03_fitting_power_laws_01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(range(1,101))\n",
    "y = [x_**(-2.6) for x_ in x] # This is the power law function with alpha=2.6\n",
    "\n",
    "x_log = np.log10(x)\n",
    "y_log = np.log10(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VVUdofN1dS4DhJEuA5wOHaWzdVfVl4NtTdk+3xk3Ajqr6flU9Qufb7C4fxDwWQtxXAAd7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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11815f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QC7/KriAR7ACQ2OT4qH7595/Vqk+cEWn7Yxm3wAl2AEhJc+ZMr9H7kMV5Kk18BDsApCjK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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c453b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.scatter(x_log,y_log)\n",
    "#Try also \n",
    "#plt.loglog(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

03_fitting_power_laws_02.ipynb

03_networkx_transitivi.ipynb