notebooks/unit04/unit04_04.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was prepared by Cayetano Benavent, 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib: plotting 02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building functions with plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic improvements in plots through a simple example with a logistic function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LogitFuncException(Exception):\n",
    "    pass\n",
    "\n",
    "class LogitFunc(object):\n",
    "    \"\"\"\n",
    "    Logistic function implementation:\n",
    "    https://en.wikipedia.org/wiki/Logistic_function\n",
    "    \"\"\"\n",
    "\n",
    "    def compXVar(self, xstart, xstop, xsize=100):\n",
    "        if xstart >= xstop:\n",
    "            raise LogitFuncException(\"Error: xstart must be greater than xstop...\")\n",
    "        if xsize <= 1:\n",
    "            raise LogitFuncException(\"xsize must be greater than 1...\")\n",
    "\n",
    "        return np.linspace(xstart, xstop, xsize)\n",
    "\n",
    "    def compLogitFunc(self, x, l, x0, k=1):\n",
    "        \"\"\"\n",
    "        x: x values\n",
    "        x0: x-value of the sigmoid's midpoint\n",
    "        l: maximum value of the curve\n",
    "        k: steepness of the curve\n",
    "        \"\"\"\n",
    "        return(l / (1 + np.exp(-k * (x - x0))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def runTest():\n",
    "\n",
    "    lf = LogitFunc()\n",
    "\n",
    "    xstart = -10\n",
    "    xstop = 10\n",
    "    xsize = 1000\n",
    "    x = lf.compXVar(xstart, xstop, xsize=xsize)\n",
    "\n",
    "    l = 1\n",
    "    x0 = 0\n",
    "    k = 1\n",
    "    y = lf.compLogitFunc(x, l, x0, k=k)\n",
    "\n",
    "    plt.axhline(y=l/2., color='red', linestyle='--')\n",
    "    plt.axvline(x=x0, color='red', linestyle='--')\n",
    "\n",
    "    plt.plot(x, y, linewidth=3)\n",
    "\n",
    "    ymarg = (l * 10.) / 100.\n",
    "    plt.ylim([0 - ymarg, l + ymarg])\n",
    "    plt.xlim([xstart, xstop])\n",
    "\n",
    "    plt.grid(True)\n",
    "    plt.title(\"Logistic function\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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6aH5br14wbZrK2SLlppq7dNk778DVV4cv1ti4Mb99//3h5z8PdXYR6Tzd5y5l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\nyfzJ5TptL3AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5e2d2eb9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runTest()"
   ]
  },
  {
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}