diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index ce8f01c..0000000
--- a/.gitignore
+++ /dev/null
@@ -1,7 +0,0 @@
-*.pyc
-report/index.html
-report/*.json
-report/images
-
-*.ipynb
-.ipynb_checkpoints
diff --git a/.ipynb_checkpoints/Untitled0-checkpoint.ipynb b/.ipynb_checkpoints/Untitled0-checkpoint.ipynb
new file mode 100644
index 0000000..9023908
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled0-checkpoint.ipynb
@@ -0,0 +1,139 @@
+{
+ "metadata": {
+  "name": "Untitled0"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline\n",
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
+        "For more information, type 'help(pylab)'.\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import json\n",
+      "\n",
+      "with open('benchmark_results.json') as f:\n",
+      "    data = json.load(f)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "groups = data['benchmark_results']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pairwise_group = [g for g in groups if g['group_name'] == 'pairwise'][0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plot_group(group, width=0.35):\n",
+      "    records = group['records']\n",
+      "    labels = [r['name'] for r in records]\n",
+      "    warm_time = np.asarray([r['warm_time'] for r in records])\n",
+      "    max_time = np.asarray([r['cold_time'] or r['warm_time'] for r in records])\n",
+      "    overhead = max_time - warm_time\n",
+      "    \n",
+      "    ind = np.arange(len(labels))\n",
+      "    p1 = plt.bar(ind, warm_time,   width, color='g')\n",
+      "    p2 = plt.bar(ind, overhead, width, color='b', bottom=warm_time)\n",
+      "    \n",
+      "    plt.ylabel('Time (s)')\n",
+      "    plt.title(group['group_name'])\n",
+      "    plt.xticks(ind + width /2., labels, rotation=45)\n",
+      "    plt.ylim((0, np.median(max_time) * 2))\n",
+      "    plt.legend( (p1[0], p2[0]), ('Execution time', 'Cold startup overhead'), loc='best')\n",
+      "    plt.savefig(group['group_name'] + '.png')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 58
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot_group(pairwise_group)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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p62YwVqNUKXs0bNgQjx49Eh6fPXuWx2pjtYJUmofq3kcllfKAuYy9iiolnuXL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+       "text": [
+        "<matplotlib.figure.Figure at 0x10fd85ed0>"
+       ]
+      }
+     ],
+     "prompt_number": 59
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "!open pairwise.png"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 61
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/LICENSE.TXT b/LICENSE.TXT
deleted file mode 100644
index 1e8a1d6..0000000
--- a/LICENSE.TXT
+++ /dev/null
@@ -1,19 +0,0 @@
-Copyright (C) 2013, python-benchmarks contributors
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of
-this software and associated documentation files (the "Software"), to deal in
-the Software without restriction, including without limitation the rights to
-use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
-of the Software, and to permit persons to whom the Software is furnished to do
-so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/Makefile b/Makefile
deleted file mode 100644
index 6cb2193..0000000
--- a/Makefile
+++ /dev/null
@@ -1,14 +0,0 @@
-WEB_REPO_ALIAS  ?= origin
-
-all: clean run
-
-clean:
-	rm -f report/*.json
-	rm -f report/*.html
-
-run:
-	python run_benchmarks.py
-
-github:
-	@echo "Publish report to github.io pages"
-	ghp-import -p report -r ${WEB_REPO_ALIAS}
diff --git a/README.md b/README.md
deleted file mode 100644
index cab692b..0000000
--- a/README.md
+++ /dev/null
@@ -1,127 +0,0 @@
-python-benchmarks
-=================
-
-A set of benchmark problems and implementations for Python.
-
-
-Results
--------
-
-[numfocus.github.io/python-benchmarks](
-  http://numfocus.github.io/python-benchmarks)
-
-
-Motivation
-----------
-
-This repository is the result of a discussion of started by
-[@aterrel](https://github.com/aterrel) at SciPy 2013 where people interested in
-the development of compiler technologies for the Python programming language
-shared design decisions.
-
-The goal of this repository to gather Python implementations of realistic use
-cases where:
-
-- naive code written with the [CPython](http://python.org) interpreter is too
-  slow to be of practical use,
-
-- an implementation of the algorithm cannot be efficiently vectorized using
-  NumPy primitives (for instance by involving nested `for`-loops)
-
-Initial use cases focus on **data processing** tasks such as **machine
-learning** and **signal processing**.
-
-For each benchmark, we would like to gather:
-
-- a naive pure python implementation (optionally using NumPy for large
-  homogeneous numerical datastructures) run using CPython
-
-- variants of the Python version that should be able to run the naive pure
-  Python version with minimal code change:
-
-  - JIT compiler packaged as a library for CPython such as:
-      - [Numba](http://numba.pydata.org/) or
-      - [Parakeet](http://www.parakeetpython.com)
-
-  - JIT compiler implemented in an alternative Python interpreter such as:
-      - [PyPy](http://pypy.org/) optionally with
-        [NumPyPy](https://bitbucket.org/pypy/numpypy)
-
-  - Python to C/C++ code translation + compiled extension for the CPython
-    interpreter such as done by:
-      - [Pythran](https://github.com/serge-sans-paille/pythran)
-      - [Shed Skin](http://code.google.com/p/shedskin/)
-
-- pure Python programs that explicitly represent the computation as a graph of
-  Python objects and use code generation and a compiler to dynamically build a
-  compiled extension such as done by [Theano](https://github.com/Theano/Theano)
-
-- alternative language implementations in Cython, C or Fortran with Python
-  bindings to serve as speed reference.
-
-
-Running
--------
-
-To run all the benchmarks:
-
-    python run_benchmarks.py
-
-To run the benchmarks of a specific folder:
-
-    python run_benchmarks.py --folders pairwise
-
-To run only the benchmarks with specific platforms:
-
-    python run_benchmarks.py --platforms numba parakeet cython
-
-To ignore previously collected data:
-
-    python run_benchmarks.py --ignore-data
-
-To see all the tracebacks of the collected errors:
-
-    python run_benchmarks.py --log-level debug
-
-To open a browser on the generated HTML report page:
-
-    python run_benchmarks.py --open-report
-
-To publish the generated report to github (assuming you want to push to
-origin):
-
-    make github
-
-Or to another remote alias:
-
-    WEB_ALIAS_REPO=upstream make github
-
-
-Dependencies
-------------
-
-### Using pip
-
-- Some dependencies use [llvmpy](http://www.llvmpy.org/) that require to have
-  llvm built with the `REQUIRES_RTTI=1` environment variable. Under OSX you
-  can install llvm with HomeBrew:
-
-    brew install llvm --rtti
-
-- Install the dependencies from the `requirements.txt` file:
-
-    pip install -r requirements.txt
-
-Note: some packages (pythran and ply) have a depency on SciPy which is
-complicated and slow to install from source because of the need of a gfortran
-compiler and a large C++ code base. It is recommended to install a binary
-package for SciPy (see http://scipy.org/install.html for instructions).
-
-
-### Using conda / Anaconda
-
-TODO
-
-### Non CPython dependencies
-
-You can also install PyPy from http://pypy.org
diff --git a/Untitled0.ipynb b/Untitled0.ipynb
new file mode 100644
index 0000000..d26c519
--- /dev/null
+++ b/Untitled0.ipynb
@@ -0,0 +1,221 @@
+{
+ "metadata": {
+  "name": "Untitled0"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline\n",
+      "import os\n",
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
+        "For more information, type 'help(pylab)'.\n"
+       ]
+      }
+     ],
+     "prompt_number": 104
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import json\n",
+      "\n",
+      "with open('benchmark_results.json') as f:\n",
+      "    data = json.load(f)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 105
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "groups = data['benchmark_results']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 106
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pairwise_group = [g for g in groups if g['group_name'] == 'pairwise'][0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 107
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plot_group(group, width=0.5, folder=\"report/images\"):\n",
+      "    records = group['records']\n",
+      "    name = group['group_name']\n",
+      "    labels = [r['name'][len(name) + 1:] for r in records]\n",
+      "    warm_time = np.asarray([r['warm_time'] for r in records])\n",
+      "    max_time = np.asarray([r['cold_time'] or r['warm_time'] for r in records])\n",
+      "    overhead = max_time - warm_time\n",
+      "    \n",
+      "    plt.figure(figsize=(12, 6))\n",
+      "    ind = np.arange(len(labels))\n",
+      "    p1 = plt.bar(ind, warm_time,   width, color='g', alpha=0.6)\n",
+      "    p2 = plt.bar(ind, overhead, width, color='g', alpha=0.2, bottom=warm_time)\n",
+      "    \n",
+      "    plt.ylabel('Time (s)')\n",
+      "    plt.title(group['group_name'])\n",
+      "    plt.xticks(ind + width /2., labels, rotation=15)\n",
+      "    plt.ylim((0, warm_time[0] * 5))\n",
+      "    plt.legend((p1[0], p2[0]),\n",
+      "               ('Execution time', 'Cold startup overhead'),\n",
+      "               loc='best')\n",
+      "    if not os.path.exists(folder):\n",
+      "        os.makedirs(folder)\n",
+      "    filename = os.path.join(folder, group['group_name'] + '.png')\n",
+      "    plt.savefig(filename)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 134
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for group in groups:\n",
+      "    plot_group(group)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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smTRpkvz8/NSpUycdPHhQkpSWlqb+/furXbt2at++vRYuXGitP3PmjAYOHKjW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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10fd668d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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k6dixY1q/fr31jP2iRYv0wgsvyNPTU/b29po7d64+/PDDKmfaY2Ji1KxZMzVt2lSS5Ovr\nq6ioKJlMJj344IMqKCjQ77//rgMHDmj9+vV6/fXX1axZM7Vu3VrTpk3TqlWrJEnXX3+9Bg4cKHt7\ne7m5uemxxx6z/twu9P0CAACNA0H7CtGqVSsdOnSoSsg7W35+vnx9fa3L7dq1U35+frXtvLy8qjzn\n6+tbY9hdsGCBLBaLevbsqS5dumjJkiU11nDy5ElNnDhRfn5+cnJy0oABA3T06NEq665u9pMLPTtb\nXf017evZ2/P19bUOZxk9erQ+/vhjlZaW6uOPP1a3bt2s7bOysnTnnXfKxcVFLi4u6ty5s+zs7HTg\nwIEa96Vt27bWx82bN5ckHT9+XNnZ2SorK5OHh4d1fZMmTdLBgwclSQcOHNCoUaPk7e0tJycnRURE\n6PDhw9Z9uJD3CwAANA4E7StEnz591KRJE61du7bGNp6ensrKyrIu79+/X56enue08/DwqDJuWpKy\ns7NrDLvu7u6Ki4tTXl6eFi1apIcffli//vprtW0XLlyovXv3Ki0tTUePHtWWLVusFz1WOns7Zy87\nOjpKOh3aKxUWFlZpU139Z4dR6fTPJCcnp8r2s7Oz5e3tLUnq3LmzfH19tX79eq1cuVJjxoyxtmvX\nrp2Sk5N15MgR67+TJ0/Kw8Ojxtpr4uPjoyZNmujw4cPWdR09elS7d++WJD311FOytbXVzz//rKNH\nj2rFihXWP6ou9P0CAACNA0H7CuHk5KTnnntOjzzyiBISEnTy5EmVlZVp/fr1mjlzpqTTZ2dfeOEF\nHTp0SIcOHdJzzz2niIiIc9bVp08f2dnZ6c0331RZWZk+/vhjbdu2rcZtr1mzRrm5uZIkZ2dnmUwm\n6xAWd3d37du3z9r2+PHjatasmZycnFRUVKS///3v56zv7DOxZ6+jdevW8vLy0ooVK2Q2m7V48eIq\nr0vS77//bq1/zZo1+t///qdhw4ads63evXurefPmWrBggcrKyrR582atW7dOo0aNsrYZM2aM3njj\nDX399de69957rc9PmjRJTz31lPbv3y9JOnjwoBITE2v8OZ2Ph4eHBg0apOnTp+vYsWOqqKjQvn37\nrOPcjx8/LkdHR7Vs2VJ5eXl65ZVXrH0v9P0CAACNA0H7CjJ9+nS99tpreuGFF9SmTRu1a9dOb731\nlvUCyWeeeUbdu3dX165d1bVrV3Xv3l3PPPOMtX/lGVAHBwd9/PHHWrp0qVq1aqUPPvhAd999d43b\n/eGHH9S7d2+1aNFCf/3rX/Xmm2/Kz89P0ukxypGRkXJxcdGHH36oadOm6Y8//pCbm5tuueUWDR06\ntNYz2FOnTtWHH34oV1dXTZs2TZL0zjvv6JVXXpGbm5vS09PPmb6wV69eysjIUOvWrTVnzhx9+OGH\ncnFxOad2e3t7ffrpp1q/fr1at26tKVOmaMWKFerYsaO1zejRo5WSkqKBAwfK1dW1Sl3h4eEaNGiQ\nWrZsqT59+igtLa3G/ahubuszl5cvX67S0lJ17txZrq6uuvfee61n6ufOnasff/xRTk5OGjFihO6+\n++56v18AAKBxMFmu0oGgJpOp2jGu1T1/Jd2wBqen93vvvff09ddfN3QpV5yajmsA0pD7h8g3wrf2\nhjBE9opsJa9Orr0hDMHx33DiRsTV+H8zN6yRCMEAAAC45Bg6gisOtx8HAABXA8ODdnJysgIDAxUQ\nEGC9w+HZoqOjFRAQoJCQEO3YscP6/IQJE+Tu7q7g4OAq7Z944gkFBQUpJCREd911V5U77KHxi4yM\ntF5ECAAA0FgZGrTNZrOmTJmi5ORkpaenKz4+Xnv27KnSJikpSZmZmcrIyFBcXJwmT55sfW38+PFV\nbnFdadCgQfrll1+0c+dOdezYUS+//LKRuwEAAABcMEODduVttP38/GRvb69Ro0YpISGhSpvExERF\nRkZKOj3TRHFxsXWmhn79+lU700RYWJh1+rlevXpZp6YDAAAArhSGBu28vLwqd9bz9vY+58YcdWlz\nPosXL652fmUAAACgIRk660hdL2g7e0qUuvZ78cUX5eDgUOVuf2eKiYmxPg4NDVVoaKhcXFy40A6N\nVnXf8AAAgMsnf3e+CnYX1KmtoUHby8tLOTk51uWcnBzrrbFrapObm1vtrbbPtnTpUiUlJWnjxo01\ntjkzaFcqKiqqQ+UAAADAuTyDPeUZ7Gld3h6/vca2hg4d6d69uzIyMpSVlaXS0lKtXr1a4eHhVdqE\nh4dr+fLlkqTU1FQ5OzvL3d39vOtNTk7WK6+8ooSEBDVt2tSw+gEAAID6MjRo29nZKTY2VoMHD1bn\nzp11//33KygoSIsWLdKiRYskScOGDVOHDh3k7++viRMn6q233rL2Hz16tG655Rbt3btXPj4+WrJk\niSTp0Ucf1fHjxxUWFqabbrpJDz/8sJG7AQAAAFwww+8MOXToUA0dOrTKcxMnTqyyHBsbW23f+Pj4\nap/PyMi4NMUBAAAABuHOkAAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI\n2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQja\nAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoA\nAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAA\nAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAA\ngAEMDdrJyckKDAxUQECA5s+fX22b6OhoBQQEKCQkRDt27LA+P2HCBLm7uys4OLhK+6KiIoWFhalj\nx44aNGiQiouLjdwFAAAAoF4MC9pms1lTpkxRcnKy0tPTFR8frz179lRpk5SUpMzMTGVkZCguLk6T\nJ0+2vjZ+/HglJyefs9558+YpLCxMe/fu1cCBAzVv3jyjdgEAAACoN8OCdlpamvz9/eXn5yd7e3uN\nGjVKCQkJVdokJiYqMjJSktSrVy8VFxersLBQktSvXz+5uLics94z+0RGRuqTTz4xahcAAACAejMs\naOfl5cnHx8e67O3trby8vAtuc7YDBw7I3d1dkuTu7q4DBw5cwqoBAACAS8POqBWbTKY6tbNYLPXq\nV9n2fO1jYmKsj0NDQxUaGlrndQMAAABny9+dr4LdBXVqa1jQ9vLyUk5OjnU5JydH3t7e522Tm5sr\nLy+v867X3d1dhYWFatu2rQoKCtSmTZsa254ZtAEAAICL5RnsKc9gT+vy9vjtNbY1bOhI9+7dlZGR\noaysLJWWlmr16tUKDw+v0iY8PFzLly+XJKWmpsrZ2dk6LKQm4eHhWrZsmSRp2bJlGjlypDE7AAAA\nAFwEw4K2nZ2dYmNjNXjwYHXu3Fn333+/goKCtGjRIi1atEiSNGzYMHXo0EH+/v6aOHGi3nrrLWv/\n0aNH65ZbbtHevXvl4+OjJUuWSJJmzZqlL774Qh07dtSmTZs0a9Yso3YBAAAAqDeT5exB0lcJk8l0\nzvhvAMDVa8j9Q+Qb4dvQZVyzsldkK3n1udPy4vLg+G84cSPiasyc3BkSAAAAMABBGwAAADAAQRsA\nAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAA\nADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAA\nMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAw\nAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAA\nQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwgKFBOzk5WYGBgQoICND8+fOrbRMdHa2AgACFhIRo\nx44dtfZNS0tTz549ddNNN6lHjx7atm2bkbsAAAAA1IthQdtsNmvKlClKTk5Wenq64uPjtWfPnipt\nkpKSlJmZqYyMDMXFxWny5Mm19n3yySf1/PPPa8eOHXruuef05JNPGrULAAAAQL0ZFrTT0tLk7+8v\nPz8/2dvba9SoUUpISKjSJjExUZGRkZKkXr16qbi4WIWFheft6+HhoaNHj0qSiouL5eXlZdQuAAAA\nAPVmZ9SK8/Ly5OPjY1329vbW1q1ba22Tl5en/Pz8GvvOmzdPffv21eOPP66Kigp9//33Ru0CAAAA\nUG+GBW2TyVSndhaL5YLWGxUVpTfffFN33nmn1qxZowkTJuiLL76otm1MTIz1cWhoqEJDQy9oWwAA\nAMCZ8nfnq2B3QZ3aGha0vby8lJOTY13OycmRt7f3edvk5ubK29tbZWVlNfZNS0vTl19+KUm65557\n9Le//a3GGs4M2gAAAMDF8gz2lGewp3V5e/z2GtsaNka7e/fuysjIUFZWlkpLS7V69WqFh4dXaRMe\nHq7ly5dLklJTU+Xs7Cx3d/fz9vX399eWLVskSZs2bVLHjh2N2gUAAACg3gw7o21nZ6fY2FgNHjxY\nZrNZUVFRCgoK0qJFiyRJEydO1LBhw5SUlCR/f385OjpqyZIl5+0rSXFxcXrkkUf0559/qlmzZoqL\nizNqFwAAAIB6M1kudJB0I2EymS54/DcAoPEacv8Q+Ub4NnQZ16zsFdlKXp3c0GVcszj+G07ciLga\nM+d5z2iXlZXp888/V0pKirKysmQymeTr66v+/ftr8ODBsrMz7IQ4AAAA0KjVOEb7+eefV48ePbRu\n3ToFBgZqwoQJioyMVKdOnfTpp5+qe/fueuGFFy5nrQAAAECjUeMp6ZCQED3zzDPVTtM3YcIEVVRU\naN26dYYWBwAAADRWNZ7RDg8PPydkV1RUqKSk5HRHG5tzZhEBAAAAcFqt0/uNHj1aJSUlOnHihLp0\n6aKgoCAtWLDgctQGAAAANFq1Bu309HS1bNlSn3zyiYYOHaqsrCytWLHictQGAAAANFq1Bu3y8nKV\nlZXpk08+0YgRI2Rvb1/n26sDAAAA16pag/bEiRPl5+en48ePq3///srKypKTk9PlqA0AAABotGoN\n2tHR0crLy9P69etlY2MjX19fbdq06XLUBgAAADRaNQbtpUuXqry8/JznTSaT7O3tVVpaar1lOgAA\nAICqapxH+/jx4+rRo4cCAwPVvXt3eXh4yGKxqLCwUD/88IP++9//6qGHHrqctQIAAACNRo1Be8qU\nKXrkkUf07bff6ptvvtE333wjSfL19dWUKVN0yy23cFEkAAAAUIMag7Z0ephI37591bdv38tVDwAA\nAHBVqPViSAAAAAAXjqANAAAAGICgDQAAABig1qBdWFioqKgoDRkyRNLpW7K/9957hhcGAAAANGa1\nBu1x48Zp0KBBys/PlyQFBATo9ddfN7wwAAAAoDGrNWgfOnRI999/v2xtbSVJ9vb2srM772QlAAAA\nwDWv1qB93XXX6fDhw9bl1NRUOTk5GVoUAAAA0NjVemp64cKFGjFihH799VfdcsstOnjwoD788MPL\nURsAAADQaNUatLt166YtW7Zo7969slgs6tSpk+zt7S9HbQAAAECjVWvQLi8vV1JSkrKyslReXq4N\nGzbIZDJp+vTpl6M+AAAAoFGqNWiPGDFCzZo1U3BwsGxsmHYbAAAAqItag3ZeXp527dp1OWoBAAAA\nrhq1nqIeNGiQNmzYcDlqAQAAAK4atZ7RvuWWW3TnnXeqoqLCehGkyWRSSUmJ4cUBAAAAjVWtQXv6\n9OlKTU1Vly5dGKMNAAAA1FGtybldu3a64YYbCNkAAADABaj1jHb79u112223aejQoXJwcJAkpvcD\nAAAAalGnoN2+fXuVlpaqtLRUFotFJpPpctQGAAAANFq1Bu2YmJjLUAYAAABwdakxaE+ZMkWxsbEa\nMWLEOa+ZTCYlJiYaWhgAAADQmNUYtJctW6bY2FjNmDHjnNcYOgIAAACcX41B29/fX5IUGhp6uWoB\nAAAArhpl6kedAAAgAElEQVQ1Bu2DBw/qtddek8ViOec1Zh0BAAAAzq/GoG02m3Xs2LHLWQsAAABw\n1agxaLdt21Zz5869nLUAAAAAVw1u9wgAAAAYoMag/eWXX170ypOTkxUYGKiAgADNnz+/2jbR0dEK\nCAhQSEiIduzYUae+//znPxUUFKQuXbpo5syZF10nAAAAcKnVOHSkVatWF7Vis9msKVOm6Msvv5SX\nl5d69Oih8PBwBQUFWdskJSUpMzNTGRkZ2rp1qyZPnqzU1NTz9v3qq6+UmJioXbt2yd7eXgcPHryo\nOgEAAAAjGDZ0JC0tTf7+/vLz85O9vb1GjRqlhISEKm0SExMVGRkpSerVq5eKi4tVWFh43r7//ve/\nNXv2bNnb20uSWrdubdQuAAAAAPVmWNDOy8uTj4+Pddnb21t5eXl1apOfn19j34yMDKWkpKh3794K\nDQ3VDz/8YNQuAAAAAPVW49CRi1XXu0dWN0/3+ZSXl+vIkSNKTU3Vtm3bdN999+nXX3+ttm1MTIz1\ncWhoKDffAQAAwEXJ352vgt0FdWprWND28vJSTk6OdTknJ0fe3t7nbZObmytvb2+VlZXV2Nfb21t3\n3XWXJKlHjx6ysbHR4cOHqx1TfmbQBgAAAC6WZ7CnPIM9rcvb47fX2NawoSPdu3dXRkaGsrKyVFpa\nqtWrVys8PLxKm/DwcC1fvlySlJqaKmdnZ7m7u5+378iRI7Vp0yZJ0t69e1VaWnrRF24CAAAAl5ph\nZ7Tt7OwUGxurwYMHy2w2KyoqSkFBQVq0aJEkaeLEiRo2bJiSkpLk7+8vR0dHLVmy5Lx9JWnChAma\nMGGCgoOD5eDgYA3qAAAAwJXEZLnQQdKNhMlkuuDx3wCAxmvI/UPkG+Hb0GVcs7JXZCt5dXJDl3HN\n4vhvOHEj4mrMnNwZEgAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAA\nADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAA\nMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAw\nAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAA\nQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMABBGwAAADAAQRsAAAAwAEEbAAAAMICh\nQTs5OVmBgYEKCAjQ/Pnzq20THR2tgIAAhYSEaMeOHXXuu3DhQtnY2KioqMiw+gEAAID6Mixom81m\nTZkyRcnJyUpPT1d8fLz27NlTpU1SUpIyMzOVkZGhuLg4TZ48uU59c3Jy9MUXX8jX19eo8gEAAICL\nYljQTktLk7+/v/z8/GRvb69Ro0YpISGhSpvExERFRkZKknr16qXi4mIVFhbW2nf69OlasGCBUaUD\nAAAAF83OqBXn5eXJx8fHuuzt7a2tW7fW2iYvL0/5+fk19k1ISJC3t7e6du1qVOlAozbi3hE6VHyo\nocu4Zrk5u+nTNZ82dBkAgCuAYUHbZDLVqZ3FYqnzOv/44w+99NJL+uKLL+rUPyYmxvo4NDRUoaGh\ndd4W0FiV2ZSp61T+EG0o2SuyG7oEAICB8nfnq2B3QZ3aGha0vby8lJOTY13OycmRt7f3edvk5ubK\n29tbZWVl1fbdt2+fsrKyFBISYm3frVs3paWlqU2bNufUcGbQBgAAAC6WZ7CnPIM9rcvb47fX2Naw\nMdrdu3dXRkaGsrKyVFpaqtWrVys8PLxKm/DwcC1fvlySlJqaKmdnZ7m7u9fYt0uXLjpw4IB+++03\n/fbbb/L29taPP/5YbcgGAAAAGpJhZ7Tt7OwUGxurwYMHy2w2KyoqSkFBQVq0aJEkaeLEiRo2bJiS\nkpLk7+8vR0dHLVmy5Lx9z1bX4SkAAADA5WZY0JakoUOHaujQoVWemzhxYpXl2NjYOvc926+//npx\nBQIAAAAG4c6QAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI\n2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQja\nAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoAAACAAQjaAAAAgAEI2gAAAIABCNoA\nAACAAewaugAj9Qnr09AlXJPcnN306ZpPG7oMAACABnVVB+2uU7s2dAnXpOwV2Q1dAgAAQINj6AgA\nAABgAII2AAAAYACCNgAAAGAAgjYAAABgAII2AAAAYACCNgAAAGAAgjYAAABgAII2AAAAYACCNgAA\nAGAAgjYAAABgAII2AAAAYACCNgAAAGAAgjYAAABgAMODdnJysgIDAxUQEKD58+dX2yY6OloBAQEK\nCQnRjh07au37xBNPKCgoSCEhIbrrrrt09OhRo3cDAAAAuCCGBm2z2awpU6YoOTlZ6enpio+P1549\ne6q0SUpKUmZmpjIyMhQXF6fJkyfX2nfQoEH65ZdftHPnTnXs2FEvv/yykbsBAAAAXDBDg3ZaWpr8\n/f3l5+cne3t7jRo1SgkJCVXaJCYmKjIyUpLUq1cvFRcXq7Cw8Lx9w8LCZGNjY+2Tm5tr5G4AAAAA\nF8zQoJ2XlycfHx/rsre3t/Ly8urUJj8/v9a+krR48WINGzbMgOoBAACA+rMzcuUmk6lO7SwWS73W\n/+KLL8rBwUFjxoyp9vXtK7dbH3sEe8gz2LNe2wEAAAAkKX93vgp2F9SpraFB28vLSzk5OdblnJwc\neXt7n7dNbm6uvL29VVZWdt6+S5cuVVJSkjZu3Fjj9ruN6XYpdgMAAACQJHkGe1Y5ebs9fnuNbQ0d\nOtK9e3dlZGQoKytLpaWlWr16tcLDw6u0CQ8P1/LlyyVJqampcnZ2lru7+3n7Jicn65VXXlFCQoKa\nNm1q5C4AAAAA9WLoGW07OzvFxsZq8ODBMpvNioqKUlBQkBYtWiRJmjhxooYNG6akpCT5+/vL0dFR\nS5YsOW9fSXr00UdVWlqqsLAwSVKfPn301ltvGbkrAAAAwAUxNGhL0tChQzV06NAqz02cOLHKcmxs\nbJ37SlJGRsalKxAAAAAwAHeGBAAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAGAAAA\nDEDQBgAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAM\nQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA\n0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAGAAAADEDQ\nBgAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAGAAAADEDQBgAAAAxA0AYAAAAMQNAG\nAAAADGBo0E5OTlZgYKACAgI0f/78attER0crICBAISEh2rFjR619i4qKFBYWpo4dO2rQoEEqLi42\nchcaTP7u/IYuAWgQHPu4VnHs41p1NR/7hgVts9msKVOmKDk5Wenp6YqPj9eePXuqtElKSlJmZqYy\nMjIUFxenyZMn19p33rx5CgsL0969ezVw4EDNmzfPqF1oUAW7Cxq6BKBBcOzjWsWxj2vV1XzsGxa0\n09LS5O/vLz8/P9nb22vUqFFKSEio0iYxMVGRkZGSpF69eqm4uFiFhYXn7Xtmn8jISH3yySdG7QIA\nAABQb4YF7by8PPn4+FiXvb29lZeXV6c2+fn5NfY9cOCA3N3dJUnu7u46cOCAUbsAAAAA1JudUSs2\nmUx1amexWOrUprr1mUym824nbkRcnWq4Um2P397QJdSb6YO6vf8wyAcNXcDFaczHvsTx36A49hsU\nx34Da8THf2M/9mtiWND28vJSTk6OdTknJ0fe3t7nbZObmytvb2+VlZWd87yXl5ek02exCwsL1bZt\nWxUUFKhNmzbVbr8uAR4AAAAwimFDR7p3766MjAxlZWWptLRUq1evVnh4eJU24eHhWr58uSQpNTVV\nzs7Ocnd3P2/f8PBwLVu2TJK0bNkyjRw50qhdAAAAAOrNsDPadnZ2io2N1eDBg2U2mxUVFaWgoCAt\nWrRIkjRx4kQNGzZMSUlJ8vf3l6Ojo5YsWXLevpI0a9Ys3XfffXrvvffk5+enDz5oxN+TAAAA4Kpl\nsjDGAgAASKqoqJCNDfeyAy4VfptQZxUVFTKbzQ1dBnDZVFRUqLy8nGs+cNWqqKioskzIBk5f51de\nXn7O70d98BuFGlVUVFQ5yGxsbGRraytJys/P14kTJxqqNMAQZwdqGxsb2dnZyWQyXZIPXKChnX3C\n5Oxg/dFHHykrK+syVwU0rLPzjslkkp2dnWxsbFRSUnJR6yZo4xyVB5uNjU2VD+Fjx44pOjpaN998\ns8aMGcOHMa4KZ37Anj1d6HfffacZM2ZowIAB+vbbbxuiPOCSqAzXZ54wkU4f47/88osk6Y8//tDq\n1au1ffvVOc0acLYzfy/OzDvHjx/Xk08+qf79++u2225TcXFxvbdh2MWQaBwqD7IzP3htbGxUUVGh\nlJQUrV+/XmFhYbrjjju0d+9enTp1SgkJCVVuKAQ0Jmcf82d+uO7YsUPNmzdXx44dderUKb3yyivq\n27evYmNjdf311zdIvcCFqqiokMVisR7jZrNZtra2slgs+umnn7R69WrdeuutGjFihNatW6fMzEx9\n8MEH+vXXX2Vra6tBgwY18B4Al151eafy8a5du5ScnKw777xTAQEBSklJkdls1jvvvKOAgICLGlJl\nGxMTE3NRlaNRqaioqHLW7sy/4ipvDPT+++/rjTfe0ObNm9W+fXv94x//kI+Pj06ePKktW7Zo27Zt\nKi0t1ZEjR+Tm5iZ7e/uG2h3ggp195uKXX37RJ598ojlz5mjlypX66quv1KpVK5WUlCgjI0MeHh7q\n2rWrbG1t1bRp0zrfjAu4XCq/lam8iZvJZLIe4ydPnlSTJk302WefaeDAgaqoqNCJEyeUlpam3Nxc\nPf7440pJSVFOTo769++vmJgYPf744w28R8DFO/tmh2d/9kvSa6+9pvfff1+JiYk6cuSI1q5da70b\n+bZt23T48GHZ2Njo+PHjNd63pTYE7avYqVOnZGd3+kuLygOuuq/GZ8+erblz5+rUqVPq3Lmzjh07\npri4OD355JN66KGHZLFYtG3bNt1+++3q1q2bnJyclJqaqlWrVslsNqt79+4NsXtAjcxmsywWS5UP\nVYvFotLSUq1fv14LFy7UyZMn1aVLF6Wnp+uFF17QwIEDFR8fr9LSUn322WcaMGCAmjZtqi1btigt\nLU3/+Mc/1Lx5cwUHBzfgngGnnXnSpDJYm0wmFRcXq6ysTDNnztSsWbO0Z88eDR8+XM2bN9dzzz2n\nV199VRMnTlSHDh303HPPKTIyUv3799eMGTPUvHlzXXfdderWrZscHR0beA+B+jGbzdbfhzNt375d\nzz77rGJjY2UymdS1a1ft2rVL8fHxmjdvnqZNm6aCggKtXbtWMTExMpvN+vPPP/XVV19Zv91s27bt\nBdfDGO2r1J49e/TXv/5Vubm5kk5/EJeUlGjt2rX66aefrO0+/PBDderUSZ9//rl++eUXPf3007rx\nxht16623qqysTJJ0++236/DhwyovL9ett96qiIgIvfrqq+rfv79atGjRIPsHnKmoqEjPPPOMdayp\nra2t9avy33//XdLp34FVq1bp9ddf1w033KCvvvpKf/vb39SvXz+FhITIxcVFkjR8+HC1aNFCBw4c\n0AMPPKD//Oc/evvttzV27Fjt2rVLpaWlDbafuHZZLJZzLk6vtGPHDsXHx6tv377q06ePFixYoMDA\nQH3++efatWuXPvroI3l7e8vNzU3NmzeXJN18883y8PDQ5s2b5ebmpgkTJujFF1/UyZMn1aZNGy7+\nRaOxbt06PfXUU9Y7ild+9m/dulV79+6VdHrM9dKlSxUQEKDp06dr48aNmj9/vkaMGKE2bdrI3d1d\nkvTggw/qxx9/lL29vR544AE9/vjjiouLU7du3XTkyJF61UfQvkq1bNlSHh4eSktLk3T6QAwNDdW7\n776rhQsXasGCBTpw4IDS09M1YcIEeXh4aPr06UpJSVHTpk3VqlUr/fjjj5KkTp06acuWLWrVqpW+\n+eYb3X333erZs6fS0tIUGhragHsJnFZeXq78/Hx98803kqTs7GxFREQoMDBQ0dHRWrVqlSwWi5KT\nkzV79mxNnTpVTz31lDZt2qQ//vhDfn5+OnnypCoqKuTh4aHt27fLwcFBOTk5WrJkiSZNmqS3335b\nAwcOlIODQwPvLa4FZrO5yuwgZw4HkU4PeZo7d64k6Y033lBcXJzeeOMNrV+/XrGxsWrXrp3atGmj\n4cOHWz/Lb7/9dsXFxUk6/Y1nmzZtrOscN26cxo8fr0OHDl2uXQQuSuUfg02aNNGxY8e0f/9+SdL6\n9esVGBiomTNn6u9//7s2bNigoqIirVu3TjNnztSQIUM0ffp0xcfHy9PTU3Z2dvr5558lSW3atJGt\nra1yc3O1adMmRUREqFu3bjp+/Lg6duxYrzoJ2o1U5cUulc6elszFxUWdOnXS1q1bJUkbN27U1KlT\n9dlnn+mxxx7TO++8oyZNmig3N1eOjo4qLy/XDTfcoKKiIpWUlOiGG27QmjVr9Oqrr2rMmDG64447\n1Lp1a/n6+io6Olpff/21kpKS5OnpeVn3G9cmi8Uis9lc41k2Z2dn9ezZ0zpbwrfffqtmzZrp559/\n1owZMzRjxgz98ccf2rdvn9q3b6/S0lK1a9dOzZs3V0ZGhm6++WatXLlS69at03fffScnJyd5eXnJ\nbDZr//79uvnmm5WUlKQ77rjjcu42rmGV38qc6Z577tGePXskSQcPHtTu3buVnZ2tQYMGqXXr1vL0\n9JSfn59Gjx5t/V248cYbVVhYqIMHD2rs2LF67733tGzZMj3wwAM6fvy4hg0bJklq3ry5nJycFBYW\nds6wK6ChnO+zv3JoSOfOnWVvb6+srCyZzWa9++67WrZsmTZv3qw777xTzz//vJydnWVvb2+dPcTX\n11ctW7bUgQMH1LNnT7366qv6z3/+o4iICA0fPlw+Pj5ycnLSiBEj9MknnyghIUFeXl71uqcCv0mN\ngNlsPuemGZXjj/Ly8rR169Zz5vlt1qyZbrjhBmVmZkqStm3bph49eqisrEw333yz7OzsVFRUpJtu\nukn//ve/ZWdnp9zcXPXs2VOS5OXlpYEDByo/P19hYWGaN2+eHBwc5OPjowEDBui66647Z95J4FI5\n+2tyk8kkW1tb2djY6OTJk9Y2lRwcHBQYGKi8vDxVVFQoMTFRgwYNkr29vXr06KF27dpp9+7d6tq1\nq9auXWs9K3399deroKBAHTt2VHBwsJYvX66YmBg98MADuv766+Xn56e5c+fq//2//1fvD1ngQh0/\nflwbNmzQmDFjNH78eOsZ6aKiIq1Zs0aS5OPjI19fX23fvl3BwcG67rrrdPjwYUlS//79rdNR3nTT\nTSoqKlJaWprCwsJkZ2enP/74Q0OHDtWbb75p3WZcXJxefvll9e7dmwt+0WAq53mv/Kw987P/xIkT\n2rdvn7Vt5XHq5eUlV1dXZWVlydbWVrt27bIOBbznnnvk4OCgjIwM3XTTTVq8eLEk6aefflJAQICc\nnZ0VEhIiR0dH/frrr+rUqZOmTZsmSerWrZvuu+8++fj4WP9Pqs/vBkH7CrR3717NnDlTq1atknT6\nzEblTTMkqaysTGvXrtWMGTM0ZswYffnll5KqjtkzmUy6/vrrderUKR05ckReXl5KS0uzzhDi6+ur\nH3/8UXPnztWhQ4fUt29f3X777QoNDVXbtm3l6+srBwcH3XrrrRo/frxat25tXXflL0B1V/AC9ZGe\nnm4d9mE2m8/5mjw3N1effvqp7rrrLj344IP6888/z/nAa9eunRwcHJSVlSV3d3fl5eVZX+vatau+\n+OILzZgxQ+np6Zo0aZJGjhypFi1aaNiwYXJycpKPj4+GDx+uzz//XA8++GCV2XgqP/gJILgYZw8H\nqU56erqmTp2qhQsXKiIiQm5ubnr22WclSVFRUfrss88kSa6urrr++uu1bds2BQYGqry83DpGtVev\nXvrpp59UUlKitm3bql+/fmrdurWaNm0qT09PBQYGKioqSp6entZ6+vTpo++++07dunXjD0pcNtnZ\n2Xrrrbe0efNmSf83z3vlZ63FYtHixYv1+uuv67bbbtPnn3+u8vJya//KY9XX11eHDx/WoUOH1KdP\nH23YsEGS9Oeff8rLy0slJSWaNGmSfvz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eOndzcyMgIABra+umfUOU51peXh4bNmwgOjqa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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10fd66d50>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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Q0FClpKRIkuLj4zVo0CClpqYqKChITZo00ezZs63r//Of/9SIESNUUlKiDh06VHoNAAAA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\ncHBwcHD8Bf4PujBo3G2ZPWQAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10ff42710>"
+       ]
+      }
+     ],
+     "prompt_number": 135
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from IPython.display import Image\n",
+      "Image('report/images/pairwise.png')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "png": 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fL+nCh6Aff/xRf/jDH9SvXz999NFHqqio0N69e5WVlaWM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\nAElFTkSuQmCC\n",
+       "prompt_number": 136,
+       "text": [
+        "<IPython.core.display.Image at 0x10ff96310>"
+       ]
+      }
+     ],
+     "prompt_number": 136
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 136
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 117
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.bar(range(10), range(10), bottom=0, log=True)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 140,
+       "text": [
+        "<Container object of 10 artists>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEBCAYAAACdctWRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10fd14c90>"
+       ]
+      }
+     ],
+     "prompt_number": 140
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/arc_distance/__init__.py b/arc_distance/__init__.py
deleted file mode 100644
index b4eb763..0000000
--- a/arc_distance/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Authors: Yuancheng Peng
-# License: MIT
-"""Computes the arc distance between a collection of points
-
-This code is challenging because it requires efficient vectorisation of
-trigonometric functions that are note natively supported in SSE/AVX. The numpy
-version makes use of numpy.tile and transpose, which proves to be challenging
-too.
-
-See also http://en.wikipedia.org/wiki/Great-circle_distance
-"""
-
-import numpy as np
-
-
-def make_env(n=1000):
-    rng = np.random.RandomState(42)
-    a = rng.rand(n, 2)
-    b = rng.rand(n, 2)
-    return (a, b), {}
diff --git a/arc_distance/arc_distance_numba.py b/arc_distance/arc_distance_numba.py
deleted file mode 100644
index 531de6f..0000000
--- a/arc_distance/arc_distance_numba.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Authors: Yuancheng Peng
-# License: MIT
-
-from arc_distance import arc_distance_python as adp
-from numba import autojit
-
-
-benchmarks = (("arc_distance_numba_for_loops",
-               autojit(adp.arc_distance_python_nested_for_loops)),
-              )
diff --git a/arc_distance/arc_distance_parakeet.py b/arc_distance/arc_distance_parakeet.py
deleted file mode 100644
index 4f16244..0000000
--- a/arc_distance/arc_distance_parakeet.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Authors: Alex Rubinsteyn
-# License: MIT
-
-from arc_distance import arc_distance_python as adp
-from parakeet import jit
-import numpy as np 
-
-@jit 
-def arc_distance_parakeet_comprehensions(a, b):
-  """
-  Calculates the pairwise arc distance between all points in vector a and b.
-  Uses nested list comprehensions, which are efficiently parallelized
-  by Parakeet. 
-  """
-  def arc_dist(ai, bj):
-    theta1 = ai[0]
-    phi1 = ai[1]
-    theta2 = bj[0]
-    phi2 = bj[1]
-    d_theta = theta2 - theta1
-    d_phi = phi2 - phi1
-    temp = (np.sin(d_theta / 2) ** 2) + \
-           (np.cos(theta1) * np.cos(theta2) * np.sin(d_phi / 2) ** 2)
-    return 2 * np.arctan2(np.sqrt(temp), np.sqrt(1 - temp))
-  return np.array([[arc_dist(ai, bj) for bj in b] for ai in a])
-
-benchmarks = (("arc_distance_parakeet_for_loops",
-               jit(adp.arc_distance_python_nested_for_loops)),
-              ("arc_distance_parakeet_comprehensions",
-               arc_distance_parakeet_comprehensions)
-              )
diff --git a/arc_distance/arc_distance_python.py b/arc_distance/arc_distance_python.py
deleted file mode 100644
index 6881254..0000000
--- a/arc_distance/arc_distance_python.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Authors: Federico Vaggi
-# License: MIT
-# Source: https://bitbucket.org/FedericoV/numpy-tip-complex-modeling/
-
-import numpy as np
-from math import *
-
-
-def arc_distance_python_nested_for_loops(a, b):
-    """
-    Calculates the pairwise arc distance between all points in vector a and b.
-    """
-    a_nrows = a.shape[0]
-    b_nrows = b.shape[0]
-
-    distance_matrix = np.zeros([a_nrows, b_nrows])
-
-    for i in range(a_nrows):
-        theta_1 = a[i, 0]
-        phi_1 = a[i, 1]
-        for j in range(b_nrows):
-            theta_2 = b[j, 0]
-            phi_2 = b[j, 1]
-            temp = (pow(sin((theta_2 - theta_1) / 2), 2)
-                    +
-                    cos(theta_1) * cos(theta_2)
-                    * pow(sin((phi_2 - phi_1) / 2), 2))
-            distance_matrix[i, j] = 2 * (atan2(sqrt(temp), sqrt(1 - temp)))
-    return distance_matrix
-
-
-def arc_distance_numpy_tile(a, b):
-    """
-    Calculates the pairwise arc distance between all points in vector a and b.
-    """
-    theta_1 = np.tile(a[:, 0], (b.shape[0], 1)).T
-    phi_1 = np.tile(a[:, 1], (b.shape[0], 1)).T
-
-    theta_2 = np.tile(b[:, 0], (a.shape[0], 1))
-    phi_2 = np.tile(b[:, 1], (a.shape[0], 1))
-
-    temp = (np.sin((theta_2 - theta_1) / 2)**2
-            +
-            np.cos(theta_1) * np.cos(theta_2)
-            * np.sin((phi_2 - phi_1) / 2)**2)
-    distance_matrix = 2 * (np.arctan2(np.sqrt(temp), np.sqrt(1 - temp)))
-
-    return distance_matrix
-
-
-def arc_distance_numpy_broadcast(a, b):
-    """
-    Calculates the pairwise arc distance between all points in vector a and b.
-    """
-    theta_1 = a[:, 0][:, None]
-    theta_2 = b[:, 0][None, :]
-    phi_1 = a[:, 1][:, None]
-    phi_2 = b[:, 1][None, :]
-
-    temp = (np.sin((theta_2 - theta_1) / 2)**2
-            +
-            np.cos(theta_1) * np.cos(theta_2)
-            * np.sin((phi_2 - phi_1) / 2)**2)
-    distance_matrix = 2 * (np.arctan2(np.sqrt(temp), np.sqrt(1 - temp)))
-    return distance_matrix
-
-benchmarks = (
-    arc_distance_python_nested_for_loops,
-    arc_distance_numpy_tile,
-    arc_distance_numpy_broadcast,
-)
diff --git a/arc_distance/arc_distance_pythran.py b/arc_distance/arc_distance_pythran.py
deleted file mode 100644
index 050f9d5..0000000
--- a/arc_distance/arc_distance_pythran.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Authors: Yuancheng Peng
-# License: MIT
-
-from arc_distance import arc_distance_python
-from pythran import compile_pythrancode
-from inspect import getsource
-import re
-import imp
-
-# grab imports
-imports = '''
-import numpy as np
-from math import *
-'''
-
-exports = '''
-#pythran export arc_distance_python_nested_for_loops(float [][], float [][])
-'''
-
-modname = 'arc_distance_pythran'
-
-# grab the source from original functions
-funs = (arc_distance_python.arc_distance_python_nested_for_loops,)
-sources = map(getsource, funs)
-source = '\n'.join(sources)
-
-# patch
-source = re.sub(r'\[a_nrows, b_nrows\]', '(a_nrows, b_nrows)', source)
-
-# compile to native module
-source = '\n'.join([exports, imports, source])
-native = compile_pythrancode(modname, source)
-
-# load
-native = imp.load_dynamic(modname, native)
-
-benchmarks = (("arc_distance_pythran_nested_for_loops",
-               native.arc_distance_python_nested_for_loops),
-              )
diff --git a/arc_distance/arc_distance_theano.py b/arc_distance/arc_distance_theano.py
deleted file mode 100644
index 76d2c6c..0000000
--- a/arc_distance/arc_distance_theano.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Authors: Frederic Bastien
-# License: MIT
-import theano
-import theano.tensor as tensor
-
-
-def arc_distance_theano_alloc_prepare(dtype='float64'):
-    """
-    Calculates the pairwise arc distance between all points in vector a and b.
-    """
-    a = tensor.matrix(dtype=str(dtype))
-    b = tensor.matrix(dtype=str(dtype))
-    # Theano don't implement all case of tile, so we do the equivalent with alloc.
-    #theta_1 = tensor.tile(a[:, 0], (b.shape[0], 1)).T
-    theta_1 = tensor.alloc(a[:, 0], b.shape[0], b.shape[0]).T
-    phi_1 = tensor.alloc(a[:, 1], b.shape[0], b.shape[0]).T
-
-    theta_2 = tensor.alloc(b[:, 0], a.shape[0], a.shape[0])
-    phi_2 = tensor.alloc(b[:, 1], a.shape[0], a.shape[0])
-
-    temp = (tensor.sin((theta_2 - theta_1) / 2)**2
-            +
-            tensor.cos(theta_1) * tensor.cos(theta_2)
-            * tensor.sin((phi_2 - phi_1) / 2)**2)
-    distance_matrix = 2 * (tensor.arctan2(tensor.sqrt(temp),
-                                          tensor.sqrt(1 - temp)))
-    name = "arc_distance_theano_alloc"
-    rval = theano.function([a, b],
-                           distance_matrix,
-                           name=name)
-    rval.__name__ = name
-
-    return rval
-
-
-def arc_distance_theano_broadcast_prepare(dtype='float64'):
-    """
-    Calculates the pairwise arc distance between all points in vector a and b.
-    """
-    a = tensor.matrix(dtype=str(dtype))
-    b = tensor.matrix(dtype=str(dtype))
-
-    theta_1 = a[:, 0][None, :]
-    theta_2 = b[:, 0][None, :]
-    phi_1 = a[:, 1][:, None]
-    phi_2 = b[:, 1][None, :]
-
-    temp = (tensor.sin((theta_2 - theta_1) / 2)**2
-            +
-            tensor.cos(theta_1) * tensor.cos(theta_2)
-            * tensor.sin((phi_2 - phi_1) / 2)**2)
-    distance_matrix = 2 * (tensor.arctan2(tensor.sqrt(temp),
-                                          tensor.sqrt(1 - temp)))
-    name = "arc_distance_theano_broadcast"
-    rval = theano.function([a, b],
-                           distance_matrix,
-                           name=name)
-    rval.__name__ = name
-
-    return rval
-
-
-benchmarks = (
-    arc_distance_theano_alloc_prepare('float64'),
-    arc_distance_theano_broadcast_prepare('float64'),
-)
diff --git a/benchmark_results.json b/benchmark_results.json
new file mode 100644
index 0000000..850129d
--- /dev/null
+++ b/benchmark_results.json
@@ -0,0 +1,638 @@
+{
+  "benchmark_results": [
+    {
+      "group_name": "arc_distance", 
+      "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/arc_distance", 
+      "records": [
+        {
+          "name": "arc_distance_pythran_nested_for_loops", 
+          "cold_time": 0.08391904830932617, 
+          "warm_time": 0.078933000564575195, 
+          "all_warm_times": [
+            0.08530902862548828, 
+            0.0794990062713623, 
+            0.0789330005645752, 
+            0.08436894416809082
+          ], 
+          "std_warm_times": 0.0028381331101485696, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_pythran.py", 
+          "rank": 1, 
+          "speedup": 86.867659007638892
+        }, 
+        {
+          "name": "arc_distance_parakeet_for_loops", 
+          "cold_time": 0.3778650760650635, 
+          "warm_time": 0.081580162048339844, 
+          "all_warm_times": [
+            0.08158016204833984, 
+            0.08426403999328613, 
+            0.08521890640258789, 
+            0.0838930606842041
+          ], 
+          "std_warm_times": 0.0013369841008558296, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_parakeet.py", 
+          "rank": 2, 
+          "speedup": 84.048925686496844
+        }, 
+        {
+          "name": "arc_distance_theano_broadcast", 
+          "cold_time": 0.08967018127441406, 
+          "warm_time": 0.086144208908081055, 
+          "all_warm_times": [
+            0.08614706993103027, 
+            0.08614420890808105, 
+            0.0862588882446289, 
+            0.0865941047668457
+          ], 
+          "std_warm_times": 0.00018375925416707701, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_theano.py", 
+          "rank": 3, 
+          "speedup": 79.59589001286966
+        }, 
+        {
+          "name": "arc_distance_parakeet_comprehensions", 
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+          "std_warm_times": 0.0018073388402672606, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_parakeet.py", 
+          "rank": 4, 
+          "speedup": 74.850838061527256
+        }, 
+        {
+          "name": "arc_distance_numpy_broadcast", 
+          "cold_time": null, 
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+          "all_warm_times": [
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+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_python.py", 
+          "rank": 5, 
+          "speedup": 73.38886734001235
+        }, 
+        {
+          "name": "arc_distance_theano_alloc", 
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+          "warm_time": 0.10689902305603027, 
+          "all_warm_times": [
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+          ], 
+          "std_warm_times": 0.00068110330342396961, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_theano.py", 
+          "rank": 6, 
+          "speedup": 64.142073346165077
+        }, 
+        {
+          "name": "arc_distance_numpy_tile", 
+          "cold_time": null, 
+          "warm_time": 0.17447614669799805, 
+          "all_warm_times": [
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+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_python.py", 
+          "rank": 7, 
+          "speedup": 39.29892485166834
+        }, 
+        {
+          "name": "arc_distance_python_nested_for_loops", 
+          "cold_time": null, 
+          "warm_time": 6.856724977493286, 
+          "all_warm_times": [
+            6.856724977493286
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_python.py", 
+          "rank": 8, 
+          "speedup": 1.0
+        }
+      ], 
+      "runtime_errors": [], 
+      "import_errors": [
+        {
+          "name": "arc_distance_numba", 
+          "error_type": "AttributeError", 
+          "error_message": "'NoneType' object has no attribute 'core'", 
+          "traceback": "Traceback (most recent call last):\n  File \"run_benchmarks.py\", line 98, in find_benchmarks\n    module = __import__(abs_module_name, fromlist=\"dummy\")\n  File \"/Users/ogrisel/code/python-benchmarks/arc_distance/arc_distance_numba.py\", line 5, in <module>\n    from numba import autojit\n  File \"/usr/local/lib/python2.7/site-packages/numba/__init__.py\", line 18, in <module>\n    from numba import utils, typesystem\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 102, in <module>\n    context = get_minivect_context()\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 100, in get_minivect_context\n    return NumbaContext()\n  File \"/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py\", line 140, in __init__\n    self.llvm_module = llvm.core.Module.new('default_module')\nAttributeError: 'NoneType' object has no attribute 'core'\n"
+        }
+      ]
+    }, 
+    {
+      "group_name": "growcut", 
+      "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/growcut", 
+      "records": [
+        {
+          "name": "growcut_parakeet", 
+          "cold_time": 0.42096495628356934, 
+          "warm_time": 0.0046639442443847656, 
+          "all_warm_times": [
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+            0.004951000213623047, 
+            0.004663944244384766, 
+            0.004747152328491211
+          ], 
+          "std_warm_times": 0.00024926502102072084, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/growcut/growcut_parakeet.py", 
+          "rank": 1, 
+          "speedup": 2147.5241795317452
+        }, 
+        {
+          "name": "growcut_cython", 
+          "cold_time": 0.009152889251708984, 
+          "warm_time": 0.0062019824981689453, 
+          "all_warm_times": [
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+            0.006947040557861328, 
+            0.006201982498168945, 
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+          ], 
+          "std_warm_times": 0.00061913661359338341, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/growcut/growcut_cython.pyx", 
+          "rank": 2, 
+          "speedup": 1614.9566755083997
+        }, 
+        {
+          "name": "growcut_pythran", 
+          "cold_time": 0.011638879776000977, 
+          "warm_time": 0.0097451210021972656, 
+          "all_warm_times": [
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+            0.009745121002197266, 
+            0.0120391845703125, 
+            0.011751174926757812
+          ], 
+          "std_warm_times": 0.0021962731413242773, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/growcut/growcut_pythran.py", 
+          "rank": 3, 
+          "speedup": 1027.789499437295
+        }, 
+        {
+          "name": "growcut_python", 
+          "cold_time": null, 
+          "warm_time": 10.0159330368042, 
+          "all_warm_times": [
+            10.0159330368042
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/growcut/growcut_python.py", 
+          "rank": 4, 
+          "speedup": 1.0
+        }
+      ], 
+      "runtime_errors": [], 
+      "import_errors": [
+        {
+          "name": "growcut_numba", 
+          "error_type": "AttributeError", 
+          "error_message": "'NoneType' object has no attribute 'core'", 
+          "traceback": "Traceback (most recent call last):\n  File \"run_benchmarks.py\", line 98, in find_benchmarks\n    module = __import__(abs_module_name, fromlist=\"dummy\")\n  File \"/Users/ogrisel/code/python-benchmarks/growcut/growcut_numba.py\", line 2, in <module>\n    from numba import autojit\n  File \"/usr/local/lib/python2.7/site-packages/numba/__init__.py\", line 18, in <module>\n    from numba import utils, typesystem\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 102, in <module>\n    context = get_minivect_context()\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 100, in get_minivect_context\n    return NumbaContext()\n  File \"/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py\", line 140, in __init__\n    self.llvm_module = llvm.core.Module.new('default_module')\nAttributeError: 'NoneType' object has no attribute 'core'\n"
+        }
+      ]
+    }, 
+    {
+      "group_name": "julia", 
+      "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/julia", 
+      "records": [
+        {
+          "name": "julia_pythran_for_loops", 
+          "cold_time": 0.0029201507568359375, 
+          "warm_time": 0.0024111270904541016, 
+          "all_warm_times": [
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+            0.0027158260345458984, 
+            0.002460002899169922, 
+            0.0024220943450927734
+          ], 
+          "std_warm_times": 0.00012462729745560191, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_pythran.py", 
+          "rank": 1, 
+          "speedup": 1865.0206664689015
+        }, 
+        {
+          "name": "julia_parakeet_for_loops", 
+          "cold_time": 0.43265795707702637, 
+          "warm_time": 0.003353118896484375, 
+          "all_warm_times": [
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+            0.003353118896484375, 
+            0.0035381317138671875
+          ], 
+          "std_warm_times": 0.00012080107339492268, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_parakeet.py", 
+          "rank": 2, 
+          "speedup": 1341.0803469852106
+        }, 
+        {
+          "name": "julia_cython_for_loops", 
+          "cold_time": 0.004063129425048828, 
+          "warm_time": 0.0037648677825927734, 
+          "all_warm_times": [
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+            0.004394054412841797, 
+            0.004058837890625
+          ], 
+          "std_warm_times": 0.00023580495443110928, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_cython.pyx", 
+          "rank": 3, 
+          "speedup": 1194.4116268760686
+        }, 
+        {
+          "name": "julia_pyopencl", 
+          "cold_time": null, 
+          "warm_time": 0.010384082794189453, 
+          "all_warm_times": [
+            0.010384082794189453
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_pyopencl.py", 
+          "rank": 4, 
+          "speedup": 433.0475731276117
+        }, 
+        {
+          "name": "julia_python_numpy", 
+          "cold_time": null, 
+          "warm_time": 0.18692898750305176, 
+          "all_warm_times": [
+            0.18692898750305176
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_python.py", 
+          "rank": 5, 
+          "speedup": 24.056203980169304
+        }, 
+        {
+          "name": "julia_python_for_loops", 
+          "cold_time": null, 
+          "warm_time": 4.496801853179932, 
+          "all_warm_times": [
+            4.496801853179932
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_python.py", 
+          "rank": 6, 
+          "speedup": 1.0
+        }
+      ], 
+      "runtime_errors": [], 
+      "import_errors": [
+        {
+          "name": "julia_numba", 
+          "error_type": "AttributeError", 
+          "error_message": "'NoneType' object has no attribute 'core'", 
+          "traceback": "Traceback (most recent call last):\n  File \"run_benchmarks.py\", line 98, in find_benchmarks\n    module = __import__(abs_module_name, fromlist=\"dummy\")\n  File \"/Users/ogrisel/code/python-benchmarks/julia/julia_numba.py\", line 2, in <module>\n    from numba import autojit\n  File \"/usr/local/lib/python2.7/site-packages/numba/__init__.py\", line 18, in <module>\n    from numba import utils, typesystem\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 102, in <module>\n    context = get_minivect_context()\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 100, in get_minivect_context\n    return NumbaContext()\n  File \"/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py\", line 140, in __init__\n    self.llvm_module = llvm.core.Module.new('default_module')\nAttributeError: 'NoneType' object has no attribute 'core'\n"
+        }
+      ]
+    }, 
+    {
+      "group_name": "pairwise", 
+      "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise", 
+      "records": [
+        {
+          "name": "pairwise_theano_blas_float32", 
+          "cold_time": 0.0011949539184570312, 
+          "warm_time": 0.00081992149353027344, 
+          "all_warm_times": [
+            0.0009999275207519531, 
+            0.0008628368377685547, 
+            0.0008511543273925781, 
+            0.0008199214935302734
+          ], 
+          "std_warm_times": 6.9048527315114595e-05, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_theano.py", 
+          "rank": 1, 
+          "speedup": 25834.77958708927
+        }, 
+        {
+          "name": "pairwise_theano_blas_float64", 
+          "cold_time": 0.001216888427734375, 
+          "warm_time": 0.0010321140289306641, 
+          "all_warm_times": [
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+            0.001032114028930664, 
+            0.0010790824890136719
+          ], 
+          "std_warm_times": 0.00019370569609799686, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_theano.py", 
+          "rank": 2, 
+          "speedup": 20523.401940401942
+        }, 
+        {
+          "name": "pairwise_python_numpy_dot", 
+          "cold_time": null, 
+          "warm_time": 0.0021979808807373047, 
+          "all_warm_times": [
+            0.0021979808807373047
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_python.py", 
+          "rank": 3, 
+          "speedup": 9637.249918646274
+        }, 
+        {
+          "name": "pairwise_pyopencl_cpu", 
+          "cold_time": null, 
+          "warm_time": 0.0037348270416259766, 
+          "all_warm_times": [
+            0.0037348270416259766
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_pyopencl.py", 
+          "rank": 4, 
+          "speedup": 5671.612320459623
+        }, 
+        {
+          "name": "pairwise_cython_for_loops", 
+          "cold_time": 0.012512922286987305, 
+          "warm_time": 0.012480020523071289, 
+          "all_warm_times": [
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+            0.013123035430908203, 
+            0.012962102890014648, 
+            0.012480020523071289
+          ], 
+          "std_warm_times": 0.00023679340309741665, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_cython.pyx", 
+          "rank": 5, 
+          "speedup": 1697.3121979176617
+        }, 
+        {
+          "name": "pairwise_parakeet_inner_numpy", 
+          "cold_time": 0.24580979347229004, 
+          "warm_time": 0.013036012649536133, 
+          "all_warm_times": [
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+            0.01588582992553711, 
+            0.013036012649536133
+          ], 
+          "std_warm_times": 0.0018440458943548158, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_parakeet.py", 
+          "rank": 6, 
+          "speedup": 1624.9210271229219
+        }, 
+        {
+          "name": "pairwise_parakeet_nested_for_loops", 
+          "cold_time": 0.18332195281982422, 
+          "warm_time": 0.01314997673034668, 
+          "all_warm_times": [
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+          ], 
+          "std_warm_times": 0.00044162494779938205, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_parakeet.py", 
+          "rank": 7, 
+          "speedup": 1610.8386728311123
+        }, 
+        {
+          "name": "pairwise_parakeet_comprehensions", 
+          "cold_time": 0.2990529537200928, 
+          "warm_time": 0.013530969619750977, 
+          "all_warm_times": [
+            0.014219999313354492, 
+            0.01638197898864746, 
+            0.013530969619750977, 
+            0.013609886169433594
+          ], 
+          "std_warm_times": 0.0011548846513050209, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_parakeet.py", 
+          "rank": 8, 
+          "speedup": 1565.4821242929888
+        }, 
+        {
+          "name": "pairwise_pythran_nested_for_loops", 
+          "cold_time": 0.01808619499206543, 
+          "warm_time": 0.01565098762512207, 
+          "all_warm_times": [
+            0.016075849533081055, 
+            0.020102977752685547, 
+            0.01565098762512207, 
+            0.0169069766998291
+          ], 
+          "std_warm_times": 0.0017446559311014883, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_pythran.py", 
+          "rank": 9, 
+          "speedup": 1353.428395155762
+        }, 
+        {
+          "name": "pairwise_theano_broadcast_float32", 
+          "cold_time": 0.05335593223571777, 
+          "warm_time": 0.03354191780090332, 
+          "all_warm_times": [
+            0.03354191780090332, 
+            0.03809690475463867, 
+            0.0337679386138916, 
+            0.0360410213470459
+          ], 
+          "std_warm_times": 0.0018570477180874286, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_theano.py", 
+          "rank": 10, 
+          "speedup": 631.52295553897
+        }, 
+        {
+          "name": "pairwise_theano_broadcast_float64", 
+          "cold_time": 0.04522085189819336, 
+          "warm_time": 0.042270183563232422, 
+          "all_warm_times": [
+            0.044497013092041016, 
+            0.04326295852661133, 
+            0.044862985610961914, 
+            0.04227018356323242
+          ], 
+          "std_warm_times": 0.0010272508080647448, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_theano.py", 
+          "rank": 11, 
+          "speedup": 501.12134082371654
+        }, 
+        {
+          "name": "pairwise_python_broadcast_numpy", 
+          "cold_time": null, 
+          "warm_time": 0.15924406051635742, 
+          "all_warm_times": [
+            0.15924406051635742
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_python.py", 
+          "rank": 12, 
+          "speedup": 133.01903377360694
+        }, 
+        {
+          "name": "pairwise_python_inner_numpy", 
+          "cold_time": null, 
+          "warm_time": 1.8430240154266357, 
+          "all_warm_times": [
+            1.8430240154266357
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_python.py", 
+          "rank": 13, 
+          "speedup": 11.493334263020001
+        }, 
+        {
+          "name": "pairwise_python_nested_for_loops", 
+          "cold_time": null, 
+          "warm_time": 21.182491064071655, 
+          "all_warm_times": [
+            21.182491064071655
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_python.py", 
+          "rank": 14, 
+          "speedup": 1.0
+        }
+      ], 
+      "runtime_errors": [], 
+      "import_errors": [
+        {
+          "name": "pairwise_numba", 
+          "error_type": "AttributeError", 
+          "error_message": "'NoneType' object has no attribute 'core'", 
+          "traceback": "Traceback (most recent call last):\n  File \"run_benchmarks.py\", line 98, in find_benchmarks\n    module = __import__(abs_module_name, fromlist=\"dummy\")\n  File \"/Users/ogrisel/code/python-benchmarks/pairwise/pairwise_numba.py\", line 5, in <module>\n    from numba import autojit\n  File \"/usr/local/lib/python2.7/site-packages/numba/__init__.py\", line 18, in <module>\n    from numba import utils, typesystem\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 102, in <module>\n    context = get_minivect_context()\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 100, in get_minivect_context\n    return NumbaContext()\n  File \"/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py\", line 140, in __init__\n    self.llvm_module = llvm.core.Module.new('default_module')\nAttributeError: 'NoneType' object has no attribute 'core'\n"
+        }
+      ]
+    }, 
+    {
+      "group_name": "rosen_der", 
+      "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/rosen_der", 
+      "records": [
+        {
+          "name": "rosen_der_loops_parakeet", 
+          "cold_time": 0.2613508701324463, 
+          "warm_time": 0.0041399002075195312, 
+          "all_warm_times": [
+            0.004981040954589844, 
+            0.004180908203125, 
+            0.004189968109130859, 
+            0.004139900207519531
+          ], 
+          "std_warm_times": 0.00035158542307875315, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_parakeet.py", 
+          "rank": 1, 
+          "speedup": 2459.2993549873299
+        }, 
+        {
+          "name": "rosen_der_pythran", 
+          "cold_time": 0.0047760009765625, 
+          "warm_time": 0.0051150321960449219, 
+          "all_warm_times": [
+            0.005486011505126953, 
+            0.005609989166259766, 
+            0.005115032196044922, 
+            0.008510828018188477
+          ], 
+          "std_warm_times": 0.0013577046061005772, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_pythran.py", 
+          "rank": 2, 
+          "speedup": 1990.4574438333177
+        }, 
+        {
+          "name": "rosen_der_cython", 
+          "cold_time": 0.008260965347290039, 
+          "warm_time": 0.0053920745849609375, 
+          "all_warm_times": [
+            0.005686044692993164, 
+            0.005606889724731445, 
+            0.005408048629760742, 
+            0.0053920745849609375
+          ], 
+          "std_warm_times": 0.0001264674464093316, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_cython.pyx", 
+          "rank": 3, 
+          "speedup": 1888.1886275203397
+        }, 
+        {
+          "name": "rosen_der_theano_float32", 
+          "cold_time": 0.012048006057739258, 
+          "warm_time": 0.0064139366149902344, 
+          "all_warm_times": [
+            0.008077859878540039, 
+            0.006413936614990234, 
+            0.007899045944213867, 
+            0.00681614875793457
+          ], 
+          "std_warm_times": 0.00070411827156742219, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_theano.py", 
+          "rank": 4, 
+          "speedup": 1587.3642851832578
+        }, 
+        {
+          "name": "rosen_der_theano_float64", 
+          "cold_time": 0.015202999114990234, 
+          "warm_time": 0.0079228878021240234, 
+          "all_warm_times": [
+            0.010712146759033203, 
+            0.007922887802124023, 
+            0.010877847671508789, 
+            0.0081939697265625
+          ], 
+          "std_warm_times": 0.0013728872848571068, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_theano.py", 
+          "rank": 5, 
+          "speedup": 1285.0433029400258
+        }, 
+        {
+          "name": "rosen_der_numpy_parakeet", 
+          "cold_time": 0.3740379810333252, 
+          "warm_time": 0.0090508460998535156, 
+          "all_warm_times": [
+            0.009679079055786133, 
+            0.009091854095458984, 
+            0.00919485092163086, 
+            0.009050846099853516
+          ], 
+          "std_warm_times": 0.00025087522763551386, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_parakeet.py", 
+          "rank": 6, 
+          "speedup": 1124.8952636847373
+        }, 
+        {
+          "name": "rosen_der_numpy", 
+          "cold_time": null, 
+          "warm_time": 0.04478788375854492, 
+          "all_warm_times": [
+            0.04478788375854492
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_python.py", 
+          "rank": 7, 
+          "speedup": 227.3216114642222
+        }, 
+        {
+          "name": "rosen_der_python", 
+          "cold_time": null, 
+          "warm_time": 10.181253910064697, 
+          "all_warm_times": [
+            10.181253910064697
+          ], 
+          "std_warm_times": 0.0, 
+          "source_url": "https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_python.py", 
+          "rank": 8, 
+          "speedup": 1.0
+        }
+      ], 
+      "runtime_errors": [], 
+      "import_errors": [
+        {
+          "name": "rosen_der_numba", 
+          "error_type": "AttributeError", 
+          "error_message": "'NoneType' object has no attribute 'core'", 
+          "traceback": "Traceback (most recent call last):\n  File \"run_benchmarks.py\", line 98, in find_benchmarks\n    module = __import__(abs_module_name, fromlist=\"dummy\")\n  File \"/Users/ogrisel/code/python-benchmarks/rosen_der/rosen_der_numba.py\", line 5, in <module>\n    from numba import autojit\n  File \"/usr/local/lib/python2.7/site-packages/numba/__init__.py\", line 18, in <module>\n    from numba import utils, typesystem\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 102, in <module>\n    context = get_minivect_context()\n  File \"/usr/local/lib/python2.7/site-packages/numba/utils.py\", line 100, in get_minivect_context\n    return NumbaContext()\n  File \"/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py\", line 140, in __init__\n    self.llvm_module = llvm.core.Module.new('default_module')\nAttributeError: 'NoneType' object has no attribute 'core'\n"
+        }
+      ]
+    }
+  ], 
+  "benchmark_environment": {}
+}
\ No newline at end of file
diff --git a/report/css/bootstrap-responsive.css b/css/bootstrap-responsive.css
similarity index 100%
rename from report/css/bootstrap-responsive.css
rename to css/bootstrap-responsive.css
diff --git a/report/css/bootstrap-responsive.min.css b/css/bootstrap-responsive.min.css
similarity index 100%
rename from report/css/bootstrap-responsive.min.css
rename to css/bootstrap-responsive.min.css
diff --git a/report/css/bootstrap.css b/css/bootstrap.css
similarity index 100%
rename from report/css/bootstrap.css
rename to css/bootstrap.css
diff --git a/report/css/bootstrap.min.css b/css/bootstrap.min.css
similarity index 100%
rename from report/css/bootstrap.min.css
rename to css/bootstrap.min.css
diff --git a/report/css/custom.css b/css/custom.css
similarity index 100%
rename from report/css/custom.css
rename to css/custom.css
diff --git a/growcut/__init__.py b/growcut/__init__.py
deleted file mode 100644
index 9c2b50d..0000000
--- a/growcut/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Authors: Serge Guelton
-# License: MIT
-import numpy as np
-
-def make_env(N=50):
-    dtype = np.double
-    image = np.zeros((N, N, 3), dtype=dtype)
-    state = np.zeros((N, N, 2), dtype=dtype)
-    state_next = np.empty_like(state)
-         
-    # colony 1 is strength 1 at position 0,0
-    # colony 0 is strength 0 at all other positions
-    state[0, 0, 0] = 1
-    state[0, 0, 1] = 1
-
-    window_radius = 10
-
-    return (image, state, state_next, window_radius), {}
diff --git a/growcut/growcut_cython.pyx b/growcut/growcut_cython.pyx
deleted file mode 100644
index a4db3f0..0000000
--- a/growcut/growcut_cython.pyx
+++ /dev/null
@@ -1,75 +0,0 @@
-# Authors: Stefan van der Walt, Nathan Faggian, Aron Ahmadia
-# https://github.com/stefanv/growcut_py
-from __future__ import division
- 
-import numpy as np
-cimport cython
-cimport numpy as cnp
- 
-cdef extern from "math.h" nogil:
-    double sqrt(double)
- 
-
-@cython.boundscheck(False)
-@cython.wraparound(False)
-cdef inline double distance(double[:, :, ::1] image,
-                            Py_ssize_t r0, Py_ssize_t c0,
-                            Py_ssize_t r1, Py_ssize_t c1) nogil:
-    cdef:
-        double s = 0, d
-        int i
- 
-    for i in range(3):
-        d = image[r0, c0, i] - image[r1, c1, i]
-        s += d * d
- 
-    return sqrt(s)
- 
-
-cdef double s3 = sqrt(3)
- 
-cdef inline double g(double d) nogil:
-    return 1 - (d / s3)
- 
-@cython.boundscheck(False)
-@cython.wraparound(False)
-def growcut_cython(double[:, :, ::1] image, double[:, :, ::1] state, double[:, :, ::1] state_next, Py_ssize_t window_radius):
- 
-    cdef:
-        Py_ssize_t i, j, ii, jj, width, height
-        double gc, attack_strength, defense_strength, winning_colony
-        int changes
- 
-    height, width = image.shape[0], image.shape[1]
- 
-    changes = 0
- 
-    for j in range(width):
-        for i in range(height):
- 
-            winning_colony = state[i, j, 0]
-            defense_strength = state[i, j, 1]
- 
-            for jj in xrange(max(0, j - window_radius), min(j + window_radius + 1, width)):
-                for ii in xrange(max(0, i - window_radius), min(i + window_radius + 1, height)):
-                    if ii == i and jj == j:
-                        continue
- 
-                    # p -> current cell, (i, j)
-                    # q -> attacker, (ii, jj)
- 
-                    gc = g(distance(image, i, j, ii, jj))
- 
-                    attack_strength = gc * state[ii, jj, 1]
- 
-                    if attack_strength > defense_strength:
-                        defense_strength = attack_strength
-                        winning_colony = state[ii, jj, 0]
-                        changes += 1
- 
-                    state_next[i, j, 0] = winning_colony
-                    state_next[i, j, 1] = defense_strength
- 
-    return changes
-
-benchmarks = ( growcut_cython, )
diff --git a/growcut/growcut_numba.py b/growcut/growcut_numba.py
deleted file mode 100644
index 31e9e1a..0000000
--- a/growcut/growcut_numba.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from growcut import growcut_python
-from numba import autojit
-
-
-benchmarks = (
-    ("growcut_numba",
-     autojit(growcut_python.growcut_python)),
-)
diff --git a/growcut/growcut_parakeet.py b/growcut/growcut_parakeet.py
deleted file mode 100644
index 5577e62..0000000
--- a/growcut/growcut_parakeet.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from growcut import growcut_python
-from parakeet import jit
-
-
-benchmarks = (
-    ("growcut_parakeet",
-     jit(growcut_python.growcut_python)),
-)
diff --git a/growcut/growcut_python.py b/growcut/growcut_python.py
deleted file mode 100644
index ac0ab09..0000000
--- a/growcut/growcut_python.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Authors: Nathan Faggian, Stefan van der Walt, Aron Ahmadia, Olivier Grisel
-# https://github.com/stefanv/growcut_py
-import numpy as np
-
-
-def window_floor(idx, radius):
-    if radius > idx:
-        return 0
-    else:
-        return idx - radius
-
-
-def window_ceil(idx, ceil, radius):
-    if idx + radius > ceil:
-        return ceil
-    else:
-        return idx + radius
-
-
-def growcut_python(image, state, state_next, window_radius):
-    changes = 0
-    sqrt_3 = np.sqrt(3.0)
-
-    height = image.shape[0]
-    width = image.shape[1]
-
-    for j in xrange(width):
-        for i in xrange(height):
-
-            winning_colony = state[i, j, 0]
-            defense_strength = state[i, j, 1]
-
-            for jj in xrange(window_floor(j, window_radius),
-                             window_ceil(j + 1, width, window_radius)):
-                for ii in xrange(window_floor(i, window_radius),
-                                 window_ceil(i + 1, height, window_radius)):
-                    if ii != i or jj != j:
-                        d = image[i, j, 0] - image[ii, jj, 0]
-                        s = d * d
-                        for k in range(1, 3):
-                            d = image[i, j, k] - image[ii, jj, k]
-                            s += d * d
-                        gval = 1.0 - np.sqrt(s) / sqrt_3
-
-                        attack_strength = gval * state[ii, jj, 1]
-
-                        if attack_strength > defense_strength:
-                            defense_strength = attack_strength
-                            winning_colony = state[ii, jj, 0]
-                            changes += 1
-
-            state_next[i, j, 0] = winning_colony
-            state_next[i, j, 1] = defense_strength
-
-    return changes
-
-
-benchmarks = (
-    growcut_python,
-)
diff --git a/growcut/growcut_pythran.py b/growcut/growcut_pythran.py
deleted file mode 100644
index c53755e..0000000
--- a/growcut/growcut_pythran.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from growcut import growcut_python
-from pythran import compile_pythrancode
-from inspect import getsource
-import imp
-
-# grab imports
-imports = 'import numpy as np'
-exports = '''
-#pythran export growcut_python(float[][][], float[][][], float[][][], int)
-'''
-modname = 'growcut_pythran'
-
-# grab the source from the original functions
-sources = map(getsource,
-              (growcut_python.window_floor,
-               growcut_python.window_ceil,
-               growcut_python.growcut_python)
-              )
-source = '\n'.join(sources)
-
-# compile to a native module
-native = compile_pythrancode(
-    modname, '\n'.join([imports, exports, source]))
-
-# load it
-native = imp.load_dynamic(modname, native)
-
-benchmarks = (
-    ("growcut_pythran", native.growcut_python),
-)
diff --git a/images/arc_distance_logscale.png b/images/arc_distance_logscale.png
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diff --git a/images/rosen_der_logscale.png b/images/rosen_der_logscale.png
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diff --git a/images/rosen_der_zoom_5x_best.png b/images/rosen_der_zoom_5x_best.png
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index 0000000..021bffd
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diff --git a/report/img/glyphicons-halflings-white.png b/img/glyphicons-halflings-white.png
similarity index 100%
rename from report/img/glyphicons-halflings-white.png
rename to img/glyphicons-halflings-white.png
diff --git a/report/img/glyphicons-halflings.png b/img/glyphicons-halflings.png
similarity index 100%
rename from report/img/glyphicons-halflings.png
rename to img/glyphicons-halflings.png
diff --git a/index.html b/index.html
new file mode 100644
index 0000000..bcd16e7
--- /dev/null
+++ b/index.html
@@ -0,0 +1,1203 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <link href="css/bootstrap.min.css" rel="stylesheet">
+  <link href="css/bootstrap-responsive.min.css" rel="stylesheet">
+  <link href="css/custom.css" rel="stylesheet" media="screen">
+</head>
+<body data-spy="scroll" data-target=".side-panel">
+<div class="navbar navbar-inverse">
+  <div class="navbar-inner">
+    <div class="container">
+      <a class="brand" id="brand" href="./index.html">Python Benchmarks</a>
+      <ul class="nav">
+        <li><a href="https://github.com/numfocus/python-benchmarks">Github repo</a></li>
+        <li><a href="benchmark_results.json">JSON data</a></li>
+        <li><a href="https://github.com/numfocus/python-benchmarks/blob/master/README.md#motivation">About</a></li>
+      </ul>
+    </div>
+  </div>
+</div>
+
+<div class="container-fluid">
+<div class="row-fluid">
+ <div class="span2 side-panel">
+<!--Sidebar content-->
+<ul class="nav nav-list affix">
+  <li><a href="#top">Python Benchmarks</a></li>
+  <li><a href="#benchmark-results">Results</a></li>
+  <li><ul class="nav nav-list">
+    
+    <li><a href="#results-arc_distance">
+        arc_distance</a></li>
+    
+    <li><a href="#results-growcut">
+        growcut</a></li>
+    
+    <li><a href="#results-julia">
+        julia</a></li>
+    
+    <li><a href="#results-pairwise">
+        pairwise</a></li>
+    
+    <li><a href="#results-rosen_der">
+        rosen_der</a></li>
+    
+  </ul></li>
+  <li><a href="#error-summary">Error Summary</a></li>
+  <li><ul class="nav nav-list">
+    
+    
+    <li><a href="#errors-arc_distance">
+        arc_distance</a></li>
+    
+  
+    
+    <li><a href="#errors-growcut">
+        growcut</a></li>
+    
+  
+    
+    <li><a href="#errors-julia">
+        julia</a></li>
+    
+  
+    
+    <li><a href="#errors-pairwise">
+        pairwise</a></li>
+    
+  
+    
+    <li><a href="#errors-rosen_der">
+        rosen_der</a></li>
+    
+  
+  </ul></li>
+  <li><a href="#runtime-environment">Runtime Environment</a></li>
+</ul>
+
+</div>
+<div class="span10 body-container">
+<!--Body content-->
+<h2 id="benchmark-results">Results
+  <a title="Permalink to this headline"
+     href="#benchmark-results"
+     class="headerlink">&para;</a></h2>
+
+
+
+<section id="results-arc_distance">
+<h3>arc_distance
+    (<a href="https://github.com/numfocus/python-benchmarks/tree/master/arc_distance"
+     title="Source code for arc_distance">source code</a>)
+    <a title="Permalink to this headline"
+       href="#results-arc_distance"
+       class="headerlink">&para;</a>
+</h3>
+
+<p class="plot">
+  
+  <img src="images/arc_distance_zoom_5x_best.png"
+       alt="Plot for arc_distance" />
+  
+  <img src="images/arc_distance_logscale.png"
+       alt="Plot for arc_distance" />
+  
+</p>
+
+<table class="table table-striped table-hover">
+<colgroup>
+  <col class="bench_icon">
+  <col class="bench_icon">
+  <col class="bench_name">
+  <col class="bench_time">
+  <col class="bench_time">
+  <col class="bench_time">
+</colgroup>
+<thead>
+<tr>
+  <th></th>
+  <th>Rank</th>
+  <th>Function name</th>
+  <th>Cold time (s)</th>
+  <th>Warm time (s): best (stddev)</th>
+  <th>Speedup</th>
+</tr>
+</thead>
+<tbody>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#1</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_pythran.py">arc_distance_pythran_nested_for_loops</a></td>
+  <td>
+    0.084
+    
+  </td>
+  <td>0.079 
+      (0.003)</td>
+  <td>86.9</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#2</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_parakeet.py">arc_distance_parakeet_for_loops</a></td>
+  <td>
+    0.378
+    
+  </td>
+  <td>0.082 
+      (0.001)</td>
+  <td>84.0</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#3</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_theano.py">arc_distance_theano_broadcast</a></td>
+  <td>
+    0.090
+    
+  </td>
+  <td>0.086 
+      (0.000)</td>
+  <td>79.6</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#4</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_parakeet.py">arc_distance_parakeet_comprehensions</a></td>
+  <td>
+    0.446
+    
+  </td>
+  <td>0.092 
+      (0.002)</td>
+  <td>74.9</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#5</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_python.py">arc_distance_numpy_broadcast</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>0.093 
+      (0.000)</td>
+  <td>73.4</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#6</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_theano.py">arc_distance_theano_alloc</a></td>
+  <td>
+    0.116
+    
+  </td>
+  <td>0.107 
+      (0.001)</td>
+  <td>64.1</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#7</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_python.py">arc_distance_numpy_tile</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>0.174 
+      (0.000)</td>
+  <td>39.3</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#8</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/arc_distance/arc_distance_python.py">arc_distance_python_nested_for_loops</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>6.857 
+      (0.000)</td>
+  <td>1.0</td>
+</tr>
+
+</tbody>
+</table>
+
+
+<p class="errors-summary">
+There were
+
+
+<a href="#import-errors-arc_distance">
+1 import error(s)
+</a>
+
+
+
+
+
+
+while running this benchmark.
+</p>
+
+
+</section>
+
+
+<section id="results-growcut">
+<h3>growcut
+    (<a href="https://github.com/numfocus/python-benchmarks/tree/master/growcut"
+     title="Source code for growcut">source code</a>)
+    <a title="Permalink to this headline"
+       href="#results-growcut"
+       class="headerlink">&para;</a>
+</h3>
+
+<p class="plot">
+  
+  <img src="images/growcut_zoom_5x_best.png"
+       alt="Plot for growcut" />
+  
+  <img src="images/growcut_logscale.png"
+       alt="Plot for growcut" />
+  
+</p>
+
+<table class="table table-striped table-hover">
+<colgroup>
+  <col class="bench_icon">
+  <col class="bench_icon">
+  <col class="bench_name">
+  <col class="bench_time">
+  <col class="bench_time">
+  <col class="bench_time">
+</colgroup>
+<thead>
+<tr>
+  <th></th>
+  <th>Rank</th>
+  <th>Function name</th>
+  <th>Cold time (s)</th>
+  <th>Warm time (s): best (stddev)</th>
+  <th>Speedup</th>
+</tr>
+</thead>
+<tbody>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#1</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/growcut/growcut_parakeet.py">growcut_parakeet</a></td>
+  <td>
+    0.421
+    
+  </td>
+  <td>0.005 
+      (0.000)</td>
+  <td>2147.5</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#2</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/growcut/growcut_cython.pyx">growcut_cython</a></td>
+  <td>
+    0.009
+    
+  </td>
+  <td>0.006 
+      (0.001)</td>
+  <td>1615.0</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#3</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/growcut/growcut_pythran.py">growcut_pythran</a></td>
+  <td>
+    0.012
+    
+  </td>
+  <td>0.010 
+      (0.002)</td>
+  <td>1027.8</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#4</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/growcut/growcut_python.py">growcut_python</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>10.016 
+      (0.000)</td>
+  <td>1.0</td>
+</tr>
+
+</tbody>
+</table>
+
+
+<p class="errors-summary">
+There were
+
+
+<a href="#import-errors-growcut">
+1 import error(s)
+</a>
+
+
+
+
+
+
+while running this benchmark.
+</p>
+
+
+</section>
+
+
+<section id="results-julia">
+<h3>julia
+    (<a href="https://github.com/numfocus/python-benchmarks/tree/master/julia"
+     title="Source code for julia">source code</a>)
+    <a title="Permalink to this headline"
+       href="#results-julia"
+       class="headerlink">&para;</a>
+</h3>
+
+<p class="plot">
+  
+  <img src="images/julia_zoom_5x_best.png"
+       alt="Plot for julia" />
+  
+  <img src="images/julia_logscale.png"
+       alt="Plot for julia" />
+  
+</p>
+
+<table class="table table-striped table-hover">
+<colgroup>
+  <col class="bench_icon">
+  <col class="bench_icon">
+  <col class="bench_name">
+  <col class="bench_time">
+  <col class="bench_time">
+  <col class="bench_time">
+</colgroup>
+<thead>
+<tr>
+  <th></th>
+  <th>Rank</th>
+  <th>Function name</th>
+  <th>Cold time (s)</th>
+  <th>Warm time (s): best (stddev)</th>
+  <th>Speedup</th>
+</tr>
+</thead>
+<tbody>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#1</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_pythran.py">julia_pythran_for_loops</a></td>
+  <td>
+    0.003
+    
+  </td>
+  <td>0.002 
+      (0.000)</td>
+  <td>1865.0</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#2</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_parakeet.py">julia_parakeet_for_loops</a></td>
+  <td>
+    0.433
+    
+  </td>
+  <td>0.003 
+      (0.000)</td>
+  <td>1341.1</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#3</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_cython.pyx">julia_cython_for_loops</a></td>
+  <td>
+    0.004
+    
+  </td>
+  <td>0.004 
+      (0.000)</td>
+  <td>1194.4</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#4</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_pyopencl.py">julia_pyopencl</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>0.010 
+      (0.000)</td>
+  <td>433.0</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#5</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_python.py">julia_python_numpy</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>0.187 
+      (0.000)</td>
+  <td>24.1</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#6</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/julia/julia_python.py">julia_python_for_loops</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>4.497 
+      (0.000)</td>
+  <td>1.0</td>
+</tr>
+
+</tbody>
+</table>
+
+
+<p class="errors-summary">
+There were
+
+
+<a href="#import-errors-julia">
+1 import error(s)
+</a>
+
+
+
+
+
+
+while running this benchmark.
+</p>
+
+
+</section>
+
+
+<section id="results-pairwise">
+<h3>pairwise
+    (<a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise"
+     title="Source code for pairwise">source code</a>)
+    <a title="Permalink to this headline"
+       href="#results-pairwise"
+       class="headerlink">&para;</a>
+</h3>
+
+<p class="plot">
+  
+  <img src="images/pairwise_zoom_5x_best.png"
+       alt="Plot for pairwise" />
+  
+  <img src="images/pairwise_logscale.png"
+       alt="Plot for pairwise" />
+  
+</p>
+
+<table class="table table-striped table-hover">
+<colgroup>
+  <col class="bench_icon">
+  <col class="bench_icon">
+  <col class="bench_name">
+  <col class="bench_time">
+  <col class="bench_time">
+  <col class="bench_time">
+</colgroup>
+<thead>
+<tr>
+  <th></th>
+  <th>Rank</th>
+  <th>Function name</th>
+  <th>Cold time (s)</th>
+  <th>Warm time (s): best (stddev)</th>
+  <th>Speedup</th>
+</tr>
+</thead>
+<tbody>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#1</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_theano.py">pairwise_theano_blas_float32</a></td>
+  <td>
+    0.001
+    
+  </td>
+  <td>0.001 
+      (0.000)</td>
+  <td>25834.8</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#2</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_theano.py">pairwise_theano_blas_float64</a></td>
+  <td>
+    0.001
+    
+  </td>
+  <td>0.001 
+      (0.000)</td>
+  <td>20523.4</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#3</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_python.py">pairwise_python_numpy_dot</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>0.002 
+      (0.000)</td>
+  <td>9637.2</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#4</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_pyopencl.py">pairwise_pyopencl_cpu</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>0.004 
+      (0.000)</td>
+  <td>5671.6</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#5</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_cython.pyx">pairwise_cython_for_loops</a></td>
+  <td>
+    0.013
+    
+  </td>
+  <td>0.012 
+      (0.000)</td>
+  <td>1697.3</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#6</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_parakeet.py">pairwise_parakeet_inner_numpy</a></td>
+  <td>
+    0.246
+    
+  </td>
+  <td>0.013 
+      (0.002)</td>
+  <td>1624.9</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#7</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_parakeet.py">pairwise_parakeet_nested_for_loops</a></td>
+  <td>
+    0.183
+    
+  </td>
+  <td>0.013 
+      (0.000)</td>
+  <td>1610.8</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#8</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_parakeet.py">pairwise_parakeet_comprehensions</a></td>
+  <td>
+    0.299
+    
+  </td>
+  <td>0.014 
+      (0.001)</td>
+  <td>1565.5</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#9</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_pythran.py">pairwise_pythran_nested_for_loops</a></td>
+  <td>
+    0.018
+    
+  </td>
+  <td>0.016 
+      (0.002)</td>
+  <td>1353.4</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#10</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_theano.py">pairwise_theano_broadcast_float32</a></td>
+  <td>
+    0.053
+    
+  </td>
+  <td>0.034 
+      (0.002)</td>
+  <td>631.5</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#11</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_theano.py">pairwise_theano_broadcast_float64</a></td>
+  <td>
+    0.045
+    
+  </td>
+  <td>0.042 
+      (0.001)</td>
+  <td>501.1</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#12</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_python.py">pairwise_python_broadcast_numpy</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>0.159 
+      (0.000)</td>
+  <td>133.0</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#13</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_python.py">pairwise_python_inner_numpy</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>1.843 
+      (0.000)</td>
+  <td>11.5</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#14</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/pairwise/pairwise_python.py">pairwise_python_nested_for_loops</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>21.182 
+      (0.000)</td>
+  <td>1.0</td>
+</tr>
+
+</tbody>
+</table>
+
+
+<p class="errors-summary">
+There were
+
+
+<a href="#import-errors-pairwise">
+1 import error(s)
+</a>
+
+
+
+
+
+
+while running this benchmark.
+</p>
+
+
+</section>
+
+
+<section id="results-rosen_der">
+<h3>rosen_der
+    (<a href="https://github.com/numfocus/python-benchmarks/tree/master/rosen_der"
+     title="Source code for rosen_der">source code</a>)
+    <a title="Permalink to this headline"
+       href="#results-rosen_der"
+       class="headerlink">&para;</a>
+</h3>
+
+<p class="plot">
+  
+  <img src="images/rosen_der_zoom_5x_best.png"
+       alt="Plot for rosen_der" />
+  
+  <img src="images/rosen_der_logscale.png"
+       alt="Plot for rosen_der" />
+  
+</p>
+
+<table class="table table-striped table-hover">
+<colgroup>
+  <col class="bench_icon">
+  <col class="bench_icon">
+  <col class="bench_name">
+  <col class="bench_time">
+  <col class="bench_time">
+  <col class="bench_time">
+</colgroup>
+<thead>
+<tr>
+  <th></th>
+  <th>Rank</th>
+  <th>Function name</th>
+  <th>Cold time (s)</th>
+  <th>Warm time (s): best (stddev)</th>
+  <th>Speedup</th>
+</tr>
+</thead>
+<tbody>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#1</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_parakeet.py">rosen_der_loops_parakeet</a></td>
+  <td>
+    0.261
+    
+  </td>
+  <td>0.004 
+      (0.000)</td>
+  <td>2459.3</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#2</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_pythran.py">rosen_der_pythran</a></td>
+  <td>
+    0.005
+    
+  </td>
+  <td>0.005 
+      (0.001)</td>
+  <td>1990.5</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#3</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_cython.pyx">rosen_der_cython</a></td>
+  <td>
+    0.008
+    
+  </td>
+  <td>0.005 
+      (0.000)</td>
+  <td>1888.2</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#4</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_theano.py">rosen_der_theano_float32</a></td>
+  <td>
+    0.012
+    
+  </td>
+  <td>0.006 
+      (0.001)</td>
+  <td>1587.4</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#5</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_theano.py">rosen_der_theano_float64</a></td>
+  <td>
+    0.015
+    
+  </td>
+  <td>0.008 
+      (0.001)</td>
+  <td>1285.0</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#6</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_parakeet.py">rosen_der_numpy_parakeet</a></td>
+  <td>
+    0.374
+    
+  </td>
+  <td>0.009 
+      (0.000)</td>
+  <td>1124.9</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#7</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_python.py">rosen_der_numpy</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>0.045 
+      (0.000)</td>
+  <td>227.3</td>
+</tr>
+
+<tr>
+  <td><i class="icon-tasks"></i></td>
+  <td>#8</td>
+  <td><a href="https://github.com/numfocus/python-benchmarks/tree/master/rosen_der/rosen_der_python.py">rosen_der_python</a></td>
+  <td>
+    N/A
+    
+  </td>
+  <td>10.181 
+      (0.000)</td>
+  <td>1.0</td>
+</tr>
+
+</tbody>
+</table>
+
+
+<p class="errors-summary">
+There were
+
+
+<a href="#import-errors-rosen_der">
+1 import error(s)
+</a>
+
+
+
+
+
+
+while running this benchmark.
+</p>
+
+
+</section>
+
+
+<h2 id="error-summary">Error Summary
+  <a title="Permalink to this headline"
+     href="#error-summary"
+     class="headerlink">&para;</a></h2>
+
+
+
+
+<section id="errors-arc_distance">
+<h3>arc_distance
+    <a title="Permalink to this headline"
+       href="#errors-arc_distance"
+       class="headerlink">&para;</a></h3>
+
+
+<div id="import-errors-arc_distance">
+<h4>Benchmark loading errors
+    <a title="Permalink to this headline"
+       href="#import-errors-arc_distance"
+       class="headerlink">&para;</a></h4>
+  
+    <div id="import-errors-arc_distance-arc_distance_numba">
+    <h5><a href="">arc_distance_numba</a>
+    <a title="Permalink to this headline"
+       href="#import-errors-arc_distance-arc_distance_numba"
+       class="headerlink">&para;</a></h5>
+    <pre class="pre-scrollable"
+>AttributeError: 'NoneType' object has no attribute 'core'
+
+Traceback (most recent call last):
+  File "run_benchmarks.py", line 98, in find_benchmarks
+    module = __import__(abs_module_name, fromlist="dummy")
+  File "/Users/ogrisel/code/python-benchmarks/arc_distance/arc_distance_numba.py", line 5, in <module>
+    from numba import autojit
+  File "/usr/local/lib/python2.7/site-packages/numba/__init__.py", line 18, in <module>
+    from numba import utils, typesystem
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 102, in <module>
+    context = get_minivect_context()
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 100, in get_minivect_context
+    return NumbaContext()
+  File "/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py", line 140, in __init__
+    self.llvm_module = llvm.core.Module.new('default_module')
+AttributeError: 'NoneType' object has no attribute 'core'
+</pre>
+    </div>
+  
+</div>
+
+
+
+
+</section>
+
+
+
+
+<section id="errors-growcut">
+<h3>growcut
+    <a title="Permalink to this headline"
+       href="#errors-growcut"
+       class="headerlink">&para;</a></h3>
+
+
+<div id="import-errors-growcut">
+<h4>Benchmark loading errors
+    <a title="Permalink to this headline"
+       href="#import-errors-growcut"
+       class="headerlink">&para;</a></h4>
+  
+    <div id="import-errors-growcut-growcut_numba">
+    <h5><a href="">growcut_numba</a>
+    <a title="Permalink to this headline"
+       href="#import-errors-growcut-growcut_numba"
+       class="headerlink">&para;</a></h5>
+    <pre class="pre-scrollable"
+>AttributeError: 'NoneType' object has no attribute 'core'
+
+Traceback (most recent call last):
+  File "run_benchmarks.py", line 98, in find_benchmarks
+    module = __import__(abs_module_name, fromlist="dummy")
+  File "/Users/ogrisel/code/python-benchmarks/growcut/growcut_numba.py", line 2, in <module>
+    from numba import autojit
+  File "/usr/local/lib/python2.7/site-packages/numba/__init__.py", line 18, in <module>
+    from numba import utils, typesystem
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 102, in <module>
+    context = get_minivect_context()
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 100, in get_minivect_context
+    return NumbaContext()
+  File "/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py", line 140, in __init__
+    self.llvm_module = llvm.core.Module.new('default_module')
+AttributeError: 'NoneType' object has no attribute 'core'
+</pre>
+    </div>
+  
+</div>
+
+
+
+
+</section>
+
+
+
+
+<section id="errors-julia">
+<h3>julia
+    <a title="Permalink to this headline"
+       href="#errors-julia"
+       class="headerlink">&para;</a></h3>
+
+
+<div id="import-errors-julia">
+<h4>Benchmark loading errors
+    <a title="Permalink to this headline"
+       href="#import-errors-julia"
+       class="headerlink">&para;</a></h4>
+  
+    <div id="import-errors-julia-julia_numba">
+    <h5><a href="">julia_numba</a>
+    <a title="Permalink to this headline"
+       href="#import-errors-julia-julia_numba"
+       class="headerlink">&para;</a></h5>
+    <pre class="pre-scrollable"
+>AttributeError: 'NoneType' object has no attribute 'core'
+
+Traceback (most recent call last):
+  File "run_benchmarks.py", line 98, in find_benchmarks
+    module = __import__(abs_module_name, fromlist="dummy")
+  File "/Users/ogrisel/code/python-benchmarks/julia/julia_numba.py", line 2, in <module>
+    from numba import autojit
+  File "/usr/local/lib/python2.7/site-packages/numba/__init__.py", line 18, in <module>
+    from numba import utils, typesystem
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 102, in <module>
+    context = get_minivect_context()
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 100, in get_minivect_context
+    return NumbaContext()
+  File "/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py", line 140, in __init__
+    self.llvm_module = llvm.core.Module.new('default_module')
+AttributeError: 'NoneType' object has no attribute 'core'
+</pre>
+    </div>
+  
+</div>
+
+
+
+
+</section>
+
+
+
+
+<section id="errors-pairwise">
+<h3>pairwise
+    <a title="Permalink to this headline"
+       href="#errors-pairwise"
+       class="headerlink">&para;</a></h3>
+
+
+<div id="import-errors-pairwise">
+<h4>Benchmark loading errors
+    <a title="Permalink to this headline"
+       href="#import-errors-pairwise"
+       class="headerlink">&para;</a></h4>
+  
+    <div id="import-errors-pairwise-pairwise_numba">
+    <h5><a href="">pairwise_numba</a>
+    <a title="Permalink to this headline"
+       href="#import-errors-pairwise-pairwise_numba"
+       class="headerlink">&para;</a></h5>
+    <pre class="pre-scrollable"
+>AttributeError: 'NoneType' object has no attribute 'core'
+
+Traceback (most recent call last):
+  File "run_benchmarks.py", line 98, in find_benchmarks
+    module = __import__(abs_module_name, fromlist="dummy")
+  File "/Users/ogrisel/code/python-benchmarks/pairwise/pairwise_numba.py", line 5, in <module>
+    from numba import autojit
+  File "/usr/local/lib/python2.7/site-packages/numba/__init__.py", line 18, in <module>
+    from numba import utils, typesystem
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 102, in <module>
+    context = get_minivect_context()
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 100, in get_minivect_context
+    return NumbaContext()
+  File "/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py", line 140, in __init__
+    self.llvm_module = llvm.core.Module.new('default_module')
+AttributeError: 'NoneType' object has no attribute 'core'
+</pre>
+    </div>
+  
+</div>
+
+
+
+
+</section>
+
+
+
+
+<section id="errors-rosen_der">
+<h3>rosen_der
+    <a title="Permalink to this headline"
+       href="#errors-rosen_der"
+       class="headerlink">&para;</a></h3>
+
+
+<div id="import-errors-rosen_der">
+<h4>Benchmark loading errors
+    <a title="Permalink to this headline"
+       href="#import-errors-rosen_der"
+       class="headerlink">&para;</a></h4>
+  
+    <div id="import-errors-rosen_der-rosen_der_numba">
+    <h5><a href="">rosen_der_numba</a>
+    <a title="Permalink to this headline"
+       href="#import-errors-rosen_der-rosen_der_numba"
+       class="headerlink">&para;</a></h5>
+    <pre class="pre-scrollable"
+>AttributeError: 'NoneType' object has no attribute 'core'
+
+Traceback (most recent call last):
+  File "run_benchmarks.py", line 98, in find_benchmarks
+    module = __import__(abs_module_name, fromlist="dummy")
+  File "/Users/ogrisel/code/python-benchmarks/rosen_der/rosen_der_numba.py", line 5, in <module>
+    from numba import autojit
+  File "/usr/local/lib/python2.7/site-packages/numba/__init__.py", line 18, in <module>
+    from numba import utils, typesystem
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 102, in <module>
+    context = get_minivect_context()
+  File "/usr/local/lib/python2.7/site-packages/numba/utils.py", line 100, in get_minivect_context
+    return NumbaContext()
+  File "/usr/local/lib/python2.7/site-packages/numba/minivect/miniast.py", line 140, in __init__
+    self.llvm_module = llvm.core.Module.new('default_module')
+AttributeError: 'NoneType' object has no attribute 'core'
+</pre>
+    </div>
+  
+</div>
+
+
+
+
+</section>
+
+
+
+<h2 id="runtime-environment">Runtime Environment
+  <a title="Permalink to this headline"
+     href="#runtime-environment"
+     class="headerlink">&para;</a></h2>
+
+<h3>Hardware</h3>
+
+<ul>	 
+  <li> Platform: <i>Darwin-13.0.0-x86_64-i386-64bit</i> </li>
+  <li> CPU type: <i>i386</i></li>
+  <li> CPU count: <i>4</i></li>
+</ul>
+
+<h3>Software</h3>
+<ul>
+  <li>Python version: <i>2.7.5</i></li>
+</ul>
+
+<!-- TODO: list version numbers for all the libraries and compilers. --> 
+
+</div>
+</div>
+</div>
+<script src="http://code.jquery.com/jquery.js"></script>
+<script src="js/bootstrap.min.js"></script>
+</body>
+</html>
\ No newline at end of file
diff --git a/report/js/bootstrap.js b/js/bootstrap.js
similarity index 100%
rename from report/js/bootstrap.js
rename to js/bootstrap.js
diff --git a/report/js/bootstrap.min.js b/js/bootstrap.min.js
similarity index 100%
rename from report/js/bootstrap.min.js
rename to js/bootstrap.min.js
diff --git a/julia/__init__.py b/julia/__init__.py
deleted file mode 100644
index e671153..0000000
--- a/julia/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Authors: Serge Guelton
-# License: MIT
-
-def make_env(cr=0.285, ci=0.01, N=200, bound=1.5, lim=1000., cutoff=1e6):
-    return (cr, ci, N, bound, lim, cutoff), {}
diff --git a/julia/julia_cython.pyx b/julia/julia_cython.pyx
deleted file mode 100644
index dcd4a20..0000000
--- a/julia/julia_cython.pyx
+++ /dev/null
@@ -1,62 +0,0 @@
-#-----------------------------------------------------------------------------
-# Copyright (c) 2012, 2013, Enthought, Inc.
-# All rights reserved.  Distributed under the terms of the 2-clause BSD
-# licence.  See LICENSE.txt for details.
-# 
-# Author: Kurt W. Smith
-# Date: 26 March 2012
-#-----------------------------------------------------------------------------
-
-# --- Python std lib imports -------------------------------------------------
-import numpy as np
-
-# --- Cython cimports --------------------------------------------------------
-cimport cython
-from libc.stdint cimport uint32_t, int32_t
-from cython.parallel cimport prange
-
-# --- Ctypedefs --------------------------------------------------------
-ctypedef float     real_t
-ctypedef uint32_t  uint_t
-ctypedef int32_t   int_t
-
-#-----------------------------------------------------------------------------
-# Cython functions
-#-----------------------------------------------------------------------------
-cdef real_t abs_sq(real_t zr, real_t zi) nogil:
-    return zr * zr + zi * zi
-
-cdef uint_t kernel(real_t zr, real_t zi,
-                   real_t cr, real_t ci,
-                   real_t lim, real_t cutoff) nogil:
-    cdef:
-        uint_t count = 0
-        real_t lim_sq = lim * lim
-        
-    while abs_sq(zr, zi) < lim_sq and count < cutoff:
-        zr, zi = zr * zr - zi * zi + cr, 2 * zr * zi + ci
-        count += 1
-    return count
-
-@cython.boundscheck(False)
-@cython.wraparound(False)
-def julia_cython_for_loops(real_t cr, real_t ci,
-                           uint_t N, real_t bound=1.5,
-                           real_t lim=1000., real_t cutoff=1e6):
-    cdef:
-        uint_t[:,::1] julia 
-        real_t[::1] grid
-        int_t i, j
-        real_t x
-
-    julia = np.empty((N, N), dtype=np.uint32)
-    grid = np.asarray(np.linspace(-bound, bound, N), dtype=np.float32)
-    for i in prange(N, nogil=True):
-        x = grid[i]
-        for j in range(N):
-            julia[i,j] = kernel(x, grid[j], cr, ci, lim, cutoff)
-    return julia
-
-benchmarks = (
-    julia_cython_for_loops,
-)
diff --git a/julia/julia_numba.py b/julia/julia_numba.py
deleted file mode 100644
index fa7ef95..0000000
--- a/julia/julia_numba.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from julia import julia_python
-from numba import autojit
-
-
-benchmarks = (
-    ("julia_numba_for_loops",
-     autojit(julia_python.julia_python_for_loops)),
-)
diff --git a/julia/julia_parakeet.py b/julia/julia_parakeet.py
deleted file mode 100644
index 87e8d16..0000000
--- a/julia/julia_parakeet.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from julia import julia_python
-from parakeet import jit
-
-benchmarks = (
-    ("julia_parakeet_for_loops",
-     jit(julia_python.julia_python_for_loops)),
-
-    # Can't run the NumPy version under Parakeet since the following
-    # features are not supported: 
-    #   - np.seterr 
-    #   - np.ogrid
-    #   - complex numbers 
-    #("julia_parakeet_numpy",
-    # jit(julia_python.julia_python_numpy)),
-)
diff --git a/julia/julia_pyopencl.py b/julia/julia_pyopencl.py
deleted file mode 100644
index 2bd8909..0000000
--- a/julia/julia_pyopencl.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Authors: James Bergstra
-# License: MIT
-
-import numpy as np
-import pyopencl as cl
-
-mf = cl.mem_flags
-
-PROFILING = 0
-
-_cache = {}
-
-def julia_cpu_prepare(cr, ci, N, bound, lim, cutoff):
-    ctx = cl.create_some_context()
-    if PROFILING:
-        queue = cl.CommandQueue(
-            ctx,
-            properties=cl.command_queue_properties.PROFILING_ENABLE)
-    else:
-        queue = cl.CommandQueue(ctx)
-
-    grid_x = np.linspace(-bound, bound, N)
-    x0 = grid_x[0]
-    dx = grid_x[1] - grid_x[0]
-    limlim = lim ** 2
-
-    prg = cl.Program(ctx, """
-        __kernel void foo(__global long *outbuf)
-        {
-          int ii = get_global_id(0);
-          int jj = get_global_id(1);
-
-          double zr = %(x0)s + ii * %(dx)s;
-          double zi = %(x0)s + jj * %(dx)s;
-          long count = 0;
-
-          while (((zr*zr + zi*zi) < %(limlim)s) && (count < %(cutoff)s))
-          {
-              double tmp = zr * zr - zi * zi + %(cr)s;
-              zi = 2. * zr * zi + %(ci)s;
-              zr = tmp;
-              count += 1;
-          }
-          outbuf[ii * %(N)s + jj] = count;
-          }
-
-        """ % locals()).build()
-
-    return prg.foo, queue, ctx
-
-
-def julia_pyopencl(cr, ci, N, bound=1.5, lim=4., cutoff=1e6):
-    output = np.empty((N, N), dtype='int64')
-    args = (cr, ci, N, bound, lim, cutoff)
-    try:
-        f, queue, ctx = _cache[args]
-    except:
-        f, queue, ctx = julia_cpu_prepare(*args)
-        _cache[args] = f, queue, ctx
-    dest_buf = cl.Buffer(ctx, mf.WRITE_ONLY, output.nbytes)
-    ev = f(queue, (N, N), None, dest_buf)
-    if PROFILING:
-        ev.wait()
-        print 'computation time', 1e-9 * (ev.profile.end - ev.profile.start)
-    cl.enqueue_copy(queue, output, dest_buf)
-    return output
-
-
-benchmarks = (
-    julia_pyopencl,
-)
diff --git a/julia/julia_python.py b/julia/julia_python.py
deleted file mode 100644
index 671e4f1..0000000
--- a/julia/julia_python.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Authors: Kurt W. Smith, Serge Guelton
-# License: MIT
-
-import numpy as np
-
-def kernel(zr, zi, cr, ci, lim, cutoff):
-    ''' Computes the number of iterations `n` such that 
-        |z_n| > `lim`, where `z_n = z_{n-1}**2 + c`.
-    '''
-    count = 0
-    while ((zr*zr + zi*zi) < (lim*lim)) and count < cutoff:
-        zr, zi = zr * zr - zi * zi + cr, 2 * zr * zi + ci
-        count += 1
-    return count
-
-def julia_python_for_loops(cr, ci, N, bound=1.5, lim=1000., cutoff=1e6):
-    ''' Pure Python calculation of the Julia set for a given `c`.  No NumPy
-        array operations are used.
-    '''
-    julia = np.empty((N, N), dtype=np.uint32)
-    grid_x = np.linspace(-bound, bound, N)
-    #"omp parallel for private(i, x, j, y)"
-    for i, x in enumerate(grid_x):
-        for j, y in enumerate(grid_x):
-            julia[i,j] = kernel(x, y, cr, ci, lim, cutoff=cutoff)
-    return julia
-
-def julia_python_numpy(cr, ci, N, bound=1.5, lim=4., cutoff=1e6):
-    ''' Pure Python calculation of the Julia set for a given `c` using NumPy
-    array operations.
-    '''
-    c = cr + 1j * ci
-    orig_err = np.seterr()
-    np.seterr(over='ignore', invalid='ignore')
-    julia = np.zeros((N, N), dtype=np.uint32)
-    X, Y = np.ogrid[-bound:bound:N*1j, -bound:bound:N*1j]
-    iterations = X + Y * 1j
-    count = 1
-    while not np.all(julia) and count < cutoff:
-        mask = np.logical_not(julia) & (np.abs(iterations) >= lim)
-        julia[mask] = count
-        count += 1
-        iterations = iterations**2 + c
-    if count == cutoff:
-        julia[np.logical_not(julia)] = count
-    np.seterr(**orig_err)
-    return julia
-
-benchmarks = (
-    julia_python_for_loops,
-    julia_python_numpy,
-)
diff --git a/julia/julia_pythran.py b/julia/julia_pythran.py
deleted file mode 100644
index 98441e1..0000000
--- a/julia/julia_pythran.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from julia import julia_python
-from pythran import compile_pythrancode
-from inspect import getsource
-import re, imp
-
-# grab imports
-imports = 'import numpy as np'
-exports = '''
-#pythran export julia_python_for_loops(float, float, int, float, float, float)
-'''
-modname = 'julia_pythran'
-
-# grab the source from the original functions
-sources = map(getsource,
-        (julia_python.julia_python_for_loops,
-            julia_python.kernel,
-            )
-        )
-source = '\n'.join(sources)
-
-# patch them
-source = re.sub(r'cutoff=cutoff', 'cutoff', source)
-source = re.sub(r'dtype=np.uint32', 'np.uint32', source)
-source = re.sub(r'#"omp', '"omp', source)
-
-# compile to a native module
-native = compile_pythrancode(modname,
-                             '\n'.join([imports, exports, source]),
-                             cxxflags=['-O2', '-fopenmp'])
-
-# load it
-native = imp.load_dynamic(modname, native)
-
-benchmarks = (
-    ("julia_pythran_for_loops",
-     native.julia_python_for_loops),
-)
diff --git a/pairwise/__init__.py b/pairwise/__init__.py
deleted file mode 100644
index ce70b6f..0000000
--- a/pairwise/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Authors: Olivier Grisel
-# License: MIT
-
-import numpy as np
-
-
-def make_env(shape=(300, 150), seed=0, dtype=np.double):
-    rng = np.random.RandomState(seed)
-    data = np.asarray(rng.normal(size=shape), dtype=dtype)
-    return (data,), {}
diff --git a/pairwise/pairwise_cython.pyx b/pairwise/pairwise_cython.pyx
deleted file mode 100644
index bfa5884..0000000
--- a/pairwise/pairwise_cython.pyx
+++ /dev/null
@@ -1,28 +0,0 @@
-# Authors: Jake Vanderplas, Olivier Grisel
-# License: MIT
-
-import numpy as np
-cimport cython
-from libc.math cimport sqrt
-
-@cython.boundscheck(False)
-@cython.wraparound(False)
-def pairwise_cython_for_loops(double[:, ::1] data):
-    cdef int n_samples = data.shape[0]
-    cdef int n_features = data.shape[1]
-    cdef double tmp, d
-    cdef double[:, ::1] distances = np.empty((n_samples, n_samples),
-                                             dtype=np.float64)
-    for i in range(n_samples):
-        for j in range(n_samples):
-            d = 0.0
-            for k in range(n_features):
-                tmp = data[i, k] - data[j, k]
-                d += tmp * tmp
-            distances[i, j] = sqrt(d)
-    return np.asarray(distances)
-
-
-benchmarks = (
-    pairwise_cython_for_loops,
-)
\ No newline at end of file
diff --git a/pairwise/pairwise_numba.py b/pairwise/pairwise_numba.py
deleted file mode 100644
index e735625..0000000
--- a/pairwise/pairwise_numba.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Authors: Olivier Grisel
-# License: MIT
-
-from pairwise import pairwise_python
-from numba import autojit
-
-
-benchmarks = (
-    ("pairwise_numba_nested_for_loops",
-     autojit(pairwise_python.pairwise_python_nested_for_loops)),
-)
diff --git a/pairwise/pairwise_parakeet.py b/pairwise/pairwise_parakeet.py
deleted file mode 100644
index 65295fd..0000000
--- a/pairwise/pairwise_parakeet.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Authors: Olivier Grisel
-# License: MIT
-
-from pairwise import pairwise_python
-from parakeet import jit
-import numpy as np 
-
-def pairwise_parakeet_comprehensions(data):
-  return np.array([[np.sqrt(np.sum((a-b)**2)) for b in data] for a in data])
-
-benchmarks = (
-    ("pairwise_parakeet_nested_for_loops",
-     jit(pairwise_python.pairwise_python_nested_for_loops)),
-    ("pairwise_parakeet_inner_numpy",
-     jit(pairwise_python.pairwise_python_inner_numpy)),
-    ("pairwise_parakeet_comprehensions",
-     jit(pairwise_parakeet_comprehensions)),
-)
diff --git a/pairwise/pairwise_pyopencl.py b/pairwise/pairwise_pyopencl.py
deleted file mode 100644
index 6e4e40d..0000000
--- a/pairwise/pairwise_pyopencl.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# Authors: James Bergstra
-# License: MIT
-
-import numpy as np
-import time
-import pyopencl as cl
-import numpy
-
-mf = cl.mem_flags
-
-PROFILING = 0
-
-ctx = cl.create_some_context()
-if PROFILING:
-    queue = cl.CommandQueue(
-        ctx,
-        properties=cl.command_queue_properties.PROFILING_ENABLE)
-else:
-    queue = cl.CommandQueue(ctx)
-
-_cache = {}
-
-def pairwise_pyopencl_cpu_prepare(shp, dtype):
-    N, D = shp
-    ctype = {
-            'float32': 'float',
-            'float64': 'double',
-            }[str(dtype)]
-
-    odd_d = "" if 0 == D % 2 else """
-    __global %(ctype)s * a1 = (__global %(ctype)s*) (a);
-    %(ctype)s diff = a1[(n0 + 1) * %(D)s - 1] - a1[(m0 + 1) * %(D)s - 1];
-    buf.s0 += diff * diff;
-    """
-
-    prg = cl.Program(ctx, """
-        __kernel void lower(__global %(ctype)s2 *a, __global %(ctype)s *c)
-        {
-          for(int n0 = get_global_id(0); n0 < %(N)s; n0 += get_global_size(0))
-          {
-              for(int m0 = get_global_id(1); m0 < %(N)s; m0 += get_global_size(1))
-              {
-                if (n0 < m0) continue;
-                  __global %(ctype)s2 *an = a + n0 * %(D)s / 2;
-                  __global %(ctype)s2 *am = a + m0 * %(D)s / 2;
-                  %(ctype)s2 buf = 0;
-                  for (int d = 0; d < %(D)s/2; ++d)
-                  {
-                    %(ctype)s2 diff = am[d] - an[d];
-                    buf += diff * diff;
-                  }
-                  %(odd_d)s;
-                  c[m0 * %(N)s + n0] = sqrt(buf.s0 + buf.s1);
-              }
-          }
-        }
-        __kernel void upper(__global %(ctype)s *a, __global %(ctype)s *c)
-        {
-          for(int n0 = get_global_id(0); n0 < %(N)s; n0 += get_global_size(0))
-          {
-              for(int m0 = get_global_id(1); m0 < %(N)s; m0 += get_global_size(1))
-              {
-                if (n0 >= m0) continue;
-                  c[m0 * %(N)s + n0] = c[n0 * %(N)s + m0];
-              }
-          }
-        }
-        """ % locals()).build()
-
-    return prg.lower, prg.upper
-
-
-comptimes = []
-def pairwise_pyopencl_cpu(data):
-    data = np.asarray(data, order='C')
-    N, D = data.shape
-    try:
-        lower, upper = _cache[(data.shape, data.dtype)]
-    except:
-        lower, upper = pairwise_pyopencl_cpu_prepare(data.shape, data.dtype)
-        _cache[(data.shape, data.dtype)] = lower, upper
-    data_buf = cl.Buffer(ctx, mf.COPY_HOST_PTR, hostbuf=data)
-    dest_buf = cl.Buffer(ctx, mf.WRITE_ONLY, N * N * data.dtype.itemsize)
-    try:
-        rval, _ = cl.enqueue_map_buffer(queue, dest_buf, cl.map_flags.READ,
-                offset=0, shape=(N, N), dtype=data.dtype)
-        need_copy = False
-    except TypeError: #OSX's OCL needs this?
-        rval = np.empty((N, N), dtype=data.dtype)
-        need_copy = True
-    lower(queue, (N, 1), (1, 1), data_buf, dest_buf)
-    upper(queue, (4, 4), (1, 1), data_buf, dest_buf)
-    if need_copy:
-        cl.enqueue_copy(queue, rval, dest_buf)
-    else:
-        queue.finish()
-    if PROFILING:
-        comptimes.append(1e-9 * (ev.profile.end - ev.profile.start))
-        print 'computation time', min(comptimes)
-    return rval
-
-
-benchmarks = (
-    pairwise_pyopencl_cpu,
-)
diff --git a/pairwise/pairwise_python.py b/pairwise/pairwise_python.py
deleted file mode 100644
index 21698fe..0000000
--- a/pairwise/pairwise_python.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Authors: Jake Vanderplas, Alex Rubinsteyn, Olivier Grisel
-# License: MIT
-
-import numpy as np
-
-
-def pairwise_python_nested_for_loops(data):
-    n_samples, n_features = data.shape
-    distances = np.empty((n_samples, n_samples), dtype=data.dtype)
-    #"omp parallel for private(j, d, k, tmp)"
-    for i in range(n_samples):
-        for j in range(n_samples):
-            d = 0.0
-            for k in range(n_features):
-                tmp = data[i, k] - data[j, k]
-                d += tmp * tmp
-            distances[i, j] = np.sqrt(d)
-    return distances
-
-
-def pairwise_python_inner_numpy(data):
-    n_samples = data.shape[0]
-    result = np.empty((n_samples, n_samples), dtype=data.dtype)
-    for i in xrange(n_samples):
-        for j in xrange(n_samples):
-            result[i, j] = np.sqrt(np.sum((data[i, :] - data[j, :]) ** 2))
-    return result
-
-
-def pairwise_python_broadcast_numpy(data):
-    return np.sqrt(((data[:, None, :] - data) ** 2).sum(axis=2))
-
-
-def pairwise_python_numpy_dot(data):
-    X_norm_2 = (data ** 2).sum(axis=1)
-    dists = np.sqrt(2 * X_norm_2 - np.dot(data, data.T))
-    return dists
-
-
-benchmarks = (
-    pairwise_python_nested_for_loops,
-    pairwise_python_inner_numpy,
-    pairwise_python_broadcast_numpy,
-    pairwise_python_numpy_dot,
-)
diff --git a/pairwise/pairwise_pythran.py b/pairwise/pairwise_pythran.py
deleted file mode 100644
index 28d6796..0000000
--- a/pairwise/pairwise_pythran.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Authors: Serge "Sans Paille" Guelton
-# License: MIT
-
-from pairwise import pairwise_python
-from pythran import compile_pythrancode
-from inspect import getsource
-import re
-import imp
-
-# grab imports
-imports = 'import numpy as np'
-exports = '#pythran export pairwise_python_nested_for_loops(float[][])'
-modname = 'pairwise_pythran'
-
-# grab the source from the original function
-source = getsource(pairwise_python.pairwise_python_nested_for_loops)
-
-# a few rewriting rules to prune unsupported features
-source = re.sub(r'dtype=data.dtype', 'np.double', source)
-source = re.sub(r'#"omp', '"omp', source)
-
-# compile to a native module
-native = compile_pythrancode(modname,
-                             '\n'.join([imports, exports, source]),
-                             cxxflags=['-O2', '-fopenmp']
-                             )
-
-# load it
-native = imp.load_dynamic(modname, native)
-
-benchmarks = (
-    ("pairwise_pythran_nested_for_loops",
-     native.pairwise_python_nested_for_loops),
-)
diff --git a/pairwise/pairwise_theano.py b/pairwise/pairwise_theano.py
deleted file mode 100644
index 367f545..0000000
--- a/pairwise/pairwise_theano.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Authors: James Bergstra
-# License: MIT
-import theano
-import theano.tensor as TT
-
-
-def pairwise_theano_tensor_prepare(dtype):
-    X = TT.matrix(dtype=str(dtype))
-    dists = TT.sqrt(
-        TT.sum(
-            TT.sqr(X[:, None, :] - X),
-            axis=2))
-    name = 'pairwise_theano_broadcast_' + dtype
-    rval = theano.function([X],
-                           theano.Out(dists, borrow=True),
-                           allow_input_downcast=True, name=name)
-    rval.__name__ = name
-    return rval
-
-
-def pairwise_theano_blas_prepare(dtype):
-    X = TT.matrix(dtype=str(dtype))
-    X_norm_2 = (X ** 2).sum(axis=1)
-    dists = TT.sqrt(2 * X_norm_2 - TT.dot(X, X.T))
-    name = 'pairwise_theano_blas_' + dtype
-    rval = theano.function([X],
-                           theano.Out(dists, borrow=True),
-                           allow_input_downcast=True, name=name)
-    rval.__name__ = name
-    return rval
-
-
-benchmarks = (
-    pairwise_theano_tensor_prepare('float32'),
-    pairwise_theano_tensor_prepare('float64'),
-    pairwise_theano_blas_prepare('float32'),
-    pairwise_theano_blas_prepare('float64'),
-)
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 0e1e2ae..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-numpy
-cython
-numba
-treelike
-parakeet
-Theano
-networkx
-ply
-pythran
-cython
-memory_profiler
-jinja2
-ghp-import
-
diff --git a/rosen_der/__init__.py b/rosen_der/__init__.py
deleted file mode 100644
index 04529d1..0000000
--- a/rosen_der/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Authors: Serge Guelton
-# License: MIT
-'''Compute the derivative of the Rosenbrock function
-
-This functions tests the ability of compiler to fuse numpy operators, use
-negative indexing and handle memory view instead of making copies when slicing.
-
-see also http://en.wikipedia.org/wiki/Rosenbrock_function
-'''
-
-import numpy as np
-
-
-def make_env(N=1000000):
-    rng = np.random.RandomState(42)
-    x = rng.rand(N)
-    return (x,), {}
diff --git a/rosen_der/rosen_der_cython.pyx b/rosen_der/rosen_der_cython.pyx
deleted file mode 100644
index 2c1f244..0000000
--- a/rosen_der/rosen_der_cython.pyx
+++ /dev/null
@@ -1,23 +0,0 @@
-# Authors: Serge Guelton
-# License: MIT
-
-import numpy as np
-cimport cython
-
-@cython.boundscheck(False)
-@cython.wraparound(False)
-def rosen_der_cython(double[:] x):
-    cdef int n = x.shape[0]
-    cdef int i
-    cdef double[:] der = np.zeros_like(x)
-
-    der[0] = -400*x[0]*(x[1]-x[0]**2) - 2*(1-x[0])
-    for i in range(1,n-1):
-        der[i] = (200*(x[i] - x[i - 1]**2)
-                  - 400 * (x[i + 1] - x[i]**2)*x[i]
-                  - 2 * (1 - x[i]))
-    der[n - 1] = 200 * (x[-1] - x[-2]**2)
-    return der
-
-
-benchmarks = (rosen_der_cython,)
diff --git a/rosen_der/rosen_der_numba.py b/rosen_der/rosen_der_numba.py
deleted file mode 100644
index 63bbf31..0000000
--- a/rosen_der/rosen_der_numba.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Authors: Serge Guelton
-# License: MIT
-
-from rosen_der import rosen_der_python
-from numba import autojit
-
-
-# segfaults...
-#benchmarks = (
-#    ("rosen_der_numba",
-#     autojit(rosen_der_python.rosen_der_python)),
-#)
diff --git a/rosen_der/rosen_der_parakeet.py b/rosen_der/rosen_der_parakeet.py
deleted file mode 100644
index 1799b37..0000000
--- a/rosen_der/rosen_der_parakeet.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Authors: Serge Guelton
-# License: MIT
-
-from rosen_der import rosen_der_python
-from parakeet import jit
-
-benchmarks = (
-     ("rosen_der_loops_parakeet", jit(rosen_der_python.rosen_der_python)),
-     ("rosen_der_numpy_parakeet", jit(rosen_der_python.rosen_der_numpy))
- )
diff --git a/rosen_der/rosen_der_python.py b/rosen_der/rosen_der_python.py
deleted file mode 100644
index b4d5dce..0000000
--- a/rosen_der/rosen_der_python.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Authors: Travis E. Oliphant (numpy version), Serge Guelton (python version)
-# License: BSD
-# Source: https://github.com/scipy/scipy/blob/master/scipy/optimize/optimize.py
-import numpy
-
-
-def rosen_der_numpy(x):
-    xm = x[1:-1]
-    xm_m1 = x[:-2]
-    xm_p1 = x[2:]
-    der = numpy.zeros_like(x)
-    der[1:-1] = (+ 200 * (xm - xm_m1 ** 2)
-                 - 400 * (xm_p1 - xm ** 2) * xm
-                 - 2 * (1 - xm))
-    der[0] = -400 * x[0] * (x[1] - x[0] ** 2) - 2 * (1 - x[0])
-    der[-1] = 200 * (x[-1] - x[-2] ** 2)
-    return der
-
-
-def rosen_der_python(x):
-    n = x.shape[0]
-    der = numpy.zeros_like(x)
-
-    for i in range(1, n - 1):
-        der[i] = (+ 200 * (x[i] - x[i - 1] ** 2)
-                  - 400 * (x[i + 1]
-                           - x[i] ** 2) * x[i]
-                  - 2 * (1 - x[i]))
-    der[0] = -400 * x[0] * (x[1] - x[0] ** 2) - 2 * (1 - x[0])
-    der[-1] = 200 * (x[-1] - x[-2] ** 2)
-    return der
-
-benchmarks = (rosen_der_numpy, rosen_der_python)
diff --git a/rosen_der/rosen_der_pythran.py b/rosen_der/rosen_der_pythran.py
deleted file mode 100644
index 52271c6..0000000
--- a/rosen_der/rosen_der_pythran.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Authors: Serge Guelton
-# License: MIT
-
-from rosen_der import rosen_der_python
-from pythran import compile_pythrancode
-from inspect import getsource
-import imp
-
-# grab imports
-imports = 'import numpy'
-exports = '''
-#pythran export rosen_der_numpy(float [])
-'''
-modname = 'rosen_der_pythran'
-
-# grab the source from the original functions
-sources = map(getsource,
-              (rosen_der_python.rosen_der_numpy,)
-              )
-source = '\n'.join(sources)
-
-# patch them
-
-# compile to a native module
-native = compile_pythrancode(modname,
-                             '\n'.join([imports, exports, source]),
-                             cxxflags=['-O2', '-fopenmp'])
-
-# load it
-native = imp.load_dynamic(modname, native)
-
-benchmarks = (
-    ("rosen_der_pythran",
-     native.rosen_der_numpy),
-)
diff --git a/rosen_der/rosen_der_theano.py b/rosen_der/rosen_der_theano.py
deleted file mode 100644
index 330cc68..0000000
--- a/rosen_der/rosen_der_theano.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Authors: Travis E. Oliphant (numpy version), Serge Guelton (python version)
-#          James Bergstra (theano version)
-# License: BSD
-# Source: https://github.com/scipy/scipy/blob/master/scipy/optimize/optimize.py
-import theano
-from theano import tensor as TT
-
-
-def rosen_der_theano_prepare(dtype):
-    x = TT.vector(dtype=dtype)
-    xm = x[1:-1]
-    xm_m1 = x[:-2]
-    xm_p1 = x[2:]
-    der = TT.zeros_like(x)
-    der = TT.set_subtensor(
-        der[1:-1],
-        (+ 200 * (xm - xm_m1 ** 2)
-                 - 400 * (xm_p1 - xm ** 2) * xm
-                 - 2 * (1 - xm)))
-    der = TT.set_subtensor(
-        der[0],
-        -400 * x[0] * (x[1] - x[0] ** 2) - 2 * (1 - x[0]))
-    der = TT.set_subtensor(
-        der[-1],
-        200 * (x[-1] - x[-2] ** 2))
-    rval = theano.function([x], der, allow_input_downcast=True)
-    rval.__name__ = 'rosen_der_theano_' + dtype
-    return rval
-
-benchmarks = (rosen_der_theano_prepare('float32'),
-              rosen_der_theano_prepare('float64'))
diff --git a/run_benchmarks.py b/run_benchmarks.py
deleted file mode 100644
index 6af8799..0000000
--- a/run_benchmarks.py
+++ /dev/null
@@ -1,365 +0,0 @@
-# Authors: Olivier Grisel
-# License: MIT
-from __future__ import print_function
-
-import argparse
-try:
-    from collections import OrderedDict
-except:
-    from ordereddict import OrderedDict
-import json
-import os
-import traceback
-from time import time
-import logging
-
-from jinja2 import Template
-import numpy as np
-import matplotlib.pyplot as plt
-
-# imports for machine stats 
-import multiprocessing 
-import platform
-
-# use this to check whether benchmark needs warmup
-from types import FunctionType 
-
-try:
-    # Use automated Cython support when available
-    import pyximport
-    pyximport.install(setup_args={'include_dirs': np.get_include()})
-except ImportError:
-    pass
-
-log = logging.getLogger("run_benchmarks")
-
-REPORT_FILENAME = 'report/index.html'
-DATA_FILENAME = 'report/benchmark_results.json'
-
-GROUP_URL_PATTERN = ("https://github.com/numfocus/python-benchmarks/"
-                     "tree/master/%s")
-
-MODULE_URL_PATTERN = ("https://github.com/numfocus/python-benchmarks/"
-                      "tree/master/%s")
-
-ABOUT_URL = ("https://github.com/numfocus/python-benchmarks/blob/master/"
-             "README.md#motivation")
-
-GITHUB_REPO_URL = "https://github.com/numfocus/python-benchmarks"
-
-
-MAIN_REPORT_TEMPLATE_FILENAME = "templates/index.html"
-
-LOG_FORMAT = '%(asctime)s %(levelname)-8s %(name)-8s %(message)s'
-
-
-def find_benchmarks(folders=None, platforms=None):
-    """Collect benchmarks collable and shared environment initializers."""
-
-    benchmark_groups = []
-
-    if folders is None:
-        here = os.path.dirname(os.path.abspath(__file__))
-        dir_content = os.listdir(here)
-        folders = [f for f in dir_content
-                   if (os.path.isdir(f) and
-                       os.path.exists(os.path.join(f, '__init__.py')))]
-        folders.sort()
-
-    for folder in folders:
-        group_name = os.path.basename(folder)
-        collected_benchmarks = []
-        modules_in_error = []
-
-        pkg = __import__(group_name, fromlist="dummy")
-
-        benchmark_groups.append(OrderedDict([
-            ('name', group_name),
-            ('make_env', getattr(pkg, 'make_env', None)),
-            ('benchmarks', collected_benchmarks),
-            ('import_errors', modules_in_error),
-        ]))
-
-        for module_filename in sorted(os.listdir(folder)):
-            module_name, ext = os.path.splitext(module_filename)
-            if ext and ext not in ('.py', '.so', '.dll', '.pyx'):
-                continue
-
-            if not module_name.startswith(group_name + "_"):
-                continue
-
-            platform_name = module_name[len(group_name) + 1:]
-            if platforms is not None and platform_name not in platforms:
-                continue
-
-            abs_module_name = "%s.%s" % (group_name, module_name)
-
-            try:
-                module = __import__(abs_module_name, fromlist="dummy")
-            except Exception as e:
-                error_type = type(e).__name__
-                error_message = str(e)
-                log.error("Failed to load %s: %s: %s", abs_module_name,
-                          type(e).__name__, e)
-                tb = traceback.format_exc()
-                loading_error = OrderedDict([
-                    ('name', module_name),
-                    ('error_type', error_type),
-                    ('error_message', error_message),
-                    ('traceback', tb),
-                ])
-                modules_in_error.append(loading_error)
-                module = None
-
-            module_source = "%s/%s" % (group_name, module_filename)
-            for benchmark in getattr(module, 'benchmarks', ()):
-                if callable(benchmark):
-                    collected_benchmarks.append(
-                        (module_source, benchmark.__name__, benchmark))
-                elif isinstance(benchmark, tuple) and len(benchmark) == 2:
-                    collected_benchmarks.append((module_source,) + benchmark)
-                else:
-                    raise ValueError("Found invalid benchmark %r in %s" %
-                                     benchmark, module_name)
-    return benchmark_groups
-
-
-
-def run_benchmark(name, func, args, kwargs, memory=False, n_runs=5,
-                  slow_threshold=1):
-    """Call a function with the provided arguments"""
-    # TODO: find a way to use memory_profiler on non-python, builtin functions
-    def time_once():
-        tic = time()
-        func(*args, **kwargs)
-        toc = time()
-        return toc - tic
-
-    first_timing = time_once()
-    # if we're running a user-defined pure Python function, assume there's no warmup
-    if isinstance(func, FunctionType):
-        cold = None 
-        warm = first_timing
-        all_warm_timings = [first_timing]
-    else:
-        # Give a warm/cold time for every benchmark, even if there's no JIT
-        # Take the best time of several runs for fast executions
-      other_timings = []
-      for i in range(n_runs - 1):
-        t = time_once()
-        other_timings.append(t)
-
-      all_warm_timings = other_timings
-      best_warm_timing = np.min(all_warm_timings)
-      cold = first_timing
-      warm = best_warm_timing
-
-    return OrderedDict([
-        ('name', name),
-        ('cold_time', cold),
-        ('warm_time', warm),
-        ('all_warm_times', all_warm_timings),
-        ('std_warm_times', np.std(all_warm_timings)),
-    ])
-
-
-def run_benchmarks(folders=None, platforms=None, catch_errors=True,
-                   memory=True):
-    collected = find_benchmarks(folders=folders, platforms=platforms)
-
-    bench_results = []
-
-    for group in collected:
-        log.info("Running benchmark group %s", group['name'])
-        make_env = group.get('make_env')
-        args, kwargs = make_env() if make_env is not None else ((), {})
-
-        records = []
-        runtime_errors = []
-        for module_source, name, func in group['benchmarks']:
-            log.info("Benchmarking %s", name)
-            module_source_url = MODULE_URL_PATTERN % module_source
-            try:
-                record = run_benchmark(name, func, args, kwargs, memory=memory)
-                record['source_url'] = module_source_url
-                records.append(record)
-                log.info("%s: cold: %s, warm: %s",
-                         name, record['cold_time'], record['warm_time'])
-            except Exception as e:
-                if catch_errors:
-                    error_type = type(e).__name__
-                    error_message = str(e)
-                    tb = traceback.format_exc()
-                    runtime_error = OrderedDict([
-                        ('name', name),
-                        ('source_url', module_source_url),
-                        ('error_type', error_type),
-                        ('error_message', error_message),
-                        ('traceback', tb),
-                    ])
-                    runtime_errors.append(runtime_error)
-                    log.warn("Could not run %s: %s: %s", name,
-                             error_type, e)
-                    log.debug(tb)
-                else:
-                    raise
-
-            # TODO: add special support for PyPy with a sub-process
-
-        # Rerank records by ascending warm time:
-        if len(records) > 0:
-            records.sort(key=lambda r: r['warm_time'])
-            slowest_time = records[-1]['warm_time']
-            for rank, record in enumerate(records):
-                record['rank'] = rank + 1  # start at 1 instead of 0
-                record['speedup'] = slowest_time / record['warm_time']
-
-        bench_results.append(OrderedDict([
-            ('group_name', group['name']),
-            ('source_url', GROUP_URL_PATTERN % group['name']),
-            ('records', records),
-            ('runtime_errors', runtime_errors),
-            ('import_errors', group['import_errors']),
-        ]))
-    return bench_results
-
-
-def plot_group(group, width=0.5, zoom_scale=None, log_scale=False,
-               figsize=(12, 6), folder="report/images"):
-    records = group['records']
-    if len(records) == 0:
-        return
-
-    name = group['group_name']
-    for r in records:
-        r['max_time'] = r['cold_time'] or r['warm_time']
-
-    labels = [r['name'][len(name) + 1:] for r in records]
-    best_time = records[0]['warm_time']
-    warm_time = np.asarray([r['warm_time'] for r in records])
-    std = np.asarray([r['std_warm_times'] for r in records])
-    max_time = np.asarray([r['cold_time'] or r['warm_time'] for r in records])
-
-    if log_scale:
-        bottom = best_time / 2.
-    else:
-        bottom = 0.0
-
-    plt.figure(figsize=figsize)
-    ind = np.arange(len(labels))
-    p1 = plt.bar(ind, max_time, width, color='g', alpha=0.2, log=log_scale,
-                 bottom=bottom)
-    p2 = plt.bar(ind, warm_time, width, yerr=[np.zeros(len(std)), std],
-                 color='g', ecolor='g', alpha=0.4, log=log_scale,
-                 bottom=bottom)
-
-    title = group['group_name']
-    if log_scale:
-        title += " (log scale)"
-    elif zoom_scale:
-        title += " (zoom %dx best time)" % zoom_scale
-    plt.title(title)
-    plt.ylabel('Time (s)')
-    plt.xticks(ind + width / 2., labels, rotation=10)
-
-    if log_scale:
-        plt.yscale('log')
-        plt.ylim(bottom, None)
-    else:
-        if zoom_scale:
-            plt.ylim((0, warm_time[0] * zoom_scale))
-        else:
-            plt.ylim(0, max(max_time) * 1.1)
-
-    plt.legend((p2[0], p1[0]),
-               ('Execution time', 'Cold startup overhead'),
-               loc='upper left')
-
-    # Save the image in the report folder
-    if not os.path.exists(folder):
-        os.makedirs(folder)
-
-    if log_scale:
-        suffix = "_logscale"
-    elif zoom_scale:
-        suffix = "_zoom_%dx_best" % zoom_scale if zoom_scale else ""
-    else:
-        suffix = ""
-
-    filename = "%s%s.png" % (group['group_name'], suffix)
-    filepath = os.path.join(folder, filename)
-    plt.savefig(filepath)
-
-    # Make it available to the template engine
-    group.setdefault('plot_filenames', []).append(filename)
-
-
-def build_report(bench_data, report_filename=REPORT_FILENAME,
-                 data_filename=DATA_FILENAME):
-    for group in bench_data['benchmark_results']:
-        plot_group(group, zoom_scale=5, log_scale=False)
-        plot_group(group, zoom_scale=None, log_scale=True)
-    with open(MAIN_REPORT_TEMPLATE_FILENAME, 'rb') as f:
-        rendered = Template(f.read()).render(
-            bench_results=bench_data['benchmark_results'],
-            bech_env=bench_data['benchmark_environment'],
-            about_url=ABOUT_URL,
-            github_repo_url=GITHUB_REPO_URL,
-            json_data_url=os.path.basename(bench_data_filename),
-            sysinfo = {
-              'platform': platform.platform(),
-              'python_version': platform.python_version(), 
-              'processor' : platform.processor(), 
-              'cpu_count' : multiprocessing.cpu_count()
-            }
-        )
-    report_filename = 'report/index.html'
-    log.info("Writing report to: %s", report_filename)
-    with open(report_filename, 'wb') as f:
-        f.write(rendered)
-
-
-def parse_args(args):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--no-catch-errors', action='store_true',
-                        default=False)
-    parser.add_argument('--folders', nargs='*', default=None)
-    parser.add_argument('--platforms', nargs='*', default=None)
-    parser.add_argument('--ignore-data', action='store_true', default=False)
-    parser.add_argument('--log-level', default='INFO')
-    parser.add_argument('--open-report', action='store_true',
-                        default=False)
-    return parser.parse_args(args)
-
-if __name__ == "__main__":
-    import sys
-    options = parse_args(sys.argv[1:])
-
-    log_level = getattr(logging, options.log_level.upper(), logging.INFO)
-    logging.basicConfig(level=log_level, format=LOG_FORMAT)
-
-    bench_data_filename = DATA_FILENAME
-    if os.path.exists(bench_data_filename) and not options.ignore_data:
-        log.info("Loading bench data from: %s", bench_data_filename)
-        with open(bench_data_filename, 'rb') as f:
-            bench_data = json.load(f)
-    else:
-        bench_results = run_benchmarks(
-            catch_errors=not options.no_catch_errors,
-            folders=options.folders,
-            platforms=options.platforms,
-        )
-        bench_environment = {}  # TODO
-        bench_data = OrderedDict([
-            ('benchmark_results', bench_results),
-            ('benchmark_environment', bench_environment),
-        ])
-        log.info("Writing bench data to: %s", bench_data_filename)
-        with open(bench_data_filename, 'wb') as f:
-            json.dump(bench_data, f, indent=2)
-    report_filename = os.path.abspath('report/index.html')
-    build_report(bench_data, data_filename=bench_data_filename,
-                 report_filename=report_filename)
-    if options.open_report:
-        import webbrowser
-        webbrowser.open("file://" + report_filename)
diff --git a/templates/index.html b/templates/index.html
deleted file mode 100644
index 2805970..0000000
--- a/templates/index.html
+++ /dev/null
@@ -1,232 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  <link href="css/bootstrap.min.css" rel="stylesheet">
-  <link href="css/bootstrap-responsive.min.css" rel="stylesheet">
-  <link href="css/custom.css" rel="stylesheet" media="screen">
-</head>
-<body data-spy="scroll" data-target=".side-panel">
-<div class="navbar navbar-inverse">
-  <div class="navbar-inner">
-    <div class="container">
-      <a class="brand" id="brand" href="./index.html">Python Benchmarks</a>
-      <ul class="nav">
-        <li><a href="{{ github_repo_url }}">Github repo</a></li>
-        <li><a href="{{ json_data_url }}">JSON data</a></li>
-        <li><a href="{{ about_url }}">About</a></li>
-      </ul>
-    </div>
-  </div>
-</div>
-
-<div class="container-fluid">
-<div class="row-fluid">
- <div class="span2 side-panel">
-<!--Sidebar content-->
-<ul class="nav nav-list affix">
-  <li><a href="#top">Python Benchmarks</a></li>
-  <li><a href="#benchmark-results">Results</a></li>
-  <li><ul class="nav nav-list">
-    {% for result in bench_results %}
-    <li><a href="#results-{{ result.group_name }}">
-        {{ result.group_name }}</a></li>
-    {% endfor %}
-  </ul></li>
-  <li><a href="#error-summary">Error Summary</a></li>
-  <li><ul class="nav nav-list">
-    {% for result in bench_results %}
-    {%if result.import_errors or result.runtime_errors %}
-    <li><a href="#errors-{{ result.group_name }}">
-        {{ result.group_name }}</a></li>
-    {% endif %}
-  {% endfor %}
-  </ul></li>
-  <li><a href="#runtime-environment">Runtime Environment</a></li>
-</ul>
-
-</div>
-<div class="span10 body-container">
-<!--Body content-->
-<h2 id="benchmark-results">Results
-  <a title="Permalink to this headline"
-     href="#benchmark-results"
-     class="headerlink">&para;</a></h2>
-
-{% for result in bench_results %}
-
-<section id="results-{{ result.group_name }}">
-<h3>{{ result.group_name }}
-    (<a href="{{ result.source_url }}"
-     title="Source code for {{ result.group_name }}">source code</a>)
-    <a title="Permalink to this headline"
-       href="#results-{{ result.group_name }}"
-       class="headerlink">&para;</a>
-</h3>
-
-<p class="plot">
-  {% for filename in result.plot_filenames %}
-  <img src="images/{{ filename }}"
-       alt="Plot for {{ result.group_name }}" />
-  {% endfor %}
-</p>
-
-<table class="table table-striped table-hover">
-<colgroup>
-  <col class="bench_icon">
-  <col class="bench_icon">
-  <col class="bench_name">
-  <col class="bench_time">
-  <col class="bench_time">
-  <col class="bench_time">
-</colgroup>
-<thead>
-<tr>
-  <th></th>
-  <th>Rank</th>
-  <th>Function name</th>
-  <th>Cold time (s)</th>
-  <th>Warm time (s): best (stddev)</th>
-  <th>Speedup</th>
-</tr>
-</thead>
-<tbody>
-{% for record in result.records %}
-<tr>
-  <td><i class="icon-tasks"></i></td>
-  <td>#{{ record.rank }}</td>
-  <td><a href="{{ record.source_url }}">{{ record.name }}</a></td>
-  <td>{% if record.cold_time %}
-    {{ "{:0.3f}".format(record.cold_time) }}
-    {% else %}
-    N/A
-    {% endif %}
-  </td>
-  <td>{{ "{:0.3f}".format(record.warm_time) }} 
-      ({{ "{:0.3f}".format(record.std_warm_times)}})</td>
-  <td>{{ "{:0.1f}".format(record.speedup) }}</td>
-</tr>
-{% endfor %}
-</tbody>
-</table>
-
-{%if result.import_errors or result.runtime_errors  %}
-<p class="errors-summary">
-There were
-
-{%if result.import_errors %}
-<a href="#import-errors-{{ result.group_name }}">
-{{ result.import_errors|length }} import error(s)
-</a>
-{% endif %}
-
-{%if result.import_errors and result.runtime_errors  %}
-and
-{% endif %}
-
-{%if result.runtime_errors  %}
-<a href="#runtime-errors-{{ result.group_name }}">
-{{ result.runtime_errors|length }} execution error(s)
-</a>
-{% endif %}
-
-while running this benchmark.
-</p>
-{% else %}
-
-<p class="errors-summary">
-  All methods were benchmarked without any error.
-</p>
-
-{% endif %}
-
-</section>
-{% endfor %}
-
-<h2 id="error-summary">Error Summary
-  <a title="Permalink to this headline"
-     href="#error-summary"
-     class="headerlink">&para;</a></h2>
-
-{% for result in bench_results %}
-{%if result.import_errors or result.runtime_errors %}
-
-<section id="errors-{{ result.group_name }}">
-<h3>{{ result.group_name }}
-    <a title="Permalink to this headline"
-       href="#errors-{{ result.group_name }}"
-       class="headerlink">&para;</a></h3>
-
-{%if result.import_errors  %}
-<div id="import-errors-{{ result.group_name }}">
-<h4>Benchmark loading errors
-    <a title="Permalink to this headline"
-       href="#import-errors-{{ result.group_name }}"
-       class="headerlink">&para;</a></h4>
-  {% for import_error in result.import_errors %}
-    <div id="import-errors-{{ result.group_name }}-{{ import_error.name }}">
-    <h5><a href="{{ import_error.source_url }}">{{ import_error.name }}</a>
-    <a title="Permalink to this headline"
-       href="#import-errors-{{ result.group_name }}-{{ import_error.name }}"
-       class="headerlink">&para;</a></h5>
-    <pre class="pre-scrollable"
->{{ import_error.error_type }}: {{ import_error.error_message }}
-
-{{ import_error.traceback }}</pre>
-    </div>
-  {% endfor %}
-</div>
-{% endif %}
-
-{%if result.runtime_errors  %}
-<div id="runtime-errors-{{ result.group_name }}">
-<h4>Benchmark execution errors
-    <a title="Permalink to this headline"
-       href="#runtime-errors-{{ result.group_name }}"
-       class="headerlink">&para;</a></h4>
-  {% for runtime_error in result.runtime_errors %}
-    <div id="runtime-errors-{{ result.group_name }}-{{ runtime_error.name }}">
-    <h5><a href="{{ runtime_error.source_url }}">{{ runtime_error.name }}</a>
-    <a title="Permalink to this headline"
-       href="#runtime-errors-{{ result.group_name }}-{{ runtime_error.name }}"
-       class="headerlink">&para;</a></h5>
-    <pre class="pre-scrollable"
->{{ runtime_error.error_type }}: {{ runtime_error.error_message|e }}
-
-{{ runtime_error.traceback|e }}</pre>
-    </div>
-  {% endfor %}
-</div>
-{% endif %}
-
-</section>
-{% endif %}
-{% endfor %}
-
-<h2 id="runtime-environment">Runtime Environment
-  <a title="Permalink to this headline"
-     href="#runtime-environment"
-     class="headerlink">&para;</a></h2>
-
-<h3>Hardware</h3>
-
-<ul>	 
-  <li> Platform: <i>{{sysinfo.platform}}</i> </li>
-  <li> CPU type: <i>{{sysinfo.processor}}</i></li>
-  <li> CPU count: <i>{{sysinfo.cpu_count}}</i></li>
-</ul>
-
-<h3>Software</h3>
-<ul>
-  <li>Python version: <i>{{sysinfo.python_version}}</i></li>
-</ul>
-
-<!-- TODO: list version numbers for all the libraries and compilers. --> 
-
-</div>
-</div>
-</div>
-<script src="http://code.jquery.com/jquery.js"></script>
-<script src="js/bootstrap.min.js"></script>
-</body>
-</html>