diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..e2615b4
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,10 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Uncompressed data
+data/
+
+# Jupyter Notebook
+.ipynb_checkpoints
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..b6f7e48
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,18 @@
+# After changing this file, check it on:
+# http://lint.travis-ci.org
+
+language: python
+
+sudo: false
+
+python:
+  - "3.4"
+
+install:
+    - pip install -U pip
+    - pip install -U -r requirements.txt
+
+script:
+    - tar xvzf data.tar.gz
+    - export PYTHONHASHSEED=0
+    - python -m doctest lib/experiments.py
diff --git a/README.md b/README.md
index fbc90b8..fdd06bf 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,9 @@
 
 Implementation of graph wavelets via sparse cuts with some baselines, datasets and evaluation.
 
-Evaluation is performed using python notebooks.
+Evaluation is performed using IPython Notebook.
+
+After code review some results may differ from those presented in the paper.
 
 Scalability and approximation experiments:
 -----------------------
@@ -12,10 +14,30 @@ Compression experiments:
 -----------------------
 https://nbviewer.jupyter.org/github/arleilps/sparse-wavelets/blob/master/compression-experiments.ipynb
 
+Testing:
+------
+At the moment there is only one doctest in lib/experiments.py. To run the test
+you should use python version 3.4 or 3.5, NetworkX 1.11 and set PYTHONHASHSEED=0.
+This conditions are used to constrain the behaviour of the NetworkX function
+fiedler_vector(). Once ready, enter the following command:
+```
+python -m doctest lib/experiments.py -v
+```
+
+List of supported Python versions:
+------------------
+<ul>
+<li>2.7</li>
+<li>3.4</li>
+<li>3.5</li>
+<li>3.6</li>
+</ul>
+
+<br />
+
 For more details, see the paper:  
 [Graph Wavelets via Sparse Cuts ](http://arxiv.org/abs/1602.03320 "")  
 Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K Singh, Ananthram Swami  
-ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016 (to appear). 
+ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
 
 Arlei Silva (arlei@cs.ucsb.edu)
-
diff --git a/compression-experiments.ipynb b/compression-experiments.ipynb
deleted file mode 100644
index 6ef1673..0000000
--- a/compression-experiments.ipynb
+++ /dev/null
@@ -1,780 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Compression Experiments using Real Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import networkx\n",
-    "import math\n",
-    "import scipy.optimize\n",
-    "import numpy\n",
-    "import sys\n",
-    "from scipy import linalg\n",
-    "import matplotlib.pyplot as plt\n",
-    "from IPython.display import Image\n",
-    "import pywt\n",
-    "import scipy.fftpack\n",
-    "import random\n",
-    "import operator\n",
-    "import copy\n",
-    "from collections import deque\n",
-    "from sklearn.preprocessing import normalize\n",
-    "from sklearn.cluster import SpectralClustering\n",
-    "from matplotlib.lines import Line2D\n",
-    "from lib.io import *\n",
-    "from lib.vis import *\n",
-    "from lib.graph_signal_proc import *\n",
-    "from lib.syn import *\n",
-    "from lib.experiments import *\n",
-    "from lib.static import *\n",
-    "from lib.datasets import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Small Traffic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "G = read_graph(small_traffic[\"path\"] + \"traffic.graph\", small_traffic[\"path\"] + \"traffic.data\")\n",
-    "F = read_values(small_traffic[\"path\"] + \"traffic.data\", G) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "#vertices =  100\n",
-      "#edges =  128\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"#vertices = \", G.number_of_nodes())\n",
-    "print(\"#edges = \", len(G.edges()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "algs = [OptWavelets(n=20), OptWavelets(), GRCWavelets(), Fourier(), HWavelets()]\n",
-    "\n",
-    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
-    "\n",
-    "res_smt, time_smt = compression_experiment_static(G, numpy.array(F), algs, comp_ratios, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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lFl/HxGQEkJgSvYsE3SOaYjxhd6EU4UYz3zH4y72MuMwuSmFJPuA8CkEAIEm9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\nx5q9rz59+qhLly6S/BedeN7hsHHjRq/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VuBkNRWkG984xv69re/XahRgSmZXvgAmEMQhCAIAACYvem2gtAGAiCdZJDk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uZP2h9Vhtrv392rx2c+Ki7KGQ3k174+PlU6ZzqggKgigIIiIiIiIiImL0/dBqEQSp7hQEkcrG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3kEOPw6BFIiKwuefhomTgRXv/Jq08bYHSQ+3vzeRERETuF0Olm2bxmJKYks2LQAh9P97+9r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\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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_compression_experiments(res_smt, comp_ratios, \"figs/compression_small_traffic.png\", 4)\n",
-    "Image(filename=\"figs/compression_small_traffic.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### FSWT x GWT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.37420233,  2.25282744,  2.45563924,  4.13498675,  3.74994093,\n",
-       "        5.76065338])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numpy.divide(res_smt['GWT'], res_smt['FSWT'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SWT x GWT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.19979191,  2.75735034,  3.38438616,  4.34140944,  4.56971652,\n",
-       "        5.93517785])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numpy.divide(res_smt['GWT'], res_smt['SWT'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Traffic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "G = read_graph(traffic[\"path\"] + \"traffic.graph\", traffic[\"path\"] + \"traffic.data\")\n",
-    "F = read_values(traffic[\"path\"] + \"traffic.data\", G) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "#vertices =  1923\n",
-      "#edges =  2659\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"#vertices = \", G.number_of_nodes())\n",
-    "print(\"#edges = \", len(G.edges()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n",
-    "\n",
-    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
-    "\n",
-    "res_t, time_t = compression_experiment_static(G, F, algs, comp_ratios, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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imx0TraZzMbbWefvtt80tyM8YUwSTyspKl/fHxsb6qRLvysnJcRna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\n3oM6bVdtaXXIIYdsawX9ZuzqPMmCBQu2hWPaayV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oVUp9P1QYBqAE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\nz+fjlBTesdnYlOW+8Q0g0GLh7og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zTZg+HU6edF9Xpw78618wdCgUNk1ImSqwF/D59s+Z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\nSCoicAkjkn79FYYMgcREKleuzNy5c/noo4/46KOPmDNnDiEhIbB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DlAp5p+QOHDigixcvSnJfSOS4zNqxY8fUp08f9evXL++7mqLmIsk5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\nd9F3ABiNvmO88+fPq2fPniXfKLQXhKl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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_compression_experiments(res_t, comp_ratios, \"figs/compression_traffic.png\", 10)\n",
-    "Image(filename=\"figs/compression_traffic.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### FSWT x GWT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.23442666,  1.27973632,  1.30163643,  1.28388523,  2.60036447,\n",
-       "        5.70455735])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numpy.divide(res_t['GWT'], res_t['FSWT'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "G = read_graph(human[\"path\"] + \"human.graph\", human[\"path\"] + \"human.data\")\n",
-    "F = read_values(human[\"path\"] + \"human.data\", G) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "#vertices =  845\n",
-      "#edges =  1272\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"#vertices = \", G.number_of_nodes())\n",
-    "print(\"#edges = \", len(G.edges()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n",
-    "\n",
-    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
-    "\n",
-    "res_h, time_h = compression_experiment_static(G, F, algs, comp_ratios, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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atGku5w0c50Nuu+02ff75503aJrqxUlJSNGvWLH3yySe69NJLG92O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\nvbM5yc1lF1OARUleTWu6v7tpSRuZcwq7pqmpKX/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hmuvRZatoxfvhp0SBnm5yRXGeau2B9rcxmmtOVfLiea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\nFbq/8f3y9nD83mOeLU/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OQOALORO2Uza9Ys7d+/X1LhbXAci7n11lvVokUL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\nMpbRunfvrtq1a0tyveNw/vx5vfjii34b74UXXlBeXl6hcZznMTExU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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_compression_experiments(res_h, comp_ratios, \"figs/compression_human.png\", 100.)\n",
-    "Image(filename=\"figs/compression_human.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### FSWT x GWT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.30960783,  1.46880193,  1.40233368,  1.83547972,  2.97625452,\n",
-       "        5.96806709])"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numpy.divide(res_h['GWT'], res_h['FSWT'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Wikipedia"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "G = read_graph(wiki[\"path\"] + \"wiki.graph\", wiki[\"path\"] + \"wiki.data\")\n",
-    "F = read_values(wiki[\"path\"] + \"wiki.data\", G) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n",
-    "\n",
-    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
-    "sys.setrecursionlimit(G.number_of_nodes())\n",
-    "res_w, time_w = compression_experiment_static(G, F, algs, comp_ratios, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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pSZMmafXq1Tp37pxsNpvLFxAuli5dqpycHMVJeszsYALgMUlxkrZt2+bXLS5D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\nMacAAADmu+mmm1xuc84NlJaW6pNPPgl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T9x80c3U+fpOjwy5RH+3PpnwEklSZIkSZIkSVJ2xaIMEwqFiu1NypJTGSYU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\nJElSpWEYRpIkS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/70pz9Rq1atMu3/6KOP5s9//jPHHHPMQfsODphybc+ePdx///1l2n9U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\nVKmAaeg9oalWrVpOH7NarUzbjaBG3/Efi8Xi9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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_compression_experiments(res_w, comp_ratios, \"figs/compression_wiki.png\", 20.)\n",
-    "Image(filename=\"figs/compression_wiki.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.24759097,  1.47684102,  1.64567719,  1.62750754,  1.91693145,\n",
-       "        1.75885185])"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numpy.divide(res_w['GWT'], res_w['FSWT'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## Blogs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "G = read_graph(polblogs[\"path\"] + \"polblogs.graph\", polblogs[\"path\"] + \"polblogs.data\")\n",
-    "F = read_values(polblogs[\"path\"] + \"polblogs.data\", G) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "#vertices =  1222\n",
-      "#edges =  16717\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"#vertices = \", G.number_of_nodes())\n",
-    "print(\"#edges = \", len(G.edges()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "algs = [OptWavelets(n=20), GRCWavelets(), Fourier()]\n",
-    "\n",
-    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
-    "\n",
-    "res_b, time_b = compression_experiment_static(G, F, algs, comp_ratios, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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yZYviJD1odTAmeFBSnKTNmzf79fjHYMaaIhR5ailjJHXy8/MDHJF/+Doid9++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\ny5bpwgsvdLlq9+7dtWzZMj300EOOC2b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IFjF1iOtOnjTUxZOYVIcYR5G+YR5cQ/VmuR3oIRPUYQLggzutdocrNyT0NwSZIkSZJOryDsh1pg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\n7XWIiIiI5wT4+XFNgwbMio3F2r8/Szp2xBwVRVAF177WvDxeOXKEvlu30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Hk2X7lCQX6D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RgcRE0ovxktjP1ZU2maOHbowfusvDo8AtWEVEROSWJUuWMGjQINyxjA5q7uiCSkjWUUKLFy9m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\nF0vdYY54PBwJcneYkw3FtEhIoGXF7aRm2B2mVatWLF++nEsuuYQpU6ZQsnM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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_compression_experiments(res_b, comp_ratios, \"figs/compression_blog.png\", 4000.)\n",
-    "Image(filename=\"figs/compression_blog.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### FSWT x FT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  1.18970511,   1.20880759,   1.52070644,   2.9339968 ,\n",
-       "         2.56411917,  87.36908629])"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numpy.divide(res_b['GWT'], res_b['FSWT'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## Average Computation times"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "### Small Traffic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "FT   0.0120865980784\n",
-      "FSWT   0.330213073889\n",
-      "SWT   0.693568921089\n",
-      "GWT   0.22568975687\n",
-      "HWT   8.12668774128\n"
-     ]
-    }
-   ],
-   "source": [
-    "for alg in time_smt:\n",
-    "    print(alg, \" \" ,numpy.mean(time_smt[alg]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Traffic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "FT   35.4420957287\n",
-      "FSWT   18.375430127\n",
-      "GWT   5.96227147182\n"
-     ]
-    }
-   ],
-   "source": [
-    "for alg in time_t:\n",
-    "    print(alg, \" \", numpy.mean(time_t[alg]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Human"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "FT   2.81663463513\n",
-      "FSWT   14.0157053073\n",
-      "GWT   11.5385679166\n"
-     ]
-    }
-   ],
-   "source": [
-    "for alg in time_h:\n",
-    "    print(alg, \" \", numpy.mean(time_h[alg]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Wikipedia"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "FT   381.24113421\n",
-      "FSWT   424.825737035\n",
-      "GWT   386.143908437\n"
-     ]
-    }
-   ],
-   "source": [
-    "for alg in time_w:\n",
-    "    print(alg, \" \",numpy.mean(time_w[alg]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Blogs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "FT   7.84694833755\n",
-      "FSWT   38.6995140592\n",
-      "GWT   47.0106969635\n"
-     ]
-    }
-   ],
-   "source": [
-    "for alg in time_b:\n",
-    "    print(alg, \" \", numpy.mean(time_b[alg]))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/figs/compression_blog.png b/figs/compression_blog.png
index bca7081..33442a3 100644
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diff --git a/figs/compression_blog2.png b/figs/compression_blog2.png
new file mode 100644
index 0000000..13ba16d
Binary files /dev/null and b/figs/compression_blog2.png differ
diff --git a/figs/compression_human.png b/figs/compression_human.png
index a382eaa..6a7753f 100644
Binary files a/figs/compression_human.png and b/figs/compression_human.png differ
diff --git a/figs/compression_human2.png b/figs/compression_human2.png
new file mode 100644
index 0000000..0c4cb63
Binary files /dev/null and b/figs/compression_human2.png differ
diff --git a/figs/compression_small_traffic.png b/figs/compression_small_traffic.png
index 44c3466..08ec021 100644
Binary files a/figs/compression_small_traffic.png and b/figs/compression_small_traffic.png differ
diff --git a/figs/compression_traffic.png b/figs/compression_traffic.png
index 6b9a87c..f7d1cc4 100644
Binary files a/figs/compression_traffic.png and b/figs/compression_traffic.png differ
diff --git a/figs/compression_wiki.png b/figs/compression_wiki.png
index 4ea45bb..134eaa3 100644
Binary files a/figs/compression_wiki.png and b/figs/compression_wiki.png differ
diff --git a/figs/compression_wiki2.png b/figs/compression_wiki2.png
new file mode 100644
index 0000000..85045f0
Binary files /dev/null and b/figs/compression_wiki2.png differ
diff --git a/lib/__init__.py b/lib/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/lib/datasets.py b/lib/datasets.py
index ceb7df3..6363389 100644
--- a/lib/datasets.py
+++ b/lib/datasets.py
@@ -1,35 +1,52 @@
+r"""
 
-#small traffic
-#Speeds from traffic sensors
-#vertices: 100
+This module provides path to the dataset in data.
+Each one is in a directory with the following structure:
+
+    datasetname
+      |- datasetname.data
+      |- datasetname.graph
+
+datasetname.data has info about the graph signal.
+Row format : "vertex_id, vertex_value"
+
+datasetname.graph has info about edges.
+Row format: "vertex_A, vertex_B[, edge_weight]"
+
+"""
+
+
+# Small traffic (weighted)
+# Speeds from traffic sensors
+# Vertices: 100
 
 small_traffic = {}
 small_traffic["path"] = "data/small_traffic/"
 
-#Large traffic
-#Speeds from traffic sensors
-#vertices: 1923
+# Large traffic (weigthed)
+# Speeds from traffic sensors
+# Vertices: 1923
 
 traffic = {}
 traffic["path"] = "data/traffic/"
 
-#Human
-#Gene expression data
-#vertices: 3628
+# Human (unweighted)
+# Gene expression data
+# Vertices: 3628
 
 human = {}
 human["path"] = "data/human/"
 
-#Wikipedia data
-#Number of views of wikipedia pages
-#Vertices: 4871
+# Wikipedia data (unweighted)
+# Number of views of wikipedia pages
+# Vertices: 4871
 
 wiki = {}
 wiki["path"] = "data/wiki/"
 
-#Political blogs
-#Link network of congressman's blogs with democrat/republican (0/1) as signal
-#vertices: 1490
+# Political blogs (unweighted)
+# Link network of congressman's blogs with democrat/republican (0/1) as signal
+# Vertices: 1490
 
 polblogs = {}
 polblogs["path"] = "data/polblogs/"
diff --git a/lib/experiments.py b/lib/experiments.py
index 3b86267..f0c4d78 100644
--- a/lib/experiments.py
+++ b/lib/experiments.py
@@ -1,422 +1,471 @@
-import matplotlib.pyplot as plt
-from matplotlib.lines import Line2D
-from lib.static import *
-from mpl_toolkits.mplot3d import axes3d
-import numpy
-from matplotlib.mlab import griddata
-from matplotlib import cm
-from lib.syn import *
+import math
 import time
-import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+import lib.optimal_cut as oc
+import lib.syn as syn
+
 
 def L2(F, F_approx):
-	"""
-		Sum of squared errors
-	"""
-	e = 0
-	for i in range(F.shape[0]):
-		e = e + ((F[i]-F_approx[i])**2).sum()
+    """
+        Sum of squared errors
+    """
+    return sum([(F[i] - F_approx[i])**2 for i in range(F.shape[0])])
 
-	return float(e)
 
 def size_time_experiment(sizes, balance, sparsity, energy, noise, num):
-	"""
-		Size x time experiment using synthetic data.
-		Input:
-			* sizes: many
-			* balance
-			* sparsity
-			* energy
-			* noise
-			* num: number of repetitions
-		Output:
-			* res_time: time results 
-	"""
-	res_time = []
-
-	for s in range(len(sizes)):
-		res_t = []
-		for i in range(num):
-			(G,F,cut) = synthetic_graph(sizes[s], 3*sizes[s], sparsity, energy, balance, noise)
-
-			j = 0
-			ind = {}
-			for v in G.nodes():
-				ind[v] = j
-				j = j + 1
-
-			k = int(len(G.edges())*sparsity)
-			start_time = time.time()
-			c = one_d_search(G, F, k, ind)
-			time_slow = time.time()-start_time
-
-			start_time = time.time()
-			c = fast_search(G, F, k, 5, ind)
-			time_5 = time.time()-start_time
-			
-			start_time = time.time()
-			c = fast_search(G, F, k, 20, ind)
-			time_20 = time.time()-start_time
-			
-			start_time = time.time()
-			c = fast_search(G, F, k, 50, ind)
-			time_50 = time.time()-start_time
-
-			res_t.append([time_slow, time_5, time_20, time_50])
-
-		r = numpy.mean(numpy.array(res_t), axis=0)
-		res_time.append(r)
-
-	return numpy.array(res_time)
+    """
+        Size x time experiment using synthetic data.
+        Input:
+            * sizes: list of sizes
+            * balance
+            * sparsity
+            * energy
+            * noise
+            * num: number of repetitions
+        Output:
+            * res_time: time results
+    """
+    res_time = []
+
+    for s in range(len(sizes)):
+        res_t = []
+        for i in range(num):
+            # synthetic_graph(size, num_edges, sparsity, energy, balance,
+            # noise, seed=None)
+            (G, F, k) = syn.synthetic_graph(sizes[s], 3 * sizes[s], sparsity,
+                                            energy, balance, noise)
+
+            ind = {v: i for i, v in enumerate(G.nodes())}
+
+            start_time = time.time()
+            oc.one_d_search(G, F, k, ind)
+            time_slow = time.time() - start_time
+
+            start_time = time.time()
+            oc.fast_search(G, F, k, 5, ind)
+            time_5 = time.time() - start_time
+
+            start_time = time.time()
+            oc.fast_search(G, F, k, 20, ind)
+            time_20 = time.time() - start_time
+
+            start_time = time.time()
+            oc.fast_search(G, F, k, 50, ind)
+            time_50 = time.time() - start_time
+
+            res_t.append([time_slow, time_5, time_20, time_50])
+
+        r = np.mean(np.array(res_t), axis=0)
+        res_time.append(r)
+
+    return np.array(res_time)
+
 
 def sparsity_acc_experiment(sparsity, size, balance, energy, noise, num):
-	"""
-		Sparsity x accuracy experiments using synthetic data.
-		Input:
-			* sparsity: many
-			* size
-			* balance
-			* energy
-			* noise
-			* num: number of repetitions
-		Output:
-			* res: accuracy results
-	"""
-	res = []
-
-	for s in range(len(sparsity)):
-		res_a = []
-		for i in range(num):
-			(G,F,k) = synthetic_graph(size, 3*size, sparsity[s], energy, balance, noise)
-			
-			j = 0
-			ind = {}
-			for v in G.nodes():
-				ind[v] = j
-				j = j + 1
-
-			c = one_d_search(G, F, k, ind)
-			acc_slow = c["energy"]
-			
-			c = fast_search(G, F, k, 5, ind)
-			acc_5 = c["energy"]
-			
-			c = fast_search(G, F, k, 20, ind)
-			acc_20 = c["energy"]
-			
-			c = fast_search(G, F, k, 50, ind)
-			acc_50 = c["energy"]
-
-			res_a.append([acc_slow, acc_5, acc_20, acc_50])
-		
-		r = numpy.mean(numpy.array(res_a), axis=0)
-		res.append(r)
-
-	return numpy.array(res)
+    """
+        Sparsity x accuracy experiments using synthetic data.
+        Input:
+            * sparsity: many
+            * size
+            * balance
+            * energy
+            * noise
+            * num: number of repetitions
+        Output:
+            * res: accuracy results
+    """
+    res = []
+
+    for s in range(len(sparsity)):
+        res_a = []
+        for i in range(num):
+            (G, F, k) = syn.synthetic_graph(size, 3 * size, sparsity[s],
+                                            energy, balance, noise)
+
+            ind = {v: i for i, v in enumerate(G.nodes())}
+
+            c = oc.one_d_search(G, F, k, ind)
+            acc_slow = c["energy"]
+
+            c = oc.fast_search(G, F, k, 5, ind)
+            acc_5 = c["energy"]
+
+            c = oc.fast_search(G, F, k, 20, ind)
+            acc_20 = c["energy"]
+
+            c = oc.fast_search(G, F, k, 50, ind)
+            acc_50 = c["energy"]
+
+            res_a.append([acc_slow, acc_5, acc_20, acc_50])
+
+        r = np.mean(np.array(res_a), axis=0)
+        res.append(r)
+
+    return np.array(res)
+
 
 def noise_acc_experiment(noise, size, sparsity, energy, balance, num):
-	"""
-		Noise x accuracy experiments using synthetic data. 
-		Input:
-			* noise: many
-			* size
-			* sparsity
-			* energy
-			* balance
-			* num: number of repetitions
-		Output:
-			* res: accuracy results
-	"""
-	res = []
-
-	for s in range(len(noise)):
-		res_a = []
-		for i in range(num):
-			(G,F,k) = synthetic_graph(size, 3*size, sparsity, energy, balance, noise[s])
-			
-			j = 0
-			ind = {}
-			for v in G.nodes():
-				ind[v] = j
-				j = j + 1
-			
-			c = one_d_search(G, F, k, ind)
-			acc_slow = c["energy"]
-			
-			L = networkx.laplacian_matrix(G)
-			c = fast_search(G, F, k, 5, ind)
-			acc_5 = c["energy"]
-			
-			c = fast_search(G, F, k, 20, ind)
-			acc_20 = c["energy"]
-			
-			c = fast_search(G, F, k, 50, ind)
-			acc_50 = c["energy"]
-
-			res_a.append([acc_slow, acc_5, acc_20, acc_50])
-		
-		r = numpy.mean(numpy.array(res_a), axis=0)
-		res.append(r)
-
-	return numpy.array(res)
+    """
+        Noise x accuracy experiments using synthetic data.
+        Input:
+            * noise: list of noises
+            * size
+            * sparsity
+            * energy
+            * balance
+            * num: number of repetitions
+        Output:
+            * res: accuracy results
+    """
+    res = []
+
+    for s in range(len(noise)):
+        res_a = []
+        for i in range(num):
+            (G, F, k) = syn.synthetic_graph(size, 3 * size, sparsity,
+                                            energy, balance, noise[s])
+
+            ind = {v: i for i, v in enumerate(G.nodes())}
+
+            c = oc.one_d_search(G, F, k, ind)
+            acc_slow = c["energy"]
+
+            c = oc.fast_search(G, F, k, 5, ind)
+            acc_5 = c["energy"]
+
+            c = oc.fast_search(G, F, k, 20, ind)
+            acc_20 = c["energy"]
+
+            c = oc.fast_search(G, F, k, 50, ind)
+            acc_50 = c["energy"]
+
+            res_a.append([acc_slow, acc_5, acc_20, acc_50])
+
+        r = np.mean(np.array(res_a), axis=0)
+        res.append(r)
+
+    return np.array(res)
+
 
 def energy_acc_experiment(energy, size, sparsity, noise, balance, num):
-	"""
-		Energy x accuracy experiments using synthetic data. 
-		Input:
-			* energy: many
-			* size
-			* sparsity
-			* noise
-			* balance
-			* num: number of repetitions
-		Output:
-			* res: accuracy results
-	"""
-	res = []
-
-	for s in range(len(energy)):
-		res_a = []
-		for i in range(num):
-			(G,F,k) = synthetic_graph(size, 3*size, sparsity, energy[s], balance, noise)
-			
-			j = 0
-			ind = {}
-			for v in G.nodes():
-				ind[v] = j
-				j = j + 1
-			
-			c = one_d_search(G, F, k, ind)
-			acc_slow = c["energy"]
-			
-			c = fast_search(G, F, k, 5, ind)
-			acc_5 = c["energy"]
-			
-			c = fast_search(G, F, k, 20, ind)
-			acc_20 = c["energy"]
-			
-			c = fast_search(G, F, k, 50, ind)
-			acc_50 = c["energy"]
-
-			res_a.append([acc_slow, acc_5, acc_20, acc_50])
-		
-		r = numpy.mean(numpy.array(res_a), axis=0)
-		res.append(r)
-
-	return numpy.array(res)
+    """
+        Energy x accuracy experiments using synthetic data.
+        Input:
+            * energy: list of energies
+            * size
+            * sparsity
+            * noise
+            * balance
+            * num: number of repetitions
+        Output:
+            * res: accuracy results
+    """
+    res = []
+
+    for s in range(len(energy)):
+        res_a = []
+        for i in range(num):
+            (G, F, k) = syn.synthetic_graph(size, 3 * size, sparsity,
+                                            energy[s], balance, noise)
+
+            ind = {v: i for i, v in enumerate(G.nodes())}
+
+            c = oc.one_d_search(G, F, k, ind)
+            acc_slow = c["energy"]
+
+            c = oc.fast_search(G, F, k, 5, ind)
+            acc_5 = c["energy"]
+
+            c = oc.fast_search(G, F, k, 20, ind)
+            acc_20 = c["energy"]
+
+            c = oc.fast_search(G, F, k, 50, ind)
+            acc_50 = c["energy"]
+
+            res_a.append([acc_slow, acc_5, acc_20, acc_50])
+
+        r = np.mean(np.array(res_a), axis=0)
+        res.append(r)
+
+    return np.array(res)
+
 
 def plot_size_time_experiment(results, sizes, output_file_name):
-	"""
-		Plots size x time experiment.
-		Input:
-			* results: time results
-			* sizes: graph sizes
-			* output_file_name: output file name	
-		Output:
-			* None
-	"""
-	plt.clf()
-	
-	ax = plt.subplot(111)
-	
-	ncol=2
-	ax.plot(sizes, results[:,0], marker="x", color="cyan", label="SWT", markersize=15)
-	ax.plot(sizes, results[:,1], marker="o", color="orangered", label="FSWT-5", markersize=15)
-	ax.plot(sizes, results[:,2], marker="o", color="darkgreen", label="FSWT-20", markersize=15)
-	ax.plot(sizes, results[:,3], marker="o", color="k", label="FSWT-50", markersize=15)
-	plt.gcf().subplots_adjust(bottom=0.15)
-	ax.legend(loc='upper center', prop={'size':20}, ncol=ncol)
-	ax.set_ylabel('time (sec.)', fontsize=30)
-	ax.set_xlabel('#vertices', fontsize=30)
-	ax.tick_params(labelsize=23)
-	#plt.rcParams['xtick.labelsize'] = 80 
-	#plt.rcParams['ytick.labelsize'] = 80
-	ax.set_xlim([180,1020])
-	ax.set_ylim([0.01,50000])
-	ax.set_yscale('log')
-
-	plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
+    """
+        Plots size x time experiment.
+        Input:
+            * results: time results
+            * sizes: graph sizes
+            * output_file_name: output file name
+        Output:
+            * None
+    """
+    plt.clf()
+
+    ax = plt.subplot(111)
+
+    ax.plot(sizes, results[:, 0], marker="x", color="cyan",
+            label="SWT", markersize=15)
+    ax.plot(sizes, results[:, 1], marker="o", color="orangered",
+            label="FSWT-5", markersize=15)
+    ax.plot(sizes, results[:, 2], marker="o", color="darkgreen",
+            label="FSWT-20", markersize=15)
+    ax.plot(sizes, results[:, 3], marker="o", color="k",
+            label="FSWT-50", markersize=15)
+    plt.gcf().subplots_adjust(bottom=0.15)
+    ax.legend(loc='upper center', prop={'size': 20}, ncol=2)
+    ax.set_ylabel('time (sec.)', fontsize=30)
+    ax.set_xlabel('#vertices', fontsize=30)
+    ax.tick_params(labelsize=23)
+    # plt.rcParams['xtick.labelsize'] = 80
+    # plt.rcParams['ytick.labelsize'] = 80
+    ax.set_xlim([180, 1020])
+    ax.set_ylim([0.01, 50000])
+    ax.set_yscale('log')
+
+    plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
+
 
 def plot_sparsity_acc_experiment(results, sparsity, output_file_name):
-	"""
-		Plots sparsity x accuracy experiment.
-		Input:
-			* results: accuracy results
-			* sparsity: sparsity values
-			* output_file_name: output file name
-		Output:
-			* None
-	"""
-	plt.clf()
-
-	ncol=2
-	ax = plt.subplot(111)
-	width = 0.04       # the width of the bars)
-	ax.bar(numpy.array(sparsity)-2*width, results[:,0], width, color='cyan', label="SWT", hatch="/")
-	ax.bar(numpy.array(sparsity)-width, results[:,1], width, color='orangered', label="FSWT-5", hatch="\\")
-	ax.bar(numpy.array(sparsity), results[:,2], width, color='darkgreen', label="FSWT-20", hatch="-")
-	ax.bar(numpy.array(sparsity)+width, results[:,3], width, color='k', label="FSWT-50", hatch="*")
-	plt.gcf().subplots_adjust(bottom=0.15)
-	ax.legend(loc='upper center', prop={'size':20}, ncol=ncol)
-	ax.set_ylabel(r'L$_2$ energy', fontsize=30)
-	ax.set_xlabel('sparsity', fontsize=30)
-	plt.rcParams['xtick.labelsize'] = 20 
-	plt.rcParams['ytick.labelsize'] = 20
-	ax.set_xlim([0.1,0.9])
-	ax.set_ylim([0,150])
-	
-	plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
+    """
+        Plots sparsity x accuracy experiment.
+        Input:
+            * results: accuracy results
+            * sparsity: sparsity values
+            * output_file_name: output file name
+        Output:
+            * None
+    """
+    plt.clf()
+
+    ax = plt.subplot(111)
+    width = 0.04       # the width of the bars)
+    ax.bar(np.array(sparsity) - 2 * width,
+           results[:, 0], width, color='cyan', label="SWT", hatch="/")
+    ax.bar(np.array(sparsity) - width, results[:, 1],
+           width, color='orangered', label="FSWT-5", hatch="\\")
+    ax.bar(np.array(sparsity), results[:, 2], width,
+           color='darkgreen', label="FSWT-20", hatch="-")
+    ax.bar(np.array(sparsity) + width, results[:, 3],
+           width, color='k', label="FSWT-50", hatch="*")
+    plt.gcf().subplots_adjust(bottom=0.15)
+    ax.legend(loc='upper center', prop={'size': 20}, ncol=2)
+    ax.set_ylabel(r'L$_2$ energy', fontsize=30)
+    ax.set_xlabel('sparsity', fontsize=30)
+    plt.rcParams['xtick.labelsize'] = 20
+    plt.rcParams['ytick.labelsize'] = 20
+    ax.set_xlim([0.1, 0.9])
+    ax.set_ylim([0, 150])
+
+    plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
+
 
 def plot_noise_acc_experiment(results, noise, output_file_name):
-	"""
-		Plots noise x accuracy experiment.
-		Input:
-			* results: accuracy results
-			* noise: noise values
-			* output_file_name: output file name
-		Output:
-			* None
-	"""
-	plt.clf()
-	
-	ax = plt.subplot(111)
-	ncol=2
-	width = 0.04       # the width of the bars)
-	rects1 = ax.bar(numpy.array(noise)-2*width, results[:,0], width, color='cyan', label="SWT")
-	rects2 = ax.bar(numpy.array(noise)-width, results[:,1], width, color='orangered', label="FSWT-5")
-	rects3 = ax.bar(numpy.array(noise), results[:,2], width, color='darkgreen', label="FSWT-20")
-	rects4 = ax.bar(numpy.array(noise)+width, results[:,3], width, color='k', label="FSWT-50")
-
-	plt.gcf().subplots_adjust(bottom=0.15)
-	ax.legend(loc='upper center', prop={'size':20}, ncol=ncol)
-	ax.set_ylabel(r'L$_2$ energy', fontsize=30)
-	ax.set_xlabel('noise', fontsize=30)
-	plt.rcParams['xtick.labelsize'] = 20 
-	plt.rcParams['ytick.labelsize'] = 20
-	ax.set_xlim([0.1,0.9])
-	ax.set_ylim([0,150])
-	
-	plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
+    """
+        Plots noise x accuracy experiment.
+        Input:
+            * results: accuracy results
+            * noise: noise values
+            * output_file_name: output file name
+        Output:
+            * None
+    """
+    plt.clf()
+
+    ax = plt.subplot(111)
+
+    width = 0.04       # the width of the bars)
+    ax.bar(np.array(noise) - 2 * width, results[:, 0],
+           width, color='cyan', label="SWT")
+    ax.bar(np.array(noise) - width,
+           results[:, 1], width, color='orangered', label="FSWT-5")
+    ax.bar(np.array(noise), results[:, 2],
+           width, color='darkgreen', label="FSWT-20")
+    ax.bar(np.array(noise) + width, results[:, 3],
+           width, color='k', label="FSWT-50")
+
+    plt.gcf().subplots_adjust(bottom=0.15)
+    ax.legend(loc='upper center', prop={'size': 20}, ncol=2)
+    ax.set_ylabel(r'L$_2$ energy', fontsize=30)
+    ax.set_xlabel('noise', fontsize=30)
+    plt.rcParams['xtick.labelsize'] = 20
+    plt.rcParams['ytick.labelsize'] = 20
+    ax.set_xlim([0.1, 0.9])
+    ax.set_ylim([0, 150])
+
+    plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
+
 
 def plot_energy_acc_experiment(results, energy, output_file_name):
-	"""
-		Plots energy x accuracy experiment.
-		Input:
-			* results: accuracy results
-			* energy: energy values
-			* output_file_name: output file name
-		Output:
-			* None
-	"""
-	plt.clf()
-	ncol=2
-	ind = numpy.array(list(range(4)))
-	ax = plt.subplot(111)
-	width = 0.2      # the width of the bars)
-	rects1 = ax.bar(ind-width, results[:,0], width, color='cyan', label="SWT", log=True)
-	rects2 = ax.bar(ind, results[:,1], width, color='orangered', label="FSWT-5", log=True)
-	rects3 = ax.bar(ind+width, results[:,2], width, color='darkgreen', label="FSWT-20", log=True)
-	rects4 = ax.bar(ind+2*width, results[:,3], width, color='k', label="FSWT-50", log=True)
-
-	plt.gcf().subplots_adjust(bottom=0.15)
-	ax.legend(loc='upper left', prop={'size':20}, ncol=ncol)
-	ax.set_ylabel(r'L$_2$ energy', fontsize=30)
-	ax.set_xlabel(r'L$_2$ energy (data)', fontsize=30)
-	plt.rcParams['xtick.labelsize'] = 20 
-	plt.rcParams['ytick.labelsize'] = 20
-	ax.set_yscale('log')
-	ax.set_xticks(ind + width)
-	ax.set_xticklabels((r'10$\mathregular{^1}$', r'10$\mathregular{^2}$', r'10$\mathregular{^3}$', r'10$\mathregular{^4}$'))
-	ax.set_ylim(0.1,1000000)
-	ax.set_xlim(-0.3,3.7)
-	
-	plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
-
-def plot_compression_experiments(results, comp_ratios, output_file_name, max_y):
-	"""
-		Plots compression size x accuracy experiment.
-		Input:
-			* results: accuracy results
-			* comp_ratios: compression ratios
-			* output_file_name: output file name
-			* max_y: maximum y-axis
-		Output:
-			* None
-	"""
-	plt.clf()
-	
-	for alg in results:
-		for i in range(1,results[alg].shape[0]):
-			if results[alg][i] > results[alg][i-1]:
-				results[alg][i] = results[alg][i-1]	
-
-	ax = plt.subplot(111)
-	ncol = 3
-	
-	ax.semilogy(comp_ratios, results["FSWT"], marker="o", color="r", label="FSWT", markersize=15)
-	ax.semilogy(comp_ratios, results["FT"], marker="*", color="c", label="FT", markersize=15)
-	
-	if "SWT" in results:
-		ax.semilogy(comp_ratios, results["SWT"], marker="x", color="b", label="SWT", markersize=15)
-	
-	ax.semilogy(comp_ratios, results["GWT"], marker="s", color="g", label="GWT", markersize=15)
-	
-	if "HWT" in results:
-		ax.semilogy(comp_ratios, results["HWT"], marker="v", color="y", label="HWT", markersize=15)
-	
-	plt.gcf().subplots_adjust(bottom=0.15)
-	ax.legend(loc='upper center', prop={'size':20}, ncol=ncol)
-	ax.set_ylabel(r'L$_2$ error', fontsize=30)
-	ax.set_xlabel('size', fontsize=30)
-	plt.rcParams['xtick.labelsize'] = 20 
-	plt.rcParams['ytick.labelsize'] = 20
-	ax.set_xlim(0.,0.65)
-	ax.set_ylim(0., max_y)
-
-	plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
-
-def compression_experiment_static(G, F, algs, comp_ratios, num):
-	"""
-		Runs compression experiment static.
-		Input:
-			* G: graph
-			* F: graph signal
-			* algs: compression algorithms/transforms
-			* comp_ratios: compression ratios
-			* num: number of repetitions
-		Output:
-			* results: compression results
-			* times: compression times
-	"""
-	results = {}
-	times = {}
-
-	for alg in algs:
-		results[alg.name()] = []
-		times[alg.name()] = []
-			
-		for r in range(len(comp_ratios)):
-			T = []
-			R = []
-			for i in range(num):
-				start_time = time.time()
-				alg.set_graph(G)
-				tr = alg.transform(F)
-				size = int(F.size * comp_ratios[r])
-				appx_tr = alg.drop_frequency(tr, size)
-				appx_F = alg.inverse(appx_tr)
-				t = time.time()-start_time
-				T.append(t)
-				R.append(L2(F, appx_F))
-			T = numpy.array(T)
-			R = numpy.array(R)
-			times[alg.name()].append(numpy.mean(T))
-			results[alg.name()].append(numpy.mean(R))
-
-		results[alg.name()] = numpy.array(results[alg.name()]) 
-
-		times[alg.name()] = numpy.array(times[alg.name()]) 
-              
-	return results, times
-
-	
+    """
+        Plots energy x accuracy experiment.
+        Input:
+            * results: accuracy results
+            * energy: energy values
+            * output_file_name: output file name
+        Output:
+            * None
+    """
+    plt.clf()
+
+    ind = np.array(list(range(4)))
+    ax = plt.subplot(111)
+    width = 0.2      # the width of the bars)
+    ax.bar(ind - width, results[:, 0],
+           width, color='cyan', label="SWT", log=True)
+    ax.bar(ind, results[:, 1],
+           width, color='orangered', label="FSWT-5", log=True)
+    ax.bar(ind + width, results[:, 2],
+           width, color='darkgreen', label="FSWT-20", log=True)
+    ax.bar(ind + 2 * width, results[:, 3],
+           width, color='k', label="FSWT-50", log=True)
+
+    plt.gcf().subplots_adjust(bottom=0.15)
+    ax.legend(loc='upper left', prop={'size': 20}, ncol=2)
+    ax.set_ylabel(r'L$_2$ energy', fontsize=30)
+    ax.set_xlabel(r'L$_2$ energy (data)', fontsize=30)
+    plt.rcParams['xtick.labelsize'] = 20
+    plt.rcParams['ytick.labelsize'] = 20
+    ax.set_yscale('log')
+    ax.set_xticks(ind + width)
+    ax.set_xticklabels((r'10$\mathregular{^1}$', r'10$\mathregular{^2}$',
+                        r'10$\mathregular{^3}$', r'10$\mathregular{^4}$'))
+    ax.set_ylim(0.1, 1000000)
+    ax.set_xlim(-0.3, 3.7)
+
+    plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
+
+
+def get_children(tree, chil_list):
+    c = tree.children
+    if c != []:
+        chil_list.append(c[0])
+        if len(c) == 2:
+            chil_list.append(c[1])
+        get_children(c[0], chil_list)
+        if len(c) == 2:
+            get_children(c[1], chil_list)
+
+
+def compression_experiment(G, F, algs, comp_ratios, num):
+    r"""
+    Runs compression experiment static.
+        Input:
+            * G: graph
+            * F: graph signal
+            * algs: compression algorithms/transforms
+            * comp_ratios: compression ratios
+            * num: number of repetitions
+        Output:
+            * results: compression results
+            * times: compression times
+
+    The following DOCTEST is designed for NetworkX 1.11. Different versions
+    (ex 2.0) return slightly different Fiedler vectors. Furthermore to get the
+    same vector you should also set PYTHONHASHSEED=0. 'tracemin_lu' seems to
+    be faster and give more stable results than 'lobpcg'. The resulting
+    accuracy of the two method is similar.
+
+    >>> from lib import io_utils, static
+    >>> import lib.datasets as data
+    >>>
+    >>> G = io_utils.read_graph(data.small_traffic["path"] + "traffic.graph",
+    ...                         data.small_traffic["path"] + "traffic.data")
+    >>> F = io_utils.read_values(data.small_traffic["path"] +
+    ...                         "traffic.data", G)
+    >>> algs = [static.OptWavelets(n=5, method='lobpcg'),\
+    ... static.OptWavelets(method='tracemin_lu'), static.Fourier(),\
+    ... static.GRCWavelets(method='tracemin_lu'), static.HWavelets()]
+    >>> comp_ratios = [0.1, 0.2]
+    >>> res_smt, time_smt = \
+    ...     compression_experiment(G, np.array(F), algs,
+    ...                            comp_ratios, 1)
+    >>> print(res_smt['FT'])
+    [ 0.17681431  0.1036391 ]
+    >>> print(res_smt['FSWT'])
+    [ 0.16306688  0.03567735]
+    >>> print(res_smt['SWT'])
+    [ 0.15091373  0.05312383]
+    >>> print(res_smt['GWT'])
+    [ 0.1988195   0.10399963]
+    >>> print(res_smt['HWT'])
+    [ 0.27003969  0.23748869]
+    """
+    results = {}
+    times = {}
+
+    for alg in algs:
+        results[alg.name()] = []
+        times[alg.name()] = []
+        for r in range(len(comp_ratios)):
+            T = []
+            R = []
+            for i in range(num):
+                start_time = time.time()
+                alg.set_graph(G)
+                tr = alg.transform(F)
+                size = int(F.size * comp_ratios[r])
+                appx_tr = alg.drop_frequency(tr, size)
+                appx_F = alg.inverse(appx_tr)
+                t = time.time() - start_time
+                T.append(t)
+                R.append(L2(F, appx_F))
+            T = np.array(T)
+            R = np.array(R)
+            times[alg.name()].append(np.mean(T))
+            results[alg.name()].append(np.mean(R))
+        results[alg.name()] = np.array(results[alg.name()])
+        times[alg.name()] = np.array(times[alg.name()])
+    return results, times
+
+
+def plot_compression_experiments(results, comp_ratios, output_file_name):
+    """
+        Plots compression size x accuracy experiment.
+        Input:
+            * results: accuracy results
+            * comp_ratios: compression ratios
+            * output_file_name: output file name
+        Output:
+            * None
+    """
+
+    # Get range of values
+    highest_val = max([max(results[res]) for res in results])
+    lowest_val = min([min(results[res]) for res in results])
+
+    plt.clf()
+    ax = plt.subplot(111)
+
+    legend_columns = 3
+
+    # Add some white space at the top to place the legend
+    white_space = 0.3 * (len(results) // legend_columns
+                         + bool((len(results) % legend_columns)))
+    max_y = 10**(1 * math.log(highest_val, 10)
+                 + white_space * (math.log(highest_val, 10)
+                                  - math.log(lowest_val, 10)))
+
+    ax.semilogy(comp_ratios, results["FSWT"], marker="o", color="r",
+                label="FSWT")
+    ax.semilogy(comp_ratios, results["FT"], marker="*", color="c",
+                label="FT")
+    if "SWT" in results:
+        ax.semilogy(comp_ratios, results["SWT"], marker="x", color="b",
+                    label="SWT")
+    ax.semilogy(comp_ratios, results["GWT"], marker="s", color="g",
+                label="GWT")
+    if "HWT" in results:
+        ax.semilogy(comp_ratios, results["HWT"], marker="v", color="y",
+                    label="HWT")
+
+    ax.legend(loc='upper center', ncol=legend_columns)
+    ax.set_ylabel(r'L$_2$ error')
+    ax.set_xlabel('size')
+
+    ax.set_ylim(lowest_val, max_y)
+
+    plt.savefig(output_file_name, dpi=300, bbox_inches='tight')
diff --git a/lib/graph_signal_proc.py b/lib/graph_signal_proc.py
index 4b39851..954dde8 100644
--- a/lib/graph_signal_proc.py
+++ b/lib/graph_signal_proc.py
@@ -1,988 +1,620 @@
-import networkx
 import math
+from collections import deque
+
+import networkx as nx
+import numpy as np
+from numpy import dot, diag
 import scipy.optimize
-import numpy
-import sys
 from scipy import linalg
-import matplotlib.pyplot as plt
-from IPython.display import Image
-import pywt
 import scipy.fftpack
-import random
-import operator
-import copy
-from collections import deque
-from sklearn.preprocessing import normalize
-from sklearn.cluster import SpectralClustering
-import statistics
-from numpy import log, mean, dot, diag, sqrt
-from numpy.linalg import eigh
 
 
 def compute_eigenvectors_and_eigenvalues(L):
-	"""
-		Computes eigenvectors and eigenvalues of the matrix L
-		Input: 
-			* L: matrix
-		Output:
-			* U: eigenvector matrix, one vector/column, sorted by corresponsing eigenvalue
-			* lamb: eigenvalues, sorted in increasing order
-	"""
-	lamb, U = linalg.eig(L)
-
-	idx = lamb.argsort()   
-	lamb = lamb[idx]
-	U = U[:,idx]
-
-	return U, lamb
-
-def s(x):
-	"""
-		Cubic spline.
-		Input:
-			* x
-		Output:
-			* spline(x)
-	"""
-	return -5 + 11*x - 6*pow(x, 2) + pow(x, 3)
+    """
+        Computes eigenvectors and eigenvalues of the matrix L
+        Input:
+            * L: matrix
+        Output:
+            * U: eigenvector matrix, one vector/column, sorted by corresponding
+                 eigenvalue
+            * lamb: eigenvalues, sorted in increasing order
+    """
+    lamb, U = linalg.eig(L)
+
+    idx = lamb.argsort()
+    lamb = lamb[idx]
+    U = U[:, idx]
+
+    return U, lamb
+
 
 def g(x):
-	"""
-		Wavelet kernel.
-		Input:
-			* x
-		Output:
-			* kernel of x
-	"""
-	a = 2
-	b = 2
-	x_1 = 1
-	x_2 = 2
-
-	if x < x_1:
-		return pow(x_1, -a)*pow(x, a)
-	elif x <= x_2 and x >= x_1:
-		return s(x)
-	else:
-		return pow(x_2, b)*pow(x, -b)
+    """
+        Wavelet generating kernel, see Hammond, D. K.,Vandergheynst, P.,
+        & Gribonval, R. (2011). "Wavelets on graphs via spectral graph theory".
+        Input:
+            * x
+        Output:
+            * kernel of x
+    """
+    a = 2
+    b = 2
+    x_1 = 1
+    x_2 = 2
+
+    if x < x_1:
+        return pow(x_1, -a) * pow(x, a)
+    elif x <= x_2 and x >= x_1:
+        return -5 + 11 * x - 6 * pow(x, 2) + pow(x, 3)
+    else:
+        return pow(x_2, b) * pow(x, -b)
+
 
 def comp_gamma():
-	"""
-		Computes gamma function
-		Input:
-			* None
-		Output:
-			* Gamma function (array)
-	"""
-	gn = lambda x: -1 * g(x)
-	xopt = scipy.optimize.fminbound(gn, 1, 2)
-	return xopt
+    """
+        In Hammond, D. K.,Vandergheynst, P.,& Gribonval, R. (2011).
+        "Wavelets on graphs via spectral graph theory" gamma is a parameter
+        used to determine the scaling function h. It is such that h(0) = max(g)
+        Input:
+                * None
+        Output:
+                * Gamma function (array)
+    """
+    # fminbound finds the minimum within the optimization bounds
+    xopt = scipy.optimize.fminbound(lambda x: -g(x), 1, 2)
+    return xopt
+
 
 def h(x, gamma, lamb_max, K):
-	"""
-		Scaling function (see details in the paper "Graph wavelets via spectral theory".
-		Input:
-			* x
-			* gamma
-			* lamb_max: upper bound spectrum
-			* K: normalization
-		Output:
-			* value of scaling function
-	"""
-	lamb_min = float(lamb_max) / K
-	return gamma * math.exp(-pow(float(x/(lamb_min * 0.6)), 4))
+    """
+        Scaling function see Hammond, D. K.,Vandergheynst, P.,
+        & Gribonval, R. (2011). "Wavelets on graphs via spectral graph theory".
+        Input:
+                * x
+                * gamma
+                * lamb_max: upper bound spectrum
+                * K: normalization
+        Output:
+                * value of scaling function
+    """
+    lamb_min = float(lamb_max) / K
+    return gamma * math.exp(-pow(float(x / (lamb_min * 0.6)), 4))
+
 
 def comp_scales(lamb_max, K, J):
-	"""
-		Computes wavelet scales
-		Input:
-			* lamb_max: upper bound spectrum
-			* K: normalization
-			* J: number of scales
-		Output:
-			* scales array
-	"""
-	lamb_min = float(lamb_max) / K
-	s_min = float(1)/lamb_max
-	s_max = float(2)/lamb_min
-
-	return numpy.exp(numpy.linspace(math.log(s_max), math.log(s_min), J))
-
-def graph_low_pass(lamb, U, N, T, gamma, lamb_max, K):
-	"""
-		Low-pass filter.
-		Input:
-			* lamb: eigenvalues
-			* U: eigenvector matrix
-			* N: number of nodes
-			* T: wavelet scales
-			* gamma:
-			* lamb_max: upper-bound spectrum
-			* K: normalization
-		Output:
-			* s: Low-pass filter as a #vertices x #vertices matrix
-	"""
-	s = []
-
-	for n in range(0, len(N)):
-		s.append([])
-
-	for n in range(0, len(N)):
-		for m in range(0, len(U)):    
-			s_n_m = 0
-
-			for x in range(0, len(U)):
-				s_n_m = s_n_m + U[n][x] * U[m][x] * h(T[-1] * lamb[x], gamma, lamb_max, K)
-
-			s[n].append(s_n_m)
-
-	return s
+    r"""
+        Computes wavelet scales see Hammond, D. K.,Vandergheynst, P.,
+        & Gribonval, R. (2011). "Wavelets on graphs via spectral graph theory".
+        Input:
+            * lamb_max: upper bound spectrum
+            * K: desired ratio for lambda_max / lambda_min
+            * J: number of scales
+        Output:
+            * scales array
+    """
+    lamb_min = float(lamb_max) / K
+    s_min = float(1) / lamb_max
+    s_max = float(2) / lamb_min
+
+    return np.exp(np.linspace(math.log(s_max), math.log(s_min), J))
+
+
+def graph_low_pass(lamb, U, T, gamma, lamb_max, K):
+    """
+        Low-pass spectral filter (square matrix).
+        See "The emerging field of signal processing on graph"
+        Input:
+            * lamb: eigenvalues
+            * U: eigenvector matrix
+            * N: number of nodes
+            * T: wavelet scales
+            * gamma: scaling function parameter
+            * lamb_max: upper-bound spectrum
+            * K: normalization
+        Output:
+            * s: Low-pass filter as a N x N matrix
+    """
+
+    h_vector = [h(T[-1] * l, gamma, lamb_max, K) for l in lamb]
+
+    return dot(U, dot(diag(h_vector), U.T))
+
 
 def graph_wavelets(lamb, U, N, T):
-	"""
-		Graph wavelets.
-		Input:
-			* lamb: eigenvalues
-			* U: eigenvector matrix
-			* N: number of nodes
-			* T: wavelet scales
-		Output:
-			* w: wavelets as a #vertices x #vertices x #scales matrix
-	"""
-	w = []
+    """
+        Graph wavelets.
+        Input:
+            * lamb: eigenvalues
+            * U: eigenvector matrix
+            * N: number of nodes
+            * T: wavelet scales
+        Output:
+            * w: wavelets as a len(T) x N x N matrix
+    """
 
-	for t in range(0, len(T)):
-		w.append([])
-		for n in range(0, len(N)):
-			w[t].append([])
+    w = []
 
+    for t in range(len(T)):
+        g_vector = [g(T[t] * l) for l in lamb]
+        w.append(dot(U, dot(diag(g_vector), U.T)))
 
-	for t in range(0, len(T)):
-		for n in range(0, len(N)):
-			for m in range(0, len(U)):    
-				w_t_n_m = 0
+    return np.asarray(w)
 
-				for x in range(0, len(U)):
-					w_t_n_m = w_t_n_m + U[n][x] * U[m][x] * g(T[t] * lamb[x])
 
-				w[t][n].append(w_t_n_m)
+def graph_fourier(F, U):
+    """
+        Graph Fourier transform.
+        Input:
+            * F: Signal in the vertex domain
+            * U: Eigenvectors matrix
+        Ouput:
+            * F_hat: Signal in the graph spectral domain
+    """
+    F_hat = []
 
-	return w
+    for i in range(0, len(U)):
+        F_hat.append(dot(F, U[:, i]))
+
+    F_hat = np.array(F_hat)
+
+    return F_hat
+
+
+def graph_fourier_inverse(F_hat, U):
+    """
+        Graph Fourier inverse:
+        Input:
+            * F_hat: Signal in the graph spectral domain
+            * U: Eigenvectors matrix
+        Output:
+            * F: Signal in the vertex domain
+    """
+    F = np.zeros(U.shape[0])
+    for v in range(U.shape[0]):
+        for u in range(U.shape[1]):
+            F[v] = F[v] + (F_hat[u] * U[v][u]).real
+
+    return F
 
-def graph_fourier(F, U):
-	"""
-		Graph Fourier transform.
-		Input:
-			* F: Graph signal as a #vertices size array, values ordered by G.nodes()
-			* U: Eigenvectors matrix
-		Ouput:
-			* lambdas: Graph Fourier transform
-	"""
-	lambdas = []
-
-	for i in range(0, len(U)):
-		lambdas.append(numpy.dot(F, U[:,i]))    
-
-	lambdas = numpy.array(lambdas)
-
-	return lambdas
-
-def graph_fourier_inverse(GF, U):
-	"""
-		Graph Fourier inverse:
-		Input:
-			* GF: Graph fourier transform
-			* U: Eigenvectors matrix
-		Output:
-			* F: Inverse
-	"""
-	F = numpy.zeros(U.shape[0])
-	for v in range(U.shape[0]):
-		for u in range(U.shape[1]):
-			F[v] = F[v] + (GF[u]*U[v][u]).real
-
-	return F
 
 def hammond_wavelet_transform(w, s, T, F):
-	"""
-		Hammond wavelet transform.
-		Input:
-			* w: wavelets
-			* s: low-pass wavelets
-			* T: wavelet scales
-			* F: graph signal
-		Output:
-			* C: Hammond's wavelet transform
-	"""
-	C = []
-
-	for i in range(len(T)):
-		C.append([])
-		for j in range(len(F)):
-			dotp = numpy.dot(F, w[i][j])
-			C[i].append(dotp)
-
-	C.append([])
-	for j in range(len(F)):
-		dotp = numpy.dot(F, s[j])
-		C[-1].append(dotp)
-
-	return numpy.array(C)
+    r"""
+        Hammond wavelet transform.
+        Input:
+            * w: wavelets
+            * s: low-pass wavelet (scaling function)
+            * T: wavelet scales
+            * F: graph signal
+        Output:
+            * C: Hammond's wavelet transform. (len(T) + 1) x len(F)
+                 matrix of transform coefficients
+    """
+    C = []
+
+    for i in range(len(T)):
+        # Each wavelet is represented by an N x N matrix
+        C.append(dot(F, w[i].T))
+    # Append output of scaling function application at the end
+    C.append(dot(F, s.T))
+
+    return np.asarray(C)
+
 
 def hammond_wavelets_inverse(w, s, C):
-	"""
-		Hammond's wavelet inverse.
-		Input:
-			* w: wavelets
-			* s: low-pass wavelets
-			* C: transform
-		Output:
-			* F: inverse
-	"""
-	w = numpy.array(w)
-	Wc = numpy.append(w, numpy.array([s]), axis=0)
-
-	nWc = Wc[0,:,:]
-	nC = C[0]
-	for i in range(1,Wc.shape[0]):
-		nWc = numpy.append(nWc, Wc[i,:,:], axis=0)
-		nC = numpy.append(nC, C[i], axis=0)
-
-	nWc = numpy.array(nWc)
-	nC = numpy.array(nC)
-
-	F = numpy.linalg.lstsq(nWc, nC)[0]
-
-	return F
+    r"""
+        Hammond's wavelet inverse.
+        Input:
+            * w: wavelets
+            * s: low-pass wavelet (scaling function)
+            * C: Hammond's wavelet transform. (len(T) + 1) x len(F)
+                 matrix of transform coefficients
+        Output:
+            * F: Reconstructed signal in the vertex domain
+    """
+    nC = np.ravel(C)
+    Wc = np.append(w, np.array([s]), axis=0)
+    nWc = Wc.reshape(Wc.shape[0] * Wc.shape[1], Wc.shape[2])
+    # Search a least square solution F, solving:
+    # nWc F = nC
+    F = np.linalg.lstsq(nWc, nC)[0]
+
+    return F
+
 
 class Node(object):
-	"""
-		Generic tree-structure used for hierarchical transforms.
-	"""
-	def __init__(self, data):
-		"""
-			Initialization.
-			Input:
-				* data: Anything to be stored in a node
-		"""
-		self.data = data
-		self.children = []
-		self.avgs = []
-		self.counts = []
-		self.diffs = []
-		self.scale = 0
-		self.ftr = []
-		self.L = []
-		self.U = []
-		self.cut = 0
-
-		if data is None:
-			self.count = 0
-		else:
-			self.count = 1
-
-	def add_child(self, obj):
-		"""
-			Adds obj as a child to a node.
-			Input:
-				* obj: anything
-		"""
-		obj.scale = self.scale + 1
-		self.children.append(obj)
-		self.count = self.count + obj.count
-
-def get_children(tree, part, G):
-	"""
-		Recursively gets all the children of a given node.
-		Input:
-			* tree: tree node
-			* part: list that will contain children
-			* G: graph
-		Output:
-			* None
-	"""
-	if tree.data is not None:
-		part.append(G.nodes()[tree.data])
-	else:
-		for c in tree.children:
-			get_children(c, part, G)
+    """
+        Generic tree-structure used for hierarchical transforms.
+    """
+
+    def __init__(self, data):
+        """
+            Input:
+                * data: Anything to be stored in a node.
+                    Usually only leaf nodes have data != None
+                    data != None often used as stopping condition
+        """
+        self.data = data
+        self.children = []
+        self.diffs = []
+        # Level on the tree. The root has scale = 0
+        self.scale = 0
+        # count: number of leaves (data != None) of its subtree
+        if data is None:
+            self.count = 0
+        else:
+            self.count = 1
+
+    def __str__(self):
+        descr = "Node id: {}, data: {}, scale: {}, count: {}"
+        return descr.format(id(self), self.data, self.scale, self.count)
+
+    def __repr__(self):
+        return self.__str__()
+
+    def add_child(self, obj):
+        """
+            Adds obj as a child to a node.
+            Input:
+                * obj: anything
+        """
+        obj.scale = self.scale + 1
+        self.children.append(obj)
+        self.count = self.count + obj.count
+
 
 def set_counts(tree):
-	"""
-		Sets counts for intermediate nodes in the tree.
-		Input:
-			* tree: tree node
-		Output: 
-			* count: count for the tree node
-	"""
-	if tree.data is not None:
-		tree.count = 1
-		return 1
-	else:
-		count = 0
-		for c in tree.children:
-			count = count + set_counts(c)
-
-		tree.count = count
-
-		return count
-
-def partitions_level_rec(tree, level, G, l, partitions):
-	"""
-		Recursively extracts partitions up to a certain level in the tree.
-		Input:
-			* tree: tree
-			* level: max level
-			* G: graph
-			* l: current level
-			* partitions: partitions recovered
-		Output:
-			None
-	"""
-	if l >= level:
-		part = []
-		get_children(tree, part, G)
-		if len(part) > 0:
-			partitions.append(part)
-	else:
-		if tree.data is None:
-			for c in tree.children:
-				partitions_level_rec(c, level, G, l+1, partitions)
-		else:
-			partitions.append([tree.data])
-			
-def partitions_level(tree, level, G):
-	"""
-		Recovers partitions at a certain level of the three.
-		Input:
-			* tree: tree
-			* level: level
-			* G: graph
-		Output:
-			* partitions: set of vertices in each partition
-	"""
-	partitions = []
-	partitions_level_rec(tree, level, G, 0, partitions)
-	
-	return partitions
-
-def build_matrix(G, ind):
-	"""
-		Builds graph distance matrix.
-		Input:
-			* G: graph
-			* ind: dictionary vertex: unique integer
-		Output:
-			* M: matrix
-	"""
-	M = []
-	dists = networkx.all_pairs_dijkstra_path_length(G)
-
-	M = numpy.zeros((len(G.nodes()), len(G.nodes())))
-
-	for v1 in G.nodes():
-		for v2 in G.nodes():
-			M[ind[v1]][ind[v2]] = dists[v1][v2]
-
-	return M
-
-def select_centroids(M, radius):
-	"""
-		Selects half of the vertices as centroids.
-		Input:
-			* M
-			* radius
-		Output:
-			* centroids	
-	"""
-	nodes = list(range(M.shape[0]))
-	random.shuffle(nodes)
-	nodes = nodes[:int(len(nodes)/2)]
-	cents = [nodes[0]]
-	mn = sys.float_info.min
-
-	for i in range(1, len(nodes)):
-		add = True
-		for j in range(len(cents)):
-			if M[cents[j]][nodes[i]] <= radius*mn:
-				add = False
-				break
-		if add:
-			cents.append(nodes[i])
-
-	return cents
-
-def coarse_matrix(M, H, cents, nodes):
-	"""
-		Makes matrix coarser based on centroids.
-		Input:
-			* M: distance matrix
-			* H: 
-			* cents: centroids
-			* nodes: list of nodes
-		Output:
-			* Q: new matrix
-			* J
-			* new_nodes: new node list
-	"""
-	Q = numpy.zeros((len(cents), len(cents)))
-	J = []
-	assigns = []
-	new_nodes = []
-
-	for i in range(len(cents)):
-		J.append([])
-		assigns.append([])
-	new_nodes.append(Node(None))
-
-	for i in range(M.shape[0]):
-		min_dist = M[i][cents[0]]
-		min_cent = 0
-
-		for j in range(1, len(cents)):
-			if M[i][cents[j]] < min_dist:
-				min_dist = M[i][cents[j]]
-				min_cent = j
-
-		J[min_cent].append(H[i])
-		assigns[min_cent].append(i)
-		new_nodes[min_cent].add_child(nodes[i])
-
-	for i in range(len(cents)):
-		if len(new_nodes[i].children) == 1:
-			new_nodes[i] = new_nodes[i].children[0]
-
-		for j in range(len(cents)):
-			if i != j:
-				for m in assigns[i]:
-					for k in assigns[j]:
-						Q[i][j] = Q[i][j] + pow(M[m][k], 2)
-
-	Q =  normalize(Q, axis=1, norm='l1')
-
-	return Q, J, new_nodes
-
-def get_partitions(x, node_list):
-	"""
-		Gets partitions given indicator vector. 
-			if x < 0: partition 1
-			if x <= 0: partition 2
-		Input:
-			* node_list: list of nodes
-			* x: indicator vector
-		Output:
-			* P1: partition 1
-			* P2: partition 2
-	"""
-	P1 = []
-	P2 = []
-
-	for i in range(x.shape[0]):
-		if x[i] < 0:
-			P1.append(node_list[i])
-		else:
-			P2.append(node_list[i])
-
-	return P1, P2
-
-
-def get_new_laplacians(L, P1, P2, ind):
-	"""
-		Compute new Laplacian matrices for partitions P1 and P2.
-		Input:
-			* L: Higher-level laplacian
-			* P1: partition 1
-			* P2: partition 2
-			* ind: node index vertex: unique integer
-		Output:
-			* L1: Laplacian P1
-			* L2: Laplacian P2
-	"""
-	data = []
-	row = []
-	col = []
- 
-	for i in range(len(P1)):
-		d = 0
-		for j in range(len(P1)):
-			if i != j and L[ind[P1[i]],ind[P1[j]]] != 0:
-				row.append(i)
-				col.append(j)
-				data.append(float(L[ind[P1[i]],ind[P1[j]]]))
-				d = d - L[ind[P1[i]],ind[P1[j]]]
- 
-		row.append(i)
-		col.append(i)
-		data.append(float(d))
-	
-	L1 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P1), len(P1)))
-	
-	data = []
-	row = []
-	col = []
-	
-	for i in range(len(P2)):
-		d = 0
-		for j in range(len(P2)):
-			if i != j and L[ind[P2[i]],ind[P2[j]]] != 0:
-				row.append(i)
-				col.append(j)
-				data.append(float(L[ind[P2[i]],ind[P2[j]]]))
-				d = d - L[ind[P2[i]],ind[P2[j]]]
- 
-		row.append(i)
-		col.append(i)
-		data.append(float(d))
-	
-	L2 = scipy.sparse.csr_matrix((data, (row, col)), shape=(len(P2), len(P2)))
-
-	return L1, L2
-
-def laplacian_complete(n):
-	"""
-		Laplacian of a complete graph with n vertices.
-		Input:
-			* n: size
-		Output:
-			* C: Laplacian
-	"""
-	C = numpy.ones((n, n))
-	C = -1 * C
-	D = numpy.diag(numpy.ones(n))
-	C = (n)*D + C
-	
-	return C
-
-def sqrtmi(mat):
-	"""
-		Computes the square-root inverse of a matrix.
-		Input:
-			* mat: matrix
-		Output:
-			* square root inverse
-	"""
-	eigvals, eigvecs = eigh(mat)
-	eigvecs = eigvecs[:, eigvals > 0]
-	eigvals = eigvals[eigvals > 0]
-
-	return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T))
-
-def create_linked_list(L):
-	"""
-		Creates linked list from a Laplacian matrix.
-		Input:
-			* L: matrix
-		Output:
-			* linked_list: linked list
-	"""
-	linked_list = {}
-	
-	for i in L.nonzero()[0]:
-		linked_list[i] = []
-		for j in range(L.shape[1]):
-			if L[i,j] < 0:
-				linked_list[i].append(j)
-	return linked_list
+    """
+        Input:
+            * tree: tree node
+        Output:
+            * count: count for the tree node
+    """
+    if tree.data is not None:
+        tree.count = 1
+        return 1
+    else:
+        count = 0
+        for c in tree.children:
+            count = count + set_counts(c)
+
+        tree.count = count
+
+        return count
+
+
+def set_fiedler_method(method):
+    # Set method for Fiedler vector computation
+    global _method
+    _method = method
+
 
 def sweep(x, G):
-	"""
-		Sweep algorithm for ratio-cut (2nd eigenvector of the Laplacian) based on vector x.
-		Input:
-			* x: vector
-			* G: graph	
-		Output:
-			* vec: indicator vector
-	"""
-	best_val = networkx.number_of_nodes(G)-1
-	sorted_x = numpy.argsort(x)
-	size_one = 0
-	edges_cut = 0
-	nodes_one = {}
-
-	for i in range(x.shape[0]):
-		size_one = size_one + 1
-		
-		nodes_one[G.nodes()[sorted_x[i]]] = True
-		
-		for v in G.neighbors(G.nodes()[sorted_x[i]]):
-			if v not in nodes_one:
-				edges_cut = edges_cut + 1
-			else:
-				edges_cut = edges_cut - 1
-			
-		den = size_one * (networkx.number_of_nodes(G)-size_one)
-
-		if den > 0:
-			val = float(edges_cut) / den
-		else:
-			val = networkx.number_of_nodes(G)
-		
-		if val <= best_val:
-			best_cand = i
-			best_val = val
-
-	vec = []
-
-	vec = numpy.zeros(networkx.number_of_nodes(G))
-
-	for i in range(x.shape[0]):
-		if i <= best_cand:
-			vec[sorted_x[i]] = -1.
-		else:
-			vec[sorted_x[i]] = 1.
-	
-	return vec
+    """
+        Sweep algorithm for ratio-cut (2nd eigenvector of the Laplacian).
+        Based on vector x.
+        Input:
+            * x: vector
+            * G: graph
+        Output:
+            * vec: indicator vector
+    """
+    sorted_x = np.argsort(x)
+    part_one = set()
+    N = nx.number_of_nodes(G)
+    best_val = N - 1
+    edges_cut = 0
+    nodes_list = list(G.nodes())
+
+    for i in range(N - 1):
+        part_one.add(nodes_list[sorted_x[i]])
+
+        for v in G.neighbors(nodes_list[sorted_x[i]]):
+            if v not in part_one:
+                edges_cut = edges_cut + 1
+            else:
+                edges_cut = edges_cut - 1
+
+        den = len(part_one) * (N - len(part_one))
+
+        if den > 0:
+            val = float(edges_cut) / den
+            if val <= best_val:
+                best_cand = i
+                best_val = val
+
+    vec = np.ones(nx.number_of_nodes(G))
+
+    for i in range(x.shape[0]):
+        if i <= best_cand:
+            vec[sorted_x[i]] = -1.
+
+    return vec
+
 
 def separate_lcc(G, G0):
-	"""
-		Separates vertices in G0 (LCC) from the rest in G using indicator vector.
-		Input:
-			* G: Graph
-			* G0: Subgraph
-		Output:
-			* x: indicator vector
-	"""
-	x = []
-
-	for v in G.nodes():
-		if v in G0:
-			x.append(-1)
-		else:
-			x.append(1.)
-	
-	return numpy.array(x)
+    """
+        Separate vertices in G0 (LCC) from the rest in G returning
+        an indicator vector.
+        Input:
+            * G: Graph
+            * G0: Subgraph
+        Output:
+            * x: indicator vector
+    """
+
+    return np.array([-1. if v in G0 else 1. for v in G.nodes()])
+
 
 def ratio_cut(G):
-	"""
-		Computes ratio-cut of G based on second eigenvector of the Laplacian.
-		Input:
-			* G: Graph
-		Output:
-			* x: Indicator vector
-	"""
-	
-	Gcc=sorted(networkx.connected_component_subgraphs(G), key = len, reverse=True)
-	G0=Gcc[0]
-
-	if networkx.number_of_nodes(G) == networkx.number_of_nodes(G0):
-		x = networkx.fiedler_vector(G, method='lobpcg',tol=1e-5)
-
-		x = sweep(x, G)
-	else:
-		#In case G is not connected
-		x = separate_lcc(G, G0)
-
-
-	return numpy.array(x)
-
-def eig_vis_rc(G):
-	"""
-		Second and third eigenvectors of the graph Laplacian. For visualization.
-		Input:
-			* G: Graph
-		Output:
-			* x1: Second eigenvector
-			* x2: Third eigenvector
-	"""
-	L = networkx.laplacian_matrix(G).todense()
-	(eigvals, eigvecs) = scipy.linalg.eigh(L,eigvals=(1,2))
-
-	x1 = numpy.asarray(eigvecs[:,0])
-	x2 = numpy.asarray(eigvecs[:,1])
-	
-	return x1, x2
+    """
+        Computes ratio-cut of G based on second eigenvector of the Laplacian.
+        Input:
+            * G: Graph
+        Output:
+            * x: Indicator vector
+    """
+
+    Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)
+    G0 = Gcc[0]
+
+    if nx.number_of_nodes(G) == nx.number_of_nodes(G0):
+        scipy.random.seed(1)
+        x = nx.fiedler_vector(G, method=_method, tol=1e-5)
+        x = sweep(x, G)
+    else:
+        # In case G is not connected
+        x = separate_lcc(G, G0)
+    return np.array(x)
+
 
 def get_subgraphs(G, cut):
-	"""
-		Compute subgraphs generated by a cut.
-		Input:
-			* G: Original graph
-			* cut: cut indicator vector
-		Output:
-			* G1: subgraph 1
-			* G2: subgraph 2
-	"""
-	G1 = networkx.Graph()
-	G2 = networkx.Graph()
- 
-	i = 0
-	P1 = []
-	P2 = []
-	for v in G.nodes():
-		if cut[i] < 0:
-			P1.append(v)
-		else:
-			P2.append(v)
-		i = i + 1
- 
-	G1 = G.subgraph(P1)
-	G2 = G.subgraph(P2)
-
-	return G1, G2
+    """
+        Return the two subgraphs as two lists of nodes
+        Input:
+            * G: Original graph
+            * cut: cut indicator vector
+        Output:
+            * G1: subgraph 1
+            * G2: subgraph 2
+    """
+    G1 = nx.Graph()
+    G2 = nx.Graph()
+    i = 0
+    P1 = []
+    P2 = []
+    for v in G.nodes():
+        if cut[i] < 0:
+            P1.append(v)
+        else:
+            P2.append(v)
+        i = i + 1
+
+    G1 = G.subgraph(P1)
+    G2 = G.subgraph(P2)
+
+    return G1, G2
+
 
 def rc_recursive(node, G, ind):
-	"""
-		Recursively computes ratio-cut.
-		Input:
-			* node: tree node
-			* G: graph
-			* ind: vertex index v: unique integer
-		Output:
-			* none
-	"""
-	if networkx.number_of_nodes(G) < 3:
-		n = Node(None)
-		n.add_child(Node(ind[G.nodes()[0]]))
-		n.add_child(Node(ind[G.nodes()[1]]))
-		node.add_child(n)
-	else:
-		C =  ratio_cut(G)
-	
-		(G1, G2) = get_subgraphs(G, C)
-
-		if networkx.number_of_nodes(G1) > 1:
-			l = Node(None)
-			rc_recursive(l, G1, ind)
-			node.add_child(l)
-		else:
-			l = Node(ind[G1.nodes()[0]])
-			node.add_child(l)
-	
-		if networkx.number_of_nodes(G2) > 1:
-			r = Node(None)
-			rc_recursive(r, G2, ind)
-			node.add_child(r)
-		else:
-			r = Node(ind[G2.nodes()[0]])
-			node.add_child(r)
-
-def ratio_cut_hierarchy(G):
-	"""
-		Computes ratio-cut hierarchy for a graph.
-		Input: 
-			* G: graph
-		Output:
-			* root: tree root
-			* ind: graph index v: unique integer
-	"""
-	i = 0
-	ind = {}
-	for v in G.nodes():
-		ind[v] = i
-		i = i + 1
-
-	root = Node(None)
-
-	rc_recursive(root, G, ind)
-
-	return root, ind
-
-def gavish_hierarchy(G, radius):
-	"""
-		Builds Gavish's hierarchy of a graph.
-		Input:
-			* G: graph
-			* radius: radius
-		Output:
-			* tree root
-			* ind: vertex index v: unique integer
-	"""
-	H = []
-	nodes = []
-	ind = {}
-	i = 0
-	for v in G.nodes():
-		ind[v] = i
-		nodes.append(Node(i))
-		H.append(i)
-		i = i + 1
-
-	M = build_matrix(G, ind)
-
-	while M.shape[0] > 1:
-		cents = select_centroids(M, radius)
-		Q, J, new_nodes = coarse_matrix(M, H, cents, nodes)
-		M = Q
-		H = J
-		nodes = new_nodes
-
-	return nodes[0], ind
+    """
+        Recursively computes ratio-cut.
+        The leaves store, as data, the integer returned by ind for the
+        inserted node.
+        Input:
+            * node: tree node
+            * G: graph
+            * ind: index with unique integers as values
+                (see ratio_cut_hierarchy for definition)
+        Output:
+            * none
+    """
+    if nx.number_of_nodes(G) < 3:
+        n = Node(None)
+        n.add_child(Node(ind[list(G.nodes())[0]]))
+        n.add_child(Node(ind[list(G.nodes())[1]]))
+        node.add_child(n)
+    else:
+        C = ratio_cut(G)
+
+        (G1, G2) = get_subgraphs(G, C)
+
+        if nx.number_of_nodes(G1) > 1:
+            l = Node(None)
+            rc_recursive(l, G1, ind)
+            node.add_child(l)
+        else:
+            l = Node(ind[list(G1.nodes())[0]])
+            node.add_child(l)
+
+        if nx.number_of_nodes(G2) > 1:
+            r = Node(None)
+            rc_recursive(r, G2, ind)
+            node.add_child(r)
+        else:
+            r = Node(ind[list(G2.nodes())[0]])
+            node.add_child(r)
+
+
+def ratio_cut_hierarchy(G, method='lobpcg'):
+    """
+        Computes ratio-cut hierarchy for a graph.
+        The leaves store, as data, the integer returned by ind for the
+        inserted node.
+        Input:
+            * G: graph
+            * method: method for Fiedler vector computation.
+                The default value is 'lobpcg' however 'tracemin_lu' seems
+                faster and it appears to give more stable results when used
+                with PYTHONHASHSEED set to a constant value.
+        Output:
+            * root: tree root
+            * ind: index with unique integers as values
+
+    """
+    global _method
+    _method = method
+
+    ind = {v: i for i, v in enumerate(G.nodes())}
+
+    root = Node(None)
+
+    rc_recursive(root, G, ind)
+
+    return root, ind
+
 
 def compute_coefficients(tree, F):
-	"""
-		Computes tree coefficients for Gavish's transform.
-		Input:
-			* tree: tree
-			* F: graph signal
-		Output:
-			* None
-	"""
-	if tree.data is None:
-		avg = 0
-		count = 0
-		for i in range(len(tree.children)):
-			compute_coefficients(tree.children[i], F)
-			avg = avg + tree.children[i].avg * tree.children[i].count 
-			count = count + tree.children[i].count
-
-			if i > 0:
-				tree.avgs.append(float(avg) / count)
-				tree.counts.append(count)
-				tree.diffs.append(2*tree.children[i].count*(tree.children[i].avg-float(avg)/count))
-		tree.avgs = list(reversed(tree.avgs))
-		tree.avg = float(avg) / tree.count
-	else:
-		tree.avg = F[tree.data]
+    """
+        Compute tree coefficients for Gavish's transform.
+        Input:
+            * tree: tree
+            * F: graph signal
+        Output:
+            * None
+    """
+    if tree.data is None:
+        tot = 0
+        count = 0
+        for i, child in enumerate(tree.children):
+            compute_coefficients(child, F)
+            tot += child.avg * child.count
+            count += child.count
+
+            if i > 0:
+                tree.diffs.append(2 * child.count *
+                                  (child.avg - float(tot) / count))
+        tree.avg = float(tot) / tree.count
+    else:
+        tree.avg = F[tree.data]
+
 
 def reconstruct_values(tree, F):
-	"""
-		Reconstructs values for Gavish's transform based on a tree.
-		Input:
-			* tree: tree
-			* F: graph signal
-		Output:
-			* None
-	"""
-	if tree.data is None:
-		avg = tree.avg * tree.count 
-		count = tree.count
-		for i in reversed(range(len(tree.children))):
-			if i == 0:
-				tree.children[i].avg = avg / tree.children[i].count
-				reconstruct_values(tree.children[i], F)
-			else:
-				tree.children[i].avg = float(avg)/count + 0.5*float(tree.diffs[i-1]) / tree.children[i].count
-				reconstruct_values(tree.children[i], F)
-				count = count - tree.children[i].count
-				avg = avg - tree.children[i].avg * tree.children[i].count 
-				tree.avgs.append(float(avg)/count)
-
-		tree.avgs = list(reversed(tree.avgs))
-	else:
-		F[tree.data] = tree.avg
+    """
+            Reconstruct values for Gavish's transform based on a tree.
+            Input:
+                    * tree: tree
+                    * F: graph signal
+            Output:
+                    * None
+    """
+    if tree.data is None:
+        tot = tree.avg * tree.count
+        count = tree.count
+        for i in reversed(range(len(tree.children))):
+            if i == 0:
+                tree.children[i].avg = tot / tree.children[i].count
+                reconstruct_values(tree.children[i], F)
+            else:
+                tree.children[i].avg = float(tot) / count + 0.5 * \
+                    float(tree.diffs[i - 1]) / tree.children[i].count
+                reconstruct_values(tree.children[i], F)
+                count = count - tree.children[i].count
+                tot = tot - tree.children[i].avg * tree.children[i].count
+
+    else:
+        F[tree.data] = tree.avg
+
 
 def clear_tree(tree):
-	"""
-		Clears tree info.
-		Input: 
-			* tree
-		Output: 
-			* None
-	"""
-	tree.avg = 0 
-	tree.diffs = []
-	tree.avgs = []
-
-	if tree.data is None:
-		for i in range(len(tree.children)):
-			clear_tree(tree.children[i])
+    """
+        Clear tree info.
+        tree.count is kept
+        Input:
+            * tree
+        Output:
+            * None
+    """
+    tree.avg = 0
+    tree.diffs = []
+
+    if tree.data is None:
+        for i in range(len(tree.children)):
+            clear_tree(tree.children[i])
+
 
 def get_coefficients(tree, wtr):
-	"""
-		Recovers wavelet coefficients from the wavelet tree.
-		Input:
-			* tree
-			* wtr: wavelet coefficients
-		Output:
-			* None
-	"""
-	Q = deque()
-	scales = []
-	wtr.append(tree.count*tree.avg)
-
-	Q.append(tree)
-
-	while len(Q) > 0:
-		node = Q.popleft()
-		scales.append(node.scale)
-
-		for j in range(len(node.diffs)):
-			wtr.append(node.diffs[j])
-
-		for i in range(len(node.children)):
-			Q.append(node.children[i])
-
-def get_cut_sizes(tree):
-	"""
-		Recovers cut sizes from tree.
-		Input:
-			* tree
-		Output:
-			* None
-	"""
-	Q = deque()
-	cut_sizes = []
-
-	Q.append(tree)
-
-	while len(Q) > 0:
-		node = Q.popleft()
-		cut_sizes.append(node.cut)
-
-		for i in range(len(node.children)):
-			Q.append(node.children[i])
-
-	return cut_sizes
+    """
+        Recover wavelet coefficients from the wavelet tree.
+        Input:
+            * tree
+            * wtr: list of wavelet coefficients
+        Output:
+            * None
+    """
+    Q = deque()
+    wtr.append(tree.count * tree.avg)
+
+    Q.append(tree)
+
+    while len(Q) > 0:
+        node = Q.popleft()
+
+        for j in range(len(node.diffs)):
+            wtr.append(node.diffs[j])
+
+        for i in range(len(node.children)):
+            Q.append(node.children[i])
+
 
 def set_coefficients(tree, wtr):
-	"""
-		Sets wavelet tree coefficients.
-		Input:
-			* tree
-			* wtr: wavelet coefficients
-	"""
-	Q = deque()
-	tree.avg = float(wtr[0]) / tree.count
-	p = 1
-	Q.append(tree)
-
-	while len(Q) > 0:
-		node = Q.popleft()
-
-		for j in range(len(node.children)-1):
-			node.diffs.append(wtr[p])
-			p = p + 1
-
-		for i in range(len(node.children)):
-			Q.append(node.children[i])
-
-def gavish_wavelet_transform(tree, ind, G, F):
-	"""
-		Gavish's wavelet transform.
-		Input:
-			* tree
-			* ind: vertex index v : unique integer
-			* G: graph
-			* F: graph signal
-		Output:
-			* wtr: wavelet transform.
-	"""
-	wtr = []
-	clear_tree(tree)
-	compute_coefficients(tree, F)
-	get_coefficients(tree, wtr)
-
-	return numpy.array(wtr)
+    """
+        Sets wavelet tree coefficients.
+        Input:
+            * tree
+            * wtr: list of wavelet coefficients
+    """
+    Q = deque()
+    tree.avg = float(wtr[0]) / tree.count
+    p = 1
+    Q.append(tree)
+
+    while len(Q) > 0:
+        node = Q.popleft()
+
+        for j in range(len(node.children) - 1):
+            node.diffs.append(wtr[p])
+            p += 1
+
+        for i in range(len(node.children)):
+            Q.append(node.children[i])
+
+
+def gavish_wavelet_transform(tree, G, F):
+    """
+        Gavish's wavelet transform.
+        Input:
+            * tree
+            * ind: vertex index v : unique integer
+            * G: graph
+            * F: graph signal
+        Output:
+            * wtr: wavelet transform.
+    """
+    wtr = []
+    clear_tree(tree)
+    compute_coefficients(tree, F)
+    get_coefficients(tree, wtr)
+    return np.array(wtr)
 
-def gavish_wavelet_inverse(tree, ind, G, wtr):
-	"""
-		Gavish's wavelet inverse.
-		Input:
-			* tree
-			* ind: vertex index v: unique integer
-			* G: graph
-			* wtr: wavelet transform
-		Output:
-			* F: wavelet inverse
-	"""
-	F = []
-
-	for i in range(len(G.nodes())):
-		F.append(0)
-
-	clear_tree(tree)
-	set_coefficients(tree, wtr)
-	reconstruct_values(tree, F)
-
-	return numpy.array(F)
 
+def gavish_wavelet_inverse(tree, ind, G, wtr):
+    """
+        Gavish's wavelet inverse.
+        Input:
+            * tree
+            * ind: vertex index v: unique integer
+            * G: graph
+            * wtr: wavelet transform
+        Output:
+            * F: wavelet inverse
+    """
+    F = []
+
+    for i in range(len(G.nodes())):
+        F.append(0)
+
+    clear_tree(tree)
+    set_coefficients(tree, wtr)
+    reconstruct_values(tree, F)
+
+    return np.array(F)
diff --git a/lib/io.py b/lib/io.py
deleted file mode 100644
index 10f5c72..0000000
--- a/lib/io.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import networkx
-import math
-import scipy.optimize
-import numpy
-import sys
-from scipy import linalg
-import matplotlib.pyplot as plt
-from IPython.display import Image
-import pywt
-import scipy.fftpack
-import random
-import operator
-import copy
-from collections import deque
-from sklearn.preprocessing import normalize
-from sklearn.cluster import SpectralClustering
-import pandas as pd
-from datetime import datetime, date, time, timedelta
-import statsmodels.api as sm
-
-
-def read_graph(input_graph_name, input_data_name):
-	"""
-		Reads graph from file.
-		Input:
-			* input_graph_name: csv edge list
-			* input_data_name: csv node-value pairs
-		Output:
-			* networkx graph
-	"""
-	
-	#Reading input data
-	input_data = open(input_data_name, 'r')
-	values = {}
-    
-	for line in input_data:
-		line = line.rstrip()
-		vec = line.rsplit(',')
-        
-		vertex = vec[0]
-		value = float(vec[1])
-		values[vertex] = value
-        
-	input_data.close()
-    
-	#Reading graph data
-	G = networkx.Graph()
-	input_graph = open(input_graph_name, 'r')
-	
-	for line in input_graph:
-		line = line.rstrip()
-		vec = line.rsplit(',')
-		v1 = vec[0]
-		v2 = vec[1]
-
-		if v1 in values and v2 in values:
-			G.add_edge(v1,v2, weight=1.)
-   
-	#Extracting largest connected component from graph
-	Gcc=sorted(networkx.connected_component_subgraphs(G), key = len, reverse=True)
-
-	G = Gcc[0]
-
-	values_in_graph = {}
-
-	#Setting values as node attributes
-	for v in values.keys():
-		if v in G:
-			values_in_graph[v] = values[v]
-	
-	input_graph.close()
-	networkx.set_node_attributes(G, "value", values_in_graph)
-    
-	return G
-
-def read_values(input_data_name, G):
-	"""
-		Reads node values.
-		Input: 
-			* input_data_name: csv node-value pairs
-			* G: networkx graph
-		Output:
-			* F: normalized node values array, ordered by G.nodes()
-	"""
-	D = {}
-	input_data = open(input_data_name, 'r')
-
-	#Reading file
-	for line in input_data:
-		line = line.rstrip()
-		vec = line.rsplit(',')
-
-		vertex = vec[0]
-		value = float(vec[1])
-		D[vertex] = value
-
-	input_data.close()
-    
-	F = []
-	for v in G.nodes():
-		if v in D:
-			F.append(float(D[v]))
-		else:
-			F.append(0.)
-   
-	#Normalization
-	F = numpy.array(F) 
-	F = F / numpy.max(F)
-	F = F - numpy.mean(F)
-    
-	return F 
-
-def read_dyn_graph(path, num_snapshots, G):
-	"""
-		Reads a dynamic graph.
-		Input:
-			* path: Path containing a files for each graph snapshot 
-				(e.g. folder/traffic_, for files folder/traffic_0.data ... 
-				folder/traffic_100.data)
-			* num_snapshots: number of snapshots
-			* G: networkx graph
-		Output:
-			* FT: array #snapshots x #vertices
-				
-	"""
-	FT = []
-	for t in range(num_snapshots):
-		in_file = path + "_" + str(t) + ".data"
-		F = read_values(in_file, G)
-		FT.append(F)
-        
-	return numpy.array(FT)
-
-def clean_traffic_data(FT):
-	start_time = datetime.strptime("1/04/11 00:00", "%d/%m/%y %H:%M")
-	c_FT = []
-	for i in range(FT.shape[1]):
-		#removing daily seasonality
-		data = pd.DataFrame(FT[:,i], pd.DatetimeIndex(start='1/04/11 00:00', periods=len(FT[:,i]), freq='5min'))
-		data.interpolate(inplace=True)
-		
-		res = sm.tsa.seasonal_decompose(data.values, freq=288)
-		F = FT[:,i] - res.seasonal
-
-		#removing weekly seasonality
-		data = pd.DataFrame(F, pd.DatetimeIndex(start='1/04/11 00:00', periods=len(FT[:,i]), freq='5min'))
-		res = sm.tsa.seasonal_decompose(data.values, freq=288*7)
-		F = F - res.seasonal
-		
-		c_FT.append(F)
-
-	return numpy.array(c_FT).transpose()
-
-
diff --git a/lib/io_utils.py b/lib/io_utils.py
new file mode 100644
index 0000000..1754acf
--- /dev/null
+++ b/lib/io_utils.py
@@ -0,0 +1,97 @@
+"""
+This module read graph's info from files with the following format:
+input_graph_name has info about edges.
+Row format: "node_A, node_B[, edge_weight]"
+input_data_name has info about the graph signal.
+Row format : "node_id, node_value"
+"""
+
+import networkx as nx
+import numpy as np
+
+
+def read_graph(input_graph_name, input_data_name):
+    """
+        Read graph from file.
+        Input:
+            * input_graph_name has info about edges.
+                Row format: "node_A, node_B[, edge_weight]"
+            * input_data_name has info about the graph signal.
+                Row format : "node_id, node_value"
+        Output:
+            * networkx graph
+    """
+
+    # Reading input data
+    input_data = open(input_data_name, 'r')
+    graph_signal = {}
+
+    for line in input_data:
+        line = line.rstrip()
+        node, value = line.rsplit(',')
+        value = float(value)
+        graph_signal[node] = value
+
+    input_data.close()
+
+    # Reading graph data
+    G = nx.Graph()
+    input_graph = open(input_graph_name, 'r')
+
+    for line in input_graph:
+        line = line.rstrip()
+        node_A, node_B = line.rsplit(',')[:2]
+        # Note that the edge weight is always set to 1
+        # even when provided available in input_graph_name
+        if node_A in graph_signal and node_B in graph_signal:
+            G.add_edge(node_A, node_B, weight=1.)
+
+    input_graph.close()
+
+    # Extracting largest connected component from graph
+    Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)
+
+    G = Gcc[0]
+
+    # Setting the graph_signal as node attribute
+    for node, value in graph_signal.items():
+        if node in G:
+            G.node[node]["value"] = value
+
+    return G
+
+
+def read_values(input_data_name, G):
+    """
+        Read the graph signal from file
+        Input:
+            * input_data_name has info about the graph signal.
+                Row format : "node_id, node_value"
+            * G: networkx graph
+        Output:
+            * F: normalized node values array, ordered by G.nodes()
+    """
+    graph_signal = {}
+    input_data = open(input_data_name, 'r')
+
+    # Reading file
+    for line in input_data:
+        line = line.rstrip()
+        node, value = line.rsplit(',')
+        graph_signal[node] = float(value)
+
+    input_data.close()
+
+    F = []
+    for node in G.nodes():
+        if node in graph_signal:
+            F.append(graph_signal[node])
+        else:
+            F.append(0.)
+
+    # Normalization
+    F = np.array(F)
+    F = F / np.max(F)
+    F = F - np.mean(F)
+
+    return F
diff --git a/lib/optimal_cut.py b/lib/optimal_cut.py
index 1b0c852..4ca3399 100644
--- a/lib/optimal_cut.py
+++ b/lib/optimal_cut.py
@@ -1,585 +1,535 @@
-import networkx
 import math
-import numpy
-import scipy
-import sys
-from scipy import linalg
-import time
-from numpy import log, mean, dot, diag, sqrt
+
+import networkx as nx
+import numpy as np
+from numpy import dot, diag, sqrt
 from numpy.linalg import eigh
-from lib.graph_signal_proc import *
-from scipy import sparse
-
-def sqrtm(mat):
-	"""
-	Matrix square root.
-	Input:
-		* mat: matrix
-	Output:
-		* matrix square root
-	"""
-	eigvals, eigvecs = eigh(mat)
-	
-	eigvecs = eigvecs[:, eigvals > 0]
-	eigvals = eigvals[eigvals > 0]
-	
-	return dot(eigvecs, dot(diag(sqrt(eigvals)), eigvecs.T))
-
-def sweep_opt(x, beta, F, G, k, ind):
-	"""
-		Sweep algorithm for sparse wavelets.
-		Input:
-			* x: continuous indicator vector
-			* beta: beta parameter for regularization
-			* F: graphsignal
-			* G: graph
-			* k: max number of edges to be cut
-			* ind: vertex index v: unique integer
-		Output:
-			* vec: indicator vector
-			* best_val: score value
-			* best_edges_cut: number of edges cut
-			* energy: wavelet energy value
-	"""
-	best_val = 0.
-	best_edges_cut = 0
-	sorted_x = numpy.argsort(x)
-	size_one = 0
-	sum_one = 0
-	sum_two = 0
-	
-	for v in G.nodes():
-		sum_two = sum_two + F[ind[v]]
-	
-	edges_cut = 0 
-	nodes_one = {}
-	total_size = networkx.number_of_nodes(G)
-
-	for i in range(x.shape[0]):
-		size_one = size_one + 1
-		sum_one = sum_one + F[ind[G.nodes()[sorted_x[i]]]]
-		sum_two = sum_two - F[ind[G.nodes()[sorted_x[i]]]]
-		
-		nodes_one[G.nodes()[sorted_x[i]]] = True
-		
-		for v in G.neighbors(G.nodes()[sorted_x[i]]):
-			if v not in nodes_one:
-				edges_cut = edges_cut + 1
-			else:
-				edges_cut = edges_cut - 1
-		
-		den = size_one  * (total_size-size_one) * total_size
-		if den > 0:
-			val = math.pow(sum_one*(total_size-size_one) - sum_two*size_one, 2) / den
-		else:
-			val = 0
-		
-		if val >= best_val and edges_cut <= k:
-			best_cand = i
-			best_val = val
-			best_edges_cut = edges_cut
-			
-			if total_size * size_one * (total_size-size_one) > 0:
-				energy = math.pow(sum_one*(total_size-size_one) - sum_two*size_one, 2) / (total_size * size_one * (total_size-size_one))
-			else:
-				energy = 0
-
-	vec = numpy.zeros(total_size)
-	
-	for i in range(x.shape[0]):
-		if i <= best_cand:
-			vec[sorted_x[i]] = -1.
-		else:
-			vec[sorted_x[i]] = 1.
-	
-	return vec, best_val, best_edges_cut, energy
-
-def laplacian_complete(n):
-	"""
-                Laplacian of a complete graph with n vertices.
-                Input:
-                        * n: size
-                Output:
-                        * C: Laplacian
-	"""
-	C = numpy.ones((n, n))
-	C = -1 * C
-	D = numpy.diag(numpy.ones(n))
-	C = (n)*D + C
-	
-	return C
+import scipy
 
-def weighted_adjacency_complete(G, F, ind):
-	"""
-		Computes weighted adjacency complete matrix (w(v)-w(u))^2
-		Input:
-			* G: graph
-			* F: graph signal
-			* ind: vertex index vertex: unique integer
-		Output:
-			* A: nxn matrix
-	"""
-	A = []
-	for v in G.nodes():
-		A.append([])
-		for u in G.nodes():
-			A[-1].append(pow(F[ind[v]]-F[ind[u]],2))
-			
-	return numpy.array(A)
+from lib.graph_signal_proc import Node
+import lib.graph_signal_proc as gsp
+
+
+def sweep_opt(x, F, G, k, ind):
+    """
+        Sweep algorithm for sparse wavelets.
+        Input:
+            * x: continuous indicator vector
+            * F: graph signal
+            * G: graph
+            * k: max number of edges to be cut
+            * ind: vertex index v: unique integer
+        Output:
+            * vec: indicator vector
+            * best_energy: max energy value
+            * best_cut_size: number of edges in the returned cut
+    """
+    sorted_x = np.argsort(x)
+    part_one = set()
+    N = nx.number_of_nodes(G)
+    best_energy = 0.
+    cut_size = 0
+    best_cut_size = 0
+    sum_one = 0     # sum of the graph signal values for the nodes in part_one
+    sum_two = 0     # sum of the graph signal values for the nodes in part_two
+
+    for v in G.nodes():
+        sum_two += F[ind[v]]
+
+    nodes_list = list(G.nodes())
+
+    for i in range(N):
+        part_one.add(nodes_list[sorted_x[i]])
+        sum_one += F[ind[nodes_list[sorted_x[i]]]]
+        sum_two -= F[ind[nodes_list[sorted_x[i]]]]
+
+        for v in G.neighbors(nodes_list[sorted_x[i]]):
+            if v not in part_one:
+                cut_size += 1
+            else:
+                cut_size -= 1
+
+        den = N * len(part_one) * (N - len(part_one))
+        if den > 0:
+            energy = math.pow(sum_one * (N - len(part_one)) - sum_two *
+                              len(part_one), 2) / den
+        else:
+            energy = 0
+
+        if energy >= best_energy and cut_size <= k:
+            best_cand = i
+            best_energy = energy
+            best_cut_size = cut_size
+
+    vec = np.ones(nx.number_of_nodes(G))
+
+    for i in range(x.shape[0]):
+        if i <= best_cand:
+            vec[sorted_x[i]] = -1.
+
+    return vec, best_energy, best_cut_size
+
+
+###############################################################################
+#  List of functions used only by the SWT:                                    #
+#  - sqrtmi()                                                                 #
+#  - spectral_cut()                                                           #
+#  - complete_graph_laplacian                                                 #
+#  - weighted_adjacency_complete                                              #
+#  - one_d_search                                                             #
+###############################################################################
+
+
+def sqrtmi(mat):
+    """
+        Computes the square-root inverse of a matrix.
+        Input:
+            * mat: matrix
+        Output:
+            * square root inverse
+    """
+    eigvals, eigvecs = eigh(mat)
+    eigvecs = eigvecs[:, eigvals > 0]
+    eigvals = eigvals[eigvals > 0]
+
+    return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T))
 
-def fast_cac(G, F, ind):
-	"""
-		Computes product C*A*C, where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix.
-		Input:
-			* G: graph
-			* F: graph signal
-			* ind: vertex index v: unique integer
-		Output: 
-			* CAC: matrix product
-	"""
-	CAC = []
-	for v in G.nodes():
-		CAC.append([])
-		for u in G.nodes():
-			CAC[-1].append(F[ind[v]] * F[ind[u]])
-
-	CAC = numpy.array(CAC)
-	CAC = -2 * math.pow(networkx.number_of_nodes(G), 2) * CAC
-
-	return CAC
 
-def power_method(mat, start, maxit):
-	"""
-		Power method implementation.
-		Input:
-			* mat: matrix
-			* start: initialization
-			* maxit: number of iterations
-		Output:
-			* vec: largest eigenvector of mat
-	"""
-	vec = numpy.copy(start)
-	vec = vec/numpy.linalg.norm(vec)
+def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind):
+    """
+        Spectral cut implementation.
+        Input:
+            * CAC: C*A*C where C is the Laplacian of a complete graph and
+                 A is a pairwise squared difference matrix
+            * L: graph laplacian matrix
+            * C: laplacian complete graph
+            * A: pairwise squared difference matrix
+            * start: initialization
+            * beta: regularization parameter
+            * k: max edges cut
+            * ind: vertex index vertex: unique integer
+        Output:
+            * res: dictionary with following fields:
+                - x: indicator vector
+                - size: number of edges cut
+                - energy: cut energy
+    """
+    isqrtCL = sqrtmi(C + beta * L)
+    M = np.dot(np.dot(isqrtCL, CAC), isqrtCL)
+
+    (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 0))
+    x = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :]
+
+    res = {}
+    res["x"], res["energy"], res["size"] = sweep_opt(x, F, G, k, ind)
+
+    return res
+
+
+def complete_graph_laplacian(n):
+    """
+        Laplacian of a complete graph with n vertices.
+        Input:
+            * n: size
+        Output:
+            * C: Laplacian
+    """
+    C = np.ones((n, n))
+    C = -1 * C
+    D = np.diag(np.ones(n))
+    C = (n) * D + C
+
+    return C
 
-	for i in range(maxit):
-		vec = numpy.dot(vec, mat)
 
-	return vec
+def weighted_adjacency_complete(G, F, ind):
+    """
+        Computes weighted adjacency complete matrix (w(v)-w(u))^2
+        Input:
+            * G: graph
+            * F: graph signal
+            * ind: vertex index vertex: unique integer
+        Output:
+            * A: nxn matrix
+    """
+    A = []
+    for v in G.nodes():
+        A.append([])
+        for u in G.nodes():
+            A[-1].append(pow(F[ind[v]] - F[ind[u]], 2))
+
+    return np.array(A)
 
 
-def spectral_cut(CAC, L, C, A, start, F, G, beta, k, ind):
-	"""
-		Spectral cut implementation.
-		Input:
-			* CAC: C*A*C where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix
-			* L: graph laplacian matrix
-			* C: laplacian complete graph
-			* A: pairwise squared difference matrix
-			* start: initialization
-			* beta: regularization parameter
-			* k: max edges cut
-			* ind: vertex index vertex: unique integer
-		Output:
-			* res: dictionary with following fields:
-				- x: indicator vector
-				- size: number of edges cut
-				- score: cut score
-				- energy: cut energy
-	"""
-	isqrtCL = sqrtmi( C + beta * L)
-	M = numpy.dot(numpy.dot(isqrtCL, CAC), isqrtCL)
-	
-	(eigvals, eigvecs) = scipy.linalg.eigh(M,eigvals=(0,0))
-	x = numpy.asarray(numpy.dot(eigvecs[:,0], isqrtCL))[0,:]
-
-	(x, score, size, energy) = sweep_opt(x, beta, F, G, k, ind)
-	
-	res = {}
-	res["x"] = numpy.array(x)
-	res["size"] = size
-	res["score"] = score
-	res["energy"] = energy
-	
-	return res
-
-def eig_vis_opt(G, F, beta):
-	"""
-		Computes first and second eigenvector of sqrt(C+beta*L)^T CAC sqrt(C+beta*L) matrix for visualization.
-		Input:
-			* G: graph
-			* F: graph signal
-			* beta: regularization parameter
-		Output:
-			* v1: first eigenvector
-			* v2: second eigenvector	
-	"""
-	ind = {}
-	i = 0
-	
-	for v in G.nodes():
-		ind[v] = i
-		i = i + 1
-	
-	C = laplacian_complete(networkx.number_of_nodes(G))
-	A = weighted_adjacency_complete(G, F, ind)
-	CAC = numpy.dot(numpy.dot(C,A), C)
-	L = networkx.laplacian_matrix(G).todense()
-	
-	isqrtCL = sqrtmi( C + beta * L)
-	M = numpy.dot(numpy.dot(isqrtCL, CAC), isqrtCL)
-	
-	(eigvals, eigvecs) = scipy.linalg.eigh(M,eigvals=(0,1))
-	x1 = numpy.asarray(numpy.dot(eigvecs[:,0], isqrtCL))[0,:]
-	x2 = numpy.asarray(numpy.dot(eigvecs[:,1], isqrtCL))[0,:]
-
-	return x1, x2
+def one_d_search(G, F, k, ind):
+    """
+        Cut computation. Perform 1-D search for beta using golden search.
+        Input:
+            * G: graph
+            * F: graph signal
+            * k: max edges to be cut
+            * n: number of chebyshev polynomials
+            * ind: vertex index vertex: unique integer
+        Output:
+            * cut
+    """
+    C = complete_graph_laplacian(nx.number_of_nodes(G))
+    A = weighted_adjacency_complete(G, F, ind)
+    CAC = np.dot(np.dot(C, A), C)
+    start = np.ones(nx.number_of_nodes(G))
+    L = nx.laplacian_matrix(G).todense()
+
+    # Upper and lower bounds for beta
+    gr = (math.sqrt(5) - 1) / 2
+    a = 0.
+    b = 1000.
+    c = b - gr * (b - a)
+    d = a + gr * (b - a)
+
+    # Tolerance
+    tol = 1.
+
+    resab = {}
+    resab["size"] = k + 1
+
+    # golden search
+    while abs(c - d) > tol or resab["size"] > k:
+        resc = spectral_cut(CAC, L, C, A, start, F, G, c, k, ind)
+        resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind)
+
+        if resc["size"] <= k and (resc["energy"] > resd["energy"]):
+            start = resc["x"]
+            b = d
+            d = c
+            c = b - gr * (b - a)
+        elif resd["size"] <= k and (resc["energy"] < resd["energy"]):
+            start = resd["x"]
+            a = c
+            c = d
+            d = a + gr * (b - a)
+        else:
+            start = resc["x"]
+            a = c
+            c = d
+            d = a + gr * (b - a)
+
+        resab = spectral_cut(CAC, L, C, A, start, F, G, (b + a) / 2, k, ind)
+
+    return resab
+
+
+###############################################################################
+#  List of functions used only by the FSWT:                                   #
+#  - trans()                                                                  #
+#  - isqrt()                                                                  #
+#  - coef()                                                                   #
+#  - chebyshev_approx_2d()                                                    #
+#  - chebyshev_approx_1d()                                                    #
+#  - fast_cac()                                                               #
+#  - power_method()                                                           #
+#  - cheb_spectral_cut()                                                      #
+#  - fast_search()                                                            #
+###############################################################################
+
 
 def trans(L, min_v, max_v):
-	"""
-		Chebyshev polynomial translation.
-		Input:
-			* L: Laplacian matrix
-			* min_v: lower bound
-			* max_v: upper bound
-		Output:
-			* translation
-	"""
-	return (float(2.) / (max_v-min_v)) * L, -(float(max_v+min_v) / (max_v-min_v)) 
-    
-def fun(k, n, beta, min_v, max_v, x):
-	"""
-		Function to be integrated in Chebyshev polynomial computation.
-		Input:
-			* k: coefficient number
-			* n: number of polynomials
-			* min_v: lower bound
-			* max_v: upper bound
-		Output:
-			* function value
-	"""
-	y = 0.5 * math.cos(x) * float(max_v - min_v) +  (0.5 * (max_v + min_v))
-	
-	return math.cos(k*x)*(float(1.) / math.sqrt(beta*y))
-		    
-def coef(k, n, beta, min_v, max_v):
-	"""
-		Chebyshev polynomial coefficients.
-		Input:
-			* k: coefficient number
-			* n: number of polynomials
-			* min_v: lower bound
-			* max_v: upper bound
-		Output:
-			* coefficient
-	"""
-	return float(2. * scipy.integrate.quad(lambda x: fun(k, n, beta, min_v, max_v, x), 0., math.pi)[0]) / math.pi
-			
+    """
+        Chebyshev polynomial translation.
+        Input:
+            * L: Laplacian matrix
+            * min_v: lower bound
+            * max_v: upper bound
+        Output:
+            * translation as tuple of a sparse matrix and a scalar
+    """
+    return (2. / (max_v - min_v)) * L, -(float(max_v + min_v) /
+                                         (max_v - min_v))
+
+
+def isqrt(k, beta, min_v, max_v, x):
+    """
+        Inverse square root function to be integrated in
+        Chebyshev polynomial computation.
+        Input:
+            * k: coefficient number
+            * beta: regularization parameter
+            * min_v: lower bound
+            * max_v: upper bound
+            * x: function variable
+        Output:
+            * function value
+    """
+    y = 0.5 * (math.cos(x) * float(max_v - min_v) + (max_v + min_v))
+
+    return math.cos(k * x) * (1. / math.sqrt(beta * y))
+
+
+def coef(k, beta, min_v, max_v):
+    """
+        Get the k-th Chebyshev polynomial coefficient for the inverse
+        square root
+        Input:
+            * k: coefficient number
+            * min_v: lower bound
+            * max_v: upper bound
+        Output:
+            * coefficient
+    """
+    return (2. / math.pi) * (scipy.integrate.quad(
+        lambda x: isqrt(k, beta, min_v, max_v, x), 0., math.pi)[0])
+
+
 def chebyshev_approx_2d(n, beta, X, L):
-	"""
-		Approximates sqrt((L)^+)^T * X * sqrt((L)^+)^T using Chebyshev polynomials (twice)
-		Input:
-			* n: number of polynomials
-			* beta: regularization parameter
-			* X: matrix
-			* L: Laplacian matrix
-		Output:
-			* P2: approximation
-	"""
-	max_v = beta * L.shape[0]
-	min_v = 1
-	
-	ts1, ts2 = trans(L, min_v, max_v)
-	P1 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * X
-	tkm2 = X 
-	tkm1 = scipy.sparse.csr_matrix.dot(ts1, X) + ts2 * X
-	P1 = P1 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1
-	
-	for i in range(2, n):                 
-		Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2  
-		P1 = P1 + coef(i, L.shape[0], beta, min_v, max_v) * Tk
-		tkm2 = tkm1
-		tkm1 = Tk
-	
-	P1 = P1.transpose()    
-	P2 = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * P1
-	tkm2 = P1 
-	tkm1 = scipy.sparse.csr_matrix.dot(ts1, P1) + ts2 * P1
-
-	P2 = P2 + coef(1, L.shape[0], beta, min_v, max_v) * tkm1
-										        
-	for i in range(2, n):                 
-		Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2  
-		P2 = P2 + coef(i, L.shape[0], beta, min_v, max_v) * Tk
-		tkm2 = tkm1
-		tkm1 = Tk
-
-	return P2
+    """
+        Approximates sqrt((beta * L)^+) * X * sqrt((beta * L)^+) using
+        Chebyshev polynomials (twice)
+        Input:
+            * n: number of polynomials
+            * beta: regularization parameter
+            * X: matrix
+            * L: Laplacian matrix
+        Output:
+            * P2: approximation
+    """
+    N = L.shape[0]
+    max_v = beta * N
+    min_v = 1
+    # ts1 is a sparse matrix, ts2 is a scalar
+    ts1, ts2 = trans(L, min_v, max_v)
+    # Recurrence relation for Chebyshev polynomials
+    # Tk = 2 * y * Tk_1 - Tk_2
+    # 0,5 * c_n0 * x + sum_{k=1}^{inf} (c_nk * Tk) * x
+    P1 = 0.5 * coef(0, beta, min_v, max_v) * X
+    tkm2 = X
+    tkm1 = scipy.sparse.csr_matrix.dot(ts1, X) + ts2 * X
+    P1 = P1 + coef(1, beta, min_v, max_v) * tkm1
+
+    for i in range(2, n):
+        Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2
+        P1 = P1 + coef(i, beta, min_v, max_v) * Tk
+        tkm2 = tkm1
+        tkm1 = Tk
+
+    P1 = P1.transpose()
+    P2 = 0.5 * coef(0, beta, min_v, max_v) * P1
+    tkm2 = P1
+    tkm1 = scipy.sparse.csr_matrix.dot(ts1, P1) + ts2 * P1
+
+    P2 = P2 + coef(1, beta, min_v, max_v) * tkm1
+
+    for i in range(2, n):
+        Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2
+        P2 = P2 + coef(i, beta, min_v, max_v) * Tk
+        tkm2 = tkm1
+        tkm1 = Tk
+
+    return P2
+
 
 def chebyshev_approx_1d(n, beta, x, L):
-	"""
-		Approximates x*sqrt(L^+) using Chebyshev polynomials.
-		Input:
-			* n: number of polynomials
-			* beta: regularization parameter
-			* x: vector
-			* L: graph Laplacian
-		Output: 	
-			* P: approximation
-			
-	"""
-	max_v = beta * L.shape[0]
-	min_v = 1
-	
-	ts1, ts2 = trans(L, min_v, max_v)
-	P = 0.5 * coef(0, L.shape[0], beta, min_v, max_v) * x
-	tkm2 = x
-	tkm1 = scipy.sparse.csr_matrix.dot(ts1, x) + ts2 * x
-	P = P + coef(1, L.shape[0], beta, min_v, max_v) * tkm1
-	
-	for i in range(2, n):                 
-		Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, tkm1) + ts2 * tkm1) - tkm2  
-		P = P + coef(i, L.shape[0], beta, min_v, max_v) * Tk
-		tkm2 = tkm1
-		tkm1 = Tk
-	
-	return P
+    """
+        Approximates x*sqrt((beta * L)^+) using Chebyshev polynomials.
+        Input:
+            * n: number of polynomials
+            * beta: regularization parameter
+            * x: vector
+            * L: graph Laplacian
+        Output:
+            * P: approximation
+
+    """
+    N = L.shape[0]
+    max_v = beta * N
+    min_v = 1
+    # ts1 is a sparse matrix, ts2 is a scalar
+    ts1, ts2 = trans(L, min_v, max_v)
+    # Recurrence relation for Chebyshev polynomials
+    # Tk = 2 * y * Tk_1 - Tk_2
+    # 0,5 * c_n0 * x + sum_{k=1}^{inf} (c_nk * Tk) * x
+    Tk_2 = x
+    Tk_1 = scipy.sparse.csr_matrix.dot(ts1, x) + ts2 * x
+    P = 0.5 * coef(0, beta, min_v, max_v) * x
+    P += coef(1, beta, min_v, max_v) * Tk_1
+    """(2./ (max_v - min_v)) * L, -(float(max_v + min_v) /
+                                         (max_v - min_v))"""
+    for i in range(2, n):
+        Tk = 2. * (scipy.sparse.csr_matrix.dot(ts1, Tk_1) + ts2 * Tk_1) - Tk_2
+        P += coef(i, beta, min_v, max_v) * Tk
+        Tk_2 = Tk_1
+        Tk_1 = Tk
+
+    return P
 
-def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind):
-	"""
-		Fast spectral cut implementation using chebyshev polynomials.
-		Input:
-			* CAC: C*A*C where C is the Laplacian of a complete graph and A is a pairwise squared difference matrix
-			* start: initialization
-			* F: graph signal
-			* G: graph
-			* L: graph laplacian matrix
-			* beta: regularization parameter
-			* k: max edges cut
-			* n: number of polynomials
-			* ind: vertex index vertex: unique integer
-		Output:
-			* res: dictionary with following fields:
-				- x: indicator vector
-				- size: number of edges cut
-				- score: cut score
-				- energy: cut energy
-	"""
-	L = networkx.laplacian_matrix(G)
-	M = chebyshev_approx_2d(n, beta, CAC, L)
-	
-	eigvec = power_method(-M, start, 10)
-	x = chebyshev_approx_1d(n, beta, eigvec, L)
-	
-	(x, score, size, energy) = sweep_opt(x, beta, F, G, k, ind)
-	
-	res = {}
-	res["x"] = numpy.array(x)
-	res["size"] = size
-	res["score"] = score
-	res["energy"] = energy
-	
-	return res
 
-def fast_search(G, F, k, n, ind):
-	"""
-		Efficient version of cut computation. Does not perform 1-D search for beta.
-		Input:
-			* G: graph
-			* F: graph signal
-			* k: max edges to be cut
-			* n: number of chebyshev polynomials
-			* ind: vertex index vertex: unique integer
-		Output:
-			* cut
-	"""
-	start = numpy.ones(networkx.number_of_nodes(G))
-	C = laplacian_complete(networkx.number_of_nodes(G))
-	A = weighted_adjacency_complete(G, F, ind)
-	CAC = fast_cac(G, F, ind)
-
-	return cheb_spectral_cut(CAC, start, F, G, 1., k, n, ind)
-
-gr=(math.sqrt(5)-1)/2
+def fast_cac(G, F, ind):
+    """
+        Computes product C*A*C, where C is the Laplacian of a complete graph
+        and A is a pairwise squared difference matrix.
+        Input:
+            * G: graph
+            * F: graph signal
+            * ind: vertex index v: unique integer
+        Output:
+            * CAC: matrix product
+    """
+    sorted_F = np.array([F[ind[v]] for v in G.nodes()])
+    return -2 * math.pow(len(sorted_F), 2) * np.outer(sorted_F, sorted_F)
 
-def one_d_search(G, F, k, ind):
-	"""
-		Cut computation. Perform 1-D search for beta using golden search.
-		Input:
-			* G: graph
-			* F: graph signal
-			* k: max edges to be cut
-			* n: number of chebyshev polynomials
-			* ind: vertex index vertex: unique integer
-		Output:
-			* cut
-	"""
-	C = laplacian_complete(networkx.number_of_nodes(G))
-	A = weighted_adjacency_complete(G,F, ind)
-	CAC = numpy.dot(numpy.dot(C,A), C)
-	start = numpy.ones(networkx.number_of_nodes(G))
-	L = networkx.laplacian_matrix(G).todense()
-
-	#Upper and lower bounds for search
-	a = 0.
-	b = 1000.
-	c=b-gr*(b-a)
-	d=a+gr*(b-a)
-	
-	#Tolerance
-	tol = 1.
-
-	resab = {}
-	resab["size"] = k + 1
-	
-	#golden search
-	while abs(c-d)>tol or resab["size"] > k:      
-		resc = spectral_cut(CAC, L, C, A, start, F, G, c, k, ind)
-		resd = spectral_cut(CAC, L, C, A, start, F, G, d, k, ind)
-		
-		if resc["size"] <= k: 
-			if resc["score"] > resd["score"]: 
-				start = numpy.array(resc["x"])
-				b = d
-				d = c
-				c=b-gr*(b-a)
-			else:
-				start = numpy.array(resd["x"])
-				a=c
-				c=d  
-				d=a+gr*(b-a)
-		else:
-				start = numpy.array(resc["x"])
-				a=c
-				c=d  
-				d=a+gr*(b-a)
-		
-		resab = spectral_cut(CAC, L, C, A, start, F, G, (b+a) / 2, k, ind)
-	
-	return resab
-
-def get_subgraphs(G, cut):
-	"""
-                Compute subgraphs generated by a cut.
-                Input:
-                        * G: Original graph
-                        * cut: cut indicator vector
-                Output:
-                        * G1: subgraph 1
-                        * G2: subgraph 2
-        """
-	G1 = networkx.Graph()
-	G2 = networkx.Graph()
-	i = 0
-	P1 = []
-	P2 = []
-	for v in G.nodes():
-		if cut[i] < 0:
-			P1.append(v)
-		else:
-			P2.append(v)
-		i = i + 1
-
-	G1 = G.subgraph(P1)
-	G2 = G.subgraph(P2)
-	
-	return G1, G2
-
-def optimal_wavelet_basis(G, F, k, npol):
-	"""
-		Computation of optimal graph wavelet basis.
-		Input:
-			* G: graph
-			* F: graph signal
-			* k: max edges to be cut
-			* npol: number of chebyshev polynomials, if 0 run exact version
-		Output:
-			* root: tree root
-			* ind: vertex index vertex: unique integer
-			* size: number of edges cut
-	"""
-	
-	#Creating index
-	ind = {}
-	i = 0
-	for v in G.nodes():
-		ind[v] = i
-		i = i+1
-
-	#First cut
-	root = Node(None)
-	size = 0
-	cand_cuts = []
-
-	if npol == 0:
-		c = one_d_search(G, F, k, ind)
-	else:
-		c = fast_search(G, F, k, npol, ind)
-
-	c["parent"] = root
-	c["graph"] = G
-	
-	cand_cuts.append(c)
-
-	#Recursively compute new cuts
-	while size <= k and len(cand_cuts) > 0:
-		best_cut = None
-		b = 0
-		
-		for i in range(0, len(cand_cuts)):
-			if cand_cuts[i]["size"] + size <= k and cand_cuts[i]["score"] > 0:
-				if best_cut is None or cand_cuts[i]["score"] > best_cut["score"]:
-					best_cut = cand_cuts[i]
-					b = i
-		if best_cut is None:
-			break
-		else:
-			#Compute cut on left and right side
-			(G1, G2) = get_subgraphs(best_cut["graph"], best_cut["x"])
-			best_cut["parent"].cut = best_cut["size"]
-			size = size + best_cut["size"]
-
-			if networkx.number_of_nodes(G1) == 1:
-				n = Node(ind[G1.nodes()[0]])
-				best_cut["parent"].add_child(n)
-			elif networkx.number_of_nodes(G1) > 0:
-				n = Node(None)
-				
-				if npol == 0:
-					c = one_d_search(G1, F, k, ind)
-				else:
-					c = fast_search(G1, F, k, npol, ind)
-				
-				c["parent"] = n
-				c["graph"] = G1
-				cand_cuts.append(c)
-
-				best_cut["parent"].add_child(n)
-			
-			if networkx.number_of_nodes(G2) == 1:
-				n = Node(ind[G2.nodes()[0]])
-				best_cut["parent"].add_child(n)
-			elif networkx.number_of_nodes(G2) > 0:
-				n = Node(None)
-				
-				if npol == 0:
-					c = one_d_search(G2, F, k, ind)
-				else:
-					c = fast_search(G2, F, k, npol, ind)
-
-				c["parent"] = n
-				c["graph"] = G2
-				cand_cuts.append(c)
-				
-				best_cut["parent"].add_child(n)
-			
-			del cand_cuts[b]
-	
-	#Compute remaining cuts using ratio cuts once budget is over (not optimal)
-	for i in range(0, len(cand_cuts)):
-		rc_recursive(cand_cuts[i]["parent"], cand_cuts[i]["graph"], ind)
-
-	set_counts(root)
-	
-	return root, ind, size
 
+def power_method(mat, start, maxit):
+    """
+        Power method implementation.
+        Input:
+            * mat: matrix
+            * start: initialization
+            * maxit: number of iterations
+        Output:
+            * vec: largest eigenvector of mat
+    """
+    vec = np.copy(start)
+    vec = vec / np.linalg.norm(vec)
+
+    for i in range(maxit):
+        vec = np.dot(vec, mat)
 
+    return vec
+
+
+def cheb_spectral_cut(CAC, start, F, G, beta, k, n, ind):
+    """
+        Fast spectral cut implementation using chebyshev polynomials.
+        Input:
+            * CAC: C*A*C where C is the Laplacian of a complete graph and
+                A is a pairwise squared difference matrix
+            * start: initialization
+            * F: graph signal
+            * G: graph
+            * beta: regularization parameter
+            * k: max edges cut
+            * n: number of polynomials
+            * ind: vertex index vertex: unique integer
+        Output:
+            * res: dictionary with following fields:
+                - x: indicator vector
+                - size: number of edges cut
+                - energy: cut energy
+    """
+    L = nx.laplacian_matrix(G)
+    M = chebyshev_approx_2d(n, beta, CAC, L)
+
+    eigvec = power_method(-M, start, 10)
+    x = chebyshev_approx_1d(n, beta, eigvec, L)
+    res = {}
+    res["x"], res["energy"], res["size"] = sweep_opt(x, F, G, k, ind)
+
+    return res
+
+
+def fast_search(G, F, k, n, ind):
+    """
+        Efficient version of cut computation.
+        Does not perform 1-D search for beta.
+        Input:
+            * G: graph
+            * F: graph signal
+            * k: max edges to be cut
+            * n: number of chebyshev polynomials
+            * ind: vertex index vertex: unique integer
+        Output:
+            * cut
+    """
+    start = np.ones(nx.number_of_nodes(G))
+    CAC = fast_cac(G, F, ind)
+
+    return cheb_spectral_cut(CAC, start, F, G, 1., k, n, ind)
+
+
+# optimal_wavelet_basis() builds the wavelet basis tree. To do so,
+# it calls either the SWT (one_d_search()) or the FSWT (fast_search())
+
+
+def optimal_wavelet_basis(G, F, k, npol, method='lobpcg'):
+    """
+        Computation of optimal graph wavelet basis.
+        Input:
+            * G: graph
+            * F: graph signal
+            * k: max edges to be cut
+            * npol: number of chebyshev polynomials, if 0 run exact version
+            * method: method used for Fiedler vector computation.
+                The default value is 'lobpcg' however 'tracemin_lu' seems
+                faster and it appears to give more stable results when used
+                with PYTHONHASHSEED set to a constant value.
+        Output:
+            * root: tree root
+            * ind: vertex index vertex: unique integer
+            * size: number of edges cut
+    """
+
+    gsp.set_fiedler_method(method)
+
+    # Creating index
+    ind = {v: i for i, v in enumerate(G.nodes())}
+
+    # First cut
+    root = Node(None)
+    size = 0
+    cand_cuts = []
+
+    if npol == 0:
+        c = one_d_search(G, F, k, ind)
+    else:
+        c = fast_search(G, F, k, npol, ind)
+
+    c["parent"] = root
+    c["graph"] = G
+
+    cand_cuts.append(c)
+    # Recursively find the best cut. Each time it tries first the supposedly
+    # biggest subgraphs inserted earlier in cand_cuts until the list is emptied
+    # or no new best cut is found
+    while size <= k and len(cand_cuts) > 0:
+        best_cut = None
+        for i, c in enumerate(cand_cuts):
+            if (c["size"] + size <= k and c["energy"] > 0 and
+                    (best_cut is None or c["energy"] > best_cut["energy"])):
+                best_cut = c
+                b = i
+
+        # Exit iteration if no better cut is found
+        if best_cut is None:
+            break
+        else:
+            # Compute cut on left and right side
+            G1, G2 = gsp.get_subgraphs(best_cut["graph"], best_cut["x"])
+
+            size += best_cut["size"]
+
+            for Gi in (G1, G2):
+                if nx.number_of_nodes(Gi) == 1:
+                    best_cut["parent"].add_child(
+                        Node(ind[list(Gi.nodes())[0]]))
+                elif nx.number_of_nodes(Gi) > 1:
+                    n = Node(None)
+
+                    if npol == 0:
+                        c = one_d_search(Gi, F, k, ind)
+                    else:
+                        c = fast_search(Gi, F, k, npol, ind)
+
+                    c["parent"] = n
+                    c["graph"] = Gi
+                    cand_cuts.append(c)
+
+                    best_cut["parent"].add_child(n)
+
+            del cand_cuts[b]
+
+    # Compute remaining cuts using ratio cuts once the budget k is over
+    # until the tree is completely built. (Not optimal search)
+    for i in range(0, len(cand_cuts)):
+        gsp.rc_recursive(cand_cuts[i]["parent"], cand_cuts[i]["graph"], ind)
+
+    gsp.set_counts(root)
+
+    return root, ind, size
diff --git a/lib/static.py b/lib/static.py
index eb3fd74..038b21a 100644
--- a/lib/static.py
+++ b/lib/static.py
@@ -1,233 +1,210 @@
-import networkx
 import math
-import scipy.optimize
-import numpy
-import sys
-from scipy import linalg
-import matplotlib.pyplot as plt
-from IPython.display import Image
-import pywt
-import scipy.fftpack
-import random
 import operator
-import copy
-from collections import deque
-from sklearn.preprocessing import normalize
-from sklearn.cluster import SpectralClustering
-from lib.graph_signal_proc import *
-from lib.optimal_cut import *
-
-class Fourier(object):    
-	"""
-		Graph Fourier transform.
-	"""
-	def name(self):
-		return "FT"
-
-	def set_graph(self, _G):
-		self.G = _G
-		L = networkx.laplacian_matrix(self.G)
-		L = L.todense()
-		self.U, self.lamb_str = compute_eigenvectors_and_eigenvalues(L)
-
-	def transform(self, F):
-		"""
-		"""
-		return graph_fourier(F, self.U)
-
-	def inverse(self, ftr):
-		"""
-		"""
-		return graph_fourier_inverse(ftr, self.U)
-
-	def drop_frequency(self, ftr, n):
-		"""
-			Keeps only the n top-energy coefficients of ftr.
-			Input:
-				* ftr: transform
-				* n: number of coefficients
-			Output:
-				* ftr_copy: changed transform
-		"""
-		coeffs = {}
-
-		for i in range(ftr.shape[0]):
-			coeffs[i] = abs(ftr[i])
-
-		sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True)
-
-		ftr_copy = numpy.copy(ftr)
-
-		for k in range(n, len(sorted_coeffs)):
-			i = sorted_coeffs[k][0]
-
-			ftr_copy[i] = 0
-
-		return ftr_copy
+
+import numpy as np
+import networkx as nx
+
+import lib.graph_signal_proc as gsp
+import lib.optimal_cut as oc
+
+
+class Fourier(object):
+    """
+        Graph Fourier transform.
+    """
+
+    def name(self):
+        return "FT"
+
+    def set_graph(self, _G):
+        self.G = _G
+        L = nx.laplacian_matrix(self.G)
+        L = L.todense()
+        self.U, self.lamb_str = gsp.compute_eigenvectors_and_eigenvalues(L)
+
+    def transform(self, F):
+        return gsp.graph_fourier(F, self.U)
+
+    def inverse(self, ftr):
+        return gsp.graph_fourier_inverse(ftr, self.U)
+
+    def drop_frequency(self, ftr, n):
+        """
+            Keeps only the n top-energy coefficients of ftr.
+            Input:
+                * ftr: transform
+                * n: number of coefficients
+            Output:
+                * ftr_copy: changed transform
+        """
+        coeffs = {i: abs(ftr[i]) for i in range(ftr.shape[0])}
+        sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1),
+                               reverse=True)
+        ftr_copy = np.copy(ftr)
+        # Set the other coefficients to 0
+        for k in range(n, len(sorted_coeffs)):
+            ftr_copy[sorted_coeffs[k][0]] = 0
+
+        return ftr_copy
+
 
 class HWavelets(object):
-	"""
-		Hammond's wavelets (spectral theory)
-	"""
-	def name(self):
-		return "HWT"
-
-	def set_graph(self, _G):
-		"""
-		"""
-		self.G = _G
-		L = networkx.normalized_laplacian_matrix(self.G)
-		L = L.todense()
-		self.U, self.lamb_str = compute_eigenvectors_and_eigenvalues(L)
-		lamb_max = max(self.lamb_str.real)
-		
-		#default parameters defined by author
-		K = 100
-		J = 4
-		gamma = comp_gamma()
-		self.T = comp_scales(lamb_max, K, J)
-		self.w = graph_wavelets(self.lamb_str.real, self.U.real, range(len(self.G.nodes())), self.T)
-		self.s = graph_low_pass(self.lamb_str.real, self.U.real, range(len(self.G.nodes())), self.T, gamma, lamb_max, K)
-
-	def transform(self, F):
-		"""
-		"""
-		return hammond_wavelet_transform(self.w, self.s, self.T, F)
-	
-	def inverse(self, wtr):
-		"""
-		"""
-		return hammond_wavelets_inverse(self.w, self.s, wtr)
-
-	def drop_frequency(self, wtr, n):
-		"""
-			Keeps only the n top-energy coefficients of wtr.
-			Input:
-				* wtr: transform
-				* n: number of coefficients
-			Output:
-				* wtr_copy: changed transform
-		"""
-		coeffs = {}
-		for i in range(len(wtr)):
-			for j in range(len(wtr[i])):
-				coeffs[(i,j)] = abs(wtr[i][j])
-
-		sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True)
-
-		wtr_copy = numpy.copy(wtr)
-
-		for k in range(n, len(sorted_coeffs)):
-			i = sorted_coeffs[k][0][0]
-			j = sorted_coeffs[k][0][1]
-
-			wtr_copy[i][j] = 0.0
-
-		return wtr_copy
+    """
+        Hammond's wavelets (spectral theory)
+    """
+
+    def name(self):
+        return "HWT"
+
+    def set_graph(self, _G):
+        """
+        """
+        self.G = _G
+        L = nx.normalized_laplacian_matrix(self.G)
+        L = L.todense()
+        self.U, self.lamb_str = gsp.compute_eigenvectors_and_eigenvalues(L)
+        lamb_max = max(self.lamb_str.real)
+
+        # default parameters defined by author
+        K = 100
+        J = 4
+        gamma = gsp.comp_gamma()
+        self.T = gsp.comp_scales(lamb_max, K, J)
+        self.w = gsp.graph_wavelets(self.lamb_str.real, self.U.real,
+                                    len(self.G.nodes()), self.T)
+        self.s = gsp.graph_low_pass(self.lamb_str.real, self.U.real,
+                                    self.T, gamma, lamb_max, K)
+
+    def transform(self, F):
+        return gsp.hammond_wavelet_transform(self.w, self.s, self.T, F)
+
+    def inverse(self, wtr):
+        return gsp.hammond_wavelets_inverse(self.w, self.s, wtr)
+
+    def drop_frequency(self, wtr, n):
+        """
+            Keeps only the n top-energy coefficients of wtr.
+            Input:
+                * wtr: transform
+                * n: number of coefficients
+            Output:
+                * wtr_copy: changed transform
+        """
+        coeffs = {}
+        for i in range(len(wtr)):
+            for j in range(len(wtr[i])):
+                coeffs[(i, j)] = abs(wtr[i][j])
+
+        sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1),
+                               reverse=True)
+
+        wtr_copy = np.copy(wtr)
+
+        for k in range(n, len(sorted_coeffs)):
+            i = sorted_coeffs[k][0][0]
+            j = sorted_coeffs[k][0][1]
+
+            wtr_copy[i][j] = 0.
+
+        return wtr_copy
+
 
 class GRCWavelets(object):
-	"""
-		Gavish's wavelet transform.
-	"""
-	def name(self):
-		return "GWT"
+    """
+        Gavish's wavelet transform.
+    """
 
-	def set_graph(self, _G):
-		"""
-		"""
-		self.G = _G
-		(self.tree, self.ind) = ratio_cut_hierarchy(self.G)
+    def __init__(self, method='lobpcg'):
+        # Method for Fiedler vector computation
+        self.method = method
 
-	def transform(self, F):
-		"""
-		"""
-		return gavish_wavelet_transform(self.tree, self.ind, self.G, F)
+    def name(self):
+        return "GWT"
 
-	def inverse(self, wtr):
-		"""
-		"""
-		return gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr)
+    def set_graph(self, G):
+        self.G = G
+        (self.tree, self.ind) = gsp.ratio_cut_hierarchy(self.G, self.method)
 
-	def drop_frequency(self, wtr, n):
-		"""
-			Keeps only the n top-energy coefficients of wtr.
-			Input:
-				* wtr: transform
-				* n: number of coefficients
-			Output:
-				* wtr_copy: changed transform
-		"""
-		coeffs = {}
-		for i in range(len(wtr)):
-			coeffs[i] = abs(wtr[i])
+    def transform(self, F):
+        return gsp.gavish_wavelet_transform(self.tree, self.G, F)
 
-		sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True)
+    def inverse(self, wtr):
+        return gsp.gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr)
 
-		wtr_copy = numpy.copy(wtr)
+    def drop_frequency(self, wtr, n):
+        """
+            Keeps only the n top-energy coefficients of wtr.
+            Input:
+                * wtr: transform
+                * n: number of coefficients
+            Output:
+                * wtr_copy: changed transform
+        """
+        coeffs = {i: abs(wtr[i]) for i in range(len(wtr))}
+        sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1),
+                               reverse=True)
 
-		for k in range(n, len(sorted_coeffs)):
-			i = sorted_coeffs[k][0]
+        wtr_copy = np.copy(wtr)
 
-			wtr_copy[i] = 0.0
+        for k in range(n, len(sorted_coeffs)):
+            i = sorted_coeffs[k][0]
 
-		return wtr_copy
+            wtr_copy[i] = 0.
 
+        return wtr_copy
 
-class OptWavelets(object):
-	"""
-		Sparse wavelet transform.
-	"""
-	def __init__(self, n=0):
-		"""
-		"""
-		self.n = n
-	
-	def name(self):
-		"""
-		"""
-		if self.n == 0:
-			return "SWT"
-		else:
-			return "FSWT"
-
-	def set_graph(self, _G):
-		self.G = _G
-
-	def transform(self, F):
-		"""
-		"""
-		self.F = F
-		return None
-
-	def inverse(self, wtr):
-		"""
-		"""
-		return gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr)
-
-	def drop_frequency(self, wtr, n):
-		coeffs = {}
-		
-		k = n
-		#Computing optimal basis
-		(self.tree, self.ind, s) =  optimal_wavelet_basis(self.G, self.F, k, self.n)
-		
-		#Gavish's wavelet transform
-		tr = gavish_wavelet_transform(self.tree, self.ind, self.G, self.F)
-		
-		for i in range(len(tr)):
-			coeffs[i] = abs(tr[i])
-
-		sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1), reverse=True)
-
-		wtr_copy = numpy.copy(tr)
-
-		#Computing number of integers required to represent the edges cut (rounded)
-		v = n - int(math.ceil(float(s * math.log2(len(self.G.edges()))) / 64))
-		
-		for k in range(v, len(sorted_coeffs)):
-			i = sorted_coeffs[k][0]
-
-			wtr_copy[i] = 0.0
-
-		return wtr_copy
 
+class OptWavelets(object):
+    """
+        Sparse wavelet transform.
+    """
+
+    def __init__(self, n=0, method='lobpcg'):
+        self.n = n
+        # Method for Fiedler vector computation
+        self.method = method
+
+    def name(self):
+        if self.n == 0:
+            return "SWT"
+        else:
+            return "FSWT"
+
+    def set_graph(self, G):
+        self.G = G
+
+    def transform(self, F):
+        self.F = F
+        return None
+
+    def inverse(self, wtr):
+        return gsp.gavish_wavelet_inverse(self.tree, self.ind, self.G, wtr)
+
+    def drop_frequency(self, wtr, n):
+        # The number of edges to be cut is set equal to the number of
+        # Chebyshev polynomials
+        k = n
+        # Computing optimal basis
+        (self.tree, self.ind, size) = \
+            oc.optimal_wavelet_basis(self.G, self.F, k,
+                                     self.n, self.method)
+        # Gavish's wavelet transform
+        tr = gsp.gavish_wavelet_transform(self.tree, self.G, self.F)
+
+        coeffs = {i: abs(tr[i]) for i in range(len(tr))}
+        sorted_coeffs = sorted(coeffs.items(), key=operator.itemgetter(1),
+                               reverse=True)
+
+        wtr_copy = np.copy(tr)
+
+        # Computing number of integers required to represent the
+        # edges cut (rounded)
+        v = n - int(math.ceil(float(size *
+                                    math.log(len(self.G.edges()), 2)) / 64))
+
+        for k in range(v, len(sorted_coeffs)):
+            i = sorted_coeffs[k][0]
+
+            wtr_copy[i] = 0.
+
+        return wtr_copy
diff --git a/lib/syn.py b/lib/syn.py
index f5b01f0..c31aed1 100644
--- a/lib/syn.py
+++ b/lib/syn.py
@@ -1,262 +1,107 @@
-import networkx
 import math
-import scipy.optimize
-import numpy
-import sys
-from scipy import linalg
-import matplotlib.pyplot as plt
-from IPython.display import Image
-import pywt
-import scipy.fftpack
 import random
-import operator
-import copy
-from collections import deque
-from sklearn.preprocessing import normalize
-from sklearn.cluster import SpectralClustering
 
-import random
-
-def synthetic_graph(size, num_edges, sparsity, energy, balance, noise):
-	size_part_a = int(math.ceil(float(size * balance) / 2))
-	size_part_b = size - size_part_a
-	F = []
-	edges = {}
-    
-	avg_a = float(numpy.sqrt(float(energy * size) / (size_part_a * size_part_b))) / 2.
-    
-	avg_b = -float(numpy.sqrt(float(energy * size) / (size_part_a * size_part_b))) / 2.
-    
-	for v in range(size):
-		if v < size_part_a:
-			F.append(random.gauss(avg_a, noise*avg_a))
-		else:
-			F.append(random.gauss(avg_b, noise*avg_a))
-    
-	G = networkx.Graph()
-    
-	for v in range(size-1):
-		G.add_edge(v,v+1)
-		edges[(v,v+1)] = True
-        
-	remaining_edges = num_edges - len(G.edges())
-	edges_accross = int((size_part_a * size_part_b * (1.-sparsity) * remaining_edges) / (size * (size-1)))
-	edges_within = remaining_edges - edges_accross
-    
-	for e in range(edges_accross):
-		v1 = random.randint(0, size_part_a-1)
-		v2 = random.randint(size_part_a, size-1)
-                     
-		while (v1,v2) in edges or v1 == v2:
-			v1 = random.randint(0,size_part_a-1)
-			v2 = random.randint(size_part_a, size_part_a+size_part_b-1)
-            
-		G.add_edge(v1,v2)
-		edges[(v1,v2)] = True
-        
-	for e in range(edges_within):
-		v1 = random.randint(0,size-1)
-		v2 = random.randint(0,size-1)
-        
-		if v1 > v2:
-			tmp = v1
-			v1 = v2
-			v2 = tmp
-        
-		while (v1,v2) in edges or v1 == v2 or (v1 < size_part_a and v2 >= size_part_a) or (v1 >= size_part_a and v2 < size_part_a):
-			v1 = random.randint(0,size-1)
-			v2 = random.randint(0,size-1)
-            
-			if v1 > v2:
-				tmp = v1
-				v1 = v2
-				v2 = tmp
-             
-		G.add_edge(v1,v2)
-		edges[(v1,v2)] = True
-        
-	return G, numpy.array(F), edges_accross+1
-
-def compute_distances(center, graph):
-	distances = networkx.shortest_path_length(graph, center)
-    
-	return distances
-
-def compute_embedding(distances, radius, graph):
-	B = []
-	s = 0
-	nodes = {}
-	for v in graph.nodes():
-		if distances[v] <= radius:
-			B.append(1)
-			s = s + 1
-		else:
-			B.append(0)
-            
-	return numpy.array(B)
-
-def generate_dyn_cascade(G, diam, duration, n):
-	Fs = []
-    
-	for j in range(n):
-		v = random.randint(0, len(G.nodes())-1)
-		distances = compute_distances(G.nodes()[v], G)
-
-		if diam > duration:
-			num_snaps = diam
-		else:
-			num_snaps = duration
-     
-		for i in range(num_snaps):
-			r = int(i * math.ceil(float(diam)/duration))
-        
-			F = compute_embedding(distances, r, G)
-			Fs.append(F)
-        
-	return numpy.array(Fs)
-
-def generate_dyn_heat(G, s, jump, n):
-	Fs = []
-	L = networkx.normalized_laplacian_matrix(G)
-	L = L.todense()
-	F0s = []	
-	seeds = []
-
-	for i in range(s):
-		F0 = numpy.zeros(len(G.nodes()))
-		v = random.randint(0, len(G.nodes())-1)
-		seeds.append(v)
-		F0[v] = len(G.nodes())
-		F0s.append(F0)
-
-	Fs.append(numpy.sum(F0s, axis=0))
-
-	for j in range(n):
-		FIs = []
-		for i in range(s):
-			FI = numpy.multiply(linalg.expm(-j*jump*L), F0s[i])[:,seeds[i]]
-			FIs.append(FI)
-		
-		Fs.append(numpy.sum(FIs, axis=0))
-
-	return numpy.array(Fs)[1:]
-
-def generate_dyn_gaussian_noise(G, n):
-	Fs = []
-	
-	for j in range(n):
-		F = numpy.random.rand(len(G.nodes()))
-		Fs.append(F)
-
-	return numpy.array(Fs)
-
-def generate_dyn_bursty_noise(G, n):
-	Fs = []
-	bursty_beta = 1
-	non_bursty_beta = 1000
-	bursty_bursty = 0.7
-	non_bursty_non_bursty = 0.9
-	bursty = False
-
-	for j in range(n):
-		r = random.random()
-
-		if not bursty:
-			if r > non_bursty_non_bursty:
-				bursty = True
-		else:
-			if r > bursty_bursty:
-				bursty = False
-
-		if bursty:	
-			F = numpy.random.exponential(bursty_beta, len(G.nodes()))
-		else:
-			F = numpy.random.exponential(non_bursty_beta, len(G.nodes()))
-			
-		Fs.append(F)
-
-	return numpy.array(Fs)
-
-def generate_dyn_indep_cascade(G, s, p):
-	Fs = []
-	
-	seeds = numpy.random.choice(len(G.nodes()), s, replace=False)
-	
-	F0 = numpy.zeros(len(G.nodes()))
-	
-	ind = {}
-	i = 0
-
-	for v in G.nodes():
-		ind[v] = i
-		i = i + 1
-	
-	for s in seeds:
-		F0[s] = 2.0
-
-	while True:
-		F1 = numpy.zeros(len(G.nodes()))
-		new_inf = 0
-		for v in G.nodes():
-			if F0[ind[v]] > 1.0:
-				for u in G.neighbors(v):
-					r = random.random()
-					if r <= p and F0[ind[u]] < 1.0:
-						F1[ind[u]] = 2.0
-						new_inf = new_inf + 1
-				F1[ind[v]] = 1.0
-				F0[ind[v]] = 1.0
-			elif F0[ind[v]] > 0.0:
-				F1[ind[v]] = 1.0
-		
-		Fs.append(F0)
-		
-		if new_inf == 0 and len(Fs) > 1:
-			break
-
-		F0 = numpy.copy(F1)
-	
-	return numpy.array(Fs)
-
-def generate_dyn_linear_threshold(G, s):
-	Fs = []
-	
-	seeds = numpy.random.choice(len(G.nodes()), s, replace=False)
-	
-	F0 = numpy.zeros(len(G.nodes()))
-	thresholds = numpy.random.uniform(0.0,1.0,len(G.nodes()))
-	
-	ind = {}
-	i = 0
-
-	for v in G.nodes():
-		ind[v] = i
-		i = i + 1
-	
-	for s in seeds:
-		F0[s] = 1.0
-
-	while True:
-		F1 = numpy.zeros(len(G.nodes()))
-		new_inf = 0
-		for v in G.nodes():
-			if F0[ind[v]] < 1.0:
-				n = 0			
-				for u in G.neighbors(v):
-					if F0[ind[u]] > 0:
-						n = n + 1
-				
-				if (float(n) / len(G.neighbors(v))) >= thresholds[ind[v]]:
-					F1[ind[v]] = 1.0
-					new_inf = new_inf + 1
-			else:
-				F1[ind[v]] = 1.0					
-	
-		Fs.append(F0)
-		
-		if new_inf == 0 and len(Fs) > 1:
-			break
-
-		F0 = numpy.copy(F1)
-	
-	return numpy.array(Fs)
+import networkx as nx
+import numpy as np
+
+
+def synthetic_graph(size, num_edges, sparsity, energy, balance, noise,
+                    seed=None):
+    r"""
+        Build a synthetic connected graph and a graph signal starting from two
+        sets of vertices. These sets have sizes depending on size and balance.
+        Input:
+            * size: number of vertices
+            * num_edges: number of edges
+            * sparsity: higher values penalize the creation of edges
+                having vertices in different sets
+            * energy: is the coefficient's energy obtained from the generated
+                graph signal and using as partitions the two vertex sets
+            * balance: takes values in (0,2). If 1 the two sets from which the
+                synthetic graph is built have similar size.
+            * noise: part of the std dev used for generating the noisy signal
+            * seed: optional seed for the random module
+        Output:
+            * G: connected graph
+            * np.array(F): graph signal
+            * edges_accross + 1: number of edges having vertices in different
+                sets
+    """
+    if seed:
+        random.seed(seed)
+
+    if balance <= 0 and balance >= 2:
+        raise ValueError("'balance' should be in (0,2)")
+
+    if num_edges < size:
+        raise ValueError("The graph returned by synthetic_graph() is assumed "
+                         + "to be connected: choose num_edges >= size - 1")
+
+    size_part_a = int(math.ceil(size * balance / 2.))
+    size_part_b = size - size_part_a
+
+    if size_part_a == 0:
+        raise ValueError("'size_part_a'==0 try bigger values " +
+                         "for 'size' or 'balance'")
+    if size_part_b == 0:
+        raise ValueError("'size_part_b'==0 try a bigger value " +
+                         "for 'size' or a smaller value for 'balance'")
+
+    avg_a = np.sqrt(float(energy * size) /
+                    (size_part_a * size_part_b)) / 2.
+    avg_b = - avg_a
+
+    F = []
+    for v in range(size):
+        if v < size_part_a:
+            F.append(random.gauss(avg_a, noise * avg_a))
+        else:
+            F.append(random.gauss(avg_b, noise * avg_a))
+
+    G = nx.Graph()
+
+    edges_set = set({})
+    for v in range(size - 1):
+        G.add_edge(v, v + 1)
+        edges_set.add((v, v + 1))
+
+    remaining_edges = num_edges - len(G.edges())
+    edges_accross = int((size_part_a * size_part_b * (1. - sparsity)
+                         * remaining_edges) / (size * (size - 1)))
+    edges_within = remaining_edges - edges_accross
+
+    for e in range(edges_accross):
+        v1 = random.randint(0, size_part_a - 1)
+        v2 = random.randint(size_part_a, size - 1)
+
+        while (v1, v2) in edges_set or v1 == v2:
+            v1 = random.randint(0, size_part_a - 1)
+            v2 = random.randint(size_part_a, size_part_a + size_part_b - 1)
+
+        G.add_edge(v1, v2)
+        edges_set.add((v1, v2))
+
+    for e in range(edges_within):
+        v1 = random.randint(0, size - 1)
+        v2 = random.randint(0, size - 1)
+
+        if v1 > v2:
+            tmp = v1
+            v1 = v2
+            v2 = tmp
+
+        while ((v1, v2) in edges_set or v1 == v2 or
+                (v1 < size_part_a and v2 >= size_part_a) or
+                (v1 >= size_part_a and v2 < size_part_a)):
+            v1 = random.randint(0, size - 1)
+            v2 = random.randint(0, size - 1)
+
+            if v1 > v2:
+                tmp = v1
+                v1 = v2
+                v2 = tmp
+
+        G.add_edge(v1, v2)
+        edges_set.add((v1, v2))
+
+    return G, np.array(F), edges_accross + 1
diff --git a/lib/vis.py b/lib/vis.py
index b5e9557..df29f97 100644
--- a/lib/vis.py
+++ b/lib/vis.py
@@ -1,197 +1,321 @@
-import networkx
-import math
-import scipy.optimize
-import numpy
-import sys
-from scipy import linalg
-import matplotlib.pyplot as plt
-from IPython.display import Image
-import pywt
-import scipy.fftpack
-import random
-import operator
-import copy
-from collections import deque
-from sklearn.preprocessing import normalize
-from sklearn.cluster import SpectralClustering
 import os
 
-def set_f(G,F,ids=None):
-	if ids is None:
-		i = 0
-		for v in G.nodes():
-			G.node[v]["value"] = F[i]
-			i = i + 1
-	else:
-		i = 0
-		for v in range(len(ids)):
-			G.node[ids[v]]["value"] = F[i]
-			i = i + 1
-
-def get_f(G):
-	F = []
-	for v in G.nodes():
-		F.append(G.node[v]["value"])
-	
-	return numpy.array(F)
-
-def rgb_to_hex(r,g,b):
-	return '#%02x%02x%02x' % (r,g,b)
+import numpy as np
+import networkx as nx
+import scipy
+
+import lib.optimal_cut as oc
+
+
+def add_signal_to_graph(G, F, identifiers=None):
+    r"""
+        Assign the signal to the graph's nodes.
+        Note that G.node[v] returns a dict.
+        Input:
+        * G: networkx graph
+        * F: graph signal
+        * identifiers: list of node identifiers. It's used to guarantee that
+            the graph signal values are assigned to the right nodes
+    """
+    if identifiers is None:
+        for i, v in enumerate(G.nodes()):
+            G.node[v]["value"] = F[i]
+    else:
+        for i, v in enumerate(identifiers):
+            G.node[v]["value"] = F[i]
+
+
+def get_signal_from_graph(G):
+    r"""
+        Get the signal along with the list of nodes identifiers
+    """
+    F = []
+    identifiers = []
+    for v in G.nodes():
+        identifiers.append(v)
+        F.append(G.node[v]["value"])
+
+    return np.array(F), identifiers
+
+
+def rgb_to_hex(r, g, b):
+    r"""
+        Convert integer numbers to hexadecimal format.
+        Input range for r, g, b: [0 - 255]
+        Examples:
+            black: (0, 0, 0)       -> #000000
+            white: (255, 255, 255) -> #ffffff
+    """
+    return '#%02x%02x%02x' % (r, g, b)
 
-def rgb(minimum, maximum, value):
-	mi, ma = float(minimum), float(maximum)
-	ratio = 2 * (value-mi) / (ma - mi)
-	b = int(max(0, 255*(1 - ratio)))
-	r = int(max(0, 255*(ratio - 1)))
-	g = 255 - b - r
-	
-	return rgb_to_hex(r, g, b)
-
-def draw_graph_with_values(G, dot_output_file_name, maximum=None, minimum=None):
-	output_file = open(dot_output_file_name, 'w')
-	output_file.write("graph G{\n")
-	output_file.write("rankdir=\"LR\";\n")
-	output_file.write("size=\"10,2\";\n")
-
-	if maximum is None:
-		maximum = -sys.float_info.max
-		minimum = sys.float_info.max
-
-		for v in G.nodes():
-			if G.node[v]["value"] > maximum:
-				maximum = G.node[v]["value"]
-	     
-			if G.node[v]["value"] < minimum:
-		 		minimum = G.node[v]["value"]
-
-	for v in G.nodes():
-		color = rgb(minimum, maximum, G.node[v]["value"])
-		if G.node[v]["value"] != 0.0:
-			output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"2\",fixedsize=true,width=\"1\",height=\"1\"];\n")
-		else:
-			output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"0\",fixedsize=true,width=\"1\",height=\"1\"];\n")
-
-	for edge in G.edges():
-		output_file.write("\""+str(edge[0])+"\" -- \""+str(edge[1])+"\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n")
-
-	
-	output_file.write("}")
-
-	output_file.close()
-
-def draw_graph_dynamic_values(G, FT, fig_output_file_name):
-	maximum = -sys.float_info.max
-	minimum = sys.float_info.max
-
-	for i in range(FT.shape[0]):
-		for j in range(FT.shape[1]):
-			if FT[i][j] > maximum:
-				maximum = FT[i][j]
-			
-			if FT[i][j] < minimum:
-				minimum = FT[i][j]
-
-	svg_names = ""
-
-	for i in range(FT.shape[0]):
-		set_f(G, FT[i])
-		#draw_graph_with_values(G, "dyn_graph-"+str(i)+".dot", maximum, minimum)
-		draw_graph_with_values(G, "dyn_graph-"+str(i)+".dot")
-		os.system("sfdp -Goverlap=prism -Tsvg dyn_graph-"+str(i)+".dot > dyn_graph-"+str(i)+".svg")
-		os.system("rm dyn_graph-"+str(i)+".dot")
-		svg_names = svg_names + " dyn_graph-"+str(i)+".svg"
-		
-	os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 "+svg_names+" > "+fig_output_file_name)
-
-	for i in range(FT.shape[0]):
-		os.system("rm dyn_graph-"+str(i)+".svg")
-	
-def draw_partitions_with_values(G, partitions, dot_output_file_name, maximum=None, minimum=None):
-	output_file = open(dot_output_file_name, 'w')
-	output_file.write("graph G{\n")
-	output_file.write("rankdir=\"LR\";\n")
-	output_file.write("size=\"10,2\";\n")
-
-	if maximum is None:
-		maximum = -sys.float_info.max
-		minimum = sys.float_info.max
-
-		for v in G.nodes():
-			if G.node[v]["value"] > maximum:
-				maximum = G.node[v]["value"]
-	     
-			if G.node[v]["value"] < minimum:
-		 		minimum = G.node[v]["value"]
-	
-	part_map = {}
-
-	for p in range(len(partitions)):
-		for i in range(len(partitions[p])):
-			part_map[partitions[p][i]] = p
-
-	for v in G.nodes():
-		color = rgb(minimum, maximum, G.node[v]["value"])
-		if G.node[v]["value"] != 0.0:
-			output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"2\",fixedsize=true,width=\"0.5\",height=\"0.5\"];\n")
-		else:
-			output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"0\",fixedsize=true,width=\"0.5\",height=\"0.5\"];\n")
-
-	for edge in G.edges():
-		if part_map[edge[0]] == part_map[edge[1]]:
-			output_file.write("\""+str(edge[0])+"\" -- \""+str(edge[1])+"\"[dir=\"none\",color=\"black\",penwidth=\"4\"];\n")
-		else:
-			output_file.write("\""+str(edge[0])+"\" -- \""+str(edge[1])+"\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n")
-	
-	output_file.write("}")
-
-	output_file.close()
-
-def draw_graph(G, dot_output_file_name):
-	output_file = open(dot_output_file_name, 'w')
-	output_file.write("graph G{\n")
-	output_file.write("rankdir=\"LR\";\n")
-	output_file.write("size=\"10,2\";\n")
-
-	for v in G.nodes():
-		color = rgb_to_hex(0,255,0)
-		output_file.write("\""+str(v)+"\" [shape=\"circle\",label=\"\",style=filled,fillcolor=\""+str(color)+"\",penwidth=\"2\",fixedsize=true,width=\"1\",height=\"1\"];\n")
-
-	for edge in G.edges():
-		output_file.write("\""+str(edge[0])+"\" -- \""+str(edge[1])+"\"[dir=\"none\",color=\"black\",penwidth=\"1\"];\n")
-
-	
-	output_file.write("}")
-
-	output_file.close()
-
-def draw_time_graph(G, fig_output_file_name):
-	svg_names = ""
-
-	for i in range(G.num_snaps()):
-		draw_graph(G.snap(i), "graph-"+str(i)+".dot")
-		os.system("sfdp -Goverlap=prism -Tsvg graph-"+str(i)+".dot > graph-"+str(i)+".svg")
-		os.system("rm graph-"+str(i)+".dot")
-		svg_names = svg_names + " graph-"+str(i)+".svg"
-		
-	os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 "+svg_names+" > "+fig_output_file_name)
-
-	for i in range(G.num_snaps()):
-		os.system("rm graph-"+str(i)+".svg")
-
-def draw_time_graph_eig(G, eig, fig_output_file_name):
-	svg_names = ""
-	G.set_values(eig)
-	maximum = numpy.max(eig)
-	minimum = numpy.min(eig)
-
-	for i in range(G.num_snaps()):
-		draw_graph_with_values(G.snap(i),"graph-"+str(i)+".dot", minimum, maximum)
-		os.system("sfdp -Goverlap=prism -Tsvg graph-"+str(i)+".dot > graph-"+str(i)+".svg")
-		os.system("rm graph-"+str(i)+".dot")
-		svg_names = svg_names + " graph-"+str(i)+".svg"
-		
-	os.system("python lib/svg_stack-master/svg_stack.py --direction=v --margin=0 "+svg_names+" > "+fig_output_file_name)
-
-	for i in range(G.num_snaps()):
-		os.system("rm graph-"+str(i)+".svg")
 
+def rgb(minimum, maximum, value):
+    r"""
+        Map 'value', from the range [minimum - maximum], to an RGB tuple. The
+        output color scale covers a subset of the RGB spectrum
+        Examples:
+            value = maximum                      -> red     : #ff0000
+            value = 0.5 * (maximum - minimum)    -> green   : #00ff00
+            value = minimum                      -> blue    : #0000ff
+    """
+    minimum, maximum = float(minimum), float(maximum)
+    ratio = 2 * (value - minimum) / (maximum - minimum)
+    b = int(max(0, 255 * (1 - ratio)))
+    r = int(max(0, 255 * (ratio - 1)))
+    g = 255 - b - r
+
+    return rgb_to_hex(r, g, b)
+
+
+def quote(s):
+    return '"' + str(s) + '"'
+
+
+def graph_to_dot(G, dot_output_file_name, nodes_color=(0, 255, 0)):
+    r"""
+        Write the graph description in .dot format to a file for visualization
+        The graph doesn't need to have an attached signal
+        Input:
+            * G : networkx graph
+            * dot_output_file_name : name of the output file (.dot format)
+            * nodes_color : tuple of RGB coordinates (default is green)
+    """
+    output_file = open(dot_output_file_name, 'w')
+    output_file.write('graph G{\n')
+    output_file.write('rankdir="LR";\n')
+    output_file.write('size=\"10,2\";\n')
+
+    for v in G.nodes():
+        color = rgb_to_hex(*nodes_color)
+        output_file.write(quote(v) + '[shape=circle, label="", '
+                          'style=filled, fillcolor=' + quote(color) + ', '
+                          'penwidth=2, fixedsize=true, width=1, height=1]; \n')
+
+    for edge in G.edges():
+        output_file.write(quote(edge[0]) + ' -- ' + quote(edge[1]) +
+                          ' [penwidth=1];\n')
+
+    output_file.write("}")
+
+    output_file.close()
+
+
+def graph_with_values_to_dot(G, dot_output_file_name, maximum=None,
+                             minimum=None, draw_zero_valued_nodes=False):
+    r"""
+        Write the graph description in .dot format to a file for visualization
+        The graph needs to have an attached signal
+        Input:
+            * G : networkx graph with values (attached graph signal)
+            * dot_output_file_name : name of the output file (.dot format)
+            * minimum, maximum : are passed as arguments to the rgb() function
+            * draw_zero_valued_nodes : if False, set penwidth = 0 for nodes
+                with value 0
+    """
+
+    output_file = open(dot_output_file_name, 'w')
+    output_file.write("graph G{\n")
+    output_file.write("rankdir=LR;\n")
+    output_file.write("size=\"10,2\";\n")
+
+    if maximum is None:
+        maximum = max([G.node[v]["value"] for v in list(G.nodes())])
+    if minimum is None:
+        minimum = min([G.node[v]["value"] for v in list(G.nodes())])
+
+    for v in G.nodes():
+        color = rgb(minimum, maximum, G.node[v]["value"])
+
+        msg = (quote(v) + ' [shape=circle, label="", ' +
+               'style=filled, fillcolor=' + quote(color) + ', ' +
+               'penwidth={}, fixedsize=true, ' +
+               'width=1, height=1]; \n')
+
+        if G.node[v]["value"] != 0.0 or draw_zero_valued_nodes:
+            output_file.write(msg.format(2))
+        else:
+            output_file.write(msg.format(0))
+
+    for edge in G.edges():
+        output_file.write(quote(edge[0]) + ' -- ' + quote(edge[1]) + ' ' +
+                          '[penwidth=1];\n')
+
+    output_file.write("}")
+
+    output_file.close()
+
+
+def dyn_graph_with_values_to_svg(G, FT, fig_output_file_name,
+                                 path_to_svg_stack, fixed_color_scale=False,
+                                 maximum=None, minimum=None,
+                                 draw_zero_valued_nodes=False):
+    r"""
+        Create an .svg file of a dynamic graph. It stacks the various
+        graph snapshots vertically. The graph signal change with time
+        Input:
+            * G : networkx graph
+            * FT : temporal graph signal
+            * fig_output_file_name : name of the output file (.svg format)
+            * path_to_svg_stack : path to svg_stack.py, clone it from
+                https://github.com/astraw/svg_stack
+            * fixed_color_scale : if True the same maximum and minimum are used
+                for every intermediate figure
+            * minimum, maximum : are passed as arguments to the rgb() function.
+                Not used if fixed_color_scale is set to False
+            * draw_zero_valued_nodes : if False, set penwidth = 0 for nodes
+                with value 0
+    """
+
+    if fixed_color_scale:
+        if maximum is None:
+            maximum = max([max(FT[i]) for i in range(FT.shape[0])])
+        if minimum is None:
+            minimum = min([min(FT[i]) for i in range(FT.shape[0])])
+
+    svg_names = ''
+
+    for i in range(FT.shape[0]):
+        add_signal_to_graph(G, FT[i])
+
+        dot_file_name = "dyn_graph-" + str(i) + ".dot"
+        svg_file_name = "dyn_graph-" + str(i) + ".svg"
+
+        graph_with_values_to_dot(G, dot_file_name, maximum, minimum,
+                                 draw_zero_valued_nodes)
+
+        # Use Scalable Force Directed Placement (sfdp), a fast multilevel force
+        # directed algorithm to layout very large graphs with high quality.
+        # It is available as part of the graphviz software.
+        os.system('sfdp - Goverlap=prism - Tsvg ' + dot_file_name + ' > ' +
+                  svg_file_name + ' ; rm ' + dot_file_name)
+        svg_names += ' ' + svg_file_name
+
+    # Stack all the previously created svg images vertically with no margin
+    # in between the figures
+    os.system("python " + path_to_svg_stack + " --direction=v --margin=0 " +
+              svg_names + " > " + fig_output_file_name)
+
+    for i in range(FT.shape[0]):
+        os.system("rm dyn_graph-" + str(i) + ".svg")
+
+
+def partitions_with_values_to_dot(G, partitions, dot_output_file_name,
+                                  maximum=None, minimum=None,
+                                  draw_zero_valued_nodes=False):
+    r"""
+        Create a .DOT file of a graph. The nodes within the same partition
+        are connected by edges with thicker penwidth (4 vs 1).
+        The graph doesn't need to have an attached signal
+        Input:
+            * G : networkx graph with values (attached graph signal)
+            * dot_output_file_name : name of the output file (.dot format)
+            * minimum, maximum : are passed as arguments to the rgb() function
+            * draw_zero_valued_nodes : if False, set penwidth = 0 for nodes
+                with value 0
+    """
+    output_file = open(dot_output_file_name, 'w')
+    output_file.write('graph G{\n')
+    output_file.write('rankdir=LR;\n')
+    output_file.write('size="10,2";\n')
+
+    if maximum is None:
+        maximum = max([G.node[v]["value"] for v in G.nodes])
+    if minimum is None:
+        minimum = min([G.node[v]["value"] for v in G.nodes])
+
+    part_map = {}
+
+    for p in range(len(partitions)):
+        for i in range(len(partitions[p])):
+            part_map[partitions[p][i]] = p
+
+    for v in G.nodes():
+        color = rgb(minimum, maximum, G.node[v]["value"])
+        msg = (quote(v) + '[shape=circle, label="", style=filled, fillcolor=' +
+               quote(color) + ', penwidth={}, fixedsize=true, width=0.5, '
+               'height=0.5]; \n')
+        if G.node[v]["value"] != 0.0 or draw_zero_valued_nodes:
+            output_file.write(msg.format(2))
+        else:
+            output_file.write(msg.format(0))
+
+    for edge in G.edges():
+        if part_map[edge[0]] == part_map[edge[1]]:
+            output_file.write(quote(edge[0]) + ' -- ' + quote(edge[1]) +
+                              ' [penwidth=4];\n')
+        else:
+            output_file.write(quote(edge[0]) + ' -- ' + quote(edge[1]) +
+                              ' [penwidth=1];\n')
+
+    output_file.write("}")
+
+    output_file.close()
+
+
+def time_graph_to_svg(G, fig_output_file_name, path_to_svg_stack,
+                      nodes_color=(0, 255, 0)):
+    r"""
+        Create an .svg file of a dynamic graph. It stacks the various
+        graph snapshots vertically. The edges change with time
+        Input:
+            * G : the graph has an associated list of snapshots (G.snap).
+                Each of them is a networkx graph. They all have the same
+                number of nodes and do not need to have an attached signal.
+                The associated edges can vary
+            * fig_output_file_name : name of the output file (.svg format)
+            * path_to_svg_stack : path to svg_stack.py, clone it from
+                https://github.com/astraw/svg_stack
+            * nodes_color : tuple of RGB coordinates (default is green)
+    """
+
+    svg_names = ''
+
+    for i in range(G.num_snaps()):
+        snap_dot_file_name = "graph-" + str(i) + ".dot"
+        snap_svg_file_name = "graph-" + str(i) + ".svg"
+        graph_to_dot(G.snap(i), snap_dot_file_name, nodes_color)
+
+        # Use Scalable Force Directed Placement (sfdp), a fast multilevel force
+        # directed algorithm to layout very large graphs with high quality.
+        # It is available as part of the graphviz software.
+        os.system("sfdp -Goverlap=prism -Tsvg " + snap_dot_file_name +
+                  "> " + snap_svg_file_name + " ; rm " + snap_dot_file_name)
+        svg_names += " " + snap_svg_file_name
+
+    # Stack all the previously created svg images vertically with no margin
+    # in between the figures
+    os.system("python " + path_to_svg_stack + " --direction=v --margin=0 " +
+              svg_names + " > " + fig_output_file_name)
+
+    for i in range(G.num_snaps()):
+        os.system("rm graph-" + str(i) + ".svg")
+
+
+def eig_vis_opt(G, F, beta):
+    """
+        Computes first and second eigenvector of sqrt(C+beta*L)^T CAC
+        sqrt(C+beta*L) matrix for visualization.
+        Input:
+            * G: graph
+            * F: graph signal
+            * beta: regularization parameter
+        Output:
+            * v1: first eigenvector
+            * v2: second eigenvector
+    """
+
+    ind = {v: i for i, v in enumerate(G.nodes())}
+
+    C = oc.complete_graph_laplacian(nx.number_of_nodes(G))
+    A = oc.weighted_adjacency_complete(G, F, ind)
+    CAC = np.dot(np.dot(C, A), C)
+    L = nx.laplacian_matrix(G).todense()
+
+    isqrtCL = oc.sqrtmi(C + beta * L)
+    M = np.dot(np.dot(isqrtCL, CAC), isqrtCL)
+
+    (eigvals, eigvecs) = scipy.linalg.eigh(M, eigvals=(0, 1))
+    v1 = np.asarray(np.dot(eigvecs[:, 0], isqrtCL))[0, :]
+    v2 = np.asarray(np.dot(eigvecs[:, 1], isqrtCL))[0, :]
+
+    return v1, v2
diff --git a/notebooks/compression-experiments.ipynb b/notebooks/compression-experiments.ipynb
new file mode 100644
index 0000000..68824fa
--- /dev/null
+++ b/notebooks/compression-experiments.ipynb
@@ -0,0 +1,785 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compression Experiments using Real Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import sys\n",
+    "\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "sys.path.append('../')\n",
+    "\n",
+    "import lib.io_utils as io\n",
+    "import lib.experiments as exp\n",
+    "import lib.static as static\n",
+    "import lib.datasets as data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Small Traffic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "G = io.read_graph(\"../\" + data.small_traffic[\"path\"] + \"traffic.graph\",\n",
+    "                  \"../\" + data.small_traffic[\"path\"] + \"traffic.data\")\n",
+    "F = io.read_values(\"../\" + data.small_traffic[\"path\"] + \"traffic.data\", G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#vertices =  100\n",
+      "#edges =  128\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"#vertices = \", G.number_of_nodes())\n",
+    "print(\"#edges = \", len(G.edges()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "algs = [static.OptWavelets(n=20), static.OptWavelets(), static.GRCWavelets(),\n",
+    "        static.Fourier(), static.HWavelets()]\n",
+    "\n",
+    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
+    "\n",
+    "res_smt, time_smt = exp.compression_experiment(G, np.array(F),\n",
+    "                                               algs, comp_ratios, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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/FCwecHxbRDqtkC7Pz0/HEsetNFSq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qDB0GkwtH+EwJwMInK/Jj97LWXlrn25YPvJ7Ww/uZ1nvnyGTk06kRiTSEJsAkPbDcXsZ/bwpxAR\nERERERGRxsynQ5rquPttVrvd3ii+EXth5UxZWZnTbZSWll62TZFGpWdP+Mc/4P/+D95/36iu2biR\nUIq5lwXcywL204l5TCWZ6Ryio1PN79kDf/oT/PnPcP31RmAzfjxckJWKeE3z0OZkP37x3mP7S4q5\nZvM3brW9ceAAIsz+bCksYGtBAd9arWwpKOB4WTkERLjVdhXmEMqiRnAqagR0eRRyv4VTazGdWoe9\nPMelJg/kHuCl9S/x0vqXaBXWivE9x5MYm8iozqMINDv/BQgRERERERERkfP5fEhzrnJGnBN+wUYX\nF1bW1MSFlTMXttmQPfDAA9x1111O3bNnzx4mTJjgpRGJzwgNhWnTjNc33xjVNQsWQFERnTnALGbz\nNH9hNaNIYgapJFJCyJVadbDZ4LPPjFfTpnDPPcZyaP37e+0TiQDgZ/KjRViLi84vtx6HwCZutb2r\nIoh7mreiZxOYeN75Y6WlZFitbLJaHf95qrzcrb4c/AKg2WBoNhh7999A/g44tdZ4lRx3qckThSeY\n+81c5n4zl8igSG7tcSsJMQnc2O1GwgLDrtyAiIiIiIiIiMgFfDqkWb16dV0Pod66MFApKipyuoqo\n0LHuf/VtNmQtW7akZcuWdT0M8XUDBsDcufD3v8P8+UZ1zY4d+GFnDKsYwypyieQ9JpLEDDZyjVPN\n5+TAK68Yr379jOqae+6BqCgvfR6Ranydn3/Z97sEB2MH9l/mywBf5+VxT6tWF51vExTEbUFB3Hb2\nf9R2u52DJSVVgpsMq5X8ykq3PgMmM0T2NV5d7ofCvXDySzidDoX7XWoyrzSPBd8vYMH3Cwj2D+aG\nrjeQGJvIrT1upVlIM/fGKyIiIiIiIiKNhk+HNNdee21dD6HeioqKqrKHT3l5OdnZ2bSqZpLsUo4c\nOVLlWKGFyCVERsJDD8GDD8LatUZ1zYcfQnk5Tcjjl8zll8xlO71IZjrzmUI2NX8WAbZuhd/8Bp54\nAm6/3aiuuf568Pfpn+LSEFwupLmvVSte7t4dgId272b+iRNOt3E+k8lEp5AQOoWEcOfZ/8+x2e3s\nLi42gpv8fDKsVr4pKKDYZnPykzg6gfBuxqvzDCg6DKfSjQoba6ZLTZZUlLB452IW71yM2WRmVOdR\nJMQkMCFmAtGWaNfGKSIiIiIiIiKNgl9dD0C8IyQkhA4dOlQ5d+jQIafauPD6mJgYt8cl0qCZTDBy\nJCxcCD/8AM8+C506Od7uzQ5e4Al+oB2LGM94FmGmwqkuysvho4/gllugQwf4wx9g1y4Pfw6Rs+x2\nO0XVVLFEms28ExvLvNhYIvz9ifD3563YWBbGxhJhNl90fWFlpcvLl/qZTPQMDeWeVq34Z/fupA8Y\nQH58PN/FxfFmz57cHx1NnMVCgKv7zYW2hw6TYMCrMOR96PYwNOmPq78iVdor+Xzf5zz46YO0fakt\nQ98cyt/X/Z09Z/a4Nj4RERERERERadAU0jRgF4YqO3bscOr+zMyq3yhWSCPihJYt4cknYe9e+PRT\nuO028DN+5AZQwXg+ZhEJHKEtL/AYvdjudBfHjsFzz0HPnhAfD2++CVarpz+INGYmk4nNcXE80rYt\n5yKQ+MhItg4axN3VVGZOatWKrXFxDI+IMO4HftOuHZvj4pxabvNK/P386Bsezow2bXi1Rw82DRyI\ndcQINg0YwKvduzOjdWv6hoU5/0tOUAtomwD9XoJhqdDz99B8GJgCXB7r+h/W87vPf0f3l7tz1WtX\nMXP1TLYe36o990REREREREQEAJNdswQ+Z82aNYwaNcpx3LFjRw4cOOB0O08++STPP/+84/gXv/gF\nr7/+eo3uPXbsGNHRPy7REhAQwJkzZxrVvjTO2r59O3369HEcb9u2jd69e9fhiMTnHDpk7GHzxhtw\nwbJQdmATg0hiBu8wiXwiXeoiNBTuustYDm3ECKO4R8QT0nNz+To/n0fbtcPf7/LxR4XNxks//MCw\niAjimzSppRFerLCyki0FBY5l0jZZrewqLna+ocpiOLMRTn0JpzdAZeGV77mCzk06kxibSEJMAkPb\nD8XPpO/NiIiIiIiIiNQWX5rLVUjjgzwV0qSnpzNixAjHcZcuXdizZ0+Nvs08b948pk2b5ji+/vrr\nWb58udNjaEx86cEWH1deDosWwWuvwerVF71dTDBpJJDEDFYy1uVuunaFadNg6lRo396N8Yo0IHkV\nFWy2Wh2hzab8fA6Wlta8AVsZ5HwLp9fCqXVQnuv2mFqHt2Z8z/EkxiZyXafrCDQHut2miIiIiIiI\niFyaL83l1tuQ5syZM2RmZnLmzBny8vKw2WzccMMNtKpm+ZX6xlMhjc1mo1WrVpw6dcpxbtWqVVXa\nvpSRI0eydu1ax/F//vMfHnjgAafH0Jj40oMt9UhWFrz+OqSkQO7Fk70H6Mg8ppLCNA7Q2aUuTCYY\nN86orhk/HoKD3RyzSANzsqyMjPODG6uV42VlV77RXgl52+HUWuNVeuLK91xBRFAkt/e4jYTYBG7o\negNhgWFutykiIiIiIiIiVfnSXG69Cmmys7N55ZVX+Oijj8jKyrro/RUrVjB69OiLzicnJ3P48GEA\noqOj+dnPfub1sbrDUyENwBNPPMELL7zgOL722mtZvXr1ZatpVq5cydixP35732KxsG/fPqKiolwa\nQ0OTkpJCSkrKRecLCwvJyMhwHCukEacUFcF778GcObBx40Vv2zCxhutIZjofciclhLjUTdOmMHky\nTJ8OAwZoOTSRSzlSWlplmbQMq5UzFRWXvsFuh4LdcCrdWBat6KDbY/A3BzOo4xgm97qDe3pNoGlI\nU7fbFBERERERERGFNC75+9//ztNPP01ZWVm1m+2aTKZLhjQvv/wyjzzyCCaTCbPZzOHDh3264saT\nIc2pU6fo3LkzBQUFjnPPPvssTz75ZLXXHzlyhPj4+Cr9PfXUUzzzzDMu9d8QzZo1i9mzZ1/xOoU0\n4rLNm42wZuFCI7y5QB4RvMdEkpjBBoa43M1VVxlhzT33QIsW7gxYpOGz2+3sLympskza5oICCior\nq7+h6NDZwGYtWC/+YonTTGZathjMkC63MKlXAte37k6zgAD32xURERERERFphBTSOKGyspK77rqL\nxYsXY7fbMZlMVUKac8eXC2mKioqIjo4mPz8fk8nECy+8wKOPPlqbH6Na69ato7iaDYy3bt3K448/\n7jhu1aoVb7/9drVtREdH06tXr8v28+yzz/LHP/6xyrn777+fp556iujoaMBYGu3jjz/mkUce4dCh\nQ1Xa3759O03qcONnX6NKGqk1eXkwf76xd82OHdVesoNYkpnOfKZwgtYudRMQALfdZiyHdsMN4O/v\nzqBFGg+b3c7OoqIq1TbfFhRQYrNVvbD05I+BTe5WwFZtezVngoheNG89iiFdb2FUm94MsljoHx6O\nRQ+wiIiIiIiIyBUppHHCr371K+bOnQv8GMj079+f66+/ng4dOvDggw863rtUSAMwZcoUFixYgMlk\nYvTo0axYsaLWPsOldOrUiYMH3VsOZerUqdUGBuez2WyMHz+eTz75pMp5s9lMx44diYyMZP/+/eRe\nsB9GSEgIK1asYPjw4W6NsbHwpQdbGhi7HdauNcKajz6C8vKLLinHn8+4kSRm8Am3UoFr37Bv0wam\nTDEqbGJi3B24SONTbrOxo6iITfn5juDmu8JCKs79ulWeB6e/MkKbM5vAfvHz7LSwLhA1EqLiiW3R\nh0EREQyyWBgUEUG/sDCCzWb3+xARERERERFpQHxpLtenQ5r09HRGjhzp2D8lKiqKlJQUbrrpJsc1\nfn5+jvcvF9J89NFH3HXXXQAEBweTm5tLYGCglz/B5dVWSANQUlLC9OnTeffdd2vUbvPmzfnwww+5\n7rrr3BpfY+JLD7Y0YNnZkJQEr78Ol1gGMZsWvM29JDGD7fSp9pqaGDrUqK75yU8gIsLlZkQavZLK\nSr4rLHSENpvy89lRVIS9oghyNsLJtXBmPVRevLyh04KjIWqE8YqIxd/PTN+wMOIsFgZZLMRZLPQJ\nCyPAz8/9vkRERERERETqKV+ay/XpkGb06NGsWbMGgIiICNavX0/MBV/trmlI88MPP9ChQwfAqLrZ\nsmULffv29d7ga6A2Q5pzPvroI/7617+yZcuWat8PCwtj6tSpzJw5k5YtW7o1tsbGlx5saQQqK+F/\n/zOqa5YuhQuXVwLsQAZxJDOdhUwmD9eWLQwNhTvvNKprRo4Eze2KuK+gooJvCwocwc3GvNPsPbrO\nqLA5nW5U3LgrsDlEDYfmI6DJ1eBnLIUWZDJxdXi4o+ImzmKhZ2go5rO/T4mIiIiIiIg0dL40l+uz\nIU1OTg4tW7bEdnbi8fnnn6+yT8s5NQ1pwKgOycnJwWQy8e677zoqaxqjPXv2sGHDBo4cOUJZWRlN\nmjQhNjaW4cOHExwcXNfDq5d86cGWRubQIZg7F954A06cqPaSYoJZxASSmMFKxmDHtaSlSxeYNg2m\nToWzubeIeEhOeTmbrVY25uWy/MCXfHNgGQUnvoDS6p9rp/hboPkQY1m0pnFgrvr/9eFmMwPCwx2h\nzaCICLoEBzt+xxIRERERERFpSHxpLtdnQ5olS5Ywfvx4wNg75eTJk9VuXu9MSBMbG8vOnTsxmUz8\n+9//duxnI+IJvvRgSyNVVgaLFxvVNatXX/Kyg3RgHlNJYRr76eJSVyYTjB1rVNdMmAAhIa4OWkQu\n53hpKe/tS+ejzFS+PfAZBdZ97jfqFwzNBhlLojUfCv7h1V7W1N+/yjJpgywW2gYFKbgRERERERGR\nes+X5nL966TXGjh69ChgLE3WpUuXagMaZ0VGRjr+bbVa3W5PRMSnBAbCXXcZr6wsY9+alBTIza1y\nWUcO8TTP8BR/5QuuJZnpfMidFBNa467sdlixwng1aQKTJhn71wwcaAQ4IuIZrYOCeCR2DI/EjgEg\n82QmKds+5KPMVPaerH7p0iuylcCptcbLZIYmAyAq3ngFNnNcllNRwYqcHFbk5Pw4nsDAi4KbFnW8\nx5+IiIiIiIhIfeazOwucOXPG8e9mzZpd5sqaKy0tdfw7ICDAI22KiPikmBj4xz/gyBFISoJBgy66\nxA87o1jDW0zlGG2Yy88ZyldOd5WbaxTvDBoEV11ldHvypCc+hIhcKLZFLM+P+jN7HviWg785yL9u\n/BfXdbwOP5OLv9LZKyFnE+z+B3x9J3z7azj8PhQfq/by42VlfHL6NDMPHOCW77+n5Vdf0fHrr7lz\n2zaeO3iQlTk55JaXu/EJRURERERERBoXnw1pvFH1kp2d7fh3VFSUR9oUEfFpoaHGmmQbN0JGBvzs\nZ8a5C0SSz895g68Yzg5i+R3P05rqJ2kvZ9s2+O1vIToaEhNhyRKoqPDEBxGRC3WI7MDD1zzM6mmr\nOf7Ycd68/U1u6X4LgWZXK1vskL8N9r0GGyfD5p/DgXlQuN8on7uEQ6WlfHTqFH/Yv5+xW7fSdN06\nemzYwD07dvCPw4dJz82lsLLSxTGJiIiIiIiINGw+uyfNBx98wMSJEwEICwsjLy8PP7+LM6Wa7klz\n+PBhOnbsCBhLqH322WeMGzfOS6OXxsiX1jEUuazcXJg/H+bMgR07LnlZBWY+40aSmMESbqMC1yoQ\nW7eGKVOMrCg21tVBi0hN5Zfms2z3MtKy0li6eykFZQXuNxrS1tjDJmoEWGLAycodP6BXWFiVZdKu\nCg8nqJrf7URERERERES8zZfmcn02pNm1axcxMTGAEaqsWbOGESNGXHRdTUOal156iccffxwAf39/\nTp8+jcVi8dLopSFLSUkhJSXlovOFhYVkZGQ4jhXSiM+z22HtWmOtso8+gsssUXSSKN7mXpKZzvdc\n5XKXQ4YYYc3EiXBewaSIeElJRQkr960kNTOVj3d9zKmiU+43GhgFUcONwCayH/i5tsVhgMnEVWFh\nRmgTEUGcxULv0FD8FdyIiIiIiIiIlymkqaGuXbty4MABAG6//XbS0tIuuqYmIUuoi5YAACAASURB\nVE1+fj69e/fm6NGjAAwZMoR169Z5b+DSoM2aNYvZs2df8TqFNFKvnDgBycnw+utw9ududezAZgaS\nzHQWMplcmrrUXUgI3HEHzJgB114LmpMV8b4KWwXph9JJy0wjNSuVH/J/cL9Rfws0HwZR8dB0EJiD\n3GouxM+P/uHhjmqbOIuFHqGh+J39XU9ERERERETEExTS1NDs2bMdk+Emk4mkpCSmTp1a5ZorhTSV\nlZXccccdfPzxx4523nrrLe65555a+ATSEKmSRhq0ykpYvtyorlm69LL7UJQQxCImkMx0VjAOu4vb\nnHXuDNOmwdSpcHZVShHxMrvdzuZjm0nNTCU1M5Wdp3e636hfMDQbbFTYNB8C/uHutwlYzGYGng1t\nzgU3nYKDHb//iYiIiIiIiDhLIU0NFRYW0rVrV06ePIndbsdsNvP//t//47e//S1msxm4fEiTlZXF\nL3/5S9LT0x3nevTowY4dO/SHvXicLz3YIh5x8CD897/wxhtGpc1lHKI9b3EfyUxnH11d6s5kgjFj\njOXQEhKMahsRqR2ZJzNJy0ojNTOVzcc2u9+gyR+aDDAqbKKGQ2Az99s8T3N//yrLpA2yWIgOcq+K\nR0RERERERBoPX5rL9emQBuCTTz4hISEBm82G3W7HZDLRoUMHJk2axMCBA7nrrrsAo0Lmueeeo3Pn\nzuzZs4dVq1axatUq7HY75z5iSEgI6enp9O/fvy4/kjRQvvRgi3hUWRksWgRz5sDq1Ze91IaJtYwg\niRl8yJ0UEeZSl5GRMGmSEdgMGmQEOCJSOw7mHmRR1iLSstJYe2gtNrvNzRZNENEHWowwqmyCW3tk\nnBeKDgysskxanMVCVGCgV/oSERERERGR+s2X5nJ9PqQBmDt3Lg888ECVwOVcJcz5w7+wOuZcqGO3\n2wkICGD+/Pn85Cc/qb2BS6PiSw+2iNdkZRlhzbx5kJt72UvzsfA+PyGZ6XzFcJe77N3bCGvuvRda\ntXK5GRFxwcnCk3y882NSs1L5fN/nlFWWud2mKbw79qh4I7AJ7eTVFLZTcLAjtBlksTDQYiHC399r\n/YmIiIiIiEj94EtzufUipAFYuXIlU6ZM4fjx41UCmvODmeoCG7vdTqtWrfjggw+Ij4+v3UFLo+JL\nD7aI1xUVwXvvGXvXbNp0xcuz6EkK05jHVI7TxqUu/f3hlltgxgy46SYICHCpGRFxUX5pPp/u/pS0\nrDSW7lpKYXmh221GWjoR0vJaTkZeQ2V4TzC5treVM3qGhFRZJu3q8HBCzy6jKyIiIiIiIo2DL83l\n1puQBiAvL4/XXnuNV155haNHj17x+qZNm/Kb3/yGRx55hIiIiFoYoTRmvvRgi9SqzZuNsGbhQigu\nvuylFZhZzg0kMYMl3EY5ri1F1KoVTJliVNj06uVSEyLihpKKEj7f9zmpmaks3rmYM8Vn3G6zZXg0\nvTpcT3DLazkS3JMdxaVUemCsV2IGeoeF/VhxExFB37AwAv28HxiJiIiIiIhI3fCludx6FdKcY7PZ\n2Lp1K2vXriUzM5PTp0+Tm5tLaGgoUVFRdO7cmVGjRjF48GD8taSF1BJferBF6kRuLsyfbwQ2mZlX\nvPwUzVnAPSQxg+/o53K311xjhDV3323sZSMitavCVsHag2tJzUwlLSuNI9YjbrfZPKQ5N3W/lT6d\nbsTcNI4tRWVkWK3svEIQ7CmBJhP9wsOr7HHTKywMszbIEhERERERaRB8aS63XoY0Ir7Ilx5skTpl\nt8OXXxp713z0EZSXX/5y4Fv6k8QMFjKZHJq51G1wMNxxhxHYjBoF+hK8SO2z2W1kHM0gLTON1KxU\ndp3e5XabYQFh3Nz9ZhJiEojvcgN7ykxsslrZZLWSYbVyoKTEAyO/slA/Pwact79NnMVCt5AQ/BTc\niIiIiIiI1Du+NJerkEbEQ3zpwRbxGSdOQFISvP46HDx4xctLCGIx40lmOv/jeuy4lrR07AjTphmv\nTp1cakJE3GS328k8lemosPnm2DdutxloDmRM5zEkxiZye8/baRnWkpNlZWw+L7TZZLVyrKzMA5/g\nyiLNZuLOWyYtzmKhQ1BQlT0TRURERERExPf40lyuQhoRD/GlB1vE51RWwvLlxlJoS5ca1TZXcJh2\nvMV9JDOdvXRzuevRo2HGDEhIgNBQl5sRETcdyD3AoqxFpGamkn4oHTvu/QrqZ/IjvkM8CTEJJMQk\n0LFJR8d7R0pLjcAmP98R3JypqHD3I9RIi4CAKtU2gywWWgcF1UrfIiIiIiIiUjO+NJerkEbEQ3zp\nwRbxaQcPwty58MYbkJ19xcvtwFpGkMx03ucnFBHmUrcRETBpkrEc2uDBoC+6i9SdEwUn+Hjnx6Rm\npbJy30rKbZdfFrEmBrQZQGJMIomxicS2iK3ynt1u50BJSZVl0jZbrVgrK93utybaBQVVCW7iLBaa\nBQTUSt8iIiIiIiJyMV+ay1VII+IhvvRgi9QLZWWwaJFRXbNmTY1usRLOB9xFEjNYR7zLXffqZYQ1\nU6ZAq1YuNyMiHpBXksfS3UtJy0rj092fUlRe5HabPZv3JDE2kYSYBOKi46pdfsxmt7OrqKjKMmnf\nFhRQYrO53X9NdAkOZtB5y6QNCA/H4u9fK3274kx5ORN37Khy7r1evRQ2iYiIiIhIveRLc7kKaUSc\nlJKSQkpKykXnCwsLycjIcBwrpBFxQmamsW9NSgrk5dXolp30IIVpvMV9HKWtS92azXDLLUZgc8st\noLlGkbpVXF7Min0rSM1MZcmuJZwpPuN2m+0j2jMhZgKJsYnEd4jH3+/SQUiFzcb2oqIqy6R9V1hI\nRS38umwCYkJDqyyTdnV4OMFms9f7rolXjxzhwd27q57r3p3727r281dERERERKQuKaQRqcdmzZrF\n7Nmzr3idQhoRFxQVwbvvGtU154Wel1OBmf9xPclMZzHjKSfQpa5btoR77zX2r9GjK1L3yivL+fLg\nl6RlpZGWlcZR61G322we0pzxPceTEJvA2C5jCfYPvuI9JZWVfFdY6AhtNuXnk1lURG3U2/ibTPQJ\nC6sS3PQJCyPAz68Weq9q8ObNbLJaq56zWNgwcGCtj0VERERERMRdCmlE6jFV0ojUkowMmDMHFi6E\n4uIa3XKK5ixkMknMYCtXu9z1oEFGWHP33dCkicvNiIiH2Ow2Nh3ZRGpmKqlZqew5s8ftNsMDw7m5\n+80kxiRyc/ebsQRZanxvQUUF3xYUOIKbDKuV3TX8OeWuIJOJq8PDjdAmIoJBFgs9Q0Mxe3Gjre2F\nhfTZtKn69wYNoleYa3uFiYiIiIiI1BWFNCINkC892CINSm4uzJ9vVNdkZtb4tm+5mmSm8zb3kkMz\nl7oODobERGM5tNGjoQ6+vC4iF7Db7Ww/uZ20zDRSs1LZcnyL220GmgMZ12UcCTEJ3N7zdlqEtXC6\njZzycjaft79NhtXKodJSt8dWE+FmMwPOBTdnq266hoRUuxePK363dy9/P3y4+vfat+f5rl090o+I\niIiIiEht8aW5XIU0Ih7iSw+2SINkt8OXXxphTWoqlJfX6LZSAvmY20liBsu5ATuuJS0dO8LUqTBt\nGnTu7FITIuIF+3P2O5ZEW3doHXbc+9XWz+THiA4jSIxNZELMBDpEdnC5rRNlZWw+b5m0TVYr2TX8\n2eWupv7+xJ23TFqcxUK7oCCng5sKm43269dzvKys2vfbBAZyaMgQ/JVii4iIiIhIPeJLc7kKaUQ8\nxJcebJEG78QJSEqC11+HgwdrfNsPtOUt7iOZ6eyhu8vdjxplVNfccQeEhrrcjIh42PGC43y882NS\nM1NZtX8V5Tb3A5G46DgSYhJIjE0kJirGrbbsdjs/lJZWqbbJsFrJqahwe5w10SogoMoyaXEWCy0C\nAjh1meBodW4uE3fsuGy77/fqxXWXWRsyKiDAY1U9IiIiIiIinuBLc7kKaUQ8xJcebJFGo7ISPvvM\n2Ltm6VKj2qYG7EA68SQznfdNEym0u7afQkQETJxo7F9zzTWgOUgR35FbksvSXUtJy0pj2Z5lFJUX\nud1mTFQMiTGJJMQmMLDNQI8ED3a7nX0lJY5Kmwyrlc1WK4U2m9tt10TrgACOe7m6Z0tcHP3Cw73a\nh4iIiIiIiDN8aS5XIY2Ih/jSgy3SKB08CHPnwhtvQHZ2jW8rIIwPuIskZpDOCJe7j4kxwpopU6B1\na5ebEREvKCovYsXeFaRmpbJk5xJySnLcbrNDZAcm9JxAYmwi8R3iMfuZPTBSQ6Xdzs6iIkdosyk/\nny0FBZTW01/bn+7YkdlaJ1JERERERHyIL83lKqQR8RBferBFGrWyMkhLM6pr1qxx6tbddCOFaaSY\nf8rRSteSFrMZbr7ZWA7tllsgMNClZkTES8ory/ni4BekZRr72BwrOOZ2m1GhUYzvOZ6EmATGdhlL\nkH+QB0ZaVZnNxvbCwh+DG6uV7wsKqPR4T57XJyyM7wcNquthiIiIiIiIOPjSXK5CGhEP8aUHW0TO\nysw0wpp58yAvr8a3VeLHCsaR5PczFjOeMluAS923aAH33msENn37utSEiHiRzW5j45GNpGamkpqZ\nyt6cvW63aQm0cHP3m0mMTeSmbjdhCbJ4YKTVK66sZGtBQZXgJquoCF/75d4MHB02jJZKrUVERERE\nxEf40lyuQhoRD/GlB1tELlBUBO++C6+9BhkZTt16mmYsZDLJwffzbUkvl4cQF2csh3b33dC0qcvN\niIiX2O12tmVvIy0rjdTMVLae2Op2m0HmIMZ1HUdCTAK397ydqNAoD4z08qwVFXxTUOBYJi3DamVv\nSYnX+72UzsHBLIyNZUhkZJ2NQURERERE5EK+NJerkEbEQ3zpwRaRy8jIMKprFi6E4mKnbt1CP5L9\nf8Hb5vs4U+raJthBQZCQYAQ2o0cby6OJiO/Zl7OPtMw0UrNS+frw19jdrE/xM/kxsuNIEmMSmRAz\ngfaR7T000is7U15OxnnVNhlWKz+Ulnq933A/P25r3pzRTZsyokkTeoSEYDKZvN6viIiIiIjIlfjS\nXK5CGhEP8aUHW0RqIDcX3nrLCGwyM526tZRAlnAbSZG/ZXn+EGx2P5eG0L49TJtmvLp0cakJEakF\nx6zHWLxzMWlZaazav4oKW4XbbQ6KHkRibCIJMQn0jOrpgVE651hpaZXgZpPVyqnycq/2GRUQQHxk\nJCMiI4mPjKR/eDgBfq79/BQREREREXGHL83lKqQR8RBferBFxAl2O3z5pbEUWmoqODlJeYRo3gr+\nJcnBv2J3bkuXh3HddcbeNXfcAWFhLjcjIl6WU5zD0t1LSc1M5bM9n1Fc4VxFXnV6tehFQkwCibGJ\n9G/dv06qTex2O4dKS/n09Gn+evAgR8vKvN5nqJ8fQyIiHMHNkIgIwv39vd6viIiIiIiIL83lNpiQ\npqSkhM8//5xdu3ZhNpvp3bs3o0aNwlyDdWSOHj3KU089hclk4s0336yF0UpD5EsPtoi46MQJePNN\nmDsXDh506lY7sI7hJLf5E+/njKWgJMClIVgsMHGisRzakCGglYFEfFdReRHL9ywnLSuNJbuWkFuS\n63abHSM7khCTQEJsAsPbD8fsV/trIj538CB/2L+/1vs1A/0tFuLPVtrER0bSKjCw1schIiIiIiIN\nny/N5TaIkOaDDz7goYce4tSpU1XOt23blueee47Jkydf9v7t27fTt29fTCYTlZWV3hyqNGC+9GCL\niJsqK+Gzz4zqmk8/NaptnFBAGB9G/ozkyN/w5aFOLg+jZ08jrJkyBdq0cbkZEakF5ZXlrDmwhtTM\nVBbtXMTxguNut9kitAXje44nMTaR0Z1HE+Qf5IGRGmx2G6eLTlf73pTMHSw/k+OxvqoIiABTzZc4\n6x4S4lgeLT4ykm7a10ZERERERDzAl+Zy631Is2DBAqZOnYrdbqe6j2IymZg0aRL//e9/CQkJqbYN\nhTTijJSUFFJSUi46X1hYSEZGhuNYIY1IA3HwoFFZ88YbkJ3t9O17TN1J6f5XUk7fzpHTwS4NwWyG\nG280AptbbwV9sVzEt9nsNtb/sJ60zDRSs1LZl7PP7TYjgiK4pfstJMYmcmO3GwkPDHervZOFJ2n5\ngutLNLpsaBoENnH59lbn9rVp0oT4yEj6hYXhr31tRERERETESQppPCQ7O5sePXqQn58PwIQJExgz\nZgxlZWWsXr2aZcuWUVlZiclkYsiQISxbtoyIiIiL2lFII86YNWsWs2fPvuJ1CmlEGpiyMkhLM6pr\nvvjC6dsr8ePz1lNIavkki7J6Ulbm2jfBo6Lg3nuN/WuuusqlJkSkFtntdr7P/p7UzFTSstL47sR3\nbrcZZA7ihm43kBCTwG09bqN5aHOn26ivIc2Fws1mhp63r801ERGE1mC5YxERERERadwU0njIs88+\ny5/+9Cf8/PxYsGABEydOrPJ+RkYG06ZNY8eOHZhMJgYMGMD//vc/mjZtWuU6hTTiDFXSiAiZmTBn\nDsybB3l5Tt9+JqAVC696jmTrnXyzy/Vvww8caIQ1kyZBs2YuNyMitWjvmb2kZaWRmpnK1z987XZ7\nZpOZaztdS2JMIhNiJtA2om2N7qurkGb4jSv5ptSfYpvNK+37m0wMCA+vskRalMoPRURERETkAgpp\nPGTUqFF8+eWX3HvvvcybN6/aawoLC5k8eTJLlizBZDLRr18/Pv/8c5qdN5ulkEY8wZcebBGpJYWF\n8N57RnXNeSGtM7Z2TSS5w9O8vbUvp8+4tmRPUBBMmGAshzZmjLE8moj4vqPWoyzOWkxaVhqrD6ym\nwlbhdpvXtL2GhJgEEmIT6NG8xyWvq6uQJvvxbCJDmvNtQQHpeXmszc0lPS+P0xXuf/ZLiQkNdVTa\nxEdG0jk4WPvaiIiIiIg0cr40l1uvQ5pWrVpx6tQpFi9ezK233nrJ6+x2Oz/72c9ITk7GZDJx1VVX\n8fnnn9O8ubE0hEIa8QRferBFpA5kZBhhzTvvQHGx07eXhTZhybBnSS6dxLJ1kbj6JfN27WDqVJg2\nDbp1c60NgK++gmHDXL+/ttsVqe9yinP4ZNcnpGalsnzPcoornP85cqHeLXqTGJtIQkwCV7e+ukow\nUZchTYuwFlXO2e12soqKSM/LM4KbvDz2l5R4bQzRgYGOKpsRkZH0DQ/HrNBGRERERKRR8aW53Hod\n0gQFBVFRUcE333xDv379rnj9/fffz+uvv47JZKJPnz6sXLmSqKgohTTiEb70YItIHcrJgfnzjcAm\nK8ulJo72v4X53WaTtKU/u3a7viH2yJFGdc2dd0JYWM3vmzULZs+GF16Axx5zufuLvPgiPP44zJxp\n9CEi1SssK2T53uWkZaWxZOcS8kqdX1bxQp2adCIhJoHE2ESGthvKmeIzPhPSVOdIaakjtEnPy2Nr\nQQHe+qMlwmxm2HnLow22WAhRSaKIiIiISIPmS3O59TqkiYiIoLCwkC+++IL4+Pga3fPQQw/x6quv\nOoKaVatWcfz4cYU04jZferBFxAfY7fDFF8beNampUF7ufBNNm/H1DbNIqriP9z6LpKDAtaGEh8PE\nicb+NcOGweW+MH4uoDnHU0HNuYDmHAU1IjVTVlnGmgNrSM1MZVHWIk4UnnC7zZZhLbmhyw3M/36+\nB0bonJqGNBfKq6jg67NVNul5eWzIz6fUS3/GBJhMxFksjkqb4ZGRNAsI8EpfIiIiIiJSN3xpLrde\nhzS9evVi586dzJ07l5/+9Kc1vu/Xv/41//nPfzCZTPTu3Zt///vfjB49WiGNuMWXHmwR8TEnTsCb\nb8Lrr8OhQy41UXjdLXzYZybJ3w3kiy9dr67p0cMIa+67D6Kjq7731VcwfPjF97gb1FwY0Jyzbp2W\nPhNxRqWtkvU/rCctK43UzFT25+6v6yE5zdWQ5kKlNhubrVbH8mjr8vLI8eK+Nr3P7msTHxnJiCZN\n6BAUpH1tRERERETqMV+ay63XIc3dd9/N+++/z6RJk1iwYIFT9z788MO88sormEwmWrRoQXZ2tkIa\ncYsvPdgi4qMqK2HZMqO65tNPjWobZ7Vpw97EJ0gxz2BeWiSHD7s2FD8/uPFGYzm0226DwEDj/KUC\nFVeDGk+3JyIGu93O1hNbSctMIzUrlW3Z2+p6SDXiqZDmQja7ncyiItbm5jqCm0OlpR7v55x2QUGM\nOG+JtD5hYfgptBERERERqTd8aS63Xoc0r776Kg899BDh4eEcP36c0NBQp+5/5JFHePnllzGZTNjt\ndoU04hZferBFpB44cADmzjUqbLKznb/fz4/KW8ez8po/kvzdQNIWmXB1PrJ5c7jnHiOw6dfPc8GK\nAhqR2rP79G7SstJIy0pj/Q/r63o4l+StkKY6h0pKWHfeEmnbCgu9tq9NE39/hkVEOIKbOIuFYO1r\nIyIiIiLis3xpLrdehzT79u2jW7dumEwm/vWvf/HQQw853cajjz7Kv/71LwCFNOIWX3qwRaQeKSuD\ntDR47TVjDxtXdOlCzpSHeSdkBkkfWNi82fXh9O9vhDU5OfD00xe/X9OARQGNSN05kn+ExTsXk5qZ\nypoDa6i0+87vt7UZ0lwop7ycr/LzjUqb3Fw2Wa2UeelPoSCTiUEREY59bYZFRNBE+9qIiIiIiPgM\nX5rLrdchDcC0adM4cuQIbdu2JSUlxaU2fv/73/P+++8DsH9//VvbW3yDLz3YIlJP7dhh7Fszbx7k\n5Tl/f2Ag3HUX3439Lclb+vP2AhOnTrk2lMBAiI2FrVsvfs8RtNjtYLUaQVNgIFgsYDIpoBHxIWeK\nz7Bk5xLSstJYvnc5JRUldTqeugxpLlRSWUmG1eqotFmXl0eel76wZQL6hIU5Km1GREbSLjjYK32J\niIiIiMiV+dJcbr0PaUR8hS892CJSzxUWwrvvGtU1rpbF9OlD2c8f5JNm95H8XijLlhlb4njKC93m\n8NjpPxolN+c0bcqLzf8fj+/51cXXK6ARqXOFZYV8tuczUrNS+XjnxxSUFdT6GHwppLlQpd3O9sJC\nx542a3NzOVJW5rX+OgYFGYFNkybER0YSGxqqfW1ERERERGqJL83lKqQR8RBferBFpAHJyDDCmnfe\ngeJi5+8PC4N77uHYnb9m/rd9SE6GrCzPDO0FHuMxXnIcv8hveZwXL75OAY2IzzmSf4R2/2hX6/36\nckhzIbvdzsGSEtLPVtqszctjR1GR1/pr5u/P8LNVNvGRkQy0WAj08/NafyIiIiIijZkvzeU2mpBm\n5cqVXH/99YCx90xFRUUdj0gaGl96sEWkAcrJgbfegjlzXE9ZhgzB/qv7Wd9xIskLg3j3XWO1Mnc8\nyCu8zK956VIBDY/x2OTj8O9/Q/Pm7nUmIh5zsvAkLV9oWev91qeQpjqny8tZdza0Sc/LI8NqpdxL\nf04F+/lxjcXiqLYZGhFBhL+/V/oSEREREWlsfGkut1GFNOPGjQOMkKbSS+tNS+PlSw+2iDRgdjt8\n8YVRXZOaCq586aBZM5g2jcL77if1u24kJcGaNa4PyUwFlVw8cVil0iY6Gj77DPr2db0jEfGYugpp\nrm51NTd1v4mxXcYyrP0wgv3r974sRZWVbLJaWZubS3peHl/l52P10t8ZfsBV4eGOSpv4yEiig4K8\n0peIiIiISEPnS3O5CmlEPMSXHmwRaSSOH4ekJHj9dTh0yLU2xo6FX/2KfX1uJ2VBACkpcPiw+0P7\nHc/zPE9WPdm0qREwKagRqXN1FdKcL8Q/hBEdRzC281jGdhlLv9b98DPV7+W9Ku12viso+HFfm7w8\njntxX5suwcFGpc3Z0KZnaCgm7WsjIiIiInJFvjSXq5BGxEN86cEWkUamshKWLTOqa5YtM6ptnNWm\nDfz851TO+DmrMiJInrKS1OKbKMX1b7lfxVYm8h4TeY+u7DNORkfDd99p6TOROuYLIc2FokKjGNN5\nDGO7jGVcl3F0bNKxrofkNrvdzv6SEtae29cmN5edruwvVkNRAQGOKpsRkZH0Dw8nQPvaiIiIiIhc\nxJfmchXSiHiILz3YItKIHTgAc+fCm29Cdrbz9/v5GYHNkSPk0IR3uZtkprOJwW4NayAZTOQ9fsL7\ndJwcDwsWuNWeiLjHF0OaC3Vr1o2xnccyrus4RnUaRdOQpnU9JI84WVbGurNVNul5eXxTUECFl/4k\nC/XzY0hEhCO4GRoRQbj2tRERERER8am5XIU0Ih7iSw+2iAhlZcaeNXPmGEuMuWkbvUlmOvOZwknc\nm9i9hvVM/FkEd83sRbt2bg9NRFxQH0Ka8/mZ/BjYZqCjymZY+2EE+TeM/VgKKyvZkJ/vWCLt67w8\nCm02r/RlBq4OD2dEkyaO4KZVYKBX+hIRERER8WW+NJerkEbESSkpKaSkpFx0vrCwkIyMDMexQhoR\n8Rk7dhhhzbx5kJ/vVlPP8wRP8jcPDQzi42HiRLjzTmjd2mPNisgV1LeQ5kLn9rMZ12UcY7uM5apW\nV9X7/WzOqbDZ2HJ2X5tzwU12ebnX+useElJlX5tuISHa10ZEREREGjyFNHVAIY14yqxZs5g9e/YV\nr1NIIyI+p7AQ3n3X2Ltm82anb3+R3/I4L150PpBSynDvG+1+fnDttUZgk5gILVq41ZyIXEFdhTQ3\nd7uZdYfXkVea59F2W4S2YEyXMYztPJaxXcY2iP1szrHb7ewpLnYsj5ael8duL+5r0+r8fW2aNKFf\nWBj+2tdGRERERBoYhTR1QCGNeIoqaUSkQdi0yQhr3n0XajDZd6mA5gUe4zFe4mlm8QwzPTI0sxlG\njzYCm4QEaNbMI82KyHnqKqTJfjybZiHN2HxsM5/v+5wV+1aw7tA6ym2erRTp3qw7Y7sYgU1D2s/m\nnOOlpaw7t0Rabi7fFhTgnQXSIMzPj6HnVdpcExFBmNnspd5ERERERGqHQpo6oJBGvM2XHmwRkRrL\nyYG33jKWQ8vKqvaSKwU0V7rOHf7+cP31RmAzfjxERnq0eZFGqy5DmhZh8hfcvwAAIABJREFUVUvl\nCssKST+Uzop9K/h83+dsPbHVo336mfyIi45jbOexjOs6jqHthjaY/WzOsVZUsP5saJOel8f6/HyK\nvLSvjb/JxIDwcMcSacMjI2mhfW1EREREpJ7xpblchTQiHuJLD7aIiNNsNmjSBKzWKqdrGtBc6fpo\njnCUtm4NMTAQbrzRCGxuuw0sFreaE2nUfCmkueiawmxW7lvpqLQ5nH/Yo2MI8Q9hZMeRjO0ylnFd\nxtG3Vd8Gs5/NOeW2/8/enQdFfeb74n93Nw0N9Ia44oLgblQUXFAhGIVMErOMUSSaZSYLzJIxp85J\n7uSee+reMVW36nfunJlT9UvOmTMXNGMm2yAxkzhxMhlao+KuoBH3KLihoii9sdP9vX986QZ3u7/f\nLzzA+1VFVdLL53lS8QF83v08Hz8Oer03XZFWp2Ffm4kxMZ1XpNlsSDKZ2NeGiIiIiIQm0l5uRI+M\nSkRERGLxehUHNACCj9/6vksYjrfxrzDDi2Lk4QimhjzF1lZg40b5y2QCFi+WA5vFi4GYmJDLEZGg\nBscOxoqpK7Bi6gpIkoTvb3wPR5UDjioHtlRvUdzPpqm9Cd+c+QbfnPkGQGc/m5zkHGQnZ2OUbZQa\n/xk9yqjXY7bVitlWK94cORKSJOFkY6N8PVpHaFPV3KzaeCcaG3GisRFrLl8GAAyLjAxej5Zhs2Ga\n2QwDQxsiIiIiojviSRoilYiUvhIRhayuDhjU+en2cAKaru73/mOYhGLkoXjsv+DkaWWfGYmNlU/W\nLF8OPP64HOAQ0b2JfJLmXtr97Si/JPezcVQ7NOtnEwhsHkl6BHaTXdX6orjU0hI8ZVPmcuE7rxda\n/cXQYjBgntUqn7Sx2zHbYkE0+9oQERERUQ8SaS+XIQ2RSkRa2EREIXO7gw1flAY0AQ9SR3K5cbja\ngvXrgeJi4MyZMOffwWKRe9fk5cm9bNgmgejOemtIc6uG1gaUnS8LXo12uPawarWBzn42gdCmL/az\nCXB19LUpczqxw+XCXo8HzRr1tTHqdJhpsQRP2sy32RBvNGoyFhERERHRnYi0l9ujIc327du7bawD\nBw7grbfeAsCQhrQh0sImIgqZJAHx8fht/cuqBDQB9w1q5s6Vj8AsWwZp+AhUVMhhzfr1wLlzYf2X\nBNntwJIlcmCzcCHA/T+iTn0lpLlVrbcWW6q3oLSqFI4qh+r9bGKMMXI/m6RsZCdn98l+NgEtfj8q\nPJ6brkirb2/XbLzJMTE3XZGWyL42RERERKQhkfZyezSk0ev13f6LtyRJDGlIEyItbCKicOya+Qbm\nl7972+PhBjQBdwtqdmIe5mF35wO3BDZ798qBTUkJUFMT9vAAgPh44Nln5cBmwQKAt+xQf+eX/Lje\neL3bx42Pie+2UKNrP5vSqlJ8W/2t4n42txocOxiLkhYhOzm7z/SzuRu/JOF4R1+bHS4XypxOnGtp\n0Wy8EVFR8vVoHaHNlNhY6BnaEBEREZFKRNrLFSKk6a4pBMZiSENaEGlhExGF5X/8D6z+/yLxDlYH\nH1Ia0ATcGtT8CquxGu/c/Q1dAht/wgjs2tUZ2NTWKpvL4MHAsmVyYJORAej75ofgiegWgX42gVM2\nuy7sUr2fzfj48chOykbOmBwsGL2gz/azCbjQ3BwMbXa4XKhsaNCsr43NYMD8jsAm02bDTIsFJibu\nRERERBQmkfZyhQhpuhNDGtKKSAubiCgslZXAtGlYjV/hHaxWLaAJCAQ19w1objV3LpCbCyxbBl/C\nSGzfLgc2GzYAdXXK5jRsmFw6Lw9IT2dgQ9SfNLQ2YPu57XBUOeCodmjSz2ZWwixkJ2cjJzkH6SPS\n+2w/m4D6tjbsdruD16Ptc7vRqtFfNyN1OsyyWJBpt8t9baxW2HmvJRERERE9IJH2cns0pBk9enSP\n3TNcXV3dI+NS3yXSwiYiCtvDDwNlZdiFuTdfRaYSxXW7BDbtw0Ziyxa5f83nnwP19crmNnKkfHgn\nLw+YORPgrTpE/UuttxabqzcHr0e76L6oav1AP5uc5By5n83gqX2+50qzz4cDHX1tdrhc2Ol2w6lR\nXxsdgCmxsTddkTbSZNJkLCIiIiLq/UTay+3RkIaoLxFpYRMRhW3TJuDJJ3t6Fg+mS2DTOmQkHA75\nhM0XXwBut7LSycmdgU1KCgMbov5GkiScun4qeMpmS/UWuFsUfmO5xeDYwXIvmyS5n81I20hV64vI\nL0k42tAQPGlT5nLhooZ9bRI7+tpk2GzItNsxKSaGfW2IiIiICIBYe7kMaYhUItLCJiJSZOVK4NNP\nta3/P/+n3GCmpES+Zk2p9PRgD5vmQSPxzTdyYLNxI9DQoKz0+PGdgU2Xb/NE1I+0+9tx4NKB4Cmb\n3Rd2q97PZkL8BDm0Sc7uF/1sAs519LUpczqxw+XC0cZGzcYaEBER7GuT0dHXJpL3XBIRERH1SyLt\n5TKkIVKJSAubiEiR69eBadOAS5fUr52QABw+DMTHdz528qQc1qxfr15g03HCpnHgKHz9tRzYfPUV\n0NSkrPTkyXJYk5cHTJigfKpE1Dt5W70oO1eG0qpSOKocqLyqwveuLvQ6PWYPnx08ZTN35FxEGiJV\nHUNUN9rasLPLSZsDHg/aNPorq0mvx2yLJXg92lybDbaICE3GIiIiIiKxiLSXy5CGSCUiLWwiIsUq\nK4GsLOWNXrqKiwO2bQOmTr37awKBTUmJHOYo1SWw8Q4Yha++kgObr78GlN6wk5LSecJmzBjlUyWi\n3uuK9wq2VG9BaVUpSs+UosZTo2r9GGMMshKzkJ2cjZzkHEwZPKXP97MJaPL5sN/jCV6Rtsvlgtvn\n02QsPYBpZvNNfW0SoqI0GYuIiIiIepZIe7kMaYhUItLCJiJSRWUl8Nhj6pyoSUgA/va3ewc0t9Iw\nsHHbR2HjRjmw+eYboE3hrUVpaXJYs3w5kJiofKpE1HsF+tkETtl8e/Zb1fvZDIkdgkXJi5CTnIPs\n5GyMsI5Qtb7IfJKESq83eNKmzOXC5dZWzcZLMpmCgU2mzYYJMTH9JiAjIiIi6stE2stlSEOkEpEW\nNhGRaq5fB954A/jkk/BrrFwJvPvuzVechUrtwGbOnGAPm3rLKHzxhRzYOByA0g9op6fLgc2yZcCI\n/rNvSkR30e5vx/6a/XBUOeCodmjWzyYQ2CwYvQA2k03V+iKTJAnVHX1tAsHNCQ372gw0GjHfakWm\n3Y4Mmw2pZjOM7GtDRERE1OuItJfLkIZIJSItbCIi1W3aBPz618D27Q/+nocfBt5+G3jiCXXncupU\nZw8blQObuphR+PxzufS33wJ+v7LSGRmdgc3QocqnSkS9n7fVi+3ntsNR5UBpVSmOXD2ian2DziD3\ns0mW+9mkj0jvN/1sAq61tgb72uxwuVDu9aJdo7/2Ruv1SLdagydt0q1WWNjXhoiIiEh4Iu3lMqQh\nUolIC5uISDNHjgCffgrs2weUl9/csyYuTr73a/ZsYMUKoMv3RM1oEdh0XIlWa0rEhg3yCZuyMkDJ\nb0x6vdziJy8PePZZYNAg5VMlor7hivcKNldthqPaoUk/m1hjLLJGZyE7SQ5t+lM/m4AGnw/73O5g\nX5vdbje8GvW1MQCY3tHXJvA1lH1tiIiIiIQj0l4uQxoilYi0sImIuoUkAV4v0NICREUBZjPQkxt/\ngcCmpAT47jvl9boENjURifjsMzmw2b1bWVmDAVi4UA5sliwBBgxQPlUi6hskScLJ6yeDp2y+rf4W\nnlaPqmMMiR0SPGXT3/rZBLT7/fiuoUG+Hs3pxA6XC7VKm5Pdw9jo6GBfmwybDeOio/tdUEZEREQk\nGpH2chnSEKlEpIVNRNTvaRjYnNcloqREDmz271dW1mgEcnLkwOaZZwBb/2kjQUQPINDPprSqFI4q\nB3Zf3I12f7uqY0wcOBHZSdnIGZODrMSsftXPJkCSJJxuarqpr833TU2ajTfYaAxej5Zhs2G62YwI\n9rUhIiIi6lYi7eUypCEK0bp167Bu3brbHm9oaMCBAweC/86QhohIEGoHNrNnB3vYVPkSsX69HNgc\nOqSsbGQk8Pjjcumnn5YPJhERdRXoZ1N6phSOaoem/WxyknMwZ8ScftfPJqC2o69N4Iq0gx4PtLkg\nDYjV6zG3I7DJtNkwx2pFrMGg0WgP7kZbG/KOHbvpseLJkzHAaOyhGRERERGphyENUS+2evVqvPPO\nO/d9HUMaIiIBnToFfPaZ3MNG5cDmVEsiiovl0kcU7puaTMDixfIJm8WLgZgY5VMlor7nsucyNldv\nDl6PdslzSdX6gX42Ock5yE7OxkODHuq313R529uxx+0OnrTZ43aj0e/XZCwDgFSL5aYr0gZFdn9Y\n9ruaGrz+/fc3PzZuHH42fHi3z4WIiIhIbQxpiHoxnqQhIuojvv9ePl2jZmCTmwvk5uKot/OEzcmT\nysrGxgJPPSUHNo89Jgc4RES3kiQJJ+pOwFHlgKPaoUk/m6HmoXIvmyS5n81wa//drG/z+3HI6w2e\ntNnhcuGahn1tJkRHyydt7HZk2GxINpk0D8xml5djv+fmP0OzLRbsTUvTdFwiIiKi7sCQhqgPEmlh\nExFRiAKBTUmJ8nvLgGBgIy1dhsPu0SgulgObqiplZS0WuXdNXh7w6KPyFWlERHfS5mvD/kv7g6ds\n9lzco3o/m0kDJ8mhTXI2FoxeAGuUVdX6vYkkSTjV1IQypzMY2pxpbtZsvGGRkcFTNpk2G6aZzTCo\nGNocbWjAlLs0Xjs6axYmx8aqNhYRERFRTxBpL5chDZFKRFrYRESkgIaBTcWN0cEr0c6dU1bWbgeW\nLJEDm4ULAbYIIKJ78bR4sP3c9mBoc/TaUVXrG3QGzBkxJ3jKJn1EOoyG/v2N6VJLy019bb7zeqHN\nBWmAxWDAXKs1eEXaHKsV0Qr62vzyzBn824ULd35u5Ej8nzFjwq5NREREJAKR9nIZ0hCpRKSFTURE\nKlE7sJk1C1i+HNLSZdhbKwc2JSVATY2ysvHxwNKlcnucBQsAAfpNE5HgAv1sSqtK4ahyqN7Pxhxp\nRlZiFrKTs5GTnIPJgyb32342Ae72duwO9LVxOrHX40GzRn1tjDod0rr0tZlvsyH+AdP8dr8fI/fs\nwZXW1js+PywyEufT0xGh16s5ZSIiIqJuJdJeLkMaIpWItLCJiEgD338PfPaZfAxGrcAmNxf+pbnY\nWTMa69fLgU1trbKygwcDy5bJJ2wyMgDuoRHR/QT62QQCm61nt2rWzyYnOQeLkhb16342Aa1+Pyo8\nnpv62txoV/dKuq4mx8TIV6RZrZgcG4uRUVF3DM6+dTqRd+zYPWutnzwZC+z2uz4/0Gjs96EcERER\niU2kvVyGNEQqEWlhExGRxk6f7jxhc/Cg8nodgY3v2VxsPy+fsNmwAairU1Z22DAgN1cObNLTGdgQ\n0YMJ9LMpPVMKR7VDs342Ock5yE7ORtborH7dzybAL0k40dgon7TpCG3OatjXRkuHZs5Eitnc09Mg\nIiIiuiuR9nIZ0hCpRKSFTURE3UijwKZ9SS62VMmBzZ//DNTXKys7cqR8HVpeHjBzJsAPOBPRg/K0\neLDt3DY4qhxwVDk062cTCG3mDJ/T7/vZBFxsbg6esilzuVDZ0IDe8Bf4/5WYiHeSknp6GkRERER3\nJdJeLkMaIpWItLCJiKiHqB3YzJwJLF+O1qeXwXEmCcXFwBdfAG63srLJyZ2BTUoKAxsiCs0lzyVs\nrtoMR7UDpWdKcdl7WdX65kgzFoxegOykbGQnZ7OfTRfOtjbs6uhrs8Plwj63Gy0C/pV+SmwsKmfN\n6ulpEBEREd2VSHu5DGmIVCLSwiYiIgGcPt3Zw0bFwKb5qVx8c1I+YbNxI9DQoKzs+PGdgU2XH2NE\nRA9EkiQcrzsOR5UDpVWl2Hp2K7ytXlXHGGYehuzk7OBXgiVB1fq9WbPPh3KvF2VOJ3a4XNjpdsOp\nYV+bB2UAcGnePAyOjOzpqRARERHdkUh7uQxpiFQi0sImIiLBaBHY5Oai8cnl+Pq4HNh89RXQ1KSs\n7OTJcliTlwdMmKB8mkTU/7T52rCvZl8wtNlzcQ98kk/VMSYPmozspGzkjMlBVmIWLFEWVev3Zn5J\nwtGGhpuuSLvQ0tKtc0gymfDJpElIt9m6dVwiIiKiUIi0l8uQhkglIi1sIiISWCCwKSkBKiqU1+sI\nbLxPLMdXR+TA5uuvAaV7cikpnSdsxoxRPk0i6p/cLW5sP7cdpWdK4ah24Ni1Y6rWj9BHYM7wOchO\nzkZOcg5mD5/Nfja3ON/cjLKO0GaHy4UjSo9g3kOUTofsuDgsjItDps2GGWYzIvR6zcYjIiIiCpdI\ne7kMaYhUItLCJiKiXuLMmc4eNioGNu7HluPL7+TA5u9/B9ralJVNS5PDmuXLgcRE5dMkov7rkucS\nHFWO4JdW/WxyknOQnZyNSQMnsZ/NLW60tWFXxymbP9fV4XulxzDvIVavR7rViky7HZk2G9KtVsQY\nDJqNR0RERPSgRNrLZUhDpBKRFjYREfVCagc2aWnA8uWofzQPXxxMRHEx4HAAPoW3DqWny4HNsmXA\niBHKp0lE/Vegn03glI0W/WwSLAlyL5ukbCxKXsR+NndwoqEBy44exdHGRs3HitDpkGY2I8NmQ6bd\njgybDfFGnnwiIiKi7ifSXi5DGiKViLSwiYiolztzprOHjYqBTd2iPHxeLgc2W7cCfr+yshkZnYHN\n0KHKp0lE/Vubrw17a/YGT9lo0c/moUEPyaFNcjb72XTxr+fO4Z+rq3tk7MkxMcjsEtokmkw9Mg8i\nIiLqX0Tay2VIQ6QSkRY2ERH1IYHApqQEKC9XXi8tDcjNRe0jz2HDATmwKSsDlPxGqNcDWVlyYPPs\ns8CgQcqnSUTkbnFj29ltcmijUT+b9BHpyE6SQ5v+3M/mmcpKbLx+vaenAQAYGRUlhzYdwc2kmBjo\neWUdERERqUykvVyGNEQqEWlhExFRH1VV1XklmoqBTU3mc/hsvxzY7N6trKTBACxcKAc2S5YAAwYo\nnyYREQDUuGuwuXozSqtK4ahy4Ir3iqr1LZEWLBi9ANnJ2chJzsHEgRP7RT8bSZIwZNcuXAuzgVmE\nTge/JEHh4cy7GhARgfmB0MZmQ6rFgki9XqPRiIiIqL8QaS+XIQ2RSkRa2ERE1A9oFNicn/8c1u9J\nxPr1wP79ykoajUBOjhzYPPMMYLMpnyYRESAHC8euHYOjyoHSqlJsPbsVDW0Nqo4R6GeTk5yDRUmL\nMMwyTNX6ojjT1ISxe/cqqvHdzJmobW1FmcuFMpcLe91uNCm9U/MuovV6zLFag6HNXKsV5ogITcYi\nIiKivkukvVyGNEQqEWlhExFRP1NV1dnDRo3AJjUVWL4cVXNWYP2eUSguBg4dUlYyMhJ4/HFg+XLg\n6acBs1n5NImIAlp9rdhXsw+lZ0rhqHZg78W9mvSzyUnOQXZyNh5OfLjP9LP56MoVvHjihLIakybh\n+SFDgv/e6vejwuMJhjY7XC7Ut7crneodGQDMsFiQ0RHaZNhsGBwZqclYRERE1HeItJfLkIZIJSIt\nbCIi6se0CGxyc3Fq5koU7x6F9euBI0eUlTSZgMWL5RM2ixcDMTHKp0lE1JW7xY2tZ7fK/WyqHDhe\nd1zV+hH6CMwdMRfZyXI/m1kJs3ptP5vXT53C7y5duuvzySYTJADVzc13r5GQgP8YP/6uz/slCccb\nG1HmdAaDmwstLUqmfU8ToqORabcHg5skk6lfXF1HRERED06kvVyGNEQqEWlhExERAegMbEpKgAMH\nlNfrCGyOpqwMnrA5eVJZydhY4Kmn5MDmscfkAIeISG017ho5sKl2aNbP5pGkR5CdJIc2vamfTeqB\nAzjo9d7xuZeGDMF748YBAH7x/ff4sLb2zjXMZpTPnBnSuOeam7HD5QoGN8caG0ObeAgSIiODp2wy\n7XZMiY2FoZf8/yEiIiJtiLSXy5CGSCUiLWwiIqLbaBDYSMtycXjq8yjeNRLFxfIQSlgscu+avDzg\n0UflK9KIiNQmSRKOXjsaPGWjRT+b4ZbhwVM2IvezkSQJk/btw8mmppsetxkM+P348XiuyxVmAPBp\nbS1+euoU3L6br5KbEB2N47NnKwqm6lpbsdPtDgY35V4v2jXarrAZDJjf5Xq0WVYrovR6TcYiIiIi\nMYm0l8uQhkglIi1sIiKie9IosCmf9ALW7xqB4mLg/HllJe12YMkSObBZuBAw9s5bhIioF2j1tWLv\nxb3BkzZa9LOZMnhK8JRN1ugsmCPFaczV4PPhX6qq8G5NDSQAGTYbPpo0CYl3Odp4tqkJLxw/jp1u\nN3QA/mHECPzvpCTEGgyqz2tvILRxubDb5UKD36/qGAFROh1mW63I7Ahu5tpssEVEaDIWERERiUGk\nvVyGNEQqEWlhExERPbDq6s4eNmoENjNmQMpdjr3jXkDxzhEoKQFqapSVjI8Hli4Fli8HFiwAVN4H\nJCK6iavZhW3ntsFR5UBpVSlO1J1QtX7XfjY5yTmYNXwWIvTaBwJ+yY/rjdfv+vwelwsHvB78dFgC\nIu5zqqTd78d/Xb6EWWYL0m22e742PiYeep3yUyptfj8Oeb0oc7mCwU1dW5viuneiBzDNbA6GNpk2\nG4ZGRWkyFhEREfUMkfZyGdIQqUSkhU1ERBQWDQIb/9Jc7Ex+EcU7R+Czz4C7tDN4YIMHA8uWySds\nMjIA3k5DRFq76L6IzVWbUVpVCkeVA7UNCr+R3cIaZcWC0QuQk5yD7ORsTIifoEk/m2sN1zD4N4NV\nr3s/V9+6ikGxg1SvK0kSTjY2oqwjsNnhcqG6uVn1cQLGmEzItNuDoc3Y6Ohe03eIiIiIbifSXi5D\nGiKViLSwiYiIFAsENiUlwP79yuvNmAHf0uXYnvgiincMx4YNQF2dspIJCUBurnzCJj2dgQ0RaS/Q\nz6b0TCkc1Q5sO7tNs342Ock5WJS8CEPNQ1Wp29dCmju52NwcPGVT5nLhSEMDtNrwGGI0IsNmCwY3\nKWYzDAxtiIiIeg2R9nIZ0hCpRKSFTUREpCoNApv2Z5djy3A5sPn8c8DpVFZy5Eg5rMnLA2bOBLhP\nRkTdodXXij0X98j9bKoc2FezT/V+NlMHT0V2stzP5uHEh8PuZ9MfQppb1be1YZfbjTKnE2UuF/Z7\nPGjTaAvEYjBgntUaDG5mWyyI5v2cREREwhJpL5chDZFKRFrYREREmlE7sJk+Ha3PPgfHsBdRXJaA\nL74A3G5lJZOTOwOblBQGNkTUfVzNLmw9u1UObaodqvezMeqNmDtyLrKT5NAmlH42/TGkuVWTz4f9\nHo980sbpxC63Gx6fuqFagFGnwyyLBZk2GzJsNsy32RBnNGoyFhEREYVOpL1chjREKhFpYRMREXWL\ns2c7e9ioFNg0L1mBbwa/iOLtw7BxI9Cg8Bah8eM7A5suP6aJiLrFBdcFbK7eHDxpo0U/m0dGPxK8\nHm18/Pi79klhSHO7dr8fhxsaOq9IczpR29amyVg6AFNiY4M9bTJsNowwmTQZi4iIiO5PpL1chjRE\nIVq3bh3WrVt32+MNDQ040KXJMkMaIiLqVwKBTUkJsG+f8nrTp6PxmRX4a/yLWF82DF99BTQ1KSs5\nebIc1uTlARMmKJ8iEVEoJEnCkatHgqdstOhnM8I6orOfTdIiDDEPCT7HkOb+JEnC6aYmlLlcweDm\ntNIfPvcw2mQKhjaZNhsmxMTcNWQjIiIidTGkIerFVq9ejXfeeee+r2NIQ0RE/ZYGgY336ZX4yv4C\nircPw9dfAy0tykqmpMhhzfLlwJgxyqdIRBSqrv1sSqtKsa9mH/ySX9UxAv1scpJzMGnQJCT9/0mq\n1n8QvSmkuZPLLS3Y0SW0+c7rhbr/lzoNNBrlnjYdXzPMZkTo9RqNRkRE1L8xpCHqxXiShoiIKAQa\nBDbup57Hl5YXULxtKP7+d0DpzTRpaZ2BTWKi8ikSEYXD2ezEtrPbUFpVCkeVAyevn1S1foQuAu1S\nu6o1H0RvD2lu5Wpvx+6OwGaHy4W9bjdaNNpWidXrkW61ItNuR6bNhnSrFTEGgyZjERER9TcMaYj6\nIJEWNhERkZDOnevsYaNGYJOSgvqnXsIXsc+jeOsQOByA0v7P6elyYJObCwwfrnyKREThuuC6ELwa\nzVHlwNWGqz09pbD0tZDmVi1+Pw54PChzOlHmcmGnywWX0h9GdxGh0yHNbJZP29jtyLDZEG80ajIW\nERFRXyfSXi5DGiKViLSwiYiIhKdBYFO3+Ef43LQSxVuHYOtWwK/wPpqMDDmwWbYMGDpU+RSJiMIl\nSRIqr1bKoU2VA9vObUNjW2NPT+uB9PWQ5lY+ScLRhgaUuVzB4OZSa6tm402OiUGmzRYMbhJNJs3G\nIiIi6ktE2stlSEOkEpEWNhERUa8SCGxKSoC9e5XXS0lB7eM/xmeRK7B+6xCUlQFKfuPV64GsLDmw\nWboUGDhQ+RSJiJRo9bVi94XdwZM2WvSzUUt/C2luJUkSqpubgz1typxOnGxq0my8kVFRwZ42GTYb\nJsfGQq/TaTYeERFRbyXSXi5DGiKViLSwiYiIei0NApuaH7yCzyKeQ/G3g7F7t7JyBgOwaJHcv2bJ\nEmDAAOVTJCJSytnsxNazW4MnbdTuZ6NEfw9p7uRqayt2dPS0KXO5cNDjgTYXpAEDIiIwvyO0ybTZ\nkGqxIFKv12g0IiKi3kOkvVyGNEQqEWlhExER9QkaBDbnc17Fev1zKN4yCAcOKCtnNAI5OfIJm2ee\nAWw25VMkIlLDedd5bK7aLEQ/G4Y09+dpb8cetxtlHcHNHrcbTUrv7LyLaL0ec6zWYGgz12qFOSJC\nk7GIiIhEJtJeLkMaIpWItLCJiIj6nPPnO3vYqBHYTJuGquwCrNctR/HmQTh0SFm5yEjg8cflwOap\npwCzWfkUiYjU4Jf8OHL1CL448QV+tfVX3T4+Q5rQtfr9qPB45Ov+z1TDAAAgAElEQVTROoKb+vZ2\nTcYyAJhuNiPTbg9ekTY4MlKTsYiIiEQi0l4uQxoilYi0sImIiPq0QGBTUgLs2aO83rRpOPXIT1As\nLUfx5oE4elRZOZMJWLxYDmwWLwZiYpRPkYhIqWsN1zD4N4O7fVyGNMr5JQnHGxtR5nQGg5sLLS2a\njTc+Olo+adMR3CSZTNCxrw0REfUxIu3lMqQhUolIC5uIiKjf0CCwOfrwz7DevwzFmwfipMK2DrGx\n8smavDzgscfkAIeIqCcwpOlbzjc3y4FNR3BzrLFRs7ESIiOREehrY7djSmwsDAxtiIiolxNpL5ch\nDZFKRFrYRERE/ZLKgY00dRoOZ76O4valKHbEo6pKWT2LRe5dk5cHPPqofEUaEVF36amQ5mDBQUwf\nNr3bx+1vrre1YWfHKZsypxPlXi/aNdrusRkMmN9xNVqmzYZZViui9HpNxiIiItKKSHu5DGmIVCLS\nwiYiIur3zp8HNmyQe9ioEdhMmYryjH9AcesSrHcMwPnzyurZ7cCSJXJgs3AhYDQqniIR0T31VEij\ngw6Lxy9Gfmo+nhj3BCL0bFLfHRp8Puxzu4PXo+12udDg92syVpROh9lWa7CnzTybDbYI/n8mIiKx\nibSXy5CGSCUiLWwiIiLq4sIF+YSNioHN3nn/iOKWZ7C+dAAuXVJWLz4eWLpUDmyysgCDQfEUiYhu\n01MhTVcJlgS8OuNVvDrjVSTaE3t0Lv1Nm9+PQ14vdgRO27hcqGtr02QsPYBpZrN8PVrH19CoKE3G\nIiIiCpdIe7kMaYhUItLCJiIiorsIBDYlJcDu3YrL+adMw87Z/4ji5mfw2eY41NYqqzd4MLBsmRzY\nZGQAvD2GiNQiQkgToIMOPxj7AxSkFuDJ8U/CaOBxwu4mSRJONjaizOUKBjfVzc2ajTfGZEKm3R4M\nbcZGR0PHvjZERNSDRNrLZUhDpBKRFjYRERE9AJUDG9+UFGyf+U8obnoKGzbHoa5OWb2EBCA3Vw5s\n0tMB7mURkRIihTRdDTUPxcvTX8Zrqa8hOS65p6fTr9W0tKDM6QwGN5UNDdBqw2iI0Sj3tOkIblLM\nZhj4g46IiLqRSHu5DGmIVCLSwiYiIqIQqRzYtE2ZgW9T30SxdzE+32KH06ms3qhRnYHNzJkMbIgo\ndKKGNF3lJOcgPzUfz0x8BpGGyJ6eTr9X39aGXW53MLjZ7/GgTaMtJIvBgHlWazC4mW2xIJr3fxIR\nkYZE2stlSEOkEpEWNhERESlw4QKwYYPcw0aFwKZ18nSUzvglij1P4ItvbfB4lNVLTgaWL5cDm5QU\ndQKbXbuAefOU1+muukQUut4Q0gQMihkUPF0zLn5cT0+HOjT5fNjv8cg9bZxO7HK74fH5NBnLqNNh\nlsWCTJsNGTYb5ttsiDPyWjwiIlKPSHu5DGmIVCLSwiYiIiKVqBzYNE9OxTcpv0Sx+zFs3GpDQ4Oy\neuPHy2HN8uVAl19DQrJ6NfDOO8BvfgO8+aay+XT1298Cb70F/OpX8hhE1LN6KqTJeygPX578Es3t\n4fU7eWT0IyhIK8CSiUsQFcHm8yLxSRIOe71yaNMR3NS2tWkylg7AlNjYYGiTabNhhMmkyVhERNQ/\niLSXy5CGSCUiLWwiIiLSQCCwKSmRj4go1Dh5Jv465Zcodv4Am8qsaGpSVm/yZDmwycsDJkx4sPcE\nApoAtYKaQEATwKCGqOf1VEhz9a2riNBH4OPKj1FYXojKq5Vh1YmPjsePUn6E/LR8TBw4UeVZkhok\nScKZpqbO0MblwmmlP9zuYbTJhMyOwCbDZsPEmBjoeB8oERE9IJH2chnSEKlEpIVNREREGlM5sPFO\nmoW/PPQ21tc/iq93WNDSoqxeSkrnCZsxY+78ml27gPnzb39caVBza0ATsHMnrz4j6kk9GdIMih0E\nQN7E31ezD4XlhfjT0T+hsa0xrJqZozJRkFaApZOWItoYreZ0SWWXW1qws0to853XC79GYw00GoOn\nbDJtNkw3m2HU6zUajYiIejuR9nIZ0hCpRKSFTURERN3o4kXgs89UC2xck9KxcdIvUXw9G3/fZYHS\nm2PS0joDm8TEm5+7W6ASblCjdj0iUo8IIU1X7hY3Pqn8BIXlhTh45WBYteNMcXhx2osoSCvAQ4P5\nd6/ewN3ejl0uF3Z0hDZ73W60aLQtFavXI91qRabdjkybDXOsVsQaDJqMRUREvY9Ie7kMaYhUItLC\nJiIioh5y8WJnDxsVApv6iXPx5wn/HcV1C7F5jxlK+zOnp8uBTW4uMHy4/JhawQoDGiKxiRbSdFV+\nqRyF5YX45Mgn8LZ6wxpn3sh5yE/Nx/KHliPGGBNWDep+LX4/Dng8KHM6UeZyYafLBZfSH3Z3EaHT\nIdVslk/a2O3IsNkQbzRqMhYREYlPpL1chjREKhFpYRMREZEAAoFNSYl815dCdRPm4/Nxb6P42kJs\n3R8Lv4L7YnQ6ICNDPl2zbBnw8ccPELBIEuDxAK2tQGQkYLHIhcCAhqg3EDmkCfC0ePCnI39CUUUR\n9l/aH9Z4tigbXpj2AvJT85EyNCWsGtRz/JKEIw0N8vVoHcHNpdZWzcabHBPTeUWa3Y5Ek0mzsYiI\nSCwi7eUypCFSiUgLm4iIiASjcmBzZfzD2DD2bRTXZmFHRSyU/Eav1wNZWcCAAfIUb/WbhX/Fm7p/\nByoqgPr6zifi4oDUVPxW+ie8teWJ29/HgIZIKH7Jj+uN17t93PiYeOh1ofcFOXj5IIoqivBx5cdw\nt7jDGnv28NkoSC1A3pQ8mCPNYdWgniVJEs42Nwd72pQ5nTjZ1KTZeCOjopBpswWDm8mxsdB3fCCB\niIj6FpH2chnSEKlEpIVNREREAlM5sKkZ/wg+S/4liq88jN2HlF3xo9PhjoHPb/Am3sS/3/b4b/FP\neAu/vf31DGiISCUNrQ1Yf3Q9CisKsefinrBqWCItWDl1JQrSCpA6LFXlGVJ3u9raip2B0MblwkGP\nB9pckAYMiIjA/I7AJsNmQ5rFgkh96KEjERGJR6S9XIY0RCoRaWETERFRL1FT09nDRoXA5tzYRShJ\n+iWKL2XiwNFoFSbY6dag5q4BzYyP8WbpY0B8vKrjExFV1laiqKIIHx7+EM5mZ1g10oalIT81Hyum\nroA1yqryDKkneNvbsdvtxo6O0GaP240mJXeC3kO0Xo85VmswtJlrtcISEaHJWEREpC2R9nIZ0hCp\nRKSFTURERL2QyoHNmbE/wPrE/4b1NfNw6IQ6gc3z+Ai/x0/xf/GTOwc0gSAnIQH429+AqVNVGZeI\nqKumtiZ8duwzFFYUYsf5HWHViDXGYsWUFShIK8DMhJnQ8UqrPqPV70eFxxMMbXa4XLjR3q7JWAYA\n081mZNrtweBmcGSkJmMREZG6RNrLZUhDpBKRFjYRERH1coHApqQE2BHeBmRXJ8c8gfUj30TxxXk4\nelppU2QJwO2bmbddiRYXB2zbxqCGiDR17NoxFJUX4Y+H/4gbTTfCqpEyJAUFaQV4furzsJlsKs+Q\neppfknC8sRFlTmcwuDnf0qLZeOOjo5FpswWDmySTiSEgEZGARNrLZUhDpBKRFjYRERH1IV0Dm507\n79w0JgRHk59C8Yg3UXw+HafORqkyxX/F23gbv779iYQE4PBhXn1GRJprbm/G58c/R2F5Ibad2xZW\njeiIaORNyUNBagHSR6RzY70PO9/cLPe06QhujjY2ajZWQmQkMjr62mTa7ZgSGwsD/2wREfU4kfZy\nGdIQqUSkhU1ERER9lIqBjQTgcPISFLsfR3HdQlRhTNi1BuEqClCIn+G/MByXbn5y5Urg44/Drk1E\nFKqTdSexpmIN1n23DnWNdWHVmDJ4CvJT8/HitBcRFx2n8gxJNNfb2rCz45RNmdOJcq8X7Rptl9kM\nBszvuBot02bDLKsVUXq9JmOF4kZbG/KOHbvpseLJkzHAaOyhGRERaUukvVyGNEQqEWlhExERUT9w\n6dLNPWwUBjblSEMx8rAey3EeiWHViUAbnsXneAPvYh52dV6K9tVXwOLFYc+PiCgcLe0t+PLklygs\nL8Tm6s1h1TBFmJA7ORf5qfnIGJXB0zX9RKPPh71utxzauFzY7XKhwe/XZKwonQ6zrdZgT5t5Nhts\nERGajHUvv6upwevff3/zY+PG4WfDh3f7XIiIuoNIe7kMaYhUItLCJiIion4mENgEetgoDGz2IB3F\nyEMJcnEJ4W3OzEAFVuE9rMCnMD08R+5PQ0TUQ07fOI21FWvx/qH3cbXhalg1Jg6ciILUAryU8hLi\nY3iNY3/S7vfjkNcbDG12uFy41tamyVh6ANPMZvl6tI7gZliUOteT3svs8nLs93hufsxiwd60NM3H\nJiLqCSLt5TKkIVKJSAubiIiI+jEVA5vf4E38N/xG0XQG4hryUYSflS7FyOwJimoRESnV6mvFX07+\nBUUVRfj7mb9DQujfIyMNkVg6aSkK0gqQlZjF0zX9kCRJONnYiB2BK9JcLlQ3N2s23hiTCZl2ezC4\nGRsdreqfu6MNDZiyf/+dn5s1C5NjY1Ubi4hIFCLt5TKkIVKJSAubiIiICICiwOa3+Ce8hd/e9ngU\nmtECU8hTMeh8WLLUgFWrgMxMgHuaRNTTquursfbgWrx/8H1c9l4Oq8a4AeOQn5qPH03/EQbHDlZ5\nhtSb1LS0oMzpDAY3lQ0NYUSAD2aI0Sj3tOkIblLMZhgU/GD95Zkz+LcLF+783MiR+D9jwu9bR0Qk\nKpH2chnSEKlEpIVNREREdJsQApu7BTS/wZt4E/+Ot/Gv+DXeDnsqKSnAqlXAypVAdHTYZYiIVNHu\nb8emU5tQWFGIr7//OqzTNUa9EUsmLUF+aj4WJi2EXtfzjeCpZ9W3tWGX2y2HNk4n9ns8aNVoC85i\nMGBuR1+bTLsdsy0WRBsMD/Tedr8fI/fswZXW1js+PywyEufT0xGh559pIupbRNrLZUhDpBKRFjYR\nERHRPdXUAOPHA42Ntz11v4Dmfq8LxYABQH4+8POfA6NGKSpFRKSK867zeP/g+1hTsQY1npqwaiTH\nJSM/NR8/nv5jDDUPVXmG1Fs1+XzY7/EEe9rsdLng8fk0Gcuo02GmxSL3tLFaMTE2FvaIiDu+9lun\nE3nHjt2z3vrJk7HAbr/r8wONRl77R0S9jkh7uQxpiFQi0sImIiIiuie3G7DZbnv4QQOa+70+VHo9\n8MMfyqdrsrJ4FRoR9bx2fzv+dvpvKCwvxKbvN8Ev+UOuEaGPwNMTnkZBagFyxuTwdA3dxCdJOOz1\nBnvalDmdqG1r6+lpheXQzJlIMZt7ehpERCERaS+XIQ2RSkRa2ERERET3VFcHDBp000OhBjT3e1+4\nvWumTpXDmuefB2JiQn47EZHqLrov4g8H/4A1B9fgvOt8WDUSbYl4LfU1vDLjFSRYElSeIfUFkiTh\nTFNTZ2jjcuF0U1NPT+uB/K/ERLyTlNTT0yAiColIe7kMaYhUItLCJiIiIrqnW07ShBvQ3O/9P8Tn\nOIaHcAoTQp5iXBzw2mvyVWijR4f8diIi1fn8PpRWlaKwvBAbT26ETwr9qiqDzoDF4xejILUAj419\nDAb9g/UNof7pSkuL3NOm4+s7rxehn+nS3pTYWFTOmtXT0yAiColIe7kMaYhUItLCJiIiIronSQLi\n44H6esUBTcDd6vwb3sJUVOJdvIG/YnHIU9Xrgaeekk/XLFzIq9CISAyXPZex7tA6FFUUodpZHVaN\nkdaReHXGq3hlxisYaRup8gypL3K3t2O3240ypxNlLhf2ut1oEWBbzwDg0rx5GBwZ2dNTISJ6YCLt\n5TKkIVKJSAubiIiI6L6ys/HbzSmqBDQB9wt8TmMM/hOv4339a3D7LSHXf+gh4Be/AF58EYiNDfnt\nRESq80t+bK7ajKKKIvz5xJ/R7m8PuYZep8fjYx9HQVoBnhj3BCL0d27wTnSrFr8fBzwe+bSN04md\nbjec7aH/GVQiyWTCJ5MmIf0Ove6IiEQm0l4uQxoilYi0sImIiIjuZ9dLv8f8D3962+PhBjQBdwtq\ndmIe5mE3AMADMz7Ei3gPb+AEJoY8ht0OvPIK8PrrQHJy2FMlIlJVrbcWH3z3AYoqinD6xumwaiRY\nEvDK9FfwauqrGG0fre4Eqc/zSxKONDTI16N1nLa51Nqq2Xhjo6PxzyNH4smBA3mKhoh6HZH2chnS\nEIVo3bp1WLdu3W2PNzQ04MCBA8F/Z0hDREREQqusxOppG/AOVgcfUhrQBNwa1PwKq7Ea79z2OgmA\nA9l4D6vwFZ6EBH1I4+h0wJNPylehZWfzKjQiEoNf8mPb2W0orCjE58c/R6sv9E1yHXR4dMyjKEgr\nwFPjn4LRYNRgptTXSZKEs83NwZ42m+rqcLmtTZOxpsbGYlFcHBbZ7ciy22GJ4IkwIhIbQxqiXmz1\n6tV4553bNxluxZCGiIiIhPfww1hdthDvYLVqAU1AIKi5W0Bzqyok4T/xOtbiVbhgD3m8SZPkq9Be\negkwm8OZMRGR+uoa6/DH7/6IwvJCnLx+MqwaQ2KH4OXpL+O11NcwZsAYlWdI/U25243njx/HyaYm\nzcYwAJhjtcqhTVwc0q1WROlD+yAGEZHWGNIQ9WI8SUNERER9xqZNwJNPYhfmBq8iU1M4db2IxUd4\nAe9hFY4h9N+lrNbOq9DGjg357UREmpAkCWXny1BUUYSSoyVo8bWEVSc7ORv5qfn44cQfItLA66Uo\nPP967hz+ubq628aL1uuRabMFQ5vpZjMMPP5KRD2MIQ1RHyTSwiYiIiJ6YCtXAp9+ql39554DVqwA\nioqAv/4V8Psf6G0SgG/xCN7FG9iIp8O6Cu2JJ+Sr0HJyAH6Al4hEcaPpBj787kMUVhTi2LVjYdUY\nFDMIP57+Y7yW+hrGx49XeYbU1z1TWYmN16/32PhxERF4xG4Phjbjo6OhY2hDRN1MpL1chjREKhFp\nYRMRERE9sOvXgWnTgEuX1K+dkAAcPgzEx8v/fvEi8Ic/AGvXAufOPXCZaozG7/BzrMFrcCIu5GmM\nHy+HNT/6EWCxhPx2IiJNSJKE3Rd3o7C8EMVHi9Hc3hxWnQWjF6AgtQBLJi2BKcKk8iypr5EkCUN2\n7cI1jXrThGNEVBQW2u3I7ghtEqKienpKRNQPiLSXy5CGSCUiLWwiIiKikFRWAllZQH29ejXj4oBt\n24CpU29/zucDHA75dM2XXwLt7Q9UsgEx+BjP4z2swhHcoe59WCzAj38s964Zzw+eE5FAnM1OfHz4\nYxRWFOJw7eGwasRHx+OllJeQn5qPSYMmqTxD6ivONDVh7N69imq8N3YsjjQ0wFFfjzPN4YWL9zIx\nJgaLOk7aLLDbEWc0qj4GEZFIe7kMaYhUItLCJiIiIgpZZSXw2GPqnKhJSAD+9rc7BzS3qq0FPvhA\nDmxOn36g8hKAbcjCu3gDX+IZ+GEIeYqPPQa88Qbwgx/wKjQiEockSdh/aT8Kywvx6ZFP0djWGFad\nzFGZyE/Nx7LJyxBtjFZ5ltSbfXTlCl48cUJZjUmT8PyQIQCAc83N2FxfH/yqVfmEjh5AqsUSDG0y\nbDZEG0L/uU9EdCuR9nIZ0hCpRKSFTURERBSW69fl5OKTT8KvsXIl8O67nVecPShJArZulcOaDRuA\n1tYHets5jMJ/4WcoQj5uIMQxAYwbB7z+unzCxmYL+e1ERJpxt7jxSeUnKCwvxMErB8OqYTfZ8dK0\nl5Cflo8pg6fc/w3U571+6hR+d48PZCSbTJAAVN/jhMzrCQn4jzscSZUkCccaG4OBzVanE26fT41p\nB0XpdJhns8n9bOx2zLRYEMFPWxBRGETay2VIQ6QSkRY2ERERkSKbNgG//jWwffuDv+fhh4G33wae\neEL5+NevAx9+KAc2xx6sqXYTTPgEK/EeVuE7TA95SLNZ7lnzi18AEyeG/HYiIk2VXypHYXkhPjny\nCbyt3rBqzB0xFwVpBVj+0HLEGGNUniH1FqkHDuCg985/hl4aMgTvjRsHAPjF99/jw9raO9cwm1E+\nc+Z9x2r3+1Hu9QZDm50uF1pU3oa0GgzI6jhls8hux0OxsdDpdKqOQUR9k0h7uQxpiFQi0sImIiIi\nUsWRI8CnnwL79gHl5Tf3rImLA9LSgNmzgRUrgCkafEJbkoDdu+WwprgYaGq6/1sAlCET72EV/owl\n8CEi5GEffRRYtUrOm/jhXCISibfViz8d+RMKywux/9L+sGpYo6x4YeoLKEgrQMrQFJVnSCKTJAmT\n9u3DyVt+ntoMBvx+/Hg813GFWcCntbX46alTt52GmRAdjeOzZ4cchjT5fNjpcmGz04nN9fUo93jg\nD+8/5a6GGI1YGBcXDG1GR/O6PyK6M5H2chnSEKlEpIVNREREpDpJArxeoKUFiIqSj5505ydVXS75\nGraiIuDgg137cwEj8F/4GQpRgOsYGPKQY8bIV6G9/DJgt4f8diIiTR26cghF5UX4qPIjuFvcYdWY\nlTALBWkFeG7KczBHmlWeIYmowefDv1RV4d2aGkgAMmw2fDRpEhJNpju+/mxTE144fhw73W7oAPzD\niBH430lJiFWhL4yzrQ1bnc5gaHO8MbweTPeSbDIhuyO0ecRux6DISNXHIKLeSaS9XIY0RCoRaWET\nERER9Wnl5XJY8/HHcnB0H00w4U94Du9hFQ4iNeThYmOBl16Sr0KbPDmcCRMRaaehtQHrj65HUUUR\ndl/cHVYNc6QZz099Hvmp+UhLSFN5hiSiHU4ndrvd+McRI+7b06Xd78e/X7yIeVYrMjT81MKllhZs\nqa8PhjYXWlpUHyMlNlY+ZRMXh4dtNpgjQj9xS0R9g0h7uQxpiFQi0sImIiIi6he8XvkatKIiYO/e\n+75cArAT8/EeVmEDloZ1FdqiRcAbbwCLFwMqfIiYiEhVlbWVKKoowoeHP4Sz2RlWjdRhqShILcCK\nqStgjbKqPEOiByNJEk43NcHR0c/mW6cTN9rbVR0jQqfDHIslGNqkW62I5D2nRP2GSHu5DGmIVCLS\nwiYiIiLqdyor5bDmww8B5/03Ji9iOH6Pn6IQBbiGwSEPl5QkX4X2yityex4iIpE0tTXhs2OfobCi\nEDvO7wirRowxBiumrEBBWgFmJcxiM3bqUX5JwiGvF5s7QpsylwuNfnU72sTo9ci02bAoLg7ZcXFI\nMZuh5597oj5LpL1chjREKhFpYRMRERH1W01NwIYNcmCzfft9X96MKKzHcryLN1COmSEPFxMDvPAC\nsGoV0OVXQSIiYRy/dhxFFUX44LsPcKPpRlg1pg2ZhoLUAjw/7XnYTWzSRT2vxe/HXrc7GNrs9XjQ\nrvIWZ3xEBB6Ji8Miux2L4uIwNjqaYSVRHyLSXi5DGiKViLSwiYiIiAjAyZPAmjXAunVAXd09XyoB\n2IN0vIdVKEEu2mEMebhHHpGvQnvqKV6FRkTiaW5vxp+P/xmFFYXYenZrWDWiI6KRNyUP+an5mDti\nLjesSRie9nZsd7mCoc3hhgbVxxgZFSVfjdYR2gyLilJ9DCLqPiLt5TKkIVKJSAubiIiIiLpobQW+\n/FI+XVNaet+XX8Iw/F/8BL/HT3EVQ0IeLjFRvgrt1VeBAQPCmTARkbZOXT+FNRVr8IdDf0Bd471D\n7Lt5aNBDKEgrwAvTXsCAaH6zI7FcbW3Ft05nMLSpam5WfYzJMTHBfjZZNhvsxtA/4EFEPUekvVyG\nNEQqEWlhExEREdFdVFcDa9cC778PXL58z5e2IBIlyMW7eAP7MTvkoaKjgeefl69CmzYt3AkTEWmn\npb0FX578EoXlhdhcvTmsGlGGKOQ+lIuC1AJkjMrg6RoS0tmmJmzuEtpcbWtTtb4ewEyLJRjazLda\nYeKxWiKhibSXy5CGSCUiLWwiIiIiuo/2dmDTJvl0zddfA/dpPrwXs/EeVmE9lqMNkSEPl5UlhzXP\nPANERIQ7aSIi7Zy5cSZ4uqa2oTasGhMHTkR+aj5eSnkJA2MGqjxDInVIkoQjDQ1yYON0YpvTCY/P\np+oYUTod5ttswdAmzWxGhF6v6hhEpIxIe7kMaYhUItLCJiIiIqIQXLwon6xZuxY4f/6eL72CIcGr\n0K5gWMhDjRwJ/PznwGuvAQO5f0lEAmrzteEvp/6CwvJC/P3M3yEh9G2jSEMknp30LApSC7Bg9AKe\nriGhtfv92O/xBEObXS4XWlXeLrUZDMiy25HdEdpMionhuiDqYSLt5TKkIVKJSAubiIiIiMLg88k9\na4qKgI0b5dM2d9EKIz7DMryHVdiDuSEPZTIBK1fKp2umT1cyaSIi7Zx1nsXairVYe3AtLnvvfUXk\n3YwbMA75qfn40fQfYXDsYJVnSKS+Rp8PO12uYGhT7vGEEVXe27DISCy024MnbUaZTCqPQET3I9Je\nLkMaIpWItLCJiIiISKErV4APPgDWrAFOn77nS/djJt7DKhQjD62ICnmozEw5rPnhDwH2HCYiEbX7\n27Hp1CYUVRThr9//NazTNUa9ET+c+EPkp+ZjUfIi6HW8+ol6h/q2Nmx1OuHo6GdzsqlJ9THGRkdj\nUUdo84jdjoGRoV+tSkShEWkvlyENkUpEWthEREREpBK/H9i2TT5ds2ED0Np615fWYjAKUYD/ws9w\nGQkhDzV8uHwVWn4+MGiQkkkTEWnnvOs83j/4PtYeXIuL7oth1UiyJyE/NR8vz3gZQ81DVZ4hkbZq\nWlrkUzYdXzX3+N0gHDoAKWazfDWa3Y5Mux2xBoOqYxCRWHu5DGmIVCLSwiYiIiIiDVy/Dnz4oRzY\nHDt215e1IQKf41m8izewC/NDHiYqClixQj5dk5qqZMJERNrx+X342+m/obCiEF+d+gp+yR9yjQh9\nBJ4a/xQK0gqQk5wDg54b0dS7SJKEU01NwcBmi9MJ5z2uSw2HUadDutUqX41mt2OO1QqjnifRiJQS\naS+XIQ2RSkRa2ERERESkIUkCdu8GCguB9euBe1x7Uo5UvO+zkc8AACAASURBVIdV+BQrwroKbd48\n4I03gGef5VVoRCSuGncN/nDoD1hTsQbnXOfCqpFoS8SrM17FKzNewXDrcJVnSNQ9fJKEgx4PNjud\n2Fxfjx0uF5r8oQeY9xKr1+PhQD8bux3TzGbodTpVxyDqD0Tay2VIQ6QSkRY2EREREXUTpxP45BP5\ndM2hQ3d92TUMRBHy8Tv8HDUYEfIwCQnAT38KFBQAQ4YomTARkXZ8fh9Kq0pRVFGEL098CZ/kC7mG\nXqfHk+OfRH5qPh4f+zhP11Cv1uL3Y7fLFQxt9rndCH1V3NtAoxELA6FNXBySTSboGNoQ3ZdIe7kM\naYhUItLCJiIiIqJuJklAebkc1nzyCeD13vFlbYjAF/gh3sUb2IHMkIeJjATy8uSr0GbNUjppIiLt\nXPZcxrpD67Dm4BpU1VeFVWOEdUTwdM0o2yiVZ0jU/dzt7djudAZDm8qGBtXHSIyKCgY2C+12DI0K\n/SQvUX8g0l4uQxoilYi0sImIiIioB3m9QHGxHNjs3XvXlx3EdPwHfoGP8TxaYAp5mPR0OaxZtkwO\nb4iIROSX/NhSvQWF5YX44sQXaPO3hVxDr9PjsbGPoSC1AIvHL0aEPkKDmRJ1v9rWVmzp6Gez2enE\n2eZm1cd4KCYmGNpk2e2wRXD9EAFi7eUypCFSiUgLm4iIiIgEcfgwsGYN8OGH8tVod1CHeKzBa/gd\nfo4LCP2T4kOHyleh/eQn8j8TEYnqasNVfHDoAxRWFOL0jdNh1RhmHoZXZryC11Jfw2j7aHUnSNTD\nqpqa5MCmvh5bnE5caws91LwXA4CZFguyO0KbuVYrTAZeKUj9k0h7uQxpiFQi0sImIiIiIsE0NQEb\nNgCFhUBZ2R1f0g4DvsQzeA+rsA0LQh7CaASWL5dP18yZo3C+REQakiQJ285tQ2F5ITYc34BWX2vI\nNXTQ4dExjyI/NR9PT3gaRoNRg5kS9Ry/JOFIQ0MwtNnmcsHrU7ejjUmvR4bNhkUdPW1SLRYY2M+G\n+gmR9nIZ0hCpRKSFTUREREQCO3FCPl3zwQdAXd0dX3IYU/EeVuEjvIBmRIc8xOzZcliTmwvwKnoi\nElldYx3++N0fUVRRhBN1J8KqMSR2CF6e/jJeS30NYwaMUXmGRGJo8/ux3+PB5vp6OOrrsdvtRpvK\n27r2iAgssNuDoc3EmBjoGNpQHyXSXi5DGiKViLSwiYiIiKgXaGkBvvxS7l3jcNzxJdcxAGvxKv4T\nr+M8EkMeYsgQ+Rq0n/wESEhQOmEiIu1IkoQd53egsKIQJUdL0OJrCavOoqRFKEgrwDMTnkFUBFNq\n6rsafD7scLmCJ20Oer1Qe5M3ITIy2M9mkd2OEabQe+gRiUqkvVyGNEQqEWlhExEREVEvU1UFrF0L\n/OEPwOXLtz3dDgP+gqfwHlbhWywMuXxEBLBsGfDGG0B6OsAPxRKRyG403cBHhz9CYXkhjl47GlaN\ngTED8eOUHyM/LR/j48erPEMi8Vxva8NWpzMY2pxqalJ9jPHR0cHQ5hG7HQOMvGaQei+R9nIZ0hCp\nRKSFTURERES9VHs7sGmTfLrm668Bv/+2l1RiCv4Dv8CHeBFNiAl5iLQ0OazJy+NVaEQkNkmSsPvi\nbhRVFKH4SDGa2sPbdM5KzEJBWgGenfQsTBE8CUD9w4XmZjmw6QhuLreG3vvpXnQAZpjNwdAm02ZD\njMGg6hhEWhJpL5chDZFKRFrYRERERNQHXLggn6xZuxY4f/62p28gDu/jFfwnXsdZJIVcftAgoKAA\n+NnPgOHD1ZgwEZF2nM1OfHz4YxRWFOJw7eGwagyIHoCXpr2E/LR8TB40WeUZEolLkiScaGwMhjbf\n1tfD5fOpOoZRp8NcqxXZHaHNLIsFRr1e1TGI1CTSXi5DGiKViLSwiYiIiKgP8fmA0lL5dM3GjfJp\nm65PQ49NWIx38QY2Izvk8hERwLPPyqdr5s3jVWhEJDZJkrD/0n4UlhfiT0f+hIa2hrDqZIzKQH5q\nPnIn5yLaGK3yLInE5pMkVHg8wdBmh8uF5juc3lXCbDAgy2YLnrSZEhsLPX/JIIGItJfLkIZIJSIt\nbCIiIiLqo65cAdatA9asAc6cue3pY5iE/8Av8AF+hEbEhlx+xgxg1SpgxQqAvYGJSHTuFjc+rfwU\nhRWFqLhcEVYNu8mOF6e9iPzUfEwdMlXlGRL1Ds0+H3a73XB09LPZ7/FA3cgGGGQ0YqHdHgxtkqMZ\njlLPEmkvlyENkUpEWthE/4+9+w6Pqs77Pv6eNAhJSGgJvSM1oYlKU7pYEVREmChKedaC+6y6t673\n7r2wzXVXfPYWdXepgUyICEtRQAQCUQQUKZLQRAi9BTAdSJt5/jiaSM+cnCQn4fO6Li+dyZzv+eJe\nh5XfZ76/n4iIiFRxbjckJhrTNYsXwxX7zKcTyhye4T1eJIVWXpevWxcmTDC2QmvSxKKeRUTK0LaT\n25ixfQZxyXFk52WbqnFX47uY2G0iIzuOJCjA+6BbpKrIKCjg8x/PsklIS2P3hQuW36N59eoMDAtj\nUK1aDKhVi/CAAMvvIXIjdlrLVUgjYhE7PdgiIiIicgs5dw5iY43AZu/ey35UiA+fch/TmMRq7vW6\ntK8vDB9uTNf07aut0ETE/rLzslmwawHTt09ny4ktpmrUrFaTMZFjmNh9Il3qd7G4Q5HK53RuLut+\nFtocyc21/B6RQUHGlE1YGPeEhRHi52f5PUR+zk5ruQppRCxipwdbRERERG5BHg9s2mSENR99BBcv\nXvbjfbTlPV4khrHkEOx1+c6djbBm9GjQDiUiUhl8e/pbZmybgSvZRWZupqkaPRr2YEK3CYzqNIqQ\naiEWdyhS+Xg8HlIuXSoKbBLS0jh/xXl5peUL3FGzZlFo0zM0lGo+PpbeQ8ROa7kKaUQsYqcHW0RE\nRERucenpMH++Edh8++1lP8qgJjGM5T1e5ABtvC5du7axFdrzz0PTplY1LCJSdnLycli4ZyHTt01n\n8/HNpmoEBwQzutNoJnafSPeG3S3uUKTycns8JGVnk/DjpM0X6enkuK090SbQx4e+oaFF59l0CQ7G\nV+O9Ukp2WstVSCNiETs92CIiIiIigDFds22bEdbMnw/Zxec0uHGwiqFMYxKruM/r0j4+8MgjxnTN\nPfdoKzQRqRx2pe5ixrYZzEuaR/qldFM1utbvysTuExkdOZqa1Wpa3KFI5ZbndrMlM7MotPkqM5N8\ni5efa/n50T8srCi0uS0wEIf+Q0S8ZKe1XIU0Ihax04MtIiIiInKV7GxYsACmT4ctl5/T8B238T4v\nEMNYsvB+wTEy0ghrxoyBGjWsalhEpOxczL/Ioj2LmLF9BhuObjBVo4Z/DUZ1HMXE7hO5o9EdWiQW\nuYbsggI2ZGQYW6Olp/Ptz74wYpVGAQFFgc3AWrVoVK2a5feQqsdOa7kKaUQsYqcHW0RERETkhpKS\njOkal8vYGu1HmYQwj6eYxiT209brsrVqwfjxxlZozZtb2K+ISBnae3YvM7bPYO7Oufxw8QdTNaIi\nopjQbQLOKCdh1cMs7lCk6jiXl8f6H6dsEtLTOXDFGXpWaBsYyKAfA5t+YWHU8ve3/B5S+dlpLVch\njYhF7PRgi4iIiIiUyMWLsGiREdhsKP4muRsHaxjMu7zESh7wuqyPDzz0kDFdM2CAtkITkcrhUsEl\nluxdwvTt00k8nGiqRqBfICM7jmRCtwn0atJL0zUiN3H00iUjsPkxtDmdl2dpfR+gW0gIA3/cHq13\naCg1fH0tvYdUTnZay1VII2IROz3YIiIiIiJe27cPZs6EuXPh3Lmitw/Qivd5gdk8SyahXpft2BFe\nfBGioyEoyMqGRUTKzv7z+5m5fSZzvp3DuQvnbn7BNXSo14GJ3SYS3Tma2oG1Le5QpOrxeDzsvXCB\nhLQ01qalkZieTmZhoaX3CHA46BUaWhTa9AgJwc/Hx9J7SOVgp7VchTQiFrHTgy0iIiIiYlpuLixb\nZkzXrF1b9HYWwcQSzTQmsY/2XpcNC4Nnn4UXXoCWLa1sWESk7OQV5rFs3zKmb5/O2pS1N7/gGqr5\nVuOxDo8xsftE+jbtq+kakRIqcLvZlp1dNGmzMSODXIuXskN8fen3Y2AzMCyMjkFBekZvEXZay1VI\nI2IROz3YIiIiIiKWSEmBWbNg9mw4fRoAD7CWQUxjEst5EA/effvU4YAHHzS2Qhs0SFuhiUjlcfCH\ng8zaMYvZO2ZzJueMqRpt67RlQrcJPN3laerWqGtxhyJV28XCQjZlZhaFNluzsnBbfI8If38G/Hie\nzcCwMJoHBlp8B7ELO63lKqQRsYidHmwREREREUvl58OKFcZ0zapV4DaWRA7Skg94nlmMIwPvD8pu\n397YCu2ppyA42OqmRUTKRn5hPp/s/4QZ22fw2YHP8OD90lqAbwAj2o9gQrcJ9GveDx+HtlsS8VZ6\nfj6fZ2QUbY+298IFy+/Rsnp1I7CpVYsBYWHUCwiw/B5SMey0lquQRsQidnqwRURERETKzLFjxmTN\nrFnGPwPZBOHCyTQmsQfv/xu4Zs3irdBat7a6YRGRsnM4/TCzts9i1o5ZnMo+ZapG69qtmdBtAmO7\njCU8KNziDkVuHSdzc1mXlkZCejoJaWkcy821/B6dg4KKQpu7Q0MJ9vOz/B5SPuy0lquQRsQidnqw\nRURERETKXGEhrF5tTNd88gkUFOAB1tOfd3mJj3nY1FZo999vbIU2eDDoHF8RqSwK3AWs/H4l07dN\n59MDn+L2eL8Jk5+PH4+0e4SJ3SYysOVATdeIlILH4+HAxYvG1mjp6axLS+OHggJL7+HncHBnSEhR\naHNXzZoE6D9eKg07reUqpBGxiJ0ebBERERGRcnX6NMTEwMyZcPAgAIdozgc8z0zGk04tr0vedpsR\n1jz9NISEWNyviEgZOpZxjNk7ZjNzx0yOZx43VaNFWAvGdxvPM12eoUFIA4s7FLn1uD0edmZns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WrVtHeHh4hfUjIiIiIiJVSFgYAb98jlGH/8rGr/3Z+sgfedrPRQAlX1TMpTox+3vR3dmB3sHf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\nX6RVq1Y5WoeUKJKh74sXL84RRxzBJZdcwpgxY/jss8/C/oE3ffp0Lrvsshy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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp.plot_compression_experiments(res_smt, comp_ratios,\n",
+    "                                 \"../figs/compression_small_traffic.png\")\n",
+    "Image(filename=\"../figs/compression_small_traffic.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reconstruction Error: SWT vs GWT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      GWT error|      SWT error|      Reduction\n",
+      "-----------------------------------------------\n",
+      "      0.2258367|      0.1280286|     -0.4330920\n",
+      "      0.1142181|      0.0512101|     -0.5516460\n",
+      "      0.0530751|      0.0226301|     -0.5736211\n",
+      "      0.0279236|      0.0080920|     -0.7102105\n",
+      "      0.0107338|      0.0031385|     -0.7076054\n",
+      "      0.0045087|      0.0015076|     -0.6656293\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "reduction = np.divide(res_smt['SWT'], res_smt['GWT']) - 1\n",
+    "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'SWT error', 'Reduction')\n",
+    "text += \"-\"*47 + \"\\n\"\n",
+    "for i in range(len(comp_ratios)):\n",
+    "    text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_smt['GWT'][i], res_smt['SWT'][i], reduction[i])\n",
+    "print(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reconstruction Error: FSWT vs GWT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      GWT error|      SWT error|      Reduction\n",
+      "-----------------------------------------------\n",
+      "      0.2258367|      0.1269770|     -0.4377488\n",
+      "      0.1142181|      0.0474275|     -0.5847640\n",
+      "      0.0530751|      0.0212761|     -0.5991323\n",
+      "      0.0279236|      0.0082267|     -0.7053843\n",
+      "      0.0107338|      0.0042162|     -0.6072049\n",
+      "      0.0045087|      0.0012525|     -0.7222055\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "reduction = np.divide(res_smt['FSWT'], res_smt['GWT']) - 1\n",
+    "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n",
+    "text += \"-\"*47 + \"\\n\"\n",
+    "for i in range(len(comp_ratios)):\n",
+    "    text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_smt['GWT'][i], res_smt['FSWT'][i], reduction[i])\n",
+    "print(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Traffic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "G = io.read_graph(\"../\" + data.traffic[\"path\"] + \"traffic.graph\",\n",
+    "                  \"../\" + data.traffic[\"path\"] + \"traffic.data\")\n",
+    "F = io.read_values(\"../\" + data.traffic[\"path\"] + \"traffic.data\", G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#vertices =  1923\n",
+      "#edges =  2659\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"#vertices = \", G.number_of_nodes())\n",
+    "print(\"#edges = \", len(G.edges()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier(), static.HWavelets()]\n",
+    "\n",
+    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
+    "\n",
+    "res_t, time_t = exp.compression_experiment(G, F, algs, comp_ratios, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Hb0x7/RNgOoZhyOeLyOeLSFo57fHJ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88mPyQnklOl+danU4rc1ppCalcmqbU6ldrXYpJVVRlad7uZY0UikpT9/YkiRJkiRVSBs3wqhR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\nMHIkPPYYFOZ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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp.plot_compression_experiments(res_t, comp_ratios,\n",
+    "                                 \"../figs/compression_traffic.png\")\n",
+    "Image(filename=\"../figs/compression_traffic.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reconstruction Error: FSWT vs GWT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      GWT error|      SWT error|      Reduction\n",
+      "-----------------------------------------------\n",
+      "      2.2200923|      1.5877000|     -0.2848496\n",
+      "      1.1735329|      0.8091524|     -0.3104988\n",
+      "      0.6413987|      0.4088434|     -0.3625753\n",
+      "      0.3337205|      0.2063473|     -0.3816763\n",
+      "      0.1571017|      0.0444101|     -0.7173164\n",
+      "      0.0660063|      0.0084063|     -0.8726447\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "reduction = np.divide(res_t['FSWT'], res_t['GWT']) - 1\n",
+    "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n",
+    "text += \"-\"*47 + \"\\n\"\n",
+    "for i in range(len(comp_ratios)):\n",
+    "    text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_t['GWT'][i], res_t['FSWT'][i], reduction[i])\n",
+    "print(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Human"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "G = io.read_graph(\"../\" + data.human[\"path\"] + \"human.graph\",\n",
+    "                  \"../\" + data.human[\"path\"] + \"human.data\")\n",
+    "F = io.read_values(\"../\" + data.human[\"path\"] + \"human.data\", G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#vertices =  845\n",
+      "#edges =  1272\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"#vertices = \", G.number_of_nodes())\n",
+    "print(\"#edges = \", len(G.edges()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier(), static.HWavelets()]\n",
+    "\n",
+    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
+    "\n",
+    "res_h, time_h = exp.compression_experiment(G, F, algs, comp_ratios, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp.plot_compression_experiments(res_h, comp_ratios,\n",
+    "                                 \"../figs/compression_human.png\")\n",
+    "Image(filename=\"../figs/compression_human.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ABgAAAAAAdAmENACAOzN4sPT1r0vv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\nb9O+tKrWilAoFEBCBcGSRiqAEukbW5IkSZIk7cfkydC1K6xYE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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_h_copy = dict(res_h)\n",
+    "res_h_copy.pop('HWT')\n",
+    "\n",
+    "exp.plot_compression_experiments(res_h_copy, comp_ratios,\n",
+    "                                 \"../figs/compression_human2.png\")\n",
+    "Image(filename=\"../figs/compression_human2.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reconstruction Error: FSWT vs GWT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      GWT error|     FSWT error|      Reduction\n",
+      "-----------------------------------------------\n",
+      "      6.8382095|      5.0477291|     -0.2618347\n",
+      "      3.8316544|      2.7098935|     -0.2927615\n",
+      "      2.1117686|      1.7143273|     -0.1882031\n",
+      "      1.1529724|      0.6686821|     -0.4200363\n",
+      "      0.5595893|      0.2221975|     -0.6029275\n",
+      "      0.2478246|      0.0556169|     -0.7755798\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "reduction = np.divide(res_h['FSWT'], res_h['GWT']) - 1\n",
+    "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n",
+    "text += \"-\"*47 + \"\\n\"\n",
+    "for i in range(len(comp_ratios)):\n",
+    "    text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_h['GWT'][i], res_h['FSWT'][i], reduction[i])\n",
+    "print(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wikipedia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "G = io.read_graph(\"../\" + data.wiki[\"path\"] + \"wiki.graph\",\n",
+    "                  \"../\" + data.wiki[\"path\"] + \"wiki.data\")\n",
+    "F = io.read_values(\"../\" + data.wiki[\"path\"] + \"wiki.data\", G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#vertices =  4881\n",
+      "#edges =  11937\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"#vertices = \", G.number_of_nodes())\n",
+    "print(\"#edges = \", len(G.edges()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier(), static.HWavelets()]\n",
+    "\n",
+    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
+    "sys.setrecursionlimit(G.number_of_nodes())\n",
+    "res_w, time_w = exp.compression_experiment(G, F, algs, comp_ratios, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp.plot_compression_experiments(res_w, comp_ratios,\n",
+    "                                 \"../figs/compression_wiki.png\")\n",
+    "Image(filename=\"../figs/compression_wiki.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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c9Yu1Z66ikpLS1wfY0hF/ovqOpMle/Mm/eXUTvtOm11ex6R9kjNTJ05ZICT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\nvve97ykrKysoawIAAAAAEJGSkkx7sgMHzGu7QhuXy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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_w_copy = dict(res_w)\n",
+    "res_w_copy.pop('HWT')\n",
+    "\n",
+    "exp.plot_compression_experiments(res_w_copy, comp_ratios,\n",
+    "                                 \"../figs/compression_wiki2.png\")\n",
+    "Image(filename=\"../figs/compression_wiki2.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reconstruction Error: FSWT vs GWT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      GWT error|     FSWT error|      Reduction\n",
+      "-----------------------------------------------\n",
+      "      1.1236578|      0.9424534|     -0.1612630\n",
+      "      0.5947244|      0.4116692|     -0.3077982\n",
+      "      0.3650694|      0.2490409|     -0.3178259\n",
+      "      0.2259673|      0.1528849|     -0.3234200\n",
+      "      0.1347382|      0.0679602|     -0.4956130\n",
+      "      0.0702006|      0.0388420|     -0.4466996\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "reduction = np.divide(res_w['FSWT'], res_w['GWT']) - 1\n",
+    "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n",
+    "text += \"-\"*47 + \"\\n\"\n",
+    "for i in range(len(comp_ratios)):\n",
+    "    text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_w['GWT'][i], res_w['FSWT'][i], reduction[i])\n",
+    "print(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Blogs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "G = io.read_graph(\"../\" + data.polblogs[\"path\"] + \"polblogs.graph\",\n",
+    "                  \"../\" + data.polblogs[\"path\"] + \"polblogs.data\")\n",
+    "F = io.read_values(\"../\" + data.polblogs[\"path\"] + \"polblogs.data\", G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#vertices =  1222\n",
+      "#edges =  16717\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"#vertices = \", G.number_of_nodes())\n",
+    "print(\"#edges = \", len(G.edges()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "algs = [static.OptWavelets(n=20), static.GRCWavelets(), static.Fourier(), static.HWavelets()]\n",
+    "\n",
+    "comp_ratios = [0.1, 0.20, 0.30, 0.40, 0.50, 0.60]\n",
+    "\n",
+    "res_b, time_b = exp.compression_experiment(G, F, algs, comp_ratios, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rcsXhwf3LQpHj0wM4tjzmcd+Xx05POxs4IPP2qnWNUzXsjTnMlEQzptegFKcuvLL4+5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\ndc3zzz8f733ve2doR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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp.plot_compression_experiments(res_b, comp_ratios,\n",
+    "                                 \"../figs/compression_blog.png\")\n",
+    "Image(filename=\"../figs/compression_blog.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Vmk9Ew6GGaDjUEOt2rUvsUzShqM/ZLfPK5nV9P6d0ThRO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\nnMYNHFetW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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_b.pop('HWT')\n",
+    "exp.plot_compression_experiments(res_b, comp_ratios,\n",
+    "                                 \"../figs/compression_blog2.png\")\n",
+    "Image(filename=\"../figs/compression_blog2.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reconstruction Error: FSWT vs GWT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      GWT error|     FSWT error|      Reduction\n",
+      "-----------------------------------------------\n",
+      "    760.4954199|    667.9291131|     -0.1217184\n",
+      "    552.5279946|    483.1301899|     -0.1256005\n",
+      "    408.4589303|    310.3847051|     -0.2401079\n",
+      "    230.1754795|    130.1136140|     -0.4347199\n",
+      "    132.3424893|     27.8478488|     -0.7895774\n",
+      "     55.7708549|      0.1880823|     -0.9966276\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "reduction = np.divide(res_b['FSWT'], res_b['GWT']) - 1\n",
+    "text = \"{:>15s}|{:>15s}|{:>15s}\\n\".format('GWT error', 'FSWT error', 'Reduction')\n",
+    "text += \"-\"*47 + \"\\n\"\n",
+    "for i in range(len(comp_ratios)):\n",
+    "    text += \"{:>15.7f}|{:>15.7f}|{:>15.7f}\\n\".format(res_b['GWT'][i], res_b['FSWT'][i], reduction[i])\n",
+    "print(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Average Computation Time (in seconds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transform |  Small Traffic        Traffic      Wikipedia          Human          Blogs\n",
+      "HWT       |      0.1105079     53.0783952    379.8365982      5.1249488     10.7540500\n",
+      "FT        |      0.0714019     33.3342006    168.3058656      3.8720605      8.3236080\n",
+      "GWT       |      0.5620325     32.1109771    513.3351845     19.0809389     51.8282435\n",
+      "SWT       |      1.5199850    Unavailable    Unavailable    Unavailable    Unavailable\n",
+      "FSWT      |      0.5427060     23.3150091    517.8988918     20.3815869     52.4716242\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "times = [time_smt, time_t, time_w, time_h, time_b]\n",
+    "algs = ['HWT', 'FT', 'GWT', 'SWT', 'FSWT']\n",
+    "transforms = ('Transform', 'Small Traffic', 'Traffic', 'Wikipedia', 'Human', 'Blogs')\n",
+    "text = \"{:10s}|{:>15s}{:>15s}{:>15s}{:>15s}{:>15s}\".format(*transforms) + \"\\n\"\n",
+    "for alg in algs:\n",
+    "    text += \"{:10s}|\".format(alg)\n",
+    "    for time in times:\n",
+    "        msg = np.mean(time.get(alg, 0.))\n",
+    "        if msg != 0.:\n",
+    "            text += \"{:>15.7f}\".format(msg)\n",
+    "        else:\n",
+    "            text += \"{:>15s}\".format('Unavailable')\n",
+    "    text += \"\\n\"\n",
+    "\n",
+    "print(text)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/synthetic-data.ipynb b/notebooks/synthetic-data.ipynb
similarity index 99%
rename from synthetic-data.ipynb
rename to notebooks/synthetic-data.ipynb
index 11743bb..becd603 100644
--- a/synthetic-data.ipynb
+++ b/notebooks/synthetic-data.ipynb
@@ -18,34 +18,19 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
-    "import networkx\n",
-    "import math\n",
-    "import scipy.optimize\n",
-    "import numpy\n",
+    "import numpy as np\n",
+    "import random\n",
     "import sys\n",
-    "from scipy import linalg\n",
-    "import matplotlib.pyplot as plt\n",
+    "\n",
     "from IPython.display import Image\n",
-    "import pywt\n",
-    "import scipy.fftpack\n",
-    "import random\n",
-    "import operator\n",
-    "import copy\n",
-    "from collections import deque\n",
-    "from sklearn.preprocessing import normalize\n",
-    "from sklearn.cluster import SpectralClustering\n",
-    "from matplotlib.lines import Line2D\n",
-    "from lib.io import *\n",
-    "from lib.vis import *\n",
-    "from lib.graph_signal_proc import *\n",
-    "from lib.syn import *\n",
-    "from lib.experiments import *\n",
-    "from lib.static import *\n",
-    "from lib.datasets import *"
+    "\n",
+    "sys.path.append('../')\n",
+    "\n",
+    "import lib.experiments as exp"
    ]
   },
   {
@@ -59,26 +44,26 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
     "sizes = range(200, 1001, 200)\n",
     "\n",
-    "num = 1\n",
+    "num = 10\n",
     "balance = 1.\n",
     "sparsity = 0.8\n",
     "noise = .1\n",
     "energy = 100\n",
-    "res_t = size_time_experiment(sizes, balance, sparsity, energy, noise, num)"
+    "random.seed(3)\n",
+    "np.random.seed(7)\n",
+    "res_t = exp.size_time_experiment(sizes, balance, sparsity, energy, noise, num)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -93,8 +78,9 @@
     }
    ],
    "source": [
-    "plot_size_time_experiment(numpy.array(res_t), sizes, \"figs/size_time_synthetic.png\")\n",
-    "Image(filename=\"figs/size_time_synthetic.png\")"
+    "exp.plot_size_time_experiment(np.array(res_t), sizes,\n",
+    "                              \"../figs/size_time_synthetic.png\")\n",
+    "Image(filename=\"../figs/size_time_synthetic.png\")"
    ]
   },
   {
@@ -108,7 +94,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -118,15 +104,16 @@
     "balance = 1.\n",
     "noise = .5\n",
     "energy = 100\n",
-    "res_acc = sparsity_acc_experiment(sparsity, size, balance, energy, noise, num)"
+    "random.seed(3)\n",
+    "np.random.seed(7)\n",
+    "res_acc = exp.sparsity_acc_experiment(sparsity, size, balance,\n",
+    "                                      energy, noise, num)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -141,8 +128,9 @@
     }
    ],
    "source": [
-    "plot_sparsity_acc_experiment(res_acc, sparsity, \"figs/sparsity_acc_synthetic.png\")\n",
-    "Image(filename=\"figs/sparsity_acc_synthetic.png\")"
+    "exp.plot_sparsity_acc_experiment(res_acc, sparsity,\n",
+    "                                 \"../figs/sparsity_acc_synthetic.png\")\n",
+    "Image(filename=\"../figs/sparsity_acc_synthetic.png\")"
    ]
   },
   {
@@ -156,7 +144,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -166,15 +154,16 @@
     "num = 10\n",
     "sparsity = .5\n",
     "balance = 1.\n",
-    "res_acc = energy_acc_experiment(energy, size, sparsity, noise, balance, num)"
+    "random.seed(3)\n",
+    "np.random.seed(7)\n",
+    "res_acc = exp.energy_acc_experiment(energy, size, sparsity,\n",
+    "                                    noise, balance, num)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -189,9 +178,10 @@
     }
    ],
    "source": [
-    "plot_energy_acc_experiment(res_acc, energy, \"figs/energy_acc_synthetic.png\")\n",
+    "exp.plot_energy_acc_experiment(res_acc, energy,\n",
+    "                               \"../figs/energy_acc_synthetic.png\")\n",
     "\n",
-    "Image(filename=\"figs/energy_acc_synthetic.png\")"
+    "Image(filename=\"../figs/energy_acc_synthetic.png\")"
    ]
   },
   {
@@ -205,7 +195,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -215,15 +205,15 @@
     "sparsity = 1.\n",
     "balance = 1.\n",
     "energy = 100\n",
-    "res_acc = noise_acc_experiment(noise, size, sparsity, energy, balance, num)"
+    "random.seed(3)\n",
+    "np.random.seed(7)\n",
+    "res_acc = exp.noise_acc_experiment(noise, size, sparsity, energy, balance, num)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -238,8 +228,8 @@
     }
    ],
    "source": [
-    "plot_noise_acc_experiment(res_acc, noise, \"figs/noise_acc_synthetic.png\")\n",
-    "Image(filename=\"figs/noise_acc_synthetic.png\")"
+    "exp.plot_noise_acc_experiment(res_acc, noise, \"../figs/noise_acc_synthetic.png\")\n",
+    "Image(filename=\"../figs/noise_acc_synthetic.png\")"
    ]
   }
  ],
@@ -259,9 +249,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.4.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..b1bf9e2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+matplotlib
+# The code also supports NetworkX 2.0 but different versions
+# could lead to a test failure due to a slightly different output
+networkx==1.11
+numpy
+scipy