYz/DqZP54LTad2Oh4y0XGYi3sRmf02lgPp63v1MjWqA\np2HNjRdpaYD5MdatuAEKLg7pfi9dXcCePcX3Ww3wBOSaBMg6N4OD/LHV1friJldvYxiHlpikpcXm\ntyJBkmKRgcRNnAir5iaVyufvipk1ekQ0LU0KM3ET9IwbsT3HjkW/5iaVsr76qEd3N4/RrvN0+jSv\nzSor4/nf5N4QRHLRaypg1CkN4M7NwYNAWxsXRm4xq7vxQtxYpUN3dQG7dxffL3NOTaWsmwSoj+Vm\n596uLr72SKX0xY2dehvxXmK7IixuiPhC4sZDfM1pFB1iRo82FzfZLL/KUlrq6u106zsyGedpabI5\ntT6kpbmuuQl6xg3AxdTgoHXNjVdpaU67pQHms4YMGGhvByZPtu+8ZDL5hc1ZZ0Wi7qbltdf4Yoog\nYkCscu/1mgqonBvdmptdu/hC3OU5EID5BSS3NTdAwcW5olgGB/lxVc+5sTNewewYq34ds3OvSEkD\neM3N9u2Fbr3KuaGam2iSpFhkIHETF7q7+QFbUcwbCviVQiUW0lZpaXpX8O3k1PqQliaFmXMRdDMB\nIC+mou7ciOfaqbthDCWnTvGFiF1xJlIzAe7cREDcjN20CfjMZ8LeDIJIHnpNBcycm/Hj+bnPi5Q0\nwP+0NLOLat3d/LyzZ09x2q9Xs+O0zo2REFKLm3Hj+DH/yJH83+06N6NH83NGNks1N4QvkLjxEF9z\nGtWLOjPnxqOFeFEsNTX8SpVZYX1E09Jc19xERNz4XnPjRtzYcW7OnIFSWsoXInadm87O/O/grLMi\nkZY2r76enBsiNsQq976xsdi5UTUUKIpFUfgi220zAUGYNTddXTz+6mouHtR4JW5kGwqoxQ1QnJpm\nt+YmleLnoJ6eSKelxeq3YkGSYpEheeJm715n7WWjjqi3AcwbCvi1EK+p4VfQysuNXSGjOTdx75YW\nEXFTRBQaCgD220GL/G0naXVqkR+RtDS0tjqf2UMQhDEWaWm6TJrknXNjdozyIi3N7LgrjpMzZxbX\n3VjNuBFYDfKUbShgJW7sOjfq94uwuCHiS/LEzbXXAps3h/LWvuY0BuzcFMVSW8sL3M3EQkS7pXlS\ncxMBceNZzY3efAS3aWl2nJuuLvSWlvITr5OaG3VaWgScm4ObNpG4IWJDrHLvLdLSdGOZNClezk3u\nuFsUS1cXP9edc05x3Y1XNTeyDQVsODfS+1d1NReuZWV8eHcEidVvxYIkxSJDssTNwAC/kqs3+Cvu\niBk3QCDipgi1c2OE3+KGseLiwyC6pYUhbsT7mV2hjKtz092NwcrKxDg3pZ2dJG4Iwg8s0tJ0mTaN\nNyvxAqNj7MAAPw6KY5FTzM5fVs6NV2lp4nXMXB4/nJuqKi6KyLUhfCBZ4ubgQV6gpu2LHxCB1dyY\nNRTws+bm6FHnzo0X3dJ6e3kHHPVVHom0tNjW3IwZUxCrrzU3dpwb7fMdODfVEyc6d27ESXbiRL7w\nGRiw9xoeM0FR+H4v+/kRRIjEKvdez7kxm3MDAA8/DCxb5s37Gx1jhcBy27hHdd7RrbkZM8a9c2On\noYCsuDn/fGDrVn7BcXCQu1g5t0x6/xK1RBHtlAbE7LdiQZJikSFZ4mbfPv7/JBb3RqHm5tgxZ86N\nV93StClpgLFrIBa8su1AoyhurPK5a2uDbwXNmH4raJvODaqr+f7sxrkpKeGpEIcO2XsNrxFXlsm9\nIQhvMXJujLqlAfz86FXrfiN3ec8enhbrFr+dG6uaG6cNBRoauLBrbeXis74eSKett0eNEDfk3BA+\nkCxxs3cv/39Izk2ia25EWlqYNTd64sbo5KBKSZOuuTE6yYQ1xFMjbnS/k85O+w003Dg3vb38s1Cf\nyBw4N8d7e/n2u+mWBkSi7qbv0CH+GZC4IWJArHLv6+v5+Vx9jDObc+M1Rs7NK68AH/uY+9dXXVTT\nrbkR4ubDDws/A3UjADNknBsnDQUUJZ+apqq30Y3DbNsinpYWq9+KBUmKRQbpKq5sNosHH3wQdXV1\neOCBB9DT04PHH38cJ06cQENDA1asWIHK3IKpubkZa9euRTqdxt1334158+b5FkAB+/bxH1lI4sZX\nMpl8nrGZuBFzbrxGODfnnGP8GKM5N3bEjVkdhp64ESeHbJa3lxSYzePRI2pDPOvqrPPGS0u50JA9\n0QlOny7+bGSdG229DeCoW9rQ6NHunRsg/LobxnjNzXnn2RdqBEGYM2oUP750dPBjYjZbfAzwEzNx\n8/DD7l9fxrkZM4Yfc9V1LXbS0rRtpNU4TUsD8uJmyhT79TbqbYtwWhoRX6Sdm5deegmTJk0avr1m\nzRrMnTsXq1evxpw5c9Dc3AwAOHToEDZs2IBVq1bhwQcfxFNPPQUWVGvmffuAyy5LZs1NwA0FdOs7\nWlvNF/letIKePJnXTumhJ25SKX1honJuYllzc9VVwK9+VXCXbhxO6m7cODd64sauc9PdjYnnnuvM\nudEubMJ2bjo7kRo9ml9hJueGiAGxy71Xp6Z1d/NjV64W0fdY9I6vmQzw/vv8GO0WmZoboLjuxoua\nm/5+7gaJi6FOxc3RowXOje2amwg7N7H7rZiQpFhkkBI3J0+exLvvvoslS5YM37dx40YsWrQIAP/Q\n3n777eH7Fy5ciHQ6jYaGBjQ1NWGPthjOL/btAy69NPk1NwE0FCiipoZfNbOblsaY/oLYiClTgMOH\neZGiFj1xA+hf/bLTKQ2InrhRFLn2mE46jrkZ4umRczN8RdKJc6M+yYbt3LS28vxzp225CYIwR91U\nwGrGjdeI1F81LS3AFVfYO78YIePcAMV1N16IG3EsF00RnIqbI0fcOTcRFjdEfJESN08//TTuuusu\nKKrOIJlMBrW5nb22thaZ3Im9vb0d48ePH35cXV0d2tvbvdxmY0J2bgKruRGLST1HzM+aG8C+uBE1\nGrJ97EeN4lfBjxwp/puRuKmsLHYOnNTcREnc6KAbh13nJpvVT7Ozk5am5/rYbCiw5/hxb5ybCIib\nzKhR8uKmtxe4+OJkDhomYkHscu/Vgzw1zQQCqbnR/q5feQW47jpvXl+m5gbQd27cDvHUvoZHzo2t\nmptjxyItbmL3WzEhSbHIYClu3nnnHdTU1GDatGmm6WWK25aIbjl1ih8MLrwwuTU3YlGXTvPFtt6C\n0k/nBrDfLc1OpzTBtGnA/v3F948k50YWu+Kmt5fHmtL89GXT0k6f1ndubDYUGKys9KbmJuy0tNZW\n9I8dKy9uTpwA3n2Xt04lCMIadVqa1Ywbr6ms5J031bWkr7wCXH+9N68fhHNjdAFJW6spznF6dbN6\n4mbiRH7O2LzZuXMzMEA1N4QvWIqbHTt2YOPGjbj//vuxevVqbNmyBT/4wQ9QW1uLztyiqrOzEzW5\nBUddXR3aVGlhJ0+eRJ1FS1u1omxpaXF2e/9+4Kyz8PqWLch2dABDQ+5ez8HtxYsX+/f6uZqb4du5\nuhvt43e8/z6OdnS4fj+Rnzn899z3e7ijw/j5ZWXoz22T4M1XX0WvyrWRef9jlZXD4qbg793dOJjJ\nFD2+J5sdPkEMPz4nbtSPNX3/khKAMbz+2mtFf9+7ffvwgT/I/Ul7W2//Ot7Xh23r10u/3h9feQUD\nqvbYw3/POTdWz/9gwwacVJ38WlpasOvgweLP32R7Tnz4Ic677DKgqgqstxctr70mvf0DbW1Yt3nz\n8O03DhzA0P79w05I0N/PrnXrMFBbOyxurB7/9u9/z/+Ra1sf5v6kd5tIPrHLvTdJS/M9FkUpdG8O\nHOAC68ILvXn9MGtu9FKM9R4/OMgvXmlFiOiY9vbbzmtugEg7N7H7rZiQpFikYDbYunUr+853vsMY\nY+xnP/sZa25uZowx1tzczJ599lnGGGMHDx5kX/3qV9nAwAA7fvw4u//++1k2mzV8zVdffdXOJhjz\n/POM3XAD/3ddHWMnTnjzulFh6lTG9u0zvi340Y8Y+7u/8/79s1nGUinGVqwwfkx7O2O1tYX3vfce\nY3Pn2nuvr3+dsW98o/j+b3yDsYceKr7/8ssZW7++8L7nn2fsppvsvW9lJWPd3cX3/6//xdjDD9t7\nraD44hcZe+IJ+cfv38/YlCnF97/3HmMXXmj9/OeeY+z22wvv+7d/Y+xzn5PfhuuuY+y3v+X/rqnh\n+40MYh/s7y+8v66OsdZW+ff3ErFPfve75r8NQUsLYwBjv/iFs/f75S/9+X0zD4/FCYQ+mxBRn9N+\n8hPG7r472PefMYOxnTv5v//P/2HsU5/y7rWffpqxu+7S/9vs2Yxt3sz/nckwNno0PwYyxtjkyYwd\nOGD9+vv388fq0dLC2NVXF9531lmM7d1beN/Jk4yNHav/Gvfcw49nhw5Zb4uWF17gz121yv5ziRGN\nzPHY8ZybW2+9FZs3b8ayZcuwZcsW3HrrrQCAyZMnY8GCBVixYgUeeeQRfP7znw8mZW3fPmD6dP5v\n0Rs/YHy9AqpNxzFqKuBXzY2i8Pe3SkvTWtp2OqUJPE5Lk/5ejFKzwphzo4NuHHbT0vSaCQCBt4J+\nR1yFtFN309PDt1M7mDXMupvWVuwWv02ZtDThqurt3zJs2eL8uQQBn89TfqCuudE4N4HEonZuvKy3\nAeRrbkQ76GPH+G0vhnjKOjd6KWmC2bP52qCxcfgu6e9EPV8nosTut2JCkmKRQXrODQDMnj0bs2fP\nBgBUVVXhoYce0n3c0qVLsXTpUvdbZwetuHHSMY0x+/NRgiCb5QcccaAD+EFJr87Bz/qQmhrzz0av\nFbRTcfPss8X3d3fz3GMtXtTcAMZ1N1GvubFTv2EkbgJuBT0otsFO3Y22U5pA1N3Mny+/DVoGBuQ7\n1KlpbUX/7Nk8DhmR5lbc7NtH9TrEyEKdlqZpKBAI4gJSNgu89hrwz//s3Wub1dxoz/mi7qapyf4Q\nT8byXdEEeudmJ+KmocH+cVO8FxDptDQivjh2biLH3r15cTN+vDPn5oMPgPPP5wsdB/iW0yg6VKmn\nwhvNuvFoiKduLFbiJp3mJ4BcvRMAfgC1e2XGrnNj0S1N+nsxEzdBD/HUwXDOjZ2ifC+cG+3zHTg3\nl4m28nacG6PhfW6cm2wW+NnP+Gv8y7/Yf35rK+Zcc40952bGjOGaG9vs30/ihnBF7HLvTRoKBBKL\nEDfvvssX8lbDle1gVHPDWPH5TtTdiAuIMuf5khL+OL2LT144NwsWAMuXF9xFNTfRJEmxyJAcceNF\nWtrhw3yR9POfe7ttblHPuBEYiRu/nRuzRb6i8AOpWhw66ZY2ZQpvBa2ddeNntzQgns6N3hwGM9w6\nN3rd0uw6N9p0C1lx09lpLG6cdEz705+AK68Evv99YPFi4+GxZtidc9PRAVx0ETk3BCFLmHNugPwx\n1uuUNMDYuTl1iv9NfUFTODd2syGMmgp4IW7GjgX+x/+Q3xbte6n/TxAekgxxw5g3aWltbXxh/eij\njuZQ+JbTqHfFOuiaG8DauQGK20E7SUsrK+MntMOHC+/3u+Ym4uLG95qbIIZ4Mgb09OD1TZv4bTvD\nL42cm6lT7Tk3ra3APfcAt94KfOELwFtvAUuW5K8O26G1FX/cvVs+js7OvLixe4zp7+c596dOOXaX\nCSJ2ufc1NfwY3Nsb/JwbIO+O//733osbo5ob9QUggXBuvBI3XqSl6SD9ncTAuYndb8WEJMUiQzLE\nTXs7n9shrug4TUs7eRJYupRbuS+/7O02ukFvUReGc7NkCTBnjvljvBA3gH5qmsO0NGmqq/UXqBER\nN7o0NhaLQDOMxE1JCU/R0rplWtzW3Jw6BZSXg4krknacG6/S0v7hH7g42LGDi5xUypnbOzAAdHVh\noLrannMzZQr/DuyKqYMH+TyJsWPztTsEkXQUhV/sOnEi+Dk3AF/YHznCWx57ndpj5NzoiRu1cyMz\nwFNg1FTAC+fGDZWV/NgbYXFDxJdkiBvh2oiCOadpaW1tXBh97WvcvbGJbzmNuRk3BfjcUEA3lmXL\ngCuuMH9iGOLGwrmR/l6MFskRETe6ccyezevNZMWFkbhRFLnUNLfOTa5IdjgWu86NWUMBWY4dA+64\no3DxIBZPdmhrA8aNw+KPfcyeuBk71riuzAxxnBs3jlLTCMfEMvdepKYFPecG4Mec3/wGuPhi7xfi\nRjU3RuJmzx77qd5Ggzx9cm6kvxNF4e8XYXETy9+KAUmKRYZkiRuBU3Fz8iQXN3fcwRceb73l2Sa6\nIio1NzJo20E7FTfTpxcXXftdczNtmn6hd9ifqRmjRnE37d135R5vJG4AuaYCbp2brq7C79AL56ah\nge9ner8HPU6e5AJBjZNjhqi3AfKTzK3SxYS4mT7dubipqyNxQ4wsRFOBsLqlffih9ylpgD3npqaG\nH2f27PG35kZ7PPbLuQGA3/2uYAAoQXhFMsXN+PHOa27Gj+dzNL70JeCxx2w9PdCaG5/FjeNY9Jwb\nJwWDHqalScditOCMiLgxjGP+fGDjRrkXMRM3ss6Nm25puZP2cCx2W0HriRtF4alesg0BTp7kAkGN\nC3HT0tLCt0EmFrE4MxLSZuzfz59Hzg3hgljm3hs4N4HEIo45fogbs5obvXPdOecA770XnYYCOtj6\nTi6/vLhFdYSI5W/FgCTFIkMyxY2btDRxRfdv/gb4wx94jmvYhNBQwDHaWTdOuqUBxeImm+ULaL1c\nY7+dm4gM8TTk0kt5PrgMbp0bo25pvb1yBfJagWqnFbRRtzTAXt2NnnNTXc33W5mmCgK1cwPIuVBi\ncUZpaQQhT0MDTz3NZoNvy19by/9zM0fLCDvODcBT0+yKG6OaG5/S0ggiCiRT3IiGAna7EYm0NIAv\n4O69F/jud6WfLp3TeN993FqWxajmJug5NzL4VXPT05MvQNTiVc2N0YIzbMGYwzCOoJ0brbhJpYrT\nEY3InbSHY/HCuQHk20Fns/xkrXVuFMX+RZGcuLFVP+RFWtq4cbyJCkE4IJa5942NwM6d/LejutIf\nSCyXXAI880xhW2avGDWKHzcZs665Abhz8+67kXZuYrl/GUCxxJdkihsx8FI2B1+gdm4A4P77gV/+\nkhcge8XmzcCPfgRs2SL/HKOaGx8bCjjGK3EzeTJw9Gi+e5dRShrgXbe0SZP44la7SI/IEE9DZs8G\nDh2SEwl+1NwA5pO21WhP2l4M8QTk20FnMnz7S0uL/1Zfb6+Dmda5sRI34vOpqHDm3FBaGjFSaWjI\ni5ugGT0auOUWf17b6MKQmXNz8mSkGwoQRBSIv7jJZvmiZtq0wvvtXoVlrDhdpb4e+NSngB/8QOol\npHIaV6/mBzM7i6i419w4ETdlZfxq3aFD/LaZuPFqzk1JCRc4WgcgbMGYwzCOkhJg3jzgnXesX8QP\n50Y8V6apQO57dFxzY3SSlXVu9FLSBA6dm+FYrMSNuo2tSKPLZuXeS8z4mDiRGgoQrohl7n1jI7Br\nV9HvP5axaMnV3VjOuQG4cwNE2rlJxHeSg2KJL/EXN0eO8B+edsFmd6HS3c0X1Nor9F/+MvDkk46G\nehZx4gTwq18Bn/tcfuKyDFGZcyODV+IGKLy6bSVuvHBuAP10obA/UxlkU9N6e/1xbmSbCoTt3Pgg\nboaRETdikTB6NP8cZI8D+/fzGFMpcm6IkUdDAz/2hOHc+I3exTkz5wawN+fGjrjRq88h54aIIfEX\nN/v2AWefXXy/3Y5p6nobNWefzYWNxNA8y5zGH/8YuO024IIL3IsbnxsKeFpz46RbGiAZhULPAAAg\nAElEQVQvbvQW1k5qbsR7apsKRETcmMYh21TAKi3NSbc0wLZz46jmxqyhQFOTXPqombixO+vGbs2N\nptOTrdQ0kZIGkLghXBHL3HtxEUEjbmIZi5bccVeq5qamhl+ECaqhwOAgP67beL9EfCc5KJb4kgxx\no663Edi9Cqutt1HT1MQdIjf09/Nam2XLuMVuR9zoHeii7Nyo84eddksD7Dk3XnRLA5Lv3LhJSxsc\n5P/p1R+F7dxMmCAvbrTNBARBODdacSPbDlp9nKOGAsRIo76e/z+JDoId5wbg7o0XNTcyaWliO/Qa\n+RBEhIn/HuuVuDFybgAubo4etXwJ05zGX/yCF37PnZvv2S+LUVqajw0FHOdnqltBM2acxiSDevHn\nIi3NVix6V9MjIm5M45g1i+/DVm6lm4YC4rl6cwlknRvtnJtRo3jdiVWntWyWX2k0OuHX1PDXsNqG\n9nb/am6sXCg9cSPr3OzbR84N4QmxzL0vLeUXJTTOTSxj0WKn5gYA7roLuOgi+dfXS0sToxW054Kq\nKn7OFmn4DlLSEvGd5KBY4ktyxY3dtDQr50ZC3BjCGLBqFbB8Ob8tpi3LEteam95efrukxNlrqV0U\nJ2lpTjqcaa+mZ7N86rwH7bV9JZUCLr4Y2LTJ/HFunBszoSrr3Gi/R0WRc2+6u/NdEPVQFO7eWF00\n8KrmRiwA1J+HVRxacWOnHfT+/YXOzcmT3tQBEkRcaGgg5wbgIyrmzZN/fT1xc/o0f1+tI5NO8/Om\nWFtQvQ0RU5IrbkJwbgxzGv/4R35wufFGfttOWhpjoYgbT2pu3DQTADxLS7MVi3bB2dfHY4rAFGXL\nOObPt667cePcmIkbm85NQSwydTdmndIEQYobkZKmKPI1N+qGAoDztLSKCr4/ynzeBKEhtrn3jY1U\nc+MEvZqbU6eMz81qMaQ9ZkmQiO8kB8USX0jcCNrajMXNxInunJvHH+e1NuIqydix/OAiM/Swr48v\nZLSCxaihgEdDPB3jpbiZPJnXUQwM2EtLY8z5bJqmJn5AF4v8qM+4UXPppdZ1N+KKnR5BOTfak7aM\nc2NWbyOQqbuxEjeyjqq23gYIrqEAQKlpxMhj8WKe2p007Do3dtFzbnp6jI/l6seTc0PElHiLm/5+\nfqV2ypTivzlxblympenmNO7fD7S08PbPglRKfiFltKirqODxDw0V3h92zY1W3DjtlAbwPOsJE/is\nGxnnRqTpnDnDtyMnJm3FkkoVthUOO81PhWUcMk0F3Do3Ziltss6Nes4NIOfcmHVKE3ghbuw6N1B9\nL3YbCpx1FnDwoPWsm+5u/r2oxRQ1FSAcEtvc+298A1iwoOCu2MaiRltzw5j34kZ78cjsQpVLcZOI\n7yQHxRJf4i1uPvqIuyp6NR1Oam5cpqXp8sMfAvfcU+xgyKamGYkbReELTa1jESXnxk2nNIG4um0m\nbkpLuSgZGOC3nXZKU7+nSBeKkLixZPp0HrvZvuqmFfTp0+6dG72TtlfOTWOjnLgx6pZWW8tjkHFU\nnTg3WnFTUcFvW3ViFM0E1KmR5NwQRDLQOjdG2RpOKS/nF0Fl58+Rc0MkgHiLm7179VPSgFCcm6Kc\nRsaAZ57hBYBa3IoboLjupr8/v9B3iauaG7E4dJuWBsiJG6DQOdCIG9uxqNOFIiRuLONQFHP3ZmCA\nn+SMxK9eeoQaL2putHNuAPmaGy+cG7NuaYoif1FEJW5s1dxohxDKpKZpU9IAEjeEY5KUe5+IWLQ1\nN3qpu25QlOLUNB+dm0R8JzkolvgSb3FjVG8D8B/kqVOFVyvMkHFu7HYnOnCAiw29IaMNDXJpaWb2\ntFbcRGEh7mXNDSAvbtTOgVvnRt1UIAqfqR3MmgqI1p9GzRGsnBsrcWPl3GSz+gPhZJ0bmYYCbtLS\nAPmLInrOjUwraG0MMh3T9I5zdXUkbggiCWiPnV6mpAm0TQXIuSESTvzFjZ5wAPgCzs7VTbNW0GJR\nrTflV0VRTuOmTcAll+g/2AvnRttUwMOFuCdzboJ2bgzEje1YIpqWJhWHWVMBs5Q0wP+GAqKINZWy\nX3PjhXPT38/jM1s4OBA3w7FUV/PPWFsHJ9A2FADknBs9cUPODeGQJOXeJyIWbc2NH+JGW3dDNTdS\nUCzxJf7ixsi5AeQXKoxZX9F1Unfjt7gZKc7Nvn2u0tJsE3fnZuNGfZfRStz43Qo610ygiDFjgumW\nJuptzNp6u3FuUim+vxtdBDFKS7NqB22UlkYNBQgi/gTh3ASYlkYQUYDEDcB/6KmU+cJPQtwU5TRu\n3Oi/uFEvKD1ciHs258ZNtzQgLzRcpKU5qrkRC84zZyIjbqTimDSJD2L76KPiv4Xt3KhO2gWxWNWq\nAHLd0sRvyih91OoCBiDfxVCv5gYwjmVggP8+tWKfnBsiYJKUe5+IWLQ1N0GIGx/T0hLxneSgWOJL\nssWNbHGw2QBPgV3nhjFvnBs7NTf9/eEvxL3uljZ5Mv+c2tsdp6XZprGRf649PfFzbsyaCnjh3Lhp\nBW0kUL1ybior+f5n9FpmndIEbpwbwFjciHobrWtkVXPDGIkbgkgyYdTckHNDJJz4ipueHv4DbWw0\nfozsQsWs3kYgIW4KchoPHOCL4qYm/Qc3NrqbcwPop6V51Abaszk3bsVNSQn/DDMZx2lptmNRFD6D\n5MCBSA3xlI7j0kv1mwq4dW7ctoJWnbQLYpFxbmTEDWDeDtqsU5pA5piRzRY0IJGKxWjS99SpfI6T\nUZ1ORwffH7XPpYYChEOSlHufiFiCqrmx49yIC0RUcxP2JnhGkmKRIb7i5sABvgD1In9exrmZONGe\nc7NpE7+CbkRDQ6QbCjjGa3ED8NSddNpcZHjZLU2857590fhM7eKnc+O25kbvpC3r3MicZM3qbmTS\n0hoarI8ZHR18AaB3IcFI3Og1EwD4vjV+PHD4sP57CddGe5wj54YgkoGec+M2nVtLgA0FCCIKxFfc\niMF2ZsimpXnk3BTkNJqlpIlt6+gABgfN3zeONTdezrkB+PdcXW0uZE3S0hzFItKFIiRupOMQ4kbr\nBsiIGz9rblRpabZrbmSdG7fiRuaCiCYlTSoWvWYCArPUNKPUWxI3hEOSlHufiFjCqLk5dcq65mZw\nUL91vwWJ+E5yUCzxJb7iRq+DkBYvnRu7NTdW4qakhC92rMRX3ObceN0KGsiLGzO87JYm3jOuzk1D\nA093euutwvtl0tKi6tzINBQAQhE3BRi1tTYTN2Yd04yOc2PH8vfJZs23lSCIaBNGtzTRlt/sseKC\nkgdDwQkiaOK71+7fb95MALBXc+OBuBnOaWTMvFOaQKapQEitoD2rufHCXpcRNyZpaY5iiaBzYyuO\nm28GXnih8L4IOTe+1dyEIG6ka27MxI1d56akhH+WnZ3m20oQGpKUe5+IWIKouXHSUMBhSloivpMc\nFEt8ibe4kUlLk3VuZNLSjhyR2zarZgKCCIsbx3jdLQ0Azj2XLzrN8LJbGpBfcEbhM3XCTTcBL75Y\neJ8XraDddEszOmmLk6lRC+ehIf7aMkJ5wgTj35TM73zsWL4tAwPGjzFzbmpq9F0oo4YCgLO0NIBS\n01Q8+eST+Nu//Vt85StfGb6vp6cHK1euxLJly/Dtb38bp1X7Z3NzM/7+7/8eK1aswPvvvx/GJhME\nJyjnRn1ckmkFTfU2RIxJtripr5evubFybsaO5Qs/k6vTwzmNVilpApmOaSE1FPBszo0X4uaKK4CX\nXjJ/jElamqNYRKpQ3ObcCK64ghepHzyYv89tQwEPu6UVxFJayj9jI3EkCmxl0iPMnJv2dutW0KkU\nf4zZccNJzY1RQwHA3LkxO85Rx7RhrrnmGvzDP/xDwX1r1qzB3LlzsXr1asyZMwfNzc0AgEOHDmHD\nhg1YtWoVHnzwQTz11FNgRsI6gSQp9z4RsYRVc+OTc5OI7yQHxRJf4ituZBsKnDxpnZcuc0VXUfjC\nSabuRlbcyHRMy2TMa258aijgGD/EjaKYL8oB77uljR/P4zhxIvzP1AnpNHDDDYXujZW4GTWKOxZG\nvxe3NTdmg1iNalUA+ZQ0wH1aGmCdmnbihLlz41XNDWPm4oacm2HOO+88jNbsmxs3bsSiRYsA8BP7\n27n26Bs3bsTChQuRTqfR0NCApqYm7NmzJ/BtJggA4dTcyDQUIOeGiDHxFDddXfyKulWqUmkpX4xZ\n5aXLODeAZd3NcE6jVRtogVVa2uAgj9PoIOTjEM/IzLmRxSQtzVEsisIXlTt3Rkbc2I5Dm5pmJW4U\nhcdqlJpmJW56e41TywDjOTeAcToXYE/cmM25kRU3Vu2gva65mTKFH6O+/W3+mxEcP86/LyNBOG4c\nd6MIXTKZDGpzi7Pa2lpkct9Le3s7xquO93V1dWgfQZ9jknLvExFLGDU3Zg0FKiv5ObytjWpuKJbY\nEk9xc+AAX3iatQYWyKSmyS56ZDqmMWYvLc1M3IiDnFGcPg7xdIxoBc1Y8OLGy25pAK912LEjMkM8\nbfPxjwOvv54XfVbiBjCvuzETN+k0v5gghK0e3d3GJ20z50a2UxqQFybaNtiMyQ3xBKydG6uaG7vi\npqyMD13dsgU45xzgiSf452jVNIWcG1soMucLE9SLg5aWFrpNt725XVGBvkwG7733Hr/d1YX1W7Z4\n+n5/2r4dp1Up8IOZDNa9+67+4xUFgxUVOLBu3bC4idTnRbcd3R7evyKyPW5uS8FC5tVXX7X/pN/8\nhrEbb5R77IIFjK1bZ/6YigrGenqsX+u++xj7/vfNH7NvH2NNTXLb9uKLjH3848Z/37uXsbPOMv77\nH/7A2MKF+duPPsrYl78s995+sXcvY9OmMXb6NGPl5cG9789+xtinP83/ffvtjD33nPvXvP9+xhSF\nsSeecP9aYXH11Xw/Y4yxO+9k7JlnzB/f1MTYoUPF92ezjKVSjPX3Gz+3poax9nbjv198MWNvv63/\nt2uvZez3v9f/269/zdhNNxm/rpbx4xk7frzwvkyGsdGj5Z5v9Ts/91zGtm/X/9u77zI2d27x/X/2\nZ4xt3Gj93u+8w9gnPsHY9OmMffazjN1xh/Fj//EfGfv6161fUxJHx+II0drayr6sOv4tX76cdXR0\nMMYY6+joYMuXL2eMMdbc3Myam5uHH7dy5Uq2a9cu09eO+2dDRJiTJxmrq8vfrqxkrLvb2/c4dIix\nCRP4v7NZxtJpxvr6jB8/eTI//vzjP3q7HQThATLH43g6NzLNBARWHdNOn+Y1BlZXtAE550bWtQGs\nGwqY1dsA0e6W5lWnNFm87pYG8KvmjIX/mbrh5pvzqWm9vdb7uVE76L6+vDtjhFVTAbN0C69qbgD9\nuhtZdxbwx7kxayig5qKLePOMn/4U2LvX/FhCDQUKYIwVNAa45JJLhq/2tbS0YH4uVXj+/PlYv349\nBgcH0draimPHjmHmzJlhbDJBFDZyEanoRg65U9Q1N/39vHFKWZn54w8epJobIrbEU9zINBMQWKWl\niQGeMikLMjU3dsWNVVqa2aLOx4YCti1AgRA3QaakAaZpaY5jEftYRMSNozhE3Q1j8mlpegLFrFOa\n+rlmTQWM5twA1jU3dk6yeuJGplOawEzciH1btT0FsThJS9Pjz/8ceOMN4IEHjB9DaWnDrF69Gg89\n9BCOHj2Ke++9F2vXrsWtt96KzZs3Y9myZdiyZQtuvfVWAMDkyZOxYMECrFixAo888gg+//nPu05Z\nixOOj4cRJBGxiJqbtWvzx0iv98eqqvyFXLNmAgIX4iYR30kOiiW+lIS9AY7Yvx9YuFDusVZXYdva\n5K/oyjo3999vb9uyWf02t1ZXrPWcmyAFhR5hiRuvu6UBkRM3jpg9m/9/61Y5cWPk3JjV2wii5Nxo\nLxrYdW6MHFXRfMSoLbUo3GUsv0AZGuK/BzsxyEANBYZZtmyZ7v0PPfSQ7v1Lly7F0qVL/dwkgpAj\nlQJKS6EMDPjTTEC8x+jR/Dhk1kxAUF0NfPABOTdEbLEUNwMDA3j44YcxODiIwcFBzJ8/H5/+9KfR\n09ODxx9/HCdOnEBDQwNWrFiBytzCqbm5GWvXrkU6ncbdd9+NefPmebvVdtPSzIZvCudGBotBnosX\nLQJuv13euRk1ih9kOjr0F15OxE1U5tyE4dwYiBvHsYhi7oiIG0dxKEo+Nc1NQwEZcWPm3AwO8v0i\n9/5FsYwZY+7cyAoTwN+0NJ2UtIJYSkr459DTk+9ylsnIz+mxAzk3hAOSNO8iMbFUVGDR5Zdzt8QP\ncQPkB3nKHMurq/l5gObchL0JnpGkWGSwPNuWlpbi4YcfxqOPPorvfve72Lp1K3bs2BHugDQ74sYq\nLc1L5+ajj/jifuJEudcDzFPTrGpufBzi6ZgIpqU5praWf/5hf6ZuEalpss6Nnvvi1rkRNVhG6RZG\n6VyAvW5pgH47aB/FTRHaWDo6/LkCSuKGIJKBOO765dwA+bobmXOzuDBDzg0RU6QuJY7KLe4GBgaQ\nzWZRVVUV3oC0TIYvnr0qDrbj3NTX599fhy0//am8ayMwaypgVXNTVsbTX8T2RKHmJp3m/xdXq4PC\nJC3NcSyKwt2biLSCdhzHNdcA773HhXlYzo3mpF0Ui5VzE2RDAbM5N8eOFc3X0q0fUosb2WYCdiFx\nQzggSbn3iYmlogJvtrQEI25knRuAam4oltgiJW6y2Sy+9rWv4Qtf+ALmzJmDyZMnhzcgTcx+kC24\ns+qWJjvAE+BpJfX1hk5L9a5dzsSNmXNjtqhTlMKmAh4O8XRFWRlfdMXduQGAZ54BFizw5rXCorwc\nWLyYOwhWn4uZc2MljKycGzOxa+bcBC1u6ur4e2pn5QC8pu7CC82fr+fc+CFuqqr4b76vz/vXJggi\nOMrLkerr81fciHpA2YYCADk3RGyREjepVAqPPvoonnzySWzfvh1bt24teoybbjN2hvdsfv55tKkW\nSVaPf/PDD3Hm0CHDvx96/33sVokvq9frrqrCphde0P37WW1t2FxWZiueQ4OD2L1unf7fMxnsOn7c\n9Pl9paVY/8oruRt92Lp7t6vhSOK2yM908vzBdHpY3Dh9f9u3czU3LS0tGOzpGV7Ea69W2H799na0\nrF/v//ZL3F68eLHz5990EwDgjU2bTB9/LJPBdr3hbrluaabvV1GB7Uavnztpi9tF+1fOudF7/Yyq\na49MvG8fPDgsbob/nuuWJvV5vfEGf7+TJ4v+3vPCC9ikOf6oaWlpQfvg4LC4aWlpwdZ164bFjaf7\nh6Kgv6oK6w2OR05uE8knSbn3iYmlogKXXXhhcGlpMs5NKuXoAmVivhNQLLHG7vCcX/7yl+zXv/61\nZwPSbA9He/xxPlxRlu5uPhTLiE99irFnn5V/vVtuYWzNmuL7h4YYGzeOscOH5V+LMca++U3G/uf/\n1P/bf/tvfDilGTNnMrZzJ//3zTfzgYdhU1/P2Je+xNhXvxrce/b2MlZWxv+dTpsPmxyJHDxoPYST\nMcb+9m8Z+5d/Kb7/6acZ+8xnrJ/74x/r/+3llxm7/nrj565fz9jll+v/7fzzGduyxfy91Zw4UTgU\njzHGbriBsRdekH8Nvfdsa2Osutr6M7zjDsZ+/vP87R//mLHPf17+ve0wezZjmzd78lI0qNIY+mwI\nX1mwgLE//pGxf/5nxlas8Oc97ryTH8d/8hPG7rnH/LGrVzM2dqw/20EQLvFkiGdXVxdO59J9+vv7\nsXnzZkyfPj28AWl2mgkA/ArF0JBxLYCddBXAuKnAu+/idGWlvWYCgHla2qZNgFWnOXVTgSjU3AA8\nLa29Pdi0tFGjgIGBfL2IathkUq5Mu4pj8mRg+3bzIZyAu1bQZjU3mrS0oljMam7sNhSoq+Pvp66N\ns/s716vV+8MfgCuvLPoMi2IJqqEAQHU3hG2ScjwEEhRLRQXee/PNaDk3Do9ZiflOQLHEGctW0J2d\nnXjiiSeGpz9fffXVmDt3LqZPn45Vq1Zh7dq1qK+vx4oVKwAUDkgrKSnxfkDavn3AVVfJP15R8guV\ns84q/rudmhvAWNy8+CJOXnEFLKoSijESN4cO8UXRnDnmz1e3g45CtzQgL27mzg3uPRWFL65l6kpG\nKrNmWT/GaIinrLgxqrmxOml7WXOTSvGmAK2tXNQBzsSNttFHSwuvXbJCO5DUr4YCAIkbgkgC6pqb\nSZP8eQ8hbhjzVdwQRBSwFDdTp07FP/3TPxXdX1VVFc6ANNFQwA4NDVyQ6IkbJ87Npk3F97/wAqZ8\n5zv2tgsw7pb2+uvAokXWszHUDQWiMOcGyIubILulAXxxffJkkbhJSq5pIHH4NcRT49xIz7kRbpxd\nF1C0g3YjbrTOTUsL8K//WvTQolj0nJupU+Xf2w51dSRuCFsk5XgIJCiWigpcOGsW8OGHwPnn+/Me\noqGAolgfTxsaeGMWByTmOwHFEmc8nioXAHbT0gAuEn73O/2/OXFutIM8jx8Hdu+25ygJjJyblhbe\nwteKKDs3QaalAVzUtLeTc+MGs1bQMm2kJVtBF1FVxZ+r7VAm2qHbdX/VHdMGB3kqhp0rkdp20CdP\nctf44outnxtUtzSACzYvu1ESBBE8Qc256eqSS0u7+mrgP//Tn+0giACIl7jp7ASyWfsLhdtuA/7j\nP4rvP3OG5+XbWYTrpaW9/DJw7bUFXbWkaWjg4kY76FQ2BcYnceO65iboVtAAX3zriJuk5JoGEodR\nK+hctzRTzJwbqzk3ojNPd3fh/XZT0gRqcdPezoWNlQuqRuvcGNTbAJI1N5SWRkSEpBwPgQTFUlGB\nne+/H9ycG6tzs6I4nu+WmO8EFEuciZe4Ea6N3au4CxbwBcDOnYX3iwGedl5PT9y8+OJwq13bjB7N\nB1+qF3WHDnEhN3u29fPVDQWiMudm1KhwxI1BWhphA7+GeFrNuQH06268Ejd1dfaerxU3shcbAL44\noYYCBEHIEsScGzsNBQhChy9jAD1g1g+MAPESN/v22a+3AfgV29tuA371q8L729rs5eEDPI3sxIl8\n+kx/P/DKK8AnPuE8p1GbmiZbbwMUOzdlZc62QYPrmpvBwcikpSUl1zSwmhunDQVsODe6sejV3djt\nlCZQixu79TaALXFjWXNDDQWICJGU4yGQoFgqKnDOlCnBDfH0Udwk5jsBxaLlJxjEhyRufMBJvY3g\n9tuLU9OEc2OH0lK+UBELn3XreBeqxkZn2wUUNxVYu1b+KrFPDQVcIQRWGGlp1C3NHX46N1Ynbb+c\nG7fipq1Nvt4GCDYtjRoKEET8CarmRjYtjSA0ZMHQBeA4iRsfcCNurroKOHyYdyMR2G0mIJg4MZ+a\n9uKLwM03A3CR06h1buykwES15gaITLe0pOSahlpz44VzYzbnBtB3btatc3bhYMKE/G/KrbgxqbcB\nLGpuGOPODaWlEREhKcdDIEGxVFTgwM6d0Wko4ILEfCegWNR0A2AAWknc+IAbcZNOA0uXFqamOVn0\nAIV1N27qbQRqcXPwIF8YydTbANHtlgZEJi2NsIFZK2g/u6UBxbUqP/gB8OtfA9/6lvnz9BCtoAFn\nv3MhGrJZexcbgMI5N93d/HOxGp7qFOqWRhDxp7wc6b4+udpEp5BzQ7igMydqyLnxg337nIsbgKem\nqcWNU+dGiJs9e/hi7KKLALjIaVSLGzv1NkC+oQBjnjYUcF1zAwRftGjQLS0pebOBxOFmiKebOTdA\noSh45hngscd4PZuTeQtu09JKS7nYam+3FDeGNTeM+dtMAMiLG223RYIwICnHQyBBsVRUYHJFBb+4\nVGI5ftAZATUUSMx3AopFjbjsqDOVMZLER9ww5myAp5pFi4C9e4EDB/htt87Niy8CN95or8WsHqId\nNGD/KrFwbgYGuDvldlu8oKyMiyy/rlYbQd3S3KPn3Hz0Ed8/rX57Xjk3zc3AAw/w2VROL2ZUV/Om\nHz09zrqlAfx3uX07P+7I1tsAfP8vKeFCz89mAuK9Ro0qbqFNEER8KC/nx1i/UtKAQueGuqURNiHn\nxi86OvjC3c1V0NJS4C/+Ij+cyo1zc+RIUUqaJzU3TsTN6dOep6S5ys8cNSoc29sgLS0pebOBxKHn\n3Dz5JHDXXe6dG7M5NwB3PF56Cfi7v+O/LTeTuhUlX3fj9CJGfT13ek3qbQCTWDIZf5sJCKjuhrBB\nUo6HQIJiqahAz4cf+ituxEUXnwdsJ+Y7AcWiphNACUjceI+behs16q5pbpyb3buBDRuA665zv02i\nW5rdehsg79xEpd4G4AfRMMSNQVoaYQOtc9PbC/zkJ8B991k/18i56evjLorV/jlmDPDWW1xQ2HFK\njBCpaW7EzX/8h72LDQLhQgUhbqhjGkHEm/JylHZ2+ituAO7eMEbnSMI2GTDMgEINBTzHK3GzZAlP\nNTl82J1z81//BVxxhXUdgQzCubFbbwPkxY3HAzxd19wE3SkN4AfsU6eo5sYN2lbQ/+//AfPnA+ec\nI/dcPedGuDaqYbm6sXzyk7w72tVX299uPbwQN4cPW4obw/qhIJ0baipASJKU4yGQoFgqKjAqkwlG\n3IwebX8Qug0S852AYlHTCWAWFHJuPMfpAE8tZWXALbfwvH43zk02675LmkCIm5YW4Jpr7D1XNBTw\ncICna8JyboSooatSzlG3gmaMdyy7/3655wrnRlvcLtsBqKkJuOQSe9trhhdpaVVVzlwktbjxs6EA\nQGlpBBF3ysu5u+23uBkzhuptCEdkwHAOUmgFwGIgcOIjbrxybgDgttt4uokb5wYoEjeOcxrHjOHO\ny29/az8Fxqe0NNdzbsJKSwOo5sYN6rS0DRu4MLnhBrnnlpTw//r7C+/XaSYQSCwTJvDGH07FTUMD\nn49l0RjDtObG74YCAIkbwhZJOR4CCYpFnLOCcG58Pjcn5jsBxaKmE0AjgMrcv6POyBQ3118PvPce\nv0Lt5GBSXs7TZ2RSdWRQFL6Q6uuzV28D+NZQwBXk3MQXIW6Ea3PfffbSJPWaCmdal54AACAASURB\nVGiaCQRGYyN3fBXFekaPHp/+NLB6tbP3Fm2tqaEAQRBWlJfz/weVlkYQNukEQy0UNMQkNW1kipvy\nct7Cedw457mnV15ZdJernMbGRu7a2N0en5wb1zU3ERI3ScmbDSSOdJq7L/v381bM99xj7/l6TQW6\nuorS0gKJZcIEYOtWZ22gAe7qzppl+bDQa26ooQBhg6QcD4EExRKkc+OzuEnMdwKKRU0GDLUAGknc\neMjQkPsBnlpuv91ZSppfNDU568pUWRk956a8PJwr9QZpaYRNKiq4Y/FXf8UX6XbQc25kZtz4wYQJ\nwI4dzlLS3EINBQiCkCVI5yaMC49E7OkEUAMFjYjHIM94iJu1a4Fzz7W/0DLjL/8S+OUvvXs9uMxp\n/P737V8lB/J1DplMdGpuPvtZ4Otf92xbpDFwbpKSNxtYHOXlwFNPybV/1qLn3Og0FAis5ubMGd/F\njW4s6lbQfjcU+NjHgOXL/X0PIjEk5XgIJCiWoJybABoKJOY7AcWiJm5paSVhb4AU//f/Ap/5jLev\nmU67GxLoNW5cqdGj+ZXbqDg3tbX+L+j0oJobb6ioAC64AJgzx/5zo+TcNDby/4fp3ATRUGDSJP4f\nQRDxRDg3fo9QIOeGcEgGoLQ02wwNGf/t9GlgzRqeIhNxQsvPHD2aXyGOSs1NWIi0NHGiyBHLWHQI\nLI6qKuC//3dnz9U6N+3tfCaUZoEfSCzl5Vxk+yxuQq+5IQgbJOV4CCQolgQ1FEjMdwKKRU0nWC4t\nLR6DPKMhbtasMf7b888Dl16ab79MFOODuIkl5Nx4w29/y9M2nSCcm8FB4IknuDs6dar8rByvmTAh\n+TU3BEHEm3Sat5z3W9z81V8By5b5+x5E4mBgw85NXNLSoiFuvvc94789+6z3KWk+EVp+ZmUlv0Lu\n4RDPWOaaUs2NN0ye7LyLYEUF8PLLwLx5fFDuq68CTz5ZtMAPLJbGRufd0iQxnHNz7Bhvo61xEgki\nTJJyPASSFctgWZn/4mbyZN/T8ZP0nVAsnNzkO5RTQwGbHD0KvPlm8f1tbcAbbwBLlwa/TXEiajU3\nYUHd0sJn7Fgubh55BHjlFWDu3HC3Z+rUcFzfmhreTjuM2jOCIGJHNghxQxAO6AR3bYD4ODcKYyzU\nrXzttdewZMsWYP164LnnCv/4ox9xcfPzn4ezcXHhhhv4FeKzzwZ++MOwtyY8enp4TnFbWzipSASf\nuVRa6qmL6IqeHu6clATcO+XYMS6qzj8f2LYt2Pd2yGuvvYYlS5aEvRmRhD4bwneefpoPDi4tDXtL\nCKKAHcjiL9GPnShHFxgm4gx6EN5FZJnjcTScm7/+a56+sn9/4f0xSkkLFaq54VDNTfiMHh0dYQPw\n5ghBCxsgfwWW6m0IgpDhc58jYUNEEj7jhlMNYAjAqYi7N9EQN9XVXOB8//v5+/buBfbsAa6/Przt\nsklo+Zk+pKXFMtc0nQZWraKamxiQ+FgqKrioInFDRIzE//ZiSlJiSUocQHCxHANDm89iwU0smdyM\nGwBQcrNuot4xLRriBuCtZ3/6U95hCOCzbT75SbqSIYNoKDDSnRuADzN0WgxPEF6hKLzuhsQNQRAE\nYcI3MYBVGAx7MwzhbaDzNAI4HtbGSBIdcTN1KvDxjwM/+QnAWCxT0mjOTTRJSixJiQMYIbHU1FBD\nASJyjIjfXgxJSixJiQMILpZtYNjjsxPiJhbeUCB/0TgOs25CSEY34ctfBm67DVi4kA/2vPzysLco\nHowezT8vcm4IIjqQc0MQBEFYsA1ZdCO6GSfqtDQgHh3TouPcAMD8+cBZZwF33w3ceWfs0otCrbkB\nqObGgKTEkpQ4gBESC4kbIoKMiN9eDElKLEmJAwgmljYwdAHYAwbmo2BwE4u6oQDAnRsSN3b50peA\nnTu5uCHkEOImSl2qCGKkQ+KGIAiCMGE7srgICtIA2sLeGAO0zg2lpTnhlluAF14AZs0Ke0tsE1qu\nqRheSTU3uiQllqTEAYyQWB58EJg2LchNIQhLRsRvL4YkJZakxAEEE8s2MMxGCkPIYg+yqEfal/dx\nX3OTpwHAepfb4zfREzfpNHDTTWFvRbzwIS2NIAiXUM0gQRAEYcJ2ZHE+FPQihQ/BsCDsDdKBd0uL\nl3MTvbS0GEM1N9EkKbEkJQ6AYiGIsEjS/kqxRI+kxAEEE4twbmZC8bVjmrs5N4XODdXcEMFAzg1B\nEARBEESs2IYsZkPxXdy4QevcULe0EUaoc24AqrkxICmxJCUOgGIhiLBI0v5KsUSPpMQB+B9LFxg6\nAUyFghlQsAdZ397LXc0NK3BuxgHoAjAQYYFD4iYJ+NBQgCAIgiAIgvCH7WA4FwpSUDATqcg6Nzwt\nLe/cpKBgHIAToW2RNSRuPIRqbqJJUmJJShwAxUIQYZGk/ZViiR5JiQPwPxaeksaX4Q0A+gB0+CRw\nnMYyCIbTAKo090e97obETRKgmhuCIAiCIIjYwJsJcEdEydXdfBgxwdAFYAy4W6Mm6h3TSNx4SOg1\nNx4O8aS82eiRlDgAioUgwiJJ+yvFEj2SEgfgfyy8DXR+Gc7FjT91N05j6dQM8BREvamA5ZybkydP\n4oc//CEymQwURcGSJUtw4403oqenB48//jhOnDiBhoYGrFixApW52o/m5masXbsW6XQad999N+bN\nm+d7ICOaigr+f3JuCIIgCIIgIo/auQEQybqbTgA1OvfHPi0tnU7jc5/7HL73ve/h29/+Nn73u9/h\n8OHDWLNmDebOnYvVq1djzpw5aG5uBgAcOnQIGzZswKpVq/Dggw/iqaeeAmPR/QC8JLRc01SKCxyq\nudElKbEkJQ6AYiEIGXrB8HUMePqaSdpfKZbokZQ4AH9jOQ2Go2A4WyVuZvjYDtppLBkD56YRQKu7\nTfIVS3FTW1uLadOmAQDKy8sxadIknDx5Ehs3bsSiRYsAcLvr7bffBgBs3LgRCxcuRDqdRkNDA5qa\nmrBnzx7/IiA4X/kKUFcX9lYQBEEQHvEmsvjfGERfhK+QEgRhn51gmAkFJQXOTfRm3Rg5N1FPS7NV\nc9Pa2ooDBw5g1qxZyGQyqK3lna9ra2uRyWQAAO3t7Rg/fvzwc+rq6tDe3u7hJkeXUHNNv/lNqrkx\nICmxJCUOgGIhCBk2IAsGYL+Hi4gk7a8US/RIShyAv7GoO6UJeFpatGpujJ2bhIibM2fO4Hvf+x7u\nvvtulJeXF/1dUYqDl0Vtl7W0tNBtuk236TbdDuE2ES02IItSIHJXcwmCcIe23gYAJoLPlOmJ0O+9\nEygY4CmIerc0MAkGBwfZypUr2Ysvvjh83/Lly1lHRwdjjLGOjg62fPlyxhhjzc3NrLm5efhxK1eu\nZLt27TJ87VdffVVmE2LB2rVrw94Ez6BYokdS4mCMYokiSToWe00Yn02WZdk4dpotZWfY42zAs9dN\nyv7KGMUSRZISB2P+xrKUnWHP6fyu57Be9j4b8vz9nMbyMOtnD7H+ovsPsixrYqddbpUzZI7HUs7N\nk08+icmTJ+PGG28cvu+SSy4ZvuLX0tKC+fPnAwDmz5+P9evXY3BwEK2trTh27BhmzpzpvSojCIIg\niISyGwyjoeBqpH1rD0sQRDhw56Z4Ce5nUwEn8LS0YhoAtAG5xNnooTBm3spsx44dePjhhzF16lQo\nigJFUfCpT30KM2fOxKpVq9DW1ob6+nqsWLECo3PzVpqbm/Ff//VfKCkpsWwF/dprr2HJkiXeRkUQ\nBEHYgo7FxoTx2TyNQbyMLO5EGk9iEC/BvBvmbzGE65EqGrZHEES06AfDGJxBBuUYpfm9fhkDaATw\nNZSGs3Ea7kE/rkIKf6MzOaYOvdiNcowL+Jgjczy2nHNz3nnn4bnnntP920MPPaR7/9KlS7F06VKJ\nTSQIgiAIQssGZLEAKamp5QNg+Av0YztGYQaJG4KINLvBMA1KkbABeMe0dyPk1Bo1FADyHdOCFjcy\n2OqWRpiTpMJciiV6JCUOgGIhCCs2IIuFSGE6FBwAw5CJwNkFhgFAqntRkvZXiiV6JCUOwL9YtoHh\nfANB4Fc7aKexGDUUAKLdVIDEDUEQBEFEiC4wfAiGeVBQDgUNUHDQZBGxOXel93hQG0gQhGP02kAL\nojbrphMMNYbOjdwFlTAgceMh1N89miQllqTEAVAsBGHGn5DFRUihLLeosCoy3pz72zGJhUaS9leK\nJXokJQ7Av1i267SBFkzJuSFnPBYNzufcmDs3Ub2gQuKGIAiCICKEqLcRWF3N3YIs5kR8qF6YrMUQ\n/h2DYW8GQQDgzs35BsvvEiiYCgX7IvJb7jSpubGTlrYFWXwR/V5umikkbjyEck2jSVJiSUocAMVC\nEGaIehvBDIumApvBcB1SUs5NkvZX2VheQxYvRqhIW4+wvhcGhjYPF9Ijcf+ywyAYdoPhPJMifD9S\n05zEwsCQAVBj8PcGGxdU3kcWHba3wDkkbgiCIAgiImTB8GaRc5PCHoPFeTcYjoHhSqSlxM1I5DAY\njtBno8tGMFyPvrA3Y8SwFwxNUFAZsLhxwikAowCUmjg3suJmu0kTBT8gceMhlGsaTZISS1LiACgW\ngjBiZy4NpFG1EDBrB701t2iYCLni3iTtr7KxHAbD0QgsFs0I63v5CAx7PfxsRuL+ZQeZRb7ZxQyn\nOImlE8auDQA0AtJpaduRNawz8gMSNwRBEAQREbT1NkA+LY3pLCS2IIsLkMIEKDgW1EbGjDiIm7A4\nmks9ytDnEwhmndIEUXFuzGbcACItTQ4u6oKTHCRuPIRyTaNJUmJJShwAxUIQRuiJm2ooqAJ0xctm\nZDE35/QcNxBAapK0v8rGchgMPQBORWDBaERY34tI1zNrNW6Hkbh/2WGbSac0gczgXrs4icWsmQAA\n6WPOABj2gWEWOTcEQRAEMfJYr2kmIJhhkKqyGQxzkcJoKEgD6A5gG+PEKTD0AzgLCrk3OojP5CP6\nbAJhu0mnNMG03FyrgZC/E6u0tCooUMBrc8zYA4bJUDCKxE08oVzTaJKUWJISB0CxEIQenWD4CAwX\n6iwC9FJVGFjOueGncpkC3yTtrzKxHAbDRCiYCCXSTQXC+l6OgmESvBM3I23/skMWTKrmpiy3vx4I\nuRbKKi0NkOuYFnQzAQAoCfTdCIIgCCJA7rvvPlRWVkJRFKTTaTzyyCPo6enB448/jhMnTqChoQEr\nVqxAZWVl2JuKt5DFJUihRGchoNcOuhXAEICm3G1ed8Nwju9bGh8Og2ESFDQA5NzocBQMlyNFzk0A\nHARDLYAaiYW+uJgxAwwfgOHXGMJvMIRbkMbDKPV/Y8GdG6MBngLRVGCGyWNk3CqvIefGQyjXNJok\nJZakxAFQLERwKIqChx9+GI8++igeeeQRAMCaNWswd+5crF69GnPmzEFzc3PIW8nRq7cR6Dk3wrVR\ncoulCYBlgW+S9leZWIS4mQgFR/3fJMeE9b0cBcMVEuLmFBjel+jgNdL2LzvssFFUPxMKvoVBnI0+\nLEU/OsDwdyjBMxiyrHHRw0ksGTBLISbTVCAM54bEDUEQBJFYGGNgrHAxsHHjRixatAgAT9d4++23\nw9i0IozqbQDeHvZDzeKS19vkFw2NOeeGyCPETVPE09LCYAAMnQAukRA3z2MI92IgmA1LKDvAcK7k\nIv9OpHEjUvgNyvAhRmEVyvB5pDGQe50gkHNuZNLSrDvEeQ2lpXkI5ZpGk6TEkpQ4AIqFCA5FUbBy\n5UqkUilce+21WLJkCTKZDGpr+Wm7trYWmUwm5K3k+fh/QhZXGCwCZkDB7lxnIuHUbEEWl6oeP0FC\n3CRpf5WtuTkbKdQB2Orx7BAvCeN7OQaGevACditxswusSFzrMdL2LzvstJGedRXSuArpgvsUKLgZ\nKTyPIdtpXs7m3DBMs3ifRiims26yYNgJhvOo5oYgCIIgvOFb3/oWxo4di66uLqxcuRITJ04seoyi\nWJ9417a04JrcAkGkeCz28Pa+0RWov3Qe6qHo/p0BwOLL0Q5gc+7vmxdfgb9GevjxjYuvwtvI+rJ9\ncb19GAx1W7dhcHAQR+bNCX17onS7cvGfowkK9rz+Bg5fPR9DKYa0wf637rwZaJ0wHt1g2NTyeiS2\nP263dyxegFsNPl/Z27cgjQc623DZe9t9397M4oWotXh8AxSsPXwQLbsP6P79IzBU9PXjnQ1vebZ9\nUrCQefXVV8PeBM9Yu3Zt2JvgGRRL9EhKHIxRLFEkScdiI37xi1+w3/zmN2z58uWso6ODMcZYR0cH\nW758uenzXn31VXaUZX3dtn9lA+wu1mf6mItZL3uLDTHGGBtiWTaanWadqu36NRtkN7Mzpq+RlP2V\nMblYLme97I9skG1mQ+x81uv/RjkkjO9ljWp/aWKn2UGTffxy1stS7DR7L7f/GTHS9i87NLHT7IDF\n52dFL8uyMew0a7N5PHISy8fZGfYSGzR9zPNskC0xOea8xAbZtRbHJLvInKuo5oYgCIJIJH19fThz\n5gwA4MyZM/jggw8wdepUXHLJJcNXA1taWjB//nzL19rpc0rTH02aCQhmqmbd7AXDOCgFBb8y+e8j\njcPAcCto6pZWyFEwNOX2n6lQ8JHJPr4bDJf5MFxShkcxYJr6FAe6wJABMNllelY5FHwMKbyEIW82\nzATeUMCcP0cKbyGLXoPvh3dKCzYlDaC0NE+xZZlFHIoleiQlDoBiIYIhk8ngscceg6IoGBoawtVX\nX4158+ZhxowZWLVqFdauXYv6+nqsWLHC8rV2gmGRT9vZB4YXMIRvWZyS1ZPLt4DhAs2iYQKAYxbv\nlaT91SqWITAcz825KQXQC6AXDBUhLLasCON7KRQ3vKnAQp3HnQTDIIAFSGEvsoCmFkSNH3E8hkHM\nRgo3m7yvH3gZy04wzIKClAf73s1I4wVkcZeN5ziJhTcUMN/eMVDwZ0jhDWRxvc73sx0MF4fQu4zE\nDUEQBJFIGhoa8NhjjxXdX1VVhYceesjWa+308crxy7mWzlMsFgEzoOD13NV19fBOgXBu1E0HRjKt\n4N2eynKfRVPOvTmbPhsAwBEwzM/tQ1Oh4KDBPr4bDOdAwQyksCXgpgwnwNAGYF/MnZudyOI8jxb5\nNyGNr2AA/WDD+7YfdEq0ggaA65DC703EzZ0h/N4oLc1DRJpDEqBYokdS4gAoFiJ+7PBxUfcsBvEZ\niavSaudG2wYa4CkrFeBXXI1I0v5qFYtoAy2IcjvoML6X4rQ0I3GTxTlI6Q6S1eJ1HNuH0zCD73Qn\nG0srGH6BQdPH2GkDbcUEKJgFBW/Y+EycfC8ZWLeCBoDrkcbvddLkGBi2hdAGGiBxQxAEQRCW+OXc\ndILhFWRxm4S4maGqudmCLC7QOYVT3U2eYnGDSA/yDBp5ccOdm7NDqLnZBoZxiLZz8zsM4QELccOd\nG+8cjFuQxvM+1t30gWEAQKXEY+dDwSGwopq2VgAKgHofts8KEjceMpJymeNEUmJJShwAxULEj0Ng\n6PNhgfUfGMJ1SFnmtgN8cd4FoA0M+wxmR1jNuona/nol+vCOw6vyVrFoxU2UmwroxXID+vCew8/m\nqxjAzywW3HbFzbTcInYgwP1rG7K4AelQxI1sLNvBsD9X32UEd268W3JzcZMFk/xc7H4vGQA1gFR6\na0muycGrGrG1PTfXJ4wUWRI3BEEQBGHBVImr1r/FkOnCT49nMYTPSJa/pnJXz1/EEM6GglE6i4ZG\niUGefrMXWamBmf1geAtZPGGxCHdKnNLStGTB8Adk8SeH4uYNDGGTyXOHwHACQGPutkxaWhkUNEkM\n/PSSbWC4GSnsy9WS2eUDZE27wHnBdmRRCuAtg/cZAsOeXEMBr7gQCgbARZMfZMCkLrgIeGpaYfzb\nwULplAaQuPGUkZTLHCeSEktS4gAoFiJ+nAvFNDVtEAxL0Y91NhZSHyGLLcjiEzZOxTOhYA2GipoJ\nCCYAOG7y/CD211UYxHckBMtuMNQD+E8ModPBIs1uzU2UnRttLPvA0As4KuDPgmFzbkFtxAkAYwGU\n5j6fceDd5Ho0z2Fgw84NAMu6Gz9qbhYihTSAkw6evxKD+DeH6VuysWwHw1/kWiLrcQAM9VAw2sOF\nvgIFNyMlnZpm93vhndLkuQ4pvIIhZFX7RlhtoAESNwRBEARhyblImc662QaGM8BwNzMZ/h1DuB1p\nXQfGiJlI4XfIFrWBFlilpQXBOmTxrsTnsB0MlyOFG5DGMz7UD8TZudkChqrc/+2yFwz94OLRiCO5\nFtkCBYpux7QT4I2fx0mKGy/pzM2GmQIF06E4Sk3bjCy2+bi9/bmUtM+gBG8a7PM7DFJI3SJS0/wg\nI9kpTTAdKVRDweYCccNwfkgyg8SNh0Qtl9kNFEv0SEocAMVCxA8r52YjspgAoEVyscHA8DMMSXVJ\nUzMDCnoBQ+fGqqGA3/trV+5K/14ww8F+gu259rj3Io1/waDttCOrWI7oiJuoOjfaWLYgi1uRduTc\nfACGa5DCATAMGsSrrrcR6KWm8ZS0/OOsxI2X+5e48q8Mixt7n8WZ3L643aEAkIllNxjOgoKrkcJG\nZDGk89ns9LBTmpprkMIHyOKkxD5t93ux69wAwPU590awjZwbgiAIgoguMuLmXpRgI7I4I7HYeB8M\npwEstHkanplbLGjbQAvCdm7eRBaXIIVZUCxdhx25nPyrkUIK9lwvGeKUlqZlCxiuQwpDgO3udx8g\ni8uQQiMUHHApbnaBYZZqH52BFD4MqC3zdrDhNsJnI4W9Nj+HHWCYCgV7TESeW0RdyTgoaICiWwOz\nw8MZN2rKc4X8L/vgesrOuFGjrrvJ5Fy3qSRu4k+Scu8pluiRlDgAioWIH1ZpaRuRxceQwmwohrn3\nap7FEO5E2vbE8llQUA1gmsHzrJwbv/fXdcjiSqRwEVKWqWnqbkpfRAmetLlIM4vlVC41a6zqvnEA\negAp8Rk02li25Ia0XuBgcOYHyOJCKDgnt7DXQ965YbacGy/3r21gmJ17bydpaZtzIq8Jim1hBADP\nvrUB38aA6WPEPgwAVyClm5q206e0NICnpr0gsX/Y/V54QwF7LEYKG5BFL9jwXB+7xzevIHFDEARB\nEBY0ABgCb8OspR8MW8DwZ0hhMdKWDsQQGP4dg7jTZkoaAJyFFLZilOGiIWzn5o/I4iqk8GdImbYx\nzoIVLPruyg0C9GrbD+dqStRtaBUogX8+Jxy8V3+uGcB5UHCBhAOm5X0wXIgUzoFiWHfjJi1tr8PO\nZXbZphIOTsTNFjBcAAWzoWC7g+19p7YG37dIl1R3BLvcoKnADmQ9bQOt5hqk8AcMSX0fdvZFnpZm\nT5jUQME8pPAGsgWiLwxI3HhIknLvKZbokZQ4AIqFiB8KlFxqWvHiZQsYzoaCKihYhJRl3c1aZNEE\nxfHJf4rJ8xrAi8CzBgsZP/fXATD8CVksQAoXQTF1bj4Cw1gAY3ILqBoouB1p/JuNttBmsWhT0gR8\nkGcw4uZDZDEdZ6Tag6tj2Z1Lp6qAYtu56QYfpjgz59zsNniuPecmv7+NgYIKGHfk83L/8sK5mYsU\nzkcK2xyk0g2cOxOtgGnr623IDqfO6YmbTjCcAjDJ9rvLIRxco/RDwajFV+NS9Em/rt2GAgJed5MN\ntQ00QOKGIAiCIKTgqWnFi4hNyGJ+7nR6FVJ4G1nTgZ8/szHbxi5lubS1dl9e3Zz3wDANCsbmruBu\nBtMtsAZEB6nCJci9KMGPMVT0nNNgWIkBfBb9Rf8ZzXIxFjcKjjiMzy4vIItTgO1uXVtU3fC4cyO/\nMN+CLGZDQQkUzETK0Lk5AoYmzX1accNyDtI5ms+Rp6b5W3dzCgytYJiee+9puW0z2p/0EM7N+Q6d\nm805Af4ng+cOgWGXyn2cl0sDVLfT3gk+38avQZYKFCxECn+0+D5eQxYHcp+pDE4aCgCi7maowHUL\nAxI3HpKk3HuKJXokJQ6AYiHiiVFTgY0qcVOTc3iMhi92gOHXuXobvzCru/FzfxUpaQD/HBpN0qL0\nruxejBQmAHhZ9dm9jCFcgD5sBsO1SBX813rsGP4/A6fHSNyYNRVgYIaOlxNexBDGA3jHZk0EX5Tz\nz/ECpLDVxnZ9kEtJA2BRc4OCVtAAMBkKDqne6yiASqDoCj5vKuDv/rUjJ6rSufcuh4Lx4N+rDJ1g\naM+Jo9kOnBsGhncH+vAZpA1/ywfAMD7n2AL8wsK8XNe0fBz+NBNQcyXSluKmueMkKgDTwa5qnDQU\nAID54O3EN4TYKQ0gcUMQBEEQUhg1FVCLGwCmdTdPYwifQBoNPp74w6q7WYchXKn6HMxS04xy8nlj\ngUEcBsMd6MP9GMCPUIrnUIbPoqTgv6WHjxt+zubOjf5n8xv8/+zde5xM9f/A8dc5M7uzX7dY13XZ\n5Jo7UQghUkmlbymUbz/dJaXSveSrq1RSSvl2LyVdKHRDJKQoitBFhFxDbruzM3PO5/fHnJlmx5nb\n2t2ZHe/n49Hj0dq5fD7nzJ7zec/n/Xl/THJw82YRylKHO4hiGSY34IwruAnln7nxH5sqaFQiempU\nqB8xaROsMOaf7QivFKZQ7ERRK+z4ZKFRmX9SzvzrbY48R6Wx101oulfACehxp6b9hEkLa0F7M6uK\nWSKB605AU9APR8QCIXYBesewgiIlVQY6VBd0lkb5jBWgWFexAkNwxB3c7KdoMzdONHqis59/Kjsm\ngwQ3xSidcu+lL6knXfoB0hdRNp1oM3PjRrEORZuQG3mkdTcKxfP4GFaCszbgn7mJFNyU1OdVoVgc\nMnMD0A6dVQnM3ABcYg0m2+DmRHTW4OKsCMfryvYd2IKyXShdlJmbLzA4CwdP4qMXHtYfRerVPEw6\nodMdne8TXHMTSKcK8K+7SWTmxv9cFxo5aGwKe+4eoDz+YCZcaGraLzYpaRA9uCmuz9e6kPU2AScQ\nf9Wz1SGzX8dZAeLWBIKb1Zi0y3RxCjrfY9qWkl5rpQCGCq+Y5p+5KdlBmKsi3AAAIABJREFUfltr\nhu5AhP59i0lzp5Ne6AnN3CRaUCCgDw4ao5EhwY0QQgiR2hpZA8XQgc5qK6f+XyE38m7WwmJP2GBj\nISZO6/clqRaJ741ytH5H4QSODzkObaOUgw5s4BmuHBpTyeQrXDxARqHjGs6JRhd0Ftm8hz+4OVK0\njTyXYHI1Dr7FRX8cdKOAe/CSV4RjOQeDc3DQFp0fImzuaCcPxZ9hQUW8RQUUih+tRfQBjWxSA+2K\nCQSEBjfhZaADSmPNjd3MTYMEigqsCTsO/tS0xNbrtEKjMhp10Wyf6w/QC7cxUFQgMPPnrwhYsn/v\nmWi0j1CGGvzXnZ44aI/OijiPwX7guCK25xIcPElGEZ9dPCS4KUbplHsvfUk96dIPkL6IsinL+iY8\ndIAVnpIG/hKqjdFYHjbYmIzBdThLbHFxgH/mxl7451Wh6EUBvSngaXxsKuKg1b+/jaNQ3wJ73YSn\neP2Fwoc/CLNzJo64FiMvXLiQHugstNkf509IKC3tkDUD1wEdJxo34uQHsvgVxdl4YrYllImyghud\nyvjLT/8SY1AZOC/rrGDZWSi4ia8c9GYUFYBqIc+1Kwe9LWZw4/8M/IpZaAPPgNJYc7PWZmYvkYpp\nq8NmvxItKrAGk4yffwXglAglntfZrCs5Hg0D/yyRD8WGCAFicTs1SmraAkyyf/yJBmgcInZRAQPF\nXyiyi9juymgRZ1tLiwQ3QgghRJzCiwqswKS9za20B3qh9SA7UHyOwZBSuOnXilJQINznmOxEcQNO\nVmFyCgW0xs29ePk7gcFgeEoa+Msu63BEdbLAoLA4grweOI5IATSsNSV2A/hIaWnLrbUqoalatdGY\nSgY/YiZUPnqltRi7kXU8TrJSm+IRut4moCU6q+N4fmgxgYDG6PwW9tzoMzd6zJmbWvg3Qz1YQrOD\nBSi2oI5YsxFvcKNQR8zcJFoOejUmJxzOB/zBTXhRAWUFw+FBuIZGR3SWodhoHedos4/FpUuEimlu\n/OXZW+8/iIbGSXGkpq3CX4q8KAUFUoUEN8UonXLvpS+pJ136AdIXUXaF73VjN3MD0D2sqMBL+BiA\no1QGDNEKCoR/Xsfh43ac9MfBy2SynSyeJ4PlmDybwJ4zS2yCGw3NNjXNblBYFD169KCdlUYVurnq\nLqAK/nSdcNXwp9yEpwwuweRUmzZloHEmDj62mR2KZLaVkhZwElrMdTeB8xK+3gaguTXzE2u/nB8x\nbYIb+7S08EppAYG0NNOadbBbFK6h0SDCupviuB7+gr/KWfj586+5iR2gbMc/uK0R8m+JbORpoliL\n4j8ntQf8qWbhwc0OIJPCs2QBnayZntIoJhDQ2XrP8LVB31jrgs7p1g2A9nEENwswOL2Mhwdlu/VC\nCCFEKWqKznprAJGH4teQBdyhuqHzNSZe/HtzTLFS0kpDtFLQoZZj8huKQSEDcQcap+JgFE7mxPlN\n9x4UW601CuHa2QY3xbfI2olG17B1N5GKCQDoaLYFF5ZiFqr0FuocdGYnENwEUtICjnbmppy17iNS\nWeeAH20+i0Vdc7MV/x4vFSI8LlJRgV8w+T88Ce1HE25thDLCddDYC+THeO3ArE3ozGBg5iaeKnib\nUFRFC24w29o6hodDnhttH5fAupvSKCYQUBWNOjbpi4H1NgHt0WIGN19gcnqS08qOVszgZvLkyVx9\n9dWMGjUq+G+HDh3iwQcf5KabbuKhhx4iLy8v+LsZM2Zw4403cvPNN/PDDz+UTKtTVDrl3ktfUk+6\n9AOkL6LsCk1L+8EahLlsBjDZ1rfb36H4BJOaYJu+VhKizdyEfl4fw8stOG2rGp2Gzk+YtpXIwi21\nKoM5bYMbjVUlNHMT6Ev3sHU30YIb8KfLbQ/52cS/L4fdzA3A2Tj4gugbswbstALe8KpxdmuP7Ppi\nN3MD8a27sZu5aYB/35HQWR9/cGMv13q8PyUt8jmKVFRg5PYtvIvBMwkEg+HWoo4oJgD+wLseGn/E\nOA6rMY84hjUADf+sXiyB9TqBc+JCo1VYUBCp2h/Aydb5XoOiaSnOIdilpi3A9K9Ls/rin7mJfPy8\n+Ksedi/jcx8xW9+zZ0/uueeeQv82c+ZMWrVqxcSJE2nRogUzZswAYOvWrXz99ddMmDCBu+66ixdf\nfBGlSr/WvhBCCFESQve6WWEtQI8ksNh9Mj6GldKsDUB1/OV+o317/ismCzG5KsI3tC40eqPzSRyD\n1MVRZj38aWmF27E+ysCwKMLX3cQObgoXFVhrfVNfM8JzqqHRImwNVSQfY9AbvVBKVXU0KsaxXuRv\nFH9b6x3CxaqYlo9ik00aVKb1jf6mQsENEWduqgMH8M8CRVsIb1dUYDuKr6pl8wUuHsQbVwqZHbsy\n0AHxrLvxVzo7MkWyGTrr4miT3exZeGpapH2aACqhUR+NjzBKbeYG/EUFQoMbN4oVYemiDdE4iH35\ndPCn2TZEo2oZXm8DcQQ3J554IuXLly/0bytWrKB79+6AP79y+fLlwX8/9dRTcTgc1KhRg5ycHH77\n7bcSaHZqSqfce+lL6kmXfoD0RZRdtYE8/APRSOttArrjYCoG32BySSmmeTjRqAL8ZfO7wOf1cXxc\nhzNi2hH4NzCcHcdg0K6YQEBjNHZbg3aAw9Zi/xOKo5iA1ZeTrG/z91jvESu4CS8qEC0lLaAfOnPi\nCPTmhK23CYi17qZHjx6FNp4M1xItalGBn/BXWbNbZxS+7iZatTTdmh2ZjxEjuDkyLe0ZfFye4aIj\nOnfi5Bq8caWBhbMrAx0Qz143q8OKCQQ0w76kc7hAGejQa7u/qMA/z40WgIE/GNoHpT5zE1ox7Wtr\nBqtiSF9iFRX4ApOeZXzWBoq45mb//v1Uruzfu7Ry5crs378fgL1791KtWrXg47Kzs9m7d28xNFMI\nIYRIPg2NJlZqWqzg5jT8my8OwUG5Uv4mNNpGnjtQTMdgRIzZpL44mItxxOL7UG4UqzDpGOE46Gi0\nxr/XC/j3/WiEhqMYj0f4fjeJztxEKiYQ6hwr0Is2WPegmIfJ2bbBTex1N/6UNPt2xNrI0y4lLSA0\nuFGoqGtuwJ+a9iVmHGlp/7TnIIop+LjZ+kyNxMl+FK8kmJ4WKJ/cJEL7Yu11Y+CvYtbC5vnN45y5\nWW0zcxNeDjramhvwBzcVIWL6X0logr/U85/W8QlfbxPg3+8mcnBT1tfbQDEVFNC0o7tIheYAL1y4\nsMz+HPj/VGnP0fwc3qdkt+dofn7qqadSqj1F/Tn83CS7PUfzc3ifkt2eo/k5XT5fIn5NrYHqxgiD\nqICqaFyBg+GlmJIWEKkc9MKFC3kaH4NxUCNGgFHTCuQWRxkQrrDWHUWbAWqLFkxNi5bOk6jQz27o\nfjeJztwsiWPmpjUaHihUBjzcV5g0jZDeFiu4WbhwoW06VEATay1MpMX0P2LSJkKfG6Hzq/Xe+4EM\nIhcKAH9wcwiiztwcbwWIgcD3ZQx6orNl4SLAH3C+RCZ34rXdVyiSDda5i1Q+2Z+WFvk4/o6ihjVb\nES6evW48VnB1YsiaG/Afi4ModqDYhyIPbDeJDeiNzoCwfZ9KmoZmpab5/w4WWuttoPDfSqSiAoGy\n0aelwcwNKg67du1St956a/DnkSNHqn379imllNq3b58aOXKkUkqpGTNmqBkzZgQf9+CDD6pffvkl\n6mvPmzcvniaUCQsWLEh2E4qN9CX1pEs/lJK+pKJ0uhYXt/BjM0Z51GnKrU5R+UlqUWyXqgL1mvIe\n8e+zFy1SVVWe2qCMuF5nrPKoW5Qn4u8fUh51kyqI+hr/U151ufWYe5VHjY7yeokI/dv7RhmqtXU+\nmql8tTpK/2YrnzpbuZVSSu1Qpqqs8pShzJjvd60qUOOjtP1mVaD+G+H3W5Wpqqs8ZUZ4nwULFqge\nyq3mKl/E12+l8tV3EfrVU7nVZxGeO1v5VB+rv2uVoZrE+NyOVh6FylN5MY5JfZWvflWG8ipTHa/y\n1TJlHHE9vE95VH/ljtjvcB8onzrXaqudb5Wh2kZp/wfKp/pFeP4mZagclRf1/VcrQ51ovX54X/oo\nt/pI+dQS5VMnp+jf/qPW32OeMlV5lacOWsc9tC+/KkPVs2n/AuVL6WtaQDz3qrjCM6VUocIA7du3\nL/TNa4cOHQDo0KEDS5cuxefzsWvXLnbs2EGjRo2KPyJLUemUey99ST3p0g+QvoiyrSkai2KkpCVb\npJmbdd06cQYOGsTZdn86ln1qUR6K5/AxKMbMVOheN3a7uhdV6N/eSdai+T1WWk6kfVygcFpaoNKb\n3TqXcP1wRC2PPRuTfhFSemrjr9b1Z4Tndu/R3TYdKlSkogIKFTMtLVBGOlZKGvhnbupFmT0JCKSm\nvYdBLv7NK8Ovh/fg5GcU78VZXCDaehuIXVAg0nobgHpoHICom9OGVloL70sgNW1dhGpuqSBQVOBr\n6/NQwaYvDdE4YFNUYEGapKQBsefKJ06cyNq1azl48CDDhg3j4osvpn///kyYMIEFCxZQvXp1br75\nZgDq1q1L586dufnmm3E6nVx11VVHnbImhBBCpJLAIuFUDm5q4t9oMNSfKJ7Ax8e44n6ddlYe/y+Y\nNAnr7yR8dESPuN4moKW15qPAWg9xYgkcN6eVkvMJBh78m3hGEpqWFk9KWsDp6AzG5G8UlcMG/h9j\nUID/eNkJLOT+HpO6NgPIQInimlHeP1I56MCmlZGeWx+NrVYKWbRiAv+8jx5zDRL8E9y8hI8xZNg+\nxoXGS2RwIR7OIss2XSzUOhRnRHnvqoAJ7ENRxea1VmNyQYQBuo7GiWisw6RzhMdEW/fUEZ2n8ZGP\nXqzV/opTB3TWopgTpTBAaFGBs0KOwxeYjE5CCm1JiPnpvemmm3jhhRd46623mDx5Mj179qRChQrc\nd999TJw4kXvvvbdQNbULLriAZ555hgkTJtCmTZsSbXyqSafcdelL6kmXfoD0RZRtgcXOqRzchM/c\nuFH8mwL6/b6Fdgm0W0PjHJsZi30oxuPjoQiD2lBZaDRC4wdrPUNx7doe/rfXA52pGNRBi7rWoTqw\nF/+eHvFUSgsoh0Y3dD4Pm8n61dq48k0yor5vtHU3b65aSUv0qM9viW5bMS0waxPpuZn4NwHdGCwm\nEF1HdKaRGeNR/uDmZXzkQXDTUrvrYWcc9MbBeHwxX/OnGDM3GlrU2Ru7MtCh/OWgI8/crAmZ+Qnv\ny8noLMeMWUwgmf6FRms0puALrreBI/vSPqxi2mEUKxP4W0h16dELIYQQopSUR+N/ZEQtBZtsodXS\nFIrheMlFY/DmbQm/Vj+b1LRx+OiPI+5ZmLbovI9B7TjSnYqqBw7mYkYtJgD+zSBrAH+g+AGTUxIY\nCp0TVh77IIr+ePgvGXSLkdITLbjZWKGc7eadoSLN3PiDm+jPDVRMiyctLV4N8W8IeSvOmGl9D+Lk\nWXzBSl52PsNgH9AqxmtFKgftxn6vn1DNYxQVCGzgaacmGsehsbAYUytLwqk4KICos28dwooKLMHk\nJPRSr+pYUiS4KUbplHsvfUk96dIPkL6Isu8qnMVazri4hc7cPIfBckxeIZOeRfi89rK+sd4fso/M\n//BxfxyzNgHt0Hkbo3g37wzry0lo/AtiBjfgX3cz2xqklk+gTedYqW8GChPF5Xg4FZ3r4lir4N/r\nxj64cTdqEHW9DfjTy/bh3zMosM5mLF6exeDkGM8NrLuJtoFnopqhURu4LKTvka6Huehci5N78dr+\n/hCK6/DyAhlkxRHc2FVMW4+iIfZ7/fzTZp21Ec7BIasaWsMIa27AP6ulrDakqp5WWmHo5zq8L/5y\n0P8Eef4S0OkTEqRPT4QQQggB/DNzswiDsXiZQWbU8r/RlEejKzqfW4PC/+LlSpzUTeD12uEvZVyS\n6TwZVjvjDW7ew0g4Ded4dGqh8S0mD+NjO4pJMdLRAuqjcRhsCz2ELmSPREejBRrX4KUhBZyPh30o\nXiODgTGCq8ZWOejtMYotJKI5Or+RFTMYCbgTJ59gsMomuLgXL93QOTOOILEBum1aWrRiAv+0OfLM\nzVoUzYi+B9Mp+MujO1M4uDkHnU9jpBWGFxX4wirlnS7SpycpIJ1y76UvqSdd+gHSFyFKWjXgb2Ag\nHt4gk4ZR1kTEw18pzOBnTGZgcGeCC4/bWO9/YjEOCu36cgNOzo1jaFMbLa7NO+30w8Ed+JiMj/dx\n4YqzT4GF3CvDBveP4mVrfj5t42jLMJy0QGMGmfyOiwlk0iOO/VQalUBaGnBEimG0z1clNEaTwSi8\nhTZDXYbJOxhMiHMmMNKamzVRUsoCGljFJPIiBpiR16kAnI8j5ua3yaahHRFwhvcltKjAfqvQR6c0\nCgnSpydCCCGEAPzrSmqhcTNO+hRDeddz0PkYg7vwcitOshMcIFdBo4E181CSzsHBaXH0NzDA71KE\nY3MuOssweY/MhGdBQtfdKBR34eUNDJ5auTaumbX/w8n9ZNAmRvGBcIE1N/FUSytJV+NgK4pPrWPg\nQXEVHiaQQdU422W35mYLJh9jRCyHHeBEoyGa7Wasa+KYPWuEztUpHtzEK1BUYJFVDj3eIL0s0FTo\nBjZJMH/+fHr16pXMJohj0M+YNIlRUUeIY4lciyMrq8dmB4qaUGzXuda42YPiV7KKtPB4K4o6xdie\nozEFHw/h4w+yivT8bUVM73obH+9jMJ1MhuNlOSaf4qJaCR8TL4ryuHEAeWQl9Rx8ZAXJP+DiIXys\nwOQjMuNu02EUVXGTRxYKeAaDB/FyE07uwhkzZexiPJyPzqVhQcoZFHArzkLlkdPZNHxMx6A+GtXQ\nuDuBNXTJFM/1WGZuxDHnVXycSAHL4txUTAghyqJaxfwFzkicPENmkSsq1U2hL5RORufaoxjEFnXd\nyknWQu4heFmL4otSCGzAvx7peDRyUuAcnItOdTRux8skfExOILAB/xqwysAcTDpSwEcYLMXFfWTE\ntRbmbHRuw8tUfIXS42Jtoppu2lvV7r5Io807A46ds1gK0in3Pl378i4Gd+NlIA4+LGPBTbqek7Iu\nnfoi0t/RfF6vwMm/U2gQdDR9aYeelG+qG6Oxx6p49imZVLIG46VxHWlsBTclKZ5+aGg8TgYTMBhL\nRkKFKQIaoDMUDyNwMp/MIzaYjWYoTj7AxeP46IWHdZjsRuEG6iTYl7LCri8N0diPYiOKDinypUNx\nSY/EQSHiMAeDG/DwOS4KgMvx8GgZmYYVQghR9uloLMBF6xgli0tCI2sxfSrogM43uIo8qJ5CBjXR\nqF7E53dCZzkunsPgNArohk6rBNcxlXW6VVTgX5DS1d+KQtbciGPCAgwuwcMsXHREx0RRFzcLcSX0\njY8Q6UquxZHJsRHp4B18/IHidvlSr5DtKEbh5UQ07jvGjs0TeMlGY2gZmuuI53pcdnojRBEtw+QS\nPEwnk45WIKOjcR4OPsTgNgluhBBCpLlLZMhnKweNqTH2hUlXt6ZpMCejumKU7vmZZVEBirM9h3jN\n2gsg1PllbN1NupwTkL4IkSzp9HmVvqSedOkHSF/KMgluRFr7HpPqBR7OtlkEezo6azDZlSI5yEII\nIYQQ4ujImhuR1p7AyyYUz0SYch5AAWfj4AqZrhfHOLkWRybHRgghUoPscyMSlofiaXw0wM3nGMlu\nzlFbismpUT7m51vrbo5FBoqWuPlTZq6EEEIIkSYkuClGZTmn8QCKR/HSADcLMGi+bSezyvigX6FY\nion29bcRH9MXBwswySsDA/zi/nx9jclPKOYm4TyX5b+VcOnUF5H+0unzKn1JPenSD5C+lGUS3KQx\nM84B+7PWTM1qFPNwMQMX52/bybw4FtubqEI7/KaSTSh0oGaBJ+JjstHogM7cEigssA3FpykcIH6I\nSX20Eum7EEIIIUQyyJqbNOVB0YQCPiOTplFi2M2YtKWAZWH7vZgoauJmJVlRdw++DS9VgTtLuZzg\nfhQfYTAkylqZqfiYicG7uKK+1kR8/IDJy8VcCvJWvLyDj81koafgBllNcfMwGVyPhx1kHVObl4kj\nybU4Mjk2QgiRGmTNzTFsOSZ/oHgQX9THPYGPK3EesZGljsbpOJgfZebBRDEVH68nYXbieXxcgZe9\nUWaN/OttjqySFu58dGZjYBTjDJTPOjY+4NsUnNlaj8lhFP9GpyIaa1KwjUIIIYQQiZLgphilUk7j\nQkyuwsGnGPwcIe3oLxRvYHCzzezHwoUL6Y0eNTXtG0wqo3EQWFuE1KZ8FIeKMKj2oXgWg6ZozIwS\nWAWKCcQ6L/XRyUHj62JMz/ockxPQuAYn78UI/lZjxlXUoDg/Xx9hcB4ONLSY57kkpNLfytFKp76I\n9JdOn1fpS+pJl36A9KUsk+AmTS3E5DwcjMQZcfbmaXxchIPaEdKR/INeI+KamhmY/BsHF6LHHMB7\nUSzA4Em8DMFDS9xk46YBbt7Cl9C6nZmY5KJxH07ejfC+B1H8iqJdnKlWxb2h52sY/AcnF+Hg/SjH\nEOBevFyDB3cpzp58iMn51qzWGTiYV4TZt9fx8aus1xFCCCFECpE1N2nIg6IqbraQhQ40xM2SsDU1\nB1E0wM3XuGgUJcZtiJuPyKRF2GOUtaZnGpnkobgBLz+QFfF17sHLBxj0Qqed9V8LNFajuBwPTdF4\nnkxqxBGMnEYBI3ByNjp1cLORLLLDnjcPg7H4WBRjvU3Ad5gMwsPPuI567cnfKI632lUFgsepvc1x\n3oJJGwpoh84lOLimFPbb2YWiCW52koULjb0o6uPmL7LIjLPvf6HIxc0lOHilmNcqxWKiOABUljVC\nxUquxZHJsRFCiNQga26OUSswaYxGZTQqoXGTzezNFHycjiNqYANETFn6CYUHOAmNU9HZheKXCN/i\n56OYgo9ZZDKJTK7EyUnouKxKZd9ZgVdr3DFngFZishFFf3QqWClVdildsfa3CXcSGvnAD8Uwe/Iu\nBmegk42GhhacvbHzPwwuxcF9OHkCX9wV7o7GbAz64MBlBQfZaDRFY1kCszDP4uMsdGZisKeU1+v8\nFx/NcLNZZo2EEEIIEUaCm2JUGjmNL+PjixgBwJeY9Ag5tSNw8glGMPgoQPEkPu6MMksQ6EtvHLb7\noMzAoD86GhoONP4dZQD/FganoEcMpLLQeJQMZpDJPXi5Ek/EQf7T+BiOgwxrYD4Ah21qWmhwE895\n0dAYhZNReGOmyO1E4Y3ymNcwuDzk2F6Ig/dsUtO8KF7Ex3U46Y5OJWBWlAF7cX2+PsTgvLBz0RtH\n3Otu8lA8i49HyOBcHLwao2iFnblfflmkoOgXTJ7Fx+U46YuH/SlQCOFYy2UWZVs6fV6lL6knXfoB\n0peyTIKbMuQ7TIbj5d4Yg8mFmHQPqRJ2XNjszRsYtLJSw2Lpic5XmEcM5mdgcEHIe1xkDeDDKRTP\n4OPGONKtOuNgJS5+RnGPTR93oZiJwdUhr3UODpZgsi+kfSaKZZh0TvDjPRwHu1ER1/GAP7Bpg5vh\neG1/vwGTXzA5K+S926PhgSMqkn2ESSN0WlhB4m04GV+EQCEReSgWYNI3rIpcIkUFXsGgCzpN0bke\nJ5MxEp5xmtToeE6jIGqQGE6hGI6Xu3HyCE56oHMRnoReQwghhBDpLe2Cm7WY+JI02OnRo0eJvXY+\nisvw8D8y2IzJqggDUS+KrzHpFnZqA7M36zF5DB93xQg2An2phkYjNL4Jeb9NmGxB0TXkPbqhswXF\nxrB2fYVJPnBGnB+1cmjMIJN3MXjJJpXuIhxUDVlrURGN08NS09aiqIFGdetx8Z4XJxrPksEteDlo\n8xkyrXMwGAdfYjLVJhB5HYNBITNL4J8VutAm+JuMj+tCgox/4+BPFF9HCK5C++FG8RkGN+GhFwUM\nwsPNeBiHl9fwsTTCa8zDpL2VMheqCzqrMWPOhPisWb/brc9PRzQqQkIbga7EZFmdmlRH45kEgrl3\nMdiJYgRONDSeIgMXcF0cs20lyd2jG7cnuQ1CxKsk71OlTfqSetKlHyB9KcvSJrg5gOIaPLSlgMvw\nFunbXC+KxRjsSsFByp14aYfOZTi5FieTIwwKv0PRAO2IwetxaNyIk354qAqclsCpD09ZmmlVYnOG\nvIcTjQtsUtOewccInAltYlkdjTlkcjfe4D47HhSTI8wAhaemJbreJlRXHPTGwVib4/soPgqAx8jg\nXTIZiZd1IcfFRPF6WEpawIXohY7NL5isxuTCkODGicYtOHk8wrk9aB2DcymgBm4ewEdNa8bnXHTq\nobEHmI/JILw8ZDO79BFGsEpaqCw0OqOzMEaQ8gEGOWh0tl5DQ+N6nDwXZ5CiUNyIl7FkMIUMHsHH\n9jj+3g6guAUvz5ERDBydaEwjk5WYPJJAkDQGL6fgPuK/SH9T0byPweV4+ACDaXFUnFMovscsciD0\nt80XCEIIIYT4R1oEN3MxaEUBAFvJ4hCKi/FQkMAAwkAxBC+X46UpbmqTT18KuAcvs2OU8g0oqZzG\nuRjMwORZMgC4CifTMWy/ZV+IUWi9TagbcbIPxd1kxKwIFtqX8JSlD8JS0gLCZye2YDIfk8vj2Egz\nXFN03iGTQXhYi8n7GDRFp5VN387FwVeY/G0dj/DgJtHzMo4MXsXHTyF9XozB0/h4i0ycaLRG5xEy\nGICHw9b7LsakPNiWn+6Ezj4U663XfAGDoTiDi/oDhuJgEeYRJZbXY9Ly8D7mWgUINpHFYlzcTQZn\n4WAwTm4hg8fI4HUyWYaLqRiMDplRMFDMsllvExAo/R2JQvFYyKxNwGAcLMbkjzgG3e9gcAhFo4WL\naYLO1Ti5LUKKX6j78dIHB13DPksV0JiNixcwbGfSwr2Gj7cxmEgGk6wCF5PI5DEyGIePFxIIcN7A\nxwg8PLRiNdOsYDdWoPYUBh0p4Dw8cQV1oZZj0o4COlHAbxLgiCJKp9x76UvqSZd+gPSlLCvTwc1B\nFNfi4Uq8TCGDKVYp4Q/IRAcuwEN+HAOIQC7/LhQ/4WIvWXyNi2uc+XpOAAAgAElEQVRxkol/1uRa\nvEed7mai+BCDvhTwTpyDqL0ohuLhZTKoYg2Ec9A4Awdv2AxEw9fbhDoOjd/J4twEg42u6KzC5ACK\nXSh+xKS3zUenJzq/othiDbwmYzAEBxWLWLK3Bw4eJ4N+eBgXZd1OIDXtI+t4HM3MDUBNNO4ng+FW\nYLAHxWC8vEQmdUP6ciUOTkLnBmtw7p+1cdgGjnpI0YV8FK/j4xqb81ABjWtxMiHk8/E+BqdRwCVb\ntvMBLgbiPGJmLlwOGgtxMRODO619hL7FpAYaDSIGN9GLCizA5DDQL+z55dG4DAdTYsxcHEZxGz6e\nISPY83twsgiTL6M8dxUmUzF4zAruw9VGYzaZjMIbdQbpO0xG4WUGmXTGwSnowf964GAemTyAl9fj\n+Nt8Hh9342M+LhodyqMDOtfi5Go8Eb8I+QyDx/CyDhdt0WmLm2lx7PGkUDyHj3Mo4AkyGEsG5+Bh\nbwrOMAshhBBJp5Js3rx5CT8nX5lqsvKqeipfXaUK1N/KPOIxXmWqQapAna7c6pDN70PdpTzqZJWv\nDkR43AFlqrOUW52t3BEfE41XmepN5VUtVL5qp/LVc1bbH1IeZUZ5PVOZ6mJVoG5SBUf8boHyqWYq\nv9DzPcpUFVWe+qsIbYzldOVWs5RP/U951QCb9gQMVQXqKeVVecpU1VWe+lkZR/3eo5VHNVT5yhel\nX28qr+qn3GqXMtVxKk8ZR3kMfMpU7VS+esN63VuVx/Zxh5Spmql89azyqioqT/0Z5X0XKp9qq/LV\na8qrzlLuiI/boUxVWeWp7cpUtyuPOl7lq+VFPI5/KVOdpPLVjapA3a486u4I/VBKKUOZqprKU1si\n9OEs5VYvKa/t79YpQ9VUecodpf/3KI8abPPZeVf5VEuVrzw2zzWUqTort5oS4X1D/aYM1UTlq1HK\nc8T536VMdbzKV+8qX9TXWKsMVUvlqelRHveE8qgTVL7aEHZOCpSp2qp89bJNW39Whqqh8tRXIa/7\nrTJUM5WvLrI+t3YOKlMNVAWqtcpXv4S8363Ko7ortyooxr/1olyLjxVybIQQIjXEcz0uUzM3eSie\nwkdD3MzG4B0y+B+ZHGfzLbYTjTfIIBeNs/CwHBPD5pvO8XiZicHHuCLOMFREYxaZ1EGjOwUJpZO8\ngY8mFPACBo+TwXe4GIaTZbh4D4OrI6wP8qGYhMFqTB6x+ca6u/Xt+aKQb9q/R3ECWqEF98UlkLLk\nr5IW+WMTqJo2DYMO6IU2Di2qMTj5EReOKP0610rn+gSDTugJrfGx47CKC1xlzeg9HGHWqDwa75HJ\n7Xg5BZ3aUd63KzrbUIy1yj9HUtPaG6c1br7HZAUuOhTxOFZFYz4uvsHkSXy2620CdDROxxFc5xTq\nR0x+xOTSCM8/0ar69kGEGZjfMXkeH+NsPssXolMLjWdDnutDMQeD86yZkCvjmG1siM5SXCzDZCBe\n3NbflQ/FQDwMxMFFMV6nGTqf4uIGPMwKac+vmDyFj97W3/KXZB4xA5aJxmvWZ2FLyN/l3yjOw8ND\nZBRKqzsZne9xUR+dJrhpj5u+FPB/eLgDL4/j5WQKKA8sw0XjkPd7DCfZwNVSyEAIIYQopEwENwrF\n43hpgJuvMJiFi9m4gouaI3Gg8RIZ9EFnKB6q4+YCCpiEj3WYvIiP5zD4HBfVYgyGnWhMIYOLcNCZ\ngkLrMQLCcxpXYnIbXl4jg0W4OCskZak2GotwsRNVaL+ODZjcjZdc3EzD4B0y+ZdN2zQ0huHkuZAB\n2EKMYNBztML70hsHszD5yqaMcKhe6PyEycNWIYHioKFRLsb5qYRGD3Tux3dESlpRc00742ASGUwn\nk8wo798cnffJZGyEtKkAh1V0oQA4J8Z5ug8nd5HBp2QGP5tF7UdlNObi4kGcdIhxHHujH1H57E8U\n9+LlRps1QqECZaHt3IqXW3AG0/pC+6Kh8QwZPISXrzG4Dy/1cfMAXs7Hwee44g5Wq6Ix10pL7Y2H\nPSjuwocDeCjOz2MbdGbh4ko8XIOHJrjpTgFrMBmOk+9xUS/Cmq7W6IzEyRVW0GGgGISHM9G5yub9\ns9AYTwZryeJ5MrkeJ93QqQxsRXEfTl60uQboaLxJJusweaiEy4eL9JJOuffSl9STLv0A6UtZVjyj\nzxI2Hh/TMJiPixYJDt51NO4jg/vIYAeKLzCYj8l4fHhRfImr0DqKaDQ07iaD49E4nQKW4yI3Snvu\nwMtoMugWIRiogMZMayHyqRRQE401mAzByTxcNI/R1//gYDRedqCohcaXmLYDqOJwEhr7UHRBt50p\nC3ChcQ4OvsHkzFKOnQfg4CO8R7XeJly8x/PMONcxjcLJBahClebs5KJzczH2oyIad8QIvsAf3Nxn\nBRlzMJmNwRYUfXEwLMaxOA+dGzEZi7fQLOhuFD+ieDvK809E5xqrmt9/cPApLloWsf9ZaLxFBnfh\nozVuMtFYEWPmL9wp6MzGxXwMriOTdmgxi3AE3IGTDyngeQw2oPABT8Q49jlo5CQ421gOjY9w0YkC\nGqAxuGxczoUQQogSpSmlkprTMH/+fHr16hXx959j8H94+Cbs29KjpVCYkNCAJ9QDeFmGyWwybQc9\nn2NwA15+wlVoz5NI3ra+XT4fR9Rvx8Ndg4dcNO7ESVXcbCAr5ixUUV2Khz7otqWOQ63HZDcqYlBX\nUvajaIab9WRRqYSOwbGgBW50oB8OzkGnE3rMYCzgMww+tZm9GYSTU2L8/fpnOoj7veIxDR+trJS5\n0rQOk84UUB2Nb3DFLABxNNZgshgzaqpjPGJdi49lcmyEECI1xHM9Tomv+uZgcI7NQPh3TIbg4V0y\nizWwAf8szNEMve/ASXsKmIbBoLDDaKC4HS+PhuzJEUv4a8Trepyci4de6OSilVhgA/BmHN/8g/9b\n+BNLrBWRHYfGn2TF/Q27sLc6gTSwcGfiiHsWK5yGVuwXpIFJusQ1Q2cqmTThyD2niltL9CLPcgkh\nhBDpJiXuiNfi4X68hRb8H0bRHw/3ksFppTwDEI9MNF4kk5vx8pfV7kBO41QMyqFFXXhfXNpamzfe\nga/Y1tuAfX6mlkBqTrLYtS9dck1Lqx9HW4whHulyTiByX87BUagIgBCp4Fj42yuL0qUv6dIPkL6U\nZSlx511BFl9ico61ANhfHclr7SGSeoFNQEd0BuHglpBNCPNR3IuP8ThLLRAYZm1i2SOFj5UQQggh\nhBAlLWXW3PhQ3ImP9zHoh87XmHyFy7ZSWCo5hKIVBUy2dop/zFqL8wGuUmuDG0VvPMwMqawlhBCJ\nkHUlkcmxEUKI1FBm1tyAfxHx42TQEZ2H8fJhhBLIqaYCGi+QwTV4WYTGeHwsLsXABvzVoUr7PYUQ\nQgghhEg1KZGWFmoADlaSFbXEcqrpg4Pu6LT1HGQADpqWobZHkk75menSl3TpB0hfhEiWdPq8Sl9S\nT7r0A6QvZVmJzdysWrWKV199FaUUPXv2pH///iX1VinhSTJYc3gv92dWSnZThBBCxOFYu08JIcSx\noETW3JimyU033cTo0aOpUqUKd911FyNHjqROnTpHPFZymYUQIvmOtWux3KeEEKLsied6XCL5U7/9\n9hs5OTlUr14dp9NJly5dWL58eUm8lRBCCJEwuU8JIUR6KpHgZu/evVStWjX4c3Z2Nnv37i2Jt0op\n6ZTTKH1JPenSD5C+iOST+1TZJ31JPenSD5C+lGUpUS1t/vz5yW5CsZG+pKZ06Uu69AOkL6JsSadz\nLH1JTenSl3TpB0hfyqoSCW6ys7P566+/gj/v3buX7Oxs28dKHrMQQojSJvcpIYRITyWSltaoUSN2\n7NjB7t278fl8LFmyhA4dOpTEWwkhhBAJk/uUEEKkpxKplgb+EpuvvPIKSilOP/10KbEphBAipch9\nSggh0k+JBTdCCCGEEEIIUZpKJC1NCCGEEEIIIUqbBDdCCCGEEEKItJDUUtCrVq3i1VdfRSlFz549\ny2y+8+TJk/n+++857rjjePzxx5PdnCLbs2cPkyZNYv/+/WiaRq9evejbt2+ym1UkXq+X+++/H5/P\nh8/no0OHDgwePDjZzSoy0zS56667yM7O5o477kh2c47K8OHDKVeuHJqm4XA4eOSRR5LdpCLJy8vj\n+eefZ8uWLWiaxrBhw2jcuHGym5Wwbdu28dRTT6FpGkopdu7cySWXXFJm//aLm9ynUovcp1Jbutyr\n5D6VWhK+T6kkMQxD3XDDDWrXrl3K6/WqUaNGqa1btyarOUdl3bp1auPGjerWW29NdlOOyr59+9TG\njRuVUkrl5+erG2+8scyeE6WUcrvdSin/Z+3uu+9W69atS3KLim7WrFlq4sSJ6tFHH012U47a8OHD\n1cGDB5PdjKM2adIk9cUXXyillPL5fOrw4cNJbtHRMwxDXXPNNWr37t3JbkpKkPtU6pH7VGpLl3uV\n3KdSVzz3qaSlpf3222/k5ORQvXp1nE4nXbp0Yfny5clqzlE58cQTKV++fLKbcdQqV65M/fr1AcjK\nyqJOnTplesdul8sF+L8dM02TChUqJLlFRbNnzx5WrlyZNnttKKVQZbyOSV5eHuvXr6dnz54AOBwO\nypUrl+RWHb3Vq1dTs2ZNqlWrluympAS5T6UeuU+lrnS6V8l9KnXFc59KWlra3r17qVq1avDn7Oxs\nfvvtt2Q1R4TZtWsXf/zxR5mcvgwwTZM777yTnTt3csYZZ1C3bt1kN6lIXnvtNYYMGUJeXl6ym1Is\nNE3jwQcfRNd1evXqRe/evZPdpITt2rWLihUr8txzz/HHH3/QoEEDhg4dSmZmZrKbdlSWLl1Kly5d\nkt2MlCH3qdQm96nUkk73KrlPpa547lNSUEAcwe128+STT/J///d/ZGVlJbs5RabrOo899hiTJ09m\n3bp1rF27NtlNSlggR75+/fpp8U0SwAMPPMC4ceO46667+Oyzz1i/fn2ym5Qw0zTZuHEjZ555JuPG\njcPlcjFz5sxkN+uo+Hw+VqxYQefOnZPdFCFikvtUakm3e5Xcp1JTvPeppAU32dnZ/PXXX8Gf9+7d\nS3Z2drKaIyyGYfDEE09w2mmncfLJJye7OcWiXLlytGvXjg0bNiS7KQlbv349K1as4IYbbmDixIn8\n9NNPTJo0KdnNOipVqlQBoFKlSpxyyill8pvw7OxsqlatSsOGDQHo1KkTv//+e5JbdXRWrVpFgwYN\nqFSpUrKbkjLkPpWa5D6VetLtXiX3qdQU730qacFNo0aN2LFjB7t378bn87FkyRI6dOiQrOYctXT4\npgL8FXXq1q1bZqvPBBw4cCA4Ne7xeFi9enUwT7ssGTx4MJMnT2bSpEmMHDmSli1bcsMNNyS7WUVW\nUFCA2+0G/N+8/vjjj9SrVy/JrUpc5cqVqVq1Ktu2bQP8OcBlOZ0EYPHixZKSFkbuU6lJ7lOpJ53u\nVXKfSl3x3qeStuZG13WuvPJKHnzwQZRSnH766WX2oE+cOJG1a9dy8OBBhg0bxsUXXxxcwFWWrF+/\nnq+++orc3Fxuv/12NE1j0KBBtG3bNtlNS9jff//Ns88+G7yZd+vWjVatWiW7Wce8/fv3M378eDRN\nwzAMunXrRps2bZLdrCIZOnQozzzzDD6fj5o1a3L99dcnu0lFVlBQwOrVq7n22muT3ZSUIvep1CP3\nKVHS5D6VmhK5T2kqHb7GEUIIIYQQQhzzpKCAEEIIIYQQIi1IcCOEEEIIIYRICxLcCCGEEEIIIdKC\nBDdCCCGEEEKItCDBjRBCCCGEECItSHAjhBBCCCGESAsS3AghhBBCCCHSggQ3QgghhBBCiLQgwY0Q\nQgghhBAiLUhwI4QQQgghhEgLEtwIIYQQQggh0oIEN0IIIYQQQoi0IMGNEEIIIYQQIi1IcCOEEEII\nIYRICxLcCCGEEEIIIdKCBDdCCCGEEEKItCDBjRBCCCGEECItSHAjhBBCCCGESAsS3AghhBBCCCHS\nggQ3QgghhBBCiLQgwY0QQgghhBAiLUhwI4QQQgghhEgLEtwIIYQQQggh0oIEN0IIIYQQQoi0IMGN\nEEIIIYQQIi1IcCOEEEIIIYRICxLcCCGEEEIIIdKCBDdCCCGEEEKItCDBjRBCCCGEECItSHAjhBBC\nCCGESAsS3AghhBBCCCHSgjPZDRAiID8/n61btya7GUIIIYRIgFKK8uXLU6dOnWQ3RQg0pZRKdiOE\nyM/PZ+PGjdSrVw9dlwlFIYQQoizZvXs3xx13HFWqVEl2U8QxTkaRIiVs3bpVAhshhBCijKpWrRq7\nd+9OdjOEkOBGpA4JbIQQQoiySdM0NE1LdjOEkOBGCCGEEEIIkR4kuBFCCCGEEEKkBQluhBAlol27\ndnz99dfJbkapGzt2LMOGDUt2MwrZtGkTOTk5yW6GEKXiWLn2nHHGGbz11lvF/rqvvfYaZ599drG/\nrhClRYIbIWKoVasWOTk55OTkUKlSJapXrx78t+nTpye1bfPnz6ddu3bUrFmTfv36pVQp7ZUrV9K5\nc+dkN0MA9evXZ/v27cluhkiQXHuKRq49Ry907cyYMWM45ZRTqFy5MuPHj09iq4SIjwQ3QsSwY8cO\ntm/fzvbt28nNzeW9994L/tvFF198xOMNwyiVdu3atYshQ4bwwAMPsHnzZlq1asXQoUNL5b2PRUop\npHJ+aiitv7Fkk2uPgORfe5o0acIjjzxCnz59ktYGIRIhwY0QCbC7yYwdO5bLL7+coUOHUrt2bd55\n5x2+/fZbTj/9dOrWrUvjxo257bbbCg081qxZw7nnnktubi6NGjVi4sSJwdcfP348rVu3pn79+gwd\nOpT9+/fbtuXDDz+kdevW9OvXD5fLxT333MPKlSvZuHGj7ePz8/O54447aNasGXXr1uWss87C6/Xy\n+++/U7FiRV599VUaN25MkyZNePbZZ4PPu+qqq3jkkUeCPy9YsIAWLVrEPFZNmzZl8eLFwWM0dOhQ\nrrrqKnJycujYsSM//vhjXK8xYcIE2rdvT25uLjfccANerzf4+xdffJE2bdpw/PHHM2jQIHbu3AnA\nf//7X+68804APB4P1atXZ8yYMQAcPnyYatWqcfDgQQC+/vrr4Lnq0qULS5YsCb7+GWecwQMPPECv\nXr2oWbOm7bfTmzZtok+fPtSuXZsLLriAvXv3Bn/Xv39/XnrppUKP79ChA59++imGYVCxYkVefvll\n2rRpQ25uLrfddlvwcRs2bKBv377k5uZSv359rr766mCbA8fm6aef5pRTTiEnJ4cRI0awc+dOzj//\n/GBbDhw4ABA8xwF79+7luuuuo1GjRuTm5nLZZZdFPQ+Bc/7oo4+Sm5tLq1ateO+994K/379/P1de\neSX169enZcuWPPHEE4XauWbNGgCmTp1KxYoV+e233wB4+eWXGTJkCBD9sx9o/5tvvkmzZs04//zz\no7Y3Hcm1R6490SileOSRR2jevDkNGjRg2LBhwfdRSnHZZZfRsGFD6tWrR9++ffn555+Dz/3rr7+4\n6KKLqF27Nr169WLTpk2FXnvw4MH06tWLcuXKxTxuQqQCCW6EKAazZ89m4MCBbNu2jQsvvJCMjAzG\njx/Pli1bmDdvHvPmzQsOcg8cOMB5553HOeecw4YNG1i1ahXdunUD4JlnnuHzzz9n7ty5/PLLL5Qv\nX55Ro0bZvue6deto1apV8OcKFSpw/PHHs27dOgCmTZsWfF2A22+/nbVr1/Lll1+yZcsWxowZU6j8\n9tKlS1mzZg0ffPAB48aNCw4O7BSl3OecOXO49NJL2bZtG2eccQa33nprXM+bPn06c+bMYdWqVaxd\nu5bHH38c8KfFPPzww0ydOpVff/2VWrVqccUVVwDQrVu3YPuXL19OTk5OcODwzTff0Lx5cypWrMjW\nrVu55JJLuO+++9i6dStjx45l8ODB7Nu3L/j+77zzDs8//zzbt2+ndu3ajB8/nkGDBgV/f/nll9Op\nUyf++OMPbrnlFqZNmxb83eDBgwv9vHLlSvbu3cuZZ54Z/LfPP/+cJUuWsHjxYt555x0WLlwI+Ack\nd9xxBxs3bmTFihVs2rSJcePGFTo2s2bN4pNPPuH777/nww8/ZMCAATz88MNs2rSJ/Px8pkyZEnxs\n6DkbOnQoXq+X77//no0bN3L99dfHPA9//vkneXl5bNiwgUmTJnH99dcHB7MjR47E7Xazdu1aZs+e\nzeuvv87bb78NQJcuXfjqq68AWLJkCQ0aNAiei8WLF9O1a1cgvs/+0qVLWblyJe+//37M9h4r5NoT\nW7pee0K98sorTJ8+nc8++4zVq1ezb98+br/99uDv+/bty+rVq9mwYQPNmzfn6quvDv7upptuolKl\nSmzcuJGJEyfyxhtvJHB0hUg9EtyIMqN8hQrF8l9J6Ny5c3DA6nK5aNeuHe3bt0fTNI4//niGDh0a\nvOF9/PHH5Obmct1115GRkUGFChU46aSTAP832WPGjKFmzZpkZmZy5513MmPGDNv3PHz4MJUqVSr0\nb5UqVeLQoUMADBw4MDioNE2Tt956i8cff5waNWqgaRqdOnXC4XAA/gHD3XffjcvlomXLllx66aW8\n++67xXqMunbtSs+ePdE0jYEDBwa/zY/l+uuvp1atWmRnZzNq1Khgu6ZPn87ll19Oy5YtyczMZOzY\nsSxevJjt27fTsWNH1q9fz4EDB1iyZAlDhw7ljz/+wO1289VXXwUH1G+//Tb9+vWjZ8+eAPTq1YtW\nrVoxd+7c4PsPGTKExo0b43A4cDgc3HbbbcGB+6ZNm1izZg133303GRkZdOvWrVDqxnnnnce6devY\nvHkz4B/0XXTRRYUGaLfddhsVKlQgNzeXrl27snr1agAaNWpE9+7dcTgcVKtWjeHDhx8x6Lv++uup\nWrUqOTk5nHrqqXTs2JEWLVqQmZnJueeeyw8//HDE8dy6dSuLFi1i4sSJVKpUCYfDwamnnhrzPDgc\njmA/u3fvTu/evZkxYwY+n48ZM2bwwAMPUK5cOerXr8/w4cODx6hr167Bdi9dupRbb701+PPixYuD\ng+BYn31N07j33nvJysrC5XLFbG9xqlBeL5b/SoJce2JLx2tPuOnTp3PTTTdRr149ypcvz/333x9c\nl6VpGoMHD6ZcuXLBc7ty5Ury8/Px+XzMmTOH0aNHB89BpABKiLLCmewGCBGvw9aNMxXVrVu30M+/\n/PILd999d/AGYhgGHTp0APyDyxNOOMH2dbZs2cLFF18c/FZTKYWu6+zatYsaNWoUemz58uULpSmB\nPz2ogk0At2vXLrxeb8T3BahTp07w/+vVq8eXX34ZpceJq1mzZvD/y5Urx+HDh+N6Xmi7cnNz2bFj\nBwDbt2+nU6dOwd9VrFiRypUrs23bNnJycmjTpg2LFy9myZIljB49mhUrVrBs2TIWL17MzTffDMDm\nzZuZPn06s2bNAvzH2+fzFZpZCT+3obZv3052djZZWVmF2rhnzx4AsrKy6N+/P9OmTWPUqFG89957\nhdK5gELntVy5csEB4s6dO7nttttYtmwZhw8fxjAMqlevXui5oT9nZWUVeq2srCzbY/znn39StWpV\n289JNHb93L59O7t378Y0TerVq1fod9u2bQP8A8uxY8eyfft2nE4n/fv359FHH+X333+noKCA5s2b\nA9E/+wG1a9dOqM3F5dBhMynvGw+59sSWjteecDt27CA3N7dQez0eD7t376Zq1aqMHj2aDz/8kL17\n9wY329yzZw+6rmOa5hHn4Lvvvov7vYVINTJzI0QxCE+VuPHGG2nRogVr1qxh27Zt3HPPPcF8+bp1\n6/L777/bvk7dunX56KOP2LJlC1u2bGHr1q22gwuAZs2aFcodP3DgAH/88QfNmjU74rE1atQgMzMz\n4vsChXK6t27dSq1atQD/YCA/Pz/4u0BueWkJbdfmzZuD7crJyQnOiAAcPHiQv//+OzgA7tKlCwsW\nLGDt2rW0a9eOLl26MHfuXFatWhWcqahbty5DhgwpdLx37NjBiBEjgq8bLQ2mVq1a7N27l4KCguC/\nbdmypdBjBg0axLRp05g/fz5VqlShXbt2cfV79OjRZGVlsXz5cv7880+ef/75YllUXLduXfbs2RMM\nouJl18+cnByqV6+OruuFzsXmzZuD56FJkyY4HA5eeOEFunbtSqVKlahSpQqvv/56oRmjRD774h9y\n7Sk5qXztCVerVq0j/gZdLhfVq1dn6tSpzJ07l08++YQ///yTH374IbiGq0aNGui6fsQ5EKIsk+BG\niBJw6NAhKlWqxL/+9S/Wr1/Pyy+/HPxd37592bp1K1OmTMHj8XDw4MHgt2RXXHEF999/f/DmsmvX\nLj7++GPb9zj//PNZs2YNc+bMoaCggIcffpj27dvbfkOq6zqXXnopd9xxBzt37sQ0TZYtWxZcaKyU\nYty4cbjdbtasWcPUqVO56KKLAGjdujWffvopf//9Nzt27OD5558vlmMU70D9hRdeYPv27ezZs4cn\nn3wy2K4BAwbw+uuv89NPP1FQUMD9999Ply5dgvu5dO3alalTp9KyZUt0Xee0007jlVdeoXHjxlSu\nXBnwBx6zZs1iwYIFmKaJ2+1m0aJFcQ+iTjjhBFq2bMnDDz+M1+tl8eLFfPbZZ4Ue06VLF7xeL/fd\nd19C6R4HDx6kXLlywfz8p59+Ou7n2gkc7zp16tCzZ09uueUW9u/fj8/nK7SQORLDMIL9XLRoEfPm\nzeOCCy7A6XRywQUXMGbMGA4fPsymTZt47rnnCvW1S5cuTJkyJZiS061bt0I/Q+zPvlSqi49ce2JL\nh2tPuAEDBvDMM8+wefNmDh48yNixY4MV9Q4dOoTL5aJy5cocPnyYMWPGBAMnp9NJv379eOihh3C7\n3fz0009HpL75fD7cbjemaeL1eikoKJC/R5HSJLgRIgHxfpP28MMP8+abb5KTk8PIkSO58MILg7+r\nVKkSH330ETNnzqRBgwa0a9cuOLgcMWIEffr0oV+/ftSuXRzZ49oAAALNSURBVJs+ffrw/fff275H\njRo1eP3117n33nupV68ea9as4ZVXXgn+/q233ir0zfi4ceNo2rQp3bp1Izc3l//+97+FblCdO3em\nZcuWXHDBBdx+++3BtRCXXXYZTZo0oXnz5vz73/9mwIABxXKs4j2WAwYMoF+/frRt25amTZsGFzn3\n7t2bO++8k0GDBtG4cWO2bdtWaCDXuXNn8vPzgwPoFi1a4HQ6Cw2oc3Nzefvtt3n00UepX78+zZs3\nZ9KkSZimGbGN48aN45JLLgn+/Oqrr7J06VJyc3N54oknbAOYgQMHsm7dukLPs3v90J/vueceVqxY\nQZ06dRg4cCD9+/eP+7l2Qn//4osvopSibdu2NGjQgBdeeCHqc8H/TXO5cuVo2LAhw4YN49lnnw0O\nZp988kkyMjJo3rw5ffv25bLLLit0HLp27cqhQ4eCxz78Z/DPOET77BdlIXk6kWuPXHvCrz3hRUIu\nvPBC+vTpQ+vWrTnuuOOCBUiGDBlCrVq1aNy4MR07djxijd2ECRPYt28fDRs2ZMSIEcEKhgHXXXcd\n1atXZ+bMmTz66KNUr1692NdFCVGcNCXht0gBv/76a9Ly6Y91v//+O23btg2WDU4lTZs25aWXXio0\nKCiL3nzzTd56662I34SnugULFjBixIi4F2ILEQ+59qSfbdu20bhx42Q3QxzjZOZGCCEpBiXo8OHD\n/O9//wuWihVC/EOuPUKI4ibV0oQQRUr52bRpE506dSr0XKUUmqaxatWqQhWKivIaK1euLPOpSJ99\n9hn/+c9/OOOMMwqlB6WicePGMWHChOAxD5yH0047jeuuuy7JrRPpSq49QojiJmlpIiVIWpoQQghR\ntklamkgFkpYmhBBCCCGESAsS3IiUIBOIQgghhBDi/9uvQyMAYBiGgSZG3n9K4/AOUZD2Tj+BqG4x\nN3hCErVlcgAA+NDMyPZ2BqADBeIay9MHZBYAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb409ebb350>"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here is the same plot using signals:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot(trace1, signals=[\"cpu_in_power:dynamic_power\", \"cpu_in_power:load1\"],\n",
+      "                    filters={\"cdev_state\": [1, 0]}, pivot=\"cpus\")\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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SlHPjCJxkC+F8FM/X6mqgTx9+v5owgf87c4aLHRvjpGvPKbbYxo7KSu6FBMzl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\nN3hCErVlcgAA+NDMyPZ2BqADBeIay9MHZBYAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb40850ff10>"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Permute Multi Trace with Multi Columns"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The permute parameter allows to plot all the permutations of the specified traces with the specified columns. Each figure will plot the values of each column for those traces. Hence, with N traces and M columns, you will get M figures each displaying N traces."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "l = trappy.LinePlot([trace1, trace2], trappy.thermal.Thermal, column=[\"temp\", \"temp_prev\"], permute=True)\n",
+      "l.view()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ziERE5LjoXFw+7ZsIYowzh6O4sVI8ab94y811GizFw5aKt337nDkfe/c6v6dGDafRcegn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\nBR+tuen78PYAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fb409ef89d0>"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
diff --git a/doc/Stats.ipynb b/doc/Stats.ipynb
new file mode 100644
index 0000000..762031c
--- /dev/null
+++ b/doc/Stats.ipynb
@@ -0,0 +1,904 @@
+{
+ "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": "2.7.6"
+  },
+  "name": "",
+  "signature": "sha256:dcfeb99f9e93abe777bae0325930f56f8d0c34764f8fdecbc280d30c969954ba"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Creating a Trace Object"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline \n",
+      "\n",
+      "import trappy\n",
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "trace = trappy.FTrace(\"./trace_stats.dat\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "View the trace"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Execute to view it\n",
+      "trappy.plotter.plot_trace(trace)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<style>\n",
+        "/*\n",
+        "\n",
+        " *    Copyright 2015-2015 ARM Limited\n",
+        "\n",
+        " *\n",
+        "\n",
+        " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "\n",
+        " * you may not use this file except in compliance with the License.\n",
+        "\n",
+        " * You may obtain a copy of the License at\n",
+        "\n",
+        " *\n",
+        "\n",
+        " *     http://www.apache.org/licenses/LICENSE-2.0\n",
+        "\n",
+        " *\n",
+        "\n",
+        " * Unless required by applicable law or agreed to in writing, software\n",
+        "\n",
+        " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "\n",
+        " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "\n",
+        " * See the License for the specific language governing permissions and\n",
+        "\n",
+        " * limitations under the License.\n",
+        "\n",
+        " */\n",
+        "\n",
+        "\n",
+        "\n",
+        ".d3-tip {\n",
+        "\n",
+        "  line-height: 1;\n",
+        "\n",
+        "  padding: 12px;\n",
+        "\n",
+        "  background: rgba(0, 0, 0, 0.6);\n",
+        "\n",
+        "  color: #fff;\n",
+        "\n",
+        "  border-radius: 2px;\n",
+        "\n",
+        "  position: absolute !important;\n",
+        "\n",
+        "  z-index: 99999;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "\n",
+        "\n",
+        ".d3-tip:after {\n",
+        "\n",
+        "  box-sizing: border-box;\n",
+        "\n",
+        "  pointer-events: none;\n",
+        "\n",
+        "  display: inline;\n",
+        "\n",
+        "  font-size: 10px;\n",
+        "\n",
+        "  width: 100%;\n",
+        "\n",
+        "  line-height: 1;\n",
+        "\n",
+        "  color: rgba(0, 0, 0, 0.6);\n",
+        "\n",
+        "  content: \"\\25BC\";\n",
+        "\n",
+        "  position: absolute !important;\n",
+        "\n",
+        "  z-index: 99999;\n",
+        "\n",
+        "  text-align: center;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "\n",
+        "\n",
+        ".d3-tip.n:after {\n",
+        "\n",
+        "  margin: -1px 0 0 0;\n",
+        "\n",
+        "  top: 100%;\n",
+        "\n",
+        "  left: 0;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "\n",
+        "\n",
+        ".contextRect {\n",
+        "\n",
+        "  fill: lightgray;\n",
+        "\n",
+        "  fill-opacity: 0.5;\n",
+        "\n",
+        "  stroke: black;\n",
+        "\n",
+        "  stroke-width: 1;\n",
+        "\n",
+        "  stroke-opacity: 1;\n",
+        "\n",
+        "  pointer-events: none;\n",
+        "\n",
+        "  shape-rendering: crispEdges;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "\n",
+        "\n",
+        ".chart {\n",
+        "\n",
+        "  shape-rendering: crispEdges;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "\n",
+        "\n",
+        ".mini text {\n",
+        "\n",
+        "  font: 9px sans-serif;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "\n",
+        "\n",
+        ".main text {\n",
+        "\n",
+        "  font: 12px sans-serif;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "\n",
+        "\n",
+        ".axis line, .axis path {\n",
+        "\n",
+        "  stroke: black;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "\n",
+        "\n",
+        ".miniItem {\n",
+        "\n",
+        "  stroke-width: 8;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "\n",
+        "\n",
+        ".brush .extent {\n",
+        "\n",
+        "\n",
+        "\n",
+        "  stroke: #000;\n",
+        "\n",
+        "  fill-opacity: .125;\n",
+        "\n",
+        "  shape-rendering: crispEdges;\n",
+        "\n",
+        "}\n",
+        "\n",
+        "</style>\n",
+        "<div id=\"fig_7fd921020f474531a31bab1d2f121748\" class=\"eventplot\">\n",
+        "        <script>\n",
+        "            var req = require.config( {\n",
+        "\n",
+        "                paths: {\n",
+        "\n",
+        "                    \"EventPlot\": '/static/plotter_scripts/EventPlot/EventPlot',\n",
+        "                    \"d3-tip\": '/static/plotter_scripts/EventPlot/d3.tip.v0.6.3',\n",
+        "                    \"d3-plotter\": '/static/plotter_scripts/EventPlot/d3.min'\n",
+        "                },\n",
+        "                shim: {\n",
+        "                    \"d3-plotter\" : {\n",
+        "                        \"exports\" : \"d3\"\n",
+        "                    },\n",
+        "                    \"d3-tip\": [\"d3-plotter\"],\n",
+        "                    \"EventPlot\": {\n",
+        "\n",
+        "                        \"deps\": [\"d3-tip\", \"d3-plotter\" ],\n",
+        "                        \"exports\":  \"EventPlot\"\n",
+        "                    }\n",
+        "                }\n",
+        "            });\n",
+        "            req([\"require\", \"EventPlot\"], function() {\n",
+        "               EventPlot.generate('fig_7fd921020f474531a31bab1d2f121748', '/static/');\n",
+        "            });\n",
+        "        </script>\n",
+        "        </div>"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.HTML at 0x7f3e12d29e10>"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "What is a Trigger?"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A trigger is the combination of the following:\n",
+      "\n",
+      "    * A TRAPpy event\n",
+      "    * A pivot for the event\n",
+      "    * A set of filters\n",
+      "    * A value"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Introduction to Triggers "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "* The example below explains how to create a trigger for the event when a particular process is switched in or out.\n",
+      "\n",
+      "* This uses the <code>trappy.sched.SchedSwitch</code> event in the trace object. This event looks for the unique_word <code>sched_switch</code> in the trace file.\n",
+      "\n",
+      "* Here is sample line in the text trace that corresponds to the <code>SchedSwitch<code> event.\n",
+      "\n",
+      "<pre>\n",
+      "sched_switch: prev_comm=trace-cmd prev_pid=4731 prev_prio=120 prev_state=1 next_comm=trace-cmd next_pid=4730 next_prio=120\n",
+      "</pre>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can see that this event is populated in the above trace object as:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trace.sched_switch.data_frame.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>__comm</th>\n",
+        "      <th>__cpu</th>\n",
+        "      <th>__pid</th>\n",
+        "      <th>next_comm</th>\n",
+        "      <th>next_pid</th>\n",
+        "      <th>next_prio</th>\n",
+        "      <th>prev_comm</th>\n",
+        "      <th>prev_pid</th>\n",
+        "      <th>prev_prio</th>\n",
+        "      <th>prev_state</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Time</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0.000000</th>\n",
+        "      <td>ls</td>\n",
+        "      <td>2</td>\n",
+        "      <td>4734</td>\n",
+        "      <td>migration/2</td>\n",
+        "      <td>18</td>\n",
+        "      <td>0</td>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>4734</td>\n",
+        "      <td>120</td>\n",
+        "      <td>1024</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.000022</th>\n",
+        "      <td>migration/2</td>\n",
+        "      <td>2</td>\n",
+        "      <td>18</td>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>4732</td>\n",
+        "      <td>120</td>\n",
+        "      <td>migration/2</td>\n",
+        "      <td>18</td>\n",
+        "      <td>0</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.000107</th>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>1</td>\n",
+        "      <td>4731</td>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>4730</td>\n",
+        "      <td>120</td>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>4731</td>\n",
+        "      <td>120</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.000127</th>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>1</td>\n",
+        "      <td>4730</td>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>4729</td>\n",
+        "      <td>120</td>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>4730</td>\n",
+        "      <td>120</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>0.000142</th>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>1</td>\n",
+        "      <td>4729</td>\n",
+        "      <td>swapper/1</td>\n",
+        "      <td>0</td>\n",
+        "      <td>120</td>\n",
+        "      <td>trace-cmd</td>\n",
+        "      <td>4729</td>\n",
+        "      <td>120</td>\n",
+        "      <td>1</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "               __comm  __cpu  __pid    next_comm  next_pid  next_prio  \\\n",
+        "Time                                                                    \n",
+        "0.000000           ls      2   4734  migration/2        18          0   \n",
+        "0.000022  migration/2      2     18    trace-cmd      4732        120   \n",
+        "0.000107    trace-cmd      1   4731    trace-cmd      4730        120   \n",
+        "0.000127    trace-cmd      1   4730    trace-cmd      4729        120   \n",
+        "0.000142    trace-cmd      1   4729    swapper/1         0        120   \n",
+        "\n",
+        "            prev_comm  prev_pid  prev_prio  prev_state  \n",
+        "Time                                                    \n",
+        "0.000000    trace-cmd      4734        120        1024  \n",
+        "0.000022  migration/2        18          0           1  \n",
+        "0.000107    trace-cmd      4731        120           1  \n",
+        "0.000127    trace-cmd      4730        120           1  \n",
+        "0.000142    trace-cmd      4729        120           1  "
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now, let us suppose we want to create a trigger for the event when the task <code>trace-cmd (pid=4729)</code>\n",
+      "is switched in and assign a value of 1 to the Trigger signal. In pseudo code:\n",
+      "\n",
+      "    if next_pid == 4729:\n",
+      "        Trigger_In(t) = 1\n",
+      "\n",
+      "We can similarly create a Trigger for the event when the task is switched out:\n",
+      "\n",
+      "\n",
+      "    if prev_pid == 4729:\n",
+      "        Trigger_Out(t) = -1\n",
+      "        \n",
+      "A pivot is the column along which the data is orthogonal. If the data is pivotable it is possibly a super-position of smaller data elements corresponding to each unique pivot value. In our case the pivot value is <code>\"__cpu\"</code> and the data can be split into the scheduler switches happening on the different CPUs"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from trappy.stats.Trigger import Trigger\n",
+      "\n",
+      "task_pid = 4729\n",
+      "\n",
+      "trigger_switch_in = Trigger(trace, \n",
+      "                            trappy.sched.SchedSwitch,\n",
+      "                            pivot = \"__cpu\",\n",
+      "                            filters = {\n",
+      "                                \"next_pid\" : task_pid\n",
+      "                            },\n",
+      "                            value = 1)\n",
+      "\n",
+      "trigger_switch_out = Trigger(trace, \n",
+      "                            trappy.sched.SchedSwitch,\n",
+      "                            pivot = \"__cpu\",\n",
+      "                            filters = {\n",
+      "                                \"prev_pid\" : task_pid\n",
+      "                            },\n",
+      "                            value = -1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Topology and Aggregation"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Creating a CPU (pivot) Topology"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A topology can be explained as a collection of different arrangements of groups of nodes. These different arrangements are called **levels**. Each level has a multiple groups of nodes. For example for a CPU topology can have the following levels:\n",
+      "\n",
+      "* CPU\n",
+      "\n",
+      "        [\n",
+      "            [cpu0],\n",
+      "            [cpu1],\n",
+      "            .\n",
+      "            .\n",
+      "            [cpuN],\n",
+      "        ]\n",
+      "\n",
+      "* Cluster\n",
+      "\n",
+      "        [\n",
+      "            [custer1_cpu1, cluster1_cpu2, ... cluster1_cpuM)],\n",
+      "            .\n",
+      "            .\n",
+      "            .\n",
+      "            [custerK_cpu1, clusterK_cpu2, ... clusterK_cpuP)]\n",
+      "        ]\n",
+      "\n",
+      "* System\n",
+      "\n",
+      "        [\n",
+      "            [cpu0,\n",
+      "             cpu1,\n",
+      "             .\n",
+      "             .\n",
+      "             .\n",
+      "             cpuN]\n",
+      "        ]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from trappy.stats.Topology import Topology\n",
+      "\n",
+      "cluster_0 = [0, 3, 4, 5]\n",
+      "cluster_1 = [1, 2]\n",
+      "clusters = [cluster_0, cluster_1]\n",
+      "\n",
+      "topology = Topology(clusters=clusters)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "* Aggregator facilitates the aggregation of signals from different triggers. The aggregator also understands a topology over which the aggregation can be performed and this aggregation can be expressed as a function which takes <code>pandas.Series</code> as an input. For example.\n",
+      "\n",
+      "\n",
+      "        def aggfunc(series):\n",
+      "               return modify(series)\n",
+      "       \n",
+      "\n",
+      "* The elements in the topology should be a superset of the pivot values of the Triggers. In our case the pivot value is <code>\"__cpu\"</code>, so, the Topology corresponds to a CPU topology. \n",
+      "\n",
+      "### Base Aggregation\n",
+      "\n",
+      "* Topology.flatten() gives the list of all the nodes in the Topology.\n",
+      "* The node in the Topology corresponds to a pivot value in the Trigger\n",
+      "* The signals for the pivot value are super imposed and stored in a dictionary."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "topology.flatten()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "[0, 1, 2, 3, 4, 5]"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here is an informal pseudo-code for this base aggregation:\n",
+      "\n",
+      "\n",
+      "    base_signals = {}\n",
+      "    \n",
+      "    for node in topology.flatten():\n",
+      "        node_signal = init_signal()\n",
+      "            \n",
+      "        for trigger in triggers:\n",
+      "            node_signal.union(trigger.get_signal_for_pivot_val(node))\n",
+      "            \n",
+      "        base_signal[node] = node_signal\n",
+      "            \n",
+      "### Aggregation over the topology\n",
+      "\n",
+      "\n",
+      "The `Aggregator.aggregate` accepts a level parameter:\n",
+      "\n",
+      "* Each level in the topology contains a group of nodes\n",
+      "* These can be accessed as\n",
+      "    \n",
+      "          topology.get_level(level)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "topology.get_level(\"cluster\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "[[0, 3, 4, 5], [1, 2]]"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here is an informal psuedo-code for this aggregation over the Topology:\n",
+      "\n",
+      "    agg_signal = []\n",
+      "    \n",
+      "    for group_of_nodes in Topology.get_level(level):\n",
+      "        \n",
+      "        for node in group_of_nodes:\n",
+      "        \n",
+      "            group_signal = init_signal()\n",
+      "            group_signal += aggfunc(base_signals[node])\n",
+      "            \n",
+      "        agg_signal.append(group_signal)\n",
+      "        \n",
+      "            "
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Create the Aggregator"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": true,
+     "input": [
+      "from trappy.stats.Aggregator import MultiTriggerAggregator\n",
+      "\n",
+      "def no_operation_aggfunc(series):\n",
+      "    return series\n",
+      "\n",
+      "triggers = [\n",
+      "            trigger_switch_in,\n",
+      "            trigger_switch_out\n",
+      "           ]\n",
+      "\n",
+      "\n",
+      "vector_agg = MultiTriggerAggregator(triggers, topology, no_operation_aggfunc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Examples of Aggregation"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Aggregate at Cluster level"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "level = \"cluster\"\n",
+      "result = vector_agg.aggregate(level=level)\n",
+      "\n",
+      "\n",
+      "# Utility Code for Viewing the data\n",
+      "\n",
+      "clusters = (str(c) for c in topology.get_level(level))\n",
+      "\n",
+      "for series, cluster in zip(result, clusters):\n",
+      "    plt.figure(figsize=(15,7))\n",
+      "    plt.plot(series.index, series.values)\n",
+      "    plt.title(cluster)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3e12ce7450>"
+       ]
+      },
+      {
+       "metadata": {},
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+       "png": 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ZD1QvFNF5LL8Aw0V1/M61j18ENOLHEi27hu6jxBN1QUE3O9Uz0kdvX2KS8seK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voWFKpHwY/1sfdaOjvt7Dv+Vm2qsu8Kp7Mv9izz27W70t4qjxUvii2u+LOKntqXV/vhpG9UZXb8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DZV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\noh8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L9U650wPPI+1VBxJH3pXRQYb3LnsY1u7Oeead+i1vd0b8Kt9V/VXuTj3XJfOfM4Ky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\n0KFDhw4dOnTo0KFDhw4dOnToEKD/D7MnztOKhRiGAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3e12b81b90>"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Aggregate at CPU level"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "level = \"cpu\"\n",
+      "result = vector_agg.aggregate(level=level)\n",
+      "\n",
+      "\n",
+      "# Utility Code for Viewing the data\n",
+      "\n",
+      "cpus = (str(c) for c in topology.get_level(level))\n",
+      "\n",
+      "for series, cpu in zip(result, cpus):\n",
+      "    plt.figure(figsize=(15,7))\n",
+      "    plt.plot(series.index, series.values)\n",
+      "    plt.title(cpu)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3e1265bb90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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2Dshnmf8xvUdG/JS84utni7soekRxn+UUJSf4/1mMM1lHt3PIbFuNfWYzi1vV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\nTBYvot1a/EZWpc1s+9a0nUN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+s9s5qPUi/ZiNkNz+POKN6lZtwGZ2KDGZ4VZiXOWJ6HVQ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\nCKy2X6XuyHYOmZ9VZfPXK6MgkcxVWVb4N5NhFb0RXh5n1eI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neip5YdVUz0xHVJZN9ezH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HAAAg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\noFSaioeuzmowrtqW1a3gVpOKasCv6lLboRsw0F8lA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7SZ5zt+Ojzf8Xt5e88Nly0CHZBR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x4yrattp+/DH2SfVDhP85h3UBXOUgexC2LLfmjBsvtY7sn+hGvu+m5qNt+T/y7Rnx43Qu/IxO\nu66zdlluXUfykF72m/I6d2kY1ameiFgH29mt+CY8Sv7ElkXqcw6dnUq3i1/xVFJyHdsj8J3FTraT\nn5McVvIVT7XXia9zwrijv26f5vjE165N6s1wk7yr29TJF1pOTpKdGKNtKt47z7052Fz8zF4lL/9n\ndjvxdjFPcLhyOh4lT/kEYXVqC/mls9/lnehEWHfjipZTPV37nVx17Kk8COsd+ZOJ6UWYVDzr0hnx\nW9snH3BHcpwRP7eO1T7lZ5UfLI873y/crK5VDq31X/s1/GjVIUznwk8UFetUUBKqu0Rdp5KXaH3S\nxpkq0VGe6snu1nRFyex1O/au0NV+x5ZK6Ps5TsfYHZjUcsLDfO/gR5h3Xn7h2N61c0YS13p3c0bl\nv4P3Dv1Tf6m+Y+JHZjfCquxRdlW5rC+psUDbdvKM+ZnZkUn5oNPn8ri8br0yHaqOEdapjVV3h3na\nJyCeTt4EK7LTGYHdqVXlM6WTYa3rHVYnRszeLgY79bGbMwzrlRvybl7n35W3ej49fZVHXZBN3upZ\n5SjZzjnLZHQb4WLys69YbqgazLi7YxYaSFnL/OKcRWfEj9Obv/BTJ13udiavFk930HE6NLQPFSzS\njXAgYlM9EV63I1ltuo7a7fgZr2tL9UEe8VN+dv1wR6w7ne6UJQdv167zS9fWvYib5D1rj/DWh+an\nOnb0M55uqqfrP1bL6gUB03X0Q/s7zIiH8bl+RnyLVH6jdTSamW1EbdWSyXP95fSDi5zc7mTv1kPG\n6uarq8vBWts52DqenTZTn6m8ZFgnuebYNrnB7ejYvdFdsTKZjmzX72s9P+O3oxPlPvNTHpVSfqjT\n9NW5ZV3v8qza3rVDMpw6RxjRvoy7vtWV2YB8X1/sUuUf+pDe/IWfk/B1PbdHc74XdW9bqnK6onXm\n5HcXA+gAU2nycheng9g5IDgdyG5nlZeL0CvwHT9PDjoq1uhibuIrhR/F3H2L2iTnJvF17qaiNpO8\nZ3hVjd+lX/F2NjttEM+iaS67tYLkofbVPmQ3a+uMDY9J4wAAIABJREFUmnS2RfijJU7uTm7uTORN\npsJOsSIbnQseNBPGkbeLtcpjfnWwKv91NjrxnPqsy/MrcVU2Vbudep/m/tRHjLdinfaJXf7UttXv\n6MLvSt9Q9eZ97jN+06me+ZyTnf9leZN4Lf5pDjJ/TnJ9UttoNPWM+HE6F35NkaPtuV1t7474uSMg\njCcTkouGxVn7SusZvy+//NCe+mP2VvwOFvdg1MWgs0UdeNTnHKqNjv3qIKYwMrlKN7JBERoRcfKz\n+sLBww46ClvmZXmEdNS8z3jR5xzUSI+jv4sf4md9B/Mjy3kW7+5zDkzHpM5VW5ef+c35LV5Wz2gE\nn2F3fIJsz+3cHHH/q1xAWPNb7VzMjA/xsBx8hDwmi2FF/Og42OUe4ptg7PoppJPFi2HNvAibu65s\nuFIfDFPnc9Y/T3HUbQprF0d0cxLd6Gc+qseWyVs9c9u1nu2ZjvjtjApOYpDtrTIc39cLXpTzSh4a\n8TsXfpzOhZ+Z3F3HiDrjyUFHFYgqVFa86oK1Fl0l9gH3id3MN04xMx84elxbUMcz/ei10j31Wda5\nI0/hRzG/6865q1NdSCofuD5hbTPezuZd/Sp+V+LY+Q/FdWHtXlTU+cKtL2Q/k8381dk0WWYfdCN+\n+b+6G93VF7JD3VCo2DtcLMcyVqet2o/sZ/idvNgZsenqkmGtcXT05qXrq85nr4EVLbsRGZY/E3un\nM1DcHJxg7WoY6WH2dDyuf9wRvwj/5S5otkZeZr15O7MdYVX4EA7EW23aqQuFu/Kg/3nbmerp05u/\n8EMHCjW1Z1HuENF/tE+16wrG5e3udDoFgaZ6Ovo7W1Tng2TsdNA7tkTok2Vm+65udBBV+dDJVx0i\n41EXup09OwdIhJfZXdug6ZOVvxuhZCcWbCqbq3+R4y/lI9W2iyeKbb573ull6x3/9P9kWbd1eVrJ\n/d6buqC+o39Bdju17uTCoprbzMadPHOmu011qTYsbxjWTibD2dnv8jKf7eicxnVycV3tdW2a5upU\nXsXq3JBXslF9MHkR85e7qJliFVe1/cpUz9xvVNu6Cz8nF+s+NZrsyJ3kecRz/OuIb5c7Nd5nqqdP\nb/7CDyWVKl60Pbd3p3p2xecWam0zOSgwqp9zcDuLTh868HQHIcRTMezYUom93MXBxHTvxG2q08Vf\nafKyk4ntV+Or7FfxV7wLb9XL7Jjon/hv17eO/xTWTG6slJ86OS7/9D+Kj4q3qmemp8rueJgdDgYH\nj8KfsSLfuTZ2Oe/YqHw2lcdsY1jvxDbBqOTWfUruBKuLw60f1X4nV13eSjsjfko2i1eXb3m7+4wf\n01X3oRE/5Ac01bPa4071rDwIg4o/4lP54dabynWlS8XijPj5dC78muR01rO87uUu7IKTjYBUnspb\n26j56dUWRs7LXZQt7gEhE8OKeJgtrg15uYhNsejaOzFx/DDxq5MPDl72zNsUn4oZw4cOEPl/PaDl\n7d2BGFE9sLA7z3fpd3i6ZbWp8rBacqeIVdlO3i5SObfWKz+ym8Vjd7mI5bfC6OT3Tl+nat3NEdQ+\nY3VsUnlWc17lsep/EMYJ1lpfldAomJKZMUxsusNnne8YRoZV1co0f6rdO/Xh5ENX3xUr8zfqM5xY\nL2K25VEhpZP53B3xi8AvIEF+6F7uUtd3X+7i1EHdX+lqvVUMCLf6n/Wczzn41HxV5NtL6kSQdXZO\n53NlxE/97uZVxD7nwPTs2sLaO3J3fsjvi9TnHFy5d8Tk0X7NeDv/79hz1QeODRO7F01G/O7Sv4Nv\nYhOLrRoVuePXyWZ+7vy/azOKt8rvKZ7dmD5S5268uxy6Ixev+gPlxQSrwraTa7s+YzruiOvE/6+Z\nqxOdDKvy6SR/FikcV9/quV7uguypNzLyt+aYH9YU9Trip3B2dro8V2OvtjGeRbsjfvn/GfHz6c2P\n+KE7Nt1FYOXPS3ZXpiZi5Ve8zGYkD3VamQcVXaU61RPJR77obFIdqJLLOkmly7ElE7rTNhl1RDJ3\nMKp8uBKLSt0BSNnDeCa2qlzoZDn2dgeW3O6KfhQ/Nwcnd7UdXy/qXiCA2k/igexA/UrHr3R0tjH+\nRSzenR7E4/TNO32dGimb9INdLS9yRtvVaNJEntOfMXlIziJ3dLPLx84W1Z84PuvkX8GK2jGbnPad\nbKZj4qPOFjbip3ILyUX/az+U5aBRoZ3zLKV3+e7LL/dG/JA93boz4ufkdF2/Um+TET/Uf1a76wXx\necbPpzc74qemFnXrrF2WW3Wsn7rgRCc7uQ3b39nBbGdUp3qiH5uek3U5U1Lqvm76AbNF8WRb2IWB\nM+KHtqlpSrt+QDwqhzr8qFNmIyJdfnY5hzp21sbBWm3p8LHcrnjZlKsd/cpPCBvrezqbkK8R3smo\niGM7i69qj/gVNuXjbnteLrprRJv5XOUIklfxMh43P12sDEeVPclndgLG/Nrh7mLfYVX/lVwVTxbf\nXZ8xrHWdYc186uSe5ewiZIs6djm5ynykckblMLN/mj+ZOh5lI/N5J9eNaSb1kjk0myyvM9vr0okX\n0l/tdetNXYQyvygMSH/EGfGb0Jsf8WMF7K6jk/DF49wxqvtUx8p4WWeu7FaUp3oiDAwH6zy6AxUr\n9q4jmvJUezOtjgfdwevisOMHZXuV0+mc4F/kPiuD7JnGJWNn/xmu2qazl9nvvPJ+R3+WwbazHFcY\nmP+QDCQP2aTi08VByWPrbu6iJdPTYVzkTh3qRqTUelfjDIODUeVCh5XZOJHN8sLNK3bs6+QxOxjW\n2t65CGB63bztfMb6qQ5/hxXZkjFnmQqXqjm3PpzaRhjZ+YSbwy42JKOjzq91ufzD8h5dUNdRqUp5\n5FP1KWyAAWFS9Vjbub6utjl1obapm+NMfv1/nvHz6Vz4kYJe5Bxg8lK93AV1UOgCLe9HvOwkJcuZ\nXsBkmkz1VIXu8HZtEI/aPrElEzrw7N5Jc2xgHV834jnVzYjdeWT5qXLOOfCofEcHlZzDjC/r6EZ0\nuzfkIfmTUU82QlPbszZov+M/VlPv3vX5yHDnbQh/xcl80tnN8Kn9Lib29jwVG+R3ZDtrz+TVbTs1\nntur3FYYnTxTo3lKXsfDZgMgXSq373i5S93uyHF8NjkJdkZyd6a1MruRXao98gfT3520K9srVnXj\nuspSuJSteRt6xq+7eY6wI9uQP7oRP9RvdYMK6uUuyP5JvJYs5XOmr/N9pY8/9nzOlvmiGsk/9J7e\n/IWfKoJaJKyomTynGFgn3nViqOPOP6eAGK2pnl9+ye1Ruplehc+Ru/NzfVCnELl+dnmR7h2eHd6a\nuxH6pkQnW9mudHa2Kt68ZPZUng4v+r+jf1IPXfwmNjGehdfN1938ztvZuuJnfLv2VvzTdg5mJS/j\n7PQ48hFP1aGwdjYq2Wp2xiRvFQ+TV+2dYlW2dD/Et4NR6ezkOViVv7r8QXZP6mPioy5nEFa27OKG\nsHV2q5e7dLWz7O7yKPM7Uz2Zzd1Uzyv5Wn/dFNtKXb0hPZWWL1H7ahOSf0b8fHrzF34raSZTPXO7\n2p4NxyM967/qcJDsSZsOB6L6Vs/Ops6WXax3yp0eeB5przqQOPKujA4yvHfZw7B2d84z79Rvebsz\n4lf5ruqvcnfquS6Z/5wRlIV3mo+THHJH/DJ/bsd8codtC7+LneFhtu/IQ1h2aqraNsHq5FB3I3Qq\n547+Y4J1MmJw17HX8dmdWJ1zCCd/pucTd/sIYd0912H9xCKn32T9sbJf4ULHonpx0vmhs6fjXT5R\nPF0uusf1nZyv2FkfzORmvvqM37nw43Re7iI6lZrMaF1dQLK7ppXP+THbmJ3OiS0j5+UunS3ZBvdg\nrPzt+ErxIFsydSN+u3qzTpUPu1indjK8zF9uzjnxzct6sFZ5ii4aHP9lYiN+7gn6RP8kZ9jS5UGx\njXg/1fNK7t7VRvn2qi0s3u4LT7qadG27o8aVTtR+0dXPOTC9SqaDw5Hj+LfD6vjtym/qM9ZP3IHV\nyV0nf1xb3PMUR36Vx7AyWU9P+pwE6VYxUzwIO9rmHsfWtslUz5pP7NyyO7+a5IOLw60Lx/eL0IXf\nJNZnqqdPH/Us305SF2xryQqm8iE5db0SK3y2zWmDOqi6X8lfhJ7xc21U9nYdQ5XbdRRVl+Jhtixy\nPiCq5CqblG6Hh+l044lo53X3iAfFr+usMx+SN7GhtmM86pmvLhfv0F/5O/md/zqq02Y6u5V9dTuz\nY8rftXG3o31Ofiu/3hVTpG9S46htbcdyu7Mxy6487ATdlbfTRzk87E2XyCbnuOHa5PhsF+u0z1K2\nMfmqf2Oyp7m6UyeLumfOs03K3imhUSHHr7VO6v66b213PufA2jojfsx/igcRyxN1XLpab/m5dGSn\n6qMjzlTPCZ0Lv6ZQnANM5u9e7tKNpKBtbpu8D92py3yK2FRPpHc6CtPtq3IV7oy1s7PDz14n3MWG\n+YFhdPyAeJyYKx9V3OwEyomPqhnk5+ontX+S95M411pEB8Jd/RMeJ/bZj8g/mQfFduGd5s9OnnW4\nlN2ZxxlZcHy46Gp+V/8jn0/rRfmN2YlyoZLzBlNko5oGqGRNpjZ2+eLEvsPa+Y3Jncw6cHzm5PBk\nJg6L6+R8gPUlyFfTfO50qpypVC94utxy8idjnvJUW5ypnkhmxdxdzKtHTpA9uQ3TiWJWMTCfsjZ1\ne9U9iU/GgezMfkAY1/J8zsGnNz/VUyVTVzCoPSvOrkOt6zttameO5LCiy8Smejq2ID3dRWgn/yqP\nartITfV05LgxcfirPhe3w7toMmXKxTeJ2fRCGfmP8XR4p7Z2+iexdGze9W3FO4ll3rao7sv70b6O\n3/FFl9tdDmb8TF9ev3OaVIR3Yj/JOSfebv+6m/uTGDl+2tG1SL384i5sE8xon9NPqRphWF1bdnJt\nWh+7Pr8y1VPJ7vKG8eRRIWUj08luZFXb1zZ3xC+3qeeq1cZJH6awsG0MF5K/G5+FnfkO+VI94xdx\nRvwUnRE/UuwqoStfbq+G4FEB1DtyVZfqAJg9tTNAMhVd+ZzDTgcz5UV8ikfpWaQ+5+DIqX5QPNM8\ncn3W2Zqpe9ZgknPTk2fE6x6gGM4ut51Pq+zo7+rZibXrm9wG7cvEPufQ1RCLRbXLsQPxI99P/1cd\naLv6IDBqh2Kv8ruTp3SwXOnsUn5Wo+2LulksDg/zC7Nf9WdMHrMtYu8TB0pvF89dnymduQ2TGXEN\nq7pgq7YpXzk8k5xh9apevsVsyrK7WDIb1Fs9u+NathvhVlM9u35LTfVc9Y70oOM145/Eq9qC/N7x\nKtwR+K2eqk3Vf57x8+lc+DWdqdNpZnnunRhneovT0TttWFEz+uSTiO9/X3/OQQ3fIx7mB9XGiYnj\njyyX3XGcjPg5fsht1fRGJ/+Yzrx0c2gROmAxXI6PEU8m5guUjypmHc7JHWVl665+VQ/Mt0wP8p/y\nG8Or7GGyqy+y3Uxux4/sdvKf6VQxZCdkk7iptnWbM/2O+aKrP9Ye4XXi7YzidP17Xu5MFZzUDsLp\n2K90O766y2d3Yt3Rh/IH5djE3ok9VU6HNe9nucX8Vvd1tqsRv+7iq9qXdTFb3Ld6olyumNZyd9aC\ncw7HYp3tmOYKkrOwq3zp8uCM+Pn05qd6so5IJShqV4uprncdKtrmdsJ5W50PruQxenp6P+rnXiB3\nGJkdLka3U5rYkok9VO3KmXS6rp2OTkc3ivn0dfcdHqbTxef62vFxtbvidey8on+ag52vXfkVb76b\nrWzdzcndHHZzyPWhm9+Onin+KWalk82wUDma8U5rg9l0FfPVXED7Mk4X43Rf54ddn92JdaIP2XtX\nbCb2qLYO1s6mKzFZlPdPblirkTXUfvIdv0VqUCFvV7grVoVvJ5928qsSe8bPsWW1rxgPYXrzI35s\nJKi7k8MuiNjLXRZ1yYzWJ21qB8TkdrQu/JSOiU3dPiTXldl1bJ0tEXpqWOWdHEwYf2fXrk5HVsTe\nVDiHx+msKy/T78pi2zKxu+dK3hX9KjZ3ymPbujekOetdfOo+h7/z9WSUENmxqHtL7x0xuFte5pvU\ne0Q/TYrp3cn5uzBO8GWcSneXMxO9jGe3n0C8Ci/L4WmuOb5S7RmPa4/j6+4it8Pn9k0THGokba2r\nKbtqqicjNtUz66p61ctdHCxoqaZmo6Xic+v744/9WXLrf5ZVfXtG/DidEb+NThvt7+7K1A6KFYZT\nME4b1iFVHIw+/TTiV3+V231lSqbC4z73NOFhHXem7pmIqR+mNimeLtfcqVaZdr+H5eJT/Du+cH3p\nTOV1lrv6d2xEcVR6UW5Umr7cBWFnP7RftWG6lY+nfq5+QNOfO+zTdceuqWyWIyhGmep3G5WNO1Pw\nO1smfnJ1obg6U/KvYkPb7/aZyhuGdcdvind67Jr4yMkZB2t3A175IRNrh6Z6VjzK/ulUz8mIX7WD\nPafnXKDW/1XGJD+7mp7UQ6Z3754fL0J2I19WWecD7j6dC7+mY2cdMytKd6pnPdljulUbVWTOMwmK\nPvvs/YVflY/0Ifk7B4RJB886HmRL94zf09NX75pPLqgUTxdrZJ/rI6Ub+WdRdyHEbL4a3x25E/+x\nA4v7cper+h1cXZ/T2eTU83Tqn1PHtZ6m/K4u1xa0zFRfcLObu5Pnu1SO7PAquzI58VazQFwelAOO\n77rjHOJBue1Of+x86+aZU6Po2PIaWN3RkSs5Nq0PxNvlIMLq2t/lH7IhL9XLXRCeOsrEbqAyDO6F\nHzoPcKZ6slxkF40Iu5szqO2Up2J344FsPVM9fXqzUz2nr3evRe10gtPOq/J2uvJ+1sFWHtQeUZ7q\nyX7IfqVPyVJy0X6F37WlUr5rjngmfngUzySXFDkvg1AypzHrcoHpdXCivK/2Ot8629E/rQckV910\n6vyHsC68Tk1MdGQZylaXn/lW2afyTflAYWd2TWN7xX5Hp4t1x8bKw0ZMa3vHr0qn4kE6nGd1O98i\neyc4mM9eE+vEJuWfzldTngnuDiuT2cXRiSnjiehvwuZ29UKwy43dzzk8Pe2N+Ln5qnyqYtDlV8dX\nsbvtkd3n5S4+vfkRPzZS0N3RRvx5WddZh/ZaI36qU0SUp3oi/zBfMJsUVkdu5x+nw0PLTI8a8XN4\nq05XjtKtOlp36uPVEb+1rkbkutpycGZZiJwRP7bu2Lbo7hG/ta5GFBGhzzk4uezUFLJDyatYUeyn\ndevUszPip/J8kTPi5+ZItXPiQ/R/0XTEz83JTl7XJ1wZ8UN0ZRSM5TmyxfUH4lF54eSbi1X5ras3\nx95Oh4u/qwmE9epNc4QZtd/5nENeZ+dZDKt6RjUCP3uPzlWnI35InqpxdryubR41awHx1MGM3P9E\nnM85TOjNjvjVxJ50XqoduytT96PC3O1YVRvUzimKzz7TI37uRSjzA+scJicK7KCkOnE2IhSBR/yU\nDa4fJlMFWT4wnS7+ire7mzzJTxaHK7ysZhx7O7yP1q/8leO3678utgsvs9WtK7Yd2aH0IIwq/5lM\nB4vyAeKf9sOMx8kRJgf50ukzFFYHB/M34lG5jnR0eTutnYwTyWD/kVwnnp0/rujsfKew1tzo/Kbs\nnhy7VK52NVD3uVg7+51cXaRqLvNUPXVfHeFD/ZmKtTvihwYNdkf8mC3ueUNX56rNsq/z/cLOZHS6\ns+8WnRE/Tm9+xE8l1SKUrIw/d2Domy2sk696uiJRvKgzULIQobd6Vv0VR2c/K2rmV8XT+amzAVG9\n44Tk7Pqhs1vFuNPp4EdYq91MlsLlxqWzueNl/ujaILxuHBz9znYmF+mZ5LbS7eRyFxe1Tdmhcs/N\nA2S3woLInTqU7enwIhxOPDp7Ve5NsTo4Orldu06Wi7HzcZWjvseJtu9gc+I3zUnXdworwlj3T2Lt\n5L6jg9mFcKv8ZVjvzmNEyMZuxA/xdDmFRvSYH6odbMSvnmcqbGi7UwddnXcxcEh9zsHJ3/OMn09n\nxI8kEUs8tC/L60b8FuV2TqfEdHS8ikdRvfBz/NTpU7zqYNTZXTuoXfn1FfjIhzt+6A56eR/KBycG\nlZfJX9Q9ZL6Tny4+x+Z8cHN83eV9h3dXP+oH3DxwRqFrWzSSjwhN9exkdwdsVhtOezX1bGfJMGSq\n9azaqfpV/TiSU3mRXzqfTftBJ95dnqGcVzbuHoecPGM2shNnVltKphPPic9cnYgX0eRNxFWPY/f0\n2NVhcXhZjlasyv6d/GF99vQD7lWO+iA7q1NluzPV01lXF6STGnf6TxYj53iZiX2CSP3Pss/nHHx6\n8yN+7OSrmz7ADjTsrkyVgWSr37SNGvHrijniw7d67shn9l+1+8oPxS1TfRnEnfbexZN1dtPLVMyn\nU6buxLfDm9uw9iq+j/iA+9X4VfldHJX8Smzq3x2/SV6ouF/Jtzt94OhFPMr+q1geFW+VZyxOd/ps\nJ18qzk6uE6upPTs+YzrduD7q5S4Z052xds45JnFVfnfzp7N9+lbPGqdu+n/dNnmrZ9XJpnd2Uz0Z\nj/Ltjt93cqZiV23ZtNr1/4z4+XRG/AbFXjsTtGR3YhR/Z0OV3T2bUPEpHIw+/TTi+9/XtnXyO96J\nXOYXpdPRlwmN+LlyrvI4z/RM5aqO9s6XuzwivtM27jMkix6p3/UXuzurbFD5UfFOasLVvcipezX7\nYepzlz/T5OUuTpwczFdjOJGX6REvd3HkOTnuxNaNq/vCEyfWkxy+4jMk/9FYJ33KVR414tfFl2Gd\n+lTJVzxXRvyQnSy+a33yVs+q4xEjfl2ePjp3KvaJ3HoheJ7x8+lc+G10XpWPyVEnO2rf9ACR+Vcx\noGknaJ3Rp59+KA/9mL3VJreYkVzWXulCtjjT6j7+GJ+oVD2OH2pMFGbHD0ynaquoe8ulOnAg25lt\nbN3B1NWm8iPCq9oz+zr9bgzc+FXdDF8X6/pB70l9uvmN5Ez4GV+n34179zkH5U/HB1mekyNTHyt/\nKaxTzIxHYchLR4eD0cntnZe7uHrR9qnPFPY7sGbe7hyC2ct4VF259aFwo6XC2tmrZHcxVXFU54J1\nKr/6j+zoRvzqh+yzzCsjfpPYIn8hPCxWqL3y/aJ1/tXJqPtz+7zvEKc3P9Wz67QXb01wNlU0Jx96\n6JYVvLKB8aqO2OkwFa0LP8bfFXv+r+5CqU68K3iXB9mLcKFnglQMlB9QW3b3u5Pd6UQyuo6WXQB3\n+ekeCDr/q2d/VBtmb2f/zos+HP1IV97OYsNsVrnTtWd4mT0Ic7UHLZFe5T9EDKfKO9UeUTfi9/Sk\n7+4zm6v+vFQ54vij8qv8zOTEG9nS9Q+dvM6vLkbFU3Hm7bU9O8ZMcSDbO5+pGCmsqs9isvKy6xvc\n/q1u260P5dduRoaTqw42pp8RkqFG+tf6dGRNPROY93ezxXK77uUuVV6WO+kz0H633hxiI37IBsRz\nnvHz6Yz4mZ2h22l305sQv9KJZHejUtWOqt8pxM8+0zhZR6hs6g6sSoZji2svI3bh5y5d/MgPj5jq\niZbZJjY9LC+dqYVdzPKvmzbHeBFf3uZM9UR54tqq9Ge9k/ipqZ7IJiSf1ZQTW4a72sL0uduZ3cw2\nlhsdhokPajulc2fanPLHTo1X/ROsCkeVfXXacZbHeFSeoH6g4lT6KtYqU+lVchyfsdkxnU4m3z1H\nQTJRP6hsm9aH0uX4tZIz1RPZxHCxWNV2aKonyyFkj/ouH2ozeavnoitTPZHN0/4M2ev42smHTNOp\nntXXdcTvXPhxevMXfuyEsTvBYp0EGoLPMth69x/pVB2f0xkryiN+qlNF9qJ2iF/JdvZ39k2xs+lS\nri5lf5WjeDo/ZF8yeWj/BK8jcxJLZPOEF+2bxNcZ8dvR78QP8Sv5U/8pvFN7HFxdrjh+YH68uo35\noMOu7J9gmmLr5Ct/M6yujcwHzE4lR8mbYlQ4mf86vcj2zlc7PstLVyfDGuF9n5HpQfK7WDM7d/2H\n2lU7Fk5l7yQfmW/Q766Xuzg+idh7ucvuVM+8Pp0OWm9iMD6Gu/M7ynd0czb/umm1Z6qnT2/+wm9S\n7KhjYReQdb3Kn4wQIB1um7yt4lJUp3oqPRP77+S9akMl9Er0O+yd4r9TNzvAdHhfIw6L7uCtbSpe\n9uKeicy74odwdW0dGxheZ1n7u+lStUd2V5trXe7aNvWBM4vi7hqf4Lkz3lMci74JfZ6L9ZG1+k3B\n6pwP3GX3I3IGYWWfc7jq00UuD8PDzh+688i6vvM5h+oTZ131tzvH9qs1tJPrlafrR89UT5/OM37m\nwTkn4Q9+0HeCT09fvctSf2of43XbOLoV5amed/x2sN79Q7HMtPsKfMfeu3iu+nWC9zXi8AjevKxY\np5iqzEfhuir/rlx+7Xjv+OFuHyCdjl132v51Yv06ea5ideJyh/5vAlakT+n9psW62lxxVhlXfbpI\n8agRP3ckzfVJhD/il9vcMeKn1r+uX6XuBWxs21o/I34+nRE/UORridYj9IUfe7lLTUQmG/GiNt2+\nOprQta/Ujfjt2Is6VyXf0c141X7G43wE2bFX8d7Bo7ZPOjyF1/HXTsycXHBscH20iL38wtVf96v8\n3amHiXzEXwk98+XYw3Rd2c7WHf5dmyL0B9x36+6OWkC5r+Q7/eBOvJV+ZePU1glGxbPIeZ5xV2/d\nvuuzic7dOnb1TusTtXHqY7dOIvpPV6i2yn7XXsfGtS1vr+dZax/b7n7OAY2eslE+dJ5Z61eNxiI7\n3P7W7cumfZjTpuo+n3Pw6c2P+LGRPzVUji78qlwm2z0ZyD8mh8mrbZyDeCX0Vk+kn40sMoxIVt7G\n/F95qg2Kh+mr1M0xZ35Y9tZtbqw7HrQd5YvykdvRdvawXKpxcOPh4qv7HF6G1bVV2YbadTjydtWG\n2VTX0f9F9aLHiV/H59qxw99NGep0IXI+4dHpyvvcGu/y3MVX/aLwOn2XyjPX7mWTo2OCkdmA8LKL\nIdYWrXc4OhnKZwwH0on4GVZlB9vnHBt3j11u8EkfAAAboElEQVTKRwo3InXhp2Kt8ifzsvM8d8QP\n+ajarWxlOCuhqZ5qxC/bifCh7ZN4oTZou9LBfF+pe6snG6xZlC+qmY5Dz3RG/JqObq2zQspD/VUu\nK0jnANXZ1q07uhWhqZ6OLWhbdxE6PeDs2KI6lEX122edHKWbxWTC7+isfE4+d3iVPZ29ii8v6xQZ\nB1/Hk6n+d+4mTnLnKk/l3ZGlYluniCmslQ/Z49jC+BVv3Z//dzYwP3Y+yOvdSe80tuwC9kqOsDYO\nVqV/N8+6GHTrDjYXq9N+uu9qLigcKm934lrb7vp4Wh+7OcMuAHe+z+hgy/5xeLKN6hxqUT3/6+yf\nfMBd2bM71RNh2cn3u+OzsDPbmK15/Yz4+XQu/EpxLVKF4zzjV9dRh7GWXSelbKzri98ZbVTkTPWs\nhahsYjaoA0OnG9nAbFH+XsSmPipM7E6g4unkMTm1nTpAsXaZ1DQi5q+d+KI4sIOHipmyQ+UBwurY\niuKnsGdbmZ0Kl3OzBvUpKL5qVETpYLFQ+rrtnd3sP9tXdaDtEXjUk7VDse9qnOV1p8PBU/nR/0xO\nvLv+gmF1/MIwq/5MYZpi7bA7ertaZXJ3+qlq1zSunU2qlmp7ZhOTU3mc45Cq0wj+OQdniqITS2TD\n09OHI35MT835vJ2d43VTPbuYqqmeVS9aV3ZeiddOX8Z8X2nncw55/Tzj59ObnerZfQhWdZj1wo8d\nDN27Zeg34c0/NIWV4VK0LvzqyMzuz7Vf8U5kdO0RffzxHl7HrsVT43AV39SvFe9dsjudzBfItit+\nY9RNfbyybzcWU72Ih9H05S5VB9LFpsZ1/MxuhB3xu37d9QGSz6aAqXadncwXkxhVvy2qo/dXcjRC\nx3oiZxcrwjjFetdvgnU3xo/AivITbbsTv8uDcO7Y1mHrZNTv+D097Y/4rbYMb5Zd5eb/6IZgbbs7\n4rfjW9ffV3OeHaOrHrb/jPj59GYv/Gpx1zuT+T+6a8lGcXJx1jsQNVGrbJbkHS9qq066UNFVuuMD\n7jv2T583uMJb6crnHFwbWGynMc66J22meCeyGS61Hfll6je1f9FkxG8nbpNcVCN+XT2j6cK7sVU+\n3PUP84Wyu2JkueFg2fWBynNmF5v54frZsUv5ItPkA+47WBnvFV2dz3aw3tFfTXGwi+RdnS5WN4+Y\nT7/uWFecV2Xu+r3uVzfPq/1oZlXentugZ9CQL9690yN0T0/8PHM64ufkNFsuuVfqLNO68c7aVJuq\n7vOMn08f9SzfTkKJzTqCuo+N+NW7Oe4BAe1zt6uCZDicoug+4N79qk3IFuYHxevodPyDaHcEDG3f\nwa/kODYp3glex+eOzYxH2Tzh7dogrIr3qn7HXxOcyjbUHuHdiZ+b4+72zm5HnoPB9YHy64RnJ/4O\nz51YlX5mk4qX2j7x6+TXYXVsmerr/LHjM1eXE1elY+KPu+Lo4K88FeeO3x1fOrnlYEfrqu6qbPfC\nj73Qh43yXR3x63w08Y0rq8O9k29nxM+nc+FHOiTWKdT9bK51LshKLPGrXrUN7UdF6MqohN7qqXQr\nm9j0BmSr6mA6G5Qtjhz1+nfWtvN31q3uok3ygeme2s7wKnuYbmWPw4PsdPE59rqvikb23KGf9S2I\nx8E9xdvpUjxrvbPjEfyIt25j7VR+r6Xqo5W9lRcdD5i8nb6/w+rG25WJnt9Tdjo2TuUxYrV8hy2u\nvLt07taxm0eTvhht7/KZtWP7mDz2tstH+X1RvThgtczqd3JuUy9MGNWYq2dpkQ2IduOl+J1cYtsQ\nqbpG+V7/1xl258KP05uf6sk6JHXCNx3xq3x5XzfVs7ZBPKit84yGou7lLlmPY5PCusNb/a54lrwq\ntxKbVqMwOfY6I7+7U1vyf+UjhJl9/0fZsxNfFAdkv/KNk++qZivWnSnWV7ErucgnFR/Sw2LL7i67\nuZJluu0ZP7M787P9SjZaZnKm2k/qV/nenTancs/hVbVcX1SxY2PHg3KR1Qzy2cQfCuuiSRwnvt3x\n2aSfUvbdidXFOJXDll0cnZgufUpm3qf8vtaZ/dPPOdTzB3Rsz9vzvkdN9XSmcSIeN15s6eaz4snU\nvaCqy7cz1dOnNz/ih4pgbVedFSoUNuKHOj703yk6p33Gxdp3hD7n4OhXNilZU17lF8ffiNiD9Dt+\nUDx3+sjBn5eZdqa2TmycyGF4pn5bVPF23zlz8N2FXdk8jfU0th0GpWvHBw62iS8cXcwHE0x12y7P\nFf89It5XbUT5cSVXXF0O1mmuuLZMcezIeSTWR+bDlZypOK/ahtovUu0rjxphq23YzTv0v37KAclb\nvkDt2cXb1ameyCf1Yno3L7r2Du6MX2E5Uz19evMXfqxDYgkWwR/qd0b8kH7WAWX5qw3qjBAONq2w\nrjN6xOcclDzHBwpvh9MZ8dv5nAPLkbzPiUXtcJEclZ+OrZUY3myzm3OuTeygqtp1U126A0qE94zf\njv4JdhZrp121pW7v8Dr2VD+ymnTys7Ob6a3tnH4CbY+4/jmHRayvdvIabWMx73iRDYsmn3Ng+x0e\nFNPd/izLU7lTafrCE1fvTt+TeSb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LRuX56KPrfb5bQ9M+rOJmbes5evbv09O58FMErrs/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\ngg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3e12b81750>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3e128a9590>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3e128a9a10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3e12a7d4d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f3e12a611d0>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Aggregator Function can return Scalars"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "An aggregator function can return a scalar, for example the number of \"switch ins\". Each value in the result has a one-to-one correspondence with the groups in `Topology.get_level(level)`"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def num_switch_ins(series):\n",
+      "    return len(series[series == 1])\n",
+      "\n",
+      "scalar_agg = MultiTriggerAggregator(triggers, topology, num_switch_ins)\n",
+      "\n",
+      "print scalar_agg.aggregate(level=\"cpu\")\n",
+      "print scalar_agg.aggregate(level=\"cluster\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[0, 364, 0, 0, 0, 0]\n",
+        "[0, 364]\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/doc/api_reference/.gitignore b/doc/api_reference/.gitignore
new file mode 100644
index 0000000..588039e
--- /dev/null
+++ b/doc/api_reference/.gitignore
@@ -0,0 +1,3 @@
+_build
+*.rst
+!index.rst
diff --git a/doc/api_reference/Makefile b/doc/api_reference/Makefile
new file mode 100644
index 0000000..f219a85
--- /dev/null
+++ b/doc/api_reference/Makefile
@@ -0,0 +1,196 @@
+# Makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+PAPER         =
+BUILDDIR      = _build
+
+# User-friendly check for sphinx-build
+ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
+$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
+endif
+
+# Internal variables.
+PAPEROPT_a4     = -D latex_paper_size=a4
+PAPEROPT_letter = -D latex_paper_size=letter
+ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+# the i18n builder cannot share the environment and doctrees with the others
+I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+
+.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
+
+help:
+	@echo "Please use \`make <target>' where <target> is one of"
+	@echo "  html       to make standalone HTML files"
+	@echo "  dirhtml    to make HTML files named index.html in directories"
+	@echo "  singlehtml to make a single large HTML file"
+	@echo "  pickle     to make pickle files"
+	@echo "  json       to make JSON files"
+	@echo "  htmlhelp   to make HTML files and a HTML help project"
+	@echo "  qthelp     to make HTML files and a qthelp project"
+	@echo "  applehelp  to make an Apple Help Book"
+	@echo "  devhelp    to make HTML files and a Devhelp project"
+	@echo "  epub       to make an epub"
+	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
+	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
+	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
+	@echo "  text       to make text files"
+	@echo "  man        to make manual pages"
+	@echo "  texinfo    to make Texinfo files"
+	@echo "  info       to make Texinfo files and run them through makeinfo"
+	@echo "  gettext    to make PO message catalogs"
+	@echo "  changes    to make an overview of all changed/added/deprecated items"
+	@echo "  xml        to make Docutils-native XML files"
+	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
+	@echo "  linkcheck  to check all external links for integrity"
+	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
+	@echo "  coverage   to run coverage check of the documentation (if enabled)"
+
+clean:
+	rm -rf $(BUILDDIR)/*
+	ls *.rst | grep -v index.rst | xargs rm -f
+
+reference:
+	sphinx-apidoc -f -e -o . ../../trappy
+
+html: reference
+	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+dirhtml: reference
+	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
+
+singlehtml: reference
+	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
+	@echo
+	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
+
+pickle: reference
+	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
+	@echo
+	@echo "Build finished; now you can process the pickle files."
+
+json: reference
+	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
+	@echo
+	@echo "Build finished; now you can process the JSON files."
+
+htmlhelp: reference
+	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
+	@echo
+	@echo "Build finished; now you can run HTML Help Workshop with the" \
+	      ".hhp project file in $(BUILDDIR)/htmlhelp."
+
+qthelp: reference
+	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
+	@echo
+	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
+	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
+	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/TRAPpy.qhcp"
+	@echo "To view the help file:"
+	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/TRAPpy.qhc"
+
+applehelp: reference
+	$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
+	@echo
+	@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
+	@echo "N.B. You won't be able to view it unless you put it in" \
+	      "~/Library/Documentation/Help or install it in your application" \
+	      "bundle."
+
+devhelp: reference
+	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
+	@echo
+	@echo "Build finished."
+	@echo "To view the help file:"
+	@echo "# mkdir -p $$HOME/.local/share/devhelp/TRAPpy"
+	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/TRAPpy"
+	@echo "# devhelp"
+
+epub: reference
+	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
+	@echo
+	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
+
+latex: reference
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo
+	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
+	@echo "Run \`make' in that directory to run these through (pdf)latex" \
+	      "(use \`make latexpdf' here to do that automatically)."
+
+latexpdf: reference
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through pdflatex..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+latexpdfja: reference
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through platex and dvipdfmx..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+text: reference
+	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
+	@echo
+	@echo "Build finished. The text files are in $(BUILDDIR)/text."
+
+man: reference
+	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
+	@echo
+	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
+
+texinfo: reference
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo
+	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
+	@echo "Run \`make' in that directory to run these through makeinfo" \
+	      "(use \`make info' here to do that automatically)."
+
+info: reference
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo "Running Texinfo files through makeinfo..."
+	make -C $(BUILDDIR)/texinfo info
+	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
+
+gettext: reference
+	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
+	@echo
+	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
+
+changes: reference
+	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
+	@echo
+	@echo "The overview file is in $(BUILDDIR)/changes."
+
+linkcheck: reference
+	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
+	@echo
+	@echo "Link check complete; look for any errors in the above output " \
+	      "or in $(BUILDDIR)/linkcheck/output.txt."
+
+doctest: reference
+	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
+	@echo "Testing of doctests in the sources finished, look at the " \
+	      "results in $(BUILDDIR)/doctest/output.txt."
+
+coverage: reference
+	$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
+	@echo "Testing of coverage in the sources finished, look at the " \
+	      "results in $(BUILDDIR)/coverage/python.txt."
+
+xml: reference
+	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
+	@echo
+	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
+
+pseudoxml: reference
+	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
+	@echo
+	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/doc/api_reference/conf.py b/doc/api_reference/conf.py
new file mode 100644
index 0000000..ea2c909
--- /dev/null
+++ b/doc/api_reference/conf.py
@@ -0,0 +1,381 @@
+# -*- coding: utf-8 -*-
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# TRAPpy documentation build configuration file, created by
+# sphinx-quickstart on Fri Sep  4 11:30:35 2015.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+import shlex
+
+this_dir = os.path.dirname(__file__)
+sys.path.insert(0, os.path.join(this_dir, '../..'))
+import trappy
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#sys.path.insert(0, os.path.abspath('.'))
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.todo',
+    'sphinx.ext.coverage',
+    'sphinx.ext.mathjax',
+    'sphinx.ext.ifconfig',
+    'sphinx.ext.viewcode']
+
+# Update MathJax path to use the cdnjs using HTTPS
+mathjax_path = "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+# source_suffix = ['.rst', '.md']
+source_suffix = '.rst'
+
+# The encoding of source files.
+#source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = u'TRAPpy'
+copyright = u'2017, ARM Ltd.'
+author = u'Javi Merino, Kapileshwar Singh(KP)'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version. Drop everything after the last "."
+version = trappy.__version__[:trappy.__version__.rindex(".")]
+# The full version, including alpha/beta/rc tags.
+release = trappy.__version__
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = 'en'
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+#today = ''
+# Else, today_fmt is used as the format for a strftime call.
+#today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = ['_build']
+
+# The reST default role (used for this markup: `text`) to use for all
+# documents.
+#default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+#add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+#add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+#show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# A list of ignored prefixes for module index sorting.
+#modindex_common_prefix = []
+
+# If true, keep warnings as "system message" paragraphs in the built documents.
+#keep_warnings = False
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = True
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+html_theme = 'classic'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#html_theme_options = {}
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = []
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<release> documentation".
+#html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+#html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+#html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+#html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+#html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+#html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Language to be used for generating the HTML full-text search index.
+# Sphinx supports the following languages:
+#   'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'
+#   'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'
+#html_search_language = 'en'
+
+# A dictionary with options for the search language support, empty by default.
+# Now only 'ja' uses this config value
+#html_search_options = {'type': 'default'}
+
+# The name of a javascript file (relative to the configuration directory) that
+# implements a search results scorer. If empty, the default will be used.
+#html_search_scorer = 'scorer.js'
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'TRAPpydoc'
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+# The paper size ('letterpaper' or 'a4paper').
+#'papersize': 'letterpaper',
+
+# The font size ('10pt', '11pt' or '12pt').
+#'pointsize': '10pt',
+
+# Additional stuff for the LaTeX preamble.
+#'preamble': '',
+
+# Latex figure (float) alignment
+#'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+  (master_doc, 'TRAPpy.tex', u'TRAPpy Documentation',
+   u'Javi Merino, Kapileshwar Singh(KP)', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+#latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+#latex_use_parts = False
+
+# If true, show page references after internal links.
+#latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+#latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+#latex_appendices = []
+
+# If false, no module index is generated.
+#latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    (master_doc, 'trappy', u'TRAPpy Documentation',
+     [author], 1)
+]
+
+# If true, show URL addresses after external links.
+#man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+  (master_doc, 'TRAPpy', u'TRAPpy Documentation',
+   author, 'TRAPpy', 'One line description of project.',
+   'Miscellaneous'),
+]
+
+# Documents to append as an appendix to all manuals.
+#texinfo_appendices = []
+
+# If false, no module index is generated.
+#texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+#texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+#texinfo_no_detailmenu = False
+
+
+# -- Options for Epub output ----------------------------------------------
+
+# Bibliographic Dublin Core info.
+epub_title = project
+epub_author = author
+epub_publisher = author
+epub_copyright = copyright
+
+# The basename for the epub file. It defaults to the project name.
+#epub_basename = project
+
+# The HTML theme for the epub output. Since the default themes are not optimized
+# for small screen space, using the same theme for HTML and epub output is
+# usually not wise. This defaults to 'epub', a theme designed to save visual
+# space.
+#epub_theme = 'epub'
+
+# The language of the text. It defaults to the language option
+# or 'en' if the language is not set.
+#epub_language = ''
+
+# The scheme of the identifier. Typical schemes are ISBN or URL.
+#epub_scheme = ''
+
+# The unique identifier of the text. This can be a ISBN number
+# or the project homepage.
+#epub_identifier = ''
+
+# A unique identification for the text.
+#epub_uid = ''
+
+# A tuple containing the cover image and cover page html template filenames.
+#epub_cover = ()
+
+# A sequence of (type, uri, title) tuples for the guide element of content.opf.
+#epub_guide = ()
+
+# HTML files that should be inserted before the pages created by sphinx.
+# The format is a list of tuples containing the path and title.
+#epub_pre_files = []
+
+# HTML files shat should be inserted after the pages created by sphinx.
+# The format is a list of tuples containing the path and title.
+#epub_post_files = []
+
+# A list of files that should not be packed into the epub file.
+epub_exclude_files = ['search.html']
+
+# The depth of the table of contents in toc.ncx.
+#epub_tocdepth = 3
+
+# Allow duplicate toc entries.
+#epub_tocdup = True
+
+# Choose between 'default' and 'includehidden'.
+#epub_tocscope = 'default'
+
+# Fix unsupported image types using the Pillow.
+#epub_fix_images = False
+
+# Scale large images.
+#epub_max_image_width = 0
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+#epub_show_urls = 'inline'
+
+# If false, no index is generated.
+#epub_use_index = True
diff --git a/doc/api_reference/index.rst b/doc/api_reference/index.rst
new file mode 100644
index 0000000..e60e913
--- /dev/null
+++ b/doc/api_reference/index.rst
@@ -0,0 +1,22 @@
+.. TRAPpy documentation master file, created by
+   sphinx-quickstart on Fri Sep  4 12:40:17 2015.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to TRAPpy's documentation!
+==================================
+
+Contents:
+
+.. toctree::
+   :maxdepth: 4
+
+   trappy
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/doc/compare runs.ipynb b/doc/compare runs.ipynb
new file mode 100644
index 0000000..8657776
--- /dev/null
+++ b/doc/compare runs.ipynb
@@ -0,0 +1,191 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:567ecb47c78aa19c7fee70a56d3d2260e63edb25c8645d6f48988621ebc6a624"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Compare runs"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`trappy.compare_runs()` is a function to compare two or more runs."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First some hacks to allow importing trappy from here"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "sys.path.append(\"..\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "import trappy"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`map_label` is a dict that maps cpumasks to a name.  This name will be used as a label for the load and frequency plots."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "map_label = {\"00000000,00000039\": \"LITTLE\", \"00000000,00000006\": \"big\"}"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`actor_order` is an array that with the order in which the actors appear in the `thermal_power_allocator` tracepoint."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "actor_order = [\"GPU\", \"big\", \"LITTLE\"]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`runs` is an array of tuples consisting of a name and the path to the directory where the `trace.dat` or `trace.txt` can be found.  The path can be relative to the location of the notebook or an absolute path.  We use a relative path in this example so that it works regardless of the path of trappy in your system.  Note that you can specify the path to the directory containing the `trace.dat` or `trace.txt` or you can specify the path to the file if you have called it something else (e.g. `/work/ipa/latest_trace.dat`)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "runs = [(\"good\", \"./trace.txt\"), (\"experiment\", \".\")]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.compare_runs(actor_order, map_label, runs)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ErInWRIyWeIxkcxPxWNRdw9qUucUsX33hNN985SQvXj/J+aWTzIRPUExeIray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\nsXnzZr755humT59OWloaAHPnzmX58uWsXbuWrKwsrFYrDz3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lPj6e5s2bn7KVgt1uZ/To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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbb18784b10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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5XXstnHNOukn/ujrYbjt44YXixyVJktoPr1AWUFk9YlaujG9ulPaqi898Jtxe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nLi7P7iJri9n/ww4iH2Xt+rSjbANwDFchazs0bW8zL/S+AOjeFKlMpVygEapWTd+XUIzROKSnkr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\nv/8PypxY6xGbtqIAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbaf3204110>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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84HdZTXKpSS41yaUembA1WzTXJwAAsDUZHBwc/agY88Dg4OBcnwIAwLygTp5/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\n9Z1L9J4DAKAAPZoBAKA7PZo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aabnuuut2uOJ5EamTAYAFpU6ua0et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\n5NokT5vnawMAQGVqZQAAdjqD9J17VZKzktyd5PNJTkwrktck+b0k1yf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EAAAg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\ndbXtNQAA0HEzMzNJrVpZnQwAQAmDamVTegEAAAAA2BWNZoZaW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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbaf2a46d90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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nr1ON3kY1Vjeq0du4K/tO21qX2x11sRN1KVxzc7MCgUCp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\n9ea9vEfvxjUb86wtz8rI1pHs2bJnRbdyuLxQPvx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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbaf1e96250>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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xVnZiNjjsi5UDiv4vFUcVNh0wkMxMLkQxcKB7bbJF9+78gooHJ6dsLFnCIK5P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BhbPsPYhQABwN/dHxcLLlr0jXFyQTIOpR/CnN5zTD52QvcJ2JZknZEaWxO3aqcnDvQZKEvtuey2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\nERERERERERERERERERGp6v8BImhvF/Ynn7gAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbaf1daa890>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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qVNtUa/9utH97nldZX6ltpduOOyjItBHT3EemToxIZEXQ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\nMDHNZoZaWFhoewkMIJd6ZFKTXGqSS01ygfHYMzXJpSa51CS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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbaf1615150>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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SOiBTz2Qpeysl9UqGvsoW67Ck3slQi51xlyRJkjrAgfsMZO2fypgrYybImctM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jI4D/HjGHya93BiBmFX4l3gOQze+T2UmGHbhRiw30D/voRkEpspb+AuaXVNUoePJ2Yum3VYq6e0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\n3NaW+ZnUdD1rG+bHfE73T8/Ozja6/nR/nmNCrMN+jp2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XAk9d0TkdzxOI05ebidN7FxGX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\nkiRJkqRp9f8DQuElPmpk2RYAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbaf1215550>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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kSSrM5rmFrHM9GXNnzAzmLiljZnUj47rPmBnMXVLGzJA3dxs2z5IkSVJDfc/c\nOWcnafCceZak/jnzLEmSJBVm89xC1rmejLkzZgZzl5Qxs7qRcd1nzAzmLiljZsibuw2bZ0mSJKmh\nvmfunLOTNHjOPEtS/5x5liRJkgqzeW4h61xPxtwZM4O5S8qYWd3IuO4zZgZzl5QxM+TN3YbNsyRJ\nktRQ3zN3ztlJGjxnniWpf848S5IkSYXZPLeQda4nY+6MmcHcJWXMrG5kXPcZM4O5S8qYGfLmbsPm\nWZIkSWqo75k75+wkDZ4zz5LUP2eeJUmSpMJsnlvIOteTMXfGzGDukjJmVjcyrvuMmcHcJWXMDHlz\nt2HzLEmSJDVUYuZuL+AC4DLgpydOc85O0uDNyMyztVjSHi3TzPMLgEsAK7Mk9cdaLEkd2Ojm+Qjg\nOOCvma13lu+WrHM9GXNnzAzmLilj5h5Yi2dExsxg7pIyZoa8udvY6Ob5NcCLgTs2+HokSdNZiyWp\nIxu5B+J44InAbwA7gN/COTtJe6CeZ56txZJEdzPPm7uLtMyxwJOIQ4V3BvYH3go8s36mhYUF5ufn\nAdi2bRvbt29nx44dwPgQgMsuu+xy9uWFhQUA5uaKf8iRtdhll13e45dHRrU4g0cD713h94sZnXvu\nuX1HaCVj7oyZFxfNXVKGzKNaR7xZb/TVB2txzzJmXlw0d0kZMy8u5sjN8lrcSsldIB4TlKT+WYsl\naTf0/a7r6gWAJA3XCnN20H/9rbMWSxq8TJ/zLEmSJA2CzXMLk8PnWWTMnTEzmLukjJnVjYzrPmNm\nMHdJGTND3txt2DxLkiRJDfU9c+ecnaTBc+ZZkvrnzLMkSZJUmM1zC1nnejLmzpgZzF1SxszqRsZ1\nnzEzmLukjJkhb+42bJ4lSZKkhvqeuXPOTtLgOfMsSf1z5lmSJEkqzOa5haxzPRlzZ8wM5i4pY2Z1\nI+O6z5gZzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2nwnHmWpP458yxJkiQVZvPcQta5noy5M2YGc5eU\nMbO6kXHdZ8wM5i4pY2bIm7sNm2dJkiSpob5n7pyzkzR4zjxLUv+ceZYkSZIKs3luIetcT8bcGTOD\nuUvKmFndyLjuM2YGc5eUMTPkzd2GzbMkSZLUUN8zd87ZSRo8Z54lqX/OPEuSJEmF2Ty3kHWuJ2Pu\njJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fsJA2eM8+S1D9nniVJkqTCbJ5byDrX\nkzF3xszZ1KWPAAAZsUlEQVRg7pIyZlY3Mq77jJnB3CVlzAx5c7dh8yxJkiQ11PfMnXN2kgbPmWdJ\n6p8zz5IkSVJhNs8tZJ3ryZg7Y2Ywd0kZM6sbGdd9xsxg7pIyZoa8uduweZYkSZIa6nvmzjk7SYPn\nzLMk9c+ZZ0mSJKkwm+cWss71ZMydMTOYu6SMmdWNjOs+Y2Ywd0kZM0Pe3G3YPEuSJEkN9T1z55yd\npMFz5lmS+ufMsyRJklSYzXMLWed6MubOmBnMXVLGzOpGxnWfMTOYu6SMmSFv7jZsniVJkqSG+p65\nc85O0uA58yxJ/XPmWZIkSSrM5rmFrHM9GXNnzAzmLiljZnUj47rPmBnMXVLGzJA3dxs2z5IkSVJD\nfc/cOWcnafCceZak/jnzLEmSJBVm89xC1rmejLkzZgZzl5Qxs7qRcd1nzAzmLiljZsibuw2bZ0mS\nJKmhvmfunLOTNHjOPEtS/5x5liRJkgpr0jyf0/B3e4yscz0Zc2fMDOYuKWPmlqzFEzKu+4yZwdwl\nZcwMeXO3sXmV0+4C7AMcBBxQ+/3+wOEbGUqS9P9ZiyVphqw26/FC4AXAYcC3ar+/EfjfwOs7uH7n\n7CQN3m7OPFuLJakDXc08N/mjk4DXtbnwBizYkgavozcMWoslaTeUfMPg64BjgacBz6x97bGyzvVk\nzJ0xM5i7pIyZW7IWT8i47jNmBnOXlDEz5M3dxmozzyNvA74P2AXcXvv9WzckkSRpJdZiSZoBTXZX\nfw64P7ARx/Q8VChp8Doa27AWS9JuKDm28Vng0DYXLknqjLVYkmZAk+b5IOAS4CzgvdXXGQ0v/87A\n+cRhxkuAV7bIOHOyzvVkzJ0xM5i7pIyZW2pbiwdZhyHnus+YGcxdUsbMkDd3G01mnnfuxuXfCjwG\nuKW6rn8FHlF9lyQ1t7Pl31mHJalDrWY9WtoH+AhwArH3A5yzk7QH6GjmuQsr1WGwFkvaA5Sceb6J\n+DD+G4HvAncAN6zzOnYBVwLnsrRgS9JgLS4ucvXVV3d1cbtTi63DkvZYN910E7feemtnl9dkbGPf\n2s+bgCcBP7aO67gD2A7cFfhnYAdw3ujEhYUF5ufnAdi2bRvbt29nx44dwHh+ZtaWR7+blTxNl1/7\n2temuH/ry7t27eKFL3zhzORpujz5WOk7T9PljPf36Hezkqe+fNFFF3HSSScBsH379irtHC0/MGN3\navGqdRisxaWWrQ3e32stZ9xWj8zq8/E5z/mvHHLIwcAce++9hb7savl3fwD8dm15MaNzzz237wit\nZMydMfPiorlLmuXMZ5555iLRKa/01YU2tXiyDoO1uJiMmRcXzV1SxsyLi7OdG1g8+OCDO6vDTWY9\nfrb28ybgIcCjgYc1+Nu7A98DrgfuQuzx+EPgnOr06jZJ0vB84AMf4Ljjjpt28npn7drW4rXqMFiL\nJQ3Y3NwcBx98MFddddWyk9pcXpOxjZ9m3J1/D7gU+JmGl38ocBpR6DcBf8PSgi1JaqZtLbYOS1KH\nNjU4zwLwrOrrucDLgWWt+xQXAQ8mZu1+GPjT9UecPfX5nkwy5s6YGcxdUsbMLS3QrhYPsg5DznWf\nMTOYu6SMmSFv7jaaNM9HAu8Gvl19vQs4YiNDSZKWsRZL0gxoMutxNnA68LZq+enV1+M7uH7n7CQN\nVsczz9ZiSWqh65nnJnueDwJOAW6rvk4FDm5zZZKk1qzFkjQDmjTP1wC/DOxFvMHwGUBnn/qfUda5\nnoy5M2YGc5eUMXNL1uIJGdd9xsxg7pIyZoa8udto0jw/C/gvwBXA5cDPV7+TJJVjLZakGdBk1uM0\n4IXAddXyAcCfAc/u4Pqds5M0WB3PPFuLJamFPmaej2ZcrAGuJT72SJJUjrVYkmZAk+Z5jtjDMXIA\nMXO3x8o615Mxd8bMYO6SMmZuyVo8IeO6z5gZzF1SxsyQN3cbTf7D4KuBjwHvIIr3zxMfzi9JKsda\nLEkzoOmsxw8BjyX+NeyHgUs6un7n7CQNVsczz2AtlqR163rmucmeZ4CLqy9JUn+sxZLUsyYzz5qQ\nda4nY+6MmcHcJWXMrG5kXPcZM4O5S8qYGfLmbsPmWZIkSWqo1axHh5yzkzRYGzDzvFGsxZIGq4/P\neZYkSZKEzXMrWed6MubOmBnMXVLGzOpGxnWfMTOYu6SMmSFv7jZsniVJkqSG+p65c85O0mA58yxJ\n/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWfMTOYu6SMmSFv7jZsniVJkqSG+p65c85O0mA5\n8yxJ/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWfMTOYu6SMmSFv7jZsniVJkqSG+p65c85O\n0mA58yxJ/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWfMTOYu6SMmSFv7jZsniVJkqSG+p65\nc85O0mA58yxJ/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWfMTOYu6SMmSFv7jZsniVJkqSG\n+p65c85O0mA58yxJ/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWfMTOYu6SMmSFv7jZsniVJ\nkqSG+p65c85O0mA58yxJ/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWfMTOYu6SMmSFv7jZs\nniVJkqSG+p65c85O0mA58yxJ/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWfMTOYu6SMmSFv\n7jZsniVJkqSG+p65c85O0mA58yxJ/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWfMTOYu6SM\nmSFv7jZsniVJkqSG+p65c85O0mA58yxJ/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWfMTOY\nu6SMmSFv7jZsniVJkqSG+p65c85O0mA58yxJ/XPmWZIkSeqJzXMLWed6MubOmBnMXVLGzOpGxnWf\nMTOYu6SMmSFv7jY2unk+EjgXuBj4LHDSBl+fJGkp67AkdWijZ+4Oqb52AfsCnwSeDHyuOt05O0mD\nNSMzz2vVYbAWSxqwbDPPVxAFG+AmolgftsHXKUkasw5LUodKzjzPAw8Czi94nRsi61xPxtwZM4O5\nS8qYuUfzDKQOQ851nzEzmLukjJkhb+42SjXP+wLvBF5A7PmQJJVlHZakDmwucB17A+8C3ga8Z/LE\nhYUF5ufnAdi2bRvbt29nx44dwPhVjMvdLI9+Nyt5mi7Xs89CnibLO3bsmKk861kemZU8mZcvvPBC\nZsSqdRisxaWWrQ3e32stj343K3mGsvyd73yHrmz0G1bmgNOAa4DfXOF036QiabBm5A2Da9VhsBZL\nGrC5uanldibfMPhw4BnAY4BPV19P2ODr3HCTr8KzyJg7Y2Ywd0kZMxc2yDoMOdd9xsxg7pIyZoa8\nudvY6LGNf8V/xCJJfbIOS1KHSh02nMZDhZIGa0bGNpqwFksarGxjG5IkSdJg2Dy3kHWuJ2PujJnB\n3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fsJA2WM8+S1D9nniVJkqSe2Dy3kHWuJ2Pu\njJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fsJA2WM8+S1D9nniVJkqSe2Dy3kHWu\nJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fsJA2WM8+S1D9nniVJkqSe2Dy3\nkHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fsJA2WM8+S1D9nniVJkqSe\n2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fsJA2WM8+S1D9nniVJ\nkqSe2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fsJA2WM8+S1D9n\nniVJkqSe2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fsJA2WM8+S\n1D9nniVJkqSe2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fsJA2W\nM8+S1D9nniVJkqSe2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+ZO+fs\nJA2WM8+S1D9nniVJkqSe2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElqqO+Z\nO+fsJA2WM8+S1D9nniVJkqSe2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmSJElq\nqO+ZO+fsJA2WM8+S1D9nniVJkqSe2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D5lmS\nJElqqO+ZO+fsJA2WM8+S1D9nniVJkqSe2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny5m7D\n5lmSJElqqO+ZO+fsJA2WM8+S1D9nniVJkqSe2Dy3kHWuJ2PujJnB3CVlzKxuZFz3GTODuUvKmBny\n5m7D5lmSJElqqO+ZO+fsJA2WM8+S1L9sM89vAa4ELtrg65EkTWctlqSObHTzfArwhA2+juKyzvVk\nzJ0xM5i7pIyZe2AtnhEZM4O5S8qYGfLmbmOjm+ePAtdt8HVIklZnLZakjpSYuZsH3gs8cIXTnLOT\nNFgzNvM8j7VY0h6o65nnze2jdGNhYYH5+XkAtm3bxvbt29mxYwcwPgTgsssuu5xx+cILLyTK7PeY\nddZil112ecjL2cwz/U0qixmde+65fUdoJWPujJkXF81d0ixnPvPMMxeBaV+lzWMt7l3GzIuL5i4p\nY+bFxdnOTcd1eFPbP5QkSZL2NBs9c/d24NHAgcBVwH8j3vU9Ur0gkKThmaGZZ2uxpD1WtpnnX9rg\ny5ckrc1aLEkdcWyjhazD5xlzZ8wM5i4pY2Z1I+O6z5gZzF1SxsyQN3cbNs+SJElSQ6U/Z3SSc3aS\nBmuGZp7XYi2WNFhdzzy751mSJElqyOa5haxzPRlzZ8wM5i4pY2Z1I+O6z5gZzF1SxsyQN3cbNs+S\nJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxzPRlzZ8wM5i4pY2Z1I+O6z5gZzF1SxsyQN3cb\nNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxzPRlzZ8wM5i4pY2Z1I+O6z5gZzF1SxsyQ\nN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxzPRlzZ8wM5i4pY2Z1I+O6z5gZzF1S\nxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxzPRlzZ8wM5i4pY2Z1I+O6z5gZ\nzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxzPRlzZ8wM5i4pY2Z1I+O6\nz5gZzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxzPRlzZ8wM5i4pY2Z1\nI+O6z5gZzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxzPRlzZ8wM5i4p\nY2Z1I+O6z5gZzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxzPRlzZ8wM\n5i4pY2Z1I+O6z5gZzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxzPRlz\nZ8wM5i4pY2Z1I+O6z5gZzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5haxz\nPRlzZ8wM5i4pY2Z1I+O6z5gZzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1xOa5\nhaxzPRlzZ8wM5i4pY2Z1I+O6z5gZzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJkiT1\nxOa5haxzPRlzZ8wM5i4pY2Z1I+O6z5gZzF1SxsyQN3cbNs+SJElSQ33P3DlnJ2mwnHmWpP458yxJ\nkiT1ZKOb5ycAnwe+BLx0g6+rmKxzPRlzZ8wM5i4pY+YeWItnRMbMYO6SMmaGvLnb2MjmeS/g9UTR\nvj/wS8D9NvD6itm1a1ffEVrJmDtjZjB3SRkzF2YtniEZM4O5S8qYGfLmbmMjm+djgC8DlwK3AX8H\n/MwGXl8x119/fd8RWsmYO2NmMHdJGTMXZi2eIRkzg7lLypgZ8uZuYyOb58OBb9SWL6t+J0kqx1os\nSR3ayOZ5sG/dvvTSS/uO0ErG3Bkzg7lLmuXMW7Zs6TsCWItnSsbMYO6SMmaG2c592GFHdnp5G/lR\nST8G7CTm7ABeBtwBvKp2ni8D37+BGSRpFn0G2F7ouqzFkrRcyTrc2GbgP4F5YAuwi4G8SUWSErEW\nS1IiTwS+QOzVeFnPWSRpT2UtliRJkiRJe44sH9r/FuBK4KLa7w4APgR8ETgL2NZDrtUcCZwLXAx8\nFjip+v2s574zcD5xWPkS4JXV72c9N8Rn6X4aeG+1nCHzpcCFRO6PV7/LkHsb8E7gc8Tj5EfJkXtW\nZajFGesw5KzFmeswWItLsQ73YC/i8OE8sDezPYP3SOBBLC3afwK8pPr5pcAflw61hkMYD8HvSxyu\nvR+znxtgn+r7ZuA/gEeQI/eLgNOBM6rlDJm/ShS7ugy5TwOeXf28GbgrOXLPoiy1OGMdhry1OGsd\nBmtxKdbhHjwM+GBt+Xeqr1k1z9Ki/XngHtXPh1TLs+w9wI+TK/c+wCeAH2L2cx8BnA08hvHejlnP\nDFGwD5z43aznvivwlRV+P+u5Z1WmWjxP7joM+WpxpjoM1uJS9vg6vJGf87ya7B/afw/iECLV93us\nct6+zRN7bM4nR+5NxN6vKxkf7pz13K8BXkx8/NfIrGeG+Pzfs4ELgOdWv5v13PcCvg2cAnwKeBOw\nldnPPasy1+Js63yePLU4Yx0Ga3Epe3wd7qt5HtKH9i8yu7dnX+BdwAuAGydOm9XcdxCHOY8AHkXs\nQaibtdzHA1cRs2rTPjd91jKPPJzYmD8R+A3i0HjdLObeDDwYeEP1/WaW7ymdxdyzaij306yv82y1\nOFsdBmtxSXt8He6ref4m8UaKkSOJPR5ZXEkckgA4lHjCzpq9iWL9N8ShQsiRe+Q7wPuBhzDbuY8F\nnkQcdns78FjiPp/lzCOXV9+/DbwbOIbZz31Z9fWJavmdRPG+gtnOPasy1+JZf6yOZK7FWeowWItL\n2uPrcF/N8wXADzD+0P5fYDzcn8EZwAnVzycwLoizYg54M/EO2NfWfj/rue/O+N25dwEeT+xFmOXc\nv0s0HPcCfhH4MPDLzHZmiFnG/aqftwI/QcyTznruK4gxg/tUyz9OHFJ+L7Ode1ZlrsWz/liFnLU4\nYx0Ga3FJ1uEeZfnQ/rcD3wL+L/FgeRbxrtizmd2PY3kEcdhtF1H0Pk18HNWs534gMT+1i/jYnhdX\nv5/13COPZtx4zHrmexH38y7iI7RGz8FZzw1wNLHH4zPAPxJvXsmQe1ZlqMUZ6zDkrMXZ6zBYi0uw\nDkuSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJJVxIOPPNr2c+I9EnyY+Q3Rzj7lW8mjgYRt4\n+XcCPsL438beBziT+DzMTwJ/DxxMfHbmmzcwh6Q9i3V4zDosKZWTgRf1nGGvVU7bCfzWOi9vPRue\nZzP+ZwN3Jor1T9VOfzTwQ9XP5xEFXJK6ZB22DktK5GSiKD6EKEoXAB8EDqlOPw/4n8R/LPoc8FDg\n3URx+6PqPPPA54G3Ef/u9h+Ifx/LGpf7mupyXwQcD/wHsdflQ0RxnGe8R+ZTxH8FOxX42Vr+m6rv\nO4CPAv9UZdkE/CnwceI/Lf3qlNv/Icb/yvTZ1eVP81LgN1Y5XZLasA5bhyUlcjLw28C/AXevfvcL\njA+NnQu8svr5JOJf8d4D2EL8O967EcX1DsaH9d5MbAg2A/9OHJ5c6XJfX8tR/3ehzwH+rJavvkfm\nFJYW7Rur7zuIAn5UtfyrwO9VP9+J2DjML73p7EVsFEZeDTyf6R5DHD6UpC5Zh8esw2pt1uadNGx3\nAh5AvPqHKGbfqp1+RvX9s9XXldXyV4AjgRuIAv6x6vdvIwr8B4lDbWdPudx6ATwSeAexR2RLddkj\nczTzceBr1c8/ATwQ+LlqeX/g3sCltfPfnXHRb3Jdl7O88EtSF6zDza7LOqypbJ5V0hxwMXDslNO/\nW32/o/bzaHn0WF2cuLzFBpd7c+3nvyD2cryPmG/bOeVvvkccCqT6vmXK5QE8j/GGaJp6kb64uu7V\nzru4yumS1JZ1OFiH1dqmtc8idea7wEHAj1XLewP3X+dl3LP2908j5t6+sMbl1gvm/oz3hizUfn8j\nsF9t+VJifg/gSdVlruSfgV9nvFG5D7DPxHmuBvatLf8tsYE5rva7RzF+o8qhjPeoSFKXrMPBOqzW\nbJ5V0u3EYbVXAbuIj0ta6WOJFpn+iv8LxJs4LgHuCrwRuG2Ny61f1k7iDS4XAN+unfZe4CnV3z4c\neBOxV2IXsTG4qXYZ9cv76yrLp4CLqjyTR3RuJw5//mC1fCvxhpnnE2/EuRg4EbiqOv0Y4F+m3H5J\n2h3W4WAdlrRHmCcKY0YLxLu3mzgPPyJJ0myaxzqsPZx7npVN1hm0vyU+T3StN8P8MPBlxns/JGnW\nWIclSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZLG/h9W2iguB71KAgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fbaf0d14750>"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/doc/sample_trace.csv b/doc/sample_trace.csv
new file mode 100644
index 0000000..d486058
--- /dev/null
+++ b/doc/sample_trace.csv
@@ -0,0 +1,7 @@
+Time,host,type,file,protocol,response_code,response_time
+0.5536,62.242.88.10,GET,/index.html,HTTP/1.0,200,945
+0.748,62.242.88.10,GET,/robots.txt,HTTP/1.0,200,567
+0.956,67.131.107.5,GET,/admin.html,HTTP/1.0,401,340
+1.5455,217.140.96.140,GET,/razor.html,HTTP/1.1,404,120
+1.7304,216.139.185.45,GET,/mailman/admindb/ppwc,HTTP/1.1,200,1012
+2.0729,216.139.185.45,POST,/mailman/admindb/ppwc,HTTP/1.1,200,1103
diff --git a/doc/summary plots.ipynb b/doc/summary plots.ipynb
new file mode 100644
index 0000000..070588c
--- /dev/null
+++ b/doc/summary plots.ipynb
@@ -0,0 +1,165 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:24a7d057c19146437c8b7621d8326a347752d849dcc232a1136551636f8e9b81"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Summary plots"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`trappy.summary_plots()` shows all the relevant (for the power allocator governor) plots for a particular run."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First some hacks to allow importing trappy from here"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "sys.path.append(\"..\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "import trappy"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "For a description of `map_label` and `actor_order` see the compare runs notebook."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "map_label = {\"00000000,00000006\": \"big\", \"00000000,00000039\": \"LITTLE\"}\n",
+      "actor_order = [\"GPU\", \"big\", \"LITTLE\"]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "trappy.summary_plots(actor_order, map_label, path=\"./trace.txt\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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G8Hn79rLKeLiQuHd/ItzIsLu7fA/ZurXMvt66tW//xz/uf2zPnmTp0uFD6N5j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\nrAf20nhfj1o92erPfOYz/v2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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe2cfb228d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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qWI+YoUNtCP0999jG6f77bYXoeurMunU2snXa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\nyUAOISgKygAAoMR54bwXvMb7fOnnGjpjqNeYAOLTE52eiPU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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe2cf9fdc90>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Hmpqa9PDDD+sb3/iGmpub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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe2cf5c4810>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe2cee906d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+9Nf51/Tp0uuvWxFf+/aWy8oOf42hgwetF/JHH9n5c+/eVoVdpIjz505JsedP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\n262Q85JLrHvBBRfkskTzgQO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KGO4SEhISEhKZAUlolpDIIkixlCTE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hSgj/EKiMefPvFEvEQJIgeHzLVQ7+fHYUBmzSIqVUr0BbIsz/v3LDAVKcJexSLvAFxzfQ212NdC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\nrkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe2ce5fb910>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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A+cnOZgAA5q1KpZKXzrw0vAt6qBD97PFns65jXW5feXtuX3l7tq3flm3rttkF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\nHc0duXvt3bl77d1j5gcrgzn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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe2cfa1afd0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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90Ndx3qiZ6TOT3C/JLZJ8LeVMHktJtqUUyRcmeWJz3/OS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\nkqtSDsjIb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WyfmnIu8ieuc79XbLDf35G1Ss8Xun/DanXj6oz/3P5ZypPRh6QMrC/Ixr9T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\nc5iWJEmS2mRnWpIkSSqwMy1JkiT1gcO0SsPeVt7MN2/mmy+zz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jiDWIlemfbdQzxWkjSl\nOExLUu891rT9WeAbTfs+wPjViKeo1/I2bPq89wKXNu0bAf5c2H6a+Pd+vO7y4+k+jiQG3L1b3GZ1\n0zEBLAE+CZwNvBq4dZz7L37edGAh8M9Nt5+2luOTpNKyMy1J/XUxsfq7SdreDtgauIIYZms1j8MK\nnzMGvDBdfl1h/8XAe6gvjOwKbDzO111N9LFnFe5rM+oVjZOBLxEr2g+3+PzfAdsWtmsD8tXAu4Ef\nEX3wiRxHVEDObto/K30NSZpSXJmWpO5qtbpa3HcpsBsxhELUHN5K1D7OIl5IeB/RW64NrCcQw+ex\nwI8L93cyUY24Id32PuC1NJ5po+hJ4I3EiwY3IlakX0WsnN9ADNGtKh6k43pu0/dU+xo/IqobFwIv\nHef7r932Q8CvqNdc/il9/lziCYUkSZLUsU9RP5tHP8wmVq7X5jSiH90Li4hT40nSlGLNQ5LKq18d\n4qOI0+R9fILbnQC8qwdf/wXAUjzHtCRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkrSu/h8Q\nfxWMBproTgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe2cdfed790>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fe2cdb0df50>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/doc/trace.txt b/doc/trace.txt
new file mode 100644
index 0000000..d4d4b98
--- /dev/null
+++ b/doc/trace.txt
@@ -0,0 +1,811 @@
+version = 6
+CPU 0 is empty
+CPU 1 is empty
+CPU 2 is empty
+CPU 3 is empty
+CPU 5 is empty
+cpus=6
+     kworker/4:1-51    [004]  1583.765274: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68794 temp=68786
+     kworker/4:1-51    [004]  1583.767518: thermal_power_devfreq_get_power: type=devfreq freq=525000000 load=94 dynamic_power=1288 static_power=210
+     kworker/4:1-51    [004]  1583.767538: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={24 19} dynamic_power=250 static_power=178
+     kworker/4:1-51    [004]  1583.767553: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={36 49 48 7} dynamic_power=166 static_power=131
+     kworker/4:1-51    [004]  1583.767558: thermal_power_allocator_pid: thermal_zone_id=0 err=3225 err_integral=0 p=1612 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1583.767563: thermal_power_devfreq_limit: type=devfreq freq=525000000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1583.767572: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1583.767577: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1583.767580: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1583.767583: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1583.767585: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1583.767587: thermal_power_allocator: thermal_zone_id=0 req_power={1498 428 594} total_req_power=2520 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68775 delta_temperature=3225
+     kworker/4:1-51    [004]  1583.864813: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68786 temp=68781
+     kworker/4:1-51    [004]  1583.866201: thermal_power_devfreq_get_power: type=devfreq freq=525000000 load=100 dynamic_power=1364 static_power=210
+     kworker/4:1-51    [004]  1583.866223: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={28 11} dynamic_power=227 static_power=178
+     kworker/4:1-51    [004]  1583.866244: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={32 28 46 44} dynamic_power=178 static_power=131
+     kworker/4:1-51    [004]  1583.866250: thermal_power_allocator_pid: thermal_zone_id=0 err=3231 err_integral=0 p=1615 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1583.866256: thermal_power_devfreq_limit: type=devfreq freq=525000000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1583.866259: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1583.866264: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1583.866267: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1583.866270: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1583.866272: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1583.866275: thermal_power_allocator: thermal_zone_id=0 req_power={1574 405 618} total_req_power=2597 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68769 delta_temperature=3231
+     kworker/4:1-51    [004]  1583.965231: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68781 temp=68782
+     kworker/4:1-51    [004]  1583.966169: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=95 dynamic_power=1480 static_power=229
+     kworker/4:1-51    [004]  1583.966188: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={20 24} dynamic_power=256 static_power=178
+     kworker/4:1-51    [004]  1583.966203: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={28 44 42 33} dynamic_power=174 static_power=131
+     kworker/4:1-51    [004]  1583.966208: thermal_power_allocator_pid: thermal_zone_id=0 err=3197 err_integral=0 p=1598 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1583.966213: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1583.966216: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1583.966222: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1583.966224: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1583.966228: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1583.966229: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1583.966231: thermal_power_allocator: thermal_zone_id=0 req_power={1709 434 610} total_req_power=2753 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68803 delta_temperature=3197
+     kworker/4:1-51    [004]  1584.064386: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68782 temp=68835
+     kworker/4:1-51    [004]  1584.065497: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=88 dynamic_power=1364 static_power=229
+     kworker/4:1-51    [004]  1584.065514: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={29 29} dynamic_power=338 static_power=178
+     kworker/4:1-51    [004]  1584.065527: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={52 31 49 8} dynamic_power=166 static_power=131
+     kworker/4:1-51    [004]  1584.065532: thermal_power_allocator_pid: thermal_zone_id=0 err=3137 err_integral=0 p=1568 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.065537: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=2 power=1937
+     kworker/4:1-51    [004]  1584.067035: cdev_update:          type=devfreq target=2
+     kworker/4:1-51    [004]  1584.067045: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.067048: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.067052: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.067054: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.067056: thermal_power_allocator: thermal_zone_id=0 req_power={1593 516 594} total_req_power=2703 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68863 delta_temperature=3137
+     kworker/4:1-51    [004]  1584.164920: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68835 temp=68882
+     kworker/4:1-51    [004]  1584.166274: thermal_power_devfreq_get_power: type=devfreq freq=525000000 load=93 dynamic_power=1274 static_power=210
+     kworker/4:1-51    [004]  1584.166293: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={36 36} dynamic_power=419 static_power=178
+     kworker/4:1-51    [004]  1584.166307: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={38 39 34 55} dynamic_power=197 static_power=131
+     kworker/4:1-51    [004]  1584.166312: thermal_power_allocator_pid: thermal_zone_id=0 err=3114 err_integral=0 p=1557 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.166316: thermal_power_devfreq_limit: type=devfreq freq=525000000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1584.168033: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1584.168043: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.168047: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.168050: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.168052: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.168055: thermal_power_allocator: thermal_zone_id=0 req_power={1484 597 656} total_req_power=2737 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68886 delta_temperature=3114
+     kworker/4:1-51    [004]  1584.264925: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68882 temp=68907
+     kworker/4:1-51    [004]  1584.265839: thermal_power_devfreq_get_power: type=devfreq freq=525000000 load=93 dynamic_power=1278 static_power=210
+     kworker/4:1-51    [004]  1584.265855: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={24 26} dynamic_power=291 static_power=178
+     kworker/4:1-51    [004]  1584.265869: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={46 48 4 28} dynamic_power=149 static_power=131
+     kworker/4:1-51    [004]  1584.265874: thermal_power_allocator_pid: thermal_zone_id=0 err=3069 err_integral=0 p=1534 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.265879: thermal_power_devfreq_limit: type=devfreq freq=525000000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1584.265883: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1584.265887: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.265890: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.265893: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.265895: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.265897: thermal_power_allocator: thermal_zone_id=0 req_power={1488 469 560} total_req_power=2517 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68931 delta_temperature=3069
+     kworker/4:1-51    [004]  1584.374919: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68907 temp=68943
+     kworker/4:1-51    [004]  1584.376011: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=95 dynamic_power=1471 static_power=229
+     kworker/4:1-51    [004]  1584.376029: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={21 23} dynamic_power=256 static_power=178
+     kworker/4:1-51    [004]  1584.376045: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={42 64 20 16} dynamic_power=168 static_power=131
+     kworker/4:1-51    [004]  1584.376050: thermal_power_allocator_pid: thermal_zone_id=0 err=3051 err_integral=0 p=1525 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.376055: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1584.376058: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1584.376063: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.376066: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.376070: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.376071: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.376074: thermal_power_allocator: thermal_zone_id=0 req_power={1700 434 598} total_req_power=2732 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68949 delta_temperature=3051
+     kworker/4:1-51    [004]  1584.473635: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68943 temp=68959
+     kworker/4:1-51    [004]  1584.474422: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=89 dynamic_power=1380 static_power=229
+     kworker/4:1-51    [004]  1584.474446: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={33 34} dynamic_power=390 static_power=178
+     kworker/4:1-51    [004]  1584.474464: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={80 41 23 16} dynamic_power=190 static_power=131
+     kworker/4:1-51    [004]  1584.474472: thermal_power_allocator_pid: thermal_zone_id=0 err=3031 err_integral=0 p=1515 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.474478: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=2 power=1937
+     kworker/4:1-51    [004]  1584.477586: cdev_update:          type=devfreq target=2
+     kworker/4:1-51    [004]  1584.477602: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.477606: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.477611: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.477613: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.477616: thermal_power_allocator: thermal_zone_id=0 req_power={1609 568 642} total_req_power=2819 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68969 delta_temperature=3031
+     kworker/4:1-51    [004]  1584.575317: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68959 temp=68947
+     kworker/4:1-51    [004]  1584.576621: thermal_power_devfreq_get_power: type=devfreq freq=525000000 load=79 dynamic_power=1084 static_power=210
+     kworker/4:1-51    [004]  1584.576643: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={31 32} dynamic_power=367 static_power=178
+     kworker/4:1-51    [004]  1584.576662: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={56 60 10 30} dynamic_power=185 static_power=131
+     kworker/4:1-51    [004]  1584.576668: thermal_power_allocator_pid: thermal_zone_id=0 err=3053 err_integral=0 p=1526 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.576674: thermal_power_devfreq_limit: type=devfreq freq=525000000 cdev_state=2 power=1937
+     kworker/4:1-51    [004]  1584.576678: cdev_update:          type=devfreq target=2
+     kworker/4:1-51    [004]  1584.576684: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.576688: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.576691: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.576693: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.576696: thermal_power_allocator: thermal_zone_id=0 req_power={1294 545 632} total_req_power=2471 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68947 delta_temperature=3053
+     kworker/4:1-51    [004]  1584.674734: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68947 temp=68950
+     kworker/4:1-51    [004]  1584.675750: thermal_power_devfreq_get_power: type=devfreq freq=487500000 load=95 dynamic_power=1141 static_power=192
+     kworker/4:1-51    [004]  1584.675767: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={22 23} dynamic_power=262 static_power=178
+     kworker/4:1-51    [004]  1584.675781: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={29 25 43 47} dynamic_power=171 static_power=131
+     kworker/4:1-51    [004]  1584.675785: thermal_power_allocator_pid: thermal_zone_id=0 err=3055 err_integral=0 p=1527 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.675790: thermal_power_devfreq_limit: type=devfreq freq=487500000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1584.677314: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1584.677322: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.677325: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.677328: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.677330: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.677333: thermal_power_allocator: thermal_zone_id=0 req_power={1333 440 604} total_req_power=2377 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68945 delta_temperature=3055
+     kworker/4:1-51    [004]  1584.773997: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68950 temp=68952
+     kworker/4:1-51    [004]  1584.774742: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=229
+     kworker/4:1-51    [004]  1584.774761: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={44 7} dynamic_power=297 static_power=178
+     kworker/4:1-51    [004]  1584.774775: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={33 29 52 20} dynamic_power=159 static_power=131
+     kworker/4:1-51    [004]  1584.774780: thermal_power_allocator_pid: thermal_zone_id=0 err=3047 err_integral=0 p=1523 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.774784: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1584.774788: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1584.774792: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.774795: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.774798: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.774800: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.774802: thermal_power_allocator: thermal_zone_id=0 req_power={1776 475 580} total_req_power=2831 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68953 delta_temperature=3047
+     kworker/4:1-51    [004]  1584.873993: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68952 temp=68965
+     kworker/4:1-51    [004]  1584.874742: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=98 dynamic_power=1530 static_power=229
+     kworker/4:1-51    [004]  1584.874759: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={34 26} dynamic_power=349 static_power=178
+     kworker/4:1-51    [004]  1584.874775: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={38 36 93 8} dynamic_power=208 static_power=131
+     kworker/4:1-51    [004]  1584.874780: thermal_power_allocator_pid: thermal_zone_id=0 err=3025 err_integral=0 p=1512 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.874785: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1584.874788: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1584.874793: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.874795: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.874798: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.874800: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.874803: thermal_power_allocator: thermal_zone_id=0 req_power={1759 527 678} total_req_power=2964 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68975 delta_temperature=3025
+     kworker/4:1-51    [004]  1584.974544: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68965 temp=68978
+     kworker/4:1-51    [004]  1584.975854: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=93 dynamic_power=1448 static_power=229
+     kworker/4:1-51    [004]  1584.975874: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={33 31} dynamic_power=373 static_power=178
+     kworker/4:1-51    [004]  1584.975889: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={38 35 27 74} dynamic_power=207 static_power=131
+     kworker/4:1-51    [004]  1584.975894: thermal_power_allocator_pid: thermal_zone_id=0 err=3010 err_integral=0 p=1505 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1584.975900: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1584.975903: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1584.975909: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1584.975912: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1584.975915: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1584.975917: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1584.975919: thermal_power_allocator: thermal_zone_id=0 req_power={1677 551 676} total_req_power=2904 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68990 delta_temperature=3010
+     kworker/4:1-51    [004]  1585.075093: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68978 temp=68989
+     kworker/4:1-51    [004]  1585.076259: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1496 static_power=229
+     kworker/4:1-51    [004]  1585.076277: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={22 22} dynamic_power=256 static_power=178
+     kworker/4:1-51    [004]  1585.076291: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={22 24 68 24} dynamic_power=164 static_power=131
+     kworker/4:1-51    [004]  1585.076295: thermal_power_allocator_pid: thermal_zone_id=0 err=2993 err_integral=0 p=1496 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1585.076300: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1585.076303: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1585.076308: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1585.076310: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.076313: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1585.076315: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.076317: thermal_power_allocator: thermal_zone_id=0 req_power={1725 434 590} total_req_power=2749 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=69007 delta_temperature=2993
+     kworker/4:1-51    [004]  1585.174092: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68989 temp=69007
+     kworker/4:1-51    [004]  1585.175033: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1498 static_power=237
+     kworker/4:1-51    [004]  1585.175052: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={25 26} dynamic_power=297 static_power=185
+     kworker/4:1-51    [004]  1585.175067: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={48 30 7 67} dynamic_power=180 static_power=137
+     kworker/4:1-51    [004]  1585.175072: thermal_power_allocator_pid: thermal_zone_id=0 err=2994 err_integral=0 p=1497 i=0 d=0 output=3908
+     kworker/4:1-51    [004]  1585.175078: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1585.175081: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1585.175087: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1351
+     kworker/4:1-51    [004]  1585.175090: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.175093: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1585.175095: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.175097: thermal_power_allocator: thermal_zone_id=0 req_power={1735 482 634} total_req_power=2851 granted_power={1944 1351 613} total_granted_power=3908 power_range=3908 max_allocatable_power=3908 current_temperature=69006 delta_temperature=2994
+     kworker/4:1-51    [004]  1585.274456: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69007 temp=69008
+     kworker/4:1-51    [004]  1585.275473: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=90 dynamic_power=1396 static_power=237
+     kworker/4:1-51    [004]  1585.275502: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={41 41} dynamic_power=478 static_power=185
+     kworker/4:1-51    [004]  1585.275521: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={92 44 11 17} dynamic_power=195 static_power=137
+     kworker/4:1-51    [004]  1585.275527: thermal_power_allocator_pid: thermal_zone_id=0 err=2992 err_integral=0 p=1496 i=0 d=0 output=3908
+     kworker/4:1-51    [004]  1585.275532: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=2 power=1944
+     kworker/4:1-51    [004]  1585.277245: cdev_update:          type=devfreq target=2
+     kworker/4:1-51    [004]  1585.277260: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1351
+     kworker/4:1-51    [004]  1585.277263: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.277267: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1585.277269: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.277271: thermal_power_allocator: thermal_zone_id=0 req_power={1633 663 664} total_req_power=2960 granted_power={1944 1351 613} total_granted_power=3908 power_range=3908 max_allocatable_power=3908 current_temperature=69008 delta_temperature=2992
+     kworker/4:1-51    [004]  1585.385163: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69008 temp=68996
+     kworker/4:1-51    [004]  1585.386673: thermal_power_devfreq_get_power: type=devfreq freq=525000000 load=100 dynamic_power=1364 static_power=210
+     kworker/4:1-51    [004]  1585.386697: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={22 13} dynamic_power=204 static_power=178
+     kworker/4:1-51    [004]  1585.386716: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={25 25 63 32} dynamic_power=172 static_power=131
+     kworker/4:1-51    [004]  1585.386721: thermal_power_allocator_pid: thermal_zone_id=0 err=3032 err_integral=0 p=1516 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1585.386727: thermal_power_devfreq_limit: type=devfreq freq=525000000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1585.388276: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1585.388286: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1585.388289: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.388292: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1585.388295: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.388297: thermal_power_allocator: thermal_zone_id=0 req_power={1574 382 606} total_req_power=2562 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=68968 delta_temperature=3032
+     kworker/4:1-51    [004]  1585.484008: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68996 temp=68990
+     kworker/4:1-51    [004]  1585.484755: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=229
+     kworker/4:1-51    [004]  1585.484773: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={46 54} dynamic_power=583 static_power=178
+     kworker/4:1-51    [004]  1585.484787: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={69 45 18 7} dynamic_power=165 static_power=131
+     kworker/4:1-51    [004]  1585.484793: thermal_power_allocator_pid: thermal_zone_id=0 err=2997 err_integral=0 p=1498 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1585.484797: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1937
+     kworker/4:1-51    [004]  1585.484801: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1585.484805: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1344
+     kworker/4:1-51    [004]  1585.484808: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.484811: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=607
+     kworker/4:1-51    [004]  1585.484813: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.484816: thermal_power_allocator: thermal_zone_id=0 req_power={1776 761 592} total_req_power=3129 granted_power={1937 1344 607} total_granted_power=3888 power_range=3888 max_allocatable_power=3888 current_temperature=69003 delta_temperature=2997
+     kworker/4:1-51    [004]  1585.584353: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=68990 temp=69007
+     kworker/4:1-51    [004]  1585.585611: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=98 dynamic_power=1527 static_power=237
+     kworker/4:1-51    [004]  1585.585627: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={52 33} dynamic_power=495 static_power=185
+     kworker/4:1-51    [004]  1585.585641: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={41 37 40 7} dynamic_power=148 static_power=137
+     kworker/4:1-51    [004]  1585.585645: thermal_power_allocator_pid: thermal_zone_id=0 err=2997 err_integral=0 p=1498 i=0 d=0 output=3908
+     kworker/4:1-51    [004]  1585.585650: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1585.585653: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1585.585658: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1351
+     kworker/4:1-51    [004]  1585.585663: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.585666: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1585.585668: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.585671: thermal_power_allocator: thermal_zone_id=0 req_power={1764 680 570} total_req_power=3014 granted_power={1944 1351 613} total_granted_power=3908 power_range=3908 max_allocatable_power=3908 current_temperature=69003 delta_temperature=2997
+     kworker/4:1-51    [004]  1585.683966: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69007 temp=69026
+     kworker/4:1-51    [004]  1585.685239: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1585.685256: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={49 55} dynamic_power=606 static_power=185
+     kworker/4:1-51    [004]  1585.685270: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={42 39 6 7} dynamic_power=111 static_power=137
+     kworker/4:1-51    [004]  1585.685275: thermal_power_allocator_pid: thermal_zone_id=0 err=2948 err_integral=0 p=1474 i=0 d=0 output=3908
+     kworker/4:1-51    [004]  1585.685279: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1585.685283: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1585.685288: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1351
+     kworker/4:1-51    [004]  1585.685291: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.685294: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1585.685296: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.685298: thermal_power_allocator: thermal_zone_id=0 req_power={1784 791 496} total_req_power=3071 granted_power={1944 1351 613} total_granted_power=3908 power_range=3908 max_allocatable_power=3908 current_temperature=69052 delta_temperature=2948
+     kworker/4:1-51    [004]  1585.784021: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69026 temp=69074
+     kworker/4:1-51    [004]  1585.785277: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1585.785294: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={36 60} dynamic_power=559 static_power=185
+     kworker/4:1-51    [004]  1585.785308: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={44 39 8 45} dynamic_power=161 static_power=137
+     kworker/4:1-51    [004]  1585.785313: thermal_power_allocator_pid: thermal_zone_id=0 err=2897 err_integral=0 p=1448 i=0 d=0 output=3908
+     kworker/4:1-51    [004]  1585.785318: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1585.785321: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1585.785326: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1351
+     kworker/4:1-51    [004]  1585.785329: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.785332: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1585.785334: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.785336: thermal_power_allocator: thermal_zone_id=0 req_power={1784 744 596} total_req_power=3124 granted_power={1944 1351 613} total_granted_power=3908 power_range=3908 max_allocatable_power=3908 current_temperature=69103 delta_temperature=2897
+     kworker/4:1-51    [004]  1585.883996: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69074 temp=69113
+     kworker/4:1-51    [004]  1585.885081: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=99 dynamic_power=1546 static_power=237
+     kworker/4:1-51    [004]  1585.885100: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={45 21} dynamic_power=384 static_power=185
+     kworker/4:1-51    [004]  1585.885116: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={31 26 6 47} dynamic_power=130 static_power=137
+     kworker/4:1-51    [004]  1585.885122: thermal_power_allocator_pid: thermal_zone_id=0 err=2884 err_integral=0 p=1442 i=0 d=0 output=3908
+     kworker/4:1-51    [004]  1585.885126: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1585.885129: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1585.885135: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1351
+     kworker/4:1-51    [004]  1585.885138: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.885141: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1585.885143: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.885146: thermal_power_allocator: thermal_zone_id=0 req_power={1783 569 534} total_req_power=2886 granted_power={1944 1351 613} total_granted_power=3908 power_range=3908 max_allocatable_power=3908 current_temperature=69116 delta_temperature=2884
+     kworker/4:1-51    [004]  1585.984555: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69113 temp=69131
+     kworker/4:1-51    [004]  1585.985671: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1494 static_power=237
+     kworker/4:1-51    [004]  1585.985688: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={65 54} dynamic_power=693 static_power=185
+     kworker/4:1-51    [004]  1585.985704: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={61 57 17 6} dynamic_power=167 static_power=137
+     kworker/4:1-51    [004]  1585.985709: thermal_power_allocator_pid: thermal_zone_id=0 err=2849 err_integral=0 p=1424 i=0 d=0 output=3908
+     kworker/4:1-51    [004]  1585.985713: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1585.985716: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1585.985721: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1351
+     kworker/4:1-51    [004]  1585.985724: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1585.985727: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1585.985729: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1585.985731: thermal_power_allocator: thermal_zone_id=0 req_power={1731 878 608} total_req_power=3217 granted_power={1944 1351 613} total_granted_power=3908 power_range=3908 max_allocatable_power=3908 current_temperature=69151 delta_temperature=2849
+     kworker/4:1-51    [004]  1586.085038: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69131 temp=69161
+     kworker/4:1-51    [004]  1586.086494: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1586.086513: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={22 22} dynamic_power=256 static_power=185
+     kworker/4:1-51    [004]  1586.086529: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={40 24 57 19} dynamic_power=166 static_power=137
+     kworker/4:1-51    [004]  1586.086535: thermal_power_allocator_pid: thermal_zone_id=0 err=2847 err_integral=0 p=1423 i=0 d=0 output=3908
+     kworker/4:1-51    [004]  1586.086541: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.086545: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.086551: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1351
+     kworker/4:1-51    [004]  1586.086554: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.086557: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1586.086559: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.086561: thermal_power_allocator: thermal_zone_id=0 req_power={1784 441 606} total_req_power=2831 granted_power={1944 1351 613} total_granted_power=3908 power_range=3908 max_allocatable_power=3908 current_temperature=69153 delta_temperature=2847
+     kworker/4:1-51    [004]  1586.184518: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69161 temp=69155
+     kworker/4:1-51    [004]  1586.185276: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1493 static_power=237
+     kworker/4:1-51    [004]  1586.185295: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={35 46} dynamic_power=472 static_power=185
+     kworker/4:1-51    [004]  1586.185310: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={57 38 38 10} dynamic_power=170 static_power=137
+     kworker/4:1-51    [004]  1586.185316: thermal_power_allocator_pid: thermal_zone_id=0 err=2817 err_integral=0 p=1408 i=0 d=0 output=3908
+     kworker/4:1-51    [004]  1586.185320: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.185324: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.185330: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1351
+     kworker/4:1-51    [004]  1586.185332: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.185336: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1586.185337: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.185341: thermal_power_allocator: thermal_zone_id=0 req_power={1730 657 614} total_req_power=3001 granted_power={1944 1351 613} total_granted_power=3908 power_range=3908 max_allocatable_power=3908 current_temperature=69183 delta_temperature=2817
+     kworker/4:1-51    [004]  1586.284676: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69155 temp=69203
+     kworker/4:1-51    [004]  1586.285753: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1492 static_power=237
+     kworker/4:1-51    [004]  1586.285769: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={57 48} dynamic_power=612 static_power=185
+     kworker/4:1-51    [004]  1586.285783: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={40 36 17 6} dynamic_power=117 static_power=137
+     kworker/4:1-51    [004]  1586.285789: thermal_power_allocator_pid: thermal_zone_id=0 err=2777 err_integral=0 p=1388 i=0 d=0 output=3888
+     kworker/4:1-51    [004]  1586.285793: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.285796: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.285801: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1332
+     kworker/4:1-51    [004]  1586.285805: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.285807: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1586.285809: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.285812: thermal_power_allocator: thermal_zone_id=0 req_power={1729 797 508} total_req_power=3034 granted_power={1944 1332 613} total_granted_power=3889 power_range=3888 max_allocatable_power=3908 current_temperature=69223 delta_temperature=2777
+     kworker/4:1-51    [004]  1586.384144: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69203 temp=69229
+     kworker/4:1-51    [004]  1586.385667: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=99 dynamic_power=1539 static_power=237
+     kworker/4:1-51    [004]  1586.385686: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={29 41} dynamic_power=408 static_power=185
+     kworker/4:1-51    [004]  1586.385700: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={56 25 22 6} dynamic_power=129 static_power=137
+     kworker/4:1-51    [004]  1586.385705: thermal_power_allocator_pid: thermal_zone_id=0 err=2759 err_integral=0 p=1379 i=0 d=0 output=3879
+     kworker/4:1-51    [004]  1586.385709: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.385713: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.385718: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1323
+     kworker/4:1-51    [004]  1586.385721: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.385724: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1586.385726: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.385728: thermal_power_allocator: thermal_zone_id=0 req_power={1776 593 532} total_req_power=2901 granted_power={1944 1323 613} total_granted_power=3880 power_range=3879 max_allocatable_power=3908 current_temperature=69241 delta_temperature=2759
+     kworker/4:1-51    [004]  1586.483938: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69229 temp=69269
+     kworker/4:1-51    [004]  1586.484670: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=97 dynamic_power=1504 static_power=237
+     kworker/4:1-51    [004]  1586.484687: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={27 29} dynamic_power=326 static_power=185
+     kworker/4:1-51    [004]  1586.484702: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={44 29 66 7} dynamic_power=173 static_power=137
+     kworker/4:1-51    [004]  1586.484707: thermal_power_allocator_pid: thermal_zone_id=0 err=2707 err_integral=0 p=1353 i=0 d=0 output=3853
+     kworker/4:1-51    [004]  1586.484711: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.484715: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.484720: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1297
+     kworker/4:1-51    [004]  1586.484723: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.484726: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1586.484728: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.484730: thermal_power_allocator: thermal_zone_id=0 req_power={1741 511 620} total_req_power=2872 granted_power={1944 1297 613} total_granted_power=3854 power_range=3853 max_allocatable_power=3908 current_temperature=69293 delta_temperature=2707
+     kworker/4:1-51    [004]  1586.583667: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69269 temp=69294
+     kworker/4:1-51    [004]  1586.584425: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1494 static_power=237
+     kworker/4:1-51    [004]  1586.584443: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={38 53} dynamic_power=530 static_power=185
+     kworker/4:1-51    [004]  1586.584458: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={42 40 62 12} dynamic_power=185 static_power=137
+     kworker/4:1-51    [004]  1586.584463: thermal_power_allocator_pid: thermal_zone_id=0 err=2695 err_integral=0 p=1347 i=0 d=0 output=3847
+     kworker/4:1-51    [004]  1586.584467: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.584471: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.584476: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1292
+     kworker/4:1-51    [004]  1586.584478: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.584481: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1586.584483: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.584485: thermal_power_allocator: thermal_zone_id=0 req_power={1731 715 644} total_req_power=3090 granted_power={1944 1292 613} total_granted_power=3849 power_range=3847 max_allocatable_power=3908 current_temperature=69305 delta_temperature=2695
+     kworker/4:1-51    [004]  1586.684805: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69294 temp=69313
+     kworker/4:1-51    [004]  1586.686577: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1498 static_power=237
+     kworker/4:1-51    [004]  1586.686594: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={20 21} dynamic_power=239 static_power=185
+     kworker/4:1-51    [004]  1586.686608: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={26 23 30 66} dynamic_power=172 static_power=137
+     kworker/4:1-51    [004]  1586.686613: thermal_power_allocator_pid: thermal_zone_id=0 err=2683 err_integral=0 p=1341 i=0 d=0 output=3841
+     kworker/4:1-51    [004]  1586.686617: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.686621: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.686626: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1285
+     kworker/4:1-51    [004]  1586.686629: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.686632: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1586.686634: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.686636: thermal_power_allocator: thermal_zone_id=0 req_power={1735 424 618} total_req_power=2777 granted_power={1944 1285 613} total_granted_power=3842 power_range=3841 max_allocatable_power=3908 current_temperature=69317 delta_temperature=2683
+     kworker/4:1-51    [004]  1586.783948: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69313 temp=69335
+     kworker/4:1-51    [004]  1586.784867: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1495 static_power=237
+     kworker/4:1-51    [004]  1586.784885: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={25 66} dynamic_power=530 static_power=185
+     kworker/4:1-51    [004]  1586.784900: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={29 26 6 3} dynamic_power=76 static_power=137
+     kworker/4:1-51    [004]  1586.784905: thermal_power_allocator_pid: thermal_zone_id=0 err=2657 err_integral=0 p=1328 i=0 d=0 output=3828
+     kworker/4:1-51    [004]  1586.784910: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.784913: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.784918: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1280
+     kworker/4:1-51    [004]  1586.784921: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.784924: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=604
+     kworker/4:1-51    [004]  1586.784926: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.784928: thermal_power_allocator: thermal_zone_id=0 req_power={1732 715 426} total_req_power=2873 granted_power={1944 1280 604} total_granted_power=3828 power_range=3828 max_allocatable_power=3908 current_temperature=69343 delta_temperature=2657
+     kworker/4:1-51    [004]  1586.884422: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69335 temp=69344
+     kworker/4:1-51    [004]  1586.885532: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=97 dynamic_power=1509 static_power=237
+     kworker/4:1-51    [004]  1586.885548: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={46 77} dynamic_power=717 static_power=185
+     kworker/4:1-51    [004]  1586.885563: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={55 45 10 5} dynamic_power=136 static_power=137
+     kworker/4:1-51    [004]  1586.885568: thermal_power_allocator_pid: thermal_zone_id=0 err=2669 err_integral=0 p=1334 i=0 d=0 output=3834
+     kworker/4:1-51    [004]  1586.885573: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.885576: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.885581: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1278
+     kworker/4:1-51    [004]  1586.885584: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.885587: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1586.885589: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.885591: thermal_power_allocator: thermal_zone_id=0 req_power={1746 902 546} total_req_power=3194 granted_power={1944 1278 613} total_granted_power=3835 power_range=3834 max_allocatable_power=3908 current_temperature=69331 delta_temperature=2669
+     kworker/4:1-51    [004]  1586.984772: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69344 temp=69360
+     kworker/4:1-51    [004]  1586.986525: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1586.986543: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={23 43} dynamic_power=384 static_power=185
+     kworker/4:1-51    [004]  1586.986558: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={31 26 34 4} dynamic_power=113 static_power=137
+     kworker/4:1-51    [004]  1586.986563: thermal_power_allocator_pid: thermal_zone_id=0 err=2622 err_integral=0 p=1311 i=0 d=0 output=3811
+     kworker/4:1-51    [004]  1586.986567: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1586.986571: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1586.986575: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1256
+     kworker/4:1-51    [004]  1586.986578: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1586.986581: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1586.986583: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1586.986585: thermal_power_allocator: thermal_zone_id=0 req_power={1784 569 500} total_req_power=2853 granted_power={1944 1256 613} total_granted_power=3813 power_range=3811 max_allocatable_power=3908 current_temperature=69378 delta_temperature=2622
+     kworker/4:1-51    [004]  1587.084057: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69360 temp=69414
+     kworker/4:1-51    [004]  1587.085203: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1499 static_power=237
+     kworker/4:1-51    [004]  1587.085222: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={26 26} dynamic_power=303 static_power=185
+     kworker/4:1-51    [004]  1587.085237: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={30 27 62 34} dynamic_power=182 static_power=137
+     kworker/4:1-51    [004]  1587.085243: thermal_power_allocator_pid: thermal_zone_id=0 err=2568 err_integral=0 p=1284 i=0 d=0 output=3784
+     kworker/4:1-51    [004]  1587.085247: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.085251: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.085256: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1229
+     kworker/4:1-51    [004]  1587.085259: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.085262: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1587.085264: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.085266: thermal_power_allocator: thermal_zone_id=0 req_power={1736 488 638} total_req_power=2862 granted_power={1944 1229 613} total_granted_power=3786 power_range=3784 max_allocatable_power=3908 current_temperature=69432 delta_temperature=2568
+     kworker/4:1-51    [004]  1587.184112: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69414 temp=69455
+     kworker/4:1-51    [004]  1587.185014: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=97 dynamic_power=1503 static_power=237
+     kworker/4:1-51    [004]  1587.185030: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={76 40} dynamic_power=676 static_power=185
+     kworker/4:1-51    [004]  1587.185044: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={46 45 19 10} dynamic_power=142 static_power=137
+     kworker/4:1-51    [004]  1587.185049: thermal_power_allocator_pid: thermal_zone_id=0 err=2533 err_integral=0 p=1266 i=0 d=0 output=3766
+     kworker/4:1-51    [004]  1587.185053: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.185056: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.185062: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1211
+     kworker/4:1-51    [004]  1587.185065: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.185067: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1587.185069: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.185072: thermal_power_allocator: thermal_zone_id=0 req_power={1740 861 558} total_req_power=3159 granted_power={1944 1211 613} total_granted_power=3768 power_range=3766 max_allocatable_power=3908 current_temperature=69467 delta_temperature=2533
+     kworker/4:1-51    [004]  1587.285312: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69455 temp=69477
+     kworker/4:1-51    [004]  1587.286832: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=99 dynamic_power=1532 static_power=237
+     kworker/4:1-51    [004]  1587.286849: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={61 26} dynamic_power=507 static_power=185
+     kworker/4:1-51    [004]  1587.286865: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={32 22 7 5} dynamic_power=78 static_power=137
+     kworker/4:1-51    [004]  1587.286870: thermal_power_allocator_pid: thermal_zone_id=0 err=2533 err_integral=0 p=1266 i=0 d=0 output=3766
+     kworker/4:1-51    [004]  1587.286875: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.286879: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.286885: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1225
+     kworker/4:1-51    [004]  1587.286888: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.286890: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=598
+     kworker/4:1-51    [004]  1587.286892: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.286895: thermal_power_allocator: thermal_zone_id=0 req_power={1769 692 430} total_req_power=2891 granted_power={1944 1225 598} total_granted_power=3767 power_range=3766 max_allocatable_power=3908 current_temperature=69467 delta_temperature=2533
+     kworker/4:1-51    [004]  1587.384142: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69477 temp=69480
+     kworker/4:1-51    [004]  1587.389791: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1587.389811: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={67 55} dynamic_power=711 static_power=185
+     kworker/4:1-51    [004]  1587.389826: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={59 56 8 6} dynamic_power=153 static_power=137
+     kworker/4:1-51    [004]  1587.389831: thermal_power_allocator_pid: thermal_zone_id=0 err=2506 err_integral=0 p=1253 i=0 d=0 output=3753
+     kworker/4:1-51    [004]  1587.389836: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.389840: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.389845: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1197
+     kworker/4:1-51    [004]  1587.389847: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.389850: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1587.389852: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.389854: thermal_power_allocator: thermal_zone_id=0 req_power={1784 896 580} total_req_power=3260 granted_power={1944 1197 613} total_granted_power=3754 power_range=3753 max_allocatable_power=3908 current_temperature=69494 delta_temperature=2506
+     kworker/4:1-51    [004]  1587.485247: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69480 temp=69498
+     kworker/4:1-51    [004]  1587.487099: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1587.487118: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={67 24} dynamic_power=530 static_power=185
+     kworker/4:1-51    [004]  1587.487136: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={33 29 9 6} dynamic_power=91 static_power=137
+     kworker/4:1-51    [004]  1587.487141: thermal_power_allocator_pid: thermal_zone_id=0 err=2495 err_integral=0 p=1247 i=0 d=0 output=3747
+     kworker/4:1-51    [004]  1587.487146: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.487150: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.487156: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1203
+     kworker/4:1-51    [004]  1587.487159: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.487161: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=601
+     kworker/4:1-51    [004]  1587.487163: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.487166: thermal_power_allocator: thermal_zone_id=0 req_power={1784 715 456} total_req_power=2955 granted_power={1944 1203 601} total_granted_power=3748 power_range=3747 max_allocatable_power=3908 current_temperature=69505 delta_temperature=2495
+     kworker/4:1-51    [004]  1587.584120: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69498 temp=69513
+     kworker/4:1-51    [004]  1587.585342: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=97 dynamic_power=1511 static_power=237
+     kworker/4:1-51    [004]  1587.585358: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={80 49} dynamic_power=752 static_power=185
+     kworker/4:1-51    [004]  1587.585373: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={54 51 9 6} dynamic_power=142 static_power=137
+     kworker/4:1-51    [004]  1587.585378: thermal_power_allocator_pid: thermal_zone_id=0 err=2465 err_integral=0 p=1232 i=0 d=0 output=3732
+     kworker/4:1-51    [004]  1587.585383: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.585387: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.585391: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1177
+     kworker/4:1-51    [004]  1587.585394: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.585397: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1587.585399: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.585401: thermal_power_allocator: thermal_zone_id=0 req_power={1748 937 558} total_req_power=3243 granted_power={1944 1177 613} total_granted_power=3734 power_range=3732 max_allocatable_power=3908 current_temperature=69535 delta_temperature=2465
+     kworker/4:1-51    [004]  1587.684852: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69513 temp=69556
+     kworker/4:1-51    [004]  1587.686230: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1587.686246: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={44 26} dynamic_power=408 static_power=185
+     kworker/4:1-51    [004]  1587.686261: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={34 27 8 34} dynamic_power=122 static_power=137
+     kworker/4:1-51    [004]  1587.686266: thermal_power_allocator_pid: thermal_zone_id=0 err=2415 err_integral=0 p=1207 i=0 d=0 output=3707
+     kworker/4:1-51    [004]  1587.686270: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.686273: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.686278: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1152
+     kworker/4:1-51    [004]  1587.686281: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.686284: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1587.686286: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.686288: thermal_power_allocator: thermal_zone_id=0 req_power={1784 593 518} total_req_power=2895 granted_power={1944 1152 613} total_granted_power=3709 power_range=3707 max_allocatable_power=3908 current_temperature=69585 delta_temperature=2415
+     kworker/4:1-51    [004]  1587.783607: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69556 temp=69607
+     kworker/4:1-51    [004]  1587.784344: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=97 dynamic_power=1501 static_power=237
+     kworker/4:1-51    [004]  1587.784362: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={61 34} dynamic_power=553 static_power=185
+     kworker/4:1-51    [004]  1587.784376: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={38 34 19 39} dynamic_power=154 static_power=137
+     kworker/4:1-51    [004]  1587.784381: thermal_power_allocator_pid: thermal_zone_id=0 err=2381 err_integral=0 p=1190 i=0 d=0 output=3690
+     kworker/4:1-51    [004]  1587.784386: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.784390: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.784394: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1135
+     kworker/4:1-51    [004]  1587.784397: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.784400: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1587.784402: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.784405: thermal_power_allocator: thermal_zone_id=0 req_power={1738 738 582} total_req_power=3058 granted_power={1944 1135 613} total_granted_power=3692 power_range=3690 max_allocatable_power=3908 current_temperature=69619 delta_temperature=2381
+     kworker/4:1-51    [004]  1587.883565: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69607 temp=69647
+     kworker/4:1-51    [004]  1587.884279: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1587.884296: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={29 37} dynamic_power=384 static_power=185
+     kworker/4:1-51    [004]  1587.884311: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={36 29 79 12} dynamic_power=185 static_power=137
+     kworker/4:1-51    [004]  1587.884315: thermal_power_allocator_pid: thermal_zone_id=0 err=2341 err_integral=0 p=1170 i=0 d=0 output=3670
+     kworker/4:1-51    [004]  1587.884319: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.884323: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.884328: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1114
+     kworker/4:1-51    [004]  1587.884330: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.884334: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1587.884336: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.884338: thermal_power_allocator: thermal_zone_id=0 req_power={1784 569 644} total_req_power=2997 granted_power={1944 1114 613} total_granted_power=3671 power_range=3670 max_allocatable_power=3908 current_temperature=69659 delta_temperature=2341
+     kworker/4:1-51    [004]  1587.985047: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69647 temp=69657
+     kworker/4:1-51    [004]  1587.986786: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=97 dynamic_power=1502 static_power=237
+     kworker/4:1-51    [004]  1587.986805: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={21 36} dynamic_power=332 static_power=185
+     kworker/4:1-51    [004]  1587.986819: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={25 23 32 20} dynamic_power=119 static_power=137
+     kworker/4:1-51    [004]  1587.986824: thermal_power_allocator_pid: thermal_zone_id=0 err=2338 err_integral=0 p=1169 i=0 d=0 output=3669
+     kworker/4:1-51    [004]  1587.986829: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1587.986832: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1587.986837: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1114
+     kworker/4:1-51    [004]  1587.986840: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1587.986843: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1587.986845: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1587.986847: thermal_power_allocator: thermal_zone_id=0 req_power={1739 517 512} total_req_power=2768 granted_power={1944 1114 613} total_granted_power=3671 power_range=3669 max_allocatable_power=3908 current_temperature=69662 delta_temperature=2338
+     kworker/4:1-51    [004]  1588.084225: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69657 temp=69663
+     kworker/4:1-51    [004]  1588.085970: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=95 dynamic_power=1483 static_power=237
+     kworker/4:1-51    [004]  1588.085988: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={25 26} dynamic_power=297 static_power=185
+     kworker/4:1-51    [004]  1588.086003: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={30 28 64 35} dynamic_power=186 static_power=137
+     kworker/4:1-51    [004]  1588.086009: thermal_power_allocator_pid: thermal_zone_id=0 err=2332 err_integral=0 p=1166 i=0 d=0 output=3666
+     kworker/4:1-51    [004]  1588.086014: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1588.086017: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1588.086023: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1111
+     kworker/4:1-51    [004]  1588.086025: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.086029: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.086031: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.086033: thermal_power_allocator: thermal_zone_id=0 req_power={1720 482 646} total_req_power=2848 granted_power={1944 1111 613} total_granted_power=3668 power_range=3666 max_allocatable_power=3908 current_temperature=69668 delta_temperature=2332
+     kworker/4:1-51    [004]  1588.184851: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69663 temp=69685
+     kworker/4:1-51    [004]  1588.186350: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1497 static_power=237
+     kworker/4:1-51    [004]  1588.186368: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={37 60} dynamic_power=565 static_power=185
+     kworker/4:1-51    [004]  1588.186383: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={52 48 11 31} dynamic_power=168 static_power=137
+     kworker/4:1-51    [004]  1588.186388: thermal_power_allocator_pid: thermal_zone_id=0 err=2295 err_integral=0 p=1147 i=0 d=0 output=3647
+     kworker/4:1-51    [004]  1588.186393: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1588.186396: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1588.186402: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1092
+     kworker/4:1-51    [004]  1588.186404: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.186407: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.186409: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.186412: thermal_power_allocator: thermal_zone_id=0 req_power={1734 750 610} total_req_power=3094 granted_power={1944 1092 613} total_granted_power=3649 power_range=3647 max_allocatable_power=3908 current_temperature=69705 delta_temperature=2295
+     kworker/4:1-51    [004]  1588.285116: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69685 temp=69710
+     kworker/4:1-51    [004]  1588.286371: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1588.286389: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={22 21} dynamic_power=250 static_power=185
+     kworker/4:1-51    [004]  1588.286403: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={28 23 10 83} dynamic_power=171 static_power=137
+     kworker/4:1-51    [004]  1588.286408: thermal_power_allocator_pid: thermal_zone_id=0 err=2274 err_integral=0 p=1137 i=0 d=0 output=3637
+     kworker/4:1-51    [004]  1588.286413: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1588.286416: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1588.286421: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1082
+     kworker/4:1-51    [004]  1588.286424: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.286427: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.286429: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.286431: thermal_power_allocator: thermal_zone_id=0 req_power={1784 435 616} total_req_power=2835 granted_power={1944 1082 613} total_granted_power=3639 power_range=3637 max_allocatable_power=3908 current_temperature=69726 delta_temperature=2274
+     kworker/4:1-51    [004]  1588.383583: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69710 temp=69733
+     kworker/4:1-51    [004]  1588.384530: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=93 dynamic_power=1440 static_power=237
+     kworker/4:1-51    [004]  1588.384549: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={23 24} dynamic_power=274 static_power=185
+     kworker/4:1-51    [004]  1588.384563: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={29 25 54 43} dynamic_power=179 static_power=137
+     kworker/4:1-51    [004]  1588.384568: thermal_power_allocator_pid: thermal_zone_id=0 err=2272 err_integral=0 p=1136 i=0 d=0 output=3636
+     kworker/4:1-51    [004]  1588.384572: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1588.384576: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1588.384581: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1080
+     kworker/4:1-51    [004]  1588.384584: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.384587: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.384589: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.384591: thermal_power_allocator: thermal_zone_id=0 req_power={1677 459 632} total_req_power=2768 granted_power={1944 1080 613} total_granted_power=3637 power_range=3636 max_allocatable_power=3908 current_temperature=69728 delta_temperature=2272
+     kworker/4:1-51    [004]  1588.485108: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69733 temp=69714
+     kworker/4:1-51    [004]  1588.486368: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1496 static_power=237
+     kworker/4:1-51    [004]  1588.486386: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={43 47} dynamic_power=524 static_power=185
+     kworker/4:1-51    [004]  1588.486400: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={45 44 84 16} dynamic_power=224 static_power=137
+     kworker/4:1-51    [004]  1588.486405: thermal_power_allocator_pid: thermal_zone_id=0 err=2288 err_integral=0 p=1144 i=0 d=0 output=3644
+     kworker/4:1-51    [004]  1588.486410: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1588.486413: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1588.486418: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1088
+     kworker/4:1-51    [004]  1588.486421: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.486424: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.486426: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.486428: thermal_power_allocator: thermal_zone_id=0 req_power={1733 709 722} total_req_power=3164 granted_power={1944 1088 613} total_granted_power=3645 power_range=3644 max_allocatable_power=3908 current_temperature=69712 delta_temperature=2288
+     kworker/4:1-51    [004]  1588.584043: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69714 temp=69700
+     kworker/4:1-51    [004]  1588.585946: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=96 dynamic_power=1486 static_power=237
+     kworker/4:1-51    [004]  1588.585963: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={20 22} dynamic_power=244 static_power=185
+     kworker/4:1-51    [004]  1588.585977: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={26 24 7 83} dynamic_power=166 static_power=137
+     kworker/4:1-51    [004]  1588.585982: thermal_power_allocator_pid: thermal_zone_id=0 err=2319 err_integral=0 p=1159 i=0 d=0 output=3659
+     kworker/4:1-51    [004]  1588.585987: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1588.585990: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1588.585995: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1103
+     kworker/4:1-51    [004]  1588.585997: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.586000: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.586003: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.586005: thermal_power_allocator: thermal_zone_id=0 req_power={1723 429 606} total_req_power=2758 granted_power={1944 1103 613} total_granted_power=3660 power_range=3659 max_allocatable_power=3908 current_temperature=69681 delta_temperature=2319
+     kworker/4:1-51    [004]  1588.685462: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69700 temp=69667
+     kworker/4:1-51    [004]  1588.686912: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=93 dynamic_power=1450 static_power=237
+     kworker/4:1-51    [004]  1588.686929: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={21 22} dynamic_power=250 static_power=185
+     kworker/4:1-51    [004]  1588.686944: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={44 22 7 64} dynamic_power=163 static_power=137
+     kworker/4:1-51    [004]  1588.686948: thermal_power_allocator_pid: thermal_zone_id=0 err=2366 err_integral=0 p=1183 i=0 d=0 output=3683
+     kworker/4:1-51    [004]  1588.686954: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1588.686957: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1588.686962: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1127
+     kworker/4:1-51    [004]  1588.686966: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.686969: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.686971: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.686974: thermal_power_allocator: thermal_zone_id=0 req_power={1687 435 600} total_req_power=2722 granted_power={1944 1127 613} total_granted_power=3684 power_range=3683 max_allocatable_power=3908 current_temperature=69634 delta_temperature=2366
+     kworker/4:1-51    [004]  1588.787388: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69667 temp=69621
+     kworker/4:1-51    [004]  1588.788239: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=87 dynamic_power=1357 static_power=237
+     kworker/4:1-51    [004]  1588.788258: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={37 40} dynamic_power=448 static_power=185
+     kworker/4:1-51    [004]  1588.788272: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={46 41 84 11} dynamic_power=216 static_power=137
+     kworker/4:1-51    [004]  1588.788277: thermal_power_allocator_pid: thermal_zone_id=0 err=2395 err_integral=0 p=1197 i=0 d=0 output=3697
+     kworker/4:1-51    [004]  1588.788281: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=2 power=1944
+     kworker/4:1-51    [004]  1588.789943: cdev_update:          type=devfreq target=2
+     kworker/4:1-51    [004]  1588.789959: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1141
+     kworker/4:1-51    [004]  1588.789963: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.789968: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.789970: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.789973: thermal_power_allocator: thermal_zone_id=0 req_power={1594 633 706} total_req_power=2933 granted_power={1944 1141 613} total_granted_power=3698 power_range=3697 max_allocatable_power=3908 current_temperature=69605 delta_temperature=2395
+     kworker/4:1-51    [004]  1588.884873: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69621 temp=69577
+     kworker/4:1-51    [004]  1588.886209: thermal_power_devfreq_get_power: type=devfreq freq=525000000 load=100 dynamic_power=1364 static_power=217
+     kworker/4:1-51    [004]  1588.886226: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={23 23} dynamic_power=268 static_power=185
+     kworker/4:1-51    [004]  1588.886242: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={49 26 37 35} dynamic_power=174 static_power=137
+     kworker/4:1-51    [004]  1588.886247: thermal_power_allocator_pid: thermal_zone_id=0 err=2451 err_integral=0 p=1225 i=0 d=0 output=3725
+     kworker/4:1-51    [004]  1588.886252: thermal_power_devfreq_limit: type=devfreq freq=525000000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1588.886261: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1588.886265: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1170
+     kworker/4:1-51    [004]  1588.886268: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.886271: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.886273: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.886276: thermal_power_allocator: thermal_zone_id=0 req_power={1581 453 622} total_req_power=2656 granted_power={1944 1170 613} total_granted_power=3727 power_range=3725 max_allocatable_power=3908 current_temperature=69549 delta_temperature=2451
+     kworker/4:1-51    [004]  1588.984220: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69577 temp=69546
+     kworker/4:1-51    [004]  1588.985411: thermal_power_devfreq_get_power: type=devfreq freq=525000000 load=100 dynamic_power=1364 static_power=217
+     kworker/4:1-51    [004]  1588.985428: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={21 27} dynamic_power=279 static_power=185
+     kworker/4:1-51    [004]  1588.985442: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={25 46 8 49} dynamic_power=152 static_power=137
+     kworker/4:1-51    [004]  1588.985447: thermal_power_allocator_pid: thermal_zone_id=0 err=2454 err_integral=0 p=1227 i=0 d=0 output=3727
+     kworker/4:1-51    [004]  1588.985451: thermal_power_devfreq_limit: type=devfreq freq=525000000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1588.985455: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1588.985460: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1172
+     kworker/4:1-51    [004]  1588.985463: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1588.985466: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1588.985468: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1588.985471: thermal_power_allocator: thermal_zone_id=0 req_power={1581 464 578} total_req_power=2623 granted_power={1944 1172 613} total_granted_power=3729 power_range=3727 max_allocatable_power=3908 current_temperature=69546 delta_temperature=2454
+     kworker/4:1-51    [004]  1589.084334: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69546 temp=69530
+     kworker/4:1-51    [004]  1589.085278: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=93 dynamic_power=1442 static_power=237
+     kworker/4:1-51    [004]  1589.085297: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={34 44} dynamic_power=454 static_power=185
+     kworker/4:1-51    [004]  1589.085312: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={78 31 28 4} dynamic_power=167 static_power=137
+     kworker/4:1-51    [004]  1589.085318: thermal_power_allocator_pid: thermal_zone_id=0 err=2478 err_integral=0 p=1239 i=0 d=0 output=3739
+     kworker/4:1-51    [004]  1589.085322: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1589.085325: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1589.085330: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1183
+     kworker/4:1-51    [004]  1589.085333: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1589.085336: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1589.085338: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1589.085340: thermal_power_allocator: thermal_zone_id=0 req_power={1679 639 608} total_req_power=2926 granted_power={1944 1183 613} total_granted_power=3740 power_range=3739 max_allocatable_power=3908 current_temperature=69522 delta_temperature=2478
+     kworker/4:1-51    [004]  1589.184047: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69530 temp=69509
+     kworker/4:1-51    [004]  1589.185238: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=94 dynamic_power=1463 static_power=237
+     kworker/4:1-51    [004]  1589.185256: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={31 32} dynamic_power=367 static_power=185
+     kworker/4:1-51    [004]  1589.185270: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={47 52 17 44} dynamic_power=190 static_power=137
+     kworker/4:1-51    [004]  1589.185275: thermal_power_allocator_pid: thermal_zone_id=0 err=2491 err_integral=0 p=1245 i=0 d=0 output=3745
+     kworker/4:1-51    [004]  1589.185280: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1589.185283: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1589.185289: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1189
+     kworker/4:1-51    [004]  1589.185291: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1589.185294: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1589.185296: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1589.185298: thermal_power_allocator: thermal_zone_id=0 req_power={1700 552 654} total_req_power=2906 granted_power={1944 1189 613} total_granted_power=3746 power_range=3745 max_allocatable_power=3908 current_temperature=69509 delta_temperature=2491
+     kworker/4:1-51    [004]  1589.294165: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69509 temp=69523
+     kworker/4:1-51    [004]  1589.295605: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=94 dynamic_power=1454 static_power=237
+     kworker/4:1-51    [004]  1589.295624: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={20 22} dynamic_power=244 static_power=185
+     kworker/4:1-51    [004]  1589.295638: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={34 74 7 4} dynamic_power=141 static_power=137
+     kworker/4:1-51    [004]  1589.295644: thermal_power_allocator_pid: thermal_zone_id=0 err=2485 err_integral=0 p=1242 i=0 d=0 output=3742
+     kworker/4:1-51    [004]  1589.295649: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1589.295652: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1589.295657: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1186
+     kworker/4:1-51    [004]  1589.295660: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1589.295663: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1589.295665: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1589.295668: thermal_power_allocator: thermal_zone_id=0 req_power={1691 429 556} total_req_power=2676 granted_power={1944 1186 613} total_granted_power=3743 power_range=3742 max_allocatable_power=3908 current_temperature=69515 delta_temperature=2485
+     kworker/4:1-51    [004]  1589.394011: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69523 temp=69515
+     kworker/4:1-51    [004]  1589.394765: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=78 dynamic_power=1219 static_power=237
+     kworker/4:1-51    [004]  1589.394782: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={31 35} dynamic_power=384 static_power=185
+     kworker/4:1-51    [004]  1589.394797: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={34 51 5 59} dynamic_power=177 static_power=137
+     kworker/4:1-51    [004]  1589.394803: thermal_power_allocator_pid: thermal_zone_id=0 err=2491 err_integral=0 p=1245 i=0 d=0 output=3745
+     kworker/4:1-51    [004]  1589.394808: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=3 power=1944
+     kworker/4:1-51    [004]  1589.396344: cdev_update:          type=devfreq target=3
+     kworker/4:1-51    [004]  1589.396354: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1190
+     kworker/4:1-51    [004]  1589.396357: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1589.396361: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1589.396363: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1589.396365: thermal_power_allocator: thermal_zone_id=0 req_power={1456 569 628} total_req_power=2653 granted_power={1944 1190 613} total_granted_power=3747 power_range=3745 max_allocatable_power=3908 current_temperature=69509 delta_temperature=2491
+     kworker/4:1-51    [004]  1589.493593: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69515 temp=69489
+     kworker/4:1-51    [004]  1589.494325: thermal_power_devfreq_get_power: type=devfreq freq=487500000 load=93 dynamic_power=1115 static_power=199
+     kworker/4:1-51    [004]  1589.494341: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={32 35} dynamic_power=390 static_power=185
+     kworker/4:1-51    [004]  1589.494356: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={40 52 26 50} dynamic_power=199 static_power=137
+     kworker/4:1-51    [004]  1589.494361: thermal_power_allocator_pid: thermal_zone_id=0 err=2532 err_integral=0 p=1266 i=0 d=0 output=3766
+     kworker/4:1-51    [004]  1589.494365: thermal_power_devfreq_limit: type=devfreq freq=487500000 cdev_state=1 power=1941
+     kworker/4:1-51    [004]  1589.495857: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1589.495866: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1213
+     kworker/4:1-51    [004]  1589.495869: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1589.495872: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1589.495874: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1589.495877: thermal_power_allocator: thermal_zone_id=0 req_power={1314 575 672} total_req_power=2561 granted_power={1941 1213 613} total_granted_power=3767 power_range=3766 max_allocatable_power=3908 current_temperature=69468 delta_temperature=2532
+     kworker/4:1-51    [004]  1589.594736: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69489 temp=69469
+     kworker/4:1-51    [004]  1589.596013: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1589.596030: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={21 24} dynamic_power=262 static_power=185
+     kworker/4:1-51    [004]  1589.596045: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={26 41 31 33} dynamic_power=155 static_power=137
+     kworker/4:1-51    [004]  1589.596050: thermal_power_allocator_pid: thermal_zone_id=0 err=2517 err_integral=0 p=1258 i=0 d=0 output=3758
+     kworker/4:1-51    [004]  1589.596054: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1589.596058: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1589.596062: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1202
+     kworker/4:1-51    [004]  1589.596065: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1589.596068: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1589.596070: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1589.596073: thermal_power_allocator: thermal_zone_id=0 req_power={1784 447 584} total_req_power=2815 granted_power={1944 1202 613} total_granted_power=3759 power_range=3758 max_allocatable_power=3908 current_temperature=69483 delta_temperature=2517
+     kworker/4:1-51    [004]  1589.693620: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69469 temp=69478
+     kworker/4:1-51    [004]  1589.694771: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=94 dynamic_power=1457 static_power=237
+     kworker/4:1-51    [004]  1589.694789: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={25 27} dynamic_power=303 static_power=185
+     kworker/4:1-51    [004]  1589.694805: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={34 27 31 63} dynamic_power=184 static_power=137
+     kworker/4:1-51    [004]  1589.694810: thermal_power_allocator_pid: thermal_zone_id=0 err=2532 err_integral=0 p=1266 i=0 d=0 output=3766
+     kworker/4:1-51    [004]  1589.694814: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1589.694818: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1589.694823: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1211
+     kworker/4:1-51    [004]  1589.694825: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1589.694829: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1589.694831: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1589.694833: thermal_power_allocator: thermal_zone_id=0 req_power={1694 488 642} total_req_power=2824 granted_power={1944 1211 613} total_granted_power=3768 power_range=3766 max_allocatable_power=3908 current_temperature=69468 delta_temperature=2532
+     kworker/4:1-51    [004]  1589.794353: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69478 temp=69453
+     kworker/4:1-51    [004]  1589.795601: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=97 dynamic_power=1503 static_power=237
+     kworker/4:1-51    [004]  1589.795619: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={36 37} dynamic_power=425 static_power=185
+     kworker/4:1-51    [004]  1589.795633: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={61 40 40 27} dynamic_power=199 static_power=137
+     kworker/4:1-51    [004]  1589.795639: thermal_power_allocator_pid: thermal_zone_id=0 err=2544 err_integral=0 p=1272 i=0 d=0 output=3772
+     kworker/4:1-51    [004]  1589.795643: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1589.795647: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1589.795652: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1216
+     kworker/4:1-51    [004]  1589.795655: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1589.795658: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1589.795660: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1589.795662: thermal_power_allocator: thermal_zone_id=0 req_power={1740 610 672} total_req_power=3022 granted_power={1944 1216 613} total_granted_power=3773 power_range=3772 max_allocatable_power=3908 current_temperature=69456 delta_temperature=2544
+     kworker/4:1-51    [004]  1589.904947: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69453 temp=69442
+     kworker/4:1-51    [004]  1589.906311: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=93 dynamic_power=1444 static_power=237
+     kworker/4:1-51    [004]  1589.906328: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={19 20} dynamic_power=227 static_power=185
+     kworker/4:1-51    [004]  1589.906343: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={43 22 38 24} dynamic_power=151 static_power=137
+     kworker/4:1-51    [004]  1589.906348: thermal_power_allocator_pid: thermal_zone_id=0 err=2560 err_integral=0 p=1280 i=0 d=0 output=3780
+     kworker/4:1-51    [004]  1589.906353: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1589.906356: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1589.906361: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1224
+     kworker/4:1-51    [004]  1589.906364: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1589.906367: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1589.906369: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1589.906371: thermal_power_allocator: thermal_zone_id=0 req_power={1681 412 576} total_req_power=2669 granted_power={1944 1224 613} total_granted_power=3781 power_range=3780 max_allocatable_power=3908 current_temperature=69440 delta_temperature=2560
+     kworker/4:1-51    [004]  1590.014034: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69442 temp=69427
+     kworker/4:1-51    [004]  1590.016888: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=93 dynamic_power=1453 static_power=237
+     kworker/4:1-51    [004]  1590.016913: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={35 34} dynamic_power=402 static_power=185
+     kworker/4:1-51    [004]  1590.016934: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={40 36 55 38} dynamic_power=201 static_power=137
+     kworker/4:1-51    [004]  1590.016942: thermal_power_allocator_pid: thermal_zone_id=0 err=2585 err_integral=0 p=1292 i=0 d=0 output=3792
+     kworker/4:1-51    [004]  1590.016948: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1590.016954: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1590.016964: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1237
+     kworker/4:1-51    [004]  1590.016970: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1590.016973: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1590.016975: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1590.016979: thermal_power_allocator: thermal_zone_id=0 req_power={1690 587 676} total_req_power=2953 granted_power={1944 1237 613} total_granted_power=3794 power_range=3792 max_allocatable_power=3908 current_temperature=69415 delta_temperature=2585
+     kworker/4:1-51    [004]  1590.115011: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69427 temp=69406
+     kworker/4:1-51    [004]  1590.116462: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=97 dynamic_power=1509 static_power=237
+     kworker/4:1-51    [004]  1590.116479: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={28 27} dynamic_power=320 static_power=185
+     kworker/4:1-51    [004]  1590.116494: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={34 89 6 6} dynamic_power=160 static_power=137
+     kworker/4:1-51    [004]  1590.116498: thermal_power_allocator_pid: thermal_zone_id=0 err=2592 err_integral=0 p=1296 i=0 d=0 output=3796
+     kworker/4:1-51    [004]  1590.116503: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1590.116506: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1590.116512: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1240
+     kworker/4:1-51    [004]  1590.116514: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1590.116517: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1590.116519: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1590.116521: thermal_power_allocator: thermal_zone_id=0 req_power={1746 505 594} total_req_power=2845 granted_power={1944 1240 613} total_granted_power=3797 power_range=3796 max_allocatable_power=3908 current_temperature=69408 delta_temperature=2592
+     kworker/4:1-51    [004]  1590.224193: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69406 temp=69424
+     kworker/4:1-51    [004]  1590.225470: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=100 dynamic_power=1547 static_power=237
+     kworker/4:1-51    [004]  1590.225487: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={28 26} dynamic_power=314 static_power=185
+     kworker/4:1-51    [004]  1590.225501: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={48 49 20 26} dynamic_power=170 static_power=137
+     kworker/4:1-51    [004]  1590.225506: thermal_power_allocator_pid: thermal_zone_id=0 err=2560 err_integral=0 p=1280 i=0 d=0 output=3780
+     kworker/4:1-51    [004]  1590.225511: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1590.225514: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1590.225519: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1225
+     kworker/4:1-51    [004]  1590.225522: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1590.225525: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1590.225527: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1590.225529: thermal_power_allocator: thermal_zone_id=0 req_power={1784 499 614} total_req_power=2897 granted_power={1944 1225 613} total_granted_power=3782 power_range=3780 max_allocatable_power=3908 current_temperature=69440 delta_temperature=2560
+     kworker/4:1-51    [004]  1590.324730: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69424 temp=69418
+     kworker/4:1-51    [004]  1590.326162: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=92 dynamic_power=1430 static_power=237
+     kworker/4:1-51    [004]  1590.326179: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={34 34} dynamic_power=396 static_power=185
+     kworker/4:1-51    [004]  1590.326192: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={64 37 42 9} dynamic_power=180 static_power=137
+     kworker/4:1-51    [004]  1590.326197: thermal_power_allocator_pid: thermal_zone_id=0 err=2603 err_integral=0 p=1301 i=0 d=0 output=3801
+     kworker/4:1-51    [004]  1590.326202: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=1 power=1944
+     kworker/4:1-51    [004]  1590.326206: cdev_update:          type=devfreq target=1
+     kworker/4:1-51    [004]  1590.326211: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1246
+     kworker/4:1-51    [004]  1590.326214: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1590.326217: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1590.326219: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1590.326221: thermal_power_allocator: thermal_zone_id=0 req_power={1667 581 634} total_req_power=2882 granted_power={1944 1246 613} total_granted_power=3803 power_range=3801 max_allocatable_power=3908 current_temperature=69397 delta_temperature=2603
+     kworker/4:1-51    [004]  1590.424251: thermal_temperature:  thermal_zone=soc_thermal id=0 temp_prev=69418 temp=69417
+     kworker/4:1-51    [004]  1590.425200: thermal_power_devfreq_get_power: type=devfreq freq=562500000 load=86 dynamic_power=1330 static_power=237
+     kworker/4:1-51    [004]  1590.425219: thermal_power_cpu_get_power: cpus=00000000,00000006 freq=1100000 load={22 22} dynamic_power=256 static_power=185
+     kworker/4:1-51    [004]  1590.425233: thermal_power_cpu_get_power: cpus=00000000,00000039 freq=850000 load={26 57 20 35} dynamic_power=164 static_power=137
+     kworker/4:1-51    [004]  1590.425238: thermal_power_allocator_pid: thermal_zone_id=0 err=2587 err_integral=0 p=1293 i=0 d=0 output=3793
+     kworker/4:1-51    [004]  1590.425243: thermal_power_devfreq_limit: type=devfreq freq=562500000 cdev_state=2 power=1944
+     kworker/4:1-51    [004]  1590.426809: cdev_update:          type=devfreq target=2
+     kworker/4:1-51    [004]  1590.426818: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1100000 cdev_state=0 power=1237
+     kworker/4:1-51    [004]  1590.426821: cdev_update:          type=thermal-cpufreq-0 target=0
+     kworker/4:1-51    [004]  1590.426824: thermal_power_cpu_limit: cpus=00000000,00000039 freq=850000 cdev_state=0 power=613
+     kworker/4:1-51    [004]  1590.426826: cdev_update:          type=thermal-cpufreq-1 target=0
+     kworker/4:1-51    [004]  1590.426828: thermal_power_allocator: thermal_zone_id=0 req_power={1567 441 602} total_req_power=2610 granted_power={1944 1237 613} total_granted_power=3794 power_range=3793 max_allocatable_power=3908 current_temperature=69413 delta_temperature=2587
diff --git a/doc/trace_stats.dat b/doc/trace_stats.dat
new file mode 100644
index 0000000..adfb449
--- /dev/null
+++ b/doc/trace_stats.dat
diff --git a/hooks/pre-commit b/hooks/pre-commit
new file mode 100755
index 0000000..9231bc2
--- /dev/null
+++ b/hooks/pre-commit
@@ -0,0 +1,29 @@
+#!/bin/sh
+#    Copyright 2015-2015 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+#
+# git pre-commit hook.  There's no way to automate them when cloning a
+# repository.  You need to run this by hand in the root of the
+# repository to activate it:
+#
+#   ln -s ../../hooks/pre-commit .git/hooks/
+
+set -e
+
+# Fail if there's whitespace errors
+git diff-index --check --cached HEAD --
+
+# Run testsuite
+exec nosetests
diff --git a/scripts/publish_interactive_plots.py b/scripts/publish_interactive_plots.py
new file mode 100755
index 0000000..7711556
--- /dev/null
+++ b/scripts/publish_interactive_plots.py
@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+"""This is a script to publish a notebook containing Ipython graphs
+The static data is published as an anonymous gist. GitHub does not
+allow easy deletions of anonymous gists.
+"""
+
+import os
+import argparse
+from IPython.nbformat.sign import TrustNotebookApp
+from argparse import RawTextHelpFormatter
+
+# Logging Configuration
+import logging
+from trappy.plotter import IPythonConf
+
+logging.basicConfig(level=logging.INFO)
+
+
+def change_resource_paths(txt):
+    """Change the resource paths from local to
+       Web URLs
+    """
+
+    # Replace the path for d3-tip
+    txt = txt.replace(
+        IPythonConf.add_web_base("plotter_scripts/EventPlot/d3.tip.v0.6.3"),
+        IPythonConf.D3_TIP_URL)
+    txt = txt.replace(
+        IPythonConf.add_web_base("plotter_scripts/EventPlot/d3.v3.min"),
+        IPythonConf.D3_PLOTTER_URL)
+    txt = txt.replace(
+        IPythonConf.add_web_base("plotter_scripts/EventPlot/EventPlot"),
+        "https://rawgit.com/sinkap/7f89de3e558856b81f10/raw/46144f8f8c5da670c54f826f0c634762107afc66/EventPlot")
+    txt = txt.replace(
+        IPythonConf.add_web_base("plotter_scripts/ILinePlot/synchronizer"),
+        IPythonConf.DYGRAPH_SYNC_URL)
+    txt = txt.replace(
+        IPythonConf.add_web_base("plotter_scripts/ILinePlot/dygraph-combined"),
+        IPythonConf.DYGRAPH_COMBINED_URL)
+    txt = txt.replace(
+        IPythonConf.add_web_base("plotter_scripts/ILinePlot/ILinePlot"),
+        "https://rawgit.com/sinkap/648927dfd6985d4540a9/raw/69d6f1f9031ae3624c15707315ce04be1a9d1ac3/ILinePlot")
+    txt = txt.replace(
+        IPythonConf.add_web_base("plotter_scripts/ILinePlot/underscore-min"),
+        IPythonConf.UNDERSCORE_URL)
+
+    logging.info("Updated Library Paths...")
+    return txt
+
+
+def publish(source, target):
+    """Publish the notebook for globally viewable interactive
+     plots
+    """
+
+    txt = ""
+
+    with open(source, 'r') as file_fh:
+        txt = change_resource_paths(file_fh.read())
+
+    with open(target, 'w') as file_fh:
+        file_fh.write(txt)
+
+    trust = TrustNotebookApp()
+    trust.sign_notebook(target)
+    logging.info("Signed and Saved: %s", target)
+
+def main():
+    """Command Line Invocation Routine"""
+
+    parser = argparse.ArgumentParser(description="""
+    The data for the interactive plots is stored in the  ipython profile.
+    In order to make it accessible when the notebook is published or shared,
+    a github gist of the data is created and the links in the notebook are
+    updated. The library links are also updated to their corresponding publicly
+    accessible URLs.
+    """,
+    prog="publish_interactive_plots.py", formatter_class=RawTextHelpFormatter)
+
+    parser.add_argument(
+        "-p",
+        "--profile",
+        help="ipython profile",
+        default="default",
+        type=str)
+
+    parser.add_argument(
+        "-o",
+        "--outfile",
+        help="name of the output notebook",
+        default="",
+        type=str)
+
+    parser.add_argument("notebook")
+    args = parser.parse_args()
+
+    notebook = args.notebook
+    outfile = args.outfile
+
+    if outfile == "":
+        outfile = "published_" + os.path.basename(notebook)
+        logging.info("Setting outfile as %s", outfile)
+
+    elif not outfile.endswith(".ipynb"):
+        outfile += ".ipynb"
+
+    publish(notebook, outfile)
+
+if __name__ == "__main__":
+    main()
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..852be06
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,6 @@
+[upload_sphinx]
+upload-dir = doc/api_reference/_build/html
+
+[nosetests]
+processes=-1
+process-timeout=60
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..b3a93dd
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from setuptools import setup, find_packages
+
+
+execfile("trappy/version.py")
+
+LONG_DESCRIPTION = """TRAPpy is a framework written in python for
+analysing and plotting FTrace data by converting it into standardised
+PANDAS DataFrames (tabular times series data representation).The goal is to
+allow developers easy and systematic access to FTrace data and leverage
+the flexibility of PANDAS for the analysis.
+
+TRAPpy also provides functionality to build complex statistical analysis
+based on the underlying FTrace data.
+"""
+REQUIRES = [
+    "numpy",
+    "pyparsing",
+    "pandas>=0.13.1",
+]
+
+EXTRAS = {
+    "notebook": ["matplotlib>=1.3.1",
+                 "ipython>=3.0.0",
+                 "jupyter>=1.0.0",
+             ],
+}
+
+data_files = {"trappy.plotter": ["js/EventPlot.js",
+                                 "js/ILinePlot.js",
+                                 "css/EventPlot.css",
+                                 "css/EventPlot_help.jpg",
+                             ]
+}
+
+setup(name='TRAPpy',
+      version=__version__,
+      license="Apache v2",
+      author="ARM-TRAPPY",
+      author_email="trappy@arm.com",
+      description="Trace Analysis and Plotting",
+      long_description=LONG_DESCRIPTION,
+      url="http://arm-software.github.io/trappy",
+      packages=find_packages(),
+      package_data=data_files,
+      scripts=["scripts/publish_interactive_plots.py"],
+      classifiers=[
+          "Development Status :: 5 - Production/Stable",
+          "Environment :: Web Environment",
+          "Environment :: Console",
+          "License :: OSI Approved :: Apache Software License",
+          "Operating System :: POSIX :: Linux",
+          "Programming Language :: Python :: 2.7",
+          # As we depend on trace data from the Linux Kernel/FTrace
+          "Topic :: System :: Operating System Kernels :: Linux",
+          "Topic :: Scientific/Engineering :: Visualization"
+      ],
+      install_requires=REQUIRES,
+      extras_require=EXTRAS
+      )
diff --git a/tests/WA_sysfs_extract.tar.xz b/tests/WA_sysfs_extract.tar.xz
new file mode 100644
index 0000000..25e332d
--- /dev/null
+++ b/tests/WA_sysfs_extract.tar.xz
diff --git a/tests/constrained.csv b/tests/constrained.csv
new file mode 100644
index 0000000..fa01266
--- /dev/null
+++ b/tests/constrained.csv
@@ -0,0 +1,147 @@
+id,workload,iteration,metric,value,units

+power_allocator_1,antutu,1,float_2,8,

+power_allocator_1,antutu,1,multitask_3,8,

+power_allocator_1,antutu,1,float,7,

+power_allocator_1,antutu,1,3d_5,9,

+power_allocator_1,antutu,1,float_4,3,

+power_allocator_1,antutu,1,floatsingle_3,7,

+power_allocator_1,antutu,1,integer_5,1,

+power_allocator_1,antutu,1,integer_4,9,

+power_allocator_1,antutu,1,integer_3,3,

+power_allocator_1,antutu,1,integer_2,9,

+power_allocator_1,antutu,1,integer,9,

+power_allocator_1,antutu,1,ram_5,8,

+power_allocator_1,antutu,1,ram_4,5,

+power_allocator_1,antutu,1,multitask,9,

+power_allocator_1,antutu,1,ram_3,2,

+power_allocator_1,antutu,1,ram_2,6,

+power_allocator_1,antutu,1,integersingle_4,7,

+power_allocator_1,antutu,1,memory_3,5,

+power_allocator_1,antutu,1,memory_2,1,

+power_allocator_1,antutu,1,memory_5,6,

+power_allocator_1,antutu,1,memory_4,4,

+power_allocator_1,antutu,1,float_3,2,

+power_allocator_1,antutu,1,integersingle_2,3,

+power_allocator_1,antutu,1,storage,4,

+power_allocator_1,antutu,1,renderscript,3,

+power_allocator_1,antutu,1,score_3,4,

+power_allocator_1,antutu,1,score,2,

+power_allocator_1,antutu,1,score_5,8,

+power_allocator_1,antutu,1,score_4,2,

+power_allocator_1,antutu,1,memory,6,

+power_allocator_1,antutu,1,renderscript_5,7,

+power_allocator_1,antutu,1,score_2,7,

+power_allocator_1,antutu,1,storage_5,9,

+power_allocator_1,antutu,1,3d,8,

+power_allocator_1,antutu,1,storage_3,6,

+power_allocator_1,antutu,1,integersingle,9,

+power_allocator_1,antutu,1,integersingle_3,9,

+power_allocator_1,antutu,1,float_5,9,

+power_allocator_1,antutu,1,2d_4,9,

+power_allocator_1,antutu,1,2d_5,4,

+power_allocator_1,antutu,1,2d_2,4,

+power_allocator_1,antutu,1,2d_3,3,

+power_allocator_1,antutu,1,renderscript_4,3,

+power_allocator_1,antutu,1,storage_2,5,

+power_allocator_1,antutu,1,floatsingle_2,6,

+power_allocator_1,antutu,1,floatsingle_5,3,

+power_allocator_1,antutu,1,floatsingle,8,

+power_allocator_1,antutu,1,storage_4,5,

+power_allocator_1,antutu,1,floatsingle_4,5,

+power_allocator_1,antutu,1,database,5,

+power_allocator_1,antutu,1,3d_4,2,

+power_allocator_1,antutu,1,dalvik,8,

+power_allocator_1,antutu,1,multitask_5,9,

+power_allocator_1,antutu,1,multitask_4,3,

+power_allocator_1,antutu,1,ram,8,

+power_allocator_1,antutu,1,multitask_2,3,

+power_allocator_1,antutu,1,2d,3,

+power_allocator_1,antutu,1,3d_3,8,

+power_allocator_1,antutu,1,3d_2,2,

+power_allocator_1,antutu,1,renderscript_3,2,

+power_allocator_1,antutu,1,renderscript_2,3,

+power_allocator_1,antutu,1,integersingle_5,6,

+power_allocator_1,antutu,1,database_5,5,

+power_allocator_1,antutu,1,database_4,5,

+power_allocator_1,antutu,1,dalvik_5,9,

+power_allocator_1,antutu,1,dalvik_4,9,

+power_allocator_1,antutu,1,dalvik_3,5,

+power_allocator_1,antutu,1,dalvik_2,8,

+power_allocator_1,antutu,1,database_3,4,

+power_allocator_1,antutu,1,database_2,5,

+power_allocator_1,antutu,1,execution_time,1234.69688511,seconds

+power_allocator_1,antutu,1,FPS,nan,

+power_allocator_1,antutu,1,frame_count,0,

+power_allocator_1,antutu,1,janks,0,

+power_allocator_1,antutu,1,not_at_vsync,0,

+power_allocator_2,egypt_offscreen,1,Frames_0,5,frames

+power_allocator_2,egypt_offscreen,1,FPS_0,334,fps

+power_allocator_2,egypt_offscreen,1,Frames_1,9,frames

+power_allocator_2,egypt_offscreen,1,FPS_1,242,fps

+power_allocator_2,egypt_offscreen,1,Frames_2,8,frames

+power_allocator_2,egypt_offscreen,1,FPS_2,190,fps

+power_allocator_2,egypt_offscreen,1,execution_time,1234.855288029,seconds

+power_allocator_2,egypt_offscreen,1,FPS,123.0000024,

+power_allocator_2,egypt_offscreen,1,frame_count,0,

+power_allocator_2,egypt_offscreen,1,janks,0,

+power_allocator_2,egypt_offscreen,1,not_at_vsync,0,

+power_allocator_3,t-rex_offscreen,1,Frames_0,8,frames

+power_allocator_3,t-rex_offscreen,1,FPS_0,560,fps

+power_allocator_3,t-rex_offscreen,1,Frames_1,5,frames

+power_allocator_3,t-rex_offscreen,1,FPS_1,872,fps

+power_allocator_3,t-rex_offscreen,1,Frames_2,1,frames

+power_allocator_3,t-rex_offscreen,1,FPS_2,918,fps

+power_allocator_3,t-rex_offscreen,1,Frames_3,3,frames

+power_allocator_3,t-rex_offscreen,1,FPS_3,5,fps

+power_allocator_3,t-rex_offscreen,1,Frames_4,4,frames

+power_allocator_3,t-rex_offscreen,1,FPS_4,494,fps

+power_allocator_3,t-rex_offscreen,1,execution_time,1234.807990074,seconds

+power_allocator_3,t-rex_offscreen,1,FPS,123.0000024,

+power_allocator_3,t-rex_offscreen,1,frame_count,0,

+power_allocator_3,t-rex_offscreen,1,janks,0,

+power_allocator_3,t-rex_offscreen,1,not_at_vsync,0,

+power_allocator_4,geekbench,1,score,1,

+power_allocator_4,geekbench,1,multicore_score,7,

+power_allocator_4,geekbench,1,Integer_score,3,

+power_allocator_4,geekbench,1,Integer_multicore_score,2,

+power_allocator_4,geekbench,1,Floating Point_score,3,

+power_allocator_4,geekbench,1,Floating Point_multicore_score,4,

+power_allocator_4,geekbench,1,Memory_score,2,

+power_allocator_4,geekbench,1,Memory_multicore_score,7,

+power_allocator_4,geekbench,1,score 1,1,

+power_allocator_4,geekbench,1,multicore_score 1,9,

+power_allocator_4,geekbench,1,Integer_score 1,6,

+power_allocator_4,geekbench,1,Integer_multicore_score 1,4,

+power_allocator_4,geekbench,1,Floating Point_score 1,6,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 1,5,

+power_allocator_4,geekbench,1,Memory_score 1,5,

+power_allocator_4,geekbench,1,Memory_multicore_score 1,8,

+power_allocator_4,geekbench,1,score 2,3,

+power_allocator_4,geekbench,1,multicore_score 2,7,

+power_allocator_4,geekbench,1,Integer_score 2,5,

+power_allocator_4,geekbench,1,Integer_multicore_score 2,9,

+power_allocator_4,geekbench,1,Floating Point_score 2,5,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 2,7,

+power_allocator_4,geekbench,1,Memory_score 2,8,

+power_allocator_4,geekbench,1,Memory_multicore_score 2,5,

+power_allocator_4,geekbench,1,score 3,2,

+power_allocator_4,geekbench,1,multicore_score 3,7,

+power_allocator_4,geekbench,1,Integer_score 3,5,

+power_allocator_4,geekbench,1,Integer_multicore_score 3,2,

+power_allocator_4,geekbench,1,Floating Point_score 3,5,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 3,5,

+power_allocator_4,geekbench,1,Memory_score 3,2,

+power_allocator_4,geekbench,1,Memory_multicore_score 3,3,

+power_allocator_4,geekbench,1,score 4,8,

+power_allocator_4,geekbench,1,multicore_score 4,4,

+power_allocator_4,geekbench,1,Integer_score 4,4,

+power_allocator_4,geekbench,1,Integer_multicore_score 4,9,

+power_allocator_4,geekbench,1,Floating Point_score 4,5,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 4,7,

+power_allocator_4,geekbench,1,Memory_score 4,6,

+power_allocator_4,geekbench,1,Memory_multicore_score 4,9,

+power_allocator_4,geekbench,1,execution_time,123.642524958,seconds

+power_allocator_4,geekbench,1,FPS,nan,

+power_allocator_4,geekbench,1,frame_count,0,

+power_allocator_4,geekbench,1,janks,0,

+power_allocator_4,geekbench,1,not_at_vsync,0,

diff --git a/tests/raw_trace.dat b/tests/raw_trace.dat
new file mode 100644
index 0000000..adfb449
--- /dev/null
+++ b/tests/raw_trace.dat
diff --git a/tests/raw_trace.raw.txt b/tests/raw_trace.raw.txt
new file mode 100644
index 0000000..f66d55b
--- /dev/null
+++ b/tests/raw_trace.raw.txt
@@ -0,0 +1,7 @@
+version = 6
+CPU 3 is empty
+CPU 4 is empty
+cpus=6
+              ls-4734  [002] 106439.675591: sched_switch:          prev_comm=trace-cmd prev_pid=4734 prev_prio=120 prev_state=1024 next_comm=migration/2 next_pid=18 next_prio=0
+     migration/2-18    [002] 106439.675613: sched_switch:          prev_comm=migration/2 prev_pid=18 prev_prio=0 prev_state=1 next_comm=trace-cmd next_pid=4732 next_prio=120
+       trace-cmd-4730  [001] 106439.675718: sched_switch:          prev_comm=trace-cmd prev_pid=4730 prev_prio=120 prev_state=1 next_comm=trace-cmd next_pid=4729 next_prio=120
diff --git a/tests/raw_trace.txt b/tests/raw_trace.txt
new file mode 100644
index 0000000..4fbf4c9
--- /dev/null
+++ b/tests/raw_trace.txt
@@ -0,0 +1,7 @@
+version = 6
+CPU 3 is empty
+CPU 4 is empty
+cpus=6
+              ls-4734  [002] 106439.675591: sched_switch:         trace-cmd:4734 [120] R ==> migration/2:18 [0]
+     migration/2-18    [002] 106439.675613: sched_switch:         migration/2:18 [0] S ==> trace-cmd:4732 [120]
+       trace-cmd-4731  [001] 106439.675698: sched_switch:         trace-cmd:4731 [120] S ==> trace-cmd:4730 [120]
diff --git a/tests/results.csv b/tests/results.csv
new file mode 100644
index 0000000..9928528
--- /dev/null
+++ b/tests/results.csv
@@ -0,0 +1,161 @@
+id,workload,iteration,metric,value,units

+power_allocator_1,antutu,1,Overall_Score,5,

+power_allocator_1,antutu,1,Multitask,4,

+power_allocator_1,antutu,1,Dalvik,9,

+power_allocator_1,antutu,1,CPU_integer,3,

+power_allocator_1,antutu,1,CPU_float-point,2,

+power_allocator_1,antutu,1,RAM_Operation,3,

+power_allocator_1,antutu,1,RAM_Speed,6,

+power_allocator_1,antutu,1,2D_graphics,6,

+power_allocator_1,antutu,1,3D_graphics,9,

+power_allocator_1,antutu,1,Overall_Score_2,3,

+power_allocator_1,antutu,1,Multitask_2,7,

+power_allocator_1,antutu,1,Dalvik_2,2,

+power_allocator_1,antutu,1,CPU_integer_2,6,

+power_allocator_1,antutu,1,CPU_float-point_2,7,

+power_allocator_1,antutu,1,RAM_Operation_2,2,

+power_allocator_1,antutu,1,RAM_Speed_2,4,

+power_allocator_1,antutu,1,2D_graphics_2,7,

+power_allocator_1,antutu,1,3D_graphics_2,8,

+power_allocator_1,antutu,1,Overall_Score_3,2,

+power_allocator_1,antutu,1,Multitask_3,1,

+power_allocator_1,antutu,1,Dalvik_3,5,

+power_allocator_1,antutu,1,CPU_integer_3,2,

+power_allocator_1,antutu,1,CPU_float-point_3,6,

+power_allocator_1,antutu,1,RAM_Operation_3,4,

+power_allocator_1,antutu,1,RAM_Speed_3,5,

+power_allocator_1,antutu,1,2D_graphics_3,2,

+power_allocator_1,antutu,1,3D_graphics_3,2,

+power_allocator_1,antutu,1,execution_time,1234.026520967,seconds

+power_allocator_1,antutu,1,FPS,nan,

+power_allocator_1,antutu,1,frame_count,0,

+power_allocator_1,antutu,1,janks,0,

+power_allocator_1,antutu,1,not_at_vsync,0,

+power_allocator_2,egypt_offscreen,1,Frames_0,6,frames

+power_allocator_2,egypt_offscreen,1,FPS_0,652,fps

+power_allocator_2,egypt_offscreen,1,Frames_1,6,frames

+power_allocator_2,egypt_offscreen,1,FPS_1,555,fps

+power_allocator_2,egypt_offscreen,1,Frames_2,5,frames

+power_allocator_2,egypt_offscreen,1,FPS_2,790,fps

+power_allocator_2,egypt_offscreen,1,execution_time,1234.440778971,seconds

+power_allocator_2,egypt_offscreen,1,FPS,60.0000024,

+power_allocator_2,egypt_offscreen,1,frame_count,0,

+power_allocator_2,egypt_offscreen,1,janks,0,

+power_allocator_2,egypt_offscreen,1,not_at_vsync,0,

+power_allocator_3,t-rex_offscreen,1,Frames_0,4,frames

+power_allocator_3,t-rex_offscreen,1,FPS_0,1777,fps

+power_allocator_3,t-rex_offscreen,1,Frames_1,3,frames

+power_allocator_3,t-rex_offscreen,1,FPS_1,397,fps

+power_allocator_3,t-rex_offscreen,1,Frames_2,9,frames

+power_allocator_3,t-rex_offscreen,1,FPS_2,512,fps

+power_allocator_3,t-rex_offscreen,1,execution_time,4321.995774984,seconds

+power_allocator_3,t-rex_offscreen,1,FPS,60.0000024,

+power_allocator_3,t-rex_offscreen,1,frame_count,0,

+power_allocator_3,t-rex_offscreen,1,janks,0,

+power_allocator_3,t-rex_offscreen,1,not_at_vsync,0,

+power_allocator_4,geekbench,1,score,3,

+power_allocator_4,geekbench,1,multicore_score,2,

+power_allocator_4,geekbench,1,Integer_score,4,

+power_allocator_4,geekbench,1,Integer_multicore_score,7,

+power_allocator_4,geekbench,1,Floating Point_score,9,

+power_allocator_4,geekbench,1,Floating Point_multicore_score,6,

+power_allocator_4,geekbench,1,Memory_score,3,

+power_allocator_4,geekbench,1,Memory_multicore_score,8,

+power_allocator_4,geekbench,1,score 1,1,

+power_allocator_4,geekbench,1,multicore_score 1,7,

+power_allocator_4,geekbench,1,Integer_score 1,9,

+power_allocator_4,geekbench,1,Integer_multicore_score 1,1,

+power_allocator_4,geekbench,1,Floating Point_score 1,5,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 1,8,

+power_allocator_4,geekbench,1,Memory_score 1,8,

+power_allocator_4,geekbench,1,Memory_multicore_score 1,7,

+power_allocator_4,geekbench,1,score 2,5,

+power_allocator_4,geekbench,1,multicore_score 2,5,

+power_allocator_4,geekbench,1,Integer_score 2,5,

+power_allocator_4,geekbench,1,Integer_multicore_score 2,6,

+power_allocator_4,geekbench,1,Floating Point_score 2,6,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 2,6,

+power_allocator_4,geekbench,1,Memory_score 2,6,

+power_allocator_4,geekbench,1,Memory_multicore_score 2,3,

+power_allocator_4,geekbench,1,execution_time,1234.685024023,seconds

+power_allocator_4,geekbench,1,FPS,nan,

+power_allocator_4,geekbench,1,frame_count,0,

+power_allocator_4,geekbench,1,janks,0,

+power_allocator_4,geekbench,1,not_at_vsync,0,

+power_allocator_6,thechase,1,execution_time,600.09159112,seconds

+power_allocator_6,thechase,1,FPS,491.6156693693,

+power_allocator_6,thechase,1,frame_count,1,

+power_allocator_6,thechase,1,janks,0,

+power_allocator_6,thechase,1,not_at_vsync,0,

+step_wise_1,antutu,1,Overall_Score,4,

+step_wise_1,antutu,1,Multitask,3,

+step_wise_1,antutu,1,Dalvik,3,

+step_wise_1,antutu,1,CPU_integer,3,

+step_wise_1,antutu,1,CPU_float-point,3,

+step_wise_1,antutu,1,RAM_Operation,6,

+step_wise_1,antutu,1,RAM_Speed,5,

+step_wise_1,antutu,1,2D_graphics,6,

+step_wise_1,antutu,1,3D_graphics,5,

+step_wise_1,antutu,1,Overall_Score_2,9,

+step_wise_1,antutu,1,Multitask_2,3,

+step_wise_1,antutu,1,Dalvik_2,5,

+step_wise_1,antutu,1,CPU_integer_2,5,

+step_wise_1,antutu,1,CPU_float-point_2,9,

+step_wise_1,antutu,1,RAM_Operation_2,1,

+step_wise_1,antutu,1,RAM_Speed_2,9,

+step_wise_1,antutu,1,2D_graphics_2,4,

+step_wise_1,antutu,1,3D_graphics_2,7,

+step_wise_1,antutu,1,Overall_Score_3,7,

+step_wise_1,antutu,1,Multitask_3,4,

+step_wise_1,antutu,1,Dalvik_3,6,

+step_wise_1,antutu,1,CPU_integer_3,9,

+step_wise_1,antutu,1,CPU_float-point_3,1,

+step_wise_1,antutu,1,RAM_Operation_3,5,

+step_wise_1,antutu,1,RAM_Speed_3,1,

+step_wise_1,antutu,1,2D_graphics_3,9,

+step_wise_1,antutu,1,3D_graphics_3,6,

+step_wise_1,antutu,1,execution_time,1234.652857065,seconds

+step_wise_2,egypt_offscreen,1,Frames_0,5,frames

+step_wise_2,egypt_offscreen,1,FPS_0,504,fps

+step_wise_2,egypt_offscreen,1,Frames_1,5,frames

+step_wise_2,egypt_offscreen,1,FPS_1,2507,fps

+step_wise_2,egypt_offscreen,1,Frames_2,4,frames

+step_wise_2,egypt_offscreen,1,FPS_2,325,fps

+step_wise_2,egypt_offscreen,1,execution_time,1234.862871885,seconds

+step_wise_3,t-rex_offscreen,1,Frames_0,6,frames

+step_wise_3,t-rex_offscreen,1,FPS_0,2365,fps

+step_wise_3,t-rex_offscreen,1,Frames_1,8,frames

+step_wise_3,t-rex_offscreen,1,FPS_1,429,fps

+step_wise_3,t-rex_offscreen,1,Frames_2,7,frames

+step_wise_3,t-rex_offscreen,1,FPS_2,424,fps

+step_wise_3,t-rex_offscreen,1,execution_time,1234.510928154,seconds

+step_wise_4,geekbench,1,score,8,

+step_wise_4,geekbench,1,multicore_score,5,

+step_wise_4,geekbench,1,Integer_score,2,

+step_wise_4,geekbench,1,Integer_multicore_score,6,

+step_wise_4,geekbench,1,Floating Point_score,7,

+step_wise_4,geekbench,1,Floating Point_multicore_score,4,

+step_wise_4,geekbench,1,Memory_score,4,

+step_wise_4,geekbench,1,Memory_multicore_score,6,

+step_wise_4,geekbench,1,score 1,4,

+step_wise_4,geekbench,1,multicore_score 1,9,

+step_wise_4,geekbench,1,Integer_score 1,8,

+step_wise_4,geekbench,1,Integer_multicore_score 1,8,

+step_wise_4,geekbench,1,Floating Point_score 1,1,

+step_wise_4,geekbench,1,Floating Point_multicore_score 1,5,

+step_wise_4,geekbench,1,Memory_score 1,3,

+step_wise_4,geekbench,1,Memory_multicore_score 1,2,

+step_wise_4,geekbench,1,score 2,2,

+step_wise_4,geekbench,1,multicore_score 2,5,

+step_wise_4,geekbench,1,Integer_score 2,3,

+step_wise_4,geekbench,1,Integer_multicore_score 2,2,

+step_wise_4,geekbench,1,Floating Point_score 2,7,

+step_wise_4,geekbench,1,Floating Point_multicore_score 2,3,

+step_wise_4,geekbench,1,Memory_score 2,9,

+step_wise_4,geekbench,1,Memory_multicore_score 2,5,

+step_wise_4,geekbench,1,execution_time,1234.436671972,seconds

+step_wise_6,thechase,1,execution_time,1234.086979151,seconds

+step_wise_6,thechase,1,FPS,242.0522258138,

+step_wise_6,thechase,1,frame_count,7,

+step_wise_6,thechase,1,janks,0,

+step_wise_6,thechase,1,not_at_vsync,0,

diff --git a/tests/test_baretrace.py b/tests/test_baretrace.py
new file mode 100644
index 0000000..0f5d5c2
--- /dev/null
+++ b/tests/test_baretrace.py
@@ -0,0 +1,90 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import pandas as pd
+import trappy
+import unittest
+
+class TestBareTrace(unittest.TestCase):
+    def __init__(self, *args, **kwargs):
+        super(TestBareTrace, self).__init__(*args, **kwargs)
+        dfr0 = pd.DataFrame({"l1_misses": [24, 535,  41],
+                             "l2_misses": [155, 11, 200],
+                             "cpu":       [ 0,   1,   0]},
+                            index=pd.Series([1.020, 1.342, 1.451], name="Time"))
+
+        dfr1 = pd.DataFrame({"load": [ 35,  16,  21,  28],
+                             "util": [279, 831, 554, 843]},
+                            index=pd.Series([1.279, 1.718, 2.243, 2.465], name="Time"))
+
+        self.dfr = [dfr0, dfr1]
+
+    def test_bare_trace_accepts_name(self):
+        """The BareTrace() accepts a name parameter"""
+
+        trace = trappy.BareTrace(name="foo")
+
+        self.assertEquals(trace.name, "foo")
+
+    def test_bare_trace_can_add_parsed_event(self):
+        """The BareTrace() class can add parsed events to its collection of trace events"""
+        trace = trappy.BareTrace()
+        trace.add_parsed_event("pmu_counters", self.dfr[0])
+
+        self.assertEquals(len(trace.pmu_counters.data_frame), 3)
+        self.assertEquals(trace.pmu_counters.data_frame["l1_misses"].iloc[0], 24)
+
+        trace.add_parsed_event("pivoted_counters", self.dfr[0], pivot="cpu")
+        self.assertEquals(trace.pivoted_counters.pivot, "cpu")
+
+    def test_bare_trace_get_duration(self):
+        """BareTrace.get_duration() works for a simple case"""
+
+        trace = trappy.BareTrace()
+        trace.add_parsed_event("pmu_counter", self.dfr[0])
+        trace.add_parsed_event("load_event", self.dfr[1])
+
+        self.assertEquals(trace.get_duration(), self.dfr[1].index[-1])
+
+    def test_bare_trace_get_duration_normalized(self):
+        """BareTrace.get_duration() works if the trace has been normalized"""
+        return # HACK: Test no longer valid
+
+        trace = trappy.BareTrace()
+        trace.add_parsed_event("pmu_counter", self.dfr[0].copy())
+        trace.add_parsed_event("load_event", self.dfr[1].copy())
+
+        basetime = self.dfr[0].index[0]
+        trace.normalize_time(basetime)
+
+        expected_duration = self.dfr[1].index[-1] - basetime
+        self.assertEquals(trace.get_duration(), expected_duration)
+
+    def test_bare_trace_normalize_time_accepts_basetime(self):
+        """BareTrace().normalize_time() accepts an arbitrary basetime"""
+        return # HACK: Test no longer valid
+
+        trace = trappy.BareTrace()
+        trace.add_parsed_event("pmu_counter", self.dfr[0].copy())
+
+        prev_first_time = trace.pmu_counter.data_frame.index[0]
+        basetime = 3
+
+        trace.normalize_time(basetime)
+
+        self.assertEquals(trace.basetime, basetime)
+
+        exp_first_time = prev_first_time - basetime
+        self.assertEquals(round(trace.pmu_counter.data_frame.index[0] - exp_first_time, 7), 0)
diff --git a/tests/test_base.py b/tests/test_base.py
new file mode 100644
index 0000000..e1fec6d
--- /dev/null
+++ b/tests/test_base.py
@@ -0,0 +1,215 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import os
+import sys
+import unittest
+import utils_tests
+import trappy
+from trappy.base import trace_parser_explode_array
+
+sys.path.append(os.path.join(utils_tests.TESTS_DIRECTORY, "..", "trappy"))
+
+class TestBaseMethods(unittest.TestCase):
+    """Test simple methods that don't need to set up a directory"""
+    def test_trace_parser_explode_array(self):
+        """TestBaseMethods: Basic test of trace_parser_explode_array()"""
+
+        line = "cpus=0000000f freq=1400000 raw_cpu_power=189 load={3 2 12 2} power=14"
+        expected = "cpus=0000000f freq=1400000 raw_cpu_power=189 load0=3 load1=2 load2=12 load3=2 power=14"
+        array_lengths = {"load": 4}
+
+        result = trace_parser_explode_array(line, array_lengths)
+        self.assertEquals(result, expected)
+
+    def test_trace_parser_explode_array_nop(self):
+        """TestBaseMethods: trace_parser_explode_array() returns the same string if there's no array in it"""
+
+        line = "cpus=0000000f freq=1400000 raw_cpu_power=189 load0=3 load1=2 load2=12 load3=2 power=14"
+        array_lengths = {"load": 0}
+
+        result = trace_parser_explode_array(line, array_lengths)
+        self.assertEquals(result, line)
+
+    def test_trace_parser_explode_array_2(self):
+        """TestBaseMethods: trace_parser_explode_array() works if there's two arrays in the string"""
+
+        line = "cpus=0000000f freq=1400000 load={3 2 12 2} power=14 req_power={10 7 2 34}"
+        expected = "cpus=0000000f freq=1400000 load0=3 load1=2 load2=12 load3=2 power=14 req_power0=10 req_power1=7 req_power2=2 req_power3=34"
+        array_lengths = {'load': 4, 'req_power': 4}
+
+        result = trace_parser_explode_array(line, array_lengths)
+        self.assertEquals(result, expected)
+
+    def test_trace_parser_explode_array_diff_lengths(self):
+        """TestBaseMethods: trace_parser_explode_array() expands arrays that are shorter than the expected length
+
+        trace_parser_explode_array() has to be able to deal with an array of
+        size 2 if we tell it in other parts of the trace it is four.
+
+        """
+
+        line = "cpus=0000000f freq=1400000 load={3 2} power=14"
+        expected = "cpus=0000000f freq=1400000 load0=3 load1=2 load2=0 load3=0 power=14"
+        array_lengths = {'load': 4}
+
+        result = trace_parser_explode_array(line, array_lengths)
+        self.assertEquals(result, expected)
+
+class TestBase(utils_tests.SetupDirectory):
+    """Incomplete tests for the Base class"""
+
+    def __init__(self, *args, **kwargs):
+        super(TestBase, self).__init__(
+             [("../doc/trace.txt", "trace.txt"),
+              ("trace_equals.txt", "trace_equals.txt")],
+             *args,
+             **kwargs)
+
+    def test_parse_empty_array(self):
+        """TestBase: Trace with empty array creates a valid DataFrame"""
+
+        in_data = """     kworker/4:1-397   [004]   720.741315: thermal_power_cpu_get: cpus=000000f0 freq=1900000 raw_cpu_power=1259 load={} power=61
+     kworker/4:1-397   [004]   720.741349: thermal_power_cpu_get: cpus=0000000f freq=1400000 raw_cpu_power=189 load={} power=14"""
+
+        expected_columns = set(["__comm", "__pid", "__tgid", "__cpu", "cpus", "freq",
+                                "raw_cpu_power", "power"])
+
+        with open("trace.txt", "w") as fout:
+            fout.write(in_data)
+
+        trace = trappy.FTrace()
+        dfr = trace.cpu_in_power.data_frame
+
+        self.assertEquals(set(dfr.columns), expected_columns)
+        self.assertEquals(dfr["power"].iloc[0], 61)
+
+    def test_parse_special_fields(self):
+        """TestBase: Task name, PID, CPU and timestamp are properly paresed """
+
+        events = {
+                1001.456789 : { 'task': 'rcu_preempt',       'pid': 1123, 'cpu': 001 },
+                1002.456789 : { 'task': 'rs:main',           'pid': 2123, 'cpu': 002 },
+                1003.456789 : { 'task': 'AsyncTask #1',      'pid': 3123, 'cpu': 003 },
+                1004.456789 : { 'task': 'kworker/1:1H',      'pid': 4123, 'cpu': 004 },
+                1005.456789 : { 'task': 'jbd2/sda2-8',       'pid': 5123, 'cpu': 005 },
+                1006.456789 : { 'task': 'IntentService[',    'pid': 6123, 'cpu': 005 },
+                1006.456789 : { 'task': r'/system/bin/.s$_?.u- \a]}c\./ef[.12]*[[l]in]ger',
+                                'pid': 1234, 'cpu': 666 },
+        }
+
+        in_data = """"""
+        for timestamp in sorted(events):
+            in_data+="{0:>16s}-{1:d} [{2:04d}] {3}: event0:   tag=value\n".\
+                    format(
+                        events[timestamp]['task'],
+                        events[timestamp]['pid'],
+                        events[timestamp]['cpu'],
+                        timestamp
+                        )
+
+        expected_columns = set(["__comm", "__pid", "__tgid", "__cpu", "tag"])
+
+        with open("trace.txt", "w") as fout:
+            fout.write(in_data)
+
+        ftrace_parser = trappy.register_dynamic_ftrace("Event0", "event0", scope="sched")
+        trace = trappy.FTrace(normalize_time=False)
+        dfr = trace.event0.data_frame
+
+        self.assertEquals(set(dfr.columns), expected_columns)
+
+        for timestamp, event in events.iteritems():
+            self.assertEquals(dfr["__comm"].loc[timestamp], event['task'])
+            self.assertEquals(dfr["__pid"].loc[timestamp],  event['pid'])
+            self.assertEquals(dfr["__cpu"].loc[timestamp],  event['cpu'])
+
+        trappy.unregister_dynamic_ftrace(ftrace_parser)
+
+
+    def test_parse_values_concatenation(self):
+        """TestBase: Trace with space separated values created a valid DataFrame"""
+
+        in_data = """     rcu_preempt-7     [000]    73.604532: my_sched_stat_runtime:   comm=Space separated taskname pid=7 runtime=262875 [ns] vruntime=17096359856 [ns]"""
+
+        expected_columns = set(["__comm", "__pid", "__tgid", "__cpu", "comm", "pid", "runtime", "vruntime"])
+
+        with open("trace.txt", "w") as fout:
+            fout.write(in_data)
+
+        ftrace_parser = trappy.register_dynamic_ftrace("sched_stat_runtime",
+                                       "my_sched_stat_runtime", scope="sched")
+        trace = trappy.FTrace()
+        dfr = trace.sched_stat_runtime.data_frame
+
+        self.assertEquals(set(dfr.columns), expected_columns)
+        self.assertEquals(dfr["comm"].iloc[0], "Space separated taskname")
+        self.assertEquals(dfr["pid"].iloc[0], 7)
+        self.assertEquals(dfr["runtime"].iloc[0], 262875)
+        self.assertEquals(dfr["vruntime"].iloc[0], 17096359856)
+
+        trappy.unregister_dynamic_ftrace(ftrace_parser)
+
+    def test_get_dataframe(self):
+        """TestBase: Thermal.data_frame["thermal_zone"] exists and
+           it contains a known value"""
+        dfr = trappy.FTrace().thermal.data_frame
+
+        self.assertTrue("thermal_zone" in dfr.columns)
+        self.assertEquals(dfr["temp"].iloc[0], 68786)
+
+    def test_write_csv(self):
+        """TestBase: Base::write_csv() creates a valid csv"""
+        from csv import DictReader
+
+        fname = "thermal.csv"
+        trappy.FTrace().thermal.write_csv(fname)
+
+        with open(fname) as fin:
+            csv_reader = DictReader(fin)
+
+            self.assertTrue("Time" in csv_reader.fieldnames)
+            self.assertTrue("temp" in csv_reader.fieldnames)
+
+            first_data = csv_reader.next()
+            self.assertEquals(first_data["Time"], "0.0")
+            self.assertEquals(first_data["temp"], "68786")
+
+    def test_normalize_time(self):
+        """TestBase: Base::normalize_time() normalizes the time of the trace"""
+        thrm = trappy.FTrace().thermal
+
+        last_prev_time = thrm.data_frame.index[-1]
+
+        basetime = thrm.data_frame.index[0]
+        thrm.normalize_time(basetime)
+
+        last_time = thrm.data_frame.index[-1]
+        expected_last_time = last_prev_time - basetime
+
+        self.assertEquals(round(thrm.data_frame.index[0], 7), 0)
+        self.assertEquals(round(last_time - expected_last_time, 7), 0)
+
+    def test_equals_in_field_value(self):
+        """TestBase: Can parse events with fields with values containing '='"""
+        trace = trappy.FTrace("trace_equals.txt", events=['equals_event'])
+
+        df = trace.equals_event.data_frame
+        self.assertSetEqual(set(df.columns),
+                            set(["__comm", "__pid", "__tgid", "__cpu", "my_field"]))
+        self.assertListEqual(df["my_field"].tolist(),
+                             ["foo", "foo=bar", "foo=bar=baz", 1,
+                              "1=2", "1=foo", "1foo=2"])
diff --git a/tests/test_constraint.py b/tests/test_constraint.py
new file mode 100644
index 0000000..29ed79a
--- /dev/null
+++ b/tests/test_constraint.py
@@ -0,0 +1,196 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import pandas as pd
+import unittest
+
+from trappy.plotter import AttrConf
+from trappy.plotter.Constraint import Constraint, ConstraintManager
+
+class TestConstraintManager(unittest.TestCase):
+    """Test trappy.plotter.ConstraintManager"""
+
+    def __init__(self, *args, **kwargs):
+        """Init some common data for the tests"""
+
+        self.dfrs = [pd.DataFrame({"load": [1, 2, 2, 3],
+                                   "freq": [2, 3, 3, 4],
+                                   "cpu": [0, 1, 0, 1]}),
+                     pd.DataFrame({"load": [2, 3, 2, 1],
+                                   "freq": [1, 2, 2, 1],
+                                   "cpu": [1, 0, 1, 0]})]
+        self.cols = ["load", "freq"]
+        super(TestConstraintManager, self).__init__(*args, **kwargs)
+
+    def test_one_constraint(self):
+        """Test that the constraint manager works with one constraint"""
+
+        dfr = self.dfrs[0]
+
+        c_mgr = ConstraintManager(dfr, "load", None, AttrConf.PIVOT, {})
+
+        self.assertEquals(len(c_mgr), 1)
+
+        constraint = iter(c_mgr).next()
+        series = constraint.result[AttrConf.PIVOT_VAL]
+        self.assertEquals(series.to_dict().values(),
+                          dfr["load"].to_dict().values())
+
+    def test_no_pivot_multiple_traces(self):
+        """Test that the constraint manager works with multiple traces and no pivots"""
+
+        c_mgr = ConstraintManager(self.dfrs, "load", None, AttrConf.PIVOT, {})
+
+        self.assertEquals(len(c_mgr), 2)
+
+        for constraint, orig_dfr in zip(c_mgr, self.dfrs):
+            series = constraint.result[AttrConf.PIVOT_VAL]
+            self.assertEquals(series.to_dict().values(),
+                              orig_dfr["load"].to_dict().values())
+
+    def test_no_pivot_zipped_columns_and_traces(self):
+        """Test the constraint manager with multiple columns and traces zipped"""
+
+        c_mgr = ConstraintManager(self.dfrs, self.cols, None, AttrConf.PIVOT, {})
+
+        self.assertEquals(len(c_mgr), 2)
+
+        for constraint, orig_dfr, col in zip(c_mgr, self.dfrs, self.cols):
+            series = constraint.result[AttrConf.PIVOT_VAL]
+            self.assertEquals(series.to_dict().values(),
+                              orig_dfr[col].to_dict().values())
+
+    def test_no_pivot_multicolumns_multitraces(self):
+        """Test the constraint manager with multiple traces that can have each multiple columns"""
+
+        c_mgr = ConstraintManager(self.dfrs, self.cols, None, AttrConf.PIVOT,
+                                  {}, zip_constraints=False)
+
+        self.assertEquals(len(c_mgr), 4)
+
+        expected_series = [dfr[col] for dfr in self.dfrs for col in self.cols]
+        for constraint, orig_series in zip(c_mgr, expected_series):
+            series = constraint.result[AttrConf.PIVOT_VAL]
+            self.assertEquals(series.to_dict(), orig_series.to_dict())
+
+    def test_no_pivot_filters(self):
+        """Test the constraint manager with filters"""
+
+        simple_filter = {"freq": [2]}
+
+        c_mgr = ConstraintManager(self.dfrs, "load", None, AttrConf.PIVOT,
+                                  simple_filter)
+
+        num_constraints = len(c_mgr)
+        self.assertEquals(num_constraints, 2)
+
+        constraint_iter = iter(c_mgr)
+        constraint = constraint_iter.next()
+        self.assertEquals(len(constraint.result), 1)
+
+        constraint = constraint_iter.next()
+        series_second_frame = constraint.result[AttrConf.PIVOT_VAL]
+        self.assertEquals(series_second_frame.to_dict().values(), [3, 2])
+
+    def test_pivoted_data(self):
+        """Test the constraint manager with a pivot and one trace"""
+
+        c_mgr = ConstraintManager(self.dfrs[0], "load", None, "cpu", {})
+
+        self.assertEquals(len(c_mgr), 1)
+
+        constraint = iter(c_mgr).next()
+        results = dict([(k, v.to_dict().values()) for k, v in constraint.result.items()])
+        expected_results = {0: [1, 2], 1: [2, 3]}
+
+        self.assertEquals(results, expected_results)
+
+    def test_pivoted_multitrace(self):
+        """Test the constraint manager with a pivot and multiple traces"""
+
+        c_mgr = ConstraintManager(self.dfrs, "load", None, "cpu", {})
+
+        self.assertEquals(len(c_mgr), 2)
+
+        constraint_iter = iter(c_mgr)
+        constraint = constraint_iter.next()
+        self.assertEquals(constraint.result[0].to_dict().values(), [1, 2])
+
+        constraint = constraint_iter.next()
+        self.assertEquals(constraint.result[1].to_dict().values(), [2, 2])
+
+    def test_pivoted_multitraces_multicolumns(self):
+        """Test the constraint manager with multiple traces and columns"""
+
+        c_mgr = ConstraintManager(self.dfrs, ["load", "freq"], None, "cpu", {})
+        self.assertEquals(len(c_mgr), 2)
+
+        constraint_iter = iter(c_mgr)
+        constraint = constraint_iter.next()
+        self.assertEquals(constraint.result[1].to_dict().values(), [2, 3])
+
+        constraint = constraint_iter.next()
+        self.assertEquals(constraint.result[0].to_dict().values(), [2, 1])
+
+    def test_pivoted_with_filters(self):
+        """Test the constraint manager with pivoted data and filters"""
+
+        simple_filter = {"load": [2]}
+        c_mgr = ConstraintManager(self.dfrs[0], "freq", None, "cpu",
+                                  simple_filter)
+
+        self.assertEquals(len(c_mgr), 1)
+
+        constraint = iter(c_mgr).next()
+        result = constraint.result
+
+        self.assertEquals(result[0].iloc[0], 3)
+        self.assertEquals(result[1].iloc[0], 3)
+
+    def test_constraint_with_window(self):
+        """Test that the constraint manager can constraint to a window of time"""
+        c_mgr = ConstraintManager(self.dfrs[0], "freq", None, AttrConf.PIVOT, {},
+                                  window=(1, 3))
+
+        constraint = iter(c_mgr).next()
+        series = constraint.result[AttrConf.PIVOT_VAL]
+        self.assertEquals(len(series), 3)
+
+        # For the graph to plot a value at 0.75, the resulting series
+        # must contain the value before 0.75.  Same for the upper limit.
+        c_mgr = ConstraintManager(self.dfrs[0], "freq", None, AttrConf.PIVOT, {},
+                                  window=(0.75, 1.5))
+
+        constraint = iter(c_mgr).next()
+        series = constraint.result[AttrConf.PIVOT_VAL]
+        self.assertEquals(series.index.tolist(), [0, 1, 2])
+
+        c_mgr = ConstraintManager(self.dfrs[0], "freq", None, AttrConf.PIVOT, {},
+                                  window=(0, 2))
+
+        constraint = iter(c_mgr).next()
+        series = constraint.result[AttrConf.PIVOT_VAL]
+        self.assertEquals(len(series), 3)
+
+class TestConstraint(unittest.TestCase):
+    def test_str_constraint(self):
+        """str(constraint) doesn't fail when the column is not a string"""
+        dfr = pd.DataFrame({12: [1, 2, 3], 13: [3, 4, 5]})
+
+        constraint = Constraint(dfr, AttrConf.PIVOT, 12, template=None,
+                                trace_index=0, filters={}, window=None)
+
+        self.assertEqual(str(constraint), "DataFrame 0:12")
diff --git a/tests/test_copyright.py b/tests/test_copyright.py
new file mode 100644
index 0000000..888cd6e
--- /dev/null
+++ b/tests/test_copyright.py
@@ -0,0 +1,104 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+from datetime import date
+from glob import glob
+import os
+import re
+import unittest
+
+
+def copyright_is_valid(fname):
+    """Return True if fname has a valid copyright"""
+    with open(fname) as fin:
+        # Read the first 2K of the file.  If the copyright is not there, you
+        # are probably doing something wrong
+        lines = fin.readlines(2048)
+
+    # Either the first or the second line must have a "Copyright:" line
+    first_line = re.compile(r"(#| \*)    Copyright")
+    try:
+        if not first_line.search(lines[0]):
+            if first_line.search(lines[1]):
+                # Drop the first line to align the copyright to lines[0]
+                lines = lines[1:]
+            else:
+                return False
+    except IndexError:
+        return False
+
+    # The copyright mentions ARM Limited
+    if "ARM Limited" not in lines[0]:
+        return False
+
+    apache_line = 6
+    if "Google Inc" in lines[1]:
+        apache_line += 1
+
+    # The Copyright includes the current year
+    current_year = date.today().year
+    if str(current_year) not in lines[0]:
+        return False
+
+    # It's the apache license
+    if "http://www.apache.org/licenses/LICENSE-2.0" not in lines[apache_line]:
+        return False
+
+    return True
+
+
+class TestCopyRight(unittest.TestCase):
+    def test_copyrights(self):
+        """Check that all files have valid copyrights"""
+
+        tests_dir = os.path.dirname(os.path.abspath(__file__))
+        base_dir = os.path.dirname(tests_dir)
+        patterns_to_ignore = {}
+
+        for root, dirs, files in os.walk(base_dir):
+            if ".gitignore" in files:
+                fname = os.path.join(root, ".gitignore")
+                with open(fname) as fin:
+                    lines = fin.readlines()
+
+            patterns_to_ignore[root] = [l.strip() for l in lines]
+
+            files_to_ignore = []
+            for directory, patterns in patterns_to_ignore.iteritems():
+                if root.startswith(directory):
+                    for pat in patterns:
+                        pat = os.path.join(root, pat)
+                        files_to_ignore.extend(glob(pat))
+
+            for dirname in dirs:
+                full_dirname = os.path.join(root, dirname)
+                if full_dirname in files_to_ignore:
+                    dirs.remove(dirname)
+
+
+            for fname in files:
+                fname = os.path.join(root, fname)
+                if fname in files_to_ignore:
+                    continue
+
+                extension = os.path.splitext(fname)[1]
+                if extension in [".py", ".js", ".css"]:
+                    if not copyright_is_valid(fname):
+                        print("Invalid copyright in {}".format(fname))
+                        self.fail()
+
+            if '.git' in dirs:
+                dirs.remove('.git')
diff --git a/tests/test_cpu_power.py b/tests/test_cpu_power.py
new file mode 100644
index 0000000..3ab3114
--- /dev/null
+++ b/tests/test_cpu_power.py
@@ -0,0 +1,145 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import matplotlib
+import pandas as pd
+
+from test_thermal import BaseTestThermal
+import trappy
+import cpu_power
+
+class TestCpuPower(BaseTestThermal):
+    def __init__(self, *args, **kwargs):
+        super(TestCpuPower, self).__init__(*args, **kwargs)
+        self.map_label = {"00000000,00000039": "A53", "00000000,00000006": "A57"}
+
+    def test_pivot_with_labels(self):
+        """Test pivot_with_labels()"""
+        map_label = {"000000f0": "A15", "0000000f": "A7"}
+        dfr_in = pd.DataFrame({'cpus': ["000000f0", "0000000f", "000000f0", "0000000f"],
+                               'freq': [1, 3, 2, 6]})
+
+        dfr_out = cpu_power.pivot_with_labels(dfr_in, "freq", "cpus", map_label)
+
+        self.assertEquals(dfr_out["A15"].iloc[0], 1)
+        self.assertEquals(dfr_out["A15"].iloc[1], 1)
+        self.assertEquals(dfr_out["A15"].iloc[2], 2)
+        self.assertEquals(dfr_out["A7"].iloc[1], 3)
+
+    def test_num_cpus_in_mask(self):
+        """num_cpus_in_mask() works with the masks we usually use"""
+        mask = "000000f0"
+        self.assertEquals(cpu_power.num_cpus_in_mask(mask), 4)
+
+        mask = sorted(self.map_label)[0]
+        self.assertEquals(cpu_power.num_cpus_in_mask(mask), 2)
+
+        mask = sorted(self.map_label)[1]
+        self.assertEquals(cpu_power.num_cpus_in_mask(mask), 4)
+
+    def test_cpuoutpower_dataframe(self):
+        """Test that CpuOutPower() creates a proper data_frame"""
+        outp = trappy.FTrace().cpu_out_power
+
+        self.assertEquals(outp.data_frame["power"].iloc[0], 1344)
+        self.assertTrue("cdev_state" in outp.data_frame.columns)
+
+    def test_cpuoutpower_get_all_freqs(self):
+        """Test CpuOutPower.get_all_freqs()"""
+        dfr = trappy.FTrace().cpu_out_power.get_all_freqs(self.map_label)
+
+        self.assertEquals(dfr["A57"].iloc[0], 1100)
+        self.assertEquals(dfr["A53"].iloc[1], 850)
+
+    def test_cpuinpower_get_dataframe(self):
+        """Test that CpuInPower() creates a proper data_frame()"""
+        inp = trappy.FTrace().cpu_in_power
+
+        self.assertTrue("load0" in inp.data_frame.columns)
+        self.assertEquals(inp.data_frame["load0"].iloc[0], 24)
+
+    def test_cpuinpower_big_cpumask(self):
+        """CpuInPower()'s data_frame is not confused by 64-bit cpumasks"""
+        in_data = """     kworker/2:2-679   [002]   676.256284: thermal_power_cpu_get:  cpus=00000000,0000000f freq=261888 cdev_state=5 power=12
+     kworker/2:2-679   [002]   676.276200: thermal_power_cpu_get:  cpus=00000000,00000030 freq=261888 cdev_state=5 power=0
+     kworker/2:2-679   [002]   676.416202: thermal_power_cpu_get:  cpus=00000000,0000000f freq=261888 cdev_state=5 power=0
+        """
+        with open("trace.txt", "w") as fout:
+            fout.write(in_data)
+
+        inp = trappy.FTrace(normalize_time=False).cpu_in_power
+        self.assertEquals(round(inp.data_frame.index[0], 6), 676.256284)
+        self.assertEquals(inp.data_frame["cpus"].iloc[1], "00000000,00000030")
+
+    def test_cpuinpower_data_frame_asymmetric_clusters(self):
+        """Test that CpuInPower()'s data_frame can handle asymmetric clusters
+
+        That is 2 cpus in one cluster and 4 in another, like Juno
+        """
+        in_data = """
+     kworker/2:2-679   [002]   676.256261: thermal_power_cpu_get:   cpus=00000000,00000030 freq=1900000 raw_cpu_power=1259 load={74 49} power=451
+     kworker/2:2-679   [002]   676.256271: thermal_power_cpu_get:   cpus=00000000,0000000f freq=450000 raw_cpu_power=36 load={1 2 1 3} power=9
+"""
+
+        with open("trace.txt", "w") as fout:
+            fout.write(in_data)
+
+        inp = trappy.FTrace(normalize_time=False).cpu_in_power
+
+        self.assertEquals(inp.data_frame["load0"].iloc[0], 74)
+        self.assertEquals(inp.data_frame["load1"].iloc[0], 49)
+        self.assertEquals(inp.data_frame["load2"].iloc[0], 0)
+        self.assertEquals(inp.data_frame["load3"].iloc[0], 0)
+        self.assertEquals(inp.data_frame["load0"].iloc[1], 1)
+        self.assertEquals(inp.data_frame["load1"].iloc[1], 2)
+        self.assertEquals(inp.data_frame["load2"].iloc[1], 1)
+        self.assertEquals(inp.data_frame["load3"].iloc[1], 3)
+
+    def test_cpuinpower_get_all_freqs(self):
+        """Test CpuInPower.get_all_freqs()"""
+        dfr = trappy.FTrace().cpu_in_power.get_all_freqs(self.map_label)
+
+        self.assertEquals(dfr["A57"].iloc[0], 1100)
+        self.assertEquals(dfr["A53"].iloc[1], 850)
+        self.assertEquals(dfr["A57"].iloc[5], 1100)
+
+    def test_cpuinpower_get_load_data(self):
+        """Test CpuInPower.get_load_data()"""
+        trace = trappy.FTrace()
+        first_load = trace.cpu_in_power.data_frame["load0"].iloc[0]
+        load_data = trace.cpu_in_power.get_load_data(self.map_label)
+
+        self.assertEquals(load_data["A57"].iloc[0], 24 + 19)
+        self.assertEquals(load_data["A53"].iloc[3], 32 + 28 + 46 + 44)
+        self.assertEquals(load_data["A57"].iloc[0], load_data["A57"].iloc[1])
+
+        self.assertEquals(trace.cpu_in_power.data_frame["load0"].iloc[0],
+                          first_load)
+
+    def test_cpuinpower_get_normalized_load_data(self):
+        """Test CpuInPower.get_normalized_load_data()"""
+        trace = trappy.FTrace()
+        first_load = trace.cpu_in_power.data_frame["load0"].iloc[0]
+        load_data = trace.cpu_in_power.get_normalized_load_data(self.map_label)
+
+        # Ideally the trace should have an event in which the cpus are
+        # not running at maximum frequency
+        self.assertEquals(load_data["A57"].iloc[0],
+                          (24. + 19) * 1100000 / (1100000 * 2))
+        self.assertEquals(load_data["A53"].iloc[1],
+                          (36. + 49 + 48 + 7) * 850000 / (850000 * 4))
+        self.assertEquals(trace.cpu_in_power.data_frame["load0"].iloc[0],
+                          first_load)
diff --git a/tests/test_devfreq.py b/tests/test_devfreq.py
new file mode 100644
index 0000000..dc91fbf
--- /dev/null
+++ b/tests/test_devfreq.py
@@ -0,0 +1,50 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import pandas as pd
+
+import trappy
+from test_thermal import BaseTestThermal
+
+class TestDevfreqPower(BaseTestThermal):
+    """Tests for the DevfreqInPower and DevfreqOutPower classes"""
+
+    def test_devfreq_inp_dataframe(self):
+        """Test that DevfreqInPower creates proper data frames"""
+        devfreq_in_power = trappy.FTrace().devfreq_in_power
+
+        self.assertTrue("freq" in devfreq_in_power.data_frame.columns)
+
+    def test_devfreq_outp_dataframe(self):
+        """Test that DevfreqOutPower creates proper data frames"""
+        devfreq_out_power = trappy.FTrace().devfreq_out_power
+
+        self.assertTrue("freq" in devfreq_out_power.data_frame.columns)
+
+    def test_get_inp_all_freqs(self):
+        """Test that DevfreqInPower get_all_freqs() work"""
+
+        all_freqs = trappy.FTrace().devfreq_in_power.get_all_freqs()
+        self.assertTrue(isinstance(all_freqs, pd.DataFrame))
+
+        self.assertEquals(all_freqs["freq"].iloc[0], 525)
+
+    def test_get_outp_all_freqs(self):
+        """Test that DevfreqOutPower get_all_freqs() work"""
+
+        all_freqs = trappy.FTrace().devfreq_out_power.get_all_freqs()
+        self.assertTrue(isinstance(all_freqs, pd.DataFrame))
+
+        self.assertEquals(all_freqs["freq"].iloc[0], 525)
diff --git a/tests/test_duplicates.py b/tests/test_duplicates.py
new file mode 100644
index 0000000..32ce8e3
--- /dev/null
+++ b/tests/test_duplicates.py
@@ -0,0 +1,96 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import unittest
+import matplotlib
+import pandas as pd
+import utils_tests
+import trappy
+import shutil
+
+from test_thermal import BaseTestThermal
+
+
+class TestPlotterDupVals(BaseTestThermal):
+
+    """Test Duplicate Entries in plotter"""
+
+    def __init__(self, *args, **kwargs):
+        super(TestPlotterDupVals, self).__init__(*args, **kwargs)
+
+    def test_plotter_duplicates(self):
+        """Test that plotter handles duplicates fine"""
+        with open("trace.txt", "w") as fout:
+            fout.write("""version = 6
+cpus=6
+       rcuos/2-22 [001] 0000.018510: sched_load_avg_sg: cpus=00000001 load=0 utilization=0
+       rcuos/2-22 [001] 6550.018611: sched_load_avg_sg: cpus=00000002 load=1 utilization=1
+       rcuos/2-22 [001] 6550.018611: sched_load_avg_sg: cpus=00000004 load=2 utilization=2
+       rcuos/2-22 [001] 6550.018612: sched_load_avg_sg: cpus=00000001 load=2 utilization=3
+       rcuos/2-22 [001] 6550.018624: sched_load_avg_sg: cpus=00000002 load=1 utilization=4
+       rcuos/2-22 [001] 6550.018625: sched_load_avg_sg: cpus=00000002 load=2 utilization=5
+       rcuos/2-22 [001] 6550.018626: sched_load_avg_sg: cpus=00000002 load=3 utilization=6
+       rcuos/2-22 [001] 6550.018627: sched_load_avg_sg: cpus=00000002 load=1 utilization=7
+       rcuos/2-22 [001] 6550.018628: sched_load_avg_sg: cpus=00000004 load=2 utilization=8\n""")
+            fout.close()
+        trace1 = trappy.FTrace(name="first")
+        l = trappy.LinePlot(
+            trace1,
+            trappy.sched.SchedLoadAvgSchedGroup,
+            column=['utilization'],
+            filters={
+                "load": [
+                    1,
+                    2]},
+            pivot="cpus",
+            marker='o',
+            linestyle='none',
+            per_line=3)
+        l.view(test=True)
+
+    def test_plotter_triplicates(self):
+
+        """Test that plotter handles triplicates fine"""
+
+        with open("trace.txt", "w") as fout:
+            fout.write("""version = 6
+cpus=6
+       rcuos/2-22 [001] 0000.018510: sched_load_avg_sg: cpus=00000001 load=0 utilization=0
+       rcuos/2-22 [001] 6550.018611: sched_load_avg_sg: cpus=00000002 load=1 utilization=1
+       rcuos/2-22 [001] 6550.018611: sched_load_avg_sg: cpus=00000004 load=2 utilization=2
+       rcuos/2-22 [001] 6550.018611: sched_load_avg_sg: cpus=00000004 load=2 utilization=2
+       rcuos/2-22 [001] 6550.018612: sched_load_avg_sg: cpus=00000001 load=2 utilization=3
+       rcuos/2-22 [001] 6550.018624: sched_load_avg_sg: cpus=00000002 load=1 utilization=4
+       rcuos/2-22 [001] 6550.018625: sched_load_avg_sg: cpus=00000002 load=2 utilization=5
+       rcuos/2-22 [001] 6550.018626: sched_load_avg_sg: cpus=00000002 load=3 utilization=6
+       rcuos/2-22 [001] 6550.018627: sched_load_avg_sg: cpus=00000002 load=1 utilization=7
+       rcuos/2-22 [001] 6550.018628: sched_load_avg_sg: cpus=00000004 load=2 utilization=8\n""")
+            fout.close()
+
+        trace1 = trappy.FTrace(name="first")
+        l = trappy.LinePlot(
+            trace1,
+            trappy.sched.SchedLoadAvgSchedGroup,
+            column=['utilization'],
+            filters={
+                "load": [
+                    1,
+                    2]},
+            pivot="cpus",
+            marker='o',
+            linestyle='none',
+            per_line=3)
+        l.view(test=True)
diff --git a/tests/test_dynamic.py b/tests/test_dynamic.py
new file mode 100644
index 0000000..d69dbcd
--- /dev/null
+++ b/tests/test_dynamic.py
@@ -0,0 +1,127 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import unittest
+import matplotlib
+from test_sched import BaseTestSched
+from trappy.base import Base
+import trappy
+
+
+class DynamicEvent(Base):
+
+    """Test the ability to register
+       specific classes to trappy"""
+
+    unique_word = "dynamic_test_key"
+    name = "dynamic_event"
+
+
+class TestDynamicEvents(BaseTestSched):
+
+    def __init__(self, *args, **kwargs):
+        super(TestDynamicEvents, self).__init__(*args, **kwargs)
+
+    def test_dynamic_data_frame(self):
+        """
+           Test if the dynamic events are populated
+           in the data frame
+        """
+        parse_class = trappy.register_dynamic_ftrace("DynamicEvent", "dynamic_test_key")
+        t = trappy.FTrace(name="first")
+        self.assertTrue(len(t.dynamic_event.data_frame) == 1)
+
+        trappy.unregister_dynamic_ftrace(parse_class)
+
+    def test_dynamic_class_attr(self):
+        """
+           Test the attibutes of the dynamically
+           generated class
+        """
+        cls = trappy.register_dynamic_ftrace("DynamicEvent", "dynamic_test_key",
+              pivot="test_pivot")
+        self.assertEquals(cls.__name__, "DynamicEvent")
+        self.assertEquals(cls.name, "dynamic_event")
+        self.assertEquals(cls.unique_word, "dynamic_test_key")
+        self.assertEquals(cls.pivot, "test_pivot")
+
+        trappy.unregister_dynamic_ftrace(cls)
+
+    def test_dynamic_event_plot(self):
+        """Test if plotter can accept a dynamic class
+            for a template argument"""
+
+        cls = trappy.register_dynamic_ftrace("DynamicEvent", "dynamic_test_key")
+        t = trappy.FTrace(name="first")
+        l = trappy.LinePlot(t, cls, column="load")
+        l.view(test=True)
+
+        trappy.unregister_dynamic_ftrace(cls)
+
+    def test_dynamic_event_scope(self):
+	"""Test the case when an "all" scope class is
+	registered. it should appear in both thermal and sched
+	ftrace class definitions when scoped ftrace objects are created
+	"""
+        cls = trappy.register_dynamic_ftrace("DynamicEvent", "dynamic_test_key")
+        t1 = trappy.FTrace(name="first")
+	self.assertTrue(t1.class_definitions.has_key(cls.name))
+
+        trappy.unregister_dynamic_ftrace(cls)
+
+    def test_register_ftrace_parser(self):
+        trappy.register_ftrace_parser(DynamicEvent)
+        t = trappy.FTrace(name="first")
+        self.assertTrue(len(t.dynamic_event.data_frame) == 1)
+
+        trappy.unregister_ftrace_parser(DynamicEvent)
+
+    def test_no_none_pivot(self):
+        """register_dynamic_ftrace() with default value for pivot doesn't create a class with a pivot=None"""
+        cls = trappy.register_dynamic_ftrace("MyEvent", "my_dyn_test_key")
+        self.assertFalse(hasattr(cls, "pivot"))
+
+        trappy.unregister_dynamic_ftrace(cls)
+
+    def test_unregister_dynamic_ftrace(self):
+        """Test that dynamic events can be unregistered"""
+        dyn_event = trappy.register_dynamic_ftrace("DynamicEvent",
+                                                   "dynamic_test_key")
+        trace = trappy.FTrace(name="first")
+        self.assertTrue(len(trace.dynamic_event.data_frame) == 1)
+
+        trappy.unregister_dynamic_ftrace(dyn_event)
+        trace = trappy.FTrace(name="first")
+
+        self.assertFalse(hasattr(trace, "dynamic_event"))
+
+        dyn_event = trappy.register_dynamic_ftrace("DynamicEvent",
+                                                   "dynamic_test_key",
+                                                   scope="sched")
+        trace = trappy.FTrace(name="first")
+        self.assertTrue(len(trace.dynamic_event.data_frame) == 1)
+
+        trappy.unregister_dynamic_ftrace(dyn_event)
+        trace = trappy.FTrace(name="first")
+
+        self.assertFalse(hasattr(trace, "dynamic_event"))
+
+    def test_unregister_ftrace_parser(self):
+        """unregister_ftrace_parser() works"""
+        trappy.register_ftrace_parser(DynamicEvent)
+        trappy.unregister_ftrace_parser(DynamicEvent)
+        trace = trappy.FTrace()
+        self.assertFalse(hasattr(trace, "dynamic_event"))
diff --git a/tests/test_ftrace.py b/tests/test_ftrace.py
new file mode 100644
index 0000000..021d70b
--- /dev/null
+++ b/tests/test_ftrace.py
@@ -0,0 +1,516 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import matplotlib
+import os
+import pandas as pd
+import re
+import shutil
+import subprocess
+import tempfile
+import time
+import unittest
+
+from test_thermal import BaseTestThermal
+import trappy
+import utils_tests
+
+class TestFTrace(BaseTestThermal):
+    def __init__(self, *args, **kwargs):
+        super(TestFTrace, self).__init__(*args, **kwargs)
+        self.map_label = {"00000000,00000006": "A57", "00000000,00000039": "A53"}
+
+    def test_ftrace_has_all_classes(self):
+        """The FTrace() class has members for all classes"""
+
+        trace = trappy.FTrace()
+
+        for attr in trace.class_definitions.iterkeys():
+            self.assertTrue(hasattr(trace, attr))
+
+    def test_ftrace_has_all_classes_scope_all(self):
+        """The FTrace() class has members for all classes with scope=all"""
+
+        trace = trappy.FTrace(scope="all")
+
+        for attr in trace.class_definitions.iterkeys():
+            self.assertTrue(hasattr(trace, attr))
+
+    def test_ftrace_has_all_classes_scope_thermal(self):
+        """The FTrace() class has only members for thermal classes with scope=thermal"""
+
+        trace = trappy.FTrace(scope="thermal")
+
+        for attr in trace.thermal_classes.iterkeys():
+            self.assertTrue(hasattr(trace, attr))
+
+        for attr in trace.sched_classes.iterkeys():
+            self.assertFalse(hasattr(trace, attr))
+
+    def test_ftrace_has_all_classes_scope_sched(self):
+        """The FTrace() class has only members for sched classes with scope=sched"""
+
+        trace = trappy.FTrace(scope="sched")
+
+        for attr in trace.thermal_classes.iterkeys():
+            self.assertFalse(hasattr(trace, attr))
+
+        for attr in trace.sched_classes.iterkeys():
+            self.assertTrue(hasattr(trace, attr))
+
+    def test_ftrace_has_no_classes_scope_dynamic(self):
+        """The FTrace() class has only dynamically registered classes with scope=custom"""
+
+        trace = trappy.FTrace(scope="custom")
+
+        for attr in trace.thermal_classes.iterkeys():
+            self.assertFalse(hasattr(trace, attr))
+
+        for attr in trace.sched_classes.iterkeys():
+            self.assertFalse(hasattr(trace, attr))
+
+        ftrace_parser = trappy.register_dynamic_ftrace("ADynamicEvent",
+                                                       "a_dynamic_event")
+        trace = trappy.FTrace(scope="custom")
+
+        self.assertTrue(hasattr(trace, "a_dynamic_event"))
+
+        trappy.unregister_dynamic_ftrace(ftrace_parser)
+
+
+    def test_ftrace_doesnt_overwrite_parsed_event(self):
+        """FTrace().add_parsed_event() should not override an event that's already present"""
+        trace = trappy.FTrace()
+        dfr = pd.DataFrame({"temp": [45000, 46724, 45520]},
+                           index=pd.Series([1.020, 1.342, 1.451], name="Time"))
+
+        with self.assertRaises(ValueError):
+            trace.add_parsed_event("sched_switch", dfr)
+
+    def test_fail_if_no_trace_dat(self):
+        """Raise an IOError with the path if there's no trace.dat and trace.txt"""
+        os.remove("trace.txt")
+        self.assertRaises(IOError, trappy.FTrace)
+
+        cwd = os.getcwd()
+
+        try:
+            trappy.FTrace(cwd)
+        except IOError as exception:
+            pass
+
+        self.assertTrue(cwd in str(exception))
+
+    def test_other_directory(self):
+        """FTrace() can grab the trace.dat from other directories"""
+
+        other_random_dir = tempfile.mkdtemp()
+        os.chdir(other_random_dir)
+
+        dfr = trappy.FTrace(self.out_dir).thermal.data_frame
+
+        self.assertTrue(len(dfr) > 0)
+        self.assertEquals(os.getcwd(), other_random_dir)
+
+    def test_ftrace_arbitrary_trace_txt(self):
+        """FTrace() works if the trace is called something other than trace.txt"""
+        arbitrary_trace_name = "my_trace.txt"
+        shutil.move("trace.txt", arbitrary_trace_name)
+
+        dfr = trappy.FTrace(arbitrary_trace_name).thermal.data_frame
+
+        self.assertTrue(len(dfr) > 0)
+        self.assertFalse(os.path.exists("trace.txt"))
+        # As there is no raw trace requested. The mytrace.raw.txt
+        # Should not have been generated
+        self.assertFalse(os.path.exists("mytrace.raw.txt"))
+
+    def test_ftrace_autonormalize_time(self):
+        """FTrace() normalizes by default"""
+
+        trace = trappy.FTrace()
+
+        self.assertEquals(round(trace.thermal.data_frame.index[0], 7), 0)
+
+    def test_ftrace_dont_normalize_time(self):
+        """FTrace() doesn't normalize if asked not to"""
+
+        trace = trappy.FTrace(normalize_time=False)
+
+        self.assertNotEquals(round(trace.thermal.data_frame.index[0], 7), 0)
+
+    def test_ftrace_basetime(self):
+        """Test that basetime calculation is correct"""
+
+        trace = trappy.FTrace(normalize_time=False)
+
+        basetime = trace.thermal.data_frame.index[0]
+
+        self.assertEqual(trace.basetime, basetime)
+
+    def test_ftrace_duration(self):
+        """Test get_duration: normalize_time=False"""
+
+        trace = trappy.FTrace(normalize_time=True)
+
+        duration = trace.thermal_governor.data_frame.index[-1] - trace.thermal.data_frame.index[0]
+
+        self.assertEqual(trace.get_duration(), duration)
+
+    def test_ftrace_duration_not_normalized(self):
+        """Test get_duration: normalize_time=True"""
+
+        trace = trappy.FTrace(normalize_time=False)
+
+        duration = trace.thermal_governor.data_frame.index[-1] - trace.thermal.data_frame.index[0]
+
+        self.assertEqual(trace.get_duration(), duration)
+
+
+    def test_ftrace_normalize_time(self):
+        """FTrace().normalize_time() works accross all classes"""
+
+        return # HACK: Time normalization test no longer valid
+
+        trace = trappy.FTrace(normalize_time=False)
+
+        prev_inpower_basetime = trace.cpu_in_power.data_frame.index[0]
+        prev_inpower_last = trace.cpu_in_power.data_frame.index[-1]
+
+        trace.normalize_time()
+
+        self.assertEquals(round(trace.thermal.data_frame.index[0], 7), 0)
+
+        exp_inpower_first = prev_inpower_basetime - trace.basetime
+        self.assertEquals(round(trace.cpu_in_power.data_frame.index[0] - exp_inpower_first, 7), 0)
+
+        exp_inpower_last = prev_inpower_last - trace.basetime
+        self.assertEquals(round(trace.cpu_in_power.data_frame.index[-1] - exp_inpower_last, 7), 0)
+
+    def test_ftrace_accepts_events(self):
+        """The FTrace class accepts an events parameter with only the parameters interesting for a trace"""
+
+        trace = trappy.FTrace(scope="custom", events=["cdev_update"])
+
+        self.assertGreater(len(trace.cdev_update.data_frame), 1)
+
+        # If you specify events as a string by mistake, trappy does the right thing
+        trace = trappy.FTrace(scope="custom", events="foo")
+        self.assertTrue(hasattr(trace, "foo"))
+
+    def test_ftrace_already_registered_events_are_not_registered_again(self):
+        """FTrace(events="foo") uses class for foo if it is a known class for trappy"""
+        events = ["sched_switch", "sched_load_avg_sg"]
+        trace = trappy.FTrace(scope="custom", events=events)
+
+        self.assertTrue(trace.sched_switch.parse_raw)
+        self.assertEquals(trace.sched_load_avg_sg.pivot, "cpus")
+
+    def test_get_all_freqs_data(self):
+        """Test get_all_freqs_data()"""
+
+        allfreqs = trappy.FTrace().get_all_freqs_data(self.map_label)
+
+        self.assertEquals(allfreqs[1][1]["A53_freq_out"].iloc[3], 850)
+        self.assertEquals(allfreqs[1][1]["A53_freq_in"].iloc[1], 850)
+        self.assertEquals(allfreqs[0][1]["A57_freq_out"].iloc[2], 1100)
+        self.assertTrue("gpu_freq_in" in allfreqs[2][1].columns)
+
+        # Make sure there are no NaNs in the middle of the array
+        self.assertTrue(allfreqs[0][1]["A57_freq_in"].notnull().all())
+
+    def test_plot_freq_hists(self):
+        """Test that plot_freq_hists() doesn't bomb"""
+
+        trace = trappy.FTrace()
+
+        _, axis = matplotlib.pyplot.subplots(nrows=2)
+        trace.plot_freq_hists(self.map_label, axis)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_load(self):
+        """Test that plot_load() doesn't explode"""
+        trace = trappy.FTrace()
+        trace.plot_load(self.map_label, title="Util")
+
+        _, ax = matplotlib.pyplot.subplots()
+        trace.plot_load(self.map_label, ax=ax)
+
+    def test_plot_normalized_load(self):
+        """Test that plot_normalized_load() doesn't explode"""
+
+        trace = trappy.FTrace()
+
+        _, ax = matplotlib.pyplot.subplots()
+        trace.plot_normalized_load(self.map_label, ax=ax)
+
+    def test_plot_allfreqs(self):
+        """Test that plot_allfreqs() doesn't bomb"""
+
+        trace = trappy.FTrace()
+
+        trace.plot_allfreqs(self.map_label)
+        matplotlib.pyplot.close('all')
+
+        _, axis = matplotlib.pyplot.subplots(nrows=2)
+
+        trace.plot_allfreqs(self.map_label, ax=axis)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_allfreqs_with_one_actor(self):
+        """Check that plot_allfreqs() works with one actor"""
+
+        in_data = """     kworker/4:1-397   [004]   720.741349: thermal_power_cpu_get: cpus=00000000,00000006 freq=1400000 raw_cpu_power=189 load={23, 12} power=14
+     kworker/4:1-397   [004]   720.741679: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1400000 cdev_state=1 power=14"""
+
+        with open("trace.txt", "w") as fout:
+            fout.write(in_data)
+
+        trace = trappy.FTrace()
+        map_label = {"00000000,00000006": "A57"}
+        _, axis = matplotlib.pyplot.subplots(nrows=1)
+
+        trace.plot_allfreqs(map_label, ax=[axis])
+        matplotlib.pyplot.close('all')
+
+    def test_trace_metadata(self):
+        """Test if metadata gets populated correctly"""
+
+        expected_metadata = {}
+        expected_metadata["version"] = "6"
+        expected_metadata["cpus"] = "6"
+
+        trace = trappy.FTrace()
+        for key, value in expected_metadata.items():
+            self.assertTrue(hasattr(trace, "_" + key))
+            self.assertEquals(getattr(trace, "_" + key), value)
+
+    def test_missing_metadata(self):
+        """Test if trappy.FTrace() works with a trace missing metadata info"""
+        lines = []
+
+        with open("trace.txt", "r") as fil:
+            lines += fil.readlines()
+            lines = lines[7:]
+            fil.close()
+
+        with open("trace.txt", "w") as fil:
+            fil.write("".join(lines))
+            fil.close()
+
+        trace = trappy.FTrace()
+        self.assertEquals(trace._cpus, None)
+        self.assertEquals(trace._version, None)
+        self.assertTrue(len(trace.thermal.data_frame) > 0)
+
+    def test_ftrace_accepts_window(self):
+        """FTrace class accepts a window parameter"""
+        trace = trappy.FTrace(window=(1.234726, 5.334726))
+        self.assertEquals(trace.thermal.data_frame.iloc[0]["temp"], 68989)
+        self.assertEquals(trace.thermal.data_frame.iloc[-1]["temp"], 69530)
+
+    def test_ftrace_accepts_abs_window(self):
+        """FTrace class accepts an abs_window parameter"""
+        trace = trappy.FTrace(abs_window=(1585, 1589.1))
+        self.assertEquals(trace.thermal.data_frame.iloc[0]["temp"], 68989)
+        self.assertEquals(trace.thermal.data_frame.iloc[-1]["temp"], 69530)
+
+
+@unittest.skipUnless(utils_tests.trace_cmd_installed(),
+                     "trace-cmd not installed")
+class TestFTraceRawDat(utils_tests.SetupDirectory):
+
+    def __init__(self, *args, **kwargs):
+        super(TestFTraceRawDat, self).__init__(
+             [("raw_trace.dat", "trace.dat")],
+             *args,
+             **kwargs)
+
+    def test_raw_dat(self):
+        """Tests an event that relies on raw parsing"""
+
+        trace = trappy.FTrace()
+        self.assertTrue(hasattr(trace, "sched_switch"))
+        self.assertTrue(len(trace.sched_switch.data_frame) > 0)
+        self.assertTrue("prev_comm" in trace.sched_switch.data_frame.columns)
+
+    def test_raw_dat_arb_name(self):
+        """Tests an event that relies on raw parsing with arbitrary .dat file name"""
+
+        arbitrary_name = "my_trace.dat"
+        shutil.move("trace.dat", arbitrary_name)
+
+        trace = trappy.FTrace(arbitrary_name)
+        self.assertTrue(os.path.isfile("my_trace.raw.txt"))
+        self.assertTrue(hasattr(trace, "sched_switch"))
+        self.assertTrue(len(trace.sched_switch.data_frame) > 0)
+
+    def test_raw_created_if_dat_and_txt_exist(self):
+        """trace.raw.txt is created when both trace.dat and trace.txt exist"""
+
+        # Create the trace.txt
+        cmd = ["trace-cmd", "report", "trace.dat"]
+        with open(os.devnull) as devnull:
+            out = subprocess.check_output(cmd, stderr=devnull)
+
+        with open("trace.txt", "w") as fout:
+            fout.write(out)
+
+        # Now check that the raw trace is created and analyzed when creating the trace
+        trace = trappy.FTrace()
+
+        self.assertTrue(hasattr(trace, "sched_switch"))
+        self.assertTrue(len(trace.sched_switch.data_frame) > 0)
+        self.assertTrue("prev_comm" in trace.sched_switch.data_frame.columns)
+
+class TestFTraceRawBothTxt(utils_tests.SetupDirectory):
+
+    def __init__(self, *args, **kwargs):
+        super(TestFTraceRawBothTxt, self).__init__(
+             [("raw_trace.txt", "trace.txt"),
+              ("raw_trace.raw.txt", "trace.raw.txt")],
+             *args,
+             **kwargs)
+
+    def test_both_txt_files(self):
+        """test raw parsing for txt files"""
+
+        self.assertFalse(os.path.isfile("trace.dat"))
+        trace = trappy.FTrace()
+        self.assertTrue(hasattr(trace, "sched_switch"))
+        self.assertTrue(len(trace.sched_switch.data_frame) > 0)
+
+    def test_both_txt_arb_name(self):
+        """Test raw parsing for txt files arbitrary name"""
+
+        arbitrary_name = "my_trace.txt"
+        arbitrary_name_raw = "my_trace.raw.txt"
+
+        shutil.move("trace.txt", arbitrary_name)
+        shutil.move("trace.raw.txt", arbitrary_name_raw)
+
+        trace = trappy.FTrace(arbitrary_name)
+        self.assertTrue(hasattr(trace, "sched_switch"))
+        self.assertTrue(len(trace.sched_switch.data_frame) > 0)
+
+class TestFTraceSched(utils_tests.SetupDirectory):
+    """Tests using a trace with only sched info and no (or partial) thermal"""
+
+    def __init__(self, *args, **kwargs):
+        super(TestFTraceSched, self).__init__(
+             [("trace_empty.txt", "trace.txt")],
+             *args,
+             **kwargs)
+
+    def test_ftrace_basetime_empty(self):
+        """Test that basetime is 0 if data frame of all data objects is empty"""
+
+        trace = trappy.FTrace(normalize_time=False)
+
+        self.assertEqual(trace.basetime, 0)
+
+    def test_ftrace_unique_but_no_fields(self):
+        """Test with a matching unique but no special fields"""
+        version_parser = trappy.register_dynamic_ftrace("Version", "version")
+
+        with self.assertRaises(ValueError):
+            trappy.FTrace(scope="custom")
+
+        trappy.unregister_dynamic_ftrace(version_parser)
+
+    def test_ftrace_normalize_some_tracepoints(self):
+        """Test that normalizing time works if not all the tracepoints are in the trace"""
+
+        with open("trace.txt", "a") as fil:
+            fil.write("     kworker/4:1-1219  [004]   508.424826: thermal_temperature:  thermal_zone=exynos-therm id=0 temp_prev=24000 temp=24000")
+
+        trace = trappy.FTrace()
+
+        self.assertEqual(trace.thermal.data_frame.index[0], 0)
+
+@unittest.skipUnless(utils_tests.trace_cmd_installed(),
+                     "trace-cmd not installed")
+class TestTraceDat(utils_tests.SetupDirectory):
+    """Test that trace.dat handling work"""
+    def __init__(self, *args, **kwargs):
+        super(TestTraceDat, self).__init__(
+            [("trace.dat", "trace.dat")],
+            *args, **kwargs)
+
+    def assert_thermal_in_trace(self, fname):
+        """Assert that the thermal event is in the trace
+
+        fname is the trace file, usually "trace.txt" or "trace.raw.txt"
+        """
+
+        found = False
+        with open(fname) as fin:
+            for line in fin:
+                if re.search("thermal", line):
+                    found = True
+                    break
+
+        self.assertTrue(found)
+
+    def test_do_txt_if_not_there(self):
+        """Create trace.txt if it's not there"""
+        self.assertFalse(os.path.isfile("trace.txt"))
+
+        trappy.FTrace()
+
+        self.assert_thermal_in_trace("trace.txt")
+
+    def test_do_raw_txt_if_not_there(self):
+        """Create trace.raw.txt if it's not there"""
+        self.assertFalse(os.path.isfile("trace.raw.txt"))
+
+        trappy.FTrace()
+
+        self.assert_thermal_in_trace("trace.raw.txt")
+
+    def test_ftrace_arbitrary_trace_dat(self):
+        """FTrace() works if asked to parse a binary trace with a filename other than trace.dat"""
+        arbitrary_trace_name = "my_trace.dat"
+        shutil.move("trace.dat", arbitrary_trace_name)
+
+        dfr = trappy.FTrace(arbitrary_trace_name).thermal.data_frame
+
+        self.assertTrue(os.path.exists("my_trace.txt"))
+        self.assertTrue(os.path.exists("my_trace.raw.txt"))
+        self.assertTrue(len(dfr) > 0)
+        self.assertFalse(os.path.exists("trace.dat"))
+        self.assertFalse(os.path.exists("trace.txt"))
+        self.assertFalse(os.path.exists("trace.raw.txt"))
+
+    def test_regenerate_txt_if_outdated(self):
+        """Regenerate the trace.txt if it's older than the trace.dat"""
+
+        trappy.FTrace()
+
+        # Empty the trace.txt
+        with open("trace.txt", "w") as fout:
+            fout.write("")
+
+        # Set access and modified time of trace.txt to 10 seconds ago
+        now = time.time()
+        os.utime("trace.txt", (now - 10, now - 10))
+
+        # touch trace.dat
+        os.utime("trace.dat", None)
+
+        trappy.FTrace()
+
+        self.assert_thermal_in_trace("trace.txt")
diff --git a/tests/test_idle.py b/tests/test_idle.py
new file mode 100644
index 0000000..483d528
--- /dev/null
+++ b/tests/test_idle.py
@@ -0,0 +1,68 @@
+#    Copyright 2016-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import sys
+import unittest
+
+import pandas as pd
+from pandas.util.testing import assert_series_equal
+
+import utils_tests
+import trappy
+
+@unittest.skipUnless(utils_tests.trace_cmd_installed(),
+                     "trace-cmd not installed")
+class TestCpuIdle(utils_tests.SetupDirectory):
+    def __init__(self, *args, **kwargs):
+        super(TestCpuIdle, self).__init__(
+            [("trace_idle.dat", "trace.dat")],
+            *args,
+            **kwargs)
+
+    def test_get_dataframe(self):
+        """Test that CpuIdle creates a proper data_frame"""
+
+        df = trappy.FTrace(normalize_time=False).cpu_idle.data_frame
+
+        exp_index = pd.Float64Index([
+            162534.215764,
+            162534.216001,
+            162534.216552,
+            162534.216568,
+            162534.217401,
+            162534.217521,
+            162534.217655,
+            162534.219077,
+            162534.219252,
+            162534.219268,
+            162534.219329,
+            162534.219336,
+            162534.219587,
+            162534.219763,
+            162534.219853,
+            162534.220947,
+            162534.220947
+        ], name="Time")
+
+        exp_states = pd.Series([
+            2, -1, 2, -1, -1, -1, 2, -1, 2, -1, 0, 0, 2, -1, 2, -1, -1
+        ], index=exp_index, name="state")
+        exp_cpus = pd.Series([
+            5,  2, 2,  1,  3,  0, 0,  0, 0,  0, 1, 3, 0,  0, 0,  3,  1
+        ], index=exp_index, name="cpu_id")
+
+        assert_series_equal(df["state"], exp_states, check_exact=True)
+        assert_series_equal(df["cpu_id"], exp_cpus, check_exact=True)
diff --git a/tests/test_pid.py b/tests/test_pid.py
new file mode 100644
index 0000000..6a11280
--- /dev/null
+++ b/tests/test_pid.py
@@ -0,0 +1,45 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import matplotlib
+
+from test_thermal import BaseTestThermal
+import trappy
+
+class TestPIDController(BaseTestThermal):
+    def test_dataframe(self):
+        """Test that PIDController() generates a valid data_frame"""
+        pid = trappy.FTrace().pid_controller
+
+        self.assertTrue(len(pid.data_frame) > 0)
+        self.assertTrue("err_integral" in pid.data_frame.columns)
+        self.assertEquals(pid.data_frame["err"].iloc[0], 3225)
+
+    def test_plot_controller(self):
+        """Test PIDController.plot_controller()
+
+        As it happens with all plot functions, just test that it doesn't explode"""
+        pid = trappy.FTrace().pid_controller
+
+        pid.plot_controller()
+        matplotlib.pyplot.close('all')
+
+        pid.plot_controller(title="Antutu", width=20, height=5)
+        matplotlib.pyplot.close('all')
+
+        _, ax = matplotlib.pyplot.subplots()
+        pid.plot_controller(ax=ax)
+        matplotlib.pyplot.close('all')
diff --git a/tests/test_plot_utils.py b/tests/test_plot_utils.py
new file mode 100644
index 0000000..8ecae2f
--- /dev/null
+++ b/tests/test_plot_utils.py
@@ -0,0 +1,255 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import unittest
+import matplotlib
+import pandas as pd
+
+from test_thermal import BaseTestThermal
+import trappy
+import plot_utils
+
+class TestPlotUtils(unittest.TestCase):
+    def test_normalize_title(self):
+        """Test normalize_title"""
+        self.assertEquals(plot_utils.normalize_title("Foo", ""), "Foo")
+        self.assertEquals(plot_utils.normalize_title("Foo", "Bar"), "Bar - Foo")
+
+    def test_set_lim(self):
+        """Test set_lim()"""
+
+        class GetSet(object):
+            def __init__(self):
+                self.min = 1
+                self.max = 2
+
+            def get(self):
+                return (self.min, self.max)
+
+            def set(self, minimum, maximum):
+                self.min = minimum
+                self.max = maximum
+
+        gs = GetSet()
+
+        plot_utils.set_lim("default", gs.get, gs.set)
+        self.assertEquals(gs.min, 1)
+        self.assertEquals(gs.max, 2)
+
+        plot_utils.set_lim("range", gs.get, gs.set)
+        self.assertEquals(gs.min, 0.9)
+        self.assertEquals(gs.max, 2.1)
+
+        plot_utils.set_lim((0, 100), gs.get, gs.set)
+        self.assertEquals(gs.min, 0)
+        self.assertEquals(gs.max, 100)
+
+    def test_set_ylim(self):
+        """Test that set_ylim() doesn't bomb"""
+
+        _, ax = matplotlib.pyplot.subplots()
+
+        plot_utils.set_ylim(ax, "default")
+        plot_utils.set_ylim(ax, (0, 5))
+
+    def test_set_xlim(self):
+        """Test that set_xlim() doesn't bomb"""
+
+        _, ax = matplotlib.pyplot.subplots()
+
+        plot_utils.set_xlim(ax, "default")
+        plot_utils.set_xlim(ax, (0, 5))
+
+    def test_pre_plot_setup(self):
+        """Test that plot_utils.pre_plot_setup() doesn't bomb"""
+        plot_utils.pre_plot_setup(None, None)
+        plot_utils.pre_plot_setup(height=9, width=None)
+        plot_utils.pre_plot_setup(height=None, width=9)
+        plot_utils.pre_plot_setup(3, 9)
+
+        axis = plot_utils.pre_plot_setup(ncols=2)
+        self.assertEquals(len(axis), 2)
+
+        axis = plot_utils.pre_plot_setup(nrows=2, ncols=3)
+        self.assertEquals(len(axis), 2)
+        self.assertEquals(len(axis[0]), 3)
+        self.assertEquals(len(axis[1]), 3)
+
+    def test_post_plot_setup(self):
+        """Test that post_plot_setup() doesn't bomb"""
+
+        _, ax = matplotlib.pyplot.subplots()
+
+        plot_utils.post_plot_setup(ax)
+        plot_utils.post_plot_setup(ax, title="Foo")
+        plot_utils.post_plot_setup(ax, ylim=(0, 72))
+        plot_utils.post_plot_setup(ax, ylim="range")
+        plot_utils.post_plot_setup(ax, xlabel="Bar")
+        plot_utils.post_plot_setup(ax, xlim=(0, 100))
+        plot_utils.post_plot_setup(ax, xlim="default")
+
+    def test_plot_hist(self):
+        """Test that plost_hist doesn't bomb"""
+        data = pd.Series([1, 1, 2, 4])
+
+        _, ax = matplotlib.pyplot.subplots()
+        plot_utils.plot_hist(data, ax, "Foo", "m", 20, "numbers", (0, 4), "default")
+
+class TestPlotUtilsNeedTrace(BaseTestThermal):
+    def __init__(self, *args, **kwargs):
+        super(TestPlotUtilsNeedTrace, self).__init__(*args, **kwargs)
+        self.map_label = {"00000000,00000039": "A53", "00000000,00000006": "A57"}
+        self.actor_order = ["GPU", "A57", "A53"]
+
+    def test_number_freq_plots(self):
+        """Calculate the number of frequency plots correctly"""
+        trace_out = ""
+
+        trace = trappy.FTrace()
+        self.assertEquals(plot_utils.number_freq_plots([trace], self.map_label),
+                          3)
+
+        # Strip out devfreq traces
+        with open("trace.txt") as fin:
+            for line in fin:
+                if ("thermal_power_devfreq_get_power:" not in line) and \
+                   ("thermal_power_devfreq_limit:" not in line):
+                    trace_out += line
+
+        with open("trace.txt", "w") as fout:
+            fout.write(trace_out)
+
+        # Without devfreq there should only be two plots
+        trace = trappy.FTrace()
+        self.assertEquals(plot_utils.number_freq_plots([trace], self.map_label),
+                          2)
+
+    def test_plot_temperature(self):
+        """Test that plot_utils.plot_temperature() doesn't bomb"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        traces = [trace1, trace2]
+
+        plot_utils.plot_temperature(traces, ylim="default")
+        matplotlib.pyplot.close('all')
+
+    def test_plot_load(self):
+        """Test that plot_utils.plot_load() doesn't bomb"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        traces = [trace1, trace2]
+
+        plot_utils.plot_load(traces, self.map_label, height=5)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_load_single_trace(self):
+        """plot_utils.plot_load() can be used with a single trace"""
+        trace = trappy.FTrace()
+
+        plot_utils.plot_load([trace], self.map_label)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_allfreqs(self):
+        """Test that plot_utils.plot_allfreqs() doesn't bomb"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        traces = [trace1, trace2]
+
+        plot_utils.plot_allfreqs(traces, self.map_label, width=20)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_allfreqs_single_trace(self):
+        """plot_utils.plot_allfreqs() can be used with a single trace"""
+        trace = trappy.FTrace()
+
+        plot_utils.plot_allfreqs([trace], self.map_label)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_allfreqs_one_actor(self):
+        """plot_utils.plot_allfreqs work when there is only one actor"""
+
+        in_data = """     kworker/4:1-397   [004]   720.741349: thermal_power_cpu_get: cpus=00000000,00000006 freq=1400000 raw_cpu_power=189 load={23, 12} power=14
+     kworker/4:1-397   [004]   720.741679: thermal_power_cpu_limit: cpus=00000000,00000006 freq=1400000 cdev_state=1 power=14"""
+
+        with open("trace.txt", "w") as fout:
+            fout.write(in_data)
+
+        traces = [trappy.FTrace(name="first"), trappy.FTrace(name="second")]
+        map_label = {"00000000,00000006": "A57"}
+
+        plot_utils.plot_allfreqs(traces, map_label)
+        matplotlib.pyplot.close("all")
+
+    def test_plot_controller(self):
+        """plot_utils.plot_controller() doesn't bomb"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        traces = [trace1, trace2]
+
+        plot_utils.plot_controller(traces, height=5)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_input_power(self):
+        """plot_utils.plot_input_power() doesn't bomb"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        traces = [trace1, trace2]
+
+        plot_utils.plot_input_power(traces, self.actor_order, width=20)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_output_power(self):
+        """plot_utils.plot_output_power() doesn't bomb"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        traces = [trace1, trace2]
+
+        plot_utils.plot_output_power(traces, self.actor_order, width=20)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_freq_hists(self):
+        """plot_utils.plot_freq_hists() doesn't bomb"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        traces = [trace1, trace2]
+
+        plot_utils.plot_freq_hists(traces, self.map_label)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_freq_hists_single_trace(self):
+        """plot_utils.plot_freq_hists() works with a single trace"""
+
+        trace = trappy.FTrace()
+
+        plot_utils.plot_freq_hists([trace], self.map_label)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_temperature_hist(self):
+        """plot_utils.plot_temperature_hist() doesn't bomb"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        traces = [trace1, trace2]
+
+        plot_utils.plot_temperature_hist(traces)
+        matplotlib.pyplot.close('all')
diff --git a/tests/test_plotter.py b/tests/test_plotter.py
new file mode 100644
index 0000000..dae91c4
--- /dev/null
+++ b/tests/test_plotter.py
@@ -0,0 +1,380 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import unittest
+import matplotlib
+import numpy as np
+import pandas as pd
+import tempfile
+import os
+import warnings
+
+from test_thermal import BaseTestThermal
+import trappy
+
+
+class TestPlotter(BaseTestThermal):
+
+    """No Bombing testcases for plotter"""
+
+    def __init__(self, *args, **kwargs):
+        super(TestPlotter, self).__init__(*args, **kwargs)
+
+    def test_plot_no_pivot(self):
+        """Tests LinePlot with no pivot"""
+        trace1 = trappy.FTrace(name="first")
+        l = trappy.LinePlot(trace1, trappy.thermal.Thermal, column="temp")
+        l.view(test=True)
+
+    def test_plot_multi_trace(self):
+        """Tests LinePlot with no Pivot multi traces"""
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        l = trappy.LinePlot(
+            [trace1, trace2], trappy.thermal.Thermal, column="temp")
+        l.view(test=True)
+
+    def test_plot_multi(self):
+        """Tests LinePlot with no Pivot multi attrs"""
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        l = trappy.LinePlot([trace1,
+                          trace2],
+                         [trappy.thermal.Thermal,
+                          trappy.thermal.ThermalGovernor],
+                         column=["temp",
+                                 "power_range"])
+        l.view(test=True)
+
+    def test_plot_filter(self):
+        """Tests LinePlot with no Pivot with filters"""
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        l = trappy.LinePlot([trace1,
+                          trace2],
+                         [trappy.cpu_power.CpuOutPower],
+                         column=["power"],
+                         filters={"cdev_state": [0]})
+        l.view(test=True)
+
+    def test_plot_pivot(self):
+        """Tests LinePlot with Pivot"""
+        trace1 = trappy.FTrace(name="first")
+        l = trappy.LinePlot(
+            trace1,
+            trappy.thermal.Thermal,
+            column="temp",
+            pivot="thermal_zone")
+        l.view(test=True)
+
+    def test_plot_multi_trace_pivot(self):
+        """Tests LinePlot with Pivot multi traces"""
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        l = trappy.LinePlot(
+            [trace1, trace2], trappy.cpu_power.CpuOutPower, column="power", pivot="cpus")
+        l.view(test=True)
+
+    def test_plot_multi_pivot(self):
+        """Tests LinePlot with Pivot with multi attrs"""
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        l = trappy.LinePlot([trace1,
+                          trace2],
+                         [trappy.cpu_power.CpuInPower,
+                          trappy.cpu_power.CpuOutPower],
+                         column=["dynamic_power",
+                                 "power"],
+                         pivot="cpus")
+        l.view(test=True)
+
+    def test_plot_multi_pivot_filter(self):
+        """Tests LinePlot with Pivot and filters"""
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        l = trappy.LinePlot(
+            trace1,
+            trappy.cpu_power.CpuInPower,
+            column=[
+                "dynamic_power",
+                "load1"],
+            filters={
+                "cdev_state": [
+                    1,
+                    0]},
+            pivot="cpus")
+        l.view(test=True)
+
+    def test_plot_savefig(self):
+        """Tests plotter: savefig"""
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+        l = trappy.LinePlot(
+            trace1,
+            trappy.cpu_power.CpuInPower,
+            column=[
+                "dynamic_power",
+                "load1"],
+            filters={
+                "cdev_state": [
+                    1,
+                    0]},
+            pivot="cpus")
+        png_file = tempfile.mktemp(dir="/tmp", suffix=".png")
+        l.savefig(png_file)
+        self.assertTrue(os.path.isfile(png_file))
+        os.remove(png_file)
+
+
+    def test_signals(self):
+        """Test signals input for LinePlot"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+
+        l = trappy.LinePlot([trace1,
+                          trace2],
+                         signals=["cpu_in_power:dynamic_power",
+                                 "cpu_out_power:power"],
+                         pivot="cpus")
+
+        self.assertTrue(isinstance(l.templates[0], type(trappy.cpu_power.CpuInPower)))
+        self.assertEquals(l._attr["column"][0], "dynamic_power")
+        self.assertTrue(l.templates[1], type(trappy.cpu_power.CpuOutPower))
+        self.assertEquals(l._attr["column"][1], "power")
+        self.assertTrue("colors" not in l._attr)
+
+        # Check that plotting doesn't barf
+        l.view(test=True)
+
+
+    def test_signals_exceptions(self):
+        """Test incorrect input combinations: signals"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+
+        with self.assertRaises(ValueError):
+            l = trappy.LinePlot([trace1, trace2],
+                            column=[
+                                "dynamic_power",
+                                "load1"],
+                            signals=["cpu_in_power:dynamic_power",
+                                 "cpu_out_power:power"],
+                            pivot="cpus")
+
+        with self.assertRaises(ValueError):
+            l = trappy.LinePlot([trace1, trace2],
+                            trappy.cpu_power.CpuInPower,
+                            signals=["cpu_in_power:dynamic_power",
+                                 "cpu_out_power:power"],
+                            pivot="cpus")
+
+        with self.assertRaises(ValueError):
+            l = trappy.LinePlot([trace1, trace2],
+                            trappy.cpu_power.CpuInPower,
+                            column=[
+                                "dynamic_power",
+                                "load1"],
+                            signals=["cpu_in_power:dynamic_power",
+                                 "cpu_out_power:power"],
+                            pivot="cpus")
+
+    def test_signals_invalid(self):
+        """Test that invalid signal defs result in a helpful errror"""
+        trace = trappy.FTrace()
+
+        with self.assertRaises(ValueError) as assertion:
+            l = trappy.LinePlot(trace, signals=["INVALID_SIGNAL_DEF"])
+        msg = str(assertion.exception)
+        self.assertIn("Invalid signal definition", msg)
+        self.assertIn("INVALID_SIGNAL_DEF", msg)
+
+    def test_signals_colors(self):
+        """Test signals with colors in LinePlot"""
+
+        trace1 = trappy.FTrace(name="first")
+        trace2 = trappy.FTrace(name="second")
+
+        l = trappy.LinePlot([trace1, trace2],
+                         signals=["thermal:temp:1,2,3",
+                                 "cpu_in_power:load2:200,100,0"],
+                         pivot="cpus")
+
+        self.assertTrue(isinstance(l.templates[0], type(trappy.thermal.Thermal)))
+        self.assertEquals(l._attr["column"][0], "temp")
+        self.assertEquals(l._attr["colors"][0], [1, 2, 3])
+        self.assertTrue(l.templates[1], type(trappy.cpu_power.CpuInPower))
+        self.assertEquals(l._attr["column"][1], "load2")
+        self.assertEquals(l._attr["colors"][1], [200, 100, 0])
+
+        # Check that plotting doesn't barf
+        l.view(test=True)
+
+        # Test hex color
+        l = trappy.LinePlot([trace1, trace2],
+                         signals=["thermal:prev_temp:0xff,0x3a,0x3"],
+                         pivot="cpus")
+        self.assertEquals(l._attr["colors"][0], [0xff, 0x3a, 0x3])
+
+    def test_lineplot_dataframe(self):
+        """LinePlot plots DataFrames without exploding"""
+        data = np.random.randn(4, 2)
+        dfr = pd.DataFrame(data, columns=["tick", "tock"]).cumsum()
+        trappy.LinePlot(dfr, column=["tick"]).view(test=True)
+
+    def test_get_trace_event_data_corrupted_trace(self):
+        """get_trace_event_data() works with a corrupted trace"""
+        from trappy.plotter.Utils import get_trace_event_data
+
+        trace = trappy.FTrace()
+
+        # We create this trace:
+        #
+        # 1 15414 -> 15411
+        # 2 15411 -> 15414
+        # 3 15414 -> 15411 (corrupted, should be dropped)
+        # 4 15413 -> 15411
+        # 5 15411 -> 15413
+        #
+        # Which should plot like:
+        #
+        # CPU
+        #    +-------+-------+
+        #  0 | 15411 | 15414 |
+        #    +-------+-------+       +-------+
+        #  1                         | 15411 |
+        #                            +-------+
+        #    +-------+-------+-------+-------+
+        #   0.1     0.2     0.3     0.4     0.5
+
+        broken_trace = pd.DataFrame({
+            '__comm': ["task2", "task1", "task2", "task3", "task1"],
+            '__cpu':  [0, 0, 0, 1, 1],
+            '__pid':  [15414, 15411, 15414, 15413, 15411],
+            'next_comm': ["task1", "task2", "task1", "task1", "task3"],
+            'next_pid':  [15411, 15414, 15411, 15411, 15413],
+            'prev_comm': ["task2", "task1", "task2", "task3", "task1"],
+            'prev_pid':  [15414, 15411, 15414, 15413, 15411],
+            'prev_state': ["S", "R", "S", "S", "S"]},
+            index=pd.Series(range(1, 6), name="Time"))
+
+        trace.sched_switch.data_frame = broken_trace
+
+        with warnings.catch_warnings(record=True) as warn:
+            data, procs, window = get_trace_event_data(trace)
+            self.assertEquals(len(warn), 1)
+
+            warn_str = str(warn[-1])
+            self.assertTrue("15411" in warn_str)
+            self.assertTrue("4" in warn_str)
+
+        zipped_comms = zip(broken_trace["next_comm"], broken_trace["next_pid"])
+        expected_procs = set("-".join([comm, str(pid)]) for comm, pid in zipped_comms)
+
+        self.assertTrue([1, 2, 0] in data["task1-15411"])
+        self.assertTrue([2, 3, 0] in data["task2-15414"])
+        self.assertTrue([4, 5, 1] in data["task1-15411"])
+        self.assertEquals(procs, expected_procs)
+        self.assertEquals(window, [1, 5])
+
+class TestILinePlotter(unittest.TestCase):
+    def test_simple_dfr(self):
+        """ILinePlot doesn't barf when plotting DataFrames"""
+        dfr1 = pd.DataFrame([1, 2, 3, 4], columns=["a"])
+        dfr2 = pd.DataFrame([2, 3, 4, 5], columns=["a"])
+
+        trappy.ILinePlot([dfr1, dfr2], column=["a", "a"]).view(test=True)
+
+        with self.assertRaises(ValueError):
+            trappy.ILinePlot([dfr1, dfr2]).view(test=True)
+
+    def test_df_to_dygraph(self):
+        """Test the ILinePlot util function: df_to_dygraph"""
+
+        dfr1 = pd.DataFrame([[1, 2],
+                             [3, 4],
+                             [5, 6]],
+                             index=[0., 1., 2.], columns=["a", "b"])
+
+        dfr2 = pd.DataFrame([1, 2, 3, 4],
+                            index=[0., 1., 2., 3.], columns=["a"])
+
+        expected_result_1 = {
+            'labels': ['index', 'a', 'b'],
+            'data': [[0.0, 1, 2], [1.0, 3, 4], [2.0, 5, 6]]
+        }
+        expected_result_2 = {
+            'labels': ['index', 'a'],
+            'data': [[0.0, 1], [1.0, 2], [2.0, 3], [3.0, 4]]
+        }
+
+        result_1 = trappy.plotter.ILinePlotGen.df_to_dygraph(dfr1)
+        result_2 = trappy.plotter.ILinePlotGen.df_to_dygraph(dfr2)
+
+        self.assertDictEqual(result_1, expected_result_1)
+        self.assertDictEqual(result_2, expected_result_2)
+
+    def test_duplicate_merging(self):
+        dfr1 = pd.DataFrame([1, 2, 3, 4], index=[0., 0., 1., 2.], columns=["a"])
+        dfr2 = pd.DataFrame([2, 3, 4, 5], index=[1., 1., 1., 2.], columns=["a"])
+
+        trappy.ILinePlot([dfr1, dfr2], column=["a", "a"]).view(test=True)
+
+    def test_independent_series_merging(self):
+        """ILinePlot fixes indexes of independent series"""
+        index1 = [0., 1., 2., 3.]
+        s1 = pd.Series([1, 2, 3, 4], index=index1)
+        index2 = [0.5, 1.5, 2.5, 3.5]
+        s2 = pd.Series([2, 3, 4, 5], index=index2)
+
+        dfr = pd.DataFrame([0, 1, 2, 3], columns=["a"])
+        iplot = trappy.ILinePlot(dfr, column=["a"])
+        s = {"s1": s1, "s2": s2}
+        merged = iplot._fix_indexes(s)
+
+        expected_index = index1 + index2
+        expected_index.sort()
+        self.assertEquals(expected_index, sorted(merged["s1"].keys()))
+
+    def test_dygraph_colors(self):
+        """Check that to_dygraph_colors() constructs a valid dygraph colors argument"""
+        from trappy.plotter.ColorMap import to_dygraph_colors
+
+        color_map = [[86, 58, 206]]
+        expected = '["rgb(86, 58, 206)"]'
+        self.assertEquals(to_dygraph_colors(color_map), expected)
+
+        color_map = [[0, 0, 0], [123, 23, 45]]
+        expected = '["rgb(0, 0, 0)", "rgb(123, 23, 45)"]'
+        self.assertEquals(to_dygraph_colors(color_map), expected)
+
+class TestBarPlot(unittest.TestCase):
+    def setUp(self):
+        self.dfr = pd.DataFrame({"foo": [1, 2, 3],
+                                 "bar": [2, 3, 1],
+                                 "baz": [3, 2, 1]})
+
+    def test_barplot_dfr(self):
+        """BarPlot plots dataframes without exploding"""
+        trappy.BarPlot(self.dfr, column=["foo", "bar"]).view(test=True)
+
+    def test_barplot_trace(self):
+        """BarPlot plots traces without exploding"""
+        trace = trappy.BareTrace()
+        trace.add_parsed_event("event", self.dfr)
+
+        trappy.BarPlot(trace, signals=["event:foo", "event:bar"]).view(test=True)
diff --git a/tests/test_results.py b/tests/test_results.py
new file mode 100644
index 0000000..e375daf
--- /dev/null
+++ b/tests/test_results.py
@@ -0,0 +1,135 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import os, sys
+import shutil
+import tempfile
+import matplotlib
+import pandas as pd
+
+import utils_tests
+sys.path.append(os.path.join(utils_tests.TESTS_DIRECTORY, "..", "trappy"))
+from trappy.wa import Result, get_results, combine_results
+
+class TestResults(utils_tests.SetupDirectory):
+    def __init__(self, *args, **kwargs):
+        super(TestResults, self).__init__(
+            [("results.csv", "results.csv")],
+            *args, **kwargs)
+
+    def test_get_results(self):
+        results_frame = get_results()
+
+        self.assertEquals(type(results_frame), Result)
+        self.assertEquals(type(results_frame.columns), pd.core.index.MultiIndex)
+        self.assertEquals(results_frame["antutu"]["power_allocator"][0], 5)
+        self.assertEquals(results_frame["antutu"]["step_wise"][1], 9)
+        self.assertEquals(results_frame["antutu"]["step_wise"][2], 7)
+        self.assertEquals(results_frame["t-rex_offscreen"]["power_allocator"][0], 1777)
+        self.assertEquals(results_frame["geekbench"]["step_wise"][0], 8)
+        self.assertEquals(results_frame["geekbench"]["power_allocator"][1], 1)
+        self.assertAlmostEquals(results_frame["thechase"]["step_wise"][0], 242.0522258138)
+
+    def test_get_results_path(self):
+        """get_results() can be given a directory for the results.csv"""
+
+        other_random_dir = tempfile.mkdtemp()
+        os.chdir(other_random_dir)
+
+        results_frame = get_results(self.out_dir)
+
+        self.assertEquals(len(results_frame.columns), 10)
+
+    def test_get_results_filename(self):
+        """get_results() can be given a specific filename"""
+
+        old_path = os.path.join(self.out_dir, "results.csv")
+        new_path = os.path.join(self.out_dir, "new_results.csv")
+        os.rename(old_path, new_path)
+
+        results_frame = get_results(new_path)
+
+        self.assertEquals(len(results_frame.columns), 10)
+
+    def test_get_results_name(self):
+        """get_results() optional name argument overrides the one in the results file"""
+        res = get_results(name="malkovich")
+        self.assertIsNotNone(res["antutu"]["malkovich"])
+
+    def test_combine_results(self):
+        res1 = get_results()
+        res2 = get_results()
+
+        # First split them
+        res1.drop('step_wise', axis=1, level=1, inplace=True)
+        res2.drop('power_allocator', axis=1, level=1, inplace=True)
+
+        # Now combine them again
+        combined = combine_results([res1, res2])
+
+        self.assertEquals(type(combined), Result)
+        self.assertEquals(combined["antutu"]["step_wise"][0], 4)
+        self.assertEquals(combined["antutu"]["power_allocator"][0], 5)
+        self.assertEquals(combined["geekbench"]["power_allocator"][1], 1)
+        self.assertEquals(combined["t-rex_offscreen"]["step_wise"][2], 424)
+
+    def test_plot_results_benchmark(self):
+        """Test Result.plot_results_benchmark()
+
+        Can't test it, so just check that it doens't bomb
+        """
+
+        res = get_results()
+
+        res.plot_results_benchmark("antutu")
+        res.plot_results_benchmark("t-rex_offscreen", title="Glbench TRex")
+
+        (_, _, y_min, y_max) = matplotlib.pyplot.axis()
+
+        trex_data = pd.concat(res["t-rex_offscreen"][s] for s in res["t-rex_offscreen"])
+        data_min = min(trex_data)
+        data_max = max(trex_data)
+
+        # Fail if the axes are within the limits of the data.
+        self.assertTrue(data_min > y_min)
+        self.assertTrue(data_max < y_max)
+        matplotlib.pyplot.close('all')
+
+    def test_get_run_number(self):
+        from trappy.wa.results import get_run_number
+
+        self.assertEquals(get_run_number("score_2"), (True, 2))
+        self.assertEquals(get_run_number("score"), (True, 0))
+        self.assertEquals(get_run_number("score 3"), (True, 3))
+        self.assertEquals(get_run_number("FPS_1"), (True, 1))
+        self.assertEquals(get_run_number("Overall_Score"), (True, 0))
+        self.assertEquals(get_run_number("Overall_Score_2"), (True, 1))
+        self.assertEquals(get_run_number("Memory_score")[0], False)
+
+    def test_plot_results(self):
+        """Test Result.plot_results()
+
+        Can't test it, so just check that it doens't bomb
+        """
+
+        res = get_results()
+
+        res.plot_results()
+        matplotlib.pyplot.close('all')
+
+    def test_init_fig(self):
+        r1 = get_results()
+        r1.init_fig()
diff --git a/tests/test_sched.py b/tests/test_sched.py
new file mode 100644
index 0000000..6527779
--- /dev/null
+++ b/tests/test_sched.py
@@ -0,0 +1,179 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import os
+import sys
+
+import utils_tests
+import trappy
+
+sys.path.append(os.path.join(utils_tests.TESTS_DIRECTORY, "..", "trappy"))
+
+class BaseTestSched(utils_tests.SetupDirectory):
+    def __init__(self, *args, **kwargs):
+        super(BaseTestSched, self).__init__(
+             [("trace_sched.txt", "trace.txt"),
+              ("trace_sched.txt", "trace.raw.txt")],
+             *args,
+             **kwargs)
+
+class TestSchedLoadAvgSchedGroup(BaseTestSched):
+
+    def test_get_dataframe(self):
+        """Test that SchedLoadAvgSchedGroup creates a proper data_frame"""
+        dfr = trappy.FTrace().sched_load_avg_sg.data_frame
+
+        self.assertTrue(len(dfr) == 1)
+        self.assertEquals(dfr["cpus"].iloc[0], "00000002")
+        self.assertEquals(dfr["load"].iloc[0], 0)
+        self.assertEquals(dfr["utilization"].iloc[0], 0)
+
+class TestSchedLoadAvgTask(BaseTestSched):
+
+    def test_get_dataframe(self):
+        """Test that SchedLoadAvgTask creates a proper data_frame"""
+        dfr = trappy.FTrace().sched_load_avg_task.data_frame
+
+        self.assertTrue(len(dfr) == 1)
+        self.assertEquals(dfr["comm"].iloc[0], "sshd")
+        self.assertEquals(dfr["pid"].iloc[0], 2962)
+        self.assertEquals(dfr["load"].iloc[0], 0)
+        self.assertEquals(dfr["utilization"].iloc[0], 0)
+        self.assertEquals(dfr["runnable_avg_sum"].iloc[0], 0)
+        self.assertEquals(dfr["running_avg_sum"].iloc[0], 0)
+        self.assertEquals(dfr["avg_period"].iloc[0], 48595)
+
+class TestSchedLoadAvgCpu(BaseTestSched):
+
+    def test_get_dataframe(self):
+        """Test that SchedLoadAvgCpu creates a proper data_frame"""
+        dfr = trappy.FTrace().sched_load_avg_cpu.data_frame
+
+        self.assertTrue(len(dfr) == 1)
+        self.assertEquals(dfr["cpu"].iloc[0], 0)
+        self.assertEquals(dfr["load"].iloc[0], 13)
+        self.assertEquals(dfr["utilization"].iloc[0], 18)
+
+class TestSchedContribScaleFactor(BaseTestSched):
+
+    def test_get_dataframe(self):
+        """Test that SchedContribScaleFactor creates a proper data_frame"""
+        dfr = trappy.FTrace().sched_contrib_scale_factor.data_frame
+
+        self.assertTrue(len(dfr) == 1)
+        self.assertEquals(dfr["cpu"].iloc[0], 0)
+        self.assertEquals(dfr["freq_scale_factor"].iloc[0], 426)
+        self.assertEquals(dfr["cpu_scale_factor"].iloc[0], 1024)
+
+class TestSchedCpuCapacity(BaseTestSched):
+
+    def test_get_dataframe(self):
+        """Test that SchedCpuCapacity creates a proper data_frame"""
+        dfr = trappy.FTrace().cpu_capacity.data_frame
+
+        self.assertTrue(len(dfr) == 1)
+        self.assertEquals(dfr["cpu"].iloc[0], 3)
+        self.assertEquals(dfr["capacity"].iloc[0], 430)
+        self.assertEquals(dfr["rt_capacity"].iloc[0], 1024)
+
+class TestSchedCpuFrequency(BaseTestSched):
+
+    def test_get_dataframe(self):
+        """Test that SchedCpuFrequency creates a proper data_frame"""
+        dfr = trappy.FTrace().cpu_frequency.data_frame
+
+        self.assertTrue(len(dfr) == 1)
+        self.assertEquals(dfr["cpu"].iloc[0], 0)
+        self.assertEquals(dfr["frequency"].iloc[0], 600000)
+        self.assertFalse("cpu_id" in dfr.columns)
+
+class TestSchedWakeup(BaseTestSched):
+
+    def test_get_dataframe(self):
+        """Test that SchedWakeup creates a proper data_frame"""
+        dfr = trappy.FTrace().sched_wakeup.data_frame
+
+        self.assertTrue(len(dfr) == 2)
+        self.assertEquals(dfr["comm"].iloc[0], "rcu_preempt")
+        self.assertEquals(dfr["pid"].iloc[0], 7)
+        self.assertEquals(dfr["prio"].iloc[0], 120)
+        self.assertEquals(dfr["success"].iloc[0], 1)
+        self.assertEquals(dfr["target_cpu"].iloc[0], 1)
+
+class TestSchedWakeupNew(BaseTestSched):
+
+    def test_get_dataframe(self):
+        """Test that SchedWakeupNew creates a proper data_frame"""
+        dfr = trappy.FTrace().sched_wakeup_new.data_frame
+
+        self.assertTrue(len(dfr) == 2)
+        self.assertEquals(dfr["comm"].iloc[0], "shutils")
+        self.assertEquals(dfr["pid"].iloc[0], 19428)
+        self.assertEquals(dfr["prio"].iloc[0], 120)
+        self.assertEquals(dfr["success"].iloc[0], 1)
+        self.assertEquals(dfr["target_cpu"].iloc[0], 2)
+
+
+class TestGetFilters(BaseTestSched):
+
+    def test_get_filters(self):
+        """Test that FTrace::get_filters returns correct list of filters"""
+
+        trace = trappy.FTrace()
+        classes = trace.class_definitions
+        filters = trace.get_filters()
+        self.assertTrue(len(classes) == len(filters))
+        self.assertTrue(sorted(classes) == sorted(filters))
+
+        sched_classes = trace.sched_classes.copy()
+        sched_filters = trace.get_filters("sched")
+
+        # cpu_capacity and cpu_frequency are in the sched scope but they should
+        # not be captured by get_filters("sched")
+        del sched_classes["cpu_capacity"]
+        del sched_classes["cpu_frequency"]
+
+        self.assertTrue(len(sched_classes) == len(sched_filters))
+        self.assertTrue(sorted(sched_classes) == sorted(sched_filters))
+
+class TestSpacedValueAttributes(BaseTestSched):
+
+    def test_spaced_value_attr(self):
+        """Test that FTrace object parses spaced value attributes correctly"""
+
+        with open("trace.txt", "a") as fout:
+            fout.write("       <...>-2971  [004]  6550.056871: sched_load_avg_task:  comm=AsyncTask #2 pid=6163 ")
+
+        dfr = trappy.FTrace().sched_load_avg_task.data_frame
+        self.assertTrue(len(dfr) == 2)
+        self.assertEquals(dfr["comm"].iloc[1], "AsyncTask #2")
+        self.assertEquals(dfr["pid"].iloc[1], 6163)
+
+class TestNoSchedTraces(utils_tests.SetupDirectory):
+
+    def __init__(self, *args, **kwargs):
+        super(TestNoSchedTraces, self).__init__(
+             [("trace_empty.txt", "trace.txt")],
+             *args,
+             **kwargs)
+
+    def test_empty_trace_txt(self):
+        """Test that empty objects are created with empty trace file"""
+
+        trace = trappy.FTrace()
+
+        for attr in trace.sched_classes.iterkeys():
+            self.assertTrue(len(getattr(trace, attr).data_frame) == 0)
diff --git a/tests/test_stats.py b/tests/test_stats.py
new file mode 100644
index 0000000..e15046e
--- /dev/null
+++ b/tests/test_stats.py
@@ -0,0 +1,363 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import unittest
+from trappy.stats.Topology import Topology
+from trappy.stats.Trigger import Trigger
+from trappy.stats.Aggregator import MultiTriggerAggregator
+
+import collections
+import trappy
+from trappy.base import Base
+import pandas as pd
+from pandas.util.testing import assert_series_equal
+
+
+class TestTopology(unittest.TestCase):
+
+    def test_add_to_level(self):
+        """Test level creation"""
+
+        level_groups = [[1, 2], [0, 3, 4, 5]]
+        level = "test_level"
+        topology = Topology()
+        topology.add_to_level(level, level_groups)
+        check_groups = topology.get_level(level)
+
+        self.assertTrue(topology.has_level(level))
+        self.assertEqual(level_groups, check_groups)
+
+    def test_flatten(self):
+        """Test Topology: flatten"""
+
+        level_groups = [[1, 2], [0, 3, 4, 5]]
+        level = "test_level"
+        topology = Topology()
+        topology.add_to_level(level, level_groups)
+        flattened = [0, 1, 2, 3, 4, 5]
+
+        self.assertEqual(flattened, topology.flatten())
+
+    def test_cpu_topology_construction(self):
+        """Test CPU Topology Construction"""
+
+        cluster_0 = [0, 3, 4, 5]
+        cluster_1 = [1, 2]
+        clusters = [cluster_0, cluster_1]
+        topology = Topology(clusters=clusters)
+
+        # Check cluster level creation
+        cluster_groups = [[0, 3, 4, 5], [1, 2]]
+        self.assertTrue(topology.has_level("cluster"))
+        self.assertEqual(cluster_groups, topology.get_level("cluster"))
+
+        # Check cpu level creation
+        cpu_groups = [[0], [1], [2], [3], [4], [5]]
+        self.assertTrue(topology.has_level("cpu"))
+        self.assertEqual(cpu_groups, topology.get_level("cpu"))
+
+        # Check "all" level
+        all_groups = [[0, 1, 2, 3, 4, 5]]
+        self.assertEqual(all_groups, topology.get_level("all"))
+
+    def test_level_span(self):
+        """TestTopology: level_span"""
+
+        level_groups = [[1, 2], [0, 3, 4, 5]]
+        level = "test_level"
+        topology = Topology()
+        topology.add_to_level(level, level_groups)
+
+        self.assertEqual(topology.level_span(level), 2)
+
+    def test_group_index(self):
+        """TestTopology: get_index"""
+
+        level_groups = [[1, 2], [0, 3, 4, 5]]
+        level = "test_level"
+        topology = Topology()
+        topology.add_to_level(level, level_groups)
+
+        self.assertEqual(topology.get_index(level, [1, 2]), 0)
+        self.assertEqual(topology.get_index(level, [0, 3, 4, 5]), 1)
+
+class BaseTestStats(unittest.TestCase):
+    def setUp(self):
+        trace = trappy.BareTrace()
+        data = {
+
+            "identifier": [
+                0,
+                0,
+                0,
+                1,
+                1,
+                1,
+            ],
+            "result": [
+                "fire",
+                "blank",
+                "fire",
+                "blank",
+                "fire",
+                "blank",
+            ],
+        }
+
+        index = pd.Series([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], name="Time")
+        data_frame = pd.DataFrame(data, index=index)
+        trace.add_parsed_event("aim_and_fire", data_frame)
+        self._trace = trace
+        self.topology = Topology(clusters=[[0], [1]])
+
+
+class TestTrigger(BaseTestStats):
+
+    def test_trigger_generation(self):
+        """TestTrigger: generate"""
+
+        filters = {
+            "result": "fire"
+        }
+
+        event_class = self._trace.aim_and_fire
+        value = 1
+        pivot = "identifier"
+
+        trigger = Trigger(self._trace,
+                          event_class,
+                          filters,
+                          value,
+                          pivot)
+
+        expected = pd.Series([1, 1], index=pd.Index([0.1, 0.3], name="Time"))
+        assert_series_equal(expected, trigger.generate(0))
+
+        expected = pd.Series([1], index=pd.Index([0.5], name="Time"))
+        assert_series_equal(expected, trigger.generate(1))
+
+    def test_trigger_with_func(self):
+        """Trigger works with a function or lambda as filter"""
+
+        def my_filter(val):
+            return val.startswith("fi")
+
+        trigger = Trigger(self._trace, self._trace.aim_and_fire,
+                          filters={"result": my_filter}, value=1,
+                          pivot="identifier")
+
+        expected = pd.Series([1], index=pd.Index([0.5], name="Time"))
+        assert_series_equal(expected, trigger.generate(1))
+
+        my_filters = {"result": lambda x: x.startswith("bl")}
+        trigger = Trigger(self._trace, self._trace.aim_and_fire,
+                          filters=my_filters, value=1, pivot="identifier")
+
+        expected = pd.Series([1, 1], index=pd.Index([0.4, 0.6], name="Time"))
+        assert_series_equal(expected, trigger.generate(1))
+
+    def test_trigger_with_callable_class(self):
+        """Trigger works with a callable class as filter"""
+
+        class my_filter(object):
+            def __init__(self, val_out):
+                self.prev_val = 0
+                self.val_out = val_out
+
+            def __call__(self, val):
+                ret = self.prev_val == self.val_out
+                self.prev_val = val
+
+                return ret
+
+        trigger = Trigger(self._trace, self._trace.aim_and_fire,
+                          filters={"identifier": my_filter(1)}, value=1,
+                          pivot="result")
+
+        expected = pd.Series([1], index=pd.Index([0.6], name="Time"))
+        assert_series_equal(expected, trigger.generate("blank"))
+
+    def test_filter_prev_values(self):
+        """Trigger works with a filter that depends on previous values of the same pivot"""
+
+        # We generate an example in which we want a trigger whenever the
+        # identifier is no longer 1 for blank
+
+        class my_filter(object):
+            def __init__(self, val_out):
+                self.prev_val = 0
+                self.val_out = val_out
+
+            def __call__(self, val):
+                ret = self.prev_val == self.val_out
+                self.prev_val = val
+
+                return ret
+
+        trace = trappy.BareTrace()
+        data = collections.OrderedDict([
+            (0.1, ["blank", 1]),
+            (0.2, ["fire",  1]),
+            (0.3, ["blank", 0]), # value is no longer 1, trigger
+            (0.4, ["blank", 1]),
+            (0.5, ["fire",  0]), # This should NOT trigger
+            (0.6, ["blank", 0]), # value is no longer 1 for blank, trigger
+        ])
+        data_frame = pd.DataFrame.from_dict(data, orient="index", )
+        data_frame.columns = ["result", "identifier"]
+        trace.add_parsed_event("aim_and_fire", data_frame)
+
+        trigger = Trigger(trace, trace.aim_and_fire,
+                          filters={"identifier": my_filter(1)}, value=-1,
+                          pivot="result")
+
+        expected = pd.Series([-1, -1], index=[0.3, 0.6])
+        assert_series_equal(expected, trigger.generate("blank"))
+
+
+
+class TestAggregator(BaseTestStats):
+
+    def test_scalar_aggfunc_single_trigger(self):
+        """TestAggregator: 1 trigger scalar aggfunc"""
+
+        def aggfunc(series):
+            return series.sum()
+
+        filters = {
+            "result": "fire"
+        }
+
+        event_class = self._trace.aim_and_fire
+        value = 1
+        pivot = "identifier"
+
+        trigger = Trigger(self._trace,
+                          event_class,
+                          filters,
+                          value,
+                          pivot)
+
+        aggregator = MultiTriggerAggregator([trigger],
+                        self.topology,
+                        aggfunc=aggfunc)
+
+        # There are three "fire" in total
+        # The all level in topology looks like
+        # [[0, 1]]
+        result = aggregator.aggregate(level="all")
+        self.assertEqual(result, [3.0])
+
+        # There are two "fire" on the first node group and a
+        # a single "fire" on the second node group at the cluster
+        # level which looks like
+        # [[0], [1]]
+        result = aggregator.aggregate(level="cluster")
+        self.assertEqual(result, [2.0, 1.0])
+
+    def test_vector_aggfunc_single_trigger(self):
+        """TestAggregator: 1 trigger vector aggfunc"""
+
+        def aggfunc(series):
+            return series.cumsum()
+
+        filters = {
+            "result": "fire"
+        }
+
+        event_class = self._trace.aim_and_fire
+        value = 1
+        pivot = "identifier"
+
+        trigger = Trigger(self._trace, event_class, filters, value, pivot)
+
+        aggregator = MultiTriggerAggregator([trigger],
+                        self.topology,
+                        aggfunc=aggfunc)
+
+        # There are three "fire" in total
+        # The all level in topology looks like
+        # [[0, 1]]
+        result = aggregator.aggregate(level="all")
+        expected_result = pd.Series([1.0, 1.0, 2.0, 2.0, 3.0, 3.0],
+                                      index=pd.Index([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
+                          )
+        assert_series_equal(result[0], expected_result)
+
+    def test_vector_aggfunc_multiple_trigger(self):
+        """TestAggregator: multi trigger vector aggfunc"""
+
+        def aggfunc(series):
+            return series.cumsum()
+
+        filters = {
+            "result": "fire"
+        }
+
+        event_class = self._trace.aim_and_fire
+        value = 1
+        pivot = "identifier"
+
+        trigger_fire = Trigger(self._trace,
+                          event_class,
+                          filters,
+                          value,
+                          pivot)
+
+        filters = {
+            "result": "blank"
+        }
+        value = -1
+        trigger_blank = Trigger(self._trace, event_class, filters, value,
+                                pivot)
+
+        aggregator = MultiTriggerAggregator([trigger_fire, trigger_blank],
+                        self.topology,
+                        aggfunc=aggfunc)
+
+        # There are three "fire" in total
+        # The all level in topology looks like
+        # [[0, 1]]
+        result = aggregator.aggregate(level="all")
+        expected_result = pd.Series([1.0, 0.0, 1.0, 0.0, 1.0, 0.0],
+                                      index=pd.Index([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
+                          )
+        assert_series_equal(result[0], expected_result)
+
+    def test_default_aggfunc_multiple_trigger(self):
+        """MultiTriggerAggregator with the default aggfunc"""
+
+        trigger_fire = Trigger(self._trace, self._trace.aim_and_fire,
+                          filters={"result": "fire"},
+                          pivot="identifier", value=1)
+
+        trigger_blank = Trigger(self._trace, self._trace.aim_and_fire,
+                          filters={"result": "blank"},
+                          pivot="identifier", value=2)
+
+        aggregator = MultiTriggerAggregator([trigger_fire, trigger_blank],
+                                            self.topology)
+
+        results = aggregator.aggregate(level="cpu")
+        expected_results = [
+            pd.Series([1., 2., 1., 0., 0., 0.],
+                      index=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]),
+            pd.Series([0., 0., 0., 2., 1., 2.],
+                      index=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6]),
+            ]
+
+        self.assertEquals(len(results), len(expected_results))
+        for result, expected in zip(results, expected_results):
+            assert_series_equal(result, expected)
diff --git a/tests/test_stats_grammar.py b/tests/test_stats_grammar.py
new file mode 100644
index 0000000..5138efc
--- /dev/null
+++ b/tests/test_stats_grammar.py
@@ -0,0 +1,256 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+from test_thermal import BaseTestThermal
+import trappy
+from trappy.stats.grammar import Parser
+from pandas.util.testing import assert_series_equal
+import numpy as np
+import pandas
+from distutils.version import LooseVersion as V
+import unittest
+
+
+class TestStatsGrammar(BaseTestThermal):
+
+    def __init__(self, *args, **kwargs):
+        super(TestStatsGrammar, self).__init__(*args, **kwargs)
+
+    def test_sum_operator(self):
+        """Test Addition And Subtraction: Numeric"""
+
+        parser = Parser(trappy.BareTrace())
+        # Simple equation
+        eqn = "10 + 2 - 3"
+        self.assertEquals(parser.solve(eqn), 9)
+        # Equation with bracket and unary ops
+        eqn = "(10 + 2) - (-3 + 2)"
+        self.assertEquals(parser.solve(eqn), 13)
+
+    @unittest.skipIf(V(pandas.__version__) < V('0.16.1'),
+                     "check_names is not supported in pandas < 0.16.1")
+    def test_accessors_sum(self):
+        """Test Addition And Subtraction: Data"""
+
+        thermal_zone_id = 0
+        parser = Parser(trappy.FTrace())
+        # Equation with dataframe accessors
+        eqn = "trappy.thermal.Thermal:temp + \
+trappy.thermal.Thermal:temp"
+
+        assert_series_equal(
+            parser.solve(eqn)[thermal_zone_id],
+            2 *
+            parser.data.thermal.data_frame["temp"], check_names=False)
+
+    def test_funcparams_sum(self):
+        """Test Addition And Subtraction: Functions"""
+
+        thermal_zone_id = 0
+        parser = Parser(trappy.FTrace())
+        # Equation with functions as parameters (Mixed)
+        eqn = "numpy.mean(trappy.thermal.Thermal:temp) + 1000"
+        self.assertEquals(
+            parser.solve(eqn)[thermal_zone_id],
+            np.mean(
+                parser.data.thermal.data_frame["temp"]) +
+            1000)
+        # Multiple func params
+        eqn = "numpy.mean(trappy.thermal.Thermal:temp) + numpy.mean(trappy.thermal.Thermal:temp)"
+        self.assertEquals(
+            parser.solve(eqn)[thermal_zone_id],
+            np.mean(
+                parser.data.thermal.data_frame["temp"]) *
+            2)
+
+    def test_parser_with_name(self):
+        """Test equation using event name"""
+
+        thermal_zone_id = 0
+        parser = Parser(trappy.FTrace())
+        # Equation with functions as parameters (Mixed)
+        eqn = "numpy.mean(thermal:temp) + 1000"
+        self.assertEquals(
+            parser.solve(eqn)[thermal_zone_id],
+            np.mean(
+                parser.data.thermal.data_frame["temp"]) + 1000)
+
+    def test_bool_ops_vector(self):
+        """Test Logical Operations: Vector"""
+
+        thermal_zone_id = 0
+        # The equation returns a vector mask
+        parser = Parser(trappy.FTrace())
+        eqn = "(trappy.thermal.ThermalGovernor:current_temperature > 77000)\
+                & (trappy.pid_controller.PIDController:output > 2500)"
+        mask = parser.solve(eqn)
+        self.assertEquals(len(parser.ref(mask.dropna()[0])), 0)
+
+    def test_bool_ops_scalar(self):
+        """Test Logical Operations: Vector"""
+
+        thermal_zone_id=0
+        parser = Parser(trappy.FTrace())
+        # The equation returns a boolean scalar
+        eqn = "(numpy.mean(trappy.thermal.Thermal:temp) > 65000) && (numpy.mean(trappy.cpu_power.CpuOutPower) > 500)"
+        self.assertTrue(parser.solve(eqn)[thermal_zone_id])
+        eqn = "(numpy.mean(trappy.thermal.Thermal:temp) > 65000) || (numpy.mean(trappy.cpu_power.CpuOutPower) < 500)"
+        self.assertTrue(parser.solve(eqn)[thermal_zone_id])
+
+    def test_super_indexing(self):
+        "Test if super-indexing works correctly"""
+
+        trace = trappy.FTrace()
+        parser = Parser(trace)
+        # The first event has less index values
+        sol1 = parser.solve("trappy.thermal.Thermal:temp")
+        # The second index has more index values
+        sol2 = parser.solve("trappy.pid_controller.PIDController:output")
+        # Super Indexing should result in len(sol2) > len(sol1)
+        self.assertGreater(len(sol2), len(sol1))
+
+    def test_single_func_call(self):
+        """Test Single Function Call"""
+
+        thermal_zone_id = 0
+        parser = Parser(trappy.FTrace())
+        eqn = "numpy.mean(trappy.thermal.Thermal:temp)"
+        self.assertEquals(
+            parser.solve(eqn)[thermal_zone_id],
+            np.mean(
+                parser.data.thermal.data_frame["temp"]))
+
+    def test_mul_ops(self):
+        """Test Mult and Division: Numeric"""
+
+        parser = Parser(trappy.BareTrace())
+        eqn = "(10 * 2 / 10)"
+        self.assertEquals(parser.solve(eqn), 2)
+        eqn = "-2 * 2 + 2 * 10 / 10"
+        self.assertEquals(parser.solve(eqn), -2)
+        eqn = "3.5 // 2"
+        self.assertEquals(parser.solve(eqn), 1)
+        eqn = "5 % 2"
+        self.assertEquals(parser.solve(eqn), 1)
+
+    def test_exp_ops(self):
+        """Test exponentiation: Numeric"""
+        parser = Parser(trappy.BareTrace())
+        eqn = "3**3 * 2**4"
+        self.assertEquals(parser.solve(eqn), 432)
+        eqn = "3**(4/2)"
+        self.assertEquals(parser.solve(eqn), 9)
+
+    @unittest.skipIf(V(pandas.__version__) < V('0.16.1'),
+                     "check_names is not supported in pandas < 0.16.1")
+    def test_funcparams_mul(self):
+        """Test Mult and Division: Data"""
+
+        thermal_zone_id = 0
+        parser = Parser(trappy.FTrace())
+        eqn = "trappy.thermal.Thermal:temp * 10.0"
+        series = parser.data.thermal.data_frame["temp"]
+        assert_series_equal(parser.solve(eqn)[thermal_zone_id], series * 10.0, check_names=False)
+        eqn = "trappy.thermal.Thermal:temp / trappy.thermal.Thermal:temp * 10"
+        assert_series_equal(parser.solve(eqn)[thermal_zone_id], series / series * 10, check_names=False)
+
+    def test_var_forward(self):
+        """Test Forwarding: Variable"""
+
+        thermal_zone_id = 0
+        pvars = {}
+        pvars["control_temp"] = 78000
+        parser = Parser(trappy.FTrace(), pvars=pvars)
+        eqn = "numpy.mean(trappy.thermal.Thermal:temp) < control_temp"
+        self.assertTrue(parser.solve(eqn)[thermal_zone_id])
+
+    def test_func_forward(self):
+        """Test Forwarding: Mixed"""
+
+        thermal_zone_id = 0
+        pvars = {}
+        pvars["mean"] = np.mean
+        pvars["control_temp"] = 78000
+        parser = Parser(trappy.FTrace(), pvars=pvars)
+        eqn = "mean(trappy.thermal.Thermal:temp) < control_temp"
+        self.assertTrue(parser.solve(eqn)[thermal_zone_id])
+
+    def test_cls_forward(self):
+        """Test Forwarding: Classes"""
+
+        cls = trappy.thermal.Thermal
+        pvars = {}
+        pvars["mean"] = np.mean
+        pvars["control_temp"] = 78000
+        pvars["therm"] = cls
+
+        thermal_zone_id = 0
+        parser = Parser(trappy.FTrace(), pvars=pvars)
+        eqn = "mean(therm:temp) < control_temp"
+        self.assertTrue(parser.solve(eqn)[thermal_zone_id])
+
+    def test_for_parsed_event(self):
+        """Test if an added parsed event can be accessed"""
+
+        trace = trappy.FTrace(scope="custom")
+        dfr = pandas.DataFrame({"l1_misses": [24, 535,  41],
+                                "l2_misses": [155, 11, 200],
+                                "cpu":       [ 0,   1,   0]},
+                           index=pandas.Series([1.020, 1.342, 1.451], name="Time"))
+        trace.add_parsed_event("pmu_counters", dfr)
+
+        p = Parser(trace)
+        self.assertTrue(len(p.solve("pmu_counters:cpu")), 3)
+
+    def test_windowed_parse(self):
+        """Test that the parser can operate on a window of the trace"""
+        trace = trappy.FTrace()
+
+        prs = Parser(trace, window=(2, 3))
+        dfr_res = prs.solve("thermal:temp")
+
+        self.assertGreater(dfr_res.index[0], 2)
+        self.assertLess(dfr_res.index[-1], 3)
+
+        prs = Parser(trace, window=(4, None))
+        dfr_res = prs.solve("thermal:temp")
+
+        self.assertGreater(dfr_res.index[0], 4)
+        self.assertEquals(dfr_res.index[-1], trace.thermal.data_frame.index[-1])
+
+        prs = Parser(trace, window=(0, 1))
+        dfr_res = prs.solve("thermal:temp")
+
+        self.assertEquals(dfr_res.index[0], trace.thermal.data_frame.index[0])
+        self.assertLess(dfr_res.index[-1], 1)
+
+    def test_filtered_parse(self):
+        """The Parser can filter a trace"""
+        trace = trappy.FTrace()
+
+        prs = Parser(trace, filters={"cdev_state": 3})
+        dfr_res = prs.solve("devfreq_out_power:freq")
+        self.assertEquals(len(dfr_res), 1)
+
+    def test_no_events(self):
+        """Test trying to parse absent data"""
+        trace = trappy.FTrace()
+        prs = Parser(trace)
+
+        # cpu_frequency is an event we know how to parse, but it isn't present
+        # in the test trace.
+        self.assertRaisesRegexp(ValueError, "No events found for cpu_frequency",
+                                prs.solve, "cpu_frequency:frequency")
diff --git a/tests/test_systrace.py b/tests/test_systrace.py
new file mode 100644
index 0000000..608ed8e
--- /dev/null
+++ b/tests/test_systrace.py
@@ -0,0 +1,87 @@
+#    Copyright 2016-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import utils_tests
+
+import trappy
+
+class TestSystrace(utils_tests.SetupDirectory):
+
+    def __init__(self, *args, **kwargs):
+        super(TestSystrace, self).__init__(
+             [("trace_systrace.html", "trace.html")],
+             *args,
+             **kwargs)
+
+    def test_systrace_html(self):
+        """Tests parsing of a systrace embedded textual trace """
+
+        events = ["sched_switch", "sched_wakeup", "trace_event_clock_sync"]
+        trace = trappy.SysTrace("trace.html", events=events)
+
+        self.assertTrue(hasattr(trace, "sched_switch"))
+        self.assertEquals(len(trace.sched_switch.data_frame), 4)
+        self.assertTrue("prev_comm" in trace.sched_switch.data_frame.columns)
+
+        self.assertTrue(hasattr(trace, "sched_wakeup"))
+        self.assertEquals(len(trace.sched_wakeup.data_frame), 4)
+        self.assertTrue("target_cpu" in trace.sched_wakeup.data_frame.columns)
+
+        self.assertTrue(hasattr(trace, "trace_event_clock_sync"))
+        self.assertEquals(len(trace.trace_event_clock_sync.data_frame), 1)
+        self.assertTrue("realtime_ts" in trace.trace_event_clock_sync.data_frame.columns)
+
+    def test_cpu_counting(self):
+        """SysTrace traces know the number of cpus"""
+
+        trace = trappy.SysTrace("trace.html")
+
+        self.assertTrue(hasattr(trace, "_cpus"))
+        self.assertEquals(trace._cpus, 3)
+
+
+class TestLegacySystrace(utils_tests.SetupDirectory):
+
+    def __init__(self, *args, **kwargs):
+        super(TestLegacySystrace, self).__init__(
+             [("trace_legacy_systrace.html", "trace.html")],
+             *args,
+             **kwargs)
+
+    def test_systrace_html(self):
+        """Tests parsing of a legacy systrace embedded textual trace """
+
+        events = ["sched_switch", "sched_wakeup", "sched_contrib_scale_f"]
+        trace = trappy.SysTrace("trace.html", events=events)
+
+        self.assertTrue(hasattr(trace, "sched_switch"))
+        self.assertEquals(len(trace.sched_switch.data_frame), 3)
+        self.assertTrue("prev_comm" in trace.sched_switch.data_frame.columns)
+
+        self.assertTrue(hasattr(trace, "sched_wakeup"))
+        self.assertEquals(len(trace.sched_wakeup.data_frame), 2)
+        self.assertTrue("target_cpu" in trace.sched_wakeup.data_frame.columns)
+
+        self.assertTrue(hasattr(trace, "sched_contrib_scale_f"))
+        self.assertEquals(len(trace.sched_contrib_scale_f.data_frame), 2)
+        self.assertTrue("freq_scale_factor" in trace.sched_contrib_scale_f.data_frame.columns)
+
+    def test_cpu_counting(self):
+        """In a legacy SysTrace trace, trappy gets the number of cpus"""
+
+        trace = trappy.SysTrace("trace.html")
+
+        self.assertTrue(hasattr(trace, "_cpus"))
+        self.assertEquals(trace._cpus, 8)
diff --git a/tests/test_thermal.py b/tests/test_thermal.py
new file mode 100644
index 0000000..d564639
--- /dev/null
+++ b/tests/test_thermal.py
@@ -0,0 +1,181 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import unittest
+import matplotlib
+import os
+import pandas as pd
+import shutil
+import sys
+import tempfile
+
+import utils_tests
+import trappy
+
+sys.path.append(os.path.join(utils_tests.TESTS_DIRECTORY, "..", "trappy"))
+
+class BaseTestThermal(utils_tests.SetupDirectory):
+    def __init__(self, *args, **kwargs):
+        super(BaseTestThermal, self).__init__(
+             [("../doc/trace.txt", "trace.txt")],
+             *args,
+             **kwargs)
+
+class TestThermal(BaseTestThermal):
+
+    def test_plot_temperature(self):
+        """Test ThermalGovernor.plot_temperature()
+
+        Can't check that the graph is ok, so just see that the method
+        doesn't blow up
+
+        """
+
+        th_data = trappy.FTrace().thermal
+        dfr = th_data.data_frame
+        ct_series = pd.Series([57, 57], index=(dfr.index[0], dfr.index[-1]))
+
+        th_data.plot_temperature()
+        matplotlib.pyplot.close('all')
+
+        th_data.plot_temperature(title="Antutu", control_temperature=ct_series)
+        matplotlib.pyplot.close('all')
+
+        th_data.plot_temperature(title="Antutu", ylim=[0, 60])
+        matplotlib.pyplot.close('all')
+
+        _, ax = matplotlib.pyplot.subplots()
+        th_data.plot_temperature(ax=ax)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_temperature_hist(self):
+        """Test that plot_temperature_hist() doesn't bomb"""
+
+        _, ax = matplotlib.pyplot.subplots()
+        trappy.FTrace().thermal.plot_temperature_hist(ax, "Foo")
+        matplotlib.pyplot.close('all')
+
+class TestThermalGovernor(BaseTestThermal):
+    def __init__(self, *args, **kwargs):
+        super(TestThermalGovernor, self).__init__(*args, **kwargs)
+        self.actor_order = ["GPU", "A15", "A7"]
+
+    def test_get_dataframe(self):
+        dfr = trappy.FTrace().thermal_governor.data_frame
+
+        self.assertTrue(len(dfr) > 0)
+        self.assertEquals(dfr["current_temperature"].iloc[0], 68775)
+        self.assertTrue("total_granted_power" in dfr.columns)
+        self.assertFalse("time" in dfr.columns)
+
+    def test_plot_temperature(self):
+        """Test ThermalGovernor.plot_temperature()
+
+        Can't check that the graph is ok, so just see that the method doesn't blow up"""
+        gov = trappy.FTrace().thermal_governor
+
+        gov.plot_temperature()
+        gov.plot_temperature(legend_label="power allocator", ylim=(0, 72))
+        matplotlib.pyplot.close('all')
+
+    def test_plot_input_power(self):
+        """plot_input_power() doesn't bomb"""
+        gov = trappy.FTrace().thermal_governor
+
+        gov.plot_input_power(self.actor_order)
+        matplotlib.pyplot.close('all')
+
+        gov.plot_input_power(self.actor_order, title="Antutu")
+        matplotlib.pyplot.close('all')
+
+        _, ax = matplotlib.pyplot.subplots()
+        gov.plot_input_power(self.actor_order, ax=ax)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_weighted_input_power(self):
+        """plot_weighted_input_power() doesn't bomb"""
+
+        gov = trappy.FTrace().thermal_governor
+        weights = zip(self.actor_order, [1024, 256, 512])
+
+        _, ax = matplotlib.pyplot.subplots()
+        gov.plot_weighted_input_power(weights, ax=ax)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_output_power(self):
+        """Test plot_output_power()
+
+        Can't check that the graph is ok, so just see that the method doesn't blow up"""
+        gov = trappy.FTrace().thermal_governor
+
+        gov.plot_output_power(self.actor_order)
+        matplotlib.pyplot.close('all')
+
+        gov.plot_output_power(self.actor_order, title="Antutu")
+        matplotlib.pyplot.close('all')
+
+        _, ax = matplotlib.pyplot.subplots()
+        gov.plot_output_power(self.actor_order, title="Antutu", ax=ax)
+        matplotlib.pyplot.close('all')
+
+    def test_plot_inout_power(self):
+        """Test plot_inout_power()
+
+        Can't check that the graph is ok, so just see that the method doesn't blow up"""
+        trappy.FTrace().thermal_governor.plot_inout_power()
+        trappy.FTrace().thermal_governor.plot_inout_power(title="Antutu")
+        matplotlib.pyplot.close('all')
+
+class TestEmptyThermalGovernor(unittest.TestCase):
+    def setUp(self):
+        self.previous_dir = os.getcwd()
+        self.out_dir = tempfile.mkdtemp()
+        os.chdir(self.out_dir)
+        with open("trace.txt", "w") as fout:
+            fout.write("""version = 6
+cpus=8
+CPU:7 [204600 EVENTS DROPPED]
+           <...>-3979  [007]   217.975284: sched_stat_runtime:   comm=Thread-103 pid=3979 runtime=5014167 [ns] vruntime=244334517704 [ns]
+           <...>-3979  [007]   217.975298: sched_task_load_contrib: comm=Thread-103 pid=3979 load_contrib=2500
+           <...>-3979  [007]   217.975314: sched_task_runnable_ratio: comm=Thread-103 pid=3979 ratio=1023
+           <...>-3979  [007]   217.975332: sched_rq_runnable_ratio: cpu=7 ratio=1023
+           <...>-3979  [007]   217.975345: sched_rq_runnable_load: cpu=7 load=127
+           <...>-3979  [007]   217.975366: softirq_raise:        vec=7 [action=SCHED]
+           <...>-3979  [007]   217.975446: irq_handler_exit:     irq=163 ret=handled
+           <...>-3979  [007]   217.975502: softirq_entry:        vec=1 [action=TIMER]
+           <...>-3979  [007]   217.975523: softirq_exit:         vec=1 [action=TIMER]
+           <...>-3979  [007]   217.975535: softirq_entry:        vec=7 [action=SCHED]
+           <...>-3979  [007]   217.975559: sched_rq_runnable_ratio: cpu=7 ratio=1023
+           <...>-3979  [007]   217.975571: sched_rq_runnable_load: cpu=7 load=127
+           <...>-3979  [007]   217.975584: softirq_exit:         vec=7 [action=SCHED]
+           <...>-3979  [007]   217.980139: irq_handler_entry:    irq=163 name=mct_tick7
+           <...>-3979  [007]   217.980216: softirq_raise:        vec=1 [action=TIMER]
+           <...>-3979  [007]   217.980253: sched_stat_runtime:   comm=Thread-103 pid=3979 runtime=4990542 [ns] vruntime=244336561007 [ns]
+           <...>-3979  [007]   217.980268: sched_task_load_contrib: comm=Thread-103 pid=3979 load_contrib=2500""")
+
+    def tearDown(self):
+        os.chdir(self.previous_dir)
+        shutil.rmtree(self.out_dir)
+
+    def test_empty_trace_txt(self):
+        dfr = trappy.FTrace(normalize_time=False).thermal_governor.data_frame
+        self.assertEquals(len(dfr), 0)
+
+    def test_empty_plot_temperature(self):
+        """trace.thermal.plot_temperature() raises ValueError() on an empty
+        thermal trace"""
+        trace = trappy.FTrace()
+        self.assertRaises(ValueError, trace.thermal.plot_temperature)
diff --git a/tests/test_trappy.py b/tests/test_trappy.py
new file mode 100644
index 0000000..9204c07
--- /dev/null
+++ b/tests/test_trappy.py
@@ -0,0 +1,135 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import os
+import re
+import matplotlib, tempfile
+
+import trappy
+from test_thermal import BaseTestThermal
+
+class TestTrappy(BaseTestThermal):
+    def __init__(self, *args, **kwargs):
+        super(TestTrappy, self).__init__(*args, **kwargs)
+        self.map_label = {"00000000,00000039": "A53", "00000000,00000006": "A57"}
+        self.actor_order = ["GPU", "A57", "A53"]
+
+    def test_summary_plots(self):
+        """Test summary_plots()
+
+        Can't check that the graphs are ok, so just see that the method doesn't blow up"""
+
+        trappy.summary_plots(self.actor_order, self.map_label)
+        matplotlib.pyplot.close('all')
+
+        trappy.summary_plots(self.actor_order, self.map_label, width=14,
+                          title="Foo")
+        matplotlib.pyplot.close('all')
+
+    def test_summary_plots_bad_parameters(self):
+        """When summary_plots() receives bad parameters, it offers an understandable error"""
+
+        self.assertRaises(TypeError, trappy.summary_plots,
+                          (self.map_label, self.actor_order))
+
+        try:
+            trappy.summary_plots(self.map_label, self.actor_order)
+            self.fail()
+        except TypeError as exception:
+            pass
+
+        self.assertTrue("actor_order" in str(exception))
+
+        try:
+            trappy.summary_plots(self.actor_order, self.actor_order)
+            self.fail()
+        except TypeError as exception:
+            pass
+
+        self.assertTrue("map_label" in str(exception))
+
+    def test_summary_other_dir(self):
+        """Test summary_plots() with another directory"""
+
+        other_random_dir = tempfile.mkdtemp()
+        os.chdir(other_random_dir)
+
+        trappy.summary_plots(self.actor_order, self.map_label, path=self.out_dir)
+        matplotlib.pyplot.close('all')
+
+        # Sanity check that the test actually ran from another directory
+        self.assertEquals(os.getcwd(), other_random_dir)
+
+    def test_summary_plots_only_power_allocator_trace(self):
+        """Test that summary_plots() work if there is only power allocator
+        trace"""
+
+        # Strip out "thermal_temperature" from the trace
+        trace_out = ""
+        with open("trace.txt") as fin:
+            for line in fin:
+                if not re.search("thermal_temperature:", line):
+                    trace_out += line
+
+        with open("trace.txt", "w") as fout:
+            fout.write(trace_out)
+
+        trappy.summary_plots(self.actor_order, self.map_label)
+        matplotlib.pyplot.close('all')
+
+    def test_summary_plots_no_gpu(self):
+        """summary_plots() works if there is no GPU trace"""
+
+        # Strip out devfreq traces
+        trace_out = ""
+        with open("trace.txt") as fin:
+            for line in fin:
+                if ("thermal_power_devfreq_get_power:" not in line) and \
+                   ("thermal_power_devfreq_limit:" not in line):
+                    trace_out += line
+
+        with open("trace.txt", "w") as fout:
+            fout.write(trace_out)
+
+        trappy.summary_plots(self.actor_order, self.map_label)
+        matplotlib.pyplot.close('all')
+
+    def test_summary_plots_one_actor(self):
+        """summary_plots() works if there is only one actor"""
+
+        # Strip out devfreq and little traces
+        trace_out = ""
+        with open("trace.txt") as fin:
+            for line in fin:
+                if ("thermal_power_devfreq_get_power:" not in line) and \
+                   ("thermal_power_devfreq_limit:" not in line) and \
+                   ("thermal_power_cpu_get_power: cpus=00000000,00000039" not in line) and \
+                   ("thermal_power_cpu_limit: cpus=00000000,00000039" not in line):
+                    trace_out += line
+
+        with open("trace.txt", "w") as fout:
+            fout.write(trace_out)
+
+        map_label = {"00000000,00000006": "A57"}
+        trappy.summary_plots(self.actor_order, map_label)
+        matplotlib.pyplot.close('all')
+
+    def test_compare_runs(self):
+        """Basic compare_runs() functionality"""
+
+        trappy.compare_runs(self.actor_order, self.map_label,
+                        runs=[("new", "."), ("old", self.out_dir)])
+        matplotlib.pyplot.close('all')
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000..bf53091
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,59 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import unittest
+from trappy import utils
+import pandas
+from pandas.util.testing import assert_series_equal
+
+
+class TestUtils(unittest.TestCase):
+
+    def test_handle_duplicate_index(self):
+        """Test Util Function: handle_duplicate_index
+        """
+
+        # Refer to the example in the function doc string
+        values = [0, 1, 2, 3, 4]
+        index = [0.0, 1.0, 1.0, 6.0, 7.0]
+        series = pandas.Series(values, index=index)
+        new_index = [0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
+
+        with self.assertRaises(ValueError):
+            series.reindex(new_index)
+
+        max_delta = 0.001
+        expected_index = [0.0, 1.0, 1 + max_delta, 6.0, 7.0]
+        expected_series = pandas.Series(values, index=expected_index)
+        series = utils.handle_duplicate_index(series, max_delta)
+        assert_series_equal(series, expected_series)
+
+        # Make sure that the reindex doesn't raise ValueError any more
+        series.reindex(new_index)
+
+    def test_handle_duplicate_index_duplicate_end(self):
+        """handle_duplicate_index copes with duplicates at the end of the series"""
+
+        max_delta = 0.001
+        values = [0, 1, 2, 3, 4]
+        index = [0.0, 1.0, 2.0, 6.0, 6.0]
+        expected_index = index[:]
+        expected_index[-1] += max_delta
+        series = pandas.Series(values, index=index)
+        expected_series = pandas.Series(values, index=expected_index)
+
+        series = utils.handle_duplicate_index(series, max_delta)
+        assert_series_equal(series, expected_series)
diff --git a/tests/test_wa_sysfs_extractor.py b/tests/test_wa_sysfs_extractor.py
new file mode 100644
index 0000000..25ef8c7
--- /dev/null
+++ b/tests/test_wa_sysfs_extractor.py
@@ -0,0 +1,61 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import os
+import subprocess
+import unittest
+
+import utils_tests
+
+import trappy.wa
+
+class TestWASysfsExtractor(utils_tests.SetupDirectory):
+    """Test the WA specific interface to get parameters from a sysfs extractor"""
+    def __init__(self, *args, **kwargs):
+        self.wa_sysfs_fname = "WA_sysfs_extract.tar.xz"
+        super(TestWASysfsExtractor, self).__init__(
+            [(self.wa_sysfs_fname, self.wa_sysfs_fname)],
+            *args, **kwargs)
+
+    def setUp(self):
+        super(TestWASysfsExtractor, self).setUp()
+        subprocess.check_call(["tar", "xf", self.wa_sysfs_fname])
+
+    def test_get_parameters(self):
+        """Test that we can get the parameters of a sysfs extractor output"""
+
+        os.chdir("..")
+        thermal_params = trappy.wa.SysfsExtractor(self.out_dir).get_parameters()
+        self.assertEquals(thermal_params["cdev0_weight"], 1024)
+        self.assertEquals(thermal_params["cdev1_weight"], 768)
+        self.assertEquals(thermal_params["trip_point_0_temp"], 72000)
+        self.assertEquals(thermal_params["policy"], "power_allocator")
+
+    def test_print_thermal_params(self):
+        """Test that printing the thermal params doesn't bomb"""
+
+        trappy.wa.SysfsExtractor(".").pretty_print_in_ipython()
+
+class TestWASysfsExtractorFailMode(unittest.TestCase):
+    """Test the failure modes of the Workload Automation sysfs extractor"""
+
+    def test_get_params_invalid_directory(self):
+        """An invalid directory for trappy.wa.SysfsExtractor doesn't bomb"""
+
+        sysfs_extractor = trappy.wa.SysfsExtractor(".")
+        self.assertEquals(sysfs_extractor.get_parameters(), {})
+
+        sysfs_extractor.pretty_print_in_ipython()
diff --git a/tests/trace.dat b/tests/trace.dat
new file mode 100644
index 0000000..c54b5e2
--- /dev/null
+++ b/tests/trace.dat
diff --git a/tests/trace_empty.txt b/tests/trace_empty.txt
new file mode 100644
index 0000000..b35ba6f
--- /dev/null
+++ b/tests/trace_empty.txt
@@ -0,0 +1,85 @@
+version = 6
+cpus=5
+       trace-cmd-2461  [000]    34.896920: sched_stat_runtime:   comm=trace-cmd pid=2461 runtime=7778999 [ns] vruntime=6087430069 [ns]
+       trace-cmd-2461  [000]    34.896941: sched_stat_sleep:     comm=sshd pid=2450 delay=1802051076 [ns]
+       trace-cmd-2461  [000]    34.896972: sched_wakeup:         sshd:2450 [120] success=1 CPU:000
+       trace-cmd-2461  [000]    34.897022: sched_stat_wait:      comm=sshd pid=2450 delay=0 [ns]
+       trace-cmd-2461  [000]    34.897053: sched_switch:         trace-cmd:2461 [120] R ==> sshd:2450 [120]
+          <idle>-0     [001]    34.897147: sched_stat_wait:      comm=rcuos/0 pid=9 delay=0 [ns]
+          <idle>-0     [001]    34.897153: sched_switch:         swapper/1:0 [120] R ==> rcuos/0:9 [120]
+         rcuos/0-9     [001]    34.897169: sched_stat_runtime:   comm=sshd pid=2450 runtime=258292 [ns] vruntime=6078688361 [ns]
+         rcuos/0-9     [001]    34.897175: sched_stat_sleep:     comm=rcu_sched pid=7 delay=26617125 [ns]
+         rcuos/0-9     [001]    34.897182: sched_wakeup:         rcu_sched:7 [120] success=1 CPU:000
+         rcuos/0-9     [001]    34.897186: sched_stat_runtime:   comm=rcuos/0 pid=9 runtime=445750 [ns] vruntime=6730976621 [ns]
+         rcuos/0-9     [001]    34.897230: sched_switch:         rcuos/0:9 [120] S ==> swapper/1:0 [120]
+            sshd-2450  [000]    34.897303: sched_stat_runtime:   comm=sshd pid=2450 runtime=134042 [ns] vruntime=6078822403 [ns]
+            sshd-2450  [000]    34.897313: sched_stat_wait:      comm=rcu_sched pid=7 delay=134042 [ns]
+            sshd-2450  [000]    34.897319: sched_switch:         sshd:2450 [120] S ==> rcu_sched:7 [120]
+       rcu_sched-7     [000]    34.897329: sched_stat_runtime:   comm=rcu_sched pid=7 runtime=25916 [ns] vruntime=6078455985 [ns]
+       rcu_sched-7     [000]    34.897338: sched_stat_wait:      comm=trace-cmd pid=2461 delay=418250 [ns]
+       rcu_sched-7     [000]    34.897343: sched_switch:         rcu_sched:7 [120] S ==> trace-cmd:2461 [120]
+       trace-cmd-2461  [000]    34.897641: sched_process_exit:   comm=trace-cmd pid=2461 prio=120
+       trace-cmd-2461  [000]    34.897665: sched_stat_runtime:   comm=trace-cmd pid=2461 runtime=335875 [ns] vruntime=6087765944 [ns]
+       trace-cmd-2461  [000]    34.897675: sched_stat_runtime:   comm=trace-cmd pid=2461 runtime=10459 [ns] vruntime=6087776403 [ns]
+       trace-cmd-2461  [000]    34.897697: sched_stat_sleep:     comm=bash pid=2459 delay=696151164 [ns]
+       trace-cmd-2461  [000]    34.897703: sched_wakeup:         bash:2459 [120] success=1 CPU:001
+       trace-cmd-2461  [000]    34.897708: sched_stat_runtime:   comm=trace-cmd pid=2461 runtime=33500 [ns] vruntime=6087809903 [ns]
+       trace-cmd-2461  [000]    34.897738: sched_switch:         trace-cmd:2461 [120] x ==> swapper/0:0 [120]
+          <idle>-0     [001]    34.898076: sched_stat_wait:      comm=bash pid=2459 delay=0 [ns]
+          <idle>-0     [001]    34.898082: sched_switch:         swapper/1:0 [120] R ==> bash:2459 [120]
+            bash-2459  [001]    34.898188: sched_process_wait:   comm=bash pid=0 prio=120
+            bash-2459  [001]    34.898653: sched_process_exit:   comm=bash pid=2459 prio=120
+            bash-2459  [001]    34.898702: sched_stat_sleep:     comm=sshd pid=2450 delay=1394541 [ns]
+            bash-2459  [001]    34.898708: sched_wakeup:         sshd:2450 [120] success=1 CPU:000
+          <idle>-0     [000]    34.898720: sched_stat_wait:      comm=sshd pid=2450 delay=0 [ns]
+            bash-2459  [001]    34.898721: sched_stat_runtime:   comm=bash pid=2459 runtime=1027542 [ns] vruntime=6723004163 [ns]
+          <idle>-0     [000]    34.898726: sched_switch:         swapper/0:0 [120] R ==> sshd:2450 [120]
+            bash-2459  [001]    34.898732: sched_stat_runtime:   comm=bash pid=2459 runtime=11000 [ns] vruntime=6723015163 [ns]
+            bash-2459  [001]    34.898745: sched_stat_runtime:   comm=bash pid=2459 runtime=13458 [ns] vruntime=6723028621 [ns]
+            bash-2459  [001]    34.898758: sched_switch:         bash:2459 [120] x ==> swapper/1:0 [120]
+            sshd-2450  [000]    34.898825: sched_process_wait:   comm=sshd pid=0 prio=120
+            sshd-2450  [000]    34.898927: sched_process_wait:   comm=sshd pid=0 prio=120
+            sshd-2450  [000]    34.898983: sched_stat_runtime:   comm=sshd pid=2450 runtime=285875 [ns] vruntime=6079108278 [ns]
+            sshd-2450  [000]    34.898996: sched_switch:         sshd:2450 [120] S ==> swapper/0:0 [120]
+          <idle>-0     [000]    34.899144: sched_stat_sleep:     comm=rcu_sched pid=7 delay=1810584 [ns]
+          <idle>-0     [000]    34.899149: sched_wakeup:         rcu_sched:7 [120] success=1 CPU:000
+          <idle>-0     [000]    34.899175: sched_stat_wait:      comm=rcu_sched pid=7 delay=0 [ns]
+          <idle>-0     [000]    34.899180: sched_switch:         swapper/0:0 [120] R ==> rcu_sched:7 [120]
+       rcu_sched-7     [000]    34.899194: sched_stat_sleep:     comm=rcuos/0 pid=9 delay=2004042 [ns]
+       rcu_sched-7     [000]    34.899200: sched_wakeup:         rcuos/0:9 [120] success=1 CPU:001
+       rcu_sched-7     [000]    34.899205: sched_stat_runtime:   comm=rcu_sched pid=7 runtime=65375 [ns] vruntime=6078875278 [ns]
+       rcu_sched-7     [000]    34.899216: sched_switch:         rcu_sched:7 [120] S ==> swapper/0:0 [120]
+          <idle>-0     [001]    34.899620: sched_stat_wait:      comm=rcuos/0 pid=9 delay=0 [ns]
+          <idle>-0     [001]    34.899628: sched_switch:         swapper/1:0 [120] R ==> rcuos/0:9 [120]
+         rcuos/0-9     [001]    34.899770: sched_stat_sleep:     comm=rcu_sched pid=7 delay=538708 [ns]
+         rcuos/0-9     [001]    34.899807: sched_wakeup:         rcu_sched:7 [120] success=1 CPU:000
+         rcuos/0-9     [001]    34.899848: sched_stat_runtime:   comm=rcuos/0 pid=9 runtime=653458 [ns] vruntime=6731630079 [ns]
+         rcuos/0-9     [001]    34.899877: sched_switch:         rcuos/0:9 [120] S ==> swapper/1:0 [120]
+          <idle>-0     [000]    34.899988: sched_stat_sleep:     comm=sshd pid=2450 delay=1003959 [ns]
+          <idle>-0     [000]    34.899995: sched_wakeup:         sshd:2450 [120] success=1 CPU:001
+          <idle>-0     [000]    34.900052: sched_stat_wait:      comm=rcu_sched pid=7 delay=0 [ns]
+          <idle>-0     [000]    34.900058: sched_switch:         swapper/0:0 [120] R ==> rcu_sched:7 [120]
+       rcu_sched-7     [000]    34.900069: sched_stat_runtime:   comm=rcu_sched pid=7 runtime=325042 [ns] vruntime=6079200320 [ns]
+       rcu_sched-7     [000]    34.900086: sched_switch:         rcu_sched:7 [120] S ==> swapper/0:0 [120]
+          <idle>-0     [001]    34.900452: sched_stat_wait:      comm=sshd pid=2450 delay=0 [ns]
+          <idle>-0     [001]    34.900460: sched_switch:         swapper/1:0 [120] R ==> sshd:2450 [120]
+            sshd-2450  [001]    34.900756: sched_stat_sleep:     comm=in:imuxsock pid=1767 delay=1926798326 [ns]
+            sshd-2450  [001]    34.900767: sched_wakeup:         in:imuxsock:1767 [120] success=1 CPU:000
+          <idle>-0     [000]    34.901148: sched_stat_wait:      comm=in:imuxsock pid=1767 delay=0 [ns]
+          <idle>-0     [000]    34.901155: sched_switch:         swapper/0:0 [120] R ==> in:imuxsock:1767 [120]
+     in:imuxsock-1767  [000]    34.901243: sched_stat_runtime:   comm=in:imuxsock pid=1767 runtime=496084 [ns] vruntime=6079305987 [ns]
+     in:imuxsock-1767  [000]    34.901250: sched_stat_sleep:     comm=rs:main Q:Reg pid=1769 delay=1925639660 [ns]
+     in:imuxsock-1767  [000]    34.901256: sched_wakeup:         rs:main Q:Reg:1769 [120] success=1 CPU:000
+     in:imuxsock-1767  [000]    34.901271: sched_stat_runtime:   comm=in:imuxsock pid=1767 runtime=28500 [ns] vruntime=6079334487 [ns]
+     in:imuxsock-1767  [000]    34.901282: sched_stat_wait:      comm=rs:main Q:Reg pid=1769 delay=28500 [ns]
+     in:imuxsock-1767  [000]    34.901287: sched_switch:         in:imuxsock:1767 [120] S ==> rs:main Q:Reg:1769 [120]
+            sshd-2450  [001]    34.901431: sched_stat_runtime:   comm=rs:main Q:Reg pid=1769 runtime=160500 [ns] vruntime=6078970403 [ns]
+            sshd-2450  [001]    34.901437: sched_stat_sleep:     comm=in:imuxsock pid=1767 delay=160500 [ns]
+            sshd-2450  [001]    34.901443: sched_wakeup:         in:imuxsock:1767 [120] success=1 CPU:000
+         rs:main-1769  [000]    34.901447: sched_stat_runtime:   comm=rs:main Q:Reg pid=1769 runtime=15833 [ns] vruntime=6078986236 [ns]
+         rs:main-1769  [000]    34.901457: sched_stat_wait:      comm=in:imuxsock pid=1767 delay=15833 [ns]
+         rs:main-1769  [000]    34.901462: sched_switch:         rs:main Q:Reg:1769 [120] S ==> in:imuxsock:1767 [120]
+     in:imuxsock-1767  [000]    34.901490: sched_stat_runtime:   comm=in:imuxsock pid=1767 runtime=43125 [ns] vruntime=6079377612 [ns]
+     in:imuxsock-1767  [000]    34.901495: sched_stat_sleep:     comm=rs:main Q:Reg pid=1769 delay=43125 [ns]
+     in:imuxsock-1767  [000]    34.901501: sched_wakeup:         rs:main Q:Reg:1769 [120] success=1 CPU:000
+     in:imuxsock-1767  [000]    34.901511: sched_stat_runtime:   comm=in:imuxsock pid=1767 runtime=21333 [ns] vruntime=6079398945 [ns]
diff --git a/tests/trace_equals.txt b/tests/trace_equals.txt
new file mode 100644
index 0000000..01e1ce1
--- /dev/null
+++ b/tests/trace_equals.txt
@@ -0,0 +1,7 @@
+ systemd-journal-1662  [003]   653.065292: bputs:                0xffff0000080feeb8s: equals_event: my_field=foo
+ systemd-journal-1662  [003]   653.065293: bputs:                0xffff0000080feeb8s: equals_event: my_field=foo=bar
+ systemd-journal-1662  [003]   653.065294: bputs:                0xffff0000080feeb8s: equals_event: my_field=foo=bar=baz
+ systemd-journal-1662  [003]   653.065295: bputs:                0xffff0000080feeb8s: equals_event: my_field=1
+ systemd-journal-1662  [003]   653.065296: bputs:                0xffff0000080feeb8s: equals_event: my_field=1=2
+ systemd-journal-1662  [003]   653.065297: bputs:                0xffff0000080feeb8s: equals_event: my_field=1=foo
+ systemd-journal-1662  [003]   653.065298: bputs:                0xffff0000080feeb8s: equals_event: my_field=1foo=2
diff --git a/tests/trace_idle.dat b/tests/trace_idle.dat
new file mode 100644
index 0000000..a2b1d66
--- /dev/null
+++ b/tests/trace_idle.dat
diff --git a/tests/trace_legacy_systrace.html b/tests/trace_legacy_systrace.html
new file mode 100644
index 0000000..73ea663
--- /dev/null
+++ b/tests/trace_legacy_systrace.html
@@ -0,0 +1,52 @@
+<!DOCTYPE HTML>
+<html>
+<head i18n-values="dir:textdirection;">
+<title>Android System Trace</title>
+#
+# Remove HTML content...
+#
+<style>
+  .view {
+    overflow: hidden;
+    position: absolute;
+    top: 0;
+    bottom: 0;
+    left: 0;
+    right: 0;
+  }
+</style>
+</head>
+<body>
+  <div class="view">
+  </div>
+  <script>
+  var linuxPerfData = "\
+# tracer: nop
+#
+# entries-in-buffer/entries-written: 1023529/1023529   #P:8
+#
+#                              _-----=> irqs-off
+#                             / _----=> need-resched
+#                            | / _---=> hardirq/softirq
+#                            || / _--=> preempt-depth
+#                            ||| /     delay
+#           TASK-PID   CPU#  ||||    TIMESTAMP  FUNCTION
+#              | |       |   ||||       |         |
+           <...>-10144 [007] dn.4  7480.992787: sched_wakeup: comm=kworker/7:0 pid=9996 prio=120 success=1 target_cpu=007
+           <...>-10144 [007] dn.3  7480.992804: sched_contrib_scale_f: cpu=7 freq_scale_factor=358 cpu_scale_factor=1024
+           <...>-10144 [007] dn.3  7480.992806: sched_contrib_scale_f: cpu=7 freq_scale_factor=358 cpu_scale_factor=1024
+           <...>-10144 [007] dn.3  7480.992807: sched_load_avg_task: comm=sh pid=10144 cpu=7 load_avg=860 util_avg=859 load_sum=41069599 util_sum=41057230 period_contrib=985
+           <...>-10144 [007] dn.3  7480.992808: sched_load_avg_cpu: cpu=7 load_avg=842 util_avg=842
+           <...>-10144 [007] dn.3  7480.992814: sched_load_avg_cpu: cpu=7 load_avg=879 util_avg=880
+           <...>-10144 [007] d..3  7480.992816: sched_switch: prev_comm=sh prev_pid=10144 prev_prio=120 prev_state=R+ ==> next_comm=kworker/7:0 next_pid=9996 next_prio=120
+           <...>-9996  [007] d..3  7480.992848: sched_load_avg_task: comm=kworker/7:0 pid=9996 cpu=7 load_avg=0 util_avg=0 load_sum=26624 util_sum=26624 period_contrib=662
+           <...>-9996  [007] d..3  7480.992849: sched_load_avg_cpu: cpu=7 load_avg=879 util_avg=880
+           <...>-9996  [007] d..3  7480.992860: sched_load_avg_cpu: cpu=7 load_avg=879 util_avg=880
+           <...>-9996  [007] d..3  7480.992862: sched_switch: prev_comm=kworker/7:0 prev_pid=9996 prev_prio=120 prev_state=S ==> next_comm=sh next_pid=10144 next_prio=120
+            adbd-315   [002] dn.4  7480.993239: sched_wakeup: comm=kworker/2:1 pid=5748 prio=120 success=1 target_cpu=002
+            adbd-315   [002] dn.3  7480.993255: sched_load_avg_task: comm=kworker/2:1 pid=5748 cpu=2 load_avg=0 util_avg=0 load_sum=0 util_sum=0 period_contrib=622
+            adbd-315   [002] dn.3  7480.993256: sched_load_avg_cpu: cpu=2 load_avg=63 util_avg=17
+            adbd-315   [002] d..3  7480.993259: sched_switch: prev_comm=adbd prev_pid=315 prev_prio=120 prev_state=R+ ==> next_comm=kworker/2:1 next_pid=5748 next_prio=120
+  </script>
+</body>
+</html>
diff --git a/tests/trace_sched.txt b/tests/trace_sched.txt
new file mode 100644
index 0000000..a0b4081
--- /dev/null
+++ b/tests/trace_sched.txt
@@ -0,0 +1,13 @@
+version = 6
+cpus=6
+         rcuos/2-22    [001]  6550.018511: sched_load_avg_sg: cpus=00000002 load=0 utilization=0
+       trace-cmd-2971  [004]  6550.018512: sched_load_avg_task:  comm=sshd pid=2962 load=0 utilization=0 runnable_avg_sum=0 running_avg_sum=0 avg_period=48595
+            sshd-2962  [000]  6550.018513: sched_load_avg_cpu:   cpu=0 load=13 utilization=18
+            sshd-2962  [000]  6550.018514: dynamic_test_key:   cpu=0 load=13 utilization=18
+           <...>-19427 [000]  6550.018664: sched_wakeup_new:      comm=shutils pid=19428 prio=120 success=1 target_cpu=2
+          <idle>-0     [000]  6550.018679: sched_contrib_scale_f: cpu=0 freq_scale_factor=426 cpu_scale_factor=1024
+       trace-cmd-3519  [003]  6550.018805: sched_cpu_capacity:   cpu=3 capacity=430 rt_capacity=1024
+     kworker/0:0-3410  [000]  6550.056870: cpu_frequency:        state=600000 cpu_id=0
+          <idle>-0     [001]  6550.100000: sched_wakeup:          comm=rcu_preempt pid=7 prio=120 success=1 target_cpu=1
+           <...>-19427 [000]  6551.993884: sched_wakeup_new:      comm=shutils pid=19428 prio=120 success=1 target_cpu=2
+          <idle>-0     [001]  6552.000002: sched_wakeup:          comm=rcu_preempt pid=7 prio=120 success=1 target_cpu=1
diff --git a/tests/trace_systrace.html b/tests/trace_systrace.html
new file mode 100644
index 0000000..94f2aaa
--- /dev/null
+++ b/tests/trace_systrace.html
@@ -0,0 +1,39 @@
+<!DOCTYPE html>
+#
+# Remove HTML content...
+#
+        overlay.textContent = tr.b.normalizeException(err).message;
+        overlay.title = 'Import error';
+        overlay.visible = true;
+      });
+  }
+  window.addEventListener('load', onLoad);
+  </script>
+<!-- BEGIN TRACE -->
+  <script class="trace-data" type="application/text">
+# tracer: nop
+#
+# entries-in-buffer/entries-written: 71906/71906   #P:6
+# enabled events: sched:sched_switch sched:sched_wakeup
+#
+#                                      _-----=> irqs-off
+#                                     / _----=> need-resched
+#                                    | / _---=> hardirq/softirq
+#                                    || / _--=> preempt-depth
+#                                    ||| /     delay
+#           TASK-PID    TGID   CPU#  ||||    TIMESTAMP  FUNCTION
+#              | |        |      |   ||||       |         |
+RenderThread-1143  (  611) [004] ...1 514769.085985: tracing_mark_write: E
+com.android.systemui-611   (  611) [001] d..5 514769.086090: sched_wakeup: comm=Binder_5 pid=679 prio=120 success=1 target_cpu=001 state=W
+com.android.systemui-611   (  611) [001] d..3 514769.086139: sched_switch: prev_comm=ndroid.systemui prev_pid=611 prev_prio=120 prev_state=S ==> next_comm=Binder_5 next_pid=679 next_prio=120
+atrace-15227 (15227) [000] ...1 514769.086243: tracing_mark_write: trace_event_clock_sync: realtime_ts=1448979311493
+atrace-15227 (15227) [000] d..3 514769.086283: sched_switch: prev_comm=atrace prev_pid=15227 prev_prio=120 prev_state=S ==> next_comm=Binder_4 next_pid=280 next_prio=120
+Binder_4-280   (  184) [000] d..5 514769.086330: sched_wakeup: comm=Binder_5 pid=1158 prio=120 success=1 target_cpu=002 state=W
+          <idle>-0     (-----) [002] d..3 514769.086339: sched_switch: prev_comm=swapper/2 prev_pid=0 prev_prio=120 prev_state=R ==> next_comm=Binder_5 next_pid=1158 next_prio=120
+Binder_4-280   (  184) [000] d..3 514769.086417: sched_wakeup: comm=Binder_5 pid=1158 prio=120 success=1 target_cpu=002 state=W|m
+          <idle>-0     (-----) [002] d..3 514769.086424: sched_switch: prev_comm=swapper/2 prev_pid=0 prev_prio=120 prev_state=R ==> next_comm=Binder_5 next_pid=1158 next_prio=120
+Binder_4-280   (  184) [000] d..4 514769.086473: sched_wakeup: comm=EventThread pid=238 prio=111 success=1 target_cpu=003 state=W
+  </script>
+<!-- END TRACE -->
+</body>
+</html>
diff --git a/tests/trappy b/tests/trappy
new file mode 120000
index 0000000..33a15b9
--- /dev/null
+++ b/tests/trappy
@@ -0,0 +1 @@
+../trappy
\ No newline at end of file
diff --git a/tests/unconstrained.csv b/tests/unconstrained.csv
new file mode 100644
index 0000000..583aaf8
--- /dev/null
+++ b/tests/unconstrained.csv
@@ -0,0 +1,147 @@
+id,workload,iteration,metric,value,units

+power_allocator_1,antutu,1,float_2,4,

+power_allocator_1,antutu,1,multitask_3,4,

+power_allocator_1,antutu,1,float,2,

+power_allocator_1,antutu,1,3d_5,6,

+power_allocator_1,antutu,1,float_4,9,

+power_allocator_1,antutu,1,floatsingle_3,1,

+power_allocator_1,antutu,1,integer_5,8,

+power_allocator_1,antutu,1,integer_4,2,

+power_allocator_1,antutu,1,integer_3,7,

+power_allocator_1,antutu,1,integer_2,0,

+power_allocator_1,antutu,1,integer,9,

+power_allocator_1,antutu,1,ram_5,1,

+power_allocator_1,antutu,1,ram_4,1,

+power_allocator_1,antutu,1,multitask,4,

+power_allocator_1,antutu,1,ram_3,2,

+power_allocator_1,antutu,1,ram_2,2,

+power_allocator_1,antutu,1,integersingle_4,9,

+power_allocator_1,antutu,1,memory_3,3,

+power_allocator_1,antutu,1,memory_2,3,

+power_allocator_1,antutu,1,memory_5,9,

+power_allocator_1,antutu,1,memory_4,6,

+power_allocator_1,antutu,1,float_3,7,

+power_allocator_1,antutu,1,integersingle_2,8,

+power_allocator_1,antutu,1,storage,8,

+power_allocator_1,antutu,1,renderscript,5,

+power_allocator_1,antutu,1,score_3,4,

+power_allocator_1,antutu,1,score,2,

+power_allocator_1,antutu,1,score_5,9,

+power_allocator_1,antutu,1,score_4,6,

+power_allocator_1,antutu,1,memory,6,

+power_allocator_1,antutu,1,renderscript_5,3,

+power_allocator_1,antutu,1,score_2,6,

+power_allocator_1,antutu,1,storage_5,5,

+power_allocator_1,antutu,1,3d,4,

+power_allocator_1,antutu,1,storage_3,3,

+power_allocator_1,antutu,1,integersingle,8,

+power_allocator_1,antutu,1,integersingle_3,6,

+power_allocator_1,antutu,1,float_5,3,

+power_allocator_1,antutu,1,2d_4,9,

+power_allocator_1,antutu,1,2d_5,5,

+power_allocator_1,antutu,1,2d_2,5,

+power_allocator_1,antutu,1,2d_3,5,

+power_allocator_1,antutu,1,renderscript_4,3,

+power_allocator_1,antutu,1,storage_2,8,

+power_allocator_1,antutu,1,floatsingle_2,7,

+power_allocator_1,antutu,1,floatsingle_5,7,

+power_allocator_1,antutu,1,floatsingle,9,

+power_allocator_1,antutu,1,storage_4,4,

+power_allocator_1,antutu,1,floatsingle_4,4,

+power_allocator_1,antutu,1,database,0,

+power_allocator_1,antutu,1,3d_4,5,

+power_allocator_1,antutu,1,dalvik,9,

+power_allocator_1,antutu,1,multitask_5,9,

+power_allocator_1,antutu,1,multitask_4,8,

+power_allocator_1,antutu,1,ram,2,

+power_allocator_1,antutu,1,multitask_2,1,

+power_allocator_1,antutu,1,2d,5,

+power_allocator_1,antutu,1,3d_3,4,

+power_allocator_1,antutu,1,3d_2,9,

+power_allocator_1,antutu,1,renderscript_3,2,

+power_allocator_1,antutu,1,renderscript_2,3,

+power_allocator_1,antutu,1,integersingle_5,5,

+power_allocator_1,antutu,1,database_5,5,

+power_allocator_1,antutu,1,database_4,3,

+power_allocator_1,antutu,1,dalvik_5,2,

+power_allocator_1,antutu,1,dalvik_4,9,

+power_allocator_1,antutu,1,dalvik_3,7,

+power_allocator_1,antutu,1,dalvik_2,9,

+power_allocator_1,antutu,1,database_3,4,

+power_allocator_1,antutu,1,database_2,5,

+power_allocator_1,antutu,1,execution_time,1234.67110705,seconds

+power_allocator_1,antutu,1,FPS,nan,

+power_allocator_1,antutu,1,frame_count,0,

+power_allocator_1,antutu,1,janks,0,

+power_allocator_1,antutu,1,not_at_vsync,0,

+power_allocator_2,egypt_offscreen,1,Frames_0,3,frames

+power_allocator_2,egypt_offscreen,1,FPS_0,864,fps

+power_allocator_2,egypt_offscreen,1,Frames_1,1,frames

+power_allocator_2,egypt_offscreen,1,FPS_1,802,fps

+power_allocator_2,egypt_offscreen,1,Frames_2,1,frames

+power_allocator_2,egypt_offscreen,1,FPS_2,47,fps

+power_allocator_2,egypt_offscreen,1,execution_time,1234.934822083,seconds

+power_allocator_2,egypt_offscreen,1,FPS,123.0000024,

+power_allocator_2,egypt_offscreen,1,frame_count,0,

+power_allocator_2,egypt_offscreen,1,janks,0,

+power_allocator_2,egypt_offscreen,1,not_at_vsync,0,

+power_allocator_3,t-rex_offscreen,1,Frames_0,6,frames

+power_allocator_3,t-rex_offscreen,1,FPS_0,185,fps

+power_allocator_3,t-rex_offscreen,1,Frames_1,4,frames

+power_allocator_3,t-rex_offscreen,1,FPS_1,878,fps

+power_allocator_3,t-rex_offscreen,1,Frames_2,1203,frames

+power_allocator_3,t-rex_offscreen,1,FPS_2,262,fps

+power_allocator_3,t-rex_offscreen,1,Frames_3,7,frames

+power_allocator_3,t-rex_offscreen,1,FPS_3,588,fps

+power_allocator_3,t-rex_offscreen,1,Frames_4,5,frames

+power_allocator_3,t-rex_offscreen,1,FPS_4,559,fps

+power_allocator_3,t-rex_offscreen,1,execution_time,1234.8131001,seconds

+power_allocator_3,t-rex_offscreen,1,FPS,123.0000024,

+power_allocator_3,t-rex_offscreen,1,frame_count,0,

+power_allocator_3,t-rex_offscreen,1,janks,0,

+power_allocator_3,t-rex_offscreen,1,not_at_vsync,0,

+power_allocator_4,geekbench,1,score,6,

+power_allocator_4,geekbench,1,multicore_score,2,

+power_allocator_4,geekbench,1,Integer_score,5,

+power_allocator_4,geekbench,1,Integer_multicore_score,9,

+power_allocator_4,geekbench,1,Floating Point_score,7,

+power_allocator_4,geekbench,1,Floating Point_multicore_score,2,

+power_allocator_4,geekbench,1,Memory_score,6,

+power_allocator_4,geekbench,1,Memory_multicore_score,9,

+power_allocator_4,geekbench,1,score 1,6,

+power_allocator_4,geekbench,1,multicore_score 1,4,

+power_allocator_4,geekbench,1,Integer_score 1,6,

+power_allocator_4,geekbench,1,Integer_multicore_score 1,1,

+power_allocator_4,geekbench,1,Floating Point_score 1,7,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 1,1,

+power_allocator_4,geekbench,1,Memory_score 1,5,

+power_allocator_4,geekbench,1,Memory_multicore_score 1,9,

+power_allocator_4,geekbench,1,score 2,1,

+power_allocator_4,geekbench,1,multicore_score 2,9,

+power_allocator_4,geekbench,1,Integer_score 2,5,

+power_allocator_4,geekbench,1,Integer_multicore_score 2,8,

+power_allocator_4,geekbench,1,Floating Point_score 2,5,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 2,2,

+power_allocator_4,geekbench,1,Memory_score 2,9,

+power_allocator_4,geekbench,1,Memory_multicore_score 2,3,

+power_allocator_4,geekbench,1,score 3,7,

+power_allocator_4,geekbench,1,multicore_score 3,6,

+power_allocator_4,geekbench,1,Integer_score 3,4,

+power_allocator_4,geekbench,1,Integer_multicore_score 3,8,

+power_allocator_4,geekbench,1,Floating Point_score 3,6,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 3,2,

+power_allocator_4,geekbench,1,Memory_score 3,7,

+power_allocator_4,geekbench,1,Memory_multicore_score 3,2,

+power_allocator_4,geekbench,1,score 4,9,

+power_allocator_4,geekbench,1,multicore_score 4,9,

+power_allocator_4,geekbench,1,Integer_score 4,3,

+power_allocator_4,geekbench,1,Integer_multicore_score 4,2,

+power_allocator_4,geekbench,1,Floating Point_score 4,8,

+power_allocator_4,geekbench,1,Floating Point_multicore_score 4,8,

+power_allocator_4,geekbench,1,Memory_score 4,4,

+power_allocator_4,geekbench,1,Memory_multicore_score 4,2,

+power_allocator_4,geekbench,1,execution_time,1234.361968994,seconds

+power_allocator_4,geekbench,1,FPS,nan,

+power_allocator_4,geekbench,1,frame_count,0,

+power_allocator_4,geekbench,1,janks,0,

+power_allocator_4,geekbench,1,not_at_vsync,0,

diff --git a/tests/utils_tests.py b/tests/utils_tests.py
new file mode 100644
index 0000000..617cfa3
--- /dev/null
+++ b/tests/utils_tests.py
@@ -0,0 +1,52 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import unittest
+import os
+import shutil
+import subprocess
+import tempfile
+
+TESTS_DIRECTORY = os.path.dirname(os.path.realpath(__file__))
+
+def trace_cmd_installed():
+    """Return true if trace-cmd is installed, false otherwise"""
+    with open(os.devnull) as devnull:
+        try:
+            subprocess.check_call(["trace-cmd", "options"], stdout=devnull)
+        except OSError:
+            return False
+
+    return True
+
+class SetupDirectory(unittest.TestCase):
+    def __init__(self, files_to_copy, *args, **kwargs):
+        self.files_to_copy = files_to_copy
+        super(SetupDirectory, self).__init__(*args, **kwargs)
+
+    def setUp(self):
+        self.previous_dir = os.getcwd()
+
+        self.out_dir = tempfile.mkdtemp()
+        os.chdir(self.out_dir)
+
+        for src_fname, dst_fname in self.files_to_copy:
+            src_fname = os.path.join(TESTS_DIRECTORY, src_fname)
+            shutil.copy(src_fname, os.path.join(self.out_dir, dst_fname))
+
+    def tearDown(self):
+        os.chdir(self.previous_dir)
+        shutil.rmtree(self.out_dir)
diff --git a/trappy/__init__.py b/trappy/__init__.py
new file mode 100644
index 0000000..3b3c419
--- /dev/null
+++ b/trappy/__init__.py
@@ -0,0 +1,54 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import warnings
+from trappy.bare_trace import BareTrace
+from trappy.compare_runs import summary_plots, compare_runs
+from trappy.ftrace import FTrace
+from trappy.systrace import SysTrace
+from trappy.version import __version__
+try:
+    from trappy.plotter.LinePlot import LinePlot
+except ImportError as exc:
+    class LinePlot(object):
+        def __init__(self, *args, **kwargs):
+            raise exc
+try:
+    from trappy.plotter.ILinePlot import ILinePlot
+    from trappy.plotter.EventPlot import EventPlot
+    from trappy.plotter.BarPlot import BarPlot
+except ImportError:
+    pass
+from trappy.dynamic import register_dynamic_ftrace, register_ftrace_parser, \
+    unregister_ftrace_parser
+import trappy.nbexport
+
+# We define unregister_dynamic_ftrace() because it undoes what
+# register_dynamic_ftrace().  Internally it does exactly the same as
+# unregister_ftrace_parser() though but with these two names the API
+# makes more sense: register with register_dynamic_ftrace(),
+# unregister with unregister_dynamic_ftrace()
+unregister_dynamic_ftrace = unregister_ftrace_parser
+
+# Load all the modules to make sure all classes are registered with FTrace
+import os
+for fname in os.listdir(os.path.dirname(__file__)):
+    import_name, extension = os.path.splitext(fname)
+    if (extension == ".py") and (fname != "__init__.py") and \
+       (fname != "plot_utils.py"):
+        __import__("trappy.{}".format(import_name))
+
+del fname, import_name, extension
diff --git a/trappy/bare_trace.py b/trappy/bare_trace.py
new file mode 100644
index 0000000..4900e8a
--- /dev/null
+++ b/trappy/bare_trace.py
@@ -0,0 +1,142 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import re
+
+class BareTrace(object):
+    """A wrapper class that holds dataframes for all the events in a trace.
+
+    BareTrace doesn't parse any file so it's a class that should
+    either be (a) subclassed to parse a particular trace (like FTrace)
+    or (b) be instantiated and the events added with add_parsed_event()
+
+    :param name: is a string describing the trace.
+    :type name: str
+
+    """
+
+    def __init__(self, name="", build_df=True):
+        self.name = name
+        self.normalized_time = False
+        self.class_definitions = {}
+        self.trace_classes = []
+        self.basetime = 0
+        self.build_df = build_df
+
+    def get_duration(self):
+        """Returns the largest time value of all classes,
+        returns 0 if the data frames of all classes are empty"""
+        durations = []
+
+        for trace_class in self.trace_classes:
+            try:
+                durations.append(trace_class.data_frame.index[-1])
+            except IndexError:
+                pass
+
+        if len(durations) == 0:
+            return 0
+
+        if self.normalized_time:
+            return max(durations)
+        else:
+            return max(durations) - self.basetime
+
+    def get_filters(self, key=""):
+        """Returns an array with the available filters.
+
+        :param key: If specified, returns a subset of the available filters
+            that contain 'key' in their name (e.g., :code:`key="sched"` returns
+            only the :code:`"sched"` related filters)."""
+        filters = []
+
+        for cls in self.class_definitions:
+            if re.search(key, cls):
+                filters.append(cls)
+
+        return filters
+
+    def normalize_time(self, basetime=None):
+        """Normalize the time of all the trace classes
+
+        :param basetime: The offset which needs to be subtracted from
+            the time index
+        :type basetime: float
+        """
+        return # HACK: Since we're not normalizing anymore after the fact
+
+        if basetime is not None:
+            self.basetime = basetime
+
+        for trace_class in self.trace_classes:
+            trace_class.normalize_time(self.basetime)
+
+        self.normalized_time = True
+
+    def add_parsed_event(self, name, dfr, pivot=None):
+        """Add a dataframe to the events in this trace
+
+        This function lets you add other events that have been parsed
+        by other tools to the collection of events in this instance.  For
+        example, assuming you have some events in a csv, you could add
+        them to a trace instance like this:
+
+        >>> trace = trappy.BareTrace()
+        >>> counters_dfr = pd.DataFrame.from_csv("counters.csv")
+        >>> trace.add_parsed_event("pmu_counters", counters_dfr)
+
+        Now you can access :code:`trace.pmu_counters` as you would with any
+        other trace event and other trappy classes can interact with
+        them.
+
+        :param name: The attribute name in this trace instance.  As in the example above, if :code:`name` is "pmu_counters", the parsed event will be accessible using :code:`trace.pmu_counters`.
+        :type name: str
+
+        :param dfr: :mod:`pandas.DataFrame` containing the events.  Its index should be time in seconds.  Its columns are the events.
+        :type dfr: :mod:`pandas.DataFrame`
+
+        :param pivot: The data column about which the data can be grouped
+        :type pivot: str
+
+        """
+        from trappy.base import Base
+        from trappy.dynamic import DynamicTypeFactory, default_init
+
+        if hasattr(self, name):
+            raise ValueError("event {} already present".format(name))
+
+        kwords = {
+            "__init__": default_init,
+            "unique_word": name + ":",
+            "name": name,
+        }
+
+        trace_class = DynamicTypeFactory(name, (Base,), kwords)
+        self.class_definitions[name] = trace_class
+
+        event = trace_class()
+        self.trace_classes.append(event)
+        event.data_frame = dfr
+        if pivot:
+            event.pivot = pivot
+
+        setattr(self, name, event)
+
+    def finalize_objects(self):
+        if not self.build_df:
+            return
+        for trace_class in self.trace_classes:
+            trace_class.create_dataframe()
+            trace_class.finalize_object()
diff --git a/trappy/base.py b/trappy/base.py
new file mode 100644
index 0000000..c0238cf
--- /dev/null
+++ b/trappy/base.py
@@ -0,0 +1,273 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Base class to parse trace.dat dumps"""
+
+import re
+import pandas as pd
+import warnings
+
+from resource import getrusage, RUSAGE_SELF
+
+def _get_free_memory_kb():
+    try:
+        with open("/proc/meminfo") as f:
+            memfree_line = [l for l in f.readlines() if "MemFree" in l][0]
+            _, num_kb, _ = memfree_line.split()
+            return int(num_kb)
+    except:
+        # Probably either not running on Linux (no /proc/meminfo), or format has
+        # changed (we didn't find num_kb).
+        return None
+
+def trace_parser_explode_array(string, array_lengths):
+    """Explode an array in the trace into individual elements for easy parsing
+
+    Basically, turn :code:`load={1 1 2 2}` into :code:`load0=1 load1=1 load2=2
+    load3=2`.
+
+    :param string: Input string from the trace
+    :type string: str
+
+    :param array_lengths: A dictionary of array names and their
+        expected length.  If we get array that's shorter than the expected
+        length, additional keys have to be introduced with value 0 to
+        compensate.
+    :type array_lengths: dict
+
+    For example:
+    ::
+
+        trace_parser_explode_array(string="load={1 2}",
+                                   array_lengths={"load": 4})
+        "load0=1 load1=2 load2=0 load3=0"
+    """
+
+    while True:
+        match = re.search(r"[^ ]+={[^}]+}", string)
+        if match is None:
+            break
+
+        to_explode = match.group()
+        col_basename = re.match(r"([^=]+)=", to_explode).groups()[0]
+        vals_str = re.search(r"{(.+)}", to_explode).groups()[0]
+        vals_array = vals_str.split(' ')
+
+        exploded_str = ""
+        for (idx, val) in enumerate(vals_array):
+            exploded_str += "{}{}={} ".format(col_basename, idx, val)
+
+        vals_added = len(vals_array)
+        if vals_added < array_lengths[col_basename]:
+            for idx in range(vals_added, array_lengths[col_basename]):
+                exploded_str += "{}{}=0 ".format(col_basename, idx)
+
+        exploded_str = exploded_str[:-1]
+        begin_idx = match.start()
+        end_idx = match.end()
+
+        string = string[:begin_idx] + exploded_str + string[end_idx:]
+
+    return string
+
+class Base(object):
+    """Base class to parse trace.dat dumps.
+
+    Don't use directly, create a subclass that has a unique_word class
+    variable.  unique_word is a string that can uniquely identify
+    lines in the trace that correspond to this event.  This is usually
+    the trace_name (optionally followed by a semicolong,
+    e.g. "sched_switch:") but it can be anything else for trace points
+    generated using trace_printk().
+
+    :param parse_raw: If :code:`True`, raw trace data (-R option) to
+        trace-cmd will be used
+
+    This class acts as a base class for all TRAPpy events
+
+    """
+    def __init__(self, parse_raw=False):
+        self.data_frame = pd.DataFrame()
+        self.data_array = []
+        self.time_array = []
+        self.comm_array = []
+        self.pid_array = []
+        self.tgid_array = []
+        self.cpu_array = []
+        self.callback = None
+        self.parse_raw = parse_raw
+
+    def finalize_object(self):
+        pass
+
+    def __get_trace_array_lengths(self):
+        """Calculate the lengths of all arrays in the trace
+
+        Returns a dict with the name of each array found in the trace
+        as keys and their corresponding length as value
+
+        """
+        from collections import defaultdict
+
+        pat_array = re.compile(r"([A-Za-z0-9_]+)={([^}]+)}")
+
+        ret = defaultdict(int)
+
+        for line in self.data_array:
+            while True:
+                match = re.search(pat_array, line)
+                if not match:
+                    break
+
+                (array_name, array_elements) = match.groups()
+
+                array_len = len(array_elements.split(' '))
+
+                if array_len > ret[array_name]:
+                    ret[array_name] = array_len
+
+                line = line[match.end():]
+
+            # Stop scanning if the trace doesn't have arrays
+            if len(ret) == 0:
+                break
+
+        return ret
+
+    def append_data(self, time, comm, pid, tgid, cpu, data):
+        """Append data parsed from a line to the corresponding arrays
+
+        The :mod:`DataFrame` will be created from this when the whole trace
+        has been parsed.
+
+        :param time: The time for the line that was printed in the trace
+        :type time: float
+
+        :param comm: The command name or the execname from which the trace
+            line originated
+        :type comm: str
+
+        :param pid: The PID of the process from which the trace
+            line originated
+        :type pid: int
+
+        :param data: The data for matching line in the trace
+        :type data: str
+        """
+
+        self.time_array.append(time)
+        self.comm_array.append(comm)
+        self.pid_array.append(pid)
+        self.tgid_array.append(tgid)
+        self.cpu_array.append(cpu)
+        self.data_array.append(data)
+
+	if not self.callback:
+            return
+        data_dict = self.generate_data_dict(comm, pid, cpu, data)
+        self.callback(time, data_dict)
+
+    def generate_data_dict(self, comm, pid, tgid, cpu, data_str):
+        data_dict = {"__comm": comm, "__pid": pid, "__tgid": tgid, "__cpu": cpu}
+        prev_key = None
+        for field in data_str.split():
+            if "=" not in field:
+                # Concatenation is supported only for "string" values
+                if type(data_dict[prev_key]) is not str:
+                    continue
+                data_dict[prev_key] += ' ' + field
+                continue
+            (key, value) = field.split('=', 1)
+            try:
+                value = int(value)
+            except ValueError:
+                pass
+            data_dict[key] = value
+            prev_key = key
+        return data_dict
+
+    def generate_parsed_data(self):
+
+        # Get a rough idea of how much memory we have to play with
+        CHECK_MEM_COUNT = 10000
+        kb_free = _get_free_memory_kb()
+        starting_maxrss = getrusage(RUSAGE_SELF).ru_maxrss
+        check_memory_usage = True
+        check_memory_count = 1
+
+        for (comm, pid, tgid, cpu, data_str) in zip(self.comm_array, self.pid_array,
+                                              self.tgid_array, self.cpu_array,
+                                              self.data_array):
+            data_dict = self.generate_data_dict(comm, pid, tgid, cpu, data_str)
+
+            # When running out of memory, Pandas has been observed to segfault
+            # rather than throwing a proper Python error.
+            # Look at how much memory our process is using and warn if we seem
+            # to be getting close to the system's limit, check it only once
+            # in the beginning and then every CHECK_MEM_COUNT events
+            check_memory_count -= 1
+            if check_memory_usage and check_memory_count == 0:
+                kb_used = (getrusage(RUSAGE_SELF).ru_maxrss - starting_maxrss)
+                if kb_free and kb_used > kb_free * 0.9:
+                    warnings.warn("TRAPpy: Appear to be low on memory. "
+                                  "If errors arise, try providing more RAM")
+                    check_memory_usage = False
+                check_memory_count = CHECK_MEM_COUNT
+
+            yield data_dict
+
+    def create_dataframe(self):
+        """Create the final :mod:`pandas.DataFrame`"""
+        if not self.time_array:
+            return
+
+        trace_arr_lengths = self.__get_trace_array_lengths()
+
+        if trace_arr_lengths.items():
+            for (idx, val) in enumerate(self.data_array):
+                expl_val = trace_parser_explode_array(val, trace_arr_lengths)
+                self.data_array[idx] = expl_val
+
+        time_idx = pd.Index(self.time_array, name="Time")
+        self.data_frame = pd.DataFrame(self.generate_parsed_data(), index=time_idx)
+
+        self.time_array = []
+        self.comm_array = []
+        self.pid_array = []
+        self.cpu_array = []
+        self.data_array = []
+
+    def write_csv(self, fname):
+        """Write the csv info into a CSV file
+
+        :param fname: The name of the CSV file
+        :type fname: str
+        """
+        self.data_frame.to_csv(fname)
+
+    def normalize_time(self, basetime):
+        """Substract basetime from the Time of the data frame
+
+        :param basetime: The offset which needs to be subtracted from
+            the time index
+        :type basetime: float
+        """
+        # HACK: We don't normalize anymore after the fact
+        return
+
+        if basetime and not self.data_frame.empty:
+            self.data_frame.reset_index(inplace=True)
+            self.data_frame["Time"] = self.data_frame["Time"] - basetime
+            self.data_frame.set_index("Time", inplace=True)
diff --git a/trappy/compare_runs.py b/trappy/compare_runs.py
new file mode 100644
index 0000000..c99f2f3
--- /dev/null
+++ b/trappy/compare_runs.py
@@ -0,0 +1,107 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import trappy.ftrace
+
+def compare_runs(actor_order, map_label, runs, **kwords):
+    """A side by side comparison of multiple runs
+
+    Plots include temperature, utilization, frequencies, PID
+    controller and power.
+
+    :param actor_order: An array showing the order in which the actors
+        were registered.  The array values are the labels that
+        will be used in the input and output power plots.
+
+        For Example:
+        ::
+
+            ["GPU", "A15", "A7"]
+
+    :param map_label: A dict that matches cpumasks (as found in the
+        trace) with their proper name.  This "proper name" will be used as
+        a label for the load and allfreqs plots.  It's recommended that
+        the names of the cpus matches those in actor_order.
+
+        For Example:
+        ::
+
+            {"0000000f": "A7", "000000f0": "A15"}
+
+    :param runs: An array of tuples consisting of a name and the path to
+        the directory where the trace.dat is.
+
+        For example:
+        ::
+
+            [("experiment1", "wa_output/antutu_antutu_1"),
+             ("known good", "good/antutu_antutu_1")]
+
+    :param tz_id: thermal zone id as it appears in the id field of the
+            thermal_temperature trace event
+
+    :type actor_order: list
+    :type map_label: dict
+    :type runs: list
+    :type tz_id: int
+
+    """
+    import trappy.plot_utils
+    import trappy.wa
+
+    if not isinstance(actor_order, list):
+        raise TypeError("actor_order has to be an array")
+
+    if not isinstance(map_label, dict):
+        raise TypeError("map_label has to be a dict")
+
+    if "width" not in kwords:
+        kwords["width"] = 20
+    if "height" not in kwords:
+        kwords["height"] = 5
+
+    run_data = []
+    for name, path in runs:
+        run_data.append(trappy.FTrace(name=name, path=path, scope="thermal"))
+        trappy.wa.SysfsExtractor(path).pretty_print_in_ipython()
+
+    trappy.plot_utils.plot_temperature(run_data, **kwords)
+    if "tz_id" in kwords:
+        del kwords["tz_id"]
+
+    try:
+        trappy.plot_utils.plot_load(run_data, map_label, **kwords)
+    except IndexError:
+        raise ValueError("No power allocator traces found.  Was IPA active (temp above switch on temperature) and FTrace configured to collect all thermal events?")
+    trappy.plot_utils.plot_allfreqs(run_data, map_label, **kwords)
+    trappy.plot_utils.plot_controller(run_data, **kwords)
+    trappy.plot_utils.plot_input_power(run_data, actor_order, **kwords)
+    trappy.plot_utils.plot_output_power(run_data, actor_order, **kwords)
+    trappy.plot_utils.plot_freq_hists(run_data, map_label)
+    trappy.plot_utils.plot_temperature_hist(run_data)
+
+def summary_plots(actor_order, map_label, **kwords):
+    """A summary of plots for a given run
+
+    .. warning::
+
+        This is a wrapper around compare_runs().  Use that instead.
+    """
+
+    path = kwords.pop("path", ".")
+    title = kwords.pop("title", "")
+
+    return compare_runs(actor_order, map_label, [(title, path)], **kwords)
diff --git a/trappy/cpu_power.py b/trappy/cpu_power.py
new file mode 100644
index 0000000..9cd0bba
--- /dev/null
+++ b/trappy/cpu_power.py
@@ -0,0 +1,198 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Process the output of the cpu_cooling devices in the current
+directory's trace.dat"""
+
+import pandas as pd
+
+from trappy.base import Base
+from trappy.dynamic import register_ftrace_parser
+
+def pivot_with_labels(dfr, data_col_name, new_col_name, mapping_label):
+    """Pivot a :mod:`pandas.DataFrame` row into columns
+
+    :param dfr: The :mod:`pandas.DataFrame` to operate on.
+
+    :param data_col_name: The name of the column in the :mod:`pandas.DataFrame`
+        which contains the values.
+
+    :param new_col_name: The name of the column in the :mod:`pandas.DataFrame` that will
+        become the new columns.
+
+    :param mapping_label: A dictionary whose keys are the values in
+        new_col_name and whose values are their
+        corresponding name in the :mod:`pandas.DataFrame` to be returned.
+
+    :type dfr: :mod:`pandas.DataFrame`
+    :type data_col_name: str
+    :type new_col_name: str
+    :type mapping_label: dict
+
+    Example:
+
+        >>> dfr_in = pd.DataFrame({'cpus': ["000000f0",
+        >>>                                 "0000000f",
+        >>>                                 "000000f0",
+        >>>                                 "0000000f"
+        >>>                                 ],
+        >>>                        'freq': [1, 3, 2, 6]})
+        >>> dfr_in
+               cpus  freq
+        0  000000f0     1
+        1  0000000f     3
+        2  000000f0     2
+        3  0000000f     6
+
+        >>> map_label = {"000000f0": "A15", "0000000f": "A7"}
+        >>> power.pivot_with_labels(dfr_in, "freq", "cpus", map_label)
+           A15  A7
+        0    1 NaN
+        1    1   3
+        2    2   3
+        3    2   6
+
+    """
+
+    # There has to be a more "pandas" way of doing this.
+
+    col_set = set(dfr[new_col_name])
+
+    ret_series = {}
+    for col in col_set:
+        try:
+            label = mapping_label[col]
+        except KeyError:
+            available_keys = ", ".join(mapping_label.keys())
+            error_str = '"{}" not found, available keys: {}'.format(col,
+                                                                 available_keys)
+            raise KeyError(error_str)
+        data = dfr[dfr[new_col_name] == col][data_col_name]
+
+        ret_series[label] = data
+
+    return pd.DataFrame(ret_series).fillna(method="pad")
+
+def num_cpus_in_mask(mask):
+    """Return the number of cpus in a cpumask"""
+
+    mask = mask.replace(",", "")
+    value = int(mask, 16)
+
+    return bin(value).count("1")
+
+class CpuOutPower(Base):
+    """Process the cpufreq cooling power actor data in a ftrace dump"""
+
+    unique_word = "thermal_power_cpu_limit"
+    """The unique word that will be matched in a trace line"""
+
+    name = "cpu_out_power"
+    """The name of the :mod:`pandas.DataFrame` member that will be created in a
+    :mod:`trappy.ftrace.FTrace` object"""
+
+    pivot = "cpus"
+    """The Pivot along which the data is orthogonal"""
+
+    def get_all_freqs(self, mapping_label):
+        """Get a :mod:`pandas.DataFrame` with the maximum frequencies allowed by the governor
+
+        :param mapping_label: A dictionary that maps cpumasks to name
+            of the cpu.
+        :type mapping_label: dict
+
+        :return: freqs are in MHz
+        """
+
+        dfr = self.data_frame
+
+        return pivot_with_labels(dfr, "freq", "cpus", mapping_label) / 1000
+
+register_ftrace_parser(CpuOutPower, "thermal")
+
+class CpuInPower(Base):
+    """Process the cpufreq cooling power actor data in a ftrace dump
+    """
+
+    unique_word = "thermal_power_cpu_get"
+    """The unique word that will be matched in a trace line"""
+
+    name = "cpu_in_power"
+    """The name of the :mod:`pandas.DataFrame` member that will be created in a
+    :mod:`trappy.ftrace.FTrace` object"""
+
+    pivot = "cpus"
+    """The Pivot along which the data is orthogonal"""
+
+    def _get_load_series(self):
+        """get a :mod:`pandas.Series` with the aggregated load"""
+
+        dfr = self.data_frame
+        load_cols = [s for s in dfr.columns if s.startswith("load")]
+
+        load_series = dfr[load_cols[0]].copy()
+        for col in load_cols[1:]:
+            load_series += dfr[col]
+
+        return load_series
+
+    def get_load_data(self, mapping_label):
+        """Return :mod:`pandas.DataFrame` suitable for plot_load()
+
+        :param mapping_label: A Dictionary mapping cluster cpumasks to labels
+        :type mapping_label: dict
+        """
+
+        dfr = self.data_frame
+        load_series = self._get_load_series()
+        load_dfr = pd.DataFrame({"cpus": dfr["cpus"], "load": load_series})
+
+        return pivot_with_labels(load_dfr, "load", "cpus", mapping_label)
+
+    def get_normalized_load_data(self, mapping_label):
+        """Return a :mod:`pandas.DataFrame` for plotting normalized load data
+
+        :param mapping_label: should be a dictionary mapping cluster cpumasks
+            to labels
+        :type mapping_label: dict
+        """
+
+        dfr = self.data_frame
+        load_series = self._get_load_series()
+
+        load_series *= dfr['freq']
+        for cpumask in mapping_label:
+            num_cpus = num_cpus_in_mask(cpumask)
+            idx = dfr["cpus"] == cpumask
+            max_freq = max(dfr[idx]["freq"])
+            load_series[idx] = load_series[idx] / (max_freq * num_cpus)
+
+        load_dfr = pd.DataFrame({"cpus": dfr["cpus"], "load": load_series})
+
+        return pivot_with_labels(load_dfr, "load", "cpus", mapping_label)
+
+    def get_all_freqs(self, mapping_label):
+        """get a :mod:`pandas.DataFrame` with the "in" frequencies as seen by the governor
+
+        .. note::
+
+            Frequencies are in MHz
+        """
+
+        dfr = self.data_frame
+
+        return pivot_with_labels(dfr, "freq", "cpus", mapping_label) / 1000
+
+register_ftrace_parser(CpuInPower, "thermal")
diff --git a/trappy/devfreq_power.py b/trappy/devfreq_power.py
new file mode 100644
index 0000000..a8a0ac1
--- /dev/null
+++ b/trappy/devfreq_power.py
@@ -0,0 +1,75 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+"""Process the output of the devfreq_cooling devices in the current
+directory's trace.dat"""
+
+import pandas as pd
+
+from trappy.base import Base
+from trappy.dynamic import register_ftrace_parser
+
+
+class DevfreqInPower(Base):
+    """Process de devfreq cooling device data regarding get_power in an
+FTrace dump"""
+
+    name = "devfreq_in_power"
+    """The name of the :mod:`pandas.DataFrame` member that will be created in a
+    :mod:`trappy.ftrace.FTrace` object"""
+
+    unique_word="thermal_power_devfreq_get_power:"
+    """The event name in the trace"""
+
+    def get_all_freqs(self):
+        """Return a :mod:`pandas.DataFrame` with
+        the frequencies for the devfreq device
+
+        The format should be the same as the one for
+        :code:`CpuInPower().get_all_freqs()`.
+
+        .. note:: Frequencies are in MHz.
+        """
+
+        return pd.DataFrame(self.data_frame["freq"] / 1000000)
+
+register_ftrace_parser(DevfreqInPower, "thermal")
+
+
+class DevfreqOutPower(Base):
+    """Process de devfreq cooling device data regarding power2state in an
+ftrace dump"""
+
+    name = "devfreq_out_power"
+    """The name of the :mod:`pandas.DataFrame` member that will be created in a
+    :mod:`trappy.ftrace.FTrace` object"""
+
+    unique_word="thermal_power_devfreq_limit:"
+    """The event name in the trace"""
+
+    def get_all_freqs(self):
+        """Return a :mod:`pandas.DataFrame` with
+        the output frequencies for the devfreq device
+
+        The format should be the same as the one for
+        :code:`CpuOutPower().get_all_freqs()`.
+
+        .. note:: Frequencies are in MHz.
+        """
+
+        return pd.DataFrame(self.data_frame["freq"] / 1000000)
+
+register_ftrace_parser(DevfreqOutPower, "thermal")
diff --git a/trappy/dynamic.py b/trappy/dynamic.py
new file mode 100644
index 0000000..f764232
--- /dev/null
+++ b/trappy/dynamic.py
@@ -0,0 +1,153 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+"""The idea is to create a wrapper class that
+returns a Type of a Class dynamically created based
+on the input parameters. Similar to a factory design
+pattern
+"""
+from trappy.base import Base
+import re
+from trappy.ftrace import GenericFTrace
+
+
+def default_init(self):
+    """Default Constructor for the
+    Dynamic MetaClass. This is used for
+    the dynamic object creation in
+    :mod:`trappy.dynamic.DynamicTypeFactory`
+    """
+
+    kwords = {}
+
+    try:
+        kwords["parse_raw"] = self.parse_raw
+    except AttributeError:
+        pass
+
+    super(type(self), self).__init__(**kwords)
+
+
+class DynamicTypeFactory(type):
+
+    """Override the type class to create
+    a dynamic type on the fly. This Factory
+    class is used internally by
+    :mod:`trappy.dynamic.register_dynamic_ftrace`
+    """
+
+    def __new__(mcs, name, bases, dct):
+        """Override the new method"""
+        return type.__new__(mcs, name, bases, dct)
+
+    def __init__(cls, name, bases, dct):
+        """Override the constructor"""
+        super(DynamicTypeFactory, cls).__init__(name, bases, dct)
+
+
+def _get_name(name):
+    """Internal Method to Change camelcase to
+    underscores. CamelCase -> camel_case
+    """
+    return re.sub('(?!^)([A-Z]+)', r'_\1', name).lower()
+
+
+def register_dynamic_ftrace(class_name, unique_word, scope="all",
+                            parse_raw=False, pivot=None):
+    """Create a Dynamic FTrace parser and register it with any FTrace parsing classes
+
+    :param class_name: The name of the class to be registered
+        (Should be in CamelCase)
+    :type class_name: str
+
+    :param unique_word: The unique_word to be matched in the
+        trace
+    :type unique_word: str
+
+    :param scope: Registry Scope (Can be used to constrain
+        the parsing of events and group them together)
+    :type scope: str
+
+    :param parse_raw: If, true, raw trace output (-R flag)
+        will be used
+    :type parse_raw: bool
+
+    :param pivot: The data column about which the data can be grouped
+    :type pivot: str
+
+    For example if a new unique word :code:`my_unique_word` has
+    to be registered with TRAPpy:
+    ::
+
+        import trappy
+        custom_class = trappy.register_dynamic_ftrace("MyEvent", "my_unique_word")
+        trace = trappy.FTrace("/path/to/trace_file")
+
+        # New data member created in the ftrace object
+        trace.my_event
+
+    .. note:: The name of the member is :code:`my_event` from **MyEvent**
+
+
+    :return: A class object of type :mod:`trappy.base.Base`
+    """
+
+    kwords = {
+            "__init__": default_init,
+            "unique_word": unique_word,
+            "name": _get_name(class_name),
+            "parse_raw" : parse_raw,
+        }
+
+    if pivot:
+        kwords["pivot"] = pivot
+
+    dyn_class = DynamicTypeFactory(class_name, (Base,), kwords)
+    GenericFTrace.register_parser(dyn_class, scope)
+    return dyn_class
+
+
+def register_ftrace_parser(cls, scope="all"):
+    """Register a new FTrace parser class implementation
+
+    Should be used when the class has complex helper methods and does
+    not expect to use the default constructor.
+
+    :param cls: The class to be registered for
+        enabling the parsing of an event in trace
+    :type cls: :mod:`trappy.base.Base`
+
+    :param scope: scope of this parser class.  The scope can be used
+        to restrict the parsing done on an individual file.  Currently
+        the only scopes available are "sched", "thermal" or "all"
+    :type scope: string
+
+    """
+
+    # Check the argspec of the class
+    GenericFTrace.register_parser(cls, scope)
+
+def unregister_ftrace_parser(ftrace_parser):
+    """Unregister an ftrace parser
+
+    :param ftrace_parser: An ftrace parser class that was registered
+        with register_ftrace_parser() or register_dynamic_ftrace().
+        If done with the latter, the cls parameter is the return value
+        of register_dynamic_ftrace()
+    :type ftrace_parser: class derived from :mod:`trappy.base.Base`
+
+    """
+    GenericFTrace.unregister_parser(ftrace_parser)
diff --git a/trappy/ftrace.py b/trappy/ftrace.py
new file mode 100644
index 0000000..07cb94b
--- /dev/null
+++ b/trappy/ftrace.py
@@ -0,0 +1,605 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+# pylint can't see any of the dynamically allocated classes of FTrace
+# pylint: disable=no-member
+
+import itertools
+import os
+import re
+import pandas as pd
+
+from trappy.bare_trace import BareTrace
+from trappy.utils import listify
+
+class FTraceParseError(Exception):
+    pass
+
+def _plot_freq_hists(allfreqs, what, axis, title):
+    """Helper function for plot_freq_hists
+
+    allfreqs is the output of a Cpu*Power().get_all_freqs() (for
+    example, CpuInPower.get_all_freqs()).  what is a string: "in" or
+    "out"
+
+    """
+    import trappy.plot_utils
+
+    for ax, actor in zip(axis, allfreqs):
+        this_title = "freq {} {}".format(what, actor)
+        this_title = trappy.plot_utils.normalize_title(this_title, title)
+        xlim = (0, allfreqs[actor].max())
+
+        trappy.plot_utils.plot_hist(allfreqs[actor], ax, this_title, "KHz", 20,
+                             "Frequency", xlim, "default")
+
+class GenericFTrace(BareTrace):
+    """Generic class to parse output of FTrace.  This class is meant to be
+subclassed by FTrace (for parsing FTrace coming from trace-cmd) and SysTrace."""
+
+    thermal_classes = {}
+
+    sched_classes = {}
+
+    dynamic_classes = {}
+
+    def __init__(self, name="", normalize_time=True, scope="all",
+                 events=[], event_callbacks={}, window=(0, None),
+                 abs_window=(0, None), build_df=True):
+        super(GenericFTrace, self).__init__(name, build_df)
+
+        self.normalized_time = normalize_time
+
+        if not hasattr(self, "needs_raw_parsing"):
+            self.needs_raw_parsing = False
+
+        self.class_definitions.update(self.dynamic_classes.items())
+        self.__add_events(listify(events))
+
+        if scope == "thermal":
+            self.class_definitions.update(self.thermal_classes.items())
+        elif scope == "sched":
+            self.class_definitions.update(self.sched_classes.items())
+        elif scope != "custom":
+            self.class_definitions.update(self.thermal_classes.items() +
+                                          self.sched_classes.items())
+
+        for attr, class_def in self.class_definitions.iteritems():
+            trace_class = class_def()
+            if event_callbacks.has_key(attr):
+                trace_class.callback = event_callbacks[attr]
+            setattr(self, attr, trace_class)
+            self.trace_classes.append(trace_class)
+
+        self.__parse_trace_file(self.trace_path, window, abs_window)
+        if self.needs_raw_parsing and (self.trace_path_raw is not None):
+            self.__parse_trace_file(self.trace_path_raw, window, abs_window,
+                                    raw=True)
+        self.finalize_objects()
+
+    @classmethod
+    def register_parser(cls, cobject, scope):
+        """Register the class as an Event. This function
+        can be used to register a class which is associated
+        with an FTrace unique word.
+
+        .. seealso::
+
+            :mod:`trappy.dynamic.register_dynamic_ftrace` :mod:`trappy.dynamic.register_ftrace_parser`
+
+        """
+
+        if not hasattr(cobject, "name"):
+            cobject.name = cobject.unique_word.split(":")[0]
+
+        # Add the class to the classes dictionary
+        if scope == "all":
+            cls.dynamic_classes[cobject.name] = cobject
+        else:
+            getattr(cls, scope + "_classes")[cobject.name] = cobject
+
+    @classmethod
+    def unregister_parser(cls, cobject):
+        """Unregister a parser
+
+        This is the opposite of FTrace.register_parser(), it removes a class
+        from the list of classes that will be parsed on the trace
+
+        """
+
+        # TODO: scopes should not be hardcoded (nor here nor in the FTrace object)
+        all_scopes = [cls.thermal_classes, cls.sched_classes,
+                      cls.dynamic_classes]
+        known_events = ((n, c, sc) for sc in all_scopes for n, c in sc.items())
+
+        for name, obj, scope_classes in known_events:
+            if cobject == obj:
+                del scope_classes[name]
+
+    def __add_events(self, events):
+        """Add events to the class_definitions
+
+        If the events are known to trappy just add that class to the
+        class definitions list.  Otherwise, register a class to parse
+        that event
+
+        """
+
+        from trappy.dynamic import DynamicTypeFactory, default_init
+        from trappy.base import Base
+
+        # TODO: scopes should not be hardcoded (nor here nor in the FTrace object)
+        all_scopes = [self.thermal_classes, self.sched_classes,
+                      self.dynamic_classes]
+        known_events = {k: v for sc in all_scopes for k, v in sc.iteritems()}
+
+        for event_name in events:
+            for cls in known_events.itervalues():
+                if (event_name == cls.unique_word) or \
+                   (event_name + ":" == cls.unique_word):
+                    self.class_definitions[event_name] = cls
+                    break
+            else:
+                kwords = {
+                    "__init__": default_init,
+                    "unique_word": event_name + ":",
+                    "name": event_name,
+                }
+                trace_class = DynamicTypeFactory(event_name, (Base,), kwords)
+                self.class_definitions[event_name] = trace_class
+
+    def __populate_data(self, fin, cls_for_unique_word, window, abs_window):
+        """Append to trace data from a txt trace"""
+
+        def contains_unique_word(line, unique_words=cls_for_unique_word.keys()):
+            for unique_word in unique_words:
+                if unique_word in line:
+                    return True
+            return False
+
+        special_fields_regexp = r"^\s*(?P<comm>.*)-(?P<pid>\d+)\s+\(?(?P<tgid>.*?)?\)"\
+                                r"?\s*\[(?P<cpu>\d+)\](?:\s+....)?\s+(?P<timestamp>[0-9]+\.[0-9]+):"
+        special_fields_regexp = re.compile(special_fields_regexp)
+        start_match = re.compile(r"[A-Za-z0-9_]+=")
+
+        actual_trace = itertools.dropwhile(self.trace_hasnt_started(), fin)
+        actual_trace = itertools.takewhile(self.trace_hasnt_finished(),
+                                           actual_trace)
+
+        for line in itertools.ifilter(contains_unique_word, actual_trace):
+            for unique_word, cls in cls_for_unique_word.iteritems():
+                if unique_word in line:
+                    trace_class = cls
+                    break
+            else:
+                raise FTraceParseError("No unique word in '{}'".format(line))
+
+            line = line[:-1]
+
+            special_fields_match = special_fields_regexp.match(line)
+            if not special_fields_match:
+                raise FTraceParseError("Couldn't match special fields in '{}'".format(line))
+            comm = special_fields_match.group('comm')
+            pid = int(special_fields_match.group('pid'))
+            cpu = int(special_fields_match.group('cpu'))
+            tgid = special_fields_match.group('tgid')
+            if not tgid or tgid[0] == '-':
+                tgid = -1
+            else:
+                tgid = int(tgid)
+
+            timestamp = float(special_fields_match.group('timestamp'))
+
+            if not self.basetime:
+                self.basetime = timestamp
+
+            if (timestamp < window[0] + self.basetime) or \
+               (timestamp < abs_window[0]):
+                continue
+
+            if (window[1] and timestamp > window[1] + self.basetime) or \
+               (abs_window[1] and timestamp > abs_window[1]):
+                return
+
+            try:
+                data_start_idx =  start_match.search(line).start()
+            except AttributeError:
+                continue
+
+            if self.normalized_time:
+                timestamp = timestamp - self.basetime
+
+            data_str = line[data_start_idx:]
+
+            # Remove empty arrays from the trace
+            data_str = re.sub(r"[A-Za-z0-9_]+=\{\} ", r"", data_str)
+
+            trace_class.append_data(timestamp, comm, pid, tgid, cpu, data_str)
+
+    def trace_hasnt_started(self):
+        """Return a function that accepts a line and returns true if this line
+is not part of the trace.
+
+        Subclasses of GenericFTrace may override this to skip the
+        beginning of a file that is not part of the trace.  The first
+        time the returned function returns False it will be considered
+        the beginning of the trace and this function will never be
+        called again (because once it returns False, the trace has
+        started).
+
+        """
+        return lambda x: False
+
+    def trace_hasnt_finished(self):
+        """Return a function that accepts a line and returns true if this line
+is part of the trace.
+
+        This function is called with each line of the file *after*
+        trace_hasnt_started() returns True so the first line it sees
+        is part of the trace.  The returned function should return
+        True as long as the line it receives is part of the trace.  As
+        soon as this function returns False, the rest of the file will
+        be dropped.  Subclasses of GenericFTrace may override this to
+        stop processing after the end of the trace is found to skip
+        parsing the end of the file if it contains anything other than
+        trace.
+
+        """
+        return lambda x: True
+
+    def __parse_trace_file(self, trace_file, window, abs_window, raw=False):
+        """parse the trace and create a pandas DataFrame"""
+
+        # Memoize the unique words to speed up parsing the trace file
+        cls_for_unique_word = {}
+        for trace_name in self.class_definitions.iterkeys():
+            trace_class = getattr(self, trace_name)
+
+            if self.needs_raw_parsing and (trace_class.parse_raw != raw):
+                continue
+
+            unique_word = trace_class.unique_word
+            cls_for_unique_word[unique_word] = trace_class
+
+        if len(cls_for_unique_word) == 0:
+            return
+
+        try:
+            with open(trace_file) as fin:
+                self.__populate_data(
+                    fin, cls_for_unique_word, window, abs_window)
+        except FTraceParseError as e:
+            raise ValueError('Failed to parse ftrace file {}:\n{}'.format(
+                trace_file, str(e)))
+
+    # TODO: Move thermal specific functionality
+
+    def get_all_freqs_data(self, map_label):
+        """get an array of tuple of names and DataFrames suitable for the
+        allfreqs plot"""
+
+        cpu_in_freqs = self.cpu_in_power.get_all_freqs(map_label)
+        cpu_out_freqs = self.cpu_out_power.get_all_freqs(map_label)
+
+        ret = []
+        for label in map_label.values():
+            in_label = label + "_freq_in"
+            out_label = label + "_freq_out"
+
+            cpu_inout_freq_dict = {in_label: cpu_in_freqs[label],
+                                   out_label: cpu_out_freqs[label]}
+            dfr = pd.DataFrame(cpu_inout_freq_dict).fillna(method="pad")
+            ret.append((label, dfr))
+
+        try:
+            gpu_freq_in_data = self.devfreq_in_power.get_all_freqs()
+            gpu_freq_out_data = self.devfreq_out_power.get_all_freqs()
+        except KeyError:
+            gpu_freq_in_data = gpu_freq_out_data = None
+
+        if gpu_freq_in_data is not None:
+            inout_freq_dict = {"gpu_freq_in": gpu_freq_in_data["freq"],
+                               "gpu_freq_out": gpu_freq_out_data["freq"]
+                           }
+            dfr = pd.DataFrame(inout_freq_dict).fillna(method="pad")
+            ret.append(("GPU", dfr))
+
+        return ret
+
+    def plot_freq_hists(self, map_label, ax):
+        """Plot histograms for each actor input and output frequency
+
+        ax is an array of axis, one for the input power and one for
+        the output power
+
+        """
+
+        in_base_idx = len(ax) / 2
+
+        try:
+            devfreq_out_all_freqs = self.devfreq_out_power.get_all_freqs()
+            devfreq_in_all_freqs = self.devfreq_in_power.get_all_freqs()
+        except KeyError:
+            devfreq_out_all_freqs = None
+            devfreq_in_all_freqs = None
+
+        out_allfreqs = (self.cpu_out_power.get_all_freqs(map_label),
+                        devfreq_out_all_freqs, ax[0:in_base_idx])
+        in_allfreqs = (self.cpu_in_power.get_all_freqs(map_label),
+                       devfreq_in_all_freqs, ax[in_base_idx:])
+
+        for cpu_allfreqs, devfreq_freqs, axis in (out_allfreqs, in_allfreqs):
+            if devfreq_freqs is not None:
+                devfreq_freqs.name = "GPU"
+                allfreqs = pd.concat([cpu_allfreqs, devfreq_freqs], axis=1)
+            else:
+                allfreqs = cpu_allfreqs
+
+            allfreqs.fillna(method="pad", inplace=True)
+            _plot_freq_hists(allfreqs, "out", axis, self.name)
+
+    def plot_load(self, mapping_label, title="", width=None, height=None,
+                  ax=None):
+        """plot the load of all the clusters, similar to how compare runs did it
+
+        the mapping_label has to be a dict whose keys are the cluster
+        numbers as found in the trace and values are the names that
+        will appear in the legend.
+
+        """
+        import trappy.plot_utils
+
+        load_data = self.cpu_in_power.get_load_data(mapping_label)
+        try:
+            gpu_data = pd.DataFrame({"GPU":
+                                     self.devfreq_in_power.data_frame["load"]})
+            load_data = pd.concat([load_data, gpu_data], axis=1)
+        except KeyError:
+            pass
+
+        load_data = load_data.fillna(method="pad")
+        title = trappy.plot_utils.normalize_title("Utilization", title)
+
+        if not ax:
+            ax = trappy.plot_utils.pre_plot_setup(width=width, height=height)
+
+        load_data.plot(ax=ax)
+
+        trappy.plot_utils.post_plot_setup(ax, title=title)
+
+    def plot_normalized_load(self, mapping_label, title="", width=None,
+                             height=None, ax=None):
+        """plot the normalized load of all the clusters, similar to how compare runs did it
+
+        the mapping_label has to be a dict whose keys are the cluster
+        numbers as found in the trace and values are the names that
+        will appear in the legend.
+
+        """
+        import trappy.plot_utils
+
+        load_data = self.cpu_in_power.get_normalized_load_data(mapping_label)
+        if "load" in self.devfreq_in_power.data_frame:
+            gpu_dfr = self.devfreq_in_power.data_frame
+            gpu_max_freq = max(gpu_dfr["freq"])
+            gpu_load = gpu_dfr["load"] * gpu_dfr["freq"] / gpu_max_freq
+
+            gpu_data = pd.DataFrame({"GPU": gpu_load})
+            load_data = pd.concat([load_data, gpu_data], axis=1)
+
+        load_data = load_data.fillna(method="pad")
+        title = trappy.plot_utils.normalize_title("Normalized Utilization", title)
+
+        if not ax:
+            ax = trappy.plot_utils.pre_plot_setup(width=width, height=height)
+
+        load_data.plot(ax=ax)
+
+        trappy.plot_utils.post_plot_setup(ax, title=title)
+
+    def plot_allfreqs(self, map_label, width=None, height=None, ax=None):
+        """Do allfreqs plots similar to those of CompareRuns
+
+        if ax is not none, it must be an array of the same size as
+        map_label.  Each plot will be done in each of the axis in
+        ax
+
+        """
+        import trappy.plot_utils
+
+        all_freqs = self.get_all_freqs_data(map_label)
+
+        setup_plot = False
+        if ax is None:
+            ax = [None] * len(all_freqs)
+            setup_plot = True
+
+        for this_ax, (label, dfr) in zip(ax, all_freqs):
+            this_title = trappy.plot_utils.normalize_title("allfreqs " + label,
+                                                        self.name)
+
+            if setup_plot:
+                this_ax = trappy.plot_utils.pre_plot_setup(width=width,
+                                                        height=height)
+
+            dfr.plot(ax=this_ax)
+            trappy.plot_utils.post_plot_setup(this_ax, title=this_title)
+
+class FTrace(GenericFTrace):
+    """A wrapper class that initializes all the classes of a given run
+
+    - The FTrace class can receive the following optional parameters.
+
+    :param path: Path contains the path to the trace file.  If no path is given, it
+        uses the current directory by default.  If path is a file, and ends in
+        .dat, it's run through "trace-cmd report".  If it doesn't end in
+        ".dat", then it must be the output of a trace-cmd report run.  If path
+        is a directory that contains a trace.txt, that is assumed to be the
+        output of "trace-cmd report".  If path is a directory that doesn't
+        have a trace.txt but has a trace.dat, it runs trace-cmd report on the
+        trace.dat, saves it in trace.txt and then uses that.
+
+    :param name: is a string describing the trace.
+
+    :param normalize_time: is used to make all traces start from time 0 (the
+        default).  If normalize_time is False, the trace times are the same as
+        in the trace file.
+
+    :param scope: can be used to limit the parsing done on the trace.  The default
+        scope parses all the traces known to trappy.  If scope is thermal, only
+        the thermal classes are parsed.  If scope is sched, only the sched
+        classes are parsed.
+
+    :param events: A list of strings containing the name of the trace
+        events that you want to include in this FTrace object.  The
+        string must correspond to the event name (what you would pass
+        to "trace-cmd -e", i.e. 4th field in trace.txt)
+
+    :param window: a tuple indicating a time window.  The first
+        element in the tuple is the start timestamp and the second one
+        the end timestamp.  Timestamps are relative to the first trace
+        event that's parsed.  If you want to trace until the end of
+        the trace, set the second element to None.  If you want to use
+        timestamps extracted from the trace file use "abs_window". The
+        window is inclusive: trace events exactly matching the start
+        or end timestamps will be included.
+
+    :param abs_window: a tuple indicating an absolute time window.
+        This parameter is similar to the "window" one but its values
+        represent timestamps that are not normalized, (i.e. the ones
+        you find in the trace file). The window is inclusive.
+
+
+    :type path: str
+    :type name: str
+    :type normalize_time: bool
+    :type scope: str
+    :type events: list
+    :type window: tuple
+    :type abs_window: tuple
+
+    This is a simple example:
+    ::
+
+        import trappy
+        trappy.FTrace("trace_dir")
+
+    """
+
+    def __init__(self, path=".", name="", normalize_time=True, scope="all",
+                 events=[], event_callbacks={}, window=(0, None),
+                 abs_window=(0, None), build_df=True):
+        self.trace_path, self.trace_path_raw = self.__process_path(path)
+        self.needs_raw_parsing = True
+
+        self.__populate_metadata()
+
+        super(FTrace, self).__init__(name, normalize_time, scope, events,
+                                     event_callbacks, window, abs_window,
+                                     build_df)
+
+    def __process_path(self, basepath):
+        """Process the path and return the path to the trace text file"""
+
+        if os.path.isfile(basepath):
+            trace_name = os.path.splitext(basepath)[0]
+        else:
+            trace_name = os.path.join(basepath, "trace")
+
+        trace_txt = trace_name + ".txt"
+        trace_raw = trace_name + ".raw.txt"
+        trace_dat = trace_name + ".dat"
+
+        if os.path.isfile(trace_dat):
+            # Both TXT and RAW traces must always be generated
+            if not os.path.isfile(trace_txt) or \
+               not os.path.isfile(trace_raw):
+                self.__run_trace_cmd_report(trace_dat)
+            # TXT (and RAW) traces must match the most recent binary trace
+            elif os.path.getmtime(trace_txt) < os.path.getmtime(trace_dat):
+                self.__run_trace_cmd_report(trace_dat)
+
+        if not os.path.isfile(trace_raw):
+            trace_raw = None
+
+        return trace_txt, trace_raw
+
+    def __run_trace_cmd_report(self, fname):
+        """Run "trace-cmd report fname > fname.txt"
+           and "trace-cmd report -R fname > fname.raw.txt"
+
+        The resulting traces are stored in files with extension ".txt"
+        and ".raw.txt" respectively.  If fname is "my_trace.dat", the
+        trace is stored in "my_trace.txt" and "my_trace.raw.txt".  The
+        contents of the destination files are overwritten if they
+        exist.
+
+        """
+        from subprocess import check_output
+
+        cmd = ["trace-cmd", "report"]
+
+        if not os.path.isfile(fname):
+            raise IOError("No such file or directory: {}".format(fname))
+
+        raw_trace_output = os.path.splitext(fname)[0] + ".raw.txt"
+        trace_output = os.path.splitext(fname)[0] + ".txt"
+        cmd.append(fname)
+
+        with open(os.devnull) as devnull:
+            try:
+                out = check_output(cmd, stderr=devnull)
+            except OSError as exc:
+                if exc.errno == 2 and not exc.filename:
+                    raise OSError(2, "trace-cmd not found in PATH, is it installed?")
+                else:
+                    raise
+
+            # Add the -R flag to the trace-cmd
+            # for raw parsing
+            cmd.insert(-1, "-R")
+            raw_out = check_output(cmd, stderr=devnull)
+
+        with open(trace_output, "w") as fout:
+            fout.write(out)
+
+        with open(raw_trace_output, "w") as fout:
+            fout.write(raw_out)
+
+    def __populate_metadata(self):
+        """Populates trace metadata"""
+
+        # Meta Data as expected to be found in the parsed trace header
+        metadata_keys = ["version", "cpus"]
+
+        for key in metadata_keys:
+            setattr(self, "_" + key, None)
+
+        with open(self.trace_path) as fin:
+            for line in fin:
+                if not metadata_keys:
+                    return
+
+                metadata_pattern = r"^\b(" + "|".join(metadata_keys) + \
+                                   r")\b\s*=\s*([0-9]+)"
+                match = re.search(metadata_pattern, line)
+                if match:
+                    setattr(self, "_" + match.group(1), match.group(2))
+                    metadata_keys.remove(match.group(1))
+
+                if re.search(r"^\s+[^\[]+-\d+\s+\[\d+\]\s+\d+\.\d+:", line):
+                    # Reached a valid trace line, abort metadata population
+                    return
diff --git a/trappy/idle.py b/trappy/idle.py
new file mode 100644
index 0000000..a2c5069
--- /dev/null
+++ b/trappy/idle.py
@@ -0,0 +1,32 @@
+#    Copyright 2016-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from trappy.base import Base
+from trappy.dynamic import register_ftrace_parser
+
+class CpuIdle(Base):
+    """Parse cpu_idle"""
+
+    unique_word = "cpu_idle"
+    pivot = "cpu_id"
+
+    def finalize_object(self):
+        # The trace contains "4294967295" instead of "-1" when exiting an idle
+        # state.
+        uint32_max = (2 ** 32) - 1
+        self.data_frame.replace(uint32_max, -1, inplace=True)
+        super(CpuIdle, self).finalize_object()
+
+register_ftrace_parser(CpuIdle)
diff --git a/trappy/nbexport/__init__.py b/trappy/nbexport/__init__.py
new file mode 100644
index 0000000..548ef6d
--- /dev/null
+++ b/trappy/nbexport/__init__.py
@@ -0,0 +1,25 @@
+#    Copyright 2015-2017 ARM Limited
+#    Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""HTML Exporter for TRAPPY plotter data. This allows
+* Custom Preprocessing
+"""
+
+try:
+    from trappy.nbexport.exporter import HTML
+except ImportError:
+    # Avoid testsuite errors when the testsuite is run in an environment without
+    # ipython
+    HTML = object
diff --git a/trappy/nbexport/exporter.py b/trappy/nbexport/exporter.py
new file mode 100644
index 0000000..c271fa8
--- /dev/null
+++ b/trappy/nbexport/exporter.py
@@ -0,0 +1,103 @@
+#    Copyright 2015-2017 ARM Limited
+#    Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""Preprocessor to remove Marked Lines from IPython Output Cells"""
+
+
+from nbconvert.exporters.html import HTMLExporter
+from nbconvert.preprocessors import Preprocessor
+import os
+import re
+
+REMOVE_START = '/* TRAPPY_PUBLISH_REMOVE_START */'
+REMOVE_STOP = '/* TRAPPY_PUBLISH_REMOVE_STOP */'
+REMOVE_LINE = '/* TRAPPY_PUBLISH_REMOVE_LINE */'
+IMPORT_SCRIPT = r'/\* TRAPPY_PUBLISH_IMPORT = "([^"]+)" \*/'
+SOURCE_LIB = r'<!-- TRAPPY_PUBLISH_SOURCE_LIB = "([^"]+)" -->'
+
+
+class HTML(HTMLExporter):
+    """HTML Exporter class for TRAPpy notebooks"""
+
+    def __init__(self, **kwargs):
+        super(HTML, self).__init__(**kwargs)
+        self.register_preprocessor(TrappyPlotterPreprocessor, enabled=True)
+
+
+class TrappyPlotterPreprocessor(Preprocessor):
+    """Preprocessor to remove Marked Lines from IPython Output Cells"""
+
+    def __init__(self, *args, **kwargs):
+        super(Preprocessor, self).__init__(*args, **kwargs)
+        self.inlined_files = []
+        self.sourced_libs = []
+
+    def preprocess_cell(self, cell, resources, cell_index):
+        """Check if cell has text/html output and filter it"""
+
+        if cell.cell_type == 'code' and hasattr(cell, "outputs"):
+            for output in cell.outputs:
+                if output.output_type == "display_data" and \
+                   hasattr( output.data, "text/html"):
+                    filtered =  self.filter_output(output.data["text/html"])
+                    output.data["text/html"] = filtered
+        return cell, resources
+
+    def filter_output(self, output):
+        """Function to remove marked lines"""
+
+        lines = output.split('\n')
+
+        final_lines = []
+        multi_line_remove = False
+        for line in lines:
+            if REMOVE_START in line:
+                multi_line_remove = True
+                continue
+            if REMOVE_STOP in line:
+                multi_line_remove = False
+                continue
+            if multi_line_remove or REMOVE_LINE in line:
+                continue
+
+            import_match = re.search(IMPORT_SCRIPT, line)
+            if import_match:
+                trappy_base = os.path.dirname(os.path.dirname(__file__))
+                import_file = os.path.join(trappy_base, import_match.group(1))
+                if import_file in self.inlined_files:
+                    continue
+
+                with open(import_file) as fin:
+                    final_lines.extend([l[:-1] for l in fin.readlines()])
+
+                self.inlined_files.append(import_file)
+                continue
+
+            source_match = re.search(SOURCE_LIB, line)
+            if source_match:
+                lib_url = source_match.group(1)
+                if lib_url in self.sourced_libs:
+                    continue
+
+                scl = '<script src="{}" type="text/javascript" charset="utf-8"></script>'.\
+                      format(lib_url)
+                final_lines.append(scl)
+
+                self.sourced_libs.append(lib_url)
+                continue
+
+            final_lines.append(line)
+
+        return '\n'.join(final_lines)
diff --git a/trappy/pid_controller.py b/trappy/pid_controller.py
new file mode 100644
index 0000000..16fe00b
--- /dev/null
+++ b/trappy/pid_controller.py
@@ -0,0 +1,60 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Process the output of the power allocator's PID controller in the
+current directory's trace.dat"""
+
+from trappy.base import Base
+from trappy.dynamic import register_ftrace_parser
+
+class PIDController(Base):
+    """Process the power allocator PID controller data in a FTrace dump"""
+
+    name = "pid_controller"
+    """The name of the :mod:`pandas.DataFrame` member that will be created in a
+    :mod:`trappy.ftrace.FTrace` object"""
+
+    pivot = "thermal_zone_id"
+    """The Pivot along which the data is orthogonal"""
+
+    unique_word="thermal_power_allocator_pid"
+    """The event name in the trace"""
+
+    def plot_controller(self, title="", width=None, height=None, ax=None):
+        """Plot a summary of the controller data
+
+        :param ax: Axis instance
+        :type ax: :mod:`matplotlib.Axis`
+
+        :param title: The title of the plot
+        :type title: str
+
+        :param width: The width of the plot
+        :type width: int
+
+        :param height: The height of the plot
+        :type int: int
+        """
+        import trappy.plot_utils
+
+        title = trappy.plot_utils.normalize_title("PID", title)
+
+        if not ax:
+            ax = trappy.plot_utils.pre_plot_setup(width, height)
+
+        self.data_frame[["output", "p", "i", "d"]].plot(ax=ax)
+        trappy.plot_utils.post_plot_setup(ax, title=title)
+
+register_ftrace_parser(PIDController, "thermal")
diff --git a/trappy/plot_utils.py b/trappy/plot_utils.py
new file mode 100644
index 0000000..412d554
--- /dev/null
+++ b/trappy/plot_utils.py
@@ -0,0 +1,322 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Small functions to help with plots"""
+
+# pylint disable=star-args
+
+from matplotlib import pyplot as plt
+import os
+import re
+
+from trappy.wa import SysfsExtractor
+
+GOLDEN_RATIO = 1.618034
+
+def normalize_title(title, opt_title):
+    """Return a string with that contains the title and opt_title if it's
+not the empty string
+
+    See test_normalize_title() for usage
+
+    """
+    if opt_title is not "":
+        title = opt_title + " - " + title
+
+    return title
+
+def set_lim(lim, get_lim_f, set_lim_f):
+    """Set x or y limitis of the plot
+
+    lim can be a tuple containing the limits or the string "default"
+    or "range".  "default" does nothing and uses matplotlib default.
+    "range" extends the current margin by 10%.  This is useful since
+    the default xlim and ylim of the plots sometimes make it harder to
+    see data that is just in the margin.
+
+    """
+    if lim == "default":
+        return
+
+    if lim == "range":
+        cur_lim = get_lim_f()
+        lim = (cur_lim[0] - 0.1 * (cur_lim[1] - cur_lim[0]),
+               cur_lim[1] + 0.1 * (cur_lim[1] - cur_lim[0]))
+
+    set_lim_f(lim[0], lim[1])
+
+def set_xlim(ax, xlim):
+    """Set the xlim of the plot
+
+    See set_lim() for the details
+    """
+    set_lim(xlim, ax.get_xlim, ax.set_xlim)
+
+def set_ylim(ax, ylim):
+    """Set the ylim of the plot
+
+    See set_lim() for the details
+    """
+    set_lim(ylim, ax.get_ylim, ax.set_ylim)
+
+def pre_plot_setup(width=None, height=None, ncols=1, nrows=1):
+    """initialize a figure
+
+    width and height are the height and width of each row of plots.
+    For 1x1 plots, that's the height and width of the plot.  This
+    function should be called before any calls to plot()
+
+    """
+
+    if height is None:
+        if width is None:
+            height = 6
+            width = 10
+        else:
+            height = width / GOLDEN_RATIO
+    else:
+        if width is None:
+            width = height * GOLDEN_RATIO
+
+    height *= nrows
+
+    _, axis = plt.subplots(ncols=ncols, nrows=nrows, figsize=(width, height))
+
+    # Needed for multirow blots to not overlap with each other
+    plt.tight_layout(h_pad=3.5)
+
+    return axis
+
+def post_plot_setup(ax, title="", xlabel=None, ylabel=None, xlim="default",
+                    ylim="range"):
+    """Set xlabel, ylabel title, xlim and ylim of the plot
+
+    This has to be called after calls to .plot().  The default ylim is
+    to extend it by 10% because matplotlib default makes it hard
+    values that are close to the margins
+
+    """
+
+    if xlabel is not None:
+        ax.set_xlabel(xlabel)
+
+    if ylabel is not None:
+        ax.set_ylabel(ylabel)
+
+    if title:
+        ax.set_title(title)
+
+    set_ylim(ax, ylim)
+    set_xlim(ax, xlim)
+
+def number_freq_plots(runs, map_label):
+    """Calculate the number of plots needed for allfreq plots and frequency
+    histogram plots
+
+    """
+    num_cpu_plots = len(map_label)
+
+    has_devfreq_data = False
+    for run in runs:
+        if len(run.devfreq_in_power.data_frame) > 0:
+            has_devfreq_data = True
+            break
+
+    num_freq_plots = num_cpu_plots
+    if has_devfreq_data:
+        num_freq_plots += 1
+
+    return num_freq_plots
+
+def plot_temperature(runs, width=None, height=None, ylim="range", tz_id=None):
+    """Plot temperatures
+
+    runs is an array of FTrace() instances.  Extract the control_temp
+    from the governor data and plot the temperatures reported by the
+    thermal framework.  The governor doesn't track temperature when
+    it's off, so the thermal framework trace is more reliable.
+
+    """
+
+    ax = pre_plot_setup(width, height)
+
+    for run in runs:
+        gov_dfr = run.thermal_governor.data_frame
+        if tz_id:
+            gov_dfr = gov_dfr[gov_dfr["thermal_zone_id"] == tz_id]
+
+        try:
+            current_temp = gov_dfr["current_temperature"]
+            delta_temp = gov_dfr["delta_temperature"]
+            control_series = (current_temp + delta_temp) / 1000
+        except KeyError:
+            control_series = None
+
+        try:
+            run.thermal.plot_temperature(control_temperature=control_series,
+                                         ax=ax, legend_label=run.name,
+                                         tz_id=tz_id)
+        except ValueError:
+            run.thermal_governor.plot_temperature(ax=ax, legend_label=run.name)
+
+    post_plot_setup(ax, title="Temperature", ylim=ylim)
+    plt.legend(loc="best")
+
+def plot_hist(data, ax, title, unit, bins, xlabel, xlim, ylim):
+    """Plot a histogram"""
+
+    mean = data.mean()
+    std = data.std()
+    title += " (mean = {:.2f}{}, std = {:.2f})".format(mean, unit, std)
+    xlabel += " ({})".format(unit)
+
+    data.hist(ax=ax, bins=bins)
+    post_plot_setup(ax, title=title, xlabel=xlabel, ylabel="count", xlim=xlim,
+                    ylim=ylim)
+
+def plot_load(runs, map_label, width=None, height=None):
+    """Make a multiplot of all the loads"""
+    num_runs = len(runs)
+    axis = pre_plot_setup(width=width, height=height, ncols=num_runs, nrows=2)
+
+    if num_runs == 1:
+        axis = [axis]
+    else:
+        axis = zip(*axis)
+
+    for ax, run in zip(axis, runs):
+        run.plot_load(map_label, title=run.name, ax=ax[0])
+        run.plot_normalized_load(map_label, title=run.name, ax=ax[1])
+
+def plot_allfreqs(runs, map_label, width=None, height=None):
+    """Make a multicolumn plots of the allfreqs plots of each run"""
+    num_runs = len(runs)
+    nrows = number_freq_plots(runs, map_label)
+
+    axis = pre_plot_setup(width=width, height=height, nrows=nrows,
+                          ncols=num_runs)
+
+    if num_runs == 1:
+        if nrows == 1:
+            axis = [[axis]]
+        else:
+            axis = [axis]
+    elif nrows == 1:
+        axis = [[ax] for ax in axis]
+    else:
+        axis = zip(*axis)
+
+    for ax, run in zip(axis, runs):
+        run.plot_allfreqs(map_label, ax=ax)
+
+def plot_controller(runs, width=None, height=None):
+    """Make a multicolumn plot of the pid controller of each run"""
+    num_runs = len(runs)
+    axis = pre_plot_setup(width=width, height=height, ncols=num_runs)
+
+    if num_runs == 1:
+        axis = [axis]
+
+    for ax, run in zip(axis, runs):
+        run.pid_controller.plot_controller(title=run.name, ax=ax)
+
+def plot_weighted_input_power(runs, actor_order, width=None, height=None):
+    """Make a multicolumn plot of the weighted input power of each run"""
+
+    actor_weights = []
+    for run in runs:
+        run_path = os.path.dirname(run.trace_path)
+        sysfs = SysfsExtractor(run_path)
+
+        thermal_params = sysfs.get_parameters()
+
+        sorted_weights = []
+        for param in sorted(thermal_params):
+            if re.match(r"cdev\d+_weight", param):
+                sorted_weights.append(thermal_params[param])
+
+        actor_weights.append(zip(actor_order, sorted_weights))
+
+    # Do nothing if we don't have actor weights for any run
+    if not any(actor_weights):
+        return
+
+    num_runs = len(runs)
+    axis = pre_plot_setup(width=width, height=height, ncols=num_runs)
+
+    if num_runs == 1:
+        axis = [axis]
+
+    for ax, run, weights in zip(axis, runs, actor_weights):
+        run.thermal_governor.plot_weighted_input_power(weights, title=run.name,
+                                                       ax=ax)
+
+def plot_input_power(runs, actor_order, width=None, height=None):
+    """Make a multicolumn plot of the input power of each run"""
+    num_runs = len(runs)
+    axis = pre_plot_setup(width=width, height=height, ncols=num_runs)
+
+    if num_runs == 1:
+        axis = [axis]
+
+    for ax, run in zip(axis, runs):
+        run.thermal_governor.plot_input_power(actor_order, title=run.name,
+                                              ax=ax)
+
+    plot_weighted_input_power(runs, actor_order, width, height)
+
+def plot_output_power(runs, actor_order, width=None, height=None):
+    """Make a multicolumn plot of the output power of each run"""
+    num_runs = len(runs)
+    axis = pre_plot_setup(width=width, height=height, ncols=num_runs)
+
+    if num_runs == 1:
+        axis = [axis]
+
+    for ax, run in zip(axis, runs):
+        run.thermal_governor.plot_output_power(actor_order, title=run.name,
+                                               ax=ax)
+
+def plot_freq_hists(runs, map_label):
+    """Plot frequency histograms of multiple runs"""
+    num_runs = len(runs)
+    nrows = 2 * number_freq_plots(runs, map_label)
+    axis = pre_plot_setup(ncols=num_runs, nrows=nrows)
+
+    if num_runs == 1:
+        axis = [axis]
+    else:
+        axis = zip(*axis)
+
+    for ax, run in zip(axis, runs):
+        run.plot_freq_hists(map_label, ax=ax)
+
+def plot_temperature_hist(runs):
+    """Plot temperature histograms for all the runs"""
+    num_runs = 0
+    for run in runs:
+        if len(run.thermal.data_frame):
+            num_runs += 1
+
+    if num_runs == 0:
+        return
+
+    axis = pre_plot_setup(ncols=num_runs)
+
+    if num_runs == 1:
+        axis = [axis]
+
+    for ax, run in zip(axis, runs):
+        run.thermal.plot_temperature_hist(ax, run.name)
diff --git a/trappy/plotter/AbstractDataPlotter.py b/trappy/plotter/AbstractDataPlotter.py
new file mode 100644
index 0000000..254709c
--- /dev/null
+++ b/trappy/plotter/AbstractDataPlotter.py
@@ -0,0 +1,126 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""This is the template class that all Plotters inherit"""
+from abc import abstractmethod, ABCMeta
+from pandas import DataFrame
+import re
+from trappy.utils import listify
+from functools import reduce
+# pylint: disable=R0921
+# pylint: disable=R0903
+
+
+class AbstractDataPlotter(object):
+    """This is an abstract data plotting Class defining an interface
+       for the various Plotting Classes"""
+
+    __metaclass__ = ABCMeta
+
+    def __init__(self, traces=None, attr=None, templates=None):
+        self._event_map = {}
+        self._attr = attr if attr else {}
+        self.traces = traces
+        self.templates = templates
+
+    @abstractmethod
+    def view(self):
+        """View the graph"""
+        raise NotImplementedError("Method Not Implemented")
+
+    @abstractmethod
+    def savefig(self, path):
+        """Save the image as a file
+
+        :param path: Location of the Saved File
+        :type path: str
+        """
+        raise NotImplementedError("Method Not Implemented")
+
+    def _check_data(self):
+        """Internal function to check the received data"""
+
+        data = listify(self.traces)
+
+        if len(data):
+            mask = map(lambda x: isinstance(x, DataFrame), data)
+            data_frame = reduce(lambda x, y: x and y, mask)
+            sig_or_template = self.templates or "signals" in self._attr
+
+            if not data_frame and not sig_or_template:
+                raise ValueError(
+                    "Cannot understand data. Accepted DataFormats are pandas.DataFrame or trappy.FTrace/BareTrace/SysTrace (with templates)")
+            elif data_frame and "column" not in self._attr:
+                raise ValueError("Column not specified for DataFrame input")
+        else:
+            raise ValueError("Empty Data received")
+
+    def _parse_value(self, signal_def):
+        """Parse a signal definition into a (template, column) tuple
+
+        :param signal_def: A signal definition. E.g. "trace_class:column"
+        :type signal_def: str
+        """
+
+        match = re.match(r"(?P<event>[^:]+):(?P<column>[^:]+)(?P<color>:.+)?",
+                         signal_def)
+        if not match:
+            raise ValueError(
+                'Invalid signal definition "{}". '
+                'Should have the form "trace_class:column" '
+                'e.g. "cpu_frequency:frequency"'.format(signal_def))
+        event = match.group("event")
+        column = match.group("column")
+        color_match = match.group("color")
+        if color_match:
+            color_list = color_match[1:].split(",", 2)
+            color = [int(n, 16) if n.startswith("0x") else int(n) for n in color_list]
+        else:
+            color = None
+
+        try:
+            return self._event_map[event], column, color
+        except KeyError:
+            for trace in listify(self.traces):
+
+                if event in trace.class_definitions:
+                    self._event_map[event] = trace.class_definitions[event]
+                    return self._event_map[event], column, color
+
+            raise ValueError(
+                "Event: " +
+                event +
+                " not found in Trace Object")
+
+    def _describe_signals(self):
+        """Internal Function for populating templates and columns
+        from signals
+        """
+
+        if "column" in self._attr or self.templates:
+            raise ValueError("column/templates specified with values")
+
+        self._attr["column"] = []
+        self.templates = []
+        colors = []
+
+        for value in listify(self._attr["signals"]):
+            template, column, color = self._parse_value(value)
+            self.templates.append(template)
+            self._attr["column"].append(column)
+            colors.append(color)
+
+        if any(colors):
+            self._attr["colors"] = colors
diff --git a/trappy/plotter/AttrConf.py b/trappy/plotter/AttrConf.py
new file mode 100644
index 0000000..3342939
--- /dev/null
+++ b/trappy/plotter/AttrConf.py
@@ -0,0 +1,129 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""These are the default plotting Attributes"""
+WIDTH = 7
+"""Default Width of a MatPlotlib Plot"""
+LENGTH = 7
+"""Default Length of a MatPlotlib Plot"""
+PER_LINE = 2
+"""Default Graphs per line"""
+CONCAT = False
+"""Default value for concat in :mod:`trappy.plotter.LinePlot`
+and :mod:`trappy.plotter.ILinePlot`
+"""
+PIVOT = "__TRAPPY_PIVOT_DEFAULT"
+"""Default pivot when None specified"""
+PIVOT_VAL = "__TRAPPY_DEFAULT_PIVOT_VAL"
+"""Default PivotValue for the default pivot"""
+XLIM = None
+"""Default value for xlimit"""
+YLIM = None
+"""Default value for ylim"""
+FILL = False
+"""Default value for "fill" in :mod:`trappy.plotter.LinePlot`
+and :mod:`trappy.plotter.ILinePlot`"""
+ALPHA = 0.75
+"""Default value for the alpha channel"""
+TITLE = None
+"""Default figure title (no title)"""
+TITLE_SIZE = 24
+"""Default size for the figure title"""
+LEGEND_NCOL = 3
+"""Default number of columns in the legend"""
+
+MPL_STYLE = {
+    'axes.axisbelow': True,
+    'axes.edgecolor': '#bcbcbc',
+    'axes.facecolor': 'white',
+    'axes.grid': True,
+    'axes.labelcolor': '#555555',
+    'axes.labelsize': 'large',
+    'axes.linewidth': 1.0,
+    'axes.titlesize': 'x-large',
+    'figure.edgecolor': 'white',
+    'figure.facecolor': 'white',
+    'figure.figsize': (6.0, 4.0),
+    'figure.subplot.hspace': 0.5,
+    'font.size': 10,
+    'interactive': True,
+    'keymap.all_axes': ['a'],
+    'keymap.back': ['left', 'c', 'backspace'],
+    'keymap.forward': ['right', 'v'],
+    'keymap.fullscreen': ['f'],
+    'keymap.grid': ['g'],
+    'keymap.home': ['h', 'r', 'home'],
+    'keymap.pan': ['p'],
+    'keymap.save': ['s'],
+    'keymap.xscale': ['L', 'k'],
+    'keymap.yscale': ['l'],
+    'keymap.zoom': ['o'],
+    'legend.fancybox': True,
+    'lines.antialiased': True,
+    'lines.linewidth': 1.0,
+    'patch.antialiased': True,
+    'patch.edgecolor': '#EEEEEE',
+    'patch.facecolor': '#348ABD',
+    'patch.linewidth': 0.5,
+    'toolbar': 'toolbar2',
+    'xtick.color': '#555555',
+    'xtick.direction': 'in',
+    'xtick.major.pad': 6.0,
+    'xtick.major.size': 0.0,
+    'xtick.minor.pad': 6.0,
+    'xtick.minor.size': 0.0,
+    'ytick.color': '#555555',
+    'ytick.direction': 'in',
+    'ytick.major.pad': 6.0,
+    'ytick.major.size': 0.0,
+    'ytick.minor.pad': 6.0,
+    'ytick.minor.size': 0.0
+}
+
+from distutils.version import LooseVersion
+import matplotlib
+
+colors = ['#348ABD', '#7A68A6', '#A60628', '#467821', '#CF4457', '#188487',
+          '#E24A33']
+if LooseVersion(matplotlib.__version__) < LooseVersion("1.5.1"):
+    MPL_STYLE['axes.color_cycle'] = colors
+else:
+    MPL_STYLE['axes.prop_cycle'] = matplotlib.cycler("color", colors)
+
+ARGS_TO_FORWARD = [
+    "marker",
+    "markersize",
+    "markevery",
+    "linestyle",
+    "linewidth",
+    "drawstyle"]
+"""kwargs that will be forwarded to matplotlib API calls
+"""
+HTML_HEIGHT = 400
+"""Default height for HTML based plots"""
+DEFAULT_SYNC_ZOOM = False
+"""Sync Graph zoom by default in
+:mod:`trappy.plotter.ILinePlot` graph groups
+"""
+EVENT_PLOT_STRIDE = False
+"""Default value for stride which enables sampled
+EventPlots for :mod:`trappy.plotter.EventPlot`
+"""
+PLOT_SCATTER = False
+"""Default value for creating Scatter Plots"""
+POINT_SIZE = 2
+"""Default Point Size for plots (in pts)"""
+LINE_WIDTH = 1.0
+"""Default Line Width for plotter"""
diff --git a/trappy/plotter/BarPlot.py b/trappy/plotter/BarPlot.py
new file mode 100644
index 0000000..6c45104
--- /dev/null
+++ b/trappy/plotter/BarPlot.py
@@ -0,0 +1,120 @@
+#    Copyright 2016-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""
+This class sublclasses :mod:`trappy.plotter.StaticPlot.StaticPlot` to
+implement a bar plot.
+
+"""
+import numpy as np
+from trappy.plotter.StaticPlot import StaticPlot
+
+class BarPlot(StaticPlot):
+    """BarPlot can plot data as vertical bars
+
+    Values are plotted against their position in the list of data.
+
+    :param traces: The input data
+    :type traces: A single instance or a list of :mod:`trappy.trace.FTrace`,
+        :mod:`trappy.trace.SysTrace`, :mod:`trappy.trace.BareTrace` or
+        :mod:`pandas.DataFrame`.
+
+    :param column: specifies the name of the column to be plotted.
+    :type column: str or list(str)
+
+    :param templates: TRAPpy events
+
+        .. note::
+
+                This is not required if a :mod:`pandas.DataFrame` is
+                used
+
+    :type templates: :mod:`trappy.base.Base`
+
+    :param signals: A string of the type event_name:column
+        to indicate the value that needs to be plotted
+
+        .. note::
+
+            - Only one of `signals` or both `templates` and
+              `columns` should be specified
+            - Signals format won't work for :mod:`pandas.DataFrame`
+              input
+
+    :type signals: str or list(string)
+
+    :param title: A title describing the generated plots
+    :type title: str
+
+    :param stacked: The series are grouped by default.  If you want a
+        stacked plot, set stacked to True.
+    :type stacked: bool
+
+    :param spacing: A proportion of the size of each group which
+        should be used as the spacing between the groups. e.g. 0.2
+        (default) means that 1/5 of the groups total width is used as
+        a spacing between groups.
+    :type spacing: float
+    """
+
+    def __init__(self, traces, templates=None, **kwargs):
+        # Default keys, each can be overridden in kwargs
+
+        super(BarPlot, self).__init__(
+            traces=traces,
+            templates=templates,
+            **kwargs)
+
+    def set_defaults(self):
+        """Sets the default attrs"""
+        super(BarPlot, self).set_defaults()
+        self._attr["spacing"] = 0.2
+        self._attr["stacked"] = False
+
+    def plot_axis(self, axis, series_list, permute, concat, args_to_forward):
+        """Internal Method called to plot data (series_list) on a given axis"""
+        stacked = self._attr["stacked"]
+        #Figure out how many bars per group
+        bars_in_group = 1 if stacked else len(series_list)
+
+        #Get the width of a group
+        group_width = 1.0 - self._attr["spacing"]
+        bar_width = group_width / bars_in_group
+
+        #Keep a list of the tops of bars to plot stacks
+        #Start with a list of 0s to put the first bars at the bottom
+        value_list = [c.result[p].values for (c, p) in series_list]
+        end_of_previous = [0] * max(len(x) for x in value_list)
+
+        for i, (constraint, pivot) in enumerate(series_list):
+            result = constraint.result
+            bar_anchor = np.arange(len(result[pivot].values))
+            if not stacked:
+                bar_anchor = bar_anchor + i * bar_width
+
+            line_2d_list = axis.bar(
+                bar_anchor,
+                result[pivot].values,
+                bottom=end_of_previous,
+                width=bar_width,
+                color=self._cmap.cmap(i),
+                **args_to_forward
+            )
+
+            if stacked:
+                end_of_previous = [x + y for (x, y) in zip(end_of_previous, result[pivot].values)]
+
+            axis.set_title(self.make_title(constraint, pivot, permute, concat))
+
+            self.add_to_legend(i, line_2d_list[0], constraint, pivot, concat, permute)
diff --git a/trappy/plotter/ColorMap.py b/trappy/plotter/ColorMap.py
new file mode 100644
index 0000000..bbd571c
--- /dev/null
+++ b/trappy/plotter/ColorMap.py
@@ -0,0 +1,80 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Defines a generic indexable ColorMap Class"""
+import matplotlib.colors as clrs
+import matplotlib.cm as cmx
+from matplotlib.colors import ListedColormap, Normalize
+
+
+def to_dygraph_colors(color_map):
+    """Convert a color_map specified as a list of rgb tuples to the
+    syntax that dygraphs expect: ["rgb(1, 2, 3)", "rgb(4, 5, 6)",...]
+
+    :param color_map: a list of rgb tuples
+    :type color_map: list of tuples
+    """
+
+    rgb_list = ["rgb(" + ", ".join(str(i) for i in e) + ")" for e in color_map]
+
+    return '["' + '", "'.join(rgb_list) + '"]'
+
+class ColorMap(object):
+
+    """The Color Map Class to return a gradient method
+
+    :param num_colors: Number or colors for which a gradient
+        is needed
+    :type num_colors: int
+    """
+
+    def __init__(self, num_colors, cmap='hsv'):
+        self.color_norm = clrs.Normalize(vmin=0, vmax=num_colors)
+        self.scalar_map = cmx.ScalarMappable(norm=self.color_norm, cmap=cmap)
+        self.num_colors = num_colors
+
+    def cmap(self, index):
+        """
+        :param index: Index for the gradient array
+        :type index: int
+
+        :return: The color at specified index
+        """
+        return self.scalar_map.to_rgba(index)
+
+    def cmap_inv(self, index):
+        """
+        :param index: Index for the gradient array
+        :type index: int
+
+        :return: The color at :math:`N_{colors} - i`
+        """
+        return self.cmap(self.num_colors - index)
+
+    @classmethod
+    def rgb_cmap(cls, rgb_list):
+        """Constructor for a ColorMap from an rgb_list
+
+        :param rgb_list: A list of rgb tuples for red, green and blue.
+            The rgb values should be in the range 0-255.
+        :type rgb_list: list of tuples
+        """
+
+        rgb_list = [[x / 255.0 for x in rgb[:3]] for rgb in rgb_list]
+
+        rgb_map = ListedColormap(rgb_list, name='default_color_map', N=None)
+        num_colors = len(rgb_list)
+
+        return cls(num_colors, cmap=rgb_map)
diff --git a/trappy/plotter/Constraint.py b/trappy/plotter/Constraint.py
new file mode 100644
index 0000000..1a5adc9
--- /dev/null
+++ b/trappy/plotter/Constraint.py
@@ -0,0 +1,386 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""This module provides the Constraint class for handling
+filters and pivots in a modular fashion. This enable easy
+constraint application.
+
+An implementation of :mod:`trappy.plotter.AbstractDataPlotter`
+is expected to use the :mod:`trappy.plotter.Constraint.ConstraintManager`
+class to pivot and filter data and handle multiple column,
+trace and event inputs.
+
+The underlying object that encapsulates a unique set of
+a data column, data event and the requisite filters is
+:mod:`trappy.plotter.Constraint.Constraint`
+"""
+# pylint: disable=R0913
+from trappy.plotter.Utils import decolonize, normalize_list
+from trappy.utils import listify
+from trappy.plotter import AttrConf
+
+
+class Constraint(object):
+
+    """
+    What is a Constraint?
+        It is collection of data based on two rules:
+
+        - A Pivot
+
+        - A Set of Filters
+
+        - A Data Column
+
+    For Example a :mod:`pandas.DataFrame`
+
+    =====  ======== =========
+    Time    CPU       Latency
+    =====  ======== =========
+    1       x           <val>
+    2       y           <val>
+    3       z           <val>
+    4       a           <val>
+    =====  ======== =========
+
+    The resultant data will be split for each unique pivot value
+    with the filters applied
+    ::
+
+        result["x"] = pd.Series.filtered()
+        result["y"] = pd.Series.filtered()
+        result["z"] = pd.Series.filtered()
+        result["a"] = pd.Series.filtered()
+
+
+    :param trappy_trace: Input Data
+    :type trappy_trace: :mod:`pandas.DataFrame` or a class derived from
+        :mod:`trappy.trace.BareTrace`
+
+    :param column: The data column
+    :type column: str
+
+    :param template: TRAPpy Event
+    :type template: :mod:`trappy.base.Base` event
+
+    :param trace_index: The index of the trace/data in the overall constraint
+        data
+    :type trace_index: int
+
+    :param filters: A dictionary of filter values
+    :type filters: dict
+
+    :param window: A time window to apply to the constraint.
+    E.g. window=(5, 20) will constraint to events that happened
+    between Time=5 to Time=20.
+    :type window: tuple of two ints
+
+    """
+
+    def __init__(self, trappy_trace, pivot, column, template, trace_index,
+                 filters, window):
+        self._trappy_trace = trappy_trace
+        self._filters = filters
+        self._pivot = pivot
+        self.column = column
+        self._template = template
+        self._dup_resolved = False
+        self._data = self.populate_data_frame()
+
+        if window:
+            # We want to include the previous value before the window
+            # and the next after the window in the dataset
+            min_idx = self._data.loc[:window[0]].index.max()
+            max_idx = self._data.loc[window[1]:].index.min()
+            self._data = self._data.loc[min_idx:max_idx]
+
+        self.result = self._apply()
+        self.trace_index = trace_index
+
+    def _apply(self):
+        """This method applies the filter on the resultant data
+        on the input column.
+        """
+        data = self._data
+        result = {}
+
+        try:
+            values = data[self.column]
+        except KeyError:
+            return result
+
+        if self._pivot == AttrConf.PIVOT:
+            pivot_vals = [AttrConf.PIVOT_VAL]
+        else:
+            pivot_vals = self.pivot_vals(data)
+
+        for pivot_val in pivot_vals:
+            criterion = values.map(lambda x: True)
+
+            for key in self._filters.keys():
+                if key != self._pivot and key in data.columns:
+                    criterion = criterion & data[key].map(
+                        lambda x: x in self._filters[key])
+
+            if pivot_val != AttrConf.PIVOT_VAL:
+                criterion &= data[self._pivot] == pivot_val
+
+            val_series = values[criterion]
+            if len(val_series) != 0:
+                result[pivot_val] = val_series
+
+        return result
+
+    def _uses_trappy_trace(self):
+        if not self._template:
+            return False
+        else:
+            return True
+
+    def populate_data_frame(self):
+        """Return the populated :mod:`pandas.DataFrame`"""
+        if not self._uses_trappy_trace():
+            return self._trappy_trace
+
+        data_container = getattr(
+            self._trappy_trace,
+            decolonize(self._template.name))
+        return data_container.data_frame
+
+    def pivot_vals(self, data):
+        """This method returns the unique pivot values for the
+        Constraint's pivot and the column
+
+        :param data: Input Data
+        :type data: :mod:`pandas.DataFrame`
+        """
+        if self._pivot == AttrConf.PIVOT:
+            return AttrConf.PIVOT_VAL
+
+        if self._pivot not in data.columns:
+            return []
+
+        pivot_vals = set(data[self._pivot])
+        if self._pivot in self._filters:
+            pivot_vals = pivot_vals & set(self._filters[self._pivot])
+
+        return list(pivot_vals)
+
+    def __str__(self):
+
+        name = self.get_data_name()
+
+        if not self._uses_trappy_trace():
+            return name + ":" + str(self.column)
+
+        return name + ":" + \
+            self._template.name + ":" + self.column
+
+
+    def get_data_name(self):
+        """Get name for the data member. This method
+        relies on the "name" attribute for the name.
+        If the name attribute is absent, it associates
+        a numeric name to the respective data element
+
+        :returns: The name of the data member
+        """
+        if self._uses_trappy_trace():
+            if self._trappy_trace.name != "":
+                return self._trappy_trace.name
+            else:
+                return "Trace {}".format(self.trace_index)
+        else:
+            return "DataFrame {}".format(self.trace_index)
+
+class ConstraintManager(object):
+
+    """A class responsible for converting inputs
+    to constraints and also ensuring sanity
+
+
+    :param traces: Input Trace data
+    :type traces: :mod:`trappy.trace.BareTrace`, list(:mod:`trappy.trace.BareTrace`)
+        (or a class derived from :mod:`trappy.trace.BareTrace`)
+    :param columns: The column values from the corresponding
+        :mod:`pandas.DataFrame`
+    :type columns: str, list(str)
+    :param pivot: The column around which the data will be
+        pivoted:
+    :type pivot: str
+    :param templates: TRAPpy events
+    :type templates: :mod:`trappy.base.Base`
+    :param filters: A dictionary of values to be applied on the
+        respective columns
+    :type filters: dict
+    :param window: A time window to apply to the constraints
+    :type window: tuple of ints
+    :param zip_constraints: Permutes the columns and traces instead
+        of a one-to-one correspondence
+    :type zip_constraints: bool
+    """
+
+    def __init__(self, traces, columns, templates, pivot, filters,
+                 window=None, zip_constraints=True):
+
+        self._ip_vec = []
+        self._ip_vec.append(listify(traces))
+        self._ip_vec.append(listify(columns))
+        self._ip_vec.append(listify(templates))
+
+        self._lens = map(len, self._ip_vec)
+        self._max_len = max(self._lens)
+        self._pivot = pivot
+        self._filters = filters
+        self.window = window
+        self._constraints = []
+
+        self._trace_expanded = False
+        self._expand()
+        if zip_constraints:
+            self._populate_zip_constraints()
+        else:
+            self._populate_constraints()
+
+    def _expand(self):
+        """This is really important. We need to
+        meet the following criteria for constraint
+        expansion:
+        ::
+
+            Len[traces] == Len[columns] == Len[templates]
+
+        Or:
+        ::
+
+            Permute(
+                Len[traces] = 1
+                Len[columns] = 1
+                Len[templates] != 1
+            )
+
+            Permute(
+                   Len[traces] = 1
+                   Len[columns] != 1
+                   Len[templates] != 1
+            )
+        """
+        min_len = min(self._lens)
+        max_pos_comp = [
+            i for i,
+            j in enumerate(
+                self._lens) if j != self._max_len]
+
+        if self._max_len == 1 and min_len != 1:
+            raise RuntimeError("Essential Arg Missing")
+
+        if self._max_len > 1:
+
+            # Are they all equal?
+            if len(set(self._lens)) == 1:
+                return
+
+            if min_len > 1:
+                raise RuntimeError("Cannot Expand a list of Constraints")
+
+            for val in max_pos_comp:
+                if val == 0:
+                    self._trace_expanded = True
+                self._ip_vec[val] = normalize_list(self._max_len,
+                                                   self._ip_vec[val])
+
+    def _populate_constraints(self):
+        """Populate the constraints creating one for each column in
+        each trace
+
+        In a multi-trace, multicolumn scenario, constraints are created for
+        all the columns in each of the traces.  _populate_constraints()
+        creates one constraint for the first trace and first column, the
+        next for the second trace and second column,...  This function
+        creates a constraint for every combination of traces and columns
+        possible.
+        """
+
+        for trace_idx, trace in enumerate(self._ip_vec[0]):
+            for col in self._ip_vec[1]:
+                template = self._ip_vec[2][trace_idx]
+                constraint = Constraint(trace, self._pivot, col, template,
+                                        trace_idx, self._filters, self.window)
+                self._constraints.append(constraint)
+
+    def get_column_index(self, constraint):
+        return self._ip_vec[1].index(constraint.column)
+
+    def _populate_zip_constraints(self):
+        """Populate the expanded constraints
+
+        In a multitrace, multicolumn scenario, create constraints for
+        the first trace and the first column, second trace and second
+        column,... that is, as if you run zip(traces, columns)
+        """
+
+        for idx in range(self._max_len):
+            if self._trace_expanded:
+                trace_idx = 0
+            else:
+                trace_idx = idx
+
+            trace = self._ip_vec[0][idx]
+            col = self._ip_vec[1][idx]
+            template = self._ip_vec[2][idx]
+            self._constraints.append(
+                Constraint(trace, self._pivot, col, template, trace_idx,
+                           self._filters, self.window))
+
+    def generate_pivots(self, permute=False):
+        """Return a union of the pivot values
+
+        :param permute: Permute the Traces and Columns
+        :type permute: bool
+        """
+        pivot_vals = []
+        for constraint in self._constraints:
+            pivot_vals += constraint.result.keys()
+
+        p_list = list(set(pivot_vals))
+        traces = range(self._lens[0])
+
+        try:
+            sorted_plist = sorted(p_list, key=int)
+        except (ValueError, TypeError):
+            try:
+                sorted_plist = sorted(p_list, key=lambda x: int(x, 16))
+            except (ValueError, TypeError):
+                sorted_plist = sorted(p_list)
+
+        if permute:
+            pivot_gen = ((trace_idx, pivot) for trace_idx in traces for pivot in sorted_plist)
+            return pivot_gen, len(sorted_plist) * self._lens[0]
+        else:
+            return sorted_plist, len(sorted_plist)
+
+    def constraint_labels(self):
+        """
+        :return: string to represent the
+            set of Constraints
+
+        """
+        return map(str, self._constraints)
+
+    def __len__(self):
+        return len(self._constraints)
+
+    def __iter__(self):
+        return iter(self._constraints)
diff --git a/trappy/plotter/EventPlot.py b/trappy/plotter/EventPlot.py
new file mode 100644
index 0000000..a360e60
--- /dev/null
+++ b/trappy/plotter/EventPlot.py
@@ -0,0 +1,282 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+The EventPlot is used to represent Events with two characteristics:
+
+    - A name, which determines the colour on the plot
+    - A lane, which determines the lane in which the event occurred
+
+In the case of a cpu residency plot, the term lane can be equated to
+a CPU and the name attribute can be the PID of the task
+"""
+
+from trappy.plotter import AttrConf
+import uuid
+import json
+import os
+from trappy.plotter.AbstractDataPlotter import AbstractDataPlotter
+from trappy.plotter import IPythonConf
+from collections import defaultdict
+from copy import deepcopy
+
+if not IPythonConf.check_ipython():
+    raise ImportError("Ipython Environment not Found")
+
+from IPython.display import display, HTML
+# pylint: disable=R0201
+# pylint: disable=R0921
+
+
+class EventPlot(AbstractDataPlotter):
+    """
+        Input Data should be of the format
+        ::
+
+                { "<name1>" : [
+                                 [event_start, event_end, lane],
+                                  .
+                                  .
+                                 [event_start, event_end, lane],
+                              ],
+                 .
+                 .
+                 .
+
+                 "<nameN>" : [
+                                [event_start, event_end, lane],
+                                 .
+                                 .
+                                [event_start, event_end, lane],
+                             ],
+                }
+
+        :param data: Input Data
+        :type data: dict
+
+        :param keys: List of unique names in the data dictionary
+        :type keys: list
+
+        :param domain: Domain of the event data
+        :type domain: tuple
+
+        :param lane_prefix: A string prefix to be used to name each lane
+        :type lane_prefix: str
+
+        :param num_lanes: Total number of expected lanes
+        :type num_lanes: int
+
+        :param summary: Show a mini plot below the main plot with an
+            overview of where your current view is with respect to the
+            whole trace
+        :type summary: bool
+
+        :param stride: Stride can be used if the trace is very large.
+            It results in sampled rendering
+        :type stride: bool
+
+        :param lanes: The sorted order of lanes
+        :type lanes: list
+
+        :param color_map: A mapping between events and colours
+            ::
+                { "<name1>" : "colour1",
+                  .
+                  .
+                  .
+                  "<nameN>" : "colourN"
+                }
+
+            Colour string can be:
+
+            - Colour names (supported colours are listed in
+            https://www.w3.org/TR/SVG/types.html#ColorKeywords)
+
+            - HEX representation of colour, like #FF0000 for "red", #008000 for
+            "green", #0000FF for "blue" and so on
+
+        :type color_map: dict
+    """
+
+    def __init__(
+            self,
+            data,
+            keys,
+            domain,
+            lane_prefix="Lane: ",
+            num_lanes=0,
+            summary=True,
+            stride=False,
+            lanes=None,
+            color_map=None):
+
+        _data = deepcopy(data)
+        self._html = []
+        self._fig_name = self._generate_fig_name()
+        # Function to get the average duration of each event
+        avgFunc = lambda x: sum([(evt[1] - evt[0]) for evt in x]) / float(len(x) + 1)
+        avg = {k: avgFunc(v) for k, v in data.iteritems()}
+        # Filter keys with zero average time
+        keys = filter(lambda x : avg[x] != 0, avg)
+        graph = {}
+        graph["lanes"] = self._get_lanes(lanes, lane_prefix, num_lanes, _data)
+        graph["xDomain"] = domain
+        graph["keys"] = sorted(keys, key=lambda x: avg[x], reverse=True)
+        graph["showSummary"] = summary
+        graph["stride"] = AttrConf.EVENT_PLOT_STRIDE
+        graph["colorMap"] = color_map
+        graph["data"] = self._group_data_by_lanes(_data)
+        self._data = json.dumps(graph)
+
+        # Initialize the HTML, CSS and JS Components
+        self._add_css()
+        self._init_html()
+
+    def _group_data_by_lanes(self, data):
+        """Group data by lanes.
+
+        This enables the Javascript code to handle the same event
+        occuring simultaneously in different lanes.
+        """
+        lane_data = {}
+        for key, value in data.items():
+            lane_data[key] = defaultdict(list)
+            for tsinfo in value:
+                lane_data[key][tsinfo[2]].append(tsinfo[:2])
+        return lane_data
+
+    def view(self):
+        """Views the Graph Object"""
+
+        # Defer installation of IPython components
+        # to the .view call to avoid any errors at
+        # when importing the module. This facilitates
+        # the importing of the module from outside
+        # an IPython notebook
+        IPythonConf.iplot_install("EventPlot")
+        display(HTML(self.html()))
+
+    def savefig(self, path):
+        """Save the plot in the provided path
+
+        .. warning:: Not Implemented for :mod:`trappy.plotter.EventPlot`
+        """
+
+        raise NotImplementedError(
+            "Save is not currently implemented for EventPlot")
+
+    def _get_lanes(self,
+                   input_lanes,
+                   lane_prefix,
+                   num_lanes,
+                   data):
+        """Populate the lanes for the plot"""
+
+        # If the user has specified lanes explicitly
+        lanes = []
+        if input_lanes:
+            lane_map = {}
+            for idx, lane in enumerate(input_lanes):
+                lane_map[lane] = idx
+
+            for name in data:
+                for event in data[name]:
+                    lane = event[2]
+
+                    try:
+                        event[2] = lane_map[lane]
+                    except KeyError:
+                        raise RuntimeError("Invalid Lane %s" % lane)
+
+            for idx, lane in enumerate(input_lanes):
+                lanes.append({"id": idx, "label": lane})
+
+        else:
+
+            if not num_lanes:
+                raise RuntimeError("Either lanes or num_lanes must be specified")
+
+            for idx in range(num_lanes):
+                lanes.append({"id": idx, "label": "{}{}".format(lane_prefix, idx)})
+
+        return lanes
+
+    def _generate_fig_name(self):
+        """Generate a unqiue name for the figure"""
+
+        fig_name = "fig_" + uuid.uuid4().hex
+        return fig_name
+
+    def _init_html(self):
+        """Initialize HTML for the plot"""
+        div_js = ''
+        for url in [IPythonConf.D3_PLOTTER_URL, IPythonConf.D3_TIP_URL]:
+            div_js += '<!-- TRAPPY_PUBLISH_SOURCE_LIB = "{}" -->\n'.format(url)
+
+        div_js += """
+        <script>
+            /* TRAPPY_PUBLISH_IMPORT = "plotter/js/EventPlot.js" */
+            /* TRAPPY_PUBLISH_REMOVE_START */
+            var req = require.config( {
+
+                paths: {
+
+                    "EventPlot": '""" + IPythonConf.add_web_base("plotter_scripts/EventPlot/EventPlot") + """',
+                    "d3-tip": '""" + IPythonConf.add_web_base("plotter_scripts/EventPlot/d3.tip.v0.6.3") + """',
+                    "d3-plotter": '""" + IPythonConf.add_web_base("plotter_scripts/EventPlot/d3.min") + """'
+                },
+                waitSeconds: 15,
+                shim: {
+                    "d3-plotter" : {
+                        "exports" : "d3"
+                    },
+                    "d3-tip": ["d3-plotter"],
+                    "EventPlot": {
+
+                        "deps": ["d3-tip", "d3-plotter" ],
+                        "exports":  "EventPlot"
+                    }
+                }
+            });
+            /* TRAPPY_PUBLISH_REMOVE_STOP */
+            """
+
+        div_js += """
+        req(["require", "EventPlot"], function() { /* TRAPPY_PUBLISH_REMOVE_LINE */
+            EventPlot.generate('""" + self._fig_name + "', '" + IPythonConf.add_web_base("") + "', " + self._data + """);
+        }); /* TRAPPY_PUBLISH_REMOVE_LINE */
+        </script>
+        """
+
+        self._html.append(
+            '<div id="{}" class="eventplot">\n{}</div>'.format(self._fig_name,
+                                                             div_js))
+
+    def _add_css(self):
+        """Append the CSS to the HTML code generated"""
+
+        base_dir = os.path.dirname(os.path.realpath(__file__))
+        css_file = os.path.join(base_dir, "css/EventPlot.css")
+        self._html.append("<style>")
+
+        with open(css_file, 'r') as css_fh:
+            self._html += [l[:-1] for l in css_fh.readlines()]
+
+        self._html.append("</style>")
+
+    def html(self):
+        """Return a Raw HTML string for the plot"""
+
+        return "\n".join(self._html)
diff --git a/trappy/plotter/ILinePlot.py b/trappy/plotter/ILinePlot.py
new file mode 100644
index 0000000..2bcd6aa
--- /dev/null
+++ b/trappy/plotter/ILinePlot.py
@@ -0,0 +1,314 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""This module contains the class for plotting and customizing
+Line/Linear Plots with :mod:`trappy.trace.BareTrace` or derived
+classes.  This plot only works when run from an IPython notebook
+
+"""
+
+from collections import OrderedDict
+import matplotlib.pyplot as plt
+from trappy.plotter import AttrConf
+from trappy.plotter import Utils
+from trappy.plotter.Constraint import ConstraintManager
+from trappy.plotter.ILinePlotGen import ILinePlotGen
+from trappy.plotter.AbstractDataPlotter import AbstractDataPlotter
+from trappy.plotter.ColorMap import ColorMap
+from trappy.plotter import IPythonConf
+from trappy.utils import handle_duplicate_index
+import pandas as pd
+
+if not IPythonConf.check_ipython():
+    raise ImportError("Ipython Environment not Found")
+
+class ILinePlot(AbstractDataPlotter):
+    """
+    This class uses :mod:`trappy.plotter.Constraint.Constraint` to
+    represent different permutations of input parameters. These
+    constraints are generated by creating an instance of
+    :mod:`trappy.plotter.Constraint.ConstraintManager`.
+
+    :param traces: The input data
+    :type traces: a list of :mod:`trappy.trace.FTrace`,
+        :mod:`trappy.trace.SysTrace`, :mod:`trappy.trace.BareTrace`
+        or :mod:`pandas.DataFrame` or a single instance of them.
+
+    :param column: specifies the name of the column to
+           be plotted.
+    :type column: (str, list(str))
+
+    :param templates: TRAPpy events
+
+        .. note::
+
+                This is not required if a :mod:`pandas.DataFrame` is
+                used
+
+    :type templates: :mod:`trappy.base.Base`
+
+    :param filters: Filter the column to be plotted as per the
+        specified criteria. For Example:
+        ::
+
+            filters =
+                    {
+                        "pid": [ 3338 ],
+                        "cpu": [0, 2, 4],
+                    }
+    :type filters: dict
+
+    :param per_line: Used to control the number of graphs
+        in each graph subplot row
+    :type per_line: int
+
+    :param concat: Draw all the pivots on a single graph
+    :type concat: bool
+
+    :param permute: Draw one plot for each of the traces specified
+    :type permute: bool
+
+    :param fill: Fill the area under the plots
+    :type fill: bool
+
+    :param fill_alpha: Opacity of filled area under the plots.
+        Implies fill=True.
+    :type fill_alpha: float
+
+    :param xlim: A tuple representing the upper and lower xlimits
+    :type xlim: tuple
+
+    :param ylim: A tuple representing the upper and lower ylimits
+    :type ylim: tuple
+
+    :param drawstyle: Set the drawstyle to a matplotlib compatible
+        drawing style.
+
+        .. note::
+
+            Only "steps-post" is supported as a valid value for
+            the drawstyle. This creates a step plot.
+
+    :type drawstyle: str
+
+    :param sync_zoom: Synchronize the zoom of a group of plots.
+        Zooming in one plot of a group (see below) will zoom in every
+        plot of that group.  Defaults to False.
+    :type sync_zoom: boolean
+
+    :param group: Name given to the plots created by this ILinePlot
+        instance.  This name is only used for synchronized zoom.  If
+        you zoom on any plot in a group all plots will zoom at the
+        same time.
+    :type group: string
+
+    :param signals: A string of the type event_name:column to indicate
+        the value that needs to be plotted.  You can add an additional
+        parameter to specify the color of the lin in rgb:
+        "event_name:column:color".  The color is specified as a comma
+        separated list of rgb values, from 0 to 255 or from 0x0 to
+        0xff.  E.g. 0xff,0x0,0x0 is red and 100,40,32 is brown.
+
+        .. note::
+
+            - Only one of `signals` or both `templates` and
+              `columns` should be specified
+            - Signals format won't work for :mod:`pandas.DataFrame`
+              input
+
+    :type signals: str
+    """
+
+    def __init__(self, traces, templates=None, **kwargs):
+        # Default keys, each can be overridden in kwargs
+        self._layout = None
+        super(ILinePlot, self).__init__(traces=traces,
+                                        templates=templates)
+
+        self.set_defaults()
+
+        for key in kwargs:
+            self._attr[key] = kwargs[key]
+
+        if "signals" in self._attr:
+            self._describe_signals()
+
+        self._check_data()
+
+        if "column" not in self._attr:
+            raise RuntimeError("Value Column not specified")
+
+        if self._attr["drawstyle"] and self._attr["drawstyle"].startswith("steps"):
+            self._attr["step_plot"] = True
+
+        zip_constraints = not self._attr["permute"]
+
+        window = self._attr["xlim"] if "xlim" in self._attr else None
+
+        self.c_mgr = ConstraintManager(traces, self._attr["column"], self.templates,
+                                       self._attr["pivot"],
+                                       self._attr["filters"],
+                                       window=window,
+                                       zip_constraints=zip_constraints)
+
+
+    def savefig(self, *args, **kwargs):
+        raise NotImplementedError("Not Available for ILinePlot")
+
+    def view(self, max_datapoints=75000, test=False):
+        """Displays the graph
+
+        :param max_datapoints: Maximum number of datapoints to plot.
+        Dygraph can make the browser unresponsive if it tries to plot
+        too many datapoints.  Chrome 50 chokes at around 75000 on an
+        i7-4770 @ 3.4GHz, Firefox 47 can handle up to 200000 before
+        becoming too slow in the same machine.  You can increase this
+        number if you know what you're doing and are happy to wait for
+        the plot to render.  :type max_datapoints: int
+
+        :param test: For testing purposes.  Only set to true if run
+        from the testsuite.
+        :type test: boolean
+        """
+
+        # Defer installation of IPython components
+        # to the .view call to avoid any errors at
+        # when importing the module. This facilitates
+        # the importing of the module from outside
+        # an IPython notebook
+        if not test:
+            IPythonConf.iplot_install("ILinePlot")
+
+        self._attr["max_datapoints"] = max_datapoints
+
+        if self._attr["concat"]:
+            self._plot_concat()
+        else:
+            self._plot(self._attr["permute"], test)
+
+    def set_defaults(self):
+        """Sets the default attrs"""
+        self._attr["per_line"] = AttrConf.PER_LINE
+        self._attr["concat"] = AttrConf.CONCAT
+        self._attr["filters"] = {}
+        self._attr["pivot"] = AttrConf.PIVOT
+        self._attr["permute"] = False
+        self._attr["drawstyle"] = None
+        self._attr["step_plot"] = False
+        self._attr["fill"] = AttrConf.FILL
+        self._attr["scatter"] = AttrConf.PLOT_SCATTER
+        self._attr["point_size"] = AttrConf.POINT_SIZE
+        self._attr["map_label"] = {}
+        self._attr["title"] = AttrConf.TITLE
+
+    def _plot(self, permute, test):
+        """Internal Method called to draw the plot"""
+        pivot_vals, len_pivots = self.c_mgr.generate_pivots(permute)
+
+        self._layout = ILinePlotGen(len_pivots, **self._attr)
+        plot_index = 0
+        for p_val in pivot_vals:
+            data_dict = OrderedDict()
+            for constraint in self.c_mgr:
+                if permute:
+                    trace_idx, pivot = p_val
+                    if constraint.trace_index != trace_idx:
+                        continue
+                    legend = constraint._template.name + ":" + constraint.column
+                else:
+                    pivot = p_val
+                    legend = str(constraint)
+
+                result = constraint.result
+                if pivot in result:
+                    data_dict[legend] = result[pivot]
+
+            if permute:
+                title = self.traces[plot_index].name
+            elif pivot != AttrConf.PIVOT_VAL:
+                title = "{0}: {1}".format(self._attr["pivot"], self._attr["map_label"].get(pivot, pivot))
+            else:
+                title = ""
+
+            if len(data_dict) > 1:
+                data_frame = self._fix_indexes(data_dict)
+            else:
+                data_frame = pd.DataFrame(data_dict)
+
+            self._layout.add_plot(plot_index, data_frame, title, test=test)
+            plot_index += 1
+
+        self._layout.finish()
+
+    def _plot_concat(self):
+        """Plot all lines on a single figure"""
+
+        pivot_vals, _ = self.c_mgr.generate_pivots()
+        plot_index = 0
+
+        self._layout = ILinePlotGen(len(self.c_mgr), **self._attr)
+
+        for constraint in self.c_mgr:
+            result = constraint.result
+            title = str(constraint)
+            data_dict = OrderedDict()
+
+            for pivot in pivot_vals:
+                if pivot in result:
+                    if pivot == AttrConf.PIVOT_VAL:
+                        key = ",".join(self._attr["column"])
+                    else:
+                        key = "{0}: {1}".format(self._attr["pivot"], self._attr["map_label"].get(pivot, pivot))
+
+                    data_dict[key] = result[pivot]
+
+            if len(data_dict) > 1:
+                data_frame = self._fix_indexes(data_dict)
+            else:
+                data_frame = pd.DataFrame(data_dict)
+
+            self._layout.add_plot(plot_index, data_frame, title)
+            plot_index += 1
+
+        self._layout.finish()
+
+    def _fix_indexes(self, data_dict):
+        """
+        In case of multiple traces with different indexes (i.e. x-axis values),
+        create new ones with same indexes
+        """
+        # 1) Check if we are processing multiple traces
+        if len(data_dict) <= 1:
+            raise ValueError("Cannot fix indexes for single trace. "\
+                             "Expecting multiple traces!")
+
+        # 2) Merge the data frames to obtain common indexes
+        df_columns = list(data_dict.keys())
+        dedup_data = [handle_duplicate_index(s) for s in data_dict.values()]
+        ret = pd.Series(dedup_data, index=df_columns)
+        merged_df = pd.concat(ret.get_values(), axis=1)
+        merged_df.columns = df_columns
+        # 3) Fill NaN values depending on drawstyle
+        if self._attr["drawstyle"] == "steps-post":
+            merged_df = merged_df.ffill()
+        elif self._attr["drawstyle"] == "steps-pre":
+            merged_df = merged_df.bfill()
+        elif self._attr["drawstyle"] == "steps-mid":
+            merged_df = merged_df.ffill()
+        else:
+            # default
+            merged_df = merged_df.interpolate()
+
+        return merged_df
diff --git a/trappy/plotter/ILinePlotGen.py b/trappy/plotter/ILinePlotGen.py
new file mode 100644
index 0000000..a748e19
--- /dev/null
+++ b/trappy/plotter/ILinePlotGen.py
@@ -0,0 +1,273 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""This is helper module for :mod:`trappy.plotter.ILinePlot`
+for adding HTML and javascript necessary for interactive
+plotting. The Linear to 2-D co-ordination transformations
+are done by using the functionality in
+:mod:`trappy.plotter.PlotLayout`
+"""
+
+from trappy.plotter import AttrConf
+import uuid
+from collections import OrderedDict
+import json
+import os
+from trappy.plotter import IPythonConf
+from trappy.plotter.ColorMap import to_dygraph_colors
+
+
+if not IPythonConf.check_ipython():
+    raise ImportError("No Ipython Environment found")
+
+from IPython.display import display, HTML
+
+def df_to_dygraph(data_frame):
+    """Helper function to convert a :mod:`pandas.DataFrame` to
+    dygraph data
+
+    :param data_frame: The DataFrame to be converted
+    :type data_frame: :mod:`pandas.DataFrame`
+    """
+
+    values = data_frame.values.tolist()
+    data = [[x] for x in data_frame.index.tolist()]
+
+    for idx, (_, val) in enumerate(zip(data, values)):
+        data[idx] += val
+
+    return {
+        "data": data,
+        "labels": ["index"] + data_frame.columns.tolist(),
+    }
+
+class ILinePlotGen(object):
+    """
+    :param num_plots: The total number of plots
+    :type num_plots: int
+
+    The linear co-ordinate system :math:`[0, N_{plots}]` is
+    mapped to a 2-D coordinate system with :math:`N_{rows}`
+    and :math:`N_{cols}` such that:
+
+    .. math::
+
+        N_{rows} = \\frac{N_{cols}}{N_{plots}}
+    """
+
+    def _add_graph_cell(self, fig_name, color_map):
+        """Add a HTML table cell to hold the plot"""
+
+        colors_opt_arg = ", " + to_dygraph_colors(color_map) if color_map else ""
+
+        graph_js = ''
+        lib_urls =  [IPythonConf.DYGRAPH_COMBINED_URL, IPythonConf.DYGRAPH_SYNC_URL,
+                     IPythonConf.UNDERSCORE_URL]
+        for url in lib_urls:
+            graph_js += '<!-- TRAPPY_PUBLISH_SOURCE_LIB = "{}" -->\n'.format(url)
+
+        graph_js += """
+            <script>
+            /* TRAPPY_PUBLISH_IMPORT = "plotter/js/ILinePlot.js" */
+            /* TRAPPY_PUBLISH_REMOVE_START */
+            var ilp_req = require.config( {
+
+                paths: {
+                    "dygraph-sync": '""" + IPythonConf.add_web_base("plotter_scripts/ILinePlot/synchronizer") + """',
+                    "dygraph": '""" + IPythonConf.add_web_base("plotter_scripts/ILinePlot/dygraph-combined") + """',
+                    "ILinePlot": '""" + IPythonConf.add_web_base("plotter_scripts/ILinePlot/ILinePlot") + """',
+                    "underscore": '""" + IPythonConf.add_web_base("plotter_scripts/ILinePlot/underscore-min") + """',
+                },
+
+                shim: {
+                    "dygraph-sync": ["dygraph"],
+                    "ILinePlot": {
+
+                        "deps": ["dygraph-sync", "dygraph", "underscore"],
+                        "exports":  "ILinePlot"
+                    }
+                }
+            });
+                /* TRAPPY_PUBLISH_REMOVE_STOP */
+                ilp_req(["require", "ILinePlot"], function() { /* TRAPPY_PUBLISH_REMOVE_LINE */
+                ILinePlot.generate(""" + fig_name + "_data" + colors_opt_arg + """);
+            }); /* TRAPPY_PUBLISH_REMOVE_LINE */
+            </script>
+        """
+
+        cell = '<td style="border-style: hidden;"><div class="ilineplot" id="{}"></div></td>'.format(fig_name)
+
+        self._html.append(cell)
+        self._js.append(graph_js)
+
+    def _add_legend_cell(self, fig_name):
+        """Add HTML table cell for the legend"""
+
+        legend_div_name = fig_name + "_legend"
+        cell = '<td style="border-style: hidden;"><div style="text-align:center" id="{}"></div></td>'.format(legend_div_name)
+
+        self._html.append(cell)
+
+    def _begin_row(self):
+        """Add the opening tag for HTML row"""
+
+        self._html.append("<tr>")
+
+    def _end_row(self):
+        """Add the closing tag for the HTML row"""
+
+        self._html.append("</tr>")
+
+    def _end_table(self):
+        """Add the closing tag for the HTML table"""
+
+        self._html.append("</table>")
+
+    def _generate_fig_name(self):
+        """Generate a unique figure name"""
+
+        fig_name = "fig_" + uuid.uuid4().hex
+        self._fig_map[self._fig_index] = fig_name
+        self._fig_index += 1
+        return fig_name
+
+    def _init_html(self, color_map):
+        """Initialize HTML code for the plots"""
+
+        table = '<table style="border-style: hidden;">'
+        self._html.append(table)
+        if self._attr["title"]:
+            cell = '<caption style="text-align:center; font: 24px sans-serif bold; color: black">{}</caption>'.format(self._attr["title"])
+            self._html.append(cell)
+
+        for _ in range(self._rows):
+            self._begin_row()
+            legend_figs = []
+            for _ in range(self._attr["per_line"]):
+                fig_name = self._generate_fig_name()
+                legend_figs.append(fig_name)
+                self._add_graph_cell(fig_name, color_map)
+
+            self._end_row()
+            self._begin_row()
+
+            for l_fig in legend_figs:
+                self._add_legend_cell(l_fig)
+
+            self._end_row()
+
+        self._end_table()
+
+    def __init__(self, num_plots, **kwargs):
+
+        self._attr = kwargs
+        self._html = []
+        self._js = []
+        self._js_plot_data = []
+        self.num_plots = num_plots
+        self._fig_map = {}
+        self._fig_index = 0
+
+        self._single_plot = False
+        if self.num_plots == 0:
+            raise RuntimeError("No plots for the given constraints")
+
+        if self.num_plots < self._attr["per_line"]:
+            self._attr["per_line"] = self.num_plots
+        self._rows = (self.num_plots / self._attr["per_line"])
+
+        if self.num_plots % self._attr["per_line"] != 0:
+            self._rows += 1
+
+        self._attr["height"] = AttrConf.HTML_HEIGHT
+        self._init_html(kwargs.pop("colors", None))
+
+    def _check_add_scatter(self, fig_params):
+        """Check if a scatter plot is needed
+        and augment the fig_params accordingly"""
+
+        if self._attr["scatter"]:
+            fig_params["drawPoints"] = True
+            fig_params["strokeWidth"] = 0.0
+        else:
+            fig_params["drawPoints"] = False
+            fig_params["strokeWidth"] = AttrConf.LINE_WIDTH
+
+        fig_params["pointSize"] = self._attr["point_size"]
+
+    def add_plot(self, plot_num, data_frame, title="", test=False):
+        """Add a plot for the corresponding index
+
+        :param plot_num: The linear index of the plot
+        :type plot_num: int
+
+        :param data_frame: The data for the plot
+        :type data_frame: :mod:`pandas.DataFrame`
+
+        :param title: The title for the plot
+        :type title: str
+        """
+
+        datapoints = sum(len(v) for _, v in data_frame.iteritems())
+        if datapoints > self._attr["max_datapoints"]:
+            msg = "This plot is too big and will probably make your browser unresponsive.  If you are happy to wait, pass max_datapoints={} to view()".\
+                  format(datapoints + 1)
+            raise ValueError(msg)
+
+        fig_name = self._fig_map[plot_num]
+        fig_params = {}
+        fig_params["data"] = df_to_dygraph(data_frame)
+        fig_params["name"] = fig_name
+        fig_params["rangesel"] = False
+        fig_params["logscale"] = False
+        fig_params["title"] = title
+        fig_params["step_plot"] = self._attr["step_plot"]
+        fig_params["fill_graph"] = self._attr["fill"]
+        if "fill_alpha" in self._attr:
+            fig_params["fill_alpha"] = self._attr["fill_alpha"]
+            fig_params["fill_graph"] = True
+        fig_params["per_line"] = self._attr["per_line"]
+        fig_params["height"] = self._attr["height"]
+
+        self._check_add_scatter(fig_params)
+
+        if "group" in self._attr:
+            fig_params["syncGroup"] = self._attr["group"]
+            if "sync_zoom" in self._attr:
+                fig_params["syncZoom"] = self._attr["sync_zoom"]
+            else:
+                fig_params["syncZoom"] = AttrConf.DEFAULT_SYNC_ZOOM
+
+        if "ylim" in self._attr:
+            fig_params["valueRange"] = self._attr["ylim"]
+
+        if "xlim" in self._attr:
+            fig_params["dateWindow"] = self._attr["xlim"]
+
+        fig_data = "var {}_data = {};".format(fig_name, json.dumps(fig_params))
+
+        self._js_plot_data.append("<script>")
+        self._js_plot_data.append(fig_data)
+        self._js_plot_data.append("</script>")
+
+    def finish(self):
+        """Called when the Plotting is finished"""
+
+        display(HTML(self.html()))
+
+    def html(self):
+        """Return the raw HTML text"""
+
+        return "\n".join(self._html + self._js_plot_data + self._js)
diff --git a/trappy/plotter/IPythonConf.py b/trappy/plotter/IPythonConf.py
new file mode 100644
index 0000000..1cfb72f
--- /dev/null
+++ b/trappy/plotter/IPythonConf.py
@@ -0,0 +1,206 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""IPythonConf provides abstraction for the varying configurations in
+different versions of ipython/jupyter packages.
+"""
+import urllib
+import os
+import shutil
+from distutils.version import StrictVersion as V
+
+D3_PLOTTER_URL = "https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js"
+D3_TIP_URL = "http://labratrevenge.com/d3-tip/javascripts/d3.tip.v0.6.3.js"
+DYGRAPH_COMBINED_URL = "http://cdnjs.cloudflare.com/ajax/libs/dygraph/1.1.1/dygraph-combined.js"
+DYGRAPH_SYNC_URL = "http://dygraphs.com/extras/synchronizer.js"
+UNDERSCORE_URL = "https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.8.3/underscore-min.js"
+
+IPLOT_RESOURCES = {
+    "ILinePlot": [
+        DYGRAPH_COMBINED_URL,
+        "js/ILinePlot.js",
+        DYGRAPH_SYNC_URL,
+        UNDERSCORE_URL],
+    "EventPlot": [
+        D3_PLOTTER_URL,
+        D3_TIP_URL,
+        "js/EventPlot.js",
+        "css/EventPlot_help.jpg"]}
+
+"""The location of the IPython webserver in IPython version 4.0+"""
+IPYTHON_V4_BASE = "/nbextensions"
+"""The webserver base directory for IPython version 4.0+"""
+IPYTHON_V3_BASE = "/static"
+"""The webserver base directory for IPython version < 4.0"""
+PLOTTER_SCRIPTS = "plotter_scripts"
+"""The installation directory of plotter JS files in the
+IPython webserver"""
+
+def install_http_resource(url, to_path):
+    """Install a HTTP Resource (eg. javascript) to
+    a destination on the disk
+
+    :param url: HTTP URL
+    :type url: str
+
+    :param to_path: Destination path on the disk
+    :type to_path: str
+    """
+    try:
+        urllib.urlretrieve(url, filename=to_path)
+    except IOError:
+        raise ImportError("Could not receive Web Resource {}"
+                          .format(to_path))
+
+
+def install_local_resource(from_path, to_path):
+    """Move a local resource  to the desired
+    a destination.
+
+    :param from_path: Path relative to this file
+    :type from_path: str
+
+    :param to_path: Destination path on the disk
+    :type to_path: str
+    """
+    base_dir = os.path.dirname(__file__)
+    from_path = os.path.join(base_dir, from_path)
+    shutil.copy(from_path, to_path)
+
+
+def install_resource(from_path, to_path):
+    """Install a resource to a location on the disk
+
+    :param from_path: URL or relative path
+    :type from_path: str
+
+    :param to_path: Destination path on the disk
+    :type to_path: str
+    """
+
+    if from_path.startswith("http"):
+        if not os.path.isfile(to_path):
+            install_http_resource(from_path, to_path)
+    else:
+        install_local_resource(from_path, to_path)
+
+
+def iplot_install(module_name):
+    """Install the resources for the module to the Ipython
+    profile directory
+
+    :param module_name: Name of the module
+    :type module_name: str
+
+    :return: A list than can be consumed by requirejs or
+        any relative resource dependency resolver
+    """
+
+    resources = IPLOT_RESOURCES[module_name]
+    for resource in resources:
+        resource_name = os.path.basename(resource)
+        resource_dest_dir = os.path.join(
+            get_scripts_path(),
+            module_name)
+
+        # Ensure if the directory exists
+        if not os.path.isdir(resource_dest_dir):
+            os.mkdir(resource_dest_dir)
+        resource_dest_path = os.path.join(resource_dest_dir, resource_name)
+        install_resource(resource, resource_dest_path)
+
+
+def get_ipython():
+    """Return an IPython instance. Returns None
+    if IPython is not installed"""
+
+    try:
+        import IPython
+        return IPython.get_ipython()
+    except ImportError:
+        return None
+
+def check_ipython():
+    """A boolean function to check if IPython
+    is available"""
+
+    try:
+        import IPython
+    except ImportError:
+        return False
+
+    return True
+
+def get_profile_name():
+    """Get the name of the profile of the current IPython
+     notebook. This is only relevant to V <= 4.0.0"""
+
+    ipy = get_ipython()
+    if not ipy:
+        raise ImportError("Cannot Find IPython Profile")
+
+    return ipy.profile
+
+def get_ipython_dir(profile=None):
+    """Returns the base directory of the IPython server
+
+    :param profile: The name of the IPython profile
+    :type profile: str
+    """
+
+    if not check_ipython():
+        raise ImportError("Cannot Find IPython Environment")
+
+    import IPython
+    # IPython 4.0+ changes the position of files in the profile
+    # directory
+    if V(IPython.__version__) >= V('4.0.0'):
+        from jupyter_core.paths import jupyter_data_dir
+        return os.path.join(
+            jupyter_data_dir(),
+            IPYTHON_V4_BASE.strip("/"))
+    else:
+        if not profile:
+            profile = get_profile_name()
+        return os.path.join(
+            IPython.utils.path.locate_profile(
+                profile),
+            IPYTHON_V3_BASE.strip("/"))
+
+def add_web_base(path):
+    """Add the base of the IPython dependency URLs
+
+    :param path: The path to be augmented with the
+        webserver base
+    :type path: str
+    """
+
+    import IPython
+    if V(IPython.__version__) >= V('4.0.0'):
+        return os.path.join(IPYTHON_V4_BASE, path)
+    else:
+        return os.path.join(IPYTHON_V3_BASE, path)
+
+def get_scripts_path(profile=None):
+    """Directory where plotter scripts are installed
+
+    :param profile: The name of the IPython profile
+    :type profile: str
+    """
+
+    dir_name = os.path.join(get_ipython_dir(profile), PLOTTER_SCRIPTS)
+    if not os.path.isdir(dir_name):
+        os.makedirs(dir_name)
+    return dir_name
diff --git a/trappy/plotter/LinePlot.py b/trappy/plotter/LinePlot.py
new file mode 100644
index 0000000..7f96594
--- /dev/null
+++ b/trappy/plotter/LinePlot.py
@@ -0,0 +1,172 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+This class sublclasses :mod:`trappy.plotter.StaticPlot.StaticPlot` to
+implement a line plot.
+"""
+
+from trappy.plotter import AttrConf
+from trappy.plotter.StaticPlot import StaticPlot
+
+class LinePlot(StaticPlot):
+    """
+    This class uses :mod:`trappy.plotter.Constraint.Constraint` to
+    represent different permutations of input parameters. These
+    constraints are generated by creating an instance of
+    :mod:`trappy.plotter.Constraint.ConstraintManager`.
+
+    :param traces: The input data
+    :type traces: a list of :mod:`trappy.trace.FTrace`,
+        :mod:`trappy.trace.SysTrace`, :mod:`trappy.trace.BareTrace`
+        or :mod:`pandas.DataFrame` or a single instance of them.
+
+    :param column: specifies the name of the column to
+           be plotted.
+    :type column: (str, list(str))
+
+    :param templates: TRAPpy events
+
+        .. note::
+
+                This is not required if a :mod:`pandas.DataFrame` is
+                used
+
+    :type templates: :mod:`trappy.base.Base`
+
+    :param filters: Filter the column to be plotted as per the
+        specified criteria. For Example:
+        ::
+
+            filters =
+                    {
+                        "pid": [ 3338 ],
+                        "cpu": [0, 2, 4],
+                    }
+    :type filters: dict
+
+    :param per_line: Used to control the number of graphs
+        in each graph subplot row
+    :type per_line: int
+
+    :param concat: Draw all the pivots on a single graph
+    :type concat: bool
+
+    :param fill: Fill the area under the plots
+    :type fill: bool
+
+    :param permute: Draw one plot for each of the traces specified
+    :type permute: bool
+
+    :param drawstyle: This argument is forwarded to the matplotlib
+        corresponding :func:`matplotlib.pyplot.plot` call
+
+        drawing style.
+
+        .. note::
+
+            step plots are not currently supported for filled
+            graphs
+
+    :param xlim: A tuple representing the upper and lower xlimits
+    :type xlim: tuple
+
+    :param ylim: A tuple representing the upper and lower ylimits
+    :type ylim: tuple
+
+    :param title: A title describing all the generated plots
+    :type title: str
+
+    :param style: Created pre-styled graphs loaded from
+        :mod:`trappy.plotter.AttrConf.MPL_STYLE`
+    :type style: bool
+
+    :param signals: A string of the type event_name:column to indicate
+        the value that needs to be plotted.  You can add an additional
+        parameter to specify the color of the lin in rgb:
+        "event_name:column:color".  The color is specified as a comma
+        separated list of rgb values, from 0 to 255 or from 0x0 to
+        0xff.  E.g. 0xff,0x0,0x0 is red and 100,40,32 is brown.
+
+        .. note::
+
+            - Only one of `signals` or both `templates` and
+              `columns` should be specified
+            - Signals format won't work for :mod:`pandas.DataFrame`
+              input
+
+    :type signals: str
+
+    """
+
+    def __init__(self, traces, templates=None, **kwargs):
+        # Default keys, each can be overridden in kwargs
+
+        super(LinePlot, self).__init__(
+            traces=traces,
+            templates=templates,
+            **kwargs)
+
+        self._check_add_scatter()
+
+    def set_defaults(self):
+        """Sets the default attrs"""
+        super(LinePlot, self).set_defaults()
+        self._attr["scatter"] = AttrConf.PLOT_SCATTER
+        self._attr["fill"] = AttrConf.FILL
+
+    def _check_add_scatter(self):
+        """Check if a scatter plot is needed
+        and augment the forwarded args accordingly"""
+
+        if self._attr["scatter"]:
+            self._attr["args_to_forward"]["linestyle"] = ""
+            self._attr["args_to_forward"]["marker"] = "o"
+            if "point_size" in self._attr:
+                self._attr["args_to_forward"]["markersize"] = \
+                    self._attr["point_size"]
+
+    def fill_line(self, axis, line_2d, cmap_index):
+        """Fill the area under a line"""
+        drawstyle = line_2d.get_drawstyle()
+        if drawstyle.startswith("steps"):
+            # This has been fixed in upstream matplotlib
+            raise UserWarning("matplotlib does not support fill for step plots")
+
+        xdat, ydat = line_2d.get_data(orig=False)
+        axis.fill_between(
+            xdat,
+            axis.get_ylim()[0],
+            ydat,
+            facecolor=self._cmap.cmap(cmap_index),
+            alpha=AttrConf.ALPHA)
+
+    def plot_axis(self, axis, series_list, permute, concat, args_to_forward):
+        """Internal Method called to plot data (series_list) on a given axis"""
+        for i, (constraint, pivot) in enumerate(series_list):
+            result = constraint.result
+            line_2d_list = axis.plot(
+                result[pivot].index,
+                result[pivot].values,
+                color=self._cmap.cmap(i),
+                **args_to_forward
+            )
+
+            if self._attr["fill"]:
+                self.fill_line(axis, line_2d_list[0], i)
+
+            axis.set_title(self.make_title(constraint, pivot, permute, concat))
+
+            self.add_to_legend(i, line_2d_list[0], constraint, pivot, concat, permute)
diff --git a/trappy/plotter/PlotLayout.py b/trappy/plotter/PlotLayout.py
new file mode 100644
index 0000000..78bbda9
--- /dev/null
+++ b/trappy/plotter/PlotLayout.py
@@ -0,0 +1,153 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""This module implements functionality related to
+the arrangement of the plots on the underlying
+plotting backend.
+"""
+
+import matplotlib.pyplot as plt
+from trappy.plotter import AttrConf
+
+
+class PlotLayout(object):
+
+    """
+    :param cols: The number of columns to draw
+    :type cols: int
+
+    :param num_plots: The total number of plots
+    :type num_plots: int
+
+    The linear co-ordinate system :math:`[0, N_{plots}]` is
+    mapped to a 2-D coordinate system with math:`N_{rows}`
+    and :math:`N_{cols}` such that:
+
+    .. math::
+
+        N_{rows} = \\frac{N_{cols}}{N_{plots}}
+    """
+
+    def __init__(self, cols, num_plots, **kwargs):
+
+        self.cols = cols
+        self._attr = {}
+        self.num_plots = num_plots
+        self._single_plot = False
+        if self.num_plots == 0:
+            raise RuntimeError("No plots for the given constraints")
+
+        if self.num_plots < self.cols:
+            self.cols = self.num_plots
+        self.rows = (self.num_plots / self.cols)
+        # Avoid Extra Allocation (shows up in savefig!)
+        if self.num_plots % self.cols != 0:
+            self.rows += 1
+
+        self.usecol = False
+        self.userow = False
+        self._set_defaults()
+
+        for key in kwargs:
+            self._attr[key] = kwargs[key]
+
+        # Scale the plots if there is a single plot and
+        # Set boolean variables
+        if num_plots == 1:
+            self._single_plot = True
+            self._scale_plot()
+        elif self.rows == 1:
+            self.usecol = True
+        elif self.cols == 1:
+            self.userow = True
+            self._scale_plot()
+
+        self._attr["figure"], self._attr["axes"] = plt.subplots(
+            self.rows, self.cols, figsize=(
+                self._attr["width"] * self.cols,
+                self._attr["length"] * self.rows))
+
+        if self._attr['title']:
+            self._attr["figure"].suptitle(
+                    self._attr['title'],
+                    fontsize=AttrConf.TITLE_SIZE,
+                    horizontalalignment='center')
+
+    def _scale_plot(self):
+        """Scale the graph in one
+           plot per line use case"""
+
+        self._attr["width"] = int(self._attr["width"] * 2.5)
+        self._attr["length"] = int(self._attr["length"] * 1.25)
+
+    def _set_defaults(self):
+        """set the default attrs"""
+        self._attr["width"] = AttrConf.WIDTH
+        self._attr["length"] = AttrConf.LENGTH
+
+    def get_2d(self, linear_val):
+        """Convert Linear to 2D coordinates
+
+        :param linear_val: The value in 1-D
+            co-ordinate
+        :type linear_val: int
+
+        :return: Converted 2-D tuple
+        """
+        if self.usecol:
+            return linear_val % self.cols
+
+        if self.userow:
+            return linear_val % self.rows
+
+        val_x = linear_val % self.cols
+        val_y = linear_val / self.cols
+        return val_y, val_x
+
+    def finish(self, plot_index):
+        """Delete the empty cells
+
+        :param plot_index: Linear index at which the
+            last plot was created. This is used to
+            delete the leftover empty plots that
+            were generated.
+        :type plot_index: int
+        """
+        while plot_index < (self.rows * self.cols):
+            self._attr["figure"].delaxes(
+                self._attr["axes"][
+                    self.get_2d(plot_index)])
+            plot_index += 1
+
+    def get_axis(self, plot_index):
+        """Get the axes for the plots
+
+        :param plot_index: The index for
+            which the axis is required. This
+            internally is mapped to a 2-D co-ordinate
+
+        :return: :mod:`matplotlib.axes.Axes`
+            instance is returned
+        """
+        if self._single_plot:
+            return self._attr["axes"]
+        else:
+            return self._attr["axes"][self.get_2d(plot_index)]
+
+    def get_fig(self):
+        """Return the matplotlib figure object
+
+        :return: :mod:`matplotlib.figure`
+        """
+        return self._attr["figure"]
diff --git a/trappy/plotter/StaticPlot.py b/trappy/plotter/StaticPlot.py
new file mode 100644
index 0000000..9b58108
--- /dev/null
+++ b/trappy/plotter/StaticPlot.py
@@ -0,0 +1,289 @@
+#    Copyright 2016-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""Base matplotlib plotter module"""
+from abc import abstractmethod, ABCMeta
+from collections import defaultdict as ddict
+import matplotlib.pyplot as plt
+from trappy.plotter import AttrConf
+from trappy.plotter.Constraint import ConstraintManager
+from trappy.plotter.PlotLayout import PlotLayout
+from trappy.plotter.AbstractDataPlotter import AbstractDataPlotter
+from trappy.plotter.ColorMap import ColorMap
+
+
+
+class StaticPlot(AbstractDataPlotter):
+    """
+    This class uses :mod:`trappy.plotter.Constraint.Constraint` to
+    represent different permutations of input parameters. These
+    constraints are generated by creating an instance of
+    :mod:`trappy.plotter.Constraint.ConstraintManager`.
+
+    :param traces: The input data
+    :type traces: a list of :mod:`trappy.trace.FTrace`,
+        :mod:`trappy.trace.SysTrace`, :mod:`trappy.trace.BareTrace`
+        or :mod:`pandas.DataFrame` or a single instance of them.
+
+    :param column: specifies the name of the column to
+           be plotted.
+    :type column: (str, list(str))
+
+    :param templates: TRAPpy events
+
+        .. note::
+
+                This is not required if a :mod:`pandas.DataFrame` is
+                used
+
+    :type templates: :mod:`trappy.base.Base`
+
+    :param filters: Filter the column to be plotted as per the
+        specified criteria. For Example:
+        ::
+
+            filters =
+                    {
+                        "pid": [ 3338 ],
+                        "cpu": [0, 2, 4],
+                    }
+    :type filters: dict
+
+    :param per_line: Used to control the number of graphs
+        in each graph subplot row
+    :type per_line: int
+
+    :param concat: Draw all the pivots on a single graph
+    :type concat: bool
+
+    :param permute: Draw one plot for each of the traces specified
+    :type permute: bool
+
+    :param drawstyle: This argument is forwarded to the matplotlib
+        corresponding :func:`matplotlib.pyplot.plot` call
+
+        drawing style.
+
+        .. note::
+
+            step plots are not currently supported for filled
+            graphs
+
+    :param xlim: A tuple representing the upper and lower xlimits
+    :type xlim: tuple
+
+    :param ylim: A tuple representing the upper and lower ylimits
+    :type ylim: tuple
+
+    :param title: A title describing all the generated plots
+    :type title: str
+
+    :param style: Created pre-styled graphs loaded from
+        :mod:`trappy.plotter.AttrConf.MPL_STYLE`
+    :type style: bool
+
+    :param signals: A string of the type event_name:column
+        to indicate the value that needs to be plotted
+
+        .. note::
+
+            - Only one of `signals` or both `templates` and
+              `columns` should be specified
+            - Signals format won't work for :mod:`pandas.DataFrame`
+              input
+
+    :type signals: str
+
+    :param legend_ncol: A positive integer that represents the
+        number of columns in the legend
+    :type legend_ncol: int
+    """
+    __metaclass__ = ABCMeta
+
+    def __init__(self, traces, templates, **kwargs):
+        self._fig = None
+        self._layout = None
+        super(StaticPlot, self).__init__(traces=traces,
+                                         templates=templates)
+
+        self.set_defaults()
+
+        for key in kwargs:
+            if key in AttrConf.ARGS_TO_FORWARD:
+                self._attr["args_to_forward"][key] = kwargs[key]
+            else:
+                self._attr[key] = kwargs[key]
+
+        if "signals" in self._attr:
+            self._describe_signals()
+
+        self._check_data()
+
+        if "column" not in self._attr:
+            raise RuntimeError("Value Column not specified")
+
+        zip_constraints = not self._attr["permute"]
+        self.c_mgr = ConstraintManager(traces, self._attr["column"],
+                                       self.templates, self._attr["pivot"],
+                                       self._attr["filters"],
+                                       zip_constraints=zip_constraints)
+
+    def savefig(self, *args, **kwargs):
+        """Save the plot as a PNG fill. This calls into
+        :mod:`matplotlib.figure.savefig`
+        """
+
+        if self._fig is None:
+            self.view()
+        self._fig.savefig(*args, **kwargs)
+
+    @abstractmethod
+    def set_defaults(self):
+        """Sets the default attrs"""
+        self._attr["width"] = AttrConf.WIDTH
+        self._attr["length"] = AttrConf.LENGTH
+        self._attr["per_line"] = AttrConf.PER_LINE
+        self._attr["concat"] = AttrConf.CONCAT
+        self._attr["filters"] = {}
+        self._attr["style"] = True
+        self._attr["permute"] = False
+        self._attr["pivot"] = AttrConf.PIVOT
+        self._attr["xlim"] = AttrConf.XLIM
+        self._attr["ylim"] = AttrConf.YLIM
+        self._attr["title"] = AttrConf.TITLE
+        self._attr["args_to_forward"] = {}
+        self._attr["map_label"] = {}
+        self._attr["_legend_handles"] = []
+        self._attr["_legend_labels"] = []
+        self._attr["legend_ncol"] = AttrConf.LEGEND_NCOL
+
+    def view(self, test=False):
+        """Displays the graph"""
+
+        if test:
+            self._attr["style"] = True
+            AttrConf.MPL_STYLE["interactive"] = False
+
+        permute = self._attr["permute"] and not self._attr["concat"]
+        if self._attr["style"]:
+            with plt.rc_context(AttrConf.MPL_STYLE):
+                self._resolve(permute, self._attr["concat"])
+        else:
+            self._resolve(permute, self._attr["concat"])
+
+    def make_title(self, constraint, pivot, permute, concat):
+        """Generates a title string for an axis"""
+        if concat:
+            return str(constraint)
+
+        if permute:
+            return constraint.get_data_name()
+        elif pivot != AttrConf.PIVOT_VAL:
+            return "{0}: {1}".format(self._attr["pivot"], self._attr["map_label"].get(pivot, pivot))
+        else:
+            return ""
+
+    def add_to_legend(self, series_index, handle, constraint, pivot, concat, permute):
+        """
+        Add series handles and names to the legend
+        A handle is returned from a plot on an axis
+        e.g. Line2D from axis.plot()
+        """
+        self._attr["_legend_handles"][series_index] = handle
+        legend_labels = self._attr["_legend_labels"]
+
+        if concat and pivot == AttrConf.PIVOT_VAL:
+            legend_labels[series_index] = self._attr["column"]
+        elif concat:
+            legend_labels[series_index] = "{0}: {1}".format(
+                self._attr["pivot"],
+                self._attr["map_label"].get(pivot, pivot)
+            )
+        elif permute:
+            legend_labels[series_index] = constraint._template.name + ":" + constraint.column
+        else:
+            legend_labels[series_index] = str(constraint)
+
+    def _resolve(self, permute, concat):
+        """Determine what data to plot on which axis"""
+        pivot_vals, len_pivots = self.c_mgr.generate_pivots(permute)
+        pivot_vals = list(pivot_vals)
+
+        num_of_axes = len(self.c_mgr) if concat else len_pivots
+
+        # Create a 2D Layout
+        self._layout = PlotLayout(
+            self._attr["per_line"],
+            num_of_axes,
+            width=self._attr["width"],
+            length=self._attr["length"],
+            title=self._attr['title'])
+
+        self._fig = self._layout.get_fig()
+
+        # Determine what constraint to plot and the corresponding pivot value
+        if permute:
+            legend_len = self.c_mgr._max_len
+            pivots = [y for _, y in pivot_vals]
+            c_dict = {c : str(c) for c in self.c_mgr}
+            c_list = sorted(c_dict.items(), key=lambda x: (x[1].split(":")[-1], x[1].split(":")[0]))
+            constraints = [c[0] for c in c_list]
+            cp_pairs = [(c, p) for c in constraints for p in sorted(set(pivots))]
+        else:
+            legend_len = len_pivots if concat else len(self.c_mgr)
+            pivots = pivot_vals
+            cp_pairs = [(c, p) for c in self.c_mgr for p in pivots if p in c.result]
+
+        # Initialise legend data and colormap
+        self._attr["_legend_handles"] = [None] * legend_len
+        self._attr["_legend_labels"] = [None] * legend_len
+
+        if "colors" in self._attr:
+            self._cmap = ColorMap.rgb_cmap(self._attr["colors"])
+        else:
+            self._cmap = ColorMap(legend_len)
+
+        # Group constraints/series with the axis they are to be plotted on
+        figure_data = ddict(list)
+        for i, (constraint, pivot) in enumerate(cp_pairs):
+            axis = self._layout.get_axis(constraint.trace_index if concat else i)
+            figure_data[axis].append((constraint, pivot))
+
+        # Plot each axis
+        for axis, series_list in figure_data.iteritems():
+            self.plot_axis(
+                axis,
+                series_list,
+                permute,
+                self._attr["concat"],
+                self._attr["args_to_forward"]
+            )
+            if self._attr["xlim"]:
+                axis.set_xlim(self._attr["xlim"])
+            if self._attr["ylim"]:
+                axis.set_ylim(self._attr["ylim"])
+
+        # Show legend
+        legend = self._fig.legend(self._attr["_legend_handles"],
+                         self._attr["_legend_labels"],
+                         loc='lower center',
+                         ncol=self._attr["legend_ncol"],
+                         borderaxespad=0.)
+        legend.get_frame().set_facecolor('#F4F4F4')
+
+        self._layout.finish(num_of_axes)
+
+    def plot_axis(self, axis, series_list, permute, concat, args_to_forward):
+        """Internal Method called to plot data (series_list) on a given axis"""
+        raise NotImplementedError("Method Not Implemented")
diff --git a/trappy/plotter/Utils.py b/trappy/plotter/Utils.py
new file mode 100644
index 0000000..2a47580
--- /dev/null
+++ b/trappy/plotter/Utils.py
@@ -0,0 +1,97 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Utils module has generic utils that will be used across
+objects
+"""
+import collections
+import warnings
+from trappy.utils import listify
+
+
+def normalize_list(val, lst):
+    """Normalize a unitary list"""
+
+    if len(lst) != 1:
+        raise RuntimeError("Cannot Normalize a non-unitary list")
+
+    return lst * val
+
+
+def decolonize(val):
+    """Remove the colon at the end of the word
+    This will be used by the unique word of
+    template class to sanitize attr accesses
+    """
+
+    return val.strip(":")
+
+
+def get_trace_event_data(trace, execnames=None, pids=None):
+    """Create a list of objects that can be consumed by EventPlot to plot
+    task residency like kernelshark
+
+    """
+
+    if execnames:
+        execnames = listify(execnames)
+
+    if pids:
+        pids = listify(pids)
+
+    data = collections.defaultdict(list)
+    pmap = {}
+
+    data_frame = trace.sched_switch.data_frame
+    start_idx = data_frame.index.values[0]
+    end_idx = data_frame.index.values[-1]
+
+    procs = set()
+
+    for index, row in data_frame.iterrows():
+        prev_pid = row["prev_pid"]
+        next_pid = row["next_pid"]
+        next_comm = row["next_comm"]
+
+        if prev_pid in pmap:
+            name = pmap[prev_pid]
+            data[name][-1][1] = index
+            del pmap[prev_pid]
+
+        name = "{}-{}".format(next_comm, next_pid)
+
+        if next_pid in pmap:
+            # Corrupted trace probably due to dropped events.  We
+            # don't know when the pid in pmap finished.  We just
+            # ignore it and don't plot it
+            warn_str = "Corrupted trace (dropped events) for PID {} at time {}". \
+                       format(next_pid, index)
+            warnings.warn(warn_str)
+            del pmap[next_pid]
+            del data[name][-1]
+
+        if next_pid != 0 and not next_comm.startswith("migration"):
+
+            if execnames and next_comm not in execnames:
+                continue
+
+            if pids and next_pid not in pids:
+                continue
+
+            data[name].append([index, end_idx, row["__cpu"]])
+            pmap[next_pid] = name
+            procs.add(name)
+
+    return data, procs, [start_idx, end_idx]
diff --git a/trappy/plotter/__init__.py b/trappy/plotter/__init__.py
new file mode 100644
index 0000000..a628b9b
--- /dev/null
+++ b/trappy/plotter/__init__.py
@@ -0,0 +1,83 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Init Module for the Plotter Code"""
+
+
+import pandas as pd
+import LinePlot
+import AttrConf
+try:
+    import trappy.plotter.EventPlot
+except ImportError:
+    pass
+import Utils
+import trappy
+import IPythonConf
+
+def register_forwarding_arg(arg_name):
+    """Allows the user to register args to
+       be forwarded to matplotlib
+
+    :param arg_name: The arg to register
+    :type arg_name: str
+    """
+    if arg_name not in AttrConf.ARGS_TO_FORWARD:
+        AttrConf.ARGS_TO_FORWARD.append(arg_name)
+
+def unregister_forwarding_arg(arg_name):
+    """Unregisters arg_name from being passed to
+       plotter matplotlib calls
+
+    :param arg_name: The arg to register
+    :type arg_name: str
+    """
+    try:
+        AttrConf.ARGS_TO_FORWARD.remove(arg_name)
+    except ValueError:
+        pass
+
+def plot_trace(trace,
+               execnames=None,
+               pids=None):
+    """Creates a kernelshark like plot of the trace file
+
+    :param trace: The path to the trace or a trace object
+    :type trace: str, :mod:`trappy.trace.FTrace`, :mod:`trappy.trace.SysTrace`
+        or :mod:`trappy.trace.BareTrace`.
+
+    :param execnames: List of execnames to be filtered. If not
+        specified all execnames will be plotted
+    :type execnames: list, str
+
+    :param pids: List of pids to be filtered. If not specified
+        all pids will be plotted
+    :type pids: list, str
+    """
+
+    if not IPythonConf.check_ipython():
+        raise RuntimeError("plot_trace needs ipython environment")
+
+    if not isinstance(trace, trappy.BareTrace):
+        if trace.endswith("html"):
+            trace = trappy.SysTrace(trace)
+        else:
+            trace = trappy.FTrace(trace)
+
+    data, procs, domain = Utils.get_trace_event_data(trace, execnames, pids)
+    trace_graph = EventPlot.EventPlot(data, procs, domain,
+                                      lane_prefix="CPU :",
+                                      num_lanes=int(trace._cpus))
+    trace_graph.view()
diff --git a/trappy/plotter/css/EventPlot.css b/trappy/plotter/css/EventPlot.css
new file mode 100644
index 0000000..98bb51d
--- /dev/null
+++ b/trappy/plotter/css/EventPlot.css
@@ -0,0 +1,82 @@
+/*
+ *    Copyright 2015-2017 ARM Limited
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+.d3-tip {
+  line-height: 1;
+  padding: 12px;
+  background: rgba(0, 0, 0, 0.6);
+  color: #fff;
+  border-radius: 2px;
+  position: absolute !important;
+  z-index: 99999;
+}
+
+.d3-tip:after {
+  box-sizing: border-box;
+  pointer-events: none;
+  display: inline;
+  font-size: 10px;
+  width: 100%;
+  line-height: 1;
+  color: rgba(0, 0, 0, 0.6);
+  content: "\25BC";
+  position: absolute !important;
+  z-index: 99999;
+  text-align: center;
+}
+
+.d3-tip.n:after {
+  margin: -1px 0 0 0;
+  top: 100%;
+  left: 0;
+}
+
+.contextRect {
+  fill: lightgray;
+  fill-opacity: 0.5;
+  stroke: black;
+  stroke-width: 1;
+  stroke-opacity: 1;
+  pointer-events: none;
+  shape-rendering: crispEdges;
+}
+
+.chart {
+  shape-rendering: crispEdges;
+}
+
+.mini text {
+  font: 9px sans-serif;
+}
+
+.main text {
+  font: 12px sans-serif;
+}
+
+.axis line, .axis path {
+  stroke: black;
+}
+
+.miniItem {
+  stroke-width: 8;
+}
+
+.brush .extent {
+
+  stroke: #000;
+  fill-opacity: .125;
+  shape-rendering: crispEdges;
+}
diff --git a/trappy/plotter/css/EventPlot_help.jpg b/trappy/plotter/css/EventPlot_help.jpg
new file mode 100644
index 0000000..712eeae
--- /dev/null
+++ b/trappy/plotter/css/EventPlot_help.jpg
diff --git a/trappy/plotter/js/EventPlot.js b/trappy/plotter/js/EventPlot.js
new file mode 100644
index 0000000..14a0024
--- /dev/null
+++ b/trappy/plotter/js/EventPlot.js
@@ -0,0 +1,1065 @@
+/*
+ *    Copyright 2015-2017 ARM Limited
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+var EventPlot = (function () {
+
+    /* EventPlot receives data that is hashed by the keys
+     * and each element in the data is sorted by start time.
+     * Since events on each lane are mutually exclusive, they
+     * they are also sorted by the end time. We use this information
+     * and binary search on the input data for filtering events
+     * This maintains filtering complexity to O[KLogN]
+     */
+
+    var GUIDER_WIDTH = 2;
+
+    infoProps = {
+        START_GUIDER_COLOR: "green",
+        END_GUIDER_COLOR: "red",
+        DELTA_COLOR: "blue",
+        GUIDER_WIDTH: 2,
+        TOP_MARGIN: 20,
+        HEIGHT: 30,
+        START_PREFIX: "A = ",
+        END_PREFIX: "B = ",
+        DELTA_PREFIX: "A - B = ",
+        XPAD: 10,
+        YPAD: 5,
+        BOX_BUFFER: 2,
+        BOX_WIDTH_RATIO: 0.6
+    }
+
+    var search_data = function (data, key, value, left, right) {
+
+        var mid;
+
+        while (left < right) {
+
+            mid = Math.floor((left + right) / 2)
+            if (data[mid][key] > value)
+                right = mid;
+            else
+                left = mid + 1;
+        }
+        return left;
+    }
+
+
+    /* Return the information for the current process
+     * pointed by the mouse
+     */
+    var getCurrentInfo = function(ePlot, x0, y0) {
+
+        for (name in ePlot.items) {
+
+            var data = ePlot.items[name];
+            var xMax = ePlot.zoomScale.domain()[1];
+            var right = search_data(data, 0, xMax, 0, data.length - 1);
+            var left = search_data(data, 1, x0, 0, right);
+
+            if (data) {
+                var candidate = data[left];
+                if (candidate[0] <= x0 &&
+                        candidate[1] >= x0 &&
+                        candidate[2] == y0)
+                    return {
+                            name: name,
+                            info: candidate
+                        };
+            }
+        }
+    }
+
+    var generate = function (div_name, base, chart_data) {
+
+        var margin, brush, x, ext, yMain, chart, main,
+            mainAxis,
+            itemRects, items, colourAxis, tip, lanes;
+
+        var process_chart_data = function (d) {
+            items = d.data;
+            lanes = d.lanes;
+            var names = d.keys;
+            var showSummary = d.showSummary;
+            var div = $("#" + div_name);
+
+            margin = {
+                    top: 15,
+                    right: 15,
+                    bottom: 15,
+                    left: 70
+                }, width = div.width() - margin.left - margin.right,
+
+                mainHeight = 50 * lanes.length - margin.top - margin.bottom;
+
+            x = d3.scale.linear()
+                .domain(d.xDomain)
+                .range([0, width]);
+
+            var zoomScale = d3.scale.linear()
+                .domain(d.xDomain)
+                .range([0, width]);
+
+            var xMin = x.domain()[0];
+            var xMax = x.domain()[1];
+
+            if (!d.colorMap) {
+                // Colour Ordinal scale. Uses Category20 Colors
+                colours = d3.scale.category20().range();
+            } else {
+                // Use colours provided by user
+                var colours = [];
+                for (var i in names)
+                    if (names[i] in d.colorMap)
+                        colours.push(d.colorMap[names[i]]);
+            }
+            colourAxis = d3.scale.ordinal()
+                .range(colours)
+                .domain(names);
+
+            brushScale = d3.scale.linear()
+                .range([0, width]);
+            ext = d3.extent(lanes, function (d) {
+                return d.id;
+            });
+            yMain = d3.scale.linear()
+                .domain([ext[0], ext[1] +
+                    1
+                ])
+                .range([0, mainHeight]);
+
+
+            var ePlot;
+
+
+            $("#" + div_name)
+                .append('<div class="pull-right">' +
+                    '<button type="button" class="btn btn-sm btn-info" ' +
+                    'onclick="EventPlot.create_help_dialog(' + base +
+                    ')">Help</button></div>')
+
+            var iDesc = drawInfo(div_name, margin, width);
+
+            chart = d3.select('#' + div_name)
+                .append('svg:svg')
+                .attr('width', width + margin.right +
+                    margin.left)
+                .attr('height', mainHeight + margin.top +
+                    margin.bottom + 5)
+                .attr('class', 'chart')
+
+
+            main = chart.append('g')
+                .attr('transform', 'translate(' + margin.left +
+                    ',' + margin.top + ')')
+                .attr('width', width)
+                .attr('height', mainHeight)
+                .attr('class', 'main')
+
+            main.append('g')
+                .selectAll('.laneLines')
+                .data(lanes)
+                .enter()
+                .append('line')
+                .attr('x1', 0)
+                .attr('y1', function (d) {
+                    return d3.round(yMain(d.id)) + 0.5;
+                })
+                .attr('x2', width)
+                .attr('y2', function (d) {
+                    return d3.round(yMain(d.id)) + 0.5;
+                })
+                .attr('stroke', function (d) {
+                    return d.label === '' ? 'white' :
+                        'lightgray'
+                });
+
+            main.append('g')
+                .selectAll('.laneText')
+                .data(lanes)
+                .enter()
+                .append('text')
+                .attr('x', 0)
+                .text(function (d) {
+                    return d.label;
+                })
+                .attr('y', function (d) {
+                    return yMain(d.id + .5);
+                })
+                .attr('dy', '0.5ex')
+                .attr('text-anchor', 'end')
+                .attr('class', 'laneText');
+
+            mainAxis = d3.svg.axis()
+                .scale(brushScale)
+                .orient('bottom');
+
+            tip = d3.tip()
+                .attr('class', 'd3-tip')
+                .html(function (d) {
+                    return "<span style='color:white'>" +
+                        d.name + "</span>";
+                })
+
+            main.append('g')
+                .attr('transform', 'translate(0,' +
+                    mainHeight + ')')
+                .attr('class', 'main axis')
+                .call(mainAxis);
+
+            var ePlot;
+
+            ePlot = {
+                div: div,
+                div_name: div_name,
+                margin: margin,
+                chart: chart,
+                mainHeight: mainHeight,
+                width: width,
+                x: x,
+                brushScale: brushScale,
+                ext: ext,
+                yMain: yMain,
+                main: main,
+                mainAxis: mainAxis,
+                items: items,
+                colourAxis: colourAxis,
+                tip: tip,
+                lanes: lanes,
+                names: names,
+                iDesc: iDesc,
+            };
+            ePlot.zoomScale = zoomScale;
+
+            if (showSummary)
+                drawMini(ePlot);
+
+            var outgoing;
+            var zoomed = function () {
+
+                if (zoomScale.domain()[0] < xMin) {
+                    zoom.translate([zoom.translate()[
+                            0] - zoomScale(
+                            xMin) +
+                        zoomScale.range()[0],
+                        zoom.translate()[
+                            1]
+                    ]);
+                } else if (zoomScale.domain()[1] >
+                    xMax) {
+                    zoom.translate([zoom.translate()[
+                            0] - zoomScale(
+                            xMax) +
+                        zoomScale.range()[1],
+                        zoom.translate()[
+                            1]
+                    ]);
+
+                }
+
+                outgoing = main.selectAll(".mItem")
+                    .attr("visibility", "hidden");
+                drawMain(ePlot, zoomScale.domain()[0],
+                    zoomScale.domain()[1]);
+                if (showSummary) {
+                    brush.extent(zoomScale.domain());
+                    ePlot.mini.select(".brush")
+                        .call(
+                            brush);
+                }
+
+                brushScale.domain(zoomScale.domain());
+                ePlot.main.select('.main.axis')
+                    .call(ePlot.mainAxis)
+
+                updateInfo(ePlot);
+            };
+
+            var rightClickCtrlAltHandler = function(x0, y0) {
+
+                x0 = ePlot.zoomScale.invert(x0);
+                y0 = Math.floor(ePlot.yMain.invert(y0));
+                var current = getCurrentInfo(ePlot, x0, y0);
+
+                if (current) {
+                    ePlot.iDesc.currentProc.text(current.name)
+                    ePlot.iDesc.currentInfo.text(
+                        current.info[0].toFixed(6)
+                        + " to " +
+                        current.info[1].toFixed(6) +
+                        " (" + (current.info[1] - current.info[0])
+                        .toFixed(6) + ")")
+
+                    removeContextRect(ePlot);
+                    ePlot.contextRect = drawContextRect(ePlot, current.info[0], current.info[1], current.info[2], true)
+                    ePlot.iDesc.currentDisp.attr("stroke", ePlot.colourAxis(current.name));
+                }
+            }
+
+            var contextMenuHandler = function() {
+
+                var e = d3.event;
+                var x0 = d3.mouse(this)[0] - ePlot.margin.left;
+                var y0 = d3.mouse(this)[1] - ePlot.margin.top;
+
+                if (e.ctrlKey && e.altKey)
+                    rightClickCtrlAltHandler(x0, y0);
+
+                else if (e.ctrlKey) {
+
+                    if (ePlot.endGuider)
+                        ePlot.endGuider = ePlot.endGuider.remove();
+
+                    ePlot.endGuider = drawVerticalLine(ePlot, x0,
+                        infoProps.END_GUIDER_COLOR, "B");
+                    ePlot.endGuider._x_pos = ePlot.zoomScale.invert(x0);
+                    iDesc.endText.text(infoProps.END_PREFIX + ePlot.endGuider._x_pos.toFixed(6))
+
+                } else {
+
+                    if (ePlot.startGuider)
+                        ePlot.startGuider = ePlot.startGuider.remove();
+
+                    ePlot.startGuider = drawVerticalLine(ePlot, x0,
+                        infoProps.START_GUIDER_COLOR, "A");
+                    ePlot.startGuider._x_pos = ePlot.zoomScale.invert(x0);
+                    iDesc.startText.text(infoProps.START_PREFIX + ePlot.startGuider._x_pos.toFixed(6))
+                }
+
+                if (ePlot.endGuider && ePlot.startGuider)
+                    iDesc.deltaText.text(infoProps.DELTA_PREFIX +
+                            (ePlot.endGuider._x_pos - ePlot.startGuider._x_pos)
+                            .toFixed(6)
+                        )
+
+                d3.event.preventDefault();
+            }
+
+            chart.on("contextmenu", contextMenuHandler);
+
+            if (showSummary) {
+                var _brushed_event = function () {
+                    main.selectAll("path")
+                        .remove();
+                    var brush_xmin = brush.extent()[0];
+                    var brush_xmax = brush.extent()[1];
+
+                    var t = zoom.translate(),
+                        new_domain = brush.extent(),
+                        scale;
+
+                    /*
+                     *    scale = x.range()[1] - x.range[0]
+                     *          --------------------------
+                     *          x(x.domain()[1] - x.domain()[0])
+                     *
+                     *                             _                                   _
+                     *  new_domain[0] =  x.invert | x.range()[0]  -   z.translate()[0]  |
+                     *                            |                 ------------------- |
+                     *                            |_                     z.scale()     _|
+                     *
+                     *
+                     *
+                     *  translate[0] = x.range()[0] - x(new_domain[0])) * zoom.scale()
+                     */
+
+                    scale = (width) / x(x.domain()[0] +
+                        new_domain[1] -
+                        new_domain[0]);
+                    zoom.scale(scale);
+                    t[0] = x.range()[0] - (x(new_domain[
+                        0]) * scale);
+                    zoom.translate(t);
+
+
+                    brushScale.domain(brush.extent())
+                    drawMain(ePlot, brush_xmin,
+                        brush_xmax);
+                    ePlot.main.select('.main.axis')
+                        .call(ePlot.mainAxis)
+
+                    updateInfo(ePlot);
+                };
+
+                brush = d3.svg.brush()
+                    .x(x)
+                    .extent(x.domain())
+                    .on("brush", _brushed_event);
+
+                ePlot.mini.append('g')
+                    .attr('class', 'brush')
+                    .call(brush)
+                    .selectAll('rect')
+                    .attr('y', 1)
+                    .attr('height', ePlot.miniHeight - 1);
+            }
+
+            var zoom = d3.behavior.zoom()
+                .x(zoomScale)
+                .on(
+                    "zoom", zoomed)
+                .on("zoomend", function () {
+                    if (outgoing)
+                        outgoing.remove()
+                })
+                .scaleExtent([1, 4096]);
+            chart.call(zoom);
+
+            drawMain(ePlot, xMin, xMax);
+            ePlot.main.select('.main.axis')
+                .call(ePlot.mainAxis)
+
+            var resize = function() {
+
+                var width = div.width() - margin.left
+                    - margin.right;
+
+                /* Update scale ranges */
+                x.range([0, width]);
+                zoomScale.range([0, width]);
+                brushScale.range([0, width]);
+                ePlot.width = width;
+
+                resize_main(ePlot);
+                resize_info(ePlot);
+                resize_mini(ePlot);
+                zoomed();
+
+            }
+
+            d3.select(window)
+                .on("resize." + ePlot.div_name, resize)
+
+            return ePlot;
+
+        }
+
+        /*
+         * If chart_data is passed, process data directly
+         */
+        process_chart_data(chart_data);
+    };
+
+
+    var resize_mini = function(ePlot) {
+
+        d3.select(ePlot.mini.node().parentNode)
+            .attr("width", ePlot.div.width());
+        ePlot.iDesc.info_svg
+            .attr("width", ePlot.div.width());
+        ePlot.mini.selectAll("line")
+            .attr("x2", ePlot.width);
+        ePlot.mini.call(ePlot.miniAxis);
+        ePlot.mini.selectAll(".miniItem").remove();
+        drawMiniPaths(ePlot);
+    }
+
+    var resize_main = function(ePlot) {
+
+        d3.select(ePlot.main.node().parentNode)
+            .attr("width", ePlot.div.width());
+        ePlot.main.selectAll("line")
+            .attr("x2", ePlot.width);
+    }
+
+    var resize_info = function(ePlot) {
+
+        var width_box_one = infoProps.BOX_WIDTH_RATIO * ePlot.width;
+        var width_box_two = ePlot.width - width_box_one;
+
+        ePlot.iDesc.info
+            .attr("width", width);
+        ePlot.iDesc.guiderInfo
+            .attr("width", width_box_one - infoProps.BOX_BUFFER);
+        ePlot.iDesc.currentDisp
+            .attr("x", width_box_one + infoProps.BOX_BUFFER);
+        ePlot.iDesc.currentDisp
+            .attr("width", width_box_two - infoProps.BOX_BUFFER);
+        ePlot.iDesc.deltaText
+            .attr("x", (width_box_one / 2) - infoProps.XPAD)
+        ePlot.iDesc.endText
+            .attr("x", width_box_one - infoProps.XPAD)
+        ePlot.iDesc.currentProc
+            .attr("x", width_box_one + infoProps.XPAD + infoProps.BOX_BUFFER)
+        ePlot.iDesc.currentInfo
+            .attr("x", ePlot.width - infoProps.XPAD)
+    }
+
+    var drawInfo = function (div_name, margin, width) {
+
+        var infoHeight = 30;
+        var _top = 20;
+        var LINE_WIDTH = 2
+
+        var iDesc = {};
+
+        var width_box_one = infoProps.BOX_WIDTH_RATIO * width;
+        var width_box_two = width - width_box_one
+
+        iDesc.info_svg = d3.select("#" + div_name)
+            .append(
+                "svg:svg")
+            .attr('width', width + margin.right +
+                margin.left)
+            .attr('height', infoHeight + infoProps.TOP_MARGIN + LINE_WIDTH)
+            .attr('class', 'info')
+
+        iDesc.info = iDesc.info_svg.append("g")
+            .attr("transform", "translate(" + margin.left +
+                 "," + infoProps.TOP_MARGIN + ")")
+            .attr('width', width)
+            .attr("class", "main")
+            .attr('height', infoProps.HEIGHT)
+
+        iDesc.guiderInfo = iDesc.info.append("rect")
+            .attr("x", 0)
+            .attr("y", 0)
+            .attr("width", width_box_one - infoProps.BOX_BUFFER)
+            .attr("height", infoHeight)
+            .attr("stroke", "lightgray")
+            .attr("fill", "none")
+            .attr("stroke-width", 1);
+
+        iDesc.currentDisp = iDesc.info.append("rect")
+            .attr("x", width_box_one + infoProps.BOX_BUFFER)
+            .attr("y", 0)
+            .attr("width", width_box_two - infoProps.BOX_BUFFER)
+            .attr("height", infoHeight)
+            .attr("stroke", "lightgray")
+            .attr("fill", "none")
+            .attr("stroke-width", 1);
+
+       iDesc.startText = iDesc.info.append("text")
+            .text("")
+            .attr("x", infoProps.XPAD)
+            .attr("y", infoProps.HEIGHT / 2 + infoProps.YPAD)
+            .attr("fill", infoProps.START_GUIDER_COLOR);
+
+       iDesc.deltaText = iDesc.info.append("text")
+            .text("")
+            .attr("x", (width_box_one / 2) - infoProps.XPAD)
+            .attr("y", infoProps.HEIGHT / 2 + infoProps.YPAD)
+            .attr("fill", infoProps.DELTA_COLOR);
+
+       iDesc.endText = iDesc.info.append("text")
+            .text("")
+            .attr("x", width_box_one - infoProps.XPAD)
+            .attr("text-anchor", "end")
+            .attr("y", infoProps.HEIGHT / 2 + infoProps.YPAD)
+            .attr("fill", infoProps.END_GUIDER_COLOR);
+
+        iDesc.currentProc = iDesc.info.append("text")
+            .text("")
+            .attr("x", width_box_one + infoProps.XPAD + infoProps.BOX_BUFFER)
+            .attr("text-anchor", "start")
+            .attr("y", infoProps.HEIGHT / 2 + infoProps.YPAD)
+
+        iDesc.currentInfo = iDesc.info.append("text")
+            .text("")
+            .attr("x", width - infoProps.XPAD)
+            .attr("text-anchor", "end")
+            .attr("y", infoProps.HEIGHT / 2 + infoProps.YPAD)
+
+        return iDesc;
+
+    }
+
+    var drawVerticalLine = function (ePlot, x, color, text) {
+
+        var line = ePlot.main.append("g")
+
+        line.append("line")
+            .style("stroke", color)
+            .style("stroke-width", GUIDER_WIDTH)
+            .attr("x1", x)
+            .attr("x2", x)
+            .attr("y1", 0)
+            .attr("y2", ePlot.mainHeight)
+
+        line.append("text")
+            .text(text)
+            .attr("y", -1)
+            .attr("x", x)
+            .attr("text-anchor", "middle")
+            .attr("fill", color)
+
+        return line;
+    };
+
+    var removeContextRect = function(ePlot) {
+        if (ePlot.contextRect && ePlot.contextRect.rect)
+            ePlot.contextRect.rect.remove();
+    }
+
+    var drawContextRect = function (ePlot, x0, x1, y, animate) {
+
+        var xMin = ePlot.zoomScale.domain()[0];
+        var xMax = ePlot.zoomScale.domain()[1];
+        var bounds = [Math.max(x0, xMin), Math.min(x1,
+                xMax)]
+
+        if (bounds[0] >= bounds[1])
+            return {
+                rect: false,
+                x0: x0,
+                x1: x1,
+                y: y,
+            }
+
+        var rect = ePlot.main.selectAll(".contextRect").data([""])
+
+        if (animate)
+            rect.enter().append("rect")
+                .attr("x", ePlot.zoomScale(bounds[0]))
+                .attr("y", ePlot.yMain(y))
+                .attr("height", ePlot.yMain(1))
+                .attr("class", "contextRect")
+                .attr("width", 0)
+                .transition()
+                .attr("width", ePlot.zoomScale(bounds[1]) - ePlot.zoomScale(bounds[0]))
+        else
+            rect.enter().append("rect")
+                .attr("x", ePlot.zoomScale(bounds[0]))
+                .attr("y", ePlot.yMain(y))
+                .attr("class", "contextRect")
+                .attr("height", ePlot.yMain(1))
+                .attr("width", ePlot.zoomScale(bounds[1]) - ePlot.zoomScale(bounds[0]))
+
+        return {
+                rect: rect,
+                x0: x0,
+                x1: x1,
+                y: y,
+        }
+    }
+
+    var checkGuiderRange = function (ePlot, xpos) {
+
+        if (xpos >= ePlot.zoomScale.domain()[0] &&
+            xpos <= ePlot.zoomScale.domain()[1])
+            return true;
+        else
+            return false;
+    }
+
+    var updateInfo = function (ePlot) {
+
+        if (ePlot.endGuider) {
+
+            var xpos = ePlot.endGuider._x_pos;
+            ePlot.endGuider.remove();
+
+            if (checkGuiderRange(ePlot, xpos)) {
+                ePlot.endGuider = drawVerticalLine(ePlot, ePlot.zoomScale(xpos),
+                    infoProps.END_GUIDER_COLOR, "B");
+                ePlot.endGuider._x_pos = xpos;
+            }
+        }
+
+        if (ePlot.startGuider) {
+
+            var xpos = ePlot.startGuider._x_pos;
+            ePlot.startGuider.remove();
+
+            if (checkGuiderRange(ePlot, xpos)) {
+                ePlot.startGuider = drawVerticalLine(ePlot, ePlot.zoomScale(xpos),
+                    infoProps.START_GUIDER_COLOR, "A");
+                ePlot.startGuider._x_pos = xpos
+            }
+        }
+
+        if (ePlot.contextRect) {
+            removeContextRect(ePlot);
+            ePlot.contextRect = drawContextRect(ePlot, ePlot.contextRect.x0,
+                                    ePlot.contextRect.x1,
+                                    ePlot.contextRect.y,
+                                    false);
+        }
+
+    }
+
+    var drawMiniPaths = function(ePlot) {
+
+        ePlot.mini.append('g')
+            .selectAll('miniItems')
+            .data(getPaths(ePlot, ePlot.x, ePlot.yMini))
+            .enter()
+            .append('path')
+            .attr('class', function (d) {
+                return 'miniItem'
+            })
+            .attr('d', function (d) {
+                return d.path;
+            })
+            .attr("stroke", function (d) {
+                return d.color
+            })
+            .attr("class", "miniItem");
+    }
+
+    var drawMini = function (ePlot) {
+
+        var miniHeight = ePlot.lanes.length * 12 + 50;
+
+        var miniAxis = d3.svg.axis()
+            .scale(ePlot.x)
+            .orient('bottom');
+
+        var yMini = d3.scale.linear()
+            .domain([ePlot.ext[0], ePlot.ext[1] +
+                1
+            ])
+            .range([0, miniHeight]);
+
+        ePlot.yMini = yMini;
+        ePlot.miniAxis = miniAxis;
+        ePlot.miniHeight = miniHeight;
+
+        var summary = d3.select("#" + ePlot.div_name)
+            .append(
+                "svg:svg")
+            .attr('width', ePlot.width + ePlot.margin.right +
+                ePlot.margin.left)
+            .attr('height', miniHeight + ePlot.margin.bottom +
+                ePlot.margin.top)
+            .attr('class', 'chart')
+
+        var mini = summary.append('g')
+            .attr("transform", "translate(" + ePlot.margin.left +
+                "," + ePlot.margin.top + ")")
+            .attr('width', ePlot.width)
+            .attr('height', ePlot.miniHeight)
+            .attr('class', 'mini');
+
+        mini.append('g')
+            .selectAll('.laneLines')
+            .data(ePlot.lanes)
+            .enter()
+            .append('line')
+            .attr('x1', 0)
+            .attr('y1', function (d) {
+                return d3.round(ePlot.yMini(d.id)) + 0.5;
+            })
+            .attr('x2', ePlot.width)
+            .attr('y2', function (d) {
+                return d3.round(ePlot.yMini(d.id)) + 0.5;
+            })
+            .attr('stroke', function (d) {
+                return d.label === '' ? 'white' :
+                    'lightgray'
+            });
+
+        mini.append('g')
+            .attr('transform', 'translate(0,' +
+                ePlot.miniHeight + ')')
+            .attr('class', 'axis')
+            .call(ePlot.miniAxis);
+
+        ePlot.mini = mini
+        drawMiniPaths(ePlot);
+
+        mini.append('g')
+            .selectAll('.laneText')
+            .data(ePlot.lanes)
+            .enter()
+            .append('text')
+            .text(function (d) {
+                return d.label;
+            })
+            .attr('x', -10)
+            .attr('y', function (d) {
+                return ePlot.yMini(d.id + .5);
+            })
+            .attr('dy', '0.5ex')
+            .attr('text-anchor', 'end')
+            .attr('class', 'laneText');
+
+        return mini;
+    };
+
+
+    var drawMain = function (ePlot, xMin, xMax) {
+
+        var rects, labels;
+        var dMin = 10000;
+        var paths = getPaths(ePlot, ePlot.zoomScale, ePlot.yMain);
+        ePlot.brushScale.domain([xMin, xMax]);
+
+        if (paths.length == 0)
+            return;
+
+        ePlot.main
+            .selectAll('mainItems')
+            .data(paths)
+            .enter()
+            .append('path')
+            .attr("shape-rendering", "crispEdges")
+            .attr('d', function (d) {
+                return d.path;
+            })
+            .attr("class", "mItem")
+            .attr("stroke-width", function(d) {
+               return  0.8 * ePlot.yMain(1);
+            })
+            .attr("stroke", function (d) {
+                return d.color
+            })
+            .call(ePlot.tip)
+            .on("mouseover", ePlot.tip.show)
+            .on('mouseout', ePlot.tip.hide)
+            .on('mousemove', function () {
+                var xDisp = parseFloat(ePlot.tip.style("width")) /
+                    2.0
+                ePlot.tip.style("left", (d3.event.pageX - xDisp) +
+                        "px")
+                    .style("top", Math.max(0, d3.event.pageY -
+                        47) + "px");
+            })
+    };
+
+
+   function  _handle_equality(d, xMin, xMax, x, y, lane) {
+        var offset = 0.5 * y(1) + 0.5
+        var bounds = [Math.max(d[0], xMin), Math.min(d[1],
+            xMax)]
+        if (bounds[0] < bounds[1])
+            return 'M' + ' ' + x(bounds[0]) + ' ' + (y(lane) + offset) + ' H ' +  x(bounds[1]);
+        else
+            return '';
+    };
+
+    function _process(path, d, xMin, xMax, x, y, offset, lane) {
+
+        var start = d[0];
+        if (start < xMin)
+            start = xMin;
+        var end = d[1];
+        if (end > xMax)
+            end = xMax;
+
+        start = x(start);
+        end = x(end);
+
+        if ((end - start) < 0.01)
+            return path;
+        else if ((end - start) < 1)
+            end = start + 1;
+
+        path += 'M' + ' ' + start + ' ' + (y(lane) + offset) + ' H ' +  end;
+        return path;
+    }
+
+    var _get_path = function(new_data, xMin, xMax, offset, x, y, stride) {
+
+            var path = ''
+            var max_rects = 2000;
+
+            for (var lane in new_data) {
+                var data = new_data[lane];
+                var right = search_data(data, 0, xMax, 0, data.length - 1)
+                var left = search_data(data, 1, xMin, 0, right)
+
+                //Handle Equality
+                if (left == right)
+                    path += _handle_equality(data[left], xMin, xMax, x, y, lane);
+
+                data = data.slice(left, right + 1);
+
+                var stride_length = 1;
+                if (stride)
+                    stride_length = Math.max(Math.ceil(data.length / max_rects), 1);
+
+                for (var i = 0; i < data.length; i+= stride_length)
+                    path = _process(path, data[i], xMin, xMax, x, y, offset, lane);
+        }
+
+        return path;
+    }
+
+    var getPaths = function (ePlot, x, y, stride) {
+
+        var keys = ePlot.names;
+        var items = ePlot.items;
+        var colourAxis = ePlot.colourAxis;
+
+        var xMin = x.domain()[0];
+        var xMax = x.domain()[1];
+        var paths = {},
+            d, offset = 0.5 * y(1) + 0.5,
+            result = [];
+
+        for (var i in keys) {
+            var name = keys[i];
+            var path = _get_path(items[name], xMin, xMax, offset, x, y, stride)
+            /* This is critical. Adding paths for non
+             * existent processes in the window* can be
+             * very expensive as there is one SVG per process
+             * and SVG rendering is expensive
+             */
+            if (!path || path == "")
+                continue
+
+            result.push({
+                color: colourAxis(name),
+                path: path,
+                name: name
+            });
+        }
+
+        return result;
+
+    }
+
+    var create_dialog_body = function (body, title) {
+
+        var element = $("<div/>")
+            .addClass("modal fade")
+            .attr("role", "dialog")
+            .attr("tabindex", -1)
+
+        element.append(
+            $("<div/>")
+            .addClass("modal-dialog")
+            .attr("role", "document")
+            .append(
+                $("<div/>")
+                .addClass("modal-content")
+                .append(
+                    $("<div/>")
+                    .addClass("modal-header")
+                    .append(
+                        $("<button>")
+                        .addClass("close")
+                        .attr("data-dismiss",
+                            "modal")
+                        .append($("<span/>")
+                            .html("&times;"))
+                    )
+                    .append($("<h4/>")
+                        .addClass("modal-title")
+                        .text(title)
+                    )
+                    .append($("<div/>")
+                        .addClass("modal-body")
+                        .append(body)
+                    )
+                    .append(
+                        $("<div/>")
+                        .addClass("modal-footer")
+                        .append(
+                            $("<button>")
+                            .addClass("btn btn-default")
+                            .attr("data-dismiss", "modal")
+                            .text("Close")
+                        )
+                    )
+                )
+            )
+        )
+
+        return element.modal();
+
+    }
+
+    var create_help_dialog = function (base) {
+
+        var HELP_IMAGE = "plotter_scripts/EventPlot/EventPlot_help.jpg"
+
+        var element = $('<div/>');
+
+        // The documentation
+        var doc = $('<div/>')
+            .addClass('alert alert-info');
+
+        doc.append(
+            'EventPlot is a multi-lane timeline plot ' +
+            'which supports interative zooming and timing calculation'
+        );
+
+        element.append(doc);
+
+        var zoom = $("<div/>")
+            .addClass("media-left");
+
+        var addLabel = function (txt, cls) {
+            return '<span class="label label-' + cls + '" + ">' +
+                txt + '</span>'
+        }
+
+        var addListItem = function (txt) {
+            return '<li class="list-group-item">' + txt +
+                '</li>'
+        }
+
+        var addPlus = function () {
+            return " + "
+        }
+
+        var addBadge = function (txt) {
+            return '<span class="label label-default" style="border-radius: 10px">' +
+                txt + '</span>'
+        }
+
+        element.append(
+            '<img style="width: 100%;" class="media-object" src="' + base +
+            HELP_IMAGE + '"/>'
+        )
+
+        element.append('<ul class="list-group">')
+        element.append(addListItem('Scroll in the main area ' +
+            addBadge("1") + " to zoom interactively"))
+        element.append(addListItem(
+            'Click and drag in the main area ' + addBadge(
+                "1") + " to pan the zoom"))
+        element.append(addListItem(
+            'The summary of the plot is shown in ' +
+            addBadge("2")))
+        element.append(addListItem('Adjust the size of window ' +
+            addBadge("4") +
+            " set the X-Limits of the chart"))
+
+        element.append(addListItem(addLabel("Right-Click",
+                "default") +
+            " to place marker " + addLabel("A", "success")))
+
+        element.append(addListItem(addLabel("Ctrl", "primary") +
+            " + " + addLabel("Right-Click", "default") +
+            " to place marker " + addLabel("B", "danger")))
+
+        element.append(addListItem(
+            "The marker positions and delta will be shown in " +
+            addBadge("3")))
+
+        element.append(
+            addListItem(addLabel("Ctrl", "primary") + addPlus() +
+                addLabel("Alt", "primary") + addPlus() +
+                addLabel("Right-Click", "default") +
+                " on the rectange (eg. " + addBadge("6") +
+                " ) to show info in " + addBadge("5")))
+
+        element.append('</ul>')
+
+        var dialog = create_dialog_body(element, "Help: EventPlot");
+        dialog.show();
+
+    }
+
+    return {
+        generate: generate,
+        create_help_dialog: create_help_dialog
+    };
+
+}());
diff --git a/trappy/plotter/js/ILinePlot.js b/trappy/plotter/js/ILinePlot.js
new file mode 100644
index 0000000..309e90d
--- /dev/null
+++ b/trappy/plotter/js/ILinePlot.js
@@ -0,0 +1,173 @@
+/*
+ *    Copyright 2015-2017 ARM Limited
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+var ILinePlot = ( function() {
+
+   var graphs = new Array();
+   var syncObjs = new Array();
+
+    var purge = function() {
+        for (var div_name in graphs) {
+            if (document.getElementById(div_name) == null) {
+                delete graphs[div_name];
+            }
+        }
+    };
+
+    var sync = function(group) {
+
+        var syncGraphs = Array();
+        var xRange;
+        var yRange;
+        var syncZoom = true;
+
+        for (var div_name in graphs) {
+
+            if (graphs[div_name].group == group) {
+                syncGraphs.push(graphs[div_name].graph);
+                syncZoom = syncZoom & graphs[div_name].syncZoom;
+
+                var xR = graphs[div_name].graph.xAxisRange();
+                var yR = graphs[div_name].graph.yAxisRange();
+
+                if (xRange != undefined) {
+                    if (xR[0] < xRange[0])
+                        xRange[0] = xR[0];
+                    if (xR[1] > xRange[1])
+                        xRange[1] = xR[1];
+                } else
+                    xRange = xR;
+
+                if (yRange != undefined) {
+                    if (yR[0] < yRange[0])
+                        yRange[0] = yR[0];
+                    if (yR[1] > yRange[1])
+                        yRange[1] = yR[1];
+                } else
+                    yRange = yR;
+            }
+        }
+
+        if (syncGraphs.length >= 2) {
+            if (syncZoom) {
+                if (syncObjs[group] != undefined)
+                    syncObjs[group].detach();
+
+                syncObjs[group] = Dygraph.synchronize(syncGraphs, {
+                    zoom: true,
+                    selection: false,
+                    range: true
+                });
+            }
+
+            $.each(syncGraphs, function(g) {
+                var graph = syncGraphs[g];
+
+                graph.updateOptions({
+                    valueRange: yRange,
+                    dateWindow: xRange
+                });
+
+                if (graph.padFront_ == undefined) {
+                    graph.padFront_ = true;
+                    var _decoy_elem = new Array(graph.rawData_[0].length);
+                    graph.rawData_.unshift(_decoy_elem);
+                }
+                graph.rawData_[0][0] = xRange[0];
+
+                if (graph.padBack_ == undefined) {
+                    graph.padBack_ = true;
+                    var _decoy_elem = new Array(graph.rawData_[0].length);
+                    graph.rawData_.push(_decoy_elem);
+                }
+                graph.rawData_[graph.rawData_.length - 1][0] = xRange[1];
+            });
+        }
+    };
+
+    var generate = function(data, colors) {
+        create_graph(data, colors);
+        purge();
+        if (data.syncGroup != undefined)
+            sync(data.syncGroup);
+    };
+
+    var create_graph = function(t_info, colors) {
+        var tabular = t_info.data;
+
+        var options = {
+            legend: 'always',
+            title: t_info.title,
+            labels: tabular.labels,
+            labelsDivStyles: {
+                'textAlign': 'right'
+            },
+            rollPeriod: 1,
+            animatedZooms: true,
+            connectSeparatedPoints: true,
+            showRangeSelector: t_info.rangesel,
+            rangeSelectorHeight: 50,
+            stepPlot: t_info.step_plot,
+            logscale: t_info.logscale,
+            fillGraph: t_info.fill_graph,
+            labelsDiv: t_info.name + "_legend",
+            errorBars: false,
+            labelsSeparateLines: true,
+            valueRange: t_info.valueRange,
+            drawPoints: t_info.drawPoints,
+            strokeWidth: t_info.strokeWidth,
+            pointSize: t_info.pointSize,
+            dateWindow: t_info.dateWindow
+        };
+
+        if (typeof t_info.fill_alpha !== 'undefined')
+            options.fillAlpha = t_info.fill_alpha;
+
+        if (typeof colors !== 'undefined')
+            options["colors"] = colors;
+
+        var graph = new Dygraph(document.getElementById(t_info.name), tabular.data, options);
+
+        var width = $("#" + t_info.name)
+            .closest(".output_subarea").width() / t_info.per_line
+
+        /*
+         * Remove 3 pixels from width to avoid unnecessary horizontal scrollbar
+         */
+        graph.resize(width - 3, t_info.height);
+
+        $(window).on("resize." + t_info.name, function() {
+
+            var width = $("#" + t_info.name)
+                .closest(".output_subarea").width() / t_info.per_line
+
+            graph.resize(width, t_info.height);
+        });
+
+        graphs[t_info.name] =
+            {
+                graph: graph,
+                group: t_info.syncGroup,
+                syncZoom: t_info.syncZoom
+            };
+
+    };
+
+    return {
+        generate: generate
+    };
+
+}());
diff --git a/trappy/sched.py b/trappy/sched.py
new file mode 100644
index 0000000..ebcb79d
--- /dev/null
+++ b/trappy/sched.py
@@ -0,0 +1,137 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+"""Definitions of scheduler events registered by the FTrace class"""
+
+from trappy.base import Base
+from trappy.dynamic import register_ftrace_parser, register_dynamic_ftrace
+
+class SchedLoadAvgSchedGroup(Base):
+    """Corresponds to Linux kernel trace event sched_load_avg_sched_group"""
+
+    unique_word = "sched_load_avg_sg:"
+    """The unique word that will be matched in a trace line"""
+
+    _cpu_mask_column = "cpus"
+
+    pivot = "cpus"
+    """The Pivot along which the data is orthogonal"""
+
+    def finalize_object(self):
+        """This condition is necessary to force column 'cpus' to be printed
+        as 8 digits w/ leading 0
+        """
+        if self._cpu_mask_column in self.data_frame.columns:
+            dfr = self.data_frame[self._cpu_mask_column].apply('{:0>8}'.format)
+            self.data_frame[self._cpu_mask_column] = dfr
+
+register_ftrace_parser(SchedLoadAvgSchedGroup, "sched")
+
+class SchedLoadAvgTask(Base):
+    """Corresponds to Linux kernel trace event sched_load_avg_task"""
+
+    unique_word = "sched_load_avg_task:"
+    """The unique word that will be matched in a trace line"""
+
+    pivot = "pid"
+    """The Pivot along which the data is orthogonal"""
+
+    def get_pids(self, key=""):
+        """Returns a list of (comm, pid) that contain
+        'key' in their 'comm'."""
+        dfr = self.data_frame.drop_duplicates(subset=['comm', 'pid'])
+        dfr = dfr.ix[:, ['comm', 'pid']]
+
+        return dfr[dfr['comm'].str.contains(key)].values.tolist()
+
+register_ftrace_parser(SchedLoadAvgTask, "sched")
+
+# pylint doesn't like globals that are not ALL_CAPS
+# pylint: disable=invalid-name
+SchedLoadAvgCpu = register_dynamic_ftrace("SchedLoadAvgCpu",
+                                          "sched_load_avg_cpu:", "sched",
+                                          pivot="cpu")
+"""Load and Utilization Signals for CPUs"""
+
+SchedContribScaleFactor = register_dynamic_ftrace("SchedContribScaleFactor",
+                                                  "sched_contrib_scale_f:",
+                                                  "sched")
+"""Event to register tracing of contrib factor"""
+
+class SchedCpuCapacity(Base):
+    """Corresponds to Linux kernel trace event sched/cpu_capacity"""
+
+    unique_word = "cpu_capacity:"
+    """The unique word that will be matched in a trace line"""
+
+    pivot = "cpu"
+    """The Pivot along which the data is orthogonal"""
+
+    def finalize_object(self):
+        """This renaming is necessary because our cpu related pivot is 'cpu'
+        and not 'cpu_id'. Otherwise you cannot 'mix and match' with other
+        classes
+        """
+        self.data_frame.rename(columns={'cpu_id':'cpu'}, inplace=True)
+        self.data_frame.rename(columns={'state' :'capacity'}, inplace=True)
+
+register_ftrace_parser(SchedCpuCapacity, "sched")
+
+SchedWakeup = register_dynamic_ftrace("SchedWakeup", "sched_wakeup:", "sched",
+                                       parse_raw=True)
+"""Register SchedWakeup Event"""
+
+SchedWakeupNew = register_dynamic_ftrace("SchedWakeupNew", "sched_wakeup_new:",
+                                         "sched", parse_raw=True)
+"""Register SchedWakeupNew Event"""
+
+# pylint: enable=invalid-name
+
+class SchedSwitch(Base):
+    """Parse sched_switch"""
+
+    unique_word = "sched_switch:"
+
+    def __init__(self):
+        super(SchedSwitch, self).__init__(parse_raw=True)
+
+    def append_data(self, time, comm, pid, tgid, cpu, data):
+        data_rep = data.replace(" ==> ", " ")
+        super(SchedSwitch, self).append_data(time, comm, pid, tgid, cpu,
+                                             data_rep)
+
+register_ftrace_parser(SchedSwitch, "sched")
+
+class SchedCpuFrequency(Base):
+    """Corresponds to Linux kernel trace event power/cpu_frequency"""
+
+    unique_word = "cpu_frequency:"
+    """The unique word that will be matched in a trace line"""
+
+    pivot = "cpu"
+    """The Pivot along which the data is orthogonal"""
+
+    def finalize_object(self):
+        """This renaming is necessary because our cpu related pivot is 'cpu'
+        and not 'cpu_id'. Otherwise you cannot 'mix and match' with other
+        classes
+        """
+        self.data_frame.rename(columns={'cpu_id':'cpu'}, inplace=True)
+        self.data_frame.rename(columns={'state' :'frequency'}, inplace=True)
+
+register_ftrace_parser(SchedCpuFrequency, "sched")
+
+register_dynamic_ftrace("SchedMigrateTask", "sched_migrate_task:", "sched")
diff --git a/trappy/stats/Aggregator.py b/trappy/stats/Aggregator.py
new file mode 100644
index 0000000..0ca9525
--- /dev/null
+++ b/trappy/stats/Aggregator.py
@@ -0,0 +1,174 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Aggregators are responsible for aggregating information
+for further analysis. These aggregations can produce
+both scalars and vectors and each aggregator implementation
+is expected to handle its "aggregation" mechanism.
+"""
+
+
+from trappy.utils import listify
+from trappy.stats.Indexer import MultiTriggerIndexer
+from abc import ABCMeta, abstractmethod
+
+
+class AbstractAggregator(object):
+    """Abstract class for all aggregators
+
+    :param indexer: Indexer is passed on by the Child class
+        for handling indices during correlation
+    :type indexer: :mod:`trappy.stats.Indexer.Indexer`
+
+    :param aggfunc: Function that accepts a pandas.Series and
+        process it for aggregation.
+    :type aggfunc: function
+    """
+
+    __metaclass__ = ABCMeta
+
+    # The current implementation needs the index to
+    # be unified across data frames to account for
+    # variable sampling across data frames
+    def __init__(self, indexer, aggfunc=None):
+
+        self._result = {}
+        self._aggregated = False
+        self._aggfunc = aggfunc
+        self.indexer = indexer
+
+    def _add_result(self, pivot, series):
+        """Add the result for the given pivot and trace
+
+        :param pivot: The pivot for which the result is being generated
+        :type pivot(hashable)
+
+        :param series: series to be added to result
+        :type series: :mod:`pandas.Series`
+        """
+
+        if pivot not in self._result:
+            self._result[pivot] = self.indexer.series()
+
+        for idx in series.index:
+                self._result[pivot][idx] = series[idx]
+
+    @abstractmethod
+    def aggregate(self, trace_idx, **kwargs):
+        """Abstract Method for aggregating data for various
+        pivots.
+
+        :param trace_idx: Index of the trace to be aggregated
+        :type trace_idx: int
+
+        :return: The aggregated result
+
+        """
+
+        raise NotImplementedError("Method Not Implemented")
+
+
+class MultiTriggerAggregator(AbstractAggregator):
+
+    """This aggregator accepts a list of triggers and each trigger has
+     a value associated with it.
+    """
+
+    def __init__(self, triggers, topology, aggfunc=None):
+        """
+        :param triggers: trappy.stat.Trigger): A list or a singular trigger object
+        :type triggers: :mod:`trappy.stat.Trigger.Trigger`
+
+        :param topology (trappy.stat.Topology): A topology object for aggregation
+                levels
+        :type topology: :mod:`trappy.stat.Topology`
+
+        :param aggfunc: A function to be applied on each series being aggregated.
+                For each topology node, a series will be generated and this
+                will be processed by the aggfunc
+        :type aggfunc: function
+        """
+        self._triggers = triggers
+        self.topology = topology
+        super(
+            MultiTriggerAggregator,
+            self).__init__(MultiTriggerIndexer(triggers), aggfunc)
+
+    def aggregate(self, **kwargs):
+        """
+        Aggregate implementation that aggregates
+        triggers for a given topological level. All the arguments passed to
+        it are forwarded to the aggregator function except level (if present)
+
+        :return: A scalar or a vector aggregated result. Each group in the
+            level produces an element in the result list with a one to one
+            index correspondence
+            ::
+
+                groups["level"] = [[1,2], [3,4]]
+                result = [result_1, result_2]
+        """
+
+        level = kwargs.pop("level", "all")
+
+        # This function is a hot spot in the code. It is
+        # worth considering a memoize decorator to cache
+        # the function. The memoization can also be
+        # maintained by the aggregator object. This will
+        # help the code scale efficeintly
+        level_groups = self.topology.get_level(level)
+        result = []
+
+
+        if not self._aggregated:
+            self._aggregate_base()
+
+        for group in level_groups:
+            group = listify(group)
+            if self._aggfunc is not None:
+                level_res = self._aggfunc(self._result[group[0]], **kwargs)
+            else:
+                level_res = self._result[group[0]]
+
+            for node in group[1:]:
+                if self._aggfunc is not None:
+                    node_res = self._aggfunc(self._result[node], **kwargs)
+                else:
+                    node_res = self._result[node]
+
+                level_res += node_res
+
+            result.append(level_res)
+
+        return result
+
+    def _aggregate_base(self):
+        """A memoized function to generate the base series
+        for each node in the flattened topology
+        ::
+
+            topo["level_1"] = [[1, 2], [3, 4]]
+
+       This function will generate the fundamental
+       aggregations for all nodes 1, 2, 3, 4 and
+       store the result in _agg_result
+       """
+
+        for trigger in self._triggers:
+            for node in self.topology.flatten():
+                result_series = trigger.generate(node)
+                self._add_result(node, result_series)
+
+        self._aggregated = True
diff --git a/trappy/stats/Correlator.py b/trappy/stats/Correlator.py
new file mode 100644
index 0000000..191cb06
--- /dev/null
+++ b/trappy/stats/Correlator.py
@@ -0,0 +1,231 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""The module responsible for correlation
+and related functionality
+"""
+from trappy.stats import StatConf
+from trappy.stats.Indexer import get_unified_indexer
+import numpy as np
+import math
+
+
+class Correlator(object):
+    """Class that allows to align and correlate two traces
+    :param first: First Aggregator
+    :type first: :mod:`trappy.stats.Aggregator`
+
+    :param second: Second Aggregator
+    :type second: :mod:`trappy.stats.Aggregator`
+    """
+
+    def __init__(self, first, second, **kwargs):
+
+        self._first_agg = first
+        self._second_agg = second
+        self.indexer = get_unified_indexer([first.indexer, second.indexer])
+        self._corrfunc = kwargs.pop("corrfunc", None)
+        self._agg_kwargs = kwargs
+        self.corr_graphs = {}
+        self._shift = self._align_top_level()
+
+    def _resample(self, series, delta=StatConf.DELTA_DEFAULT):
+        """Internal method to resample the series
+        to a uniformly spaced index
+
+        :param series: Series io be resampled
+        :type series: :mod:`pandas.Series`
+
+        :param delta: spacing between indices
+        :type delta: float
+
+        :return: resampled :mod:`pandas.Series`
+        """
+
+        new_index = self.indexer.get_uniform(delta)
+        return series.reindex(index=new_index, method="pad")
+
+    def correlate(self, level, resample=True):
+        """This function returns the correlation between two traces
+
+        :param level: The level at which the correlation is
+            required
+        :type level: str
+
+        :param resample: Resample data
+        :type resample: bool
+
+        :return: A normalized correlation value is returned
+            for each group in the level
+        """
+        result_1 = self._first_agg.aggregate(level=level, **self._agg_kwargs)
+        result_2 = self._second_agg.aggregate(level=level, **self._agg_kwargs)
+
+
+        corr_output = []
+        weights = []
+
+        for group_id, result_group in enumerate(result_1):
+            series_x = result_group
+            series_y = result_2[group_id]
+
+            if resample:
+                series_x = self._resample(series_x)
+                series_y = self._resample(series_y)
+
+            series_x, series_y = shift_series(series_x, series_y, self._shift)
+            corr_output.append(self._correlate(series_x, series_y))
+            weights.append(len(series_x[series_x != 0]) + len(series_y[series_y != 0]))
+
+        total = 0
+        for weight, corr in zip(weights, corr_output):
+            if math.isnan(corr):
+                continue
+            total += (weight * corr) / sum(weights)
+
+        return corr_output, total
+
+
+    def plot(self, level, per_line=3):
+        """Temporary function to plot data. Expected to be
+        implemented in plotter
+
+        :param level: Topological Level (level in :mod:`trappy.stats.Topology`)
+        :type level: str
+
+        :param per_line: Number of plots per line
+        :type per_line: int
+        """
+        from trappy.plotter.PlotLayout import PlotLayout
+
+        num_plots = self._first_agg.topology.level_span(level)
+        result_1 = self._first_agg.aggregate(level=level, **self._agg_kwargs)
+        result_2 = self._second_agg.aggregate(level=level, **self._agg_kwargs)
+        layout = PlotLayout(per_line, num_plots)
+
+        plot_index = 0
+
+        for group_id, result_group in enumerate(result_1):
+            s_x = result_group
+            s_y = result_2[group_id]
+
+            s_x = self._resample(s_x)
+            s_y = self._resample(s_y)
+
+            s_x, s_y = shift_series(s_x, s_y, self._shift)
+
+            ymax = 1.25 + max(max(s_x.values), max(s_y.values)) + 1
+            ymin = min(min(s_x.values), min(s_y.values)) - 1
+            ylim = [ymin, ymax]
+            ylim = [-1, 3]
+
+            axis = layout.get_axis(plot_index)
+
+            axis.plot(s_x.index, s_x.values)
+            axis.plot(s_y.index, s_y.values)
+
+            axis.set_ylim(ylim)
+            plot_index += 1
+        layout.finish(plot_index)
+
+    def _correlate(self, s_x, s_y):
+
+        if self._corrfunc != None:
+            f = self._corrfunc
+            return f(s_x, s_y)
+        else:
+            return s_x.corr(s_y)
+
+    def _align_top_level(self):
+        """Temporary function to plot data. Expected to be
+        implemented in plotter
+        """
+
+        result_1 = self._first_agg.aggregate(level="all")
+        result_2 = self._second_agg.aggregate(level="all")
+
+        s_x = self._resample(result_1[0])
+        s_y = self._resample(result_2[0])
+
+
+        front_x, front_y, front_shift = align(s_x, s_y, mode="front")
+        front_corr = self._correlate(front_x, front_y)
+
+        back_x, back_y, back_shift = align(s_x, s_y, mode="back")
+        back_corr = self._correlate(back_x, back_y)
+
+        if math.isnan(back_corr):
+            back_corr = 0
+        if math.isnan(front_corr):
+            front_corr = 0
+
+        if front_corr >= back_corr:
+            return front_shift
+        else:
+            return back_shift
+
+
+
+def align(s_x, s_y, mode="front"):
+    """Function to align the input series
+
+    :param s_x: First Series
+    :type s_x: :mod:`pandas.Series`
+
+    :param s_y: Second Series
+    :type s_y: :mod:`pandas.Series`
+
+    :param mode: Align Front/Back
+    :type mode: str
+    """
+
+    p_x = np.flatnonzero(s_x)
+    p_y = np.flatnonzero(s_y)
+
+    if not len(p_x) or not len(p_y):
+        return s_x, s_y, 0
+
+    if mode == "front":
+        p_x = p_x[0]
+        p_y = p_y[0]
+
+    if mode == "back":
+        p_x = p_x[-1]
+        p_y = p_y[-1]
+
+    shift = p_x - p_y
+
+    s_x, s_y = shift_series(s_x, s_y, shift)
+    return s_x, s_y, shift
+
+def shift_series(s_x, s_y, shift):
+    """Shift series to align
+    :param s_x: First Series
+    :type s_x: :mod:`pandas.Series`
+
+    :param s_y: Second Series
+    :type s_y: :mod:`pandas.Series`
+
+    :param shift: The number of index
+        positions to be shifted
+    :type shift: int
+    """
+
+    if shift > 0:
+        s_y = s_y.shift(shift)
+    else:
+        s_x = s_x.shift(-1 * shift)
+
+    return s_x, s_y
diff --git a/trappy/stats/Indexer.py b/trappy/stats/Indexer.py
new file mode 100644
index 0000000..32b5ee1
--- /dev/null
+++ b/trappy/stats/Indexer.py
@@ -0,0 +1,102 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Indexers are responsible for providing indexes for
+   aggregations and provide specific functions like
+   unification and resampling.
+"""
+
+import pandas as pd
+import numpy as np
+from trappy.utils import listify
+from trappy.stats import StatConf
+
+class Indexer(object):
+    """Indexer base class is an encapsulation
+    around the pandas Index object with some
+    special functionality
+
+    :param index: Pandas index object. This can be
+                    non-unoform and non-unique
+    :type index: :mod:`pandas.Index`
+
+    :param traces: trappy FTrace list/singular object
+    :type traces: :mod:`trappy.trace.FTrace`
+    """
+
+    def __init__(self, index):
+        self.index = index
+
+    def series(self):
+        """Returns an empty series with the initialized index
+        """
+        return pd.Series(np.zeros(len(self.index)), index=self.index)
+
+    def get_uniform(self, delta=StatConf.DELTA_DEFAULT):
+        """
+        :param delta: Difference between two indices. This has a
+            default value specified in StatConf.DELTA_DEFAULT
+        :type delta: float
+
+        :return: A uniformly spaced index.
+        """
+
+        uniform_start = self.index.values[0]
+        uniform_end = self.index.values[-1]
+        new_index = np.arange(uniform_start, uniform_end, delta)
+        return new_index
+
+def get_unified_indexer(indexers):
+    """Unify the List of Indexers
+
+    :param indexers: A list of indexers
+    :type indexers: :mod:`trappy.stats.Indexer.Indexer`
+
+    :return: A :mod:`pandas.Indexer.Indexer`
+        with a unfied index
+    """
+
+    new_index = indexers[0].index
+
+    for idx in indexers[1:]:
+        new_index = new_index.union(idx.index)
+
+    return Indexer(new_index)
+
+class MultiTriggerIndexer(Indexer):
+    """"The index unifies the indices of all trigger
+     events.
+
+    :param triggers: A (list or single) trigger
+    :type triggers: :mod:`trappy.stats.Trigger.Trigger`
+    """
+
+    def __init__(self, triggers):
+
+        self._triggers = listify(triggers)
+        super(MultiTriggerIndexer, self).__init__(self._unify())
+
+    def _unify(self):
+        """Function to unify all the indices of each trigger
+        """
+
+        idx = pd.Index([])
+        for trigger in self._triggers:
+            trace = trigger.trace
+            trappy_event = getattr(trace, trigger.template.name)
+            idx = idx.union(trappy_event.data_frame.index)
+
+
+        return pd.Index(np.unique(idx.values))
diff --git a/trappy/stats/StatConf.py b/trappy/stats/StatConf.py
new file mode 100644
index 0000000..1437af4
--- /dev/null
+++ b/trappy/stats/StatConf.py
@@ -0,0 +1,28 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Config Parameters for the Statistics Framework"""
+
+# Default interval between a uniform time series
+DELTA_DEFAULT = 0.000025
+"""The default delta for uniformly resampled data"""
+GRAMMAR_DEFAULT_PIVOT = "NO_PIVOT"
+"""Default pivot value for :mod:`trappy.stats.grammar`"""
+REINDEX_METHOD_DEFAULT = "pad"
+"""Default method for reindexing and filling up NaNs"""
+REINDEX_LIMIT_DEFAULT = None
+"""Number or indices a value will be propagated forward when reindexing"""
+NAN_FILL_DEFAULT = True
+"""Fill NaN values by default"""
diff --git a/trappy/stats/Topology.py b/trappy/stats/Topology.py
new file mode 100644
index 0000000..014581e
--- /dev/null
+++ b/trappy/stats/Topology.py
@@ -0,0 +1,179 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""A Topology can be defined as an arrangement of
+fundamental nodes, in various levels. Each topology
+has a default level "all" which has each node represented
+as a group. For example:
+
+    +--------+---------------------------------------+
+    | level  |     groups                            |
+    +========+=======================================+
+    | all    | :code:`[[0, 1, 2, 3, 4]]`             |
+    +--------+---------------------------------------+
+    | cluster| :code:`[[0, 1], [2, 3, 4]]`           |
+    +--------+---------------------------------------+
+    | cpu    | :code:`[[0], [1], [2], [3], [4], [5]]`|
+    +--------+---------------------------------------+
+
+"""
+
+class Topology(object):
+    """Topology object allows grouping of
+    pivot values (called nodes) at multiple levels.
+    The implementation is targeted towards CPU topologies
+    but can be used generically as well
+
+    :param clusters: clusters can be defined as a
+        list of groups which are again lists of nodes.
+
+        .. note::
+
+            This is not a mandatory
+            argument but can be used to quickly create typical
+            CPU topologies.
+
+        For Example:
+        ::
+
+            from trappy.stats.Topology import Topology
+
+            CLUSTER_A = [0, 1]
+            CLUTSER_B = [2, 3]
+
+            clusters = [CLUSTER_A, CLUSTER_B]
+            topology = Topology(clusters=clusters)
+
+    :type clusters: list
+    """
+
+    def __init__(self, clusters=[]):
+        self._levels = {}
+        self._nodes = set()
+
+        if len(clusters):
+            self.add_to_level("cluster", clusters)
+            cpu_level = []
+            for node in self.flatten():
+                cpu_level.append([node])
+            self.add_to_level("cpu", cpu_level)
+
+    def __repr__(self):
+        repr_str = ""
+        for level_name in self._levels:
+            repr_str += level_name + " " + \
+                        self.get_level(level_name).__repr__() + \
+                        "\n"
+        return repr_str
+
+    def add_to_level(self, level_name, level_vals):
+        """Add a group to a level
+
+        This function allows to append a
+        group of nodes to a level. If the level
+        does not exist a new level is created
+
+        :param level_name: The name of the level
+        :type level_name: str
+
+        :level_vals: groups containing nodes
+        :type level_vals: list of lists:
+        """
+
+        if level_name not in self._levels:
+            self._levels[level_name] = []
+
+        self._levels[level_name] += level_vals
+
+        for group in level_vals:
+            self._nodes = self._nodes.union(set(group))
+
+    def get_level(self, level_name):
+        """Returns the groups of nodes associated
+        with a level
+
+        :param level_name: The name of the level
+        :type level_name: str
+        """
+
+        if level_name == "all":
+            return [self.flatten()]
+        else:
+            return self._levels[level_name]
+
+    def get_index(self, level, node):
+        """Return the index of the node in the
+        level's list of nodes
+
+        :param level: The name of the level
+        :type level_name: str
+
+        :param node: The group for which the inde
+            is required
+
+            .. todo::
+
+                Change name of the arg to group
+
+        :type node: list
+        """
+
+        nodes = self.get_level(level)
+        return nodes.index(node)
+
+    def get_node(self, level, index):
+        """Get the group at the index in
+        the level
+
+        :param level: The name of the level
+        :type level_name: str
+
+        :param index: Index of the group in
+            the list
+        :type index: int
+        """
+
+        nodes = self.get_level(level)
+        return nodes[index]
+
+    def __iter__(self):
+        return self._levels.__iter__()
+
+    def flatten(self):
+        """Return a flattened list of nodes in the
+        topology
+        """
+        return list(self._nodes)
+
+    def level_span(self, level):
+        """Return the number of groups in a level
+
+        :param level: The name of the level
+        :type level_name: str
+        """
+        if level == "all":
+            return len(self._nodes)
+        else:
+            return len(self._levels[level])
+
+    def has_level(self, level):
+        """Returns true if level is present
+
+        :param level: The name of the level
+        :type level_name: str
+        """
+
+        return (level in self._levels)
+
diff --git a/trappy/stats/Trigger.py b/trappy/stats/Trigger.py
new file mode 100644
index 0000000..b2860d7
--- /dev/null
+++ b/trappy/stats/Trigger.py
@@ -0,0 +1,130 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Trigger is a representation of the following:
+
+    - Event(s) (:mod:`trappy.base.Base`)
+    - An associated value
+        - scalar
+        - vector
+    - A set of filters
+        - value based
+        - function based
+"""
+
+import types
+from trappy.utils import listify
+import pandas as pd
+
+
+class Trigger(object):
+    """Trigger is an event-value relationship which
+    accepts a trace object to "generate" qualified data
+
+    :param trace: A trappy FTrace object
+    :type trace: :mod:`trappy.trace.FTrace`
+
+    :param template: A trappy Event to act as a trigger
+    :type template: trappy.Base
+
+    :param filters: Key value filter pairs
+    :type filters: dict
+
+    The filter can either have a function:
+    ::
+
+        def function_based_filter(elem):
+            if condition:
+                return True
+            else:
+                return False
+
+    or a value/list of values
+    ::
+
+        f = {}
+        f["data_column_a"] = function_based_filter
+        f["data_column_b"] = value
+
+    function_based_filter is anything that behaves like a function,
+    i.e. a callable.
+
+    :param value: Value can be a string or a numeric
+    :type value: str, int, float
+
+    :param pivot: This is the column around which the data will be
+        pivoted
+    :type pivot: str
+    """
+
+    def __init__(self, trace, template, filters, value, pivot):
+
+        self.template = template
+        self._filters = filters
+        self._value = value
+        self._pivot = pivot
+        self.trace = trace
+
+    def generate(self, pivot_val):
+        """Generate the trigger data for a given pivot value
+        and a trace index
+
+        :param pivot_val: The pivot to generate data for
+        :type pivot_val: hashable
+        """
+
+        trappy_event = getattr(self.trace, self.template.name)
+        data_frame = trappy_event.data_frame
+        data_frame = data_frame[data_frame[self._pivot] == pivot_val]
+
+        mask = [True for _ in range(len(data_frame))]
+
+        for key, value in self._filters.iteritems():
+            if hasattr(value, "__call__"):
+                mask = mask & (data_frame[key].apply(value))
+            else:
+                mask = apply_filter_kv(key, value, data_frame, mask)
+
+        data_frame = data_frame[mask]
+
+        if isinstance(self._value, str):
+            return data_frame[value]
+        else:
+            return pd.Series(self._value, index=data_frame.index)
+
+
+def apply_filter_kv(key, value, data_frame, mask):
+    """Internal function to apply a key value
+    filter to a data_frame and update the initial
+    condition provided in mask.
+
+    :param value: The value to checked for
+
+    :param data_frame: The data to be filtered
+    :type data_frame: :mod:`pandas.DataFrame`
+
+    :param mask: Initial Condition Mask
+    :type mask: :mod:`pandas.Series`
+
+    :return: A **mask** to index the data frame
+    """
+
+    value = listify(value)
+    if key not in data_frame.columns:
+        return mask
+    else:
+        for val in value:
+            mask = mask & (data_frame[key] == val)
+        return mask
diff --git a/trappy/stats/__init__.py b/trappy/stats/__init__.py
new file mode 100644
index 0000000..82de515
--- /dev/null
+++ b/trappy/stats/__init__.py
@@ -0,0 +1,22 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+from trappy.stats import Aggregator
+from trappy.stats import Correlator
+from trappy.stats import Trigger
+from trappy.stats import Topology
+from trappy.stats import Indexer
+from trappy.stats import StatConf
diff --git a/trappy/stats/grammar.py b/trappy/stats/grammar.py
new file mode 100644
index 0000000..c0a9a63
--- /dev/null
+++ b/trappy/stats/grammar.py
@@ -0,0 +1,580 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Grammar module allows the user to easily define relations
+between data events and perform basic logical and arithmetic
+operations on the data. The parser also handles super-indexing
+and variable forwarding.
+"""
+from pyparsing import Literal, delimitedList, Optional, oneOf, nums,\
+    alphas, alphanums, Forward, Word, opAssoc, operatorPrecedence, Combine, Group
+import importlib
+import pandas as pd
+import types
+import numpy as np
+from trappy.stats.Topology import Topology
+from trappy.stats import StatConf
+from trappy.utils import handle_duplicate_index, listify
+
+
+def parse_num(tokens):
+    """Parser function for numerical data
+
+    :param tokens: The grammar tokens
+    :type tokens: list
+    """
+    return float(tokens[0])
+
+# Suppressed Literals
+LPAREN = Literal("(").suppress()
+RPAREN = Literal(")").suppress()
+COLON = Literal(":").suppress()
+EXP_START = Literal("[").suppress()
+EXP_END = Literal("]").suppress()
+
+# Grammar Tokens
+
+# DataFrame Accessor
+INTEGER = Combine(Optional(oneOf("+ -")) + Word(nums))\
+    .setParseAction(parse_num)
+REAL = Combine(Optional(oneOf("+ -")) + Word(nums) + "." +
+               Optional(Word(nums)) +
+               Optional(oneOf("e E") + Optional(oneOf("+ -")) + Word(nums)))\
+    .setParseAction(parse_num)
+
+# Generic Identifier
+IDENTIFIER = Word(alphas + '_', alphanums + '_')
+# Python Like Function Name
+FUNC_NAME = delimitedList(IDENTIFIER, delim=".", combine=True)
+# Exponentiation operators
+EXPONENTIATION_OPS = "**"
+# Unary Operators
+UNARY_OPS = oneOf("+ -")
+# Multiplication/Division Operators
+MULT_OPS = oneOf("* / // %")
+# Addition/Subtraction Operators
+SUM_OPS = oneOf("+ -")
+# Relational Operators
+REL_OPS = oneOf("> < >= <= == !=")
+# Logical Operators
+LOGICAL_OPS = oneOf("&& || & |")
+
+# Operator to function mapping
+OPERATOR_MAP = {
+    "+": lambda a, b: a + b,
+    "-": lambda a, b: a - b,
+    "*": lambda a, b: a * b,
+    "/": lambda a, b: a / b,
+    "//": lambda a, b: a // b,
+    "%": lambda a, b: a % b,
+    "**": lambda a, b: a ** b,
+    ">": lambda a, b: a > b,
+    "<": lambda a, b: a < b,
+    ">=": lambda a, b: a >= b,
+    "<=": lambda a, b: a <= b,
+    "||": lambda a, b: a or b,
+    "&&": lambda a, b: a and b,
+    "|": lambda a, b: a | b,
+    "==": lambda a, b: a == b,
+    "!=": lambda a, b: a != b,
+    "&": lambda a, b: a & b
+}
+
+
+def eval_unary_op(tokens):
+    """Unary Op Evaluation
+
+    :param tokens: The grammar tokens
+    :type tokens: list
+    """
+
+    params = tokens[0]
+    if params[0] == "-":
+        return -1 * params[1]
+    else:
+        return params[1]
+
+
+def iterate_binary_ops(tokens):
+    """An iterator for Binary Operation tokens
+
+    :param tokens: The grammar tokens
+    :type tokens: list
+    """
+
+    itr = iter(tokens)
+    while True:
+        try:
+            yield(itr.next(), itr.next())
+        except StopIteration:
+            break
+
+
+def eval_binary_op(tokens):
+    """Evaluate Binary operators
+
+    :param tokens: The grammar tokens
+    :type tokens: list
+    """
+
+    params = tokens[0]
+    result = params[0]
+
+    for opr, val in iterate_binary_ops(params[1:]):
+        result = OPERATOR_MAP[opr](result, val)
+
+    return result
+
+
+def str_to_attr(cls_str):
+    """Bring the attr specified into current scope
+       and return a handler
+
+    :param cls_str: A string representing the class
+    :type cls_str: str
+
+    :return: A class object
+    """
+    attr_name = cls_str.rsplit(".", 1)
+    if len(attr_name) == 2:
+        module_name, attr_name = attr_name
+        mod = importlib.import_module(module_name)
+        return getattr(mod, attr_name)
+    else:
+        attr_name = attr_name[0]
+        return globals()[attr_name]
+
+
+def get_parse_expression(parse_func, parse_var_id):
+    """return a parse expression with for the
+    input parseActions
+    """
+
+    var_id = Group(
+        FUNC_NAME + COLON + IDENTIFIER) | REAL | INTEGER | IDENTIFIER
+    var_id.setParseAction(parse_var_id)
+
+    # Forward declaration for an Arithmetic Expression
+    arith_expr = Forward()
+    func_call = Group(
+        FUNC_NAME +
+        LPAREN +
+        Optional(
+            Group(
+                delimitedList(arith_expr))) +
+        RPAREN)
+    # An Arithmetic expression can have a var_id or
+    # a function call as an operand
+    # pylint: disable=expression-not-assigned
+    arith_expr << operatorPrecedence(func_call | var_id,
+                                     [
+                                         (EXPONENTIATION_OPS, 2, opAssoc.LEFT,
+                                          eval_binary_op),
+                                         (UNARY_OPS, 1,
+                                          opAssoc.RIGHT, eval_unary_op),
+                                         (MULT_OPS, 2, opAssoc.LEFT,
+                                          eval_binary_op),
+                                         (SUM_OPS, 2, opAssoc.LEFT,
+                                          eval_binary_op),
+                                         (REL_OPS, 2, opAssoc.LEFT,
+                                          eval_binary_op),
+                                         (LOGICAL_OPS, 2,
+                                          opAssoc.LEFT, eval_binary_op)
+                                     ])
+
+    # pylint: enable=expression-not-assigned
+    # Argument expression for a function call
+    # An argument to a function can be an
+    # IDENTIFIER, Arithmetic expression, REAL number, INTEGER or a
+    # Function call itself
+    func_call.setParseAction(parse_func)
+    return arith_expr
+
+
+class Parser(object):
+
+    """A parser class for solving simple
+    data accesses and super-indexing data
+
+    :param data: Trace Object
+    :type data: instance of :mod:`trappy.ftrace.BareTrace` or a child
+        class (like :mod:`trappy.ftrace.FTrace`)
+
+    :param pvars: A dictionary of variables that need to be
+        accessed from within the grammar
+    :type pvars: dict
+
+    :param method: The method to be used for reindexing data
+        This can be one of the standas :mod:`pandas.DataFrame`
+        methods (eg. pad, bfill, nearest). The default is pad
+        or use the last valid observation.
+    :type method: str
+
+    :param limit: The number of indices a value will be propagated
+        when reindexing. The default is None
+    :type limit: int
+
+    :param fill: Whether to fill the NaNs in the data.
+        The default value is True.
+    :type fill: bool
+
+    :param window: A window of time in which to apply the data
+        accesses.  By default the data accesses happen accross the
+        whole trace.  With the window parameter you can limit it to a
+        window of time inside the trace.  The first element of the
+        tuple is the starting time and the second the ending time (set
+        to None for end of trace).
+
+    :type window: tuple
+
+    :param filters: Restrict the parsing to the rows that match the
+        specified criteria. For Example:
+        ::
+
+            filters =
+                    {
+                        "pid": 3338,
+                        "cpu": [0, 2, 4],
+                    }
+
+        will only consider rows whose pid column is 3338 and cpu is
+        either 0, 2 or 4.
+    :type filters: dict
+
+    - **Operators**
+
+        +----------------+----------------------+---------------+
+        | Operation      |      operator        | Associativity |
+        +================+======================+===============+
+        | Exponentiation | \*\*                 |    Left       |
+        +----------------+----------------------+---------------+
+        |Unary           | \-                   |    Right      |
+        +----------------+----------------------+---------------+
+        | Multiply/Divide| \*, /, //, %         |    Left       |
+        +----------------+----------------------+---------------+
+        | Add/Subtract   | +, \-,               |    Left       |
+        +----------------+----------------------+---------------+
+        | Comparison     | >, <, >=, <=, ==, != |    Left       |
+        +----------------+----------------------+---------------+
+        | Logical        | &&, ||, \|, &        |    Left       |
+        +----------------+----------------------+---------------+
+
+    - **Data Accessors**
+
+        Since the goal of the grammar is to provide an
+        easy language to access and compare data
+        from a :mod:`trappy.trace.FTrace` object. The parser provides
+        a simple notation to access this data.
+
+        *Statically Defined Events*
+        ::
+
+            import trappy
+            from trappy.stats.grammar import Parser
+
+            trace = trappy.FTrace("path/to/trace/file")
+            parser = Parser(trace)
+            parser.solve("trappy.thermal.Thermal:temp * 2")
+
+        *Aliasing*
+        ::
+
+            import trappy
+            from trappy.stats.grammar import Parser
+
+            pvars = {"THERMAL": trappy.thermal.Thermal}
+            trace = trappy.FTrace("path/to/trace/file")
+            parser = Parser(trace, pvars=pvars)
+            parser.solve("THERMAL:temp * 2")
+
+        *Using Event Name*
+        ::
+
+            import trappy
+            from trappy.stats.grammar import Parser
+            trace = trappy.FTrace("path/to/trace/file")
+            parser = Parser(trace)
+            parser.solve("thermal:temp * 2")
+
+        The event :mod:`trappy.thermal.Thermal` is aliased
+        as **thermal** in the grammar
+
+        *Dynamic Events*
+        ::
+
+            import trappy
+            from trappy.stats.grammar import Parser
+
+            # Register Dynamic Event
+            cls = trappy.register_dynamic_ftrace("my_unique_word", "event_name")
+
+            pvars = {"CUSTOM": cls}
+            trace = trappy.FTrace("path/to/trace/file")
+            parser = Parser(trace, pvars=pvars)
+            parser.solve("CUSTOM:col * 2")
+
+        .. seealso:: :mod:`trappy.dynamic.register_dynamic_ftrace`
+
+    """
+
+    def __init__(self, data, pvars=None, window=(0, None), filters=None, **kwargs):
+        if pvars is None:
+            pvars = {}
+
+        self.data = data
+        self._pvars = pvars
+        self._accessor = Group(
+            FUNC_NAME + COLON + IDENTIFIER).setParseAction(self._pre_process)
+        self._inspect = Group(
+            FUNC_NAME + COLON + IDENTIFIER).setParseAction(self._parse_for_info)
+        self._parse_expr = get_parse_expression(
+            self._parse_func, self._parse_var_id)
+        self._agg_df = pd.DataFrame()
+        self._pivot_set = set()
+        self._limit = kwargs.get("limit", StatConf.REINDEX_LIMIT_DEFAULT)
+        self._method = kwargs.get("method", StatConf.REINDEX_METHOD_DEFAULT)
+        self._fill = kwargs.get("fill", StatConf.NAN_FILL_DEFAULT)
+        self._window = window
+        self._filters = filters
+
+    def solve(self, expr):
+        """Parses and solves the input expression
+
+        :param expr: The input expression
+        :type expr: str
+
+        :return: The return type may vary depending on
+            the expression. For example:
+
+            **Vector**
+            ::
+
+                import trappy
+                from trappy.stats.grammar import Parser
+
+                trace = trappy.FTrace("path/to/trace/file")
+                parser = Parser(trace)
+                parser.solve("trappy.thermal.Thermal:temp * 2")
+
+            **Scalar**
+            ::
+
+                import trappy
+                from trappy.stats.grammar import Parser
+
+                trace = trappy.FTrace("path/to/trace/file")
+                parser = Parser(trace)
+                parser.solve("numpy.mean(trappy.thermal.Thermal:temp)")
+
+            **Vector Mask**
+            ::
+
+                import trappy
+                from trappy.stats.grammar import Parser
+
+                trace = trappy.FTrace("path/to/trace/file")
+                parser = Parser(trace)
+                parser.solve("trappy.thermal.Thermal:temp > 65000")
+        """
+
+        # Pre-process accessors for indexing
+        self._accessor.searchString(expr)
+        return self._parse_expr.parseString(expr)[0]
+
+
+        """
+
+        # Pre-process accessors for indexing
+        self._accessor.searchString(expr)
+        return self._parse_expr.parseString(expr)[0]
+
+
+        """
+
+        # Pre-process accessors for indexing
+        self._accessor.searchString(expr)
+        return self._parse_expr.parseString(expr)[0]
+
+    def _pivot(self, cls, column):
+        """Pivot Data for concatenation"""
+
+        data_frame = self._get_data_frame(cls)
+        if data_frame.empty:
+            raise ValueError("No events found for {}".format(cls.name))
+
+        data_frame = handle_duplicate_index(data_frame)
+        new_index = self._agg_df.index.union(data_frame.index)
+
+        if hasattr(cls, "pivot") and cls.pivot:
+            pivot = cls.pivot
+            pivot_vals = list(np.unique(data_frame[pivot].values))
+            data = {}
+
+
+            for val in pivot_vals:
+                data[val] = data_frame[data_frame[pivot] == val][[column]]
+                if len(self._agg_df):
+                    data[val] = data[val].reindex(
+                        index=new_index,
+                        method=self._method,
+                        limit=self._limit)
+
+            return pd.concat(data, axis=1).swaplevel(0, 1, axis=1)
+
+        if len(self._agg_df):
+            data_frame = data_frame.reindex(
+                index=new_index,
+                method=self._method,
+                limit=self._limit)
+
+        return pd.concat({StatConf.GRAMMAR_DEFAULT_PIVOT: data_frame[
+                         [column]]}, axis=1).swaplevel(0, 1, axis=1)
+
+    def _pre_process(self, tokens):
+        """Pre-process accessors for super-indexing"""
+
+        params = tokens[0]
+        if params[1] in self._agg_df.columns:
+            return self._agg_df[params[1]]
+
+        event = params[0]
+        column = params[1]
+
+        if event in self._pvars:
+            cls = self._pvars[event]
+        elif event in self.data.class_definitions:
+            cls = self.data.class_definitions[event]
+        else:
+            try:
+                cls = str_to_attr(event)
+            except KeyError:
+                raise ValueError(
+                    "Can't find parser class for event {}".format(event))
+
+        data_frame = self._pivot(cls, column)
+        self._agg_df = pd.concat(
+            [self._agg_df, data_frame], axis=1)
+
+        if self._fill:
+            self._agg_df = self._agg_df.fillna(method="pad")
+
+        return self._agg_df[params[1]]
+
+    def _parse_for_info(self, tokens):
+        """Parse Action for inspecting data accessors"""
+
+        params = tokens[0]
+        cls = params[0]
+        column = params[1]
+        info = {}
+        info["pivot"] = None
+        info["pivot_values"] = None
+
+        if cls in self._pvars:
+            cls = self._pvars[cls]
+        elif cls in self.data.class_definitions:
+            cls = self.data.class_definitions[cls]
+        else:
+            cls = str_to_attr(cls)
+
+        data_frame = self._get_data_frame(cls)
+
+        info["class"] = cls
+        info["length"] = len(data_frame)
+        if hasattr(cls, "pivot") and cls.pivot:
+            info["pivot"] = cls.pivot
+            info["pivot_values"] = list(np.unique(data_frame[cls.pivot]))
+        info["column"] = column
+        info["column_present"] = column in data_frame.columns
+        return info
+
+    def _parse_var_id(self, tokens):
+        """A function to parse a variable identifier
+        """
+
+        params = tokens[0]
+        try:
+            return float(params)
+        except (ValueError, TypeError):
+            try:
+                return self._pvars[params]
+            except KeyError:
+                return self._agg_df[params[1]]
+
+    def _parse_func(self, tokens):
+        """A function to parse a function string"""
+
+        params = tokens[0]
+        func_name = params[0]
+        if func_name in self._pvars and isinstance(
+                self._pvars[func_name],
+                types.FunctionType):
+            func = self._pvars[func_name]
+        else:
+            func = str_to_attr(params[0])
+        return func(*params[1])
+
+    def _get_data_frame(self, cls):
+        """Get the data frame from the BareTrace object, applying the window
+        and the filters"""
+
+        data_frame = getattr(self.data, cls.name).data_frame
+
+        if data_frame.empty:
+            return data_frame
+        elif self._window[1] is None:
+            data_frame = data_frame.loc[self._window[0]:]
+        else:
+            data_frame = data_frame.loc[self._window[0]:self._window[1]]
+
+        if self._filters:
+            criterion = pd.Series([True] * len(data_frame),
+                                  index=data_frame.index)
+
+            for filter_col, wanted_vals in self._filters.iteritems():
+                try:
+                    dfr_col = data_frame[filter_col]
+                except KeyError:
+                    continue
+
+                criterion &= dfr_col.isin(listify(wanted_vals))
+
+            data_frame = data_frame[criterion]
+
+        return data_frame
+
+    def ref(self, mask):
+        """Reference super indexed data with a boolean mask
+
+        :param mask: A boolean :mod:`pandas.Series` that
+            can be used to reference the aggregated data in
+            the parser
+        :type mask: :mod:`pandas.Series`
+
+        :return: aggregated_data[mask]
+        """
+
+        return self._agg_df[mask]
+
+    def inspect(self, accessor):
+        """A function to inspect the accessor for information
+
+        :param accessor: A data accessor of the format
+            <event>:<column>
+        :type accessor: str
+
+        :return: A dictionary of information
+        """
+        return self._inspect.parseString(accessor)[0]
diff --git a/trappy/systrace.py b/trappy/systrace.py
new file mode 100644
index 0000000..c11601d
--- /dev/null
+++ b/trappy/systrace.py
@@ -0,0 +1,80 @@
+#    Copyright 2016-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from trappy.ftrace import GenericFTrace
+
+class drop_before_trace(object):
+    """Object that, when called, returns True if the line is not part of
+the trace
+
+    We have to first look for the "<!-- BEGIN TRACE -->" and then skip
+    the headers that start with #
+
+    """
+    def __init__(self):
+        self.before_begin_trace = True
+        self.before_script_trace_data = True
+        self.before_actual_trace = True
+
+    def __call__(self, line):
+        if self.before_begin_trace:
+            if line.startswith("<!-- BEGIN TRACE -->") or \
+               line.startswith("<title>Android System Trace</title>"):
+                self.before_begin_trace = False
+        elif self.before_script_trace_data:
+            if line.startswith('  <script class="trace-data"') or \
+               line.startswith("  var linuxPerfData"):
+                self.before_script_trace_data = False
+        elif not line.startswith("#"):
+            self.before_actual_trace = False
+
+        return self.before_actual_trace
+
+class SysTrace(GenericFTrace):
+    """A wrapper that parses all events of a SysTrace run
+
+    It receives the same parameters as :mod:`trappy.ftrace.FTrace`.
+
+    """
+
+    def __init__(self, path=".", name="", normalize_time=True, scope="all",
+                 events=[], event_callbacks={}, window=(0, None),
+                 abs_window=(0, None), build_df=True):
+
+        self.trace_path = path
+
+        super(SysTrace, self).__init__(name, normalize_time, scope, events,
+                                       event_callbacks, window, abs_window,
+                                       build_df)
+        if not build_df:
+            return
+        try:
+            self._cpus = 1 + self.sched_switch.data_frame["__cpu"].max()
+        except AttributeError:
+            pass
+
+    def trace_hasnt_started(self):
+        return drop_before_trace()
+
+    def trace_hasnt_finished(self):
+        """Return a function that returns True while the current line is still part of the trace
+
+        In Systrace, the first line that is not part of the trace is
+        </script>.  There's a further "<!-- END TRACE -->" but there's
+        not point scanning for it, we should stop parsing as soon as
+        we see the </script>
+
+        """
+        return lambda x: not x.endswith("</script>\n")
diff --git a/trappy/thermal.py b/trappy/thermal.py
new file mode 100644
index 0000000..bde69be
--- /dev/null
+++ b/trappy/thermal.py
@@ -0,0 +1,310 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Process the output of the power allocator trace in the current
+directory's trace.dat"""
+
+from collections import OrderedDict
+import pandas as pd
+import re
+
+from trappy.base import Base
+from trappy.dynamic import register_ftrace_parser
+
+class Thermal(Base):
+    """Process the thermal framework data in a FTrace dump"""
+
+    unique_word = "thermal_temperature:"
+    """The unique word that will be matched in a trace line"""
+
+    name = "thermal"
+    """The name of the :mod:`pandas.DataFrame` member that will be created in a
+    :mod:`trappy.ftrace.FTrace` object"""
+
+    pivot = "id"
+    """The Pivot along which the data is orthogonal"""
+
+    def plot_temperature(self, control_temperature=None, title="", width=None,
+                         height=None, ylim="range", ax=None, legend_label="",
+                         tz_id=None):
+        """Plot the temperature.
+
+        :param ax: Axis instance
+        :type ax: :mod:`matplotlib.Axis`
+
+        :param legend_label: Label for the legend
+        :type legend_label: str
+
+        :param title: The title of the plot
+        :type title: str
+
+        :param control_temperature: If control_temp is a
+            :mod:`pd.Series` representing the (possible)
+            variation of :code:`control_temp` during the
+            run, draw it using a dashed yellow line.
+            Otherwise, only the temperature is plotted.
+        :type control_temperature: :mod:`pandas.Series`
+
+        :param width: The width of the plot
+        :type width: int
+
+        :param height: The height of the plot
+        :type height: int
+
+        :param tz_id: thermal zone id as it appears in the id field of
+            the thermal_temperature trace event
+        :type tz_id: int
+
+        """
+        from matplotlib import pyplot as plt
+        from trappy.plot_utils import normalize_title, pre_plot_setup, post_plot_setup
+
+        title = normalize_title("Temperature", title)
+
+        if len(self.data_frame) == 0:
+            raise ValueError("Empty DataFrame")
+
+        thermal_dfr = self.data_frame
+        if tz_id is not None:
+            thermal_dfr = thermal_dfr[thermal_dfr["id"] == tz_id]
+            if len(thermal_dfr) == 0:
+                raise ValueError("No thermal_temperature trace for thermal zone {}".format(tz_id))
+
+        setup_plot = False
+        if not ax:
+            ax = pre_plot_setup(width, height)
+            setup_plot = True
+
+        temp_label = normalize_title("Temperature", legend_label)
+        (thermal_dfr["temp"] / 1000).plot(ax=ax, label=temp_label)
+        if control_temperature is not None:
+            ct_label = normalize_title("Control", legend_label)
+            control_temperature.plot(ax=ax, color="y", linestyle="--",
+                           label=ct_label)
+
+        if setup_plot:
+            post_plot_setup(ax, title=title, ylim=ylim)
+            plt.legend()
+
+    def plot_temperature_hist(self, ax, title):
+        """Plot a temperature histogram
+
+        :param ax: Axis instance
+        :type ax: :mod:`matplotlib.Axis`
+
+        :param title: The title of the plot
+        :type title: str
+        """
+        from trappy.plot_utils import normalize_title, plot_hist
+
+        temps = self.data_frame["temp"] / 1000
+        title = normalize_title("Temperature", title)
+        xlim = (0, temps.max())
+
+        plot_hist(temps, ax, title, "C", 30, "Temperature", xlim, "default")
+
+register_ftrace_parser(Thermal, "thermal")
+
+class ThermalGovernor(Base):
+    """Process the power allocator data in a ftrace dump"""
+
+    unique_word = "thermal_power_allocator:"
+    """The unique word that will be matched in a trace line"""
+
+    name = "thermal_governor"
+    """The name of the :mod:`pandas.DataFrame` member that will be created in a
+    :mod:`trappy.ftrace.FTrace` object"""
+
+    pivot = "thermal_zone_id"
+    """The Pivot along which the data is orthogonal"""
+
+    def plot_temperature(self, title="", width=None, height=None, ylim="range",
+                         ax=None, legend_label=""):
+        """Plot the temperature"""
+        from matplotlib import pyplot as plt
+        from trappy.plot_utils import normalize_title, pre_plot_setup, post_plot_setup
+
+        dfr = self.data_frame
+        curr_temp = dfr["current_temperature"]
+        control_temp_series = (curr_temp + dfr["delta_temperature"]) / 1000
+        title = normalize_title("Temperature", title)
+
+        setup_plot = False
+        if not ax:
+            ax = pre_plot_setup(width, height)
+            setup_plot = True
+
+        temp_label = normalize_title("Temperature", legend_label)
+        (curr_temp / 1000).plot(ax=ax, label=temp_label)
+        control_temp_series.plot(ax=ax, color="y", linestyle="--",
+                                 label="control temperature")
+
+        if setup_plot:
+            post_plot_setup(ax, title=title, ylim=ylim)
+            plt.legend()
+
+    def plot_input_power(self, actor_order, title="", width=None, height=None,
+                         ax=None):
+        """Plot input power
+
+        :param ax: Axis instance
+        :type ax: :mod:`matplotlib.Axis`
+
+        :param title: The title of the plot
+        :type title: str
+
+        :param width: The width of the plot
+        :type width: int
+
+        :param height: The height of the plot
+        :type int: int
+
+        :param actor_order: An array showing the order in which the actors
+           were registered.  The array values are the labels that
+           will be used in the input and output power plots.
+
+           For Example:
+           ::
+
+                ["GPU", "A15", "A7"]
+
+        :type actor_order: list
+        """
+        from trappy.plot_utils import normalize_title, pre_plot_setup, post_plot_setup
+
+        dfr = self.data_frame
+        in_cols = [s for s in dfr.columns if re.match("req_power[0-9]+", s)]
+
+        plot_dfr = dfr[in_cols]
+        # Rename the columns from "req_power0" to "A15" or whatever is
+        # in actor_order.  Note that we can do it just with an
+        # assignment because the columns are already sorted (i.e.:
+        # req_power0, req_power1...)
+        plot_dfr.columns = actor_order
+
+        title = normalize_title("Input Power", title)
+
+        if not ax:
+            ax = pre_plot_setup(width, height)
+
+        plot_dfr.plot(ax=ax)
+        post_plot_setup(ax, title=title)
+
+    def plot_weighted_input_power(self, actor_weights, title="", width=None,
+                                  height=None, ax=None):
+        """Plot weighted input power
+
+        :param actor_weights: An array of tuples.  First element of the
+            tuple is the name of the actor, the second is the weight.  The
+            array is in the same order as the :code:`req_power` appear in the
+            trace.
+        :type actor_weights: list
+
+        :param ax: Axis instance
+        :type ax: :mod:`matplotlib.Axis`
+
+        :param title: The title of the plot
+        :type title: str
+
+        :param width: The width of the plot
+        :type width: int
+
+        :param height: The height of the plot
+        :type int: int
+        """
+        from trappy.plot_utils import normalize_title, pre_plot_setup, post_plot_setup
+
+        dfr = self.data_frame
+        in_cols = [s for s in dfr.columns if re.match(r"req_power\d+", s)]
+
+        plot_dfr_dict = OrderedDict()
+        for in_col, (name, weight) in zip(in_cols, actor_weights):
+            plot_dfr_dict[name] = dfr[in_col] * weight / 1024
+
+        plot_dfr = pd.DataFrame(plot_dfr_dict)
+
+        title = normalize_title("Weighted Input Power", title)
+
+        if not ax:
+            ax = pre_plot_setup(width, height)
+
+        plot_dfr.plot(ax=ax)
+        post_plot_setup(ax, title=title)
+
+    def plot_output_power(self, actor_order, title="", width=None, height=None,
+                          ax=None):
+        """Plot output power
+
+        :param ax: Axis instance
+        :type ax: :mod:`matplotlib.Axis`
+
+        :param title: The title of the plot
+        :type title: str
+
+        :param width: The width of the plot
+        :type width: int
+
+        :param height: The height of the plot
+        :type int: int
+
+        :param actor_order: An array showing the order in which the actors
+            were registered.  The array values are the labels that
+            will be used in the input and output power plots.
+
+            For Example:
+            ::
+
+                ["GPU", "A15", "A7"]
+
+        :type actor_order: list
+        """
+        from trappy.plot_utils import normalize_title, pre_plot_setup, post_plot_setup
+
+        out_cols = [s for s in self.data_frame.columns
+                    if re.match("granted_power[0-9]+", s)]
+
+        # See the note in plot_input_power()
+        plot_dfr = self.data_frame[out_cols]
+        plot_dfr.columns = actor_order
+
+        title = normalize_title("Output Power", title)
+
+        if not ax:
+            ax = pre_plot_setup(width, height)
+
+        plot_dfr.plot(ax=ax)
+        post_plot_setup(ax, title=title)
+
+    def plot_inout_power(self, title=""):
+        """Make multiple plots showing input and output power for each actor
+
+        :param title: The title of the plot
+        :type title: str
+        """
+        from trappy.plot_utils import normalize_title
+        dfr = self.data_frame
+
+        actors = []
+        for col in dfr.columns:
+            match = re.match("P(.*)_in", col)
+            if match and col != "Ptot_in":
+                actors.append(match.group(1))
+
+        for actor in actors:
+            cols = ["P" + actor + "_in", "P" + actor + "_out"]
+            this_title = normalize_title(actor, title)
+            dfr[cols].plot(title=this_title)
+
+register_ftrace_parser(ThermalGovernor, "thermal")
diff --git a/trappy/utils.py b/trappy/utils.py
new file mode 100644
index 0000000..eb73752
--- /dev/null
+++ b/trappy/utils.py
@@ -0,0 +1,104 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Generic functions that can be used in multiple places in trappy
+"""
+
+def listify(to_select):
+    """Utitlity function to handle both single and
+    list inputs
+    """
+
+    if not isinstance(to_select, list):
+        to_select = [to_select]
+
+    return to_select
+
+def handle_duplicate_index(data,
+                           max_delta=0.000001):
+    """Handle duplicate values in index
+
+    :param data: The timeseries input
+    :type data: :mod:`pandas.Series`
+
+    :param max_delta: Maximum interval adjustment value that
+        will be added to duplicate indices
+    :type max_delta: float
+
+    Consider the following case where a series needs to be reindexed
+    to a new index (which can be required when different series need to
+    be combined and compared):
+    ::
+
+        import pandas
+        values = [0, 1, 2, 3, 4]
+        index = [0.0, 1.0, 1.0, 6.0, 7.0]
+        series = pandas.Series(values, index=index)
+        new_index = [0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 7.0]
+        series.reindex(new_index)
+
+    The above code fails with:
+    ::
+
+        ValueError: cannot reindex from a duplicate axis
+
+    The function :func:`handle_duplicate_axis` changes the duplicate values
+    to
+    ::
+
+        >>> import pandas
+        >>> from trappy.utils import handle_duplicate_index
+
+        >>> values = [0, 1, 2, 3, 4]
+        index = [0.0, 1.0, 1.0, 6.0, 7.0]
+        series = pandas.Series(values, index=index)
+        series = handle_duplicate_index(series)
+        print series.index.values
+        >>> [ 0.        1.        1.000001  6.        7.      ]
+
+    """
+
+    index = data.index
+    new_index = index.values
+
+    dups = index.get_duplicates()
+
+    for dup in dups:
+        # Leave one of the values intact
+        dup_index_left = index.searchsorted(dup, side="left")
+        dup_index_right = index.searchsorted(dup, side="right") - 1
+        num_dups = dup_index_right - dup_index_left + 1
+
+        # Calculate delta that needs to be added to each duplicate
+        # index
+        try:
+            delta = (index[dup_index_right + 1] - dup) / num_dups
+        except IndexError:
+            # dup_index_right + 1 is outside of the series (i.e. the
+            # dup is at the end of the series).
+            delta = max_delta
+
+        # Clamp the maximum delta added to max_delta
+        if delta > max_delta:
+            delta = max_delta
+
+        # Add a delta to the others
+        dup_index_left += 1
+        while dup_index_left <= dup_index_right:
+            new_index[dup_index_left] += delta
+            delta += delta
+            dup_index_left += 1
+
+    return data.reindex(new_index)
diff --git a/trappy/version.py b/trappy/version.py
new file mode 100644
index 0000000..76a3dd3
--- /dev/null
+++ b/trappy/version.py
@@ -0,0 +1,16 @@
+#    Copyright 2016-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+__version__ = "5.10.0"
diff --git a/trappy/wa/__init__.py b/trappy/wa/__init__.py
new file mode 100644
index 0000000..cb29fd3
--- /dev/null
+++ b/trappy/wa/__init__.py
@@ -0,0 +1,26 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""A helper module to extract data from WorkloadAutomation
+output directories. WorkloadAutomation is a tool for
+automating the execution of workloads. For more information
+please visit https://github.com/ARM-software/workload-automation
+
+.. note::
+
+    TRAPpy does not have a dependency on workload automation
+"""
+
+from trappy.wa.results import Result, get_results, combine_results
+from trappy.wa.sysfs_extractor import SysfsExtractor
diff --git a/trappy/wa/results.py b/trappy/wa/results.py
new file mode 100644
index 0000000..4b0889c
--- /dev/null
+++ b/trappy/wa/results.py
@@ -0,0 +1,166 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Parse the results from a Workload Automation run and show it in a
+"pretty" table
+
+"""
+
+import os
+import collections, csv, re
+import pandas as pd
+from matplotlib import pyplot as plt
+
+class Result(pd.DataFrame):
+    """A DataFrame-like class for storing benchmark results"""
+    def __init__(self, *args, **kwargs):
+        super(Result, self).__init__(*args, **kwargs)
+        self.ax = None
+
+    def init_fig(self):
+        _, self.ax = plt.subplots()
+
+    def enlarge_axis(self, data):
+        """Make sure that the axis don't clobber some of the data"""
+
+        (_, _, plot_y_min, plot_y_max) = plt.axis()
+
+        concat_data = pd.concat(data[s] for s in data)
+        data_min = min(concat_data)
+        data_max = max(concat_data)
+
+        # A good margin can be 10% of the data range
+        margin = (data_max - data_min) / 10
+        if margin < 1:
+            margin = 1
+
+        update_axis = False
+
+        if data_min <= plot_y_min:
+            plot_y_min = data_min - margin
+            update_axis = True
+
+        if data_max >= plot_y_max:
+            plot_y_max = data_max + margin
+            update_axis = True
+
+        if update_axis:
+            self.ax.set_ylim(plot_y_min, plot_y_max)
+
+    def plot_results_benchmark(self, benchmark, title=None):
+        """Plot the results of the execution of a given benchmark
+
+        A title is added to the plot if title is not supplied
+        """
+
+        if title is None:
+            title = benchmark.replace('_', ' ')
+            title = title.title()
+
+        self[benchmark].plot(ax=self.ax, kind="bar", title=title)
+        plt.legend(bbox_to_anchor=(1.05, .5), loc=6)
+
+    def plot_results(self):
+        for bench in self.columns.levels[0]:
+            self.plot_results_benchmark(bench)
+
+def get_run_number(metric):
+    found = False
+    run_number = None
+
+    if re.match("Overall_Score|score|FPS", metric):
+        found = True
+
+        match = re.search(r"(.+)[ _](\d+)", metric)
+        if match:
+            run_number = int(match.group(2))
+            if match.group(1) == "Overall_Score":
+                run_number -= 1
+        else:
+            run_number = 0
+
+    return (found, run_number)
+
+def get_results(path=".", name=None):
+    """Return a pd.DataFrame with the results
+
+    The DataFrame's rows are the scores.  The first column is the
+    benchmark name and the second the id within it.  For benchmarks
+    that have a score result, that's what's used.  For benchmarks with
+    FPS_* result, that's the score.  E.g. glbenchmarks "score" is it's
+    fps.
+
+    An optional name argument can be passed.  If supplied, it overrides
+    the name in the results file.
+
+    """
+
+    bench_dict = collections.OrderedDict()
+
+    if os.path.isdir(path):
+        path = os.path.join(path, "results.csv")
+
+    with open(path) as fin:
+        results = csv.reader(fin)
+
+        for row in results:
+            (is_result, run_number) = get_run_number(row[3])
+
+            if is_result:
+                if name:
+                    run_id = name
+                else:
+                    run_id = re.sub(r"_\d+", r"", row[0])
+
+                bench = row[1]
+                try:
+                    result = int(row[4])
+                except ValueError:
+                    result = float(row[4])
+
+                if bench in bench_dict:
+                    if run_id in bench_dict[bench]:
+                        if run_number not in bench_dict[bench][run_id]:
+                            bench_dict[bench][run_id][run_number] = result
+                    else:
+                        bench_dict[bench][run_id] = {run_number: result}
+                else:
+                    bench_dict[bench] = {run_id: {run_number: result}}
+
+    bench_dfrs = {}
+    for bench, run_id_dict in bench_dict.iteritems():
+        bench_dfrs[bench] = pd.DataFrame(run_id_dict)
+
+    return Result(pd.concat(bench_dfrs.values(), axis=1,
+                            keys=bench_dfrs.keys()))
+
+def combine_results(data):
+    """Combine two DataFrame results into one
+
+    The data should be an array of results like the ones returned by
+    get_results() or have the same structure.  The returned DataFrame
+    has two column indexes.  The first one is the benchmark and the
+    second one is the key for the result.
+
+    """
+
+    res_dict = {}
+    for benchmark in data[0].columns.levels[0]:
+        concat_objs = [d[benchmark] for d in data]
+        res_dict[benchmark] = pd.concat(concat_objs, axis=1)
+
+    combined = pd.concat(res_dict.values(), axis=1, keys=res_dict.keys())
+
+    return Result(combined)
diff --git a/trappy/wa/sysfs_extractor.py b/trappy/wa/sysfs_extractor.py
new file mode 100644
index 0000000..f45ed77
--- /dev/null
+++ b/trappy/wa/sysfs_extractor.py
@@ -0,0 +1,93 @@
+#    Copyright 2015-2017 ARM Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import os
+import pandas as pd
+import re
+
+class SysfsExtractor(object):
+    """Operate on the parameters of a dump of Workload Automation's
+       sysfs extractor instrumentation.
+
+    :param path: The path to the workload in a output directory created by
+        WA.
+    :type path: str
+    """
+
+    def __init__(self, path):
+        self.thermal_path = os.path.join(path, "after", "sys", "devices",
+                                         "virtual", "thermal", "thermal_zone0")
+        self.properties = ["integral_cutoff", "k_d", "k_i", "k_po", "k_pu",
+                           "policy", "sustainable_power"]
+
+        try:
+            sysfs_files = os.listdir(self.thermal_path)
+        except OSError:
+            sysfs_files = []
+
+        for fname in sysfs_files:
+            if re.search(r"cdev\d+_weight", fname):
+                self.properties.append(fname)
+            elif re.search(r"trip_point_\d+_temp", fname):
+                self.properties.append(fname)
+
+    def get_parameters(self):
+        """Get the parameters from a sysfs extractor dump
+
+        WorkloadAutomation (WA) can dump sysfs values using its
+        sysfs_extractor instrumentation.  Parse the tree and return the
+        thermal parameters as a dict of key and values where the keys are
+        the names of the files and values its corresponding values.
+
+        """
+
+        ret = {}
+
+        for property_name in self.properties:
+            property_path = os.path.join(self.thermal_path, property_name)
+
+            if not os.path.isfile(property_path):
+                continue
+
+            with open(property_path) as fin:
+                contents = fin.read()
+                # Trim trailing newline
+                contents = contents[:-1]
+
+                try:
+                    ret[property_name] = int(contents)
+                except ValueError:
+                    ret[property_name] = contents
+
+        return ret
+
+    def pretty_print_in_ipython(self):
+        """Print parameters extracted from sysfs from a WA run in a pretty HTML table.
+
+        This won't work if the code is not running in an ipython notebook."""
+
+        from IPython.display import display, HTML
+
+        params = self.get_parameters()
+
+        # Don't print anything if we couldn't find any parameters
+        if len(params) == 0:
+            return
+
+        params_items = [(key, [value]) for key, value in sorted(params.items())]
+        dfr = pd.DataFrame.from_items(params_items, orient="index",
+                                      columns=["Value"])
+        display(HTML(dfr.to_html(header=False)))