matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib\n",
    "\n",
    "## Matlab interface vs Object oriented interface\n",
    "\n",
    "While most `plt` functions translate directly to `ax` methods (such as `plt.plot()` -> `ax.plot`, `plt.legend()` -> `ax.legend()`, etc.), this is not the case for all commands. In particular, functions to set limits, labels, and titles are slightly modified. For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\n",
    "\n",
    " - `plt.xlabel()` -> `ax.set_xlabel()`\n",
    " - `plt.ylabel()` -> `ax.set_ylabel()`\n",
    " - `plt.xlim()` -> `ax.set_xlim()`\n",
    " - `plt.ylim()` -> `ax.set_ylim()`\n",
    " - `plt.title()` -> `ax.set_title()`\n",
    " \n",
    "**Note: ** We can also do this:\n",
    "\n",
    "`ax.set(xlim=(0, 10), ylim=(-2, 2), title=\"Plot\")`\n",
    "\n",
    "## Example Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 50)\n",
    "dy = 0.8\n",
    "y = np.sin(x) + dy * np.random.randn(50)\n",
    "plt.errorbar(x, y, yerr=dy, fmt=\".k\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.randn(1000)\n",
    "plt.hist(data, bins=30, alpha=0.5, histtype=\"stepfilled\", color=\"steelblue\", edgecolor=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.random.normal(0, 0.8, 1000)\n",
    "x2 = np.random.normal(-2, 1, 1000)\n",
    "x3 = np.random.normal(3, 2, 1000)\n",
    "\n",
    "kwargs = dict(histtype=\"stepfilled\", alpha=0.3, density=True, bins=40)\n",
    "\n",
    "plt.hist(x1, **kwargs)\n",
    "plt.hist(x2, **kwargs)\n",
    "plt.hist(x3, **kwargs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean = [0, 0]\n",
    "cov = [[1, 1], [1, 2]]\n",
    "x, y = np.random.multivariate_normal(mean, cov, 10000).T\n",
    "\n",
    "plt.hist2d(x, y, bins=30, cmap=\"Blues\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colormap\n",
    "\n",
    "Look at this : https://matplotlib.org/users/colormaps.html and this https://matplotlib.org/examples/color/colormaps_reference.html ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}